Systems and methods for measuring, predicting, and optimizing brain function

Abstract

Methods and devices for changing a brain state of a person from an initial brain state to a target brain state are described. The method comprises the following steps: receiving information representing the initial brain state, wherein the information comprises the structural composition and the functional architecture of the brain; estimating a likelihood of the brain changing from the initial brain state to the target brain state based at least in part on the received information; determining a non-invasive brain stimulation scheme based at least in part on the received information and the estimated likelihood of the brain changing from the initial brain state to the target brain state; and controlling at least one non-invasive brain stimulation device to stimulate the brain according to the non-invasive brain stimulation scheme. The method also includes using the brain information to provide information for a design to compute a generic artificial intelligence agent.

Description

Technical Field

[0001] The present disclosure relates to systems and methods for measuring, predicting, and optimizing brain function in humans. Background Art

[0002] The brain is a complex dynamic system characterized by topological organization in a distributed neural network with specific temporal and spatial properties. The brain of each individual is different in terms of its structural and functional properties, and only a partial understanding of how brain activity explains cognition and behavior is obtained today. In addition, the available knowledge about how the brain adapts over time or in response to external and internal disturbances is very limited, such as in normal healthy aging, trauma, dementia, brain cancer, or even exposure to cognitive training aimed at improving abilities (such as memory or attention). Additionally, models that can simulate and predict such changes are generally not available at present, and the models that have been developed so far do not use artificial intelligence algorithms that take into account the principles of brain function. Summary of the invention

[0003] Some embodiments of the present disclosure relate to techniques for mapping, characterizing, predicting, and / or optimizing brain function. The systems and methods described herein include data analysis and visualization tools; algorithms for estimating brain likelihood and corresponding strategies for brain, cognitive, and behavioral enhancement; hardware for data collection and neuromodulation; and application-specific algorithms for generating digital assets based on individual brain activity signatures.

[0004] In some embodiments, a method for changing the state of a person's brain from an initial brain state to a target brain state is provided. The method includes receiving information characterizing the initial brain state, the information including the structural composition and functional architecture of the brain of the person, wherein the information includes passive data and active data recorded from the brain of the person; estimating the possibility of the brain of the person changing from the initial brain state to the target brain state based at least in part on the received information characterizing the initial brain state; determining a non-invasive brain stimulation scheme based at least in part on the received information characterizing the initial brain state and the estimated possibility of the brain of the person changing from the initial brain state to the target brain state; and controlling at least one non-invasive brain stimulation device to stimulate the brain of the person according to the non-invasive brain stimulation scheme to change the state of the brain of the person from the initial brain state to the target brain state.

[0005] On the one hand, estimating the likelihood that the brain of the person changes from the initial brain state to the target brain state includes using an algorithm to analyze the received information characterizing the initial brain state. On the other hand, the algorithm is configured to enhance cognition and brain health. On the other hand, the algorithm is configured to optimize interventions intended to treat neurological or psychiatric conditions. On the other hand, the method further includes extracting at least one indicator from the algorithm and storing the at least one indicator as a non-fungible token. On the other hand, the method further includes using the non-fungible token to perform one or more of the following: tracking the progress of interventions associated with the non-invasive brain stimulation regimen; defining brain and cognitive stimulation methods; or comparing a dynamic multi-layer digital twin (DMDT) associated with the person with a population-level DMDT to calculate a distance matrix.

[0006] On the other hand, the method further includes extracting at least one metric from the algorithm and using the extracted at least one metric to provide information for the neuromorphic AI platform. On the other hand, the method further includes extracting at least one metric from the algorithm and using the extracted at least one metric to set the parameters of the non-invasive brain stimulation protocol. On the other hand, the method further includes extracting at least one metric from the algorithm and using the extracted at least one metric to create avatars and content for games and / or metaverse applications. On the other hand, the method further includes extracting at least one metric from the algorithm and using the extracted at least one metric to define a personalized learning trajectory for skill acquisition, wherein the likelihood of the brain of the person changing from the initial brain state to the target brain state is estimated at least in part based on the personalized learning trajectory for skill acquisition. On the other hand, the method further includes extracting at least one metric from the algorithm and using the extracted at least one metric to derive a measure of brain plasticity and elasticity, wherein the likelihood of the brain of the person changing from the initial brain state to the target brain state is estimated at least in part based on the measure of brain plasticity and elasticity. On the other hand, the method further includes extracting at least one indicator from the algorithm and using the extracted at least one indicator to derive an intervention aimed at improving one or more of brain elasticity, brain plasticity, or overall brain health, wherein the non-invasive brain stimulation regimen is determined at least in part based on the derived intervention aimed at improving one or more of brain elasticity, brain plasticity, or overall brain health.

[0007] On the other hand, the method further includes extracting at least one indicator from the algorithm, and using the extracted at least one indicator to estimate the possibility of brain evolution and / or an indicator of state / trait transition, wherein the possibility of the brain of the person changing from the initial brain state to the target brain state is estimated at least in part based on the estimated possibility of brain evolution and / or the indicator of state / trait transition. On the other hand, the method further includes extracting at least one indicator from the algorithm, and using the extracted at least one indicator to estimate a specific brain state associated with one or more of the following: thinking about the future, the past, emotional content, or a specific memory, wherein the possibility of the brain of the person changing from the initial brain state to the target brain state is estimated at least in part based on the estimated specific brain state associated with one or more of the following: thinking about the future, the past, emotional content, or a specific memory. On the other hand, the method further includes extracting at least one indicator from the algorithm and using the extracted at least one indicator to create a template of a specific brain state associated with one or more of the following: thinking about the future, the past, emotional content, or a specific memory, wherein the likelihood of the brain of the person changing from the initial brain state to the target brain state is estimated at least in part based on the template of the specific brain state associated with one or more of the following: thinking about the future, the past, emotional content, or a specific memory. On the other hand, the method further includes using the specific brain state to classify data from healthy controls and patients to assess brain health of the healthy controls and patients.

[0008] On the other hand, the method further includes extracting at least one indicator from the algorithm and using the extracted at least one indicator to estimate disease progression of a neurodegenerative condition, wherein the likelihood of the brain of the person changing from the initial brain state to the target brain state is estimated at least in part based on the estimated progression of the neurodegenerative condition. On the other hand, the method further includes extracting at least one indicator from the algorithm and using the extracted at least one indicator to estimate disease progression of a patient with a brain tumor, wherein the likelihood of the brain of the person changing from the initial brain state to the target brain state is estimated at least in part based on the estimated disease progression of the patient with a brain tumor. On the other hand, the method further includes extracting at least one indicator from the algorithm and using the extracted at least one indicator to estimate disease progression and symptoms of a patient with depression, wherein the likelihood of the brain of the person changing from the initial brain state to the target brain state is estimated at least in part based on the estimated disease progression and symptoms of the patient with depression.

[0009] In another aspect, the method further includes extracting at least one indicator from the algorithm and using the extracted at least one indicator to estimate disease progression and symptoms of a patient with brain cancer, wherein the likelihood of the brain of the person changing from the initial brain state to the target brain state is estimated based at least in part on the estimated disease progression and symptoms of the patient with brain cancer. In another aspect, the target brain state is a brain state that is susceptible to the greatest clinical benefit of brain surgery in a patient with brain cancer. In another aspect, the target brain state is a brain state that has the least probability of experiencing tumor recurrence in a patient with brain cancer.

[0010] In another aspect, the method further includes extracting at least one indicator from the algorithm and using the extracted at least one indicator to estimate disease progression and symptoms of a patient with a sleep disorder, wherein the likelihood of the brain of the person changing from the initial brain state to the target brain state is estimated based at least in part on the estimated disease progression and symptoms of the patient with a sleep disorder. In another aspect, the target brain state is a brain state that tends to have the longest duration of a deep sleep stage during sleep in a patient with a sleep disorder.

[0011] In some embodiments, a method of changing the state of a person's brain to a target brain state is provided. The method includes receiving information associated with a digital replica of an initial brain state of the person, the digital replica including a plurality of brain indicators; determining a non-invasive brain stimulation protocol based at least in part on the received information associated with the digital replica of the initial brain state of the person to change the state of the brain of the person from the initial brain state to the target brain state; and stimulating the brain of the person with at least one non-invasive brain stimulation device according to the determined non-invasive brain stimulation protocol to change the state of the brain of the person from the initial brain state to the target brain state.

[0012] In some embodiments, a method for creating digital content and architecture for a video game or metaverse using brain data is provided. The method includes receiving information characterizing the structural and functional architecture of a human brain, wherein the information includes electrophysiological and neuroimaging data recorded from the human; analyzing the received information to create indicators of brain activity and performance in the form of a digital twin; and creating a first video game or metaverse content based at least in part on the indicators of brain activity and performance in the form of the digital twin.

[0013] On the one hand, the received information includes data sensed by a wearable device worn by the person. On the other hand, the method further includes receiving updated information from a networked device, updating the digital twin based on the received updated information, and creating a second video game or metaverse content based at least in part on the updated digital twin. On the other hand, the first video game or metaverse content includes multiple layers of avatars. On the other hand, the first video game or metaverse content includes in-game mechanisms. On the other hand, the in-game mechanisms include a physical and / or avatar hierarchical system in the game. On the other hand, the first video game or metaverse content includes a unique digital asset of the person. On the other hand, the unique digital asset of the person includes equipment in a video game. On the other hand, the first video game or metaverse content includes artificial intelligence for controlling one or more non-player characters in a video game. On the other hand, the first video game or metaverse content includes at least one in-game progression sequence or storyline. On the other hand, the first video game or metaverse content includes clinical metaverse applications for patients with neurological and psychiatric conditions.

[0014] In some embodiments, a method for creating a digital replica of a brain state of a person is provided. The method includes receiving information associated with the brain of the person, the information including brain structure information and brain function information, the brain function information including electrophysiological data recorded from the person; processing the received information to determine a plurality of brain indicators characterizing the brain state of the person; and creating a digital replica of the brain state of the person based at least in part on the plurality of brain indicators determined based on the received information.

[0015] In one aspect, the method further comprises designing a neuromorphic artificial intelligence agent based on the digital replica of the brain state of the person. In another aspect, the method further comprises updating the digital replica of the brain state of the person based at least in part on an updated plurality of brain metrics determined based on updated information associated with the brain of the person; and updating the neuromorphic artificial intelligence agent based on the updated digital replica of the brain state of the person. In another aspect, the neuromorphic artificial intelligence agent is based on an oscillation generator that reflects the brain state and brain architecture of the person. In another aspect, the neuromorphic artificial intelligence agent is used as a conversational agent in a therapeutic setting. In another aspect, the neuromorphic artificial intelligence agent is used to improve the efficacy of mental health interventions, including cognitive behavioral therapy.

[0016] In some embodiments, a device for modifying a brain state of a person is provided. The device includes at least one sensor configured to sense brain activity signals from the brain of the person; a stimulation device configured to provide non-invasive stimulation to the brain of the person; and a controller configured to control the operation of the stimulation device based at least in part on one or more characteristics of the brain activity signals sensed from the at least one sensor after stimulating the brain of the person with the stimulation device.

[0017] In some embodiments, a method of providing personalized modulation of brain activity to a person to alter a brain state of the person's brain is provided. The method includes sensing a plurality of evoked potentials using a plurality of electrophysiological sensors in response to providing non-invasive stimulation to the brain of the person; determining a personalized modulation plan for the person based at least in part on at least one characteristic of the plurality of evoked potentials, the personalized modulation plan including a location of stimulation and one or more stimulation characteristics; and stimulating the brain of the person to alter the brain state of the person according to the personalized modulation plan.

[0018] In some embodiments, a method of personalizing a digital experience within a digitally created environment is provided. The method includes receiving information associated with a digital replica of a user's brain, the digital replica including a plurality of brain indicators of the user; configuring at least one aspect of the digitally created environment based at least in part on the plurality of brain indicators of the user included in the digital replica to create a personalized digitally created environment for the user; and displaying the personalized digitally created environment to the user.

[0019] In some embodiments, a method of inducing plasticity in a human brain is provided. The method includes receiving information associated with a digital replica of a human brain, the digital replica including a plurality of brain indicators of the human, the plurality of brain indicators including a first plasticity level of the brain of the human; controlling at least one non-invasive brain stimulation device to deliver non-invasive stimulation to at least one location in the brain of the human, the at least one location having been identified as a location for inducing plasticity in the brain of the human; and determining a second plasticity level of the brain of the human based at least in part on feedback received after stimulating the at least one location of the brain of the human.

[0020] In some embodiments, a method for characterizing a person's brain health index is provided. The method includes receiving information that at least partially characterizes the brain structural composition and functional architecture of the person's brain, wherein the information includes first data, the first data including passive data and active data recorded from the person; processing the first data to at least partially extract an index of performance and efficiency of the person's brain and cognitive system; and defining an intervention for regulating brain activity of the person's brain based on the index of performance and efficiency of the person's brain and cognitive system.

[0021] In one aspect, the information is collected through a wearable device, a personal computer, or a portable headset. In another aspect, the information is collected through at least one electrophysiological technique. In another aspect, the at least one electrophysiological technique includes electroencephalography. In another aspect, the information is collected through at least one neuroimaging method. In another aspect, the at least one neuroimaging method includes one or more of magnetic resonance imaging (MRI), positron emission tomography (PET), or near infrared spectroscopy. In another aspect, the information comes from multiple sources, and processing the first data to at least partially extract indicators of the performance and efficiency of the person's brain and cognitive system includes using an algorithm to tune the information from multiple sources into a digital twin. In another aspect, defining an intervention to regulate brain activity of the brain of the person includes using the digital twin to define an intervention to enhance cognition and brain health of the brain. In another aspect, defining an intervention to regulate brain activity of the brain of the person includes using the digital twin to define an intervention to treat one or more neurological or psychiatric conditions.

[0022] On the other hand, the method further includes using the digital twin to predict the trajectory of a neurological or psychiatric disease, wherein defining an intervention for regulating brain activity of the brain of the person includes defining the intervention based at least in part on the predicted trajectory. On the other hand, the neurological disease is Alzheimer's disease, dementia, or brain cancer. On the other hand, the method further includes receiving second data sensed by a wearable device or a brain activity recording device; and updating the digital twin based at least in part on the second data. On the other hand, defining an intervention for regulating brain activity of the brain of the person includes using the digital twin to set one or more parameters of non-invasive brain stimulation. On the other hand, defining an intervention for regulating brain activity of the brain of the person includes using the digital twin to define a personalized learning trajectory for skill acquisition. On the other hand, defining an intervention for regulating brain activity of the brain of the person includes using the digital twin to derive a measure of brain plasticity and elasticity.

[0023] On the other hand, the received information is formalized into a network, and processing the first data to at least partially extract indicators of the performance and efficiency of the human brain and cognitive system includes extracting characteristics of the network using graph theory indicators and network control theory indicators. On the other hand, the received information is formalized into a network, and processing the first data to at least partially extract indicators of the performance and efficiency of the human brain and cognitive system includes extracting characteristics of the network by analyzing at least one of the amplitude, shape or frequency of the first data. On the other hand, processing the first data to at least partially extract indicators of the performance and efficiency of the human brain and cognitive system includes calculating the structural elasticity, functional elasticity and dynamic elasticity of the brain. On the other hand, processing the first data to at least partially extract indicators of the performance and efficiency of the human brain and cognitive system includes calculating the structural connection group of the brain. On the other hand, the method further includes monitoring the disease progression of the brain and predicting the level of neuroinflammation in the brain based at least in part on the structural connection group of the brain. On the other hand, the method further includes monitoring demyelination in the brain and / or identifying white matter lesions in the brain based at least in part on the structural connection group of the brain.

[0024] In another aspect, the information includes information about an emotional state of the person recorded by a camera, and the method further includes storing a non-fungible token representing the information, the information including the information about the emotional state of the person. In another aspect, the method further includes creating an emotional virtual agent based on the information about the emotional state of the person. In another aspect, the method further includes using the emotional virtual agent to monitor and support patients with dementia or enhance brain health of healthy people. In another aspect, the method further includes guiding at least one cognitive or behavioral intervention based on the information about the emotional state of the person to improve brain health. In another aspect, the method further includes guiding the dosage of a therapeutic intervention based on the information about the emotional state of the person.

[0025] On the other hand, the information includes spontaneous and / or stimulus-driven eye movements recorded by a camera, and the method further includes storing non-fungible tokens representing an analysis of the spontaneous and / or stimulus-driven eye movements. On the other hand, the spontaneous and / or stimulus-driven eye movements are collected during the person's performance of a cognitive task. On the other hand, the method further includes assessing the person's brain health and overall cognitive performance based on the non-fungible tokens. On the other hand, the method further includes assessing improvements in the person's cognitive performance and learning ability based on the non-fungible tokens. On the other hand, the spontaneous and / or stimulus-driven eye movements are collected by the person while playing a video game, and the method further includes using the non-fungible tokens to assess the person's in-game performance.

[0026] In some embodiments, a method of stimulating one or more brain regions of a person to guide the state of the brain of the person to a target state is provided. The method includes receiving non-invasive brain stimulation parameters, wherein the non-invasive brain stimulation parameters are determined using information included in a dynamic multi-layer digital twin (DMDT) associated with the brain of the person; controlling at least one non-invasive brain stimulation device to stimulate the one or more brain regions of the person according to the non-invasive brain stimulation parameters; and receiving feedback on the effect of the stimulation of the one or more brain regions to determine whether the state of the brain of the person is in the target state.

[0027] On the one hand, the at least one non-invasive brain stimulation device is configured to provide one or more of transcranial electrical stimulation, transcranial magnetic stimulation or transcranial focused ultrasound stimulation. On the other hand, the at least one non-invasive brain stimulation device is configured to provide sensory stimulation, including auditory stimulation and / or visual stimulation. On the other hand, the sensory stimulation is embedded in multimedia content, and the multimedia content includes movies, songs, television programs, video games or virtual reality applications. On the other hand, the at least one non-invasive brain stimulation device is configured to provide sinusoidal stimulation of a pattern of noise bursts applied at a specific frequency or random noise stimulation limited to a specific frequency band. On the other hand, the method further includes providing non-invasive stimulation intervention to the person sequentially or simultaneously with controlling the at least one non-invasive stimulation device to stimulate the one or more brain regions of the person. On the other hand, the non-invasive stimulation intervention includes cognitive rehabilitation or cognitive training intervention, and the cognitive rehabilitation or cognitive training intervention includes video games. On the other hand, the non-invasive stimulation intervention includes behavioral intervention or physical training intervention. On the other hand, the non-invasive stimulation intervention includes drug intervention.

[0028] On the other hand, controlling the at least one non-invasive brain stimulation device to stimulate the one or more brain regions of the person includes stimulating the one or more brain regions to selectively modify specific brain connections between the one or more brain regions. On the other hand, controlling the at least one non-invasive brain stimulation device to stimulate the one or more brain regions of the person includes stimulating the one or more brain regions to increase brain plasticity and / or accelerate learning rate. On the other hand, the one or more brain regions are defined based on data associated with the person. On the other hand, the one or more brain regions are defined based on group level data. On the other hand, the one or more brain regions are part of a functional brain network. On the other hand, the one or more brain regions include subcortical brain structures. On the other hand, the one or more brain regions are defined based on neuroimaging data. On the other hand, the neuroimaging data includes functional MRI data. On the other hand, the neuroimaging data includes diffusion MRI data. On the other hand, the neuroimaging data includes structural MRI data. On the other hand, the neuroimaging data includes positron emission tomography (PET) data. On the other hand, the neuroimaging data includes near infrared spectroscopy data. On the other hand, the one or more brain regions are defined based on electrophysiological data. On the other hand, the electrophysiological data includes scalp electroencephalography data. In another aspect, the one or more brain regions are defined by mapping white matter tracts in the brain of the person. In another aspect, the one or more brain regions are regions that are connected or not connected to a brain cancer. In another aspect, the one or more brain regions are regions responsible for sleep induction.

[0029] In some embodiments, a method for determining the health and performance of a person's brain is provided. The method includes sensing a plurality of evoked potentials in response to a first non-invasive stimulation of each of a plurality of locations in the brain of the person; determining personalized stimulation parameters for the person based at least in part on at least one characteristic of the plurality of evoked potentials, wherein the personalized stimulation parameters include stimulation locations and one or more stimulation characteristics; providing a second non-invasive stimulation to the person based on the personalized stimulation parameters; and receiving feedback on the effect of the second non-invasive stimulation to extract indicators of performance and brain health.

[0030] In one aspect, the method further includes controlling a non-invasive stimulation device to provide the first non-invasive stimulation to each of the plurality of locations in the brain of the person. In another aspect, providing the first non-invasive stimulation includes providing at least one of transcranial electrical stimulation, transcranial magnetic stimulation, or transcranial focused ultrasound stimulation. In another aspect, providing the first non-invasive stimulation includes providing at least one sensory or cognitive stimulation. In another aspect, the personalized stimulation parameters are determined based at least in part on a comparison of at least one characteristic of the plurality of evoked potentials with spontaneous activity in the brain of the person. In another aspect, the first non-invasive stimulation is delivered to a plurality of locations in the plurality of locations simultaneously or in a predefined order. In another aspect, the plurality of locations are arranged in a grid for systematic spatial assessment of brain dynamics. In another aspect, the at least one characteristic of the plurality of evoked potentials includes a peak amplitude. In another aspect, determining the personalized stimulation parameters includes selecting the stimulation location as a location in the plurality of locations associated with the maximum peak amplitude of the plurality of evoked potentials. On the other hand, the multiple locations include locations in a brain network that includes one or more of the following: a default mode network, a frontoparietal control network, a sensorimotor network, a pre-salience network, a dorsal attention network, a ventral attention network, a visual network, an auditory network, or a language network.

[0031] On the other hand, the method further includes treating or alleviating a neurological or psychiatric disease by providing the second non-invasive stimulation to the brain of the person based on the personalized stimulation parameters. On the other hand, the neurological or psychiatric disease is Alzheimer's disease. On the other hand, the neurological or psychiatric disease is mild cognitive impairment (MCI). On the other hand, the neurological or psychiatric disease is frontotemporal dementia. On the other hand, the neurological or psychiatric disease is one of the following: depression (DEP), schizophrenia (SCZ), autism (AUT), attention deficit and hyperactivity disorder (ADHD), bipolar disorder (BP), amyotrophic lateral sclerosis (ALS), Parkinson's disease (Parkinson's disease, PD), traumatic brain injury (TBI), insomnia (INS), disorder of consciousness (DOC), headache (HD), multiple sclerosis (MS), stroke (STR) or brain tumor (BT). On the other hand, the neurological or psychiatric disease is characterized by memory deficits. On the other hand, the neurological or psychiatric disease is characterized by cognitive control deficits. On the other hand, the neurological or psychiatric disease is characterized by reduced functional independence. On the other hand, the neurological or psychiatric disease is brain cancer. In another aspect, the neurological or psychiatric disease is a brain tumor. In another aspect, the second non-invasive stimulation is provided to the brain of the human prior to surgery to provide information for surgical planning associated with removal of at least a portion of the brain tumor. In another aspect, the second non-invasive stimulation is provided to the brain of the human after surgery to detect recurrence of the brain tumor.

[0032] On the other hand, the method further includes providing a drug intervention that affects the central nervous system to the person, wherein the drug intervention is provided sequentially or simultaneously with the second non-invasive stimulation. On the other hand, the method further includes providing a cognitive assessment, cognitive training, or cognitive enhancement intervention to the person, wherein the cognitive assessment, cognitive training, or cognitive enhancement intervention is provided sequentially or simultaneously with the second non-invasive stimulation. On the other hand, the method further includes providing a behavioral intervention to the person, wherein the behavioral intervention is provided sequentially or simultaneously with the second non-invasive stimulation. On the other hand, the method further includes providing a third non-invasive stimulation to the person, wherein the third non-invasive stimulation is provided sequentially or simultaneously with the second non-invasive stimulation. On the other hand, the third non-invasive stimulation is transcranial electrical stimulation.

[0033] In some embodiments, a method for creating a plasticity-inducing intervention for a person is provided. The method includes receiving information characterizing a brain state and a plasticity level of the person, wherein the information includes electrophysiological data, behavioral data, and neuroimaging data recorded from the person; identifying a plasticity-inducing intervention for the person, the plasticity-inducing intervention including one or more brain stimulation targets; controlling at least one non-invasive brain stimulation device to deliver stimulation to the one or more brain stimulation targets; and receiving feedback about the effect of the stimulation of the one or more brain stimulation targets to determine whether the plasticity level of the brain of the person has changed.

[0034] On the one hand, the plasticity level of the person is measured by brain and cognitive indicators obtained using a dynamic multi-layer digital twin (DMDT) of the brain of the person. On the other hand, identifying plasticity-inducing interventions is based on an enriched environment, which is determined based on the DMDT of the brain of the person. On the other hand, the enriched environment is a virtual environment, an augmented environment, or a mixed reality environment. On the other hand, the enriched environment is delivered in the form of a room equipped with a sensory stimulation device and a data recording device. On the other hand, the enriched environment is delivered in the form of a video game. On the other hand, the enriched environment is represented by a dual-task platform.

[0035] On the other hand, the plasticity-inducing intervention is configured to target the peripheral neuronal network and extracellular matrix of the brain of the person. On the other hand, the plasticity-inducing intervention is based on regulating CSPG by injecting ChABC. On the other hand, the plasticity-inducing intervention is based on regulating CSPG by manipulating BDNF. On the other hand, the plasticity-inducing intervention is based on regulating CSPG by administering ketamine. On the other hand, the plasticity-inducing intervention is based on drugs that act on the GABAergic circuits of the brain. On the other hand, the plasticity-inducing intervention includes using drugs to regulate one or more of PNN, ECM or CSPG. On the other hand, the plasticity-inducing intervention is based on a non-invasive brain stimulation method. On the other hand, the non-invasive brain stimulation method includes a scheme for inducing rapid oscillatory activity in the gamma band in the brain. On the other hand, the non-invasive brain stimulation method includes a scheme for inducing activity in the gamma band to activate inhibitory interneurons and glial cells. On the other hand, the non-invasive brain stimulation method includes a scheme for inducing gamma activity in the brain, and the non-invasive brain stimulation method includes transcranial alternating current stimulation (tACS), transcranial pulsed gamma stimulation (tPGS) and / or narrow-band transcranial random noise stimulation (nb-tRNS).

[0036] On the other hand, the plasticity-inducing intervention is applied to patients with neurological and psychiatric symptoms. On the other hand, the plasticity-inducing intervention is applied to athletes to improve performance and response to training. On the other hand, the plasticity-inducing intervention is applied to improve sleep quality and memory consolidation. On the other hand, the plasticity-inducing intervention is applied to patients with post-traumatic stress disorder (PTSD) to promote the manipulation and elimination of traumatic memories and promote a healthy brain state. On the other hand, the plasticity-inducing intervention is applied to astronauts to improve the learning and retention of skills and promote protection from brain and cognitive changes associated with space flight. On the other hand, the plasticity-inducing intervention is applied during sleep to enhance memory consolidation and overall brain plasticity.

[0037] In some embodiments, a method for enhancing cognitive performance and learning ability by stimulating a person's brain in a predefined sequence is provided. The method includes receiving information characterizing an initial brain state of the person, wherein the information includes electrophysiological data and cognitive data recorded from the person; defining an optimal sequence of cognitive changes to be induced to change the state of the person from the initial brain state to a target brain state; delivering one or more cognitive modulators targeting specific brain structures and cognitive functions based on the optimal sequence of cognitive changes to be induced; and receiving feedback on the effect of the delivered one or more cognitive modulators to determine whether the state of the brain of the person is in the target brain state.

[0038] On the one hand, the optimal order of cognitive regulators is defined to achieve an optimal learning state (OLS). On the other hand, the OLS is defined based on the dynamic multi-layer digital twin (DMDT) of the brain of the person. On the other hand, the target brain state is a state of high cognitive performance, and the state of high cognitive performance is defined by a specific activation and deactivation pattern of a brain region based on neuroimaging or electrophysiological diagrams. On the other hand, the optimal order of cognitive regulators is combined with brain regulators to improve brain excitability, regulate plasticity and / or improve brain efficiency or modularity. On the other hand, the brain regulator is in the form of non-invasive stimulation. On the other hand, the one or more cognitive regulators include cognitive training that acts on sensory and cognitive functions. On the other hand, the target brain state is a brain state in which the learning rate is accelerated. On the other hand, the learning rate is associated with learning sensory motor skills, including one or more of the performance of musical instruments, the enhancement of memory performance or the learning of coded language. On the other hand, in patients with neurological or psychiatric conditions, the learning rate is associated with learning rehabilitation skills. On the other hand, the learning rate is associated with improving physical and / or mental performance associated with sports. On the other hand, the learning rate is associated with improving educational performance. In another aspect, the learning rate is associated with cognitive-behavioral therapy, speech therapy, or dynamic therapy. In another aspect, the learning rate is associated with enhancing convergent thinking in the form of fluid intelligence and abstract reasoning. In another aspect, the learning rate is associated with enhancing divergent thinking in the form of creativity and insight. In another aspect, the learning rate is associated with enhancing general intelligence by targeting brain regions and networks of a multi-layered convergent-divergent thinking (CDt) model of human cognition.

[0039] In some embodiments, a system for managing a brain optimization program is provided. The system includes a data collection platform configured to collect and store data for identifying an individual with an optimal optimization target; a data analysis platform configured to process the data of the individual and derive optimal stimulation parameters; and a database including the data of the individual collected before and / or during brain stimulation therapy based on the optimal stimulation parameters.

[0040] In one aspect, the system is accessible by a clinician prescribing the brain stimulation therapy. In another aspect, the system is accessible by an end user via a portable computing device. In another aspect, the data analysis platform is a cloud-based computing platform in communication with the data collection platform via at least one network. In another aspect, the system is controlled via a blockchain environment.

[0041] In some embodiments, a system for recording and regulating human brain activity is provided. The system includes a stimulation device configured to provide non-invasive stimulation to each of a plurality of locations in a brain region of the person; a sensor device configured to sense at least one induced response in response to the non-invasive stimulation provided by the stimulation device; and a computer processor configured to select one of the plurality of locations suitable for regulating brain activity by non-invasive stimulation as a personalized stimulation target, wherein the selection is based on at least one characteristic of the at least one induced response.

[0042] On the one hand, the stimulation device and the sensor device are included as part of a portable device in the form of a wearable headset. On the other hand, the portable device is configured to be controlled by a separate computing device, thereby allowing remote control of the portable device. On the other hand, the stimulation device is configured to provide at least one of transcranial electrical stimulation, transcranial magnetic stimulation or transcranial focused ultrasound stimulation to the personalized stimulation target. On the other hand, the stimulation device is configured to treat or alleviate the neurological or psychiatric disease of the person by providing the non-invasive stimulation to the selected personalized stimulation target. On the other hand, the neurological or psychiatric disease is Alzheimer's disease. On the other hand, the neurological or psychiatric disease is frontotemporal dementia. On the other hand, the neurological or psychiatric disease is mild cognitive impairment (MCI). On the other hand, the neurological or psychiatric disease is brain cancer. On the other hand, the neurological or psychiatric disease is glioma. On the other hand, the neurological or psychiatric disease is depression. On the other hand, the neurological or psychiatric disease is ADHD. On the other hand, the neurological or psychiatric disease is a sleep disorder. In another aspect, the neurological or psychiatric disease is characterized by changes in brain networks, such as depression (DEP), schizophrenia (SCZ), autism (AUT), attention deficit and hyperactivity disorder (ADHD), bipolar disorder (BP), amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), traumatic brain injury (TBI), insomnia (INS), disorders of consciousness (DOC), headache (HD), multiple sclerosis (MS), stroke (STR) and brain tumor (BT). In another aspect, the neurological or psychiatric disease is characterized by memory deficits. In another aspect, the neurological or psychiatric disease is characterized by cognitive control deficits. In another aspect, the neurological or psychiatric disease is characterized by reduced functional independence.

[0043] On the other hand, the non-invasive stimulation provided to the selected personalized stimulation target is combined with a drug intervention that affects the central nervous system, and the drug intervention is delivered sequentially or simultaneously. On the other hand, the non-invasive stimulation provided to the selected personalized stimulation target is combined with one or more of cognitive assessment, cognitive training or cognitive enhancement intervention, and the cognitive assessment, cognitive training or cognitive enhancement intervention are delivered sequentially or simultaneously. On the other hand, the non-invasive stimulation provided to the selected personalized stimulation target is combined with a behavioral intervention, and the behavioral intervention is delivered sequentially or simultaneously. On the other hand, the non-invasive stimulation provided to the selected personalized stimulation target is combined with different non-invasive stimulation interventions, and the different non-invasive stimulation interventions are delivered sequentially or simultaneously, including transcranial electrical stimulation.

[0044] In another aspect, the computer processor is further configured to elaborate a sequence of combined automatic and semi-automatic signal processing algorithms installed on local hardware to achieve specific indications of one or more of stimulation location, frequency, or intensity. In another aspect, the computer processor is further configured to elaborate a sequence of combined automatic and semi-automatic signal processing algorithms installed on remote hardware with connectivity to achieve specific indications of stimulation location, frequency, or intensity. In another aspect, the sensor device includes electrodes configured to be placed on the scalp of the person. In another aspect, the sensor device includes electrodes configured to be placed inside the ear of the person. In another aspect, the sensor device includes electrodes configured to be placed on the face of the person. In another aspect, the sensor device includes high-density electrodes based on carbon nanotubes, the high-density electrodes being placed inside a stimulation cap placed on the head of the person. In another aspect, the system is configured for use within a metaverse.

[0045] In some embodiments, a method of collecting neurophysiological data and cognitive-behavioral data of an individual to characterize brain health indicators is provided. The method includes receiving information that at least partially characterizes the structural composition and functional architecture of the brain of an individual, wherein the information includes passive data and active data recorded from the person; processing the data to at least partially extract indicators of the performance and efficiency of the brain and cognitive systems; and defining interventions to modulate brain activity and optimize the performance and efficiency of the brain.

[0046] On the one hand, the information is received from multiple sources and tuned by an algorithm and combined as part of a DMDT into indicators of brain performance and brain health. On the other hand, the information included in the DMDT is used to optimize interventions aimed at enhancing cognition and brain health through the DARWIN algorithm described herein. On the other hand, the information included in the DMDT is used to optimize interventions aimed at treating neurological or psychiatric conditions through the DARWIN algorithm. On the other hand, the information included in the DMDT is used to predict the trajectory of neurological or psychiatric diseases in the form of diagnostic markers and prognostic markers. On the other hand, the information included in the DMDT is updated based on new data collected by wearable devices or brain activity recording devices. On the other hand, the information included in the DMDT is used to set parameters for non-invasive brain stimulation. On the other hand, the information included in the DMDT is used to create avatars and content for games and metaverse applications. On the other hand, the information included in the DMDT is used to define personalized learning trajectories for skill acquisition. On the other hand, the information included in the DMDT is used to derive measures of brain plasticity and elasticity.

[0047] On the other hand, the information included in the DMDT is stored as a non-fungible token (NFT) and is subsequently used to track the progress of therapeutic interventions, recovery interventions, or enhancement interventions. On the other hand, the information included in the DMDT is stored as a non-fungible token (NFT) and is subsequently used to define brain and cognitive stimulation methods. On the other hand, the information included in the DMDT is stored as a non-fungible token (NFT) and is subsequently used to compare individual DMDTs with group-level DMDTs and calculate distance matrices. On the other hand, the information included in the DMDT is stored as a non-fungible token (NFT) and is used as biometric data for encryption and network security applications. On the other hand, the information included in the DMDT is stored as a non-fungible token (NFT) and is used as an asset for digital commerce and financial transactions in the metaverse. On the other hand, the information included in the DMDT is stored as a non-fungible token (NFT) and is used as an asset for generating personalized digital content in a game application, the personalized digital content including but not limited to equipment, weapons, and clothing. On the other hand, specific patterns of brain activity are stored as non-fungible tokens (NFTs) and used as passwords for encryption and cybersecurity applications in the context of online commercial and financial transactions.

[0048] In another aspect, specific patterns of brain activity associated with specific stimuli are stored as non-fungible tokens (NFTs). In another aspect, individual cognitive architectures, including but not limited to information about brain network dynamics and cognitive processing strategies are stored as NFTs. In another aspect, individual cognitive architectures are used as targets for cognitive enhancement, cognitive training, and psychotherapy applications. In another aspect, individual mind wandering patterns, including but not limited to sequences of recorded brain states associated with specific events or stimuli are stored as NFTs. In another aspect, individual mind wandering patterns are used as targets for cognitive enhancement, cognitive training, and psychotherapy applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Advantages of the present invention, as well as further advantages, may be better understood by referring to the following description taken in conjunction with the accompanying drawings, which are not necessarily drawn to scale, emphasis instead generally being placed upon illustrating the principles of the invention.

[0050] Figure 1 Exemplary components of a neuromodulation and enhancement platform according to some embodiments of the present disclosure are schematically illustrated.

[0051] Figure 2 An example cloud computing and blockchain architecture is shown for use with some embodiments of the present disclosure.

[0052] Figures 3A-3C Schematically illustrates the creation of a multi-layer NFT based on neural data according to some embodiments of the present disclosure.

[0053] Figure 4 Components of a system for capturing information about an individual according to some embodiments of the present disclosure are schematically illustrated.

[0054] Figure 5 An example data analysis pipeline including data processing components that may be used according to some embodiments of the present disclosure is presented.

[0055] Fig. 6A Examples of individual differences in state or trait transitions according to some embodiments of the present disclosure are shown.

[0056] Figure 6B Indicators of state transitions for a group of subjects according to some embodiments of the present disclosure are shown.

[0057] Figure 7 Results of studies in which the brain was perturbed to improve measures of brain elasticity are shown according to some embodiments of the present disclosure.

[0058] Figure 8 Graphical metrics representing integration and separation of information processing according to some embodiments of the present disclosure are shown.

[0059] Figures 9A-9C A process for mapping the connectivity profile of a brain tumor according to some embodiments of the present disclosure is schematically illustrated.

[0060] Fig.10 Components of a system for monitoring a brain tumor according to some embodiments of the present disclosure are schematically illustrated.

[0061] Fig.11A and 11B An example of a dynamic decomposition for a psycho-cognitive assessment according to some embodiments of the present disclosure is schematically illustrated.

[0062] Fig.12 Brain regions identified as supporting abstract reasoning and problem solving in humans for providing information to a neuromorphic artificial intelligence agent according to some embodiments of the present disclosure are shown.

[0063] Fig.13 Brain networks identified as supporting humans in abstract reasoning and problem solving are shown for providing information to a neuromorphic artificial intelligence agent in accordance with some embodiments of the present disclosure.

[0064] Figures 14A-14C Major clusters of brain networks supporting human intelligence for providing information to neuromorphic artificial intelligence agents according to some embodiments of the present disclosure are shown.

[0065] Figures 15A-15B Activation foci and brain connectivity patterns according to some embodiments of the present disclosure are shown.

[0066] Figures 16A-16D Functional connections across overlapping nodes of convergent and divergent thinking in the brain for providing information to a neuromorphic artificial intelligence agent are shown in accordance with some embodiments of the present disclosure.

[0067] Figures 17A-17B According to some embodiments of the present disclosure, Figure 1 Example analysis, connection, and cognitive outputs of the DARWIN module of the architecture.

[0068] Fig.18 and 19 Components of a brain-to-command engine module for converting information from brain data into a coding language for a brain-computer interface application according to some embodiments of the present disclosure are schematically illustrated.

[0069] Fig. 20 Components of a brain-activity-inspired neuromorphic oscillatory multi-scale adaptive artificial intelligence module are shown, according to some embodiments of the present disclosure.

[0070] Figures 21A-21FExamples of controlled perturbations and corresponding brain responses for mapping brain activity according to some embodiments of the present disclosure are shown.

[0071] Figures 22A-22C Results of a study in which transcranial magnetic stimulation (TMS) was delivered through multiple brain networks based on individual MRI and fMRI data collected in patients with Alzheimer's disease are shown according to some embodiments of the present disclosure.

[0072] Figures 23A-23B Evoked oscillatory activity of an Alzheimer's disease patient after TMS according to some embodiments of the present disclosure is shown.

[0073] Figures 24A-24B Network level responses to perturbations for performing fingerprinting of brain activity are shown, according to some embodiments of the present disclosure.

[0074] Figures 25A-25E The clinical application of a combined neuroplasticity protocol and non-invasive brain stimulation for inducing brain plasticity according to some embodiments of the present disclosure is shown.

[0075] Figures 26A-26B Enhancement of brain plasticity through brain stimulation according to some embodiments of the present disclosure is shown.

[0076] Figures 27A-27B Clusters of brain activation and deactivation during anticipation and / or placebo response are shown according to some embodiments of the present disclosure.

[0077] Figures 28A-28B Brain networks corresponding to placebo activity are shown according to some embodiments of the present disclosure.

[0078] Fig.29 A hierarchical approach to a modular, adaptive, neural approach to psychological change (MANP) for improving the efficacy of mental health interventions is shown in accordance with some embodiments of the present disclosure.

[0079] Fig.30 A schematic representation of an example software architecture of a CLARITY module for virtual assistants and caregivers according to some embodiments of the present disclosure is shown.

[0080] Fig.31 is a diagram illustrating modulation of brain and cognitive plasticity as a function of manipulation of environmental complexity in a virtual reality environment, according to some embodiments of the present disclosure.

[0081] Fig.32A A network of brain regions identified as potential targets for neuromodulation of motion sickness, vertigo, and nausea during VR applications is shown in accordance with some embodiments of the present disclosure.

[0082] Fig.32B An optimized electrical stimulation pattern for tACS application for reducing nausea and vertigo during VR applications is shown in accordance with some embodiments of the present disclosure.

[0083] Fig.32C Schematically illustrated are tACS electrodes placed beneath an elastic band holding headphones and near a person's right and left ears, according to some embodiments of the present disclosure.

[0084] Fig.33A and 33B Results of a study that stimulated a person's brain in a particular way to reduce feelings of motion sickness are shown, according to some embodiments of the present disclosure.

[0085] Fig.34A -C shows the response to Adaptive Brain State Optimization (ABSO) according to some embodiments of the present disclosure. DETAILED DESCRIPTION

[0086] The inventors have recognized and believed that quantifying the complexity of the brain is important for: understanding and modifying human cognitive abilities and behavioral patterns; enhancing brain function; identifying personalized interventions to improve mental health and healthy aging; and providing individuals with knowledge about their brain's ability to learn, adapt, and evolve. Some embodiments of the present disclosure relate to techniques for mapping, characterizing, predicting, and / or optimizing brain function using an integrated software-hardware platform. Systems and methods include data analysis and visualization tools; algorithms for estimating brain likelihood and corresponding strategies for brain, cognitive, and behavioral enhancement; hardware for data collection and neuromodulation; and application-specific algorithms for generating digital assets based on individual brain activity features. The data processing techniques and algorithms described herein have a wide range of applications, including but not limited to creating digital twins of patients or healthy individuals; digital health biometrics; solutions to enhance brain plasticity and learning capabilities; algorithms for brain state-state and trait-trait transformations through personalized neuromodulation; enhancement of brain health and cognitive performance; deriving personalized interventions to manipulate brain activity and alleviate symptoms in patients with neurological and psychiatric diseases; acceleration of skill acquisition; and for creating brain-inspired game mechanics and content for the gaming industry and metaverse.

[0087] The inventors have recognized that even as solutions for mapping brain complexity and generating models of brain activity and behavior become available, solutions for precisely manipulating brain activity through controlled perturbations will be needed. Data collected on individual brains can be used to generate computational evolutionary biology models and identify optimal strategies for improving brain performance and behavior, while intervening to modify (e.g., through brain plasticity) the learning and evolutionary capabilities of the brain. The inventors have recognized that a new platform is needed for data collection, tuning, analysis, and simulation of brain, cognitive, and behavioral data. Some systems and methods of the present disclosure may be referred to herein as a neural modeling and enhancement platform (NEP), which covers software and hardware solutions for performing one or more of the following: brain fingerprinting; brain likelihood estimation; development of digital health assessment tools based on individual brain and cognitive characteristics; and brain optimization.

[0088] Some embodiments relate to a platform (e.g., NEP) for data collection, data tuning, data processing, and manipulation, for creating digital replicas of the human brain and computer models that can be used to simulate brain and human cognitive behavior. NEP may include software and hardware methods to at least partially: characterize individual brain complexity and generate digital twins of healthy and pathological brain functions; generate artificial intelligence agents (neuromorphic artificial intelligence) that reflect the brain dynamics and cognitive architecture of individuals; personalize solutions for state-state and trait-trait brain transformations used to estimate brain likelihood and create personalized trajectories for brain / cognitive optimization; use cortical plasticity mechanisms to derive cognitive / brain enhancement solutions; improve interventions aimed at maintaining brain health and treatment plans for patients with neurological and psychiatric conditions; generate assets for the metaverse and gaming industry by using neural data in open and closed-loop environments. These solutions can have direct applications in various fields, including the medical and human performance fields and the metaverse, in which newly generated applications and assets can be created based on brain activity and individual brain digital twins.

[0089] An overview of the NEP framework and example components of its specific applications are described in more detail below. The framework includes raw data and algorithms involving principles and tools for regulating brain plasticity; techniques for mapping the human connectome; learning science applied to video games and cognitive training; virtual reality applications; and novel tools for non-invasive brain stimulation of healthy and diseased brains. Following a general discussion of the NEP, the details of the procedures and associated data for each example component are described in more detail.

[0090] Mapping, predicting, and regulating brain and behavior

[0091] The platform described herein provides a map of the complexity of an individual brain, opening up scenarios where such "brain fingerprints" can be used to define or guide personalized solutions across multiple fields and markets, ranging from discovering novel disease biomarkers to defining personalized therapies within a precision medicine framework, to creating brain-inspired artificial intelligence, implementing personalized training and dietary guidelines, to personalized cognition and behavior to accelerate skill acquisition, to algorithms for creating brain-inspired game mechanics and content for the video game industry and the metaverse.

[0092] Once such complexity is mapped and recorded in digital form, a model of how the brain of a given individual or a group of individuals responds to external or internal disturbances can be derived. In this context, a solution to quantify the ability of the brain to adapt to changes (commonly referred to as "neuroplasticity", or simply "plasticity"), form memories, evolve in response to experience, and the cognitive possibilities of the brain becomes possible, and the solution can be used to improve the application of the brain fingerprint mentioned above. Recently, knowledge has been accumulated in the methods and approaches of mapping individual brains by neuroimaging and electrophysiology techniques, and particularly in modifying brain structure and function with the help of non-invasive brain stimulation techniques. Although the basic principles of how to effectively map brain structure and function, as well as behavioral or brain stimulation solutions to improve brain capacity have been proposed, methods and principles for parameterizing such processes, and configuring the necessary steps and changes to transform the brain from a given state A to another state B (e.g., from pathology to normal function, from a relaxed state to an abstract reasoning state) are not available. For example, the systems and methods described herein allow the definition of:

[0093] (i) the state-to-state transition steps for each brain (e.g., for a given brain with a baseline state of A, the best strategy to reach state B might be to increase the overall brain plasticity level and improve network modularity; whereas for a brain with a baseline state of C, the focus might be on improving flexibility and cortical excitability levels);

[0094] (ii) the order of state-to-state transition steps (e.g., stronger connections should be changed first, followed by weaker connections; the focus should be on increasing left hemisphere connections first, followed by right hemisphere connections);

[0095] (iii) the intensity / amplitude of stimulation required to achieve each change based on individually defined brain states;

[0096] (iv) Delivering a given type of brain stimulation based on the optimal time frame for brain change and adaptation (e.g., daily, weekly, multiple times a day, overnight, interleaved with cognitive and / or behavioral training).

[0097] By parameterizing such steps and concepts, and estimating how much different interventions might "change" the brain (e.g., each session of cognitive training might change brain connectivity by 0.5%; 10 hours of meditation stimulation might increase the spectral power of a given oscillation by 1%), it becomes possible to predict the number of "steps" required to move from state A to state B. A specific module of NEP (also referred to herein as the "DARWIN" module) includes solutions for such analysis and model building that apply to both clinical and non-clinical contexts.

[0098] Improves the brain's ability to learn and change

[0099] The opportunity to regulate brain states and traits also utilizes knowledge about brain plasticity and related opportunities to reactivate / manipulate the plasticity level of the human brain through pharmacological methods, behavioral methods, and cognitive methods. In humans, brain plasticity peaks during early childhood, after which the level of plasticity decreases, and at the same time, even in response to external stimuli, it is possible to observe significant modifications in brain wiring. The core of such changes in the brain's "learning" ability is at least in part a structural change in the surrounding neuronal network (PNN) (i.e., an extracellular matrix (ECM) structure rich in chondroitin sulfate proteoglycans (CSPGs)). The main event that reduces adult CNS plasticity is the accumulation of extracellular matrix molecules (such as CSPGs) around the soma and dendrites of neurons. The systems and methods described herein include algorithms and hardware to further amplify the effects of ECM / PNN regulation, thereby improving learning and brain physiology (e.g., brain elasticity, flexibility, efficiency) and state-trait transitions. Some of the systems and methods described herein combine interventions (including PNN modulators) with brain optimization and brain modifiers (including but not limited to non-invasive brain magnetic and electrical stimulation) as a platform for enhancing brain physiology, improving cognitive function, accelerating recovery from injury and brain pathology, and promoting brain health. The following section on brain plasticity and learning applications of NEP provides further details.

[0100] Personalized content generation based on neural data

[0101] In some embodiments, information about a given individual's brain activity, behavior, and cognition can be used for applications in the video game industry and the metaverse. For example, individual brain activity can guide virtual reality / augmented reality / extended reality (VR / AR / XR) experiences for generating assets and content based on raw and processed brain data acquired in real time or processed offline, guiding the automatic creation of "worlds" and corresponding physics based on brain activity and other physiological data (e.g., neural architecture). Similar techniques can be applied to the gaming industry, where information about each individual player can be used to characterize in-game parameters and adaptive game dynamics, and information about the evolvability of each player can be used to derive individual trajectories for growth, avatar optimization, creation of personalized artificial intelligence (AI), and / or control of other players or non-player characters (NPCs) in the game. The following sections describing the NEUROCREATOR module provide additional details on how to use the NEP, such as metaverse and gaming applications.

[0102] Clinical Application

[0103] The systems and methods described herein include algorithms with applications in the medical field, including but not limited to techniques for identifying novel biomarkers or novel therapeutic targets based on brain evolvability and connectome analysis. In addition, data obtained from each individual can be stored and subsequently used as a template for trait-trait transformation; for example, a snapshot of brain activity obtained from a patient with dementia before the onset of clinical symptoms (including but not limited to memory problems) can be used to guide sensory, cognitive or brain stimulation interventions designed to restore pre-pathological healthy brain dynamics. Similar applications can be used to guide surgical and post-surgical recovery in patients with brain tumors. Similar applications can be used for neurological and / or psychiatric conditions, including but not limited to post-traumatic stress disorder, depression and anxiety disorders.

[0104] Data protection and storage

[0105] In view of the uniqueness of each individual human brain, the ability of some of the technologies described herein to synthesize, quantify and model brain structure and function can allow non-homogeneous tokens (NFTs) to be stored based on individual brain / cognitive / behavioral data. NFTs can be used as target brain states and traits for brain transformation (e.g., returning to a high memory performance state), as a form of digital currency, as a template for creating digital avatars, and as wiring diagrams for biologically inspired artificial intelligence. Dynamic brain data collected by portable devices at specific, meaningful moments of life can be stored and reproduced through applications related to online transactions from art to games. Systems and methods for collecting and storing data are also described herein. Data can be stored by a data management and protection system (referred to herein as "DATANET"), which is responsible for data storage, quality assurance, and protected data sharing. DATANET can be based on a multi-key, decentralized ledger technology (e.g., blockchain) algorithm with enhanced compression capabilities to allow storage and transmission of large DMDT and / or DARWIN files.

[0106] Neuromorphic AI

[0107] In some embodiments, the accumulated knowledge about brain function, its association with behavior and cognition, and strategies for brain enhancement and optimization are also used to create neuromorphic artificial intelligence (AI). Group-level brain activity patterns (e.g., high abstract reasoning performance) for the best solution to a given problem or a specific skill set can be used to design AI tools for specific tasks, as well as general AI tools that can generate independent thinking and synthetic consciousness. In some embodiments, the AI ​​tool can be based on knowledge related to the computational and topological properties of brain function, including the role of oscillatory networks. In some embodiments, the AI ​​tool may include an auxiliary (e.g., conversational) agent [CLARITY] that helps patients with reduced functional independence, an AI tool that integrates patient psychotherapy treatment through personalized digital consultation, and an AI tool for generative digital asset creation through natural language processing for interactive video game applications. The following section entitled NEURO-AI on neuromorphic AI applications of NEP provides additional details.

[0108] Example Architecture

[0109] Figure 1 Exemplary components of the NEP platform 100 according to some embodiments of the present disclosure are schematically illustrated.

[0110] 1) [BRAINPRINT] is a tuning unit 102 for characterizing each individual user of the platform through a combination of multimodal data collected with the help of dedicated or third-party systems;

[0111] 2) [PERCEPTRON] is a monitoring and stimulation unit 104 in the form of a portable device that allows for the capture of brain data, physiological data, cognitive and behavioral performance data, and allows for brain stimulation through multiple stimulation modalities, including but not limited to sensory stimulation, electrical stimulation, or magnetic stimulation;

[0112] 3) [DARWIN] is a brain enhancement platform 106 that includes algorithms and computational toolsets for multi-purpose analysis of BRAINPRINT data to estimate one or more of brain likelihood, brain evolution trajectory, brain plasticity level, and other properties related to brain health.

[0113] DARWIN modules include:

[0114] (i) [SYNAPSE] is module 108, which includes systems and methods for measuring, predicting, and modulating neuroplasticity, including behavioral and brain stimulation solutions to disrupt peripheral neural networks (PNNs) and reopen the plasticity window of the human brain;

[0115] (ii) [IMPROVE] is module 110, which includes systems and methods for enhancing / optimizing the learning process, and solutions for cognitive-behavioral enhancement, including knowledge about optimal brain targets for neuromodulation of specific cognitive functions and behaviors, and the optimal hierarchy of cognitive and behavioral steps required to accelerate learning;

[0116] (iii) [PREPARE] is a module 112 that includes algorithms for data processing and analysis, including but not limited to solutions for cleaning, preprocessing, and analyzing brain data from a single individual or a group of individuals to provide input to secondary algorithms such as OPTI-BRAIN, OPTI-COG, and NEUROCREATOR, which are described in more detail below;

[0117] (iv) [SCREEN] is a digital biometric platform 114 that includes, but is not limited to, methods and tasks for assessing cognitive, psychological, and brain health, as well as the development of novel assessment tools based on DMDT data and SYNAPSE, DARWIN, and IMPROVE principles;

[0118] (v) [OPTI-BRAIN] is module 116, which includes systems and methods for optimizing brain function, including but not limited to solutions to improve brain elasticity, information processing, modularity, brain network flexibility; solutions also include applications to promote and guide transitions from / to a given brain state or trait, and identify optimal stimulation targets for therapeutic applications of neurological and psychiatric conditions;

[0119] (vi) [OPTI-COG] is a module 118 that includes systems and methods for assessing and enhancing human cognition, including methods for regulating brain function and specific brain networks that support specific cognitive abilities (such as abstract reasoning, memory, and attention). The module also includes algorithms for defining cognitive assessment tools based on individual properties captured by DARWIN, including brain properties quantified by the principles of plasticity according to SYNAPSE, the principles of learning according to IMPROVE, and OPTI-BRAIN. The modules constitute a digital health platform for assessing and regulating brain activity and cognition.

[0120] (vii) [NEUROCREATOR] is a module 120 that includes systems and methods for generating unique assets and content for the Metaverse and video game industry based on individual features extracted by BRAINPRINT and processed by DARWIN;

[0121] (viii) [NEURO-AI] is module 122, which includes systems and methods for creating neuromorphic artificial intelligence, including algorithms for generating AI tools that resemble individual brain properties or group-level properties of specific clusters of individuals captured by DMDT, and evolutionary algorithms for improving AI performance after DARWIN generates data on human brain data.

[0122] 4) [DATANET] is a data management and protection system 124 responsible for data storage, quality assurance, and protected data sharing. DATANET is based on decentralized ledger technology (e.g., blockchain) algorithms with enhanced compression capabilities to allow storage and transmission of large DMDT and DARWIN files. DATANET includes multi-key encryption for commercial and non-profit organizations to access data individually, allowing data owners to share their data with multiple users for different purposes without ever sharing all of their data. DATANET includes a compensation system for data owners to receive compensation based on their DMDT data usage in perpetuity.

[0123] 5) [STIMOLA] is module 126, which includes systems and methods for manipulating brain activity, cognition, and behavior through neuromodulation; these systems and methods include, but are not limited to, non-invasive brain stimulation techniques, cognitive training, video games, and augmented, mixed, and virtual reality tools;

[0124] 6) [TRAINER] includes systems and methods for specific skill enhancement, in which information from BRAINPRINT is combined with models from the DARWIN module to develop personalized training policies to maximize learning and cognitive performance; these training programs include but are not limited to language acquisition, music training, visual motor training, behavioral training, and cognitive therapy. TRAINER can be combined with methods from STIMOLA for additive effect.

[0125] Each of the components listed above is described in more detail below, and example data sets are provided to demonstrate the feasibility and functionality of the components and methods.

[0126] Workflow

[0127] The following describes a general example application in which data flows through the NEP platform:

[0128] 1) Data collected from people through PERCEPTRON and / or other wearable systems can be stored in a cloud-based database as part of BRAINPRINT. These data (including inputs from multiple modalities) include but are not limited to neuroimaging data (including but not limited to electroencephalography (EEG), magnetic resonance imaging (MRI), positron emission tomography (PET) and magnetoencephalography (MEG)), cognitive-behavioral measures (including but not limited to memory scores, language proficiency, reaction time and emotional response to stimuli) and physiological data (including but not limited to heart rate variability, skin galvanic response and facial thermal response), which can be pre-processed for further analysis and feature extraction to create a dynamic multi-layer digital twin (DMDT). Preprocessing steps may include, for example, removal of data recording artifacts, resampling, filtering, relabeling and averaging of signal traces; other preprocessing steps may include algorithms for tuning the data based on distribution fitting and alignment, reweighting, normalization; feature selection based on adaptive machine learning (AML) can be used to identify features that carry unique information about a person by comparing with a population-average template of the data distribution / variability portion of the human template library (HTR); preprocessed brain, physiological, cognitive and behavioral data can be used to extract performance indices (e.g., brain plasticity index, learning index) based on data collected at baseline; in some embodiments, the performance indices are updated with new incoming data collected by PERCEPTRON and / or other devices.

[0129] 2) DMDT data is sent to the DARWIN module for use, at least in part, in creating predictive models, estimating brain likelihoods, extracting features for content generation in the metaverse, and / or personalized neuromodulatory interventions.

[0130] 3) DMDT data are sent to PREPARE for data preprocessing, tuning and database building; the data are processed and formatted to allow further analysis by the DARWIN module.

[0131] 4) OPTI-BRAIN can simulate DMDT data to establish the best model for enhancing specific cognitive functions (e.g., logical reasoning). In this example, OPTI-BRAIN uses the organization of cognitive and functional brain networks as the main reference to estimate a person's learning potential and identify the best personalized learning trajectory.

[0132] 5) OPTI-BRAIN solutions can be further improved based on algorithms and proprietary knowledge about the neuroscience of learning (e.g., through IMPROVE) and neuroplasticity (through SYNAPSE); for example,

[0133] - Apply algorithmic data from IMPROVE that describes the optimal sequence of enhancement steps to achieve general cognitive enhancement; for example, based on specific cognitive and brain qualities described in the DMDT data and initial analyses from OPTI-BRAIN, IMPROVE may suggest improving visual-spatial attention levels first, followed by enhancements of two executive functions (e.g., inhibition and flexibility), followed by improvements in semantic verbal fluency;

[0134] - Based on the DMDT data describing individual qualities associated with neuroplasticity, the revised OPTI-BRAIN solution for enhancing logical reasoning may be further constrained by SYNAPSE; given the low levels of brain plasticity indexed by EEG and cognitive data as described in the DMDT, SYNAPSE recommends that global neuromodulatory interventions aimed at increasing global brain plasticity (e.g., drugs that disrupt peripheral neuronal networks) be performed first before implementing the solution suggested by IMPROVE, followed by selective / localized more plasticity modulation targeting the frontoparietal control network (FPCN) and dorsal attention network (DAN) of the brain (e.g., by transcranial electrical stimulation - tES);

[0135] 6) Based on input from IMPROVE and SYNAPSE, the OPTI-BRAIN model for enhancing logical reasoning may be sent to STIMOLA for implementation, including, for example, the execution of specific transcranial electrical stimulation (tES) protocols to enhance plasticity, and cognitive training protocols to enhance cognitive function (e.g., executive function, visual-spatial attention);

[0136] 7) Based on algorithms that evaluate dose-response curves and identify personalized training guidelines, STIMOLA can be used to implement enhanced logical reasoning programs. PERCEPTRON can be used to collect brain, cognitive and behavioral data;

[0137] 8) STIMOLA can provide feedback to DARWIN on the efficacy of the enhancement regimen, while also updating the individual's DMDT which can be used to update the OPTI-BRAIN solution in a closed-loop manner.

[0138] 9) PERCEPTRON can be used to track brain, behavioral and cognitive changes during periods of enhancement by continuously or periodically updating an individual's DMDT and providing information to the DARWIN model.

[0139] Different (but potentially similar) workflows may be used in different scenarios. For example, in some embodiments, DMDT data and the DARWIN model are used to create personalized avatar information for the metaverse, and to generate digital assets based on, for example, a person's brain efficiency, resilience, or cognitive profile.

[0140] In some embodiments, DMDT data and the DARWIN model are used to generate assets and content for video game development in which, for example, a player's specific brain and cognitive characteristics are reflected in an avatar, or the specific game dynamics of a game are regulated by individual DMDT characteristics and real-time data streaming through PERCEPTRON.

[0141] In some embodiments, DMDT data and DARWIN models (e.g., SYNAPSE, IMPROVE, OPTI-BRAIN) are used to create customized, optimized and / or improved in-game training strategies and learning schemes to enhance gaming performance.

[0142] In some embodiments, DMDT data and DARWIN models (eg, SYNAPSE, IMPROVE, OPTI-BRAIN) are used to track brain health through PERCEPTRON and identify customized, optimal, and / or improved strategies to improve brain health of an individual.

[0143] In some embodiments, DMDT data and the DARWIN model are used to create VR applications to enhance brain plasticity through a virtual augmented environment (VAE) in which specific combinations of stimuli and their features are presented to individuals to induce neuroplastic effects in the brain by modulating PNNs and plasticity circuits, which have implications for learning, cognition, and rehabilitation programs.

[0144] In some embodiments, DMDT data and the DARWIN model are used to guide a STIMOLA regimen delivered in VR to enhance the sensing of embodiments, reduce motion sickness, and / or enhance the effectiveness of cognitive interventions delivered in a virtual environment.

[0145] In some embodiments, DMDT data and the DARWIN model can be used to plan personalized psychotherapy interventions in which the sequence of cognitive and behavioral exercises / tasks is defined based on the optimal evolutionary path for each individual. Methods and algorithms from OPTI-BRAIN, OPTI-COG, NEUROCREATOR, and STIMOLA can be used to identify interventions that directly act on brain activity to be interleaved with cognitive and behavioral exercises in order to improve or maximize the impact of therapy.

[0146] In some embodiments, OPTI-BRAIN can simulate DMDT data to identify brain targets for non-invasive brain stimulation therapeutic interventions in patients with neurological and psychiatric conditions.

[0147] In some embodiments, OPTI-BRAIN can be configured to simulate DMDT data to identify brain targets for non-invasive brain stimulation interventions to enhance plasticity through sleep modulation.

[0148] In some embodiments, OPTI-BRAIN can be configured to simulate DMDT data to identify brain targets for non-invasive brain stimulation interventions to slow or prevent the migration and recurrence of brain tumors.

[0149] In some embodiments, OPTI-BRAIN can be configured to perform simulations on DMDT data to identify brain regions affected by brain tumor recurrence, wherein the information is used to guide surgical intervention.

[0150] In some embodiments, DMDT data and the DARWIN model can be used to inform a general AI agent through NEURO AI based on the brain and cognitive properties of an individual; the agent can be trained based on similar DMDTs from multiple individuals to generate group-average behaviors for specific problem-solving and decision-making processes.

[0151] BRAINPRINT

[0152] Some embodiments relate to solutions for capturing individual characteristics of a given brain and combining the individual characteristics with one or more of cognitive data, behavioral data, and biometric data to provide a comprehensive summary of the capabilities and possibilities of a given individual, and to design personalized interventions to at least partially enhance cognitive function and brain health, optimize brain function, enhance neuroplasticity, and / or accelerate learning. The combined information can form a dynamic multi-layer digital twin (DMDT) of an individual, representing a snapshot of the profile of the individual at a specific moment in time and its observed time evolution in the data and the time evolution predicted by the DARWIN model. The DMDT can represent a digital replica of local and distributed brain dynamics constrained by structural information derived from brain data. The mechanisms of brain activity are described by equations and combinations of equations that describe local and distributed activity over time.

[0153] Some embodiments involve creating a DMDT based at least in part on passive data, induced data, active data, or any combination thereof. For example, passive data can be obtained by recording passive data from the brain using non-invasive electrophysiology in the form of electroencephalography (EEG), and the dynamics of brain activity can be summarized as a series of indices and indicators that describe the topology, activity, and dynamics of a given brain under the name "Electrome" based on its similarity to the brain connectome.

[0154] Induced data can be obtained by, for example, recording active data from the brain, including but not limited to electrical and / or hemodynamic signals measured in response to external stimuli (e.g., in response to a flashing light or sound) or by manipulating brain states through specific instructions (e.g., inducing a meditative state).

[0155] The induced data can be obtained, for example, by recording active data from the brain, including, but not limited to, electrical and hemodynamic signals measured in response to cognitive stimulation, as described as part of a cognitive assessment program in which tests and tasks are administered to infer information about an individual's cognitive profile. The program can also include measuring responses to cognitive interventions designed to enhance or restore cognitive function in healthy individuals or individuals with neurological or psychiatric conditions.

[0156] The induced data can be obtained, for example, by recording active data from the brain, including, but not limited to, electrical and hemodynamic signals measured in response to a psychological assessment procedure in which tests and tasks are administered to infer information about the individual's psychological profile. The procedure can also include measuring responses to psychological interventions designed to provide psychological relief and / or alleviate symptoms of specific psychological cognitions, including symptoms associated with mood disorders and anxiety disorders.

[0157] The induced data can be obtained, for example, by recording active data from the brain, including, but not limited to, electrical and hemodynamic signals measured during tasks completed as part of a video game. Brain activity associated with specific aspects of the video game experience can be recorded in order to infer the neural correlates of performance in the game. In a similar embodiment, data is collected during physical performance, such as during exercise-related activities or meditation sessions.

[0158] The induced data can be obtained by, for example, recording data from the brain, including, but not limited to, electrical and hemodynamic signals measured in response to, but not limited to, external electrical, magnetic, or ultrasonic perturbations of the brain using a device mounted on the individual's head. Brain activity and corresponding cognitive and behavioral data can be collected before, during, and after the perturbation to derive an indicator of the individual's response to the targeted non-invasive stimulation of the brain.

[0159] In some embodiments, brain perturbations are delivered by methods and techniques of non-invasive brain stimulation (NIBS), including but not limited to transcranial magnetic stimulation (TMS), in the form of but not limited to repetitive TMS (rTMS), patterned rTMS protocols (such as theta burst stimulation), multi-pulse TMS, and paired associative stimulation; transcranial electrical stimulation (tES), in the form of but not limited to transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), and transcranial random noise stimulation (tRNS); focused ultrasound (FUS). The following description of the STIMOLA module provides additional details on methods and approaches for providing stimulation according to some embodiments.

[0160] For any of the modalities and applications described above and without limitation, a DMDT can be created at least in part by examining the properties of brain activity recorded at meso- and macro-scale levels, locally and across multiple brain structures, circuits, and networks. The resulting model can be a digital replica of the meso-macroscale dynamics of an individual brain, in which information at different spatial and temporal scales is summarized at multiple levels, including but not limited to (i) a structural level, which includes information about brain wiring and local tissue properties, (ii) a functional level, which describes spontaneous and / or induced activity measured in a brain region or local circuit, (iii) a dynamic level, in which multi-region activity occurring over time (e.g., functional connectivity and spectral coherence data across brain regions or networks) is summarized, and (iv) a mechanism level, in which algorithmic patterns that explain and predict functional and dynamic level data are stored. Models include mathematical models that explain layer-to-layer gray matter activity in the brain, as well as models that explain long-distance network level activity (such as activity captured by high-resolution EEG). Machine learning and / or deep learning models can be used to validate predictions for null distributions and / or random brain activity templates. Information from different data modalities can be summarized using indicators applicable to different data types, such as indicators related to network-level activity summarized by graph theory and network control theory indicators. Anatomical and functional templates can be used to ensure symmetry between data collected across modalities and different spatial resolutions (e.g., EEG and MRI). Indicators related to local activity can also be extracted and integrated with network-level indicators. Activity-related indicators can be applied to both static and dynamic data (e.g., time series data) as a summary of activity over time and / or a continuous discrete indicator of activity over time.

[0161] For example, in some embodiments, DMDT can be obtained at least in part by viewing the synchronization between the activities of two or more brain regions using indicators of static (functional) connectivity, dynamic connectivity, and effective connectivity. In some embodiments, DMDT can be obtained at least in part by viewing the organization of brain networks using indicators derived from graph theory, network topology analysis, and network control theory. In some embodiments, DMDT can be obtained at least in part by viewing eye movement patterns recorded by a camera during the performance of one or more cognitive tasks. In some embodiments, DMDT can be used to define a quantitative template of "healthy" brain and cognitive dynamics for use as a reference for DARWIN applications, which involve but are not limited to estimating brain likelihood, performing brain optimization programs through OPTI-BRAIN, or defining therapeutic interventions.

[0162] Data can be assimilated and tuned across data input modalities and data formats, including but not limited to inputs from EEG and neuroimaging data, cognitive indices, and heart rate variability. Subsequent analysis of individual data can be performed in a module (e.g., OPTI-BRAIN) that can summarize individual data as performance indices divided in a macroscopic domain, including but not limited to (i) brain performance index, (ii) cognitive performance index, (iii) perturbation index, (iv) learning index, (v) plasticity index. In some embodiments, DMDT can be used to calculate a series of indices that capture brain, cognitive, and behavioral performance.

[0163] A brain performance index can be derived from individual data, which represents the ability of the human brain to effectively propagate information within brain regions and networks and to appropriately and dynamically allocate metabolic resources for different tasks.

[0164] A perturbation index can be derived from individual data, which represents the ability of the human brain to respond to endogenous and exogenous perturbations in the form of, but not limited to, cognitive stimulation, electro-magnetic stimulation, behavioral interventions (such as psychotherapy or meditation), or drugs.

[0165] Plasticity indices can be derived from individual data, which represent the ability of the human brain to adapt to novel stimuli by, but not limited to, creating new brain connections or rearranging existing connections at the micro-meso-macro scale.

[0166] A learning index can be derived from individual data that represents a person's ability to generate, organize, and apply new knowledge through, but not limited to, information acquisition and manipulation of new inputs, including a person's strategic learning ability (e.g., learning how to learn).

[0167] In some embodiments, DMDTs from multiple individuals are stored in a database, and the stored DMDTs are used to estimate brain patterns that represent specific brain configurations for building neuromorphic artificial intelligence (AI) algorithms; for example, the average of DMDTs collected in individuals with high IQ can be used to generate a general AI module; DMDTs collected in individuals with high creativity can be used to generate a divergent thinking AI module; DMDTs collected in individuals with high executive function can be used to generate a decision-making AI module; DMDTs collected in individuals with a high brain performance index can be used to generate a backbone AI infrastructure in which modules are connected and synchronized.

[0168] In some embodiments, DMDTs from multiple individuals are stored in a database, and the stored DMDTs are used to estimate brain patterns that represent specific brain configurations suitable for specific tasks; for example, the average of DMDTs collected in individuals with a high learning index can be used to identify brain targets for neuromodulatory interventions aimed at enhancing learning and memory performance in individuals with a low learning index.

[0169] DMDT data may be stored at multiple levels for different and / or overlapping purposes. For example, DMDT data may be stored at least partially locally in a PERCEPTRON device that is used to collect data as part of the DMDT creation process. The device may be configured to store data and host local routines for data tuning and analysis, including at least some of the routines described in DARWIN. Figure 2 As shown, DMDT data can be stored at least in part in a database DATANET (e.g., a cloud-based biorepository), where DMDT data from multiple individuals are analyzed and tuned to create, for example, population-based DMDTs of brain health, cognitive performance, and resilience to pathology, as well as to identify candidate brain targets for neuromodulatory interventions.

[0170] Figure 2 An example cloud computing and blockchain architecture for data storage, processing, verification, and distributed sharing across multiple platforms, as well as different scenarios involving different users and clients requesting access to data, according to some embodiments, are shown.

[0171] Generation and use of non-fungible tokens (NFTs)

[0172] In some embodiments, the DMDT is stored as a non-fungible token (NFT) in the form of a digital package that contains information about the structural and functional properties of an individual brain. In one instance, the NFT may be associated with the global characteristics of a given brain and may not be associated with a specific state or trait. In another instance, the NFT may be associated with one or more specific characteristics of a given brain recorded during a specific state or moment in time, such as during the successful completion of a cognitive task (e.g., successful memory encoding and retrieval), a behavioral task (e.g., a meditative state, music playing); during an emotionally activated state (e.g., during retrieval of past trauma or during experience of trauma); during a state of high creativity or abstract reasoning (e.g., during the conception of a solution to a problem or during the generation of new ideas). The NFT can then be used as a template to derive a DARWIN solution using approaches and methods from STIMOLA (e.g., using brain stimulation to re-enter a highly creative brain state), thereby transforming from the current DMDT to a past brain state / trait.

[0173] In some embodiments, the NFT can be used as a template in a clinical setting to derive DARWIN solutions using STIMOLA and including interventions (including but not limited to transcranial electrical stimulation and magnetic stimulation) to transform from a patient's current BRAINPRINT to past brain states / traits. Applications may include but are not limited to using a patient's preclinical DMDT (e.g., prior to symptom onset or detection of pathological biomarkers) to optimize neuromodulatory interventions [STIMOLA] in an effort to alleviate clinical symptoms or modify the course of the disease (e.g., using a patient's DMDT collected prior to the onset of memory deficits to optimize current brain processing toward efficient memory encoding and retrieval patterns; using a patient's DMDT collected prior to a PTSD-inducing trauma to identify changing patterns of brain activity and derive helpful interventions via DARWIN).

[0174] In some embodiments, NFTs can be used in human performance applications as a template for using STIMOLA to derive DARWIN solutions to transition from an individual's current DMDT to past brain states / traits. Applications can include, but are not limited to, using an athlete's DMDT collected during a peak state and / or during peak performance to optimize neuromodulation interventions to re-enter a high performance state; or using DMDTs collected during a deep meditative state to guide the gradual modification of brain dynamics required to achieve such a state.

[0175] In some embodiments, the NFTs may be enriched with data related to, but not limited to, psychosomatic responses (e.g., stress response, galvanic skin response), cardiac activity (e.g., heart rate, heart rate variability), behavioral patterns (e.g., sleep activity and efficiency), cognitive states (e.g., memory performance, attention levels, intelligence quotient - IQ), to further personalize the creation of the DMDT and define parameters for brain optimization. In some embodiments, a group of NFTs from individuals with similar profiles may be used to create a group-level representation of brain and cognitive activity that identifies a particular group of individuals.

[0176] In some embodiments, patterns of brain activity recorded during specific moments in time that are particularly relevant to an individual may be stored as NFTs for their value as memories. In some embodiments, patterns of brain activity associated with specific moments in time during a video game performance that are particularly relevant to an individual may be stored as NFTs for their replay value, their value as a template for a memorable performance, their value as training material to improve performance and train other individuals in terms of in-game dynamics and performance.

[0177] The NFT may be represented by brain data alone, brain data combined with behavioral data capturing one or more performance characteristics, brain data combined with cognitive data, or brain data combined with other physiological data. In some embodiments, the NFT may include both the data and its generation program, including data processing techniques and data visualization solutions.

[0178] In some embodiments, NFTs may contain a set of brain states in the form of brain data, cognitive data, and physiological data collected by PERCEPTRON or similar devices. NFTs are marked by individuals as specific memories with unique value, and NFTs are then used as templates to recreate patterns of brain activity that generate such memories. The DARWIN algorithm, which suggests the best connection group adjustment methods and tools from STIMOLA, can be used to achieve the recreation of memory. NFTs for unique memories, moments, and brain states can be manually generated by individuals by assigning tags to moments in a specific time, or automatically generated by algorithms based on thresholds of brain, psychological, and physiological activation, which mark the beginning and end of moments in time with unique value to be stored as NFTs. For example, NFTs can be automatically generated during high-performance video game tournaments, thereby forming memories of brain patterns that support peak performance or brain patterns that occur during celebrations. NFTs can be automatically generated at moments with high emotional activation (e.g., when meeting a partner or family member after a long separation; when interacting with a pet or child; when recalling positive or negative memories). The detection, analysis, and storage of brain states can be performed by algorithms from DARWIN and stored in DATANET.

[0179] As part of DATANET, individuals can maintain control over NFTs generated by them and / or based on their brain, physiological and cognitive data. Individuals can have the possibility to share their NFTs, edit and modify their NFTs, exchange and trade their NFTs using DATANET or other associated platforms.

[0180] In general, models of brain activity, behavior, cognitive performance, psychological profiles, and physiological profiles collected as part of a DMDT and / or analyzed by DARWIN can be stored as NFTs representing specific, unique characteristics of a given individual. The DARWIN model obtained by extracting features from an individual's DMDT can constitute a unique information set that describes an individual phenotype associated with one or more indices in the DARWIN index (an example of the index is described herein), including but not limited to indices of brain and psychological resilience, flexibility, and evolvability. The characteristics of a given brain that provide, for example, a high degree of evolvability and plasticity in a given individual can constitute an NFT that uniquely identifies an individual whose data is used to generate such brain characteristics. In some embodiments, data from multiple individuals and DMDTs collected in DATANET can be used to generate group-level NFTs that represent unique configurations of brain activity describing a specific group of individuals, such as a group of patients with similar clinical characteristics or a group of healthy individuals with high working memory performance.

[0181] Figures 3A-3C Schematically illustrates the creation of a multi-layer NFT from neural data according to some embodiments. Figure 3A It is shown that NFTs can represent structural properties of the brain 302; spontaneous activity patterns 304 that are unique to each individual; patterns of induced activity 306 induced by various types of perturbations (including but not limited to electrical stimulation; magnetic stimulation; sensory stimulation as in the case of visual and auditory stimulation; emotional stimulation that induces psychosomatic responses); 2D graphical representations 308 of the activity; 3D renderings 310 of brain activity using high-resolution brain data or 3D templates; connectivity patterns 312 and network-level activity that characterizes how brain regions / networks / circuits interact over time; and / or art generated from brain data 314 by generative algorithms, AI, or human-directed programs. Figure 3B It is shown that evoked activity can be recorded in an individual while solving a cognitive task or performing a specific activity (e.g., playing a video game or solving a math problem); changes in brain activity may occur over time due to normal aging processes, lack of training, and other factors; an individual may wish to restore a previous pattern of brain activity in order to regain his / her performance; the NFT used at a first time point can be used as a template to guide the use of DARWIN to rewire brain activity patterns. Figure 3C It is shown that similar procedures can be implemented to rewire the brain activity of individuals to match the brain activity of different individuals with desired brain activity patterns and corresponding behaviors / performances.

[0182] System and Hardware [PERCEPTRON]

[0183] Some embodiments relate to a system for capturing information about an individual (referred to herein as a "PERCEPTRON"). For example, Figure 4 It is shown that the PERCEPTRON system can include, but is not limited to, a portable wearable headset configured to record brain signals (e.g., EEG, blood flow), voice via a microphone, galvanic skin response (GSR) or similar data of stress-related responses, and / or heart / cardiac data (e.g., heart rate, heart rate variability), and configured to deliver the stimulation regimen received from STIMOLA.

[0184] In some embodiments, PERCEPTRON is a battery-powered headset 410 having electrodes 412 for recording electroencephalography (EEG), which are arranged to be placed in contact with the individual's scalp. Based on the collected data, the electrodes can be arranged to cover the entire scalp according to a pre-specified position. The PERCEPTRON system can have a modular structure having a main frame that accommodates electrodes and a power source (e.g., a battery), and a set of additional components that can be attached to the frame to amplify the number of electrodes on the scalp, as well as other components (including but not limited to microphones, VR headsets, or AR goggles). The PERCEPTRON system may include a processing unit with computing capabilities (e.g., embedded in the frame) and a memory unit for local data storage. The PERCEPTRON system may have wireless communication capabilities, including but not limited to circuit systems capable of communicating via Wi-Fi and / or Bluetooth. The PERCEPTRON system can be configured to connect directly to external hardware, including but not limited to smartphones, desktop computers, laptops, tablet computers, and televisions. The PERCEPTRON system may have a connection capability that allows for direct network (e.g., Internet) connection without the need for external devices; this capability may be used for, but is not limited to, cloud-based computing, real-time storage and processing of data, or interaction with other PERCEPTRON systems. Interaction with other devices allows information about stored data or real-time data to be received and sent directly from a given PERCEPTRON system, including, but not limited to, EEG activity, heart rate, and GSR. The connection capability may be used to interact with hardware other than the PERCEPTRON system through one or more brain-computer interface (BCI) applications, including, but not limited to, controlling televisions, laptops, mobile phones, and more direct interaction with content (such as controlling avatars in video games or metaverse applications).

[0185] In some embodiments, the PERCEPTRON system also includes a battery powered neuromodulation unit that allows various forms of transcranial electrical stimulation (tES) through the same EEG electrodes included in the PERCEPTRON system. Neuromodulation also includes, for example, transcranial focused ultrasound (tFUS) and near infrared spectroscopy.

[0186] In some embodiments, the PERCEPTRON system includes composite electrodes made of carbon nanotubes that allow dry recording and dry brain stimulation without the need to apply gel on the scalp. The carbon nanotubes can be positioned as conductive filaments inside a fabric cap positioned on the scalp. The filaments can be positioned across the entire scalp, and subsections of each filament can be activated by a microcontroller capable of activating millimeter-resolution segments of the filament; this solution allows for high spatial resolution for both brain recording and brain stimulation, allowing a 1-1 match between brain data observed on the scalp and injected brain stimulation.

[0187] In some embodiments, the PERCEPTRON system includes electrodes for gel-based brain recording and brain stimulation, the electrodes being equipped with a pressure-based dispenser system and an active impedance monitoring system; the gel can be stored inside the electrode and released when the impedance level assessed on the electrode surface contacting the scalp reaches a pre-specified threshold level. The system allows for minimal gel usage, thereby avoiding bridging effects and signal contamination.

[0188] In some embodiments, the PERCEPTRON system is controlled via a multi-platform software package in the form of an application available for use with personal computing (PC) devices and / or mobile devices.

[0189] In some embodiments, the PERCEPTRON system can be used to acquire data of an individual's DMDT and / or can be subsequently used to perform longitudinal assessments of individuals over time. Baseline and longitudinal assessments can be completed during resting state activity and / or during performance of specific tasks to assess induced brain activity.

[0190] In some embodiments, the PERCEPTRON system can capture data for creating a DMDT, as described above. For example, an individual may be exposed to a VR or AR environment, in which the PERCEPTRON system is used to capture behavioral and cognitive responses, and biometrics are recorded by a wearable data collection unit. The physical features of the VR / AR environment can be manipulated to elicit a target response and provide meaningful data for creating a DMDT. VR and AR can be used to overcome the limitations of standard cognitive and behavioral assessments, as well as administer special, unconventional stimuli customized to detect brain and mental function (e.g., exposing patients to rich environments and unconventional stimulation patterns, including sensory stimulation, auditory stimulation, and visual stimulation).

[0191] In some embodiments, the PERCEPTRON system can be connected and / or controlled via a mobile device (e.g., smartphone, tablet) for remote monitoring and / or real-time data collection during daily activities, performance of specific tasks, etc.

[0192] In some embodiments, stimulus presentation and response recording using the PERCEPTRON system can be performed in a dedicated physical space, allowing for more immersive sensory and cognitive stimulation, as well as exposure to more complex stimuli. For example, the physical space can be a room that includes tools for audio and video stimulus presentation and recording sensors mounted on each wall of the room. The sensors and stimulus presentation equipment of the physical space can be connected to a wearable device that is used to guide or update the perturbation pattern provided to the individual in real time based on the individual's response.

[0193] In some embodiments, the PERCEPTRON system may be configured to deliver non-invasive brain stimulation including, but not limited to, transcranial electrical stimulation. The stimulation may be delivered prior to, concurrently with, and / or after brain data collection.

[0194] In some embodiments, the PERCEPTRON system may be used in gaming applications to monitor, guide / regulate brain and biometric data during gaming performance, and / or extract DMDT data to generate gaming content and assets.

[0195] In some embodiments, the PERCEPTRON system can be used as a digital biometric assessment tool to monitor daily living activities as well as daily cognitive and brain activity in healthy individuals. The data collected by the PERCEPTRON system and the metrics extracted by DARWIN can be used to trigger in-depth assessments performed in a clinical setting, such as where individuals who show changes in brain oscillatory activity during weekly home-based assessments are invited to a local hospital for a clinical EEG assessment.

[0196] In some embodiments, one or more PERCEPTRON systems may be used to monitor brain activity from multiple individuals simultaneously, thereby providing data for brain-to-brain communication analysis relevant to applications in the fields of decision making and cognitive neuroscience.

[0197] In some embodiments, the PERCEPTRON system can be used for clinical evaluation of individuals suffering from neurological or psychiatric conditions, as well as other medical conditions affecting the central nervous system.

[0198] Data processing and analysis [PREPARE]

[0199] Information collected as part of BRAINPRINT and constituting individual DMDTs can be processed and analyzed as part of module PREPARE. Data cleaning methods, preprocessing methods, tuning methods, and analysis methods can be implemented to convert raw data collected from information into data suitable for data analysis and visualization. PREPARE includes a pipeline for data conversion, which facilitates secondary analysis such as by DARWIN modules, brain-computer interface (BCI) applications (e.g., by STIMOLA and / or PERCEPTRON) that provide information for brain stimulation interventions, applications involving VR or metaverses and neurogames (e.g., NEUROCREATOR). The pipeline can be applied to a variety of data formats and modalities, including but not limited to brain scans, electroencephalography data, and behavioral data. In some embodiments, the pipeline is used to analyze different brain states, including analyzing spontaneous brain data and behavior; analyzing induced activity to external or internal events / stimuli; analyzing brain data in response to external disturbances in the form of transcranial electrical stimulation, magnetic stimulation, or ultrasonic stimulation. The pipeline of data preprocessing and analysis may include both unsupervised algorithms for automatic processing and semi-automatic, supervised algorithms for data interpretation and decision making. Figure 5 An example data analysis pipeline including data processing components that may be included in PREPARE according to some embodiments is presented.

[0200] Data Processing

[0201] Data processing includes solutions for automatic and semi-automatic cleaning / preprocessing of data, with the possibility to manually identify artifacts. Processing and analysis can be performed as part of separate modules covering, for example: (i) data collection, (ii) data validation and format conversion, (iii) data cleaning and preprocessing, (iv) data tuning, (v) data analysis, and (vi) detailed report creation (including optimal stimulation targets / parameters and summary of processing steps).

[0202] Brain scan processing

[0203] When brain scans can be used for stimulation target selection, two types of information can be used: (i) structural properties of the brain, including but not limited to density / volume / thickness / folds / sulcus depth of gray / white matter, CSF distribution, diffusivity and anisotropy of white matter, and spectral distribution of neurotransmitters; and (ii) functional properties of the brain, including but not limited to hemodynamic response, blood perfusion, metabolic activity (e.g., glucose consumption), neuroinflammation levels, and protein load. Steps for preparing brain scans for statistical analysis include but are not limited to converting single images to 3D volume formats; segmentation of brain tissue classes; spatial and temporal filtering; removing physiological noise; removing image artifacts; extracting mean values ​​and / or time series of brain activity, co-registering to common anatomical or functional templates for group-level analysis; in the case of block-fMRI data, calculating induced activity when multiple scanning conditions are present. Subsequent analysis can be performed on both voxel-based volume data or vertex-based surface images, and includes masking clean data based on anatomical or functional maps describing relevant networks or brain regions.

[0204] Electrophysiology data processing

[0205] Data collected by electroencephalography (EEG) is often contaminated by recording artifacts, including but not limited to blinking, heart rate, motion-related artifacts. The systems and methods described herein provide data cleaning, preprocessing and / or tuning to at least partially reduce or eliminate the effects of such artifacts.

[0206] Preprocessing and cleaning steps include, but are not limited to: raw data conversion (e.g., in .edf format); trimming of raw data to epochs of predefined length; automated or semi-automated data inspection to identify EEG channels with excessive noise or artifacts; zero removal of muscle artifacts (e.g., based on voltage-based thresholds, kurtosis, and joint probability); independent component analysis (ICA) to identify and remove components, with additional data reduction via principal component analysis (PCA) to minimize overfitting and noise components; data interpolation; bandpass filtering using a forward-backward filter; notch filtering to account for line noise; referencing to a global average; a second ICA to manually remove all remaining artifact components, including eye movements / blinks, muscle noise (EMG), single electrode noise, and auditory induced artifacts (artifacts are identified and labeled based on their spectral frequency distribution, power spectrum, amplitude, scalp topography, and time course); application of machine learning and deep learning algorithms to the identification of residual artifacts; interpolation of previously removed electrodes. Data cleaning and processing can be performed as a supervised approach, such as with human verification of processing steps and visual inspection of the data, or as an unsupervised procedure based on ML and AI, without human interaction.

[0207] Simultaneous brain stimulation and EEG recording

[0208] In some embodiments, brain activity can be recorded during the delivery of non-invasive brain stimulation, as described in the STIMOLA section. Unique adjustments to the standard EEG processing pipeline can be used to ensure that stimulation-induced artifacts are properly handled and the data interpreted.

[0209] In the examples below, a procedure is described for cleaning, preprocessing and analyzing EEG data collected before, during and after the delivery of TMS pulses. The same procedure can be applied to simultaneous tCS and / or focused ultrasound during EEG recording.

[0210] TMS-EEG analysis: In addition to typical artifacts present during EEG recordings (e.g., eye movements and heartbeats), EEG data collected during TMS can be contaminated by TMS-specific artifacts, including but not limited to magnetic artifacts that change the impedance of the EEG electrodes, TMS-induced muscle artifacts characterized by high-frequency activity, and artifacts related to the TMS machine recharging process between TMS pulses. These artifacts typically have amplitudes several orders of magnitude larger than the EEG data, thereby confounding the brain signals in the EEG.

[0211] Preprocessing and cleaning steps include, but are not limited to, raw data conversion (e.g., to .edf format); trimming of raw data to epochs of predefined length, including segments capturing pre-TMS and post-TMS brain activity; normalization of post-TMS activity by subtracting the mean signal amplitude of the EEG data collected pre-TMS; automated or semi-automated data inspection to identify EEG channels with excessive noise or artifacts; zero-filling of activity concurrent with a single TMS pulse to remove early signal decay and muscle artifacts induced by the TMS pulse (e.g., based on voltage-based thresholds, kurtosis, and joint probability); independent component analysis (ICA) to identify and remove components including early TMS-evoked high-amplitude electrodes, and principal component analysis (PFA) to identify and remove components including early TMS-evoked high-amplitude electrodes; and additional data reduction (ICA) to minimize overfitting and noise components; interpolation of previously zero-filled signals across TMS pulses; bandpass filtering using a forward-backward filter typically between 1 Hz and 150 Hz; notch filtering to account for line noise; reference to a global average; a second ICA to manually remove all remaining artifact components, including eye movements / blinks, muscle noise (EMG), single electrode noise, TMS-induced muscle activity, cardiac signals (EKG), and auditory-induced artifacts (artifacts were identified and labeled based on their spectral frequency distribution, power spectrum, amplitude, scalp topography, and time course); application of machine learning and deep learning algorithms to identification of residual artifacts; interpolation of previously removed electrodes.

[0212] Data cleaning and processing can be performed as a supervised approach, such as with human verification of processing steps and visual inspection of the data, or as an unsupervised procedure based on machine learning (ML) and AI, without human interaction.

[0213] Brain Enhancement Platform [DARWIN]

[0214] According to some embodiments, once DMDT is available, the DARWIN module can be configured to estimate the possibility of growth, evolution, adaptation and / or learning of an individual brain. DARWIN can be configured to create a digital representation of an individual brain, including its structural and functional properties and corresponding cognitive and psychological layers and abilities, thereby allowing simulations that describe responses to stimuli, such as normal and pathological aging, external disturbances or psychological stressors (such as trauma) in the case of traumatic brain injury or brain tumors or magnetic / electric stimulation. For example, DARWIN can be configured to identify the best trajectory for guiding brain changes, both under the target being a modifiable transient target state or a new stable brain configuration (trait). DARWIN also allows special modification of one or more brain properties associated with a given target, such as learning a new musical instrument or inducing a meditative state. The following describes the details of exemplary different applications using DARWIN. DARWIN can be provided with information by data collected by PERCEPTRON, by models and information from SYNAPSE and IMPROVE, can generate assessment tools through SCREEN, and / or guide the programs used in STIMOLA.

[0215] Digital Bioassay [SCREEN]

[0216] Through SCREEN, DARWIN can be configured to generate data-driven digital biometric assessment tools and indicators.SCREEN can include a method for generating an assessment tool, which is quantitative to cognitive, brain and psychological dimensions, structure and function, thereby promoting the personalized digital biometrics of brain, cognitive and mental health.In an embodiment, the assessment tool is deployed by a portable device in the form of a digital task or game, and the portable device includes but is not limited to a mobile phone, a tablet computer, a notebook computer and a wearable device.The mechanism and design of the assessment tool can be based on the analysis of the data collected as a part of DMDT, and information is provided by the design principles and neurobiology / neurophysiology / neurology concepts and models from SYNAPSE and IMPROVE.The assessment tool can be based at least in part on the knowledge related to brain plasticity and learning science and the combination of the raw data (e.g., referring to IMPROVE) related to human cognitive architecture and individual psychological well-being.

[0217] In some embodiments, the information obtained by OPTI-BRAIN and OPTI-COG can be used to design a special cognitive and brain health assessment tool based on the individual quality derived from brain and cognitive data. The information used to create the assessment tool may be related to a single individual or a group of individuals sharing the specific features of interest, such as similar performances of cognitive functions (e.g., high working memory capacity) or neurological pathologies (e.g., patients with dementia). Brain properties associated with the function or pathology can be modeled according to spontaneous activity data or from activities induced in response to external disturbances. In an example application, DMDT data from individual samples diagnosed with dementia and memory problems can be integrated by PREPARE, and features related to brain plasticity and elasticity can be extracted by DARWIN. Changes in the plasticity mechanisms of specific brain regions and low levels of brain elasticity may indicate that cognitive tests that capture these brain features may be valuable for the diagnosis and prognosis of individuals with dementia and memory problems. Therefore, the stimulus set can be combined in a specific order to perform stress tests on those brain regions with low plasticity and elasticity, thereby measuring the elasticity of the brain to disturbances.

[0218] In some embodiments, cognitive assessments, brain assessments, and psychological assessments are delivered via VR or AR platforms, including in the form of gamified applications. In some embodiments, cognitive, brain, and psychological assessments are performed while recording biometric data via devices including, but not limited to, PERCEPTRON; individual performance in assessment tasks may be associated with specific brain activity patterns to generate induced brain activity indicators of cognitive, brain, and psychological function and health.

[0219] In some embodiments, perturbation-based assessments are performed, where brain and physiological activity recorded in a bioassay is modulated by external stimuli, thereby generating perturbation-based biomarkers that index an individual's brain / cognitive / psychological ability to adapt to perturbations as a measure of plasticity.

[0220] In some embodiments, the physiological data collected in conjunction with the brain data may include, but is not limited to, EKG, GSR, eye movements obtained through eye tracking technology, and body temperature.

[0221] In some embodiments, assessment tasks are developed to index indices calculated by DARWIN, such as those described in the following sections, including but not limited to indices of brain potential, health, resilience, adaptability, evolvability, stability, and plasticity. SCREEN can be configured to generate assessment tasks for each indicator and index created as part of the BRAINPRINT and DARWIN modules and combined with the scheme included in STIMOLA.

[0222] In some embodiments, an assessment of brain properties is performed to derive recommendations for personalized beneficial activities and interventions for a given individual based on the individual's DMDT. Interventions and activities may include, but are not limited to, psychotherapy, cognitive training, brain stimulation programs, meditation, and physical exercise.

[0223] Brain likelihood estimation

[0224] Each brain is characterized by the various elements that constitute its structure and functional connection group, including the amount of gray matter in each brain region, the number and intensity of white matter fibers connecting different regions and networks, the vascular system for supplying nutrition to brain structure, and the functional organization (synchronous mode, oscillation dynamics, local coherence mode) of interaction between brain elements. This complexity spans the microscopic, mesoscopic and macroscopic scales of the human brain, and many of the dynamics also occur across scales. The connection group of each individual is the result of genetic and environmental factors at any scale, wherein the latter is related to the continuous practice and learning activities that gradually shape brain structure and function with early life experience and over time. Although functional connection and dynamics are generally considered to be the most general and flexible, such as being easier to change, structural connection is often more robust and less responsive to changes, thereby providing "trunk" for brain activity and computational process. A fine understanding of the structural and functional properties of the brain and its interaction (e.g., as provided by DMDT) may be important for understanding the possibility of change of a given brain. The systems and techniques described herein allow for computational estimation of the likelihood of change in a given brain based on a series of tests and simulated manipulations of a given structural and functional connectome, thereby enabling quantification of a range of indices with predictive power over both narrow (e.g., learning a new instrument, improving a behavior) and broader (e.g., successful aging, overcoming post-traumatic stress disorder, avoiding delirium episodes after elective surgery) ranges. The estimation process can include analysis of existing activity patterns and connections, and quantification of potential new activity patterns under the guidance of the structural and functional constraints of current DMDT.

[0225] Some of the systems and methods described herein relate to methods for adaptive system rewiring (ASR) for estimating multiple parameters and indices. The indices may include, but are not limited to, a brain evolvability index, a brain optimization index, a brain adaptability index, a brain stability index, a brain neuroinflammation index, or a brain associative plasticity index, each of which is described in further detail below.

[0226] Brain Evolvability Index

[0227] Given at least in part the number, location, and strength of brain connections and the magnitude and frequency of local activity in the brain, a set of constraints can be imposed on an evolutionary algorithm that can compute mutations (e.g., all possible mutations) of an original set of features until full development of the resulting network is achieved in which no additional manipulation is possible. The performance of a given brain can be summarized using several metrics, including but not limited to the number of modifications a brain can withstand, the number of configurations the brain can achieve, and the energy level required to achieve each configuration.

[0228] In some embodiments, the simulated evolution can be based on different targeting strategies, including but not limited to random targeting; topology-based targeting; modularity-based targeting, including separate simulations of manipulation of intra-module and inter-module connections; or hierarchical targeting.

[0229] In some embodiments, the simulated evolution is performed by a connection group rewiring process, in which existing connections, active connections or silent connections of a network composed of brain data are modified. Modifications may include, but are not limited to, changing the strength of a given connection, changing its input and / or output, reactivating it or redistributing it to different target brain structures. The fixed amount of energy available for rewiring can be set based on one or more parameters, including but not limited to the amount of circulating blood oxygen level dependent (BOLD) signal when considering functional MRI data, the total available metabolic energy estimated by techniques including PET or SPECT, vascular activity and perfusion levels. In view of the limited amount of energy used for rewiring, each connection group is exposed to a rewiring program in which each modification is assigned a cost. Therefore, each connection group (e.g., each individual brain of an individual or each brain state) can undergo a limited amount of rewiring steps, thereby determining the intrinsic possibility of a given brain rewiring itself, modifying and optimizing its connection group, and regulating its resulting behavior, cognition and performance. Based on energy constraints, the number of possible rewiring steps and the overall possibility of the connection group being rewired can constitute a "rewiring index."

[0230] In some embodiments, the simulated evolution can be based on a fixed amount of energy used to induce manipulation of the connectome, based on brain metabolic data and estimates of brain response to perturbations. In some embodiments, the simulated evolution can be based on increasing or decreasing a given connection between brain regions, increasing or decreasing brain activity in a given brain region, increasing global connectivity of a brain region or network, or increasing or decreasing synchronization between brain networks. The magnitude of the increase and decrease can be defined based on, but not limited to, brain perturbations and plasticity indices.

[0231] In some embodiments, the simulated evolution can be based on amplifying existing connectivity patterns and local brain activity, reducing noise, and improving intrinsic properties of a given brain (e.g., amplifying individual DMDTs). In some embodiments, the simulated evolution can be based on different levels of compensation for connectivity and activity based on past changes induced by DMDTs, including but not limited to redistribution of functional connectivity to adjacent regions, to positively connected regions, to regions belonging to the manipulated network, or to regions belonging to the manipulated module.

[0232] In some embodiments, the simulated evolution may be constrained by information about the structural wiring of the brain, represented by the density of white matter fibers, their anisotropy and mean diffusivity, and myelination levels. In some embodiments, the simulated evolution may be constrained by information about local metabolic brain activity and perfusion levels, measured by, but not limited to, PET imaging and arterial spin labeling (ASL) MRI.

[0233] Brain Optimization Index

[0234] At least in part in view of the number, location and strength of brain connections and the amplitude and frequency of local activity in the brain, a target configuration representing the optimal functional configuration of the human brain can be considered as a reference target towards which brain modifications are made. The optimal configuration may include features related to network function, such as but not limited to a high small-world state (representing an optimal ratio between separation and integration) or a high network modularity. Some features of network function can be selected based on their association with favorable traits, such as but not limited to high intelligence quotient (IQ), high memory performance, or high information processing rate. From a cognitive and computational perspective, the brain optimization index can at least partially provide an indicator of the distance between an individual's current state and the most desired state.

[0235] Brain Adaptability Index

[0236] Given the number, location and strength of brain connections and the amplitude and frequency of local activity in the brain, at least in part, a given brain is exposed to a series of state changes, and its ability to adapt to such changes can be quantified in a computational model that takes into account the amount of energy required to adapt to the state change and the duration of the induced adaptation. Brain adaptability can be used as a measure of neuroplasticity and network plasticity, including measures related to network memory and network generalization of previous state changes (e.g., the ability of the brain to predict a wide range of upcoming changes given the history of past changes, thereby obtaining generalization of knowledge and enhanced preparedness for future changes). State changes can be local or distributed. Brain adaptability can provide information on the trend of brain changes over time and the amplitude of such changes, thereby then constraining analysis related to state or trait changes.

[0237] Brain stability index

[0238] Given, at least in part, the number, location, and strength of brain connections and the amplitude and frequency of local activity in the brain, a given brain is exposed to a range of state changes, and its average distance from any possible state transition can be quantified. As observed in analyses of network criticality and avalanches, brain stability can be used as an indicator of the brain's current tendency to approach changes in its ongoing dynamics.

[0239] Cranial neuroinflammatory index

[0240] At least in part in view of the number, location and strength of brain connections and the amplitude and frequency of local activity in the brain, individuals are exposed to a series of stimuli in two brain regions at the receiving end of the white matter brain bundle. The delay between the stimulation of one end (e.g., the first brain region) and the generation of a brain response at the second end (e.g., the second brain region) can be calculated, and used as a substitute for the conduction delay between the two target brain regions; in view of the relationship between the conduction velocity and myelination of white matter fibers in the human brain, the conduction delay can be calculated repeatedly within a given time window, and the given time window includes seconds, minutes, hours, days, weeks, months and years. The longitudinal change of the delay can be used as an index of signal propagation changes in white matter fibers, which may indicate the presence of pathological conditions including but not limited to changes in myelination and the presence of lesions. In some embodiments, the level of brain inflammation is quantified by analyzing spontaneous and induced brain activity, which is recorded by non-invasive methods, including but not limited to EEG and NIRS. Activity in a specific frequency band (e.g., between 1Hz and 9Hz) can be measured and is related to activity in a high-frequency EEG band (e.g., between 19Hz and 85Hz). Activity may reflect both local and long-range distributed connectivity in the brain, capturing both processing speed and fiber conductivity. An index, the electrical-neuroinflammatory index (ENI 2 ) is calculated both: globally by looking at oscillatory activity across the brain / scalp, and locally by selecting specific electrodes located in regions at specific distances across the scalp; specifically, the ratio between electrodes located in frontal and parietal regions relative to electrodes located in occipital and temporal cortices is calculated as an index of cognitive versus sensory processing streams that capture different processing speeds. Visual and auditory stimulation can be delivered to amplify signals across regions; in some embodiments, low voltage alternating current stimulation is delivered to each electrode at a resonant frequency to elicit localized and distributed responses; the propagation of the signal is tracked across the selected electrodes (detected by electrical tomography); the amplitude and frequency of the early and late potentials generated by the perturbations are combined and input to the individual's ENI 2 Rating in progress. ENI 2 The scores can be used to confirm or inform cognitive and neuroimaging assessments to investigate the presence of neurodegeneration and neuroinflammation in an individual's brain and central nervous system.

[0241] Brain Associative Plasticity Index

[0242] At least in part in view of the number, location and strength of brain connections, as well as the amplitude and frequency of local activity in the brain, two brain regions can be stimulated by two quasi-simultaneous magnetic or electrical stimulations to induce paired associative plasticity. Repeated quasi-simultaneous stimulation generates long-term possibilities in each region and generates Hebbian plasticity processes across two brain regions. The rate at which the two regions show Hebbian plasticity depends on the state of the two regions and the integrity of the monosynaptic white matter fibers connecting the two regions. By measuring the associative rate during a fixed number of stimulations, a dose-response curve for any given pair of brain regions in any given brain of any individual can be obtained. Each curve represents the ability of the corresponding brain to increase inter-regional connectivity and generate plasticity, and may serve as an index of changes in brain plasticity in neurodegenerative conditions characterized by increased neuroinflammation or neurodegenerative processes, such as dementia, Alzheimer's disease or multiple sclerosis.

[0243] In some embodiments, after a detailed map of brain structure and function has been obtained (e.g., as represented by a DMDT), the likelihood of the brain changing (evolving) to other configurations can be estimated. For example, the estimate can be based on the number, location, strength, configuration of brain regions and connections, and the finite number of iterations ("changes") that the brain can withstand / support based on its past and current behavior / activity.

[0244] In some embodiments, OPTI-BRAIN may be based on concepts from evolutionary biology, evolutionary game theory, network control theory, network theory, agent-based modeling, artificial intelligence (AI), thermal diffusion models, and / or epidemiological transmission models.

[0245] In some embodiments, estimates of brain likelihood may be used to identify optimal enhancement strategies by calculating similarity indices to specific brain states / traits and identifying a hierarchy of brain modifications to apply to reach a target state / brain.

[0246] Changes in brain and cognitive states and traits

[0247] In some embodiments, starting from the same DMDT, DARWIN can be used to parameterize the transition trajectory from the current brain state or trait to the target brain state or trait by simulating multiple modifications of the structural and functional connectome (e.g., every possible modification), and derive a sequence of events represented by, for example, changes in connection strength, reduction in local activity in specific brain regions, etc. (e.g., an optimal sequence of events), thereby allowing the transition from the current state / trait to the desired state / trait using the least amount of energy and time.

[0248] Fig. 6A An example of individual differences in state or trait transitions is shown, where due to differences at the baseline, two individuals aiming to reach the same goal state follow two different trajectories that require different numbers of steps to evolve toward the desired state. Figure 6B Indicators of state transitions in a sample of 500 subjects are shown, highlighting individual differences in the likelihood of brain changes.

[0249] In this framework, state transition is used as a term that indicates more acute and transient changes in brain function, as well as changes in a given "brain state" (e.g., being in an optimal brain state for performing task X). When using techniques that are capable of inducing lasting changes and promoting brain plasticity (such as those of non-invasive brain stimulation methods), it is expected that the application of non-invasive brain stimulation (NIBS) techniques and their lasting brain changes, and their effects may result in trait transitions, such as more lasting changes in brain structure and / or function leading to changes in trait-level measures (e.g., intelligence, memory ability, creativity).

[0250] In some embodiments, the estimated brain likelihood can be used to guide the planning of interventions (e.g., using STIMOLA, SYNAPSE, NEUROCREATOR) aimed at promoting brain transformation in a particular direction (e.g., a target state or trait). Brain properties can be used to identify the most appropriate brain targets (e.g., regions, connections) for modulation to achieve a target state or target trait.

[0251] In some embodiments, a target state may be defined as a state associated with a particular cognitive activity (e.g., memory processing; meditative state). In some embodiments, a target state may represent a previous state of the same individual recorded in the past, including but not limited to a brain state of successful memory encoding or retrieval in a patient suffering from dementia. In some embodiments, a target state may represent a specific brain state associated with a high physical performance state.

[0252] In some embodiments, the target trait can be defined as a previous brain configuration of the same individual recorded in the past (e.g., a brain configuration before the onset of dementia), which can be used to customize brain modifications and restore the brain to a state before the onset of the disease. In some embodiments, the target trait can be represented by a template trait that represents a specific configuration associated with a cognitive trait (e.g., a high IQ brain), or a cognitive pattern associated with a specific skill set (e.g., a mechanical engineer's brain). In some embodiments, the target state or trait can be defined as a previous brain configuration of the same individual stored as an NFT.

[0253] Brain Optimization

[0254] Changes in state or traits can apply to modifications in brain activity that are not explicitly related to measurable cognitive or behavioral abilities, but rather to changes in brain physiology itself that have an impact on brain health or disease. In some embodiments, through careful brain mapping and network analysis, the resilience (e.g., the ability of a system to absorb external attacks or internal failures without loss of performance), adaptability (e.g., the ability of a system to quickly adapt to attacks and failures based on its wiring / connectivity profile), and flexibility (e.g., the ability of a system to change from one given state to another without changing its structure, which is critical to adaptability) of each brain can be quantified. DARWIN can be used to identify and plan targeted brain optimization (e.g., brain stimulation, cognitive programming, behavioral therapy) solutions to, for example, (i) improve resilience to brain changes that occur during physiological and pathological aging (e.g., brain atrophy, loss of key network nodes, decreased metabolism in areas associated with memory performance); (ii) improve resilience to external events, thereby reducing the likelihood of stress-related pathological responses (e.g., PTSD); (iii) optimize resilience and adaptability prior to general surgery to avoid the onset of post-surgical delirium; (iv) improve brain resilience under pathological conditions where the effects of external stimuli are not properly tolerated (e.g., psychiatric disorders and conditions associated with high anxiety and hypersensitivity); All of these approaches and others fall under the concept of "brain shielding," with other possible impacts extending to health and overall brain health.

[0255] In some embodiments, DMDT is used to identify the best target to improve the ability of the brain to respond to external disturbances, such as brain elasticity. The best brain target (e.g., area, connection) and the order of changes required to enhance elasticity can be identified as part of a brain shielding program (BSP). According to the principles defined in DARWIN, brain modifiers can then be used to modify brain configuration by steps involving at least partially modifying the activity of a specific area, the strength of a specific connection, and / or the organization of a brain functional network. In an example, the DMDT of an individual preparing for a stressful activity (e.g., a soldier preparing for deployment in a mission or a patient with PTSD who is about to relive a traumatic experience) can be used to define a set of target locations in the brain, and manipulating the target location may cause the brain to increase its elasticity against disturbances. A specific sequence of actions can be defined according to the DARWIN elastic optimization algorithm, including but not limited to delivering non-invasive brain stimulation to specific brain areas and brain connections in multiple physical therapies, and exposure to cognitive and sensory retraining programs, including VR-based applications. Subsequently, the individual may receive treatment, and multiple measurements of brain elasticity may be captured over time to evaluate the impact of BSP, including measurements using PERCEPTRON. The duration, frequency, and intensity of BSP can be defined according to the desired effect based on the expected duration of effect (acute - minutes, transient - hours, prolonged - weeks - months).

[0256] In some embodiments, BSP includes a scheme consisting of progressive, adaptive perturbations in the form of magnetic, electrical, and ultrasonic stimulations that expose a given brain. Perturbations may target nodes, regions, and networks of the brain previously identified by DMDT and DARWIN. Continuous perturbation stimulations of regulated intensity are applied to induce responses of brain networks and circuits in the desired direction (e.g., by conducting guided rewiring). The overall improvement of the brain's resilience to external perturbations can be established through long-term potentiation (LTP) plasticity processes and network remodeling. In some embodiments, perturbations are delivered using sensory stimulations to cause specific responses in sensory areas of the brain. Perturbations include visual, auditory, and tactile stimulations, wherein visual and auditory perturbations are delivered through various media, including VR and AR technologies.

[0257] In an example embodiment, DMDT is used to identify optimal targets to increase brain modularity by modifying the strength, direction, and / or temporal dynamics of specific connections between brain regions belonging to known brain networks and circuits. In an example embodiment, DMDT is used to identify optimal targets to increase brain flexibility by reducing the strength of connections between brain networks responsible for redirecting attention load during perception and networks responsible for cognitive processing including memory and inhibitory functions.

[0258] In an exemplary embodiment, the DMDT and DARWIN index are used to determine a personalized dietary policy for an individual based on his / her brain activity profile and concepts related to the gut-brain axis, including but not limited to his / her brain elasticity level, modularity, flexibility, and neuroinflammation level. A dietary policy can be determined by selecting nutrients and specialized supplements associated with effects on brain activity, including but not limited to vitamins, probiotics, minerals, and proteins. Personalized dietary interventions can be defined by looking at the effects of specific nutrients on the gut microbiome and on the brain, including but not limited to anti-inflammatory effects, blood flow and perfusion effects, effects on intracranial pressure, and effects on brain oscillation patterns. In some embodiments, a specific dietary policy is selected for its anti-aging properties, including but not limited to vitamins B1, B6, B12, and folic acid (B9); potassium; calcium that affects brain plasticity; magnesium; and beta-carotene that interacts with the ApoE status of healthy individuals and patients with Alzheimer's disease.

[0259] In certain embodiments, DMDT and DARWIN are used to personalize dietary information for individuals, thereby analyzing the response of the brain to different nutrients and estimating the optimal nutrient set, to cause desired brain changes, including state-to-state transitions and trait-to-trait transitions. In an example application, before, during and after taking in specific foods and nutrients, individual brain activity is recorded by PERCEPTRON, and his / her brain activity is processed by PREPARE. Individuals can also be exposed to systematic disturbances of brain, cognitive and sensory activities by SCREEN, including but not limited to brain electrical stimulation, visual stimulation and / or auditory stimulation. The response of the brain to disturbances can be quantified, and used to track individual brain and cognitive states in response to different nutrients and food products over time.

[0260] In some embodiments, the resulting brain and cognitive profiles obtained in response to different nutrients and food products can be used to (i) identify a dietary plan that can optimize brain and cognitive performance by increasing the brain health and cognitive health indexes identified in IMPROVE, OPTI-BRAIN, and OPTI-COG. Over time, longitudinal assessments can be performed on the brain and cognitive profiles to further optimize personalized dietary groups. Example applications include, but are not limited to, optimizing dietary guidelines to achieve (i) enhanced brain capacity and cognitive performance in healthy individuals, (ii) accelerated recovery after injury, (iii) accelerated post-surgical recovery by reducing neuroinflammation, (iv) improved concentration in special operators, and (v) improved muscle growth and improved physical performance in athletes by looking at dietary guidelines that can improve synchronization between brain activity and muscle response.

[0261] In some embodiments, the resulting brain and cognitive profiles obtained in response to different nutrients and food products can be used to (i) identify existing nutrients and combinations of nutrients, or to (ii) design new nutrients and combinations of nutrients that can optimize brain and cognitive performance by increasing the brain health and cognitive health indices identified in IMPROVE, OPTI-BRAIN, and OPTI-COG. Example applications include, but are not limited to, designing nutrients and combinations of nutrients to achieve (i) enhancing brain capacity and cognitive performance in healthy individuals, (ii) accelerating recovery after injury, (iii) accelerating recovery after surgery by reducing neuroinflammation, (iv) improving concentration in special operators, and (v) improving muscle growth and improving physical performance in athletes by looking at dietary guidelines that can improve synchronization between brain activity and muscle response.

[0262] Example use case based on experimental data

[0263] Some embodiments of the systems and methods described herein are further illustrated by the following examples and detailed schemes. However, the examples are intended only to illustrate the embodiments and should not be interpreted as limiting the scope of the technology described herein.

[0264] Brain optimization: improving brain resilience to disturbance / stress

[0265] Resilience is the ability of a complex system to maintain its functional properties and performance in the face of external disturbances (such as environmental factors such as disease, stress and aging). In the case of brain and pathology, resilience can be considered as the individual ability of the brain to withstand high-intensity damage and / or long-term damage before showing obvious symptoms. The ability to recruit other areas to compensate and / or postpone damage and consequences is one of the potential factors that can improve brain resilience. The intrinsic properties of brain structural wiring and its functional organization may be related to different levels of resilience. The possibility of mapping the resilience of individual brains, identifying candidate target areas / networks / systems for their enhancement, and artificially manipulating the level of resilience through external adjustment tools represents a method that has an impact on both brain health and brain pathology. Such methods can also allow the development of new cognitive enhancement applications, clinical treatment tools or solutions to enhance brain health and overall health improvement technology. The data reported below show examples of the effects of targeted personalized non-invasive brain stimulation on the brain resilience of healthy individuals, thereby proving that the application of BRAINPRINT, DARWIN and STIMOLA (within the framework of adaptive system rewiring) can change the ability of the brain to respond to external disturbances.

[0266] DMDTs were created for healthy control samples. DARWIN was used to estimate the optimal regulatory target for enhancing brain elasticity based on simulated lesions for each possible connection and node of each individual's brain network and subsequent estimates of the changes induced in brain performance under pressure. Once the optimal target brain area was identified, a transcranial magnetic stimulation (TMS) was performed before, during and after each TMS pulse while EEG was recorded. EEG data was analyzed to examine the indexed elasticity of brain efficiency and other measures to external perturbations. These measures included the maximum connected component (LCC) corresponding to the largest set of nodes, the pairs of nodes being connected by at least one edge as the primary result. The corresponding decrease in LCC was recorded as a measure of the induced damage. In each lesion iteration, the degree of the node was recalculated, and the order of removal was adjusted based on the effect of the previous removal. The metrics also include: decay velocity, which represents the individual velocity of lesions in the brain connectivity matrix, calculated as the decay slope of the LCC after targeted edge removal; early edge droop, which results from the total amount of connections that need to be lost before the LCC is destroyed; late edge droop, which corresponds to the total amount of connection loss required to completely destroy the LCC; and the collapse point or point of maximum deflection in the lesion curve of the LCC, based on the targeted edge removal.

[0267] Figure 7 It is shown that perturbation of the brain by STIMOLA induces a significant increase in elasticity measures across the entire brain (Note: *p<0.05). After targeted TMS, the entire brain network is more tightly connected and more elastic, gradually returning to its original state after stimulation. This result is explained by two: graphical metrics, in which the characteristic path length of the network is reduced after the TMS pulse; and elasticity indicators, in which the number of links required to reduce network efficiency (late edge decline) after TMS is significantly increased. As shown in this case, higher elasticity can be explained by greater network stability, such as the maximum deflection point and the complete network depletion point both occur at more advanced lesion stages. Repeated application of elasticity-enhancing perturbations leads to a sustained state of elevated elasticity that lasts longer than the perturbation period, thereby helping to improve elasticity in non-experimental environments (e.g., daily life, stressful work environments).

[0268] In some embodiments, resilience enhancement and brain shielding may be performed using techniques other than TMS, including but not limited to transcranial electrical stimulation. In some embodiments, resilience enhancement and brain shielding may be performed by delivering cognitive perturbations in the form of visual, auditory, tactile, and cognitive stimulation in a temporal sequence to activate and strengthen specific brain activity patterns associated with brain resilience.

[0269] In some embodiments, an analysis of brain elasticity and brain health is performed before a surgical operation, including but not limited to brain surgery, cardiac surgery, and orthopedic surgery. Brain data can be collected by PERCEPTRON to establish an individual DMDT through PRINT. The data in the DMDT include but are not limited to brain data collected by PERCEPTRON and magnetic resonance imaging (MRI); cognitive data; medical history; perturbation-based biomarkers collected by PERCEPTRON when an individual receives sensory, electrical, and magnetic stimulation. The DMDT can be used to define an optimal brain state that can reduce post-surgical complications (e.g., delirium, cognitive deficits, neuroinflammation) and / or accelerate recovery through OPTI-BRAIN. Recovery can include but is not limited to physical rehabilitation, including assisted robotic rehabilitation, physical therapy, cognitive training, and cognitive rehabilitation. In an example application, a brain state analysis can be performed to quantify brain elasticity and optimal brain state for general anesthesia for hip replacement surgery; brain data can be collected by PERCEPTRON, cognitive data can be collected by SCREEN, data can be processed by PREPARE, and the patient's DMDT can be created. DARWIN can then be used to create a differential matrix between the patient's current pre-surgical brain and cognitive state and the optimal state that maximizes recovery; in one example application, the system estimates that high brain modularity and high separation of motor networks, including lower limb representation, are preferred, thereby avoiding overconnectivity that could cause motor brain regions to participate in non-motor functions during recovery.

[0270] In some embodiments, assessments are repeated over time following surgery to create a longitudinal prediction of recovery that can be used to adjust training and rehabilitation regimens. OPTI-BRAIN and OPTI-COG can be used to maximize cognitive and motor recovery over time, with applications including, but not limited to, stroke recovery, motor rehabilitation of athletes following orthopedic surgery, and patients with brain tumors.

[0271] Optimization of brain function and brain health

[0272] In this example, data showing the effects of targeted personalized non-invasive brain stimulation on brain functional networks associated with high cognitive performance are provided. The data refer to a scheme similar to one described for enhancing the resilience of the brain to perturbations, in which external perturbations are delivered to the brain in the form of electromagnetic pulses, and brain data are collected before and after the perturbations. Analysis of functional network properties related to, but not limited to, information processing, integration and separation of brain networks, synchronization (connectivity) within and between functional networks is reported, wherein brain health is significantly improved after targeted personalized perturbations.

[0273] Analysis of brain efficiency and network dynamics included the properties path length (the average distance between a system's nodes and all other nodes), which can represent how easily information can move through the network; node degree (the total number of edges connected to a given node); global and local efficiency; diffusion efficiency; clustering coefficient (as the fraction of nodes that are neighbors to surrounding nodes, forming triangular triplets); and small-worldness, or the property that a system has a concomitant high clustering coefficient and low path length. Changes in the number of modules and the corresponding modularity index, which represents the degree to which each structural connectivity matrix is ​​arranged in submodules as calculated using the Louvain algorithm, were also examined.

[0274] Figure 8 Three different graphical measures are shown that represent the integration and separation of information processing (e.g., characteristic path length, modularity, global and local efficiency). Brain networks appear to be more tightly connected shortly after brain perturbations and then return to baseline after stimulation. The reduction in characteristic path length represents the ease with which information can move within the network after each perturbation. This result is also confirmed by the modification of the topology of the brain network, as captured by node degree, clustering coefficient, modularity score, and number of modules. Note: *p<0.05.

[0275] Personalization of neuromodulation targets for optimal brain control

[0276] In some embodiments, DARWIN can be used to identify the best brain structures, regions and networks that exert control on brain activity, and thus may be important for inducing changes in brain states. Identifying such structural and functional networks can allow for selective perturbations intended to improve brain control levels, thereby inducing, for example, specific changes in brain states or an overall reduction in brain controllability (e.g., resulting in enhanced shielding for perturbations). Based on both the structural and functional connectomes of the human brain, network controllability analysis is performed by representing the human brain as a network. The resulting connectome matrix is ​​used to define priority connection pathways that support hierarchical organization of information processing and brain activity.

[0277] DARWIN can be used to perform brain control analysis (BCA). BCA allows the ability of the system to drive its output towards the desired result by applying appropriate input signals to the selected nodes. The temporal evolution of the basic nervous system represents the state of N nodes (neurons or brain regions) at a given time, describes the nonlinear dynamics of each node / region, and takes into account the influence of external stimuli (e.g., in the form of non-invasive brain stimulation) applied to the input nodes. The goal of BCA is to identify M nodes (driver nodes) that can guide the system towards the desired final state. When applied to the brain by BCA, identifying the driver nodes can provide information for the selection of which brain regions should be stimulated by NIBS to ensure that the brain can be affected to the desired direction / state.

[0278] Within the DARWIN framework, BCA is applied to functional EEG data of healthy individuals to identify optimal network nodes capable of inducing a transition from a spontaneous mind wandering state to a state of enhanced cognitive control. The analysis is performed by identifying personalized driver nodes for each individual, thereby minimizing the control energy required to achieve the desired brain state. The research involves the optimization of non-invasive brain stimulation-based interventions, where a single node / region / network is targeted at any given time, or alternatively, multiple nodes / regions / networks are targeted.

[0279] BCA was able to identify specific driver nodes for each individual, with a variability of about 78% across individuals, and only about 1 / 4 of the samples received simulated stimulation on the same node / area / network. Comparison of brain controllability results in response to non-invasive brain stimulation on personalized targets relative to standard targets (i.e., the same for each individual) showed that brain controllability was improved by 81% when personalized targets were used relative to standard targets. The most representative brain targets for successful transitions from mind wandering states to enhanced cognitive control states were identified as the anterior cingulate cortex, left dorsolateral prefrontal cortex, inferior frontal gyrus, and superior parietal lobe. Given the location and morphology of brain targets in the brain, biophysical modeling performed in brain scans of participants was used to identify brain targets with the highest likelihood of effective targeting. The results showed that 79% of participants used the left dorsolateral prefrontal cortex and superior parietal lobe as the primary targets for achieving brain state control, while ensuring that the induced electric field (i.e., stimulation) required for inducing brain activity was at least 0.25V / m.

[0280] Additional analyses were performed to investigate the potential for transitions to other brain states, including transitions to a memory retrieval state associated with patients with Alzheimer's disease, and to a state of high creativity and mood control associated with patients with depression. BCA was able to identify optimal brain targets for each condition, with 88%, 68%, and 79% successful state transition rates achieved, respectively.

[0281] In this example, it has been demonstrated that BCA can be used to identify brain targets that, when stimulated, can induce controlled changes in human brain state. Data processed with PREPARE and analyzed by DARWIN provide an opportunity to identify personalized brain stimulation targets suitable for state control for both cognitive enhancement and therapeutic applications.

[0282] Connectome-based analysis of tumor migration pathways and prediction of clinical outcomes

[0283] Malignant brain tumors, despite accounting for only 2% of all cancers, rank fourth in years of life lost among all cancers due to limited treatment options, onset at a relatively young age, delayed diagnosis, and infiltration into the brain parenchyma. Specifically, glioblastoma (GBM) is the most common and aggressive high-grade glioma (HGG, WHO glioma IV), with a median survival of approximately 16-18 months from diagnosis.

[0284] The recently discovered neuron to glioma synapse generation provides possibility for the new strategy of intervening this novel pathological physiological behavior.The communication of neuron to glioma is not one-sided, and even if glioma cells cannot form spikes, it has been found to promote neuronal discharge, and its purpose is to generate positive feedback with neurons for further activation by multiple mechanisms, such as synapse generation factor secretion, non-synaptic glutamate release and by reducing the activity of inhibitory interneurons in the surrounding microenvironment.Animal experiments have been further confirmed and expanded these data by showing that the tissue infiltrated by patients with HGG (70Hz-110Hz) activity (a kind of index of neuronal activation).These changes extend to both mesoscopic and macroscopic brain levels, thereby causing the change of brain function network activity.

[0285] The systems and methods described herein using the DMDT and DARWIN submodules include algorithms to localize and map brain tumors based on brain scan and electrophysiological data, characterize their behavior and relationship to clinical symptoms, predict their migration trajectory in the brain, and can be used to inform optimal targets for therapeutic neuromodulation interventions. When targets are identified by DARWIN, brain stimulation can be performed, such as according to STIMOLA.

[0286] In some embodiments, NiBS can be used to regulate the positive feedback mechanism between the increase in neuronal excitation triggered by glioma and its effect on mitosis and migration. Controlling the neuronal excitability of patients with HGG may inhibit the growth and proliferation of tumors, thereby ultimately extending the survival of patients. In some embodiments, NiBS technology (such as TMS and tES) can be used for its ability to induce LTD-like changes in synaptic excitability related to the context of neurons to gliomas, wherein the reduction in the possibility of neuronal discharge after presynaptic events will reduce the activation of Ca2+AMPA-R in postsynaptic glioma cells, resulting in a limited influx of Ca2+ signals that promote mitosis, and thereby may limit the contribution of neurons to glioma growth. In some embodiments, NiBS can be used to regulate / inhibit synaptic signaling of neurons around HGG tumors, such as slowing down their mitosis and migration rates. In some embodiments, NiBS can be applied to regulate tumor perfusion and blood-brain barrier permeability, such as to improve the efficacy of chemotherapy. In some embodiments, inhibitory tDCS can be used to reduce the likelihood of neuronal spikes in targeted cortical areas through an LTD-like effect, thereby, for example, disrupting diffuse neuronal communication to gliomas.

[0287] In some embodiments, STIMOLA can be used to temporarily increase the permeability of the blood-brain barrier to small and large molecules, for example, thereby enhancing the delivery of drugs across the barrier.

[0288] In some embodiments, STIMOLA can be used to control the migration and spread of tumor cells using the principle of galvanotaxis. When exposed to an electric field, cells can be directed and guided in their migration (galvanotaxis). In some embodiments, the cathode field generated by tDCS in the area surrounding a brain tumor can be used to limit the migration of cancer cells located in the cathode field (e.g., in the immediate vicinity of the tumor boundary), thereby, for example, interfering with migration and infiltration across the brain. In some embodiments, STIMOLA can be used to inhibit the tumor-promoting activity of neurons through an inhibitory NiBS protocol, including but not limited to cathode tDCS, closed-loop inverted tACS, and / or continuous theta burst stimulation.

[0289] In some embodiments, BRAINPRINT, DARWIN, and STIMOLA can be used to identify optimal stimulation targets for inhibiting tumor activity based on the location of the tumor and its brain connectivity profile relative to known brain functional networks. The possibility of interacting with entire networks rather than individual brain regions may help inhibit neural activity that regulates cancer growth and tumor spread. Inhibition of entire networks may be more effective in slowing neuron-related cancer growth, where tools derived from network control theory may represent a valuable approach for selecting the most relevant stimulation targets.

[0290] In some embodiments, BRAINPRINT and DARWIN can be used to identify the best stimulation targets for inhibiting tumor activity. Based on the functional synchronization / connectivity profiles of white matter tract connections and tumors, information from the structural and functional connectome data of patients can be used to map potential migration pathways for solid tumors. Tools may include, but are not limited to, network analysis, network control theory, graph theory, and evolutionary biology. Analysis of longitudinal changes in brain network dynamics (including changes in network topology) collected by neuroimaging and electrophysiological techniques can be used to provide information for predictive models that identify brain regions most likely to be affected by tumor migration.

[0291] In some embodiments, BRAINPRINT and DARWIN can be used to identify connectivity patterns between tumor masses and other parts of the brain, thereby having predictive power for clinical status and disease trajectory, including but not limited to overall survival of patients. The resulting connectivity patterns can be used as targets for NIBS interventions aimed at closing the connections, thereby slowing tumor aggressiveness and improving survival.

[0292] In some embodiments, BRAINPRINT, DARWIN, and STIMOLA can be used to identify brain networks associated with tumor symptoms, including but not limited to cognitive deficits. STIMOLA can be used to modulate network activity to slow cognitive impairment or enhance recovery.

[0293] In some embodiments, NiBS can be used to restore the excitation / inhibition (E / I) balance of brain networks. The cortical E / I ratio is often altered in patients with gliomas and in other neurological and psychiatric conditions that are usually associated with cognitive deficits and symptoms. In patients with gliomas, E / I imbalance involves the emergence of epileptiform activity and subsequent neuronal death, which is parallel to tumor progression.

[0294] In some embodiments, STIMOLA can be used to enhance the effects of drug therapies and chemotherapies, including the use of magnetic or electrical stimulation to modulate the permeability of the blood-brain barrier (BBB) ​​and improve the transfer and absorption of molecules (e.g., chemotherapeutic agents). STIMOLA can be used to modulate local or distributed brain perfusion by modulating neurovascular coupling in the tumor or surrounding environment to enhance the effects of chemotherapeutic agents.

[0295] In some embodiments, DMDT and DARWIN can be used to extract indices of tumor functional behavior by analyzing dynamic connectivity patterns in tumor masses and surrounding brain tissue (including but not limited to white matter and gray matter). Dynamic connectivity indices can include measures of the similarity of tumor activity to activity in other brain regions, including brain regions near the tumor and brain regions located in other parts of the brain. The similarity of spontaneous and induced activity is an index of tumor aggressiveness, and in some embodiments, is calculated as the proportion of data records showing that tumor activity is synchronized with healthy brain structure activity over time. Segmentation of brain regions in networks and modules can also be performed, allowing estimates including but not limited to the following indicators: (i) marking tumor activity as belonging to a specific network or module over time, (ii) estimating the number of times a tumor mass switches its activity, thereby achieving increased similarity with different networks or modules, (iii) estimating the stability of tumor activity over time, and (iv) estimating the likelihood that a tumor mass will evolve its behavior over time as its complexity characteristics change, thereby providing an index of tumor aggressiveness. Additional indicators can be extracted from tumor data, including but not limited to data described herein as part of the DMDT and DARWIN modules.

[0296] In some embodiments, data from the DMDT is used to identify potential tumor recurrence areas by analyzing the functional connectivity patterns within the infiltrated tissue (e.g., edema) surrounding the tumor lesion. Functional connectivity indices representing the strength of correlation between each voxel of the edema mask and other parts of the brain can be calculated using functional MRI BOLD data, and the resulting quantitative map captures voxels and voxel clusters, thereby showing positive or negative connections with other parts of the brain; the algorithm compares the connectivity pattern of each voxel with the connectivity pattern of the voxels belonging to the solid tumor mask (containing only the tumor mass), and a similarity score can be obtained. Voxels and voxel clusters with high similarity can be marked as potential locations for tumor recurrence, due to their similar connectivity profiles reflecting tumor infiltration.

[0297] In some embodiments, the same methods described in the previous embodiments are used for diffusion imaging data instead of functional MRI BOLD data. A map of diffusion is created, resulting in a voxel-by-voxel representation of indicators, including but not limited to fractional anisotropy, mean diffusivity, free water, and number of streamlines. The algorithm can compare the diffusion pattern of each voxel in the edema mask with the diffusion pattern of the voxels belonging to the solid tumor mask (containing only the tumor mass), and a similarity score can be obtained. Voxels and voxel clusters with high similarity can be marked as potential locations for tumor recurrence, due to their similar diffusion profiles reflecting tumor infiltration.

[0298] In some embodiments, a video game application is created to guide the mental thinking and reasoning patterns of an individual so as to selectively engage specific brain regions while avoiding activation of other brain regions. The selection of regions can be based on DMDT and DARWIN data extracted from individuals with brain tumors, including but not limited to gliomas, glioblastomas, astrocytomas, oligodendrogliomas, and melanoma brain metastases, because they involve tumor networks. A tumor network can be defined as a group of brain regions located in one or both brain hemispheres, which are connected to the tumor by structural connections (including white matter fiber bundles) or functional connections (including functional connections measured by functional MRI imaging). Video games can be configured to induce selective activation of regions that are not connected to the tumor, thereby preventing the tumor from receiving additional stimulation / activation, and inducing tumor proliferation and migration. Brain tumor cells have been shown to migrate along white matter tracts and establish connections with regions that show high functional connectivity with the tumor; activation of brain regions around the tumor increases blood flow and metabolism in the region, thereby also feeding tumor cells; avoiding direct activation of the tumor and activating regions connected to the tumor are the goals of video game applications to prevent tumor growth and spread.

[0299] In some embodiments, DMDT and DARWIN data for patients with brain tumors may be analyzed to create a map of regions to be avoided and one or more regions (eg, target regions) whose activation will not affect the tumor. Figures 9A-9C The application of cognitive engagement according to some embodiments of the present disclosure is schematically described. Cognitive functions corresponding to target areas can be identified (e.g., by IMPROVE), and specialized cognitive tasks can be created to selectively activate these functions. DMDT and DARWIN can also be based on proximity to the tumor ( Fig.9A ) or due to its connection to the tumor ( Fig. 9B ) to identify brain regions (and corresponding cognitive activities) to avoid during the day. Tasks supported by the target regions can then be embedded into the game to improve playability and ensure adherence to treatment. Individuals with brain tumors may be asked to play the game every day for a predefined duration at a specific time to maximize activation of the target region and minimize possible tumor activation throughout the day.

[0300] In some embodiments, DMDT and DARWIN are used to map the connectivity profile of a brain tumor and identify regions connected to the tumor mass, such as Figures 9A-9C Depending on the location and connectivity profile of the tumor, the application may recommend avoiding one or more brain regions because of their functional connectivity profile ( Fig.9A Area "A" in the Fig. 9B and can identify clusters of regions to engage in order to minimize brain activity not associated with tumor activity ( Fig. 9Carea "D").

[0301] Monitoring brain tumors by analyzing spontaneous and evoked brain activity

[0302] Brain tumors (including but not limited to gliomas, glioblastomas, astrocytomas, and oligodendrogliomas) induce significant changes in brain structure and function due to mechanical stress, metabolic effects on surrounding healthy brain tissue, and / or changes in brain synchronization patterns that are important for cognitive function. Brain tumors are one of the most aggressive tumors, and treatment options are limited. In addition, diagnostic options are often even more limited, with physicians only able to diagnose brain tumors when patients request a consultation after symptom onset. Importantly, even after successful treatment of brain tumors by surgery, chemotherapy, and radiotherapy, brain tumors tend to reappear (tumor recurrence), with significantly lower survival rates after the first and second tumor recurrences. Being able to detect new brain tumors and post-treatment recurrences may be important for successful treatment, prolonged survival, and improved quality of life; however, solutions for detecting and monitoring brain tumor activity are often limited to expensive hospital-based examinations, including but not limited to magnetic resonance imaging (MRI) and positron emission tomography (PET), which are only requested at fixed time points after symptom onset or after surgery. Some embodiments of the present disclosure relate to a platform for remote, self-managed brain activity monitoring that is capable of capturing changes in brain activity over time and identifying patterns of abnormal brain activity that index tumor activity.

[0303] According to some embodiments, a device configured to non-invasively measure brain activity (e.g., [PERCEPTRON]) and a digital platform configured to deliver cognitive tasks that activate specific brain regions may be included in a system for detecting brain tumors. Fig.10 An example of such a system is schematically shown in . An individual's response to each cognitive task generates a map of spontaneous and evoked brain activity that is recorded over time and provides information for the individual DMDT. Some embodiments relate to a specific method that combines information from each brain response both cross-sectionally and longitudinally to generate a score of brain activity that is then used to assign the likelihood of tumor presence, recurrence, and migration.

[0304] In some embodiments, brain tumor activity is quantified by cognitive tasks performed on portable devices, including but not limited to personal computers, tablet computers, and mobile phones. Cognitive tasks can induce specific brain activity patterns that are recorded by portable devices to capture brain activity (e.g., [PERCEPTRON]), thereby generating specific response curves based on the type of stimulation presented to the individual performing the task. Tasks may include but are not limited to simple stimulus presentations, including but not limited to visual and auditory stimulation presented in a rhythmic manner on a screen and / or through speakers or headphones. Tasks may include but are not limited to specific categories of visual and auditory stimulation, including but not limited to objects, people, animals, and their subcategories (including but not limited to living objects and non-living objects). Specific stimuli can be selected to induce specific brain activity patterns detected in the form of event-related potentials (ERPs) using methods including but not limited to scalp electroencephalography (EEG) (e.g., [PERCEPTRON]). Stimuli can be selected to induce activity in specific brain regions, including all lobes and both hemispheres.

[0305] In some embodiments, EEG signals are analyzed by analyzing specific brain waves generated in specific time windows before, during, and after presentation of a stimulus. Brain wave characteristics (including but not limited to amplitude, latency, frequency, and coherence across EEG channels) can be quantified and used to create a user-specific response fingerprint (e.g., [DMDT]).

[0306] In some embodiments, the location of a particular response, its characteristics, and / or its changes over time can be used to generate a map of brain activity that reflects the effect of the presence of a brain tumor on brain activity. Longitudinal monitoring of changes in brain activity can be used to determine deviation patterns of brain activity associated with tumor recurrence, progression, or migration. The information can be used for early detection of tumor recurrence and progression, and may trigger more in-depth studies by tumor specialists including neuro-oncologists, neurologists, neurosurgeons, or neuroradiologists. Information about changes can be used to map tumor migration based on changes in brain activity patterns, thereby identifying brain regions where tumors migrate.

[0307] In some embodiments, metrics extracted from evoked and spontaneous brain activity may include, but are not limited to: the amplitude of evoked spikes and peaks in brain signals; the latency of spikes and peaks in brain signals; the frequency of oscillatory activity; the level of synchronization between activity recorded at multiple brain locations (functional connectivity); the influence of evoked activity in one brain region on evoked activity in another brain region (causal connectivity); the algorithmic complexity of the signals; and / or the delay between the start of stimulation and the time when brain activity returns to pre-stimulation activity levels (brain responsiveness).

[0308] In some embodiments, metrics extracted from evoked and spontaneous brain activity may include metrics calculated by the DARWIN module (including, at least in part, brain plasticity, resilience, efficiency, and / or evolvability), as well as other perturbation-based metrics in response to TMS and TCS described herein.

[0309] In some embodiments, assessments may be performed on varying time scales, ranging from once a day to once a year, may be performed based on an established schedule, or may be performed in response to the onset of symptoms that may be associated with tumor onset, recurrence, metastasis, including but not limited to cognitive deficits and health changes.

[0310] In some embodiments, data collected locally during task performance and brain recording (e.g., using PERCEPTRON) can be uploaded to a cloud-based analysis platform (e.g., DATANET), where spontaneous and induced brain activity can be processed and analyzed. In some embodiments, the assessment results can be summarized in a report, reporting the activity levels on multiple brain regions and networks and the likelihood of tumor onset, recurrence, and migration. In some embodiments, the report can be used to determine the goals of therapeutic or preventive interventions intended to interrupt tumor migration, slow tumor growth, or resolve cognitive symptoms.

[0311] Fig.10 A brain tumor monitoring platform 1000 is shown according to some embodiments of the present disclosure. Brain activity can be recorded before, during, and after stimulation is presented by a portable stimulation presentation device 1002. Stimuli can be presented so as to induce specific patterns of brain activity in different brain regions, networks, and systems, thereby inducing different activity patterns (e.g., stimulation A induces strong activity in a specific brain region, while stimulation B induces low brain activity in a different region). Activity can be monitored over time and evaluated over the course of a day, weeks, months, and years. The stimulation-related data and brain recordings can be processed in a cloud-based platform 1004 (e.g., DATANET), and reports of tumor-related brain activity can be generated.

[0312] DMDT and DARWIN for consciousness sampling and classification

[0313] Assessments of brain, cognitive, and psychological states, including performance-related brain states (flow states) and pathological mental states, are often based on outdated standards and tools, including questionnaires and self-report measures. The implementation of wearable devices has opened up new applications for state detection, but direct access to brain data is required to predict brain state changes and identify state markers. In addition, methods and algorithms for state analysis can also be used to evaluate the impact of interventions for modifying brain states. In this example, based on EEG data and other non-invasive electrophysiological recordings (including but not limited to galvanic skin response and cardiac output), methods from DMDT, DARWIN, and SCREEN are used for consciousness sampling and classification (CSC), thereby creating a consciousness assessment tool.

[0314] method

[0315] Electroencephalography (EEG) and galvanic skin response (GSR) data were collected from a sample of healthy participants during a variety of brain states induced by external instructions provided prior to data collection. Participants were familiar with the nature of each state and how to induce the state. Snapshots of brain and biosensing data were labeled according to each brain state, where brain states at least in part include states related to: thinking about the future, thinking about the past, recalling traumatic memories, recalling pleasant memories, thinking about current experiences, paying attention to individual body sensations, performing memory exercises, performing language exercises, thinking about preferred physical or mental activities, thinking about family and friends, thinking about previously assigned problems, thinking about art in the form of painting or sculpture, thinking about oneself, or solving creativity puzzles.

[0316] Dynamic decomposition

[0317] Data analysis was performed following the procedures and methods described as part of the PREPARE module. Fingerprints of dynamic brain states were created by sampling data segments corresponding to each specific state. Spatiotemporal fingerprints of each state were then searched for in the resting-state, uncued, mind wandering data recorded at the start of therapy. A machine learning algorithm was trained to identify each state in the spontaneous EEG and biosensory data, enabling quantification of the time spent in each state during spontaneous mind wandering. The same fingerprinting / labeling procedure was applied to data collected following a VR-based trauma deconciliation intervention designed to alleviate symptoms associated with past trauma in the context of PTSD pathology. Participants were exposed to multiple trauma deconciliation sessions, with EEG and biosensory data collected before and after a series of sessions.

[0318] Figures 11A-11B An example of dynamic decomposition for psycho-cognitive assessment is shown in accordance with some embodiments. Fig.11AData from the DMDT are shown, including brain data collected during various externally induced brain states, as well as data collected during spontaneous mental states. Previously identified brain states are mapped onto spontaneous mental state segments, thereby enabling quantification of states and associated representations during unprompted mind wandering. States are identified through data processing performed by PREPARE, thereby involving the extraction of data features related to multiple dimensions, including, at least in part, time and frequency, network metrics, and complexity. Fig. 11B showed that exposure to an intervention aimed at suppressing trauma-related memories and negative thoughts resulted in a structural remodeling of spontaneous mental states, with changes in the targeted brain state.

[0319] result

[0320] like Fig.11A As shown, characterization of brain states and their labels was achieved by machine learning with 83% accuracy, with pre-labeled states accounting for approximately 78% of the available spontaneous EEG data in participants. On average, the decomposer failed to classify 22% of the data, with a range of 11%-39%. The hierarchical estimates associated with each state were also correlated with self-reported measures of each state and an estimated % of its presence during spontaneous mental states.

[0321] like Fig. 11B As shown, overall, subjects exposed to flow training had an enhanced sense of control and awareness over their stream of consciousness, regardless of the type of training. Participants exposed to the VR intervention showed structural changes in spontaneous mental states, with a decrease in trauma-related brain states and states associated with negative thoughts and negative emotions. Changes in the ratio between the index of brain states thinking about the future and the index of brain states about the past were also observed.

[0322] The results of this example demonstrate that dynamic brain state decomposition by consciousness sampling and classification (CSC) is an effective method for passive data-driven internal mental state assessment, which can be used at least in part to identify the severity of mental illness beyond conventional verbal / interview / self-report methods. The method can be used at least in part to track longitudinal changes in brain state composition and related psychological symptoms and dimensions.

[0323] In some embodiments, CSC is performed in combination with perturbation-based biomarkers to assess the brain state of patients with disorders of consciousness, including but not limited to vegetative state, coma, newly emerged vegetative state, locked-in syndrome. Perturbations can be applied to the body and / or brain by presenting external stimuli, including but not limited to transcranial electrical stimulation, transcranial magnetic stimulation, electrical peripheral stimulation, sensory stimulation including auditory, visual and tactile stimulation (e.g., vibration, pulsating mechanical pressure). A device for recording brain data (e.g., [PERCEPTRON]) can be used to sample brain activity in response to external perturbations; brain data can be analyzed by PREPARE and then combined with time-locked information associated with external stimuli to produce brain activity patterns associated with stimulus presentation. The system can generate brain activity reports for different types of stimuli, which can be repeated over time to create a longitudinal curve for identifying changes in brain activity that represent changes in consciousness levels. In an example application, auditory and vibrotactile stimulation can be presented to different body parts (including upper and lower limbs) of a patient in a vegetative state, and brain activity can be recorded by PERCEPTRON; changes in brain activity immediately following each stimulation can be recorded and stored in the patient's DMDT for offline data analysis. The patient's brain activity in response to stimulation can be correlated with clinical information to determine an individual brain consciousness index (BCI), which is then monitored over time by repeated assessments. The method can be used to sample brain activity at different levels of consciousness to determine level-to-level thresholds for tracking consciousness levels over time, and identify trajectories of brain state changes from a current level of consciousness (e.g., a vegetative state) to a different state (e.g., an emerging vegetative state).

[0324] In some embodiments, the BCI includes information about the brain's response to stimulation associated with specific brain regions, systems, and networks, which can be used to monitor longitudinal changes in local brain activity associated with changes in levels of consciousness. In some embodiments, the information can be used to identify targets for neuromodulatory interventions via STIMOLA to accelerate state-to-state transitions via targeted brain stimulation interventions, including but not limited to electrical stimulation delivered via PERCEPTRON.

[0325] In some embodiments, VR-based interventions for reducing trauma can be used, at least in part, to reduce symptoms, and a quantitative measure of psychological distress is obtained by extracting features that are part of DARWIN. In some embodiments, dynamic brain state decomposition can be used to sample spontaneous mind wandering in healthy participants of different ages to detect fingerprints of state-space modulated by aging, thereby generating an index of brain and cognitive age associated with brain aging and brain health. In some embodiments, dynamic brain state decomposition can be used to sample spontaneous mind wandering in patients with neurological conditions, particularly patients with dementia, wherein the above-mentioned brain and cognitive age index can be used to predict the onset and progression of dementia, including but not limited to Alzheimer's disease and mild cognitive impairment. In some embodiments, dynamic brain state decomposition can be used to sample spontaneous mind wandering in patients with mood disorders including major depressive disorder and patients with anxiety-related conditions as a biomarker for disease onset, progression, and response to therapeutic intervention.

[0326] Neuromorphic Artificial Intelligence [NEURO-AI]

[0327] The lack of global human intelligence models and the need for large amounts of brain, cognitive, and behavioral data to inform AI solutions have both challenged the quest to create brain-inspired AI. When brain data is available, cognitive and / or behavioral correlations are often lacking; vice versa, when good cognitive and behavioral models of human problem solving are available, corresponding brain activity patterns are often not available, largely due to high costs and lack of opportunities to collect such data in an organized manner. As a result, true neuromorphic AI does not exist, limiting the ability of AI to show significant impact on real-life tasks and challenges. One of the issues represented is the search for a 1:1 replica of the human brain as the pinnacle of neuromorphic AI, which has limited the evolution of brain-inspired AI over the past two decades. For example, in order to simulate high-order brain functions and network-level dynamics, predict behavior and cognition, understand pathology, and derive therapeutic treatments, a full carbon-copy of brain microcircuits at the microscale (cellular) level may not be required. Instead, the optimal balance between the need to replicate brain features and the implementation of practical organizational principles that guide such complexity can be identified and used to evolve current AI modules.

[0328] In this regard, the systems and methods described herein allow (i) the creation of a structured platform for acquiring and tuning brain data at meso- and macro-scales, as well as corresponding cognitive and behavioral data (e.g., DMDT, PERCEPTRON), which is combined with a novel neuroscience model of human intelligence that follows the organizational principles derived from the analysis of DMDT data (including, at least in part, the brain efficiency and plasticity index in DARWIN). In some embodiments, this allows the creation of neuromorphic AI agents based on principles extracted from the DMDT of a single individual or a group of individuals, including modeling of oscillatory activity and modeling of connections from micro to macro scales. In some embodiments, this allows the creation of specialized AI agents specifically for specific tasks (e.g., opinion generation, emotional support, negotiation), or general AI that mimics human reasoning abilities (including logical and abstract reasoning). In some embodiments, this also allows the conceptualization of cognitive architectures for guiding the creation of novel assessment tools as part of SCREEN.

[0329] General AI

[0330] Human cognition is not produced by a crude, unorganized assembly of independent cognitive modules (e.g., memory modules, attention modules, abstract thinking modules), but by the fine coordination of multiple cognitive streams flowing into a balanced architecture. According to this reasoning, an advanced general AI capable of solving any problem may not be assembled by using, for example, the best DMDT template with high memory performance combined with the best DMDT with high attention ability. Instead, the best balance may be found when following the organizational principles.

[0331] In some embodiments, DMDT and specific aspects of its DARWIN model can be used to create a module of a general AI (referred to herein as "NEURO-AI") in which the general structure and algorithm wiring are defined based on data collected in individuals with a high degree of convergent and divergent thinking, thereby constituting the best compromise between knowledge-based and intuition-based learning and problem solving. By using brain imaging and cognitive-behavioral data from healthy individuals, a model has been created that identifies a set of main stem brain regions that support human convergent and divergent thinking, as well as their priority interaction pathways and dynamics. This model represents a wireframe for generating a new category of AI, in which elements of both knowledge-based intelligence (convergent thinking) and creativity and intuition (divergent thinking) are represented, and solutions to different categories of problems are optimally adjusted. The section entitled "Multi-layer Convergent-Divergent thinking (CDt) model of human cognition" below provides additional details.

[0332] In some embodiments, additional information may be implemented in NEURO-AI, including data on physiological and anatomical brain properties. For example, information on local brain metabolic activity may be used to allocate energy to AI processing units following the DMDT of individuals with high CDt, while the organization of local and distributed information processing of high CDt brains may be simulated for resource allocation. Furthermore, the properties captured by DARWIN (including, at least in part, brain plasticity, resilience, efficiency, and / or evolvability) may also be used to define the network topology of NEURO-AI, thereby improving the CDt model. For example, properties associated with brain resilience have been associated with higher cognitive performance in humans, suggesting how evolutionary dynamics may have shaped the human brain toward a highly resilient system in which multiple central brain regions exist, thereby reducing risk.

[0333] Task-specific AI

[0334] Following similar principles to data-inspired model creation but focusing on specific tasks and cognitive abilities, specific modules of NEURO-AI can be created by integrating DMDT and DARWIN data that captures the specific cognitive functions of interest. In some embodiments, data from individuals with high inductive reasoning and executive function are used to generate AI solutions for acute problem solving; data from individuals with high semantic access and high deductive reasoning are used to create AI solutions for medical diagnosis; data from individuals with high brain plasticity and learning ability are used to generate AI solutions for adaptive learning in gaming applications; data from individuals with high insight, semantic ability, and conceptual knowledge are used to create AI solutions for scientific inference, knowledge search, and hypothesis generation (e.g., virtual scientists).

[0335] Examples

[0336] Some embodiments described herein are further illustrated by the following examples and detailed schemes. However, the examples are intended only to illustrate the embodiments and should not be interpreted as limiting the scope of the technology described herein.

[0337] Neural substrates and enhanced correlations of high-level imagery reasoning and fluid intelligence

[0338] Analyzing the spontaneous metabolic activity patterns of the brain can provide information for the induced activities of individuals during sensorimotor and cognitive tasks, and even predict the induced activities of the individuals. Such intrinsic activities are believed to reflect not only the past experience of the brain as a system, but also the functional basis on which the brain will generate future goal-oriented behaviors. Although studies on the functional connectivity (FC) correlation of fluid intelligence have been proposed, it is not yet possible to clearly outline the role played by the areas belonging to specific resting state networks. The following are the results of an analysis, which illustrate the most relevant brain areas supporting human intelligence, and their corresponding functional brain networks. In addition, functional networks are classified into three main components that support human intelligence, thereby forming a target list for neuromodulation and enhancing intelligence. Functional and structural MRI data were analyzed to view local and interval synchrony across healthy subject samples, thereby identifying any cognitive functions related to intelligence (in red, Fig.12 ) and areas and networks involved in some cognitive functions associated with intelligence.

[0339] Fig.12 Shown are brain regions identified as supporting human abstract reasoning and problem solving using the systems and methods described herein. Fig.12 The top panel of shows the most relevant regions (in red) supporting human fluid intelligence across different cognitive domains and neuroimaging modalities. Regions (in green) where activity supports some cognitive functions involved in fluid intelligence tasks are shown. Fig.12 The bottom panel shows the regions color-coded according to their importance.

[0340] A hierarchy of regions and brain networks is also identified, providing a wiring diagram for NEURO-AI and task-specific AI related to convergent problem solving and hypothesis testing. In addition, the results provide a framework for human intelligence augmentation, using DARWIN and neuromodulatory interventions from STIMOLA to optimize specific brain targets and inter-regional synchronization patterns.

[0341] Fig.13 Brain networks identified as supporting abstract reasoning and problem solving in humans are shown. Associations between regions supporting intelligence and functional brain networks are shown.

[0342] Figures 14A-14C The main clusters of brain networks supporting human intelligence are shown. Based on their spontaneous activity and inter-network dynamics, the regions and networks associated with intelligence are grouped into three main components. The three components represent (i) regions associated with the anterior salience (AS) network, which is at least partially involved in proprioception, perception of one's own body and sensations, and attention to internal signaling; (ii) regions associated with the dorsal attention network (DAN) and the default mode network (DMN), which are associated with balancing attention to external stimuli and internal dialogue; (iii) regions of the visual network, which is a key function of the human brain. The functional connectivity patterns of each component are shown in Fig. 14C The far right side of the graph shows how each component explains the variability in intelligence between subjects through its positive or negative synchronization with the sensory area.

[0343] Multi-layered Convergent-Divergent Thinking (CDt) model of human cognition

[0344] Human cognition encompasses a wide range of abilities, allowing for different problem-solving strategies depending on the task at hand. Convergent thinking, including the so-called fluid intelligence (GF), features individuals who are able to find a unique correct solution to a problem by means of logical and deductive reasoning. Divergent thinking, more associated with creativity and intuition (so-called insight), conversely defines individuals who are able to generate a wide range of possible solutions to open-ended problems, a quality that is associated with artistic talent and intimacy. It is unclear whether these two cognitive domains share a common neuroanatomical substrate in the human brain, regardless of their conceptual opposition.

[0345] By analyzing functional activation patterns associated with convergent and divergent thinking, DMDT and DARWIN data were used to identify a subset of brain regions in the left inferior frontal gyrus, left frontal eye fields, and bilateral anterior cingulate cortices that constitute a non-domain-specific cognitive network associated with both convergent and divergent thinking tasks. These overlapping regions also had predictive value for potential convergent / divergent thinking cognitive factors. The strength of the negative correlation between these brain regions / structures and default mode network activity was also identified as the best predictor of high convergent / divergent thinking in humans.

[0346] The results of this example promote a view of the high-level backbone structure of human abstract thinking, referred to as the "Multi-layered CDt model of human cognition," that promotes human cognition as a whole, and thus represents a key building block for a new class of neuromorphic AI. Such AI provides a platform in which abilities and functions related to creativity, fluid intelligence, and insight can be tuned to obtain the heuristics best suited to the problem at hand.

[0347] Furthermore, the CDt model also allows for the identification of optimal brain targets for modulating cognitive enhancement via neuromodulatory interventions, including but not limited to non-invasive brain stimulation techniques.

[0348] Figure 15-19 relates to CDt definition and result according to some embodiments.Result is obtained by creating weighted graph of the location of the most relevant brain area reporting gf, creativity and insight.Then, compare the graph statistically, thereby derive the overlapping area between gf, creativity and insight and the area of ​​specific field, thereby produce quantitative answer for the problem that intelligence, creativity and insight overlap more than separation on neurobiological level.Result shows that both hemispheres have brain activation on both aggregation and divergent thinking, and the three functions examined do not have unique lateralization.By viewing the interaction between gf, creativity and insight nodes and other brain areas belonging to functional brain network, the similarity of its functional connection pattern is identified using clustering method to analyze functional brain data.Finally, the connection pattern of the overlapping area previously identified is related to behavioral gf, creativity and insight score and other lower level cognitive functions.DARWIN is used to identify the best predictor of global "abstract reasoning" potential factor of mankind, and its application includes but is not limited to cognitive enhancement and accelerated learning.

[0349] Figures 15A-15B Activation foci and brain connectivity patterns are shown. Fig.15A The focus of activation for each area across both brain hemispheres is shown. Fig. 15B The significant brain regions comprising each map after permutation-based testing (10,000 permutations; p<0.05 FDR; cluster-based correction p<0.001) are shown, showing the strength of their positive (yellow-red) and negative (cyan-blue) connections.

[0350] Figures 16A-16D Functional connectivity of overlapping nodes is shown. Functional connectivity analysis including three connected regions and nodes constituting 14 brain functional networks (n=90) revealed a preferential connectivity pattern between two overlapping regions (i.e., left SFG and MFG) at rest. Fig.16A In the figure, black spheres represent overlapping nodes used as seed regions, and red lines indicate the only significant connection among all possible connections. Fig. 16B The seed-based connectivity profile of each overlapping node is shown, which is similar to the attention and salience networks highlighted by the dashed lines. Fig. 16C A modularity analysis is shown, highlighting the separation between the three completely overlapping regions and the rest of the ALE fMRI node. Fig.16D The "Multi-layer CDt model of human cognition" is shown. Fig.16D The image in shows (from left to right) the connections between each node of the gf, creativity, and insight graphs (layer 3), nodes that are part of at least two graphs (layer 2), and nodes that are always reported in all three graphs (layer 1), representing a "multi-layer CDt model of human cognition" according to some embodiments.

[0351] Figures 17A-17B DARWIN analysis, connectivity, and cognition are shown. Negative correlations between Tier 1 regions and the default mode network were associated with individual CD-r scores, as shown in Fig.17A shown. Fig. 17B Similar analyses using domain-specific meta-analytic plots and corresponding cognitive scores are shown, with the same negative correlations as the best predictor signature for , and insight scores, with a less similar pattern for creativity (p. < 0.05 FDR; cluster-corrected p. < 0.001).

[0352] Brain to Command Engine

[0353] brain to command engine, shown in Fig.18 and 19 The example in includes three layers, a brain to minimum [B2M] module 1910, a minimum to command [M2C] module 1912 and an adaptive input generator [AIG] module 1914.

[0354] B2M1910 is an encoder that converts brain activity patterns into commands that follow a hierarchical structure. The encoder receives raw and processed data from an individual's DMDT, processes the received data through PREPARE to remove artifacts and noise, and extracts features of brain signals into property categories that can be used for a variety of applications, including but not limited to game creation, game design, interactive generative virtual asset creation, collaborative multi-player asset creation, AI control, hardware control.

[0355] The M2C module 1912 converts the minimum value from B2M (the basic information unit extracted from the brain data) into an operation command for generating assets and a logical language through the AIG module.

[0356] The AIG module 1914 allows the generation of assets and functions based on commands from the M2C. The generated assets include, but are not limited to, 2D and 3D objects and their physical properties; the functions include, but are not limited to, conditional statements, logical operators, and information about the time-varying properties of the objects. In some embodiments, specific properties of brain signals (including but not limited to frequency components extracted from specific brain regions or sensors (e.g., spectral power in the theta band)) are used to define properties of objects; in some embodiments, properties related to distributed brain activity (e.g., coherence of gamma activity across the entire brain) are used to define higher-level properties, such as the color of a class of objects (e.g., trees); in some embodiments, network-level properties (e.g., modularity of brain activity across all available regions or sensors) are used to define relationships between macro elements in the game (e.g., higher modularity = more villages and towns in the map; lower modularity = fewer villages and towns); in some embodiments, network-level properties related to efficiency of information transmission (e.g., minimum path length of a brain map built in a specific frequency band) are used to design infrastructure for organizing 3D maps (e.g., high efficiency = efficiently placing roads connecting villages and towns to maximize travel in the game; low efficiency = non-connected environment); in some embodiments, ratios between brain data properties associated with different assets and their properties can be combined to create more complex features; in some embodiments, the complexity of brain signals (e.g., Kolmogorov complexity of EEG time series) is used to define relationships between macro elements in the game (e.g., higher modularity = more villages and towns in the map; lower modularity = fewer villages and towns); The complexity) is used to define the resolution of objects in the game (e.g. high complexity = objects are visualized with 4K video quality).

[0357] In some embodiments, the three layers can be integrated with external software packages, including but not limited to brain-computer interface applications, to control external hardware and / or software.

[0358] In some embodiments, the three layers can be integrated with a natural language processing (NLP) agent to generate commands and control the generation of complex assets through instructions indicated using voice or text commands, such as Fig.19 shown.

[0359] Neuromorphic Oscillatory Multiscale Adaptive Engine [NOMAD-E]

[0360] NOMAD-E2000, shown in Fig. 20An example is an AGI engine that combines computational biology and computational neuroscience principles of microscale brain processing, applied to a cognitive architecture of mesoscale and macroscale processing based on a CDt model 2002 and an oscillation generator, applied to a natural language processing unit 2004 equipped with but not limited to long-term and short-term memory capabilities, a logical reasoning module 2006, a sentiment analysis core, a decision making module 2008, and an abstract reasoning module 2010.

[0361] In some embodiments, the meso-macro scale processing module comprises a cluster of independent agents that process information in parallel and provide competition-based emergent decisions. Each agent is provided with information by a unique set of prior information and an overlapping set of common information shared between agents. Each agent is configured to process new information independently, thereby providing an output that is weighted based on the current mental state of NOMAD-E. The mental state of NOMAD-E is determined by two sub-engines that capture the idiosyncratic and state characteristics of NOMAD-E's behavior. NOMAD-E's idiosyncrasies are stable, and the core properties of its behavior are determined by a combination of: (i) common sense 2012, (ii) organized knowledge 2014, which can be adjusted according to specific applications, and (iii) long-term memory 2016, including any knowledge of past behavior and its relevance to a specific context.

[0362] The state of NOMAD-E may be determined by the activity of the oscillation generator 2018. Each network in the generator represents a subsample of computational units that are designed to solve a specific problem by receiving a specific input and a collated set of information. Each network may have a baseline weight determined by the idiosyncratic properties of NOMAD-E, which include information stored in long-term memory. The baseline weight may determine the influence of the network on the other networks that make up the oscillation generator. The weights may change over time based on the competitive search for energy between networks. The oscillation generator may have a fixed metabolic (energy) budget available for each life cycle of NOMAD-E. NOMAD-E may have a life cycle engine 2020 that roughly measures the passage of time in arbitrary units. Time may be equivalent to a metabolic budget, where NOMAD-E attempts to prioritize processing efficiency and minimum metabolic consumption rather than processing speed while maximizing learning and promoting adaptation. The oscillating networks can constantly compete for energy by estimating the optimal pattern of excitation and inhibition weights assigned to all other networks in the generator. This creates an ever-changing balance of excitation and inhibition between the networks, the net numerical result of which is the primary output of the engine and the primary determinant of brain state, which can also be used to weight the decision-making process.

[0363] In some embodiments, the networks that make up the oscillation generator represent cognitive macro functions, including sensory processing, memory-related functions, global attention levels, emotional cues, and / or switching rates. In some embodiments, the networks that make up the oscillation generator can be removed, enhanced, or temporarily silenced from the generator.

[0364] In some embodiments, the activity of the network constituting the oscillation generator can be calculated by a local neural cluster model 2022 consisting of different classes of neurons with different functions. The classes of neurons can constitute independent computational units expressing a variety of behaviors, including but not limited to tonic and phasic activation and deactivation, where each neuron is connected to other neurons through connections expressing regulatory activities, including but not limited to inhibition, excitation, feedforward and feedback loops, noise amplification. Each class of neurons can have a specific oscillation frequency. The sum of multiple neurons and their connections can generate the dominant oscillation frequency of the network.

[0365] In some embodiments, the life cycle engine 2020 can be adjusted so that the perception of time is accelerated or slowed, and the metabolic budget is adjusted to increase or decrease the available energy. The initial weights of the network in the oscillation generator can also be modifiable, as well as the information included in the long-term memory and the general weights between the different modules that make up NOMAD-E. This allows for inducing changes in the maturity of the system, where the goal is to generate different outcomes and measure the evolution of behavior throughout the life cycle.

[0366] In some embodiments, the balance in the oscillatory network can determine the primary cognitive problem-solving attitude of NOMAD-E at any given time, based on knowledge from CDt 2002. The balance function between cognitive skills associated with fluid intelligence, creativity, and insight can determine the agent's default approach to problem solving, resulting in the agent's behavior being more logical, creative, or intuitive. An individual using NOMAD-E may be able to modify the agent's default behavior to generate multiple answers to the same problem using different approaches.

[0367] In some embodiments, NOMAD-E may include a module representing the sum of information from modules including but not limited to learning 2024 and plasticity 2026 modules, lifecycle engine 2020, CDt 2002, long-term memory module 2016, emotion processing module 2028, and cognition module 2030 to provide additional decision-making tools (wisdom 2032) based on past experience.

[0368] In some embodiments, NOMAD-E can be used to generate new ideas when provided with appropriate inputs. The idea formation process can be the result of the activities of the CDt module, the wisdom module, and the cognition module. Ideas (including but not limited to formal problem solutions and abstract conceptualizations) may result from a balance between knowledge accumulated by the agent and CDt-related decision making, including fluid intelligence, creativity, and insight-based problem solving.

[0369] In some embodiments, NOMAD-E can be personalized for a given individual, thereby creating a hybrid between general NOMAD-E traits and state characteristics and behaviors and the individual's personality, cognitive architecture, and brain properties. The individual can input their characteristics into the application programming interface (API) 2034, and the relevant information of different categories and their weights can be automatically assigned to the corresponding NOMAD-E modules. In some embodiments, NOMAD-E can be personalized for a given individual, thereby creating a hybrid between general NOMAD-E traits and state characteristics and behaviors and the individual's (i) DMDT and (ii) DARWIN. In some embodiments, NOMAD-E can be personalized for a given individual, thereby creating a hybrid between (i) general NOMAD-E traits and state characteristics and behaviors and (ii) real-time brain activity collected by PERCEPTRON.

[0370] In some embodiments, NOMAD-E can be used as a cognitive enhancement tool. For example, the DMDT and DARWIN information of an individual can be uploaded to NOMAD-E to generate a simulated cognitive model representing the cognitive architecture and dynamics of an individual. Then, NOMAD-E can be used to guide an individual to carry out personalized cognitive activities to enhance its cognitive performance. NOMAD-E can allow the selection of cognitive skill types to be enhanced, and select the most effective cognitive tasks to be performed. Cognitive enhancement can include various cognitive domains and skills, including but not limited to abstract reasoning, decision making, short-term and long-term memory, creativity, attention, perception, language ability, flexibility, insight, logical reasoning, perceptual reasoning and / or communication skills. Activities and tasks for enhancing cognitive functions include but are not limited to specific subject thinking, philosophical reasoning, logical reasoning, memory tasks and training, problem solving, emotion recognition tasks, ethical problem solving, persistent and distraction training and / or mind wandering.

[0371] In some embodiments, NOMAD-E can be optimized to generate the maximum number of new ideas and insights given a fixed baseline information set (e.g., IDEATION module). The weights in the NOMAD-E oscillation generator can be set based on brain data collected during idea generation and insight events in which individuals are asked to generate new solutions to existing problems. The inventors have defined a pattern of brain activity responsible for idea generation, which involves an imbalance between specific brain networks, including but not limited to (i) language, (ii) pre-salience, and (iii) executive control networks and their oscillatory activities. The NOMAD-E network in the oscillation generator can be programmed to simulate the pattern and perform pattern recognition searches in available collated and general knowledge databases. NOMAD-E can be programmed to generate answers across multiple ranges, which are defined by weights assigned to emotion processing, wisdom, CDt, cognition, and idea formation modules. The ranges can include but are not limited to (i) innovation, (ii) level of scientific evidence, (iii) feasibility, (iv) social impact, (v) ethical validity, (vi) scalability, and (vii) protectability (e.g., from existing intellectual property). NOMAD-E can provide a report and overall score for each viewpoint's profile across scopes. Users can modify the weights of networks and / or modules to optimize the resulting viewpoints across various scopes.

[0372] In some embodiments, the opinion formation module 2036 can be used to conduct research on specific market segments by providing specific collated knowledge to NOMAD-E. The research results can be used to find growth areas for a specific company or entity, identify the best strategic partners to promote innovation, and / or generate ideas for new products, etc. In this embodiment, a product can represent the result of organized thinking about a goal, resulting in a product that meets a need. This includes but is not limited to physical products in the case of devices or software, and concepts that describe the relationship between multiple sources of information. For example, the opinion formation module 2036 can be used to discover new hypotheses within a specific scientific field and identify candidates for drug repurposing (drugs used for specific diseases may be effective in other conditions based on recent scientific evidence based on the knowledge provided to NOMAD-E).

[0373] Multimodal Cognition and Plasticity (MCP) Testing

[0374] In some embodiments, cognitive testing can be used to activate CD-r.Multimodal cognition and plasticity (MCP) test is composed of the minimum number of stimuli required for activating CD-r, and is composed of audio and video stimuli delivered by display and audio device.Described test measures the ability of individual to switch across multiple cognitive fields related to sensory, emotional and cognitive processing, and the ability to solve increasingly difficult tasks with or without time pressure.In some embodiments, task can require individual to solve memory problems while visual stimulation on monitoring screen begins, thereby forcing individual to distract his / her attention while solving memory problems.Then, display stimulation with emotional valence, and also require individual to score stimulation based on its emotional activation level.In some embodiments, individual wears device for brain data collection while solving task, and closed-loop system is used to activate certain aspects of task in response to changes in brain activity, and vice versa.In some embodiments, MCP task is used as an indicator of brain plasticity and brain health, and can be applied to individuals at risk of developing dementia or other forms of neurodegenerative disorders.

[0375] Neuromodulation [STIMOLA]

[0376] STIMOLA includes methods that allow for the measurement and interaction of brain function through controlled perturbations. Given the diversity of brain states, traits, and target configurations, a single class of brain modifications is often insufficient to elicit the desired effects, and a properly timed combined approach may be more effective in inducing the desired meaningful brain changes.

[0377] In some embodiments, STIMOLA can implement a form of non-invasive brain stimulation (NIBS) in which an electric field is generated, controlled in space and time, and directed into the brain without the need for any surgical procedure. Examples of NIBS include, but are not limited to, transcranial magnetic stimulation (TMS), in the form of, but not limited to, repetitive TMS (rTMS), patterned rTMS protocols (such as theta burst stimulation), multi-pulse TMS, and paired associative stimulation; transcranial electrical stimulation (tES), in the form of, but not limited to, transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), and transcranial random noise stimulation (tRNS); and focused ultrasound (FUS).

[0378] In some embodiments, TMS and tES can be used to trigger long-term potentiation (LTP) or long-term depression (LTD)-like mechanisms, depending on the specific frequency applied. High-frequency rTMS (>1Hz) or intermittent TBS-iTBS (e.g., short pulse trains) generally increase cortical excitability and cause LTP-like effects, while low-frequency rTMS (≤1Hz) or continuous TBS-cTBS (e.g., single pulse trains) more frequently cause cortical excitability to decrease and ultimately cause LTD-like effects. TMS and tES can be used to regulate GABAergic function associated with inhibitory interneuron activity, and / or regulate the role of cholinergic systems in cognition and cognitive decline.

[0379] In some embodiments, non-invasive brain stimulation is performed by targeting parvalbumin-positive (PV+) inhibitory interneurons in the brain by means of high-frequency stimulation bursts in the form of magnetic, electrical or ultrasonic stimulation. High-frequency subcortical burst (HFSB) stimulation is intended to replicate thalamic information bursts sent from the thalamus to the neocortex, and can be considered to be converted into local activity by activating PV+ interneurons. Stimulation can be performed with short bursts of high-frequency stimulation, such as but not limited to tACS with a frequency of about 300Hz for 5 seconds and an interval of 2 seconds between bursts. Stimulation can be directly applied to the scalp transcranially by personalized electrode placement based on DMDT and DARWIN algorithms (including biophysical modeling solutions). HFSB can be used to activate PV+ interneurons, where the goal is to induce regulation of brain neuroinflammatory responses, regulate protein clearance, and regulate cognitive function by acting on the excitation / inhibition balance of the cortex. In some embodiments, stimulation can be delivered by PERCEPTRON and adjusted by closed-loop tools using real-time brain signals. HFSB can be used to activate other types of interneurons that also resonate with thalamic subcortical bursts, including somatostatin interneurons. HFSB may have specific applications in the areas of neurodegenerative disorders and cognitive enhancement.

[0380] Perturbation method

[0381] STIMOLA includes, at least in part, solutions in which external electrical, magnetic or ultrasonic perturbations are delivered to one brain region at a time, in which multiple perturbations are delivered on a single region to assess dynamic brain responses, or in which perturbations are delivered on multiple brain regions simultaneously or in a predefined sequence to examine network level rearrangements of brain dynamics. The method includes, at least in part, solutions in which the effects of the perturbations are measured by electrophysiological methods such as, but not limited to, EEG or EMG.

[0382] Figures 21A-21FExamples of controlled perturbations and brain responses according to some embodiments are shown.Different brain stimulation methods can be used to perturb brain activity and record responses that depend on individual differences in brain anatomy and function. Fig.21A An example method is shown that involves collecting brain activity with non-invasive electrodes placed on the scalp, such as in the case of EEG, then delivering electro-magnetic perturbations, such as in the case of TMS, and recording brain responses from multiple regions of the brain to look for, for example, an increase in activity after stimulation or an increase in synchronization between two or more regions or networks. For example, this method can be used to measure a variety of brain dynamics and induce specific effects on brain activity.

[0383] Example perturbation-based application

[0384] Shown in Fig.21A The perturbation protocols in can be used to induce specific changes in brain activity and / or measure specific features of brain activity that characterize an individual's brain. Figures 21B-21F Example applications using such perturbation schemes are described.

[0385] Information Processing: Fig.21B It is shown that the response to electromagnetic perturbations by TMS can be used to measure the delay of brain response between the area (or areas) targeted by TMS and other areas located at different proximity to the target area. Data analysis can provide information about information flow and effective connections in the brain, identifying areas that are critical for information processing and information conduction, and the most effective pathways connecting two distant brain areas.

[0386] Brain elasticity: Fig. 21C It was shown that analysis of network-level responses to electromagnetic perturbations can also be used to calculate brain resilience to perturbations as a measure of the brain's ability to maintain high levels of efficiency in the presence of perturbations that mimic real-life scenarios involving, for example, neurodegeneration of brain tissue, stroke, accumulation of waste products such as amyloid-β and tau proteins associated with Alzheimer's disease. Systematic analysis of the amount of "damage / damage" to brain networks induced by TMS or tCS yielded quantitative indices of brain resilience that correlated with clinical variables such as cognitive impairment, disease progression rate, brain atrophy.

[0387] Brain Efficiency: Fig.21D It is shown that targeted TMS or tCS application during EEG recording can be used to test the strength and efficiency of specific connections in the brain that are associated with activity within a specific brain functional network, multiple networks, or between networks. By looking at brain activity just before and after the electromagnetic perturbation, indicators related to global brain connectivity can be obtained.

[0388] Brain Plasticity: Fig.21EIt is shown that stimulation of two or more brain regions by TMS or tCS allows the strength of specific connections in the brain to be measured. By applying repeated TMS / tCS stimulation at specific time intervals, changes in the strength of connections may be induced; the magnitude, speed, and duration of such changes can be used as indicators of brain plasticity, such as the ability of the brain to adapt to external perturbations and rearrange its connections. For example, brain plasticity measures can be used to estimate a patient's ability to adapt to brain changes induced by Alzheimer's disease, and therefore estimate the trajectory of disease progression and cognitive decline.

[0389] Network regression: Fig.21F It is shown that perturbing specific regions and monitoring the propagation of the signal across other network nodes allows estimation of signal attenuation in the brain, which can be used as an indicator of tissue neurodegeneration caused by, for example, amyloid-β and tau protein accumulation, atrophy, neuroinflammation, and related changes in white matter tracts. Analysis of both spontaneous and induced brain activity can be used to estimate regional and global protein loads in the brain, thereby quantifying the amount of waste proteins such as amyloid-β, tau, TDP43, and alpha synuclein using indices created from EEG data.

[0390] Time frame and range of disturbance-based markers

[0391] The systems and methods described herein can be applied over a variety of scales and time frames.In summary, the following describes some applications of perturbation-based markers in patients with Alzheimer's disease and dementia.

[0392] Diagnostic Purpose

[0393] In some embodiments, the response of an individual to a disturbance can be used to identify abnormal brain activity in a patient suspected of having Alzheimer's disease or dementia. Responses to disturbances that are dissimilar to previous responses obtained from control subjects, healthy controls, or the same individual can be used to assist in the diagnosis process. A similar process can be applied to identify a subtype of Alzheimer's disease in a group of patients diagnosed with Alzheimer's disease (e.g., amnestic Alzheimer's disease). A similar process can be applied to identify individuals with brain cancer at risk of tumor recurrence after surgery. The analysis of the response can be performed by visual inspection by a trained human operator, or by a classification algorithm based on machine learning that can mark the response to the disturbance as normal or abnormal response.

[0394] Prognostic purpose

[0395] In some embodiments, individual brain data can be analyzed to predict (e.g., estimate) the course of a pathology (e.g., Alzheimer's disease) based on historical data collected from the same individual or group-level estimates of disease progression. A similar process can be applied to predict the course of a pathology in patients with brain cancer, thereby predicting recurrence rates and estimating survival.

[0396] Evaluation of treatment efficacy

[0397] In some embodiments, individual or group level brain data can be analyzed to quantify differences in brain activity before and after treatment for Alzheimer's disease or memory problems. Changes in perturbation-based metrics before and after treatment can be used to assess individual or group level responses to a given treatment, alter treatment parameters, or facilitate decisions to continue or stop treatment.

[0398] Personalized treatment

[0399] In some embodiments, individual brain data can be analyzed to personalize non-invasive brain stimulation parameters, including but not limited to stimulation location, orientation, intensity, frequency, phase, and / or noise level. Personalization can be performed by looking at the response to TMS pulses delivered, for example, at multiple locations within a target area and identifying the location that provides the highest brain response to TMS.

[0400] Disease tracking

[0401] In some embodiments, perturbation-based metrics collected over time can be used to monitor brain function in patients with neurological and psychiatric conditions as well as healthy individuals, thereby capturing significant deviations in brain activity patterns at any given time point from data collected at previous time points. For example, individual responses can be compared to normative data collected in samples of patients with Alzheimer's disease or in a group of healthy controls, thereby providing estimated deviations from, for example, expected rates of cognitive decline.

[0402] Cognitive function assessment

[0403] In some embodiments, perturbation-based data can be collected from brain regions / networks associated with or supporting specific cognitive functions. For example, TMS or tCS can be directed to regions associated with memory processing, such as the precuneus or dorsolateral prefrontal cortex. TMS-EEG and tCS-EEG data can be used in combination with neuropsychological scores to identify brain circuit changes that lead to decreased memory performance in patients with, for example, Alzheimer's disease.

[0404] Sample Application

[0405] In some embodiments, BRAINPRINT and OPTI-BRAIN can be used to simultaneously determine both the location and stimulation parameters of NIBS. In some embodiments, tACS can be delivered by a combination of a high-frequency carrier frequency in the megahertz range and a target frequency in the physiological frequency band. Stimulation in the megahertz range (>1Mhz) tends not to induce side effects of typical tES, such as scalp itching and burning sensations, thereby allowing the intensity of tACS stimulation to be increased, and thus reaching deeper brain structures or superficial brain areas with higher intensity. The combination of a high-frequency carrier with a physiological tACS frequency (such as 6Hz or 40Hz) allows the delivery of higher intensity stimulation and induces brain activity in a specific frequency manner. The combination of high-frequency tACS and physiological tACS can be used to regulate brain activity in healthy and pathological brains.

[0406] In some embodiments, NIBS can be used in combination with BRAINPRINT data to determine the location and stimulation parameters of non-invasive brain stimulation. For example, TMS or tES can be combined with EEG recordings, and one or more characteristics of the evoked response to brain stimulation at multiple brain locations can be used to determine the optimal location of the targeted NIBS and stimulation characteristics (e.g., stimulation frequency, intensity / amplitude).

[0407] In some embodiments, controlled perturbations are applied to a single brain region, and responses are measured by electrodes placed on the scalp (EEG) or body (EMG). Based on the brain state of the subject, the perturbations can have various intensity levels, including but not limited to the levels of cortical excitability, plasticity, inhibition, excitability, oscillatory activity, connectivity and / or responsiveness. In some embodiments, the response to the perturbation can be measured by EMG to analyze the longitudinal changes over time in the local excitability of the targeted brain system after the perturbation as a measure of cortical plasticity. In some embodiments, the response to the perturbation can be measured by EEG to view (a) local responses, which are represented by but not limited to the amplitude, number, frequency and timing of the so-called TMS-induced potentials measured in the stimulated brain region; (b) remote responses measured using the same (or similar) indicators calculated from remote brain regions, thereby viewing the amount of EEG electrodes or induced currents generated in the brain; (c) changes in the dynamics of the intervals of the brain regions including or excluding those receiving stimulation, wherein these measures include but are not limited to graph theory measures of correlation, connectivity, effective connectivity, dynamic connectivity, and node interactions.

[0408] In some embodiments, dual co-localized perturbations can be delivered by a sequence of at least two controlled perturbations repeated over a single region, and the response can be measured by electrodes placed on the scalp (EEG) or body (EMG). The response to the perturbations can be measured by EMG, looking at longitudinal changes in local excitability over time within the targeted brain system following the perturbation as a measure of cortical plasticity.

[0409] In some embodiments, multi-site perturbations delivered sequentially by at least two controlled perturbations can be repeated across multiple different brain regions, and the response can be measured by electrodes placed on the scalp (EEG) or body (EMG), or by neuroimaging techniques such as MRI. The response to the perturbations can be measured by EMG and EEG, looking at longitudinal changes in local excitability over time within the targeted brain system following the perturbation as a measure of cortical plasticity.

[0410] In some embodiments, the timing of each pulse delivered in a sequence of at least two controlled perturbations can be defined by looking at the conduction delay between target brain regions. The delay between regions can be used to set the delay between two regions in order to induce progressive associative stimulation (PAS) to strengthen synchronization between target regions.

[0411] In some embodiments, the timing of progressive associative stimulation is calculated by sequentially measuring the axon diameter and length of white matter fibers in the brain using diffusion MRI. White matter myelination can be calculated to estimate input propagation velocity (in mm / sec) and can be combined with information about tract length to estimate conduction delays between two or more target areas.

[0412] In some embodiments, STIMOLA can be configured to provide a stimulation modality performed to create, induce, modulate, or amplify traveling waves recorded from the human brain.

[0413] In some embodiments, multimodal perturbations can be delivered by a combination of two or more stimulation types used to maximize the effects of TMS or tCS on the brain, thereby reducing noise in brain activity data. Oscillatory electrical stimulation (e.g., transcranial alternating current stimulation - tACS) can be used to modulate oscillatory activity in a target area / network before or during the application of TMS. For example, applying 20Hz tACS on the precuneus can act as a stabilizer of spontaneous brain activity for subsequent delivery of TMS pulses synchronized with the peak of each 20Hz oscillation cycle.

[0414] In some embodiments, brain perturbations can be applied in a variety of forms, including but not limited to (i) a single pulse, (ii) a train of pulses spaced at fixed intervals or with variable jitter, (iii) a continuous waveform characterized by a given amplitude, frequency (or frequency combination), and duration, (iv) a continuous electric field with a given polarity, amplitude, and duration, or (v) a signal consisting of noise pulsed at a specific frequency.

[0415] In some embodiments, perturbations can be delivered to one or more brain regions identified based on brain scans and / or electrophysiological data. The information sources used to define optimal perturbation targets can represent population-level data and / or individual data. The data can be organized so that local and distributed brain activity can be summarized as quantifiable metrics; characteristics of brain activity can be extracted to measure features related to, but not limited to, (a) metabolic and vascular activity, (b) oscillatory activity within known frequency bands, (c) network elasticity measures, (d) dynamic connectivity, and / or (e) protein accumulation maps obtained by PET imaging.

[0416] In some embodiments, disturbances can be delivered to the nodes based on the degree of connection of the nodes to other parts of the brain, to (i) assess the integrity of the brain connection group, or (ii) induce a wide range of responses in local and remote brain regions as a measure of brain integrity and connectivity. The degree of connection can be estimated by functional imaging data or EEG (as in the case of functional connectivity), or by anatomical and diffusion MRI scans (in the case of structural connectivity). In the latter, the physical connection between two or more regions can be estimated by analyzing the properties (e.g., diameter, anisotropy, diffusivity) of the white matter fibers that constitute a specific white matter bundle in the brain. In an example scenario, the TMS or tCS stimulation target is defined by examining the structural connectivity of multiple brain regions, and then selecting the region with the most connections to other parts of the brain, where the goal is to maximize signal propagation and overall stimulation effects. Similar analysis can be performed to identify brain targets with strong structural connections to subcortical brain regions (e.g., hippocampus) that are inaccessible by TMS or tCS; stimulating accessible gray matter regions with strong structural connections to the hippocampus can also maximize the possibility of indirectly activating the hippocampus.

[0417] In some embodiments, perturbations can be delivered to two or more nodes of the same brain network to assess the integrity of connectivity within a given brain network, such as in the case of the default mode network in patients with Alzheimer's disease. Targets can be identified by analyzing brain scans (e.g., functional MRI) or electrophysiological data (e.g., EEG data).

[0418] In some embodiments, perturbations can be delivered to nodes that have a demonstrated relevance to the pathophysiology of a given disease; for example, the precuneus region can be targeted due to its demonstrated decline in functional connectivity in Alzheimer's disease, as measured using functional magnetic resonance imaging (fMRI); temporal lobe regions affected by waste proteins in Alzheimer's disease can be targets of stimulation, with particular emphasis on amyloid-β and p-tau; regions that show altered levels of neuroinflammation, as measured by, for example, diffusion MRI.

[0419] In some embodiments, the selection of perturbation targets can be based on estimates of the induced electric fields induced in candidate brain regions extracted from biophysical modeling of passive tissue conductivity. Models can be created by simulating the propagation of magnetic and electrical energy across head and brain tissues (e.g., skin, muscle fibers, bones, cerebrospinal fluid-CSF, gray matter, white matter), thereby enabling quantitative measurement of the induced electromagnetic stimulation affecting brain cells (e.g., excitatory neurons and inhibitory interneurons). The amplitude of the induced electromagnetic stimulation can be used to adjust stimulation parameters (such as intensity and phase angle) to maximize the stimulation effect on a given brain target according to known criteria for inducing, for example, neuronal discharges or neurotransmitter release.

[0420] In some embodiments, target regions may be identified as regions responsible for specific cognitive processes (e.g., long-term memory or attention). Brain scans or EEG data collected from patients with neurological or psychiatric conditions during the performance of cognitive tasks may be analyzed to identify regions whose activation correlates with performance in the task. In an example scenario, one or more of the resulting regions may then be selected as targets for TMS or tCS perturbations to assess their level of integration with other brain regions involved in the same cognitive process (e.g., long-term memory) as a measure of efficiency within the patient's long-term memory network.

[0421] In some embodiments, the target area can be identified based on a spatial and functional search algorithm, where, for example, the evoked EEG activity after at least 2 TMS pulses is averaged, revealing TEPs at different latencies between 5 and 500 milliseconds after TMS. The amplitude of the TEP can be calculated for each patient and used as a surrogate for the individual's responsiveness to TMS, and therefore as an index of the excitability and responsiveness of the target area / network. The amplitude of the TEP can then be used to correct the stimulation intensity obtained from stimulation of the motor cortex (e.g., resting motor threshold (RMT)), with the goal of adjusting the TMS stimulation intensity based on the TEP.

[0422] The stimulation intensity of TMS, tCS and tFUS stimulation depends on the individual brain anatomy and can be estimated by creating a biophysical model of high-resolution brain anatomy from an MRI scan. In some embodiments, the electric field (E field) induced on the brain target can be generated by using a real volume conductor head model generated based on the segmentation of MRI images and validation data sets. The model can be based on the anisotropic conductivity value of each brain tissue category (e.g., skin, fat, muscle, bone, cerebrospinal fluid, gray matter, white matter) expressed in seconds / meter. The resulting grid set that integrates at least gray matter and white matter, scalp, bone and cerebrospinal fluid can be used to calculate the E field distribution of a specific TMS coil design, position, angle and rotation, taking into account the coil to scalp distance and brain atrophy. The estimated E field can be used to (a) retrospectively calculate the individual differences in the amount of current delivered to the target area, and therefore explain the differences in response to treatment, or (b) adjust the stimulation position and / or intensity so that all participants receive the same amount of induced cortical stimulation.

[0423] In some embodiments, STIMOLA can be configured to provide stimulation modalities that take place in a VR or AR environment. The sections herein describing the NEUROCREATOR and the Metaverse provide additional details.

[0424] In some embodiments, STIMOLA may be used in combination with drugs that act on neuroplasticity, particularly drugs that affect PNNs. Additional details are provided in the section describing SYNAPSE herein.

[0425] In some embodiments, STIMOLA can be applied for diagnostic purposes. Individual responses to disturbances can be used to identify abnormal brain activity in patients suspected of having Alzheimer's disease or dementia. Responses to disturbances that are dissimilar to previous responses obtained from control subjects, healthy controls, or the same individual can be used to assist in the diagnostic process. A similar process can be applied to identify subtypes of Alzheimer's disease in a group of patients diagnosed with Alzheimer's disease (e.g., amnestic Alzheimer's disease). Analysis of the response can be performed by visual inspection by trained human operators, or by a classification algorithm based on machine learning that can mark the response to the disturbance as normal or abnormal response.

[0426] In some embodiments, STIMOLA can be applied for prognostic purposes. Individual brain data can be analyzed to predict (e.g., estimate) the course of a pathology (e.g., Alzheimer's disease) based on historical data collected from the same individual or group-level estimates of disease progression.

[0427] In some embodiments, STIMOLA can be applied to assess the efficacy of treatment. Individual or group level brain data can be analyzed to quantify the difference in brain activity before and after treatment for Alzheimer's disease or memory problems. Changes in perturbation-based indices before and after treatment can be used to assess individual or group level responses to a given treatment, change treatment parameters, and continue or stop treatment.

[0428] In some embodiments, STIMOLA can be applied to define personalized treatment guidelines and / or parameters. Individual brain data can be analyzed to personalize non-invasive brain stimulation parameters, including but not limited to stimulation location, orientation, intensity, frequency, phase, and / or noise level. Personalization can be performed by looking at the response to TMS pulses delivered at multiple locations, for example within a target area, and identifying the location that provides the highest brain response to TMS.

[0429] In some embodiments, STIMOLA can be applied to disease tracking. Perturbation-based metrics collected over time can be used to monitor brain function in patients with Alzheimer's disease, thereby capturing significant deviations in brain activity patterns at any given time point from data collected at previous time points. Individual responses can be compared to normative data collected in samples of patients with Alzheimer's disease or in a group of healthy controls, thereby providing estimated deviations from, for example, expected rates of cognitive decline.

[0430] In some embodiments, STIMOLA can be applied to study cognitive function. Perturbation-based data can be collected from brain regions / networks associated with or supporting specific cognitive functions. For example, TMS or tCS can be directed to areas associated with memory processing, such as the precuneus or dorsolateral prefrontal cortex. TMS-EEG and tCS-EEG data can be used in combination with neuropsychological scores to identify changes in brain circuits that lead to decreased memory performance in patients with, for example, Alzheimer's disease.

[0431] In some embodiments, STIMOLA can be applied to sleep regulation by acting on brain oscillation activities associated with, but not limited to, brain state transitions from wakefulness to sleep and specific sleep stages (including but not limited to non-rapid eye movement (REM) and REM sleep stages). STIMOLA can be used in combination with DMDT and DARWIN to select the best stimulation frequency, intensity, duration and position for a given individual so as to maximize the response to stimulation. In some embodiments, STIMOLA can be applied during sleep to increase the duration and expression of specific sleep stages associated with protein clearance and glymphatic system activity, wherein the goal is to increase protein clearance in the brain, and remove waste products associated with cognitive decline and pathological aging, including but not limited to amyloid-β, tau protein, alpha synuclein and TDP43 protein. STIMOLA can be applied to proteins intended to be removed in individuals, including healthy cognitively intact individuals, individuals at risk of developing dementia, individuals with preclinical dementia, and individuals diagnosed with dementia (including Alzheimer's disease). In some embodiments, stimulation can be performed using transcranial electrical stimulation in a specific frequency band, including but not limited to the theta band between 3 Hz and 7 Hz. In some embodiments, electrical stimulation can be monitored by continuous EEG data and regulated by the phase of the respiratory and / or cardiac cycle (in phase with CSF production in the brain). According to the state-to-state transition principle described in DARWIN, electrical stimulation can be regulated in real time based on EEG data that captures different sleep stages and the stability of each stage, thereby identifying the transition period by looking at the ratio of specific oscillatory dynamics (including but not limited to bursts of gamma activity within 30 Hz and 150 Hz).

[0432] In some embodiments, STIMOLA can be used to regulate sleep patterns monitored by EEG using PERCEPTRON and DARWIN, thereby improving sleep efficiency by stabilizing the duration and complexity of brain activity in specific sleep stages (including but not limited to REM and non-REM stages). STIMOLA can be used to improve sleep efficiency, stabilize circadian rhythms, and enhance memory consolidation and retrieval in individuals, including healthy cognitively intact individuals, individuals at risk of developing dementia, individuals with preclinical dementia, and individuals diagnosed with dementia (including Alzheimer's disease). In some embodiments, the effects of STIMOLA on sleep regulation can be assessed by DMDT measurements associated with circadian activity and behavioral patterns, which are collected by wearable devices such as activity watches and PERCEPTRON.

[0433] Examples

[0434] Some embodiments described herein are further illustrated by the following examples and detailed protocols. However, the examples are intended only to illustrate the embodiments and should not be construed as limiting the scope of the technology described herein. The examples described below demonstrate the application of perturbation-based methods to: (i) predicting the cognitive state of an individual, (ii) assessing the resilience of the brain to perturbations, and (iii) measuring brain oscillatory activity, plasticity, and neuroinflammation levels by EEG response to tCS in AD patients.

[0435] Differences in responses to perturbations and relationships to cognitive profiles

[0436] Individual responses to electromagnetic perturbations can be used to reveal individual brain properties associated with cognitive processing, and thus identify subtle changes in the cognitive profile of patients with Alzheimer's disease (AD) and related dementias. Perturbations by TMS can be delivered in selected brain regions known to play a role in specific cognitive processes or known to be affected by pathology, and their responses can be quantified and correlated with individual scores in cognitive / neuropsychological tasks, which involve functions such as memory, attention, language, abstract reasoning, etc. For example, the amplitude of the local response of a given brain region may be related to working memory capacity, while the synchronization between two nodes of the brain network associated with attention may explain the individual variability of executive functions (such as inhibition and flexibility). Given the possibility of "passively" collecting this information (e.g., without the subject actively participating in cognitive tests or specific tasks), these methods are particularly useful in patient populations characterized by cognitive impairment, behavioral disorders, and lack of compliance (such as in the case of AD patients). In addition, these methods for identifying changes in cognitive function by passive perturbations also reduce the burden on patients by potentially adding valuable information about the patient's cognitive state without the need for extensive and tiring cognitive assessments.

[0437] The following data collected in a sample of patients with Alzheimer's disease are reported. The patients underwent TMS-EEG therapy in which multiple brain networks were stimulated with the explicit goal of identifying correlations with specific cognitive scores also collected in the same group of patients. The participants also underwent MRI scans of TMS target selection following the procedures described herein.

[0438] Methods and analysis

[0439] Based on individual MRI and fMRI data collected in patients with Alzheimer's disease, TMS is delivered across multiple networks of the brain, such as Figure 24A-2Specifically, stimulation was targeted toward the angular gyrus, which is a node of: the default mode network (DMN), which is a functional network responsible for memory processing and is primarily affected by pathology in patients with Alzheimer's disease; the frontoparietal control network (FPCN), due to its role in higher-order cognitive functions; and the visual network (VN), which serves as a control network where disrupted connectivity has not been reported in patients with Alzheimer's disease.

[0440] Under neuronavigation monitoring, batches of single TMS pulses (120 pulses in each district) are delivered in each district. Before, during and after each TMS pulse, an EEG device of 128 channels is used to monitor brain activity. TMS intensity is set based on the resting-motion threshold collected on the primary motor cortex. EEG data are processed according to the pipeline described in PREPARE, thereby generating TMS-induced potentials (TEPs), which are then projected into the MRI scans of each patient to perform source level analysis on network activity. The cognitive profile of the patient is characterized by standard cognitive tools, and a comprehensive cognitive score is created. The comprehensive score allows the sample to be divided into a high cognitive comprehensive score and a low cognitive comprehensive score, so the TEP difference between the two groups is checked, and the difference may explain different cognitive profiles and cognitive decline rates.

[0441] result

[0442] Figures 23A-23B Shown is the evoked oscillatory activity of an Alzheimer's disease patient after TMS. Fig.23A Differences in brain response to stimulation of the dorsolateral prefrontal cortex were shown, with patients with higher versus lower composite cognitive scores having greater high-frequency activity immediately following the perturbation. Fig. 23B Figure 1 represents a source-level analysis of TMS-EEG data and shows how TMS-induced signals propagate more across the brain after perturbation in patients with higher composite cognitive scores compared to patients with lower composite scores where stimulation induces only localized responses. This reflects changes in brain connectivity and functional integrity that prevent signal propagation in the brains of patients with more significant cognitive decline.

[0443] Patients with high composite cognition scores showed stronger responses to TMS delivered over the DMN and FPCN, whereas no differences were noted with regard to the VN. Specifically, a significant increase in fast oscillatory activity (in the gamma band) was observed when patients were stimulated over the left dorsolateral prefrontal cortex (DLPFC), which was more significant in the group with high composite cognition scores compared to low composite cognition scores (p.<0.002), as shown in Figure 2. Fig.23ATopographic analysis of signal propagation in the brain also revealed differences in the magnitude of activation throughout the brain following TMS of the DLPFC, with individuals with higher composite scores showing a more widespread response compared to those with lower composite scores, as shown in Figure 2. Fig. 23B shown.

[0444] Figures 24A-24B The network level response to the perturbation is shown. Fig.24A It was shown that patients exhibited reduced activation in networks associated with cognitive function and affected by Alzheimer's disease, such as the DMN and FPCN, whereas no significant differences were observed in the VN. Fig. 24B It was shown that the specificity of TMS-evoked responses (as measured by the ratio between the response in the targeted network and the response in any other network of the brain) also showed significant differences between the groups in the DMN and FPCN, but not in the VN.

[0445] Comparison between activation levels of the three networks in patients relative to controls also revealed a general decrease in responses in the DMN and FPCN, with no significant differences observed in the VN. Fig.24A In addition, to identify specific differences in the brain of TMS-induced responses, the amount of activation induced in the stimulated network (e.g., DMN) was analyzed and compared with the amount of activation in other brain networks. The results showed that the specific response of patients to activity in the targeted network was significantly reduced relative to controls, indicating that the response of patients with Alzheimer's disease is more distributed, non-specific, and "disorganized", reflecting the disruption of network integrity, such as Fig. 24B shown.

[0446] In this example, it was shown that TMS-EEG studies of network dynamics in patients with Alzheimer's disease may be a valuable tool for identifying changes in brain function relevant to understanding individual differences in cognitive performance and cognitive decline in patients with Alzheimer's disease.

[0447] Response of AD patients to electrical perturbations of specific frequencies.

[0448] Alzheimer's disease (AD) is characterized by impaired inhibitory circuits and GABAergic dysfunction, resulting in reduced fast brain oscillations in the gamma band (γ, 30Hz-90Hz). Assessment of such activity can enable identification of diagnostic and / or prognostic biomarkers. In this example, a multimodal "perturbation-based" transcranial alternating current stimulation (tACS)-EEG protocol for detecting gamma changes in patients with AD was tested, and individual responses to tACS were correlated with clinical phenotypes.

[0449] Methods and Materials

[0450] Patients with mild to moderate AD (mean age = 72 years) underwent multimodal assessments, including tACS-EEG, cognitive tests, blood markers, and theta burst TMS to assess (LTP)-like brain plasticity. The tACS-EEG visit included a brief (6′) tACS block at 40 Hz or 6 Hz in 6 locations covering 4 lobes in both hemispheres, with each block having 32 channels of pre- and post-EEG recordings. The spectral power of the pre- and post-EEG in 4 frequency bands was calculated. The pre- and post-spectral power change (δ value) was calculated as an indicator of γ induction and correlated with cognitive scores, blood markers, and brain plasticity measures.

[0451] result

[0452] In terms of cognitive function and gamma induction, gamma activity induced by 40 Hz-tACS and 6 Hz-tACS correlated with measures of cognitive and memory impairment, such as episodic memory (r = .74), global cognition (r = .78), language expression (r = .74) (all ps < .01), and auditory learning skills (r = .81, p < .05). Regarding gamma induction and neuroinflammatory markers, 40 Hz-induced brain activity correlated with cytokines, tumor necrosis factor-α (TNF-α) (r = -.73), interleukin-10 (IL-10) (r = .77) (all p. < .01), respectively. Regarding gamma induction and measures of cortical plasticity, cortical plasticity correlated with 40 Hz-induced brain activity (r = .69, p < .05).

[0453] The results of this example provide evidence supporting the use of perturbation-based EEG markers combined with brain stimulation in Alzheimer's disease (AD) for detecting clinical and cognitive correlates of the disease, as well as for estimating levels of neuroinflammation and cortical plasticity in the brains of patients with AD.

[0454] Neuroplasticity [SYNAPSE]

[0455] Some of the systems and methods described herein relate to applications as part of SYNAPSE to modulate aspects of brain plasticity and neuroplasticity, including but not limited to:

[0456] (i) Platforms for combined interventions that exploit increases in global brain plasticity achieved through: (a) interventions that modulate peripheral neural networks (PNNs) and extracellular matrix (ECM), including but not limited to drugs and physical exercise, and (b) local / targeted modifications induced by external modifiers, including non-invasive brain stimulation methods; and

[0457] (ii) Platforms for simulated and accelerated rich environments by targeted systemic stimulation of brain function using non-invasive brain stimulation and other brain modifications, including cognitive stimulation and virtual reality.

[0458] Brain development and performance are generated by the synergistic effect of both genetic factors and factors related to experience. The interaction between the signals generated by the central nervous system, the extracellular environment (e.g., the physical space around brain cells) and the external world (e.g., experience) determines the adaptation of the brain to external inputs from the environment and promotes its maturation. The concept of brain plasticity represents the ability of the brain to modify its structure and function related to experience. Brain plasticity can be achieved by finely adjusting growth-promoting signals and growth-inhibiting signals. In the brain after birth, many processes involved in brain development are highly active, such as synaptic processes, glial cell maturation, neuronal growth regulation, and axon pruning. The plasticity level is at the highest level and rapidly decreases in the subsequent 4-5 years. The neonatal brain is highly responsive to external stimuli and can easily adapt its connections, thereby achieving a high ability to learn and adapt (e.g., learning language, how to walk). The end of the critical period defines a dramatic change in the brain's ability to adapt, learn, and evolve, making the search for solutions that can reopen the window of plasticity a critical unmet need in modern neuroscience, with applications including, but not limited to, cognitive enhancement, physical and cognitive rehabilitation, and accelerated learning. Some of the systems and methods described herein include tools and protocols for reactivating plasticity in healthy and diseased adult brains, enhancing normal brain function, and / or treating brain diseases.

[0459] SYNAPSE modules can include behavioral, cognitive and non-invasive brain stimulation methods to regulate, enhance and reactivate brain plasticity levels by regulating the extracellular matrix (ECM), peripheral neuronal nets (PNNs), chondroitin sulfate proteoglycans (CSPGs) and inhibitory interneurons. During the critical period, ECM supports neurogenesis, synaptogenesis, cell migration, growth and axonal elongation, and in the adult brain, it is involved in neuronal plasticity and regeneration after injury. The main component of ECM is CSPG (a proteoglycan that mainly exerts growth inhibition and therefore prevents cell adaptation and plasticity). In general, PNNs form part of the cell pericellular matrix, which wraps around the brain and more generally the cell bodies and dendrites of neurons in the central nervous system. PNNs provide structural support, while also limiting growth and inhibiting plasticity. ECM molecules and PNNs preferentially aggregate around neurons of a particular class (e.g., inhibitory interneurons). Among them, parvalbumin-expressing interneurons (responsible for the activation of microglia in the presence of viral infection or neurodegenerative diseases such as Alzheimer's disease) were particularly affected by the enriched PNN and ECM complexes.

[0460] In some embodiments, non-invasive brain stimulation methods as described in the STIMOLA section of this article can be used to induce a state of increased neuroplasticity, and the non-invasive brain stimulation methods include but are not limited to transcranial magnetic stimulation (TMS), repetitive TMS (rTMS), patterned rTMS protocols (such as theta burst stimulation), multi-pulse TMS and paired associative stimulation; transcranial electrical stimulation (tES) in the form of but not limited to transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), transcranial random noise stimulation (tRNS); and focused ultrasound (FUS).

[0461] In some embodiments, specific frequency neuromodulation targeting the activity of inhibitory interneurons can be used to modulate the activity of interneurons and cause modulation of PNNs. Given the correlation between the inhibitory activity of interneurons and the oscillation pattern of the gamma band (between 30 Hz and 120 Hz), specific frequency stimulation of the gamma band can be used to target brain systems / networks / areas. Stimulation can be in the form of, but not limited to, sinusoidal stimulation, patterned stimulation (e.g., bursts of white noise applied to a carrier frequency at 40 Hz), or multi-frequency crossover frequency stimulation.

[0462] In some embodiments, brain optimization and evolutionary algorithms (e.g., as described in the DARWIN, SYNAPSE, IMPROVE, OPTI-BRAIN, and OPTI-COG sections herein) can be combined with other interventions that can promote brain plasticity and thereby accelerate / enhance brain changes.

[0463] In some embodiments, interventions configured to promote brain plasticity may include drugs capable of disrupting PNNs and reopening the plasticity window in the brain, including but not limited to drugs that act on growth-promoting and growth-inhibitory factors, and drugs that act on specific extracellular matrix molecules (such as chondroitin sulfate proteoglycans (CSPGs), hyaluronic acid (HA) tenascin-R and connexins), as well as drugs that act on GABAergic activity, antidepressants, ketamine, drugs that act on brain-derived neurotrophic factor (BDNF). Candidate drugs include, but are not limited to, drugs based on chABC, an enzyme that is able to degrade CSPG molecules and reactivate the critical period; ketamine, for its ability to induce a dissociative state; opioids; psychoactive drugs; psychedelic compounds that are able to robustly promote neurogenesis, including, but not limited to, tryptamines (N,N-dimethyltryptamine DMT and psilocin), amphetamines (2,5-dimethoxy-4-iodoamphetamine - DOI and 3,4-methylenedioxy-methamphetamine MDMA) and ergoline (lysergic acid diethylamide - LSD).

[0464] In some embodiments, drugs capable of temporarily disrupting PNNs can be used to enhance plasticity at the whole brain level; OPTI-BRAIN can be used to identify targets and parameters for brain optimization, including regions and / or connections where further enhancement of local plasticity may be desired; STIMOLA can be used to further enhance plasticity of a given brain target.

[0465] In some embodiments, drugs capable of temporarily disrupting PNNs can be used to increase plasticity at the level of the entire brain; once increased plasticity is achieved, OPTI-BRAIN can be used to identify areas and / or connections where STIMOLA can be used to selectively modify specific brain connections, networks, regions, and / or connections to achieve a desired outcome (e.g., a change in state or trait).

[0466] In some embodiments, physical activity can be used to modulate PNNs and promote a state of increased brain plasticity. Subjects may be asked to perform aerobic physical tasks (e.g., running or walking on a treadmill) to increase brain metabolic activity and promote brain plasticity, while also performing more specific task loads on specific brain systems / networks / areas (e.g., working memory tasks to activate the dorsolateral prefrontal cortex-DLPFC).

[0467] In some embodiments, sensory deprivation protocols can be used to induce increased brain plasticity in specific systems / networks / regions of the brain. Subjects may be asked to complete a visual task while wearing a patch covering one eye, thereby inducing a distressed state in the visual system that triggers a protective response, resulting in increased plasticity (simulated deficit protocol). The same approach can be used for the auditory and motor systems and can be combined with specific training protocols to increase plasticity and enhance behavioral performance (e.g., sensory deprivation of the right arm induces neural plasticity and improves motor learning).

[0468] In some embodiments, disruption of PNNs can be used to affect long-term memory. The integrity and configuration of PNNs have been shown to be important for maintaining long-term memory, wherein their disruption leads to loss of long-term memory. SYNAPSE and STIMOLA can be used to target traumatic long-term memory by disrupting PNNs in areas associated with traumatic memory and post-traumatic stress disorder, including but not limited to the amygdala, hippocampus, precuneus and anterior cingulate cortex, insula.

[0469] In some embodiments, SYNAPSE can be used to create a virtual augmented environment (VAE), where the goal is to promote neuroplasticity. VAE refers to a virtual, enhanced or mixed reality environment, so that sensory, motor, cognitive and social stimulation is maximized, thereby achieving increased stimulation of brain cells (including but not limited to synaptic remodeling, dendrite growth to glial cell generation, angiogenesis and neurogenesis) and systems, thereby promoting adaptation and plasticity. The concept of enriched environment (EE) is used in animal research to promote learning, injury recovery and plasticity by regulating the expression of several neuronal growth genes (including but not limited to changes in the expression of neurotrophic factors). VAE is a parameter environment, in which the stimulus and its properties are selected by an algorithm based on but not limited to the following (Plasticity Maximization Algorithm-PMA): the physical properties of 3D objects (e.g., complexity, color, shape), their ability to interact with human agents, their emotional value and their effects on brain structure (including but not limited to the increase of vascular flow, metabolism and connectivity). Stimuli can be organized based on their effects on plasticity and combined into specific combinations to create the best plasticity-inducing stimulus according to individual brain properties as defined by DMDT.

[0470] In some embodiments, PMA can be used to integrate elements of VAE into video game experiences, thereby seamlessly exposing players to passive plasticity-inducing stimuli during engaging entertainment activities. Elements that make up the background, active elements in the foreground, or interactive elements in the game can be designed to include plasticity-inducing properties, thereby exposing players to a continuous plasticity-inducing experience.

[0471] In some embodiments, a VR environment can be created to induce a specific brain state in which stimuli are visualized in a specific order to induce a brain response that represents a more focused, calm state, accompanied by reduced activation of emotion-regulating areas of the brain (such as the amygdala), thereby facilitating neutral reprocessing of traumatic memories. For example, stimuli are visualized as alternating between four quadrants of the visual field with predefined trajectories along the horizontal and vertical axes. The induced brain activation simulates an evolutionary visual exploration pattern that is associated with attention to external activities and reduced mind wandering, inner dialogue, and emotional activation. The Metaverse section of this article provides additional explanations and examples.

[0472] In some embodiments, a VR environment can be created to cause parameter manipulation of sensory perception, where the goal is to disrupt the brain's sensory systems and induce plasticity. These systems include, but are not limited to, visual, somatic sensory, motor, and auditory systems. Applications include, but are not limited to, exposure to altered sensory stimuli that mimic changes in the function of the sensory cortex of the brain, including, but not limited to, changes in the field of vision, so that an object is visualized, for example, in a spatial position different from its actual physical position in the VR environment. In this particular instance, an object can be visualized to a certain degree of mismatch between what is perceived from the visual system and what is visualized in the 3D environment; individuals exposed to such stimuli can be gradually trained to adapt to the spatial mismatch and correct his / her visual motor plan to reach the 3D object in its correct position (e.g., by pointing to a position 30 degrees to the left of the actual object and grabbing the object; systematically processing this visual motor adaptation for each object in the environment). This adaptation process can induce remodeling of synaptic connections in the visual cortex and other cortices of the brain and cerebellum, which may lead to an increase in brain plasticity, as measured by an increase in the levels of brain-derived neurotrophic factor (BDNF) and other brain markers. After training, the increased level of plasticity can be used as a primer for other brain, cognitive and behavioral manipulations, including but not limited to cognitive training, behavioral therapy, and physical training. After training, the sensory cortex (e.g., the visual cortex) can slowly readjust to the stimulus displayed in its correct position and may restore normal synaptic connections. This intervention, called virtual sensory manipulation (VSM), can also be applied to other sensory systems following the same principle, for example, inducing a mismatch between the spatial position of a 3D audio source in a VR environment and the actual position of the speakers displayed as 3D objects; this can also be applied to tactile stimulation when a device providing vibrotactile feedback is used in a VR environment. VSM can be embedded in different media using VR, including existing and specialized video games. In some embodiments, VSM is also used to retrain and perturb cognitive systems by embedding cognitive tasks and decision-making processes into the sensory retraining process; for example, the user is asked to recalibrate his / her grasping movement to objects in the 3D environment by 10 degrees, 20 degrees, and then 30 degrees, but only for objects that appear red; in another instance, objects that appear blue need to be adjusted 10 degrees to the left, while objects that appear green need to be adjusted 30 degrees to the right; these additional constraints and rules induce the engagement of cognitive functions associated with brain health and healthy aging (such as attention, working memory, inhibition, and flexibility).

[0473] In some embodiments, enriched environments (EEs) induce changes in brain structure and function associated with plasticity, learning, and brain optimization. These changes can be represented by their location and / or magnitude in the brain. For example, exposure to EEs with high-dimensional color properties may induce higher activation and plasticity in brain areas associated with vision, such as the occipital lobe of the brain. SYNAPSE can be used to identify such brain targets and circumvent the need for physical or simulated stimulation by directly stimulating the brain using non-invasive brain stimulation using STIMOLA.

[0474] In some embodiments, brain states generated by EE can be synthesized and corresponding brain targets defined by OPTI-BRAIN and OPTI-COG; STIMOLA can then be used to stimulate the targets using methods such as, but not limited to, transcranial electrical stimulation, transcranial magnetic stimulation, or transcranial ultrasound stimulation.

[0475] In some embodiments, the SYNAPSE solution for brain plasticity enhancement can be used to treat Alzheimer's disease (AD) and other pathologies characterized by altered brain plasticity mechanisms, altered inhibitory interneuron activity, reduced fast oscillatory brain activity, and altered brain network dynamics. AD is characterized by diffuse amyloid-β (Aβ) plaques and phosphorylated tau (p-tau) deposits in neurofibrillary tangles generated by altered inhibitory interneuron activity in the brain, as well as widespread neurodegeneration, neuroinflammatory signs, and altered brain plasticity processes.

[0476] In some embodiments, DMDT and DARWIN can be used to derive personalized interventions to enhance brain plasticity in patients with AD; STIMOLA can be used to deliver non-invasive brain stimulation solutions designed to enhance the activity of interneurons, thereby disrupting PNNs and enhancing plasticity.

[0477] Sample Application

[0478] Some embodiments described herein are further illustrated by the following examples and detailed schemes. However, the examples are intended only to illustrate the embodiments and should not be interpreted as limiting the scope of the technology described herein.

[0479] Combination synergistic application of plasticity-inducing protocols and targeted brain stimulation

[0480] As described in the SYNAPSE section herein, some embodiments relate to systems and methods for regulating brain plasticity. These methods include solutions for enhancing overall brain plasticity by chemicals (e.g., drugs) that can regulate the extracellular matrix and perineuronal networks, thereby promoting neuronal repair and neuronal system adaptability enhancement in lesions associated with neurological conditions such as traumatic brain injury and spinal cord injury and neurodegenerative conditions such as Alzheimer's disease. These methods provide general effects for PNNs of diseased central and peripheral nervous tissues and brain cells, where the targeting of the effects is limited. The systems and methods described herein allow the combination of neuroplasticity induction methods with the non-invasive brain stimulation (NIBS) method included in STIMOLA, achieving combined targeting using drugs and NIBS, enhanced targeted plasticity, and pre-activation of neural pathways by NIBS to enhance drug effects and accelerate injury recovery. Application examples of the synergistic effects of pharmaceutical agents and STIMOLA are shown in Figure 27A-2 In 7E, this involves the application of both central and peripheral electrical and magnetic stimulation.

[0481] In some embodiments, non-invasive brain stimulation can be delivered to brain regions to control the peripheral systems of the body affected by traumatic lesions or neurodegeneration. Stimulation can be performed to induce a synergistic effect (SYNAPSE) with the effects of drugs acting on neuroplasticity and PNN. Applications include rehabilitation programs for patients with spinal cord injury, traumatic brain injury or neurodegenerative disorders, which include but are not limited to dementia, Alzheimer's disease, mild cognitive impairment, frontotemporal dementia, multiple sclerosis and Parkinson's disease. In some embodiments, non-invasive brain stimulation can be delivered in combination with peripheral nerve stimulation to produce a timing-dependent effect on the pathological spinal cord pathway located at different heights within the spinal cord. Simultaneous central (brain) and peripheral (e.g., right hand) stimulation can be used to promote bottom-up and top-down re-transmission by promoting neuroplasticity promoted by drugs acting on PNN and neuroplasticity. In some embodiments, non-invasive brain stimulation can be delivered to brain areas affected by neurodegeneration or tissue pathology (e.g., due to ...

Claims

1. A method for changing the state of a human brain from an initial brain state to a target brain state, the method comprising: receiving information characterizing the initial brain state, the information comprising structural composition and functional architecture of the brain of the person, wherein the information comprises passive data and active data recorded from the brain of the person; estimating a likelihood that the brain of the person will change from the initial brain state to the target brain state based at least in part on the received information characterizing the initial brain state; determining a non-invasive brain stimulation protocol based at least in part on the received information characterizing the initial brain state and the estimated likelihood that the brain of the person will change from the initial brain state to the target brain state; as well as At least one non-invasive brain stimulation device is controlled to stimulate the brain of the person according to the non-invasive brain stimulation protocol to change the state of the brain of the person from the initial brain state to the target brain state.

2. The method of claim 1, wherein estimating the likelihood that the brain of the person changes from the initial brain state to the target brain state comprises using an algorithm to analyze received information characterizing the initial brain state.

3. The method of claim 2, wherein the algorithm is configured to enhance cognition and brain health.

4. The method of claim 2, wherein the algorithm is configured to optimize an intervention aimed at treating a neurological or psychiatric condition.

5. The method according to claim 2, further comprising: extracting at least one metric from the algorithm; and The at least one indicator is stored as a non-fungible token.

6. The method according to claim 5, further comprising: Use the non-fungible token to perform one or more of the following: track the progress of an intervention associated with the non-invasive brain stimulation regimen; define brain and cognitive stimulation methods; or compare a dynamic multi-layer digital twin (DMDT) associated with the person with a population-level DMDT to calculate a distance matrix.

7. The method according to claim 2, further comprising: extracting at least one metric from the algorithm; and The extracted at least one metric is used to provide information to a neuromorphic AI platform.

8. The method according to claim 2, further comprising: extracting at least one metric from the algorithm; and The extracted at least one metric is used to set parameters of the non-invasive brain stimulation protocol.

9. The method according to claim 2, further comprising: extracting at least one metric from the algorithm; and The extracted at least one metric is used to create avatars and content for a game and / or a metaverse application.

10. The method according to claim 2, further comprising: extracting at least one metric from the algorithm; and defining a personalized learning trajectory for skill acquisition using the extracted at least one metric, Wherein the likelihood of the brain of the person changing from the initial brain state to the target brain state is estimated based at least in part on the personalized learning trajectory of the skill acquisition.

11. The method according to claim 2, further comprising: extracting at least one metric from the algorithm; and using at least one of the extracted indicators to derive a measure of brain plasticity and resilience, Wherein the likelihood of the brain of the person changing from the initial brain state to the target brain state is estimated based at least in part on the measure of brain plasticity and elasticity.

12. The method according to claim 2, further comprising: extracting at least one metric from the algorithm; and using the extracted at least one indicator to derive an intervention aimed at improving one or more of brain resilience, brain plasticity, or overall brain health, The non-invasive brain stimulation regimen is determined based at least in part on a derived intervention intended to enhance one or more of brain elasticity, brain plasticity, or overall brain health.

13. The method of claim 2, further comprising: extracting at least one metric from the algorithm; and using the extracted at least one indicator to estimate an indicator of brain evolution likelihood and / or state / trait transition; Wherein the likelihood of the brain of the person changing from the initial brain state to the target brain state is estimated based at least in part on the estimated likelihood of brain evolution and / or indicators of state / trait transitions.

14. The method of claim 2, further comprising: extracting at least one metric from the algorithm; and using the extracted at least one indicator to estimate a specific brain state associated with one or more of: thinking about the future, the past, emotional content, or a specific memory, Wherein the likelihood of the person's brain changing from the initial brain state to the target brain state is estimated based at least in part on an estimated specific brain state associated with one or more of: thinking about the future, the past, emotional content, or a specific memory.

15. The method of claim 2, further comprising: extracting at least one metric from the algorithm; and using the extracted at least one indicator to create a template of a specific brain state associated with one or more of: thinking about the future, the past, emotional content, a specific memory, The likelihood that the person's brain will change from the initial brain state to the target brain state is estimated based at least in part on the template of the specific brain state associated with one or more of: thinking about the future, the past, emotional content, specific memories.

16. The method according to claim 14 or 15, further comprising: The data from the healthy controls and the patients are classified using the specific brain states to assess the brain health of the healthy controls and the patients.

17. The method of claim 2, further comprising: extracting at least one metric from the algorithm; and estimating disease progression of a neurodegenerative condition using the extracted at least one indicator, Wherein the likelihood that the brain of the person will change from the initial brain state to the target brain state is estimated based at least in part on the estimated progression of the neurodegenerative condition.

18. The method of claim 2, further comprising: extracting at least one metric from the algorithm; and estimating disease progression in a patient with a brain tumor using the extracted at least one indicator, wherein the likelihood that the brain of the person will change from the initial brain state to the target brain state is estimated based at least in part on an estimated disease progression in a patient suffering from a brain tumor.

19. The method of claim 2, further comprising: extracting at least one metric from the algorithm; and using the extracted at least one indicator to estimate disease progression and symptoms in a patient suffering from depression, wherein the likelihood that the brain of the person will change from the initial brain state to the target brain state is estimated based at least in part on estimated disease progression and symptoms of a patient suffering from depression.

20. The method of claim 2, further comprising: extracting at least one metric from the algorithm; and estimating disease progression and symptoms of a patient suffering from brain cancer using the extracted at least one indicator, wherein the likelihood that the brain of the person will change from the initial brain state to the target brain state is estimated based at least in part on estimated disease progression and symptoms of a patient suffering from brain cancer.

21. The method of claim 20, wherein the target brain state is a brain state that is susceptible to maximal clinical benefit from brain surgery in a patient with brain cancer.

22. The method of claim 20, wherein the target brain state is a brain state that minimizes the probability of experiencing tumor recurrence in a patient with brain cancer.

23. The method of claim 2, further comprising: extracting at least one metric from the algorithm; and estimating disease progression and symptoms of a patient suffering from a sleep disorder using the extracted at least one indicator, Wherein the likelihood that the brain of the person will change from the initial brain state to the target brain state is estimated based at least in part on estimated disease progression and symptoms of the patient suffering from the sleep disorder.

24. The method of claim 23, wherein the target brain state is a brain state that tends to achieve the longest duration of deep sleep stages during sleep in a patient suffering from a sleep disorder.

25. A method of changing a human brain state to a target brain state, the method comprising: receiving information associated with a digital replica of an initial brain state of the person, the digital replica comprising a plurality of brain indicators; determining a non-invasive brain stimulation protocol to change the state of the brain of the person from the initial brain state to the target brain state based at least in part on the received information associated with the digital replica of the initial brain state of the person; and The brain of the person is stimulated with at least one non-invasive brain stimulation device according to the determined non-invasive brain stimulation protocol to change the state of the brain of the person from the initial brain state to the target brain state.

26. A method of using brain data to create digital content and architecture for a video game or metaverse, the method comprising: receiving information characterizing the structural and functional architecture of a brain of a person, wherein the information includes electrophysiological data and neuroimaging data recorded from the person; Analyze the information received to create indicators of brain activity and performance in the form of a digital twin; and A first video game or metaverse content is created based at least in part on the indicator of brain activity and performance in the form of the digital twin.

27. The method of claim 26, wherein the received information comprises data sensed by a wearable device worn by the person.

28. The method of claim 26, further comprising: receiving updated information from a networked device; updating the digital twin based on the received updated information; and Creating a second video game or metaverse content based at least in part on the updated digital twin.

29. The method of claim 26, wherein the first video game or metaverse content includes multiple layers of avatars.

30. The method of claim 26, wherein the first video game or metaverse content comprises an in-game mechanic.

31. The method of claim 30, wherein the in-game mechanics include an in-game physical and / or avatar leveling system.

32. The method of claim 26, wherein the first video game or metaverse content includes a unique digital asset of the person.

33. The method of claim 32, wherein the unique digital asset of the person comprises equipment in a video game.

34. The method of claim 26, wherein the first video game or metaverse content includes artificial intelligence for controlling one or more non-player characters in a video game.

35. The method of claim 26, wherein the first video game or metaverse content comprises at least one in-game progression sequence or storyline.

36. The method of claim 26, wherein the first video game or metaverse content comprises a clinical metaverse application for patients suffering from neurological and psychiatric conditions.

37. A method of creating a digital replica of a person's brain state, the method comprising: receiving information associated with a brain of the person, the information comprising brain structure information and brain function information, the brain function information comprising electrophysiological data recorded from the person; processing the received information to determine a plurality of brain indicators characterizing a brain state of the person; as well as A digital replica of the brain state of the person is created based at least in part on the plurality of brain indicators determined from the received information.

38. The method of claim 37, further comprising: A neuromorphic artificial intelligence agent is designed based on the digital replica of the brain state of the person.

39. The method of claim 38, further comprising: updating the digital replica of the brain state of the person based at least in part on an updated plurality of brain indicators determined based on updated information associated with the brain of the person; and The neuromorphic artificial intelligence agent is updated based on the updated digital replica of the brain state of the person.

40. The method of claim 38, wherein the neuromorphic artificial intelligence agent is based on an oscillation generator that reflects the brain state and brain architecture of the person.

41. The method of claim 38, wherein the neuromorphic artificial intelligence agent is used as a conversational agent in a therapeutic setting.

42. A method according to claim 41, wherein the neuromorphic artificial intelligence agent is used to improve the efficacy of a mental health intervention, wherein the mental health intervention comprises cognitive behavioral therapy.

43. An apparatus for modifying a brain state of a person, the apparatus comprising: at least one sensor configured to sense brain activity signals from a brain of the person; a stimulation device configured to provide non-invasive stimulation to the brain of the person; and A controller is configured to control operation of the stimulation device based at least in part on one or more characteristics of the brain activity signal sensed from the at least one sensor after stimulating the brain of the person with the stimulation device.

44. A method of providing personalized modulation of brain activity to a person to alter the brain state of the person's brain, the method comprising: sensing a plurality of evoked potentials using a plurality of electrophysiological sensors in response to providing non-invasive stimulation to the brain of the person; determining an individualized adjustment plan for the person based at least in part on at least one characteristic of the plurality of evoked potentials, the individualized adjustment plan comprising stimulation locations and one or more stimulation characteristics; and The brain of the person is stimulated according to the personalized regulation plan to change the brain state of the person.

45. A method of personalizing a digital experience within a digitally created environment, the method comprising: receiving information associated with a digital replica of a user's brain, the digital replica comprising a plurality of brain indicators of the user; configuring at least one aspect of the digitally created environment based at least in part on the plurality of brain indicators of the user included in the digital replica to create a personalized digitally created environment for the user; and The personalized digitally created environment is displayed to the user.

46. ​​A method of inducing plasticity in a human brain, the method comprising: receiving information associated with a digital replica of a brain of a person, the digital replica comprising a plurality of brain indicators of the person, the plurality of brain indicators comprising a first plasticity level of the brain of the person; controlling at least one non-invasive brain stimulation device to deliver non-invasive stimulation to at least one location in the brain of the person, the at least one location having been identified as a location for inducing plasticity in the brain of the person; as well as A second level of plasticity of the brain of the person is determined based at least in part on feedback received after stimulating the at least one location of the brain of the person.

47. A method for characterizing a person's brain health index, the method comprising: receiving information characterizing, at least in part, a brain structural composition and a functional architecture of a brain of a person, wherein the information comprises first data, the first data comprising passive data and active data recorded from the person; processing the first data to extract, at least in part, an indicator of the performance and efficiency of the person's brain and cognitive systems; and Interventions for modulating brain activity of the brain of the person are defined based on indicators of the performance and efficiency of the brain and cognitive systems of the person.

48. The method of claim 47, wherein the information is collected via a wearable device, a personal computer, or a portable headset.

49. The method of claim 47, wherein the information is collected by at least one electrophysiological technique.

50. The method of claim 49, wherein the at least one electrophysiological technique comprises electroencephalography.

51. The method of claim 47, wherein the information is collected by at least one neuroimaging method.

52. The method of claim 51, wherein the at least one neuroimaging method comprises one or more of magnetic resonance imaging (MRI), positron emission tomography (PET), or near infrared spectroscopy.

53. The method of claim 47, wherein The information is obtained from a variety of sources, and Processing the first data to at least partially extract indicators of the performance and efficiency of the person's brain and cognitive systems includes using an algorithm to tune the information from multiple sources into a digital twin.

54. The method of claim 53, wherein defining an intervention for modulating brain activity of the brain of the person comprises using the digital twin to define an intervention that enhances cognition and brain health of the brain.

55. The method of claim 53, wherein defining an intervention for modulating brain activity of the brain of the person comprises using the digital twin to define an intervention for treating one or more neurological or psychiatric conditions.

56. The method of claim 53, further comprising: The digital twin is used to predict a trajectory of a neurological or psychiatric disease, wherein defining an intervention for modulating brain activity of the brain of the person includes defining the intervention based at least in part on the predicted trajectory.

57. The method of claim 56, wherein the neurological disease is Alzheimer's disease, dementia, or brain cancer.

58. The method of claim 53, further comprising: receiving second data sensed by a wearable device or a brain activity recording device; and The digital twin is updated based at least in part on the second data.

59. The method of claim 53, wherein defining an intervention for modulating brain activity of the brain of the person comprises using the digital twin to set one or more parameters of non-invasive brain stimulation.

60. The method of claim 53, wherein defining an intervention for modulating brain activity of the brain of the person comprises using the digital twin to define a personalized learning trajectory for skill acquisition.

61. The method of claim 53, wherein defining an intervention for modulating brain activity of the brain of the person comprises using the digital twin to derive measures of brain plasticity and elasticity.

62. The method of claim 47, wherein The received information is formalized into a network, and Processing the first data to at least partially extract indicators of the performance and efficiency of the person's brain and cognitive system includes using graph theory indicators and network control theory indicators to extract characteristics of the network.

63. The method of claim 47, wherein The received information is formalized into a network, and Processing the first data to at least partially extract indicators of performance and efficiency of the person's brain and cognitive systems includes extracting characteristics of the network by analyzing at least one of amplitude, shape, or frequency of the first data.

64. The method of claim 47, wherein processing the first data to at least partially extract indicators of the performance and efficiency of the person's brain and cognitive systems comprises calculating structural elasticity, functional elasticity, and dynamic elasticity of the brain.

65. The method of claim 47, wherein processing the first data to extract, at least in part, indicators of performance and efficiency of the person's brain and cognitive systems comprises computing a structural connectome of the brain.

66. The method of claim 65, further comprising: Monitoring disease progression in the brain and predicting levels of neuroinflammation in the brain based at least in part on the structural connectome of the brain.

67. The method of claim 65, further comprising: Demyelination in the brain is monitored and / or white matter lesions in the brain are identified based at least in part on the structural connectome of the brain.

68. The method of claim 47, wherein the information comprises information about an emotional state of the person recorded by a camera, the method further comprising storing a non-fungible token representing the information, the information comprising the information about the emotional state of the person.

69. The method of claim 68, further comprising: An emotional virtual agent is created based on the information about the emotional state of the person.

70. The method of claim 69, further comprising: Use the emotional virtual agent to monitor and support patients with dementia or to enhance brain health in healthy people.

71. The method of claim 68, further comprising: At least one cognitive or behavioral intervention is directed based on the information about the emotional state of the person to improve brain health.

72. The method of claim 68, further comprising: Dosage of a therapeutic intervention is guided based on the information about the emotional state of the person.

73. The method of claim 47, wherein the information comprises spontaneous and / or stimulus-driven eye movements recorded by a camera, the method further comprising storing a non-homogeneous token representing an analysis of the spontaneous and / or stimulus-driven eye movements.

74. A method according to claim 73, wherein the spontaneous and / or stimulus-driven eye movements are collected while the person performs a cognitive task.

75. The method of claim 74, further comprising: The person's brain health and overall cognitive performance are assessed based on the non-fungible token.

76. The method of claim 74, further comprising: Improvements in the person's cognitive performance and learning ability are assessed based on the non-fungible tokens.

77. The method of claim 73, wherein the spontaneous and / or stimulus-driven eye movements are collected while the person is playing a video game, the method further comprising: using the non-fungible token to evaluate the person's in-game performance.

78. A method of stimulating one or more brain regions of a person to direct the state of the person's brain to a target state, the method comprising: receiving non-invasive brain stimulation parameters, wherein the non-invasive brain stimulation parameters are determined using information included in a dynamic multi-layer digital twin (DMDT) associated with the brain of the person; controlling at least one non-invasive brain stimulation device to stimulate the one or more brain regions of the person according to the non-invasive brain stimulation parameters; and Feedback is received regarding the effect of the stimulation of the one or more brain regions to determine whether the state of the brain of the person is in the target state.

79. The method of claim 78, wherein the at least one non-invasive brain stimulation device is configured to provide one or more of transcranial electrical stimulation, transcranial magnetic stimulation, or transcranial focused ultrasound stimulation.

80. The method of claim 78, wherein the at least one non-invasive brain stimulation device is configured to provide sensory stimulation comprising auditory stimulation and / or visual stimulation.

81. The method of claim 80, wherein the sensory stimulation is embedded within multimedia content, the multimedia content comprising a movie, a song, a television program, a video game, or a virtual reality application.

82. The method of claim 79 or claim 80, wherein the at least one non-invasive brain stimulation device is configured to provide sinusoidal stimulation in a pattern of noise bursts applied at a specific frequency or random noise stimulation limited to a specific frequency band.

83. The method of claim 78, further comprising: The non-invasive stimulation intervention is provided to the human sequentially or simultaneously with controlling the at least one non-invasive stimulation device to stimulate the one or more brain regions of the human.

84. The method of claim 83, wherein the non-invasive stimulation intervention comprises a cognitive rehabilitation or cognitive training intervention comprising a video game.

85. The method of claim 83, wherein the non-invasive stimulation intervention comprises a behavioral intervention or a physical training intervention.

86. The method of claim 83, wherein the non-invasive stimulation intervention comprises a drug intervention.

87. The method of claim 78, wherein controlling the at least one non-invasive brain stimulation device to stimulate the one or more brain regions of the person comprises stimulating the one or more brain regions to selectively modify specific brain connections between the one or more brain regions.

88. The method of claim 78, wherein controlling the at least one non-invasive brain stimulation device to stimulate the one or more brain regions of the person comprises stimulating the one or more brain regions to increase brain plasticity and / or accelerate learning rate.

89. The method of claim 78, wherein the one or more brain regions are defined based on data associated with the person.

90. The method of claim 78, wherein the one or more brain regions are defined based on group level data.

91. The method of claim 78, wherein the one or more brain regions are part of a functional brain network.

92. The method of claim 78, wherein the one or more brain regions comprise subcortical brain structures.

93. The method of claim 78, wherein the one or more brain regions are defined based on neuroimaging data.

94. The method of claim 93, wherein the neuroimaging data comprises functional MRI data.

95. The method of claim 93, wherein the neuroimaging data comprises diffusion MRI data.

96. The method of claim 93, wherein the neuroimaging data comprises structural MRI data.

97. The method of claim 93, wherein the neuroimaging data comprises positron emission tomography (PET) data.

98. The method of claim 93, wherein the neuroimaging data comprises near infrared spectroscopy data.

99. The method of claim 78, wherein the one or more brain regions are defined based on electrophysiological data.

100. The method of claim 99, wherein the electrophysiological data comprises scalp electroencephalography data.

101. The method of claim 78, wherein the one or more brain regions are defined by mapping white matter tracts in the brain of the person.

102. The method of claim 78, wherein the one or more brain regions are regions that are connected or not connected to a brain cancer.

103. The method of claim 78, wherein the one or more brain regions are regions responsible for sleep induction.

104. A method for determining the health and performance of a person's brain, the method comprising: sensing a plurality of evoked potentials in response to a first non-invasive stimulation of each of a plurality of locations in the brain of the person; determining personalized stimulation parameters for the person based at least in part on at least one characteristic of the plurality of evoked potentials, wherein the personalized stimulation parameters include stimulation location and one or more stimulation characteristics; providing a second non-invasive stimulation to the person based on the personalized stimulation parameters; and Feedback is received regarding the effectiveness of the second non-invasive stimulation to extract indicators of performance and brain health.

105. The method of claim 104, further comprising: A non-invasive stimulation device is controlled to provide the first non-invasive stimulation to each of the plurality of locations in the brain of the person.

106. The method of claim 105, wherein providing the first non-invasive stimulation comprises providing at least one of transcranial electrical stimulation, transcranial magnetic stimulation, or transcranial focused ultrasound stimulation.

107. The method of claim 105, wherein providing the first non-invasive stimulation comprises providing at least one sensory or cognitive stimulation.

108. The method of claim 104, wherein the personalized stimulation parameters are determined based at least in part on a comparison of the at least one characteristic of the plurality of evoked potentials with spontaneous activity in the brain of the person.

109. The method of claim 104, wherein the first non-invasive stimulation is delivered to a plurality of the plurality of locations simultaneously or in a predefined sequence.

110. The method of claim 104, wherein the plurality of locations are arranged in a grid for systematic spatial assessment of brain dynamics.

111. The method of claim 104, wherein the at least one characteristic of the plurality of evoked potentials comprises peak amplitude.

112. The method of claim 104, wherein determining the personalized stimulation parameters comprises selecting the stimulation location as the location among the plurality of locations associated with a maximum peak amplitude of the plurality of evoked potentials.

113. The method of claim 104, wherein the plurality of locations comprises locations in a brain network comprising one or more of: a default mode network, a frontoparietal control network, a sensorimotor network, a pre-salience network, a dorsal attention network, a ventral attention network, a visual network, an auditory network, or a language network.

114. The method of claim 104, further comprising: A neurological or psychiatric disorder is treated or alleviated by providing the second non-invasive stimulation to the brain of the person based on the personalized stimulation parameters.

115. The method of claim 114, wherein the neurological or psychiatric disease is Alzheimer's disease.

116. The method of claim 114, wherein the neurological or psychiatric disease is mild cognitive impairment (MCI).

117. The method of claim 114, wherein the neurological or psychiatric disease is frontotemporal dementia.

118. The method of claim 114, wherein the neurological or psychiatric disease is one of: depression (DEP), schizophrenia (SCZ), autism (AUT), attention deficit and hyperactivity disorder (ADHD), bipolar disorder (BP), amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), traumatic brain injury (TBI), insomnia (INS), disorder of consciousness (DOC), headache (HD), multiple sclerosis (MS), stroke (STR) or brain tumor (BT).

119. The method of claim 114, wherein the neurological or psychiatric disorder is characterized by memory impairment.

120. The method of claim 114, wherein the neurological or psychiatric disorder is characterized by deficits in cognitive control.

121. The method of claim 114, wherein the neurological or psychiatric disorder is characterized by reduced functional independence.

122. The method of claim 114, wherein the neurological or psychiatric disease is brain cancer.

123. The method of claim 114, wherein the neurological or psychiatric disease is a brain tumor.

124. The method of claim 123, wherein the second non-invasive stimulation is provided to the brain of the person prior to surgery to provide information for surgical planning associated with removal of at least a portion of the brain tumor.

125. The method of claim 123, wherein the second non-invasive stimulation is provided to the brain of the human after surgery to detect recurrence of the brain tumor.

126. The method of claim 104, further comprising: A pharmacological intervention affecting the central nervous system is provided to the human, wherein the pharmacological intervention is provided sequentially or simultaneously with the second non-invasive stimulation.

127. The method of claim 104, further comprising: A cognitive assessment, cognitive training, or cognitive enhancement intervention is provided to the human, wherein the cognitive assessment, cognitive training, or cognitive enhancement intervention is provided sequentially or simultaneously with the second non-invasive stimulation.

128. The method of claim 104, further comprising: A behavioral intervention is provided to the human, wherein the behavioral intervention is provided sequentially or simultaneously with the second non-invasive stimulation.

129. The method of claim 104, further comprising: A third non-invasive stimulation is provided to the human, wherein the third non-invasive stimulation is provided sequentially or simultaneously with the second non-invasive stimulation.

130. The method of claim 129, wherein the third non-invasive stimulation is transcranial electrical stimulation.

131. A method of creating a plasticity-inducing intervention for a human, the method comprising: receiving information characterizing a brain state and a level of plasticity of the person, wherein the information includes electrophysiological data, behavioral data, and neuroimaging data recorded from the person; identifying a plasticity-inducing intervention for the person, the plasticity-inducing intervention comprising one or more brain stimulation targets; controlling at least one non-invasive brain stimulation device to deliver stimulation to the one or more brain stimulation targets; as well as Feedback is received regarding the effects of the stimulation of the one or more brain stimulation targets to determine whether the level of plasticity of the person's brain has changed.

132. The method of claim 131, wherein the plasticity level of the person is measured by brain and cognitive indices obtained using a dynamic multi-layer digital twin (DMDT) of the brain of the person.

133. The method of claim 132, wherein identifying a plasticity-inducing intervention is based on an enriched environment determined based on the DMDT of the brain of the person.

134. The method of claim 133, wherein the rich environment is a virtual environment, an augmented environment, or a mixed reality environment.

135. The method of claim 133, wherein the enriched environment is delivered in the form of a room equipped with sensory stimulation devices and data recording devices.

136. The method of claim 133, wherein the rich environment is delivered in the form of a video game.

137. The method of claim 133, wherein the rich environment is represented by a dual-task platform.

138. The method of claim 131, wherein the plasticity-inducing intervention is configured to target the peripheral neuronal network and extracellular matrix of the brain of the human.

139. The method of claim 131, wherein the plasticity-inducing intervention is based on modulation of CSPGs by injection of ChABC.

140. The method of claim 131, wherein the plasticity-inducing intervention is based on regulating CSPG by manipulating BDNF.

141. The method of claim 131, wherein the plasticity-inducing intervention is based on modulation of CSPG by ketamine administration.

142. The method of claim 131, wherein the plasticity-inducing intervention is based on a drug that acts on GABAergic circuits of the brain.

143. The method of claim 131, wherein the plasticity-inducing intervention comprises using a drug to modulate one or more of PNNs, ECMs, or CSPGs.

144. The method of claim 131, wherein the plasticity-inducing intervention is based on a non-invasive brain stimulation method.

145. The method of claim 144, wherein the non-invasive brain stimulation method comprises a protocol for inducing rapid oscillatory activity in the gamma frequency band in the brain.

146. The method of claim 144, wherein the non-invasive brain stimulation method comprises a protocol for inducing activity in the gamma band to activate inhibitory interneurons and glial cells.

147. A method according to claim 144, wherein the non-invasive brain stimulation method includes a scheme for inducing gamma activity in the brain, and the non-invasive brain stimulation method includes transcranial alternating current stimulation (tACS), transcranial pulsed gamma stimulation (tPGS) and / or narrow-band transcranial random noise stimulation (nb-tRNS).

148. The method of claim 131, wherein the plasticity-inducing intervention is applied to patients suffering from neurological and psychiatric conditions.

149. The method of claim 131, wherein the plasticity-inducing intervention is applied to athletes to improve performance and response to training.

150. The method of claim 131, wherein the plasticity-inducing intervention is applied to improve sleep quality and memory consolidation.

151. The method of claim 131, wherein the plasticity-inducing intervention is applied to patients suffering from post-traumatic stress disorder (PTSD) to facilitate manipulation and elimination of traumatic memories and promote healthy brain states.

152. The method of claim 131, wherein the plasticity-inducing intervention is applied to astronauts to enhance learning and retention of skills and promote protection from brain and cognitive changes associated with space flight.

153. The method of claim 131, wherein the plasticity-inducing intervention is applied during sleep to enhance memory consolidation and overall brain plasticity.

154. A method for enhancing cognitive performance and learning ability by stimulating a person's brain in a predefined sequence, the method comprising: receiving information characterizing an initial brain state of the person, wherein the information includes electrophysiological data and cognitive data recorded from the person; defining an optimal sequence of cognitive changes to be induced to change the state of said person from said initial brain state to a target brain state; delivering one or more cognitive modulators targeting specific brain structures and cognitive functions based on the optimal sequence of cognitive changes to be induced; and Feedback is received regarding the effect of the delivered one or more cognitive modulators to determine whether the state of the brain of the person is in the target brain state.

155. The method of claim 154, wherein an optimal sequence of cognitive modulators is defined to achieve an optimal learning state (OLS).

156. The method of claim 155, wherein the OLS is defined based on a dynamic multi-layer digital twin (DMDT) of the brain of the person.

157. The method of claim 154, wherein the target brain state is a state of high cognitive performance defined by specific activation and deactivation patterns of brain regions based on neuroimaging or electrophysiological mapping.

158. The method of claim 154, wherein an optimal sequence of cognitive modulators is combined with brain modulators to increase brain excitability, modulate plasticity and / or increase brain efficiency or modularity.

159. The method of claim 158, wherein the brain modulator is in the form of a non-invasive stimulation.

160. The method of claim 154, wherein the one or more cognitive modulators include cognitive training that acts on sensory and cognitive functions.

161. The method of claim 154, wherein the target brain state is a brain state in which the learning rate is accelerated.

162. The method of claim 161, wherein the learning rate is associated with learning a sensorimotor skill comprising one or more of playing a musical instrument, enhancing memory representation, or learning a coded language.

163. The method of claim 161, wherein the learning rate is associated with learning a rehabilitation skill in a patient suffering from a neurological or psychiatric condition.

164. The method of claim 161, wherein the learning rate is associated with improving physical and / or mental performance associated with a sport.

165. The method of claim 161, wherein the learning rate is associated with improved educational performance.

166. The method of claim 161, wherein the learning rate is associated with cognitive-behavioral therapy, speech therapy, or dynamic therapy.

167. The method of claim 161, wherein the learning rate is associated with enhanced convergent thinking in the form of fluid intelligence and abstract reasoning.

168. The method of claim 161, wherein the learning rate is associated with enhanced divergent thinking in the form of creativity and insight.

169. The method of claim 161, wherein the learning rate is associated with enhancing general intelligence by targeting brain regions and networks of a multi-layered convergent-divergent thinking (CDt) model of human cognition.

170. A system for managing a brain optimization program, the system comprising: a data collection platform configured to collect and store data for identifying individuals with the best optimization goals; a data analysis platform configured to process the data of the individual and derive optimal stimulation parameters; as well as A database comprising the data of the individual collected before and / or during brain stimulation therapy based on the optimal stimulation parameters.

171. The system of claim 170, wherein the system is accessible to a clinician prescribing the brain stimulation therapy.

172. The system of claim 170, wherein the system is accessible by an end user via a portable computing device.

173. The system of claim 170, wherein the data analysis platform is a cloud-based computing platform in communication with the data collection platform via at least one network.

174. The system of claim 170, wherein the system is controlled via a blockchain environment.

175. A system for recording and regulating human brain activity, the system comprising: a stimulation device configured to provide non-invasive stimulation to each of a plurality of locations in a brain region of the person; a sensor device configured to sense at least one evoked response in response to the non-invasive stimulation provided by the stimulation device; and A computer processor configured to select one of the plurality of locations suitable for modulating brain activity by non-invasive stimulation as a personalized stimulation target, wherein the selection is based on at least one characteristic of the at least one evoked response.

176. A system according to claim 175, wherein the stimulation device and the sensor device are included as part of a portable device in the form of a wearable headset.

177. The system of claim 176, wherein the portable device is configured to be controlled by a separate computing device, thereby allowing remote control of the portable device.

178. A system according to claim 175, wherein the stimulation device is configured to provide at least one of transcranial electrical stimulation, transcranial magnetic stimulation, or transcranial focused ultrasound stimulation to the personalized stimulation target.

179. A system according to claim 175, wherein the stimulation device is configured to treat or alleviate the person's neurological or psychiatric disease by providing the non-invasive stimulation to a selected personalized stimulation target.

180. The system of claim 179, wherein the neurological or psychiatric disease is Alzheimer's disease.

181. The system of claim 179, wherein the neurological or psychiatric disease is frontotemporal dementia.

182. The system of claim 179, wherein the neurological or psychiatric disease is mild cognitive impairment (MCI).

183. The system of claim 179, wherein the neurological or psychiatric disease is brain cancer.

184. The system of claim 179, wherein the neurological or psychiatric disease is glioma.

185. The system of claim 179, wherein the neurological or psychiatric disorder is depression.

186. The system of claim 179, wherein the neurological or psychiatric disorder is ADHD.

187. The system of claim 179, wherein the neurological or psychiatric disease is a sleep disorder.

188. The system of claim 179, wherein the neurological or psychiatric disease is characterized by alterations in brain networks, such as depression (DEP), schizophrenia (SCZ), autism (AUT), attention deficit and hyperactivity disorder (ADHD), bipolar disorder (BP), amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), traumatic brain injury (TBI), insomnia disorder (INS), disorders of consciousness (DOC), headache (HD), multiple sclerosis (MS), stroke (STR) and brain tumor (BT).

189. The system of claim 179, wherein the neurological or psychiatric disorder is characterized by memory impairment.

190. The system of claim 179, wherein the neurological or psychiatric disorder is characterized by deficits in cognitive control.

191. The system of claim 179, wherein the neurological or psychiatric disorder is characterized by reduced functional independence.

192. A system according to claim 175, wherein the non-invasive stimulation provided to the selected personalized stimulation target is combined with a pharmacological intervention affecting the central nervous system, the pharmacological intervention being delivered sequentially or simultaneously.

193. A system according to claim 175, wherein the non-invasive stimulation provided to the selected personalized stimulation target is combined with one or more of cognitive assessment, cognitive training, or cognitive enhancement intervention, and the cognitive assessment, cognitive training, or cognitive enhancement intervention are delivered sequentially or simultaneously.

194. A system according to claim 175, wherein the non-invasive stimulation provided to the selected personalized stimulation target is combined with a behavioral intervention, and the behavioral intervention is delivered sequentially or simultaneously.

195. A system according to claim 175, wherein the non-invasive stimulation provided to the selected personalized stimulation target is combined with different non-invasive stimulation interventions, which are delivered sequentially or simultaneously, including transcranial electrical stimulation.

196. A system according to claim 175, wherein the computer processor is further configured to elaborate a sequence of combined automatic and semi-automatic signal processing algorithms installed on local hardware to achieve specific indication of one or more of stimulation location, frequency or intensity.

197. The system of claim 175, wherein the computer processor is further configured to elaborate a sequence of combined automatic and semi-automatic signal processing algorithms installed on remote hardware having connectivity capabilities to achieve specific instructions for stimulation location, frequency, or intensity.

198. A system according to claim 175, wherein the sensor device includes electrodes configured to be placed on the scalp of the person.

199. A system according to claim 175, wherein the sensor device includes an electrode configured to be placed inside the ear of the person.

200. A system according to claim 175, wherein the sensor device includes electrodes configured to be placed on the face of the person.

201. The system of claim 175, wherein the sensor device comprises high-density carbon nanotube-based electrodes placed inside a stimulation cap placed on the person's head.

202. The system of claim 175, wherein the system is configured for use within a metaverse.

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