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

Abstract

A method and apparatus for changing a person's brain state from an initial brain state to a target brain state are described. The method includes receiving information characterizing the initial brain state, where the information includes the structural configuration and functional architecture of the brain; estimating the likelihood that the brain will change 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 protocol based at least in part on the received information and the estimated likelihood that the brain will change 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 protocol. The method also includes using the brain information to inform the design of a computationally general artificial intelligence agent.

Description

【Technical Field】 【0001】 The present disclosure relates to systems and methods for measuring, predicting, and optimizing human brain function. 【Background Art】 【0002】 The brain is a complex dynamic system characterized by topographic organization in a distributed neural network with specific temporal and spatial properties. Individual brains differ in their structural and functional characteristics, and today only a partial understanding has been obtained regarding how brain activity explains cognition and behavior. Furthermore, very limited knowledge has been obtained regarding how the brain adapts over time or in response to external and internal perturbations, such as in the case of normal healthy aging, trauma, dementia, brain cancer, or cognitive training aimed at improving abilities such as memory or attention. In addition, models that can simulate and predict such changes are generally not currently available, and the models developed so far do not employ artificial intelligence algorithms that take into account the principles of brain function. 【Summary of the Invention】 【Means for Solving the Problems】 【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 potentials, as well as corresponding strategies for enhancing the brain, cognition, and behavior, hardware for data collection and neuromodulation, and application-specific algorithms for generating digital assets based on the characteristics of individual brain activity. 【0004】 In some embodiments, a method is provided for changing the state of a human brain from an initial brain state to a target brain state. The method includes receiving information characterizing the initial brain state, where the information includes the structural constitution and functional architecture of the human brain and includes passive and active data recorded from the human brain; estimating the likelihood that the human brain 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 human brain will change from the initial brain state to the target brain state; and controlling at least one non-invasive brain stimulation device to stimulate the human brain according to the non-invasive brain stimulation protocol to change the state of the human brain from the initial brain state to the target brain state. 【0005】 In one aspect, estimating the likelihood that the human brain will change from the initial brain state to the target brain state includes using an algorithm to analyze the received information characterizing the initial brain state. In another aspect, the algorithm is configured to enhance cognition and brain health. In another aspect, the algorithm is configured to optimize an intervention aimed at treating a neurological or psychiatric condition. In another aspect, the method further includes extracting at least one metric from the algorithm and storing the at least one metric as a non-fungible token. In another aspect, the method further includes using the non-fungible token to perform one or more of tracking the progress of an intervention associated with the non-invasive brain stimulation protocol, defining a brain and cognitive stimulation approach, or calculating a distance matrix by comparing a dynamic multi-layer digital twin (DMDT) associated with a person to a population-level DMDT. 【0006】 In another aspect, the method further includes extracting at least one metric from an algorithm and using the at least one extracted metric to notify a neuromorphic AI platform. In another aspect, the method further includes extracting at least one metric from an algorithm and using the at least one extracted metric to set parameters for a non-invasive brain stimulation protocol. In another aspect, the method further includes extracting at least one metric from an algorithm and using the at least one extracted metric to create avatars and content for a game and / or a metaverse application. In another aspect, the method further includes extracting at least one metric from an algorithm and using the at least one extracted metric to define an individualized learning trajectory for skill acquisition, wherein the likelihood that a person's brain changes from an initial brain state to a target brain state is estimated based at least in part on the individualized learning trajectory for skill acquisition. In another aspect, the method further includes extracting at least one metric from an algorithm and using the at least one extracted metric to derive a measure of brain plasticity and resilience, wherein the likelihood that a person's brain changes from an initial brain state to a target brain state is estimated based at least in part on the measure of brain plasticity and resilience. In another aspect, the method further includes extracting at least one metric from an algorithm and using the at least one extracted metric to derive an intervention aimed at increasing one or more of brain resilience, brain plasticity, or overall brain health, wherein the non-invasive brain stimulation protocol is determined based at least in part on the derived intervention aimed at increasing one or more of brain resilience, brain plasticity, or overall brain health. 【0007】 In another aspect, the method further includes extracting at least one metric from an algorithm and using the at least one extracted metric to estimate a metric of the likelihood of brain evolution and / or the transition of states / traits. The likelihood that a human brain changes from an initial brain state to a target brain state is estimated based at least in part on the estimated metric of the likelihood of brain evolution and / or the transition of states / traits. In another aspect, the method further includes extracting at least one metric from an algorithm and using the at least one extracted metric to estimate a specific brain state related to one or more of future, past, emotional content, or thoughts related to a specific memory. The likelihood that a human brain changes from an initial brain state to a target brain state is estimated based at least in part on the estimated specific brain state related to one or more of future, past, emotional content, or thoughts related to a specific memory. In another aspect, the method further includes extracting at least one metric from an algorithm and using the at least one extracted metric to create a template of a specific brain state related to one or more of future, past, emotional content, or thoughts related to a specific memory. The likelihood that a human brain changes from an initial brain state to a target brain state is estimated based at least in part on the estimated template of the specific brain state related to one or more of future, past, emotional content, or thoughts related to a specific memory. In another aspect, the method further includes using a specific brain state to classify data from healthy controls and patients to evaluate the health of the brains of healthy controls and patients. 【0008】 In another aspect, the method further includes extracting at least one metric from an algorithm and using the at least one extracted metric to estimate disease progression in a neurodegenerative condition, wherein the likelihood that a human brain changes from an initial brain state to a target brain state is estimated based at least in part on the estimated progression in the neurodegenerative condition. In another aspect, the method further includes extracting at least one metric from an algorithm and using the at least one extracted metric to estimate disease progression in a patient having a brain tumor, wherein the likelihood that a human brain changes from an initial brain state to a target brain state is estimated based at least in part on the estimated disease progression in the patient having a brain tumor. In another aspect, the method further includes extracting at least one metric from an algorithm and using the at least one extracted metric to estimate disease progression and symptoms in a patient having depression, wherein the likelihood that a human brain changes from an initial brain state to a target brain state is estimated based at least in part on the estimated disease progression and symptoms in the patient having depression. 【0009】 In another aspect, the method further includes extracting at least one metric from an algorithm and using the at least one extracted metric to estimate disease progression and symptoms in a patient having brain cancer, wherein the likelihood that a human brain changes from an initial brain state to a target brain state is estimated based at least in part on the estimated disease progression and symptoms in the patient having brain cancer. In another aspect, the target brain state is the brain state that contributes to the maximum clinical benefit of brain surgery in a patient having brain cancer. In another aspect, the target brain state is the brain state in which the probability of experiencing tumor recurrence in a patient having brain cancer is lowest. 【0010】 In another aspect, the method further includes extracting at least one metric from an algorithm and using the at least one extracted metric to estimate disease progression and symptoms in a patient having a sleep disorder, wherein the likelihood that a person's brain changes from an initial brain state to a target brain state is estimated based at least in part on the estimated disease progression and symptoms in the patient having a sleep disorder. In another aspect, the target brain state is a brain state that contributes to the longest duration of deep sleep stages during sleep in a patient having a sleep disorder. 【0011】 In some embodiments, a method of changing a 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 a person, the information including a plurality of brain metrics, determining a non-invasive brain stimulation protocol for changing the state of the person's brain 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 using at least one non-invasive brain stimulation device to stimulate the person's brain according to the determined non-invasive brain stimulation protocol to change the state of the person's brain from the initial brain state to the target brain state. 【0012】 In some embodiments, a method of creating digital content and architecture for use in a video game or a metaverse using brain data is provided. The method includes receiving information characterizing the structural and functional architecture of a person's brain, the information including electrophysiological and neuroimaging data recorded from the person, analyzing the received information to create metrics 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 metrics of brain activity and performance in the digital twin. 【0013】 In one aspect, the received information includes data sensed by a wearable device worn by a person. In another aspect, the method further includes receiving updated information from a device connected to a network, updating a digital twin based on the received updated information, and creating second video game or metaverse content based at least in part on the updated digital twin. In another aspect, the first video game or metaverse content includes a multi-layer avatar. In another aspect, the first video game or metaverse content includes in-game mechanics. In another aspect, the in-game mechanics include in-game physics and / or an avatar leveling system. In another aspect, the first video game or metaverse content includes a unique digital asset for a person. In another aspect, the unique digital asset for a person includes facilities in a video game. In another aspect, the first video game or metaverse content includes artificial intelligence used to control one or more non-player characters in a video game. In another aspect, the first video game or metaverse content includes at least one in-game progression sequence or storyline. In another aspect, the first video game or metaverse content includes a clinical metaverse application for a patient with neurological and psychiatric conditions. 【0014】 In some embodiments, a method of creating a digital replica of a person's brain state is provided. The method includes receiving information associated with a person's brain, the information including structural brain information and functional brain information, the functional brain information including electrophysiological data recorded from the person, processing the received information to determine a plurality of brain metrics characterizing the person's brain state, and creating a digital replica of the person's brain state based at least in part on the plurality of brain metrics determined from the received information. 【0015】 In one aspect, the method further includes designing a neuromorphic artificial intelligence agent based on a digital replica of a human brain state. In another aspect, the method includes updating a digital replica of a human brain state at least partially based on a plurality of updated brain metrics determined from updated information associated with the human brain, and updating a neuromorphic artificial intelligence agent based on the updated digital replica of the human brain state. In another aspect, the neuromorphic artificial intelligence agent is based on an oscillator generator that reflects a human brain state and brain architecture. In another aspect, the neuromorphic artificial intelligence agent is used as a conversation agent in a therapeutic environment. In another aspect, the neuromorphic artificial intelligence agent is used to enhance the effectiveness of mental health interventions, where the mental health interventions include cognitive behavioral therapy. 【0016】 In some embodiments, an apparatus for modifying a human brain state is provided. The apparatus includes at least one sensor configured to sense brain activity signals from a human brain, a stimulation device configured to provide a non-invasive stimulation to the human brain, and a controller configured to control the operation of the stimulation device at least partially based on one or more characteristics of the brain activity signals sensed from at least the sensor following stimulation of the human brain using the stimulation device. 【0017】 In some embodiments, a method is provided for providing individualized regulation of brain activity to a human to change the state of the human brain. The method includes sensing a plurality of evoked potentials using a plurality of electrophysiological sensors in response to providing a non-invasive stimulation to the human brain, determining an individualized regulation plan for the human at least partially based on at least one characteristic of the plurality of evoked potentials, where the individualized regulation plan includes a location of the stimulation and one or more stimulation characteristics, and stimulating the human brain according to the individualized regulation plan to change the state of the human brain. 【0018】 In some embodiments, a method for 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 metrics for the user; configuring at least one aspect of the digitally created environment at least partially based on the plurality of brain metrics for 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 for 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 metrics for the human, the plurality of brain metrics including a first level of plasticity of the human brain; controlling at least one non-invasive brain stimulation device to deliver non-invasive stimulation to at least one location in the human brain, the at least one location being identified as a location for inducing plasticity in the human brain; and determining a second level of plasticity of the human brain at least partially based on feedback received subsequent to stimulation of the at least one location in the human brain. 【0020】 In some embodiments, a method for characterizing a metric of a human brain health is provided. The method includes receiving information characterizing a brain structural composition and a functional architecture of a human brain, the information including first data including passive data and active data recorded from the human; processing the first data to extract metrics of performance and efficiency of the human brain and cognitive system; and defining an intervention for modulating brain activity of the human brain based on the metrics of performance and efficiency of the human brain and cognitive system. 【0021】 In one aspect, information is collected via a wearable device, a personal computer, or a portable headset. In another aspect, information is collected via at least one electrophysiological technique. In another aspect, at least one electrophysiological technique includes electroencephalography. In another aspect, information is collected via at least one neuroimaging method. In another aspect, 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, information is from multiple sources, and processing the first data to at least partially extract metrics of the performance and efficiency of a person's brain and cognitive system includes using an algorithm to reconcile information from multiple sources to a digital twin. In another aspect, defining an intervention for modulating brain activity of a person's brain includes using the digital twin to define an intervention for enhancing brain cognition and brain health. In another aspect, defining an intervention for modulating brain activity of a person's brain includes using the digital twin to define an intervention for treating one or more neurological or psychiatric conditions. 【0022】 In another aspect, the method further includes using a digital twin to predict the trajectory of a neurological or psychiatric disorder, and defining an intervention for modulating the brain activity of a person, including defining the intervention based at least in part on the predicted trajectory. In another aspect, the neurological disorder is Alzheimer's disease, dementia, or a brain cancer. In another aspect, 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. In another aspect, defining an intervention for modulating the brain activity of a person includes using the digital twin to set one or more parameters for non-invasive brain stimulation. In another aspect, defining an intervention for modulating the brain activity of a person includes using the digital twin to define an individualized learning trajectory for skill acquisition. In another aspect, defining an intervention for modulating the brain activity of a person includes using the digital twin to derive a measure of brain plasticity and resilience. 【0023】 In another aspect, the received information is formalized as a network, and processing the first data to at least partially extract metrics of the performance and efficiency of the human brain and cognitive system includes extracting features of the network using graph theory metrics and network control theory metrics. In another aspect, the received information is formalized as a network, and processing the first data to at least partially extract metrics of the performance and efficiency of the human brain and cognitive system includes extracting features of the network by analyzing at least one of the amplitude, shape, or frequency of the first data. In another aspect, processing the first data to at least partially extract metrics of the performance and efficiency of the human brain and cognitive system includes calculating the structural, functional, and dynamic resilience of the brain. In another aspect, processing the first data to at least partially extract metrics of the performance and efficiency of the human brain and cognitive system includes calculating the structural connectome of the brain. In another aspect, the method further includes monitoring disease progression in the brain and predicting the level of neuroinflammation, at least partially based on the structural connectome of the brain. In another aspect, the method further includes monitoring demyelination in the brain and / or identifying white matter lesions, at least partially based on the structural connectome of the brain. 【0024】 In another aspect, the information includes information regarding a person's emotional state recorded by a camera, and the method further includes storing a non-fungible token representing the information including the information regarding the person's emotional state. In another aspect, the method further includes creating an emotional virtual agent based on the information regarding the person's emotional state. In another aspect, the method further includes using the emotional virtual agent to monitor and support a patient with dementia or to enhance the brain health of a healthy person. In another aspect, the method further includes inducing at least one cognitive or behavioral intervention based on the information regarding the person's emotional state to improve brain health. In another aspect, the method further includes inducing a dosage of a therapeutic intervention based on the information regarding the person's emotional state. 【0025】 In another aspect, the information includes spontaneous and / or stimulus-driven eye movements recorded via a camera, and the method further includes storing a non-fungible token representing an analysis of the spontaneous and / or stimulus-driven eye movements. In another aspect, the spontaneous and / or stimulus-driven eye movements are collected during the performance of a cognitive task by a person. In another aspect, the method further includes evaluating the health and overall cognitive performance of a person's brain based on the non-fungible token. In another aspect, the method further includes evaluating the improvement of a person's cognitive performance and learning ability based on the non-fungible token. In another aspect, the spontaneous and / or stimulus-driven eye movements are collected while a person is playing a video game, and the method further includes using the non-fungible token to evaluate the person's in-game performance. 【0026】 In some embodiments, a method is provided for stimulating one or more brain regions of a person to induce a target state of the person's brain. 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 person's brain, controlling at least one non-invasive brain stimulation device according to the non-invasive brain stimulation parameters to stimulate one or more brain regions of the person, and receiving feedback regarding the effect of the stimulation of the one or more brain regions to determine whether the person's brain state is in the target state. 【0027】 In one aspect, 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. In another aspect, at least one non-invasive brain stimulation device is configured to provide a sensory stimulation, which includes an auditory stimulation and / or a visual stimulation. In another aspect, the sensory stimulation is embedded within multimedia content, which includes a movie, a song, a television program, a video game, or a virtual reality application. In another aspect, at least one non-invasive brain stimulation device is configured to provide a sine wave stimulation of a pattern of noise bursts applied at a specific frequency, or a random noise stimulation limited to a specific frequency band. In another aspect, the method further includes providing a non-invasive stimulation intervention to a person sequentially or simultaneously while controlling at least one non-invasive stimulation device to stimulate one or more brain regions of the person. In another aspect, the non-invasive stimulation intervention includes a cognitive rehabilitation or cognitive training intervention including a video game. In another aspect, the non-invasive stimulation intervention includes a behavioral intervention or a physical training intervention. In another aspect, the non-invasive stimulation intervention includes a drug intervention. 【0028】 In another aspect, controlling at least one non-invasive brain stimulation device to stimulate one or more brain regions of a person includes stimulating one or more brain regions to selectively modify specific brain connections between one or more brain regions. In another aspect, controlling at least one non-invasive brain stimulation device to stimulate one or more brain regions of a person includes stimulating one or more brain regions to increase brain plasticity and / or accelerate the learning rate. In another aspect, one or more brain regions are defined based on data associated with the person. In another aspect, one or more brain regions are defined based on group-level data. In another aspect, one or more brain regions are part of a functional brain network. In another aspect, one or more brain regions include subcortical brain structures. In another aspect, one or more brain regions are defined based on neuroimaging data. In another aspect, the neuroimaging data includes functional MRI data. In another aspect, the neuroimaging data includes diffusion MRI data. In another aspect, the neuroimaging data includes structural MRI data. In another aspect, the neuroimaging data includes positron emission tomography (PET) data. In another aspect, the neuroimaging data includes near-infrared spectroscopy data. In another aspect, one or more brain regions are defined based on electrophysiological data. In another aspect, the electrophysiological data includes scalp electroencephalogram data. In another aspect, one or more brain regions are defined via mapping of white matter tracts in the person's brain. In another aspect, one or more brain regions are regions connected or not connected to a brain tumor. In another aspect, one or more brain regions are regions involved in sleep induction. 【0029】 In some embodiments, a method for determining the health and performance of a human brain is provided. The method includes sensing a plurality of evoked potentials in response to a first non-invasive stimulation at each of a plurality of locations in the human brain, and determining individualized stimulation parameters for a human based at least in part on at least one characteristic of the plurality of evoked potentials, the individualized stimulation parameters including a location of the stimulation and one or more stimulation characteristics, and providing a second non-invasive stimulation to the human based on the individualized stimulation parameters, and receiving feedback regarding the effect of the second non-invasive stimulation to extract metrics of performance and brain health. 【0030】 In one aspect, the method further includes controlling a non-invasive stimulation device to provide a first non-invasive stimulation at each of a plurality of locations in the human brain. 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 individualized 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 human brain. In another aspect, the first non-invasive stimulation is delivered to a plurality of locations simultaneously or in a predetermined sequence. In another aspect, the plurality of locations are arranged in a grid for systematic spatial assessment of brain dynamics. In another aspect, at least one characteristic of the plurality of evoked potentials includes peak magnitude. In another aspect, determining the individualized stimulation parameters includes selecting the location of the stimulation as the location of the plurality of locations associated with the largest peak magnitude of the plurality of evoked potentials. In another aspect, the plurality of locations include locations in a brain network, the brain network including one or more of a default mode network, a frontoparietal control network, a sensorimotor network, a prefrontal salience network, a dorsal attention network, a ventral attention network, a visual network, an auditory network, or a language network. 【0031】 In another aspect, the method further includes treating or ameliorating a neurological or psychiatric disorder by providing a second non-invasive stimulus to a human brain based on individualized stimulation parameters. In another aspect, the neurological or psychiatric disorder is Alzheimer's disease. In another aspect, the neurological or psychiatric disorder is mild cognitive impairment (MCI). In another aspect, the neurological or psychiatric disorder is frontotemporal dementia. In another aspect, the neurological or psychiatric disorder 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). In another aspect, the neurological or psychiatric disorder is characterized by memory deficit. In another aspect, the neurological or psychiatric disorder is characterized by a deficit in cognitive control. In another aspect, the neurological or psychiatric disorder is characterized by a decrease in functional independence. In another aspect, the neurological or psychiatric disorder is brain cancer. In another aspect, the neurological or psychiatric disorder is a brain tumor. In another aspect, the second non-invasive stimulus to the human brain is provided prior to surgery to inform a surgical plan related to the removal of at least a portion of the brain tumor. In another aspect, the second non-invasive stimulus to the human brain is provided after surgery to detect recurrence of the brain tumor. 【0032】 In another aspect, the method further includes providing a drug intervention to a person that affects the central nervous system, the drug intervention being provided sequentially or simultaneously with the second non-invasive stimulus. In another aspect, the method further includes providing a cognitive assessment, cognitive training, or cognitive enhancement intervention to a person, the cognitive assessment, cognitive training, or cognitive enhancement intervention being provided sequentially or simultaneously with the second non-invasive stimulus. In another aspect, the method further includes providing a behavioral intervention to a person, the behavioral intervention being provided sequentially or simultaneously with the second non-invasive stimulus. In another aspect, the method further includes providing a third non-invasive stimulus to a person, the third non-invasive stimulus being provided sequentially or simultaneously with the second non-invasive stimulus. In another aspect, the third non-invasive stimulus is transcranial electrical stimulation. 【0033】 In some embodiments, a method of creating a plasticity-inducing intervention for a person is provided. The method includes receiving information characterizing the person's brain state and level of plasticity, the information including electrophysiological, behavioral, 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 regarding the effect 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. 【0034】 In one aspect, the level of plasticity of the person is measured via the brain and the cognitive metric is obtained using a dynamic multi-layer digital twin (DMDT) of the person's brain. In another aspect, identifying the plasticity-inducing intervention is based on a rich environment determined based on the DMDT of the person's brain. In another aspect, the rich environment is a virtual reality environment, an augmented reality environment, or a mixed reality environment. In another aspect, the rich environment is delivered in the form of a room equipped with sensory stimulation devices and data recording devices. In another aspect, the rich environment is delivered in the form of a video game. In another aspect, the rich environment is represented by a dual-task platform. 【0035】 In another aspect, the plasticity-inducing intervention is configured to target the perineuronal nets and extracellular matrix of the human brain. In another aspect, the plasticity-inducing intervention is based on the modulation of CSPGs via injection of ChABC. In another aspect, the plasticity-inducing intervention is based on the modulation of CSPGs via manipulation of BDNF. In another aspect, the plasticity-inducing intervention is based on the modulation of CSPGs via ketamine administration. In another aspect, the plasticity-inducing intervention is based on a drug that acts on the GABAergic circuits of the brain. In another aspect, the plasticity-inducing intervention involves using a drug to modulate one or more of PNN, ECM, or CSPG. In another aspect, the plasticity-inducing intervention is based on non-invasive brain stimulation methods. In another aspect, the non-invasive brain stimulation method includes a protocol that induces high-frequency oscillatory activity in the brain within the gamma frequency band. In another aspect, the non-invasive brain stimulation method includes a protocol that induces activity within the gamma frequency band to activate inhibitory interneurons and glial cells. In another aspect, the non-invasive brain stimulation method includes non-invasive brain stimulation methods that include a protocol that induces gamma activity in the brain, transcranial alternating current stimulation (tACS), transcranial pulsed gamma stimulation (tPGS), and / or narrowband transcranial random noise stimulation (nb-tRNS). 【0036】 In another aspect, the plasticity-inducing intervention is applied to patients with neurological and psychiatric conditions. In another aspect, the plasticity-inducing intervention is applied to athletes to improve their performance and response to training. In another aspect, the plasticity-inducing intervention is applied to improve the quality of sleep and memory consolidation. In another aspect, the plasticity-inducing intervention is applied to patients with post-traumatic stress disorder (PTSD) to facilitate the manipulation and removal of traumatic memories and promote a healthy brain state. In another aspect, the plasticity-inducing intervention is applied to astronauts to increase skill learning and retention and facilitate protection from brain and cognitive changes associated with spaceflight. In another aspect, the plasticity-inducing intervention is applied during sleep to enhance memory consolidation and overall brain plasticity. 【0037】 In some embodiments, a method is provided for improving cognitive performance and learning ability by stimulating a human brain in a predetermined sequence. The method includes receiving information characterizing the initial brain state of a human, where the information includes electrophysiological and cognitive data recorded from the human; defining an optimal sequence of cognitive changes that are induced to change the state of the human from the initial brain state to a target brain state; delivering one or more cognitive modulators that target specific brain structures and cognitive functions based on the optimal sequence of induced cognitive changes; and receiving feedback regarding the effect of the delivered one or more cognitive modulators to determine whether the state of the human brain is in the target brain state. 【0038】 In one aspect, the optimal sequence of cognitive modulators is defined to reach the optimal learning state (OLS). In another aspect, the OLS is defined based on the dynamic multi-layer digital twin (DMDT) of the human brain. In another aspect, the target brain state is a state of high cognitive performance defined by a specific pattern of activation and deactivation of brain regions based on neuroimaging methods or electrophysiological maps. In another aspect, the optimal sequence of cognitive modulators is combined with brain modulators to increase brain excitability, regulate plasticity, and / or increase brain efficiency or modularity. In another aspect, the brain modulators are in the form of non-invasive stimuli. In another aspect, one or more cognitive modulators include cognitive training that acts on sensory and cognitive functions. In another aspect, the target brain state is a brain state in which the learning speed is accelerated. In another aspect, the learning speed is related to the learning of sensorimotor skills including one or more of the performance of musical instruments, enhancement of memory performance, or learning of coding languages. In another aspect, the learning speed is related to the learning of rehabilitation skills in patients with neurological or psychiatric conditions. In another aspect, the learning speed is related to the improvement of physical and / or mental performance related to movement. In another aspect, the learning speed is related to the improvement of educational performance. In another aspect, the learning speed is related to cognitive behavioral therapy, speech therapy, or dynamic therapy. In another aspect, the learning speed is related to the enhancement of convergent thinking in the form of fluid intelligence and abstract reasoning. In another aspect, the learning speed is related to the enhancement of divergent thinking in the form of creativity and insight ability. In another aspect, the learning speed is related to the enhancement of general intelligence by targeting the brain regions and networks of the multi-layer convergent-divergent thinking (CDt) model of human cognition. 【0039】 In some embodiments, a system for managing a brain optimization procedure is provided. The system includes a data collection platform configured to collect and store data of an individual used to identify optimal optimization targets, a data analysis platform configured to process the individual's data to derive optimal stimulation parameters, and a database including the individual's data collected before and / or during a brain stimulation treatment based on the optimal stimulation parameters. 【0040】 In one aspect, the system is accessible to a clinician who prescribes a brain stimulation treatment. In another aspect, the system is accessible to an end user via a portable computing device. In another aspect, the data analysis platform is a cloud-based computing platform that communicates 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 brain activity in a person is provided. The system includes a stimulation device configured to provide non-invasive stimulation at each of a plurality of locations in a person's brain region, 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 regulating brain activity via the non-invasive stimulation as an individualized stimulation target, wherein the selection is based on at least one characteristic of the at least one evoked response. 【0042】 In one aspect, the stimulation device and the sensor device are included as part of a portable device in the form of a wearable headset. In another aspect, the portable device is configured to be controlled by a separate computing device that enables remote control of the portable device. In another aspect, the stimulation device is configured to provide at least one of transcranial electrical stimulation, transcranial magnetic stimulation, or transcranial focused ultrasound stimulation to an individualized stimulation target. In another aspect, the stimulation device is configured to treat or ameliorate a neurological or psychiatric disorder in a human by providing non-invasive stimulation to a selected individualized stimulation target. In another aspect, the neurological or psychiatric disorder is Alzheimer's disease. In another aspect, the neurological or psychiatric disorder is frontotemporal dementia. In another aspect, the neurological or psychiatric disorder is mild cognitive impairment (MCI). In another aspect, the neurological or psychiatric disorder is brain cancer. In another aspect, the neurological or psychiatric disorder is glioma. In another aspect, the neurological or psychiatric disorder is depression. In another aspect, the neurological or psychiatric disorder is ADHD. In another aspect, the neurological or psychiatric disorder is a sleep disorder. In another aspect, the neurological or psychiatric disorder is characterized by changes in the brain network 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), disorder of consciousness (DOC), headache (HD), multiple sclerosis (MS), stroke (STR), or brain tumor (BT). In another aspect, the neurological or psychiatric disorder is characterized by memory deficit. In another aspect, the neurological or psychiatric disorder is characterized by a deficit in cognitive control. In another aspect, the neurological or psychiatric disorder is characterized by a decrease in functional independence. 【0043】 In another aspect, the non-invasive stimulus provided to the selected individualized stimulation target is combined with a drug intervention that affects the central nervous system and delivered sequentially or simultaneously. In another aspect, the non-invasive stimulus provided to the selected individualized stimulation target is combined with one or more of cognitive evaluation, cognitive training, or cognitive enhancement interventions and delivered sequentially or simultaneously. In another aspect, the non-invasive stimulus provided to the selected individualized stimulation target is combined with a behavioral intervention and delivered sequentially or simultaneously. In another aspect, the non-invasive stimulus provided to the selected individualized stimulation target is combined with different non-invasive stimulation interventions, including transcranial electrical stimulation, and delivered sequentially or simultaneously. 【0044】 In another aspect, the computer processor is further configured to refine a sequence of combined automated and semi-automated signal processing algorithms installed on local hardware to provide specific instructions for one or more of stimulation location, frequency, or intensity. In another aspect, the computer processor is further configured to refine a sequence of combined automated and semi-automated signal processing algorithms installed on remote hardware with connectivity functions to provide specific instructions for stimulation location, frequency, or intensity. In another aspect, the sensor device includes electrodes configured to be placed on a person's scalp. In another aspect, the sensor device includes electrodes configured to be placed inside a person's ear. In another aspect, the sensor device includes electrodes configured to be placed on a person's face. In another aspect, the sensor device includes high-density electrodes based on carbon nanotubes placed inside a stimulation cap placed on a person's head. In another aspect, the system is configured to be used within a metaverse. 【0045】 In some embodiments, a method is provided for collecting an individual's neurophysiological data and cognitive behavioral data to characterize brain health metrics. The method includes receiving information that at least partially characterizes the individual's brain structural composition and functional architecture, where the information includes passive and active data recorded from a person, processing the data to at least partially extract metrics of the performance and efficiency of the brain and cognitive system, and defining an intervention that modulates brain activity and optimizes the performance and efficiency of the brain. 【0046】 In one aspect, the information is received from multiple sources, reconciled via an algorithm, and combined as part of the DMDT into metrics of brain performance and brain health. In another aspect, the information included in the DMDT is used to optimize an intervention aimed at enhancing cognition and brain health via the DARWIN algorithm described herein. In another aspect, the information included in the DMDT is used to optimize an intervention aimed at treating a neurological or psychiatric condition via the DARWIN algorithm. In another aspect, the information included in the DMDT is used to predict the trajectory of a neurological or psychiatric disease in the form of diagnostic and prognostic markers. In another aspect, the information included in the DMDT is updated based on new data collected via a wearable device or a brain activity recording device. In another aspect, the information included in the DMDT is used to set parameters for non-invasive brain stimulation. In another aspect, the information included in the DMDT is used to create avatars and content for games and metaverse applications. In another aspect, the information included in the DMDT is used to define an individualized learning trajectory for skill acquisition. In another aspect, the information included in the DMDT is used to derive a measure of brain plasticity and resilience. 【0047】 In another aspect, the information contained in the DMDT is stored as a non-fungible token (NFT) and is then used to track the progress of therapeutic, rehabilitative, or enhancement interventions. In another aspect, the information contained in the DMDT is stored as a non-fungible token (NFT) and is then used to define brain and cognitive stimulation approaches. In another aspect, the information contained in the DMDT is stored as a non-fungible token (NFT) and is then used to compare individual DMDTs to group-level DMDTs and calculate a distance matrix. In another aspect, the information contained in the DMDT is stored as a non-fungible token (NFT) and is used as biometric data for encryption and cybersecurity applications. In another aspect, the information contained 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. In another aspect, the information contained in the DMDT is stored as a non-fungible token (NFT) and is used as an asset to generate individualized digital content (including but not limited to gear, weapons, and clothing) in game applications. In another aspect, a specific pattern of brain activity associated with a specific stimulus is stored as a non-fungible token (NFT) and is used as a password for encryption and cybersecurity applications in relation to online business and financial transactions. 【0048】 In another aspect, a specific pattern of brain activity associated with a specific stimulus is stored as a non-fungible token (NFT). In another aspect, an individual's cognitive architecture (including but not limited to information regarding brain network dynamics and cognitive processing strategies) is stored as an NFT. In another aspect, an individual's cognitive architecture is used as a target for the application of cognitive enhancement, cognitive training, and psychotherapy. In another aspect, an individual's mind wandering pattern (including but not limited to a sequence of brain states recorded in relation to a specific event or stimulus) is stored as an NFT. In another aspect, an individual's mind wandering pattern is used as a target for the application of cognitive enhancement, cognitive training, and psychotherapy. BRIEF DESCRIPTION OF THE DRAWINGS 【0049】 The advantages of the present invention can be better understood, along with further advantages, by reference to the following description in conjunction with the accompanying drawings. The drawings are not necessarily to scale, and instead, generally focus on illustrating the principles of the present invention. 【0050】 【Figure 1-1】 Schematically shows exemplary components of a neuromodulation and enhancement platform according to some embodiments of the present disclosure. 【Figure 1-2】 Schematically shows exemplary components of a neuromodulation and enhancement platform according to some embodiments of the present disclosure. 【Figure 1-3】 Schematically shows exemplary components of a neuromodulation and enhancement platform according to some embodiments of the present disclosure. 【Figure 2】 Shows an exemplary cloud computing and blockchain architecture for use in some embodiments of the present disclosure. 【Figure 3A】 Schematically shows the creation of multi-layer NFTs from neural data according to some embodiments of the present disclosure. 【Figure 3B】 Schematically shows the creation of multi-layer NFTs from neural data according to some embodiments of the present disclosure. 【Figure 3C】 Schematically shows the creation of multi-layer NFTs from neural data according to some embodiments of the present disclosure. 【Figure 4】 Schematically shows the components of a system for capturing information about an individual according to some embodiments of the present disclosure. 【Figure 5】 Shows an exemplary data analysis pipeline including data processing components that can be used in accordance with some embodiments of the present disclosure. 【Figure 6A】 Shows examples of individual differences in the transition of a state or trait according to some embodiments of the present disclosure. 【Figure 6B】 Shows metrics of state transitions of a group of subjects according to some embodiments of the present disclosure. 【Figure 7】Results of a study in which perturbations of the brain were made to improve a measure of brain resilience, according to some embodiments of the present disclosure. 【Figure 8】 Graph metrics representing integration and segregation of information processing, according to some embodiments of the present disclosure. 【Figure 9A】 Schematically shows a process for mapping a connectivity profile of a brain tumor, according to some embodiments of the present disclosure. 【Figure 9B】 Schematically shows a process for mapping a connectivity profile of a brain tumor, according to some embodiments of the present disclosure. 【Figure 9C】 Schematically shows a process for mapping a connectivity profile of a brain tumor, according to some embodiments of the present disclosure. 【Figure 10】 Schematically shows the components of a system for monitoring a brain tumor, according to some embodiments of the present disclosure. 【Figure 11A】 Schematically shows an example of a dynamic decomposition for mental cognitive assessment, according to some embodiments of the present disclosure. 【Figure 11B】 Schematically shows an example of a dynamic decomposition for mental cognitive assessment, according to some embodiments of the present disclosure. 【Figure 12】 Shows brain regions identified as supporting abstract reasoning and problem solving in humans used to notify a neuromorphic artificial intelligence agent, according to some embodiments of the present disclosure. 【Figure 13】 Shows a brain network identified as supporting abstract reasoning and problem solving in humans used to notify a neuromorphic artificial intelligence agent, according to some embodiments of the present disclosure. 【Figure 14A】 Shows the major clusters of a brain network that supports human intelligence used to notify a neuromorphic artificial intelligence agent, according to some embodiments of the present disclosure. 【Figure 14B】Shows the main clusters of brain networks that support human intelligence and are used to notify neuromorphic artificial intelligence agents, according to some embodiments of the present disclosure. 【Figure 14C】 Shows the main clusters of brain networks that support human intelligence and are used to notify neuromorphic artificial intelligence agents, according to some embodiments of the present disclosure. 【Figure 15A】 Shows activation centers and brain connection patterns, according to some embodiments of the present disclosure. 【Figure 15B】 Shows activation centers and brain connection patterns, according to some embodiments of the present disclosure. 【Figure 16-1】 Shows the functional connectivity of overlapping nodes across convergent and divergent thinking in the brain, which is used to notify neuromorphic artificial intelligence agents, according to some embodiments of the present disclosure. 【Figure 16-2】 Shows the functional connectivity of overlapping nodes across convergent and divergent thinking in the brain, which is used to notify neuromorphic artificial intelligence agents, according to some embodiments of the present disclosure. 【Figure 16-3】 Shows the functional connectivity of overlapping nodes across convergent and divergent thinking in the brain, which is used to notify neuromorphic artificial intelligence agents, according to some embodiments of the present disclosure. 【Figure 17】 Shows exemplary analysis, connectivity, and cognitive output from the DARWIN module of the architecture of FIG. 1, according to some embodiments of the present disclosure. 【Figure 18-1】 Schematically shows the components of a brain-to-command engine module used to convert information from brain data into a coding language for a brain-computer interface application, according to some embodiments of the present disclosure. 【Figure 18-2】Schematic of components of a brain-to-command engine module used to convert information from brain data into a coding language for a brain-computer interface application, according to some embodiments of the present disclosure. 【Figure 19】 Schematic of components of a brain-to-command engine module used to convert information from brain data into a coding language for a brain-computer interface application, according to some embodiments of the present disclosure. 【Figure 20】 Shows components of a neuromorphic oscillatory multi-scale adaptive artificial intelligence module inspired by brain activity, according to some embodiments of the present disclosure. 【Figure 21A】 Examples of controlled perturbations used to map brain activity and corresponding brain responses are shown, according to some embodiments of the present disclosure. 【Figure 21B】 Examples of controlled perturbations used to map brain activity and corresponding brain responses are shown, according to some embodiments of the present disclosure. 【Figure 21C】 Examples of controlled perturbations used to map brain activity and corresponding brain responses are shown, according to some embodiments of the present disclosure. 【Figure 21D】 Examples of controlled perturbations used to map brain activity and corresponding brain responses are shown, according to some embodiments of the present disclosure. 【Figure 21E】 Examples of controlled perturbations used to map brain activity and corresponding brain responses are shown, according to some embodiments of the present disclosure. 【Figure 21F】 Examples of controlled perturbations used to map brain activity and corresponding brain responses are shown, according to some embodiments of the present disclosure. 【Figure 22A】Results of a study in which transcranial magnetic stimulation (TMS) was delivered across multiple networks of the brain based on individual MRI and fMRI data collected in patients with Alzheimer's disease, according to some embodiments of the present disclosure. 【Figure 22B】 Results of a study in which transcranial magnetic stimulation (TMS) was delivered across multiple networks of the brain based on individual MRI and fMRI data collected in patients with Alzheimer's disease, according to some embodiments of the present disclosure. 【Figure 22C】 Results of a study in which transcranial magnetic stimulation (TMS) was delivered across multiple networks of the brain based on individual MRI and fMRI data collected in patients with Alzheimer's disease, according to some embodiments of the present disclosure. 【Figure 23A】 Shows the induced oscillatory activity in Alzheimer's disease patients after TMS, according to some embodiments of the present disclosure. 【Figure 23B】 Shows the induced oscillatory activity in Alzheimer's disease patients after TMS, according to some embodiments of the present disclosure. 【Figure 24A】 Shows the network-level response to perturbations used to perform fingerprinting of brain activity, according to some embodiments of the present disclosure. 【Figure 24B】 Shows the network-level response to perturbations used to perform fingerprinting of brain activity, according to some embodiments of the present disclosure. 【Figure 25A】 Shows the clinical application of combined neuroplasticity protocols and non-invasive brain stimulation to induce brain plasticity, according to some embodiments of the present disclosure. 【Figure 25B】 Shows the clinical application of combined neuroplasticity protocols and non-invasive brain stimulation to induce brain plasticity, according to some embodiments of the present disclosure. 【Figure 25C】 Shows the clinical application of combined neuroplasticity protocols and non-invasive brain stimulation to induce brain plasticity, according to some embodiments of the present disclosure. 【Figure 25D】Illustrates 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. 【Figure 25E】 Illustrates 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. 【Figure 26A】 Shows the enhancement of brain plasticity via brain stimulation according to some embodiments of the present disclosure. 【Figure 26B】 Shows the enhancement of brain plasticity via brain stimulation according to some embodiments of the present disclosure. 【Figure 27A】 Shows clusters of brain activation and deactivation during prediction and / or placebo response according to some embodiments of the present disclosure. 【Figure 27B】 Shows clusters of brain activation and deactivation during prediction and / or placebo response according to some embodiments of the present disclosure. 【Figure 28A】 Shows the brain network corresponding to placebo activity according to some embodiments of the present disclosure. 【Figure 28B】 Shows the brain network corresponding to placebo activity according to some embodiments of the present disclosure. 【Figure 29】 Shows a modular adaptive neurological approach (MANP) hierarchical approach to psychological changes for increasing the effectiveness of mental health interventions according to some embodiments of the present disclosure. 【Figure 30】 Shows a schematic diagram of an exemplary software architecture of the CLARITY module for virtual assistants and caregivers according to some embodiments of the present disclosure. 【Figure 31】 Is a plot showing the regulation 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. 【Figure 32A】 Shows a network of brain regions identified as potential targets for the neuroregulation of motion sickness, dizziness, and nausea during VR applications according to some embodiments of the present disclosure. 【Figure 32B】Shows an optimized electrical stimulation pattern for a tACS application to reduce nausea and dizziness during VR applications according to some embodiments of the present disclosure. 【Figure 32C】 Schematically shows tACS electrodes disposed near a person's left and right ears under an elastic band that holds a headset according to some embodiments of the present disclosure. 【Figure 33A】 Shows the results of a study in which a person's brain was stimulated in a specific manner to reduce the feeling of motion sickness according to some embodiments of the present disclosure. 【Figure 33B】 Shows the results of a study in which a person's brain was stimulated in a specific manner to reduce the feeling of motion sickness according to some embodiments of the present disclosure. 【Figure 34A】 Shows the response to adaptive brain state optimization (ABSO) according to some embodiments of the present disclosure. 【Figure 34B】 Shows the response to adaptive brain state optimization (ABSO) according to some embodiments of the present disclosure. 【Figure 34C】 Shows the response to adaptive brain state optimization (ABSO) according to some embodiments of the present disclosure. 【MODE FOR CARRYING OUT THE INVENTION】 【0051】 The inventor recognizes and understands that quantifying brain complexity is important for understanding and modifying human cognitive abilities and behavioral patterns, enhancing brain function, promoting mental health and healthy aging, identifying individualized interventions, and providing individuals with knowledge about the brain's capabilities for learning, adaptation, and evolution. Some embodiments of the present disclosure relate to techniques for mapping, characterizing, predicting, and / or optimizing brain function using an integrated software-hardware platform. The systems and methods include data analysis and visualization tools, algorithms for estimating brain potentials, corresponding strategies for enhancing the brain, cognition, and behavior, hardware for data collection and neuromodulation, and application-specific algorithms for generating digital assets based on the characteristics of individual brain activity. 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 for enhancing brain plasticity and learning ability, algorithms for transitioning between brain states and traits via individualized neuromodulation, enhancing brain health and cognitive performance, manipulating brain activity in patients with neurological and psychiatric disorders to derive individualized interventions for symptom reduction, accelerating skill acquisition, and use in creating brain-based game mechanics and content for the gaming industry and the metaverse. 【0052】 The inventors recognize that there is a need for a solution to accurately manipulate brain activity via controlled perturbations, even when solutions are available for mapping the complexity of the brain and generating models of brain activity and behavior. Using data collected from individual brains to generate computational evolutionary biology models, for example, in combination with interventions to modify the ability of the brain to learn and evolve via brain plasticity, the best strategies for improving brain performance and behavior can be identified. The inventors recognize the need for a new platform for data collection, reconciliation, analysis, and simulation of brain, cognitive, and behavioral data. Some of the systems and methods of the present disclosure may be referred to herein as a Neuro Modeling and Enhancement Platform (NEP). This includes software and hardware solutions for performing one or more of brain fingerprinting, estimation of brain potentials, development of digital health assessment tools based on individual brain and cognitive characteristics, and brain optimization. 【0053】 Some embodiments relate to a platform (e.g., NEP) for data collection, data reconciliation, data processing, and manipulation for creating digital replicas of the human brain and in - silico models that can be used to simulate the human brain and cognitive behavior. The NEP includes, at least in part, artificial intelligence agents (neuromorphic artificial intelligence) that characterize the complexity of individual brains and reflect the brain dynamics and cognitive architecture of an individual to generate digital twins of healthy and diseased brain functions, to estimate brain potentials, to customize solutions for brain transitions between states and traits for creating individualized trajectories of brain / cognitive optimization, to derive cognitive / brain enhancement protocols leveraging cortical plasticity mechanics, to facilitate interventions aimed at maintaining brain health and treatment protocols for patients with neurological and psychiatric conditions, and software and hardware approaches for generating assets for the metaverse and gaming industries by using neural data in open - loop and closed - loop environments. These solutions can be directly applied in various fields including the medical and human performance domains, as well as the metaverse where a new generation of applications and assets can be created based on brain activity and an individual's brain digital twin. 【0054】 An exemplary configuration of the NEP framework and an overview of their specific applications are described in more detail below. This framework includes principles and tools for modulating brain plasticity, techniques for mapping the human connectome, the science of learning applied to video games and cognitive training, virtual reality applications, and novel tools for non - invasively stimulating healthy and diseased brains. Following a general discussion of the NEP, details of procedures and related data are described in more detail for each exemplary component. 【0055】 Mapping, Prediction, and Regulation of Brain and Behavior The platform described herein provides for mapping the complexity of individual brains, and such "brain prints" can be used to define individualized therapies within a precision medicine framework, create brain-type artificial intelligence, implement individualized training and diet regimens, individualized cognition and behavior to accelerate skill acquisition, and open scenarios for defining or deriving individualized solutions across multiple domains and markets, from the discovery of novel biomarkers for disease to the creation of brain-type game mechanics and content for the video game industry and the metaverse. 【0056】 When such complexity is mapped and documented in digital form, models can be derived regarding how the brain of a given individual or group of individuals responds to external or internal perturbations. In this context, solutions are enabled to quantify the brain's ability to adapt to change (often referred to as "neuroplasticity" or simply "plasticity"), form memories, and evolve in response to experience, and can be used to improve the aforementioned applications of brain prints. Recently, knowledge has accumulated regarding methods and approaches for mapping individual brains via neuroimaging methods and electrophysiological techniques, and in particular for modifying the structure and function of the brain with the aid of non-invasive brain stimulation techniques. Fundamental principles regarding methods for effectively mapping the structure and function of the brain, as well as behavioral or brain stimulation solutions for improving the brain's capabilities, have been proposed, but methods and principles for parameterizing such processes and for constructing the steps and changes necessary to transition 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. The systems and methods described herein enable, for example, the definition of: (i) Transition steps between states of each brain (e.g., for a given brain having a baseline state A, the best strategy to reach state B may be to increase the overall brain plasticity level and increase the modularity of the network; while for a brain having a baseline state C, the focus may be to increase flexibility and cortical excitability levels); (ii) The order of transition steps between states (e.g., first change the stronger connection and then the weaker connection. It is necessary to focus on first increasing the connections in the left hemisphere and then increasing the connections in the right hemisphere); (iii) The intensity / magnitude of the stimulus required to bring about each change based on individually defined brain states; (iv) The optimal temporal framework according to the ability of the brain to change and adapt (e.g., deliver a given type of brain stimulation daily, weekly, multiple times a day, overnight, while sandwiching cognitive training and / or behavioral training). 【0057】 By parameterizing such steps and concepts and estimating how much "change" different interventions can bring to the brain (e.g., cognitive training can change 0.5% of the brain's connectivity per session, and meditation can increase the spectral output of a given vibration by 1% every 10 hours of stimulation), it becomes possible to predict the number of "steps" required to transition from state A to state B. A specific module of the NEP (also referred to herein as the "DARWIN" module) includes solutions for such analysis and model building and is applicable in both clinical and non-clinical contexts. 【0058】 Increase in the learning and change abilities of the brain The opportunity to modulate brain states and traits exploits knowledge of brain plasticity and related opportunities to reactivate / manipulate the level of plasticity of the human brain via pharmacological, behavioral, and cognitive approaches. In humans, brain plasticity peaks in infancy and then, while the level of plasticity declines, large modifications of brain wiring can still be observed in response to external stimuli. Central to such changes in the ability of the brain to “learn” are structural changes in the perineuronal net (PNN), an extracellular matrix (ECM) structure rich in chondroitin sulfate proteoglycan (CSPG), at least partially. The main event that reduces plasticity in the adult CNS is the accumulation of extracellular matrix molecules (e.g., CSPG) around the cell bodies and dendrites of neurons. The systems and methods described herein include algorithms and hardware that further amplify the effects of ECM / PNN modulation, thereby improving learning, as well as brain physiology (e.g., brain resilience, flexibility, efficiency) and state-trait transitions. Some of the systems and methods described herein include an intervention comprising a PNN modulator, combined with brain optimization and brain modifiers (including, but not limited to, non-invasive magnetic and electrical brain stimulation), as a platform for enhancing brain physiological functions, increasing cognitive functions, accelerating recovery from brain injury and pathology, and promoting brain health. The following sections on brain plasticity and learning applications of NEP provide further details. 【0059】 Generation of individualized content from neurodata Information regarding the brain activity, behavior, and cognition of a given individual can, in some embodiments, be used for applications in the video game industry and the metaverse. For example, individual brain activity can be used to induce virtual reality / augmented reality / cross reality (VR / AR / XR) experiences, to generate assets and content based on raw and processed brain data acquired in real-time or processed offline, and to induce the automatic creation of "worlds" and corresponding physics based on brain activity and other physiological data (e.g., neuroarchitecture). Similar techniques can be applied to the gaming industry. Here, information about each individual player can be used to characterize in-game parameters and adaptive game dynamics, while information about each player's evolvability can be used to derive individual trajectories for in-game growth, avatar optimization, creation of individualized artificial intelligence (A.I.), and / or control of other players or non-player characters (NPCs) in the game. In the following section describing the NEUROCREATOR module, details will be provided on how NEP can be used in metaverse and game applications, among others. 【0060】 Clinical Application The systems and methods described herein include algorithms having applications in the medical field, including but not limited to techniques for identifying novel biomarkers or novel therapeutic targets based on brain evolution and connectome analysis. Further, data obtained from each individual is stored as a template for phenotypic transitions and can be used later, for example, to induce sensory, cognitive, or brain stimulation interventions aimed at restoring pre-pathological healthy brain dynamics using snapshots of brain activity obtained from patients with dementia prior to the onset of clinical symptoms (including but not limited to memory impairment). Similar applications can be used to induce surgery and postoperative 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, and depressive and anxiety disorders. 【0061】 Data protection and storage Given 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 enables the storage of non-fungible tokens (NFTs) based on individual brain / cognitive / behavioral data. NFTs can be used as target brain states and traits for brain transfer (e.g., to return to a high memory performance state), in the form of digital currency, as a template for creating digital avatars, and as a wiring diagram for biology-inspired artificial intelligence. Dynamic brain data collected via a portable device at specific meaningful life moments can be stored and reproduced in applications ranging from art to game-related online transactions. Systems and methods for collecting and storing the data are also described herein. The data is stored via a data management and protection system (referred to herein as "DATANET") involved in data storage, quality assurance, and protected data sharing. DATANET may be based on a multi-key distributed ledger technology (e.g., blockchain) algorithm with enhanced compression capabilities that enables the storage and transfer of large-scale DMDT and / or DARWIN files. 【0062】 Neuroomorphic A.I. Knowledge accumulated regarding the relationship with brain function, behavior, and cognition, as well as strategies for brain enhancement and optimization, is, in some embodiments, also used to create neuromorphic artificial intelligence (A.I.). Using group-level patterns of brain activity that are optimal for solving a given problem or related to a specific skill set (e.g., high abstract reasoning performance), A.I. tools for specific tasks and generalized A.I. tools capable of generating independent thinking and synthetic consciousness can be designed. In some embodiments, the A.I. tools may be based on knowledge related to the computational and topological characteristics of brain function, including the role of oscillatory networks. In some embodiments, the A.I. tools can include assistance (e.g., conversation) agents to help patients with reduced functional independence [CLARITY], A.I. tools to integrate patients' psychotherapy treatments via individualized digital counseling, and A.I. tools for generating digital assets via natural language processing used in interactive video game applications. The following section entitled NEURO-AI regarding the neuromorphic A.I. applications of NEP provides additional details. 【0063】 Examples of architectures Figure 1 schematically shows exemplary components of an NEP platform 100 according to some embodiments of the present disclosure. 1) [BRAINPRINT] is a harmonization unit 102 used to characterize each individual user of the platform through a combination of multimodal data collected via a dedicated or third-party system. 2) [PERCEPTRON] is a monitoring and stimulation unit 104 in the form of a portable device that enables capturing brain data, physiological data, cognitive and behavioral performance data, and enables brain stimulation via multiple stimulation modalities including, but not limited to, sensory, electrical, or magnetic stimulation. 3) [DARWIN] is a brain enhancement platform 106 that includes a set of algorithms and computational tools for the multi-purpose analysis of BRAINPRINT data to estimate one or more of electroencephalogram, brain evolutionary trajectory, brain plasticity level, and other characteristics related to brain health. The DARWIN module includes the following: (i) [SYNAPSE] is a module 108 that includes systems and methods for measuring, predicting, and modulating neural plasticity, which includes behavioral and brain stimulation solutions that disrupt the perineuronal net (PNN) in the human brain and reopen the window of plasticity. (ii) [IMPROVE] is a module 110 that includes solutions for cognitive-behavioral enhancement that include systems and methods for enhancing / optimizing the learning process, knowledge about optimal brain targets for the neuromodulation of specific cognitive functions and behaviors, and knowledge about the optimal hierarchy of cognitive and behavioral steps required to accelerate learning. (iii) [PREPARE] is a module 112 that includes algorithms for data processing and analysis, which includes, but is not limited to, solutions for cleaning, preprocessing, and analyzing brain data from a single individual or group of individuals, and provides input to second-level algorithms such as OPTI-BRAIN, OPTI-COG, and NEUROCREATOR, which are described in more detail below. (iv) [SCREEN] is a digital biometric platform 114 that includes methods and tasks for assessing cognitive, psychological, and brain health, as well as the development of novel assessment tools based on the principles from DMDT data and SYNAPSE, DARWIN, and IMPROVE, but is not limited to these. (v) [OPTI-BRAIN] is module 116 that includes systems and methods for optimizing brain function, including but not limited to solutions for increasing brain resilience, information processing, modularity, and flexibility of brain networks. The solutions include applications for facilitating and inducing transitions from / to a given brain state or trait, as well as applications for identifying optimal stimulation targets for therapeutic applications in neurological and psychiatric states. (vi) [OPTI-COG] is module 118 that includes systems and methods for evaluating and enhancing human cognition, including approaches for regulating brain functions and specific brain networks that support specific cognitive abilities such as abstract reasoning, memory, and attention. This module also includes an algorithm for defining a cognitive assessment tool based on individual characteristics captured via DARWIN, including plasticity principles from SYNAPSE, learning principles from IMPROVE, and brain characteristics quantified via OPTI-BRAIN. The module constitutes a digital health platform for the evaluation and regulation of brain activity and cognition. (vii) [NEUROCREATOR] is module 120 that includes systems and methods for generating unique assets and content for the metaverse and video game industries based on individual features extracted via BRAINPRINT and processed via DARWIN. (viii) [NEURO-AI] is module 122 that includes systems and methods for creating neuromorphic artificial intelligence, including an algorithm for generating A.I. tools that resemble the characteristics of an individual's brain captured by DMDT or group-level characteristics of a specific cluster of individuals, and an evolutionary algorithm for improving A.I. performance according to data generated by DARWIN for human brain data. 4) [DATANET] is a data management and protection system 124 involved in data storage, quality assurance, and protected data sharing. DATANET is based on a distributed ledger technology (e.g., blockchain) algorithm with enhanced compression capabilities to enable the storage and transfer of large-scale DMDT and DARWIN files. DATANET includes multi-path key encryption for separate data access by for-profit and non-profit organizations, allowing data owners to share data with multiple users for different purposes without sharing the entire data. DATANET includes a reward system for data owners to receive permanent compensation based on the use of DMDT data. 5) [STIMOLA] is a module 126 that includes systems and methods for manipulating brain activity, cognition, and behavior through neuromodulation, including, but not limited to, non-invasive brain stimulation techniques, cognitive training, video games, and augmented, mixed, and virtual reality tools. 6) [TRAINER] includes systems and methods for skill-specific enhancement, programming an individualized training regime that combines information from BRAINPRINT and models from the DARWIN module to maximize learning and cognitive performance. These include, but are not limited to, language acquisition, music training, visuomotor training, behavioral training, and cognitive therapy. TRAINER can be combined with the methods of STIMOLA for additive effects. 【0064】 Each of the above-listed components is described in more detail below, and exemplary datasets are provided to demonstrate the feasibility and functionality of the components and approaches. 【0065】 Workflow A general exemplary application through which data flows via the NEP platform is described below: 1) Data collected from a person via a PERCEPTRON and / or other wearable system can be stored in a cloud-based database as part of the BRAINPRINT. Data including, but not limited to, neuroimaging data (including electroencephalogram (EEG), magnetic resonance imaging (MRI), positron emission tomography (PET), and magnetoencephalogram (MEG)), cognitive behavioral measurements (including, but not limited to, memory scores, language proficiency, reaction times, and emotional responses to stimuli), and physiological data (including, but not limited to, heart rate variability, electrodermal response, and thermal facial response) from multiple modalities can be preprocessed for further analysis and extraction of features to create a dynamic multi-layer digital twin (DMDT). The preprocessing steps can include, for example, removal of data recording artifacts, resampling, filtering, relabeling of signal traces, and averaging. Other preprocessing steps can include algorithms for harmonizing data based on distribution fitting and alignment, reweighting, and normalization. Feature selection based on adaptive machine learning (AML) can be used to identify features carrying a person's unique information through comparison with the population average template of the data distribution / variable part of the human template repository (HTR). The preprocessed brain, physiological, cognitive, and behavioral data can be used to extract performance indices (e.g., brain plasticity index, learning index) based on the data collected at baseline. In some embodiments, the performance indices are updated with new incoming data collected via the PERCEPTRON and / or other devices. 2) The DMDT data is sent to the DARWIN module, at least in part, for creation of a prediction model, estimation of brain potentials, extraction of features for content generation in the metaverse, and / or individualization of neuromodulation interventions. 3) The DMDT data is sent to PREPARE for preprocessing, harmonizing, and database creation of the data. The data is processed and formatted to enable further analysis via the DARWIN module. 4) OPTI-BRAIN can perform simulations on DMDT data to establish optimal patterns for enhancing specific cognitive functions, such as logical inference. In this example, OPTI-BRAIN uses the organization of cognitive and functional brain networks as the main reference for estimating a person's learning potential and identifying optimal individualized learning trajectories. 5) The OPTI-BRAIN solution can be further refined based on algorithms and proprietary knowledge related to the neuroscience of learning (e.g., via IMPROVE) and neuroplasticity (e.g., via SYNAPSE). For example, - Apply algorithmic data from IMPROVE that describes the optimal sequence of enhancement steps to achieve general cognitive enhancement to the original OPTI-BRAIN model. For example, based on the specific cognitive and brain qualities described in the DMDT data and the initial analysis from OPTI-BRAIN, IMPROVE may propose first improving the visual-spatial attention level, followed by strengthening two executive functions (e.g., inhibition and flexibility), and then enhancing semantic language fluency. - The revised OPTI-BRAIN solution for enhancing logical inference can be further constrained by SYNAPSE based on DMDT data that describes individual qualities related to neuroplasticity. Considering the low-level brain plasticity indexed by the cognitive data described in EEG and DMDT, SYNAPSE proposes global neuromodulatory interventions (e.g., drugs that disrupt perineuronal nets) and aims to first increase global brain plasticity before implementing the solution proposed by IMPROVE, followed by more selective / focused modulation of plasticity targeting the brain's fronto-parietal control network (FPCN) and dorsal attention network (DAN) (e.g., via transcranial electrical stimulation - tES). 6) Following the INPUT from IMPROVE and SYNAPSE, the OPTI-BRAIN model for enhancing logical inference can be sent to STIMOLA for implementation (e.g., including the execution of a specific transcranial electrical stimulation (tES) protocol to improve plasticity and the execution of a cognitive training protocol to enhance cognitive functions (e.g., executive functions, visuospatial attention)). 7) Protocols for enhancing logical inference can be implemented via STIMOLA based on algorithms that evaluate dose-response curves and identify individualized training regimens. Brain, cognitive, and behavioral data can be collected via PERCEPTRON. 8) STIMOLA can update an individual's DMDT while providing feedback on the effectiveness of the enhancement protocol to DARWIN, and this can be used to update the OPTI-BRAIN solution in a closed-loop fashion. 9) PERCEPTRON can be used to track changes in the brain, behavior, and cognition during the enhancement period by continuously or periodically updating an individual's DMDT and notifying the DARWIN model. 【0066】 In various scenarios, different (but perhaps similar) workflows can be used. For example, in some embodiments, the DMDT data and the DARWIN model are used to create individualized avatar information about the metaverse and, for example, to generate digital assets based on a person's brain efficiency, resilience, or cognitive profile. 【0067】 In some embodiments, the DMDT data and the DARWIN model are used to generate assets and content for video game development, e.g., such that a player's specific brain and cognitive characteristics are reflected in the avatar or such that specific game dynamics of the game are adjusted by the individual DMDT characteristics and real-time streaming of data via PERCEPTRON. 【0068】 In some embodiments, DMDT data and DARWIN models (e.g., SYNAPSE, IMPROVE, OPTI - BRAIN) are used to create customized, optimal, and / or improved in - game training strategies and learning protocols to enhance game performance. 【0069】 In some embodiments, DMDT data and DARWIN models (e.g., SYNAPSE, IMPROVE, OPTI - BRAIN) are used to track brain health via PERCEPTRON and identify customized, optimal, and / or improved strategies for improving an individual's brain health. 【0070】 In some embodiments, DMDT data and DARWIN models are used to create VR applications for enhancing brain plasticity via an Extended Virtual Environment (VAE), where specific combinations of stimuli and their features are presented to an individual, inducing neuroplasticity effects in the brain via the regulation of PNN and plastic circuits, and affecting learning, cognitive, and rehabilitation protocols. 【0071】 In some embodiments, DMDT data and DARWIN models are used to induce the STIMOLA protocol delivered in VR, enhance the sensations of the embodiment, reduce motion sickness, and / or improve the effectiveness of cognitive interventions delivered in a virtual environment. 【0072】 In some embodiments, DMDT data and DARWIN models can be used to plan individualized psychotherapy interventions, and the sequence of cognitive and behavioral training / tasks is defined based on the optimal evolutionary path for each individual. Methods and algorithms from OPTI - BRAIN, OPTI - COG, NEUROCREATOR, and STIMOLA can identify interventions that act directly on brain activity, sandwiching cognitive and behavioral training, to improve or maximize the impact of the therapy. 【0073】 In some embodiments, OPTI-BRAIN can run simulations on DMDT data to identify brain targets for non-invasive brain stimulation therapeutic intervention in patients with neurological and psychiatric conditions. 【0074】 In some embodiments, OPTI-BRAIN can be configured to run simulations on DMDT data to identify brain targets for non-invasive brain stimulation interventions to enhance plasticity via sleep modulation. 【0075】 In some embodiments, OPTI-BRAIN can be configured to run simulations on the DMDT data to identify brain targets for non-invasive brain stimulation interventions to slow or prevent brain tumor migration and recurrence. 【0076】 In some embodiments, OPTI-BRAIN can be configured to run simulations on the DMDT data to identify brain regions affected by brain tumor recurrences, and the information is used to guide surgery. 【0077】 In some embodiments, DMDT data and the DARWIN model can be used to inform general AI agents based on individual brain and cognitive characteristics via NEURO AI, and the agents can be trained based on similar DMDTs from multiple individuals to generate population average behaviors to be used for specific problem-solving and decision-making processes. 【0078】 Brain print Some embodiments relate to solutions for capturing the individual characteristics of a given brain and combining the individual characteristics with one or more of cognitive, behavioral, and biometric data to provide a comprehensive summary of the capabilities and potential of a given individual, and also, at least in part, to solutions for designing individualized interventions for enhancing cognitive function and brain health, optimizing brain function, improving neuroplasticity, and / or accelerating learning. The combined information may form a dynamic multi-layer digital twin (DMDT) for an individual, which represents a snapshot of the individual's profile at a particular moment and its temporal evolution as observed in the data and predicted by the DARWIN model. The DMDT may represent a digital replica of local and distributed brain dynamics constrained by structural information derived from brain data. The mechanics of brain activity are described by equations and sets of equations that describe local and distributed activity over time. 【0079】 Some embodiments relate to creating a DMDT based at least in part on passive data, evoked data, active data, or any combination thereof. For example, passive data may 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 may be summarized into a set of indices and metrics that describe the topology, activity, and dynamics of a given brain under the name "electome" according to its similarity to the brain connectome. 【0080】 Evoked data may be obtained by recording active data from the brain, including, but not limited to, electrical signals and / or hemodynamic signals measured in response to external stimuli (e.g., in response to flashing lights or sounds) or manipulation of the brain state via specific instructions (e.g., induction of a meditation state). 【0081】 The induced data can be obtained by recording active data from the brain, including but not limited to electrical signals and hemodynamic signals measured in response to cognitive stimuli, as described, for example, as part of a cognitive assessment procedure in which tests and tasks are performed to infer information about an individual's cognitive profile. The procedure may also include measuring the response to a cognitive intervention aimed at enhancing or restoring cognitive function in a healthy individual or an individual with a neurological or psychiatric condition. 【0082】 The induced data can be obtained by recording active data from the brain, including but not limited to electrical signals and hemodynamic signals measured in response to a psychological assessment procedure in which tests and tasks are performed to infer information about an individual's psychological profile. The procedure may also include measuring the response to a psychological intervention aimed at providing psychological relief and / or reducing specific psychological and cognitive symptoms, including those related to mood and anxiety disorders. 【0083】 The induced data can be obtained by recording active data from the brain, including but not limited to electrical signals and hemodynamic signals measured during task completion as part of a video game. Brain activity related to specific aspects of video game experience can be recorded to infer the neural correlates of in-game performance. In similar embodiments, the data is collected during physical performance, such as sports-related activities, or during a meditation session. 【0084】 The induced data can be obtained by recording data from the brain, including but not limited to electrical signals and hemodynamic signals measured in response to external electrical, magnetic, or ultrasonic perturbations of the brain using a device attached to an individual's head. Brain activity and corresponding cognitive and behavioral data are collected before, during, and after the perturbation to derive metrics of an individual's response to targeted non-invasive stimulation of the brain. 【0085】 In some embodiments, perturbations to the brain are delivered via methods and techniques of non-invasive brain stimulation (NIBS). This includes, but is not limited to, transcranial magnetic stimulation (TMS) (in the form of repetitive TMS (rTMS), but not limited thereto), patterned rTMS protocols (e.g., theta burst stimulation, multi-pulse TMS, and associative paired stimulation), transcranial electrical stimulation (tES) (in the form of transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), and transcranial random noise stimulation (tRNS), but not limited thereto), focused ultrasound (FUS), among others. The following description of the STIMOLA module provides additional details regarding methods and approaches for providing stimulation according to some embodiments. 【0086】 For any of the above modalities and applications, but not limited to, DMDT can be created, at least in part, by examining the characteristics of brain activity recorded at the mesoscale and macroscale levels, locally and across multiple brain structures, circuits, and networks. The resulting models can be digital replicas of the mesomacroscale dynamics of individual brains, and information at different spatial and temporal scales is summarized at multiple levels, including but not limited to: (i) the structural level, which includes information about brain wiring and local tissue characteristics; (ii) the functional level, which describes spontaneous and / or evoked activity measured in brain regions or local circuits; (iii) the dynamic level, at which multi-region activity occurring over time is summarized (e.g., functional connectivity and spectral coherence data across brain regions or networks); and (iv) the machine level, at which algorithmic models that explain and predict functional and dynamic level data are stored. The models include mathematical models that explain gray and white matter activity from layer to layer in the brain, as opposed to those that explain activity at the long-range network level, such as that captured by high-resolution EEG. Machine learning and / or deep learning models can be used to validate predictions against templates of null distributions and / or random brain activity. Information from different data modalities can be summarized in relation to network-level activity, such as by metrics applicable to different data types, e.g., metrics of graph theory and network control theory. Anatomical and functional templates can be used to ensure symmetry between data collected across modalities and different spatial resolutions (e.g., EEG and MRI). Metrics related to local activity can also be extracted and integrated with network-level metrics. Metrics related to activity can be applied to both static and dynamic data (e.g., time series data) as a summary of activity over time and / or as successive discrete measures of activity over time. 【0087】 For example, in some embodiments, DMDT can be obtained at least in part by examining the synchronization between the activities of two or more brain regions using static (functional), dynamic, and effective connectivity metrics. In some embodiments, DMDT can be obtained at least in part by examining the organization of the brain network using metrics derived from graph theory, network topology analysis, and network control theory. In some embodiments, DMDT can be obtained at least in part by examining the pattern of eye movements recorded via a camera during the execution of one or more cognitive tasks. In some embodiments, DMDT is used to define a quantitative template of "healthy" brain and cognitive dynamics for use as a reference for DARWIN applications, including but not limited to estimating brain potentials, executing brain optimization programs via OPTI-BRAIN, or defining therapeutic interventions. 【0088】 Data can be assimilated and reconciled across data input modalities and data formats, including but not limited to EEG and neuroimaging data, cognitive metrics, and inputs from heart rate variability. Subsequent analysis of individual data can be performed within a module (e.g., OPTI-BRAIN) that can summarize the individual's data into performance indices partitioned into macrodomains, including but not limited to (i) brain performance indices, (ii) cognitive performance indices, (iii) perturbation indices, (iv) learning indices, (v) plasticity indices. In some embodiments, DMDT can be used to calculate a series of indices that capture brain, cognitive, and behavioral performance. 【0089】 Brain performance indices can be derived from individual data representing a person's brain's ability to efficiently propagate information within brain regions and networks and to appropriately and dynamically allocate metabolic resources to different tasks. 【0090】 The perturbation index can be derived from individual data representing a person's brain's ability to respond to endogenous and exogenous perturbations, which are in the form of, but not limited to, cognitive stimulation, electromagnetic stimulation, behavioral interventions such as psychotherapy or meditation, or drugs. 【0091】 The plasticity index can be derived from individual data representing a person's brain's ability to adapt to new stimuli, which is not limited to, but includes, creating new brain connections or reorganizing existing brain connections at the micro-meso-macro scale. 【0092】 The learning index can be derived from individual data representing an individual's ability to generate, organize, and apply new knowledge through the acquisition of information and the manipulation of new inputs, which includes, but is not limited to, the individual's strategic ability to learn (e.g., learning to learn). 【0093】 In some embodiments, DMDT from multiple individuals is stored in a database, and the stored DMDT is used to estimate a brain mode representing a specific brain configuration suitable for a specific task. For example, the average of DMDT collected from individuals with a high IQ can be used to generate a general-purpose AI module. DMDT collected from individuals with high creativity can be used to generate a divergent thinking AI module. DMDT collected from individuals with high executive function can be used to generate a decision-making AI module. DMDT collected from individuals with a high brain performance index can be used to generate a backbone AI infrastructure where modules are connected and synchronized. 【0094】 In some embodiments, DMDT from multiple individuals is stored in a database, and the stored DMDT is used to estimate a brain mode representing a specific brain configuration suitable for a specific task. For example, the average of DMDT collected from individuals with a high learning index can be used to identify brain targets for neuromodulation interventions aimed at improving learning and memory performance in individuals with a low learning index. 【0095】 DMDT data can be stored at multiple levels for different and / or overlapping purposes. For example, DMDT data can be stored locally on a PERCEPTRON device used to collect data as part of the DMDT creation process, at least in part. The device can be configured to store data and host local routines for the reconciliation and analysis of the data, including at least some of those described in DARWIN. As shown in FIG. 2, DMDT data can be stored, at least in part, in a database DATANET (e.g., a cloud-based biobank), where DMDT data from multiple individuals is analyzed and reconciled to create, for example, a population-based DMDT of brain health, cognitive performance, and resilience to disease states, and to identify candidate brain targets for neuromodulation interventions. 【0096】 FIG. 2 shows an exemplary cloud computing and blockchain architecture for data storage, processing, verification, and distribution sharing across multiple platforms, according to some embodiments, and different scenarios including different users and clients that request access to the data. 【0097】 Generation and Use of Non-Fungible Tokens (NFTs) In some embodiments, the DMDT is stored as a non-fungible token (NFT) in the form of a digital packet containing information about the structural and functional characteristics of an individual's brain. In one example, the NFT may be related to the overall characteristics of a given brain and may be independent of a particular state or trait. In another example, the NFT may be related to one or more specific characteristics of a given brain recorded at a particular state or moment, such as when a cognitive task (e.g., successful encoding and recall of memory), an action task (e.g., meditation state, music performance) is successfully completed, when in an emotionally heightened state (e.g., during recall of past trauma or experiencing trauma), when in a state of high creativity or abstract reasoning (e.g., conceiving a solution to a problem or generating a new idea), etc. The NFT can then be used as a template for deriving a DARWIN solution for transitioning from the current DMDT to past brain states / traits using approaches and methods from STIMOLA (e.g., using brain stimulation to re-enter a highly creative brain state). 【0098】 In some embodiments, the NFT can be used in a clinical setting as a template for deriving a DARWIN solution for transitioning from a patient's current BRAINPRINT to past brain states / traits using STIMOLA (including, but not limited to, interventions including transcranial electrical stimulation and magnetic stimulation). Applications can include, but are not limited to, optimizing neuromodulation interventions [STIMOLA] using a patient's pre-clinical DMDT (e.g., before the onset of symptoms or detection of a pathological biomarker) to improve clinical symptoms or modify the disease course (e.g., using the DMDT of a dementia patient collected before the onset of memory deficits to optimize current brain processing towards effective memory encoding and recall patterns, using the DMDT of a PTSD patient collected before the trauma that induces PTSD to identify patterns of changes in brain activity and derive useful interventions via DARWIN). 【0099】 In some embodiments, the NFT can be used in human performance applications as a template for deriving a DARWIN solution for transitioning an individual's current DMDT to past brain states / traits using STIMOLA. The application can include using the DMDT of an athlete collected during peak states and / or peak performance to optimize neuromodulatory interventions to re-enter a high-performance state, or using the DMDT collected during a deep meditation state to guide the stepwise modification of brain dynamics necessary to reach such a state, but is not limited thereto. 【0100】 In some embodiments, the NFT can be enriched with data related to, but not limited to, psychosomatic responses (e.g., stress response, electrodermal response), cardiac activity (e.g., heart rate, heart rate variability), behavioral patterns (e.g., sleep activity and efficiency), and cognitive states (e.g., memory performance, attention level, intelligence quotient - IQ) to further individualize the creation of the DMDT and define parameters for brain optimization. In some embodiments, a group of NFTs from individuals with similar profiles can be used to create a group-level representation of brain and cognitive activities representing a particular group of individuals. 【0101】 In some embodiments, patterns of brain activity recorded during specific moments particularly relevant to an individual can be stored as NFTs for their value as memory. In some embodiments, patterns of brain activity related to specific moments during video game performance particularly relevant to an individual can be stored as NFTs for their replay value, their value as a template for performance to be remembered, their value in improving performance, and their value as training material for training other individuals regarding in-game dynamics and performance. 【0102】 NFT can be represented by brain data alone, brain data combined with behavioral data that captures one or more performance characteristics, brain data combined with cognitive data, or brain data combined with other physiological data. In some embodiments, NFT can include both data and the procedures for its generation, including data processing techniques and data visualization solutions. 【0103】 In some embodiments, NFT can include a set of brain states collected via a PERCEPTRON or similar device in the form of brain, cognitive, and physiological data. The NFT is labeled as a specific memory of values unique to an individual and then used as a template to reproduce the pattern of brain activity that generated such memory. Memory reproduction can be achieved using the DARWIN algorithm that suggests optimal connectome regulation approaches and tools from STIMOLA. NFTs for unique memories, moments, and brain states can be manually generated by an individual by assigning labels at specific moments, or automatically generated via an algorithm based on thresholds of brain, psychological, and physiological activation that mark the start and end of inherently valuable moments stored as NFTs. For example, NFTs can be automatically generated during a high-performance video game tournament to store brain patterns that support peak performance, or brain patterns that occur during a celebratory event. NFTs can be automatically generated at moments of high emotional activation (e.g., when meeting a long-lost partner or family member, when interacting with a pet or child, when recalling positive or negative memories). Detection, analysis, and memory of brain states are performed via algorithms from DARWIN and can be stored in DATANET. 【0104】 As part of DATANET, an individual can maintain control over NFTs generated by themselves and / or based on their brain, physiological, and cognitive data. An individual may share, edit, modify, exchange, and sell NFTs using DATANET or other related platforms. 【0105】 Generally, models of brain activity, behavior, cognitive performance, psychological profiles, and physiological profiles that are collected as part of DMDT and / or analyzed via DARWIN can be stored as NFTs representing specific unique characteristics of a given individual. The DARWIN models obtained by extracting features from an individual's DMDT can constitute a unique set of information that describes the phenotype of the individual, including but not limited to one or more DARWIN indices (examples of which are described herein), such as indices of brain and psychological resilience, flexibility, and evolvability. For example, a given brain feature that provides a highly evolvable and plastic state for a given individual can constitute an NFT that uniquely identifies the individual for whom the data was used to generate such a brain feature. In some embodiments, data from multiple individuals collected in DATANET and from DMDT can be used to generate group-level NFTs that represent unique configurations of brain activity that describe a particular group of individuals, such as a group of patients with similar clinical characteristics or a group of healthy individuals with high working memory performance. 【0106】 Figures 3A-3C schematically show the creation of multi-layer NFTs from neural data according to some embodiments. FIG. 3A shows that an NFT can represent the structural characteristics of the brain 302, its spontaneous activity pattern 304 unique to each individual, the pattern of evoked activity 306 induced by various types of perturbations (including but not limited to sensory stimuli such as electrical, magnetic, visual, and auditory stimuli, and emotional stimuli that induce psychosomatic responses), the graphical representation 308 of such activity in 2D, the 3D rendering 310 of brain activity using high-resolution brain data or a 3D template, the connections 312 and network-level patterns of activity that characterize how brain regions / networks / circuits interact over time, and / or art 314 generated from brain data via generative algorithms, A.I., or human-guided procedures. FIG. 3B shows 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 can occur over time due to normal aging processes, lack of training, and other factors. An individual may want to restore a previous pattern of brain activity in order to recover their performance. The NFT used at the first time point can be used as a template for inducing rewiring of the brain activity pattern using DARWIN. FIG. 3C shows that a similar procedure can be implemented to rewire an individual's brain activity to match the brain activity of a different individual with a desired pattern of brain activity and corresponding behavior / performance. 【0107】 System and Hardware [PERCEPTRON] Some embodiments relate to a system for capturing information about an individual (referred to herein as "PERCEPTRON"). For example, FIG. 4 shows that the PERCEPTRON system may include, but is not limited to, a portable wearable headset configured to record brain signals (e.g., EEG, blood flow), audio via a microphone, galvanic skin response (GSR) or similar data for stress-related responses, and / or heart / cardiac data (e.g., heart rate, heart rate variability), and to deliver a stimulation protocol received from STIMOLA. 【0108】 In some embodiments, the PERCEPTRON is a battery-powered headset 410 having electroencephalogram (EEG) recording electrodes 412 arranged to be placed in contact with an individual's scalp. The electrodes can be arranged to cover the entire scalp according to a pre-specified position, depending on the data to be collected. The PERCEPTRON system can have a modular structure including a mainframe that hosts the electrodes and a power source (e.g., a battery), as well as a set of additional components that can be attached to the frame to expand the number of electrodes on the scalp, and other components including but not limited to a microphone, a VR headset, or an AR goggle. The PERCEPTRON system can include a processing unit having computing capabilities (e.g., embedded in the frame) and a memory unit for local storage of data. The PERCEPTRON system can have wireless communication capabilities including but not limited to circuits that enable communication 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, tablets, TVs. The PERCEPTRON system can have a connectivity function that enables direct network (e.g., Internet) connection without the need for an external device. This function can be used for cloud-based computing, real-time storage and processing of data, or interaction with other PERCEPTRON systems, among other things. Interaction with other devices can enable direct transmission and reception of information regarding data stored or in real-time from a given PERCEPTRON system, including but not limited to EEG activity, heart rate, and GSR. The connectivity function can be used to interact with hardware other than the PERCEPTRON system via one or more brain-computer interface (BCI) applications including but not limited to controlling a TV set, a laptop computer, a mobile phone, and for more direct interaction with content such as controlling an avatar in a video game or a metaverse application. 【0109】 In some embodiments, the PERCEPTRON system also includes a battery-powered neuromodulation unit that enables various forms of transcranial electrical stimulation (tES) via the same EEG electrodes included in the PERCEPTRON system. Neuromodulation also includes, for example, transcranial focused ultrasound (tFUS) and near-infrared spectroscopy. 【0110】 In some embodiments, the PERCEPTRON system includes composite electrodes made of carbon nanotubes that enable dry recording and dry brain stimulation without the need to apply gel to the scalp. The carbon nanotubes can be arranged as conductive filaments within a fabric cap placed on the scalp. The filaments can be arranged across the entire scalp, and sub-portions of each filament can be activated by a microcontroller capable of activating millimeter-resolution segments of the filament. This solution enables high spatial resolution for both brain recording and brain stimulation, allowing for a one-to-one correspondence between the brain data observed at the scalp and the injected brain stimulation. 【0111】 In some embodiments, the PERCEPTRON system includes electrodes for gel-based brain recording and brain stimulation with a pressure-based dispenser system and an active impedance monitoring system. The gel is stored within the electrodes and can be released when the impedance level evaluated at the surface of the electrodes in contact with the scalp reaches a pre-specified threshold level. This system minimizes the use of gel and avoids the effects of bridging and signal contamination. 【0112】 In some embodiments, the PERCEPTRON system is controlled via a multi-platform software package available for personal computing (PC) devices and / or mobile devices in the form of an app. 【0113】 In some embodiments, the PERCEPTRON system can be used to obtain data on an individual's DMDT and / or can subsequently be used for longitudinal assessment of the individual over time. The baseline and longitudinal assessments can be completed during rest state activity and / or during the execution of a specific task to evaluate the induced brain activity. 【0114】 In some embodiments, the PERCEPTRON system can capture the data used to create the DMDT as described above. For example, an individual can be exposed to a VR or AR environment. Here, the behaviors and cognitive responses are captured using the PERCEPTRON system, and the biometrics are recorded via a wearable data collection unit. The physical characteristics of the VR / AR environment can be manipulated to induce a target response and provide meaningful data for the creation of the DMDT. VR and AR can be used to overcome the limitations of standard cognitive and behavioral assessments and to administer special non - ordinary stimuli tailored to probe brain and mental functions (e.g., exposing the patient to a rich environment and non - ordinary stimulus patterns including sensory, auditory, and visual stimuli). 【0115】 In some embodiments, the PERCEPTRON system can be connected and / or controlled via a mobile device (e.g., a smartphone, a tablet) for remote monitoring and / or real - time data collection during daily activities, execution of specific tasks, etc. 【0116】 In some embodiments, the stimulus presentation and response recording using the PERCEPTRON system can be performed in a dedicated physical space that allows for more immersive sensory and cognitive stimuli and exposure to more complex stimuli. As an example, the physical space can be a room containing tools for audio and video stimulus presentation and recording sensors installed on each wall of the room. The sensors and stimulus presentation devices in the physical space can be connected to the wearable device used to induce or update the pattern of perturbation provided to the individual in real - time based on the individual's responses. 【0117】 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 before, simultaneously with, and / or after brain data collection. 【0118】 In some embodiments, the PERCEPTRON system may be used in a game application to monitor, induce / regulate brain and biometric data during game performance and / or to extract DMDT data for the generation of game content and assets. In some embodiments, the PERCEPTRON system may be used as a digital biometric assessment tool for monitoring daily life activities and daily cognitive and brain activities in healthy individuals. The data collected via the PERCEPTRON system and the metrics extracted via DARWIN may be used to induce a detailed assessment conducted in a clinical setting, such as when an individual showing changes in brain oscillatory activity in a weekly home assessment is invited for a clinical EEG assessment at a local hospital. 【0119】 In some embodiments, one or more PERCEPTRON systems may be used to provide data for the analysis of brain-to-brain communication related to applications in the fields of decision-making and cognitive neuroscience and to simultaneously monitor brain activities from multiple individuals. 【0120】 In some embodiments, the PERCEPTRON system may be used for the clinical evaluation of individuals having neurological or psychiatric conditions and other medical conditions affecting the central nervous system. 【0121】 Data Processing and Analysis [PREPARE] Information collected as part of a BRAINPRINT and constituting an individual's DMDT can be processed and analyzed as part of module PREPARE. Data cleaning, preprocessing, harmonization, and analysis methods can be implemented to transform raw data collected from a device into data suitable for data analysis and visualization. PREPARE can include, for example, a second-level analysis performed via the DARWIN module, a brain-computer interface (BCI) application (e.g., via STIMOLA and / or PERCEPTRON) that notifies of brain stimulation interventions, an application including VR, or a pipeline for data conversion that facilitates a metaverse and neurogaming (e.g., NEUROCREATOR). The pipeline can be applied to multiple data formats and modalities including, but not limited to, brain scans, electroencephalogram data, and behavioral data. In some embodiments, the pipeline is used for the analysis of different brain states including the analysis of spontaneous brain data and behavior, the analysis of evoked activity in response to external or internal events / stimuli, and the analysis of brain data in response to external perturbations in the form of transcranial electrical stimulation, magnetic stimulation, or ultrasonic stimulation. The pipeline for data preprocessing and analysis can include both unsupervised algorithms for automated processing and semi-supervised supervised algorithms for data interpretation and decision-making. FIG. 5 shows an exemplary data analysis pipeline including data processing components that can be included in PREPARE according to some embodiments. 【0122】 Data processing method Data processing includes solutions for automatic and semi-automatic cleaning / preprocessing of data and allows for manual identification of artifacts. Processing and analysis can be performed as part of separate modules covering, for example, (i) data collection, (ii) data verification and format conversion, (iii) data cleaning and preprocessing, (iv) data harmonization, (v) data analysis, and (vi) detailed report generation (including a summary of optimal stimulation targets / parameters and processing steps). 【0123】 Brain scan processing When brain scans are available for target selection, two types of information can be used: (i) structural properties of the brain, including but not limited to gray / white matter density / volume / thickness / cortical folding / sulcal depth, CSF distribution, white matter diffusivity and anisotropy, and spectroscopic profiles of neurotransmitters, and (ii) functional properties of the brain, including but not limited to hemodynamic responses, blood perfusion, metabolic activity (e.g., glucose consumption), neuroinflammatory levels, and protein load. Steps for preparing brain scans for statistical analysis include, but are not limited to, conversion to 3D volume format of a single image, segmentation in brain tissue classes, spatial and temporal filtering, removal of physiological noise, removal of image artifacts, extraction of mean values and / or time series of brain activity, co-registration to a common anatomical or functional template for group-level analysis, and calculation of evoked activity when multiple scan conditions exist, as in the case of block fMRI data. Follow-up analysis can be performed on both voxel-based volumetric measurement data or vertex-based surface images, and includes masking of clean data based on an anatomical or functional atlas that describes the relevant networks or brain regions. 【0124】 Processing of electrophysiological data Data collected via electroencephalography (EEG) is often contaminated with recording artifacts, including but not limited to blinks, heart rate, and movement-related artifacts. The systems and methods described herein provide for cleaning, preprocessing, and / or conditioning of the data to at least partially reduce or eliminate the effects of such artifacts. 【0125】 The preprocessing and cleaning steps include, but are not limited to: raw data conversion (e.g., .edf format); trimming of raw data into epochs of a predetermined length; automatic or semi-automatic data inspection to identify EEG channels containing excessive noise or artifacts; zeroing of muscle artifacts (e.g., by voltage-based threshold, kurtosis, and combination 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; band-pass filtering using forward-backward filters; notch filtering to account for line noise; reference to global mean; and a second ICA to manually remove all remaining artifact components including eye movement / blink, muscle noise (EMG), single electrode noise, and auditory evoked artifacts (artifacts are identified and labeled based on spectral frequency profile, power spectrum, amplitude, scale, scalp topography, and time course). The cleaning and processing of data can be performed as a supervised method, e.g., with human verification of processing steps and visual inspection of data, or as an unsupervised procedure based on ML and A.I. without human interaction. 【0126】 Simultaneous brain stimulation and EEG recording In some embodiments, brain activity can be recorded during the delivery of non-invasive brain stimulation, as described in the STIMOLA section. Appropriate adjustment to the standard EEG processing pipeline can be used to ensure proper processing of stimulus-induced artifacts and interpretation of the data. 【0127】 In the following example, the procedures for cleaning, preprocessing, and analysis of EEG data collected before, during, and after the delivery of TMS pulses are described. The same procedures can be applied to simultaneous tCS and / or focused ultrasound during EEG recording. 【0128】 TMS-EEG Analysis: In addition to typical artifacts present in EEG recordings (e.g., eye movements and heart rate), the EEG data collected during TMS can be contaminated with TMS-specific artifacts including, but not limited to, magnetic artifacts that change the impedance of EEG electrodes, TMS-induced muscle artifacts characterized by high-frequency activity, and artifacts associated with the TMS machine charging 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. 【0129】 Preprocessing and cleaning steps include, but are not limited to: raw data conversion (e.g., .edf format); trimming of the raw data into epochs of a predetermined length that include segments capturing brain activity before and after TMS; normalization of the activity after TMS by subtracting the average signal amplitude of the EEG data collected before TMS; automated or semi-automated data inspection to identify EEG channels containing excessive noise or artifacts; zero-padding of the activity simultaneous with a single TMS pulse to remove early signal attenuation and muscle artifacts induced by the TMS pulse (e.g., based on voltage-based thresholds, kurtosis, and combined probability); independent component analysis (ICA) to identify and remove components containing early TMS-induced high-amplitude electrodes, with additional data reduction via principal component analysis (PCA) to minimize overfitting and noise components; interpolation of the signal zero-padded prior to the TMS pulse; band-pass filtering using forward-backward filters (typically 1 - 150 Hz); notch filtering to account for line noise; reference to the global mean; manual removal of all remaining artifact components including eye movement / blink, muscle noise (EMG), single electrode noise, TMS-induced muscle activity, heart signal (EKG), and auditory-evoked artifacts (artifacts are identified and labeled based on spectral frequency profile, power spectrum, amplitude, scale, scalp topography, and time course); application of machine learning and deep learning algorithms for identification of residual artifacts. 【0130】 Data cleaning and processing can be performed, for example, as a supervised method involving human verification of processing steps and visual inspection of data, or as an unsupervised procedure based on machine learning (ML) and A.I. without human interaction. 【0131】 Brain Enhancement Platform [DARWIN] When DMDT becomes available, according to some embodiments, the DARWIN module can be configured to estimate the growth, evolution, adaptation, and / or learning potential of an individual brain. DARWIN can be configured to create a digital representation of an individual brain, including its structural and functional characteristics, as well as the corresponding cognitive and psychological hierarchies, and capabilities, and to perform simulations that describe the response to stimuli such as normal and pathological aging, external perturbations (such as in the case of traumatic brain injury or brain tumor, or magnetic / electrical stimulation), or psychological stressors such as trauma. For example, DARWIN can be configured to identify the optimal trajectory for inducing brain changes, both when the goal is a modifiable temporary target state or when the goal is a new stable brain configuration (trait). DARWIN can also specifically modify one or more brain characteristics related to a given purpose, such as learning a new musical instrument or inducing a meditative state. Details of exemplary different applications using DARWIN are described below. DARWIN can be informed by data, models, and information from SYNAPSE and IMPROVE collected via PERCEPTRON, generate evaluation tools via SCREEN, and / or guide the procedures used in STIMOLA. 【0132】 Digital Biometric [SCREEN] Through the SCREEN, DARWIN can be configured to generate data-driven digital biometric assessment tools and metrics. The SCREEN can include methods for generating assessment tools that quantify cognitive, brain, and psychological dimensions, constructs, and functions, and facilitate individualized digital biometrics for brain, cognitive, and mental health. In embodiments, the assessment tools are deployed in the form of digital tasks or games via portable devices including, but not limited to, mobile phones, tablets, laptops, and wearables. The mechanics and design of the assessment tools may be based on the analysis of data collected as part of the DMDT and may be informed by design principles and neurobiological / neurophysiological / neurological concepts and models from SYNAPSE and IMPROVE. The assessment tools may be at least partially based on knowledge related to the science of brain plasticity and learning, in combination with original data related to the architecture of human cognition and the psychological well-being of the individual (see, e.g., IMPROVE). 【0133】 In some embodiments, the information obtained via OPTI-BRAIN and OPTI-COG can be used to design ad-hoc cognitive and brain health assessment tools based on individual qualities derived from brain and cognitive data. The information used to create the assessment tools can relate to a single individual or group of individuals sharing similar performance of a particular characteristic of interest, such as a cognitive function (e.g., high working memory capacity) or neurological state (e.g., a patient with dementia). Brain characteristics related to the function or state can be modeled from spontaneous activity data or activity induced in response to external perturbation. In an exemplary application, DMDT data from samples of individuals diagnosed with dementia and memory impairment can be aggregated via PREPARE, and characteristics related to brain plasticity and resilience can be extracted via DARWIN. Changes in plasticity mechanisms in specific regions of the brain and low levels of brain resilience may suggest that cognitive tests capturing these brain characteristics can be useful for the diagnosis and prognosis of individuals with dementia and memory impairment. As a result, a series of stimuli can be combined in a specific sequence to perform a stress test on brain regions with low plasticity and resilience, and thus measure the brain's resilience to perturbation. 【0134】 In some embodiments, cognitive, brain, and psychological assessments are delivered via a VR or AR platform in the form of a gamified application. In some embodiments, cognitive, brain, and psychological assessments are performed while recording biometric data via a device including but not limited to PERCEPTRON. Individual performance on the assessment tasks can be linked to specific patterns of brain activity for the generation of cognitive, brain, and psychological function, as well as induced brain activity metrics of health. 【0135】 In some embodiments, perturbation-based assessments are performed where brain and physiological activities recorded biometrically are regulated by external stimuli to generate perturbation-based biomarkers that index an individual's brain / cognitive / psychological capabilities to adapt to perturbation as a measure of plasticity. 【0136】 In some embodiments, physiological data collected in combination with brain data can include, but is not limited to, EKG, GSR, eye movements via eye-tracking technology, and body temperature. 【0137】 In some embodiments, the assessment task is developed to index metrics calculated via DARWIN, such as those described in the following sections, including but not limited to metrics of brain potential, fitness, resilience, adaptability, evolvability, stability, and plasticity. SCREEN can be configured to generate assessment tasks for each metric and index created in combination with the BRAINPRINT and DARWIN modules, as well as the protocols included in STIMOLA. 【0138】 In some embodiments, the assessment of brain characteristics is performed to derive recommendations regarding beneficial activities and interventions individualized for a given individual based on DMDT. Interventions and activities include, but are not limited to, psychotherapy, cognitive training, brain stimulation protocols, meditation, and exercise. 【0139】 Estimation of Brain Potentials Each brain is characterized by various elements that make up its structural and functional connectome, including, for example, the amount of gray matter in each brain region, the number and strength of white matter fibers connecting different regions and networks, the vascular system that supplies nutrients to the brain structure, and the functional organization of interactions between brain elements (synchronization patterns, oscillatory dynamics, local coherence patterns). This complexity spans both the micro, meso, and macro scales of the human brain, and many dynamics also occur across scales. Individual connectomes are the result of genetic and environmental factors at any scale, with the latter related to early life experiences, as well as ongoing practice and learning activities, which gradually shape the structure and function of the brain over time. Functional connections and dynamics are typically the most general and flexible and are thought to be, for example, more susceptible to change, while structural connections are more robust and tend to be less responsive to change, providing the "backbone" for brain activity and computational processes. A detailed understanding of the structural and functional properties of the brain and their interactions (e.g., as provided by DMDT) can be important for understanding the potential for change in a given brain. The systems and techniques described herein enable computationally estimating the potential for change in a given brain based on a series of tests and simulation operations on a given structural and functional connectome, resulting in the quantification of a series of indices with predictive power across a narrow range (e.g., learning a new musical instrument, improving a specific behavior) and a broader range (e.g., healthy aging, overcoming post-traumatic stress disorder, avoiding the onset of delirium after elective surgery). The estimation process may include the analysis of existing patterns of activity as well as connectivity, but may also include the quantification of potential new patterns of activity guided by the structural and functional constraints of this DMDT. 【0140】 Some of the systems and methods described herein relate to an approach for adaptive systematic rewiring (ASR) used to estimate multiple parameters and indices. The indices can include, but are not limited to, an evolutionary index of the brain, an optimization index of the brain, an adaptability index of the brain, a stability index of the brain, a neuroinflammation index of the brain, or an associative plasticity index of the brain. Each of these is described in more detail below. 【0141】 Evolutionary index of the brain At least in part, considering the number, location of brain connections, and the magnitude and frequency of local activity in the brain, a set of constraints can impose an evolutionary algorithm that can calculate the mutations (e.g., all possible mutations) of the original feature set until a complete development of the network that cannot be further operated is reached. Several metrics can be used to summarize the performance of a given brain, including but not limited to the number of modifications the brain can maintain, the number of configurations it can reach, and the level of energy required to reach each configuration. 【0142】 In some embodiments, the simulated evolution may be based on different targeting strategies including, but not limited to, random targeting, topology-based targeting, modularity-based targeting including separate simulations for in-module and inter-module connection operations, or hierarchical targeting. 【0143】 In some embodiments, the simulated evolution is performed via a connectome rewiring process that modifies existing, active, or silenced connections in a network composed of brain data. The modifications can include, but are not limited to, changes in the strength of a given connection, changes in its inputs and / or outputs, its reactivation, or its reassignment to a different target brain structure. The fixed amount of energy available for rewiring can be set based on one or more parameters including, but not limited to, the total metabolic energy available as estimated by techniques including functional MRI data, PET or SPECT, the amount of the blood oxygenation level-dependent (BOLD) signal considering vascular activity and perfusion levels. Considering the finite amount of energy for rewiring, each connectome is exposed to a rewiring procedure where each modification is assigned a cost. As a result, each connectome (e.g., each individual brain or each brain state of an individual) can undergo a finite number of rewiring steps and thus determine the inherent potentialities of a given brain, rewire itself, modify and optimize its connectome, and regulate its resulting behavior, cognition, and performance. The overall potentialities of the connectomes rewired based on the number of possible rewiring steps and the energy constraints can constitute a "rewiring index". 【0144】 In some embodiments, the simulated evolution may be based on a fixed amount of energy used to induce operations on the connectome based on brain metabolic data and estimates of the brain's response to perturbations. In some embodiments, the simulated evolution may be based on an increase or decrease in a given connectivity between brain regions, an increase or decrease in brain activity in a given brain region, an increase in the global connectivity of a brain region or network, or an increase or decrease in the synchrony between brain networks. The magnitudes of the increases and decreases can be defined based on, but are not limited to, brain perturbations and a plasticity index. 【0145】 In some embodiments, the simulated evolution may be based on amplifying existing patterns of connectivity and local brain activity, reducing noise, and improving the inherent characteristics of a given brain (e.g., amplifying individual DMDTs). In some embodiments, the simulated evolution may be based on compensating for different levels of connectivity and activity induced by DMDTs, including, but not limited to, redistribution of functional connectivity to adjacent regions, redistribution to positively connected regions, redistribution to regions belonging to the network being manipulated, or redistribution to regions belonging to the module being manipulated. 【0146】 In some embodiments, the simulated evolution may be constrained by information regarding the structural wiring of the brain, represented as the density of white matter fibers, their anisotropy and mean diffusivity, and the level of myelination. In some embodiments, the simulated evolution may be constrained by information regarding local metabolic brain activity and perfusion levels, measured via, but not limited to, PET imaging and arterial spin labeling (ASL) MRI. 【0147】 Brain Optimization Index At least in part, considering the number, location, and strength of brain connections, and the magnitude 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 against which brain modification can be performed. The optimal configuration may include characteristics related to network function, such as, but not limited to, a state of high small-worldness (representing an optimal ratio between segregation and integration), or high network modularity. Some characteristics of network function may be selected based on their association with desirable traits such as, but not limited to, a high intelligence quotient (IQ), high memory performance, or high information processing speed. The Brain Optimization Index can provide a distance metric between an individual's current state and the most desirable state, at least in part from a cognitive and computational perspective. 【0148】 Brain Adaptability Index At least in part, considering the number, location, and strength of brain connections, as well as the magnitude and frequency of local activity in the brain, 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 considering 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 neural and network plasticity, including measures related to previous state changes and network memory of network generalization (e.g., the brain's ability to predict broad categories of future changes considering past history, leading to generalization of knowledge and enhanced preparation for future changes). State changes can be local or distributed. Brain adaptability can inform the tendency of the brain to change over time and the magnitude of such changes, and then potentially constrain the analysis related to changes in state or trait. 【0149】 Brain stability index At least in part, considering the number, location, and strength of brain connections, as well as the magnitude and frequency of local activity in the brain, a given brain is exposed to a series of state changes, and its average distance from any possible state transition can be quantified. Brain stability can be used as a metric of the brain current tendency to approach ongoing dynamic changes, as observed in the analysis of network criticality and avalanches. 【0150】 Brain neuroinflammation index At least in part, considering the number, location, and strength of brain connections, as well as the magnitude and frequency of local activity in the brain, an individual is exposed to a series of stimuli across two brain regions located at the receiving ends of white matter brain pathways. The delay between the stimulation at one end (e.g., the first brain region) and the generation of the brain response at the second end (e.g., the second brain region) is calculated and can be used as a proxy for the conduction delay between the two target brain regions. Considering the relationship between the conduction speed and the myelination of white matter fibers in the human brain, the conduction delay can be calculated multiple times over a given time frame including seconds, minutes, hours, days, weeks, months, and years. The longitudinal change in the delay can be used as an index of changes in signal propagation in white matter fibers. This may imply the existence of 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 analysis of spontaneous and evoked brain activity recorded via non-invasive methods including, but not limited to, EEG and NIRS. Activity in a specific frequency band is measured (e.g., 1 Hz - 9 Hz) and may be related to activity in the high-frequency EEG band (e.g., 19 Hz - 85 Hz). The activity reflects both local and long-distance distributed connectivity in the brain and can capture processing speed and fiber conductivity. The Electrical Neuroinflammation Index (ENI 2 ) is calculated both by examining the oscillatory activity across the entire brain / scalp at an overall level and locally by selecting specific electrodes located in regions at specific distances across the scalp. In particular, the ratio of electrodes located in the frontal and vertex regions to electrodes located in the occipital and temporal cortices is calculated and serves as an index for cognitive processing streams and sensory processing streams that capture different processing speeds. Visual and auditory stimuli can be delivered to amplify signals across the region. In some embodiments, low-voltage alternating electrical stimulation is delivered to each electrode at a resonant frequency to induce local and distributed responses. The propagation of the signal is tracked across the selected electrodes (electrical tomography detection), and the amplitudes and frequencies of the initial and late potentials generated by the perturbation are combined together and input into the individual's ENI 2 score. ENI 2Cognitive and neuroimaging assessments that can confirm or inform the presence of neurodegeneration and neuroinflammation in an individual's brain and central nervous system using scores can be used. 【0151】 Brain association plasticity index At least in part, considering the number, location, and strength of brain connections, and the magnitude and frequency of local activity in the brain, two brain regions can be stimulated via two quasi-simultaneous magnetic or electrical stimulations to induce associativity versus plasticity. Repeated quasi-simultaneous stimulations generate long-term potentiation in each region and a process of Hebbian plasticity across the two brain regions. The rate at which the two regions exhibit Hebbian plasticity depends on the state of the two regions and the integrity of the single synaptic white matter fibers connecting the two regions. By measuring the correlation rate over a fixed number of stimulations, a dose-response curve can be obtained for any given pair of brain regions in any given brain of any individual. Each curve represents the corresponding brain's ability to increase inter-regional connections and generate plasticity, and can function as an index of changes in brain plasticity in a neurodegenerative state characterized by an increase in a neuroinflammatory or neurodegenerative process such as dementia, Alzheimer's disease, or multiple sclerosis. 【0152】 In some embodiments, after obtaining a detailed map of brain structure and function (as represented, for example, as DMDT), the potential for the brain to change (evolve) towards other configurations can be estimated. The estimation may be based, for example, on the number, location, strength, configuration of brain regions and connections, and a finite number of iterations ("changes") that the brain can maintain / support based on its past and current behavior / activity. 【0153】 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 (A.I.), heat diffusion models, and / or epidemiological diffusion models. 【0154】 In some embodiments, the estimation of brain potential can be used to identify an optimal strategy for enhancement by calculating a similarity index with a specific brain state / trait and identifying the hierarchy of brain modifications applied to reach the target state / brain. 【0155】 Brain and Cognitive State and Trait Transitions Starting from the same DMDT, in some embodiments, DARWIN simulates multiple modifications (e.g., all possible modifications) of the structural and functional connectome and derives a sequence of events (e.g., an optimal sequence of events) represented as, for example, changes in connection strength, decreases in local activity in specific brain regions, etc., and enables a transition from the current state / trait to the desired state / trait using a minimum amount of energy and time, which can be used for the parametricization of the trajectory of the transition from the current brain state or trait to the target brain state or trait. 【0156】 Figure 6A shows an example of individual differences in the transition of states or traits, where two individuals aiming to reach the same target state follow two separate trajectories with different numbers of steps required to evolve towards the desired state due to differences at baseline. Figure 6B shows the metrics of state transitions within a sample of 500 subjects, highlighting individual differences in the likelihood of brain changes. 【0157】 In this framework, state transitions are used as terms to represent more acute and transient changes in brain function, as well as changes in a given "brain state" (e.g., being in the optimal brain state for performing task X). When techniques that can induce long-term changes and promote brain plasticity, such as those that are part of non-invasive brain stimulation methods, are used, the application of non-invasive brain stimulation (NIBS) techniques and their long-term brain changes are expected, and their effects may lead to more persistent changes in brain structure and / or function that lead to changes in trait transitions, such as changes in trait-level metrics (e.g., intelligence, memory ability, creativity). 【0158】 In some embodiments, the estimated brain potential can be used to guide an intervention plan (e.g., using STIMOLA, SYNAPSE, NEUROCREATOR) aimed at facilitating a transition of the brain in a specific direction (e.g., a target state or trait). Brain features can be used to identify the most appropriate brain targets (e.g., regions, connections) to be adjusted to reach the target state or trait. 【0159】 In some embodiments, the target state can be defined as a state associated with a specific cognitive activity (e.g., memory processing, meditation state). In some embodiments, the target state can represent a previous state of the same individual recorded in the past, including but not limited to the brain state of successful memory encoding or recall in a patient suffering from dementia. In some embodiments, the target state can represent a specific brain state associated with a high physical performance state. 【0160】 In some embodiments, the target trait can be defined as the previous brain configuration of the same individual recorded in the past (e.g., the brain configuration before the onset of dementia), which can be used to adjust brain modifiers and return the brain to its pre-disease state. In some embodiments, the target trait can be a template trait representing a specific configuration linked to a cognitive trait (e.g., a high-IQ brain), or can be represented by a cognitive mode associated with a specific skill set (e.g., the brain of a mechanical engineer). In some embodiments, the target state or trait can be defined as the previous brain configuration of the same individual stored as an NFT. 【0161】 Brain Optimization Changes in state or trait may not be explicitly related to measurable cognitive or motor abilities, but rather to modifications of brain activity that are changes in brain physiology itself that involve effects on brain health or disease. In some embodiments, through careful brain mapping and network analysis, the resilience of each brain (e.g., the ability of a system to absorb external attacks or internal disorders without losing performance), adaptability (e.g., the ability of a system to quickly adapt to attacks and disorders based on its wiring / connectivity profile), and flexibility (e.g., the ability of a system to shift from one given state to another without the need to change its structure, which is important for adaptability) can be quantified. DARWIN can be used, for example, (i) to enhance resilience to changes in the brain that occur during physiological and pathological aging, such as brain atrophy in regions related to memory performance, loss of important network nodes, and decreased metabolism; (ii) to enhance resilience to external events, thereby reducing the likelihood of stress-related pathological responses (e.g., PTSD); (iii) to optimize resilience and adaptability before general surgery to avoid the onset of postoperative delusions; (iv) to enhance the resilience of the brain under conditions where the effects of external stimuli, such as mental disorders and states related to high levels of anxiety and hypersensitivity, cannot be appropriately tolerated, in order to identify and plan targeted brain optimization (e.g., brain stimulation, cognitive programs, behavioral therapy) solutions. All of these approaches and other approaches fall under the concept of a "brain shield", and other effects may extend to health and overall brain health. 【0162】 In some embodiments, DMDT is used to identify optimal targets and increase the brain's ability to respond to external perturbations (e.g., the brain's resilience). The best brain targets (e.g., regions, connections) and the series of changes necessary to improve resilience can be identified as part of a Brain Shield Protocol (BSP). Brain modifiers can then be used to modify the brain's makeup through steps that include modifying the activity of specific regions, the strength of specific connections, and / or the organization of brain functional networks, according to the principles defined in DARWIN. In one example, DMDT of an individual preparing for a stressful activity, such as a soldier preparing for deployment on a mission, or a person with PTSD who is attempting to re-experience a traumatic event, can be used to define a series of target locations in the brain that can lead to an increase in the brain's resilience to perturbations. A specific series of actions, including but not limited to the delivery of non-invasive brain stimulation to specific brain regions and brain connections over multiple sessions, and exposure to cognitive and sensory retraining protocols, including VR-based applications, can be defined according to the DARWIN Resilience Optimization Algorithm. The individual may then undergo treatment, and multiple measurements of the brain's resilience can be captured over time to evaluate the impact of the BSP, including those using PERCEPTRON. The duration, frequency, and intensity of the BSP can be defined according to the desired effect, based on the expected duration of the effect (acute - a few minutes, short term - a few hours, long term - weeks to months). 【0163】 In some embodiments, the BSP includes a protocol that consists of exposing a given brain to progressive and adaptive perturbations in the form of magnetic, electrical, and ultrasonic stimuli. The perturbations can target previously identified brain nodes, regions, and networks via DMDT and DARWIN. The application of continuous perturbation stimuli of regulated intensity induces responses of brain networks and circuits in a desired direction (e.g., by performing induced rewiring). The overall increase in the brain's resilience to external perturbations can be built through long-term potentiation (LTP) plasticity processes and network reconstruction. In some embodiments, the perturbations are delivered using sensory stimuli to elicit specific responses in the sensory regions of the brain. The perturbations include visual, auditory, and tactile stimuli, and visual and auditory perturbations are delivered via various media including VR and AR technologies. 【0164】 In an exemplary embodiment, DMDT is used to identify optimal targets for increasing the modularity of the brain by modifying the strength, direction, and / or temporal dynamics of specific connections between brain regions belonging to known brain networks and circuits. In an exemplary embodiment, DMDT is used to identify optimal targets for increasing the flexibility of the brain by decreasing the strength of connections between the brain networks involved in redirecting attentional load during perception and the networks involved in cognitive processes including memory and inhibitory functions. 【0165】 In an exemplary embodiment, the DMDT and DARWIN indices are used to determine an individualized dietary regime for an individual based on concepts related to the gut-brain axis, including but not limited to his / her profile of brain activity and levels of brain resilience, modularity, flexibility, and neuroinflammation. The dietary regime can be determined by selecting nutrients and ad hoc supplements related to the effects on brain activity, including but not limited to vitamins, probiotics, minerals, and proteins. The individualized dietary intervention can be defined by examining the gut microbiota as well as the effects of specific nutrients on the brain (including but not limited to anti-inflammatory effects, blood flow and perfusion effects, effects on intracranial pressure, effects on the brain's oscillatory patterns, etc.). In some embodiments, a specific dietary regime is selected for its anti-aging properties, including but not limited to vitamin B1, B6, B12, and folic acid (B9), potassium, calcium, magnesium, and beta-carotene in the case of effects on brain plasticity, and in the case of interaction with the APOE status in healthy individuals and patients with Alzheimer's disease. 【0166】 In some embodiments, DMDT and DARWIN are used to individualize dietary information for an individual, analyze the brain's response to different nutrients, estimate an optimal set of nutrients, and induce desirable brain changes, including transitions between states and traits. In an exemplary application, an individual's brain activity is recorded via PERCEPTRON before, during, and after ingestion of specific foods and nutrients, and his / her brain activity is processed via PREPARE. The individual may also be exposed to systematic perturbations of brain, cognitive, and sensory activities via SCREEN, including but not limited to electroencephalic stimulation, visual stimulation, and / or auditory stimulation. The brain's response to the perturbations is quantified and can be used to track the individual's brain and cognitive state over time in response to different nutrients and foods. 【0167】 In some embodiments, brain and cognitive profiles obtained in response to different nutrients and foods can be used to identify (i) a diet plan that can optimize brain and cognitive performance by increasing the indices of brain health and cognitive health identified by IMPROVE, OPTI-BRAIN, and OPTI-COG. Longitudinal assessment of the brain and cognitive profiles can be performed over time to further optimize the individualized diet regimen. Exemplary applications include, but are not limited to, (i) improving brain capacity and cognitive performance in healthy individuals, (ii) accelerating recovery from injury, (iii) accelerating postoperative recovery by reducing neuroinflammation, (iv) enhancing concentration in special operators, and (v) enhancing muscle growth and physical performance in athletes by investigating a diet regimen that can enhance the synchronization between brain activity and muscle response. 【0168】 In some embodiments, brain and cognitive profiles obtained in response to different nutrients and foods can be used to identify (i) existing nutrients and combinations of nutrients or (ii) design new nutrients and combinations of nutrients that can optimize brain and cognitive performance. Exemplary applications include, but are not limited to, (i) improving brain capacity and cognitive performance in healthy individuals, (ii) accelerating recovery from injury, (iii) accelerating postoperative recovery by reducing neuroinflammation, (iv) enhancing concentration in special operators, and (v) enhancing muscle growth and physical performance in athletes by investigating a diet regimen that can enhance the synchronization between brain activity and muscle response. 【0169】 Use examples based on experimental data Some embodiments of the systems and methods described herein are further illustrated by the following examples and detailed protocols. However, the examples are intended merely to illustrate the embodiments and should not be construed as limiting the scope of the technology described herein. 【0170】 Brain Optimization: Enhancing Brain Resilience to Perturbation / Stress Resilience is the ability of a complex system to maintain its functional characteristics and performance in the face of external perturbations such as environmental factors like disease, stress, aging, etc. In the case of the brain and pathology, resilience can be regarded as the ability of an individual brain to maintain a large intensity of damage and / or for a long period before overt symptoms appear. The ability to recruit additional regions to compensate for and / or delay disruption and consequences is one of the potential factors that can enhance resilience in the brain. The inherent properties of the brain's structural wiring, as well as its functional organization, can be related to different levels of resilience. The possibility of mapping the resilience of an individual brain, identifying candidate target regions / networks / systems for its enhancement, and artificially manipulating the resilience level via external regulatory tools represents an approach that can impact both brain health and brain pathology. Such an approach may also enable the development of new cognitive enhancement applications, clinical treatment tools, or solutions for enhancing brain health and overall well-being technologies. The data reported below show examples of the effects of targeted individualized non-invasive brain stimulation on brain resilience in healthy individuals and demonstrate the application of BRAINPRINT, DARWIN, and STIMOLA (within an adaptive systematic rewiring framework) to change the brain's ability to respond to external perturbations. 【0171】 DMDTs were created for samples of healthy controls. Using DARWIN, optimal regulatory targets for improving brain resilience were estimated based on simulations of damage to each possible connection and node of each individual's brain network and the resulting estimates of induced changes in brain performance under stress. Once the optimal target brain regions were identified, transcranial magnetic stimulation (TMS) sessions were performed using simultaneous EEG recording before, during, and after each TMS pulse. EEG data were analyzed to examine other metrics that index brain efficiency and resilience to external perturbation. These metrics included, as a primary outcome, the largest connected component (LCC) corresponding to the largest set of nodes whose pairs were connected by at least an edge. The corresponding decrease in the LCC was recorded as a measure of induced damage. At each damage iteration, the node degrees were recalculated and the order of removal was adjusted based on the effect of previous removals. The measurements included the rate of decay, representing the individual pace of brain connectivity matrix damage calculated as the slope of the decay of the LCC after targeted edge removal, the initial edge reduction due to the overall amount of connections that needed to be lost before the LCC was disrupted, the late edge reduction corresponding to the overall amount of connection loss required to completely disrupt the LCC, and the break point, or maximum deflection point, in the damage curve of the LCC based on targeted removal of edges. 【0172】 Figure 7 shows that perturbation of the brain via STIMOLA induced a significant increase in the resilience measure across the whole brain (note: *p<0.05). After targeted TMS, the whole-brain network was more connected, more resilient, and gradually returned to its original state after stimulation. This result is explained by both graph measures with a characteristic decrease in path length of the network after TMS pulses and resilience metrics with a significant increase in the number of links required to reduce network efficiency after TMS (late edge reduction). As shown in this example, higher resilience can be explained by higher network stability, such that both the maximum deflection point and the complete network depletion point occur at a more advanced stage of damage. Repeated application of perturbations that improve resilience results in a sustained state of increased resilience beyond the perturbation period, thereby contributing to increased resilience even in non-experimental environments (e.g., daily life, stressful workplace environments). 【0173】 In some embodiments, improvement of resilience and shielding of the brain can be performed using techniques other than TMS, including but not limited to transcranial electrical stimulation. In some embodiments, improvement of resilience and shielding of the brain can be performed via delivery of cognitive perturbations in the form of visual, auditory, tactile, and cognitive stimuli to activate and enhance specific brain activity patterns related to brain resilience. 【0174】 In some embodiments, the analysis of brain resilience and brain health is performed prior to surgery, including but not limited to brain surgery, heart surgery, and orthopedic surgery. Brain data can be collected via PERCEPTRON and individual DMDTs can be constructed via PRINT. The data of DMDT includes, but is not limited to, brain data collected via PERCEPTRON and magnetic resonance imaging (MRI), cognitive data, medical history, perturbation-based biomarkers collected via PERCEPTRON while the individual is receiving sensory, electrical, and magnetic stimulation. The DMDT can be used to define the optimal brain state that can reduce postoperative complications (e.g., delirium, cognitive impairment, neuroinflammation) and / or accelerate recovery via 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 exemplary application, a brain state analysis can be performed to quantify the brain resilience and optimal brain state for receiving general anesthesia for hip replacement surgery. Brain data can be collected via PERCEPTRON, cognitive data can be collected via SCREEN, data can be processed via PREPARE, and the patient's DMDT can be created. Then, DARWIN can be used to create a differential matrix between the patient's current pre-operative brain and cognitive state and the optimal state for maximizing recovery. In an exemplary application, the system estimates that high brain modularity and high segregation of the motor network, including lower limb representation, are preferred to avoid excessive connectivity that may lead to the involvement of motor brain regions in non-motor functions during recovery. 【0175】 In some embodiments, the assessments are repeated over time after surgery to create longitudinal projections of recovery that can be used to adjust training and rehabilitation protocols. OPTI-BRAIN and OPTI-COG can be used to maximize cognitive and motor recovery over time, and applications include, but are not limited to, recovery from stroke, sports medicine rehabilitation after orthopedic surgery, and patients with brain tumors. 【0176】 Optimization of Brain Function and Brain Health In this example, data is provided showing the effects of targeted, individualized, non-invasive brain stimulation on brain functional networks associated with high cognitive performance. The data refers to a protocol similar to that described for enhancing the brain's resilience to perturbation, where external perturbations are sent to the brain in the form of electromagnetic pulses and brain data is collected before and after the perturbation. Analyses of functional network characteristics are reported that relate to, but are not limited to, information processing, integration and segregation of brain networks, and synchronization (connectivity) within and between functional networks, and the brain's fitness is significantly increased after targeted, individualized perturbation. 【0177】 Analysis of brain efficiency and network dynamics included characteristic path length (average distance between a node and all other nodes in the system), which can represent how easily information can move within the network, node degree (total number of edges connected to a given node), global and local efficiency, diffusion efficiency, clustering coefficient (where nodes form triplets of triangles as parts adjacent to surrounding nodes), and small-world or system properties that simultaneously have a high clustering coefficient and short path length. The corresponding modularity index, which represents how much each structural connectivity matrix is arranged into submodules as calculated using the Louvain algorithm, was also examined. 【0178】 Figure 8 shows three different graph metrics (e.g., characteristic path length, modularity, global efficiency, and local efficiency) representing the integration and segregation of information processing. Immediately after perturbation of the brain, the brain network appears more connected, and then returns to baseline after the stimulus. A decrease in characteristic path length indicates that it becomes easier for information to move within the network after each perturbation. This result is also confirmed by the modification of the brain network topology captured by the node degree, clustering coefficient, modularity score, and number of modules. Note: *p<0.05. 【0179】 Individualization of Neuromodulation Targets for Optimal Brain Control In some embodiments, DARWIN can be used to identify optimal brain structures, regions, and networks that exert a control effect on brain activity and can thus be important for inducing changes in brain states. Identification of such structural and functional networks can enable the execution of selective perturbations aimed at increasing the level of brain control and thus, for example, inducing a specific change in brain state or an overall decrease in brain controllability (e.g., resulting in an enhanced shield against perturbations). Analysis of network controllability was performed by representing the human brain as a network based on both its structural and functional connectomes. Using the resulting connectome matrix, preferential connection pathways were defined that support the hierarchical organization of information processing and brain activity. 【0180】 Using DARWIN, Brain Control Analysis (BCA) can be performed. BCA enables probing the ability of a system to drive its output towards a desired result by applying suitable input signals to selected nodes. The underlying temporal evolution of the nervous system describes the state of N nodes (neurons or brain regions) at a given time and accounts for the non-linear dynamics of each node / region, for example, the influence of external stimuli in the form of non-invasive brain stimulation applied to input nodes. The goal of BCA is to identify M nodes (driver nodes) that can lead the system to a desired final state. When applied to the brain via BCA, identifying the driver nodes can inform the selection of which brain regions should be stimulated by NIBS to ensure that the brain can be influenced in a desired direction / state. 【0181】 BCA was applied to the functional EEG data of healthy individuals for the identification of optimal network nodes that can induce a transition from a spontaneous mental wandering state to an enhanced cognitive control state within the DARWIN framework. The analysis was performed by identifying the individualized driver nodes for each individual and minimizing the control energy required to reach the desired brain state. This study involved the optimization of non-invasive brain stimulation-based interventions, where either a single node / region / network is targeted at any given time or, alternatively, multiple nodes / regions / networks are targeted. 【0182】 BCA can identify specific driver nodes for each individual, with a variation of about 78% between individuals, and only about one-fourth of the samples that received stimuli simulated on the same node / region / network. Comparing the results of brain controllability for non-invasive brain stimulation performed on individualized targets and standard (i.e., the same for all individuals) targets, it was shown that brain controllability increased by 81% when using individualized targets and standard targets. The most representative brain targets for successfully transitioning the brain state from a wandering mental state to a strengthened cognitive control state were identified as the anterior cingulate cortex, left dorsolateral prefrontal cortex, inferior frontal gyrus, and superior parietal lobe. Using biophysical modeling performed on the brain scans of the participants, the brain targets that were most likely to be effectively targeted were identified considering their positions and morphologies in the brain. The results suggested the left dorsolateral prefrontal cortex and superior parietal lobe as the main targets for achieving brain state control in 79% of the participants while ensuring an induced electric field (i.e., stimulation) of at least 0.25 V / m required to induce brain activity. 【0183】 Additional analyses were performed to investigate the possibility of transitions to other brain states, including transitions to memory recovery states associated with patients with Alzheimer's disease and transitions to high creative and mood control states associated with patients with depression. BCA can identify the optimal brain targets for each condition, and successful state transitions were achieved at 88%, 68%, and 79% respectively. 【0184】 In this example, it was demonstrated that BCA can be used to identify brain targets that can induce controlled changes in brain states in humans when stimulated. The data processed by PREPARE and analyzed via DARWIN provides an opportunity to identify individualized brain stimulation targets for state control applicable to both cognitive enhancement and therapeutic applications. 【0185】 Connectome-based analysis of tumor migratory pathways and prediction of clinical outcomes Due to limited treatment options, incidence in relatively young individuals, delayed diagnosis, and invasion into the brain parenchyma, malignant brain tumors rank fourth among all cancers in terms of years of life lost, despite accounting for only 2% of all cancers. In particular, glioblastoma (GBM) is the most frequent and highly invasive high-grade glioma (HGG, WHO glioma grade IV), with an average survival period of approximately 16 to 18 months from diagnosis. 【0186】 Recent findings regarding synapse formation between neurons and gliomas offer the possibility of new strategies to interfere with this new pathophysiological behavior. The communication between neurons and gliomas is not one-sided. Even if glioma cells cannot spike, they can promote neuronal firing through multiple mechanisms such as the secretion of synapse-forming factors, non-synaptic glutamate release, and by reducing the activity of inhibitory interneurons in the surrounding microenvironment, for the purpose of creating positive feedback for further activation of neurons. These data have been further confirmed and extended by animal experiments showing an increase in high-frequency (70 - 110 Hz) activity (an index of neuronal activation) in the infiltrating tissue of patients with HGG. These changes spread to both the meso and macro brain levels, resulting in changes in the activity of the brain functional network. 【0187】 Using the DMDT and DARWIN submodules, the systems and methods described herein can be used to localize and map brain tumors based on brain scans and electrophysiological data, characterize their behavior in relation to their clinical symptoms, predict their migratory trajectories in the brain, and inform optimal targets for therapeutic neuromodulation interventions, including algorithms. When the target is identified via DARWIN, brain stimulation can be performed, for example, according to STIMOLA. 【0188】 In some embodiments, NiBS can be used to regulate the positive feedback mechanism between the increased excitability of neurons induced by gliomas and its effects on mitosis and migration. Controlling neuronal excitability in patients with HGG may suppress tumor growth and proliferation and ultimately prolong the patient's survival. In some embodiments, NiBS techniques such as TMS and tES may be used for their ability to induce LTD-like changes in synaptic excitability related to the context from neurons to gliomas, resulting in a decrease in the probability of neuronal firing after a presynaptic event, leading to a decrease in the activation of Ca2+AMPA-R in postsynaptic glioma cells and a limitation in the influx of Ca2+ signals that promote mitosis, and thus potentially limiting the contribution of neurons to glioma growth. In some embodiments, NiBS may be used to modulate / suppress synaptic signaling of neurons surrounding the HGG tumor, thereby, for example, slowing its mitosis and migration rate. In some embodiments, NiBS can be applied to regulate tumor perfusion and blood-brain barrier permeability, thereby, for example, increasing the effectiveness of chemotherapy. In some embodiments, inhibitory tDCS can be used to reduce the probability of neuronal spikes in the target cortical region via an LTD-like effect, thereby, for example, blocking diffuse neuron-glioma communication. 【0189】 In some embodiments, STIMOLA can be used to temporarily increase the permeability of the blood-brain barrier to small and large molecules, thereby, for example, enhancing drug delivery through the barrier. 【0190】 In some embodiments, STIMOLA can be used to control the migration and spread of tumor cells by utilizing the galvanotaxis principle. Cells can be oriented and induced in their migration when exposed to an electric field (galvanotaxis). In some embodiments, the cathode region generated by tDCS in the region surrounding a brain tumor can be used to restrict the migration of cancer cells located in the cathode field (e.g., immediately adjacent to the tumor boundary), thereby preventing, for example, migration and invasion across the brain. In some embodiments, STIMOLA can be used to suppress neuronal tumor-promoting activity via an inhibitory NiBS protocol including, but not limited to, cathodal tDCS, closed-loop anti-phase tACS, and / or continuous theta burst stimulation. 【0191】 In some embodiments, BRAINPRINT, DARWIN, and STIMOLA can be used to identify optimal stimulation targets for suppressing tumor activity based on the location of the tumor relative to known brain functional networks and its brain connectivity profile. The ability to interact with an entire network rather than a single brain region may help suppress the neural activity that regulates cancer growth and tumor spread. Suppression of the entire network may be more effective in retarding the growth of neuro-related cancers because tools derived from network control theory may represent a valuable approach for selecting the most relevant stimulation targets. 【0192】 In some embodiments, BRAINPRINT and DARWIN can be used to identify optimal stimulation targets for suppressing tumor activity. Information derived from a patient's structural and functional connectome data can be used to map potential migratory pathways of solid tumors based on the tumor profile of white matter tract connections and functional synchronization / connectivity. The tools can include, but are not limited to, network analysis, network control theory, graph theory, and evolutionary biology. Analysis of longitudinal changes in brain network dynamics collected via neuroimaging methods and electrophysiological techniques, including changes in network topology, can be used to inform predictive models that identify the brain regions most likely to be affected by tumor migration. 【0193】 In some embodiments, BRAINPRINT and DARWIN can be used to identify connection patterns between tumor masses and the rest of the brain that retain predictive power for clinical states and disease trajectories, including but not limited to a patient's overall survival period. The resulting connectivity patterns can be used as targets for NIBS interventions aimed at disrupting connectivity, reducing tumor invasiveness, and increasing survival rates. 【0194】 In some embodiments, BRAINPRINT, DARWIN, and STIMOLA can be used to identify brain networks associated with tumor symptoms, including but not limited to cognitive impairment. STIMOLA can be used to modulate network activity to delay cognitive impairment or promote recovery. 【0195】 In some embodiments, NiBS can be used to restore the excitation / inhibition (E / I) balance of brain networks. The cortical E / I ratio typically varies in patients with gliomas and in other neurological and psychiatric states often associated with cognitive impairment and symptoms. In patients with gliomas, E / I imbalance is involved in the emergence of seizure-like activity and subsequent neuronal death, paralleling tumor progression. 【0196】 In some embodiments, STIMOLA can be used to enhance the effects of drug therapy and chemical agents, including using magnetic or electrical stimulation to regulate the permeability of the blood-brain barrier (BBB) and improve the movement and absorption of molecules (e.g., chemotherapeutic agents). STIMOLA can be used to regulate local or distributed cerebral perfusion to enhance the effects of chemotherapeutic agents by regulating neurovascular coupling in the tumor or the surrounding environment. 【0197】 In some embodiments, DMDT and DARWIN can be used to extract an index of the functional behavior of a tumor through analysis of dynamic connectivity patterns within the tumor mass and the surrounding brain tissue, including but not limited to white and gray matter. The dynamic connectivity metric can include a measure of the similarity of the activity of the tumor to the activity of other brain regions, including regions located near the tumor and regions located in the rest of the brain. The similarity between spontaneous and induced activity is an index of tumor invasiveness and, in some embodiments, is calculated as the proportion of data recordings indicating that the activity of the tumor is synchronized with the activity of healthy brain structures over time. Fractionation of brain regions in networks and modules may be performed to (i) label the activity of the tumor over time as belonging to a particular network or module, (ii) estimate the number of times the tumor mass switches its activity to increase its similarity to different networks or modules, (iii) estimate the stationarity of the tumor activity over time, (iv) estimate the likelihood that the tumor mass evolves its behavior over time as a function of its complexity characteristics, and thus provide an index of tumor invasiveness, including but not limited to, estimating metrics. Additional metrics can be extracted from tumor data, including but not limited to those described herein as part of the DMDT and DARWIN modules. 【0198】 In some embodiments, data from DMDT is used to identify regions of potential tumor recurrence by analyzing patterns of functional connectivity within infiltrating tissue around tumor lesions (e.g., edema). An index of functional connectivity representing the strength of correlation between each voxel of the edema mask and the rest of the brain can be calculated using functional MRI BOLD data, and the resulting quantitative map captures voxels and clusters of voxels that show positive or negative connectivity with the rest of the brain. The algorithm can compare the connectivity pattern of each voxel to the connectivity of voxels belonging to a solid tumor mask (including only the tumor mass) to obtain a similarity score. Voxels and clusters of voxels with high similarity can be labeled as locations where tumor recurrence is likely due to their similar connectivity profiles that reflect tumor infiltration. 【0199】 In some embodiments, the same approach described in the previous embodiments is used with diffusion image data instead of functional MRI BOLD data. A diffusion map is created, and a voxel-wise representation of metrics is obtained, including but not limited to fractional anisotropy, mean diffusivity, free water, and number of streamlines. The algorithm may compare the diffusion pattern of each voxel in the edema mask to the pattern of voxels belonging to a solid tumor mask (including only the tumor mass) to obtain a similarity score. Voxels and clusters of voxels with high similarity can be labeled as locations where tumor recurrence is likely due to their similar diffusion profiles that reflect tumor infiltration. 【0200】 In some embodiments, a video game application is created to guide an individual's mental thinking and inference patterns to selectively engage specific brain regions while avoiding activating other brain regions. The selection of regions may 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, due to the involvement of the tumor network. The tumor network can be defined as a set of brain regions located in one or both hemispheres that are connected to the tumor via structural connections (including white matter fiber tracts) or functional connections (including those measured via functional MRI imaging - functional connectivity). The video game can be configured to induce selective activation of regions not connected to the tumor, prevent the tumor from receiving additional stimulation / activation, and induce tumor growth 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 surrounding the tumor increases blood flow and metabolism in the region and also provides nutrients to the tumor cells. Avoiding direct activation of the tumor and activation of regions connected to the tumor is the goal of the video game application for preventing tumor growth and spread. 【0201】 In some embodiments, DMDT and DARWIN data of patients with brain tumors can be analyzed to create maps of regions to avoid and one or more regions (e.g., target regions) whose activation does not affect the tumor. FIGS. 9A - 9C schematically illustrate cognitive engagement applications according to some embodiments of the present disclosure. The cognitive functions corresponding to the target regions can be identified (e.g., via IMPROVE), and ad - hoc cognitive tasks can be created to selectively activate these functions. DMDT and DARWIN can also identify brain regions (and corresponding cognitive activities) to avoid during the day, based on their proximity to the tumor (FIG. 9A) or because of their connections to the tumor (FIG. 9B). The tasks supported by the target regions can then be embedded in games to enhance playability and ensure treatment compliance. An individual with a brain tumor can be asked to play a game for a predetermined amount of time per day at specific moments to maximize the activation of the target regions and minimize tumor activation as much as possible throughout the day. 【0202】 In some embodiments, DMDT and DARWIN are used, as shown in FIGS. 9A - 9C, to map the connectivity profile of a brain tumor and identify regions connected to the tumor mass. Depending on the location and connectivity profile of the tumor, the application can propose one or more brain regions to avoid for their functional connectivity profile (region "A" in FIG. 9A), structural connectivity profile (regions "B", "C" in FIG. 9B), and can identify clusters of regions involved in maximizing brain activity independent of tumor activity (region "D" in FIG. 9C). 【0203】 Monitoring of Brain Tumors by Analysis of Spontaneous and Evoked Brain Activity Brain tumors (including, but not limited to, gliomas, glioblastomas, astrocytomas, and oligodendrogliomas) induce significant changes in the structure and function of the brain due to mechanical pressure, metabolic effects on surrounding healthy brain tissue, and / or changes in brain synchronization patterns important for cognitive function. Brain tumors are one of the most invasive tumors, with limited treatment options. Furthermore, diagnostic options tend to be even more limited, and physicians can diagnose brain tumors only when patients request an examination after the onset of symptoms. Importantly, even after successful treatment of brain tumors by surgery, chemotherapy, and radiotherapy, brain tumors tend to recur (tumor recurrence), and the survival rates after the first and second tumor recurrences are dramatically reduced. The ability to detect new brain tumors and recurrences after treatment can be important for treatment success, extended survival, and improved quality of life. However, solutions for detecting and monitoring brain tumor activity tend to be limited to expensive hospital-based tests, including, but not limited to, magnetic resonance imaging (MRI) and positron emission tomography (PET), which are only required after symptoms appear or at a fixed point after surgery. Some embodiments of the present disclosure relate to a platform for remote self-administered brain activity monitoring that can capture changes in brain activity over time and identify abnormal patterns of brain activity that index tumor activity. 【0204】 Devices configured to non-invasively measure brain activity (e.g., [PERCEPTRON]) and digital platforms configured to deliver cognitive tasks that activate specific brain regions can be included in a system for monitoring brain tumors according to some embodiments. An example of such a system is schematically shown in FIG. 10. Individual responses to each cognitive task generate maps of spontaneous and evoked brain activity, which are recorded over time and communicated to individual DMDTs. Some embodiments relate to a specific approach that combines information from each brain response to generate a score of brain activity both cross-sectionally and longitudinally and then uses this to assign probabilities of tumor presence, recurrence, and migration. 【0205】 In some embodiments, the activity of a brain tumor is quantified via cognitive tasks executed on a portable device (including, but not limited to, personal computers, tablets, and mobile phones). The cognitive tasks can induce a specific pattern of brain activity that is recorded via a portable device (e.g., [PERCEPTRON]) to capture brain activity, and generate a specific profile of responses depending on the type of stimuli presented to the individual performing the task. The tasks can include simple stimulus presentations, including, but not limited to, visual and auditory stimuli presented rhythmically via a screen and / or speakerphone or headphones. The tasks can include specific classes of visual and auditory stimuli, including, but not limited to, objects, people, animals, and their subclasses (including, but not limited to, living and non-living things). Specific stimuli can be selected to induce a specific pattern of brain activity detected using methods including, but not limited to, scalp electroencephalography (EEG) in the form of event-related potentials (ERP) (e.g., [PERCEPTRON]). The stimuli can be selected to induce activity in specific brain regions, including all lobes and both hemispheres of the brain. 【0206】 In some embodiments, the EEG signals are analyzed by analyzing specific brainwaves generated in specific time windows before, during, and after the presentation of the stimuli. The characteristics of the brainwaves, including, but not limited to, amplitude, latency, frequency, and coherence across the EEG channels, are quantified and can be used to create a user-specific fingerprint of the response (e.g., [DMDT]). 【0207】 In some embodiments, the location of specific responses, their characteristics, and / or their changes over time can be used to generate a map of brain activity that reflects the impact of the presence of a brain tumor on brain activity. Longitudinal monitoring of changes in brain activity can be used to determine patterns of deviation in brain activity associated with tumor recurrence, progression, or migration. The information can be used for early detection of tumor recurrence and progression and can trigger more detailed investigation by a tumor specialist, including a neuro-oncologist, neurologist, neurosurgeon, or neuroradiologist. Information about the changes can be used for mapping tumor migration to identify brain regions to which the tumor has migrated, based on the changed pattern of brain activity. 【0208】 In some embodiments, metrics extracted from induced and spontaneous brain activity can include, but are not limited to, the amplitude of induced spikes and peaks in brain signals, the latency of spikes and peaks in brain signals, the frequency of oscillatory activity, the level of synchronization (functional connectivity) between activities recorded across multiple brain locations, the influence of activity induced in one brain region on activity induced in another brain region (causal connectivity), the algorithmic complexity of the signal, and / or the delay between the onset of a stimulus and the time when brain activity returns to the pre-stimulus activity level (brain responsiveness). 【0209】 In some embodiments, metrics extracted from induced and spontaneous brain activity can include metrics calculated via the DARWIN module (which includes, at least in part, brain plasticity, resilience, efficiency, and / or evolvability) and other perturbation-based metrics described herein, in response to TMS and TCS. 【0210】 In some embodiments, the assessments may be performed over different timescales 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 likely associated with tumor onset, recurrence, or migration. Assessments include, but are not limited to, cognitive impairment and changes in health. 【0211】 In some embodiments, data locally collected during task execution and brain recording (e.g., using PERCEPTRON) may be uploaded to a cloud-based analysis platform (e.g., DATANET), and spontaneous and evoked brain activities can be processed and analyzed. In some embodiments, the results of the evaluation can be summarized in a report that reports the levels and likelihoods of tumor onset, recurrence, and migration across multiple brain regions and networks. In some embodiments, the report can be used to determine targets for therapeutic or preventive interventions aimed at preventing tumor migration, delaying tumor growth, or counteracting cognitive symptoms. 【0212】 Figure 10 shows a brain tumor monitoring platform 1000 according to some embodiments of the present disclosure. Brain activity can be recorded before, during, and after a stimulus is presented via a portable stimulus presentation device 1002. The stimulus can be presented to induce specific patterns of brain activity in different brain regions, networks, and systems, and can induce different activity patterns (e.g., stimulus A induces strong activity in a specific brain region, while stimulus B induces low brain activity in a different region). Activity can be monitored over time, and the evaluation can be performed over a period of one day, several weeks, several months, and several years. Stimulus-related data and brain recordings can be processed by a cloud-based platform 1004 (e.g., DATANET), and a report of tumor-related brain activity can be generated. 【0213】 DMDT and DARWIN for Consciousness Sampling and Classification The evaluation of brain, cognitive, and psychological states, including the assessment of performance-related brain states (flow states) and pathological mental states, is typically carried out based on outdated criteria and tools that include questionnaires and self-report scales. Wearable implementations are open to new applications for state detection, but direct access to brain data is required for predicting brain state changes and identifying state markers. Furthermore, the methods and algorithms for state analysis can also be used to evaluate the impact of interventions used to modify brain states. In this example, methods from DMDT, DARWIN, and SCREEN are used for Consciousness Sampling and Classification (CSC) based on EEG data as well as other non-invasive electrophysiological recordings including electrodermal response and cardiac output, leading to the creation of a consciousness assessment tool. 【0214】 Method Electroencephalogram (EEG) and galvanic skin response (GSR) data were collected from a sample of healthy participants between multiple brain states induced by external instructions provided prior to data collection. The participants were aware of the nature of each state and the method of inducing them. Snapshots of brain and biosensing data were labeled according to each brain state. Brain states included thoughts about the future, thoughts about the past, recollection of traumatic memories, recollection of pleasant memories, thoughts about current experiences, concentration on individual body sensations, performance of memory exercises, performance of language exercises, thoughts about preferred physical or mental activities, thoughts about family and friends, thoughts about previously assigned problems, thoughts about art in the form of paintings or sculptures, thoughts about oneself, or states at least partially related to the solution of creative puzzles. 【0215】 Dynamic Decomposition The analysis of the data was performed according to the procedures and methods described as part of the PREPARE module. By sampling segments of data corresponding to each specific state, a fingerprint of the dynamic brain state was created. Subsequently, the search for the temporal fingerprint of each state was performed within the resting state and unprompted mind wandering data recorded at the start of the session. Machine learning algorithms were trained to identify each state within the spontaneous EEG and biosensing data, and the time spent in each state during spontaneous mind wandering was quantified. The same fingerprinting / labeling procedure was applied to data collected after a VR-based trauma modulation intervention aimed at reducing symptoms related to past trauma in terms of post-traumatic stress disorder states. Participants were exposed to multiple sessions of trauma modulation, and EEG and biosensing data were collected before and after a series of sessions. 【0216】 Figures 11A-11B show examples of dynamic decomposition for mental cognitive assessment according to some embodiments. FIG. 11A shows data from DMDT, including brain data collected during various externally induced brain states, as well as data collected during spontaneous mentation. Previously identified brain states were mapped to segments of spontaneous mentation, and the states and relative representations in unprompted mind wandering were quantified. The states were identified by data processing via PREPARE, including the extraction of data features related to multiple dimensions, including at least in part time and frequency, network metrics, and complexity. FIG. 11B shows that exposure to an intervention aimed at suppressing trauma-related memories and negative thoughts resulted in a structural remodeling of spontaneous mentation with changes in the target brain state. 【0217】 Results As shown in Figure 11A, the characterization of brain states and their labels via machine learning was achieved with 83% accuracy, and the pre-labeled states addressed approximately 78% of the available spontaneous EEG data across all participants. The decomposer was unable to classify on average 22% (range 11% - 39%) of the data. The hierarchical estimates associated with each state were also correlated with the self-report scale for each state and the estimated % of presence during spontaneous mentation. 【0218】 As shown in Figure 11B, overall, subjects exposed to mind-stream training showed an increase in the sense of control over and awareness of the stream of consciousness, regardless of the type of training. Participants exposed to the VR intervention showed a change in the structure of spontaneous mentation, with a decrease in trauma-related brain states and states associated with negative thoughts and negative emotions. A change in the ratio of brain states indexing thoughts about the future and thoughts about the past was also observed. 【0219】 The results of this example demonstrate that the decomposition of dynamic brain states via Consciousness Sampling and Classification (CSC) is an effective method for the evaluation of passive data-driven internal mentation that can be used, at least in part, to identify the severity of mental illness beyond traditional verbal / interview / self-report methods. This method can be used, at least in part, to track longitudinal changes in brain state composition and associated psychological symptoms and dimensions. 【0220】 In some embodiments, the CSC is performed in combination with perturbation-based biomarkers to evaluate the brain state of patients with disorders of consciousness, including, but not limited to, vegetative state, coma, emerging vegetative state, locked-in syndrome. Perturbations can be applied to the body and / or brain by the presentation of 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, pulsatile mechanical pressure)). A device for recording brain data (e.g., [PERCEPTRON]) can be used to sample brain activity in response to external perturbations. The brain data can be analyzed via PREPARE and then combined with time-locked information related to the external stimuli, resulting in a pattern of brain activity related to stimulus presentation. The system can generate reports of brain activity for different types of stimuli. This can be repeated over time to create a longitudinal profile used to identify changes in brain activity that represent changes in the level of consciousness. In an exemplary application, a patient in a vegetative state can be presented with auditory and vibrotactile stimuli to different body parts, including the upper and lower limbs, the brain activity can be recorded via PERCEPTRON, and changes in brain activity immediately following each stimulus can be recorded and stored in the patient's DMDT for offline data analysis. The brain activity of the patient in response to the stimuli can be correlated with clinical information to determine an individual brain consciousness index (BCI), which can then be monitored over time via repeated assessments. The method can be used to sample brain activity across different levels of consciousness to determine between-level thresholds for tracking the level of consciousness over time and identifying the trajectory of brain state changes from the current level of consciousness (e.g., vegetative state) to a different state (e.g., emerging vegetative state). 【0221】 In some embodiments, the BCI includes information regarding brain responses to stimuli associated with specific brain regions, systems, and networks, which can be used to monitor longitudinal changes in local brain activity related to changes in the level of consciousness. In some embodiments, this information is used to identify targets for neuromodulation interventions via STIMOLA and to accelerate transitions between states via targeted brain stimulation (including, but not limited to, electrical stimulation delivered via PERCEPTRON) interventions. 【0222】 In some embodiments, VR-based interventions for trauma reduction can be used to at least partially alleviate symptoms and to obtain a quantitative measure of psychological distress via features extracted as part of DARWIN. In some embodiments, the decomposition of dynamic brain states can be used to sample spontaneous mind wandering in healthy participants of various ages in order to detect the fingerprint of the state space regulated by aging, and thus obtain indices of brain age and cognitive age related to brain aging and brain health. In some embodiments, the decomposition of dynamic brain states can be used to sample spontaneous mind wandering in patients with neurological conditions (particularly patients with dementia), and using the above brain age and cognitive age indices, the onset and progression of dementia, including but not limited to Alzheimer's disease and mild cognitive impairment, can be predicted. In some embodiments, the decomposition of dynamic brain states can be used to sample spontaneous mind wandering in patients with mood disorders, including major depression, and patients with anxiety-related conditions as biomarkers for the onset, progression, and response to treatment interventions of the disease. 【0223】 Neuro-morphic Artificial Intelligence [NEURO-AI] The quest to create brain-like A.I. has been hampered by both the lack of a complete model of human intelligence and the need for large amounts of brain, cognitive, and behavioral data to inform A.I. solutions. When brain data is available, there is a tendency for the lack of cognitive and / or behavioral correlations, and conversely, when excellent cognitive and behavioral models of human problem-solving are available, the corresponding patterns of brain activity tend to be unavailable mainly due to the high cost and the lack of opportunities to collect such data in an organized manner. As a result, true neuromorphic A.I. does not exist, and the capabilities of A.I. that have a significant impact on real-world tasks and challenges are limited. One of the problems is represented by the search for a 1:1 replica of the human brain as the pinnacle of neuromorphic A.I., which has restricted the evolution of brain-informed A.I. over the past 20 years. For example, a complete carbon copy of brain microcircuits at the microscale (cellular) level may not be required to simulate higher brain functions and network-level dynamics, predict behavior and cognition, understand pathology, and derive treatment regimens. Rather, it is possible to identify the optimal balance between the need to replicate the features of the brain and the implementation of the actual organizational principles that lead to such complexity, and use this to evolve current A.I. modules. 【0224】 In this regard, the systems and methods described herein enable the creation of a structured platform for the acquisition and reconciliation of meso- and macro-scale brain data, as well as corresponding cognitive and behavioral data (e.g., DMDT, PERCEPTRON), combined with a novel neuroscience model of human intelligence refined by following organizational principles derived through the analysis of DMDT data (including at least in part the brain efficiency and plasticity indices in DARWIN). In some embodiments, this enables the creation of neuromorphic A.I. agents based on principles extracted from DMDT of a single individual or group of individuals, including modeling of oscillatory activity and micro-macroscale connectivity modeling. In some embodiments, this enables the creation of task-specific A.I. agents specialized for particular tasks (e.g., idea generation, emotional support, negotiation), or general-purpose A.I. that mimics human inferential capabilities including logical and abstract reasoning. In some embodiments, this also enables the conceptualization of a cognitive architecture used to guide the creation of novel evaluation tools as part of SCREEN. 【0225】 General-purpose A.I. Human cognition results not from a brute and unorganized assembly of independent cognitive modules (e.g., memory modules, attention modules, abstract thinking modules), but rather from the fine-tuning of multiple cognitive streams flowing into a balanced architecture. Following this reasoning, an excellent global-purpose A.I. that can tackle any problem may not be constructible, for example, by simply using the best DMDT templates for high memory performance in combination with the best DMDT for high attentional capabilities, etc. Rather, an optimal balance may be found when following organizational principles. 【0226】 In some embodiments, certain aspects of DMDT and its DARWIN model can be used to create a module of general-purpose A.I. (referred to herein as "NEURO-AI"), and the general structure and algorithm wiring are defined based on data collected from individuals with high convergent and divergent thinking, constituting an optimal compromise between knowledge-based and intuition-based learning and problem-solving. By using brain images and cognitive behavioral data of healthy individuals, a model has been created that identifies a series of skeletal brain regions that support human convergent and divergent thinking, along with their preferential interaction pathways and dynamics. This model represents a wireframe for the generation of a new class of A.I. in which both elements of knowledge-based intelligence (convergent thinking) and creativity and intuition (divergent thinking) are represented and optimally tuned to solve different classes of problems. Additional details are provided in the following section entitled "Multilayer Convergent Divergent Thinking (CDt) Model of Human Cognition." 【0227】 In some embodiments, additional information can be implemented in NEURO-AI, including data regarding physiological and anatomical brain characteristics. For example, information regarding local brain metabolic activity can be used to allocate energy to the A.I. processing unit following the DMDT of individuals with high CDt, while mimicking the local and distributed information processing organization of high CDt brains to allocate resources. Furthermore, the characteristics captured by DARWIN (including at least in part the plasticity, resilience, efficiency, and / or evolvability of the brain) can also be used to define the network topology of NEURO-AI and be improved in the CDt model. For example, features related to brain resilience are associated with higher cognitive performance in humans, and the dynamics of evolution suggest that the human brain is shaped into a highly resilient system with multiple hub brain regions, thus potentially reducing risk. 【0228】 Task-Specific A.I. It follows the same principle as creating a data type model, but focusing on specific tasks and cognitive abilities, specific modules of NEURO-AI can be created by aggregating DMDT and DARWIN data that capture specific cognitive functions of interest. In some embodiments, data of individuals with high inductive reasoning and execution capabilities are used to generate AI solutions for solving critical problems, and data from individuals with high semantic access and high deductive reasoning are used to create AI solutions for medical diagnosis, and data of individuals with high brain plasticity and learning capabilities are used to generate AI solutions for adaptive learning in game applications, and data from individuals with high insight, semantic capabilities, and conceptual knowledge are used to create AI solutions for scientific inference, knowledge retrieval, and hypothesis generation (e.g., Virtual Scientist). 【Example】 【0229】 Some embodiments described herein are further illustrated by the following examples and detailed protocols. However, the examples are merely intended to illustrate the embodiments and should not be construed as limiting the scope of the technology described herein. 【0230】 Neural basis of advanced abstract reasoning, and fluid intelligence, and relevance regarding enhancement Analyzing the spontaneous patterns of brain metabolic activity can be beneficial for, and even predictive of, individual evoked activities during sensorimotor and cognitive tasks. Such intrinsic activities are thought to not only reflect the brain's past experience as a system, but also potentially form the functional basis for the brain to generate future goal-directed behaviors. Although research on the functional connectivity (FC) correlations of fluid intelligence has been proposed, a clear overview of the roles played by regions belonging to specific resting-state networks is not available. The following are the results of an analysis that elucidates the most relevant brain regions supporting human intelligence, as well as the corresponding functional brain networks. Furthermore, the functional networks were classified into three main components supporting human intelligence, and a list of targets for neuromodulation and intelligence enhancement was obtained. Functional and structural MRI data were analyzed, local and interregional synchronizations across a sample of healthy subjects were analyzed, and regions and networks involved in cognitive functions related to intelligence (red, Figure 12) and regions and networks involved in some of the cognitive functions related to intelligence were identified. 【0231】 Figure 12 shows the brain regions identified as supporting abstract reasoning and problem-solving in humans using the systems and methods described herein. The upper panel of Figure 12 shows the most relevant regions (red) supporting human fluid intelligence across different cognitive domains and neuroimaging modalities. Regions are shown that support some of the cognitive functions involved in fluid intelligence tasks (green). The lower panel of Figure 12 shows the regions color-coded by their importance. 【0232】 The hierarchy of regions and brain networks was also identified, suggesting a wiring diagram for task-specific A.I. related to NEURO-AI and convergent problem-solving and hypothesis testing. Furthermore, the results provide a framework for intelligence enhancement in humans, and specific brain targets and patterns of interregional synchronization are optimized using neuromodulation interventions from DARWIN and STIMOLA. 【0233】 Figure 13 shows the brain network identified to support abstract reasoning and problem solving in humans. It shows the relevance between the regions supporting intelligence and the functional brain network. 【0234】 Figures 14A - 14C show the main clusters of the brain network that support intelligence in humans. The regions and networks related to intelligence were grouped into three main components based on their spontaneous activities and the dynamics between the networks. The three components are: (i) regions related to the anterior salience (AS) network that are at least partially involved in self - acceptance, perception of one's own body and senses, and attention to internal signaling; (ii) regions related to the dorsal attention network (DAN) and the default mode network (DMN) that are related to attention to external stimuli and the balance of internal dialogue; (iii) regions of the visual network, which is an important function of the human brain. The pattern of functional connectivity of each component is shown at the right end of Figure 14C, showing how each component explains the variation in intelligence between subjects through its positive or negative synchronization with sensory regions. 【0235】 Multilayer Convergent - Divergent Thinking (CDt) Model of Human Cognition Human cognition includes a wide range of abilities that enable different problem - solving strategies depending on the task at hand. Convergent thinking, including so - called fluid intelligence (gf), characterizes individuals who can find the single correct solution to a problem through logical and deductive reasoning. Divergent thinking, related to creativity and intuition (so - called insight), instead defines individuals who can generate various possible solutions to open - ended problems, which are qualities related to artistic talent and kindness. Despite their conceptual antagonism, it is not known whether the two cognitive domains share a common neuroanatomical basis in the human brain. 【0236】 By analyzing the functional activation patterns associated with convergent and divergent thinking, a subset of brain regions in the left inferior frontal gyrus, left frontal eye field, and bilateral anterior cingulate cortex that constitute a domain-nonspecific cognitive network correlated with both convergent and divergent thinking tasks was identified using DMDT and DARWIN data. These overlapping regions also have predictive values for potential convergent / divergent thinking cognitive factors. The strength of the negative correlation between these brain regions / structures and the activity of the default mode network was also identified as the best predictor of high convergent / divergent thinking in humans. 【0237】 The results of this example facilitate the idea of a high-level skeletal structure for abstract thinking in humans, called the "multilayer CDt model of human cognition," which subordinates human cognition as a whole and thus represents an important building block for a new class of neuromorphic A.I. Such A.I. provides a platform that can adjust creativity, fluid intelligence, and abilities and functions related to insight to obtain heuristics that best fit the problem at hand. 【0238】 Furthermore, the CDt model also enables the identification of optimal brain targets for cognitive enhancement that are modulated via neuromodulatory interventions, including but not limited to non-invasive brain stimulation techniques. 【0239】 Figures 15-19 relate to the definition and results of CDt according to several embodiments. The results were obtained by creating weighted maps that report the localization of the most relevant brain regions for gf, creativity, and insight. The maps were then statistically compared to derive the overlapping regions of gf, creativity, and insight, as well as domain-specific regions, generating a quantitative answer to the question of whether intelligence, creativity, and insight overlap more than what is separated at the neurobiological level. The results show brain activation in both hemispheres for both convergent and divergent thinking, with no inherent lateralization for the three functions investigated. The functional brain data were analyzed by examining the interactions between the gf, creativity, and insight nodes belonging to the functional brain network, as well as other brain regions, and using a clustering approach to identify the similarity of their functional connectivity patterns. Finally, the connectivity patterns of the previously identified overlapping regions were correlated with the behavioral gf, creativity, and insight scores, as well as other low-level cognitive functions. DARWIN was used to identify the best predictors of the global "abstract inference" latent factor in humans, and applications include, but are not limited to, cognitive enhancement and accelerated learning. 【0240】 Figures 15A-15B show the activation centers and brain connectivity patterns. Figure 15A shows the activation centers for each domain across the two hemispheres. Figure 15B shows the significant brain regions that make up each map following a permutation-based test (10,000 permutations, p<0.05 FDR; cluster-based correction, p<0.001), and their positive (yellow - red) and negative (cyan - blue) connectivity strengths. 【0241】 Figures 16A - 16D show the functional connectivity of overlapping nodes. Through functional connectivity analysis involving three connected regions and nodes that make up 14 brain functional networks (n = 90), the pattern of preferential connectivity at rest between two of the overlapping regions (i.e., the left SFG and MFG) was revealed. In Figure 16A, the black spheres represent the overlapping nodes used as seed regions, and the red lines indicate the only significant connections among all possible connections. Figure 16B shows the seed - based connectivity profile of each overlapping node, which is similar to the attention and salience networks highlighted by the dotted lines. Figure 16C shows the modularity analysis, emphasizing the separation between three fully overlapping regions and the rest of the ALE fMRI nodes. Figure 16D shows the "multilayer CDt model of human cognition". The graph in Figure 16D shows the connectivity between each node of gf, creativity, and insight map (layer 3), nodes that are part of at least two maps (layer 2), and nodes consistently reported in three maps (layer 1) according to some embodiments, representing the "multilayer CDt model of human cognition". 【0242】 Figures 17A - 17B show DARWIN analysis, connectivity, and cognition. As shown in Figure 17A, the negative correlation between the layer #1 region and the default mode network correlates with the individual CD - r scores. Figure 17B shows a similar analysis using domain - specific meta - analysis maps and corresponding cognitive scores, where the same negative correlation is identified as the best predictor of gf and insight scores, but there are fewer similar patterns for creativity (p < 0.05 FDR; cluster - based corrected p < 0.001). 【0243】 Brain - to - Command Engine The example of the Brain - to - Command Engine shown in Figures 18 and 19 includes three layers: the Brain - to - MiniMa [B2M] module 1910, the MiniMa - to - Command [M2C] module 1912, and the Adaptive Input Generator [AIG] module 1914. 【0244】 B2M 1910 is an encoder that converts the pattern of brain activity into commands according to a hierarchical structure. The encoder receives raw and processed data from an individual's DMDT, processes the received data through PREPARE which removes artifacts and noise, and extracts the features of brain signals into classes of properties. This can be used in multiple applications including, but not limited to, game creation, game design, interactive generative virtual asset creation, collaborative multiplayer asset creation, A.I. control, and hardware control. 【0245】 The M2C module 1912 converts minima from B2M (the basic unit of information extracted from brain data) into action commands that are used to generate assets and logical language via the AIG module. 【0246】 The AIG module 1914 enables the generation of assets and functions based on commands from 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 regarding the time-varying characteristics of the objects. In some embodiments, certain characteristics of brain signals, including but not limited to frequency components (e.g., spectral power in the theta band) extracted from specific brain regions or sensors, are used to define the characteristics of the objects. In some embodiments, characteristics related to distributed brain activity (e.g., coherence of gamma activity across the whole brain) are used to define higher-level characteristics, such as the color of an object's class (e.g., a tree). In some embodiments, network-level characteristics (e.g., modularity of brain activity across all available regions or sensors) are used to define the relationships between macro elements in the game (e.g., higher modularity = more villages and towns on the map; lower modularity = fewer). In some embodiments, network-level characteristics related to the efficiency of information transfer (e.g., minimum path length of a brain graph constructed in a specific frequency band) are used to design the infrastructure that makes up the 3D map (e.g., high efficiency = roads connecting villages and towns are efficiently placed to maximize in-game movement; low efficiency = the environment is fragmented). In some embodiments, ratios between brain data characteristics related to different assets and their combination can create more complex features. In some embodiments, the complexity of brain signals (e.g., Kolmogorov complexity of EEG time series) is used to define the resolution of in-game objects (e.g., high complexity = objects are visualized in 4K video quality). 【0247】 In some embodiments, the three layers can be integrated with an external software package, including but not limited to, a brain-computer interface application for controlling external hardware and / or software. 【0248】 In some embodiments, the three layers can be integrated with a natural language processing (NLP) agent to generate commands and to control the generation of complex assets via instructions directed using voice or text commands, as shown in FIG. 19. 【0249】 Neuromorphic Oscillatory Multiscale Adaptive Engine [NOMAD-E] In the example shown in FIG. 20, NOMAD-E2000 is an AGI engine derived from microscale brain processing that combines principles of computational biology and computational neuroscience, and is applied to a cognitive architecture for mesoscale and macroscale processing based on the CDt model 2002 and an oscillatory generator, and is applied to a natural language processing unit 2004 that includes, but is not limited to, long-term and short-term memory capacities, a logical inference module 2006, an emotion analysis core, a decision-making module 2008, and an abstract inference module 2010. 【0250】 In some embodiments, the mesomacroscale processing module includes a cluster of independent agents that process information in parallel and provide competition-based emergency decisions. Each agent is informed of a unique set of prior information and a set of overlapping common information shared among the agents. Each agent is configured to independently process new information and provide an output weighted by the current mental state of NOMAD-E. The mental state of NOMAD-E is determined by two sub-engines that capture the traits and state characteristics of NOMAD-E behavior. The traits of NOMAD-E are the stable core characteristics of its behavior and are determined by a combination of (i) general knowledge 2012, (ii) selected knowledge 2014 that can be adjusted according to a specific application, and (iii) long-term memory 2016 that includes knowledge of past actions and links to specific contexts. 【0251】 The state of NOMAD-E can be determined by the activities of the vibration generator 2018. Each network in the generator represents a subsample of computational units designed to solve specific problems by receiving specific inputs and a selected set of information. Each network may have baseline weights determined by the trait characteristics of NOMAD-E, including information stored in long-term memory. The baseline weights can determine the influence of the network on other networks that make up the vibration generator. The weights can change over time based on a competitive search for energy between the networks. The vibration 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 any unit. Time may be equal to the metabolic budget, and NOMAD-E attempts to prioritize minimal metabolic consumption over processing efficiency and speed while maximizing learning and promoting adaptation. The oscillating network may constantly compete for energy by estimating the optimal pattern of excitatory and inhibitory weights assigned to all other networks in the generator. This causes the balance of excitation and inhibition between the networks to constantly change, and its net numerical result may be the main output of the engine and a major determinant of the brain state, and may also be used to weight decision-making processes. 【0252】 In some embodiments, the networks that make up the vibration generator represent cognitive macrofunctions, including sensory processing, memory-related functions, overall attention levels, emotional cues, and / or switching rates. In some embodiments, the networks that make up the vibration generator can be removed from, expanded, or temporarily muted from the generator. 【0253】 In some embodiments, the activity of the network that constitutes the vibration generator can be calculated via a local neural mass model 2022 composed of different classes of neurons with different functions. The classes of neurons can constitute independent computational units that exhibit multiple behaviors including, but not limited to, sustained and phasic activation and deactivation, and each neuron is connected to other neurons via connections that exhibit regulatory activity including, but not limited to, inhibitory, excitatory, feedforward, and feedback loops, noise amplification. Each class of neurons can have a specific oscillation frequency. The sum of the plurality of neurons and their connections can generate the dominant oscillation frequency of the network. 【0254】 In some embodiments, the life cycle engine 2020 can be adjusted such that the perception of time is accelerated or decelerated, and the metabolic budget is adjusted to increase or decrease the available energy. The initial weighting of the network in the vibration generator can be modifiable, similar to the information contained in long-term memory and the general weights between different modules that constitute NOMAD-E. Thereby, different results can be generated, and it is possible to induce changes in the maturity of the system for the purpose of measuring the evolution of behavior throughout the life cycle. 【0255】 In some embodiments, the balance of the vibration network can determine the main cognitive problem-solving attitude of NOMAD-E at any given time based on the knowledge from CDt 2002. The function of balancing between fluid intelligence, creativity, and cognitive skills related to insight can determine the default approach of the agent to problem-solving and can result in more logical, creative, or intuitive agent behavior. An individual using NOMAD-E may be able to modify the default behavior of the agent to generate multiple answers to the same problem using different approaches. 【0256】 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, life cycle engine 2020, CDt 2002, long-term memory module 2016, emotion processing module 2028, and consciousness module 2030, and provide additional decision-making tools based on past experience (wisdom 2032). 【0257】 In some embodiments, NOMAD-E can be used to generate new ideas when appropriate input is provided. The idea formation process can be the result of the activities of the CDt module, wisdom module, and consciousness module. Ideas, including but not limited to, solutions to formal problems and abstract conceptualizations, can arise from the balance of accumulated knowledge by the agent and CDt-related decision-making, including fluid intelligence, creativity, and insight-based problem-solving. 【0258】 In some embodiments, NOMAD-E can be individualized for a given individual, creating a hybrid between the characteristics and behaviors of general NOMAD-E traits and states and the individual's personality, cognitive architecture, and brain characteristics. The individual can input those characteristics into an application programming interface (API) 2034, and different classes of relevant information and their weights can be automatically assigned to the corresponding NOMAD-E modules. In some embodiments, NOMAD-E can be individualized for a given individual, creating a hybrid between the characteristics and behaviors of general NOMAD-E traits and states and (i) the individual's DMDT and (ii) DARWIN. In some embodiments, NOMAD-E can be individualized for a given individual, creating a hybrid between (i) the characteristics and behaviors of general NOMAD-E traits and states and (ii) real-time brain activity collected via PERCEPTRON. 【0259】 In some embodiments, NOMAD-E can be used as a cognitive enhancement tool. For example, information on an individual's DMDT and DARWIN can be uploaded to NOMAD-E to generate a simulated cognitive model representing the individual's cognitive architecture and dynamics. NOMAD-E can then be used to guide the individual to perform individualized cognitive activities so that their cognitive performance is enhanced. NOMAD-E can be enabled to select the type of cognitive skills to be enhanced and the most effective cognitive tasks to be performed. Cognitive enhancement can include, but is not limited to, various cognitive domains and skills such as 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 used to enhance cognitive functions can include, but are not limited to, topic-specific thinking, philosophical reasoning, logical reasoning, memory tasks and training, problem-solving, emotion recognition tasks, ethical problem-solving, sustained and divided attention training, and / or mind wandering. 【0260】 In some embodiments, NOMAD-E can be optimized to generate as many new ideas and insights as possible when given a fixed set of baseline information (e.g., the IDEATION module). The weights in the NOMAD-E oscillator can be set based on brain data collected during idea generation and insight events, and the individual is asked to generate new solutions to existing problems. The inventors have defined patterns of brain activity involved in idea generation that involve an imbalance between specific brain networks, including but not limited to (i) the language network, (ii) the fronto-salience network, and (iii) the executive control network, and their oscillatory activity. The NOMAD-E network in the oscillator can be programmed to simulate the pattern and perform a pattern recognition search within an available curated database of general knowledge. NOMAD-E can be programmed to generate responses across multiple spectra defined by the weights assigned to the emotion processing, wisdom, CDt, consciousness, and idea formation modules. The spectra 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 of the profile of each idea across the spectra and an overall score. The user can modify the weights of the network and / or modules to optimize the ideas obtained across the various spectra. 【0261】 In some embodiments, the idea formation module 2036 can be used to conduct research on a particular market segment by providing NOMAD-E with specifically selected knowledge. The results of the research can be used, among other things, to identify growth areas for a particular company or organization, to identify the best strategic partners for fostering innovation, and / or to generate ideas for new products. In this embodiment, the products represent the results of organized thinking towards a goal, leading to products that address needs. This includes, but is not limited to, physical products such as in the case of devices or software, and concepts that describe relationships between multiple information sources. For example, the idea formation module 2036 can be used to discover new hypotheses within a particular scientific field and to identify candidates for drug repurposing (drugs used for specific diseases that may be effective under other conditions based on recent scientific evidence and the knowledge provided to NOMAD-E). 【0262】 Multimodal Cognition and Plasticity (MCP) Test In some embodiments, cognitive tests may be used to activate CD-r. The Multimodal Cognition and Plasticity (MCP) test consists of the minimum number of stimuli necessary to activate CD-r and is composed of audio and video stimuli delivered via a display and an audio device. This test switches between multiple cognitive domains related to sensory, emotional, and cognitive processing and measures an individual's ability to solve tasks of increasing difficulty, with or without time pressure. In some embodiments, the task may request the individual to solve memory problems while monitoring the onset of visual stimuli on a screen, and while solving the memory problems, the individual's attention may be distracted. Next, emotionally-valued stimuli are presented, and the individual is asked to evaluate the stimuli based on their emotional activation level. In some embodiments, the individual wears a device for collecting brain data while solving the task and uses a closed-loop system to activate specific aspects of the task in response to changes in brain activity and vice versa. In some embodiments, the MCP task is used as a metric of brain plasticity and brain health and can be applied to individuals at risk of developing dementia or other forms of neurodegenerative disorders. 【0263】 Neuroregulation [STIMOLA] STIMOLA includes ways to measure and interact with brain function through controlled perturbations. Given the diversity of brain states, properties, and target configurations, a single class of brain modifiers typically is not sufficient to induce a desired effect and, in inducing desirable meaningful brain changes, an approach combined at the appropriate timing may be more effective. 【0264】 In some embodiments, STIMOLA may implement a form of non-invasive brain stimulation (NIBS) that generates an electric field, spatially and temporally controlled, and directed into the brain, without the need for any surgical procedures. Examples of NIBS include, but are not limited to, transcranial magnetic stimulation (TMS) (in the form of repetitive TMS (rTMS), but not limited thereto), patterned rTMS protocols (such as theta burst stimulation, multipulse TMS, and paired associative stimulation), transcranial electrical stimulation (tES) (in the form of transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), and transcranial random noise stimulation (tRNS), but not limited thereto), and focused ultrasound (FUS). 【0265】 In some embodiments, TMS and tES can be used to induce long-term potentiation (LTP)- or long-term depression (LTD)-like mechanisms depending on the specific frequencies applied. High-frequency rTMS (>1 Hz) or intermittent TBS-iTBS (e.g., short trains of impulses) often increase cortical excitability and result in LTP-like effects, while low-frequency rTMS (≦1 Hz) or continuous TBS-cTBS (e.g., single trains of pulses) more frequently result in a decrease in cortical excitability and ultimately an LTD-like effect. TMS and tES can be used to modulate the GABAergic function associated with the activity of inhibitory interneurons and / or the cholinergic system with respect to their role in cognition and cognitive decline. 【0266】 In some embodiments, non-invasive brain stimulation is performed by targeting parvalbumin-positive (PV+) inhibitory interneurons in the brain by means of bursts of high-frequency stimulation in the form of magnetic, electrical, or ultrasonic stimulation. High-frequency subcortical burst (HFSB) stimulation is designed to replicate thalamic bursts of information transmitted from the thalamus to the neocortex and is thought to be converted into local activity via the activation of PV+ interneurons. The stimulation can be performed with tACS at a frequency of about 300 Hz that lasts for 5 seconds with an inter-burst interval of 2 seconds, for example, although not limited to short bursts of high-frequency stimulation. The stimulation can be applied transcranially directly to the scalp via individualized electrode placement based on DMDT and DARWIN algorithms including biophysical modeling solutions. HFSB can be used to activate PV+ interneurons for the purpose of modulating cognitive function by inducing regulation of the neuroinflammatory response, regulating protein clearance, and acting on the excitatory / inhibitory balance in the cortex. In some embodiments, the stimulation can be delivered via PERCEPTRON and adjusted via a closed-loop tool using real-time brain signals. HFSB can be used to activate other classes of interneurons (including somatostatin interneurons) that resonate with thalamocortical bursts. HFSB can have specific applications in the fields of neurodegenerative diseases and cognitive enhancement. 【0267】 Perturbation approach STIMOLA includes, at least in part, solutions in which external electrical, magnetic, or ultrasonic perturbations are delivered to one brain region at a time, solutions in which multiple perturbations are delivered to a single region to evaluate dynamic brain responses, or solutions in which perturbations are delivered simultaneously or in a predetermined sequence across multiple brain regions to examine network-level reorganization of brain dynamics. The method includes, at least in part, solutions in which the effects of the perturbations are measured via electrophysiological approaches such as EEG or EMG, but is not limited thereto. 【0268】 Figures 21A - 21F show examples of controlled perturbations and brain responses according to several embodiments. Different brain stimulation approaches can be used to perturb brain activity and record responses according to the individual differences in brain anatomy and function. Figure 21A shows an exemplary approach of collecting brain activity with non - invasive electrodes placed on the scalp as in the case of EEG, then delivering electromagnetic perturbations as in the case of TMS, and recording the brain's responses from multiple regions of the brain, for example, examining an increase in activity after stimulation or an increase in synchrony between two or more regions or networks. This approach can be used, for example, to measure multiple brain dynamics and induce specific effects on brain activity. 【0269】 Examples of perturbation - based applications The perturbation protocol shown in Figure 21A 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 illustrate exemplary applications using such a perturbation protocol. 【0270】 Information processing: Figure 21B shows that the delay of the brain response between the region (or regions) targeted by TMS and other regions located at different proximities from the target region can be measured using the response to electromagnetic perturbation via TMS. Data analysis can provide information regarding the flow of information and effective connectivity in the brain, identify regions essential for information processing and transmission, and the most efficient pathways for connecting two remote brain regions. 【0271】 Brain Resilience: FIG. 21C shows that the resilience of the brain can also be calculated using an analysis of network-level responses to electromagnetic perturbations as a measure of the brain's ability to maintain high efficiency levels in the presence of perturbations that mimic real-world scenarios, such as neurodegeneration of brain tissue, stroke, and the accumulation of waste products (such as amyloid-β and tau proteins associated with Alzheimer's disease). A systematic analysis of the amount of "damage / disruption" to the brain network induced by TMS or tCS generates quantitative metrics of brain resilience related to clinical variables such as cognitive impairment, disease progression rate, and brain atrophy. 【0272】 Brain Efficiency: FIG. 21D shows that the standardized application of TMS or tCS during EEG recording can be used to test the strength and efficiency of specific connections in the brain related to activity in a particular brain functional network, multiple networks, or activity between networks. Metrics related to overall brain connectivity can be obtained by examining brain activity immediately before and after electromagnetic perturbation. 【0273】 Brain Plasticity: FIG. 21E shows that stimulation across two or more brain regions via TMS or tCS enables the measurement of the strength of specific connections in the brain. By repeatedly applying TMS / tCS stimulation at specific time intervals, it is possible to induce changes in connection strength, and the magnitude, speed, and duration of such changes can be used as metrics of brain plasticity (e.g., the brain's ability to adapt to external perturbations and reorganize its connections). A measure of brain plasticity can be used, for example, to estimate a patient's ability to adapt to changes in the brain induced by Alzheimer's disease and thus to estimate the trajectory of disease progression and cognitive decline. 【0274】 Network degradation: FIG. 21F shows that perturbations in a specific region and monitoring of signal propagation across other network nodes enable the estimation of signal attenuation in the brain, which can be used as a metric of neurodegeneration of tissue caused, for example, by the accumulation of amyloid-β and tau proteins, atrophy, neuroinflammation, and associated changes in white matter tracts. To quantify the amount of waste proteins such as amyloid-β, tau, TDP43, and alpha-synuclein, indices created from EEG data are used to analyze both spontaneous and induced brain activity to estimate local and global protein loads in the brain. 【0275】 Temporal framework and scope of perturbation-based markers The systems and methods described herein can be applied to multiple ranges and time frames. Some applications of perturbation-based markers in patients with Alzheimer's disease and general dementia are described below. 【0276】 Diagnostic purposes In some embodiments, the individual responses to perturbations can be used to identify abnormal brain activity in patients suspected of having Alzheimer's disease or dementia. The diagnostic process can be assisted by using responses to perturbations that are different from control subjects, healthy controls, or previous responses obtained from the same individual. A similar process can be applied to identify subtypes of Alzheimer's disease within 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 responses can be performed via visual inspection by a trained human operator or via a machine learning-based classification algorithm that can label the responses to perturbations as normal or abnormal responses. 【0277】 Prognostic purposes In some embodiments, the analysis of individual brain data can be performed to predict (e.g., estimate) the course of a pathology (e.g., Alzheimer's disease) based on medical history data collected from the same individual or group-level estimates of disease progression. A similar process can be applied to predict the course of pathology, predict recurrence, and estimate survival rates in patients with brain cancer. 【0278】 Evaluation of treatment effect In some embodiments, the analysis of individual or group-level brain data may be performed to quantify the differences in brain activity before and after treatment for Alzheimer's disease or memory impairment. Changes before and after treatment in perturbation-based metrics can be used to evaluate the response at the individual or group level to a given treatment, change treatment parameters, or facilitate the decision to continue or discontinue treatment. 【0279】 Individualized treatment In some embodiments, the analysis of individual brain data can be performed to individualize non-invasive brain stimulation parameters including, but not limited to, the location, orientation, intensity, frequency, phase, and / or noise level of the stimulation. Individualization can be performed, for example, by examining the response to TMS pulses delivered over multiple locations within the target region to identify the location that provides the highest brain response to TMS. 【0280】 Disease tracking In some embodiments, perturbation-based metrics collected over time are used to monitor brain function in patients with neurological and psychiatric conditions, as well as healthy individuals, and at any given time point, capture significant deviations in brain activity patterns 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 groups of healthy controls, thus providing, for example, an estimated deviation from the expected rate of cognitive decline. 【0281】 Evaluation of cognitive function In some embodiments, perturbation-based data can be collected from brain regions / networks associated with or supporting a particular cognitive function. 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, for example, changes in brain circuits involved in memory performance decline in patients with Alzheimer's disease. 【0282】 Application Examples In some embodiments, BRAINPRINT and OPTI-BRAIN can be used to simultaneously determine both the location parameters and the stimulation parameters for NIBS. In some embodiments, tACS can be delivered via a combination of a high-frequency carrier frequency in the megahertz range and a target frequency within a physiological frequency band. Stimulation within the megahertz range (>1Mhz) tends not to induce the standard tES side effects such as scalp itching and burning sensations, increasing the intensity of the tACS stimulation and thus reaching deeper brain structures or surface brain regions at higher intensities. The combination of a high-frequency carrier wave and a physiological tACS frequency such as 6Hz or 40Hz enables the delivery of higher-intensity stimulation and the synchronization of brain activity in a frequency-specific manner. The combination of high-frequency tACS and physiological tACS can be used to modulate brain activity in healthy and diseased brains. 【0283】 In some embodiments, NIBS can be used in combination with BRAINPRINT data to determine the location and stimulation parameters for non-invasive brain stimulation. For example, TMS or tES can be combined with EEG recordings and one or more characteristics of the evoked responses to brain stimulation at multiple brain locations can be used to determine the optimal location for targeted NIBS and stimulation characteristics (e.g., stimulation frequency, intensity / amplitude). 【0284】 In some embodiments, the controlled perturbation is applied to a single brain region and the response is measured via electrodes placed on the scalp (EEG) or body (EMG). The perturbation can have various intensity levels based on the subject's brain state, including but not limited to levels of cortical excitability, plasticity, inhibition, excitability, oscillatory activity, connectivity, and / or reactivity. In some embodiments, the response to the perturbation may be measured via EMG and the longitudinal changes in local excitability of the target brain system after the perturbation may be analyzed as a measure of cortical plasticity. In some embodiments, the response to the perturbation is measured via EEG and may include, but is not limited to, local responses such as the amplitude, number, frequency, and timing of so-called TMS-evoked potentials measured in the stimulated brain region, remote responses measured using the same (or similar) metrics calculated from remote brain regions by examining the induced current flow generated in EEG electrodes or the brain, and changes in interregional dynamics including or excluding the brain region receiving the stimulus. These metrics include, but are not limited to, correlation, connectivity, effective connectivity, dynamic connectivity, and graph-theoretic metrics of node interaction. 【0285】 In some embodiments, the dual co-localized perturbation can be delivered via at least two sequences of controlled perturbations repeated over a single region, and the response can be measured via electrodes placed on the scalp (EEG) or body (EMG). The response to the perturbation can be measured via EMG by examining the longitudinal changes in local excitability within the target brain system after the perturbation as a measure of cortical plasticity. 【0286】 In some embodiments, the multi-site perturbation delivered via an array of at least two controlled perturbations can be repeated over multiple different brain regions, and the response can be measured via electrodes placed on the scalp (EEG) or body (EMG) or via neuroimaging techniques such as MRI. The response to the perturbation can be measured via EMG and EEG by examining the longitudinal changes in local excitability within the target brain system after the perturbation as a measure of cortical plasticity. 【0287】 In some embodiments, the timing of each pulse delivered in a sequence of at least two controlled perturbations can be defined by examining the conduction delays between target brain regions. To induce progressive associative stimulation (PAS) that enhances synchronization between target regions, the inter-regional delay can be used to set the delay between two regions. 【0288】 In some embodiments, the timing of progressive associative stimulation is calculated by measuring the axonal diameter and length of white matter fibers in the brain using a diffusion MRI sequence. Myelination of white matter can be calculated to estimate the input propagation speed in mm / s and can be combined with information about the path length to estimate the conduction delay between two or more target regions. 【0289】 In some embodiments, STIMOLA can be configured to provide a form of stimulation that is executed to generate, induce, regulate, or amplify traveling waves recorded from the human brain. 【0290】 In some embodiments, multimodal perturbations can be delivered via a combination of two or more types of stimulation used to maximize the effect of TMS or tCS on the brain, thereby reducing noise in the brain activity data. Oscillatory electric stimulation (e.g., transcranial alternating current stimulation (tACS)) can be used to modulate oscillatory activity in the target region / network before or during the application of TMS. For example, the application of 20 Hz tACS across the precuneus can function as a stabilizer of spontaneous brain activity for the subsequent delivery of TMS pulses synchronized with the peak of each 20 Hz oscillation cycle. 【0291】 In some embodiments, brain perturbations can be applied in multiple forms including, but not limited to, signals composed of (i) a single pulse, (ii) a train of pulses spaced at regular intervals or with variable jitter, (iii) a continuous waveform characterized by a given amplitude, frequency (or combination of frequencies), and duration, (iv) a continuous electric field with a given polarity, amplitude, and duration, or (v) noise pulsed at a specific frequency. 【0292】 In some embodiments, perturbations can be delivered to one or more brain regions identified based on brain scans and / or electrophysiological data. Information sources for defining optimal perturbation targets can represent population-level data and / or individual data. The data can be organized such that local and distributed brain activities are summarized into quantifiable metrics, characteristics of brain activity can be extracted, and measurements are made of features related to, but not limited to, (a) metabolic and vascular activity, (b) oscillatory activity within known frequency bands, (c) network resilience metrics, (d) dynamic connectivity, and / or (e) protein accumulation maps obtained via PET imaging methods. 【0293】 In some embodiments, perturbations can be delivered to nodes based on the degree of connection to the rest of the brain to (i) assess the integrity of the brain connectome or (ii) elicit widespread responses in local and remote brain regions as a measure of brain integrity and connectivity. The degree of connection can be estimated via functional imaging data or EEG as in the case of functional connectivity, or via anatomical and diffusion MRI scans in the case of structural connectivity. In the latter case, 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 make up a particular white matter tract in the brain. In an exemplary scenario, the target for TMS or tCS stimulation is defined by examining the structural connectivity of multiple brain regions and then selecting the region most connected to the rest of the brain for the purpose of maximizing signal propagation and overall stimulation effects. A similar analysis may be performed to identify brain targets with strong structural connectivity to subcortical brain regions (e.g., the hippocampus) that are not reachable via TMS or tCS. Stimulation of accessible gray and white matter regions with strong structural connections to the hippocampus may increase the probability of indirectly activating the hippocampus. 【0294】 In some embodiments, the perturbation can be delivered to two or more nodes of the same brain network to evaluate the integrity of the connectivity within a given brain network, such as in the case of the default mode network of a patient with Alzheimer's disease. The targets can be identified by analysis of brain scans (e.g., functional MRI) or electrophysiological data (e.g., EEG data). 【0295】 In some embodiments, the perturbation can be delivered to nodes whose relevance to the pathophysiology of a given disease has been demonstrated. For example, it has been demonstrated that functional connectivity decreases in Alzheimer's disease as measured using functional magnetic resonance imaging (fMRI), and the precuneus region can be targeted. Regions of the temporal lobe affected by waste proteins in Alzheimer's disease can be stimulation targets, particularly with an emphasis on amyloid-β and p-tau proteins. For example, the region shows changes in the level of neuroinflammation as measured by diffusion MRI. 【0296】 In some embodiments, the selection of perturbation targets may be based on the estimation of the induced electric field induced in candidate brain regions extracted from biophysical modeling of passive tissue conductivity. The model can be created by simulating the propagation of magnetic and electrical energy across the head and brain tissues (e.g., skin, muscle fibers, bone, cerebrospinal fluid (CSF), gray matter, white matter), resulting in a quantitative measurement of the induced electromagnetic stimulation that affects brain cells (e.g., excitatory neurons and inhibitory interneurons). The magnitude of the induced electromagnetic stimulation can be used, for example, 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 nerve firing or neurotransmitter release. 【0297】 In some embodiments, the target region can be identified as a region involved in a particular cognitive process (e.g., long-term memory or attention). Brain scans or EEG data collected during the performance of a cognitive task by a patient with a neurological or psychiatric condition can be analyzed to identify regions whose activation is related to performance on the task. In an exemplary scenario, one or more resulting regions are then selected as targets for TMS or tCS perturbation to evaluate the 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 long-term memory network in the patient. 【0298】 In some embodiments, the target region may be identified based on spatial and functional search algorithms. For example, after averaging at least two TMS pulses, the induced EEG activity is averaged and the TEP becomes apparent at different latencies between 5 milliseconds and 500 milliseconds after TMS. The amplitude of the TEP is calculated for each patient and can be used as a proxy for individual responsiveness to TMS and thus as an index of excitability and reactivity of the target region / network. Next, the amplitude of the TEP can be used to correct the stimulation intensity (e.g., resting motor threshold (RMT)) obtained from stimulation of the motor cortex for the purpose of adapting the TMS stimulation intensity based on the TEP. 【0299】 The intensities of TMS, tCS, and tFUS stimulation depend on the individual brain's anatomical structure and can be estimated by creating a high-resolution biophysical model of the brain's anatomical structure via an MRI scan. In some embodiments, the electric field (E-field) induced over the brain target can be generated by using a realistic volume conductor head model generated based on the MRI image and segmentation from a validation dataset. The model may be based on anisotropic conductivity values for each brain tissue class (e.g., skin, fat, muscle, bone, cerebrospinal fluid, gray matter, white matter) expressed in S / m. Using a set of resulting meshes, including at least gray matter, scalp, bone, and cerebrospinal fluid, the E-field distribution for a particular TMS coil's design, position, angle, and rotation can be calculated taking into account the distance from the coil to the scalp and brain atrophy. The estimated E-field can be used to (a) retrospectively calculate the individual differences in the amount of current delivered over the target region and thus to explain differences in response to treatment, or (b) adjust the position and / or intensity of the stimulation so that all participants receive the same amount of induced cortical stimulation. 【0300】 In some embodiments, STIMOLA can be configured to provide a form of stimulation that is executed in a VR or AR environment. Additional details are provided in the section describing NEUROCREATOR and the metaverse herein. 【0301】 In some embodiments, STIMOLA can 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. 【0302】 In some embodiments, STIMOLA can be applied for diagnostic purposes. The individual responses to perturbations can be used to identify abnormal brain activity in patients suspected of having Alzheimer's disease or dementia. The diagnostic process can be assisted by using responses to perturbations different from those of control subjects, healthy controls, or previous responses obtained from the same individual. A similar process can be applied to identify subtypes of Alzheimer's disease within a group of patients diagnosed with Alzheimer's disease (e.g., amnestic Alzheimer's disease). The analysis of the responses can be performed via visual inspection by a trained human operator or via a machine learning-based classification algorithm that can label responses to perturbations as normal or abnormal responses. 【0303】 In some embodiments, STIMOLA can be applied for prognostic purposes. By performing an analysis of individual brain data, it is possible to predict (e.g., estimate) the course of a pathology (e.g., Alzheimer's disease) based on medical history data collected from the same individual or group-level estimates of disease progression. 【0304】 In some embodiments, STIMOLA can be applied to evaluate treatment effects. By performing an analysis of brain data at the individual or group level, it is possible to quantify the differences in brain activity before and after treatment for Alzheimer's disease or memory impairment. Changes before and after treatment in perturbation-based metrics can be used to evaluate the response of an individual or group to a given treatment, to change treatment parameters, and to continue or discontinue treatment. 【0305】 In some embodiments, STIMOLA can be applied for the definition of individualized treatment regimens and / or parameters. Analysis of individual brain data can be performed to individualize non-invasive brain stimulation parameters including, but not limited to, the location, orientation, intensity, frequency, phase, and / or noise level of the stimulation. Individualization can be performed, for example, by examining the responses to TMS pulses delivered over multiple locations within the target region to identify the location that provides the highest brain response to TMS. 【0306】 In some embodiments, STIMOLA can be applied for disease tracking. Perturbation-based metrics collected over time can be used to monitor brain function in patients with Alzheimer's disease and capture significant deviations in brain activity patterns from data collected at previous time points at any given time point. Individual responses can be compared to normative data collected in a sample of patients with Alzheimer's disease or a group of healthy controls, thus providing, for example, an estimated deviation from the expected rate of cognitive decline. 【0307】 In some embodiments, STIMOLA can be applied for the investigation of cognitive function. Perturbation-based data can be collected from brain regions / networks that support relevant or specific cognitive functions. For example, TMS or tCS can be directed at regions related to 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, for example, changes in brain circuits involved in the decline of memory performance in patients with Alzheimer's disease. 【0308】 In some embodiments, STIMOLA can be applied to sleep regulation by acting on the transition of brain states from wakefulness to sleep, and brain oscillatory activities associated with, but not limited to, non-rapid eye movement (REM) and certain sleep stages (including but not limited to REM sleep stages). STIMOLA can be used in combination with DMDT and DARWIN to select the optimal stimulation frequency, intensity, duration, and location for a given individual to maximize the response to the stimulation. In some embodiments, to increase protein clearance in the brain and remove waste 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 during sleep to increase the duration and manifestation of specific sleep stages related to protein clearance and lymphatic system activity. STIMOLA can be applied for the purpose of protein clearance 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 stimulation can be performed using transcranial electrical stimulation in specific frequency bands (including but not limited to the theta frequency band of 3 Hz to 7 Hz). In some embodiments, the electrical stimulation can be adjusted by continuous EEG data monitoring, along with CSF production in the brain, and by the phase of the respiratory and / or cardiac cycle. According to the principle of state transition described in DARWIN, the electrical stimulation can be adjusted in real time based on EEG data capturing different sleep stages and the stability of each stage, and identify the transition period by examining the ratio of specific oscillatory dynamics, including but not limited to bursts of gamma activity within 30 Hz and 150 Hz. 【0309】 In some embodiments, STIMOLA can be used to increase sleep efficiency by regulating sleep patterns monitored via EEG using PERCEPTRON and DARWIN, 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 enhance sleep efficiency, stabilize the circadian rhythm, and strengthen memory consolidation and recall 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 for sleep regulation can be evaluated with the DMDT scale related to circadian activity and behavioral patterns collected by wearables such as Activity Watch and PERCEPTRON. 【0310】 Examples Some embodiments described herein are further illustrated by the following examples and detailed protocols. However, the examples are merely intended 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 a perturbation-based method that (i) predicts individual cognitive states, (ii) evaluates the brain's resilience to perturbations, and (iii) measures brain oscillatory activity, plasticity, and neuroinflammation levels by the EEG response to tCS in AD patients. 【0311】 Differences in responses to perturbations and relationships with cognitive profiles Using the individual responses to electromagnetic perturbations, the individual brain characteristics related to cognitive processing can be revealed, and thus, subtle changes in the cognitive profile in patients with Alzheimer's disease (AD) and related dementias can be identified. Perturbations via TMS can be delivered over the selection of brain regions known for their roles in specific cognitive processes or brain regions known to be affected by pathology, and their responses are quantified and correlated with the individual scores in cognitive / neuropsychological tasks addressing functions such as memory, attention, language, abstract reasoning, etc. For example, the amplitude of the local response in a given brain region can be correlated with working memory capacity, while the synchrony between two nodes of a brain network associated with attention can explain individual variations in executive functions such as inhibition and flexibility. Considering the possibility of "passively" collecting this information without actively engaging the subject in a cognitive test or a specific task, 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). Further, these methods for identifying cognitive functions changed via passive perturbations potentially add valuable information about the patient's cognitive state without requiring extensive and cumbersome cognitive evaluations, thus reducing the patient's burden. 【0312】 Data collected on samples of patients with Alzheimer's disease are reported below. Patients underwent TMS-EEG sessions and stimulated multiple brain networks with the explicit aim of identifying correlations with specific cognitive scores collected on the same patient group. Participants also underwent MRI scans for TMS target selection according to the procedures described herein. 【0313】 Methods and Analysis As shown in FIGS. 24A-24C, TMS was delivered across multiple networks of the brain based on individual MRI and fMRI data collected from patients with Alzheimer's disease. Specifically, the stimulation targeted the angular gyrus as a node of the default mode network (DMN), a functional network involved in memory processing in patients with Alzheimer's disease and mainly affected by the pathology, the frontoparietal control network (FPCN) with a role in higher cognitive functions, and the visual network (VN) as a control network in which no disruption of connectivity has been reported in patients with Alzheimer's disease. 【0314】 A batch of single TMS pulses was delivered across each region under the monitoring by Neuronavigation (120 pulses each). A 128-channel EEG device was used to monitor brain activities before, during, and after each TMS pulse. The TMS intensity was set based on the resting motor threshold collected across the primary motor cortex. The EEG data was processed according to the pipeline described in PREPARE, and TMS-evoked potentials (TEPs) were generated. Subsequently, the TEPs were projected onto the MRI scans of each patient, and source-level analysis of network activities was performed. The cognitive profiles of the patients were characterized via standard cognitive tools, and composite cognitive scores were created. The composite scores divided the sample into high and low cognitive composite scores, thus enabling the examination of differences in TEPs between the two groups and potentially explaining different cognitive profiles and rates of cognitive decline. 【0315】 Results Figures 23A-23B show the evoked oscillatory activity in patients with Alzheimer's disease after TMS. Figure 23A shows the differences in the brain responses to stimulation of the dorsolateral prefrontal cortex with stronger high-frequency activity immediately after perturbation in patients with higher composite cognitive scores versus those with lower composite cognitive scores. Figure 23B represents the source-level analysis of TMS-EEG data, showing how the TMS-evoked signals propagate across the whole brain after perturbation in patients with higher composite cognitive scores compared to patients with lower composite cognitive scores who only induced local responses. This reflects changes in brain connectivity and functional integrity that impede signal propagation in the brains of patients with more significant cognitive decline. 【0316】 Patients with higher composite cognitive scores showed a stronger response to TMS delivered via the DMN and FPCN, but no differences were observed regarding the VN. In particular, when patients were stimulated over the left dorsolateral prefrontal cortex (DLPFC), a significant increase in fast oscillatory activity (in the gamma band) was observed. This was more prominent in the group with higher composite cognitive scores compared to those with lower composite cognitive scores (p < 0.002), as shown in Figure 23A. Topographic analysis of signal propagation in the brain also revealed differences in the magnitude of activation across the whole brain after TMS of the DLPFC, as shown in Figure 23B, with individuals having higher composite scores showing a more distributed response compared to those with lower composite scores. 【0317】 Figures 24A-24B show the network-level responses to perturbation. Figure 24A shows that patients showed a decrease in activation in networks related to cognitive function, such as the DMN and FPCN, which are affected by Alzheimer's disease, but no significant differences were observed in the VN. Figure 24B shows that the specificity of the TMS-evoked response, measured by the ratio between the response in the targeted network and the response in any other network of the brain, showed significant differences between groups for the DMN and FPCN, but not for the VN. 【0318】 The comparison of activation levels between the three networks in patients versus controls also revealed generally decreased responses for the DMN and FPCN, as shown in Figure 24A, but no significant differences were observed for the VN. Furthermore, to identify differences in the specificity of TMS-induced responses across the brain, comparisons were made by analyzing the amount of activation induced in the stimulated network (e.g., DMN) versus the amount of activation in other networks of the brain. The results, as shown in Figure 24B, showed significantly less specific responses in patients compared to controls with respect to activation in the targeted network. This indicates a more diffuse, non-specific "disordered" response in patients with Alzheimer's disease, reflecting disruption of network integrity. 【0319】 In this example, TMS-EEG investigation of network dynamics in patients with Alzheimer's disease was shown to be a valuable tool for identifying changes in brain function relevant to understanding individual differences in cognitive performance and decline in patients with Alzheimer's disease. 【0320】 Response to frequency-specific electrical perturbations in AD patients. Alzheimer's disease (AD) is characterized by disruption of inhibitory circuits and GABAergic dysfunction, leading to a decrease in high-frequency brain oscillations in the gamma band (γ, 30 - 90 Hz). Assessment of such activity may lead to the identification of diagnostic and / or prognostic biomarkers. In this example, a multimodal "perturbation-based" transcranial alternating current stimulation (tACS)-EEG protocol for detecting γ changes in patients with AD was tested, and individual responses to tACS were correlated with clinical phenotypes. 【0321】 Methods and Materials Participants with mild to moderate AD (mean age = 72) underwent a multimodal evaluation including tACS-EEG, cognitive tests, blood markers, and theta burst TMS to assess (LTP)-like brain plasticity. For tACS-EEG visits, short (6-second) tACS blocks at 40 Hz or 6 Hz were included across six positions covering four lobes of both hemispheres, and 32-channel EEG recordings were made before / after each block. Spectral output in four frequency bands was calculated for the pre- and post-EEG. The change in spectral output (delta value) between pre- and post- was calculated as a metric of gamma induction and correlated with cognitive scores, blood markers, and measures of brain plasticity. 【0322】 Results Regarding cognitive function and gamma induction, 40Hz-tACS- and 6Hz-tACS-induced gamma activity correlated with measures of cognitive and memory impairment such as episodic memory (r = 0.74), general cognition (r = 0.78), verbal expression (r = 0.74) (all ps < 0.01), and auditory learning skills (r = 0.81, p < 0.05). Regarding gamma induction and neuroinflammatory markers, 40Hz-induced brain activity correlated with cytokine, tumor necrosis factor-α (TNF-α) (r = -0.73), and interleukin-10 (IL-10) (r = 0.77) (all p < 0.01), respectively. Regarding gamma induction and measures of cortical plasticity, cortical plasticity correlated with 40Hz-induced brain activity (r = 0.69, p < 0.05) 【0323】 The results of this example provide evidence to support the use of perturbation-based EEG markers in combination with brain stimulation in Alzheimer's disease (AD), detect clinical and cognitive correlates of the disease, and estimate levels of neuroinflammation and cortical plasticity in the brains of patients with AD. 【0324】 Neuroplasticity [SYNAPSE] Some of the systems and methods described herein relate to applications for modulating aspects of brain plasticity and neuroplasticity as part of SYNAPSE, which include (i) A platform for combined interventions that leverage the improvement of general brain plasticity obtained through interventions that regulate the perineuronal net (PNN) and extracellular matrix (ECM), including but not limited to drugs and physical exercise, and (b) local / targeted modifications induced through external modifiers including non-invasive brain stimulation methods. (ii) A platform for simulated and accelerated enriched environments through targeted systematic stimulation of brain function using non-invasive brain stimulation and other brain modifiers including cognitive stimulation and virtual reality. 【0325】 Brain development and performance result from the synergistic action of both genetic and experience-related factors. The interaction between the central nervous system, the extracellular environment (e.g., the physical space surrounding brain cells), and signals arising from the external world (e.g., experience) determines the brain's adaptation to external inputs from the environment and promotes its maturation. The concept of brain plasticity represents the brain's ability to modify its structure and function in relation to experience. Brain plasticity can be achieved through fine-tuning of growth-promoting and growth-inhibiting signals. In the postnatal brain, many processes involved in brain development are highly active, e.g., the synaptogenesis process, glial maturation, regulation of neurogrowth, and axonal pruning occur. The plasticity level is at its highest and then rapidly declines over the next 4 - 5 years. The neonatal brain is highly sensitive to external stimuli and can easily adapt its connections, resulting in high capabilities for learning and adaptation (e.g., language learning, walking). The end of the critical period defines a dramatic change in the brain's ability to adapt, learn, and evolve, and the search for solutions to reopen the window of plasticity is an important unmet need in modern neuroscience, including applications such as cognitive enhancement, physical and cognitive rehabilitation, and acceleration of learning. Some of the systems and methods described herein include tools and protocols for reactivating plasticity in the healthy and diseased adult brain to enhance normal brain function and / or treat brain diseases. 【0326】 The SYNAPSE module may include behavioral, cognitive, and non-invasive brain stimulation methods for regulating, enhancing, and reactivating the level of brain plasticity through the regulation of the extracellular matrix (ECM), perineuronal nets (PNN), chondroitin sulfate proteoglycans (CSPG), and inhibitory interneurons. During the critical period, the ECM supports neurogenesis, synaptogenesis, cell migration, growth, and axonal elongation, but in the adult brain, it is involved in neural plasticity and regeneration after injury. The main component of the ECM is CSPG, and proteoglycans mainly play a role in growth inhibition and thus prevent cell adaptation and plasticity. The PNN generally constitutes a part of the pericellular matrix that surrounds the cell bodies and dendrites of neurons in the brain and central nervous system. The PNN provides structural support while restricting growth and suppressing plasticity. ECM molecules and PNN preferentially accumulate around specific classes of neurons (e.g., inhibitory interneurons). Among these, parvalbumin-expressing interneurons (involved in activating microglia in the presence of neurodegenerative diseases such as viral infection or Alzheimer's disease) are particularly affected by the abundant PNN and ECM complexes. 【0327】 In some embodiments, the non-invasive brain stimulation approaches described in the STIMOLA section herein can be used to induce a state of increased neural plasticity, which includes, but is not limited to, transcranial magnetic stimulation (TMS), repetitive TMS (rTMS), patterned rTMS protocols (e.g., theta burst stimulation, multipulse TMS, and associative paired stimulation), transcranial electrical stimulation (tES) (in the form of transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), and transcranial random noise stimulation (tRNS)), and focused ultrasound (FUS). 【0328】 In some embodiments, frequency-specific neuromodulation targeting the activity of inhibitory interneurons can be used to regulate the activity of the interneurons and induce the regulation of PNNs. Considering the relationship between the inhibitory activity of the interneurons and the oscillatory patterns in the gamma band (between 30 Hz and 120 Hz), frequency-specific stimulation in the gamma band can be used to target brain systems / networks / regions. The stimulation can be in the form of sinusoidal stimulation, patterned stimulation (e.g., bursts of white noise imposed on a 40 Hz carrier frequency), or multi-frequency cross-frequency stimulation, but is not limited thereto. 【0329】 In some embodiments, brain optimization and evolution algorithms (e.g., those described in the DARWIN, SYNAPSE, IMPROVE, OPTI-BRAIN, and OPTI-COG sections of this specification) can be combined with other interventions that can promote brain plasticity and thus accelerate / enhance changes in the brain. 【0330】 In some embodiments, interventions configured to promote brain plasticity include drugs that act on growth promoting and growth inhibitory factors, as well as specific extracellular matrix molecules (e.g., chondroitin sulfate proteoglycan (CSPG), hyaluronan (HA), tenascin-R, and link proteins), that can disrupt PNNs and reopen the window of plasticity in the brain, and drugs that act on GABAergic activity, antidepressants, ketamine, and drugs that act on brain-derived neurotrophic factor (BDNF). Candidate drugs include chABC, an enzyme that can break down CSPG molecules and reactivate the critical period, ketamine for its ability to induce dissociative states, opioids, psychostimulants, psychedelic compounds (tryptamines (N,N-dimethyltryptamine - DMT - and psilocybin), amphetamines (2,5-dimethoxy-4-iodoamphetamine - DOI - and 3,4-methylenedioxy-methamphetamine MDMA), and ergolines (lysergic acid diethylamide - LSD)) that can strongly promote neurogenesis, but are not limited thereto. 【0331】 In some embodiments, drugs that can temporarily disrupt 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 improvement of local plasticity is desired. STIMOLA can be used to further enhance the plasticity of a given brain target. 【0332】 In some embodiments, drugs that can temporarily disrupt PNNs can be used to enhance plasticity at the whole-brain level. Once an improvement in plasticity is achieved, OPTI-BRAIN can be used to identify regions and / or connections in which specific brain connections, networks, and regions can be selectively modified using STIMOLA to achieve desired results (e.g., changes in states and traits). 【0333】 In some embodiments, physical activity can be used to regulate PNNs and promote a state of enhanced brain plasticity. The subject may be required to perform aerobic exercise (e.g., running or walking on a treadmill) to increase the metabolic activity of the brain and promote brain plasticity, and at the same time, perform a more specific task load on a particular brain system / network / region (e.g., a working memory task to activate the dorsolateral prefrontal cortex (DLPFC)). 【0334】 In some embodiments, a sensory deprivation protocol can be used to induce an improvement in brain plasticity in a particular brain system / network / region. The subject is required to wear an eye patch covering one eye and complete a visual task. This induces a state of distress in the visual system, which triggers a protective response (simulated deficit protocol) that leads to an improvement in plasticity. The same approach can be used in the auditory and motor systems and combined with specific training protocols (e.g., deprivation of sensation in the right arm to induce neuroplasticity and enhance motor learning) to enhance plasticity and improve behavioral performance. 【0335】 In some embodiments, the disruption of PNN can be used to affect long-term memory. The integrity and composition of PNN have been shown to be important for the maintenance of long-term memory, and its disruption leads to the loss of long-term memory. Using SYNAPSE and STIMOLA, traumatic long-term memory can be targeted through the disruption of PNN in regions related to traumatic memory and post-traumatic stress disorder, including but not limited to the amygdala, hippocampus, prefrontal cortex, and anterior cingulate cortex, insula. 【0336】 In some embodiments, SYNAPSE can be used to create a virtual augmented environment (VAE) for the purpose of promoting neuroplasticity. VAE refers to a virtual, augmented, or mixed reality environment that maximizes sensory, motor, cognitive, and social stimuli, resulting in an increase in the stimulation of brain cells (including but not limited to synaptic remodeling, dendritic growth, gliogenesis, angiogenesis, and neurogenesis) and systems, and promoting adaptation and plasticity. The concept of an enriched environment (EE) has been used in animal studies to promote learning, recovery from injury, and plasticity through the regulation of the expression of several neuronal growth genes, including but not limited to changes in the expression of neurotrophins. VAE is a parametric environment in which stimuli and their properties are selected via an algorithm (plasticity maximization algorithm - PMA) based on the physical properties of 3D objects (e.g., complexity, color, shape), their ability to interact with human agents, their emotional value, and their impact on brain structure (including but not limited to increased blood flow, metabolism, and connectivity). The stimuli are organized based on their impact on plasticity and combined in specific ensembles to create an optimal plasticity-inducing stimulus according to the characteristics of the individual brain, as defined by DMDT. 【0337】 In some embodiments, the VAE elements can be integrated into the video game experience using PMA to seamlessly expose the player to passive plasticity-inducing stimuli during engagement in entertainment activities. The elements that make up the background, the active elements in the foreground, or the interactive elements of the game are designed to include plasticity-inducing features and can thus provide the player with an experience that induces continuous plasticity. 【0338】 In some embodiments, the VR environment can be created to induce a particular brain state, where the stimuli are visualized in a particular sequence, inducing a brain response that represents a more mindful and calm state, accompanied by a decrease in the activation of emotion-regulating regions of the brain such as the amygdala, and thus facilitating the neutral reprocessing of traumatic memories. The stimuli are visualized, for example, alternately between the four quadrants of the visual field, along a predetermined trajectory along the horizontal and vertical axes. The induced brain activation simulates patterns of visual exploration that are evolutionarily related to increased focus on external activities, and decreased mind wandering, inner dialogue, and emotional activation. Additional explanations and examples are provided in the Metaverse section of this specification. 【0339】 In some embodiments, the VR environment can be created to induce parametric manipulation of sensory perception for the purpose of perturbing the brain's sensory systems and inducing plasticity. These systems include, but are not limited to, the visual, somatosensory, motor, and auditory systems. Applications include, but are not limited to, changes in the visual field such that objects are visualized at spatial locations that are different, for example, from their actual physical locations in the VR environment, exposure to altered sensory stimuli that mimic changes in the function of the brain's sensory cortex. In this particular example, objects can be visualized with some degree of mismatch between what is perceived from the visual system and what is visualized in the 3D environment. An individual exposed to such stimuli can gradually be trained to adapt to the spatial mismatch and modify their visuomotor plan to reach the 3D object at its correct location (e.g., by grasping the object by pointing to a location 30 degrees to the left of the actual object and systematically processing this visuomotor adaptation for any object in that environment). This adaptation process can induce the reorganization of synaptic connectivity in the visual cortex as well as other cortices of the brain and cerebellum. This can lead to an improvement in brain plasticity measured via increased levels of brain-derived neurotrophic factor (BDNF) and other brain markers. After training, the enhanced 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 be slowly readjusted to stimuli presented at their correct locations, and normal synaptic connections can be restored. This intervention, called virtual sensory manipulation (VSM), can also be applied to other sensory systems according to the same principle, for example, inducing a mismatch in the spatial location of 3D audio sources compared to the actual location of speakers presented as 3D objects in a VR environment. The same can be applied to tactile stimuli when a device providing vibrotactile feedback is used in a VR environment. VSM can be embedded in different media using VR, including existing and ad hoc video games.In some embodiments, the VSM is also used to retrain and perturb the cognitive system 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 for an object in a 3D environment by 10 degrees, 20 degrees, and then 30 degrees, but only for objects colored red. In another example, a blue object requires a 10-degree adjustment to the left, while a green object requires a 30-degree adjustment to the right. These additional constraints and rules induce the involvement of cognitive functions such as attention, working memory, inhibition, and flexibility related to brain health and healthy aging. 【0340】 In some embodiments, the enriched environment (EE) induces changes in brain structure and function related to plasticity, learning, and brain optimization. These changes can be represented by their location and / or size in the brain. For example, exposure to an EE with high-dimensional color characteristics may induce higher activation and plasticity in brain regions related to vision, such as the occipital lobe of the brain. By using SYNAPSE to identify such brain ...

Claims

[Claim 1] A method for creating a digital replica of a human brain state, wherein the method is Receiving information associated with the brain of the person, wherein the information includes structural brain information and functional brain information, and the functional brain information includes electrophysiological data recorded from the person. The process of receiving the information to determine a set of brain metrics that characterize the brain state of the person, To create a digital replica of the person's brain state based at least in part on the plurality of brain metrics determined from the received information. Methods that include... [Claim 2] The method according to claim 1, further comprising designing a brain-inspired artificial intelligence system based on the digital replica of the brain state of the person. [Claim 3] Updating the digital replica of the brain state of the person based at least in part on a plurality of updated brain metrics determined from updated information associated with the person's brain, Updating the brain-inspired artificial intelligence system based on the updated digital replica of the person's brain state. The method according to claim 2, further comprising: [Claim 4] The method according to claim 3, wherein the updated information associated with the person's brain includes data sensed by a wearable device worn by the person. [Claim 5] The method according to claim 2, wherein the brain-type artificial intelligence system is designed based on an oscillator that reflects the brain state and brain architecture of the person. [Claim 6] The method according to claim 2, wherein the brain-type artificial intelligence system is designed based on macroscale brain architecture data of the human brain. [Claim 7] The method according to claim 2, wherein the brain-inspired artificial intelligence system is designed based on a multi-layered hierarchical reasoning architecture that includes two or more layers representing convergent, divergent, and combined general cognition. [Claim 8] Decomposing the digital replica of the brain state of the person into basic units of information, Using the brain-type artificial intelligence system, generate code for controlling hardware and / or software using the basic units of information. The method according to claim 2, further comprising: [Claim 9] The method according to claim 2, wherein the brain-type artificial intelligence system is designed based on an oscillator having a network of activity computed via a neural mass model composed of different classes of neurons having different functions. [Claim 10] The method according to claim 2, wherein the brain-type artificial intelligence system is designed based on an oscillator having a fixed lifecycle that defines the metabolic budget relating to the brain-type artificial intelligence system. [Claim 11] The method according to claim 2, wherein the brain-type artificial intelligence system is configured to be used as a conversational agent in a therapeutic environment. [Claim 12] The method according to claim 1, wherein the received information includes data sensed by a wearable device worn by the person or via at least one neuroimaging method. [Claim 13] The method according to claim 1, further comprising creating a first video game or metaverse content based at least in part on the digital replica of the brain state of the person. [Claim 14] Receiving updated information from a device connected to a network, Based on the received updated information, the digital replica of the person's brain state is updated. Creating a second video game or metaverse content based at least partially on the updated digital replica of the person's brain state. The method according to claim 13, further comprising: [Claim 15] The method according to claim 14, wherein the first video game or metaverse content includes a multilayer avatar, in-game mechanics, equipment in the video game, artificial intelligence used to control one or more non-playable characters in the video game, or at least one in-game progression sequence or storyline. [Claim 16] The received information is from multiple sources, The method according to claim 1, wherein creating a digital replica of the brain state of the person comprises using an algorithm to harmonize the received information from multiple sources. [Claim 17] Receiving additional data sensed by a wearable device or a brain activity recording device, Updating the digital replica of the person's brain state based at least in part on the aforementioned additional data. The method according to claim 1, further comprising: [Claim 18] The received information is formalized as a network, The method according to claim 1, wherein processing the received information to determine a plurality of brain metrics that characterize the brain state of the person comprises extracting features of the network using graph theory metrics and network control theory metrics.

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