Systems and methods for providing personalized targeted non-invasive stimulation to a brain network

Abstract

Disclosed herein are devices, systems, and methods for improving or treating or preventing Alzheimer's Disease and related dementias. Methods can include using individual patient data to identify optimal treatment targets and stimulation intensity, frequency, or waveform or any combination thereof delivered by a device, as in the case of transcranial magnetic stimulation. Systems and methods include a platform or infrastructure for treatment delivery and data processing. In some embodiments, methods are applied to restore cognitive function, improve clinical symptoms, modify mechanisms of altered plasticity, modulate cortical excitability and reactivity, or induce changes in local brain metabolism.

Description

[0001] Cross-referencing

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 218,625, filed July 6, 2021, which is incorporated herein by reference. This application also claims the benefit of U.S. Provisional Patent Application No. 63 / 277,086, filed November 8, 2021, which is incorporated herein by reference. Background Technology

[0003] Alzheimer's disease (AD) is considered the most debilitating form of dementia in the elderly. Currently, routine care for AD patients relies solely on cholinergic and glutamatergic medications, despite their limited effectiveness and frequent adverse side effects. No effective treatment currently exists, primarily due to a lack of understanding of the fundamental pathophysiology. Overall, there is an urgent need in this field for novel non-pharmacological therapies. Summary of the Invention

[0004] The inventors have realized that existing techniques for treating neurological and psychiatric disorders with noninvasive stimulation can be improved by personalizing the location and / or stimulation parameters for individual patients. This document discloses a method related to identifying the location in the brain where noninvasive stimulation will be delivered. Multiple brain regions in a brain network (e.g., a default mode network) can be identified based on neuroimaging data (e.g., functional magnetic resonance imaging data, diffusion tensor imaging data, etc.). Within one of the multiple brain regions in the brain network, functional connectivity with one or more other brain regions in the brain network can be evaluated to determine a subregion of the brain region most closely connected to the other regions in the brain network.

[0005] The aspects disclosed herein provide a method for identifying a first location in the brain of a subject suitable for non-invasive stimulation to treat or improve a neurological or psychiatric disorder, the method comprising: a) identifying a plurality of brain regions forming a brain network based on scan data of the subject's brain; and b) within a first brain region of the plurality of brain regions, identifying a subregion of the first brain region that is closely connected to one or more other brain regions of the plurality of brain regions as a first location suitable for non-invasive stimulation to treat or improve the neurological or psychiatric disorder. In some embodiments, a target brain region is defined based on average connectivity within the origin network of the target brain region, connectivity with a specific network, and connectivity with a plurality of other networks. In some embodiments, a measure of network controllability is used instead of connectivity. In some embodiments, one or more of a measure of network efficiency, modularity, clustering, evolvability, or resilience is used instead of connectivity. In some embodiments, a target brain region is defined based on simulated data estimating the propagation of a TMS pulse over the remainder of an unstimulated brain structure, wherein the estimation takes into account the initial intensity of the pulse, the pulse shape, and the integrity of connections between the target region or other brain structures or networks. In some embodiments, the estimation takes into account a decay function that approximates the asymptotic loss of energy of the original perturbation, varying with time and quantity, region sequence, or nodes reached indirectly through stimuli.

[0006] In some embodiments, TMS-EEG functional mapping is used to simultaneously determine both the location and stimulation parameters of noninvasive brain stimulation. In some embodiments, one or more characteristics of the evoked response to brain stimulation at one or more locations within a brain region can be used to determine the optimal location for targeted noninvasive stimulation and to personalize the stimulation characteristics (e.g., stimulation frequency, intensity, amplitude) for individual patients to provide optimized brain responses, for example, to treat neurological or psychiatric disorders.

[0007] The aspects described herein provide a method for identifying personalized stimulation targets in brain regions to treat or improve a neurological or psychiatric disorder in a subject, the method comprising: a) non-invasively stimulating each of a plurality of locations in a brain region of the subject; b) sensing at least one evoked potential in response to the provided stimulation; and c) selecting one of the plurality of locations suitable for providing therapeutically effective non-invasive stimulation to treat or improve the neurological or psychiatric disorder as a personalized stimulation target for the subject, wherein the selection is based on at least one characteristic of the at least one evoked potential. In some embodiments, the method involves personalizing the brain stimulation target by identifying brain subregions and using TMS-EEG functional mapping (“TMS-EEG functional mapping”). Opti-SearchThe procedure further refines the stimulation location in the brain region. In some embodiments, stimuli can be sequentially delivered to multiple locations within a subregion, and evoked responses to the stimuli can be sensed using, for example, electroencephalography (EEG) or another suitable sensing technique. In some embodiments, personalized stimulation targets within the brain region can be selected based at least in part on the analysis of evoked responses to the stimuli. In some embodiments, the intensity or amplitude of the brain stimulation can be selected by adjusting the baseline intensity of the stimulation determined using other techniques. In some embodiments, the patient's resting motor threshold can be used to establish the baseline stimulation intensity, and the baseline stimulation intensity can be refined at least in part based on one or more characteristics of the evoked responses sensed during TMS-EEG functional mapping as described herein. In some embodiments, the determination of personalized stimulation target locations can be performed using neuroimaging data, initially using methods related to brain connectomics, brain connectivity, and network... One or more related concepts and methods from network theory, graph theory, or control theory analysis can be used to identify subregions of brain regions most closely connected to other brain regions in a brain network. In some embodiments, personalized algorithms for defining brain targets and stimulation parameters for each patient can be applied to treat or prevent neurological and psychiatric disorders associated with changes in brain networks similar to those characterizing AD patients (e.g., the default mode network) or other networks functioning in cognitive processes (e.g., the dorsal attention network, the frontoparietal cognitive control network). In some embodiments, personalized algorithms for defining brain targets and stimulation parameters for each patient include analysis of intra- and inter-network dynamics to determine the hierarchical structure of network targets. In some embodiments, personalized algorithms for defining brain stimulation targets for each patient can be applied to other neuroimaging data sources, including structural MRI, diffusion MRI, perfusion MRI, or PET imaging data.

[0008] In some embodiments, the systems and methods described herein allow for the modulation of oscillatory brain activity, including but not limited to rapid activity in the gamma band. In some embodiments, the systems and methods of the present invention can be applied to treat or prevent various diseases associated with protein deposition and pathophysiological mechanisms associated with protein accumulation and interneuronal pathology in AD. In some embodiments, the systems and methods of the present invention allow for the mitigation of cognitive decline, including identifying brain regions, further refining the stimulation location of the brain regions, and applying repetitive transcranial magnetic stimulation to the stimulation locations. In some embodiments, cognitive decline is mitigated when the methods are applied over time intervals ranging from several days to several weeks, months, or years. In some embodiments, cognitive decline is mitigated by altering the brain activity of specific brain networks, including but not limited to networks of patients with AD or subjects at risk of developing AD.

[0009] In some embodiments, optimal TMS coil location, brain target, or stimulation parameters are determined using biophysical electric field modeling of a real head model. In some embodiments, optimal TMS coil location, brain target, or stimulation parameters are determined using prescriptive data from AD patient samples. In some embodiments, optimal TMS coil location, brain target, or stimulation parameters are determined by estimating the connectivity between the white matter of an AD patient and the subcortical region of interest in the AD patient. In some embodiments, optimal TMS coil location, brain target, or stimulation parameters are determined by using a scalp coordinate system derived from individual head measurements of the patient and combining it with prescriptive maps of spontaneous brain activity or activity during a specific cognitive task (such as an episodic memory task estimated from AD patient samples). In some embodiments, optimal TMS coil location, brain target, or stimulation parameters are determined using individual brain scan data and EMG data in the absence of TMS-EEG data. In some embodiments, the real head model may be a multi-layer finite element model of a real head, which may be subject-general or subject-specific, such as from the patient's MRI. In some embodiments, tissue boundaries can be derived from MR images (e.g., scalp, skull, cerebrospinal fluid, ventricles, gray matter, and white matter) regardless of whether additional brain scans are performed, and the method can be used to more specifically calculate the induced electric field (E-field) on the head and cortex. In some embodiments, prescriptive data from AD patient samples include, but are not limited to, prescriptive brain activation patterns, functional brain network topography maps, metabolic activity maps, or brain perfusion maps during specific cognitive tasks (e.g., episodic memory tasks). In some embodiments, a non-limiting instance of the subcortical region of interest in AD patients is the hippocampus because of its role in memory processing. In some embodiments, cortical sites with strong connections to subcortical targets are chosen because they have a higher probability of contact with subcortical areas that would otherwise not be directly targeted by TMS.

[0010] In some embodiments, the optimal TMS coil location or stimulation parameters can be determined by using only brain scan and EMG data in the absence of TMS-EEG data, by comparing the patient's brain scan and EMG data with standard brain scan, EMG, and TMS-EEG data from an AD patient group. In some embodiments, estimates of the optimal stimulation intensity are inferred from the AD patient's EMG data (e.g., cortical excitability) and its relationship with TMS-EEG data, and adjusted using scalp-cortex distance values ​​extracted from the patient's brain scan. In some embodiments, physiological computational models of the subject's brain derived from electrophysiological and biophysical data can also be used to define brain stimulation targets and optimize stimulation parameters, including, but not limited to, intensity, frequency, duration, or waveform, or any combination thereof. In some embodiments, TMS-based interventions can be combined with other interventions (TMS + other interventions = T). 2 The application of TMS-based interventions (formulations) in combination includes, but is not limited to, cognitive training programs or cognitive tasks or behavioral interventions, to amplify the effects of brain stimulation, amplify the effects of a second intervention, or produce synergistic effects, or any combination thereof. In some embodiments, cognitive tasks or programs may be used to stabilize brain states and maximize the effects of TMS-based interventions, induce state-dependent effects, or improve cognitive or behavioral performance. In some embodiments, TMS-based interventions may be combined with other brain stimulation techniques to amplify the effects of TMS, amplify the effects of a second intervention, or produce synergistic effects, or any combination thereof. In embodiments, TMS may be combined with transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), or transcranial random noise stimulation (tRNS), or any combination thereof. In some embodiments, the combination with electrical stimulation may be used to amplify endogenous oscillations, amplify the long-term enhancing or inhibiting effects of other brain stimulation protocols, stabilize brain activity during or before the delivery of TMS-based interventions, modulate plastic responses, or modulate corticospinal excitability and the balance of inhibition and excitation, or any combination thereof. In some embodiments, TMS-based interventions include, but are not limited to, patterned rTMS protocols, such as theta burst stimulation, multi-pulse TMS, and paired associative stimulation. In some embodiments, TMS-based interventions may be used in combination with drugs applied to the central nervous system to amplify brain stimulation effects, amplify the effects of a second intervention, or produce synergistic effects or any combination thereof.

[0011] In some embodiments, parameters for the TMS-based intervention are calculated using signal processing software and algorithms installed on local hardware operated by trained personnel, including but not limited to research scientists, laboratory technicians, research assistants, neurologists, psychiatrists, geriatricians, neurophysiologists, clinical technicians, or psychologists, or any combination thereof. In some embodiments, parameters for the TMS-based intervention are calculated using signal processing software or algorithms installed on remote hardware with connectivity. In some embodiments, data collected on the patient is streamed to a platform hosting code and software for optimal stimulation parameter definition (TMS-EEG functional mapping), and the resulting stimulation parameters are sent back to the operator performing the TMS-based treatment. In some embodiments, parameters for the TMS-based intervention are calculated via a supervised or unsupervised feature selection procedure by applying machine learning or deep learning algorithms to the subject's EEG, TMS, EMG, or brain scan data. In some embodiments, stimulation parameters for the TMS-based intervention, details about the treatment process, data storage and processing, or scheduling of brain stimulation are hosted as part of a hybrid local or remote infrastructure and application programming interface (API). Attached Figure Description

[0012] To gain a more complete understanding of this embodiment, including its features and advantages, reference is now made to a detailed description of the embodiment and the accompanying drawings.

[0013] Figure 1 provides an exemplary method for personalized target identification and TMS intensity limiting according to embodiments of this document, in which various options are provided.

[0014] Figure 2 provides an exemplary method for target identification using standard or custom brain activity templates according to embodiments of this document, with various options provided.

[0015] Figure 3 Exemplary methods for generating predictive models for estimating stimulus parameters are provided according to embodiments of this document.

[0016] Figure 4 illustrates an exemplary method for subcortical targeting using functional or structural connectivity group data according to embodiments of this document, with various options provided.

[0017] Figure 5 An exemplary flowchart depicting a method for treating a medical condition according to embodiments described herein is provided.

[0018] Figure 6 provides an exemplary study design for measuring changes in brain reactivity after treatment, according to embodiments of this document.

[0019] Figure 7 provides an exemplary depiction of post-treatment cognitive changes according to embodiments of this document.

[0020] Figure 8 provides an exemplary depiction of post-treatment TMS-EEG results according to embodiments of this document.

[0021] Figure 9 An exemplary depiction of changes in brain activity following treatment according to embodiments of this document is provided.

[0022] Figure 10 provides an exemplary TMS research scheme according to embodiments of this document, in which various measurement results are provided.

[0023] Figure 11 An exemplary depiction of changes in brain activity in certain brain regions following treatment according to embodiments of this article is provided.

[0024] Figure 12 An exemplary depiction of changes in brain activity in certain brain regions following treatment according to embodiments of this article is provided.

[0025] Figure 13 An exemplary depiction of the lack of changes in brain activity in certain brain regions after treatment according to embodiments of this article is provided.

[0026] Figure 14 An exemplary depiction of the lack of changes in brain activity in certain brain regions after treatment according to embodiments of this article is provided. Detailed Implementation

[0027] This document provides devices, systems, and methods for improving, treating, or preventing Alzheimer's disease and related dementia, or any combination thereof, including but not limited to preclinical and early stages of the diseases (e.g., mild cognitive impairment) and later stages. In some embodiments, the methods use data from individual patients to identify optimal therapeutic targets and the intensity, frequency, or waveform of transcranial electromagnetic stimulation delivered by the device, or any combination thereof, such as in the case of transcranial magnetic stimulation (TMS). In some embodiments, the data used to define the optimal stimulation parameters are derived from sources such as electrophysiological recordings (e.g., electroencephalography-EEG, electromyography-EMG), magnetic resonance imaging (e.g., MRI, fMRI, DTI), and estimates of induced electric fields in the brain obtained through biophysical modeling. In some embodiments, the methods include, but are not limited to, solutions for targeting brain networks altered in neurological diseases such as Alzheimer's disease and related dementia. In some embodiments, as a non-limiting example, the methods target the default pattern network (DMN) and its major network node, the precuneus region. In some embodiments, the methods include, but are not limited to, details of a treatment plan based on repeated TMS sessions, which is capable of slowing cognitive and clinical decline in patients with Alzheimer's disease over a 6-month period. In some embodiments, the systems and methods include, but are not limited to, platforms or infrastructure for treatment delivery and data processing. In some embodiments, the methods are applied to restore cognitive function, improve clinical symptoms, modify altered plasticity mechanisms, modulate cortical excitability and responsiveness, or induce changes in local brain metabolism, or any combination thereof.

[0028] Synaptic dysfunction may be a central factor in the pathophysiology of Alzheimer's disease (AD). For example, synaptic collapse is an early event predicting neuronal degeneration. Impaired synaptic transmission may play a crucial role in the pathogenesis of AD. Transcranial magnetic stimulation (TMS) is a novel approach capable of identifying early features of synaptic dysfunction characterizing the pathophysiology of AD. Treatment based on multiple rTMS sessions has the potential to influence cognition in individuals with neurodegenerative diseases. rTMS application to individuals with mood disorders and depression may be able to influence cognitive processes, while also being safe and painless. Prolonged exposure (i.e., several weeks) to multiple rTMS sessions may have a greater impact on modulating long-term plasticity and behavior.

[0029] From a neurobiological perspective, rTMS can lead to substantial clinical improvements by promoting changes in synaptic plasticity, a crucial biological mechanism behind learning and memory. Specifically, long-term potentiation (LTP) is a primary target candidate due to its prior association with individual variability in cognitive function. LTP-like cortical plasticity may be impaired in the early stages of Alzheimer's disease (AD) and may be associated with verbal memory impairment. Therefore, high-frequency rTMS can be used to enhance LTP-like cortical plasticity in AD patients in the early stages of the disease to slow disease progression. For example, a combination of brain plasticity and responsiveness measures captured by a combination of TMS and electroencephalography (EEG) along with cognitive scores can confirm whether rTMS produces changes at both local and global levels. Specifically, TMS-EEG provides an innovative approach to directly probe local and widespread cortical dynamics, not only in clinical practice but also in the assessment of the effectiveness of clinical interventions. Early TMS-evoked potentials (TEPs) recorded on EEG data collected simultaneously with TMS delivery originate from GABA(A)-mediated and GABA(B)-mediated inhibitory postsynaptic potentials (IPSPs), which constitute valuable biomarkers of excitation-inhibition balance in the brain and thus constitute levels of brain plasticity. TEPs are also reproducible, capable of revealing regional characteristics when TMS targets different brain regions or networks, and are sensitive to changes in brain state, and therefore also sensitive to potential improvements in cognitive function. The application of TMS-EEG as a marker of cortical plasticity and responsiveness, and therefore the response to rTMS therapy, will contribute to elucidating the mechanism of action of rTMS in AD patients. Given its spatial and temporal resolution, TMS-EEG can also be used to explore cortical responses to TMS by sampling multiple brain regions and identifying optimal locations where TMS induces strong brain responses and maximum target involvement, which are then used as targets for rTMS therapy.

[0030] Stimulation of the prefrontal cortex may be the most effective option for improving cognitive function. When individuals engage in cognitive tasks, targeting areas such as the right and left DLPFC, Broca's and Wernicke's cortices, and the right or left parietal somatosensory cortex may also enhance the effects of stimulation. However, significant neuropathological abnormalities in the early stages of the disease (i.e., β-amyloid plaques and neurofibrillary tangles) primarily affect the posterior cortical areas of the brain, including, but not limited to, the precuneus, posterior cingulate cortex, posterior sphincter, and angular gyrus. AD patients also exhibit alterations in medial frontoparietal functional connectivity and the so-called default mode network (DMN). The precuneus is a key node in the DMN, and in the early clinical stages of AD, functional changes in the precuneus and reduced connectivity with other parts of the brain precede regional brain atrophy, which becomes more prominent in later stages of the disease.

[0031] The DMN and precuneus can be viable brain targets for treatments aimed at alleviating dementia symptoms and may have disease-modifying effects. AD patients exhibit reduced cortical thickness around the precuneus, often followed by aberrant local activation and hypometabolism, as well as reduced functional connectivity during memory performance. The involvement of the DMN and precuneus is also crucial for episodic memory retrieval, which is frequently impaired in the early stages of AD. rTMS performed on the DMN has enhanced both short-term and long-term memory function. Therefore, implementing non-invasive brain stimulation interventions targeting the DMN and precuneus in patients with AD represents a key step in slowing disease progression and potentially counteracting memory decline in AD patients.

[0032] However, strategies to maximize DMN-precuneus (DMN-p) involvement through neural stimulation are not yet available, and personalized network-level targeting methods must be developed. The invention described herein provides a novel approach to perform non-invasive stimulation of DMN-p using a precise spatial targeting method based at least partially on TMS-EEG, and a solution to personalize stimulation intensity, frequency, duration, and waveform based at least partially on electrophysiological and neuroimaging data. Results from a double-blind, randomized, placebo-controlled phase 2 clinical trial investigating the effects of rTMS on personalized DMN-p targets defined for each patient based on TMS-EEG data are reported, serving as evidence of the effectiveness of the proposed system and method for personalized treatment, as well as the treatment regimen itself.

[0033] In the case of Alzheimer's disease (AD), longer treatment durations can help significantly reduce disease progression. As a non-limiting example, the clinical trials described in the Examples section below document the effects of the longest rTMS treatment tested to date in AD patients. In some embodiments, the longest rTMS treatment tested in AD patients was 24 weeks.

[0034] The brain-targeting and therapeutic approaches for AD described in this article also utilize new insights into the histopathology and concussion activity of AD documented in patients with dementia (including, but not limited to, AD, mild cognitive impairment, and frontotemporal dementia). Specifically, AD is characterized by diffuse amyloid-β (Aβ) plaques and phosphorylated tau (p-tau) deposition in neurofibrillary tangles, along with extensive signs of neurodegeneration and neuroinflammation. Progressive Aβ deposition can begin up to 20 years before the onset of clinical symptoms and stabilizes as clinical symptoms become prominent. Even in the absence of Aβ, p-tau accumulates particularly in the middle temporal lobe and spreads extra-temporal along multiple trajectories, which also include, but are not limited to, regional portions of the DMN. Neurodegeneration and clinical symptoms may be closely related to the spread of p-tau and the level of inflammation. Therefore, Aβ and p-tau may play key roles in the pathogenesis of AD, and interventions that reliably and safely reduce the intracranial burden of either Aβ or p-tau could have significant clinical importance.

[0035] In patients with Alzheimer's disease (AD) and in a mouse model of the disease (5XFAD mice), rapid oscillatory activity in the “γ” band, ranging from approximately 30 Hz to 120 Hz, may be reduced. This pathological change in γ activity is associated with albumin-+ inhibitory interneuron pathology, where interventions designed to restore γ activity in pre-symptomatic AD mice have shown a significant ability to prevent subsequent neurodegeneration and behavioral deficits and to reduce Aβ and p-tau. Given its effects on the plastic circuitry system and on excitatory and inhibitory levels in the brain, the approach described herein for personalized rTMS may also have beneficial effects on oscillatory activity in the AD brain, leading to positive clinical, behavioral, cognitive, and neurological outcomes.

[0036] Activity within the gamma band is also associated with plasticity processes in the human brain, where gamma-induction interventions via electrical stimulation aim to enhance gamma oscillation activity (>30 Hz), thereby increasing cortical plasticity levels during and after stimulation. The combination of rTMS with gamma-induction protocols can lead to synergistic or additive effects on cortical physiology and plasticity, with potentially stronger clinical implications for patients, as described in some embodiments.

[0037] Figure 1 provides an exemplary depiction of an embodiment of the method 100 described herein, wherein the method includes identifying personalized targets and defining TMS intensity. In some embodiments, reference is made to... Figure 1AA method includes: collecting fMRI data of Alzheimer's disease (AD) patients 101; creating a mean default pattern network (DMN) map of AD patients 103, the center of which includes the precuneus 102; focusing on the most closely connected sub-region of the precuneus 104; creating a TMS-based functional search 105 via TMS-EEG 106 by measuring TMS evoked potentials (TEPs) 108 across a sub-region of the precuneus 107 (containing the most closely connected sub-region of the precuneus 104); and creating personalized targets for each brain 109 including a primary target 111 and a personalized target 110. In some embodiments, the personalized target is located dorsally to the primary target. In some embodiments, the personalized target is located ventrally to the primary target. In some embodiments, the personalized target is located caudally to the primary target. In some embodiments, the personalized target is located rostrally to the primary target. In some embodiments, the personalized target is located in the most closely connected sub-region of the precuneus 104. In some embodiments, the personalized target is in another sub-region of the precuneus 107. In some embodiments, the method involves, but is not limited to, a two-step procedure for identifying DMN network-level targets 103 and then personalizing them via a TMS-EEG functional search 105. In some embodiments, reference is made to... Figure 1B The method includes: measuring the resting motor threshold (RMT) by measuring the action potentials and activity of the index finger 116 while stimulating the precuneus 102; creating a TMS-based functional search via TMS-EEG 106 by measuring TMS-evoked potentials (TEPs) 117 across a subregion of the precuneus 107 (containing the most closely connected sub-part of the precuneus 104); and determining personalized stimulation intensity 118. In some embodiments, the resting motor threshold 116 is used as a measure of cortical excitability. In some embodiments, the RMT 116 of the detected nerve impulses can be used to determine the patient's personalized RMT as a percentage. In some embodiments, TEP 117 can be used to measure the amplitude, delay, shape, or frequency of the response to the TMS-EEG. In some embodiments, after measuring TEP 117, the patient's personalized RMT can be adjusted to different or the same percentage 118. In some embodiments, after measuring TEP 117, the patient's personalized stimulation frequency can be measured 118. In some embodiments, this method can be used to personalize the stimulation intensity for the patient. In some embodiments, reference Figure 1CThe method includes: positioning a TMS coil 119; generating a biophysical modeling result 120; and using a biophysical modeling result 121 to estimate an induced electric field on a target region 122. In some embodiments, the biophysical modeling result uses an electric field norm map measured in volts per meter (V / m). In some embodiments, the desired minimum induced electric field value is the estimated induced electric field 122 plus 27%. In some embodiments, the adjusted TMS stimulation for the induced electric field is a percentage equal to the desired minimum induced electric field value. In some embodiments, reference... Figure 1D The method includes various alternative targets for the DMN and other functional brain networks associated with AD, including, but not limited to, prefrontal nodes of the default mode network 112, the frontoparietal bone control network 113, the dorsal attention network 114, or the sensorimotor network 115. In some embodiments, the method described herein can be applied to alternative targets of the DMN.

[0038] Figure 2 illustrates an exemplary method for target identification 200 using standardized and customized brain activity templates. In some embodiments, these methods can be used in the absence of a specific brain scan. In some embodiments, reference is made to... Figure 2A The method includes a functional network atlas 201, which includes a visual network 212, a default mode network 209, a frontoparietal bone network 206, a limbic network 210, a ventral attention network 211, a somatosensory network 208, and a dorsal attention network 207; it can be applied to patients 202, 203, 204, or 205. In some embodiments, the method includes adjusting an existing anatomical atlas. In some embodiments, the method includes adjusting an existing functional atlas. In some embodiments, the shape of the brain network differs among different patients. In some embodiments, reference... Figure 2B The method includes: deforming a predefined brain activation pattern to an individual patient's anatomy by measuring brain activation 214 during episodic memory processing in an AD patient 213; and adapting the pattern of activation areas to brain activation 216 in the patient's brain 215 and activation 218 in the patient's brain 217. In some embodiments, activation areas 216 and 218 are identical. In some embodiments, activation areas 216 and 218 are different. In some embodiments, the predefined brain activation pattern is similar to the individual patient's anatomy. In some embodiments, the predefined brain activation pattern is different from the individual patient's anatomy. In some embodiments, reference is made to... Figure 2CThe method includes: using a scalp coordinate system 219, the scalp coordinate system including a vertex 220, a left tragus 221, a nasal root point 222, a right tragus 223, and an occipital protuberance 224; and locating an optimal target distance 226 based on an occipital protuberance-vertex distance 225 to find an optimal first stimulation position on the scalp 227. In some embodiments, the optimal target distance 226 is the optimal first stimulation position based on the scalp coordinate system. In some embodiments, the optimal target distance 226 is 29% of the occipital protuberance-vertex distance 225 from the vertex 220.

[0039] Figure 3 Exemplary methods are provided for generating a predictive model for stimulus parameters 300. In some embodiments, the method includes: using a canonical AD patient dataset 301 and a structural brain scan 302 including, but not limited to, scalp-cortical distance; measuring cortical excitability from a primary motor cortex 303 including theta waves 305 and alpha waves 304; measuring the induced electric field 306 in the precuneus; measuring the stimulus intensity 308 for the precuneus using a TMS-EEG Opti-Stim procedure 307; and creating a predictive model 309 for estimating the stimulus parameters. In some embodiments, the canonical AD patient dataset is a small dataset. In some embodiments, the canonical AD patient dataset is a large dataset. In some embodiments, corticospinal excitability is measured as altered neural oscillations. In some embodiments, altered neural oscillations include, but are not limited to, an increase in the frequency of theta waves. In some embodiments, altered neural oscillations include, but are not limited to, a decrease in the frequency of alpha waves.

[0040] Figure 4 illustrates an exemplary method 400 for subcortical targeting using functional and structural connective data. In some embodiments, reference is made to... Figure 4A The method includes using MRI 401 to measure episodic memory activation 402 in the brain to display activation areas 404 in resting-state functional MRI (rs-fMRI) 403. In some embodiments, episodic memory activation 402 is recorded during a memory task in task-based fMRI. In some embodiments, the activated target network 404 is the DMN. In some embodiments, methods for identifying patient-specific brain activation during a memory task in patients with Alzheimer's disease highlight the involvement of the DMN. In some embodiments, reference Figure 4BThe method includes MRI data of the hippocampus 405, parahippocampal gyrus 406, and left angular gyrus 407, as well as white matter fiber tract imaging data 408. In some embodiments, the method uses structural MRI data to identify optical cortical targets for hippocampal modulation. In some embodiments, the method uses diffuse MRI data to identify optical cortical targets for hippocampal modulation. In some embodiments, the method uses MRI data to compute and visualize white matter fiber tracts 408 connecting stimulated superficial areas (e.g., angular gyrus) to subcortical targets of interest in AD (e.g., hippocampus, due to its role in memory processing). In some embodiments, white matter fibers can be estimated from multiple subregions of the angular gyrus, wherein the subregion showing the strongest white matter fiber pathway toward the hippocampus is selected as the final personalized target. In some embodiments, reference... Figure 4C The method includes using brain biophysical modeling 409 to estimate the optimal intensity of activation of the angular gyrus and hippocampus 410 via TMS 411. In some embodiments, biophysical modeling 409 is electric field (E-field) modeling. In some embodiments, biophysical modeling 409 is used to estimate the induced electric field in the angular gyrus of an AD patient, and then used to optimize stimulation parameters, including but not limited to stimulation intensity.

[0041] Figure 5An exemplary flowchart 500 is provided for the infrastructure of devices for treatment optimization and delivery. In some embodiments, the flowchart includes a clinical staff member or user 503, a web browser or local machine 502, authentication 501, brain scan data 504, TMS session data 505, EEG matching 506, survey metadata 507, gateway entry 508, gateway 509, notification 510, billing 511, schedule 512, object 513, collection 514, cleaning or preprocessing 515, characterization 516, generation of models 517 and 518, flow 519, storage 520, exit 521, baseline survey 522, database 523, and population-based inference for treatment optimization 524. In some embodiments, TMS session data 505 and brain scan data 504 are input into survey metadata 507. In some embodiments, the clinical staff member 503 uses a web browser or local machine 502 and authenticates the staff member 501 as a user. In some embodiments, there is a set of key actions from the gateway, including but not limited to: streaming 519 – the ability to query and stream data (e.g., EEG time-series data); data collection 514 – the ability to collect, clean, characterize, and process raw EEG data, for example, to obtain EEG feedback and return to the location in the brain for stimulation; object 513 – the ability to access and query a database; scheduling 512 – the ability to book and publish patient schedules or appointments; billing 511 – the ability to access billing information and charge a third-party service provider for each session; notification 510 – the ability to send an email to the user after certain actions are completed (e.g., session completion or billing success, or target results are ready); data export 521 – the ability to request and create data packets or download information from a database; baseline user 522 – the ability to perform a baseline check on the user through survey tools (such as, but not limited to, the Mini-Mental State Exam (MMSE)) or to publish information from the electronic health record (HER) into the survey itself. In some embodiments, collection 514 includes cleaning or preprocessing 515, characterization 516, and creating multiple models 517 and 518. In some embodiments, all information in the infrastructure is collected in a database 523, which then creates population-based inference 524 for treatment optimization. In some embodiments, population-based inference 524 for treatment optimization includes the results of one or more sessions for a single patient. In some embodiments, population-based inference 524 for treatment optimization includes the results of one or more sessions for multiple patients.

[0042] Figure 6 provides an exemplary depiction 600 of the research design and results. In some embodiments, reference is made to... Figure 6AThe study design 601 includes: a clinical assessment at week 0, said clinical assessment comprising tests including, but not limited to, the Clinical Dementia Scale (CDR), the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), the Mini Mental State Examination (MMSE), the Neuropsychiatric Questionnaire (NPI), the Alzheimer's Disease Collaborative Research-Activities of Daily Living Scale (ADCS-ADL), or the Frontal Assessment Scale. Battery (FAB); neurophysiological assessment at week 0, including but not limited to TMS-EEG, long-term enhancement (LTP), and short-latency afferent inhibition (SAI); intensive phase, including 10 daily sessions of true or sham rTMS lasting two weeks; maintenance phase, including 22 weekly sessions of true or sham rTMS lasting 22 weeks; clinical assessment at week 12, including tests including but not limited to CDR, ADAS-Cog, FAB, MMSE, NPI, or ADCS-ADL; clinical assessment at week 24, including tests including but not limited to CDR, ADAS-Cog, FAB, MMSE, NPI, or ADCS-ADL; and neurophysiological assessment at week 24, including but not limited to TMS-EEG, LTP, and SAI. In some embodiments, reference is made to... Figure 6B The results of study design 602 include the average location of a TMS coil visualized on the patient's scalp on the template head, and biophysical modeling results based on the simulated induced electric field. In some embodiments, the average location of the TMS coil on the scalp is above the precuneus. In some embodiments, the biophysical modeling is electric field modeling. In some embodiments, the biophysical model is generated using precuneus rTMS. In some embodiments, additional imaging techniques (e.g., MRI) may be used to display the average stimulation location. In some embodiments, reference... Figure 6C The results of study design 603 include TMS-EEG data with temporal, spatial, and frequency mappings at target locations in one or more patients. In some embodiments, TMS-EEG data measurements are performed over 300 milliseconds (ms). In some embodiments, the temporal mapping is measured in microvolts per millisecond. In some embodiments, the frequency mapping is measured in hertz per millisecond. In some embodiments, TMS-EEG data collection is performed simultaneously across patients. In some embodiments, TMS-EEG data collection is performed at baseline to personalize rTMS intensity and target location for each patient. In some embodiments, TMS-EEG data collection is repeated at the end of the trial to observe longitudinal changes in brain reactivity following treatment.

[0043] Figure 7 provides an exemplary depiction 700 of post-treatment cognitive changes according to embodiments of this document. In some embodiments, the baseline is plotted at week zero, where the week is the average assessment time of the baseline measurement relative to the first dose of the test drug. In some embodiments, error bars indicate standard error. In some embodiments, reference... Figure 7A The depiction of cognitive changes includes a plot of the mean change in generalized linear mixture model (GLMM) estimates across three time points spanning 24 weeks, the plot including the rTMS line 701 and the spurious rTMS line 702. In some embodiments, the plot shows the mean change in the GLMM estimates relative to baseline on the Clinical Dementia Rating Scale Frame Summation (CDR-SB). In some embodiments, the score range is obtained by summing each of the domainbox scores, where the score range is 0 to 18. In some embodiments, a higher score indicates worse cognition. In some embodiments, the three time points are week 0, week 12, and week 24. In some embodiments, the asterisks include the vertical and horizontal averages of the rTMS and spurious rTMS lines. In some embodiments, reference is made to... Figure 7B The depiction of cognitive changes includes a graph of the mean change in GLMM estimates across three time points spanning 24 weeks, the graph including the rTMS line 701 and the spurious rTMS line 702. In some embodiments, the graph shows the mean change in GLMM estimates relative to baseline on the Alzheimer's Disease Assessment Scale-Cognitive 13 Item Scale (ADAS-Cog13). In some embodiments, the score range is 0 to 70. In some embodiments, a higher score indicates worse cognition. In some embodiments, the three time points are week 0, week 12, and week 24. In some embodiments, the asterisks include the vertical and horizontal averages of the rTMS and spurious rTMS lines. In some embodiments, reference... Figure 7C The depiction of cognitive change includes a graph of the mean change in GLMM estimates across three time points spanning 24 weeks, the graph including the rTMS line 701 and the spurious rTMS line 702. In some embodiments, the graph shows the mean change in GLMM estimates relative to baseline on the MMSE scale. In some embodiments, the rating range is 0 to 30. In some embodiments, a lower rating indicates poorer cognition. In some embodiments, the three time points are week 0, week 12, and week 24. In some embodiments, the asterisks include the vertical and horizontal averages of the rTMS and spurious rTMS lines. In some embodiments, reference... Figure 7DThe depiction of cognitive changes includes a graph of the average change in GLMM estimates across three time points spanning 24 weeks, the graph including the rTMS line 701 and the spurious rTMS line 702. In some embodiments, the graph shows the average change in GLMM estimates relative to baseline on the Frontal Functional Assessment Scale (FAB). In some embodiments, the score range is 0 to 18. In some embodiments, a higher score indicates better frontal cognitive function. In some embodiments, the three time points are week 0, week 12, and week 24. In some embodiments, the asterisks include the vertical and horizontal averages of the rTMS and spurious rTMS lines. In some embodiments, reference... Figure 7E The depiction of cognitive changes includes a plot of the mean change in GLMM estimates across three time points spanning 24 weeks, the plot including the rTMS line 701 and the pseudo-rTMS line 702. In some embodiments, the plot shows the mean change in GLMM estimates relative to baseline on the Alzheimer's Disease Collaborative Study Activities of Daily Living (ADCS-ADL) scale. In some embodiments, the score range is 0 to 78. In some embodiments, a lower score indicates poorer function. In some embodiments, the three time points are week 0, week 12, and week 24. In some embodiments, reference... Figure 7F The depiction of cognitive changes includes a graph of the mean change in GLMM estimates across three time points spanning 24 weeks, the graph including the rTMS line 701 and the pseudo-rTMS line 702. In some embodiments, the graph shows the mean change in GLMM estimates relative to baseline on the NPI scale. In some embodiments, the score range is 0 to 144. In some embodiments, a higher score indicates worse behavioral symptoms. In some embodiments, the three time points are week 0, week 12, and week 24.

[0044] Figure 8 provides an exemplary depiction 800 of post-treatment TMS-EEG results according to embodiments of this document. In some embodiments, reference is made to... Figure 8AThe TMS-EEG results include: a plot of TMS evoked potentials (TEPs) with error bars 801, a true rTMS line at cycle 0 803, a true rTMS line at cycle 24 802, a TMS pulse 804, and a DMN-p response activity interval 809; DMN-p response activity at cycle 0 in the first interval 805, DMN-p response activity at cycle 0 in the second interval 807, DMN-p response activity at cycle 24 in the first interval 806, and DMN-p response activity at cycle 24 in the first interval 808; a plot of TEPs with a false rTMS line 811 and error bars 810 at cycle 0, a false rTMS line 813 and error bars 812 at cycle 24, a TMS pulse 804, and a DMN-p response activity interval 819; and DMN-p response activity at cycle 0 in the first interval 815, and DMN-p response activity at cycle 0 in the second interval 815. DMN-p response activity 817, DMN-p response activity 816 at week 24 of the first interval, and DMN-p response activity 818 at week 24 of the first interval. In some embodiments, TEP is measured in microvolts. In some embodiments, the x-axis of the TEP plot is in milliseconds. In some embodiments, sham rTMS is any method designed to simulate the effect of true rTMS without actually stimulating the brain. In some embodiments, TEP is collected from DMN-p stimulation 24 weeks before (week 0) and after true or sham rTMS over DMN-p. In some embodiments, after 24 weeks, for sham rTMS patients, the amplitude of TEP induced by single-pulse TMS from DMN-p is reduced. In some embodiments, after 24 weeks, for true rTMS patients, the amplitude of TEP induced by single-pulse TMS from DMN-p is unchanged. In some embodiments, reference Figure 8B The TMS-EEG results include: a graph of DMN source activity with a true rTMS line 822 and error bar 821 at week 0, a true rTMS line 820 and error bar 823 at week 24, and a TMS pulse 804; source reconstruction 828 over the DMN at week 0 824 and week 24 825; and a graph of DMN source activity with a spurious rTMS line 831 and error bar 830 at week 0, a spurious rTMS line 832 and error bar 833 at week 24, and a TMS pulse 804. In some embodiments, spurious rTMS is any method designed to mimic the effects of true rTMS without actually stimulating the brain. In some embodiments, after 24 weeks, for spurious rTMS patients, the amplitude of DMN source activity induced by a single-pulse TMS from DMN-p is reduced. In some embodiments, after 24 weeks, for true rTMS patients, the amplitude of DMN source activity induced by a single-pulse TMS from DMN-p is unchanged.

[0045] Figure 9An exemplary depiction 900 of changes in brain oscillatory activity in the gamma band following treatment according to embodiments of this document is provided. In some embodiments, the depiction includes: TMS-related spectral perturbation (TRSP) after a single-pulse TMS 907 on DMN-p in patients receiving true rTMS at times 0 901 and 1 902, where 1 901 is after stimulation and 0 902 is before stimulation, with a difference 905; TRSP after a single-pulse TMS 907 on DMN-p in patients receiving sham rTMS at times 0 903 and 1 904, where 1 903 is after stimulation and 0 902 is before stimulation, with a difference 906; and a horizontal line 908 intersecting the letter γ on the y-axis is the natural γ frequency of 40 Hz; and a plot of event-related spectral dynamics (ERSP) including post-true rTMS 912, baseline true rTMS 913, post-sham rTMS 914, baseline sham rTMS 915, difference between post-true rTMS and baseline 910, and difference between post-sham rTMS and baseline 911. In some embodiments, ERSP is measured in spectral power (dB). In some embodiments, the shaded boxes in the ERSP plot show the differences between lines on any given plot. In some embodiments, TRSP is measured in Hertz per millisecond. In some embodiments, the y-axis of the TRSP plot shows the range of γ, β, α, and θ waves. In some embodiments, oscillatory activity increases after the true rTMS, especially in the β and γ bands, and oscillatory activity decreases slightly after the spurious rTMS. In some embodiments, there is a difference in the γ spectral power between the true rTMS and the spurious rTMS when directly compared to each other. In some embodiments, changes in the TRSP plot of the true rTMS 905 show increased activity. In some embodiments, changes in the TRSP plot of the spurious rTMS 906 show decreased activity. In some embodiments, the difference between the true and spurious rTMS in the ERSP plot is positive.

[0046] Figures 10-14 provide an exemplary TMS research scheme, research conditions, and research results. Figure 10 provides an exemplary TMS research scheme and research conditions 1000 according to embodiments of this document, in which various measurement results are provided. In some embodiments, the research protocol includes: an EEG 1001 comprising responses to transcranial gamma alternating current stimulation (γtACS) 1003, transcranial theta alternating current stimulation (θtACS) 1004, and intermittent theta burst stimulation (iTBS) 1002; a mapping of stimulation of selected brain regions 1005 using a tACS instrument 1006; mapping of the stimulated brain regions, including the left dorsolateral prefrontal cortex (DLPFC SX) 1009, the right dorsolateral prefrontal cortex (DLPFC DX) 1008, or the vertex 1007, or any combination thereof; and an experimental protocol comprising TMS-EEG 1015 prior to stimulation at T0, iTBS tACS stimulation 1016, TMS-EEG immediately after stimulation at T1 1017, and TMS-EEG 1018 20 minutes after stimulation at T2. In some embodiments, the EEG 1001 displays brain waves in Hertz per second. In some embodiments, θtACS is performed at 5 Hz. In some embodiments, γtACS is performed at 70 Hz.

[0047] Figure 11An exemplary depiction 1100 of changes in brain activity in certain brain regions after treatment according to scheme 1000 is provided. In some embodiments, changes in brain activity include changes in the left DLPFC 1101, which include: Figure 1102 of the TEP after a single-pulse TMS perturbation when iTBS and γtACS stimulation are used with spectral power representation 1105, and TEP graphical representations 1110, 1108, and 1109 for T0, T1, and T2; Figure 1103 of the TEP after a single-pulse TMS perturbation when iTBS and θtACS stimulation are used with spectral power representation 1106, and TEP graphical representations 1110, 1108, and 1109 for T0, T1, and T2; and Figure 1104 of the TEP after a single-pulse TMS perturbation when iTBS and sham tACS stimulation are used with spectral power representation 1107, and TEP graphical representations 1110, 1108, and 1109 for T0, T1, and T2. In some embodiments, changes in brain activity include changes in the right DLPFC 1115, which include: Figure 1116 of the TEP after a single-pulse TMS perturbation when iTBS and γtACS stimulation are used with spectral power representation 1119, and TEP graphical representations 1110, 1108, and 1109 for T0, T1, and T2; Figure 1117 of the TEP after a single-pulse TMS perturbation when iTBS and θtACS stimulation are used with spectral power representation 1120, and TEP graphical representations 1110, 1108, and 1109 for T0, T1, and T2; and Figure 1118 of the TEP after a single-pulse TMS perturbation when iTBS and sham tACS stimulation are used with spectral power representation 1121, and TEP graphical representations 1110, 1108, and 1109 for T0, T1, and T2. In some embodiments, changes in brain activity include changes in vertices 1125, which include: Figure 1126 of the TEP after a single-pulse TMS perturbation, and TEP graphical representations 1110, 1108, and 1109 for T0, T1, and T2, when iTBS and γtACS stimulation are used with spectral power representation 1129; Figure 1127 of the TEP after a single-pulse TMS perturbation, and TEP graphical representations 1110, 1108, and 1109 for T0, T1, and T2, when iTBS and θtACS stimulation are used with spectral power representation 1130; and Figure 1128 of the TEP after a single-pulse TMS perturbation, and TEP graphical representations 1110, 1108, and 1109 for T0, T1, and T2, when iTBS and sham tACS stimulation are used with spectral power representation 1131. In some embodiments, θtACS is performed at 5 Hz. In some embodiments, γtACS is performed at 70 Hz.In some embodiments, the amplitude of the TEP increases after a single-pulse TMS perturbation with iTBS plus γtACS compared to iTBS plus θtACS or iTBS plus sham tACS. In some embodiments, the increase is present in the left DLPFC, but not in the right DLPFC or the vertex.

[0048] Figure 12 An exemplary depiction 1200 of changes in brain activity in the left DLPFC in certain brain regions following treatment according to scheme 1000 is provided. In some embodiments, changes in brain activity include spectral perturbation images comprising: iTBS plus γtACS 1201, 1202, and 1203 and shaded scale 1204 at T0, T1, and T2; iTBS plus θtACS 1205, 1206, and 1207 and shaded scale 1204 at T0, T1, and T2; iTBS plus pseudotACS 1208, 1209, and 1210 and shaded scale 1204 at T0, T1, and T2; natural frequency power analysis plot 1215 having iTBS plus γtACS line 1216, iTBS plus θtACS line 1218, and iTBS plus pseudotACS line 1217; and natural frequency shift analysis plot 1220 having iTBS plus γtACS line 1216, iTBS plus θtACS line 1218, and iTBS plus pseudotACS line 1217. In some embodiments, the spectral perturbation plot measures frequency in Hertz per millisecond. In some embodiments, the shading scale 1204 ranges from 0 microvolts squared (μV). 2 ) to 2μV 2 In some embodiments, the natural frequency power analysis 1215 measures the percentage change at T0, T1, and T2. In some embodiments, the natural frequency shift analysis 1220 measures the frequency change at T0, T1, and T2. In some embodiments, θtACS is performed at 5 Hz. In some embodiments, γtACS is performed at 70 Hz. In some embodiments, the effect of iTBS plus γtACS is visible in spectral perturbations and oscillatory activity. In some embodiments, iTBS plus γtACS increases the spectral power of the local response in the γ band compared to iTBS plus θtACS or iTBS plus sham tACS having no visible effect.

[0049] Figure 13An exemplary description 1300 of the lack of changes in brain activity in the right DLPFC after treatment according to protocol 1000 is provided. In some embodiments, the changes in brain activity include a spectral perturbation image 1301 comprising: iTBS plus γtACS at T0, T1, and T2; iTBS plus θtACS at T0, T1, and T2; iTBS plus sham tACS at T0, T1, and T2; a natural frequency power analysis plot 1302 having iTBS plus γtACS lines 1304, iTBS plus θtACS lines 1305, and iTBS plus sham tACS lines 1306; and a natural frequency shift analysis plot 1303 having iTBS plus γtACS lines 1304, iTBS plus θtACS lines 1305, and iTBS plus sham tACS lines 1306. In some embodiments, the spectral perturbation plot measures frequency in Hertz per millisecond. In some embodiments, the shading scale ranges from 0 microvolts squared (μV). 2 ) to 2μV 2 In some embodiments, natural frequency power analysis 1302 measures the percentage change at T0, T1, and T2. In some embodiments, natural frequency shift analysis 1303 measures the frequency change at T0, T1, and T2. In some embodiments, θtACS is performed at 5 Hz. In some embodiments, γtACS is performed at 70 Hz. In some embodiments, no visible spectral power change is observed above the right DLPFC.

[0050] Figure 14 An exemplary description 1400 is provided of the lack of changes in brain activity at the peak after treatment according to scheme 1000. In some embodiments, changes in brain activity include a spectral perturbation image 1401 comprising: iTBS plus γtACS at T0, T1, and T2; iTBS plus θtACS at T0, T1, and T2; iTBS plus sham tACS at T0, T1, and T2; a natural frequency power analysis plot 1402 having iTBS plus γtACS lines 1404, iTBS plus θtACS lines 1406, and iTBS plus sham tACS lines 1405; and a natural frequency shift analysis plot 1403 having iTBS plus γtACS lines 1404, iTBS plus θtACS lines 1406, and iTBS plus sham tACS lines 1405. In some embodiments, the spectral perturbation plot measures frequency in Hertz per millisecond. In some embodiments, the shading scale ranges from 0 microvolts squared (μV). 2 ) to 2μV 2In some embodiments, natural frequency power analysis 1402 measures the percentage change at T0, T1, and T2. In some embodiments, natural frequency shift analysis 1403 measures the frequency change at T0, T1, and T2. In some embodiments, θtACS is performed at 5 Hz. In some embodiments, γtACS is performed at 70 Hz. In some embodiments, there is no visible spectral power change above the apex.

[0051] Personalized TMS targeting and intensity adjustment

[0052] In some embodiments, knowledge of the pathophysiology of the disease can be used to optimize spatial targeting (i.e., the corresponding location or placement of the brain stimulation target and stimulation coil on the scalp) or stimulation parameters, including but not limited to intensity, frequency, or waveform, to maximize the effect of rTMS on Alzheimer's patients.

[0053] Target identification methods

[0054] Figure 1 provides an exemplary method for identifying stimulus hotspots based on (i) structural and functional imaging data collected in patients with Alzheimer's disease and further refined by (ii) a "TMS-EEG functional search" based on TMS evoked potentials (TEPs). In some embodiments, the method is used to identify network-level targets or personalizations via TMS-EEG functional search. In some embodiments, biophysical modeling may be applied to stimulus intensity tuning. In some embodiments, identifying network-level targets or personalizations via TMS-EEG functional search is, for example, a two-step process. In some embodiments, the first step includes computing a group-level mapping of the morphology of the default pattern network (DMN) characterizing AD patients. Since the precuneus is the most tightly connected node in the DMN, further spatial optimization can be performed in some embodiments by using other major nodes of the DMN as seed regions for seed-based connectivity analysis to estimate their connectivity with the precuneus and identifying local maxima of intra-network connectivity within the precuneus region. In some embodiments, further spatial optimization can be repeated for each node (e.g., left angular gyrus, right angular gyrus, left medial prefrontal cortex, right medial prefrontal cortex, left temporal lobe (including but not limited to the hippocampus), right temporal lobe (including but not limited to the hippocampus)) to generate multiple connectivity maps. In some embodiments, these maps can then be combined to generate a “precuneus-weighted mask” that highlights the subregions of the precuneus that are most closely connected to the rest of the network. In some embodiments, stimulation over these subregions can provide the highest probability of local activity propagating throughout the DMN when the precuneus is stimulated. In some embodiments, the local maximum of the highest functional connectivity can be selected and its shortest path to the cortex / scalp can be projected to guide the placement of the TMS coil on the scalp. As described herein, the methods described are merely one solution for identifying the most suitable candidate nodes or subregions therein for stimulation. As a non-limiting example, other methods may include estimating the average node degree of each region, its path length, its controllability within the framework of network control theory, its evolutionary dynamics, or utilizing other scans to derive structural, metabolic, perfusion, or diffusion information for each brain region or network of interest, or any combination thereof.

[0055] In some embodiments, the second step includes each patient receiving a series of single-pulse TMS, for example, 60 pulses, over the precuneus identified based on fMRI data as described herein. In some embodiments, the patient receives a series of single-pulse TMS simultaneously with EEG recording via a 64-channel EEG system. In some embodiments, a neuronavigation system coupled to an infrared camera may be used to continuously monitor the TMS coil position. In some embodiments, the data may be preprocessed to remove artifacts and filtered to identify early-onset TMS evoked potentials (TEPs) that reflect local neuronal activity in response to each TMS pulse. As described herein, in some embodiments, target optimization may be achieved by performing a grid search over an area of ​​approximately 5 × 5 cm around a stimulation target defined by the original fMRI and by directly visualizing the TEPs after each TMS delivery, allowing for immediate examination of the amplitude and shape of each TEP and identification of its spatial location within the precuneus, thereby generating the highest response to TMS for each patient (“TMS-EEG functional search”). In some embodiments, selecting a TMS location in the brain can be done by selecting a brain region that provides the highest TEP amplitude locally (i.e., in the stimulation area, as measured by TMS-EEG) and in other nodes of the default pattern network located in the frontal and temporoparietal cortex of the brain (including, but not limited to, the medial frontal cortex, dorsolateral prefrontal cortex, angular gyrus, and middle temporal lobe). A TMS location in the precuneus that provides the highest balance between brain activity induction in the precuneus and other nodes of the default pattern network is selected as a stimulation target, with the aim of inducing maximum network-level activation above a local response in the precuneus.

[0056] Personalization of stimulus intensity

[0057] Figure 1BAn exemplary method is provided for defining the intensity applied during a repetitive TMS visit to a TMS-based metric collected at baseline for each patient, particularly (i) a combination of the resting-state motor threshold calculated via TMS of the right-hand representative in the left motor cortex and (ii) TMS-evoked potentials collected during a single-pulse TMS in the precuneus. In some embodiments, an ad-hoc algorithm may be used to weigh the different metrics to ultimately estimate the optimal personalized stimulation intensity for each patient's rTMS treatment session. In some embodiments, data for calculating the stimulation intensity may be collected during a separate TMS-EEG visit on a different day than the first rTMS visit. In some embodiments, a first step in personalizing the stimulation intensity includes calculating the resting motor threshold calculated based on the left motor cortex. In some embodiments, the target of the first step is the right-hand representative from the left motor cortex. In some embodiments, the stimulation intensity may be set to 100% of the resting motor threshold (RMT), defined as the minimum intensity that produces a motor evoked potential (MEP) greater than 50 μV in at least five out of ten trials of the relaxed right first dorsal interosseous (FDI) muscle.

[0058] In some embodiments, the second step of personalizing stimulation intensity includes using data collected during a TMS-EEG session to perform a TMS-EEG function search (“…”). Opti-Search The location of the TMS target is optimized based on the process. In some embodiments, evoked activity after sessions of more than two TMS pulses can be averaged to reveal TMS evoked potentials (TEPs) with different latencies between 5 ms and 500 ms after TMS. The TEP amplitude can be calculated for each patient. In some embodiments, the calculated amplitude can be used as a representative of an individual's responsiveness to TMS and thus as an indicator of excitability and responsiveness of the precuneus. In some embodiments, the TEP amplitude can then be used to correct for the intensity of stimulation obtained from stimulation of the motor cortex (i.e., RMT), with the aim of adjusting the TMS stimulation intensity based on the TEP.

[0059] In some embodiments, the third step of personalizing the stimulation intensity includes performing additional analysis to estimate the induced electric field above the TMS target. In some embodiments, this estimation may use a realistic volumetric conductor head model generated using MRI images and segmentation from a validation dataset, and based on anisotropic conductivity values ​​for each tissue class, expressed in S / m. In some embodiments, the resulting mesh set, which integrates gray and white matter, scalp, bone, or cerebrospinal fluid, may be used to calculate the electric field distribution for a particular TMS coil design, taking into account the coil-to-scalp distance and coil current. In some embodiments, the estimated electric field may be used to (a) retrospectively calculate individual differences in the amount of current reaching the target area and interpret differences in response to treatment, or (b) adjust the stimulation location or intensity such that all participants receive the same amount of induced cortical stimulation.

[0060] Personalization of stimulation frequency

[0061] Figure 1B Exemplary methods are provided for determining the frequency of stimulation during repetitive TMS visits by combining TMS-based measures collected for each patient at baseline. In some embodiments, the specific TMS-based measure is a TMS-EEG-evoked oscillation collected during a single-pulse TMS assessment of the precuneus. In some embodiments, ad-hoc algorithms may be used to weigh different measures to ultimately estimate the optimal personalized stimulation frequency for each patient's rTMS treatment session. In some embodiments, data for calculating the stimulation frequency may be collected during a separate TMS-EEG visit performed on a different day than the first rTMS visit.

[0062] In some embodiments, the first step of personalizing the stimulation frequency includes optimizing the location of the TMS target based on a TMS-EEG function search process using data collected during a TMS-EEG session. In some embodiments, TMS-evoked activity may be averaged after a session of more than 10 TMS pulses. In some embodiments, time or frequency analysis is performed in the period from 1 second before to 1 second after the TMS pulse to assess TMS-evoked oscillatory activity.

[0063] In some embodiments, the second step of personalizing the stimulation frequency includes calculating the Event Correlation Spectral Perturbation (ERSP) value based on Morlet wavelets to analyze the individual's time-frequency domain response. For example, in some embodiments, calculating the ERSP value includes convolving a mother wavelet across 100 linearly spaced frequencies spanning 5 to 50 Hz with 3.5 periodic wavelets and a 0.5 scaling factor. In some embodiments, determining the peak value of a single frequency for each subject at each stimulation site may include, but is not limited to, analyzing the global ERSP (gERSP) response by summing the power values ​​of each frequency within a 20-200 ms time window and then determining which frequency has the highest value. For example, in this particular case, the maximum frequency is not driven solely by a single frequency with a very high peak value, but may be captured by a frequency with a moderate but sustained response that is larger than in other frequency bands.

[0064] TMS Optimization Method Based on Limited Data

[0065] Target identification using standardized brain network templates

[0066] As described herein, in some embodiments, when only partially available brain scans of the patient are available, canonical network templates can be used to apply systems and methods for treating AD via rTMS. In some embodiments, if only structural brain scans are available, templates representing brain networks and regions of interest can be used via spherical or linear registration algorithms that adapt the morphology of the target network or region to the individual brain anatomy. Figure 2A Exemplary methods are provided for accurately identifying regions such as the precuneus and networks such as the DMN, for example, to provide initial coil placement guidance for subsequent functional searches via TMS-EEG. In some embodiments, the methods described herein also utilize ad-hoc mappings of highlighted rTMS stimulation targets created by the inventors based on their accumulated knowledge of the pathophysiology of AD and dementia. Figure 2B Exemplary graphs of pattern activation during specific cognitive tasks are provided. In some embodiments, the graphs include, but are not limited to, information on pattern activation, atrophy, low metabolism, protein accumulation, or functional connectivity breakdown during specific cognitive tasks.

[0067] Target identification using scalp-based inference and prescriptive brain scans

[0068] Figure 2CExemplary systems and methods are provided for treating AD via rTMS using scalp-based navigation when functional or structural brain scans are unavailable for the patient. In some embodiments, reference points include, but are not limited to, the locations of the bilateral tragus, occipital protuberance, and nasal root points on the patient's head, and their distances measured with a tape measure. In some embodiments, half the distance between the occipital protuberance-nasal root point and the tragus-tragus distance is calculated, where the intersection of the two points determines the location of the scalp apex. In some embodiments, the approximate locations of the EEG electrode positions can be derived using the international 10 / 20 or 10 / 10 EEG reference system. In some embodiments, the locations of the precuneus and posterior cingulate cortex regions can be estimated based on canonical functional MRI data from a dataset of AD patients. In some embodiments, the dataset provides the average location of the scalp sites corresponding to the shortest linear trajectories of the precuneus connections in the template AD brain. In some embodiments, the scalp locations are then expressed as a percentage of distance to the apex and other scalp reference points (e.g., the occipital protuberance). In some embodiments, a percentage of distance corresponding to the average optimal cortical site for TMS targeting the precuneus region is then identified on the patient's scalp. In some embodiments, the same process described herein can be applied to other brain regions.

[0069] Target identification when TMS-EEG data is missing

[0070] Figure 3 Exemplary systems and methods are provided for treating Alzheimer's disease (AD) via rTMS using a patient's brain scan when TMS-EEG data is unavailable and the Opti-Search procedure cannot be completed. In some embodiments, a canonical dataset of AD patients collected by the inventors, including but not limited to individual brain scans, EMG, EEG, and TMS-EEG data, can be used to create a set of reference measurements, including but not limited to scalp-cortical distance estimated based on structural brain scans, corticospinal excitability, TMS-EEG responsiveness, or the induced electric field estimated based on the range of coil location and stimulus intensity. In some cases, regression models can be used to estimate the stimulus intensity to be used on the patient's precuneus, based on the relationship between the average intensity of precuneus stimulation used in the AD patient dataset and its corresponding other measures (e.g., cortical excitability, scalp-cortical distance, TMS-EEG responsiveness). For example, in some embodiments, a regression model based on an AD patient database can indicate the potential for stimulation intensity to be applied to the patient's precuneus by calculating corticospinal excitability values. * β1, scalp-cortex distance * β2, the induced electric field in the precuneus * The sum of β3 is used to estimate the stimulation intensity of the precuneus in AD patients, where β1-2-3 are regression β values ​​calculated based on a normalized dataset of AD patients.

[0071] Target identification based on subcortical networks using TMS

[0072] Figure 4 illustrates an exemplary method that includes using structural and functional brain scans to estimate the connectivity between subcortical targets and cortical regions that can be targeted by non-invasive brain stimulation. In some embodiments, this method is used to iteratively test the connectivity strength and distribution of each cortical target and to identify the optimal superficial region to stimulate to maximize the chance of activation of the subcortical target. In some embodiments, as described herein, identifying patient-specific brain activation during memory tasks in patients with Alzheimer's disease highlights the involvement of the default mode network. In some embodiments, the method includes, but is not limited to, identifying the optimal cortical target for hippocampal modulation using structural and diffusion MRI data used to visualize white matter fiber bundles connecting stimulated superficial regions (e.g., the angular gyrus) to known subcortical targets of interest in Alzheimer's disease (e.g., the hippocampus, because of its role in memory processing). In some embodiments, white matter fibers can be estimated based on multiple subregions of the left angular gyrus, wherein the subregion showing the strongest white matter fiber pathway toward the hippocampus is selected as the final personalized target. The pathophysiology of AD is characterized by alterations in cortical and subcortical brain structures, the latter containing key regions such as the hippocampus and amygdala. However, while non-invasive brain stimulation methods can effectively modulate cortical activity, they may not be able to directly target subcortical areas. Therefore, the method described in this paper utilizes network neuroscience principles and methods derived from modern connectomics analysis to hypothesize the possibility of accessing desired deep targets by stimulating strongly connected cortical areas. In some cases, this could stimulate areas such as the hippocampus to induce changes in memory performance or increase local metabolism.

[0073] Data processing methods

[0074] In some embodiments, data processing can be performed using custom code in languages ​​such as Python and Matlab. In some embodiments, data processing includes, but is not limited to, solutions for automated cleaning or preprocessing, as well as semi-automated solutions with the possibility of manual artifact identification. In some embodiments, processing and analysis can be performed as part of a separate module, encompassing: (i) data collection; (ii) data validation and format conversion; (iii) data cleaning or preprocessing; (iv) data analysis; or (v) detailed report creation, including, but not limited to, a summary of optimal stimulation targets and parameters, and processing steps.

[0075] Brain scan processing

[0076] In some embodiments, brain scans for brain region targeting may include information about: (i) structural features of the brain, including but not limited to density, volume, thickness, folds, sulcus depth, CSF distribution, white matter diffusion rate, and anisotropy or spectral distribution of neurotransmitters, or any combination thereof; and (ii) functional features of the brain, including but not limited to hemodynamic response, blood perfusion, metabolic activity (e.g., glucose consumption), or protein load. In some embodiments, steps for preparing brain scans for statistical analysis include, but are not limited to: converting individual images to a 3D volumetric format; segmentation of brain tissue classification; spatial and temporal filtering; elimination of physiological noise; removal of image artifacts; extraction of mean or time series of brain activity; co-registration to a common anatomical or functional template for group-level analysis; or, in the case of block-fMRI data, calculation of evoked activity when multiple scan conditions are present. In some embodiments, both voxel-based volumetric data and vertex-based surface images may be subsequently analyzed, and may include, but are not limited to, masking of clean data based on anatomical or functional atlases describing relevant networks or brain regions that can be targeted by TMS.

[0077] EEG Data Processing

[0078] In some embodiments, EEG data collected before, during, and after a single TMS pulse during a TMS-EEG recording session can be processed to prepare for data analysis and selection of the optimal TMS target. In some embodiments, the steps include, but are not limited to: raw data conversion to .edf format; trimming the raw data to predefined length periods containing segments capturing brain activity before and after TMS; normalizing post-TMS activity by subtracting the average signal amplitude of the EEG data collected before TMS; automated or semi-automated data inspection to identify EEG channels with excessive noise or artifacts; zero-padding of activity contemporaneous with a single TMS pulse to remove early signal attenuation and muscle artifacts caused by the TMS pulse (based on voltage-based thresholds, kurtosis, and joint probabilities); independent component analysis (ICA) to identify and remove components including high-amplitude electrodes induced by early TMS, and principal component analysis (PCA). Further data reduction is performed using the first ICA step to minimize overfitting and noise components; the previous zero-fill signal is interpolated across the TMS pulse; bandpass filtering is performed using a forward-backward filter typically between 1 and 150 Hz; notch filtering is performed to account for line noise; a global average is referenced; a second ICA step is performed to manually remove all remaining artifact components, including eye movement / blinking, muscle noise (EMG), single-electrode noise, TMS-induced muscle activity, cardiac signal (EKG), and auditory evoked artifacts (artifacts are identified and labeled based on their spectral frequency distribution, power spectrum, amplitude, scalp morphology, and time progression); machine learning and deep learning algorithms are applied to the identification of residual artifacts; and interpolation is performed on the previously removed electrodes.

[0079] In some embodiments, the order of the steps described herein may be varied, and specific adjustments may be made at least in part based on individual brain properties or pathology-specific artifacts and signal characteristics. Features to be considered include, but are not limited to, for example, the level of cortical atrophy affecting the induced electric field in the brain; known alterations in neurotransmitter activity affecting the amplitude and shape of specific evoked potentials; increased movement and muscle activation during EEG recording; and elevated levels of oscillatory activity in the EEG due to drowsiness or a general slowing of brain activity typical of patients with ADD.

[0080] Infrastructure

[0081] Figure 5 Exemplary infrastructure systems and methods are provided that allow for steps such as stimulation target and parameter optimization, data storage and processing, or treatment delivery, or any combination thereof. In some embodiments, the processing flow may include, but is not limited to, the following steps:

[0082] 1) The patient receives a prescription for rTMS treatment from a clinician;

[0083] 2) Arrange for patients to undergo target-specific and optimized sessions;

[0084] 3) The patient completes a brain scanning session to identify and characterize the morphology of target brain networks or regions;

[0085] 4) Process the brain scan and identify the first mask in the precuneus region;

[0086] 5) During individual visits, collect EMG, TMS, or EEG data from the precuneus region previously identified by brain scans;

[0087] 6) The results are streamed to a data processing unit or platform for data cleaning and analysis to further identify the optimal target for stimulation;

[0088] 7) Clinicians or operators receive information about the patient's individualized target and stimulation parameters and schedule treatment sessions;

[0089] 8) The patient receives rTMS treatment for the prescribed duration; data from each visit, including but not limited to neuronavigation data, is stored locally in the infrastructure and in a remote cloud system;

[0090] 9) Patients should complete a TMS-EEG visit during treatment to monitor disease progression and treatment response;

[0091] 10) Store data and use the analytics platform to further optimize treatment based on aggregated data and group-level insights into treatment effectiveness.

[0092] In some embodiments, these steps may be combined with or replaced by any suitable steps of other methods disclosed herein. In some cases, for example, starting from step #3, clinical staff may access a URL link and use a service (e.g., Auth0) to verify their user account. The clinical staff may then set up the TMS-EEG device and pair it with a web browser. In some embodiments, following this pairing, data and requests, including but not limited to raw EEG data or survey metadata (e.g., demographic information, brain scan data, neuronavigation parameters), can be sent from the EEG to the backend via an application programming interface (API). This data and requests are then routed through a gateway ingress and gateway.

[0093] In some embodiments, the API is configured to have a set of primary actions from the gateway, which include, but are not limited to:

[0094] 1. Streaming - The ability to query and stream data (e.g., EEG time series data); for example, note that streaming includes, but is not limited to, encrypting and decrypting data using encryption keys through a service like Amazon Key Management Service (KMS).

[0095] 2. Data Collection - The ability to collect, clean, analyze, and process raw EEG data, for example, with the aim of receiving EEG feedback and returning it to a specific location in the brain for stimulation.

[0096] 3. Objects - The ability to access and query the database (MongoDB).

[0097] 4. Scheduling – The ability to book and publish patient schedules or appointments.

[0098] 5. Billing - The ability to access billing information and charge a third-party service provider (e.g., Stripe, Inc.) for each session.

[0099] 6. Notifications - The ability to send emails to users after certain actions are completed (e.g., a session has ended, billing has been successful, or a targeting result is ready).

[0100] 7. Data Export – The ability to request and create data packets or download information from a database. This is particularly useful, for example, for tracking sessions over a period of time and measuring symptom improvement.

[0101] 8. Baseline Users - The ability to conduct baseline checks on users through survey tools such as, but not limited to, the digital mini mental health check (MMSE) or the ability to publish information from electronic health records (HER) into the survey itself.

[0102] In some cases, data from the database can be formatted to be compatible with FHIR, a standard for the electronic exchange of healthcare information. In some cases, data can be published directly from the database to the EHR using an API. In some cases, the entire architecture (i.e., infrastructure) described herein, or any part of it, can be run locally or in the cloud via Docker containers. Servers and clusters can be launched relative to geographic location (e.g., India versus the United States). This is important for data provisioning to comply with GDPR, including, for example, keeping Indian data in India or European data in Europe. In some cases, the database can search for users and can delete user data upon request to comply with GDPR and other privacy laws.

[0103] Database for treatment optimization

[0104] In some embodiments, the architecture described herein can be used to store brain scan, EEG, EMG, and TMS data; individual patient characteristics (including but not limited to clinical, demographic, and cognitive profiles); optimal parameters for stimulation; or optimal parameters for group-level analysis and further optimization of TMS-based interventions, or any combination thereof. In some embodiments, machine learning and deep learning solutions (e.g., convolutional neural networks and clustering algorithms) can be used to search the database and identify, for example, individual data features related to treatment response, clusters of individuals with similar stimulation parameters, clusters of individuals with similar treatment responses, estimate dose-response curves, and build predictive models of disease progression.

[0105] Example use cases

[0106] Below are two examples of potential applications of the systems and methods described above. The first is the direct application of the proposed rTMS parameter personalization solution to the treatment of AD patients, reporting the results of a phase 2 randomized, double-blind, placebo-controlled clinical trial that demonstrated the efficacy of rTMS therapy targeting the precuneus in a sample of 50 AD patients.

[0107] T was also reported 2 Examples of formulations (i.e., combinations of TMS and other interventions). Results from studies demonstrating the effects of rTMS intervention combined with plasticity-induced brain stimulation (i.e., tACS) show increased efficacy of the combined solution in modulating brain activity and responsiveness.

[0108] Example

[0109] Data collected by the inventors demonstrate the safety, feasibility, and effectiveness of a long-term treatment process using a novel approach of rTMS targeting altered functional networks in Alzheimer's disease (AD), particularly the DMN and precuneus (DMN-p), which yielded clinical and cognitive outcomes in a phase 2, double-blind, randomized, placebo-controlled clinical trial involving 50 patients with AD. Details of the clinical trial report, information on the intervention and targeting procedures, and methods for personalized treatment based on TMS-EEG and brain scan analysis are provided below. The methods, experimental design, and other aspects of the examples below are not intended to be limiting in any way.

[0110] Example 1: Experimental Design Using rTMS

[0111] Figure 6 illustrates the exemplary study design described herein, comprising a single-center, randomized, sham-controlled, double-blind trial of rTMS versus DMN-p in patients with mild to moderate AD. Following recruitment and baseline assessment, patients were randomized 1:1 to receive either true or sham DMN-p-rTMS, in addition to their stable drug regimen of acetylcholinesterase inhibitor therapy. All treatments were administered for 24 weeks without interruption. The trial comprised a 24-week treatment period followed by a 2-week intensive chemotherapy regimen in which the rTMS of DMN-p was administered daily, five times weekly (W1 and W2) (Monday through Friday), followed by a maintenance phase in which the same stimulation was administered weekly for 22 weeks (W3–W24).

[0112] Each rTMS session consisted of forty 2-second pulses delivered at 20 Hz, with 28-second no-stimulation intervals (total stimulation: 1600), for a total duration of approximately 20 minutes. A total of 51,200 pulses were delivered to each patient over the entire 24-week period. rTMS was performed using a magnetic stimulator connected to a figure-eight coil.

[0113] Validity assessments were performed on enrolled patients and caregivers at baseline (week 0 (W0)) and repeated at week 12 (W12) and week 24 (W24) (or upon early termination) by raters unaware of the assigned group. Investigators, patients, and their caregivers were also unaware of the assignment. Adverse events (AEs), vital signs, and physical and neurological examinations were recorded at each clinical visit (or early termination). Patient safety was monitored by an independent data monitoring committee in accordance with its bylaws.

[0114] Primary and secondary outcome measures

[0115] The primary outcome measure was the change in the Clinical Dementia Rating Scale Frame Sum (CDR-SB) score over 24 weeks post-baseline (CDR-SB scores range from 0 to 18, with higher scores indicating poorer cognitive and daily functioning). The intention-to-treat analysis set included all patients with post-baseline power data. Secondary outcome measures were:

[0116] ● Changes in the Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) 11 at 24 weeks compared to baseline

[25] ;

[0117] ● Changes in MMSE score at 24 weeks compared to baseline;

[0118] ● Changes in daily living activities (ADCS-ADL) at 24 weeks compared to baseline

[26] ;

[0119] ● Changes in frontal lobe function assessment scale (FAB) at 24 weeks compared to baseline

[27] ;

[0120] ● Changes in the Neuropsychiatric Inventory (NPI) at 24 weeks compared to baseline

[28] .

[0121] TMS-EEG was used as a marker of cortical excitability, plasticity, and responsiveness by analyzing TMS-evoked potentials (TEPs) and oscillatory activity, which was measured as TMS-related spectral perturbations (TRSPs) evoked by a single-pulse TMS applied via DMN-p during simultaneous EEG recording. Source reconstruction analysis was performed to determine the spatial distribution of TMS-EEG-evoked activity around the stimulation area.

[0122] Sample size estimation and statistical analysis

[0123] Based on power calculations from previous studies, a total of 50 randomly assigned patients were planned, 25 per group, for a study evaluating the effects of two weeks of DMN-p-rTMS on cognitive function in a small sample of AD patients. Given the effect size of memory performance scores observed in the pilot study at 2 weeks of treatment (equal to 0.39, obtained as a post-pre means relative to the pooled standard deviation), it is reasonable for the current study, with a treatment duration ten times longer than the pilot study, to have an effect size at least twice that of the pilot study (i.e., approximately 0.75). With this effect size, using a two-tailed paired Wilcoxon signed-rank with a Type I error α = 0.05 and a reasonable correlation of 0.7 between the prognostic and post-prognostic measures, the minimum sample size to reach the power of 0.8 was estimated to be n = 17; and at most n = 23 to ensure the power of 0.9. The choice of N = 50 (25 per group) also ensured a sufficient size for the within-group analysis.

[0124] Adaptive randomization of covariates was performed and assigned by statisticians working in independent institutions. The normality assumption of the endpoint variables was assessed by testing the distribution plots and the Kolmogorov-Smirnov and Shapiro-Wilk tests.

[0125] Statistical analysis

[0126] The endpoints of each group were longitudinally assessed using a generalized linear mixed model (GLMM) with repeated measures of random intercept and random slope to account for individual differences at baseline and to assess individual changes through follow-up. GLMM was applied to CDR-SB and other power outcome measures, ADAS-Cog11, MMSE, ADCS-ADL, FAB, and NPI as dependent variables, and the interactions of “group,” “time,” and “group × time” were independent factors.

[0127] sample

[0128] Eighty-six patients were screened, and 50 were randomly assigned to either group. The mean age of the total patient sample was 73.7 years (SD = 6.6, range 62–84), and 52% were female. The mean raw MMSE score at baseline was 21.3 (SD = 2.5). There were no differences in baseline patient demographics and clinical characteristics between the DMN-p-rTMS and sham rTMS groups. Five patients withdrew from the trial before its end (3 in the DMN-p-rTMS group and 2 in the sham rTMS group). A total of 45 patients (90%) completed the treatment period. On average, there was no change in the mean number of rTMS sessions completed during the 24-week period for AD patients assigned to either experimental group (DMN-p-rTMS: 30.2; sham rTMS: 30.5).

[0129] patient characteristics

[0130] Patients were eligible if they met the following criteria: had been diagnosed with possible mild to moderate Alzheimer's disease (AD) according to the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association criteria; were >50 ≤85 years old; had a Clinical Dementia Scale (CDR) score of 0.5–1 and a Mini Mental State Examination (MMSE) score of 18–26 at screening, indicating mild to moderate AD; had a caregiver; had been on acetylcholinesterase inhibitor therapy for at least 6 months; and had undergone a lumbar puncture for diagnostic purposes to analyze cerebrospinal fluid biomarkers. Patients underwent medical and neurological evaluations, including magnetic resonance imaging (MRI) or computed tomography (CT) scans. Patients with extrapyramidal symptoms, a history of stroke, other neurodegenerative conditions, psychotic disorders, or who had received antipsychotic, anti-Parkinson's disease, anticholinergic, or antiepileptic medications within six months prior to enrollment were excluded. This trial was approved by the Santa Lucia Foundation Review Board and Ethics Committee and conducted in accordance with the principles of the Declaration of Helsinki and the International Conference on Harmonization Good Clinical Practice guidelines. Written informed consent was provided by all patients or their parents or legal representatives. Patients could withdraw at any time without any bias. This report follows the CONSORT guidelines for reporting randomized studies.

[0131] result

[0132] Security

[0133] Eight participants reported adverse events (AEs), seven in the active DMN-p-rTMS group and one in the sham rTMS group. All events were mild and most were relieved on the day they occurred, with little or no action taken (mild headache (n=3), scalp / skin discomfort (n=4), neck pain / stiffness (n=3), and fatigue (n=2)).

[0134] Cognitive and clinical outcomes

[0135] The mean baseline CDR-SB total score was 4.1 (SD = 1.8) in the DMN-p-rTMS group and 4.6 (SD = 1.5) in the sham rTMS group. There was a significant difference in cognitive performance as measured by the CDR-SB total score between the active rTMS group and the sham group (baseline vs. week 24). The GLMM of repeated measures of CDR-SB scores showed significant results regarding differences in the group (p = 0.038) versus time × group (p = 0.009) interaction, with patients receiving sham rTMS showing a general decline in cognitive performance over time, which was not evident in the DMN-p-rTMS group. Figure 7A An exemplary depiction of the results is provided, where the mean change (baseline–week 24) of the GLMM estimated CDR-SB score in the DMN-p-rTMS group was -0.25 (95% confidence interval (CI) [-4.8, 4.3]) and -1.42 (95% CI [-6.0, 3.3]) in the sham rTMS group. The responder rate (defined as the percentage of patients whose CDR-SB score improved by 1 point) was 68.2% in the DMN-p-rTMS group and 34.7% in the sham group.

[0136] Secondary clinical outcome analysis showed significant longitudinal differences in ADAS-COG11, MMSE, and ADCS-ADL scores between the DMN-p-rTMS and sham rTMS groups, but no differences in FAB and NPI scores. The mean baseline total ADAS-Cog11 score was 22.6 (SD = 7.4) in the DMN-p-rTMS group and 24.8 (SD = 6.5) in the sham rTMS group. GLMM (adjusted for age and education level) of the repeatability measure of ADAS-Cog11 score showed significant time-×group interaction (p = 0.035). Figure 7B An exemplary depiction of the results is provided, in which the mean change (baseline-week 24) of the GLMM estimate of the ADAS-Cog11 score for the DMN-p-rTMS was -0.67 (95% confidence interval (CI) [-21.5, 20.2]) and -4.2 (95% CI [-25.1, 16.6]) for the spurious rTMS group.

[0137] The mean baseline total MMSE score was 21.2 in the DMN-p-rTMS group (SD = 2.7) and 21.5 in the sham rTMS group (SD = 2.4). GLMM (adjusted for age and education level) of the repeated measure of MMSE scores showed significant results in terms of time × group interaction (p = 0.048). Figure 7CAn exemplary depiction of the results is provided, where the mean change (baseline-week 24) in the GLMM estimate of the MMSE score for the DMN-p-rTMS group was 0.24 (95% confidence interval (CI) [-6.5, 7.0]) and 1.8 (95% CI [-5.1, 8.8]) for the sham rTMS group. The baseline mean ADCS-ADL total score was 58.6 (SD = 9.7) for the DMN-p-rTMS group and 58.3 (SD = 9.7) for the sham rTMS group. Figure 7D An exemplary depiction of the results is provided, in which the mean change (baseline to week 24) in the estimated ADCS-ADL score of AD-DMN-p-rTMS was -0.7 (95% CI [-27.2, 25.8]) and 7.5 (95% CI [-20.5, 35.5]) in the sham rTMS group, showing an improvement of DMN-p-rTMS relative to the sham rTMS group (interaction effect: p = <0.001). Figure 7E An exemplary depiction of the results is provided, where the baseline mean of the total FAB score was 10.7 (SD = 3.9) in the DMN-p-rTMS group and 10.2 (SD = 3.4) in the pseudo-rTMS group. The estimated mean change in the FAB score for DMN-p-rTMS was -0.01 (95% CI [-7.7, 7.7]) and 0.29 (95% CI [-7.4, 8.0]) in the pseudo-rTMS group, revealing no significant effect. The baseline mean of the total NPI score was 9.8 (SD = 10.2) in the DMN-p-rTMS group and 12.6 (SD = 11.7) in the pseudo-rTMS group. Figure 7F An exemplary depiction of the results is provided, in which the mean change (baseline-week 24) in the estimated NPI score for DMN-p-rTMS was -1.4 (95% CI [-15.7, 13.6]) and -3.7 (95% CI [-25.8, 21.9]) for the sham rTMS group, revealing no significant effect.

[0138] Figure 8A An exemplary depiction of the results is provided, in which neurophysiological analysis showed that TMS-induced cortical activity was stable in the DMN-p-rTMS group but decreased in the sham rTMS group (group × time interaction [F(1,126) = 6.65; p = 0.011; post-hoc: sham W0 - sham W24 p = 0.002]).

[0139] Figure 8B An exemplary depiction of further results is provided, in which DMN source activity was not altered in patients treated with DMN-p-rTMS, but decreased in the sham rTMS patient group. Figure 9Another illustrative depiction of the results is provided, in which an increase in rapid brain oscillation activity, primarily involving the β and γ bands, was also observed after true rTMS, but no decrease in γ spectral power was observed after sham rTMS (which could be attributed to disease progression).

[0140] Example 2: T 2 Experimental study of formulation

[0141] Rapid oscillating activity in the gamma band is associated with processing plasticity in the human brain. Given the impaired plasticity mechanisms in conditions such as dementia and Alzheimer's disease, the proposed multi-session rTMS protocol based on the precuneus of AD patients can benefit from additional interventions specifically designed to enhance brain plasticity. In this context, the combination of rTMS with time-of-action alternating current stimulation (tACS) targeting gamma activity (>30 Hz) can produce additive effects on brain activity and lead to stronger clinical outcomes than applying each modality alone.

[0142] In the study described below, the effects of a combined intervention based on rTMS and tACS were tested in a sample of 20 healthy adults. Participants had no prior brain stimulation, no history of mental illness, and were on a stable dose of medication for at least 6 weeks prior to enrollment in the study. Participants received a specific form of rTMS known as intermittent theta burst TMS (iTBS) (a plasticity-inducing protocol known to increase cortical excitability and responsiveness for a certain duration after stimulation) and (ii) tACS at the target frequency in the gamma band (70 Hz), (iii) tACS at the control frequency in the theta band (5 Hz), and (iv) sham (placebo) tACS. The combined stimulation protocol was delivered above the left dorsolateral prefrontal cortex (DLPFC). Participants completed iTBS+70 Hz, iTBS+5 Hz, and iTBS+sham tACS at three separate study visits on different days.

[0143] Participants underwent TMS-EEG recordings from multiple scalp sites, including the iTBS+tACS targeting area (left DLPFC), right DLPFC, and apex, before stimulation (T0), immediately after stimulation (T1), and 20 minutes after stimulation (T2). Figure 10 provides an exemplary depiction of the results, where TMS-EEG data were used to quantify cortical reactivity under different conditions and also to extract the so-called intrinsic frequencies of each stimulated site and to determine possible changes in the area-specific oscillations following the combined intervention.

[0144] result

[0145] Compared with iTBS+sham tACS and iTBS+5Hz tACS, an increase in the amplitude of TMS evoked potentials was observed for iTBS+70Hz tACS after each single-pulse TMS perturbation during the TMS-EEG session (p<0.05). Figure 11 An exemplary depiction of the results is provided, wherein the effect exists only for the stimulated left DLPPFC and not for the right DLPPFC and apex. This indicates that the induction and modulation of brain activity has high spatial specificity.

[0146] When observing the spectral power perturbations and oscillatory activity generated by the combined intervention, the effect of iTBS plus γtACS is also visible at 70 Hz. Figure 12 An exemplary depiction of the results is provided, in which iTBS+70Hz significantly increases the spectral power of the local oscillation response in the γ band (p<0.05) compared to no visible effect for iTBS+5Hz and iTBS+pseudo tACS. Figure 13 and Figure 14 An exemplary depiction of the results is provided, where the right DLPFC ( Figure 13 ) and vertex ( Figure 14 When the pulsed TMS was used, no shift in the intrinsic frequency generated by the single pulsed TMS or in the amplitude observed using TMS-EEG above the stimulated brain region (left DLPFC) was observed.

[0147] Example

[0148] Example 1. An embodiment comprising a method for identifying a first location in the brain of a subject suitable for non-invasive stimulation to treat or improve a neurological or psychiatric disorder, the method comprising: a. identifying a plurality of brain regions forming a brain network based on scan data of the subject's brain; and b. within a first brain region of the plurality of brain regions, identifying a subregion of the first brain region that is closely connected to one or more other brain regions of the plurality of brain regions as a first location suitable for non-invasive stimulation to treat or improve the neurological or psychiatric disorder.

[0149] Example 2. The method according to Example 1, wherein the scan data is functional MRI data (fMRI).

[0150] Example 3. The method according to Example 1, wherein the brain network is a default mode network (DMN).

[0151] Example 4. The method according to Example 1, wherein the first brain region includes at least a portion of the precuneus.

[0152] Example 5. The method according to Example 1, wherein the neurological or mental illness is Alzheimer's disease.

[0153] Example 6. The method according to Example 1, wherein the identification of the plurality of brain regions forming the brain network is further based on neuroimaging data of a plurality of subjects suffering from the neurological disease.

[0154] Example 7. The method according to Example 1, wherein the scan data includes neuroimaging data.

[0155] Example 8. The method according to Example 1, further comprising: providing non-invasive stimulation to a determined first location in the brain of the subject.

[0156] Example 9. The method according to Example 1, wherein the identification of the plurality of brain regions forming the brain network is based at least in part on one or more of the following: brain connectomics, network theory, graph theory, or control theory analysis frameworks.

[0157] Example 10. The method according to Example 1 further includes providing non-invasive stimulation to the subject.

[0158] Example 11. The method according to Example 10, wherein providing non-invasive stimulation includes providing at least one of transcranial electrical stimulation or transcranial magnetic stimulation to a determined first location of the patient's brain.

[0159] Example 12. The method according to Example A1 further includes: providing non-invasive stimulation to a plurality of locations within the first brain region containing the first location; sensing at least one evoked potential in response to the provided stimulation; and selecting one of the plurality of locations suitable for non-invasive stimulation to treat or improve the neurological or psychiatric disease as a personalized stimulation target based on at least one characteristic of the at least one evoked potential.

[0160] Example 13. The method according to Example 12, wherein the plurality of positions are arranged in a grid.

[0161] Example 14. The method according to Example 12, wherein the at least one characteristic includes the peak amplitude of the at least one evoked potential.

[0162] Example 15. The method according to Example 12, wherein selecting the personalized stimulation target includes selecting the location among the plurality of locations having the maximum peak amplitude of the at least one evoked potential.

[0163] Example 16. The method according to Example 12, wherein sensing the at least one evoked potential includes using electroencephalography (EEG) to sense the at least one evoked potential.

[0164] Example 17. The method according to Example 12 further includes: treating or improving the neurological or psychiatric disease by providing the non-invasive stimulation to the identified personalized stimulation target.

[0165] Example 18. The method according to Example 17, wherein the non-invasive stimulation provided to the determined personalized stimulation target is effective in treating or improving the neurological or psychiatric disease of the subject, such that after a 12-week period, the subject has improved cognitive and clinical performance scores compared to untreated subjects.

[0166] Example 19. The method according to Example 17, wherein the non-invasive stimulation provided to the determined personalized stimulation target is effective in treating or improving the neurological or psychiatric disease of the subject, such that after a 24-week period, the subject has improved cognitive and clinical performance scores compared to untreated subjects.

[0167] Example 20. The method according to Example 17, wherein the non-invasive stimulation provided to the determined personalized stimulation target is effective in treating or improving the subject's neurological or psychiatric disease, such that after a 24-week period, the subject's cognitive decline is at least 25% less than that of an untreated subject.

[0168] Example 21. The method according to Example 17, wherein the non-invasive stimulation provided to the determined personalized stimulation target is effective in treating or improving the neurological or psychiatric disease of the subject, such that after a 24-week period, the subject's disease severity decreases by at least 25% less than the disease severity decrease of an untreated subject.

[0169] Example 22. The method according to Example 17, wherein the non-invasive stimulation provided to the determined personalized stimulation target is effective in treating or improving the neurological or psychiatric disease of the subject, such that after a 24-week period, the subject's functional independence decline is at least 25% less than that of an untreated subject.

[0170] Example 23. The method according to Example 17, wherein the non-invasive stimulation provided to the determined personalized stimulation target is effective in treating or improving the neurological or psychiatric disease of the subject, such that after a 24-week period, the subject's TEP amplitude decrease is at least 25% smaller than that of an untreated subject.

[0171] Example 24. The method according to Example 17, wherein the non-invasive stimulation provided to the determined personalized stimulation target is effective in treating or improving the neurological or psychiatric disease of the subject, such that after a 24-week period, the subject's network oscillation activity decreases by at least 25% less than the decrease in network oscillation activity of an untreated subject.

[0172] Example 25. The method according to Example 17, wherein the non-invasive stimulation provided to the determined personalized stimulation target is effective in treating or improving the neurological or psychiatric disease of the subject, such that after a 24-week period, the subject's source-level network activity decreases by at least 25% less than the decrease in source-level network activity of an untreated subject.

[0173] Example 26. The method according to Example 17, wherein the non-invasive stimulation provided to the determined personalized stimulation target is effective in treating or improving the neurological or psychiatric disease of the subject, such that after a 24-week period, the subject's network metabolic activity decreases by at least 25% less than the decrease in network metabolic activity of an untreated subject.

[0174] Example 27. The method according to Example 17, wherein the non-invasive stimulation provided to the determined personalized stimulation target is effective in treating or improving the neurological or psychiatric disease of the subject, such that the subject's brain oscillation activity changes compared to that of an untreated subject.

[0175] Example 28. The method according to Example 17, wherein the non-invasive stimulation provided to the determined personalized stimulation target is effective in treating or improving the neurological or psychiatric disease of the subject, such that the rapid oscillating brain activity in the gamma band of the subject is increased compared with the rapid oscillating brain activity in the gamma band of an untreated subject.

[0176] Example 29. The method according to Example 17, wherein the non-invasive stimulation provided to the determined personalized stimulation target is delivered on multiple consecutive days or multiple non-consecutive days at a frequency of daily, weekly, or a combination thereof.

[0177] Example 30. The method according to Example 12, wherein the non-invasive stimulation provided to the identified personalized stimulation target is combined with, sequentially or simultaneously delivered with a drug intervention affecting the central nervous system.

[0178] Example 31. The method according to Example 12, wherein the non-invasive stimulus provided to the determined personalized stimulus target is delivered sequentially or simultaneously in combination with one or more of cognitive assessment, cognitive training or cognitive enhancement intervention.

[0179] Example 32. The method according to Example 12, wherein the non-invasive stimulation provided to the identified personalized stimulation target is combined with behavioral interventions, delivered sequentially or simultaneously.

[0180] Example 33. The method according to Example 12, wherein the non-invasive stimulation provided to the determined personalized stimulation target is combined with different non-invasive stimulation interventions delivered sequentially or simultaneously, the different non-invasive stimulations including transcranial electrical stimulation.

[0181] Example 34. The method according to Example 33, wherein the second non-invasive stimulation intervention is transcranial alternating current stimulation delivered sequentially or simultaneously.

[0182] Example 35. The method according to Example 1, wherein the scan data includes diffusion MRI data.

[0183] Example 36. The method according to Example 1, wherein the scan data includes perfusion MRI data.

[0184] Example 37. The method according to Example 1, wherein the scanning data includes positron emission tomography (PET) data, the PET data being indexed to local perfusion / metabolic levels or to protein load or to measure neuroinflammation.

[0185] Example 38. The method according to Example 1, wherein the brain network includes one or more of the following: frontoparietal bone control network, sensorimotor network, anterior spur network, dorsal attention network, ventral attention network, visual network, auditory network, or language network.

[0186] Example 39. The method according to Example 1, wherein the first brain region includes at least a portion of the angular gyrus.

[0187] Example 40. The method according to Example 1, wherein the first brain region includes at least a portion of the temporal lobe.

[0188] Example 41. The method according to Example 1, wherein the first brain region includes at least a portion of the medial prefrontal cortex.

[0189] Example 42. The method according to Example 1, wherein the neurological or psychiatric disease is mild cognitive impairment (MCI).

[0190] Example 43. The method according to Example 1, wherein the neurological or psychiatric disease is frontotemporal dementia.

[0191] Example 44. The method according to Example 1, wherein the neurological or psychiatric disease is characterized by changes in brain networks, such as depression (DEP), schizophrenia (SCZ), autism (AUT), attention deficit and hyperactivity disorder (ADHD), bipolar disorder (BP), amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), traumatic brain injury (TBI), insomnia (INS), disorder of consciousness (DOC), headache (HD), multiple sclerosis (MS), stroke (STR), and brain tumor (BT).

[0192] Example 45. The method according to Example 1, wherein the neurological or psychiatric disease is characterized by memory deficits.

[0193] Example 46. The method according to Example 1, wherein the neurological or mental illness is characterized by a cognitive control deficit.

[0194] Example 47. The method according to Example 1, wherein the neurological or psychiatric disease is characterized by reduced functional independence.

[0195] Example 48. An embodiment comprising a method for determining a personalized stimulation target in a brain region to treat or improve a neurological or psychiatric disorder of a subject, the method comprising: a. non-invasively stimulating each of a plurality of locations in a brain region of the subject; b. sensing at least one evoked potential in response to the provided stimulation; and c. selecting one of the plurality of locations suitable for providing therapeutically effective non-invasive stimulation to treat or improve the neurological or psychiatric disorder as a personalized stimulation target of the subject, wherein the selection is based on at least one characteristic of the at least one evoked potential.

[0196] Example 49. The method according to Example 48, wherein the brain region is part of the default mode network (DMN).

[0197] Example 50. The method according to Example 48, wherein the brain region includes at least a portion of the precuneus.

[0198] Example 51. The method according to Example 48, wherein the neurological or mental illness is Alzheimer's disease.

[0199] Example 52. The method according to Example 48, wherein providing non-invasive stimulation includes providing at least one of transcranial electrical stimulation or transcranial magnetic stimulation to the personalized stimulation target of the subject's brain.

[0200] Example 53. The method according to Example 48, wherein the plurality of positions are arranged in a grid.

[0201] Example 54. The method according to Example 48, wherein the at least one characteristic includes the peak amplitude of the at least one evoked potential.

[0202] Example 55. The method according to Example 48, wherein selecting the personalized stimulation target includes selecting the location among the plurality of locations having the maximum peak amplitude of the at least one evoked potential.

[0203] Example 56. The method according to Example 48, wherein sensing the at least one evoked potential includes using electroencephalography (EEG) to sense the at least one evoked potential.

[0204] Example 57. The method according to Example 48 further includes: treating or improving the neurological or psychiatric disease by providing the non-invasive stimulation to a selected personalized stimulation target.

[0205] Example 58. The method according to Example 48, wherein the brain network is one or more of the following: frontoparietal control network, sensorimotor network, anterior spur network, dorsal attention network, ventral attention network, visual network, auditory network, or language network.

[0206] Example 59. The method according to Example 48, wherein the brain region includes at least a portion of the angular gyrus.

[0207] Example 60. The method according to Example 48, wherein the brain region includes at least a portion of the temporal lobe.

[0208] Example 61. The method according to Example 48, wherein the brain region includes at least a portion of the medial prefrontal cortex.

[0209] Example 62. The method according to Example 48, wherein the neurological or psychiatric disease is mild cognitive impairment (MCI).

[0210] Example 63. The method according to Example 48, wherein the neurological or psychiatric disease is frontotemporal dementia.

[0211] Example 64. The method according to Example 48, wherein the neurological or psychiatric disease is characterized by changes in brain networks, such as depression (DEP), schizophrenia (SCZ), autism (AUT), attention deficit and hyperactivity disorder (ADHD), bipolar disorder (BP), amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), traumatic brain injury (TBI), insomnia (INS), disorder of consciousness (DOC), headache (HD), multiple sclerosis (MS), stroke (STR), and brain tumor (BT).

[0212] Example 65. The method according to Example 48, wherein the neurological or psychiatric disease is characterized by memory deficits.

[0213] Example 66. The method according to Example 48, wherein the neurological or mental illness is characterized by a cognitive control deficit.

[0214] Example 67. The method according to Example 48, wherein the neurological or psychiatric disease is characterized by reduced functional independence.

[0215] Example 68. The method according to Example 48, wherein the non-invasive stimulation provided to the selected personalized stimulation target is combined with, sequentially or simultaneously delivered with a drug intervention affecting the central nervous system.

[0216] Example 69. The method according to Example 48, wherein the non-invasive stimulus provided to the selected personalized stimulus target is combined with cognitive assessment, cognitive training or cognitive enhancement intervention, delivered sequentially or simultaneously.

[0217] Example 70. The method according to Example 48, wherein the non-invasive stimulation provided to the selected personalized stimulation target is combined with behavioral interventions, delivered sequentially or simultaneously.

[0218] Example 71. The method according to Example 48, wherein the non-invasive stimulation provided to the selected personalized stimulation target is combined with different non-invasive stimulation interventions delivered sequentially or simultaneously, the different non-invasive stimulations including transcranial electrical stimulation.

[0219] Example 72. An embodiment comprising a method for determining parameters for non-invasive stimulation of a subject's brain, the method comprising: a. sensing a plurality of evoked potentials in response to non-invasive stimulation of each of a plurality of locations in a brain region of the subject; b. determining personalized stimulation parameters for the subject based at least in part on at least one characteristic of the plurality of evoked potentials, wherein the personalized stimulation parameters include stimulation location and one or more stimulation characteristics.

[0220] Example 73. The method according to Example 72, wherein the one or more stimulation characteristics include stimulation intensity and / or stimulation frequency.

[0221] Example 74. The method according to Example 72 further includes: sensing a resting motor threshold in response to providing non-invasive stimulation to the motor cortex of the subject's brain; determining a baseline stimulation intensity based at least in part on the resting motor threshold; and adjusting the baseline stimulation intensity based at least in part on the determined personalized stimulation parameters.

[0222] Example 75. The method according to Example 72, wherein the brain region is part of a brain network, and the brain network is one or more of the following: frontoparietal control network, sensorimotor network, anterior spur network, dorsal attention network, ventral attention network, visual network, auditory network, or language network.

[0223] Example 76. The method according to Example 72, wherein the brain region is part of the DMN.

[0224] Example 77. The method according to Example 72, wherein the brain region is the precuneus.

[0225] Example 78. The method according to Example 72, wherein the plurality of evoked potentials in response to stimuli are obtained by concurrent TMS-EEG recording.

[0226] Example 79. The method according to Example 72, wherein providing non-invasive stimulation includes providing at least one of transcranial electrical stimulation or transcranial magnetic stimulation to a determined stimulation site in the subject's brain.

[0227] Example 80. The method according to Example 72, wherein the plurality of positions are arranged in a grid.

[0228] Example 81. The method according to Example 72, wherein the one or more stimulation characteristics include the peak amplitude of the plurality of evoked potentials.

[0229] Example 82. The method according to Example 72, wherein determining the personalized stimulation parameters includes selecting from the plurality of locations the location having the maximum peak amplitude of the plurality of evoked potentials.

[0230] Example 83. The method according to Example 72, wherein sensing the plurality of evoked potentials includes using electroencephalography (EEG) to sense the plurality of evoked potentials.

[0231] Example 84. The method according to Example 72 further includes: treating or improving neurological or psychiatric disorders by providing the non-invasive stimulation to the determined stimulation location according to determined personalized stimulation parameters.

[0232] Example 85. The method according to Example 84, wherein the neurological or mental illness is Alzheimer's disease.

[0233] Example 86. The method according to Example 84, wherein the neurological or psychiatric disease is mild cognitive impairment (MCI).

[0234] Example 87. The method according to Example 84, wherein the neurological or psychiatric disease is frontotemporal dementia.

[0235] Example 88. The method according to Example 84, wherein the neurological or mental illness is characterized by changes in brain networks, such as depression (DEP), schizophrenia (SCZ), autism (AUT), attention deficit and hyperactivity disorder (ADHD), bipolar disorder (BP), amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), traumatic brain injury (TBI), insomnia (INS), disorder of consciousness (DOC), headache (HD), multiple sclerosis (MS), stroke (STR), and brain tumor (BT).

[0236] Example 89. The method according to Example 84, wherein the neurological or psychiatric disease is characterized by memory deficits.

[0237] Example 90. The method according to Example 84, wherein the neurological or mental illness is characterized by a cognitive control deficit.

[0238] Example 91. The method according to Example 84, wherein the neurological or psychiatric disease is characterized by reduced functional independence.

[0239] Example 92. The method according to Example 84, wherein the non-invasive stimulation provided according to the personalized stimulation parameters is combined with, sequentially or simultaneously delivered with drug interventions affecting the central nervous system.

[0240] Example 93. The method according to Example 84, wherein the non-invasive stimulus provided according to the personalized stimulus parameters is combined with cognitive assessment, cognitive training or cognitive enhancement intervention, and delivered sequentially or simultaneously.

[0241] Example 94. The method according to Example 84, wherein the non-invasive stimulation and behavioral intervention provided according to the personalized stimulation parameters are delivered in combination, sequentially or simultaneously.

[0242] Example 95. The method according to Example 84, wherein the non-invasive stimulation provided according to the personalized stimulation parameters is combined with different non-invasive stimulation interventions delivered sequentially or simultaneously, the different non-invasive stimulations including transcranial electrical stimulation.

[0243] Example 96. The method according to Example 72, wherein the brain region includes at least a portion of the angular gyrus.

[0244] Example 97. The method according to Example 72, wherein the brain region includes at least a portion of the temporal lobe.

[0245] Example 98. The method according to Example 72, wherein the brain region includes at least a portion of the medial prefrontal cortex.

[0246] Example 99. An embodiment comprising a method for determining personalized stimulation characteristics of targeted non-invasive brain stimulation for a patient’s brain, the method comprising: a. sensing a resting motor threshold in response to providing non-invasive stimulation to a motor cortex of the patient’s brain; b. non-invasively stimulating a region of the brain located outside the motor cortex; c. sensing evoked potentials in response to the stimulation of the region of the brain located outside the motor cortex; and d. adjusting the resting motor threshold of the stimulation based at least in part on at least one characteristic of the sensed evoked potentials.

[0247] Example 100. The method according to Example 99, wherein the region located outside the motor cortex is part of the DMN.

[0248] Example 101. The method according to Example 99, wherein the region located outside the motor cortex is the precuneus.

[0249] Example 102. The method according to Example 99, wherein the evoked potentials in response to stimuli are obtained by concurrent TMS-EEG recording.

[0250] Example 103. The method according to Example 99, wherein the characteristic of the evoked potential is the signal amplitude.

[0251] Example 104. The method according to Example 99, wherein one of the characteristics of the evoked potential is the stimulation intensity, and the stimulation intensity is adjusted based on biophysical modeling work simulating the electric field generated in the target area of ​​the patient.

[0252] Example 105. The method according to Example 99, wherein the non-invasive stimulation comprises providing at least one of transcranial electrical stimulation or transcranial magnetic stimulation to the location of the patient's brain.

[0253] Example 106. The method according to Example 99, wherein the at least one characteristic includes the peak amplitude of the evoked potential.

[0254] Example 107. The method according to Example 99, wherein sensing the evoked potential includes using electroencephalography (EEG) to sense the evoked potential.

[0255] Example 108. The method according to Example 99 further includes selecting a personalized stimulation target by selecting a location from a plurality of locations having the maximum peak amplitude of the at least one evoked potential.

[0256] Example 109. The method according to Example 108 further includes treating or improving neurological or psychiatric disorders by providing the non-invasive stimulation.

[0257] Example 110. The method according to Example 109, wherein the neurological or mental illness is Alzheimer's disease.

[0258] Example 111. The method according to Example 109, wherein the neurological or psychiatric disease is mild cognitive impairment (MCI).

[0259] Example 112. The method according to Example 109, wherein the neurological or mental illness is frontotemporal dementia.

[0260] Example 113. The method according to Example 109, wherein the neurological or mental illness is characterized by changes in brain networks, such as depression (DEP), schizophrenia (SCZ), autism (AUT), attention deficit and hyperactivity disorder (ADHD), bipolar disorder (BP), amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), traumatic brain injury (TBI), insomnia (INS), disorder of consciousness (DOC), headache (HD), multiple sclerosis (MS), stroke (STR), and brain tumor (BT).

[0261] Example 114. The method according to Example 109, wherein the neurological or psychiatric disease is characterized by memory deficits.

[0262] Example 115. The method according to Example 109, wherein the neurological or mental illness is characterized by a cognitive control deficit.

[0263] Example 116. The method according to Example 109, wherein the neurological or psychiatric disease is characterized by reduced functional independence.

[0264] Example 117. The method according to Example 109, wherein the non-invasive stimulation is combined with, sequentially or simultaneously delivered with a drug intervention affecting the central nervous system.

[0265] Example 118. The method according to Example 109, wherein the non-invasive stimulus is delivered sequentially or simultaneously in combination with one or more of cognitive assessment, cognitive training or cognitive enhancement intervention.

[0266] Example 119. The method according to Example 109, wherein the non-invasive stimulus and behavioral intervention are combined, delivered sequentially or simultaneously.

[0267] Example 120. The method according to Example 109, wherein the non-invasive stimulation is combined with different non-invasive stimulation interventions delivered sequentially or simultaneously, the different non-invasive stimulations including transcranial electrical stimulation.

[0268] Example 121. An embodiment comprising a method for treating or improving a neurological or psychiatric disorder in a subject, the method comprising: a. non-invasively stimulating a first location in a first brain region of a plurality of brain regions identified as forming a brain network, b. wherein the first location is a subregion of the first brain region that is closely connected to one or more other brain regions of the plurality of brain regions.

[0269] Example 122. The method according to Example 121, wherein the plurality of brain regions forming the brain network are identified based on brain scan data of the subject.

[0270] Example 123. The method according to Example 121, wherein the first brain region is part of a default mode network (DMN).

[0271] Example 124. The method according to Example 121, wherein the first brain region includes at least a portion of the precuneus.

[0272] Example 125. The method according to Example 121, wherein the neurological or mental illness is Alzheimer's disease.

[0273] Example 126. The method according to Example 121, wherein providing non-invasive stimulation includes providing at least one of transcranial electrical stimulation or transcranial magnetic stimulation to the first location of the patient's brain.

[0274] Example 127. The method according to Example 121 further includes:

[0275] The neurological or mental illness is treated or improved by providing the non-invasive stimulation to the first location.

[0276] Example 128. The method according to Example 121, wherein the brain network is one or more of the following: frontoparietal control network, sensorimotor network, anterior spur network, dorsal attention network, ventral attention network, visual network, auditory network, or language network.

[0277] Example 129. The method according to Example 121, wherein the first position includes at least a portion of the corner.

[0278] Example 130. The method according to Example 121, wherein the first location includes at least a portion of the temporal lobe.

[0279] Example 131. The method according to Example 121, wherein the first location includes at least a portion of the medial prefrontal cortex.

[0280] Example 132. The method according to Example 121, wherein the neurological or psychiatric disease is mild cognitive impairment (MCI).

[0281] Example 133. The method according to Example 121, wherein the neurological or mental illness is frontotemporal dementia.

[0282] Example 134. The method according to Example 121, wherein the neurological or psychiatric disease is characterized by changes in brain networks, such as depression (DEP), schizophrenia (SCZ), autism (AUT), attention deficit and hyperactivity disorder (ADHD), bipolar disorder (BP), amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), traumatic brain injury (TBI), insomnia (INS), disorder of consciousness (DOC), headache (HD), multiple sclerosis (MS), stroke (STR), and brain tumor (BT).

[0283] Example 135. The method according to Example 121, wherein the neurological or psychiatric disease is characterized by memory deficits.

[0284] Example 136. The method according to Example 121, wherein the neurological or mental illness is characterized by a cognitive control deficit.

[0285] Example 137. The method according to Example 121, wherein the neurological or psychiatric disease is characterized by reduced functional independence.

[0286] Example 138. The method according to Example 121, wherein the non-invasive stimulation provided to the first location is combined with, sequentially or simultaneously delivered with a drug intervention affecting the central nervous system.

[0287] Example 139. The method according to Example 121, wherein the non-invasive stimulus provided to the first location is combined with cognitive assessment, cognitive training or cognitive enhancement intervention, delivered sequentially or simultaneously.

[0288] Example 140. The method according to Example 121, wherein the non-invasive stimulus provided to the first location is combined with behavioral intervention, delivered sequentially or simultaneously.

[0289] Example 141. The method according to Example 121, wherein the non-invasive stimulation provided to the first location is combined with different non-invasive stimulation interventions delivered sequentially or simultaneously, the different non-invasive stimulations including transcranial electrical stimulation.

[0290] Example 142. An embodiment comprising a method for determining personalized stimulation characteristics of targeted non-invasive brain stimulation for a subject, the method comprising: a. non-invasively stimulating a region of the brain; b. estimating an electric field induced by the stimulation in the region of the brain; and c. adjusting the stimulation characteristics based at least in part on at least one characteristic of the induced electric field.

[0291] Example 143. The method according to Example 142, wherein the intensity of the induced electric field is used to adjust one or more of the following: stimulation intensity, frequency, pulse shape, or duration.

[0292] Example 144. The method according to Example 142, wherein the region of the brain is part of a default mode network (DMN).

[0293] Example 145. The method according to Example 142, wherein the region of the brain includes at least a portion of the precuneus.

[0294] Example 146. The method according to Example 142, wherein non-invasive stimulation of the region of the brain includes providing at least one of transcranial electrical stimulation or transcranial magnetic stimulation according to the stimulation characteristics.

[0295] Example 147. The method according to any one of Examples 142 to 146, further comprising: treating or improving a neurological or mental illness by providing the non-invasive stimulation according to the stimulation characteristics.

[0296] Example 148. The method according to Example 142 further includes identifying multiple brain regions forming a brain network based on scan data of the subject's brain, wherein the brain regions subjected to non-invasive stimulation are brain regions in the brain network.

[0297] Example 149. The method according to Example 148, wherein the brain network is one or more of the following: frontoparietal control network, sensorimotor network, anterior spur network, dorsal attention network, ventral attention network, visual network, auditory network, or language network.

[0298] Example 150. The method according to Example 148, wherein the brain region includes at least a portion of the angular gyrus.

[0299] Example 151. The method according to Example 148, wherein the brain region includes at least a portion of the temporal lobe.

[0300] Example 152. The method according to Example 148, wherein the brain region includes at least a portion of the medial prefrontal cortex.

[0301] Example 153. The method according to Example 147, wherein the neurological or mental illness is Alzheimer's disease.

[0302] Example 154. The method according to Example 147, wherein the neurological or psychiatric disease is mild cognitive impairment (MCI).

[0303] Example 155. The method according to Example 147, wherein the neurological or psychiatric disease is frontotemporal dementia.

[0304] Example 156. The method according to Example 147, wherein the neurological or psychiatric disease is characterized by changes in brain networks, such as depression (DEP), schizophrenia (SCZ), autism (AUT), attention deficit and hyperactivity disorder (ADHD), bipolar disorder (BP), amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), traumatic brain injury (TBI), insomnia (INS), disorder of consciousness (DOC), headache (HD), multiple sclerosis (MS), stroke (STR), and brain tumor (BT).

[0305] Example 157. The method according to Example 147, wherein the neurological or psychiatric disease is characterized by memory deficits.

[0306] Example 158. The method according to Example 147, wherein the neurological or mental illness is characterized by a cognitive control deficit.

[0307] Example 159. The method according to Example 147, wherein the neurological or psychiatric disease is characterized by reduced functional independence.

[0308] Example 160. The method according to Example 147, wherein the non-invasive stimulation provided according to the stimulation characteristics is combined with, sequentially or simultaneously delivered with a drug intervention affecting the central nervous system.

[0309] Example 161. The method according to Example 147, wherein the non-invasive stimulus provided according to the stimulus characteristics is combined with cognitive assessment, cognitive training or cognitive enhancement intervention, and delivered sequentially or simultaneously.

[0310] Example 162. The method according to Example 147, wherein the non-invasive stimulus provided according to the stimulus characteristics is combined with behavioral interventions, delivered sequentially or simultaneously.

[0311] Example 163. The method according to Example 147, wherein the non-invasive stimulation provided according to the stimulation characteristics is combined with different non-invasive stimulation interventions delivered sequentially or simultaneously, the different non-invasive stimulations including transcranial electrical stimulation.

[0312] Example 164. An embodiment comprising a system for identifying a first location in the brain of a subject suitable for non-invasive stimulation to treat or improve a neurological or psychiatric disorder, the system comprising: a. an imaging source configured to generate scan data of the subject's brain; and b. a processor configured to: (i) identify a plurality of brain regions forming a brain network based on the scan data, and (ii) within a first brain region of the plurality of brain regions, identify a subregion of the first brain region that is closely connected to one or more other brain regions of the plurality of brain regions as the first location suitable for non-invasive stimulation.

[0313] Example 165. The system according to Example 164 further includes a stimulation device for providing non-invasive stimulation to at least the first location of the subject's brain.

[0314] Example 166. The system according to Example 164, wherein the imaging source is magnetic resonance imaging (MRI).

[0315] Example 167. The system according to Example 164, wherein the scan data is diffusion MRI data;

[0316] Example 168. The system according to Example 164, wherein the scan data is perfusion MRI data.

[0317] Example 169. The system according to Example 164, wherein the scan data is PET data, which indexes local perfusion / metabolic levels or protein load or measures neuroinflammation.

[0318] Example 170. The system according to Example 164, wherein the scan data includes neuroimaging data of multiple subjects suffering from neurological diseases.

[0319] Example 171. The system according to Example 164, wherein the scan data includes neuroimaging data.

[0320] Example 172. The system according to Example 164, wherein the processor is configured to identify the brain network based on one or more of the following: brain connectomics, network theory, graph theory, or control theory analysis frameworks.

[0321] Example 173. The system according to Example 164, wherein the processor is further configured to treat the neurological or psychiatric disease by providing the non-invasive stimulation to the determined first location.

[0322] Example 174. The system according to Example 164, wherein the brain network is one or more of the following: frontoparietal bone control network, sensorimotor network, anterior spur network, dorsal attention network, ventral attention network, visual network, auditory network, or language network.

[0323] Example 175. The system according to Example 164, wherein the first brain region includes at least a portion of the precuneus.

[0324] Example 176. The system according to Example 164, wherein the first brain region includes at least a portion of the angular gyrus.

[0325] Example 177. The system according to Example 164, wherein the first brain region includes at least a portion of the temporal lobe.

[0326] Example 178. The system according to Example 164, wherein the first brain region includes at least a portion of the medial prefrontal cortex.

[0327] Example 179. The system according to Example 164, wherein the neurological or mental illness is Alzheimer's disease.

[0328] Example 180. The system according to Example 164, wherein the neurological or psychiatric disease is mild cognitive impairment (MCI).

[0329] Example 181. The system according to Example 164, wherein the neurological or psychiatric disease is frontotemporal dementia.

[0330] Example 182. The system according to Example 164, wherein the neurological or psychiatric disease is characterized by changes in brain networks, such as depression (DEP), schizophrenia (SCZ), autism (AUT), attention deficit and hyperactivity disorder (ADHD), bipolar disorder (BP), amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), traumatic brain injury (TBI), insomnia (INS), disorder of consciousness (DOC), headache (HD), multiple sclerosis (MS), stroke (STR), and brain tumor (BT).

[0331] Example 183. The system according to Example 164, wherein the neurological or psychiatric disease is characterized by memory deficits.

[0332] Example 184. The system according to Example 164, wherein the neurological or psychiatric disease is characterized by cognitive control deficits.

[0333] Example 185. The system according to Example 164, wherein the neurological or psychiatric disease is characterized by reduced functional independence.

[0334] Example 186. An embodiment comprising a system for identifying a personalized stimulation target in a brain region to treat or improve a neurological or psychiatric disorder of a subject, the system comprising: a. a stimulation device for providing non-invasive stimulation to each of a plurality of locations in a brain region of the subject; and b. a sensor device for sensing at least one evoked potential in response to the provided stimulation; and c. a processor configured to select one of the plurality of locations suitable for providing therapeutically effective non-invasive stimulation to treat or improve the neurological or psychiatric disorder as a personalized stimulation target, wherein the selection is based on at least one characteristic of the at least one evoked potential.

[0335] Example 187. The system according to Example 186, wherein the brain region is part of a default mode network (DMN).

[0336] Example 188. The system according to Example 186, wherein the brain region includes at least a portion of the precuneus.

[0337] Example 189. The system according to Example 186, wherein the neurological or mental illness is Alzheimer's disease.

[0338] Example 190. The system according to Example 186, wherein the stimulation device is configured to provide at least one of transcranial electrical stimulation or transcranial magnetic stimulation to the personalized stimulation target of the subject's brain.

[0339] Example 191. The system according to Example 186, wherein the plurality of locations are arranged in a grid.

[0340] Example 192. The system according to Example 186, wherein the at least one characteristic includes the peak amplitude of the at least one evoked potential.

[0341] Example 193. The system according to Example 186, wherein selecting the personalized stimulation target includes selecting the location among the plurality of locations having the maximum peak amplitude of the at least one evoked potential.

[0342] Example 194. The system according to Example 186, wherein the sensor device is configured to use electroencephalography (EEG) to sense the at least one evoked potential.

[0343] Example 195. The system according to Example 186, wherein the stimulation device is configured to treat or improve the neurological or psychiatric disease by providing the non-invasive stimulation to a selected personalized stimulation target.

[0344] Example 196. The system according to Example 186, wherein the brain network is one or more of the following: frontoparietal control network, sensorimotor network, anterior spur network, dorsal attention network, ventral attention network, visual network, auditory network, or language network.

[0345] Example 197. The system according to Example 186, wherein the personalized stimulation target is a region of the angular gyrus;

[0346] Example 198. The system according to Example 186, wherein the personalized stimulation target is a region of the temporal lobe.

[0347] Example 199. The method according to Example 186, wherein the personalized stimulation target is a region of the medial prefrontal cortex.

[0348] Example 200. The system according to Example 186, wherein the neurological or psychiatric disease is mild cognitive impairment (MCI).

[0349] Example 201. The system according to Example 186, wherein the neurological or psychiatric disease is frontotemporal dementia.

[0350] Example 202. The system according to Example 186, wherein the neurological or psychiatric disease is characterized by changes in brain networks, such as depression (DEP), schizophrenia (SCZ), autism (AUT), attention deficit and hyperactivity disorder (ADHD), bipolar disorder (BP), amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), traumatic brain injury (TBI), insomnia (INS), disorder of consciousness (DOC), headache (HD), multiple sclerosis (MS), stroke (STR), and brain tumor (BT).

[0351] Example 203. The system according to Example 186, wherein the neurological or psychiatric disease is characterized by memory deficits.

[0352] Example 204. The system according to Example 186, wherein the neurological or psychiatric disease is characterized by cognitive control deficits.

[0353] Example 205. The system according to Example 186, wherein the neurological or psychiatric disease is characterized by reduced functional independence.

[0354] Example 206. The system according to Example 186, wherein the non-invasive stimulation provided to the selected personalized stimulation target is combined with, sequentially or simultaneously delivered with a drug intervention affecting the central nervous system.

[0355] Example 207. The system according to Example 186, wherein the non-invasive stimulus provided to the selected personalized stimulus target is delivered sequentially or simultaneously in combination with one or more of cognitive assessment, cognitive training or cognitive enhancement intervention.

[0356] Example 208. The system according to Example 186, wherein the non-invasive stimulation and behavioral intervention provided to the selected personalized stimulation target are delivered in combination, sequentially or simultaneously.

[0357] Example 209. The system according to Example 186, wherein the non-invasive stimulation provided to a selected personalized stimulation target is combined with different non-invasive stimulation interventions delivered sequentially or simultaneously, the different non-invasive stimulations including transcranial electrical stimulation.

[0358] Example 210. The system according to Example 186, wherein the processor describes a sequence of combined automatic and semi-automatic signal processing algorithms mounted on local hardware, thereby enabling specific indication of the location, frequency, intensity, but not limited thereto, of a stimulus.

[0359] Example 211. The system according to Example 186, wherein the processor describes a sequence of combined automatic and semi-automatic signal processing algorithms mounted on remote hardware with connectivity capabilities, thereby enabling specific indication of the location, frequency, intensity, but not limited thereto, of a stimulus.

[0360] Example 212. The system according to Example 186, wherein data collected from individuals receiving TMS-based interventions is stored for group-level inference and further optimization of TMS optimization procedures, wherein the collected data can be used for, but is not limited to: identifying patient clusters with similar responses to treatment; refining dose estimation procedures; establishing machine learning and artificial intelligence models of responses to treatment; and predicting disease progression.

[0361] Example 213. An embodiment comprising a system for determining parameters for non-invasive stimulation of a patient's brain, the system comprising: a. a sensor device for sensing a plurality of evoked potentials in response to non-invasive stimulation of each of a plurality of locations in a brain region of the patient; and b. a processor configured to determine personalized stimulation parameters for the patient based at least in part on at least one characteristic of the plurality of evoked potentials, wherein the personalized stimulation parameters include stimulation location and one or more stimulation characteristics.

[0362] Example 214. The system according to Example 213, wherein the one or more stimulation characteristics include stimulation intensity and / or stimulation frequency.

[0363] Example 215. The system according to Example 213, wherein the sensor device is further configured to sense a resting motor threshold in response to providing non-invasive stimulation to the motor cortex of the patient's brain, wherein the processor is further configured to determine a baseline stimulation intensity based at least in part on the resting motor threshold; and wherein the processor is further configured to adjust the baseline stimulation intensity based at least in part on the determined personalized stimulation parameters.

[0364] Example 216. The system according to Example 213, wherein the brain region is part of a brain network.

[0365] Example 217. The system according to Example 213, wherein the brain region is part of the DMN.

[0366] Example 218. The system according to Example 213, wherein the brain region is the precuneus.

[0367] Example 219. The system according to Example 213, wherein the plurality of evoked potentials in response to stimuli are obtained by concurrent TMS-EEG recording.

[0368] Example 220. The system according to Example 213 further includes a stimulation device for providing non-invasive stimulation to a subject based on the personalized stimulation parameters by providing at least one of transcranial electrical stimulation or transcranial magnetic stimulation to a determined stimulation location in the patient's brain.

[0369] Example 221. The system according to Example 213, wherein the plurality of locations are arranged in a grid.

[0370] Example 222. The system according to Example 213, wherein the one or more stimulation characteristics include the peak amplitude of the plurality of evoked potentials.

[0371] Example 223. The system according to Example 213, wherein the processor is configured to determine the personalized stimulation parameters by selecting from the plurality of locations the location having the maximum peak amplitude of the plurality of evoked potentials.

[0372] Example 224. The system according to Example 213, wherein the sensor device is configured to use electroencephalography (EEG) to sense the plurality of evoked potentials.

[0373] Example 225. The system according to Example 213, wherein the brain region is the location of a brain network, and wherein the brain network is one or more of the following: frontoparietal control network, sensorimotor network, anterior convexity network, dorsal attention network, ventral attention network, visual network, auditory network, and language network.

[0374] Example 226. The system according to Example 213, wherein the brain region includes at least a portion of the angular gyrus.

[0375] Example 227. The system according to Example 213, wherein the brain region includes at least a portion of the temporal lobe.

[0376] Example 228. The system according to Example 213, wherein the brain region includes at least a portion of the medial prefrontal cortex.

[0377] Example 229. The system according to Example 213 further includes: a stimulation device for treating or improving neurological or mental illness by providing the non-invasive stimulation to a determined stimulation location according to determined personalized stimulation parameters.

[0378] Example 230. The system according to Example 229, wherein the neurological or mental illness is Alzheimer's disease.

[0379] Example 231. The system according to Example 229, wherein the neurological or psychiatric disease is mild cognitive impairment (MCI).

[0380] Example 232. The system according to Example 229, wherein the neurological or psychiatric disease is frontotemporal dementia.

[0381] Example 233. The system according to Example 229, wherein the neurological or psychiatric disease is characterized by changes in brain networks, such as depression (DEP), schizophrenia (SCZ), autism (AUT), attention deficit and hyperactivity disorder (ADHD), bipolar disorder (BP), amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), traumatic brain injury (TBI), insomnia (INS), disorder of consciousness (DOC), headache (HD), multiple sclerosis (MS), stroke (STR), and brain tumor (BT).

[0382] Example 234. The system according to Example 229, wherein the neurological or psychiatric disease is characterized by memory deficits.

[0383] Example 235. The system according to Example 229, wherein the neurological or psychiatric disease is characterized by cognitive control deficits.

[0384] Example 236. The system according to Example 229, wherein the neurological or psychiatric disease is characterized by reduced functional independence.

[0385] Example 237. The system according to Example 229, wherein the non-invasive stimulation provided according to the personalized stimulation parameters is combined with, sequentially or simultaneously delivered with drug interventions affecting the central nervous system.

[0386] Example 238. The system according to Example 229, wherein the non-invasive stimulation provided according to the personalized stimulation parameters is combined with cognitive assessment, cognitive training or cognitive enhancement intervention, and delivered sequentially or simultaneously.

[0387] Example 239. The system according to Example 213, wherein the non-invasive stimulation and behavioral intervention provided according to the personalized stimulation parameters are delivered sequentially or simultaneously.

[0388] Example 240. The system according to Example 213, wherein the non-invasive stimulation according to the personalized stimulation parameters is combined with different non-invasive stimulation interventions delivered sequentially or simultaneously, the different non-invasive stimulations including transcranial electrical stimulation.

[0389] Example 241. An embodiment comprising a system for determining personalized stimulation characteristics of targeted non-invasive brain stimulation for a patient's brain, the system comprising: a. a sensor device for sensing a resting motor threshold in response to providing non-invasive stimulation to a motor cortex of the patient's brain; and b. a stimulation device for non-invasively stimulating a region of the brain located outside the motor cortex; c. wherein the sensor device is further configured to sense evoked potentials in response to the stimulation of the region of the brain located outside the motor cortex; and d. wherein the stimulation device is further configured to adjust the resting motor threshold of the stimulation at least in part based on at least one characteristic of the sensed evoked potentials.

[0390] Example 242. The system according to Example 241, wherein the region located outside the motor cortex is part of the DMN.

[0391] Example 243. The system according to Example 241, wherein the region located outside the motor cortex is the precuneus.

[0392] Example 244. The system according to Example 241, wherein the evoked potentials in response to stimuli are obtained by concurrent TMS-EEG recording.

[0393] Example 245. The system according to Example 241, wherein the characteristic of the evoked potential is the signal amplitude.

[0394] Example 246. The system according to Example 241, wherein one of the characteristics of the evoked potential is the stimulation intensity, and the stimulation intensity is adjusted based on biophysical modeling work simulating the electric field generated in the target area of ​​the patient.

[0395] Example 247. The system according to Example 241, wherein the stimulation device is configured to provide at least one of transcranial electrical stimulation or transcranial magnetic stimulation to the location of the patient's brain.

[0396] Example 248. The system according to Example 241, wherein the plurality of locations are arranged in a grid.

[0397] Example 249. The system according to Example 241, wherein the at least one characteristic includes the peak amplitude of the at least one evoked potential.

[0398] Example 250. The system according to Example 241, wherein selecting the personalized stimulation target includes selecting the location among the plurality of locations having the maximum peak amplitude of the at least one evoked potential.

[0399] Example 251. The system according to Example 241, wherein the sensor device is configured to use electroencephalography (EEG) to sense the at least one evoked potential.

[0400] Example 252. According to the system of Example 241, the stimulation device is configured to treat or improve neurological or psychiatric disorders by providing the non-invasive stimulation to a personalized stimulation target.

[0401] Example 253. The system according to Example 252, wherein the neurological or mental illness is Alzheimer's disease.

[0402] Example 254. The system according to Example 252, wherein the neurological or psychiatric disease is mild cognitive impairment (MCI).

[0403] Example 255. The system according to Example 252, wherein the neurological or mental illness is Alzheimer's disease.

[0404] Example 256. The system according to Example 252, wherein the neurological or psychiatric disease is characterized by changes in brain networks, such as depression (DEP), schizophrenia (SCZ), autism (AUT), attention deficit and hyperactivity disorder (ADHD), bipolar disorder (BP), amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), traumatic brain injury (TBI), insomnia (INS), disorder of consciousness (DOC), headache (HD), multiple sclerosis (MS), stroke (STR), and brain tumor (BT).

[0405] Example 257. The system according to Example 252, wherein the neurological or psychiatric disease is characterized by memory deficits.

[0406] Example 258. The system according to Example 252, wherein the neurological or psychiatric disease is characterized by cognitive control deficits.

[0407] Example 259. The system according to Example 252, wherein the neurological or psychiatric disease is characterized by reduced functional independence.

[0408] Example 260. The system according to Example 252, wherein the non-invasive stimulation provided to the identified personalized stimulation target is combined with, sequentially or simultaneously with, a drug intervention affecting the central nervous system.

[0409] Example 261. The system according to Example 252, wherein the non-invasive stimulus provided to the determined personalized stimulus target is combined with cognitive assessment, cognitive training or cognitive enhancement intervention, delivered sequentially or simultaneously.

[0410] Example 262. The system according to Example 252, wherein the non-invasive stimulation and behavioral intervention provided to the determined personalized stimulation target are delivered in combination, sequentially or simultaneously.

[0411] Example 263. The system according to Example 252, wherein the non-invasive stimulation provided to the determined personalized stimulation target is combined with different non-invasive stimulation interventions delivered sequentially or simultaneously, the different non-invasive stimulations including transcranial electrical stimulation.

[0412] Example 264. An embodiment comprising a system for treating or improving a neurological or psychiatric disorder in a subject, the system comprising: a. a stimulation device for non-invasively stimulating a first location in a first brain region of a plurality of brain regions identified as forming a functional brain network; b. wherein the first location is a subregion of the first brain region that is closely connected to one or more other brain regions of the plurality of brain regions.

[0413] Example 265. The system according to Example 264, wherein the plurality of brain regions forming the brain network are identified based on brain scan data of the subject.

[0414] Example 266. The system according to Example 264, wherein the brain region is part of a default mode network (DMN).

[0415] Example 267. The system according to Example 264, wherein the first brain region includes at least a portion of the precuneus.

[0416] Example 268. The system according to Example 264, wherein the neurological or mental illness is Alzheimer's disease.

[0417] Example 269. The system according to Example 264, wherein the non-invasive stimulation comprises at least one of transcranial electrical stimulation or transcranial magnetic stimulation to the first location of the patient's brain.

[0418] Example 270. The system according to Example 264 further includes a stimulation device for treating or improving the neurological or mental illness by providing the non-invasive stimulation to the first location.

[0419] Example 271. The system according to Example 264, wherein the brain network is one or more of the following: frontoparietal bone control network, sensorimotor network, anterior spur network, dorsal attention network, ventral attention network, visual network, auditory network, or language network.

[0420] Example 272. The system according to Example 264, wherein the first position includes at least a portion of the corner.

[0421] Example 273. The system according to Example 264, wherein the first location includes at least a portion of the temporal lobe.

[0422] Example 274. The system according to Example 264, wherein the first location includes at least a portion of the medial prefrontal cortex.

[0423] Example 275. The system according to Example 264, wherein the neurological or psychiatric disease is mild cognitive impairment (MCI).

[0424] Example 276. The system according to Example 264, wherein the neurological or psychiatric disease is frontotemporal dementia.

[0425] Example 277. The system according to Example 264, wherein the neurological or psychiatric disease is characterized by changes in brain networks, such as depression (DEP), schizophrenia (SCZ), autism (AUT), attention deficit and hyperactivity disorder (ADHD), bipolar disorder (BP), amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), traumatic brain injury (TBI), insomnia (INS), disorder of consciousness (DOC), headache (HD), multiple sclerosis (MS), stroke (STR), and brain tumor (BT).

[0426] Example 278. The system according to any one of Examples 264, wherein the neurological or psychiatric disease is characterized by memory deficits.

[0427] Example 279. The system according to Example 264, wherein the neurological or psychiatric disease is characterized by cognitive control deficits.

[0428] Example 280. The system according to Example 264, wherein the neurological or psychiatric disease is characterized by reduced functional independence.

[0429] Example 281. The system according to Example 264, wherein the non-invasive stimulation provided to the first location is combined with, sequentially or simultaneously delivered with a drug intervention affecting the central nervous system.

[0430] Example 282. The system according to Example 264, wherein the non-invasive stimulus provided to the first location is combined with cognitive assessment, cognitive training or cognitive enhancement intervention, delivered sequentially or simultaneously.

[0431] Example 283. The system according to Example 264, wherein the non-invasive stimulus and behavioral intervention provided to the first location are delivered in combination, sequentially or simultaneously.

[0432] Example 284. The system according to Example 264, wherein the non-invasive stimulation provided to the first location is combined with different non-invasive stimulation interventions delivered sequentially or simultaneously, the different non-invasive stimulations including transcranial electrical stimulation.

[0433] Example 285. An embodiment comprising a system for administering brain stimulation therapy to treat or improve a neurological or psychiatric disorder, the system comprising: a. a data collection platform for collecting and storing data from a subject for identifying optimal stimulation targets; b. a data analysis platform for processing the data from the subject and deriving optimal stimulation parameters for treatment; c. an infrastructure for developing a brain stimulation session plan and monitoring the treatment; and d. a database containing the data from the subject collected prior to and / or during the treatment.

[0434] Example 286. The system according to Example 285, wherein the data collection platform for identifying the optimal brain stimulation parameters of the subject utilizes the method according to Examples 1 to 163.

[0435] Example 287. The system according to Example 285, wherein the system is accessible to a clinician who prescribes brain stimulation therapy.

[0436] Example 288. The system according to Example 285, wherein the data processing is performed on a local system.

[0437] Example 289. The system according to Example 285, wherein the data processing is performed using cloud-based computing.

[0438] Example 290. The system according to Example 285, wherein the system is connected to billing software.

[0439] Example 291. The system according to Example 285, wherein the system is connected to a health insurance provider.

[0440] Example 292. An embodiment comprising a method for delivering a combination of simultaneous noninvasive brain stimulation interventions to a brain region of a subject to treat or improve a neurological or psychiatric disorder, the method comprising: a. scanning the subject's brain; b. identifying targets for noninvasive brain stimulation; wherein the noninvasive brain stimulation is based on transcranial magnetic stimulation or transcranial electrical stimulation.

[0441] Example 293. The method according to Example 292, wherein the transcranial magnetic stimulation technique is theta burst stimulation.

[0442] Example 294. The method according to Example 292, wherein the transcranial magnetic stimulation technique is repetitive transcranial magnetic stimulation.

[0443] Example 295. The method according to Example 292, wherein the transcranial electrical stimulation technique is transcranial alternating current stimulation.

[0444] Example 296. The method according to claim M4, wherein the frequency of transcranial alternating current stimulation is within the gamma frequency EEG band.

[0445] Example 297. The method according to Example 292, wherein the identification of targets for non-invasive brain stimulation of the subject is the method according to Examples 1 to 163.

[0446] Example 298. The method according to Example 292, wherein the transcranial magnetic stimulation technique is theta burst stimulation, the transcranial electrical stimulation technique is transcranial alternating current stimulation, and the transcranial magnetic stimulation and the transcranial electrical stimulation are delivered simultaneously.

[0447] Example 299. The method according to Example 292, wherein the transcranial magnetic stimulation technique is repetitive transcranial magnetic stimulation, the transcranial electrical stimulation technique is transcranial alternating current stimulation, and the transcranial magnetic stimulation and the transcranial electrical stimulation are delivered simultaneously.

[0448] Example 300. The method according to Example 292, wherein the combined non-invasive brain stimulation intervention induces an increase in cortical plasticity.

[0449] Example 301. The method according to Example 292, wherein the target is a brain network.

[0450] Example 302. The method according to claim M10, wherein the brain network is a default mode network (DMN).

[0451] Example 303. The method according to Example 292, wherein the brain region includes at least a portion of the precuneus.

[0452] Example 304. The method according to Example 292, wherein the brain region includes at least a portion of the dorsolateral prefrontal cortex.

[0453] Example 305. The method according to Example 292, wherein the brain region includes at least a portion of the parietal lobe.

[0454] Example 306. The method according to Example 292, wherein the brain region includes at least a portion of the temporal lobe.

[0455] Example 307. The method according to Example 292, wherein the brain region includes at least a portion of the angular gyrus.

[0456] Example 308. The method according to Example 292, wherein the non-invasive stimulation of the combination provided to the determined stimulation target is effective such that, after a single stimulation session, the increase in the TEP amplitude of the subject is at least 15% greater than the increase in the TEP amplitude of an untreated subject.

[0457] Example 309. The method according to Example 292, wherein the non-invasive stimulation of the combination provided to the determined stimulation target is effective such that, after a single stimulation session, the increase in the network oscillation amplitude of the subject is at least 15% greater than the increase in the network oscillation amplitude of an untreated subject.

[0458] Example 310. The method according to Example 292, wherein the non-invasive stimulation of the combination provided to the identified stimulation target is effective such that, after a single stimulation session, the increase in the cortical plasticity level of the subject as measured by TEP is at least 15% greater than the increase in the cortical plasticity level as measured by TEP of an untreated subject.

[0459] Example 311. The method according to Example 292, wherein the non-invasive stimulation of the combination provided to the determined stimulation target is effective such that, after a single stimulation session, the increase in the frequency of oscillatory brain activity of the subject is at least 15% greater than the increase in the frequency of oscillatory brain activity of an untreated subject.

[0460] Example 312. The method according to Example 292, wherein the non-invasive stimulation of the combination provided to the determined stimulation target is delivered at a frequency of daily, weekly, or a combination thereof over multiple consecutive days or multiple non-consecutive days.

[0461] Example 313. The method according to Example 292, wherein the brain network includes one or more of the following: frontoparietal control network, sensorimotor network, anterior spur network, dorsal attention network, ventral attention network, visual network, auditory network, or language network.

[0462] Example 314. The method according to Example 292, wherein the neurological or mental illness is Alzheimer's disease.

[0463] Example 315. The method according to Example 292, wherein the neurological or psychiatric disease is mild cognitive impairment (MCI).

[0464] Example 316. The method according to Example 292, wherein the neurological or psychiatric disease is frontotemporal dementia.

[0465] Example 317. The method according to Example 292, wherein the neurological or psychiatric disease is characterized by changes in brain networks, such as depression (DEP), schizophrenia (SCZ), autism (AUT), attention deficit and hyperactivity disorder (ADHD), bipolar disorder (BP), amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), traumatic brain injury (TBI), insomnia (INS), disorder of consciousness (DOC), headache (HD), multiple sclerosis (MS), stroke (STR), and brain tumor (BT).

[0466] Example 318. The method according to Example 292, wherein the neurological or psychiatric disease is characterized by memory deficits.

[0467] Example 319. The method according to Example 292, wherein the neurological or mental illness is characterized by cognitive control deficits.

[0468] Example 320. The method according to Example 292, wherein the neurological or psychiatric disease is characterized by reduced functional independence.

[0469] Although preferred embodiments of the invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many variations, modifications, and substitutions will now occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein can be used to practice the invention. The appended claims are intended to define the scope of the invention and thereby cover the methods and structures within the scope of these claims and their equivalents.

Claims

1. A system for determining personalized stimulation parameters for non-invasive stimulation of a subject's brain, the system comprising: A stimulation device configured to sequentially stimulate each of a plurality of locations within a subject's brain region using transcranial magnetic stimulation (TMS), the brain region forming a node in a network of connecting nodes in the subject's brain; A sensor device comprising electroencephalography (EEG) electrodes configured to sense multiple TMS evoked potentials in response to sequential stimulation of multiple locations within a brain region; and A processor configured to determine personalized stimulation parameters for the subject based at least in part on at least one characteristic of the plurality of TMS evoked potentials, wherein the personalized stimulation parameters include stimulation location within a brain region and one or more stimulation features, the one or more stimulation features including personalized stimulation intensity.

2. The system of claim 1, wherein the one or more stimulation characteristics further include personalized stimulation frequencies.

3. The system of claim 1, wherein the sensor device is further configured to sense a resting motion threshold in response to providing non-invasive stimulation to the motor cortex of the subject's brain; and the processor is further configured to i) determine a baseline stimulation intensity based at least in part on the resting motion threshold; and ii) adjust the baseline stimulation intensity based at least in part on the personalized stimulation intensity.

4. The system of claim 1, wherein the brain region is a node of a default mode network (DMN).

5. The system of claim 1, wherein the stimulation device is further configured to provide non-invasive stimulation to a determined stimulation location of the subject's brain according to one or more determined stimulation characteristics, wherein the non-invasive stimulation includes at least one of transcranial electrical stimulation or transcranial magnetic stimulation.

6. The system of claim 5, wherein the non-invasive stimulation provided to the determined stimulation site is combined with different non-invasive stimuli delivered sequentially or simultaneously, the different non-invasive stimuli including transcranial electrical stimulation.

7. The system of claim 1, wherein determining the personalized stimulation parameters includes selecting from the plurality of locations the stimulation location having the maximum peak amplitude of the plurality of TMS evoked potentials.

8. The system of claim 1, wherein at least one characteristic of the plurality of TMS evoked potentials includes signal amplitude.

9. The system of claim 1, wherein the stimulation device is configured to sequentially stimulate each of a plurality of locations within a brain region of a subject using a single-pulse TMS.

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