System and method for characterizing an individual response to brain disturbance of a

Abstract

Provided herein are devices, systems, and methods for non-invasive assessment of synaptic dysfunction in a patient with Alzheimer's disease. Transcranial magnetic stimulation (TMS) is applied to at least one brain region of the patient. A plurality of TMS-evoked potentials generated in response to the TMS applied to the at least one brain region of the patient are recorded with a plurality of electroencephalogram (EEG) electrodes. At least one characteristic of the plurality of TMS-evoked potentials is analyzed to assess synaptic dysfunction of the patient. An indication of the synaptic dysfunction assessment of the patient is output.

Description

[0001] Cross-references

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 356,609, filed on June 29, 2022, which is incorporated herein by reference. Background Art

[0003] Alzheimer's disease (AD) is one of the most devastating pathologies affecting elderly individuals in the Western world population. It is considered one of the most serious medical, economic and social emergencies facing society today and even more broadly in the coming decades. Currently, there is no effective treatment and the future of patients diagnosed with AD is full of uncertainty as the course of the disease cannot currently be predicted.

[0004] In recent years, increasing evidence supports the idea that synaptic density loss may be an early event and precedes neuronal degeneration, suggesting that damage to synaptic mechanisms plays a key role in the pathogenesis of AD. These altered synaptic mechanisms are associated with reduced dendritic spines, neuronal network disorder, and cell death. Synaptic dysfunction represents a key driver of Alzheimer's disease, which ultimately leads to brain functional network dysfunction and cognitive degeneration, making it a promising therapeutic target. However, there is a lack of validated biomarkers for measuring synaptic dysfunction in AD. Summary of the invention

[0005] In light of the concept of physics and biology that complex systems, such as the human brain, are better characterized by observing their responses to external perturbations rather than by their spontaneous activity, systems and methods for stress testing the brain are described herein. Some aspects of the present disclosure combine hardware and software and are optimized for use with patients suffering from neurodegenerative disorders, particularly patients suffering from Alzheimer's disease and more generally dementia.

[0006] These methods are combined with data collection methods to capture brain activity before, during and after the disturbance, allowing the response of individuals to the disturbance to be recorded and thus reflecting the unique characteristics of each individual brain. The nature of the disturbance can vary according to the target response or brain system. These methods are related to concepts and principles from neurophysiology, neurology, neuroscience, cell biology (including but not limited to brain plasticity, Hebbian plasticity, excitation-inhibition balance, network complexity, connectomics, network elasticity, structural and functional brain network analysis). These systems and methods are particularly applicable to the fields of neurodegeneration, physiological and pathological aging, dementia and Alzheimer's disease, specifically for applications including but not limited to the identification of patients and the prediction of therapeutic responses.

[0007] The brain may be viewed as a complex dynamic system of organized distributed neural networks with specific spatial-temporal properties. The complex geometry of brain structure and functional connectivity (the so-called brain connectome) explains individual differences in cognitive performance, susceptibility to neurological and psychiatric conditions, and healthy aging trajectories. Neuroimaging and electrophysiological methods are sometimes used to characterize such complexity, observing brain anatomy and spontaneous functional properties of brain activity collected at rest. In limited cases, cognitive or behavioral stimulation is provided to elicit transient changes in brain activity (e.g., during memory tasks) that may be relevant when quantifying cognitive performance. However, these methods have many limitations, such as (i) these methods require active cooperation from the subject being evaluated (e.g., performing a memory task), which may be a limiting factor for patients with cognitive impairment; (ii) these methods often rely on perturbations (e.g., cognitive or behavioral stimulation) that affect (e.g., activate or deactivate) multiple brain regions or networks of the brain (sometimes the entire brain), limiting the specificity of the conclusions drawn from the data, and also preventing the assessment of the brain's response to perturbations at a higher spatial resolution in the range of 1 or 2 mm rather than centimeters, which is often necessary when studying the brain of patients with conditions that affect specific regions or networks of the brain; finally, (iii) the nature of the stimulation often does not allow for the engagement of specific classes of neurons in the brain, or for the induction of controlled, sustained activation or deactivation by directly inducing neuronal activity (rather than indirectly promoting neuronal activity by displaying stimulation on a screen), which may be important when studying aspects of the brain of patients with brain changes associated with specific types of neurons, neurotransmitters, or cortical circuits. These aspects may be important when studying brain changes in patients with dementia, and specifically in patients with Alzheimer's disease. Patients with AD show altered synaptic activity in specific brain networks (including the default mode network-DMN), specific regions (including the precuneus, angular gyrus, medial frontal cortex, hippocampus and temporal lobe), and specific categories of neurons (including parvalbumin-positive interneurons) and neurotransmitters (including GABA and glutamate). Although several AD biomarkers are widely used and considered useful for diagnosis, they still lack sufficient accuracy for evaluating disease severity and predicting disease progression. Few imaging biomarkers specifically developed for evaluating synaptic dysfunction have been developed. An emerging method for detecting synaptic loss in neurodegenerative dementia is based on PET tracers targeting synaptic vesicle proteins. However, there is limited scientific evidence to support this method and the PET procedure is complex, costly and requires exposure of patients to radioactive tracers. In this case, a novel method that combines neuroimaging and electrophysiological methods to measure synaptic and network dysfunction in patients with AD can at least partially solve some challenges with the above-mentioned existing methods.

[0008] For at least these reasons, the inventors have developed a system and methods in which perturbations are induced by electrical or magnetic stimulation of the brains of patients with AD, and the individual response characteristics from each patient are collected, analyzed, and summarized into metrics and indicators with diagnostic and prognostic value. Each response is captured at high spatial (i.e., 1 cubic millimeter) and temporal resolution (i.e., at least 1 millisecond) and represents the brain's response to the focused electromagnetic perturbations.

[0009] The present invention also relates to the following numbered clauses:

[0010] Clause 1. A method of non-invasively assessing synaptic dysfunction in a patient suffering from Alzheimer's disease, the method comprising:

[0011] applying transcranial magnetic stimulation (TMS) to at least one brain region of the patient;

[0012] recording a plurality of TMS-evoked potentials with a plurality of electroencephalogram (EEG) electrodes in response to applying the TMS to the at least one brain region of the patient;

[0013] analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient; and

[0014] An indication of the synaptic dysfunction assessment of the patient is output.

[0015] Clause 2. The method of Clause 1, wherein applying TMS to at least one brain region of the patient comprises applying a single pulse of TMS to the at least one brain region.

[0016] Clause 3. The method of clause 1 or 2, wherein applying TMS to at least one brain region of the patient comprises applying repetitive TMS to the at least one brain region.

[0017] Clause 4. The method of any one of clauses 1 to 3, wherein applying TMS to at least one brain region of the patient comprises applying TMS at multiple frequencies simultaneously.

[0018] Clause 5. The method of any one of clauses 1 to 4, wherein applying TMS to at least one brain region of the patient comprises applying patterned bursts of TMS to the at least one brain region.

[0019] Clause 6. The method of Clause 5, wherein the patterned bursts of TMS comprise a continuous or intermittent theta burst pattern.

[0020] Clause 7. The method of any one of Clauses 1 to 6, wherein applying TMS to at least one brain region of the patient comprises applying TMS to a single brain region.

[0021] Clause 8. The method of any one of Clauses 1 to 7, wherein applying TMS to at least one brain region of the patient comprises applying TMS to a plurality of different brain regions.

[0022] Clause 9. The method of clause 8, wherein the plurality of different brain regions are two or more nodes of the same brain network.

[0023] Clause 10. The method of clause 9, wherein the brain network is a long-term memory network.

[0024] Clause 11. The method of clause 9 or 10, wherein the brain network is a default mode network.

[0025] Clause 12. The method of any one of clauses 9 to 11, wherein the brain network is a fronto-parietal network.

[0026] Clause 13. The method of any one of Clauses 1 to 12, wherein the at least one brain region comprises at least one brain region in a temporal lobe of the patient's brain.

[0027] Clause 14. The method according to any one of clauses 1 to 13, wherein the at least one brain region comprises the precuneus region.

[0028] Clause 15. The method of any one of clauses 1 to 15, further comprising determining a location of the at least one brain region to apply the TMS based at least in part on spatial and functional searches.

[0029] Clause 16. The method of any one of clauses 1 to 15, further comprising determining a location of the at least one brain region to apply the TMS based at least in part on magnetic resonance imaging (MRI) data associated with the patient's brain.

[0030] Clause 17. A method according to clause 16, wherein determining the position of the at least one brain region to apply the TMS based at least in part on magnetic resonance imaging (MRI) data associated with the patient's brain includes generating a biophysical model of the brain anatomy of the patient's brain based on the MRI data and determining the position based at least in part on the generated biophysical model.

[0031] Clause 18. The method of any one of clauses 1 to 17, further comprising determining a location of the at least one brain region to apply the TMS based at least in part on data from a plurality of patients.

[0032] Clause 19. A method according to any one of clauses 1 to 18, further comprising recording electromyography (EMG) data in response to applying the TMS to at least one brain region of the patient, wherein the assessment of the synaptic dysfunction of the patient is further based at least in part on at least one characteristic of the recorded EMG data.

[0033] Clause 20. A method according to any one of clauses 1 to 19, further comprising determining a resting motor threshold (RMT) of the patient, wherein applying transcranial magnetic stimulation (TMS) to at least one brain region of the patient comprises applying TMS at a certain intensity based on the RMT of the patient.

[0034] Clause 21. The method of Clause 20, wherein the intensity of the applied TMS is set to 90% of the RMT of the patient.

[0035] Clause 22. The method of any one of clauses 9 to 12, wherein the brain network is a pre-salience network.

[0036] Clause 23. The method of any one of clauses 9 to 12 or 22, wherein the brain network is a sensorimotor network.

[0037] Clause 24. A method according to any one of clauses 9 to 12, 22 or 23, wherein the brain network is an attention network.

[0038] Clause 25. The method according to any one of clauses 1 to 24, wherein the at least one brain region is a cortical brain region connected to the hippocampus and the temporal polar region of the brain by white matter fibers.

[0039] Clause 26. The method according to any one of clauses 1 to 25, wherein the at least one brain region is a cortical brain region connected to a region of the default mode network by white matter fibers.

[0040] Clause 27. The method according to any one of clauses 1 to 26, wherein the at least one brain region is the dorsolateral prefrontal cortex.

[0041] Clause 28. A method according to any one of clauses 1 to 27, wherein at least one of the brain regions is a brain region with high network resilience, i.e., a brain region in the 5th percentile ranked based on its resilience that is able to maintain a high level of network communication efficiency despite simulated or real damage.

[0042] Clause 29. A method according to any one of clauses 1 to 28, wherein at least one of the brain regions is a brain region with relatively high network modularity, in which brain activity is organized in more than 2 separate networks based on functional connectivity measurements.

[0043] Clause 30. The method of any one of clauses 1 to 29, wherein the at least one brain region is a brain region or network that supports episodic memory.

[0044] Clause 31. The method of any one of clauses 1 to 30, wherein the at least one brain region is a brain region or network that supports autobiographical memory.

[0045] Clause 32. The method of any one of clauses 1 to 31, wherein the at least one brain region is a brain region or network that supports emotion processing.

[0046] Clause 33. The method of any one of clauses 1 to 32, wherein the at least one brain region is a brain region or network that supports mental rotation ability.

[0047] Clause 34. The method of any one of clauses 1 to 33, wherein the at least one brain region is a brain region or network that supports mental planning.

[0048] Clause 35. The method of any one of clauses 1 to 34, wherein the at least one brain region is a brain region or network that supports executive function.

[0049] Clause 36. The method according to any one of clauses 1 to 35, wherein the at least one brain region is a brain region or network where amyloid beta protein is present.

[0050] Clause 37. The method according to any one of clauses 1 to 36, wherein the at least one brain region is a brain region or network where tau protein is present.

[0051] Clause 38. The method according to any one of Clauses 1 to 37, wherein the at least one brain region is a brain region or network having altered levels of neuroinflammation.

[0052] Clause 39. The method according to any one of Clauses 1 to 38, wherein the at least one brain region is a brain region or network with altered synaptic plasticity.

[0053] Clause 40. The method according to any one of clauses 1 to 39, wherein the at least one brain region is a brain region or network with hypoperfusion and hypometabolism.

[0054] Clause 41. The method of any one of clauses 1 to 40, wherein the at least one brain region is a brain region or network that supports cognitive reserve.

[0055] Clause 42. The method according to any one of Clauses 1 to 41, wherein the at least one brain region is a brain region or network with elevated neuroinflammation.

[0056] Clause 43. The method of any one of clauses 1 to 42, wherein the at least one brain region is a brain region or network that supports functional independence in elderly individuals.

[0057] Clause 44. The method of any one of clauses 1 to 43, wherein the at least one brain region is a brain region or network having altered oscillatory activity.

[0058] Clause 45. A method according to any one of clauses 1 to 44, wherein recording multiple TMS-induced potentials in response to applying the TMS to the at least one brain region of the patient includes recording at least one TMS-induced potential from the at least one brain region to which the TMS is applied.

[0059] Clause 46. A method according to any one of clauses 1 to 45, wherein recording multiple TMS-induced potentials in response to applying the TMS to the at least one brain region of the patient includes recording at least one TMS-induced potential from one or more brain regions that are different from the at least one brain region to which TMS is applied.

[0060] Clause 47. A method according to any one of clauses 1 to 46, wherein analyzing at least one characteristic of the plurality of TMS-induced potentials to assess synaptic dysfunction in the patient comprises comparing the at least one characteristic of the plurality of TMS-induced potentials with data collected from individuals not suffering from Alzheimer's disease or previous data collected from the patient.

[0061] Clause 48. A method according to any one of clauses 1 to 47, wherein analyzing at least one characteristic of the multiple TMS-induced potentials to assess synaptic dysfunction in the patient comprises comparing the at least one characteristic of the multiple TMS-induced potentials with data collected from multiple patients suffering from Alzheimer's disease, wherein the assessment of synaptic dysfunction comprises determining that the patient suffers from a subtype of Alzheimer's disease.

[0062] Clause 49. A method according to any one of clauses 1 to 48, wherein the applying and the recording are performed after providing the patient with treatment for Alzheimer's disease, and wherein analyzing at least one characteristic of the multiple TMS-induced potentials to assess synaptic dysfunction in the patient comprises comparing the at least one characteristic of the multiple TMS-induced potentials with data collected from the patient before providing the patient with the treatment for Alzheimer's disease.

[0063] Clause 50. A method according to any one of clauses 1 to 49, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient comprises analyzing the plurality of TMS-evoked potentials using at least one machine learning classification model.

[0064] Clause 51. A method according to any one of clauses 1 to 50, wherein the at least one characteristic of the plurality of TMS-induced potentials comprises one or more of a shape, an amplitude, a phase or a timing of one or more of the plurality of TMS-induced potentials.

[0065] Clause 52. A method according to any one of clauses 1 to 51, wherein analyzing at least one characteristic of the multiple TMS-induced potentials to assess synaptic dysfunction in the patient comprises determining an amount of time for an energy level of one or more of the multiple TMS-induced potentials to return to a baseline energy level before application of the TMS, wherein the assessment of synaptic dysfunction is based at least in part on the amount of time for the energy level of one or more of the multiple TMS-induced potentials to return to the baseline energy level.

[0066] Clause 53. The method of Clause 52, wherein the assessment of synaptic dysfunction is further based at least in part on an amplitude of the one or more TMS-evoked potentials of the plurality of TMS-evoked potentials.

[0067] Clause 54. A method according to any one of clauses 1 to 53, wherein analyzing at least one characteristic of the plurality of TMS-induced potentials to assess synaptic dysfunction in the patient comprises projecting the plurality of TMS-induced potentials onto structural magnetic resonance imaging (MRI) data.

[0068] Clause 55. The method of clause 54, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient further comprises extracting time series data from one or more regions of interest defined based on the structural MRI data.

[0069] Clause 56. The method of clause 55, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient further comprises using the extracted time series data to evaluate synchronization across electrodes and / or brain regions.

[0070] Clause 57. The method of any one of Clauses 1 to 56, further comprising determining a treatment for the patient based at least in part on the assessment of synaptic dysfunction and providing the treatment to the patient.

[0071] Clause 58. A method according to any one of Clauses 1 to 57, wherein the assessment of synaptic dysfunction comprises an assessment of neuroinflammation, the method further comprising determining a treatment for the patient based at least in part on the assessment of neuroinflammation and providing the treatment to the patient.

[0072] Clause 59. A method according to any one of Clauses 1 to 58, wherein the assessment of synaptic dysfunction comprises an assessment of protein clearance mechanisms and brain protein load, and the method further comprises determining a treatment for the patient based at least in part on the assessment of the protein clearance mechanisms and brain protein load and providing the treatment to the patient.

[0073] Clause 60. A method according to any one of clauses 1 to 59, wherein the assessment of synaptic dysfunction comprises an assessment of brain network dysfunction and altered connectivity patterns, and the method further comprises determining a treatment for the patient based at least in part on the assessment of the brain network dysfunction and altered connectivity patterns and providing the treatment to the patient.

[0074] Clause 61. A method according to any one of clauses 1 to 60, wherein the assessment of synaptic dysfunction comprises an assessment of cortical excitability levels, the method further comprising determining a treatment for the patient based at least in part on the assessment of the cortical excitability levels and providing the treatment to the patient.

[0075] Clause 62. A method according to any one of clauses 1 to 61, wherein the assessment of synaptic dysfunction comprises an assessment of altered brain oscillatory activity, the method further comprising determining a treatment for the patient based at least in part on the assessment of altered brain oscillatory activity and providing the treatment to the patient.

[0076] Clause 63. A method according to any one of clauses 1 to 62, wherein perturbation of brain function is applied by a combination of TMS and tACS stimulation, the method further comprising delivering tACS to induce brain oscillatory activity at a specific frequency band, while simultaneously delivering TMS to tACS to assess the patient's brain plasticity and cortical excitability level.

[0077] Clause 64. The method of clause 63, wherein tACS perturbations are delivered in the gamma frequency band between 30 Hz and 300 Hz.

[0078] Clause 65. The method of clause 63 or 64, wherein the tACS perturbation is delivered using electrical noise stimulation at a frequency between 1 Hz and 1000 Hz based on an electrical pattern including but not limited to patterns of brown, pink, white and random noise.

[0079] Clause 66. A method according to any one of clauses 1 to 65, wherein perturbation of brain function is applied by a combination of TMS and tACS stimulation, the method further comprising delivering tACS to induce brain oscillatory activity at a specific frequency band, and simultaneously delivering repetitive TMS (rTMS) to tACS to modulate the patient's brain plasticity and cortical excitability.

[0080] Clause 67. The method of clause 63, wherein the TMS perturbation is delivered in the form of theta burst stimulation, including intermittent and continuous stimulation modes.

[0081] Clause 68. A non-invasive brain assessment system comprising:

[0082] a transcranial magnetic stimulation (TMS) device configured to apply TMS to at least one brain region of the patient;

[0083] a plurality of electroencephalogram (EEG) electrodes configured to record a plurality of TMS-evoked potentials in response to applying the TMS to the at least one brain region of the patient; and at least one computer processor programmed to analyze at least one characteristic of the plurality of TMS-evoked potentials to provide an assessment of synaptic dysfunction in the patient.

[0084] Clause 69. The system of Clause 68, further comprising a neuronavigation system comprising at least one camera, wherein the neuronavigation system is configured to facilitate positioning of the TMS device.

[0085] Clause 70. The system of clause 68 or 69, further comprising an electromyography (EMG) system configured to determine an EMG signal in response to applying the TMS to the at least one brain region of the patient.

[0086] Clause 71. The system of any one of clauses 68 to 70, wherein applying TMS to at least one brain region of the patient comprises applying a single pulse of TMS to the at least one brain region.

[0087] Clause 72. The system of any one of clauses 68 to 71, wherein applying TMS to at least one brain region of the patient comprises applying repetitive TMS to the at least one brain region.

[0088] Clause 73. The system of any one of clauses 68 to 72, wherein applying TMS to at least one brain region of the patient comprises applying TMS at multiple frequencies simultaneously.

[0089] Clause 74. The system of any one of clauses 68 to 73, wherein applying TMS to at least one brain region of the patient comprises applying patterned bursts of TMS to the at least one brain region.

[0090] Clause 75. The system of clause 74, wherein the patterned bursts of TMS comprise a continuous or intermittent theta burst pattern.

[0091] Clause 76. The system of any one of Clauses 68 to 75, wherein applying TMS to at least one brain region of the patient comprises applying TMS to a single brain region.

[0092] Clause 77. The system of any one of Clauses 68 to 76, wherein applying TMS to at least one brain region of the patient comprises applying TMS to a plurality of different brain regions.

[0093] Clause 78. The system of clause 77, wherein the plurality of different brain regions comprise two or more nodes of the same brain network.

[0094] Clause 79. A system according to clause 78, wherein the brain network is a long-term memory network.

[0095] Clause 80. A system according to clause 78 or 79, wherein the brain network is a default mode network.

[0096] Clause 81. The system of any one of clauses 78 to 80, wherein the brain network is a fronto-parietal network.

[0097] Clause 82. The system of any one of Clauses 68 to 81, wherein the at least one brain region comprises at least one brain region in a temporal lobe of the patient's brain.

[0098] Clause 83. The system of any one of clauses 68 to 82, wherein the at least one brain region comprises the precuneus region.

[0099] Clause 84. The system of any one of clauses 68 to 83, wherein the at least one computer processor is further programmed to determine the location of the at least one brain region to apply the TMS based at least in part on spatial and functional searches.

[0100] Clause 85. A system according to any one of clauses 68 to 84, wherein the at least one computer processor is further programmed to determine the location of the at least one brain region to apply the TMS based at least in part on magnetic resonance imaging (MRI) data associated with the patient's brain.

[0101] Clause 86. A system according to clause 85, wherein determining the position of the at least one brain region to apply the TMS based at least in part on magnetic resonance imaging (MRI) data associated with the patient's brain includes generating a biophysical model of the brain anatomy of the patient's brain based on the MRI data and determining the position based at least in part on the generated biophysical model.

[0102] Clause 87. The system of any one of clauses 68 to 86, wherein the at least one processor is further programmed to determine a location of the at least one brain region to apply the TMS based at least in part on data from a plurality of patients.

[0103] Clause 88. The system of any one of clauses 70 to 87, wherein the assessment of the synaptic dysfunction of the patient is further based at least in part on at least one characteristic of the determined EMG signal.

[0104] Clause 89. A system according to any one of clauses 68 to 88, wherein the at least one processor is further programmed to determine a resting motor threshold (RMT) of the patient, and wherein applying transcranial magnetic stimulation (TMS) to at least one brain region of the patient comprises applying TMS at a certain intensity based on the RMT of the patient.

[0105] Clause 90. The method of clause 89, wherein the intensity of the applied TMS is set to 90% of the RMT of the patient.

[0106] Clause 91. A system according to any one of clauses 78 to 81, wherein the brain network is a pre-salience network.

[0107] Clause 92. A system according to any one of clauses 78 to 81 or 91, wherein the brain network is a sensorimotor network.

[0108] Clause 93. A system according to any one of clauses 78 to 81, 91 or 92, wherein the brain network is an attention network.

[0109] Clause 94. The system of any one of clauses 68 to 93, wherein the at least one brain region is a cortical brain region connected to the hippocampus and the temporal polar regions of the brain by white matter fibers.

[0110] Clause 95. The system of any one of clauses 68 to 94, wherein the at least one brain region is a cortical brain region connected to a region of the default mode network via white matter fibers.

[0111] Clause 96. The system of any one of clauses 68 to 95, wherein the at least one brain region is a brain region within the dorsolateral prefrontal cortex.

[0112] Clause 97. A system according to any one of clauses 68 to 96, wherein at least one of the brain regions is a brain region with high network resilience, i.e., a brain region in the 5th percentile ranked based on its resilience that is capable of maintaining a high level of network communication efficiency despite simulated or real injury.

[0113] Clause 98. A system according to any one of clauses 68 to 97, wherein at least one of the brain regions is a brain region with relatively high network modularity, in which brain activity is organized in more than 2 separate networks based on functional connectivity measurements.

[0114] Clause 99. The system of any one of clauses 68 to 98, wherein the at least one brain region is a brain region or network that supports episodic memory.

[0115] Clause 100. The system of any one of clauses 68 to 99, wherein the at least one brain region is a brain region or network that supports autobiographical memory.

[0116] Clause 101. The system of any one of clauses 68 to 100, wherein the at least one brain region is a brain region or network that supports emotion processing.

[0117] Clause 102. The system of any one of clauses 68 to 101, wherein the at least one brain region is a brain region or network that supports mental rotation ability.

[0118] Clause 103. The system of any one of clauses 68 to 102, wherein the at least one brain region is a brain region or network that supports mental planning.

[0119] Clause 104. The system of any one of clauses 68 to 103, wherein the at least one brain region is a brain region or network that supports executive function.

[0120] Clause 105. The system according to any one of clauses 68 to 104, wherein the at least one brain region is a brain region or network where amyloid beta protein is present.

[0121] Clause 106. The system of any one of Clauses 68 to 105, wherein the at least one brain region is a brain region or network where tau protein is present.

[0122] Clause 107. The system of any one of Clauses 68 to 106, wherein the at least one brain region is a brain region or network having altered levels of neuroinflammation.

[0123] Clause 108. The system of any one of Clauses 68 to 107, wherein the at least one brain region is a brain region or network having altered synaptic plasticity.

[0124] Clause 109. The system of any one of Clauses 68 to 108, wherein the at least one brain region is a brain region or network having hypoperfusion and hypometabolism.

[0125] Clause 110. The system of any one of clauses 68 to 109, wherein the at least one brain region is a brain region or network that supports cognitive reserve.

[0126] Clause 111. The system of any one of Clauses 68 to 110, wherein the at least one brain region is a brain region or network with elevated neuroinflammation.

[0127] Clause 112. The system of any one of clauses 68 to 111, wherein the at least one brain region is a brain region or network that supports functional independence in elderly individuals.

[0128] Clause 113. The system of any one of clauses 68 to 112, wherein the at least one brain region is a brain region or network having altered oscillatory activity.

[0129] Clause 114. A system according to any one of clauses 68 to 113, wherein recording multiple TMS-induced potentials in response to applying the TMS to the at least one brain region of the patient includes recording at least one TMS-induced potential from the at least one brain region to which the TMS is applied.

[0130] Clause 115. A system according to any one of clauses 68 to 114, wherein recording multiple TMS-induced potentials in response to applying the TMS to the at least one brain region of the patient includes recording at least one TMS-induced potential from one or more brain regions that are different from the at least one brain region to which TMS is applied.

[0131] Clause 116. A system according to any one of clauses 68 to 115, wherein analyzing at least one characteristic of the plurality of TMS-induced potentials to assess synaptic dysfunction in the patient comprises comparing the at least one characteristic of the plurality of TMS-induced potentials with data collected from individuals who do not have Alzheimer's disease or previous data collected from the patient.

[0132] Clause 117. A system according to any one of clauses 68 to 116, wherein analyzing at least one characteristic of the plurality of TMS-induced potentials to assess synaptic dysfunction in the patient comprises comparing the at least one characteristic of the plurality of TMS-induced potentials with data collected from a plurality of patients suffering from Alzheimer's disease, wherein the assessment of synaptic dysfunction comprises determining that the patient suffers from a subtype of Alzheimer's disease.

[0133] Clause 118. A system according to any one of clauses 68 to 117, wherein the applying and the recording are performed after providing the patient with treatment for Alzheimer's disease, and wherein analyzing at least one characteristic of the multiple TMS-induced potentials to assess synaptic dysfunction in the patient comprises comparing the at least one characteristic of the multiple TMS-induced potentials with data collected from the patient before providing the patient with the treatment for Alzheimer's disease.

[0134] Clause 119. A system according to any one of clauses 68 to 118, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient comprises analyzing the plurality of TMS-evoked potentials using at least one machine learning classification model.

[0135] Clause 120. A system according to any one of clauses 68 to 119, wherein the at least one characteristic of the plurality of TMS-induced potentials comprises one or more of a shape, an amplitude, a phase, or a timing of one or more of the plurality of TMS-induced potentials.

[0136] Clause 121. The system of any one of clauses 68 to 120, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient comprises:

[0137] Determining an amount of time for an energy level of one or more of the plurality of TMS-induced potentials to return to a baseline energy level prior to application of the TMS, wherein the assessment of synaptic dysfunction is based at least in part on the amount of time for the energy level of the one or more of the plurality of TMS-induced potentials to return to the baseline energy level.

[0138] Clause 122. The system of clause 121, wherein the assessment of synaptic dysfunction is further based at least in part on an amplitude of the one or more TMS-evoked potentials of the plurality of TMS-evoked potentials.

[0139] Clause 123. A system according to any one of clauses 68 to 122, wherein analyzing at least one characteristic of the plurality of TMS-induced potentials to assess synaptic dysfunction in the patient comprises projecting the plurality of TMS-induced potentials onto structural magnetic resonance imaging (MRI) data.

[0140] Clause 124. The system of clause 123, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient further comprises extracting time series data from one or more regions of interest defined based on the structural MRI data.

[0141] Clause 125. The system of clause 123 or 124, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient further comprises using the extracted time series data to evaluate synchronization across electrodes and / or brain regions.

[0142] Clause 126. A system according to any one of clauses 68 to 125, wherein the at least one computer processor is further programmed to determine a treatment for the patient based at least in part on the assessment of synaptic dysfunction, and the TMS device is further configured to apply TMS to at least one brain region of the patient to provide the treatment to the patient.

[0143] Clause 127. A system according to any one of clauses 68 to 126, wherein the assessment of synaptic dysfunction includes an assessment of neuroinflammation, the at least one computer processor is further programmed to determine a treatment for the patient based at least in part on the assessment of neuroinflammation, and the TMS device is further configured to apply TMS to at least one brain region of the patient to provide the treatment to the patient.

[0144] Clause 128. A system according to any one of clauses 68 to 127, wherein the assessment of synaptic dysfunction includes an assessment of protein clearance mechanisms and brain protein load, the at least one computer processor is further programmed to determine a treatment for the patient based at least in part on the assessment of the protein clearance mechanisms and brain protein load, and the TMS device is further configured to apply TMS to at least one brain region of the patient to provide the treatment to the patient.

[0145] Clause 129. A system according to any one of clauses 68 to 128, wherein the assessment of synaptic dysfunction includes an assessment of brain network dysfunction and altered connectivity patterns, the at least one computer processor is further programmed to determine a treatment for the patient based at least in part on the assessment of brain network dysfunction and altered connectivity patterns, and the TMS device is further configured to apply TMS to at least one brain region of the patient to provide the treatment to the patient.

[0146] Clause 130. A system according to any one of clauses 68 to 129, wherein the assessment of synaptic dysfunction includes an assessment of cortical excitability levels, the at least one computer processor is further programmed to determine a treatment for the patient based at least in part on the assessment of cortical excitability levels, and the TMS device is further configured to apply TMS to at least one brain region of the patient to provide the treatment to the patient.

[0147] Clause 131. A system according to any one of clauses 68 to 130, wherein the assessment of synaptic dysfunction includes an assessment of altered brain oscillatory activity, the at least one computer processor is further programmed to determine a treatment for the patient based at least in part on the assessment of altered brain oscillatory activity, and the TMS device is further configured to apply TMS to at least one brain region of the patient to provide the treatment to the patient.

[0148] Clause 132. A system according to any one of clauses 68 to 131, wherein the assessment of synaptic dysfunction comprises simultaneously delivering magnetic stimulation (TMS) and electrical stimulation, and recording brain activity via EEG recording, wherein the system comprises at least one TMS device configured to apply TMS to at least one brain region of a patient; at least one electrical stimulation device for delivering transcranial electrical stimulation to at least one brain region of a patient; and at least one EEG device that records brain activity from scalp EEG electrodes.

[0149] Clause 133. The system of clause 132, wherein the electrical stimulation is delivered in the form of tACS.

[0150] Clause 134. The system of clause 133, wherein tACS perturbations are delivered in the gamma frequency band between 30 Hz and 300 Hz.

[0151] Clause 135. A system according to clause 133 or 134, wherein the tACS perturbation is delivered using electrical noise stimulation at a frequency between 1 Hz and 1000 Hz based on an electrical pattern including but not limited to patterns of brown, pink, white and random noise.

[0152] Clause 136. The system of any one of clauses 133 to 135, wherein the TMS perturbation is delivered in the form of theta burst stimulation, including intermittent and continuous stimulation modes.

[0153] Clause 137. A system according to any one of clauses 133 to 136, wherein the TMS, the electrical stimulation and the EEG recording are delivered by a single device that includes TMS, tES and EEG capabilities.

[0154] Clause 138. A method of providing non-invasive assessment of a person's brain state, the method comprising:

[0155] applying non-invasive stimulation to at least one brain region of the person;

[0156] recording brain signals using a brain activity recording device in response to applying the non-invasive stimulation to the at least one brain region of the person;

[0157] analyzing at least one characteristic of the recorded brain signals to provide said non-invasive assessment of said brain state of said person; and

[0158] An indication of the non-invasive assessment of the brain state of the person is output.

[0159] Clause 139. The method of clause 138, wherein applying non-invasive stimulation to at least one brain region of the person comprises applying one or more of transcranial magnetic stimulation (TMS), transcranial current stimulation (tCS), or focused ultrasound stimulation (tFUS).

[0160] Clause 140. The method of Clause 139, wherein applying non-invasive stimulation to at least one brain region of the human comprises applying at least two of TMS, tCS, or tFUS.

[0161] Clause 141. The method of any one of clauses 138 to 140, wherein the brain activity recording device is an electroencephalogram (EEG) device.

[0162] Clause 142. The method of any one of clauses 138 to 141, wherein applying TMS to at least one brain region of the patient comprises applying a single pulse of TMS to the at least one brain region.

[0163] Clause 143. The method of any one of clauses 138 to 142, wherein applying TMS to at least one brain region of the patient comprises applying repetitive TMS to the at least one brain region.

[0164] Clause 144. The method of any one of clauses 138 to 143, wherein applying TMS to at least one brain region of the patient comprises applying TMS at multiple frequencies simultaneously.

[0165] Clause 145. The method of any one of clauses 138 to 144, wherein applying TMS to at least one brain region of the patient comprises applying patterned bursts of TMS to the at least one brain region.

[0166] Clause 146. The method of clause 145, wherein the patterned bursts of TMS comprise a continuous or intermittent theta burst pattern.

[0167] Clause 147. The method of any one of clauses 138 to 146, wherein applying TMS to at least one brain region of the patient comprises applying TMS to a single brain region.

[0168] Clause 148. The method of any one of clauses 138 to 147, wherein applying TMS to at least one brain region of the patient comprises applying TMS to a plurality of different brain regions.

[0169] Clause 149. The method of clause 148, wherein the plurality of different brain regions are two or more nodes of the same brain network.

[0170] Clause 150. A method according to clause 149, wherein the brain network is a long-term memory network.

[0171] Clause 151. The method of clause 149 or 150, wherein the brain network is a default mode network.

[0172] Clause 152. The method of any one of clauses 149 to 151, wherein the brain network is a fronto-parietal network.

[0173] Clause 153. The method of any one of clauses 138 to 152, wherein the at least one brain region comprises at least one brain region in a temporal lobe of the patient's brain.

[0174] Clause 154. The method of any one of clauses 138 to 153, wherein the at least one brain region comprises the precuneus region.

[0175] Clause 155. The method of any one of clauses 138 to 154, further comprising determining a location of the at least one brain region to apply the TMS based at least in part on spatial and functional searches.

[0176] Clause 156. The method of any one of clauses 138 to 155, further comprising determining a location of the at least one brain region to apply the TMS based at least in part on magnetic resonance imaging (MRI) data associated with the patient's brain.

[0177] Clause 157. A method according to clause 156, wherein determining the position of the at least one brain region to apply the TMS based at least in part on magnetic resonance imaging (MRI) data associated with the patient's brain includes generating a biophysical model of the brain anatomy of the patient's brain based on the MRI data and determining the position based at least in part on the generated biophysical model.

[0178] Clause 158. The method of any one of clauses 138 to 157, further comprising determining a location of the at least one brain region to apply the TMS based at least in part on data from a plurality of patients.

[0179] Clause 159. A method according to any one of clauses 138 to 158, further comprising recording electromyography (EMG) data in response to applying the TMS to at least one brain region of the patient, wherein the assessment of the brain state of the person is further based at least in part on at least one characteristic of the recorded EMG data.

[0180] Clause 160. A method according to any one of clauses 138 to 159, further comprising determining a resting motor threshold (RMT) of the patient, wherein applying transcranial magnetic stimulation (TMS) to at least one brain region of the patient comprises applying TMS at a certain intensity based on the RMT of the patient.

[0181] Clause 161. The method of clause 160, wherein the intensity of the applied TMS is set to 90% of the RMT of the patient.

[0182] Clause 162. A method according to any one of clauses 149 to 152, wherein the brain network is a pre-salience network.

[0183] Clause 163. A method according to any one of clauses 149 to 152 or 162, wherein the brain network is a sensorimotor network.

[0184] Clause 164. A method according to any one of clauses 149 to 152, 162 or 163, wherein the brain network is an attention network.

[0185] Clause 165. The method of any one of clauses 138 to 164, wherein the at least one brain region comprises a cortical brain region connected to the hippocampus and temporal polar regions of the brain by white matter fibers.

[0186] Clause 166. The method of any one of clauses 138 to 165, wherein the at least one brain region comprises a cortical brain region connected to a region of the default mode network via white matter fibers.

[0187] Clause 167. The method of any one of clauses 138 to 166, wherein the at least one brain region comprises the dorsolateral prefrontal cortex.

[0188] Clause 168. A method according to any one of clauses 138 to 167, wherein at least one of the brain regions comprises a brain region having relatively high network resilience, i.e., a brain region in the 5th percentile ranked based on its resilience that is capable of maintaining a high level of network communication efficiency despite simulated or real injury.

[0189] Clause 169. A method according to any one of clauses 138 to 168, wherein at least one of the brain regions comprises a brain region with relatively high network modularity, in which brain activity is organized in more than 2 separate networks based on functional connectivity measurements.

[0190] Clause 170. The method of any one of clauses 138 to 169, wherein the at least one brain region comprises a brain region or network that supports episodic memory.

[0191] Clause 171. The method of any one of clauses 138 to 170, wherein the at least one brain region comprises a brain region or network that supports autobiographical memory.

[0192] Clause 172. The method of any one of clauses 138 to 171, wherein the at least one brain region comprises a brain region or network that supports emotion processing.

[0193] Clause 173. The method of any one of clauses 138 to 172, wherein the at least one brain region comprises a brain region or network that supports mental rotation ability.

[0194] Clause 174. The method of any one of clauses 138 to 173, wherein the at least one brain region comprises a brain region or network that supports mental planning.

[0195] Clause 175. The method of any one of clauses 138 to 174, wherein the at least one brain region comprises a brain region or network that supports executive function.

[0196] Clause 176. The method according to any one of clauses 138 to 175, wherein the at least one brain region comprises a brain region or network in which amyloid beta protein is present.

[0197] Clause 177. The method according to any one of clauses 138 to 176, wherein the at least one brain region comprises a brain region or network in which tau protein is present.

[0198] Clause 178. The method of any one of Clauses 138 to 177, wherein the at least one brain region comprises a brain region or network having altered levels of neuroinflammation.

[0199] Clause 179. The method of any one of clauses 138 to 178, wherein the at least one brain region comprises a brain region or network having altered synaptic plasticity.

[0200] Clause 180. The method of any one of clauses 138 to 179, wherein the at least one brain region comprises a brain region or network having hypoperfusion and hypometabolism.

[0201] Clause 181. The method of any one of clauses 138 to 180, wherein the at least one brain region comprises a brain region or network that supports cognitive reserve.

[0202] Clause 182. The method of any one of clauses 138 to 181, wherein the at least one brain region comprises a brain region or network having elevated neuroinflammation.

[0203] Clause 183. The method of any one of clauses 138 to 182, wherein the at least one brain region comprises a brain region or network that supports functional independence in an elderly individual.

[0204] Clause 184. The method of any one of clauses 138 to 183, wherein the at least one brain region comprises a brain region or network having altered oscillatory activity.

[0205] Clause 185. A method according to any one of clauses 138 to 184, wherein recording multiple TMS-induced potentials in response to applying the TMS to the at least one brain region of the patient includes recording at least one TMS-induced potential from the at least one brain region to which the TMS is applied.

[0206] Clause 186. A method according to any one of clauses 138 to 185, wherein recording multiple TMS-induced potentials in response to applying the TMS to the at least one brain region of the patient includes recording at least one TMS-induced potential from one or more brain regions that are different from the at least one brain region to which TMS is applied.

[0207] Clause 187. A method according to any one of clauses 138 to 186, wherein analyzing at least one characteristic of the plurality of TMS-induced potentials to assess synaptic dysfunction in the patient comprises analyzing the plurality of TMS-induced potentials using at least one machine learning classification model.

[0208] Clause 188. A method according to any one of clauses 138 to 187, wherein the at least one characteristic of the plurality of TMS-induced potentials comprises one or more of a shape, an amplitude, a phase, or a timing of one or more of the plurality of TMS-induced potentials.

[0209] Clause 189. A method according to any one of clauses 138 to 188, wherein analyzing at least one characteristic of the multiple TMS-induced potentials to assess synaptic dysfunction in the patient comprises determining an amount of time for an energy level of one or more of the multiple TMS-induced potentials to return to a baseline energy level before application of the TMS, wherein the assessment of the brain state of the person is based at least in part on the amount of time for the energy level of one or more of the multiple TMS-induced potentials to return to the baseline energy level.

[0210] Clause 190. The method of clause 189, wherein the assessment of the brain state of the person is further based at least in part on the amplitude of the one or more TMS-induced potentials of the plurality of TMS-induced potentials.

[0211] Clause 191. A method according to any one of clauses 138 to 190, wherein analyzing at least one characteristic of the plurality of TMS-induced potentials to assess synaptic dysfunction in the patient comprises projecting the plurality of TMS-induced potentials onto structural magnetic resonance imaging (MRI) data.

[0212] Clause 192. The method of clause 191, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient further comprises extracting time series data from one or more regions of interest defined based on the structural MRI data.

[0213] Clause 193. The method of clause 192, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient further comprises using the extracted time series data to evaluate synchronization across electrodes and / or brain regions.

[0214] Clause 194. The method of any one of clauses 138 to 193, further comprising determining a treatment for the patient based at least in part on the assessment of the brain state of the person and providing the treatment to the patient.

[0215] Clause 195. A method according to any one of clauses 138 to 194, wherein the assessment of the brain state of the person includes an assessment of neuroinflammation, the method further comprising determining a treatment for the patient based at least in part on the assessment of neuroinflammation and providing the treatment to the patient.

[0216] Clause 196. A method according to any one of clauses 138 to 195, wherein the assessment of the brain state of the person comprises an assessment of protein clearance mechanisms and brain protein load, the method further comprising determining a treatment for the patient based at least in part on the assessment of the protein clearance mechanisms and brain protein load and providing the treatment to the patient.

[0217] Clause 197. A method according to any one of clauses 138 to 196, wherein the assessment of the brain state of the person comprises an assessment of brain network dysfunction and altered connectivity patterns, the method further comprising determining a treatment for the patient based at least in part on the assessment of the brain network dysfunction and altered connectivity patterns and providing the treatment to the patient.

[0218] Clause 198. A method according to any one of clauses 138 to 197, wherein the assessment of the brain state of the person includes an assessment of a level of cortical excitability, the method further comprising determining a treatment for the patient based at least in part on the assessment of the level of cortical excitability and providing the treatment to the patient.

[0219] Clause 199. A method according to any one of clauses 138 to 198, wherein the assessment of the brain state of the person includes an assessment of altered brain oscillatory activity, the method further comprising determining a treatment for the patient based at least in part on the assessment of altered brain oscillatory activity and providing the treatment to the patient.

[0220] Clause 200. A non-invasive brain state assessment system comprising:

[0221] a non-invasive brain stimulation device configured to apply non-invasive stimulation to at least one brain region of a person;

[0222] a brain activity recording device configured to record brain signals in response to applying the non-invasive stimulation to the at least one brain region of the person; and

[0223] At least one computer processor is programmed to analyze at least one characteristic of the recorded brain signals to provide a non-invasive assessment of the brain state of the person.

[0224] Clause 201. The system of clause 200, further comprising a neuronavigation system comprising at least one camera, wherein the neuronavigation system is configured to facilitate positioning of the TMS device.

[0225] Clause 202. The system of clause 200 or 201, further comprising an electromyography (EMG) system configured to determine an EMG signal in response to applying the TMS to the at least one brain region of the patient.

[0226] Clause 203. The system of any one of clauses 200 to 202, wherein applying TMS to at least one brain region of the patient comprises applying a single pulse of TMS to the at least one brain region.

[0227] Clause 204. The system of any one of clauses 200 to 203, wherein applying TMS to at least one brain region of the patient comprises applying repetitive TMS to the at least one brain region.

[0228] Clause 205. The system of any one of clauses 200 to 204, wherein applying TMS to at least one brain region of the patient comprises applying TMS at multiple frequencies simultaneously.

[0229] Clause 206. The system of any one of clauses 200 to 205, wherein applying TMS to at least one brain region of the patient comprises applying patterned bursts of TMS to the at least one brain region.

[0230] Clause 207. The system of clause 206, wherein the patterned bursts of TMS comprise a continuous or intermittent theta burst pattern.

[0231] Clause 208. The system of any one of clauses 200 to 207, wherein applying TMS to at least one brain region of the patient comprises applying TMS to a single brain region.

[0232] Clause 209. The system of any one of clauses 200 to 208, wherein applying TMS to at least one brain region of the patient comprises applying TMS to a plurality of different brain regions.

[0233] Clause 210. The system of clause 209, wherein the plurality of different brain regions are two or more nodes of the same brain network.

[0234] Clause 211. A system according to clause 210, wherein the brain network is a long-term memory network.

[0235] Clause 212. A system according to clause 210 or 211, wherein the brain network is a default mode network.

[0236] Clause 213. The system of any one of clauses 210 to 212, wherein the brain network is a fronto-parietal network.

[0237] Clause 214. The system of any one of clauses 200 to 213, wherein the at least one brain region comprises at least one brain region in a temporal lobe of the patient's brain.

[0238] Clause 215. The system of any one of clauses 200 to 214, wherein the at least one brain region comprises a precuneus region.

[0239] Clause 216. The system of any one of clauses 200 to 215, wherein the at least one computer processor is further programmed to determine a location of the at least one brain region to apply the TMS based at least in part on spatial and functional searches.

[0240] Clause 217. A system according to any one of clauses 200 to 216, wherein the at least one computer processor is further programmed to determine the location of the at least one brain region to apply the TMS based at least in part on magnetic resonance imaging (MRI) data associated with the patient's brain.

[0241] Clause 218. A system according to clause 217, wherein determining the position of the at least one brain region to apply the TMS based at least in part on magnetic resonance imaging (MRI) data associated with the patient's brain includes generating a biophysical model of the brain anatomy of the patient's brain based on the MRI data and determining the position based at least in part on the generated biophysical model.

[0242] Clause 219. The system of any one of clauses 200 to 218, wherein the at least one computer processor is further programmed to determine a location of the at least one brain region to apply the TMS based at least in part on data from a plurality of patients.

[0243] Clause 220. A system according to any one of clauses 202 to 219, wherein the assessment of the brain state of the person is further based at least in part on at least one characteristic of the EMG signal.

[0244] Clause 221. A system according to any of clauses 200 to 220, wherein the at least one computer processor is further programmed to determine a resting motor threshold (RMT) of the patient, and wherein applying transcranial magnetic stimulation (TMS) to at least one brain region of the patient comprises applying TMS at a certain intensity based on the RMT of the patient.

[0245] Clause 222. A system according to any one of clauses 210 to 213, wherein the brain network is a pre-salience network.

[0246] Clause 223. A system according to any one of clauses 210 to 213 or 222, wherein the brain network is a sensorimotor network.

[0247] Clause 224. A system according to any one of clauses 210 to 213, 222 or 223, wherein the brain network is an attention network.

[0248] Clause 225. The system of any one of clauses 200 to 224, wherein the at least one brain region comprises a cortical brain region connected to the hippocampus and temporal polar regions of the brain via white matter fibers.

[0249] Clause 226. The system of any one of clauses 200 to 225, wherein the at least one brain region comprises a cortical brain region connected to a region of the default mode network via white matter fibers.

[0250] Clause 227. The system of any one of clauses 200 to 226, wherein the at least one brain region comprises the dorsolateral prefrontal cortex.

[0251] Clause 228. A system according to any one of clauses 200 to 227, wherein at least one of the brain regions includes a brain region having relatively high network resilience, i.e., a brain region in the 5th percentile ranked based on its resilience that is capable of maintaining a high level of network communication efficiency despite simulated or real injury.

[0252] Clause 229. A system according to any one of clauses 200 to 228, wherein at least one of the brain regions comprises a brain region with relatively high network modularity, in which brain activity is organized in more than 2 separate networks based on functional connectivity measurements.

[0253] Clause 230. The system of any one of clauses 200 to 229, wherein the at least one brain region comprises a brain region or network that supports episodic memory.

[0254] Clause 231. The system of any one of clauses 200 to 230, wherein the at least one brain region comprises a brain region or network that supports autobiographical memory.

[0255] Clause 232. The system of any one of clauses 200 to 231, wherein the at least one brain region comprises a brain region or network that supports emotion processing.

[0256] Clause 233. The system of any one of clauses 200 to 232, wherein the at least one brain region comprises a brain region or network that supports mental rotation ability.

[0257] Clause 234. A system according to any one of clauses 200 to 233, wherein the at least one brain region comprises a brain region or network that supports mental planning.

[0258] Clause 235. The system of any one of clauses 200 to 234, wherein the at least one brain region comprises a brain region or network that supports executive function.

[0259] Clause 236. The system of any one of clauses 200 to 235, wherein the at least one brain region comprises a brain region or network where amyloid beta protein is present.

[0260] Clause 237. The system of any one of clauses 200 to 236, wherein the at least one brain region comprises a brain region or network in which tau protein is present.

[0261] Clause 238. The system of any one of clauses 200 to 237, wherein the at least one brain region comprises a brain region or network having altered levels of neuroinflammation.

[0262] Clause 239. The system of any one of clauses 200 to 238, wherein the at least one brain region comprises a brain region or network having altered synaptic plasticity.

[0263] Clause 240. The system of any one of clauses 200 to 239, wherein the at least one brain region comprises a brain region or network having hypoperfusion and hypometabolism.

[0264] Clause 241. The system of any one of clauses 200 to 240, wherein the at least one brain region comprises a brain region or network that supports cognitive reserve.

[0265] Clause 242. The system of any one of clauses 200 to 241, wherein the at least one brain region comprises a brain region or network having elevated neuroinflammation.

[0266] Clause 243. The system of any one of clauses 200 to 242, wherein the at least one brain region comprises a brain region or network that supports functional independence in elderly individuals.

[0267] Clause 244. The system of any one of clauses 200 to 243, wherein the at least one brain region comprises a brain region or network having altered oscillatory activity.

[0268] Clause 245. A system according to any one of clauses 200 to 244, wherein the TMS, the electrical stimulation, and the EEG recording are delivered by a single device that includes TMS, tES, and EEG capabilities.

[0269] Clause 246. The system of clause 245, wherein the electrical stimulation and the EEG recording are delivered via a single scalp electrode having both recording and stimulation capabilities.

[0270] Clause 247. A system according to clause 246, wherein the electrodes that deliver electrical stimulation and record EEG signals are part of a TMS stimulation coil.

[0271] Clause 248. The system of clause 247, wherein the TMS stimulation coil comprises a figure eight structure capable of accommodating the EEG electrodes and the electrical stimulation electrodes for TMS delivery.

[0272] Clause 249. The system of clause 248, wherein the TMS stimulation coil comprises a figure eight structure for TMS delivery and additional structure capable of accommodating the EEG electrodes and the electrical stimulation electrodes.

[0273] Clause 250. A system according to clause 249, wherein the structure capable of accommodating the EEG electrodes and the electrical stimulation electrodes allows recording and stimulation from multiple brain regions and lobes including but not limited to the frontal lobe, temporal lobe, parietal lobe, and occipital lobe.

[0274] Clause 251. A system according to clause 249, wherein the structure capable of accommodating the EEG electrodes and the electrical stimulation electrodes is constructed of a flexible material, thereby allowing the system to be comfortably placed on the scalp of individuals with different head sizes and shapes.

[0275] Clause 252. A method of treating a patient suffering from Alzheimer's disease, the method comprising:

[0276] applying transcranial magnetic stimulation (TMS) to at least one brain region of the patient;

[0277] recording a plurality of TMS-evoked potentials with a plurality of electroencephalogram (EEG) electrodes in response to applying the TMS to the at least one brain region of the patient;

[0278] determining a treatment for the patient based at least in part on an analysis of at least one characteristic of the plurality of TMS-evoked potentials; and

[0279] The treatment is administered to the patient.

[0280] Another aspect of the present disclosure provides a system comprising one or more computer processors and a computer memory coupled thereto. The computer memory comprises machine executable code that, when executed by the one or more computer processors, implements any of the methods described above or elsewhere herein.

[0281] Further aspects and advantages of the present disclosure will be apparent to those skilled in the art from the following detailed description, wherein only illustrative embodiments of the present disclosure are shown and described. As will be appreciated, the present disclosure is capable of other and different embodiments, and its several details can be modified in various obvious aspects without departing from the present disclosure. Therefore, the drawings and description are to be regarded as illustrative rather than restrictive in nature.

[0282] Incorporated by Reference

[0283] All publications, patents, and patent applications mentioned in this specification are incorporated herein by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. In the event that the publications and patents or patent applications incorporated by reference conflict with the disclosure contained in this specification, this specification is intended to supersede and / or take precedence over any such conflicting material. BRIEF DESCRIPTION OF THE DRAWINGS

[0284] The novel features of the present disclosure are particularly set forth in the appended claims. A better understanding of the features and advantages of the present disclosure will be obtained by reference to the following detailed description setting forth illustrative embodiments in which the principles of the present disclosure are utilized, and in the accompanying drawings (also referred to herein as "Figures (Figure / FIG.)"):

[0285] Figures 1A-1F Non-limiting examples of perturbation-based assessment of brain dynamics are presented;

[0286] Figure 2A shows the spatial-temporal reconstruction of TEPs recorded over the entire scalp after L-DLPFC stimulation;

[0287] Figure 2B Local TEP waveforms recorded above the nearest electrode to the stimulated area of ​​L-DLPFC stimulation are shown;

[0288] Figure 2C shows the spatial-temporal reconstruction of TEPs recorded over the entire scalp after PC stimulation;

[0289] Figure 2D Local TEP waveforms recorded above the nearest electrode to the stimulated area of ​​PC stimulation are shown;

[0290] Figure 2E shows the spatial-temporal reconstruction of TEPs recorded over the entire scalp after L-PPC stimulation;

[0291] Figure 2F Local TEP waveforms recorded above the nearest electrode to the stimulated area of ​​l-PPC stimulation are shown;

[0292] Figure 3A shows the spatio-temporal reconstruction of TRSPs recorded over the entire scalp after L-DLPFC stimulation;

[0293] Figure 3B Graphical analysis of TRSP at local level and natural frequency of local oscillatory activity in the stimulated area of ​​L-DLPFC stimulation is shown;

[0294] Figure 3C shows the spatio-temporal reconstruction of TRSPs recorded over the entire scalp after PC stimulation;

[0295] Figure 3D Graphical analysis of TRSP at local level and natural frequency of local oscillatory activity in stimulated area with PC stimulation is shown;

[0296] Figure 3E shows the spatio-temporal reconstruction of TRSPs recorded over the entire scalp after L-PPC stimulation;

[0297] Figure 3F Graphical analysis of TRSP at local level and natural frequency of local oscillatory activity in stimulated area with L-PPC stimulation is shown;

[0298] Figure 4 A-4C shows a graphical analysis of the spectral power (4-50 Hz) in AD brain and the spectral power evoked in HV brain using TMS at L-DLPFC, PC, and L-PPC;

[0299] Figure 5 Graphical analysis showing the linear relationship between the natural frequency of the L-DLPFC (x-axis) and the change in clinical scores after 24 weeks (y-axis);

[0300] Figure 6 Graphical analysis showing linear regression between TMS-EEG features and patients’ cognitive performance;

[0301] Figure 7Graphical analysis showing linear regression between TMS-EEG features, amyloid β, and plasma levels of proinflammatory cytokines;

[0302] Figures 8A-8C presents a non-limiting example of a TMS stimulation system according to embodiments described herein;

[0303] Fig. 9 A-9D shows the experimental setup used to evaluate the effects of TMS on long-term associative memory;

[0304] Fig. 10A shows a graphical analysis of the immediate FNAT and delayed FNAT tests;

[0305] Fig. 10B Graphical analysis of STMB accuracy and RT is shown;

[0306] Fig.11A shows a graphical analysis of the immediate FNAT and delayed FNAT tests;

[0307] Fig. 11B A graphical analysis of delayed FNAT after 1 day, 2 days, and 7 days is shown;

[0308] Figures 12A-12D shows a graphical representation of a seed-to-voxel analysis on an MNI map; and

[0309] Fig.13 A computer system is shown that is programmed or otherwise configured to implement the methods provided herein. DETAILED DESCRIPTION

[0310] Although various embodiments of the present disclosure 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. Without departing from the scope of the present disclosure, those skilled in the art may conceive of many variations, changes and substitutions. It should be understood that various alternatives to the embodiments described herein may be employed.

[0311] Whenever the term "at least", "greater than", or "greater than or equal to" precedes the first value in a series of two or more values, the term "at least", "greater than", or "greater than or equal to" applies to each value in the series. For example, greater than or equal to 1, 2, or 3 is equivalent to greater than or equal to 1, greater than or equal to 2, or greater than or equal to 3.

[0312] Whenever the term "not more than," "less than," or "less than or equal to" precedes the first value in a series of two or more values, the term "not more than," "less than," or "less than or equal to" applies to each value in the series. For example, less than or equal to 3, 2, or 1 is equivalent to less than or equal to 3, less than or equal to 2, or less than or equal to 1.

[0313] Certain inventive embodiments herein contemplate numerical ranges. When there is a range, the range includes the range endpoints. In addition, each subrange and value within the range exists as if explicitly written out. The term "about" or "approximately" can mean within an acceptable error range for a particular value, which will depend in part on how the value is measured or determined, for example, limitations on the measurement system. For example, according to the practice in the art, "about" can mean within 1 or more than 1 standard deviation. Alternatively, "about" can mean a range of up to 20%, up to 10%, up to 5%, or up to 1% of a given value. When describing a specific value in the present application and claims, unless otherwise noted, it can be assumed that the term "about" means within an acceptable error range for a specific value.

[0314] The inventors have recognized that the complex network dynamics that characterize the human brain can be effectively captured by applying controlled perturbations and modeling the resulting activity. This is relevant to the field of neurodegenerative disorders, where changes in brain dynamics are difficult to detect in the preclinical stages of diseases such as Alzheimer's disease, and where these changes have been assumed to have existed for years if not decades by the time clinical symptoms appear. To this end, some embodiments relate to identifying one or more locations in the brain of a subject to which non-invasive perturbations will be provided. Some embodiments relate to how to quantify the effects of perturbations by electrophysiological methods, including but not limited to electroencephalograms (EEGs) and electromyograms (EMGs).

[0315] Several studies using EEG to explore the dynamics of brain activity during various cognitive activities have revealed correlations between activity in specific frequency bands and cognitive function. In addition, recent studies have shown that electrophysiological features measured using EEG can serve as endophenotypes for psychiatric and neurological diseases in both human and rodent models. However, one of the limitations of EEG may be that it passively records brain activity, and therefore the inferences that can be drawn about brain dynamics may be purely correlational. In addition, it may be difficult to assess key and fundamental brain properties, such as plasticity-the ability to change in response to changing environmental and system demands or stressors.

[0316] In contrast to EEG, non-invasive brain stimulation (NiBS) techniques (such as transcranial magnetic stimulation (TMS)) utilize electromagnetic principles to non-invasively regulate brain function by inducing electric fields in target cortical areas. TMS can be used to directly activate brain areas without being regulated by sensory or cognitive pathways, and therefore the functional integrity of cortical circuits in the stimulated area can be assessed. Applying TMS to neurons in the motor cortex can produce motor-evoked potentials (MEPs) that can be measured by EMG. When applied in the form of pulse pairs, TMS engages intracortical GABAergic and glutamatergic circuits and can be used to assess the balance of excitation and inhibition in vivo. Simultaneous TMS and EEG monitoring (TMS-EEG) can enable direct measurement of brain responses to TMS, and therefore assess brain reactivity. TMS-EEG studies have shown that TMS produces repeatable and reliable waves of activity, and reverberates throughout the cortex with responses of different frequencies that vary with the stimulation site. In addition, the induced activity varies with cognitive task participation and is changed by interventions that change task performance. Therefore, TMS-evoked potentials (TEPs) can be used to assess cortical network properties in health and disease. Furthermore, TEPs can be used to assess the integrity of different excitatory and inhibitory circuits and to detect alterations in the cortical excitation / inhibition balance in disease states.

[0317] It is worth noting that TMS-EEG can identify the abnormality of brain reactivity of an individual, even if the conventional EEG of the individual appears normal. Single-pulse TMS has been used to successfully test the use of perturbation-based paradigms to amplify the information that can be obtained during scalp EEG recording, thereby strengthening the idea that brain responses to external perturbations may carry more information about system dynamics than typical resting or task-induced induced activities. In addition to measuring brain reactivity and connectivity, TMS can also be used to evaluate the efficacy of brain plasticity mechanisms. When a series of repeated TMS pulses are applied at a fixed frequency or in a specific pattern based on an experimental synaptic plasticity induction scheme, repeated TMS can cause changes in cortical reactivity (as measured by MEP or TEP), and the duration of the change exceeds the duration of stimulation. Such TMS schemes are considered to achieve their effects through synaptic plasticity mechanisms, and cause extensive changes in the morphological structure of brain connectivity as measured by EEG and fMRI. Such TMS measurements provide evidence of abnormal plasticity mechanisms in diseases such as AD. Thus, real-time integration of TMS with EEG can overcome at least some of the fundamental limitations of conventional EEG analysis described above and enable the study of causal relationships. Furthermore, the same TMS and TMS-EEG protocols can be applied to both human and animal models, facilitating translational work.

[0318] Similar to TMS, transcranial current stimulation (tCS) is a form of non-invasive brain stimulation (NIBS) that uses electrodes placed on the scalp to deliver weak electrical currents to the brain. tCS can be used to stimulate or inhibit one or more target brain regions. tCS can include a range of related non-invasive techniques, such as transcranial direct stimulation (tDCS), transcranial alternating current stimulation (tACS), transcranial random noise current stimulation (tRNS), and more complex forms of stimulation that can be used to stimulate brain regions of an individual according to the systems and methods described herein.

[0319] According to some embodiments of the systems and methods described herein, combining TMS and tCS with EEG recordings may enable rapid, highly sensitive, and easy-to-operate clinical tests that can be applied to a large number of pathological and non-pathological populations. In particular, patients with dementia, particularly Alzheimer's disease, show changes in cortical brain dynamics captured by EEG and can be better characterized by combined methods that utilize controlled perturbations of the brain. Patients with dementia may also show changes in plasticity mechanisms, altered excitation / inhibition balance, and GABAergic dysfunction as well as changes in brain connectivity dynamics.

[0320] Notably, subclinical manifestations of such alterations may also exist in the general population and in subjects at risk for dementia. The systems and methods can also be used to assess brain health, thereby identifying individual deviations from standard datasets and understanding interventions to protect cognitive function and help slow disease onset and disease progression.

[0321] The systems and methods described herein can be used to diagnose, monitor and predict the course of disease in patients with dementia, particularly AD. The systems and methods include a combination of multiple methods of brain perturbation, brain recording and data analysis.

[0322] In some embodiments, perturbations may be delivered via non-invasive brain stimulation techniques such as transcranial magnetic stimulation or transcranial electrical stimulation.

[0323] In some embodiments, transcranial direct stimulation (tDCS), transcranial alternating current stimulation (tACS), or transcranial random noise current stimulation (tRNS) is used as the perturbation method.

[0324] In some embodiments, determination of perturbation targets may be performed using brain scans of the subject, including but not limited to data obtained by magnetic resonance imaging (MRI) and positron emission tomography (PET).

[0325] In some embodiments, determination of perturbation targets may be performed using electrophysiological data collected from a subject, including but not limited to data obtained via electroencephalography (EEG), electromyography (EMG), and magnetoencephalography (MEG).

[0326] In some embodiments, determination of perturbation targets may be made without the use of a brain scan of the subject, and may be based on a standard database of healthy individuals or pathology specific data, as in the case of Alzheimer's disease data.

[0327] In some embodiments, perturbations are delivered to assess the integrity of brain networks, including the resilience of brain systems to perturbations and their ability to adapt to said perturbations by rearranging their structural and functional connections.

[0328] In some embodiments, perturbations are delivered to a single brain region, brain network, or brain system to assess the brain's excitability level and response to stimulation.

[0329] In some embodiments, perturbations are delivered to multiple brain regions, networks, or systems simultaneously or in a predetermined sequence to assess the integrity of brain connectivity and its ability to adapt to perturbations.

[0330] In some embodiments, the response to the perturbation is measured during the perturbation itself, while in other embodiments, brain activity monitored before and after the perturbation is compared.

[0331] In some embodiments, the impact of the perturbation is quantified by analyzing changes in performance of a cognitive task performed via a tablet / phone / computer before and / or during and / or after the brain perturbation.

[0332] In certain embodiments, perturbations are delivered during specific brain states, e.g., to stabilize brain dynamics or elicit specific responses associated with specific brain states. Brain states include, but are not limited to, high cognitive load states, such as those observed when performing working memory or abstract reasoning tasks, sensory stimulation, and sleep.

[0333] In some embodiments, the methods involve obtaining or developing a map comprising locations where waste protein deposition commonly occurs in the brains of patients with neurodegenerative disorders, and more specifically Alzheimer's disease, and then using NIBS targeted to said locations.

[0334] In some embodiments, after identifying the perturbation target, further refinement of the stimulation location can be achieved using perturbation-based brain region functional mapping. For example, stimulation can be sequentially provided to multiple locations within the identified target, and the induced response to the stimulation can be sensed using, for example, electroencephalography (EEG) or another suitable sensing technology. Additional personalized perturbation targets can be selected based at least in part on the analysis of the induced response to the stimulation.

[0335] In some embodiments, the intensity or amplitude of the stimulation can be selected by adjusting a baseline intensity of the stimulation determined using other techniques. For example, the patient's resting motor threshold can be used to establish a baseline stimulation intensity, and the baseline stimulation intensity can be refined based at least in part on one or more characteristics of the evoked response sensed during perturbation-based functional mapping as described herein.

[0336] In some embodiments, the systems and methods can be used to aid in the diagnosis and / or prognosis of neurological or psychiatric conditions associated with neurodegenerative disorders such as mild cognitive impairment (MCI) and Alzheimer's disease.

[0337] In some embodiments, the systems and methods may be used to track brain health in patients with a diagnosis of dementia, including but not limited to mild cognitive impairment (MCI) and Alzheimer's disease.

[0338] In some embodiments, the systems and methods may be used to track brain health in subjects at risk for dementia, including but not limited to mild cognitive impairment (MCI) and Alzheimer's disease.

[0339] In some embodiments, machine learning (ML) and artificial intelligence (AI) are used to analyze data collected before, during, and after a disturbance to detect an individual's response to the disturbance, to classify individuals based on their responses, to identify predictors of response to treatment, or to identify the natural course of a disease. ML includes the use of one or more models utilizing regression models, neural networks, regularized models such as LASSO, decision trees, Bayesian models, general clustering models, associative models, deep learning models, dimensionality reduction models (such as ICA and PCA), and multilayer perceptrons. In some embodiments, the machine learning model includes a neural network.

[0340] Example System Components

[0341] In some embodiments, the system has multiple modules, including but not limited to:

[0342] (i) a non-invasive brain stimulation device configured to deliver single pulse and / or repetitive transcranial magnetic stimulation (TMS) protocols, transcranial current stimulation (tCS) and / or focused ultrasound stimulation (tFUS);

[0343] (ii) a brain activity recording device, such as an EEG system, configured to define personalized stimulation levels and monitor brain activity in response to perturbations;

[0344] (iii) a neuronavigation system equipped with an infrared camera, allowing for individualized positioning of a non-invasive brain stimulation device;

[0345] (iv) an electromyography (EMG) system configured to determine responses to perturbations in the sensorimotor system; and

[0346] (v) a software platform (e.g., implemented using one or more programmed computer processors) to perform data analysis and storage.

[0347] Non-invasive brain stimulation

[0348] Non-invasive brain stimulation (NIBS) is a method for changing the conduction of brain electrical signals at a local level. The changes in induced local signaling may cause more extensive changes in neuronal signaling throughout the brain (involving brain networks). The network-wide effects of these non-invasive brain stimulations reflect the brain effects of stimulation and the network rebound response to the burst of activity entering the system. NIBS methods include transcranial magnetic stimulation (TMS), transcranial current stimulation (tCS), and transcranial focused ultrasound stimulation (tFUS).

[0349] TMS is a non-invasive brain stimulation method that uses a magnetic field generator applied near the head to locally stimulate the current in the brain. In some embodiments, TMS includes repetitive transcranial magnetic stimulation or rTMS. Treatment with rTMS generally includes multiple periods (daily, across days, or multiple times per day / across days), during which TMS is repeatedly delivered in a pattern intended to induce plasticity (e.g., defined as changes in brain activity). rTMS can be delivered as a high-frequency regimen (involving>5Hz stimulation) or a low-frequency regimen (involving<1Hz stimulation). rTMS can also be delivered at more than one frequency at the same time. rTMS can also deliver patterned bursts of TMS, such as in the case of the theta burst rTMS regimen, involving stimulation with a continuous or intermittent theta burst pattern.

[0350] tCS includes methods that manipulate the intensity, frequency, amplitude, and phase of electrical stimulation signals to alter brain activity. Examples of tCS methods include transcranial direct current stimulation (tDCS), a non-invasive brain stimulation technique in which a low level of constant electrical current is delivered to specific areas of the brain via electrodes positioned on and around the head to modulate neural activity in targeted brain tissue. This technique can facilitate, for example, modulation of spontaneous brain activity, neurotransmitter dynamics, or blood flow properties.

[0351] Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation technique in which small pulses of alternating current are delivered to specific areas of the brain via electrodes positioned on and around the head to modulate neural activity in the targeted brain tissue. tACS allows for frequency-specific stimulation of brain tissue, inducing a resonance effect that is used to control neuronal activity and direct brain activity within specific frequency bands (e.g., alpha, gamma, beta).

[0352] Transcranial random noise stimulation (tRNS) is a non-invasive brain stimulation technique in which small electrical currents with the frequency content of white, brown or pink noise are delivered to specific areas of the brain via electrodes positioned on and around the head to modulate neural activity in the targeted brain tissue. tRNS allows for a stochastic resonance-like effect, thereby reducing the signal-to-noise ratio in the brain system and, therefore, having an impact on the spontaneous synchronization of brain activity and the likelihood of response to perturbations.

[0353] Focused ultrasound stimulation (fUS) is a non-invasive brain stimulation technique that involves the delivery of sound pressure waves to specific and potentially deep areas of the brain to modulate neural activity.

[0354] Example Hardware

[0355] Provided below is a description of some components of an example system for perturbation-based biomarker data collection based on TMS or tCS perturbations.

[0356] TMS Device The device includes (i) a metal housing housing a set of capacitors for accumulating charge and delivering energy to (ii) a stimulation coil configured to be positioned on the scalp, and (iii) an LED display for visualizing stimulation parameters. The coil can be equipped with buttons to adjust the stimulation intensity and deliver TMS pulses without having to interact directly with the LED display, thereby ensuring maximum accuracy during treatment delivery.

[0357] EEG. The EEG system can be configured to record EEG activity from the scalp with an EEG amplifier. EEG signals continuously recorded from multiple scalp sites can be positioned according to the 10-20 international system using Ag / AgCl patch electrodes mounted on an elastic cap. The EEG signals can be digitized at a sampling rate of 5 kHz. The skin / electrode impedance can be maintained below 5 kΩ. Horizontal and vertical eye movements can be detected by recording an electrooculogram (EOG) to exclude portions of the EEG signal with eye artifacts. TMS-EEG data can be pre-processed offline using algorithms included as part of the cloud-based software platform. Independent component analysis can be used to detect physiological and TMS-related artifact components and remove them based on their scalp distribution, frequency, timing, and amplitude.

[0358] EMG. Electromyographic data can be used in combination with TMS and neuronavigation to collect motor-evoked potentials after each TMS pulse and estimate the resting motor threshold (RMT) used to set the stimulation intensity for personalized perturbations. EMG devices can have up to 16 bipolar channels and can be connected to a monitor for data visualization.

[0359] Neuronavigation. A neuronavigation system can be configured to enable precise positioning of a magnetic coil over an area in the brain to be stimulated. For example, using an infrared camera to capture the activity of a magnetic tracker placed on the TMS coil and the patient's head, a neuronavigation system can be used to monitor (e.g., continuously monitor) TMS coil positioning. A neuronavigation system can be configured to provide high accuracy in positioning a TMS coil over a specific target based on a brain scan.

[0360] Perturbation Method

[0361] The methods include, but are not limited to, solutions in which perturbations are delivered to one brain region at a time, solutions in which multiple perturbations are delivered to a single brain region to assess dynamic brain responses, or solutions in which perturbations are delivered over multiple brain regions simultaneously or in a predetermined sequence to study network-level rearrangements of brain dynamics. The methods include, but are not limited to, solutions in which the effects of perturbations are measured by electrophysiological methods such as, but not limited to, EEG or EMG.

[0362] Figures 1A-1F Perturbation-based assessment of brain dynamics according to some embodiments is demonstrated. Different brain stimulation methods can be used to perturb brain activity and the responses recorded based on individual differences in brain anatomy and function. Figure 1AAn example method is shown that involves measuring activity 100 (as in the case of EEG using non-invasive electrodes 101 placed on the scalp), delivering a perturbation 102 (e.g., electromagnetic perturbation as in the case of TMS), and measuring activity 100 in multiple regions of the brain 103, e.g., observing an increase in activity after stimulation or an increase in synchronization between two or more regions or networks 104. Figure 1B The effects of the electromagnetic perturbation 102 approach on local-global connectivity 104 are demonstrated. Figure 1C The influence of electromagnetic perturbation 102 method on the temporal decay of stimulation effect is demonstrated. Figure 1D The effects of electromagnetic perturbation 102 methods on the plasticity of specific brain connections were demonstrated. Figure 1E The effects of electromagnetic perturbation methods on brain robustness are demonstrated. Figure 1F The effect of electromagnetic perturbation method on information propagation pathway is demonstrated.

[0363] Stimulus form

[0364] Single disturbance

[0365] In some embodiments, a controlled perturbation 102 is applied to a single zone and the response is measured by electrodes 101 placed on the scalp (EEG) or on another part of the body (EMG). The perturbation 102 can have various intensity levels based on the subject's brain state, including but not limited to levels of cortical excitability, plasticity, inhibition, excitation, oscillatory activity, connectivity, and reactivity. The response 100 to the perturbation 102 can be measured by EMG, observing longitudinal changes in the local excitability of the targeted brain system over time after the perturbation 102 as a measure of cortical plasticity. The response 100 to a perturbation 102 can be measured by EEG 101, observing (a) a local response represented by, but not limited to, the amplitude, number, frequency and timing of so-called TMS-evoked potentials measured in the stimulated brain region; (b) a distant response, observed as the amount of induced current generated in the EEG electrodes or brain, which can also be measured using the same or similar metrics calculated based on the distant brain region; (c) changes in interval dynamics, including or excluding the brain region receiving stimulation, where these measures include but are not limited to graph theoretical measures of correlation, connectivity, effective connectivity, dynamic connectivity, and node interactions; or (d) changes in global brain complexity, including mathematical indicators that express the global response to external perturbations.

[0366] Double colocalization perturbations

[0367] In some embodiments, a sequence of at least two controlled perturbations 102 is repeated over a single zone 103 and the response is measured by electrodes 101 placed on the scalp (EEG) or another part of the body (EMG).

[0368] Double site perturbation

[0369] In some embodiments, a sequence of at least two controlled perturbations 102 is repeated over a plurality of different brain regions 103 and the responses 100 are measured by electrodes 101 placed on the scalp (EEG) or another part of the body (EMG).

[0370] The response to the perturbation 102 can be measured by EMG, observing longitudinal changes in local excitability within the targeted brain system over time after the perturbation as a measure of cortical plasticity. The cortical-cortical paired associative stimulation protocol (cc-PAS) is a dual-site TMS protocol that can promote Hebbian spike-timing-dependent plasticity (STDP). The cc-PAS protocol mimics the neuronal pre- and postsynaptic coupling patterns that induce STDP through a series of TMS pairs on two interconnected areas with a specific inter-stimulus interval (ISI).

[0371] Multimodal perturbations

[0372] In some embodiments, a combination of two or more types of stimulation can be used to maximize the effect of TMS or tCS on the brain, thereby reducing noise in brain activity data. Oscillatory electrical stimulation (such as transcranial alternating current stimulation - tACS) can be used to regulate oscillatory activity in the target area / network before or during the application of TMS. For example, applying 20Hz tACS above the precuneus brain area can act as a stabilizer of spontaneous brain activity for subsequent delivery of TMS pulses synchronized with the peak of each 20Hz oscillation cycle. In some embodiments, tACS can be applied before TMS is applied to regulate brain oscillatory activity, for example, by driving the brain of an individual oscillating at 18.5Hz toward a stable oscillation mode of 21Hz; TMS is then delivered above the same area stimulated by tACS to enhance the effect of tACS, or to produce brain plasticity and concretize changes in tACS-induced oscillatory activity.

[0373] Perturbation stimulus

[0374] The perturbation 102 may be applied in a variety of forms, including but not limited to (i) a single pulse, (ii) a series of pulses separated by fixed intervals or variable dithering intervals, (iii) a continuous waveform characterized by a given amplitude, frequency (or combination of frequencies), and duration, (iv) a continuous electric field of given polarity, amplitude, and duration, or (v) a signal consisting of pulsed noise of a specific frequency.

[0375] Target selection method

[0376] In some embodiments, perturbations 102 are delivered to one or more brain regions 103 identified from brain scans and electrophysiological data. These sources of information defining optimal perturbation targets can represent population-level data and / or individual data. The data can be organized so that local and distributed brain activity can be summarized with quantifiable metrics; characteristics of brain activity can be extracted, measuring features related to, but not limited to, (a) metabolic and vascular activity, (b) oscillatory activity within known frequency bands, (c) network elasticity measures, (d) dynamic connectivity, (e) protein accumulation maps obtained by PET imaging.

[0377] In some embodiments, a perturbation 102 is delivered to a brain region 103 that is identified as a node based on the extent of connectivity 104 between the node and the rest of the brain to (i) assess the integrity of the brain connectome, or (ii) induce a widespread response in local and distant brain regions as a measure of brain integrity and connectivity, such as Figure 1B As shown. In the case of functional connectivity, the degree of connectivity 104 can be estimated by functional imaging data or EEG, or in the case of structural connectivity, the degree of the connection can be estimated by anatomical and diffusion MRI scans. In the latter case, the physical connection between two or more regions can be estimated by observing the properties (e.g., diameter, anisotropy, diffusivity) of the white matter fibers that constitute a specific white matter bundle in the brain. In an example scenario, the TMS or tCS stimulation target is defined by examining the structural connectivity of many brain regions, and then selecting the region with the most connections to the rest of the brain, with the goal of maximizing signal propagation and overall stimulation effects. The same analysis can be performed to identify brain targets with strong structural connectivity to subcortical brain regions (e.g., hippocampus) that are not accessible by TMS or tCS; stimulating accessible gray matter regions with strong structural connections to the hippocampus also maximizes the possibility of indirect activation of the hippocampus.

[0378] In some embodiments, perturbations are delivered to two or more nodes of the same brain network to assess the integrity of connectivity within a given brain network, such as in the case of the default mode network in patients with Alzheimer's disease. Perturbation targets within a brain network can be identified by analysis of brain scans (e.g., functional MRI) or electrophysiological data (e.g., EEG data).

[0379] In some embodiments, perturbations are delivered to nodes with demonstrated relevance to the pathophysiology of a given disease; for example, the precuneus region can be targeted for Alzheimer's disease demonstrated functional connectivity decline as measured using functional magnetic resonance imaging (fMRI); regions of the temporal lobe affected by waste proteins in Alzheimer's disease can be targeted for stimulation, with particular emphasis on amyloid beta and p-tau proteins; regions showing altered levels of neuroinflammation as measured, for example, by diffusion MRI.

[0380] In some embodiments, the selection of perturbation targets is based on an estimate of the induced electric field induced in a candidate brain region extracted from biophysical modeling of passive tissue conductivity. A model can be created by simulating the propagation of magnetic and electrical energy across the head and brain tissue (e.g., skin, muscle fibers, bones, cerebrospinal fluid-CSF, gray matter, white matter), thereby quantitatively measuring the induced electromagnetic stimulation that affects brain cells (e.g., excitatory neurons and inhibitory interneurons). The amplitude of the induced electromagnetic stimulation can be used to adjust stimulation parameters, such as intensity and phase angle, to maximize the stimulation effect on a given brain target, according to known criteria for inducing, for example, neuronal discharge or neurotransmitter release. In an example application, stimulation of the precuneus region of a patient with Alzheimer's disease is achieved by simulating the effect of a TMS pulse on an anatomical MRI scan of the patient and determining that the minimum TMS intensity that induces an electric field sufficient to induce neuronal discharge in the precuneus corresponds to 67% of the output capacity of the TMS device. The process can also identify one or more locations on the scalp that may be most suitable for placing a TMS coil by simulating different angles and rotations while taking into account fine gray matter anatomy.

[0381] In some embodiments, the target areas for perturbation are identified as areas responsible for specific cognitive processes (e.g., long-term memory or attention). Brain scans and / or EEG data collected during cognitive tasks performed by patients with Alzheimer's disease can be analyzed to identify areas where activation is associated with task performance. In an example scenario, the resulting areas are then selected as targets for TMS or tCS perturbation to evaluate the level of integration of these areas with other brain areas involved in the same cognitive process (e.g., long-term memory) as a measure of efficiency within the long-term memory network of patients with Alzheimer's disease.

[0382] Stimulation parameter selection

[0383] Selecting appropriate stimulation intensities for TMS, tCS, and tFUS stimulation may be important to ensure correct assessment.

[0384] In some embodiments, the target area for perturbation is identified based on a spatial and functional search algorithm, in which the evoked EEG activity after at least 2 TMS pulses is averaged, revealing TEPs of different latencies between 5 milliseconds and 500 milliseconds after TMS. The TEP amplitude can be calculated for each patient and used as a proxy for the individual's responsiveness to TMS, and therefore as an indicator of the excitability and responsiveness of the target area / network. The TEP amplitude can then be used to correct the stimulation intensity obtained from motor cortex stimulation (e.g., RMT), with the goal of adjusting the TMS stimulation intensity based on the TEP.

[0385] The stimulation intensity of TMS, tCS and tFUS stimulation depends on individual brain anatomy and can be estimated by creating a high-resolution biophysical model of brain anatomy through MRI scans. In some embodiments, the electric field (E field) induced above the brain target area is generated by using a real volume conductor head model generated based on MRI images and segmentation from a validation data set. The model is based on the anisotropic conductivity value of each brain tissue category (e.g., skin, fat, muscle, bone, cerebrospinal fluid, gray matter, white matter) represented by S / m. The resulting grid set that integrates at least gray matter and white matter, scalp, bone and cerebrospinal fluid can be used to calculate the E-field distribution of a specific TMS coil design, positioning, angle and rotation, taking into account the distance from the coil to the scalp and brain atrophy. The estimated E-field can be used to (a) retrospectively calculate individual differences in the amount of current delivered above the target area, and thus explain differences in responses to treatment, or (b) adjust the stimulation position and / or intensity so that all participants receive the same amount of induced cortical stimulation.

[0386] Time frame and range of disturbance-based markers

[0387] The systems and methods described herein can be applicable across multiple scopes and time frames.Example applications of perturbation-based markers in patients with Alzheimer's disease and general dementia are described below.

[0388] Diagnostic Purpose

[0389] Individual responses to disturbances can be used to identify abnormal brain activity in patients suspected of having Alzheimer's disease or dementia. Responses to disturbances that are different from control subjects, healthy controls, or previous responses obtained from the same individual can be used to assist in the diagnosis process. The same method can be used to identify patients with Alzheimer's disease subtypes in a patient group diagnosed with Alzheimer's disease (e.g., amnestic Alzheimer's disease). Response analysis can be performed by visual inspection performed by a trained operator and / or by a classification algorithm based on machine learning that is configured to mark responses to disturbances as normal or abnormal responses.

[0390] Prognostic purpose

[0391] Individual brain data can be analyzed to predict (e.g., estimate) the course of a pathology (e.g., Alzheimer's disease) based on historical data collected from the same individual or group-level disease progression estimates.

[0392] Evaluation of treatment effect

[0393] Brain data can be analyzed at the individual or group level to quantify differences in brain activity before and after treatment for Alzheimer's disease or memory problems. Changes before and after treatment based on perturbation metrics can be used to evaluate individual or group responses to a given treatment, change treatment parameters, and continue or discontinue treatment.

[0394] Personalized treatment definition

[0395] Individual brain data can be analyzed to personalize non-invasive brain stimulation parameters, including but not limited to stimulation position, orientation, intensity, frequency, phase, noise level. Personalization can be performed by observing the response to TMS pulses delivered, for example, above multiple positions in the target area and identifying the position that provides the highest brain response to TMS. Parameters such as the intensity of brain stimulation, the frequency of stimulation, the position of stimulation, the stimulation waveform, the timing of multi-pulse stimulation, the duration of the stimulation scheme, and the number of stimulation periods can be defined based on the individual response to disturbances such as using TMS and EEG. In some embodiments, the stimulation intensity is defined based on the amplitude of the induced response recorded by EEG immediately after the TMS pulse, and the amplitude of the induced potential represents the excitability of the brain tissue stimulated by TMS. The stimulation intensity can also be defined based on the characteristics of the individual's brain anatomy, including the level of cortical gray matter and white matter atrophy, which affects the amplitude of the magnetic stimulation and electrical stimulation reaching the brain during TMS due to the increase in scalp-cortex distance. In some embodiments, the stimulation frequency is defined based on the frequency domain response to TMS recorded by EEG, wherein the spectral power of brain activity in frequency bands including but not limited to δ, θ, α, β and γ is calculated on the EEG signal collected after TMS delivery. The peak value of the spectral power under the target frequency band can be determined and used to set the stimulation frequency or as a reference point to guide the adaptive regulation of oscillatory brain activity; for example, a strong response with a 38 Hz peak in the γ band can be stimulated with 38 Hz repetitive TMS after TMS to maintain ongoing oscillatory activity, or the individual is stimulated with an incrementally faster frequency of 38 Hz (e.g., 39 Hz, 40 Hz, 45 Hz) in order to guide neurons to produce faster brain activity. In some embodiments, the stimulation position is determined based on the analysis of the evoked potential collected using EEG immediately after TMS delivery, creating a higher and lower map of the response to TMS based on the amplitude of the potential. This information can be used to select an area showing a stronger response to TMS as a measure of the integrity and ongoing activity of the neural circuit stimulated by TMS. Brain regions with stronger ongoing activity may exhibit intact brain circuits and may be selected as targets for repeated TMS with the goal of maintaining activity in the brain region and counteracting neurodegeneration. Repeated TMS can target brain regions that are less responsive to TMS with the goal of restoring brain activity and repairing brain circuits. In some embodiments, the frequency, amplitude, waveform, and duration of the brain response to TMS, as recorded by EEG, are used to determine the duration of repeated TMS treatment, as well as the number of TMS pulses delivered in each repeated TMS session.Individuals with stronger responses to TMS (e.g., high amplitudes of evoked potentials above 6 mV and strong responses in the gamma band) may be assigned to shorter treatment durations than individuals with weaker responses to TMS, which indicate that brain circuits in the stimulated brain area are more damaged. Information related to EEG-evoked responses can be combined with other pathological markers, including but not limited to levels of neuroinflammation as measured by blood / plasma biomarkers, brain levels of amyloid and tau protein accumulation as measured by positron emission tomography (PET), levels of cortical brain atrophy and levels of corticospinal plasticity measured by transcranial theta burst stimulation (TBS).

[0396] Disease tracking purposes

[0397] Perturbation-based metrics collected over time can be used to monitor brain function in patients with Alzheimer's disease, capturing significant deviations in brain activity patterns at any given time point from data collected at previous time points. Individual response data can be compared to standard data collected in a sample of patients with Alzheimer's disease or in a group of healthy controls, providing estimated deviations from, for example, expected rates of cognitive decline.

[0398] Research on cognitive function

[0399] Perturbation-based data can be collected from brain regions / networks associated with or supporting specific cognitive functions. For example, TMS or tCS can be used to stimulate areas associated with memory processing, such as the precuneus or dorsolateral prefrontal cortex. TMS-EEG and tCS-EEG data can be used in combination with neuropsychological scores to identify altered brain circuits associated with decreased memory performance in, for example, patients with Alzheimer's disease.

[0400] Data processing and analysis

[0401] Data processing methods

[0402] In some embodiments, data processing includes automated cleaning / preprocessing solutions and / or semi-automated processing solutions with the possibility of manual identification of artifacts. Processing and analysis can be performed as part of separate modules covering (i) data collection, (ii) data validation and format conversion, (iii) data cleaning and preprocessing, (iv) data analysis, and (v) detailed report creation (e.g., including optimal stimulation targets / parameters and summary of processing steps).

[0403] Brain scan processing

[0404] When brain scans can be used for stimulation target selection, two types of information can be used: (i) brain structural properties, including but not limited to density / volume / thickness / folds / sulcus depth of gray / white matter, CSF distribution, diffusivity and anisotropy of white matter, and spectral distribution of neurotransmitters; and (ii) functional properties of the brain, including but not limited to hemodynamic response, blood perfusion, metabolic activity (e.g., glucose consumption), and protein load. Steps for preparing brain scans for statistical analysis include but are not limited to: converting single images to 3D volume format; segmentation of brain tissue classification; spatial and temporal filtering; removing physiological noise; removing image artifacts; extracting mean values ​​and / or time series of brain activity, co-registering to common anatomical or functional templates for group-level analysis; in the case of block-fMRI data, calculating evoked activity when multiple scanning conditions are present. Subsequent analysis can be performed on both voxel-based volume data or vertex-based surface images, and can include masking clean data based on anatomical or functional maps describing relevant networks or brain regions that can be targeted by TMS.

[0405] EEG data processing

[0406] EEG data collected before, during, and / or after a single TMS pulse during a TMS-EEG recording session can be processed for data analysis and perturbation target selection using TMS. In addition to EEG artifacts (e.g., eye movements and heartbeats), EEG data collected during TMS are often contaminated by TMS-specific artifacts, including magnetic pulse artifacts that change the impedance of the EEG electrodes, TMS-induced muscle artifacts characterized by high-frequency activity, and artifacts associated with the TMS machine recharging process between TMS pulses. The amplitudes of these artifacts are typically several orders of magnitude larger than the amplitude of the recorded EEG data, thus confounding the brain signals in the EEG.

[0407] Preprocessing and cleaning steps may include, but are not limited to: conversion of raw data in .edf format; trimming of raw data to epochs of predefined length, including segments capturing pre-TMS and post-TMS brain activity; normalizing post-TMS activity by subtracting the mean signal amplitude of the EEG data collected pre-TMS; performing automated or semi-automated data inspection to identify EEG channels with excessive noise or artifacts; zero-filling activity concurrent with a single TMS pulse to remove early signal decay and muscle artifacts induced by the TMS pulse (based on voltage-based thresholds, kurtosis, and joint probability); performing independent component analysis (ICA) to identify and remove components including early TMS-induced high amplitude electrodes, and principal component analysis (PCA) to identify and remove components including early TMS-induced high amplitude electrodes. additional data reduction using PCA to minimize overfitting and noise components; interpolation of previously zero-filled signals across TMS pulses; bandpass filtering using a forward-backward filter typically between 1 Hz and 150 Hz; notch filtering to account for line noise; reference to a global average; a second ICA to manually remove all remaining artifact components, including eye movements / blinks, muscle noise (EMG), single electrode noise, TMS-induced muscle activity, cardiac signals (EKG), and auditory-induced artifacts (artifacts can be identified and labeled based on their spectral frequency distribution, power spectrum, amplitude, scalp topography, and / or time course); identification of residual artifacts using machine learning and deep learning algorithms; or interpolation of previously removed electrodes.

[0408] Data cleaning and processing can be performed as a supervised approach, such as by manually verifying processing steps and visually inspecting the data, or as an unsupervised procedure, such as using machine learning techniques that do not require human interaction.

[0409] Covariates of interest

[0410] It should be understood that the order of steps may vary and specific adjustments may be implemented based at least in part on individual brain characteristics or pathology-specific artifacts and signal characteristics. Features to be considered include, but are not limited to, levels of cortical atrophy that affect induced electric fields in the brain; known changes in neurotransmitter activity that affect the amplitude and shape of evoked potentials; increased movement and muscle activation during EEG recording; elevated levels of oscillatory activity in the EEG due to typical sleepiness and / or general slowing of brain activity in the brain of patients with AD.

[0411] Disturbance Indicator

[0412] The response to a perturbation can be quantified by multiple non-mutually exclusive metrics and indices, including but not limited to metrics and indices based on signal shape, amplitude, phase and timing, metrics and indices related to resonant effects associated with the duration of the induced perturbation, and metrics and indices related to network-level activity and brain connectivity analysis.

[0413] Metrics based on signal characteristics

[0414] EEG signals recorded before and after TMS or tCS stimulation can be characterized by quantifying activity in specific frequency bands, metrics of activity at specific time points after TMS / tCS stimulation that are connected to specific neurotransmitters or the dynamics of cortico-cortical circuits. Non-limiting examples of metrics that may be applicable to patients with AD include:

[0415] The natural frequency of TMS-induced activity in the local area of ​​interest, expressed as the oscillatory activity showing the strongest spectral power after the TMS pulse

[0416] ●EEG theta / beta band ratio measured in the frontal lobe

[0417] The amplitude of the N100 peak

[0418] ●The area under the curve of the global mean field potential (GMFP) of the TMS-evoked response after stimulation

[0419] Absolute and relative EEG spectral power in specific frequency bands (including but not limited to delta, theta, alpha, beta, and gamma)

[0420] EEG spectral power in posterior brain regions in the alpha and delta bands

[0421] Paired-pulse TMS reduction of oscillations in the gamma band

[0422] ● Paired-pulse TMS reduces the P60 and N100 peaks of TMS-evoked potentials

[0423] Cross-frequency coupling centered around the frequency band stimulated by TMS or tCS (to assess multiplexing of underlying brain circuits and changes in information processing)

[0424] ●Changes in the P30, N45, P60, and N100 peaks of TMS-evoked potentials

[0425] ●Changes in resting EEG alpha and beta power

[0426] TMS-induced spectral perturbations in localized areas, such as evoked activity in specific frequency bands (including but not limited to delta, theta, alpha, beta, and gamma)

[0427] The magnitude and duration of TMS / tCS-induced spectral changes (as a measure of frequency-dependent plasticity within cortical areas).

[0428] ● Spectral coherence changes at the scalp and source levels, using individual MRI data or templates generated from individual samples with desired clinical or phenotypic properties.

[0429] Perturbed Resonance EEG Measure (PREM)

[0430] After TMS is applied, the time required for the EEG signal to return to baseline can be used to characterize the ability of the brain to recover after a perturbation, possibly as an indicator of plasticity mechanisms. However, the return time may not be the only indicator of recovery. Different subjects may exhibit higher response amplitudes. For example, patients with Alzheimer's disease have previously been shown to exhibit increased excitability. Therefore, the combination of the amplitude of the response and the time required to return to baseline energy can reveal important information about the pathology of such patients and can serve as a biomarker for the disease. Based on the energy of the EEG signal collected before and after the perturbation, multiple metrics can be extracted from the TMS-EEG or tCS-EEG signal.

[0431] In some embodiments, the time required for the EEG signal to return to baseline is automatically calculated based on the signal energy levels obtained over the time course of the TEP.

[0432] The absolute TEP values ​​can be extracted and normalized relative to their maximum value to make the amplitude range consistent across subjects. Next, the energy level of the signal can be calculated by moving the TEP sample by sample over a sliding window. Experiments were performed to evaluate the optimal window size. The length values ​​of the sliding window included 5 milliseconds, 10 milliseconds, 20 milliseconds, 30 milliseconds, and 40 milliseconds. A final evaluation was performed on the maximum performance obtained for AD and HC classification.

[0433] Once the power signal is calculated, the value of the EEG signal obtained after the TMS pulse is compared to the baseline. The threshold can be defined as the mean value of the power signal obtained on the baseline data plus one standard deviation. The baseline is considered to be between -500 milliseconds and -200 milliseconds before the pulse. The last 200 milliseconds before the pulse are excluded to avoid interference of residual preprocessing and filtering effects on the baseline data.

[0434] After applying the threshold, a new signal is obtained which should represent an increase in energy as a response to the stimulus.

[0435] Calculate the difference between the time points at which the energy signal crosses the threshold. Extract the maximum interval between the time points. When the difference between two time points of the signal is higher than 75% of the maximum value, the signal is considered to have returned to the baseline level after the disturbance. In some embodiments, this represents the time required for the signal to return to the baseline.

[0436] Examples of metrics that can be used to characterize TEP include, but are not limited to, metrics that measure the time required for a signal to return to baseline (e.g., a measure of target engagement duration); metrics that measure the slope of the signal between the maximum absolute value of the highest TEP between the end of engagement and after the TMS pulse, and metrics that capture the area under the curve of the signal between the TMS pulse and the end of target engagement.

[0437] Network-based metrics

[0438] The response to disturbance can be analyzed to characterize the network-level dynamics in the brain, with a particular focus on analyzing the brain functions and structural networks that change in Alzheimer's disease and dementia. The recorded electrophysiology (e.g., EEG) data can be preprocessed to eliminate noise sources and recording artifacts, and then analyzed by observing the brain connectivity patterns in the electrode and / or source space. The analysis in the electrode space involves observing the brain activity recorded by the scalp EEG electrodes without projecting the data onto the structural MRI scan of the patient. Source analysis involves projecting the pure EEG data onto the 3D brain structural MRI area, thus observing the high spatial resolution activity within the range of 1 cubic millimeter related to the morphological organization of the individual anatomy and brain functional networks. Regardless of the method, the data can be represented as a time series representing the brain activity in a specific electrode (scalp level) or brain area / network (source level). In source space, time series can be extracted from anatomically or functionally defined regions of interest, which represent specific brain regions or functional networks of interest in Alzheimer's disease pathology (e.g., default mode network, fronto-parietal control network, anterior salience network, dorsal attention network). The extracted time series can be analyzed to observe synchronization patterns across electrodes and brain regions / networks, and obtain a connectivity matrix (functional connectivity analysis) describing the direction and intensity of connectivity of each given brain region / network or electrode pair. This analysis can be performed by extracting a value for each region / network / electrode pair, and it provides a static measure of brain functional connectivity. Time series data can also be used to observe dynamic patterns of connectivity that change over time, and model its changes over time (dynamic connectivity analysis). By calculating the predictive power of time series #1 to time series #2, time series can also be used to estimate the directionality of the impact across brain regions / networks / electrodes. The resulting metric measures the causal connection between the activity in region #2 and the activity in region #1, and obtains a weighted graph describing the hierarchy of brain regions / networks / electrodes that drive brain activity in other regions / networks / electrodes.

[0439] Functional / dynamic / effective connectivity analysis can be performed to observe measures of inter- and intra-network activity, quantifying the amount of connectivity within a network node associated with Alzheimer's disease or between two or more network nodes of the brain to observe inter-network communication and information processing. Connectivity matrices based on functional / dynamic / effective connectivity can also be used to obtain network topography measures related to the field of graph theory analysis, allowing identification of regions / networks / electrodes that are particularly important in providing, for example, integrity, efficiency of information transfer, and resilience to perturbations of the network or the entire brain.

[0440] Example metrics related to network dynamics in AD patients include, but are not limited to:

[0441] ●Connectivity within the default mode network (DMN), frontoparietal control network (FPCN), dorsal attention network (DAN), and anterior salience network (AS);

[0442] ● Connectivity between any two pairs of networks, as a measure of inter-network synchronization;

[0443] The strength of negative connectivity between the DMN and AS

[0444] ●Connectivity of the precuneus region of the DMN;

[0445] Connectivity between frontal and parietal nodes of the DMN;

[0446] The connectivity of the DMN's angular gyrus, particularly the strength of its connections to the hippocampus;

[0447] ● Connectivity of brain regions with high accumulation of waste proteins as measured by PET imaging;

[0448] Connectivity between frontal and parietal nodes of the FPCN as a measure of cognitive flexibility and executive function;

[0449] ● Dynamics, time-varying connectivity of brain regions that make up the DMN and FPCN

[0450] ●Effective causal connectivity of brain regions constituting the DMN and FPCN

[0451] ● Cross-frequency coupling of theta and gamma activity in regions that constitute the DMN and FPCN

[0452] - Kinetic changes in network modularity following a TMS pulse, including but not limited to kinetic changes associated with an increase in resilience to external perturbations as measured by elastogram analysis.

[0453] Machine Learning Analysis

[0454] In some embodiments, the metrics extracted from the perturbation-based data set are analyzed by machine learning (ML) and artificial intelligence (AI) platforms. In some embodiments, the ML model can be trained to cluster one or more variables in a plurality of variables or combinations of variables to obtain unique information from the data set. The ML model can then generate clusters that group the same and / or similar variables or combinations of variables, thereby allowing the identification of response patterns within a data set, within a group of individuals, and across individual groups. In some embodiments, the ML model may include an algorithm to reduce the dimension of the data set, and apply feature selection techniques to reduce the dimension of the feature space by discarding multiple variables or metrics that provide the same type of information. Dimensionality reduction techniques include, but are not limited to, principal component analysis (PCA), independent component analysis (ICA), and spectral decomposition. In some embodiments, the ML model may include training and test segmentation for cross-validation (including, but not limited to, K-fold cross-validation and leave-one-out cross-validation) of the classification results. The ML method described herein can be applied in a supervised or unsupervised manner, depending on whether the model is trained to identify hidden clusters / groups in the data set (unsupervised) or trained to identify specific groups of features / individuals based on the target set of desired features.

[0455] Infrastructure and patient flow

[0456] In some embodiments, infrastructure is created to allow, among other steps, stimulation target and TMS parameter optimization, data storage and processing, and report creation. The session flow may include the following steps:

[0457] 1) Prescribe TMS-EEG studies to identify possible changes in brain physiology;

[0458] 2) Arrange target definition and optimization time periods for patients;

[0459] 3) Patients complete a brain scanning session to identify and characterize the topography of target brain networks / regions;

[0460] 4) Process brain scans and identify TMS target areas;

[0461] 5) Collect the patient's EMG, TMS, and EEG data from the areas previously identified by brain scans;

[0462] 6) Stream the results to a data processing unit / platform for data cleaning and analysis;

[0463] 7) optionally, comparing the data to a normative database of age-matched healthy individuals to identify altered responses to perturbations;

[0464] 8) Optionally, data are collected at multiple time points and compared to a standard longitudinal database of age-matched healthy individuals to identify altered responses to perturbations and estimate disease trajectories and, for example, in the case of patients with MCI, potential conversion from MCI to AD;

[0465] 9) Clinicians / operators receive reports on altered markers and use them to (i) prescribe medications, (ii) prescribe non-pharmacological interventions (e.g., meditation, cognitive training);

[0466] 10) The data is stored and coordinated with subsequent data collection for disease tracking.

[0467] These steps may be combined with or substituted by any suitable steps of other methods disclosed herein.

[0468] Starting from step 3, in the example scenario, clinical staff can access the URL link and authenticate their user account through a service (e.g., Auth0). Clinical staff can then set up the TMS-EEG device and pair it with a web browser. After this, data (raw EEG data and / or survey metadata (e.g., demographic information, brain scan data, neuronavigation parameters)) and requests can be sent from the EEG to the backend through an application programming interface (API). This data and requests are then routed through the gateway portal and gateway.

[0469] Definition of Brain Targets

[0470] Brain regions can refer to brain anatomical regions that follow standard neuroanatomical hierarchies. Brain regions can also include collections of brain anatomical regions or regions that have similar functions or are associated with specific cognitive functions. "Cognitive regions" can be considered to be physical parts of the brain (e.g., parts of the cerebral cortex, hippocampus, thalamus, or cerebellum) that support cognitive functions (including but not limited to attention, language, memory, visual motor function, executive function, flexibility, inhibition, abstract reasoning, creativity, emotional processing).

[0471] Brain networks may refer to functional and / or anatomical networks defined by techniques such as fMRI, diffusion tensor imaging (DTI), diffusion weighted imaging (DWI), PET, EEG, and MEG. Examples of brain networks are default mode network (DMN), ventral DMN (vDMN), dorsal DMN (dDMN), frontoparietal control network (FPCN), visual network (VN), sensorimotor network (SMN), dorsal attention network (DAN), ventral attention network (VAN), salience network (SN), limbic network (LN), executive control network (ECN), auditory network (AN), memory network (MN), cingulate-opercular network (CON), episodic memory network (EMN), precuneus network (PrecN), language network (LangN), cognitive control network (CCN), inhibition network (InhN), working memory network (WMN), insight network (InsiN), insular network (InsN), vestibular network (VestN), basal ganglia network (BGN).

[0472] System design for simultaneous TMS, electrical stimulation, and EEG recording

[0473] Brain perturbations for assessing, quantifying, and modulating brain activity can be delivered by a system that allows for simultaneous delivery of TMS (including but not limited to single pulse and repetitive TMS), electrical stimulation (including but not limited to tDCS and tACS), and scalp EEG recording. Figures 8A-8C A non-limiting example described herein is shown, wherein the system may include a TMS coil 801 (e.g., in a figure-of-8 shape) incorporating an EEG electrode 800 within a plastic housing of the coil 801, and tACS electrodes 802 distributed along the surface of the coil. In some embodiments, the TMS coil 801 may have a shape other than a figure-of-8, including a personalized shape based on an individual head shape obtained from a 3D scan of the head (including but not limited to obtained through structural MRI images). In some embodiments, the system includes multiple TMS coils 801 that allow stimulation of multiple brain regions via TMS, connected to additional EEG 800 and electrical stimulation electrodes 802.

[0474] In some embodiments, the EEG and electrical stimulation electrodes are controlled by separate generators and amplifiers, unlike the TMS coil. In some embodiments, the EEG and electrical stimulation electrodes are operated by a TMS device.

[0475] In some embodiments, the system may include a single TMS coil 800 comprising a Fig. 8AEEG 801 and electrical stimulation electrodes 802 within the housing of the illustrated figure-8 coil 800. In some embodiments, the system may include the TMS coil 800 and may also include additional structures 805 protruding from the TMS coil 800 housing and covering other head scalp areas to allow additional EEG 801 and electrical stimulation 802 of other brain regions and lobes 804, such as Figure 8B In some embodiments, the system may include multiple TMS coils 800 configured to target multiple brain regions 804 and lobes 804, and may further include additional EEG electrodes 801 and electrical stimulation electrodes 802 incorporated, such as Figure 8C What is shown.

[0476] Example Use Case

[0477] The embodiments of the present invention are further described by the following examples and detailed schemes. However, the examples are only intended to illustrate the embodiments and should not be interpreted as limiting the scope of the technology described herein.

[0478] Examples demonstrate the application of perturbation-based methods to (i) predict the response of AD patients to a 6-month repetitive TMS-based treatment and (ii) identify AD patients from healthy individuals.

[0479] Example Use Case 1

[0480] Predicting Response to Brain Stimulation Therapy in Patients with AD

[0481] Data collected as part of a phase 2, double-blind, randomized, placebo-controlled clinical trial of patients with AD demonstrate the safety, feasibility and efficacy of the proposed disturbance-based measures. This trial is based on a novel method for targeting functional networks that change in AD (particularly, DMN and precuneus area) to study the efficacy of a long-term treatment course of repeated TMS, wherein clinical effects and cognitive effects are captured within a 24-week course. After recruitment and baseline assessment, patients are randomly assigned at a ratio of 1:1 to receive true or false rTMS above the precuneus. All treatments are applied for 24 weeks without interruption. The trial includes a 24-week treatment period and a 2-week intensive course, wherein rTMS of the precuneus is applied every day, 5 times (Monday to Friday) per week (W1 and W2), followed by a maintenance phase, wherein the same stimulation is applied weekly for 22 weeks (W3-W24). During the entire 24-week period, a total of 51,200 pulses were delivered to each patient. rTMS is performed using a magnetic stimulator connected to a figure-of-eight coil.

[0482] Efficacy evaluation was performed on selected patients and caregivers at baseline (W0) and repeated at Week 12 (W12) and Week 24 (W24) (or when terminated early) by scorers who were unaware of the allocation group. The investigators, patients, and their caregivers were also unaware. At each clinical visit (or early termination), adverse events (AEs) were recorded, vital signs were measured, and physical and neurological examinations were performed. An independent data monitoring committee monitored the safety of patients according to the Data Monitoring Committee Charter.

[0483] Primary and secondary outcome measures

[0484] The primary outcome measure was the change from baseline in the Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB) score at week 24 (CDR-SB scores range from 0 to 18, with higher scores indicating worse cognition and daily functioning). The intention-to-treat analysis set included all patients with post-baseline efficacy data. Secondary outcome measures were:

[0485] Change from baseline at week 24 in the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog)11;

[0486] ●Change from baseline in MMSE score at week 24;

[0487] Change from baseline in activities of daily living (ADCS-ADL) at 24 weeks;

[0488] sample

[0489] 86 patients were screened and 50 patients were randomized. The average age of the total sample of patients was 73.7 years (SD=6.6, ranging from 62 to 84), and 52% were female. The average MMSE raw score of the patients at baseline was 21.3 (SD=2.5). A total of 45 patients (90%) completed the treatment period.

[0490] Prediction of response to rTMS therapy

[0491] The researchers selected TMS-based cortical physiology measures based on their knowledge and expertise in the pathophysiology of AD and used them to predict response to treatment. This approach includes three main single- and double-perturbation measures: resting motor threshold (RMT), short afferent inhibition (SAI), and TMS-evoked potential (TEP).

[0492] Resting Motor Threshold (RMT)

[0493] The RMT was defined as the lowest intensity that produced a MEP >50 μV in the relaxed first dorsal interosseous (FDI) muscle of the right hand in at least five of ten trials. The RMT was assessed over the optimal stimulation site to elicit MEPs in the right FDI (referred to as the "motor hotspot"), identified by positioning the coil approximately over the central sulcus and moving it over the scalp in 0.5 cm steps over the left M1.

[0494] Short afferent inhibition (SAI)

[0495] Corticospinal evaluation was performed using TMS-EMG and was designed to investigate whether short afferent latency inhibition (SAI) could predict clinical progression in AD patients in the true rTMS group. This measure is based on the amplitude of the MEP induced by stimulating the relaxed first dorsal interosseous (FDI) muscle of the contralateral hand. The coil was placed at an angle of about 45° tangential to the scalp and relative to the midline, thereby inducing a posterior-anterior current in the brain. The stimulation intensity of the single pulse TMS was adjusted to induce a MEP with a peak-to-peak amplitude of about 1mV. SAI was measured as follows: 16 milliseconds, 20 milliseconds, 24 milliseconds, and 28 milliseconds before the single pulse TMS, a conditioned electrical stimulus (200 microseconds) was applied to the right median nerve at the wrist (proximal to the cathode) via a bipolar electrode. The intensity of the electrical stimulation was set to just exceed the motor threshold to induce a visible twitch of the thenar muscle. In order to measure the SAI effect, the percentage of the average peak-to-peak amplitude of the conditioned MEP relative to the average peak-to-peak amplitude of the unconditioned MEP in the block was considered.

[0496] TMS-evoked potential (TEP)

[0497] The stimulation intensity was set at 90% of the RMT, testing the contralateral FDI muscle at rest. Each session consisted of 80 single pulses of TMS, applied over the DMN-precuneus at a random ISI of 2-4 seconds, targeted using a neuronavigation system. Each participant wore earplugs that continuously played white noise, reproducing the specific time-varying frequency of the TMS clicks.

[0498] TMS-evoked EEG activity was recorded from the scalp using a TMS-compatible DC amplifier. EEG was recorded continuously from 61 scalp sites positioned according to the 10-20 international system using TMS-compatible Ag / AgCl patch electrodes mounted on an elastic cap. EEG signals were digitized at a sampling rate of 5 kHz. Skin / electrode impedance was maintained below 5 kΩ. Horizontal and vertical eye movements were detected by recording electrooculograms (EOGs) to reject trials with eye artifacts offline.

[0499] INFOMAX-ICA was used to detect physiological and TMS-related artifact components and remove them based on their scalp distribution, frequency, timing, and amplitude. The effects of rTMS were evaluated in three domains: temporal, via TMS-evoked potentials (TEPs) from the site of stimulation (in this case, DMN-precuneus); oscillatory, via TMS-related spectral perturbations (TRSPs) from the site of stimulation (i.e., DMN-precuneus); and spatial, via source analysis performed over the entire scalp.

[0500] In calculating TEP, the time window from 100 ms before TMS to 300 ms after TMS was considered, with baseline correction of -100 ms to 0 ms (where 0 ms represents the time of TMS application). A time / frequency decomposition based on Morlet wavelet (parameter c=3; 41 linear 1 Hz steps from 4 Hz to 45 Hz) was used, and then the frequency domain analysis was performed by calculating TRSP. The spectral power of theta (4-7 Hz), alpha (8-13 Hz), beta (14-30 Hz) and gamma bands (31-45 Hz) was extracted and averaged over a time window from 20 ms to 250 ms after TMS for theta and alpha bands and over a time window from 20 ms to 70 ms after TMS for the beta and gamma bands.

[0501] TMS-EEG source reconstruction for precuneus stimulation was performed using Brainstorm, fitting a distributed source model consisting of 15,000 elementary current dipoles. These dipole sources were distributed on each vertex of the surface of a tessellated cortical grid template derived from the MNI's standard 1 mm resolution brain (Colin27) provided in the Brainstorm toolbox running in MATLAB. A head model for source imaging was first implemented according to the boundary element method (BEM). Based on this head model, the current density map was used to solve the inverse problem.

[0502] Effects of rTMS and prediction of response to treatment

[0503] Cognitive and clinical outcomes

[0504] The mean baseline CDR-SB total score for the real rTMS group was 4.1 (SD = 1.8), and for the sham rTMS group it was 4.6 (SD = 1.5). There were significant differences in cognitive performance as measured by the CDR-SB total score in the active rTMS group compared to the sham (baseline relative to week 24). The GLMM of repeated measures on the CDR-SB score showed significant results in terms of the difference in group (p = 0.038) and time × group (p = 0.009) interaction, where patients treated with sham rTMS showed a general deterioration in the patient's cognitive performance over time, which was not obvious in the DMN-p-rTMS group. The mean change in the GLMM estimate of the CDR-SB score for DMN-p-rTMS (baseline-week 24) was -0.25 (95% confidence interval (CI) [-4.8, 4.3]), and for the sham rTMS group it was -1.42 (95% CI [-6.0, 3.3]). The responder rate (defined as the percentage of patients with a 1-point improvement in CDR-SB score) was 68.2% in the DMN-p-rTMS group and 34.7% in the sham group.

[0505] The GLMM of repeated measures on ADAS-Cog11 scores (adjusted for age and education) showed significant results in terms of time × group (p = 0.035) interaction. The GLMM estimated mean change (baseline-week 24) in ADAS-Cog11 scores for 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 sham rTMS group.

[0506] 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. The estimated mean change in ADCS-ADL score (baseline-week 24) for the AD-DMN-p-rTMS group was -0.7 (95% CI [-27.2, 25.8]) and 7.5 (95% CI [-20.5, 35.5]) for the sham rTMS group, showing an improvement for the DMN-p-rTMS group relative to the sham rTMS group (interaction effect: p = <0.001).

[0507] Prediction of response to therapy based on perturbation biomarkers

[0508] A multivariate stepwise regression analysis was performed to observe whether neurophysiological measures at baseline (T0) could predict clinical progression in patients treated with rTMS. Three neurophysiological measures were selected as predictor variables, two of which had previously been tested as predictors of clinical progression of AD, namely RMT and SAI (with an ISI of 20 milliseconds, as this is the time interval at which the strongest inhibition is observed), and the third was the TEP amplitude of the first peak, which is the response regulated by the rTMS protocol described herein. The CDR improvement between W1 and W24 was selected as the dependent variable because this was the primary outcome. In order to explore whether the TEP amplitude of the first peak was linearly correlated with clinical improvement, Pearson's correlation was performed with CDR, ADAS-cog, and ADCS-ADL.

[0509] Multiple stepwise regression analysis showed that the linear model significantly predicted clinical progression, as tested with CDR, when it included all three predictors, RMT, SAI, and TEP (Model 1: R2=0.461, p=0.03), two predictors, SAI and TEP (Model 2: R2=0.394, p=0.023), and one predictor, TEP (Model 3: R2=0.355, p=0.009). Backward selection of coefficients revealed that the TEP amplitude of the first adjusted peak was the best predictor of clinical progression compared to SAI and RMT (all p<0.05). Correlation analysis revealed significant correlations between TEP amplitude and CDR (r=-.449, p=0.021), ADAS-cog (r=-0.422, p=0.018), and ADCS-ADL (r=0.514, p=0.009).

[0510] observe

[0511] Analysis of brain activity in response to TMS perturbations may allow prediction of response to brain stimulation treatments in patients with Alzheimer's disease. Refined analysis of signal propagation may allow prediction of individual response rates and may be used to screen and assign patients to specific treatments based on their perturbation-based responses.

[0512] Example Use Case #2

[0513] Responses of patients with Alzheimer's disease to electromagnetic perturbations as a marker of disease pathology and altered synaptic plasticity.

[0514] Synaptic dysfunction is a key feature of cognitive deterioration associated with Alzheimer's disease, but non-invasive in vivo methods to reliably measure such dysfunction are lacking. A new set of synaptic plasticity biomarkers was identified and validated in 50 patients with Alzheimer's disease. TMS-induced EEG activity was collected following stimulation of specific hubs of the fronto-parietal network and default mode network involved in AD pathology. Specifically, stimulation was delivered to the left dorsolateral prefrontal cortex (l-DLPFC), precuneus (PC), and left posterior parietal cortex (l-PPC).

[0515] Cortical oscillatory activity in the gamma range resulted in significant reductions in the frontal lobes of patients, including activity induced locally by stimulation of the dorsolateral prefrontal cortex or indirectly by stimulation of the precuneus.

[0516] Cortical excitability of the same region assessed by TMS-evoked potentials (TEPs) was higher in AD patients compared with age-matched healthy volunteers (HV).

[0517] Comparisons with other brain regions including the parietal lobe were identical in patients with HV and AD, identifying changes in gamma activity in the frontal lobe (DLPFC) as pathologically specific markers of AD. In addition, in the follow-up evaluation conducted 24 weeks after the TMS-EEG session, patients with more significant decreases in DLPFC gamma activity showed stronger cognitive deterioration.

[0518] Further details on the procedures and results are reported below.

[0519] sample

[0520] TMS-EEG data were collected in patients with mild to moderate AD and analyzed according to the above procedures and methods. The mean age of the total sample of AD patients was 69.2 years, of which 59% were female, while the mean age of the HV group was 67.4 years. The mean MMSE raw score of AD patients at baseline was 22.7.

[0521] Patients were eligible for this study if they had been diagnosed with mild to moderate AD according to the recommendations of the international working group.

[0522] TMS-EEG recordings

[0523] Neurophysiological characterization was performed by combining single-pulse TMS and EEG recordings according to the procedure for single-pulse TMS described above. During all TMS-EEG recordings, participants sat in a comfortable armchair in a soundproof room, facing a computer screen. They were guided to fixate on a white cross in the middle of the screen and to keep their arms relaxed.

[0524] TMS was delivered over three target brain regions: l-DLPFC, PC, and l-PPC. The order of stimulation of these regions was counterbalanced between patients. The stimulation intensity of single-pulse TMS was personalized for each patient based on the level of corticospinal excitability and brain anatomy.

[0525] Spatial / temporal domain analysis

[0526] Figures 2A-2F Shown are the spatiotemporal brain dynamics at the global and local levels after TMS. Figure 2A Depicted are TMS-evoked potentials (TEPs) 210 recorded across the scalp following TMS of the L-DLPFC for both the AD and HV groups, and Figure 2B The corresponding TMS-evoked potentials 230 , 231 at F3 recorded above the L-DLPFC are depicted. Figure 2C Depicted are TMS-evoked potentials (TEPs) 210 at a global level recorded over the entire scalp after TMS of the PC for both the AD and HV groups and Figure 2D The corresponding TMS-evoked potentials 240 , 241 at Pz recorded above the PC are depicted. Figure 2E Depicted are TMS-evoked potentials (TEPs) 210 recorded across the scalp following TMS of the L-PPC for both the AD and HV groups, and Figure 2F The corresponding TMS-evoked potentials 250, 251 at P3 recorded above the L-PPC are depicted. Error bars and hatching indicate standard error. * indicates p<0.05.

[0527] Figure 2A , 2C and 2E show the L-DLPFC ( Figure 2A )、PC( Figure 2C ) and L-PPC( Figure 2E) Spatial-temporal reconstruction of TEP 220 recorded over the entire scalp after stimulation. Regardless of the stimulation site, single pulse TMS evoked a well-known pattern of five main activity peaks (P1, P2, P3, P4, P5) lasting about 250 ms and with different latencies depending on the stimulated area (L-DLPFC: P1 at 15 ms, P2 at 30 ms, P3 at 60 ms, P4 at 100 ms, P5 at 180 ms; PC and L-PPC: P1 at 20 ms, P2 at 40 ms, P3 at 70 ms, P4 at 120 ms, P5 at 200 ms). Spatial-temporal reconstruction of TEP 220 showed similar spatial dynamics between the three areas, with significant activity concentrated in the stimulated area (P1, P2), then spreading within and between hemispheres (P3, P4), and finally leading to central bilateral positivity, probably produced by TMS-induced auditory artifacts (P5).

[0528] First, TEP analysis was performed at the global level210,220 to assess differences in excitability of the entire scalp cortex between the AD and HV groups. Figure 2A ), AD patients showed higher cortical excitability between 20 and 40 ms after TMS over a cluster of two left frontal electrodes (Monte Carlo ps < 0.01) and a cluster of right posterior electrodes (all Monte Carlo ps < 0.01). Figure 2C ), AD patients showed higher cortical excitability over a cluster of four electrodes over the stimulated medial parietal region between 30 and 50 ms after TMS (all Monte Carlo ps < 0.01); and over two clusters of electrodes, one of four electrodes over the stimulated region, and one of three frontal electrodes in the subsequent time window between 50 and 90 ms (all Monte Carlo ps < 0.01). No differences were observed when stimulating over the L-PPC in either group ( Figure 2E )(all ps>0.05).

[0529] To compare the responsiveness of the stimulated area between the AD and HV groups, the temporal analysis of TEPs induced at the local level was then performed. Figure 2B , 2D and 2F show local TEP waveforms recorded by the nearest electrode to the stimulated area: F3 for L-DLPFC stimulation 230, 231 ( Figure 2B ), Pz stimulates PC 240, 241 ( Figure 2D ) and P3 stimulates 250, 251 for L-PPC ( Figure 2FWhen stimulating above the L-DLPFC, the AD group showed higher TEP amplitudes between 20 ms and 40 ms compared with the HV group (AD: 1.602±0.286, HV: 1.137±0.235; t(59.314)=2.370, p=0.021) ( Figure 2B ). When stimulated above the PC ( Figure 2D ), AD showed higher TEP amplitudes between 10 ms and 30 ms (AD: 2.671 ± 0.222, HV: 1.252 ± 0.368; t(79) = 3.263, p = 0.002), 50 ms to 90 ms (AD: 1.671 ± 0.156, HV: 1.038 ± 0.182; t(79) = 2.208, p = 0.03), and 90 ms to 160 ms (AD: 2.801 ± 0.201, HV: 1.637 ± 0.264; t(79) = 3.118, p = 0.003). No differences were observed between the TEPs of the two groups when stimulating above the L-PPC (all ps > 0.05) ( Figure 2F ).

[0530] Time domain / frequency domain analysis

[0531] Figure 3A , 3C and 3E show the global time / frequency brain dynamics, and Figure 3B , 3D and 3F show the corresponding local-level temporal / frequency brain dynamics after TMS. Figure 3A depicts a global TMS-related spectral perturbation (TRSP) 310 and scalp map 320 of gamma activity following TMS of the L-DLPFC, and Figure 3B Depicted are the corresponding TRSPs recorded over the L-DLPFC when stimulated in the AD 330 group and the HV 332 group. Figure 3C depicts a global TMS-related spectral perturbation (TRSP) 310 and scalp map 320 of gamma activity following TMS of a PC, and Figure 3D Depicted are bar graphs of the corresponding TRSPs recorded over the PCs when stimulated in the AD group 340 and the HV group 342 . Figure 3E depicts a global TMS-related spectral perturbation (TRSP) 310 and scalp map 320 of gamma activity following TMS of the L-PPC, and Figure 3F Depicted are the corresponding TRSPs recorded above the L-PPC when stimulated in the AD group 350 and the HV group 352. Error bars indicate standard error. * indicates p<0.05.

[0532] Figure 3A , 3Cand 3E show the L-DLPFC ( Figure 3A )、PC( Figure 3C ) and L-PPC( Figure 3E ) Spatial-temporal reconstruction of TRSP 310 recorded over the entire scalp after stimulation. Regardless of the site of stimulation, a single pulse of TMS results in sustained oscillatory activity lasting about 250 milliseconds, showing a first significant activation in the beta / gamma band lasting about 80 milliseconds, and subsequent activity in the alpha / theta band lasting about 250 milliseconds. When analyzed at a local level, the significant oscillatory activity (e.g., natural frequency) differs depending on the site stimulated.

[0533] TRSP analysis was performed at the global level to assess differences in cortical oscillatory activity across the scalp between the AD and HV groups. Figure 3A ), AD patients showed evoked lower gamma frequency (30-50 Hz) activity in a cluster of six frontal bilateral electrodes (Monte Carlo ps < 0.01) 100 to 150 ms after the TMS pulse. Figure 3C ), AD patients showed evoked lower gamma frequency (30-50 Hz) activity in one cluster of five frontal bilateral electrodes (Monte Carlo ps < 0.01) 100 to 150 ms after the TMS pulse, and in two clusters of four frontal electrodes and two posteromedial electrodes (Monte Carlo ps < 0.01) 150 to 200 ms after the TMS pulse. L-PPC stimulation ( Figure 3E ) did not produce any differences in the oscillatory activity between the two groups (all ps > 0.05).

[0534] TRSP analysis was performed at the local level to assess local oscillatory activity in the stimulated area ( Figure 3B , 3D and 3F) and measure its natural frequency. When stimulating above L-DLPFC, the local horizontal oscillatory activity map at F3 331 depicts that AD patients show lower local γ activity compared with the HV group (AD: 1.283 ± 0.143dB, HV: 2.243 ± 0.267dB; t(79) = -2.977, p = 0.004), while other frequency bands do not differ between the two groups (all ps> 0.05). When stimulating above PC and L-PPC, the corresponding local horizontal oscillatory activity maps at Pz 341 and P3 351 depict that AD patients do not show any differences in oscillatory activity compared with the HV group (all ps> 0.05).

[0535] Figure 4The specificity of the reduced γ activity in the prefrontal lobe of AD patients is shown. TMS-induced TMS-related spectral disturbances by L-DLPFC 410, PC 420 and L-PPC 430. The first column in each pair depicts the spectral power (4-50Hz) in the AD brain, and the second column depicts the spectral power induced in the HV brain. The light gray box indicates the natural frequency of each area. The error bar indicates the standard error. * indicates p<0.05.

[0536] Figure 4 The TRSP averages for each of the 46 frequency layers analyzed after stimulation of the three regions are shown. L-DLPFC 410 stimulation resulted in a natural frequency of 40.8 Hz for both groups. When compared to HV, the AD group showed a lower local oscillatory activity of 35 Hz to 46 Hz, thus also containing a natural frequency of 40.8 Hz (average p value = 0.006). Stimulation of PC 420 and L-PPC430 resulted in a natural frequency of 29.8 Hz for both groups. When compared to HV, the AD group did not show any differences in the local oscillatory activity of any of the layers considered (all ps>0.05).

[0537] Linear relationships between clinical and neurophysiological measures

[0538] Figure 5 Shown are scatter plots depicting the linear relationship between the natural frequency of the L-DLPFC (x-axis) and the change in clinical scores 24 weeks from the first assessment (y-axis) in the Clinical Dementia Rating Scale Box Sum (CDR) 510; Neuropsychiatric Inventory (NPI) 520; Alzheimer's Disease Cooperative Study-Activities of Daily Living Scale (ADC-ADL) 530; and Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) 540.

[0539] Multiple stepwise regression analysis showed that the stepwise algorithm had no significant difference in predictors when it included two predictors, namely, the natural frequencies of DLPFC and PC (CDR 510: Model 1: R2 = 0.410, p = 0.012; NPI 520: Model 1: R2 = 0.417, p = 0.007; ADCS-ADL 530: Model 1: R2 = 0.328, p = 0.049; ADAS-COG 540: Model 1: R2 = 0.391, p = 0.014) and one predictor, namely, the natural frequencies of DLPFC (CDR 510: Model 2: R2 = 0.410, p = 0.003; NPI 520: Model 2: R2 = 0.417, p = 0.002; ADCS-ADL 530: Model 2: R2 = 0.305, p = 0.022; ADAS-COG 540: Model 2: R2 = 0.356, p = 0.008), the linear model significantly predicted clinical progression, as tested with CDR 510 ( Figure 5 ). Backward selection of the coefficients revealed that DLPFC natural frequency proved to be one of the best predictors of general clinical progression.

[0540] Example Use Case 3

[0541] Responses of patients with Alzheimer's disease to electromagnetic perturbations as markers of protein clearance, neuroinflammation, and altered cognition

[0542] Background and Principles

[0543] Evidence from both animal studies and human studies shows that enhanced neuronal excitability may represent an early unique feature of Alzheimer's disease. It has been shown that hyperexcitability even precedes AD etiology pathophysiological changes (such as accumulation and inflammation of amyloid plaques). However, the relationship between the biomarkers of cortical excitability, cognitive performance and AD pathology and inflammation that have been changed has not yet been extensively explored. The co-registration of transcranial magnetic stimulation (TMS) and the induced response (TMS-EEG) induced by TMS of electroencephalogram (EEG) is used to quantitatively measure cortical excitability and brain oscillation activity in patients with AD, and to verify the possibility of extracting the TMS-EEG markers of brain neuroinflammation and protein clearance of patients. TMS-EEG is performed on patients with AD, and amyloid (Aβ1-40 and Aβ1-42), p-tau and inflammation (IL-6, IL-10, IL-17 and TNFα) biomarkers are collected before the TMS-EEG period.

[0544] In this example, the TMS-EEG evoked potential (TEP) of 14 AD patients was analyzed in time and frequency domains to explore the relationship between TEP, cognition and neurodegeneration and inflammatory blood biomarkers. Stimulation was delivered above three different neuronavigation cortical sites, namely the left primary motor area (L-M1), the right dorsolateral prefrontal cortex (R-DLPFC), and the right occipital cortex (R-OC). Patients underwent extensive neuropsychological evaluation at baseline. Before TMS-EEG registration, blood amyloid (Aβ1-40 and Aβ1-42), p-tau, and inflammation (IL-6, IL-10, IL-17, and TNFα) biomarkers were collected.

[0545] Methods and samples

[0546] Patients with mild to moderate dementia due to AD were enrolled in this study. Inclusion criteria were: evidence or history of memory impairment, Mini-Mental State Examination (MMSE) ≥ 18 (mild AD ≥ 21, moderate AD = 18-20), and Clinical Dementia Rating Scale (CDR) ≥ 0.5. Participants underwent baseline assessments completed within 5-7 days. Baseline assessments included: i) neuropsychological assessments; ii) blood sample collection to evaluate neurodegeneration and neuroinflammatory biomarkers; iii) intermittent theta burst stimulation (iTBS) for LTP-like (long-term potentiation) plasticity assessment; iv) resting state EEG (rsEEG); and v) TMS-EEG co-registered sessions. iTBS, rsEEG, and TMS-EEG sessions were performed on three different days to avoid potential bias. All participants gave written informed consent.

[0547] Cognitive and biomarker assessments

[0548] Participants underwent baseline cognitive assessments to evaluate global function [Alzheimer's Disease Assessment Scale-Cognitive Subscale-ADAS-Cog, Mini-Mental State Examination-MMSE, Montreal Cognitive Assessment-MoCA]; Verbal / episodic memory and learning status [Rey Auditory Verbal Learning Test-RAVLT, Craft Story 21 Recall Immediate and Delayed, Number Span Test Forward and Backward]. Blood samples were collected at baseline and peripheral concentrations of Aβ1-40, Aβ1-42, and p-tau were assessed. Blood BDNF concentrations and levels of peripheral cytokines IL-6, IL-10, IL-17A, and TNFα were also assessed.

[0549] TMS-EEG evaluation

[0550] TMS was applied to three cortical sites using an 8-shaped coil oriented at a 45-degree angle relative to the midline. The T1-weighted MRI volume was used as a position reference, and the coil was placed on top of the target area. Participants sat in a comfortable chair and were asked to remain quiet and alert during the visit. The stimulation intensity was determined based on the individual resting motor threshold (RMT), which was defined as the minimum stimulation intensity that produced at least 50 μV of motor-induced potential (MEP) in at least 5 trials out of 10 trials. The stimulation intensity was set to 120% of the RMT using an 8-shaped coil. Single TMS pulse blocks were delivered with an interstimulus interval of 1 to 4 seconds. During the TMS protocol, the EEG activity of the scalp was recorded using a TMS-compatible 64-channel (Ag / AgCl electrode) EEG headset mounted on an elastic cap.

[0551] result

[0552] Cognitive Performance Indicators

[0553] Figure 6 The results of linear regression between TMS-EEG features and patient cognitive performance are shown. Univariate regression analysis of time domain, global TEP extracted features and cognitive performance indicators revealed a positive linear correlation 610 between the ADAS-Cog score and the peak amplitude of the second TEP peak (P2), and a negative correlation 620 between the same TEP component and the RAVLT score after right DLPFC stimulation. In addition, after stimulation, the P4 peak amplitude above the right temporal lobe showed a positive correlation with the ADAS-Cog score and a negative correlation with the MoCA score 630. Finally, the RAVLT score showed a negative correlation with the left central parietal P2. Interestingly, all of the above results were statically significant after adjusting for the normalized induced electric field calculated by biophysical modeling. Frequency domain analysis of TEP revealed a positive correlation 640 between the ADAS-Cog score and the global theta power after stimulation.

[0554] Blood biomarkers of AD pathology

[0555] Figure 7 Results of linear regression between TMS-EEG features, amyloid beta, and plasma levels of proinflammatory cytokines are shown. Linear regression of time-domain, local TEP extracted features and blood biomarkers of AD pathology showed a negative correlation between plasma concentrations of Aβ1-40 and P3 peak amplitudes in the left frontal lobe, right central parietal region, and superior temporal lobe after DLPFC stimulation 710. These findings remained significant after controlling for the induced electric field as a covariate in the regression model.

[0556] Blood biomarkers of neuroinflammation

[0557] Several global and local TEP signatures showed significant correlations with blood biomarkers of inflammation. More specifically, Figure 7 As shown, after stimulation, IL6 730 and TNFα 720 levels showed positive correlation with the P2 peak amplitude over the left central parietal and temporal regions. Similarly, after stimulation of the frontal and parietal regions, IL-6, IL-10 740 and TNFα showed positive correlation with the nearest TEP peak amplitude (P3, P4) over the bilateral central parietal and temporal regions ( Figure 7 ). All the above results are statically significant after adjusting for the normalized induced electric field.

[0558] Example Use Case 4

[0559] Combination of transcranial magnetic stimulation and electrical stimulation for the assessment and enhancement of long-term associative memory in patients with Alzheimer's disease

[0560] sample

[0561] 41 young healthy participants were recruited. The sample size was estimated based on previous experiments with similar paradigms conducted by the group. 24 participants were enrolled in Experiment 1, taking into account the dropout probability due to the large number of sessions required per subject (i.e., 4). 10 participants were enrolled in Experiment 2 to replicate Experiment 1 and to investigate long-term effects. 18 participants (10 of whom had been recruited in Experiment 1) participated in Experiments 3 and 4.

[0562] TMS and tACS safety guidelines and medical regulations were fully followed. In addition to the criteria indicated by the safety guidelines (i.e., history of epilepsy, metal implants in the head, implanted electronic devices, etc.), left-handedness, claustrophobia, psychotropic medication, and current or past psychiatric or neurological illness were considered exclusion criteria. Inclusion criteria included participants who were native or fluent Italian speakers and had normal or corrected-to-normal vision and hearing.

[0563] program

[0564] Fig. 9 An exemplary procedure for testing a combination of transcranial magnetic stimulation and electrical stimulation for assessment and enhancement of long-term associative memory in patients with Alzheimer's disease is presented, comprising at least 4 experiments 910 , 920 , 930 , and 940 .

[0565] Experiment 1 910 investigated the behavioral effects of iTBS+γtACS co-stimulation by studying changes in memory performance in two memory tasks. Participants first underwent MRI scanning 909 to individualize stimulation sites and allow neuronavigation, and then they participated in a crossover design in which three neuromodulation experimental sessions 904 were separated by a washout week 905. Each session of the experimental session 904 corresponded to a different counterbalanced and randomized stimulation condition (i.e., iTBS+γtACS, iTBS+sham tACS, sham iTBS+sham γtACS), followed by two memory tasks (see "Memory Tasks" paragraph): Face-Name Association Task (FNAT) (e.g., 906a-906d) and Visual Short-Term Memory Binding Test (STMB) 907.

[0566] Experiment 2 920. The study of behavioral effects was deepened to study long-term changes in memory performance, and the experimental design was simplified to have two experimental sessions 904 by eliminating the control condition "sham iTBS + sham γtACS" (as demonstrated in Experiment 1 910) that did not produce any effect. In fact, the participants participated in a crossover design, in which the two experimental sessions 904 corresponded to different balanced and random stimulation conditions (i.e., iTBS + γtACS, iTBS + sham tACS), separated by a washout week 905. In each session 904, participants were required to participate in three experimental test sessions. During the first test session (Day 1), participants received the neuromodulation protocol 901, and then performed FNAT 906a, 906b (without recognition phase) and STMB 907. In the second testing session (Day 2), participants performed FNAT recall 906b with a 24-hour delay according to neuromodulation protocol 901, while in the third testing session (Day 7), participants performed FNAT recall 906d and recognition with a 1-week delay according to neuromodulation protocol 901. Cognitive testing was performed only in the third session to avoid learning bias from cued recall on Days 2 and 7.

[0567] Experiment 3 930. The neurophysiological effects of the neuromodulation protocol 901 were investigated by combining the use of TMS-EEG 908. As for Experiment 1 910, participants underwent MRI 909 scanning to individualize stimulation sites and allow neuronavigation. They then participated in two randomized and balanced neuromodulation experimental sessions 904 (i.e., iTBS+γtACS, iTBS+sham tACS) separated by a washout week 905. TMS-EEG 908 recordings were performed before (T0), immediately after (T1), and 20 minutes after (T2) the neuromodulation protocol 901. Fig. 9 C).

[0568] Experiment 4 940. Functional connectivity effects of the neuromodulation protocol 901 were investigated by fMRI scans 911. After the first MRI scan 909 required for neuronavigation, participants participated in two randomized and counterbalanced neuromodulation experimental sessions 904 (i.e., iTBS+γtACS, iTBS+sham tACS) separated by a washout week 905. fMRI scans 911 were performed before (T0) and immediately after (T1) the neuromodulation protocol 901.

[0569] iTBS–γtACS neuromodulation protocol

[0570] Based on previous studies, costimulation consisted of a combination of iTBS and tACS delivered in the gamma band (γtACS). The neuromodulation protocol 901 was delivered on the individual PC based on the individual's resting state structural MRI 909 and functional MRI 911 targeted by a stereotactic neuronavigation system (see the "MRI data acquisition and preprocessing" paragraph). The active tACS electrode (anode) 903 was placed on the scalp, the iTBS coil 902 was located above it, and another tACS electrode (cathode) 912 was placed over the right shoulder muscle.

[0571] tACS was delivered via a multifunctional system for low-intensity transcranial electrical stimulation and saline-soaked sponge electrodes (7.5 cm2). The γtACS sinusoidal frequency wave was set at 70 Hz, with an intensity of 1 mA and a total duration of 190 seconds.

[0572] iTBS was delivered via a figure-of-eight coil 902 (70 mm). iTBS consisted of ten bursts of three pulses at 50 Hz for 2 s, repeated every 10 s, with an 8 s pause between consecutive trains, for a total of 600 pulses, for a total of 190 s. The stimulation intensity of iTBS was set to 80% of the active motor threshold (AMT), defined as the lowest intensity capable of producing a motor evoked potential (MEP) of approximately 200 μV in at least five out of ten trials when the participant performed a 10% maximal contraction using visual feedback. The AMT was tested over the first dorsal interosseous hotspot over the primary motor cortex (M1) in the left dominant hemisphere, with the tACS electrode 903 below the coil 902 to ensure the same scalp-coil distance for the neuromodulation protocol. Electromyographic activity of the contralateral FDI muscle was recorded using two Ag–AgCl surface disk electrodes (9 mm) in a ventral-tendon pattern.

[0573] Sham stimulation conditions were implemented to control for individual contributions of technical and placebo effects. When implementing the tACS sham condition, only 2 seconds of wave up and 2 seconds of wave down were applied to give participants a real stimulation sensation. The iTBS sham condition was implemented by adding a layer of wood under the coil.

[0574] Memory task

[0575] Face-Name Association Task (FNAT). In both Experiment 1 910 and Experiment 2 920, the first task run was the FNAT, a cross-modal associative memory test that requires participants to pair photos of unfamiliar faces with common names and occupations. In the current study, the FNAT 906 was created by taking photos from an online dataset (FACES) and associating them with Italian names and occupations, creating three parallel tables. Two versions of the task were used: the first task was implemented in Experiment 1 910 and consisted of a learning phase followed by immediate cued recall 906a and 15-minute delayed cued recall 906b in the case of recognition; the second task was implemented in Experiment 2 920 and consisted of a learning phase, immediate cued recall 906a and 15-minute delayed cued recall 906b on Day 1, 24-hour delayed cued recall 906c on Day 2, and 1-week delayed cued recall 906d on Day 7 when recognition was required.

[0576] The learning phase includes showing twelve faces associated with names and occupations on a PC screen for a period of time (e.g., up to 8 seconds or less than 8 seconds), and instructing participants to read the names and occupations aloud to ensure that their attention is focused on each item and remember the face-name-occupation association. Immediate cued recall is performed immediately after the learning phase, during which participants are asked to recall the name and occupation of each face shown again for 8 seconds. Delayed cued recall includes showing the faces shown previously, and asking participants to say the name and occupation of each face aloud. Finally, during the recognition trial, participants must first identify the face in the study from another interference item face matched with age and gender. Then, for those names and / or occupations that were not correctly recalled in the previous delayed cued recall, they must identify the correct name and / or occupation in three alternative options, where the interference item is a new name / occupation and a name / occupation associated with a different face.

[0577] Three accuracy metrics were derived for each task phase, i.e., immediate cued recall 906a, delayed cued recall 906b, and recognition 910 of Experiment 1; immediate cued recall 906a; delayed cued recall 906b (Day 1); 24-h delayed cued recall 906c (Day 2); 1-week cued recall and recognition 906d (Day 7): total number of names recalled or recognized / 12x100; total number of occupations recalled or recognized / 12x100; total number of items recalled or recognized in total / 12x100 (i.e., both names and occupations recalled / recognized).

[0578] Short-term memory binding test (STMB). This test runs between FNAT immediate prompt recall 906a and 15 minutes delayed prompt recall 906b. STMB 907 is a recognition task that relies on a change detection paradigm, which requires participants to remember a visual array of three black shapes (shape conditions only) or colored shapes (shape-color binding conditions) presented for a period of time (e.g., at least 2 seconds or less than 2 seconds) (study phase). After a 1 second delay of presenting a blank screen (keeping interval), the display of the same or different items appears in a new random position on the screen (test phase). If the items shown in the study and test phases are different (50% of the trials), the participant is asked to press the "1" button on the keyboard, or if the same, press "2". For each condition, a total of 32 random trials were presented. Conditions were balanced between participants. Before starting the test, each participant was subjected to a perceptual test, in which two shape arrays were presented on the same screen to eliminate perceptual defects and train the participant's keyboard answer. Two accuracy metrics were derived for the shape-only condition and the shape-color combination condition, respectively (total score for each condition / 32x100).

[0579] TMS–EEG neurophysiological assessment

[0580] During EEG recording, single pulse TMS is used to assess the change of local cortical oscillation and connectivity. During TMS-EEG908 assessment, the participant sits on a comfortable armchair in a soundproof room, is guided to look at the black cross on the wall, and wears earplugs that continue to play white noise, to avoid the potential response related to possible auditory events. The intensity of white noise is adjusted separately by increasing the volume (always below 90 decibels), until the participant determines that she / he can no longer hear the click of TMS induction. The TMS recorded for EEG 908 is identical with the stimulator for neuromodulation scheme 901. The stimulated area is PC and lPPC. The order of the stimulation area is balanced between the participants. Both PC and l-PPC are identified by individual resting state structural MRI 909 and functional MRI 911 (see " MRI data acquisition and preprocessing " paragraph). Coil 902 positioning is continuously detected using a neuronavigation system and has an unused orientation according to the area stimulated, so that the current flow direction in the most effective (second) stage is the back-front direction. To target the PC, the coil 902 was positioned in an orientation parallel to the midline, while to target the l-PPC, the coil 902 was positioned in an orientation 15 degrees relative to the midline. The single-pulse TMS intensity was set to 110% of the resting motor threshold (RMT), which was defined as the lowest intensity that produced a MEP of >50 μV in the relaxed right hand FDI muscle in at least five of the 10 trials. Each TMS-EEG 908 block consisted of 90 single pulses with a random inter-stimulus interval (ISI) between 2 and 42.

[0581] EEG was recorded continuously from 61 scalp sites located according to the 10-20 international system using TMS-compatible Ag / AgCl patch electrodes mounted on an elastic cap 913. Additional electrodes served as ground and reference electrodes: the ground electrode was located in the Fpz, while the reference electrode was located on the tip of the nose. The EEG signals were digitized at a sampling rate of 5 kHz. The skin / electrode impedance was maintained below 5 kΩ.

[0582] EEG preprocessing. Offline preprocessing of TMS-EEG data. The data is divided into periods starting from 1 second before and 1 2 after the TMS pulse. From 1 millisecond before the pulse to 10 milliseconds after, cubic interpolation is used to remove and replace TMS pulse artifacts. Afterwards, the data is downsampled to 1,000Hz and bandpass filtered (Butterworth zero phase filter) between 1 and 80Hz. A 50-Hz notch filter is applied to reduce noise from the power supply. Then, all periods are visually inspected and those EEGs that are too noisy are excluded from the analysis. Independent component analysis (INFOMAX-ICA) is applied to the EEG signal to identify and remove components reflecting muscle activity, eye movement and blink-related activity, as well as residual TMS-related artifacts based on previously established standards. Finally, the signal is re-referenced to the average signal of all electrodes.

[0583] Cortical oscillations. Cortical oscillation analysis was performed based on Morlet wavelets (period: 5; frequency resolution: 1 Hz, from 4 to 90 Hz) to run a time / frequency decomposition and calculate TMS-related spectral perturbations (TRSP) in theta, alpha, beta, and gamma frequency bands. To analyze local oscillatory activity, the average TRSP within the following electrode clusters was calculated: for PC, Pz, P1, P2, POz; for l-PPC, P3, P5, CP3, PO3. For each time, condition, and stimulated area, the corresponding cluster was calculated and the TRSP values ​​were averaged between 10 ms and 250 ms (as this is the averaging time window for spectral perturbations, see the “Results” paragraph) in the following frequency bands: theta (4-7 Hz), alpha (8-13 Hz), beta (14-30 Hz), low gamma (31-40 Hz), high gamma (41-90 Hz).

[0584] Connectivity. Connectivity analysis was performed by calculating the wavelet phase-locking value (PLV), a phase synchronization index ranging from 0 (complete absence of phase synchronization) to 1 (complete phase synchronization). To assess the effects of the neuromodulation protocol on DMN connectivity, PLVs were calculated between the PC cluster (see the “Cortical oscillations” section) and the medial frontal cluster (FM) composed of Fz, AFz, F1, and F2. To verify the specificity of the network effect, PLVs were also calculated for the PC and l-PPC clusters. Each calculation was run for each frequency band.

[0585] Cortical excitability was assessed using TMS-evoked potentials (TEPs) from 100 ms before to 250 ms after the TMS pulse and was calculated by averaging all time-locked EEG responses in each electrode, baseline corrected 100 ms before the TMS pulse. Pz-TEPs were calculated to assess the local effects of the neuromodulation protocol. Six peaks were identified by precise visual inspection of the TEP waveform: P1 from 10 ms to 25 ms, P2 from 26 ms to 40 ms, P3 from 41 ms to 55 ms, P4 from 56 ms to 65 ms, P5 from 66 ms to 85 ms, and P6 from 86 ms to 100 ms. The average TEP amplitude was calculated within each time window.

[0586] MRI data acquisition and preprocessing

[0587] MRI data were acquired 1) to identify and individualize stimulation targets before each experiment and 2) to assess the neuromodulation protocol effects before and after the protocol (see "Procedures" paragraph).

[0588] Structural imaging sessions were acquired using high-resolution T1-weighted (T1) anatomical images obtained with a 3D-T1w sequence (TR = 2500 msec, TE = 2 msec, TI = 1070 msec, flip angle (FA) = 8°, thickness = 1 mm, imaging matrix = 240×240). fMRI images were acquired using standard echo-planar blood oxygen level-dependent (BOLD) imaging (TR = 3200 msec, TE = 25 msec, flip angle (FA) = 90°, thickness = 2.5 mm, gap = 2.5 mm, acquisition duration: 6,46”). Subjects were instructed not to focus their thoughts on any particular topic, not to cross their arms or legs and to keep their eyes open.

[0589] For the individualization of stimulation sites, the functional regions of interest (ROIs) represent the PC nodes of the DMN and the L-PPC nodes of the frontal-parietal network (FPN). Seed-to-voxel correlation maps 1200 were calculated for each participant to obtain maps of positively and negatively correlated voxels representing the DMN and FPN, respectively. An MRI research expert examined the resting state functional connectivity (rs-FC) map and structural MRI data (i.e., T1-weighted images) to identify single hotspots according to self-organizing criteria. In particular, the stimulation site is defined as a site as close as possible to the local maximum number of rs-FC clusters, which is identified as the DMN-PC and FPN-PPC located at the top of the cortical gyrus and represents the shortest vertical path connecting the stimulation TMS coil 902 on the scalp to the cortex. Based on the best judgment, the resulting set of coordinates is selected as a separate stimulation site. Then, a nonlinear transformation is used to transform a separate set of coordinates to reconstruct the target in a separate brain space. Finally, in order to ensure coherent intra-period and inter-period stimulation, the individualized target is marked in the subject's anatomical MRI loaded into the neuronavigation system.

[0590] Data analysis

[0591] Before performing parametric or nonparametric statistical procedures, the assumption of normal distribution of data residuals was assessed using the Shapiro-Wilk test. The assumption of sphericity was tested with the Mauchly test, and if this test was significant, the Huynh-Feldt correction was used. The significance level was set at α = 0.05.

[0592] To assess memory performance in Experiment 1 910, repeated measures ANOVAs were run for each dependent variable with stimulation condition as a within-subject factor (i.e., iTBS+γtACS; iTBS+sham tACS; sham iTBS+sham tACS). In detail, separate ANOVAs were performed for each FNAT measure (i.e., name, occupation, total) for each memory process (i.e., intermediate recall 906a, delayed recall 906b, and recognition). Separate ANOVAs were also performed for both shape and combined conditions for STBM 907 accuracy and RT.

[0593] To evaluate memory performance in Experiment 2 920, repeated measures ANOVAs were run with stimulation condition as a within-subject factor (iTBS + γtACS, iTBS + sham tACS). As in Experiment 1 910, separate ANOVAs were performed for each dependent variable, except that FNAT delayed recall performance was analyzed with repeated measures ANOVAs with stimulation condition (i.e., iTBS + γtACS, iTBS + sham tACS) and time (i.e., day 1, day 2, day 7) as within-subject factors. Based on the results obtained in this analysis (see the 'Results' paragraph), an exploratory paired t-test was performed to investigate the effect of stimulation condition at each time point.

[0594] To evaluate both cortical oscillations and connectivity changes in Experiment 3 930, repeated measures ANOVA were calculated with stimulation condition (iTBS+γtACS, iTBS+sham tACS) and time T1-T0, T2-T0 (ΔT1, ΔT2) as internal factors for each frequency band. Also in this case, paired t-tests were performed to investigate the effects of stimulation condition on each time point (T0, T1, T2).

[0595] To evaluate cortical excitability effects, repeated measures ANOVA were calculated with stimulation condition (iTBS+γtACS, iTBS+sham tACS), time (T0, T1, T2), and peak (P1, P2, P3, P4, P5, P6) as within-subject factors. Post hoc comparisons were performed using paired t-tests corrected with the Bonferroni method.

[0596] To assess the potential predictive role of baseline neurophysiological outputs (ie, cortical oscillatory activity, connectivity, cortical excitability) on memory performance, a stepwise linear regression was performed using a forward algorithm.

[0597] General linear models (GLM) were used to test whether stimulation conditions modulated participants' resting state functional connectivity (rsFC). Considering stimulation conditions (i.e., iTBS+γtACS; iTBS+sham tACS) and time points (i.e., before and after) as factors, statistical analysis was performed. In particular, a seed-to-voxel analysis was calculated that considered the PC (i.e., stimulated area) as a seed. The temporal correlation between this seed and all other voxels in the brain was calculated. Age, gender, and stimulation order were included in all analyses as covariates. Results were calculated by applying cluster size correction (false discovery rate) and an alpha of 0.01.

[0598] result

[0599] The different stimulation protocols were all well tolerated. No participant reported significant side effects related to the use of the neuromodulation protocols.

[0600] iTBS-γtACS for selectively improving long-term associative memory

[0601] In Experiment 1 910, the effects of three stimulation conditions (i.e., iTBS+γtACS, iTBS+sham tACS, sham iTBS+sham tACS) on memory performance were investigated. The iTBS+γtACS condition was found to have a main effect on long-term associative memory performance by FNAT 906a, 906b. Specifically, total FNAT was found to be different in both immediate FNAT recall 906a performance as shown in Figure 1010 (F2,38=6.81; p=0.003; η2p=0.264) and delayed FNAT recall 906b performance as shown in Figure 1020 (F2,38=6.35; p=0.009; η2p=0.25). Post hoc analysis revealed that total FNAT scores in immediate recall were higher in iTBS+γtACS vs. iTBS+sham tACS (40.4±22.5% vs. 25.4±19%; post hoc p=0.011) ( Fig. 10A ), while iTBS+γtACS showed an increase in delayed recall relative to both iTBS+sham tACS (35±22.4% vs. 26.2±19.8%; p=0.046) and sham iTBS+sham tACS (40.4±22.5% vs. 26.3±17.6%; p=0.026). Figure 2A ). In addition, effects were found on FNAT name delayed recall 906b (F2,38=3.46; p=0.042; η2p=0.154), where iTBS+γtACS was found to increase relative to sham iTBS+sham tACS (42.9±21.5% vs. 33.8±19%; p=0.048), and a trend was observed in immediate recall 906a (F2,38=3.2; p=0.052; η2p=0.144) (Figure 10). These stimulation conditions were selective for the effects on long-term associative memory as studied by FNAT 906a, 906b, and no effects were found on visual short-term memory as studied by STMB RT 1030 and accuracy 1040 (all ps>0.05) ( Fig. 10B ).

[0602] iTBS-γtACS has lasting effects on associative memory

[0603] To investigate the long-term effects of the neuromodulation protocol on memory performance and replicate the results of Experiment 1 910, Experiment 2 920 was run, adding delayed FNAT cued recall 24 hours 906c and 1 week 906d after the neuromodulation protocol 901. For Experiment 1 910, it was found that iTBS+γtACS had an effect on total immediate FNAT 906a memory performance (F1,9=7.31; p=0.024; η2p=0.448, with an increase in performance after iTBS+γtACS relative to the control condition iTBS+sham tACS (26.7±10.2% vs. 17.5±8.3%; p=0.024), as shown in Figure 2 . Fig.11A The immediate recall results are shown in Figure 1110. More importantly, it was found that the stimulation condition had a lasting effect on the total delayed FNAT performance (F1,9 = 8.433; p = 0.017; η2p = 0.484), as shown in Figure 1110. Fig. 11B As shown in the name result graph 1130, the occupation result graph 1140, and the total result graph 1050. In fact, exploratory analysis revealed that such effects persisted for up to one week after neuromodulation (Day 1 906b: 25±9.6% vs. 14.2±9.7%; t9=-2.751; p=0.011; Day 2 906c: 25.8±10.7% vs. 15±9.5%; t9=-2.899; p=0.009; Day 7 906d: 23.3±12.9% vs. 14.2±12.5%; t9=-2.400; p=0.020).

[0604] For Experiment 1 910, no effects were found on FNAT 906a, 906b recognition and STMB 907 accuracy and RT (all ps>0.05).

[0605] iTBS-γtACS increases gamma oscillations and medial parieto-frontal connectivity

[0606] Experiment 3 930 was designed to investigate the effects of neuromodulation protocol 901 on cortical oscillations and connectivity, respectively, via TRSP and PLV. Analysis of local cortical oscillations showed a main effect of neuromodulation protocol 901 on γTRSP (F1,13 = 5.073; p = 0.042; η2p = 0.281). Specifically, an increase in γ-TRSP at both time points was observed after iTBS+γtACS relative to iTBS+sham tACS (ΔT1: 0.045 ± 0.09 vs. -0.013 ± 0.06; t13 = 1.96; p = 0.036; ΔT2: 0.026 ± 0.04 vs. -0.013 ± 0.05; t13 = 1.92; p = 0.039). This effect was specific to PC stimulation; in fact, no significant effects were found above the lPPC (p>.05).

[0607] Connectivity analysis showed that neuromodulation protocol 901 had significant effects on PLV in the gamma band (F1,13 = 5.07; p = 0.042; η2p = 0.281) and the alpha band (F1,13 = 13.32; p = 0.003; η2p = 0.506). Specifically, an increase in gamma PLV was observed 20 minutes after iTBS + gamma tACS relative to iTBS + sham tACS (ΔT2: 0.06 ± 0.11 vs. -0.03 ± 0.09; t13 = 2.53; p = 0.013), although a trend was observed immediately thereafter (ΔT1: 0.07 ± 0.11 vs. 0.00 ± 0.11; t13 = 1.5; p = 0.07). On the other hand, αPLV increased relative to iTBS+sham tACS immediately and 20 min after iTBS+γtACS (ΔT1: 0.06±0.09 vs. -0.09±0.11; t13=4.58; p=0.001; ΔT2: 0.02±0.07 vs. -0.06±0.13; t13=2.06; p=0.03). In addition, trends of stimulation effects on PLV indices calculated in theta (F1,13=4.309; p=0.058; η2p=0.249) and beta (F1,13=4.583; p=0.052; η2p=0.261) were found.

[0608] iTBS-γtACS increases functional connectivity

[0609] In Experiment 4 940, the effects of the neuromodulation protocol 901 on functional connectivity were investigated by MRI analysis. Seed-based functional connectivity analyses were computed using the stimulated region (i.e., PC) as a seed. The analysis revealed a significant stimulation condition × time interaction on rs-FC between the PC and bilateral insular cortex, cuneate cortex, orbitofrontal cortex, and cingulate gyrus ( Fig. 12A In addition, main effects of stimulation condition were observed in the PC and bilateral insular cortex, left temporal cortex, and cingulate gyrus ( Fig. 12B To further investigate the interaction effect, pairwise comparisons were performed, which revealed an increase in rs-FC in the insular cortex, orbitofrontal cortex, and cingulate gyrus after iTBS+γtACS compared with the same condition before stimulation ( Fig. 12C ). At the same time, it was observed that rs-FC was reduced in the bilateral insular cortex and cuneate cortex after iTBS+sham tACS conditions compared with before rs-FC stimulation ( Fig.12D ). Before stimulation, no significant differences were detected between the groups.

[0610] Fig. 12A-D shows the seed-to-voxel results of Experiment 4 940, with significant results at p < 0.02 (FDR corrected), shown on the following standard MNI maps: A) interaction between group and condition, B) main effect of condition, C) iTBS + γtACS: after > before, D) iTBS + sham tACS: after > before. Color bars represent F values ​​or t values.

[0611] Computer Systems

[0612] The present disclosure provides computer systems programmed to implement the methods of the present disclosure. Fig.13 A computer system 1301 is shown that is programmed or otherwise configured to perform one or more steps of the methods described herein and / or control one or more aspects of the systems and methods described herein. The computer system 1301 can regulate various aspects of the present disclosure, such as applying stimulation to a patient; recording a patient's biological signals; analyzing the recorded signals and / or one or more other inputs; and generating outputs based on the recorded signals and the signal analysis.

[0613] The computer system 1301 may be a user's electronic device or a computer system located remotely from the electronic device. The electronic device may be a mobile electronic device.

[0614] The computer system 1301 includes a central processing unit (CPU, also referred to herein as a "processor" and "computer processor") 1305, which may be a single-core or multi-core processor or multiple processors for parallel processing. The computer system 1301 also includes a memory or memory location 1310 (e.g., random access memory, read-only memory, flash memory), an electronic storage unit 1315 (e.g., a hard disk), a communication interface 1320 (e.g., a network adapter) for communicating with one or more other systems, and peripheral devices 1325, such as cache, other memory, data storage devices, and / or electronic display adapters. The memory 1310, storage unit 1315, interface 1320, and peripheral devices 1325 communicate with the CPU 1305 via a communication bus (solid lines) such as a motherboard. The storage unit 1315 may be a data storage unit (or data repository) for storing data. The computer system 1301 may be operably coupled to a computer network ("network") 1330 by means of the communication interface 1320. The network 1330 may be the Internet, the internet and / or an extranet or an intranet and / or an extranet in communication with the Internet. In some cases, the network 1330 is a telecommunications network and / or a data network. The network 1330 may include one or more computer servers that may implement distributed computing such as cloud computing. In some cases, the network 1330 may implement a peer-to-peer network with the aid of the computer system 1301 that may implement coupling a device to the computer system 1301 to act as a client or server.

[0615] The CPU 1305 may execute a series of machine-readable instructions, which may be embodied in a program or software. The instructions may be stored in a memory location such as the memory 1310. The instructions may be directed to the CPU 1305, which may then program or otherwise configure the CPU 1305 to implement the methods of the present disclosure. Examples of operations performed by the CPU 1305 may include fetching, decoding, executing, and writing back.

[0616] CPU 1305 may be part of a circuit, such as an integrated circuit. One or more other components of system 1301 may be included in the circuit. In some cases, the circuit is an application specific integrated circuit (ASIC).

[0617] Storage unit 1315 can store files such as drivers, libraries, and saved programs. Storage unit 1315 can store user data (e.g., user preferences and user programs). In some cases, computer system 1301 may include one or more additional data storage units that are located external to computer system 1301, such as on a remote server that communicates with computer system 1301 via an intranet or the Internet.

[0618] Computer system 1301 can communicate with one or more remote computer systems via network 1330. For example, computer system 1301 can communicate with a user's remote computer system. Examples of remote computer systems include personal computers (e.g., portable PCs), tablet computers, or tablet PCs (e.g., iPad, Galaxy Tab), phones, smartphones (e.g. iPhone, Android-supported devices, ) or a personal digital assistant. A user can access the computer system 1301 through the network 1330.

[0619] The methods described herein may be implemented by machine (e.g., computer processor) executable code stored in an electronic storage location (e.g., memory 1310 or electronic storage unit 1315) of computer system 1301. Machine executable code or machine readable code may be provided in the form of software. During use, the code may be executed by processor 1305. In some cases, the code may be retrieved from storage unit 1315 and stored on memory 1310 for access by processor 1305 at any time. In some cases, electronic storage unit 1315 may be excluded and machine executable instructions may be stored on memory 1310.

[0620] The code may be precompiled and configured for use with a machine having a processor suitable for executing the code, or may be compiled during runtime. The code may be supplied in a programming language that may be selected to enable execution of the code in a precompiled or as-compiled manner.

[0621] Aspects of the systems and methods provided herein such as computer system 1301 can be embodied in programming. Aspects of the technology can be considered as "products" or "articles" in the form of machine (or processor) executable code and / or associated data, which are carried on or embodied in a type of machine-readable medium. Machine executable code can be stored on electronic storage units such as memory (e.g., read-only memory, random access memory, flash memory) or hard disks. "Storage" type media can include any or all tangible memories, processors, etc. of a computer, or its associated modules such as various semiconductor memories, tape drives, disk drives, etc., which can provide non-temporary storage for software programming at any time. All or part of the software can sometimes communicate through the Internet or various other telecommunication networks. Such communications, for example, can realize loading software from one computer or processor into another computer or processor, such as loading from a management server or host computer into a computer platform of an application server. Therefore, another type of medium that can carry software elements includes light waves, radio waves, and electromagnetic waves used as physical interfaces between local devices over wired and optical landline networks and over various air links. Physical elements that carry such waves, such as wired or wireless links, optical links, etc., may also be considered as the medium that carries the software. As used herein, unless restricted to non-transitory, tangible "storage" media, terms such as computer or machine "readable media" refer to any medium that participates in providing instructions to a processor for execution.

[0622] Thus, machine-readable media (such as computer executable code) can take many forms, including but not limited to tangible storage media, carrier media, or physical transmission media. Non-volatile storage media include, for example, optical or magnetic disks, such as any storage device in any computer, etc., such as can be used to implement the database shown in the accompanying drawings, etc. Volatile storage media include dynamic memory, such as the main memory of such computer platforms. Tangible transmission media include coaxial cables; copper wire and optical fiber, including the wires that comprise the bus within a computer system. Carrier transmission media can take the form of electrical or electromagnetic signals, or acoustic or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Thus, common forms of computer readable media include, for example, a floppy disk, a foldable disk, a hard disk, a magnetic tape, any other magnetic medium, a CD-ROM, a DVD or DVD-ROM, any other optical medium, a punched card tape, any other physical storage medium with a pattern of holes, a RAM, a ROM, a PROM and an EPROM, a FLASH-EPROM, any other memory chip or cassette, a carrier wave that transmits data or instructions, a cable or link that transmits such a carrier wave, or any other medium from which a computer can read programming code and / or data. A variety of these forms of computer readable media may be involved in transmitting one or more sequences of one or more instructions to a processor for execution.

[0623] The computer system 1301 may include or communicate with an electronic display 1335 that includes a user interface (UI) 1340 for providing, for example, a visual representation of stimulation parameters; a graphical representation of recorded response signals, an indication of a non-invasive assessment of a person's brain state, etc. Examples of UIs include, but are not limited to, a graphical user interface (GUI) and a web-based user interface.

[0624] The methods and systems of the present disclosure may be implemented by one or more algorithms. The algorithms may be implemented by software when executed by the central processing unit 1305. The algorithms may, for example, control stimulation protocols, perform data analysis, and generate outputs based on the data analysis.

[0625] Although preferred embodiments of the present disclosure 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. The present disclosure is not intended to be limited by the specific examples provided in the specification. Although the present disclosure has been described with reference to the above description, the description and illustration of the embodiments herein are not intended to be understood in a limiting sense. Without departing from the present disclosure, those skilled in the art will now think of many variations, changes and substitutions. In addition, it should be understood that all aspects of the present disclosure are not limited to the specific description, configuration or relative proportions set forth herein, which depend on various conditions and variables. It should be understood that different alternatives of the embodiments described herein can be used to practice the present disclosure. Therefore, it is considered that the present disclosure should also cover any such alternatives, modifications, variations or equivalents. It is contemplated that the following claims define the scope of the present invention and thus cover the methods and structures within the scope of these claims and their equivalents.

Claims

1. A method for non-invasively assessing synaptic dysfunction in a patient suffering from Alzheimer's Disease, the method comprising: applying transcranial magnetic stimulation (TMS) to at least one brain region of the patient; recording a plurality of TMS-evoked potentials with a plurality of electroencephalogram (EEG) electrodes in response to applying the TMS to the at least one brain region of the patient; analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient; as well as An indication of the synaptic dysfunction assessment of the patient is output. 2 . The method of claim 1 , wherein applying TMS to at least one brain region of the patient comprises applying a single pulse of TMS to the at least one brain region. 3 . The method of claim 1 , wherein applying TMS to at least one brain region of the patient comprises applying repetitive TMS to the at least one brain region. 4 . The method of claim 1 , wherein applying TMS to at least one brain region of the patient comprises applying TMS at multiple frequencies simultaneously. 5 . The method of claim 1 , wherein applying TMS to at least one brain region of the patient comprises applying patterned bursts of TMS to the at least one brain region.

6. The method of claim 5, wherein the patterned bursts of TMS comprise a continuous or intermittent theta burst pattern.

7. The method of claim 1, wherein applying TMS to at least one brain region of the patient comprises applying TMS to a single brain region.

8. The method of claim 1, wherein applying TMS to at least one brain region of the patient comprises applying TMS to a plurality of different brain regions. 9 . The method according to claim 8 , wherein the multiple different brain regions are two or more nodes of the same brain network.

10. The method of claim 9, wherein the brain network is a long-term memory network.

11. The method of claim 9, wherein the brain network is a default mode network.

12. The method of claim 9, wherein the brain network is a fronto-parietal network.

13. The method of claim 1, wherein the at least one brain region comprises at least one brain region in a temporal lobe of the patient's brain.

14. The method of claim 1, wherein the at least one brain region comprises the precuneus region.

15. The method of claim 1, further comprising determining a location of the at least one brain region to apply the TMS based at least in part on spatial and functional searches.

16. The method of claim 1, further comprising determining a location of the at least one brain region to apply the TMS based at least in part on magnetic resonance imaging (MRI) data associated with a brain of the patient.

17. A method according to claim 16, wherein determining the position of the at least one brain region to apply the TMS based at least in part on magnetic resonance imaging (MRI) data associated with the patient's brain includes generating a biophysical model of the brain anatomy of the patient's brain based on the MRI data and determining the position based at least in part on the generated biophysical model.

18. The method of claim 1, further comprising determining a location of the at least one brain region to apply the TMS based at least in part on data from a plurality of patients.

19. The method of claim 1, further comprising recording electromyography (EMG) data in response to applying the TMS to the at least one brain region of the patient, wherein the assessment of the synaptic dysfunction of the patient is further based at least in part on at least one characteristic of the recorded EMG data.

20. The method of claim 1, further comprising determining a resting motor threshold (RMT) of the patient, wherein applying transcranial magnetic stimulation (TMS) to at least one brain region of the patient comprises applying TMS at an intensity based on the RMT of the patient.

21. The method of claim 20, wherein the intensity of the applied TMS is set to 90% of the RMT of the patient.

22. The method of claim 9, wherein the brain network is a pre-salience network.

23. The method of claim 9, wherein the brain network is a sensorimotor network.

24. The method of claim 9, wherein the brain network is an attention network.

25. The method of claim 1, wherein the at least one brain region is a cortical brain region connected to the hippocampus and temporal polar regions of the brain by white matter fibers.

26. The method of claim 1, wherein the at least one brain region is a cortical brain region connected to a region of the default mode network by white matter fibers.

27. The method of claim 1, wherein the at least one brain region is the dorsolateral prefrontal cortex.

28. The method of claim 1, wherein the at least one brain region is a brain region with high network resilience, i.e., a brain region in the 5th percentile ranked based on its resilience that is able to maintain a high level of network communication efficiency despite simulated or real injury.

29. The method of claim 1, wherein the at least one brain region is a brain region with relatively high network modularity, in which brain activity is organized in more than 2 separate networks based on functional connectivity measures.

30. The method of claim 1, wherein the at least one brain region is a brain region or network that supports episodic memory.

31. The method of claim 1, wherein the at least one brain region is a brain region or network that supports autobiographical memory.

32. The method of claim 1, wherein the at least one brain region is a brain region or network that supports emotion processing.

33. The method of claim 1, wherein the at least one brain region is a brain region or network that supports mental rotation ability.

34. The method of claim 1, wherein the at least one brain region is a brain region or network that supports mental planning.

35. The method of claim 1, wherein the at least one brain region is a brain region or network that supports executive function.

36. The method of claim 1, wherein the at least one brain region is a brain region or network where amyloid-beta protein is present.

37. The method of claim 1, wherein the at least one brain region is a brain region or network where tau protein is present.

38. The method of claim 1, wherein the at least one brain region is a brain region or network having altered levels of neuroinflammation.

39. The method of claim 1, wherein the at least one brain region is a brain region or network with altered synaptic plasticity.

40. The method of claim 1, wherein the at least one brain region is a brain region or network with hypoperfusion and hypometabolism.

41. The method of claim 1, wherein the at least one brain region is a brain region or network that supports cognitive reserve.

42. The method of claim 1, wherein the at least one brain region is a brain region or network with elevated neuroinflammation.

43. The method of claim 1, wherein the at least one brain region is a brain region or network that supports functional independence in elderly individuals.

44. The method of claim 1, wherein the at least one brain region is a brain region or network with altered oscillatory activity.

45. The method of claim 1, wherein recording a plurality of TMS-evoked potentials in response to applying the TMS to the at least one brain region of the patient comprises recording at least one TMS-evoked potential from the at least one brain region to which TMS is applied.

46. ​​The method of claim 1, wherein recording a plurality of TMS-evoked potentials in response to applying the TMS to the at least one brain region of the patient comprises recording at least one TMS-evoked potential from one or more brain regions that are different from the at least one brain region to which TMS is applied.

47. The method of claim 1, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient comprises comparing the at least one characteristic of the plurality of TMS-evoked potentials to data collected from individuals not suffering from Alzheimer's disease or previous data collected from the patient.

48. The method of claim 1, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient comprises comparing the at least one characteristic of the plurality of TMS-evoked potentials with data collected from a plurality of patients suffering from Alzheimer's disease, wherein the assessment of synaptic dysfunction comprises determining that the patient suffers from a subtype of Alzheimer's disease.

49. The method of claim 1, wherein the applying and the recording are performed after providing the patient with a treatment for Alzheimer's disease, and wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient comprises comparing the at least one characteristic of the plurality of TMS-evoked potentials with data collected from the patient before providing the patient with the treatment for Alzheimer's disease.

50. The method of claim 1, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient comprises analyzing the plurality of TMS-evoked potentials using at least one machine learning classification model.

51. The method of claim 1, wherein the at least one characteristic of the plurality of TMS-induced potentials comprises one or more of a shape, an amplitude, a phase, or a timing of one or more TMS-induced potentials of the plurality of TMS-induced potentials.

52. The method of claim 1, wherein analyzing at least one characteristic of the plurality of TMS-induced potentials to assess synaptic dysfunction in the patient comprises determining an amount of time for an energy level of one or more of the plurality of TMS-induced potentials to return to a baseline energy level prior to application of the TMS, wherein the assessment of synaptic dysfunction is based at least in part on the amount of time for the energy level of the one or more of the plurality of TMS-induced potentials to return to the baseline energy level.

53. The method of claim 52, wherein the assessment of synaptic dysfunction is further based at least in part on an amplitude of the one or more TMS-evoked potentials of the plurality of TMS-evoked potentials.

54. The method of claim 1, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient comprises projecting the plurality of TMS-evoked potentials onto structural magnetic resonance imaging (MRI) data.

55. The method of claim 54, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient further comprises extracting time series data from one or more regions of interest defined based on the structural MRI data.

56. The method of claim 55, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient further comprises using the extracted time series data to assess synchrony across electrodes and / or brain regions.

57. The method of claim 1, further comprising determining a treatment for the patient based at least in part on the assessment of synaptic dysfunction and providing the treatment to the patient.

58. The method of claim 1, wherein the assessment of synaptic dysfunction comprises an assessment of neuroinflammation, the method further comprising determining a treatment for the patient based at least in part on the assessment of neuroinflammation and providing the treatment to the patient.

59. The method of claim 1, wherein the assessment of synaptic dysfunction comprises an assessment of protein clearance mechanisms and brain protein load, the method further comprising determining a treatment for the patient based at least in part on the assessment of the protein clearance mechanisms and brain protein load and providing the treatment to the patient.

60. The method of claim 1, wherein the assessment of synaptic dysfunction comprises an assessment of brain network dysfunction and altered connectivity patterns, the method further comprising determining a treatment for the patient based at least in part on the assessment of the brain network dysfunction and altered connectivity patterns and providing the treatment to the patient.

61. The method of claim 1, wherein the assessment of synaptic dysfunction comprises an assessment of cortical excitability levels, the method further comprising determining a treatment for the patient based at least in part on the assessment of the cortical excitability levels and providing the treatment to the patient.

62. The method of claim 1, wherein the assessment of synaptic dysfunction comprises an assessment of altered brain oscillatory activity, the method further comprising determining a treatment for the patient based at least in part on the assessment of altered brain oscillatory activity and providing the treatment to the patient.

63. The method of claim 1, wherein the perturbation of brain function is applied by a combination of TMS and tACS stimulation, the method further comprising delivering tACS to induce brain oscillatory activity at a specific frequency band, while simultaneously delivering TMS to tACS to assess the patient's brain plasticity and cortical excitability level.

64. The method of claim 63, wherein tACS perturbations are delivered in the gamma frequency band between 30 Hz and 300 Hz.

65. The method of claim 63, wherein the tACS perturbation is delivered using electrical noise stimulation at a frequency between 1 Hz and 1000 Hz based on electrical patterns including but not limited to brown, pink, white and random noise.

66. The method of claim 1, wherein the perturbation of brain function is applied by a combination of the TMS and tACS stimulation, the method further comprising delivering tACS to induce brain oscillatory activity at a specific frequency band, and simultaneously delivering repetitive TMS (rTMS) to the tACS to modulate the patient's brain plasticity and cortical excitability.

67. The method of claim 63, wherein the TMS perturbation is delivered in the form of theta burst stimulation, including intermittent and continuous stimulation modes.

68. A non-invasive brain assessment system comprising: a transcranial magnetic stimulation (TMS) device configured to apply TMS to at least one brain region of the patient; a plurality of electroencephalogram (EEG) electrodes configured to record a plurality of TMS-evoked potentials in response to applying the TMS to the at least one brain region of the patient; and At least one computer processor programmed to analyze at least one characteristic of the plurality of TMS-evoked potentials to provide an assessment of synaptic dysfunction in the patient.

69. The system of claim 68, further comprising a neuronavigation system comprising at least one camera, wherein the neuronavigation system is configured to facilitate positioning of the TMS device.

70. The system of claim 68, further comprising an electromyography (EMG) system configured to determine an EMG signal in response to applying the TMS to the at least one brain region of the patient.

71. The system of claim 68, wherein applying TMS to at least one brain region of the patient comprises applying a single pulse of TMS to the at least one brain region.

72. The system of claim 68, wherein applying TMS to at least one brain region of the patient comprises applying repetitive TMS to the at least one brain region.

73. The system of claim 68, wherein applying TMS to at least one brain region of the patient comprises applying TMS at multiple frequencies simultaneously.

74. The system of claim 68, wherein applying TMS to at least one brain region of the patient comprises applying patterned bursts of TMS to the at least one brain region.

75. The system of claim 74, wherein the patterned bursts of TMS comprise continuous or intermittent theta burst patterns.

76. The system of claim 68, wherein applying TMS to at least one brain region of the patient comprises applying TMS to a single brain region.

77. The system of claim 68, wherein applying TMS to at least one brain region of the patient comprises applying TMS to a plurality of different brain regions.

78. The system of claim 77, wherein the plurality of different brain regions comprise two or more nodes of the same brain network.

79. The system of claim 78, wherein the brain network is a long-term memory network.

80. The system of claim 78, wherein the brain network is a default mode network.

81. The system of claim 78, wherein the brain network is a fronto-parietal network.

82. The system of claim 68, wherein the at least one brain region comprises at least one brain region in a temporal lobe of the patient's brain.

83. The system of claim 68, wherein the at least one brain region comprises a precuneus region.

84. The system of claim 68, wherein the at least one computer processor is further programmed to determine a location of the at least one brain region to apply the TMS based at least in part on spatial and functional searches.

85. The system of claim 68, wherein the at least one computer processor is further programmed to determine a location of the at least one brain region to apply the TMS based at least in part on magnetic resonance imaging (MRI) data associated with the patient's brain.

86. A system according to claim 85, wherein determining the position of the at least one brain region to apply the TMS based at least in part on magnetic resonance imaging (MRI) data associated with the patient's brain includes generating a biophysical model of the brain anatomy of the patient's brain based on the MRI data and determining the position based at least in part on the generated biophysical model.

87. The system of claim 68, wherein the at least one processor is further programmed to determine a location of the at least one brain region to apply the TMS based at least in part on data from a plurality of patients.

88. The system of claim 70, wherein the assessment of the synaptic dysfunction of the patient is further based at least in part on at least one characteristic of the determined EMG signal.

89. The system of claim 68, wherein the at least one processor is further programmed to determine a resting motor threshold (RMT) of the patient, and wherein applying transcranial magnetic stimulation (TMS) to at least one brain region of the patient comprises applying TMS at an intensity based on the RMT of the patient.

90. The method of claim 89, wherein the intensity of applied TMS is set to 90% of the patient's RMT.

91. The system of claim 78, wherein the brain network is a pre-salience network.

92. The system of claim 78, wherein the brain network is a sensorimotor network.

93. The system of claim 78, wherein the brain network is an attention network.

94. The system of claim 68, wherein the at least one brain region is a cortical brain region connected to the hippocampus and temporal polar regions of the brain by white matter fibers.

95. The system of claim 68, wherein the at least one brain region is a cortical brain region connected to a region of the default mode network via white matter fibers.

96. The system of claim 68, wherein the at least one brain region is a brain region within the dorsolateral prefrontal cortex.

97. The system of claim 68, wherein at least one of the brain regions is a brain region with high network resilience, i.e., a brain region in the 5th percentile ranked based on its resilience, capable of maintaining a high level of network communication efficiency despite simulated or real injury.

98. The system of claim 68, wherein at least one of the brain regions is a brain region with relatively high network modularity, in which brain activity is organized in more than 2 separate networks based on functional connectivity measures.

99. The system of claim 68, wherein the at least one brain region is a brain region or network that supports episodic memory.

100. The system of claim 68, wherein the at least one brain region is a brain region or network that supports autobiographical memory.

101. The system of claim 68, wherein the at least one brain region is a brain region or network that supports emotion processing.

102. The system of claim 68, wherein the at least one brain region is a brain region or network that supports mental rotation ability.

103. The system of claim 68, wherein the at least one brain region is a brain region or network that supports mental planning.

104. The system of claim 68, wherein the at least one brain region is a brain region or network that supports executive function.

105. The system of claim 68, wherein the at least one brain region is a brain region or network where amyloid beta protein is present.

106. The system of claim 68, wherein the at least one brain region is a brain region or network where tau protein is present.

107. The system of claim 68, wherein the at least one brain region is a brain region or network having altered levels of neuroinflammation.

108. The system of claim 68, wherein the at least one brain region is a brain region or network with altered synaptic plasticity.

109. The system of claim 68, wherein the at least one brain region is a brain region or network with hypoperfusion and hypometabolism.

110. The system of claim 68, wherein the at least one brain region is a brain region or network that supports cognitive reserve.

111. The system of claim 68, wherein the at least one brain region is a brain region or network with elevated neuroinflammation.

112. The system of claim 68, wherein the at least one brain region is a brain region or network that supports functional independence in elderly individuals.

113. The system of claim 68, wherein the at least one brain region is a brain region or network with altered oscillatory activity.

114. The system of claim 68, wherein recording a plurality of TMS-evoked potentials in response to applying the TMS to the at least one brain region of the patient comprises recording at least one TMS-evoked potential from the at least one brain region to which TMS is applied.

115. The system of claim 68, wherein recording a plurality of TMS-evoked potentials in response to applying the TMS to the at least one brain region of the patient comprises recording at least one TMS-evoked potential from one or more brain regions different from the at least one brain region to which TMS is applied.

116. The system of claim 68, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient comprises comparing the at least one characteristic of the plurality of TMS-evoked potentials to data collected from individuals not suffering from Alzheimer's disease or previous data collected from the patient.

117. The system of claim 68, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient comprises comparing the at least one characteristic of the plurality of TMS-evoked potentials with data collected from a plurality of patients suffering from Alzheimer's disease, wherein the assessment of synaptic dysfunction comprises determining that the patient suffers from a subtype of Alzheimer's disease.

118. The system of claim 68, wherein the applying and the recording are performed after providing the patient with a treatment for Alzheimer's disease, and wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient comprises comparing the at least one characteristic of the plurality of TMS-evoked potentials with data collected from the patient before providing the patient with the treatment for Alzheimer's disease.

119. The system of claim 68, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient comprises analyzing the plurality of TMS-evoked potentials using at least one machine learning classification model.

120. The system of claim 68, wherein the at least one characteristic of the plurality of TMS-induced potentials comprises one or more of a shape, an amplitude, a phase, or a timing of one or more TMS-induced potentials of the plurality of TMS-induced potentials.

121. The system of claim 68, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient comprises: Determining an amount of time for an energy level of one or more of the plurality of TMS-induced potentials to return to a baseline energy level prior to application of the TMS, wherein the assessment of synaptic dysfunction is based at least in part on the amount of time for the energy level of the one or more of the plurality of TMS-induced potentials to return to the baseline energy level.

122. The system of claim 121, wherein the assessment of synaptic dysfunction is further based at least in part on an amplitude of the one or more TMS-evoked potentials of the plurality of TMS-evoked potentials.

123. The system of claim 68, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient comprises projecting the plurality of TMS-evoked potentials onto structural magnetic resonance imaging (MRI) data.

124. The system of claim 123, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient further comprises extracting time series data from one or more regions of interest defined based on the structural MRI data.

125. The system of claim 123, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient further comprises using the extracted time series data to evaluate synchronization across electrodes and / or brain regions.

126. The system of claim 68, wherein the at least one computer processor is further programmed to determine a treatment for the patient based at least in part on the assessment of synaptic dysfunction, and the TMS device is further configured to apply TMS to at least one brain region of the patient to provide the treatment to the patient.

127. The system of claim 68, wherein the assessment of synaptic dysfunction comprises an assessment of neuroinflammation, the at least one computer processor is further programmed to determine a treatment for the patient based at least in part on the assessment of neuroinflammation, and the TMS device is further configured to apply TMS to at least one brain region of the patient to provide the treatment to the patient.

128. The system of claim 68, wherein the assessment of synaptic dysfunction comprises an assessment of protein clearance mechanisms and brain protein load, the at least one computer processor being further programmed to determine a treatment for the patient based at least in part on the assessment of protein clearance mechanisms and brain protein load, and the TMS device being further configured to apply TMS to at least one brain region of the patient to provide the treatment to the patient.

129. The system of claim 68, wherein the assessment of synaptic dysfunction comprises an assessment of brain network dysfunction and altered connectivity patterns, the at least one computer processor is further programmed to determine a treatment for the patient based at least in part on the assessment of brain network dysfunction and altered connectivity patterns, and the TMS device is further configured to apply TMS to at least one brain region of the patient to provide the treatment to the patient.

130. A system according to claim 68, wherein the assessment of synaptic dysfunction includes an assessment of cortical excitability level, the at least one computer processor is further programmed to determine a treatment for the patient based at least in part on the assessment of the cortical excitability level, and the TMS device is further configured to apply TMS to at least one brain region of the patient to provide the treatment to the patient.

131. The system of claim 68, wherein the assessment of synaptic dysfunction comprises an assessment of altered brain oscillatory activity, the at least one computer processor is further programmed to determine a treatment for the patient based at least in part on the assessment of altered brain oscillatory activity, and the TMS device is further configured to apply TMS to at least one brain region of the patient to provide the treatment to the patient.

132. The system of claim 68, wherein the assessment of synaptic dysfunction comprises simultaneously delivering magnetic stimulation (TMS) and electrical stimulation, and recording brain activity via EEG recording, wherein the system comprises at least one TMS device configured to apply TMS to at least one brain region of a patient; at least one electrical stimulation device for delivering transcranial electrical stimulation to at least one brain region of a patient; and at least one EEG device for recording brain activity from scalp EEG electrodes.

133. The system of claim 132, wherein the electrical stimulation is delivered in the form of tACS.

134. The system of claim 133, wherein tACS perturbations are delivered in the gamma frequency band between 30 Hz and 300 Hz.

135. The system of claim 133, wherein the tACS perturbations are delivered using electrical noise stimulation at an electrical pattern with a frequency between 1 Hz and 1000 Hz based on patterns including but not limited to brown, pink, white, and random noise.

136. The system of claim 133, wherein the TMS perturbation is delivered in the form of theta burst stimulation, including intermittent and continuous stimulation modes.

137. The system of claim 133, wherein the TMS, the electrical stimulation, and the EEG recording are delivered by a single device that includes TMS, tES, and EEG capabilities.

138. A method of providing a non-invasive assessment of a person's brain state, the method comprising: applying non-invasive stimulation to at least one brain region of the person; recording brain signals using a brain activity recording device in response to applying the non-invasive stimulation to the at least one brain region of the person; analyzing at least one characteristic of the recorded brain signals to provide said non-invasive assessment of said brain state of said person; as well as An indication of the non-invasive assessment of the brain state of the person is output.

139. The method of claim 138, wherein applying non-invasive stimulation to at least one brain region of the person comprises applying one or more of transcranial magnetic stimulation (TMS), transcranial current stimulation (tCS), or focused ultrasound stimulation (tFUS).

140. The method of claim 139, wherein applying non-invasive stimulation to at least one brain region of the human comprises applying at least two of TMS, tCS, or tFUS.

141. The method of claim 138, wherein the brain activity recording device is an electroencephalogram (EEG) device.

142. The method of claim 138, wherein applying TMS to at least one brain region of the patient comprises applying a single pulse of TMS to the at least one brain region.

143. The method of claim 138, wherein applying TMS to at least one brain region of the patient comprises applying repetitive TMS to the at least one brain region.

144. The method of claim 138, wherein applying TMS to at least one brain region of the patient comprises applying TMS at multiple frequencies simultaneously.

145. The method of claim 138, wherein applying TMS to at least one brain region of the patient comprises applying patterned bursts of TMS to the at least one brain region.

146. The method of claim 145, wherein the patterned bursts of TMS comprise continuous or intermittent theta burst patterns.

147. The method of claim 138, wherein applying TMS to at least one brain region of the patient comprises applying TMS to a single brain region.

148. The method of claim 138, wherein applying TMS to at least one brain region of the patient comprises applying TMS to a plurality of different brain regions.

149. The method of claim 148, wherein the plurality of different brain regions are two or more nodes of the same brain network.

150. The method of claim 149, wherein the brain network is a long-term memory network.

151. The method of claim 149, wherein the brain network is a default mode network.

152. The method of claim 149, wherein the brain network is a fronto-parietal network.

153. The method of claim 138, wherein the at least one brain region comprises at least one brain region in a temporal lobe of the patient's brain.

154. The method of claim 138, wherein the at least one brain region comprises the precuneus region.

155. The method of claim 138, further comprising determining a location of the at least one brain region to apply the TMS based at least in part on spatial and functional searches.

156. The method of claim 138, further comprising determining a location of the at least one brain region to apply the TMS based at least in part on magnetic resonance imaging (MRI) data associated with the patient's brain.

157. A method according to claim 156, wherein determining the position of the at least one brain region to apply the TMS based at least in part on magnetic resonance imaging (MRI) data associated with the patient's brain includes generating a biophysical model of the brain anatomy of the patient's brain based on the MRI data and determining the position based at least in part on the generated biophysical model.

158. The method of claim 138, further comprising determining a location of the at least one brain region to apply the TMS based at least in part on data from a plurality of patients.

159. The method of claim 138, further comprising recording electromyography (EMG) data in response to applying the TMS to the at least one brain region of the patient, wherein the assessment of the brain state of the person is further based at least in part on at least one characteristic of the recorded EMG data.

160. The method of claim 138, further comprising determining a resting motor threshold (RMT) of the patient, wherein applying transcranial magnetic stimulation (TMS) to at least one brain region of the patient comprises applying TMS at an intensity based on the RMT of the patient.

161. The method of claim 160, wherein the intensity of the applied TMS is set to 90% of the patient's RMT.

162. The method of claim 149, wherein the brain network is a pre-salience network.

163. The method of claim 149, wherein the brain network is a sensorimotor network.

164. The method of claim 149, wherein the brain network is an attention network.

165. The method of claim 138, wherein the at least one brain region comprises a cortical brain region connected to the hippocampus and temporal polar regions of the brain by white matter fibers.

166. The method of claim 138, wherein the at least one brain region comprises a cortical brain region connected to a region of the default mode network via white matter fibers.

167. The method of claim 138, wherein the at least one brain region comprises the dorsolateral prefrontal cortex.

168. The method of claim 138, wherein the at least one brain region comprises a brain region having relatively high network resilience, i.e., a brain region in the 5th percentile ranked based on its resilience, capable of maintaining a high level of network communication efficiency despite simulated or real injury.

169. The method of claim 138, wherein the at least one brain region comprises a brain region with relatively high network modularity, in which brain activity is organized in more than 2 separate networks based on functional connectivity measurements.

170. The method of claim 138, wherein the at least one brain region comprises a brain region or network that supports episodic memory.

171. The method of claim 138, wherein the at least one brain region comprises a brain region or network that supports autobiographical memory.

172. The method of claim 138, wherein the at least one brain region comprises a brain region or network that supports emotion processing.

173. The method of claim 138, wherein the at least one brain region comprises a brain region or network that supports mental rotation ability.

174. The method of claim 138, wherein the at least one brain region comprises a brain region or network that supports mental planning.

175. The method of claim 138, wherein the at least one brain region comprises a brain region or network that supports executive function.

176. The method of claim 138, wherein the at least one brain region comprises a brain region or network where amyloid-beta protein is present.

177. The method of claim 138, wherein the at least one brain region comprises a brain region or network where tau protein is present.

178. The method of claim 138, wherein the at least one brain region comprises a brain region or network having altered levels of neuroinflammation.

179. The method of claim 138, wherein the at least one brain region comprises a brain region or network having altered synaptic plasticity.

180. The method of claim 138, wherein the at least one brain region comprises a brain region or network having hypoperfusion and hypometabolism.

181. The method of claim 138, wherein the at least one brain region comprises a brain region or network that supports cognitive reserve.

182. The method of claim 138, wherein the at least one brain region comprises a brain region or network with elevated neuroinflammation.

183. The method of claim 138, wherein the at least one brain region comprises a brain region or network that supports functional independence in elderly individuals.

184. The method of claim 138, wherein the at least one brain region comprises a brain region or network having altered oscillatory activity.

185. The method of claim 138, wherein recording a plurality of TMS-evoked potentials in response to applying the TMS to the at least one brain region of the patient comprises recording at least one TMS-evoked potential from the at least one brain region to which TMS is applied.

186. The method of claim 138, wherein recording a plurality of TMS-evoked potentials in response to applying the TMS to the at least one brain region of the patient comprises recording at least one TMS-evoked potential from one or more brain regions different from the at least one brain region to which TMS is applied.

187. The method of claim 138, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient comprises analyzing the plurality of TMS-evoked potentials using at least one machine learning classification model.

188. The method of claim 138, wherein the at least one characteristic of the plurality of TMS-induced potentials comprises one or more of a shape, an amplitude, a phase, or a timing of one or more TMS-induced potentials of the plurality of TMS-induced potentials.

189. The method of claim 138, wherein analyzing at least one characteristic of the plurality of TMS-induced potentials to assess synaptic dysfunction in the patient comprises determining an amount of time for an energy level of one or more of the plurality of TMS-induced potentials to return to a baseline energy level prior to application of the TMS, wherein the assessment of the brain state of the person is based at least in part on the amount of time for the energy level of the one or more of the plurality of TMS-induced potentials to return to the baseline energy level.

190. The method of claim 189, wherein the assessment of the brain state of the person is further based at least in part on the amplitude of the one or more TMS-induced potentials of the plurality of TMS-induced potentials.

191. The method of claim 138, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient comprises projecting the plurality of TMS-evoked potentials onto structural magnetic resonance imaging (MRI) data.

192. The method of claim 191, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient further comprises extracting time series data from one or more regions of interest defined based on the structural MRI data.

193. The method of claim 192, wherein analyzing at least one characteristic of the plurality of TMS-evoked potentials to assess synaptic dysfunction in the patient further comprises using the extracted time series data to evaluate synchronization across electrodes and / or brain regions.

194. The method of claim 138, further comprising determining a treatment for the patient based at least in part on the assessment of the brain state of the person and providing the treatment to the patient.

195. The method of claim 138, wherein the assessment of the brain state of the person comprises an assessment of neuroinflammation, the method further comprising determining a treatment for the patient based at least in part on the assessment of neuroinflammation and providing the treatment to the patient.

196. The method of claim 138, wherein the assessment of the brain state of the person comprises an assessment of protein clearance mechanisms and brain protein load, the method further comprising determining a treatment for the patient based at least in part on the assessment of the protein clearance mechanisms and brain protein load and providing the treatment to the patient.

197. The method of claim 138, wherein the assessment of the brain state of the person comprises an assessment of brain network dysfunction and altered connectivity patterns, the method further comprising determining a treatment for the patient based at least in part on the assessment of the brain network dysfunction and altered connectivity patterns and providing the treatment to the patient.

198. The method of claim 138, wherein the assessment of the brain state of the person comprises an assessment of a level of cortical excitability, the method further comprising determining a treatment for the patient based at least in part on the assessment of the level of cortical excitability and providing the treatment to the patient.

199. The method of claim 138, wherein the assessment of the brain state of the person comprises an assessment of altered brain oscillatory activity, the method further comprising determining a treatment for the patient based at least in part on the assessment of altered brain oscillatory activity and providing the treatment to the patient.

200. A non-invasive brain state assessment system, comprising: a non-invasive brain stimulation device configured to apply non-invasive stimulation to at least one brain region of a person; a brain activity recording device configured to record brain signals in response to applying the non-invasive stimulation to the at least one brain region of the person; as well as At least one computer processor is programmed to analyze at least one characteristic of the recorded brain signals to provide a non-invasive assessment of the brain state of the person.

201. The system of claim 200, further comprising a neuronavigation system comprising at least one camera, wherein the neuronavigation system is configured to facilitate positioning of the TMS device.

202. The system of claim 200, further comprising an electromyography (EMG) system configured to determine an EMG signal in response to applying the TMS to the at least one brain region of the patient.

203. The system of claim 200, wherein applying TMS to at least one brain region of the patient comprises applying a single pulse of TMS to the at least one brain region.

204. The system of claim 200, wherein applying TMS to at least one brain region of the patient comprises applying repetitive TMS to the at least one brain region.

205. The system of claim 200, wherein applying TMS to at least one brain region of the patient comprises applying TMS at multiple frequencies simultaneously.

206. The system of claim 200, wherein applying TMS to at least one brain region of the patient comprises applying patterned bursts of TMS to the at least one brain region.

207. The system of claim 206, wherein the patterned bursts of TMS comprise continuous or intermittent theta burst patterns.

208. The system of claim 200, wherein applying TMS to at least one brain region of the patient comprises applying TMS to a single brain region.

209. The system of claim 200, wherein applying TMS to at least one brain region of the patient comprises applying TMS to a plurality of different brain regions.

210. The system of claim 209, wherein the plurality of different brain regions are two or more nodes of the same brain network.

211. A system according to claim 210, wherein the brain network is a long-term memory network.

212. The system of claim 210, wherein the brain network is a default mode network.

213. The system of claim 210, wherein the brain network is a fronto-parietal network.

214. The system of claim 200, wherein the at least one brain region comprises at least one brain region in a temporal lobe of the patient's brain.

215. The system of claim 200, wherein the at least one brain region comprises a precuneus region.

216. The system of claim 200, wherein the at least one computer processor is further programmed to determine a location of the at least one brain region to apply the TMS based at least in part on spatial and functional searches.

217. The system of claim 200, wherein the at least one computer processor is further programmed to determine a location of the at least one brain region to apply the TMS based at least in part on magnetic resonance imaging (MRI) data associated with the patient's brain.

218. A system according to claim 217, wherein determining the position of the at least one brain region to apply the TMS based at least in part on magnetic resonance imaging (MRI) data associated with the patient's brain includes generating a biophysical model of the brain anatomy of the patient's brain based on the MRI data and determining the position based at least in part on the generated biophysical model.

219. The system of claim 200, wherein the at least one computer processor is further programmed to determine a location of the at least one brain region to apply the TMS based at least in part on data from a plurality of patients.

220. The system of claim 202, wherein the assessment of the brain state of the person is further based at least in part on at least one characteristic of the EMG signal.

221. The system of claim 200, wherein the at least one computer processor is further programmed to determine a resting motor threshold (RMT) of the patient, and wherein applying transcranial magnetic stimulation (TMS) to at least one brain region of the patient comprises applying TMS at an intensity based on the RMT of the patient.

222. The system of claim 210, wherein the brain network is a pre-salience network.

223. A system according to claim 210, wherein the brain network is a sensorimotor network.

224. A system according to claim 210, wherein the brain network is an attention network.

225. The system of claim 200, wherein the at least one brain region comprises a cortical brain region connected to the hippocampus and temporal polar regions of the brain via white matter fibers.

226. The system of claim 200, wherein the at least one brain region comprises a cortical brain region connected to a region of the default mode network via white matter fibers.

227. The system of claim 200, wherein the at least one brain region comprises the dorsolateral prefrontal cortex.

228. A system according to claim 200, wherein at least one of the brain regions includes a brain region with relatively high network resilience, i.e., a brain region in the 5th percentile ranked based on its resilience, which is able to maintain a high level of network communication efficiency despite simulated or real injury.

229. A system according to claim 200, wherein at least one of the brain regions comprises a brain region with relatively high network modularity, in which brain activity is organized in more than 2 separate networks based on functional connectivity measurements.

230. The system of claim 200, wherein the at least one brain region comprises a brain region or network that supports episodic memory.

231. The system of claim 200, wherein the at least one brain region comprises a brain region or network that supports autobiographical memory.

232. A system according to claim 200, wherein the at least one brain region comprises a brain region or network that supports emotion processing.

233. The system of claim 200, wherein the at least one brain region comprises a brain region or network that supports mental rotation ability.

234. A system according to claim 200, wherein the at least one brain region comprises a brain region or network that supports mental planning.

235. The system of claim 200, wherein the at least one brain region comprises a brain region or network supporting executive function.

236. The system of claim 200, wherein the at least one brain region comprises a brain region or network where amyloid-beta protein is present.

237. The system of claim 200, wherein the at least one brain region comprises a brain region or network where tau protein is present.

238. The system of claim 200, wherein the at least one brain region comprises a brain region or network having altered levels of neuroinflammation.

239. The system of claim 200, wherein the at least one brain region comprises a brain region or network having altered synaptic plasticity.

240. The system of claim 200, wherein the at least one brain region comprises a brain region or network having hypoperfusion and hypometabolism.

241. The system of claim 200, wherein the at least one brain region comprises a brain region or network that supports cognitive reserve.

242. The system of claim 200, wherein the at least one brain region comprises a brain region or network having elevated neuroinflammation.

243. A system according to claim 200, wherein the at least one brain region comprises a brain region or network that supports functional independence in elderly individuals.

244. The system of claim 200, wherein the at least one brain region comprises a brain region or network having altered oscillatory activity.

245. The system of claim 200, wherein the TMS, the electrical stimulation, and the EEG recording are delivered by a single device that includes TMS, tES, and EEG capabilities.

246. The system of claim 245, wherein the electrical stimulation and the EEG recording are delivered via a single scalp electrode having both recording and stimulation capabilities.

247. A system according to claim 246, wherein the electrodes that deliver electrical stimulation and record EEG signals are part of a TMS stimulation coil.

248. The system of claim 247, wherein the TMS stimulation coil comprises a figure eight structure capable of accommodating the EEG electrodes and the electrical stimulation electrodes for TMS delivery.

249. The system of claim 248, wherein the TMS stimulation coil comprises a figure eight structure for TMS delivery and additional structure capable of accommodating the EEG electrodes and the electrical stimulation electrodes.

250. A system according to claim 249, wherein the structure capable of accommodating the EEG electrodes and the electrical stimulation electrodes allows recording and stimulation from multiple brain regions and lobes including but not limited to the frontal lobe, temporal lobe, parietal lobe and occipital lobe.

251. A system according to claim 249, wherein the structure capable of accommodating the EEG electrodes and the electro-stimulation electrodes is constructed of a flexible material, thereby allowing the system to be comfortably placed on the scalp of individuals with different head sizes and shapes.

252. A method of treating a patient suffering from Alzheimer's disease, the method comprising: applying transcranial magnetic stimulation (TMS) to at least one brain region of the patient; recording a plurality of TMS-evoked potentials with a plurality of electroencephalogram (EEG) electrodes in response to applying the TMS to the at least one brain region of the patient; determining a treatment for the patient based at least in part on an analysis of at least one characteristic of the plurality of TMS-evoked potentials; as well as The treatment is administered to the patient.

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