Systems and methods for characterizing individual responses to perturbations in the brain of patients with Alzheimer's disease

Abstract

Devices, systems, and methods are provided herein for non-invasively assessing synaptic dysfunction in patients with Alzheimer's disease. Transcranial magnetic stimulation (TMS) is applied to at least one brain region of a patient. A plurality of TMS-evoked potentials generated in response to the TMS applied to 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 evaluate synaptic dysfunction in the patient. A display of the evaluation of the patient's synaptic dysfunction is output.

Description

Technical Field

[0001] Cross-reference This application claims the benefit of U.S. Provisional Application No. 63 / 356,609, filed Jun. 29, 2022, which is incorporated herein by reference.

Background Art

[0002] Alzheimer's disease (AD) is one of the conditions that most severely affects elderly individuals among the populations of Western countries. It is considered today, and even more extensively in the coming decades, to be one of the most serious medical, economic, and social emergencies that our society faces. Currently, there is no effective treatment, and patients diagnosed with AD face an uncertain future because the course of the disease cannot be predicted at present.

[0003] In recent years, evidence has been increasing to support the concept that a decrease in synapse density is an early event and may precede neurodegeneration, suggesting that synaptic dysfunction plays an important role in the etiology of AD. These altered synaptic mechanisms are associated with spinal cord atrophy, disruption of neuronal networks, and cell death. Synaptic dysfunction is a major cause of Alzheimer's disease that ultimately leads to dysfunction of the brain functional network and cognitive decline, and is a promising therapeutic target. However, there is a lack of validated biomarkers for measuring synaptic dysfunction in AD.

Summary of the Invention

Means for Solving the Problems

[0004] In this specification, concepts from physics and biology are considered, for example, that complex systems such as the human brain are better characterized by examining their responses to external perturbations rather than through their spontaneous activities, and a system and method for performing a stress test on the brain are described. Some aspects of the present disclosure are optimized for use in patients with neurodegenerative disorders, particularly patients with Alzheimer's disease and more general dementia, by combining hardware and software.

[0005] The method combines data collection methods for capturing brain activity before, during, and after the perturbation to record individual responses to the perturbation, thereby enabling reflection of the unique characteristics of the individual brain. The nature of the perturbation can vary depending on the target response or brain system. The method is related to concepts and principles from neurophysiology, neurology, neuroscience, cell biology, including but not limited to brain plasticity, Hebbian plasticity, excitatory-inhibitory balance, network complexity, connectomics, network resilience, structural and functional brain network analysis. The system and method are particularly applicable for uses including but not limited to patient identification and prediction of response to treatment in the fields of neurodegeneration, physiological and pathological aging, dementia, and Alzheimer's disease.

[0006] The brain can be considered as a complex dynamical system of organized distributed neural networks with specific spatio-temporal characteristics. The complex geometric structure of the structural and functional connections in the brain (the so-called brain connectome) explains cognitive abilities, vulnerability to neurological and mental states, and individual differences in the trajectory of healthy aging. Neuroimaging and electrophysiological methods may be used to characterize such complexity, examining the anatomical structure of the brain and the spontaneous functional characteristics of brain activity collected at rest. In limited cases, cognitive or behavioral stimuli are presented, for example, to induce transient changes in brain activity that are likely relevant when quantifying cognitive abilities during memory tasks. However, these methods have multiple limitations. For example, (i) they require active cooperation from the subjects being evaluated (e.g., performing memory tasks), which can be a limiting factor for patients with cognitive impairments. (ii) Typically, they rely on perturbations (e.g., cognitive or behavioral stimuli) that affect (e.g., activate or deactivate) multiple brain regions or brain networks (sometimes the entire brain), limiting the specificity of the conclusions drawn from the data and preventing the evaluation of the brain's response to perturbations with a higher spatial resolution in the range of 1 - 2 millimeters rather than centimeters, which is often necessary when investigating the brains of patients with conditions affecting specific regions or networks of the brain. Finally, (iii) due to the nature of the stimuli, they typically cannot engage specific classes of neurons in the brain or induce sustained activation or inactivation controlled by directly inducing neural activity (rather than indirectly promoting neural activity by displaying stimuli on a screen), which can be important when investigating aspects of the brains of patients with brain changes related to specific types of neurons, neurotransmitters, or cortical circuits. These aspects can be important when investigating brain changes in patients with dementia, particularly Alzheimer's disease.Patients with AD have been shown to have altered synaptic activity in specific brain networks (including the default mode network - DMN), specific regions (including the precuneus, angular gyrus, medial prefrontal cortex, hippocampus, and temporal lobe), and specific classes of neurons (including parvalbumin - positive interneurons) and neurotransmitters (including GABA and glutamate). Although several AD biomarkers are widely applied and considered useful for diagnosis, they still lack sufficient accuracy to assess disease severity and predict disease progression. There are few imaging biomarkers developed to specifically evaluate synaptic dysfunction. One new method for detecting synaptic loss in neurodegenerative dementia is based on PET tracers targeting synaptic vesicle proteins. However, this approach is supported by limited scientific evidence, as well as the complexity of the PET procedure, high cost, and the need for patient exposure to radioactive tracers. In this context, a novel approach that combines neuroimaging and electrophysiological methods to measure synaptic and network dysfunction in patients with AD can at least partially address some of the challenges of the existing approaches described above.

[0007] For at least these reasons, the inventors have developed a system and method in which perturbations are induced through electrical or magnetic stimulation of the brain of patients with AD, and the characteristics of the individual responses from each patient are collected, analyzed, and summarized into metrics and indicators having diagnostic and prognostic value. Each response is captured with high spatial (i.e., 1 cubic millimeter) and temporal resolution (i.e., at least 1 millisecond) and represents the brain's response to a focused electromagnetic perturbation.

[0008] The present disclosure is also related to the following numbered clauses:

[0009] Clause 1. A method for non - invasively evaluating synaptic dysfunction in a patient having Alzheimer's disease, comprising: applying transcranial magnetic stimulation (TMS) to at least one brain region of the patient; Recording a plurality of TMS-evoked potentials in response to applying TMS to at least one brain region of a patient using a plurality of electroencephalogram (EEG) electrodes; Analyzing at least one characteristic of the plurality of TMS-evoked potentials to evaluate synaptic dysfunction in the patient; Outputting a display of the evaluation of the patient's synaptic dysfunction, a method comprising.

[0010] Clause 2. The method according to clause 1, wherein applying TMS to at least one brain region of the patient includes applying single-pulse TMS to at least one brain region.

[0011] Clause 3. The method according to clause 1 or 2, wherein applying TMS to at least one brain region of the patient includes applying repetitive TMS to at least one brain region.

[0012] Clause 4. The method according to any one of clauses 1 to 3, wherein applying TMS to at least one brain region of the patient includes simultaneously applying TMS at a plurality of frequencies.

[0013] Clause 5. The method according to any one of clauses 1 to 4, wherein applying TMS to at least one brain region of the patient includes applying a patterned burst of TMS to at least one brain region.

[0014] Clause 6. The method according to clause 5, wherein the patterned burst of TMS includes a continuous or intermittent theta burst pattern.

[0015] Clause 7. The method according to any one of clauses 1 to 6, wherein applying TMS to at least one brain region of the patient includes applying TMS to a single brain region.

[0016] Clause 8. The method according to any one of clauses 1 to 7, wherein applying TMS to at least one brain region of the patient includes applying TMS to a plurality of different brain regions.

[0017] Clause 9. The method according to clause 8, wherein a plurality of different brain regions are two or more nodes of the same brain network.

[0018] Clause 10. The method according to clause 9, wherein the brain network is a long-term memory network.

[0019] Clause 11. The method according to clause 9 or 10, wherein the brain network is a default mode network.

[0020] Clause 12. The method according to any one of clauses 9 to 11, wherein the brain network is a fronto-parietal network.

[0021] Clause 13. The method according to any one of clauses 1 to 12, wherein at least one brain region includes at least one brain region within the temporal lobe of the patient's brain.

[0022] Clause 14. The method according to any one of clauses 1 to 13, wherein at least one brain region includes the precuneus region.

[0023] Clause 15. The method according to any one of clauses 1 to 15, further comprising determining the location of at least one brain region to which TMS is applied, at least in part, based on spatial and functional search.

[0024] Clause 16. The method according to any one of clauses 1 to 15, further comprising determining the location of at least one brain region to which TMS is applied, at least in part, based on magnetic resonance imaging (MRI) data related to the patient's brain.

[0025] Clause 17. The method according to clause 16, wherein determining the location of at least one brain region to which TMS is applied, at least in part, based on magnetic resonance imaging (MRI) data related to the patient's brain includes generating a biophysical model of the brain's anatomical structure for the patient's brain based on the MRI data, and determining the location, at least in part, based on the generated biophysical model.

[0026] Clause 18. The method according to any one of Clauses 1 to 17, further comprising determining the location of at least one brain region to which TMS is applied, at least in part, based on data from a plurality of patients.

[0027] Clause 19. The method according to any one of Clauses 1 to 18, further comprising recording electromyogram (EMG) data in response to applying TMS to at least one brain region of a patient, wherein the evaluation of the synaptic dysfunction of the patient is further based at least in part on at least one characteristic of the recorded EMG data.

[0028] Clause 20. The method according to any one of Clauses 1 to 19, further comprising determining the resting motor threshold (RMT) of a patient, wherein applying transcranial magnetic stimulation (TMS) to at least one brain region of the patient includes applying TMS at an intensity based on the patient's RMT.

[0029] Clause 21. The method according to Clause 20, wherein the intensity of the applied TMS is set to 90% of the patient's RMT.

[0030] Clause 22. The method according to any one of Clauses 9 to 12, wherein the brain network is the frontoparietal salience network.

[0031] Clause 23. The method according to any one of Clauses 9 to 12 or 22, wherein the brain network is the sensorimotor network.

[0032] Clause 24. The method according to any one of Clauses 9 to 12, 22, or 23, wherein the brain network is the attention network.

[0033] Clause 25. The method according to any one of Clauses 1 to 24, wherein at least one brain region is a cortical brain region connected to the hippocampus and the temporal pole region of the brain via white matter fibers.

[0034] The method according to any one of clauses 1 to 25, wherein at least one brain region is a cortical brain region connected to a region of the default mode network via white matter fibers.

[0035] The method according to any one of clauses 1 to 26, wherein at least one brain region is the dorsolateral prefrontal cortex.

[0036] The method according to any one of clauses 1 to 27, wherein at least one brain region has high network resilience, i.e., it can maintain a high level of efficiency of network communication at the 5th percentile of brain regions ranked based on their resilience, regardless of the occurrence of simulated or actual damage.

[0037] The method according to any one of clauses 1 to 28, wherein at least one brain region is a brain region having relatively high network modularity in which brain activity is organized into more than two distinct networks based on functional connectivity measurements.

[0038] The method according to any one of clauses 1 to 29, wherein at least one brain region is a brain region or network that supports episodic memory.

[0039] The method according to any one of clauses 1 to 30, wherein at least one brain region is a brain region or network that supports autobiographical memory.

[0040] The method according to any one of clauses 1 to 31, wherein at least one brain region is a brain region or network that supports emotional processing.

[0041] The method according to any one of clauses 1 to 32, wherein at least one brain region is a brain region or network that supports mental rotation ability.

[0042] The method according to any one of clauses 1 to 33, wherein at least one brain region is a brain region or network that supports mental planning.

[0043] The method according to any one of clauses 1 to 34, wherein at least one brain region is a brain region or network that supports executive function.

[0044] The method according to any one of clauses 1 to 35, wherein at least one brain region is a brain region or network in which amyloid-β protein is present.

[0045] The method according to any one of clauses 1 to 36, wherein at least one brain region is a brain region or network in which tau protein is present.

[0046] The method according to any one of clauses 1 to 37, wherein at least one brain region is a brain region or network with a modified neuroinflammatory level.

[0047] The method according to any one of clauses 1 to 38, wherein at least one brain region is a brain region or network with modified synaptic plasticity.

[0048] The method according to any one of clauses 1 to 39, wherein at least one brain region is a brain region or network with low perfusion and low metabolism.

[0049] The method according to any one of clauses 1 to 40, wherein at least one brain region is a brain region or network that supports cognitive reserve ability.

[0050] The method according to any one of clauses 1 to 41, wherein at least one brain region is a brain region or network with elevated neuroinflammation.

[0051] Article 43. The method according to any one of Articles 1 to 42, wherein at least one brain region is a brain region or network that supports functional independence in an elderly individual.

[0052] Article 44. The method according to any one of Articles 1 to 43, wherein at least one brain region is a brain region or network associated with altered oscillatory activity.

[0053] Article 45. The method according to any one of Articles 1 to 44, comprising recording a plurality of TMS-evoked potentials in response to applying TMS to at least one brain region of a patient, wherein recording at least one TMS-evoked potential from at least one brain region to which TMS is applied.

[0054] Article 46. The method according to any one of Articles 1 to 45, comprising recording a plurality of TMS-evoked potentials in response to applying TMS to at least one brain region of a patient, wherein recording at least one TMS-evoked potential from one or more brain regions different from at least one brain region to which TMS is applied.

[0055] Article 47. The method according to any one of Articles 1 to 46, wherein analyzing at least one characteristic of a plurality of TMS-evoked potentials to evaluate synaptic dysfunction in a patient comprises comparing at least one characteristic of the plurality of TMS-evoked potentials with data collected from individuals without Alzheimer's disease or previous data collected from the patient.

[0056] Article 48. The method according to any one of Articles 1 to 47, wherein analyzing at least one characteristic of a plurality of TMS-evoked potentials to evaluate synaptic dysfunction in a patient comprises comparing at least one characteristic of the plurality of TMS-evoked potentials with data collected from a plurality of patients with Alzheimer's disease, and the evaluation of synaptic dysfunction comprises determining that the patient has a subtype of Alzheimer's disease.

[0057] Clause 49. The method according to any one of Clauses 1 to 48, wherein applying and recording are performed after providing treatment for Alzheimer's disease to a patient, analyzing at least one characteristic of a plurality of TMS-evoked potentials to evaluate synaptic dysfunction of the patient, and comparing at least one characteristic of the plurality of TMS-evoked potentials with data collected from the patient before providing treatment for Alzheimer's disease to the patient.

[0058] Clause 50. The method according to any one of Clauses 1 to 49, wherein analyzing at least one characteristic of a plurality of TMS-evoked potentials to evaluate synaptic dysfunction of the patient includes analyzing the plurality of TMS-evoked potentials using at least one machine learning classification model.

[0059] Clause 51. The method according to any one of Clauses 1 to 50, wherein at least one characteristic of the plurality of TMS-evoked potentials includes one or more of the shape, amplitude, phase, or timing of one or more of the plurality of TMS-evoked potentials.

[0060] Clause 52. The method according to any one of Clauses 1 to 51, wherein analyzing at least one characteristic of a plurality of TMS-evoked potentials to evaluate synaptic dysfunction of the patient includes determining the amount of time until the energy level of one or more of the plurality of TMS-evoked potentials returns to the baseline energy level before the application of TMS, and the evaluation of synaptic dysfunction is at least partially based on the amount of time until the energy level of one or more of the plurality of TMS-evoked potentials returns to the baseline energy level.

[0061] Clause 53. The method according to Clause 52, wherein the evaluation of synaptic dysfunction is at least partially further based on the amplitude of one or more of the plurality of TMS-evoked potentials.

[0062] Clause 54. The method according to any one of Clauses 1 to 53, wherein analyzing at least one characteristic of a plurality of TMS-evoked potentials to evaluate synaptic dysfunction of the patient includes projecting the plurality of TMS-evoked potentials onto structural magnetic resonance imaging (MRI) data.

[0063] Clause 55. The method according to clause 54, further comprising extracting time-series data from one or more target regions defined based on structural MRI data, and analyzing at least one characteristic of a plurality of TMS-evoked potentials to evaluate synaptic dysfunction in a patient.

[0064] Clause 56. The method according to clause 55, further comprising using the extracted time-series data to evaluate synchrony across electrodes and / or regions of the brain, and analyzing at least one characteristic of a plurality of TMS-evoked potentials to evaluate synaptic dysfunction in a patient.

[0065] Clause 57. The method according to any one of clauses 1 to 56, further comprising determining a treatment for a patient and providing the treatment to the patient, at least in part, based on an evaluation of synaptic dysfunction.

[0066] Clause 58. The method according to any one of clauses 1 to 57, wherein the evaluation of synaptic dysfunction includes an evaluation of neuroinflammation, and further comprising determining a treatment for a patient and providing the treatment to the patient, at least in part, based on an evaluation of neuroinflammation.

[0067] Clause 59. The method according to any one of clauses 1 to 58, wherein the evaluation of synaptic dysfunction includes an evaluation of the protein clearance mechanism and cerebral protein load, and further comprising determining a treatment for a patient and providing the treatment to the patient, at least in part, based on an evaluation of the protein clearance mechanism and cerebral protein load.

[0068] Clause 60. The method according to any one of clauses 1 to 59, wherein the evaluation of synaptic dysfunction includes an evaluation of brain network dysfunction and altered connectivity patterns, and further comprising determining a treatment for a patient and providing the treatment to the patient, at least in part, based on an evaluation of brain network dysfunction and altered connectivity patterns.

[0069] Clause 61. The method according to any one of Clauses 1 to 60, wherein the evaluation of synaptic dysfunction includes the evaluation of cortical excitability level, and the method further includes determining a treatment for a patient and providing the treatment to the patient, at least partially based on the evaluation of the cortical excitability level.

[0070] Clause 62. The method according to any one of Clauses 1 to 61, wherein the evaluation of synaptic dysfunction includes the evaluation of altered brain oscillatory activity, and the method further includes determining a treatment for a patient and providing the treatment to the patient, at least partially based on the evaluation of the altered brain oscillatory activity.

[0071] Clause 63. The method according to any one of Clauses 1 to 62, wherein the perturbation of brain function is applied via a combination of TMS and tACS stimulation, and the method further includes delivering tACS to induce brain oscillatory activity in a specific frequency band and delivering TMS simultaneously with tACS to evaluate the plasticity and cortical excitability level of the patient's brain.

[0072] Clause 64. The method according to Clause 63, wherein the perturbation of tACS is delivered in the gamma frequency band of 30 Hz to 300 Hz.

[0073] Clause 65. The method according to Clause 63 or 64, wherein the perturbation of tACS is delivered using electrical noise stimulation with an electrical pattern based on a pattern including, but not limited to, brown, pink, white, and random noise, at a frequency of 1 Hz to 1000 Hz.

[0074] Clause 66. The method according to any one of Clauses 1 to 65, wherein the perturbation of brain function is applied via a combination of TMS and tACS stimulation, and the method further includes delivering tACS to induce brain oscillatory activity in a specific frequency band and delivering repetitive TMS (rTMS) simultaneously with tACS to modulate the plasticity and cortical excitability of the patient's brain.

[0075] Clause 67. The method according to Clause 63, wherein the perturbation of TMS is delivered in the form of theta burst stimulation, including intermittent and continuous stimulation patterns.

[0076] Clause 68. A non-invasive brain evaluation system, comprising: a transcranial magnetic stimulation (TMS) device configured to apply TMS to at least one brain region of a patient; a plurality of electroencephalogram (EEG) electrodes configured to record a plurality of TMS-evoked potentials in response to applying TMS to 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 evaluation of synaptic dysfunction of the patient.

[0077] Clause 69. The system according to clause 68, further comprising a neuronavigation system including at least one camera, the neuronavigation system being configured to facilitate the placement of the TMS device.

[0078] Clause 70. The system according to clause 68 or 69, further comprising an electromyogram (EMG) system configured to determine an EMG signal in response to applying TMS to at least one brain region of the patient.

[0079] Clause 71. The system according to any one of clauses 68 to 70, wherein applying TMS to at least one brain region of the patient includes applying single-pulse TMS to at least one brain region.

[0080] Clause 72. The system according to any one of clauses 68 to 71, wherein applying TMS to at least one brain region of the patient includes applying repetitive TMS to at least one brain region.

[0081] Clause 73. The system according to any one of clauses 68 to 72, wherein applying TMS to at least one brain region of the patient includes applying TMS simultaneously at a plurality of frequencies.

[0082] Clause 74. The system according to any one of Clauses 68 to 73, wherein applying TMS to at least one brain region of a patient includes applying a patterned burst of TMS to at least one brain region.

[0083] Clause 75. The system according to Clause 74, wherein the patterned burst of TMS includes a continuous or intermittent theta burst pattern.

[0084] Clause 76. The system according to any one of Clauses 68 to 75, wherein applying TMS to at least one brain region of a patient includes applying TMS to a single brain region.

[0085] Clause 77. The system according to any one of Clauses 68 to 76, wherein applying TMS to at least one brain region of a patient includes applying TMS to a plurality of different brain regions.

[0086] Clause 78. The system according to Clause 77, wherein the plurality of different brain regions includes two or more nodes of the same brain network.

[0087] Clause 79. The system according to Clause 78, wherein the brain network is a long-term memory network.

[0088] Clause 80. The system according to Clause 78 or 79, wherein the brain network is a default mode network.

[0089] Clause 81. The system according to any one of Clauses 78 to 80, wherein the brain network is a fronto-parietal network.

[0090] Clause 82. The system according to any one of Clauses 68 to 81, wherein at least one brain region includes at least one brain region within the temporal lobe of the patient's brain.

[0091] Clause 83. The system according to any one of Clauses 68 to 82, wherein at least one brain region includes the precuneus region.

[0092] Clause 84. The system according to any one of Clauses 68 to 83, wherein at least one computer processor is further programmed to determine the location of at least one brain region to which TMS is applied, based at least in part on a spatial and functional search.

[0093] Clause 85. The system according to any one of Clauses 68 to 84, wherein at least one computer processor is further programmed to determine the location of at least one brain region to which TMS is applied, based at least in part on magnetic resonance imaging (MRI) data related to the patient's brain.

[0094] Clause 86. Determining the location of at least one brain region to which TMS is applied, based at least in part on magnetic resonance imaging (MRI) data related to the patient's brain, includes generating a biophysical model of the anatomical structure of the patient's brain based on the MRI data, and determining the location based at least in part on the generated biophysical model, for the system according to Clause 85.

[0095] Clause 87. The system according to any one of Clauses 68 to 86, wherein at least one processor is further programmed to determine the location of at least one brain region to which TMS is applied, based at least in part on data from a plurality of patients.

[0096] Clause 88. The system according to any one of Clauses 70 to 87, wherein the assessment of the patient's synaptic dysfunction is further based at least in part on at least one characteristic of the determined EMG signal.

[0097] Clause 89. The system according to any one of Clauses 68 to 88, wherein at least one processor is further programmed to determine the patient's resting motor threshold (RMT), and applying transcranial magnetic stimulation (TMS) to at least one brain region of the patient includes applying TMS at an intensity based on the patient's RMT.

[0098] Article 90. The method according to Article 89, wherein the intensity of the TMS applied is set at 90% of the patient's RMT.

[0099] Article 91. The system according to any one of Articles 78 to 81, wherein the brain network is within the frontoparietal salience network.

[0100] Article 92. The system according to any one of Articles 78 to 81 or 91, wherein the brain network is a sensorimotor network.

[0101] Article 93. The system according to any one of Articles 78 to 81, 91, or 92, wherein the brain network is an attention network.

[0102] Article 94. The system according to any one of Articles 68 to 93, wherein at least one brain region is a cortical brain region connected to the hippocampus and the temporal pole region of the brain via white matter fibers.

[0103] Article 95. The system according to any one of Articles 68 to 94, wherein at least one brain region is a cortical brain region connected to the region of the default mode network via white matter fibers.

[0104] Article 96. The system according to any one of Articles 68 to 95, wherein at least one brain region is a brain region within the dorsolateral prefrontal cortex.

[0105] Article 97. The system according to any one of Articles 68 to 96, wherein at least one brain region has high network resilience, that is, it can maintain a high level of efficiency of network communication at the 5th percentile of the brain regions ranked based on their resilience, regardless of the occurrence of simulated damage or actual damage.

[0106] Clause 98. The system according to any one of Clauses 68 to 97, wherein at least one brain region has a relatively high network modularity such that brain activity is organized into more than two distinct networks based on functional connectivity measurements.

[0107] Clause 99. The system according to any one of Clauses 68 to 98, wherein at least one brain region is a brain region or network that supports episodic memory.

[0108] Clause 100. The system according to any one of Clauses 68 to 99, wherein at least one brain region is a brain region or network that supports autobiographical memory.

[0109] Clause 101. The system according to any one of Clauses 68 to 100, wherein at least one brain region is a brain region or network that supports emotional processing.

[0110] Clause 102. The system according to any one of Clauses 68 to 101, wherein at least one brain region is a brain region or network that supports mental rotation ability.

[0111] Clause 103. The system according to any one of Clauses 68 to 102, wherein at least one brain region is a brain region or network that supports mental planning.

[0112] Clause 104. The system according to any one of Clauses 68 to 103, wherein at least one brain region is a brain region or network that supports executive function.

[0113] Clause 105. The system according to any one of Clauses 68 to 104, wherein at least one brain region is a brain region or network in which amyloid-β protein is present.

[0114] Clause 106. The system according to any one of Clauses 68 to 105, wherein at least one brain region is a brain region or network in which tau protein is present.

[0115] Clause 107. The system according to any one of Clauses 68 to 106, wherein at least one brain region is a brain region or network with an altered neuroinflammation level.

[0116] Clause 108. The system according to any one of Clauses 68 to 107, wherein at least one brain region is a brain region or network with altered synaptic plasticity.

[0117] Clause 109. The system according to any one of Clauses 68 to 108, wherein at least one brain region is a brain region or network with hypoperfusion and hypometabolism.

[0118] Clause 110. The system according to any one of Clauses 68 to 109, wherein at least one brain region is a brain region or network that supports cognitive reserve.

[0119] Clause 111. The system according to any one of Clauses 68 to 110, wherein at least one brain region is a brain region or network with elevated neuroinflammation.

[0120] Clause 112. The system according to any one of Clauses 68 to 111, wherein at least one brain region is a brain region or network that supports functional independence in an elderly individual.

[0121] Clause 113. The system according to any one of Clauses 68 to 112, wherein at least one brain region is a brain region or network with altered oscillatory activity.

[0122] Clause 114. A system according to any one of Clauses 68 to 113, wherein recording a plurality of TMS-evoked potentials in response to applying TMS to at least one brain region of a patient includes recording at least one TMS-evoked potential from at least one brain region to which the TMS is applied.

[0123] Clause 115. A system according to any one of Clauses 68 to 114, wherein recording a plurality of TMS-evoked potentials in response to applying TMS to at least one brain region of a patient includes recording at least one TMS-evoked potential from one or more brain regions different from the at least one brain region to which the TMS is applied.

[0124] Clause 116. A system according to any one of Clauses 68 to 115, wherein analyzing at least one characteristic of a plurality of TMS-evoked potentials to evaluate synaptic dysfunction in a patient includes comparing at least one characteristic of the plurality of TMS-evoked potentials with data collected from individuals without Alzheimer's disease or previous data collected from the patient.

[0125] Clause 117. A system according to any one of Clauses 68 to 116, wherein analyzing at least one characteristic of a plurality of TMS-evoked potentials to evaluate synaptic dysfunction in a patient includes comparing at least one characteristic of the plurality of TMS-evoked potentials with data collected from a plurality of patients with Alzheimer's disease, and the evaluation of synaptic dysfunction includes determining that the patient has a subtype of Alzheimer's disease.

[0126] Clause 118. A system according to any one of Clauses 68 to 117, wherein the applying and recording are performed after providing treatment for Alzheimer's disease to the patient, and analyzing at least one characteristic of a plurality of TMS-evoked potentials to evaluate synaptic dysfunction in the patient includes comparing at least one characteristic of the plurality of TMS-evoked potentials with data collected from the patient before providing treatment for Alzheimer's disease to the patient.

[0127] Clause 119. Evaluating a patient's synaptic dysfunction by analyzing at least one characteristic of a plurality of TMS-evoked potentials, including analyzing the plurality of TMS-evoked potentials using at least one machine learning classification model, the system according to any one of Clauses 68 to 118.

[0128] Clause 120. The system according to any one of Clauses 68 to 119, wherein at least one characteristic of the plurality of TMS-evoked potentials includes one or more of the shape, amplitude, phase, or timing of one or more of the plurality of TMS-evoked potentials.

[0129] Clause 121. Evaluating a patient's synaptic dysfunction by analyzing at least one characteristic of a plurality of TMS-evoked potentials, including determining the amount of time until the energy level of one or more of the plurality of TMS-evoked potentials returns to the baseline energy level before the application of TMS, and the evaluation of synaptic dysfunction being at least partially based on the amount of time until the energy level of one or more of the plurality of TMS-evoked potentials returns to the baseline energy level, the system according to any one of Clauses 68 to 120.

[0130] Clause 122. The system according to Clause 121, wherein the evaluation of synaptic dysfunction is at least partially further based on the amplitude of one or more of the plurality of TMS-evoked potentials.

[0131] Clause 123. Evaluating a patient's synaptic dysfunction by analyzing at least one characteristic of a plurality of TMS-evoked potentials, including projecting the plurality of TMS-evoked potentials onto structural magnetic resonance imaging (MRI) data, the system according to any one of Clauses 68 to 122.

[0132] Clause 124. The system according to Clause 123, wherein evaluating a patient's synaptic dysfunction by analyzing at least one characteristic of a plurality of TMS-evoked potentials further includes extracting time series data from one or more target regions defined based on the structural MRI data.

[0133] Clause 125. The system according to clause 123 or 124, further comprising evaluating the synchrony across electrodes and / or regions of the brain using the extracted time-series data to analyze at least one characteristic of a plurality of TMS-evoked potentials to evaluate synaptic dysfunction in a patient.

[0134] Clause 126. The system according to any one of clauses 68 to 125, wherein at least one computer processor is further programmed to determine treatment for the patient, at least in part, based on an evaluation of synaptic dysfunction, and the TMS device is further configured to apply TMS to at least one region of the patient's brain to provide treatment to the patient.

[0135] Clause 127. The system according to any one of clauses 68 to 126, wherein the evaluation of synaptic dysfunction includes an evaluation of neuroinflammation, at least one computer processor is further programmed to determine treatment for the patient, at least in part, based on an evaluation of neuroinflammation, and the TMS device is further configured to apply TMS to at least one region of the patient's brain to provide treatment to the patient.

[0136] Clause 128. The system according to any one of clauses 68 to 127, wherein the evaluation of synaptic dysfunction includes an evaluation of the protein clearance mechanism and cerebral protein load, at least one computer processor is further programmed to determine treatment for the patient, at least in part, based on an evaluation of the protein clearance mechanism and cerebral protein load, and the TMS device is further configured to apply TMS to at least one region of the patient's brain to provide treatment to the patient.

[0137] Clause 129. The evaluation of synaptic dysfunction includes the evaluation of brain network dysfunction and modified connectivity patterns, and at least one computer processor is further programmed to determine treatment for a patient, at least in part, based on the evaluation of brain network dysfunction and modified connectivity patterns, and the TMS device is further configured to apply TMS to at least one brain region of the patient to provide treatment to the patient, the system according to any one of Clauses 68 to 128.

[0138] Clause 130. The evaluation of synaptic dysfunction includes the evaluation of cortical excitability levels, and at least one computer processor is further programmed to determine treatment for a patient, at least in part, based on the evaluation 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 treatment to the patient, the system according to any one of Clauses 68 to 129.

[0139] Clause 131. The evaluation of synaptic dysfunction includes the evaluation of modified brain oscillatory activity, and at least one computer processor is further programmed to determine treatment for a patient, at least in part, based on the evaluation of modified brain oscillatory activity, and the TMS device is further configured to apply TMS to at least one brain region of the patient to provide treatment to the patient, the system according to any one of Clauses 68 to 130.

[0140] Clause 132. The evaluation of synaptic dysfunction includes the simultaneous delivery of magnetic (TMS) and electrical stimulation and the recording of brain activity via EEG recording, and the system comprises at least one TMS device configured to apply TMS to at least one brain region of the patient, at least one electrical stimulation device configured to deliver transcranial electrical stimulation to at least one brain region of the patient, and at least one EEG device for recording brain activity from scalp EEG electrodes, the system according to any one of Clauses 68 to 131.

[0141] Clause 133. The system according to Clause 132, wherein the electrical stimulation is delivered in the form of tACS.

[0142] Clause 134. The system according to Clause 133, wherein the perturbation of tACS is delivered in the gamma frequency band of 30 Hz to 300 Hz.

[0143] Clause 135. The system according to Clause 133 or 134, wherein the perturbation of tACS is delivered using electrical noise stimulation with an electrical pattern based on a pattern including, but not limited to, brown, pink, white, and random noise, at a frequency of 1 Hz to 1000 Hz.

[0144] Clause 136. The system according to any one of Clauses 133 to 135, wherein the perturbation of TMS is delivered in the form of theta burst stimulation, including intermittent and continuous stimulation patterns.

[0145] Clause 137. The system according to any one of Clauses 133 to 136, wherein the TMS, electrical stimulation, and EEG recording are delivered via a single device including TMS, tES, and EEG functions.

[0146] Clause 138. A method for providing a non-invasive assessment of the state of a human brain, comprising: applying non-invasive stimulation to at least one brain region of a human; using a brain activity recording device to record brain signals in response to applying non-invasive stimulation to at least one brain region of a human; analyzing at least one characteristic of the recorded brain signals to provide a non-invasive assessment of the state of the human brain; outputting a display of the non-invasive assessment of the state of the human brain.

[0147] Clause 139. The method according to Clause 138, wherein applying non-invasive stimulation to at least one brain region of a human includes applying one or more of transcranial magnetic stimulation (TMS), transcranial current stimulation (tCS), or focused ultrasound stimulation (tFUS).

[0148] Clause 140. The method according to Clause 139, wherein applying non-invasive stimulation to at least one brain region of a human includes applying at least two of TMS, tCS, or tFUS.

[0149] Clause 141. The method according to any one of Clauses 138 to 140, wherein the brain activity recording device is an electroencephalogram (EEG) recording device.

[0150] Clause 142. The method according to any one of Clauses 138 to 141, wherein applying TMS to at least one brain region of a patient includes applying single-pulse TMS to at least one brain region.

[0151] Clause 143. The method according to any one of Clauses 138 to 142, wherein applying TMS to at least one brain region of a patient includes applying repetitive TMS to at least one brain region.

[0152] Clause 144. The method according to any one of Clauses 138 to 143, wherein applying TMS to at least one brain region of a patient includes applying TMS simultaneously at multiple frequencies.

[0153] Clause 145. The method according to any one of Clauses 138 to 144, wherein applying TMS to at least one brain region of a patient includes applying a patterned burst of TMS to at least one brain region.

[0154] Clause 146. The method according to Clause 145, wherein the patterned burst of TMS includes a continuous or intermittent theta burst pattern.

[0155] Clause 147. The method according to any one of Clauses 138 to 146, wherein applying TMS to at least one brain region of a patient includes applying TMS to a single brain region.

[0156] Clause 148. The method according to any one of Clauses 138 to 147, wherein applying TMS to at least one brain region of a patient includes applying TMS to a plurality of different brain regions.

[0157] Clause 149. The method according to Clause 148, wherein the plurality of different brain regions are two or more nodes of the same brain network.

[0158] Clause 150. The method according to Clause 149, wherein the brain network is a long-term memory network.

[0159] Clause 151. The method according to Clause 149 or 150, wherein the brain network is a default mode network.

[0160] Clause 152. The method according to any one of Clauses 149 to 151, wherein the brain network is a fronto-parietal network.

[0161] Clause 153. The method according to any one of Clauses 138 to 152, wherein at least one brain region includes at least one brain region within the temporal lobe of the patient's brain.

[0162] Clause 154. The method according to any one of Clauses 138 to 153, wherein at least one brain region includes a precuneus region.

[0163] Clause 155. The method according to any one of Clauses 138 to 154, further comprising determining the location of at least one brain region to which TMS is applied, at least in part, based on spatial and functional searches.

[0164] Clause 156. The method according to any one of Clauses 138 to 155, further comprising determining the location of at least one brain region to which TMS is applied, at least in part, based on magnetic resonance imaging (MRI) data related to the patient's brain.

[0165] Clause 157. Determining the location of at least one brain region to which TMS is applied, based at least in part on magnetic resonance imaging (MRI) data related to a patient's brain, includes generating a biophysical model of the brain's anatomical structure for the patient's brain based on the MRI data, and determining the location based at least in part on the generated biophysical model, the method according to Clause 156.

[0166] Clause 158. The method according to any one of Clauses 138 to 157, further comprising determining the location of at least one brain region to which TMS is applied, based at least in part on data from multiple patients.

[0167] Clause 159. The method according to any one of Clauses 138 to 158, further comprising recording electromyogram (EMG) data in response to applying TMS to at least one brain region of a patient, and the evaluation of the state of the human brain is further based at least in part on at least one characteristic of the recorded EMG data.

[0168] Clause 160. The method according to any one of Clauses 138 to 159, further comprising determining the resting motor threshold (RMT) of a patient, and applying transcranial magnetic stimulation (TMS) to at least one brain region of the patient includes applying TMS at an intensity based on the patient's RMT.

[0169] Clause 161. The method according to Clause 160, wherein the intensity of the applied TMS is set to 90% of the patient's RMT.

[0170] Clause 162. The method according to any one of Clauses 149 to 152, wherein the brain network is the frontoparietal salience network.

[0171] Clause 163. The method according to any one of Clauses 149 to 152 or 162, wherein the brain network is the sensorimotor network.

[0172] Article 164. The method according to any one of Articles 149 to 152, 162, or 163, wherein the brain network is an attention network.

[0173] Article 165. The method according to any one of Articles 138 to 164, wherein at least one brain region includes a cortical brain region connected to the hippocampus and the temporal pole region of the brain via white matter fibers.

[0174] Article 166. The method according to any one of Articles 138 to 165, wherein at least one brain region includes a cortical brain region connected to the regions of the default mode network via white matter fibers.

[0175] Article 167. The method according to any one of Articles 138 to 166, wherein at least one brain region includes the dorsolateral prefrontal cortex.

[0176] Article 168. The method according to any one of Articles 138 to 167, wherein at least one brain region includes a brain region having a relatively high network resilience, that is, at the 5th percentile of the brain regions ranked based on their resilience, maintaining a high level of efficiency of network communication despite the occurrence of simulated damage or actual damage.

[0177] Article 169. The method according to any one of Articles 138 to 168, wherein at least one brain region includes a brain region having a relatively high network modularity in which brain activity is organized into more than two distinct networks based on functional connectivity measurements.

[0178] Article 170. The method according to any one of Articles 138 to 169, wherein at least one brain region includes a brain region or network that supports episodic memory.

[0179] Article 171. The method according to any one of Articles 138 to 170, wherein at least one brain region includes a brain region or network that supports autobiographical memory.

[0180] Article 172. The method according to any one of Articles 138 to 171, wherein at least one brain region includes a brain region or network that supports emotional processing.

[0181] Article 173. The method according to any one of Articles 138 to 172, wherein at least one brain region includes a brain region or network that supports mental rotation ability.

[0182] Article 174. The method according to any one of Articles 138 to 173, wherein at least one brain region includes a brain region or network that supports mental planning.

[0183] Article 175. The method according to any one of Articles 138 to 174, wherein at least one brain region includes a brain region or network that supports executive function.

[0184] Article 176. The method according to any one of Articles 138 to 175, wherein at least one brain region includes a brain region or network in which amyloid-β protein is present.

[0185] Article 177. The method according to any one of Articles 138 to 176, wherein at least one brain region includes a brain region or network in which tau protein is present.

[0186] Article 178. The method according to any one of Articles 138 to 177, wherein at least one brain region includes a brain region or network with an altered level of neuroinflammation.

[0187] Article 179. The method according to any one of Articles 138 to 178, wherein at least one brain region includes a brain region or network with altered synaptic plasticity.

[0188] Article 180. The method according to any one of Articles 138 to 179, wherein at least one brain region comprises a brain region or network associated with hypoperfusion and hypometabolism.

[0189] Article 181. The method according to any one of Articles 138 to 180, wherein at least one brain region comprises a brain region or network that supports cognitive reserve ability.

[0190] Article 182. The method according to any one of Articles 138 to 181, wherein at least one brain region comprises a brain region or network associated with elevated neuroinflammation.

[0191] Article 183. The method according to any one of Articles 138 to 182, wherein at least one brain region comprises a brain region or network that supports functional independence in an elderly individual.

[0192] Article 184. The method according to any one of Articles 138 to 183, wherein at least one brain region comprises a brain region or network associated with altered oscillatory activity.

[0193] Article 185. Recording a plurality of TMS-evoked potentials in response to applying TMS to at least one brain region of a patient includes recording at least one TMS-evoked potential from at least one brain region to which TMS is applied, the method according to any one of Articles 138 to 184.

[0194] Article 186. Recording a plurality of TMS-evoked potentials in response to applying TMS to at least one brain region of a patient includes recording at least one TMS-evoked potential from one or more brain regions different from at least one brain region to which TMS is applied, the method according to any one of Articles 138 to 185.

[0195] Clause 187. Evaluating a patient's synaptic dysfunction by analyzing at least one characteristic of a plurality of TMS-evoked potentials, which includes analyzing the plurality of TMS-evoked potentials using at least one machine learning classification model, the method according to any one of Clauses 138 to 186.

[0196] Clause 188. The method according to any one of Clauses 138 to 187, wherein at least one characteristic of the plurality of TMS-evoked potentials includes one or more of the shape, amplitude, phase, or timing of one or more of the plurality of TMS-evoked potentials.

[0197] Clause 189. Evaluating a patient's synaptic dysfunction by analyzing at least one characteristic of a plurality of TMS-evoked potentials, which includes determining the amount of time until the energy level of one or more of the plurality of TMS-evoked potentials returns to the baseline energy level before the application of TMS, and the evaluation of the state of the human brain is at least partially based on the amount of time until the energy level of one or more of the plurality of TMS-evoked potentials returns to the baseline energy level, the method according to any one of Clauses 138 to 188.

[0198] Clause 190. The method according to Clause 189, wherein the evaluation of the state of the human brain is at least partially further based on the amplitude of one or more of the plurality of TMS-evoked potentials.

[0199] Clause 191. Evaluating a patient's synaptic dysfunction by analyzing at least one characteristic of a plurality of TMS-evoked potentials, which includes projecting the plurality of TMS-evoked potentials onto structural magnetic resonance imaging (MRI) data, the method according to any one of Clauses 138 to 190.

[0200] Clause 192. The method according to Clause 191, wherein evaluating a patient's synaptic dysfunction by analyzing at least one characteristic of a plurality of TMS-evoked potentials further includes extracting time-series data from one or more target regions defined based on the structural MRI data.

[0201] Clause 193. The method according to clause 192, further comprising evaluating the synchrony across electrodes and / or regions of the brain using the extracted time series data, by analyzing at least one characteristic of a plurality of TMS-evoked potentials to evaluate synaptic dysfunction in a patient.

[0202] Clause 194. The method according to any one of clauses 138 to 193, further comprising determining a treatment for a patient and providing the treatment to the patient, based at least in part on an evaluation of the state of the human brain.

[0203] Clause 195. The method according to any one of clauses 138 to 194, wherein the evaluation of the state of the human brain includes an evaluation of neuroinflammation, and the method further comprises determining a treatment for a patient and providing the treatment to the patient, based at least in part on the evaluation of neuroinflammation.

[0204] Clause 196. The method according to any one of clauses 138 to 195, wherein the evaluation of the state of the human brain includes an evaluation of the protein clearance mechanism and cerebral protein load, and the method further comprises determining a treatment for a patient and providing the treatment to the patient, based at least in part on the evaluation of the protein clearance mechanism and cerebral protein load.

[0205] Clause 197. The method according to any one of clauses 138 to 196, wherein the evaluation of the state of the human brain includes an evaluation of brain network dysfunction and altered connectivity patterns, and the method further comprises determining a treatment for a patient and providing the treatment to the patient, based at least in part on the evaluation of brain network dysfunction and altered connectivity patterns.

[0206] Clause 198. The method according to any one of clauses 138 to 197, wherein the evaluation of the state of the human brain includes an evaluation of the cortical excitability level, and the method further comprises determining a treatment for a patient and providing the treatment to the patient, based at least in part on the evaluation of the cortical excitability level.

[0207] Article 199. The method according to any one of Articles 138 to 198, wherein the evaluation of the state of the human brain includes the evaluation of the modified brain oscillation activity, and the method further includes determining the treatment for the patient and providing the treatment to the patient at least partially based on the evaluation of the modified brain oscillation activity.

[0208] Article 200. A non-invasive brain state evaluation system, A non-invasive brain stimulation device configured to apply a non-invasive stimulation to at least one brain region of a human, A brain activity recording device configured to record a brain signal in response to applying a non-invasive stimulation to at least one brain region of a human, At least one computer processor programmed to analyze at least one characteristic of the recorded brain signal to provide a non-invasive evaluation of the state of the human brain.

[0209] Article 201. The system according to Article 200, further including a neuronavigation system including at least one camera, the neuronavigation system being configured to facilitate the placement of the TMS device.

[0210] Article 202. The system according to Article 200 or 201, further comprising an electromyogram (EMG) system configured to determine an EMG signal in response to applying TMS to at least one brain region of a patient.

[0211] Article 203. The system according to any one of Articles 200 to 202, wherein applying TMS to at least one brain region of a patient includes applying single-pulse TMS to at least one brain region.

[0212] Article 204. The system according to any one of Articles 200 to 203, wherein applying TMS to at least one brain region of a patient includes applying repetitive TMS to at least one brain region.

[0213] Clause 205. The system according to any one of Clauses 200 to 204, wherein applying TMS to at least one brain region of a patient includes applying TMS at multiple frequencies simultaneously.

[0214] Clause 206. The system according to any one of Clauses 200 to 205, wherein applying TMS to at least one brain region of a patient includes applying a patterned burst of TMS to at least one brain region.

[0215] Clause 207. The system according to Clause 206, wherein the patterned burst of TMS includes a continuous or intermittent theta burst pattern.

[0216] Clause 208. The system according to any one of Clauses 200 to 207, wherein applying TMS to at least one brain region of a patient includes applying TMS to a single brain region.

[0217] Clause 209. The system according to any one of Clauses 200 to 208, wherein applying TMS to at least one brain region of a patient includes applying TMS to a plurality of different brain regions.

[0218] Clause 210. The system according to Clause 209, wherein the plurality of different brain regions are two or more nodes of the same brain network.

[0219] Clause 211. The system according to Clause 210, wherein the brain network is a long-term memory network.

[0220] Clause 212. The system according to Clause 210 or 211, wherein the brain network is a default mode network.

[0221] Clause 213. The system according to any one of Clauses 210 to 212, wherein the brain network is a fronto-parietal network.

[0222] Clause 214. The system according to any one of Clauses 200 to 213, wherein at least one brain region includes at least one brain region within the temporal lobe of the patient's brain.

[0223] Clause 215. The system according to any one of Clauses 200 to 214, wherein at least one brain region includes the precuneus region.

[0224] Clause 216. The system according to any one of Clauses 200 to 215, wherein at least one computer processor is further programmed to determine the location of at least one brain region to which TMS is applied, based at least in part on a spatial and functional search.

[0225] Clause 217. The system according to any one of Clauses 200 to 216, wherein at least one computer processor is further programmed to determine the location of at least one brain region to which TMS is applied, based at least in part on magnetic resonance imaging (MRI) data related to the patient's brain.

[0226] Clause 218. The system according to Clause 217, wherein determining the location of at least one brain region to which TMS is applied, based at least in part on magnetic resonance imaging (MRI) data related to the patient's brain, includes generating a biophysical model of the anatomical structure of the patient's brain based on the MRI data, and determining the location based at least in part on the generated biophysical model.

[0227] Clause 219. The system according to any one of Clauses 200 to 218, wherein at least one computer processor is further programmed to determine the location of at least one brain region to which TMS is applied, based at least in part on data from a plurality of patients.

[0228] The system according to any one of clauses 202 to 219, wherein the evaluation of the state of the human brain is further based at least in part on at least one characteristic of the EMG signal.

[0229] Clause 221. The system according to any one of clauses 200 to 220, wherein at least one computer processor is further programmed to determine the resting motor threshold (RMT) of a patient, and applying transcranial magnetic stimulation (TMS) to at least one region of the patient's brain includes applying TMS at an intensity based on the patient's RMT.

[0230] Clause 222. The system according to any one of clauses 210 to 213, wherein the brain network is the frontoparietal salience network.

[0231] Clause 223. The system according to any one of clauses 210 to 213 or 222, wherein the brain network is the sensorimotor network.

[0232] Clause 224. The system according to any one of clauses 210 to 213, 222, or 223, wherein the brain network is the attentional network.

[0233] Clause 225. The system according to any one of clauses 200 to 224, wherein at least one region of the brain includes a cortical brain region connected to the hippocampus and temporal pole regions of the brain via white matter fibers.

[0234] Clause 226. The system according to any one of clauses 200 to 225, wherein at least one region of the brain includes a cortical brain region connected to a region of the default mode network via white matter fibers.

[0235] Clause 227. The system according to any one of clauses 200 to 226, wherein at least one region of the brain includes the dorsolateral prefrontal cortex.

[0236] Clause 228. The system according to any one of Clauses 200 to 227, comprising a brain region that has a relatively high network resilience, that is, maintains a high level of efficiency of network communication in the fifth percentile of brain regions ranked based on their resilience, despite the occurrence of simulated or actual damage.

[0237] Clause 229. The system according to any one of Clauses 200 to 228, comprising a brain region that has a relatively high network modularity in which brain activity is organized into more than two distinct networks based on functional connectivity measurements.

[0238] Clause 230. The system according to any one of Clauses 200 to 229, comprising a brain region or network that supports episodic memory.

[0239] Clause 231. The system according to any one of Clauses 200 to 230, comprising a brain region or network that supports autobiographical memory.

[0240] Clause 232. The system according to any one of Clauses 200 to 231, comprising a brain region or network that supports emotional processing.

[0241] Clause 233. The system according to any one of Clauses 200 to 232, comprising a brain region or network that supports mental rotation ability.

[0242] Clause 234. The system according to any one of Clauses 200 to 233, comprising a brain region or network that supports mental planning.

[0243] System according to any one of clauses 200 to 234, wherein at least one brain region comprises a brain region or network that supports executive function.

[0244] System according to any one of clauses 200 to 235, wherein at least one brain region comprises a brain region or network in which amyloid-β protein is present.

[0245] System according to any one of clauses 200 to 236, wherein at least one brain region comprises a brain region or network in which tau protein is present.

[0246] System according to any one of clauses 200 to 237, wherein at least one brain region comprises a brain region or network with a modified neuroinflammatory level.

[0247] System according to any one of clauses 200 to 238, wherein at least one brain region comprises a brain region or network with modified synaptic plasticity.

[0248] System according to any one of clauses 200 to 239, wherein at least one brain region comprises a brain region or network with low perfusion and low metabolism.

[0249] System according to any one of clauses 200 to 240, wherein at least one brain region comprises a brain region or network that supports cognitive reserve.

[0250] System according to any one of clauses 200 to 241, wherein at least one brain region comprises a brain region or network with elevated neuroinflammation.

[0251] System according to any one of clauses 200 to 242, wherein at least one brain region comprises a brain region or network that supports functional independence in an elderly individual.

[0252] Article 244. The system according to any one of Articles 200 to 243, wherein at least one brain region includes a brain region or network with altered oscillatory activity.

[0253] Article 245. The system according to any one of Articles 200 to 244, wherein TMS, electrical stimulation, and EEG recording are delivered via a single device including TMS, tES, and EEG functions.

[0254] Article 246. The system according to Article 245, wherein electrical stimulation and EEG recording are delivered via a single scalp electrode having both recording and stimulation functions.

[0255] Article 247. The system according to Article 246, wherein the electrode for delivering electrical stimulation and recording EEG signals is part of a TMS stimulation coil.

[0256] Article 248. The system according to Article 247, wherein the TMS stimulation coil includes the structure of FIG. 8 for TMS delivery, which can host EEG and electrical stimulation electrodes.

[0257] Article 249. The system according to Article 248, wherein the TMS stimulation coil includes the structure of FIG. 8 for TMS delivery, which can host EEG and electrical stimulation electrodes, and additional structures.

[0258] Article 250. The system according to Article 249, wherein the structure capable of hosting EEG and electrical stimulation electrodes enables recording and stimulation from brain lobes including, but not limited to, multiple brain regions, as well as the frontal lobe, temporal lobe, parietal lobe, and occipital lobe.

[0259] Article 251. The system according to Article 249, wherein the structure capable of hosting EEG and electrical stimulation electrodes is composed of a flexible material that enables the system to be comfortably placed on the scalp of individuals with different head sizes and shapes.

[0260] A method of treating a patient having Alzheimer's disease, comprising: applying transcranial magnetic stimulation (TMS) to at least one brain region of the patient; using a plurality of electroencephalogram (EEG) recording electrodes to record a plurality of TMS-evoked potentials in response to applying TMS to at least one brain region of the patient; determining treatment of the patient, at least in part, based on analysis of at least one characteristic of the plurality of TMS-evoked potentials; and administering treatment to the patient.

[0261] Another aspect of the present disclosure provides a system including one or more computer processors and a computer memory coupled thereto. The computer memory includes machine-executable code that, when executed by the one or more computer processors, implements any of the methods described herein or elsewhere.

[0262] Further aspects and advantages of the present disclosure will become readily apparent to those skilled in the art from the following detailed description, which illustrates only exemplary embodiments of the present disclosure. As will be understood, the present disclosure is capable of other and different embodiments and its several details are capable of modification in various obvious respects without departing from the present disclosure. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive.

[0263] Incorporation by reference All publications, patents, and patent applications mentioned in this specification are hereby incorporated 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. If any incorporated publication and patent or patent application contradicts the disclosure contained herein, this specification is intended to supersede and / or take precedence over any such conflicting material.

[0264] The novel features of the present disclosure are specifically 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 which illustrates exemplary embodiments in which the principles of the present disclosure are utilized, and to the following appended drawings (also, the "Figure" and "FIG." in this specification).

Brief Description of the Drawings

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DETAILED DESCRIPTION OF THE INVENTION

[0266] Although various embodiments of the present disclosure are shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, modifications, and substitutions can occur to those skilled in the art without departing from the scope of the present disclosure. It should be understood that various alternatives to the embodiments described herein may be used.

[0267] When the terms "at least", "greater than", or "greater than or equal to" are in front of the first numerical value of a series of two or more numerical values, the terms "at least", "greater than", or "greater than or equal to" apply to each of the numerical values in that series of numerical values. For example, 1, 2, or 3 or more is equal to 1 or more, 2 or more, or 3 or more.

[0268] When the terms "no more than", "less than", or "less than or equal to" are at the beginning of the first of two or more numbers in a series, the terms "no more than", "less than", or "less than or equal to" apply to each of the numbers in that series. For example, no more than 3, 2, or 1 is equal to no more than 3, no more than 2, or no more than 1.

[0269] Certain embodiments of the present invention contemplate numerical ranges. When ranges are present, the range endpoints are included in the range. In addition, all sub-ranges and values within the range exist as if they were explicitly written out. The term "about" or "approximately" can mean within an acceptable error range for a particular value, which depends in part on how the value is measured or determined, e.g., limitations of the measurement system. For example, "about" can mean within one standard deviation, or more than one standard deviation, in accordance with practice in the art. Alternatively, "about" can mean within a range of up to 20%, up to 10%, up to 5%, or up to 1% of a particular value. When a particular value is recited in the application and claims, the term "about" can be assumed to mean within an acceptable error range for the particular value, unless otherwise stated.

[0270] The inventors recognize that by applying controlled perturbations and modeling the resulting activity, the complex network dynamics that characterize the human brain can be effectively captured. This is relevant in the field of neurodegenerative disorders, where it is difficult to detect modifications in brain dynamics at the preclinical stage of diseases such as Alzheimer's disease, and it is hypothesized that they occur years or even decades before clinical symptoms appear. For this purpose, some embodiments relate to identifying the location or locations within the brain of a subject to which a non-invasive perturbation is provided. Some embodiments relate to methods of quantifying the effect of the perturbation via methods from electrophysiology, including but not limited to electroencephalography (EEG) and electromyography (EMG).

[0271] In several studies that used EEG to explore the dynamics of brain activity during various cognitive activities, correlations between activity in specific frequency bands and cognitive functions have been revealed. Furthermore, more recent studies have suggested that electrophysiological properties measured using EEG may function as endophenotypes of mental and neurological disorders in both human and rodent models. However, one of the limitations of EEG is that it can passively record brain activity, and thus, the inferences that can be drawn about brain dynamics can be purely correlational. Additionally, it can be difficult to assess important and fundamental brain properties such as plasticity - the ability to change in response to changing environmental and systemic needs or stress factors.

[0272] In contrast to EEG, non-invasive brain stimulation (NiBS) techniques, such as transcranial magnetic stimulation (TMS), utilize electromagnetic principles to non-invasively modulate brain function by inducing an electric field in the target cortical region. TMS can be used to directly activate brain regions without mediation by sensory or cognitive pathways and, thus, can evaluate the functional integrity of cortical circuits within the stimulated area. Applying TMS to neurons in the motor cortex results in motor evoked potentials (MEPs) that can be measured via EMG. When applied in pairs, TMS can be used to assess the balance of excitation and inhibition in vivo, as it engages GABAergic and glutamatergic circuits within the cortex. Simultaneous TMS and EEG monitoring (TMS-EEG) can enable a direct measure of the brain's response to TMS and, thus, can evaluate brain reactivity. TMS-EEG studies have shown that TMS is reproducible and reliable, generating waves of activity that resonate across the cortex with different frequency responses depending on the stimulation site. Furthermore, the induced activity varies in response to cognitive task engagement and is modified by interventions that alter task performance. Thus, TMS-evoked potentials (TEPs) can be used to evaluate cortical network properties in health and disease. Additionally, TEPs can be used to assess the integrity of different excitatory and inhibitory circuits and detect changes in the cortical excitation / inhibition balance in disease states.

[0273] In particular, TMS-EEG can identify abnormalities in an individual's brain reactivity even when the individual's daily EEG appears normal. The use of perturbation-based paradigms to amplify the information available during scalp EEG recording has been successfully tested using single-pulse TMS, reinforcing the idea that the brain's response to external perturbations may carry more information about the system's dynamics than standard resting or task-induced evoked activity. In addition to measuring brain reactivity and connectivity, TMS can also be used to evaluate the effectiveness of the mechanisms of brain plasticity. Based on experimental synaptic plasticity induction protocols, when applied to a train of repetitive TMS pulses at a fixed frequency or specific pattern, repetitive TMS can produce changes in cortical reactivity (measured via MEP or TEP) that outlast the duration of the stimulus. Such TMS protocols are thought to exert their effects via synaptic plasticity mechanisms, resulting in extensive changes in the topological structure of brain connectivity, as measured via EEG and fMRI. Such TMS measurements have provided evidence of abnormal mechanisms of plasticity in diseases such as AD. Thus, real-time integration of TMS and EEG can overcome at least some of the above basic limitations of conventional EEG analysis and enable causal relationship studies. Furthermore, the same TMS and TMS-EEG protocols can be applied to both human and animal models, facilitating a translational approach.

[0274] Similar to TMS, transcranial current stimulation (tCS) is a form of non-invasive brain stimulation (NIBS) that delivers weak electric currents to the brain using electrodes placed on the scalp. tCS can be used to stimulate or inhibit one or more target brain regions. tCS can include related non-invasive techniques such as transcranial direct current 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 regions of an individual's brain according to the systems and methods described herein.

[0275] According to some embodiments of the systems and methods described herein, combining TMS and tCS with EEG recordings can lead to fast, sensitive, and easy-to-handle clinical trials, which can be applied to large pathological and non-pathological populations. Specifically, patients with dementia, particularly Alzheimer's disease, exhibit modifications in cortical brain dynamics captured by EEG and can potentially be better characterized by a combined approach that utilizes controlled perturbations of the brain. Patients with dementia can also exhibit modifications in the mechanisms of plasticity, excitatory / inhibitory balance, and GABAergic dysfunction, as well as changes in brain connectivity dynamics.

[0276] In particular, asymptomatic signs of such modifications are likely to be present in the general population and in subjects at risk of developing dementia. The systems and methods can also be used to assess brain health, identify individual deviations from normative datasets, maintain cognitive function, and inform interventions to help delay the onset and progression of disease.

[0277] The systems and methods described herein are particularly applicable to the diagnosis, monitoring, and prediction of the disease course of patients with dementia and AD. The systems and methods include a combination of multiple approaches to brain perturbation, brain recording, and data analysis.

[0278] In some embodiments, the perturbation can be delivered via non-invasive brain stimulation techniques such as transcranial magnetic stimulation or transcranial electrical stimulation.

[0279] In some embodiments, transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), or transcranial random noise current stimulation (tRNS) is used as the perturbation approach.

[0280] In some embodiments, the determination of the perturbation target can be performed using brain scans of the subject, including but not limited to data obtained via magnetic resonance imaging (MRI) and positron emission tomography (PET).

[0281] In some embodiments, the determination of the perturbation target can be performed using electrophysiological data collected from a subject, including but not limited to data obtained via electroencephalography (EEG), electromyography (EMG), and magnetoencephalography (MEG).

[0282] In some embodiments, the determination of the perturbation target may be performed without a brain scan of the subject and may be based on normative databases or disease-specific data of healthy individuals, as in the case of Alzheimer's disease data.

[0283] In some embodiments, the perturbation is delivered to evaluate the integrity of the brain network, including the resilience of the brain system to the perturbation and the ability to adapt to the perturbation by reconfiguring their structural and functional connections.

[0284] In some embodiments, the perturbation is delivered to a single brain region, network, or system to evaluate the level of excitability of the brain and the response to the stimulus.

[0285] In some embodiments, the perturbation is delivered to multiple brain regions, networks, or systems either simultaneously or in a predefined order to evaluate the integrity of the brain connections and their ability to adapt to the perturbation.

[0286] In some embodiments, the response to the perturbation is measured during the perturbation itself, while in other embodiments, a comparison of the brain activity monitored before and after the perturbation is performed.

[0287] In some embodiments, the impact of the perturbation is quantified by analyzing the change in ability in a cognitive task administered via a tablet / phone / computer before, and / or during, and / or after the perturbation of the brain.

[0288] In some embodiments, the perturbation is delivered during a particular brain state, for any of, for example, stabilizing brain dynamics or inducing a particular response associated with a particular brain state. Brain states include, but are not limited to, high cognitive load states such as those observed during working memory or the execution of abstract reasoning tasks, sensory stimuli, and sleep.

[0289] In some embodiments, the method more specifically includes obtaining or developing a map that includes the location in the brain of waste protein deposits that commonly occur in patients with neurodegenerative disorders and Alzheimer's disease, and then using NIBS to target that location.

[0290] In some embodiments, after identifying the perturbation target, functional mapping based on perturbation of brain regions can be used to achieve further refinement of the stimulation location. For example, the stimulation may be provided over time at multiple locations within the identified target, and the induced response to the stimulation may be sensed using, for example, electroencephalography (EEG) or another suitable sensing technique. The further individualized perturbation target may be selected, at least in part, based on analysis of the induced response to the stimulation.

[0291] In some embodiments, the intensity or amplitude of the stimulation can be selected by adjusting the baseline intensity of the stimulation determined using other techniques. For example, the patient's resting motor threshold can be used to establish the baseline stimulation intensity, which can be refined, at least in part, based on one or more characteristics of the induced response sensed during perturbation-based functional mapping as described herein.

[0292] In some embodiments, the system and method can be used to assist 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.

[0293] In some embodiments, the system and method can be used to track the brain health of patients diagnosed with dementia, including but not limited to mild cognitive impairment (MCI) and Alzheimer's disease.

[0294] In some embodiments, the system and method can be used to track the brain health of subjects at risk of developing dementia, including but not limited to mild cognitive impairment (MCI) and Alzheimer's disease. In some embodiments, the data collected before, during, and after perturbation are analyzed using machine learning (ML) and artificial intelligence (AI) to detect individual responses to perturbation, classify individuals based on that response, identify predictors of response to treatment, or identify the natural course of the disease. ML includes using one or more models that utilize regression models, neural networks, regularization models such as LASSO, decision trees, Bayesian models, common 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.

[0295] Exemplary System Components In some embodiments, the system includes (i) a non-invasive brain stimulation device configured to deliver a single pulse and / or repetitive transcranial magnetic stimulation (TMS) protocol, transcranial current stimulation (tCS), and / or focused ultrasound stimulation (tFUS); (ii) a brain activity recording device, such as an EEG system, configured to define individualized levels of stimulation and monitor brain activity in response to perturbation; (iii) a neuronavigation system with an infrared camera that enables ensuring the individual placement of the non-invasive brain stimulation device; (iv) an electromyography (EMG) system configured to determine the response to perturbation in the sensorimotor system; (v) a software platform for data analysis and storage (e.g., implemented using one or more programmed computer processors), and has a plurality of modules including but not limited to these.

[0296] Non-invasive brain stimulation Non-invasive brain stimulation (NIBS) is a class of methods used to modify the electrical signaling of the brain at a local level. Induced local modifications in signaling can lead to broader modifications to neural signaling throughout the brain, involving brain networks. These network-wide effects of non-invasive brain stimulation reflect the effect of the stimulation on the brain, as well as the network rebound response to bursts of activity entering the system. NIBS methods include transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), and transcranial focused ultrasound stimulation (tFUS).

[0297] TMS is a non-invasive brain stimulation method that locally stimulates the current in the brain using a magnetic field generator applied near the head. In some embodiments, TMS includes repetitive transcranial magnetic stimulation or rTMS. Treatment with rTMS typically includes multiple sessions (daily over several days, or multiple times a day over several days) during which TMS is repeatedly delivered in a pattern intended to induce plasticity (e.g., defined as a change in brain activity). rTMS can be delivered as a high-frequency protocol with stimulation at >5 Hz, or as a low-frequency protocol with stimulation at <1 Hz. rTMS can also be delivered simultaneously at more than one frequency. rTMS can deliver a patterned burst of TMS, with stimulation either in a continuous or intermittent theta burst pattern, as in the case of the theta burst rTMS protocol.

[0298] tCS includes methods in which the intensity, frequency, amplitude, and phase of an electrical stimulation signal are manipulated to modify brain activity. Examples of tCS methods include transcranial direct current stimulation (tDCS), a non-invasive brain stimulation technique in which a low-level constant current is delivered through electrodes placed on and around the head to a specific region of the brain, modulating neural activity in the targeted brain tissue. This technique can, for example, facilitate the regulation of spontaneous brain activity, neurotransmitter dynamics, or blood flow characteristics.

[0299] Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation technique in which a small, pulsed, alternating current is delivered through electrodes placed on and around the head to a specific region of the brain, modulating neural activity in the targeted brain tissue. tACS enables frequency-specific stimulation of brain tissue, inducing neural activity and the resonance effects used to induce brain activity within specific frequency bands (e.g., alpha, gamma, beta).

[0300] Transcranial random noise stimulation (tRNS) is a non-invasive brain stimulation technique in which a small current with the frequency content of white, brown, or pink noise is delivered through electrodes placed on and around the head to a specific region of the brain, modulating neural activity in the targeted brain tissue. tRNS enables probabilistic resonance-like effects, reducing the signal-to-noise ratio in the brain system and thus affecting the likelihood of spontaneous synchronization and perturbation responses of brain activity.

[0301] Focused ultrasound stimulation (fUS) is a non-invasive brain stimulation technique involving the delivery of acoustic pressure waves to specific potentially deep regions of the brain to modulate neural activity.

[0302] Exemplary Hardware Descriptions of some components of an exemplary system for perturbation-based biomarker data collection based on perturbations of TMS or tCS are provided below.

[0303] TMS device. The device includes (i) a metal case that houses a set of capacitors for charge accumulation and energy delivery to a stimulation coil configured to be placed on the scalp, (ii) a stimulation coil configured to be placed on the scalp, and (iii) an LED display for visualizing stimulation parameters. Since the coil is equipped with buttons for adjusting the stimulation intensity and delivering TMS pulses without directly interacting with the LED display, maximum accuracy can be ensured during the delivery of treatment.

[0304] EEG. The EEG system can be configured to record EEG activity from the scalp using an EEG amplifier. EEG signals continuously recorded from multiple scalp sites can be arranged according to the 10-20 International System using Ag / AgCl pellet electrodes attached to 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 the electrooculogram (EOG) to exclude a part of the EEG signal including eye artifacts. TMS-EEG data can be preprocessed offline using an algorithm included as part of a cloud-based software platform. Physiological and TMS-related artificial components can be detected using independent component analysis and removed based on their scalp distribution, frequency, timing, and amplitude.

[0305] EMG. Electromyogram examination 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 individualized perturbations. The EMG device can have up to 16 bipolar channels and can be connected to a monitor for data visualization.

[0306] Neuronavigation. A neuronavigation system can be configured to enable the accurate placement of a magnetic coil over an area to be stimulated in the brain. For example, a neuronavigation system can be used to monitor (e.g., continuously monitor) the position of a TMS coil using an infrared camera that supplements the activities of the TMS coil and a magnetic tracker placed on the patient's head. The neuronavigation system can be configured to provide high accuracy when placing the TMS coil over a specified target based on a brain scan.

[0307] Perturbation approach The method includes, but is not limited to, solutions when perturbations are delivered to one brain region simultaneously, when multiple perturbations are delivered to a single brain region to evaluate the dynamic brain response, or when perturbations are delivered across multiple brain regions, either simultaneously or in a predefined order, to investigate the network-level reorganization of brain dynamics. The method includes, but is not limited to, solutions where the effect of the perturbation is measured via an electrophysiological approach such as EEG or EMG.

[0308] Figures 1A - 1F show evaluations based on perturbations of brain dynamics according to several embodiments. Different brain stimulation approaches can be used to perturb brain activity and record responses that depend on individual differences in the brain's anatomical structure and function. Figure 1A shows an exemplary approach involving measuring activity 100 (such as in the case of EEG using non - invasive electrodes 101 placed on the scalp), delivering a perturbation 102 (such as an electromagnetic wave as in the case of TMS), and examining, for example, an increase in activity after stimulation or an increase in synchrony between two or more regions or networks 104 to measure activity 100 in multiple regions of the brain 103. The effect of the electromagnetic perturbation 102 approach on the local - global connectivity 104 is shown in Figure 1B. The effect of the electromagnetic perturbation 102 approach on the decay of the effect of the stimulation over time is shown in Figure 1C. The effect of the electromagnetic perturbation 102 approach on the plasticity of specific brain connections is shown in Figure 1D. The effect of the electromagnetic perturbation approach on the robustness of the brain is shown in Figure 1E. The effect of the electromagnetic perturbation approach on the information propagation pathway is shown in Figure 1F.

[0309] Stimulation pattern Single perturbation In some embodiments, the controlled perturbation 102 is applied to a single region, and the response is measured via electrodes 101 placed on the scalp (EEG) or another part of the body (EMG). The perturbation 102 can have various intensity levels based on the state of the subject's brain, 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 via EMG by examining the longitudinal changes in the local excitability of the targeted brain system over time following the perturbation 102 as a measure of cortical plasticity. The response 100 to the perturbation 102 can be expressed as, but is not limited to, local responses such as the amplitude, number, frequency, and timing of so-called TMS-evoked potentials measured in the region of the brain being stimulated, (b) remote responses that can be measured using the same or similar metrics processed by a computer from regions of the remote brain examining the induced amount of current generated in the EEG electrodes or within the brain, (c) changes in inter-regional dynamics, including but not limited to graph-theoretic measures of correlation, connectivity, effective connectivity, dynamical connectivity, nodal interactions, in regions of the brain being stimulated or not being stimulated, or (d) changes in the overall complexity of the brain, including mathematical metrics representing the overall response to the external perturbation, which can be measured via EEG101.

[0310] Dual co-localized perturbation In some embodiments, the succession of at least two controlled perturbations 102 is repeated over a single region 103, and the response is measured via electrodes 101 placed on the scalp (EEG) or another part of the body (EMG).

[0311] Dual-site perturbation In some embodiments, the succession of at least two controlled perturbations 102 is repeated over a plurality of different brain regions 103, and the response 100 is measured via electrodes 101 placed on the scalp (EEG) or another part of the body (EMG).

[0312] The response to perturbation 102 can be measured via EMG by examining the longitudinal changes in local excitability within the targeted brain system over time after perturbation, as a measure of cortical plasticity. Cortical-interhemispheric associative stimulation (cc-PAS) is a dual-site TMS protocol that can promote Hebbian spike-timing-dependent plasticity (STDP). The cc-PAS protocol mimics the pre- and postsynaptic coupling patterns of neurons that induce STDP via a series of TMS pairs over two interconnected regions with a specific inter-stimulus interval (ISI).

[0313] Multimodal perturbation In some embodiments, combinations of two or more types of stimuli can be used to maximize the effect of TMS or tCS on the brain, thereby reducing the noise in the brain activity data. Oscillatory electrical stimulation (e.g., transcranial alternating current stimulation - tACS) can be used to control the oscillatory activity in the target region / network before or during the application of TMS. For example, the application of 20 Hz tACS over the region of the precuneus brain can function as a stabilizing factor for spontaneous brain activity for the subsequent delivery of TMS pulses synchronized with the peak of each 20 Hz oscillation cycle. In some embodiments, tACS can be applied before the application of TMS to regulate brain oscillatory activity, for example, by driving the brain of an individual oscillating at 18.5 Hz towards a stable oscillatory pattern at 21 Hz. TMS is then delivered over the same region stimulated by tACS to enhance the effect of tACS or generate cortical plasticity and embody the changes in oscillatory activity induced by tACS.

[0314] Perturbation stimulus Perturbation 102 can be applied in many forms, including but not limited to signals composed of (i) a single pulse, (ii) a train of pulses separated by fixed intervals or variable micro-vibrations, (iii) a continuous waveform characterized by a given amplitude, frequency (or combination of frequencies), and duration, (iv) a continuous electric field with a given polarity, amplitude, and duration, or (v) noise pulsed at a specific frequency.

[0315] Target selection approach In some embodiments, perturbation 102 is delivered to one or more brain regions 103 identified based on brain scans and electrophysiological data. These information sources for defining an optimal perturbation target can represent population-level data and / or individual data. The data can be compiled such that local and distributed brain activity is summarized into quantifiable metrics, extracting characteristics of brain activity and measuring characteristics related to, but not limited to, (a) metabolic and vascular activity, (b) oscillatory activity within known frequency bands, (c) network resilience measurements, (d) dynamic connectivity, (e) protein accumulation maps obtained via PET imaging.

[0316] In some embodiments, perturbation 102 is delivered to a brain region 103 determined to be a node based on the degree of connection 104 between the node and the rest of the brain in order to (i) assess the integrity of the brain connectome or (ii) induce a wide range of responses in local and remote brain regions as a measure of brain integrity and connectivity, as shown in Figure 1B. The degree of connection 104 can be estimated via functional imaging data or EEG in the case of functional connectivity, or via anatomical and diffusion MRI scans in the case of structural connectivity. In the latter case, the physical connection between two or more regions can be estimated by examining the characteristics (e.g., diameter, anisotropy, diffusivity) of the white matter fibers that make up a particular white matter tract in the brain. In an exemplary scenario, TMS or tCS stimulation targets are defined by examining the structural connectivity of many brain regions and then selecting the region that is most connected to the rest of the brain for the purpose 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., the hippocampus) that are not reachable via TMS or tCS, and stimulation of accessible gray and white matter regions with strong structural connections to the hippocampus maximizes the potential to indirectly activate the hippocampus.

[0317] In some embodiments, the perturbation is delivered to two or more nodes of the same brain network to evaluate the integrity of connectivity within a given brain network, such as in the case of the default mode network of a patient with Alzheimer's disease. Perturbation targets within the brain network can be identified via analysis of brain scans (e.g., functional MRI) or electrophysiological data (e.g., EEG data).

[0318] In some embodiments, the perturbation is delivered to nodes that have been demonstrated to be associated with the pathophysiology of a given disease. For example, the precuneus region can be targeted due to its demonstrated attenuation of functional connectivity in Alzheimer's disease when measured using functional magnetic resonance imaging (fMRI), and regions of the temporal lobe affected by the waste proteins in Alzheimer's disease can be targeted as stimulation targets with particular emphasis on amyloid-β and p-tau proteins. The regions exhibit modified levels of neuroinflammation when measured, for example, by diffusion MRI.

[0319] In some embodiments, the selection of the perturbation target is based on an estimate of the induced electric field induced in candidate brain regions extracted from a biophysical modeling of passive tissue conductivity. The model can be created by simulating the propagation of magnetic and electrical energy across the head and brain tissues (e.g., skin, muscle fibers, bone, cerebrospinal fluid - CSF, gray matter, white matter) to yield a quantitative measure of the induced electromagnetic stimulation that affects brain cells, e.g., excitatory neurons and inhibitory interneurons. The degree of induced electromagnetic stimulation can be used, for example, to adjust stimulation parameters such as intensity and phase angle to maximize the stimulation effect on a given brain target according to known criteria for inducing neuron firing or neurotransmitter release. In an exemplary application, stimulation of the precuneus region in patients with Alzheimer's disease determines that the minimum TMS intensity that simulates the effect of the TMS pulse on the patient's anatomical MRI scan and induces an electric field sufficient to induce neuron firing in the precuneus corresponds to 67% of the output capacity of the TMS device. The process can also identify one or more positions that may be optimal for placing the TMS coil on the scalp by simulating different angles and rotations while taking into account the fine anatomical structure of the gray matter.

[0320] In some embodiments, the target region for perturbation is identified as a region responsible for a particular cognitive process, e.g., long - term memory or attention. Brain scans and / or EEG data collected during the performance of cognitive tasks by patients with Alzheimer's disease can be analyzed to identify regions where activation is related to the ability of the task. In an exemplary scenario, the resulting region(s) are selected as targets for TMS or tCS perturbation to evaluate their level of integration with other brain regions involved in the same cognitive process (e.g., long - term memory) as a measure of efficiency within the long - term memory network of patients with Alzheimer's disease.

[0321] Stimulation Parameter Selection Selecting an appropriate stimulation intensity for TMS, tCS, tFUS stimulation can be important to ensure proper evaluation.

[0322] In some embodiments, the target area for perturbation is identified based on spatial and functional search algorithms, the EEG activity induced after at least two TMS pulses is averaged, and the TEP becomes apparent at different latencies between 5 ms and 500 ms after TMS. The amplitude of the TEP can be calculated for each patient and used as a surrogate for individual responsiveness to TMS, and thus can be used as an indicator of the excitability and reactivity of the target area / network. Then, the amplitude of the TEP can be used to correct the stimulation intensity obtained from the stimulation of the motor cortex (e.g., RMT) for the purpose of adapting the stimulation intensity of TMS based on the TEP.

[0323] The stimulation intensities of TMS, tCS, and tFUS stimulation depend on the individual brain's anatomical structure and can be estimated by creating a high-resolution biophysical model of the brain's anatomical structure via an MRI scan. In some embodiments, the electric field (E-field) induced over the brain target area is generated by using a realistic head volume conductor model generated based on MRI images and segmentation from a validation dataset. The model is based on anisotropic conductivity values for each brain tissue class (e.g., skin, fat, muscle, bone, cerebrospinal fluid, gray matter, white matter) expressed in S / m. Using a complex mechanism resulting from a series of results covering at least gray matter, scalp, bone, and cerebrospinal fluid, the E-field distribution of a specific TMS coil's design, position, angle, and rotation can be calculated taking into account the distance from the coil to the scalp and brain atrophy. The estimated E-field can be used to (a) retrospectively calculate individual differences in the amount of current delivered over the target area and thus to account for differences in response to treatment, or (b) adjust the stimulation location and / or intensity so that all participants receive the same amount of induced cortical stimulation.

[0324] Temporal Framework and Scope of Perturbation-Based Markers The systems and methods described herein can be applied to multiple ranges and timeframes. An exemplary application of perturbation-based markers in patients with Alzheimer's disease and general dementia is described below.

[0325] Diagnostic purposes Individual responses to perturbations can be used to identify abnormal brain activity in patients suspected of having Alzheimer's disease or dementia. The diagnostic process can be assisted by using responses to perturbations that differ from those of control subjects, healthy controls, or previous responses obtained from the same individual. The same process can be applied to identify patients with subtypes of Alzheimer's disease within a group of patients diagnosed with Alzheimer's disease (e.g., amnestic Alzheimer's disease). Analysis of the responses can be performed via visual inspection by a trained human operator and / or via a machine learning-based classification algorithm configured to label responses to perturbations as normal or abnormal responses.

[0326] Prognostic purposes Analysis of individual brain data can be performed to predict (e.g., estimate) the course of a disease (e.g., Alzheimer's disease) based on historical data collected from the same individual or group-level estimates of disease progression.

[0327] Evaluation of treatment efficacy Analysis of individual or group-level brain data can be performed to quantify differences in brain activity before and after treatment for Alzheimer's disease or memory impairment. Changes before and after treatment in perturbation-based metrics can be used to evaluate individual or group-level responses to a particular treatment, modify treatment parameters, and continue or discontinue treatment.

[0328] Individualized treatment definition The analysis of individual brain data can be performed to individualize non-invasive brain stimulation parameters including, but not limited to, the location, direction, intensity, frequency, phase, and noise level of the stimulation. Individualization can be performed, for example, by examining the responses to TMS pulses delivered over multiple positions within the target region 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 location of stimulation, the stimulation waveform, the timing of multiple pulse stimulations, the duration of the stimulation protocol, and the number of stimulation sessions can be defined based on the individual responses to perturbations performed using TMS and EEG. In some embodiments, the intensity of the stimulation is defined based on the amplitude of the evoked response recorded via EEG immediately after the TMS pulse, and the amplitude of the evoked potential represents the excitability of the brain tissue stimulated via TMS. The intensity of the stimulation can be defined based on the characteristics of the anatomical structure of the individual's brain, including the atrophy levels of the gray and white matter of the cortex that affect the magnitude of the magnetic and electrical stimulation reaching the brain during TMS due to the increase in scalp-cortex distance. In some embodiments, the frequency of the stimulation is defined based on the frequency domain response to TMS recorded via EEG, and the power spectrum of brain activity in frequency bands including, but not limited to, delta, theta, alpha, beta, and gamma is calculated based on the EEG signals collected after TMS delivery. The peak of the power spectrum within the target frequency band can be determined and used as a reference point for setting the stimulation frequency or inducing adaptive modulation of the oscillatory brain activity. For example, an individual who shows a strong response after TMS with a peak at 38 Hz within the gamma band can be stimulated using repetitive TMS at 38 Hz to maintain continuous oscillatory activity, or incrementally stimulated faster at frequencies of 38 Hz (e.g., 39 Hz, 40 Hz, 45 Hz) for the purpose of inducing neurons to generate faster brain activity. In some embodiments, the stimulation location is determined based on the analysis of the evoked potential collected using EEG immediately after TMS delivery, and maps of higher and lower responses to TMS are created based on the amplitude of the potential.This information can be used to select regions that show a stronger response to TMS as an indicator of the integrity and continuous activity of neural circuits stimulated by TMS. Brain regions showing stronger continuous activity may indicate intact brain circuits and may be selected as targets for repetitive TMS with the aim of maintaining that activity and counteracting neurodegeneration. Brain regions with a weak response to TMS can be targeted for repetitive TMS with the aim of restoring brain activity and repairing brain circuits. In some embodiments, the frequency, amplitude, waveform, and duration of the brain response to TMS recorded via EEG are used to determine the duration of repetitive TMS treatment and the number of TMS pulses delivered in each repetitive TMS session. Individuals having a stronger response to TMS (e.g., high amplitude of the evoked potential exceeding 6 mV and strong response in the gamma band) can be assigned a shorter treatment period compared to individuals having a weaker response indicating more brain circuit impairment in the stimulated brain regions. Information regarding the EEG evoked response can be combined with other markers of the disease state including, but not limited to, levels of neuroinflammation measured via blood / plasma biomarkers, brain levels of amyloid and tau protein accumulation measured via positron emission tomography (PET), levels of cortical brain atrophy, and levels of corticospinal plasticity measured via transcranial theta burst stimulation (TBS).

[0329] Purpose of disease tracking Using perturbation-based metrics collected over time, the brain function of patients with Alzheimer's disease can be monitored to capture significant deviations in the brain's activity pattern from data collected at previous time points at any given time point. Individual response data can be compared to normative data collected in samples of patients with Alzheimer's disease or healthy control groups, thus providing an estimated deviation from, for example, an expected rate of cognitive decline.

[0330] Investigation of cognitive function Perturbation-based data can be collected from brain regions / networks associated with or supporting specific cognitive functions. For example, TMS or tCS can be used to stimulate regions associated with memory processing, such as the precuneus or dorsolateral prefrontal cortex. TMS-EEG and tCS-EEG data can be used in combination with neuropsychological scores to identify modifications in brain circuits associated with memory decline in patients with Alzheimer's disease, for example.

[0331] Data Processing and Analysis Data Processing Methods In some embodiments, data processing includes automated cleaning / preprocessing solutions and / or semi-automated processing solutions capable of manual identification of artifacts. Processing and analysis can be performed as part of separate modules that cover (i) data collection, (ii) data verification and format conversion, (iii) data cleaning and preprocessing, (iv) data analysis, and (v) generation of a detailed report (including, for example, a summary of optimal stimulation targets / parameters and processing steps).

[0332] Brain Scan Processing When brain scans are available for stimulus target selection, two types of information can be used: (i) brain structural characteristics including, but not limited to, gray / white matter density / volume / thickness / gyri / sulci, CSF distribution, white matter diffusion rate and anisotropy, and neurotransmitter spectroscopic profiles, and (ii) brain functional characteristics 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, conversion to 3D volume format of a single image, segmentation in brain tissue classes, spatial and temporal filtering, removal of physiological noise, removal of image artifacts, extraction of mean values and / or time series of brain activity, co-registration to a common anatomical or functional template for group-level analysis, and calculation of evoked activity when multiple scanning conditions exist as in the case of block fMRI data. Follow-up analysis can be performed on both voxel-based volume data or vertex-based surface images and may include masking of clean data based on an anatomical or functional atlas that describes the relevant network or brain regions that can be targeted by TMS.

[0333] EEG Data Processing EEG data collected before, during, and / or after a single TMS pulse during a TMS-EEG recording session can be processed for data analysis using TMS and preparation for perturbation target selection. In addition to EEG artifacts (e.g., eye movements and heartbeats), EEG data collected during TMS is often contaminated by TMS-specific artifacts including magnetic pulse artifacts that change the impedance of EEG electrodes, TMS-evoked muscle artifacts characterized by high-frequency activity, and artifacts related to the TMS machine charging process between TMS pulses. These artifacts typically have amplitudes that are several orders of magnitude larger than the recorded EEG data and thus disrupt the brain signals within the EEG.

[0334] The preprocessing and cleaning steps include, but are not limited to, conversion of raw data in the.edf format, trimming of raw data into epochs of a predefined length that include segments capturing brain activity before and after TMS, normalization of post-TMS activity by subtracting the average signal amplitude of EEG data collected before TMS, automatic or semi-automatic data inspection to identify EEG channels with excessive noise or artifacts, zero-padding of activity simultaneous with a single TMS pulse (according to voltage-based thresholds, kurtosis, and combination probability) to remove early signal attenuation and muscle artifacts induced by the TMS pulse, identification and removal of components including the initial stages of TMS-induced high-amplitude electrodes by independent component analysis (ICA), additional data reduction via principal component analysis (PCA) to minimize overfitting and noise components, interpolation of previous zero-padded signals over the entire TMS pulse, band-pass filtering typically using pre- and post-filters of 1 - 150 Hz, notch filtering to account for line noise, manual removal of all remaining artifact components including eye movements / blinks, muscle noise (EMG), single electrode noise, TMS-induced muscle activity, cardiac signals (EKG), auditory evoked artifacts (artifacts can be identified and labeled based on spectral frequency profile, power spectrum, amplitude, scalp topography, and / or time course) with reference to the global mean, application of machine learning, and deep learning algorithms for identification of remaining artifacts, or interpolation of previously removed electrodes.

[0335] Data cleaning and processing can be performed as a supervised method, for example, using human verification of processing steps and visual inspection of the data, or as an unsupervised procedure, for example, using machine learning techniques that do not require human interaction.

[0336] Covariates of interest It should be understood that the order of steps can vary and specific adjustments can be implemented, at least in part, based on individual brain characteristics or pathology-specific artifacts and signal characteristics. Characteristics to consider include, but are not limited to, the level of cortical atrophy affecting the induced electric field in the brain, known modifications in neurotransmitter activity affecting the amplitude and shape of evoked potentials, increased motor and muscle activation during EEG recording, and increased levels of oscillatory activity in the EEG due to general slowing of typical brain activity in the brains of patients with drowsiness and / or AD.

[0337] Perturbation index The response to perturbation can be quantified through a plurality of non-mutually exclusive metrics and indices, including but not limited to those based on the shape, amplitude, phase, and timing of the signal, those related to resonance effects associated with the duration of the induced perturbation, and those related to network-level activity and brain connectivity analysis.

[0338] Metrics based on signal characteristics EEG signals recorded before and after TMS or tCS stimulation can be characterized through metrics that quantify activity in specific frequency bands and activity at specific time points after TMS / tCS stimulation related to the dynamics of specific neurotransmitters or corticocortical circuits. Non-limiting examples of metrics that can be applied to patients with AD include the following: ● The eigenfrequency of TMS-induced activity in a local region of interest, represented as oscillatory activity showing the strongest power spectrum after a TMS pulse ● The ratio of the EEG theta / beta frequency band measured in the frontal lobe of the brain ● The magnitude of the N100 peak ● The area under the curve of the global mean field power (GMFP) of the TMS-evoked response after stimulation ● The absolute and relative power spectra of the EEG in specific frequency bands including, but not limited to, delta, theta, alpha, beta, and gamma ● The EEG power spectra in the alpha and delta bands of regions in the posterior brain ● Decrease in paired-pulse TMS of oscillations in the gamma band ● Reduction of paired-pulse TMS of the P60 and N100 peaks of TMS-evoked potentials ● Inter-frequency coupling centered on the frequency band stimulated via TMS or tCS (to evaluate multiplexing of underlying brain circuits and changes in information processing) ● Changes in the P30, N45, P60, and N100 peaks of TMS-evoked potentials ● Changes in resting EEG alpha and beta power ● TMS-induced spectral perturbations in local regions, e.g., evoked activity in specific frequency bands including, but not limited to, delta, theta, alpha, beta, and gamma ● Magnitude and duration of TMS / tCS-induced spectral changes (as a measure of plasticity within the cortical region as a function of frequency). ● Changes in spectral coherence at the scalp and source levels using individual MRI data or templates generated from samples of individuals with desired clinical or phenotypic characteristics.

[0339] Perturbation resonance EEG metrics (PREM) The time required for the EEG signal to return to baseline after TMS application is useful for characterizing the brain's recovery ability after perturbation and can, in some cases, serve as an indicator of plasticity mechanisms. However, the recovery time alone may not be an indicator of recovery. Different subjects may show higher amplitudes in their responses. For example, patients with Alzheimer's disease have been shown to previously exhibit increased excitability. Thus, 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 function as a biomarker for the disease. Multiple metrics can be extracted from TMS-EEG or tCS-EEG signals based on the energy of the EEG signals collected before and after perturbation.

[0340] In some embodiments, the time required for the EEG signal to return to the baseline is automatically calculated based on the signal energy level obtained over the course of the TEP.

[0341] The absolute TEP values can be extracted and normalized with respect to their maximum values to have a uniform amplitude range across the entire subject. Next, the energy level of the signal can be calculated over a sliding window that moves sample by sample through the TEP. Experiments can be conducted to evaluate the optimal window size. Values for the length of the sliding window include 5 ms, 10 ms, 20 ms, 30 ms, and 40 ms. The final evaluation was performed with respect to the maximum ability obtained for the classification of AD and HC.

[0342] Once the energy 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 value obtained by adding one standard deviation to the average value of the energy signal obtained on the baseline data. The baseline is considered between -500 ms and -200 ms before the pulse. The last 200 ms before the pulse are excluded to avoid interference from residual preprocessing and filtering effects on the baseline data.

[0343] After applying the threshold, a new signal representing the increase in energy as a response to the stimulus is obtained.

[0344] The difference between the time points when the energy signal exceeds the threshold is calculated. The maximum interval between time points is extracted. If the difference between two time points of the signal is higher than 75% of the maximum, the signal is considered to have returned to the baseline level after perturbation. In some embodiments, this represents the time required for the signal to return to the baseline.

[0345] Examples of metrics that can be used to characterize the TEP include, but are not limited to, metrics that measure the time required for the signal to return to baseline (e.g., measurement of target engagement duration), metrics that measure the slope of the signal between the end of engagement after the TMS pulse and the maximum absolute value of the highest TEP, and measurements that capture the area under the curve of the signal between the TMS pulse and the end of target engagement.

[0346] Network-based metrics The response to perturbation can be analyzed to characterize the network-level dynamics in the brain, with a particular focus on the analysis of modified functional and structural brain networks in Alzheimer's disease and dementia. Recorded electrophysiological (e.g., EEG) data is preprocessed to remove noise sources and recording artifacts and can then be analyzed by examining the patterns of brain connectivity in electrode and / or source space. Analysis in electrode space involves examining the brain activity recorded by scalp EEG electrodes without projecting the data onto the patient's structural MRI scan. Source analysis involves projecting clean EEG data onto the 3D brain structural MRI region and thus examining high-spatial-resolution activity in a 1 cubic millimeter range related to the topography of individual anatomical structures and brain functional networks. Regardless of the approach, the data can be represented as a time series representing brain activity at specific electrodes (at the scalp level) or brain regions / networks (at the source level). In source space, the time series can be extracted from anatomically or functionally defined target regions representing specific brain regions or functional networks (e.g., default mode network, frontoparietal control network, anterior salience network, dorsal attention network, etc.) relevant to the pathophysiology of Alzheimer's disease. The extracted time series is analyzed to examine the patterns of synchrony across electrodes and brain regions / networks, and a connectivity matrix can be obtained that describes the direction and strength of connectivity for each given pair of brain regions / networks or electrodes (functional connectivity analysis). This analysis can be performed by extracting one value for each pair of regions / networks / electrodes, providing a static metric of brain functional connectivity. The time series data can also be used to examine the dynamic connection patterns that vary over time and to model their changes over time (dynamic connectivity analysis). The time series can also be used to estimate the directionality of influence across brain regions / networks / electrodes by calculating the predictive power of time series #1 over time series #2 as a whole.The resulting metrics measure the causal relationships between activities in Region #2 from Region #1 and yield a weighted map that explains the hierarchy of brain regions / networks / electrodes that drive brain activity in other regions / networks / electrodes.

[0347] Analysis of functional / dynamical / effective connectivity can examine the metrics of activity between and within networks, quantify the amount of connectivity within nodes of networks associated with Alzheimer's disease, or be performed between nodes of two or more brain networks to examine network communication and information processing. Connectivity matrices based on functional / dynamical / effective connectivity can also be used to obtain network topography measurements related to the field of graph theory analysis, for example, to identify regions / networks / electrodes that are particularly important in providing measures of coherence, efficiency of information transfer, and resilience to perturbations of the network or the brain as a whole.

[0348] Examples related to network dynamics in AD patients include, but are not limited to: ● Connectivity within the default mode network (DMN), fronto-parietal control network (FPCN), dorsal attention network (DAN), and anterior salience network (AS), ● Connectivity between any two pairs of networks as a measure of network synchronization, ● Strength of negative connectivity between the DMN and the AS, ● Connectivity of the precuneus region of the DMN, ● Connectivity between the parietal and frontal nodes of the DMN, ● Connectivity of the angular gyrus of the DMN, specifically, the strength of its connection to the hippocampus, ● Connectivity of brain regions with high accumulation of waste proteins measured via PET imaging, ● Connectivity between the frontal and parietal nodes of the FPCN as a metric of cognitive flexibility and executive function, ● Dynamical, time-varying connectivity of brain regions that make up the DMN and the FPCN ●Efficient and causal connectivity of the brain regions that make up the DMN and FPCN, ●Frequency coupling between theta and gamma waves in the regions that make up the DMN and FPCN, ●Including but not limited to those related to the increase in resilience to external perturbations measured by resilience graph analysis, dynamic changes in network modularity after TMS pulses.

[0349] Machine learning analysis In some embodiments, metrics extracted from perturbation-based datasets are analyzed via machine learning (ML) and artificial intelligence (AI) platforms. In some embodiments, an ML model can be trained to cluster one or more of a plurality of variables or combinations of variables to obtain unique information from a dataset. The ML model can then generate clusters that group together the same and / or similar variables or combinations of variables, enabling the identification of response patterns within the dataset, within groups of individuals, and across groups of individuals. In some embodiments, the ML model can include algorithms that reduce the dimensionality of the dataset and apply feature selection techniques to reduce the dimensionality of the feature space by discarding multiple variables or metrics that provide the same kind 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 can include training and test splits for cross-validation of classification results, including but not limited to k-fold and leave-one-out cross-validation. The ML methods described herein can be applied in a supervised or unsupervised manner depending on whether the model is trained to identify hidden clusters / groups within the dataset (unsupervised) or to identify specific groups of features / individuals based on a target set of desired characteristics (supervised).

[0350] Infrastructure and patient flow In some embodiments, the infrastructure is created to enable, among other things, optimization of stimulation targets and TMS parameters, data storage and processing, and report generation. The session flow may include the following steps: 1) The patient is prescribed a TMS-EEG examination to identify potential modifications to brain physiology, 2) The patient is scheduled for a target definition and optimization session, 3) The patient completes a brain scan session to identify and characterize the topography of the target brain network / region, 4) The brain scan is processed and the TMS target region is identified, 5) The patient's EMG, TMS, and EEG data are collected from the regions previously identified via the brain scan, 6) The results are streamed to a data processing unit / platform where data cleaning and analysis are performed, 7) Optionally, the data is compared to a normative database of age-matched healthy individuals to identify responses modified relative to perturbation, 8) Optionally, the data is collected over multiple time points and compared to a normative longitudinal database of age-matched healthy individuals to identify responses modified relative to perturbation and to infer potential conversion from a disease trajectory, e.g., from MCI to AD in the case of patients with MCI, 9) The clinician / operator receives a report on the modified markers and it is used to (i) prescribe medications, (ii) prescribe non-pharmacological interventions (meditation, cognitive training, etc.), 10) The data is stored and reconciled with follow-up data collection for disease tracking.

[0351] The steps can also be combined or replaced with any suitable steps of other methods disclosed herein.

[0352] Starting from step 3, in an exemplary scenario, the clinical staff may access a URL link and authenticate their user account with a service (e.g., Auth0). The clinical staff may then set up the TMS-EEG device and pair it with a web browser. Subsequently, data (raw EEG data and / or survey metadata, e.g., demographic information, brain scan data, neuronavigation parameters) and requests may be sent from the EEG to the backend via an application programming interface (API). This data and requests are then routed through the gateway ingress and gateway.

[0353] Definition of Brain Targets Brain regions may refer to anatomical regions of the brain following a standard neuroanatomical hierarchy. Brain regions may also include anatomical regions or ensembles of regions of the brain that have similar functions or are related to specific cognitive functions. "Cognitive regions" may be considered as the 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, visuomotor function, executive function, flexibility, inhibition, abstract reasoning, creativity, emotional processing).

[0354] The brain network can refer to functional and / or anatomical networks defined through techniques such as fMRI, diffusion tensor imaging (DTI), diffusion weighted imaging (DWI), PET, EEG, and MEG. Examples of brain networks are the default mode network (DMN), ventral DMN (vDMN), dorsal DMN (dDMN), fronto-parietal 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), cingulo-opercular network (CON), episodic memory network (EMN), precuneus network (PrecN), language network (LangN), cognitive control network (CCN), inhibitory network (InhN), working memory network (WMN), insight network (InsiN), insular cortex network (InsN), vestibular network (VestN), basal ganglia network (BGN).

[0355] System design for simultaneous TMS, electrical stimulation, and EEG recording Brain perturbations for evaluating, quantifying, and modulating brain activity can be delivered via systems that enable 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. FIGS. 8A-8C show non-limiting examples described herein where the system can include a TMS coil 801 (e.g., FIG. 8) that incorporates EEG electrodes 800 within the plastic casing of the coil 801, and tACS electrodes 802 distributed along the surface of the coil. In some embodiments, the TMS coil 801 can have a different shape than that shown in FIG. 8, including, but not limited to, an individualized shape based on the shape of an individual's head derived from a 3D scan of the head, including those obtained via structural MRI images. In some embodiments, the system includes multiple TMS coils 801 that enable stimulation of multiple brain regions via TMS, connected to additional EEG 800 and electrical stimulation electrodes 802.

[0356] In some embodiments, the EEG and electrical stimulation electrodes are controlled by separate generators and amplifiers different from the TMS coil. In some embodiments, the EEG and electrical stimulation electrodes are actuated by a TMS device.

[0357] In some embodiments, the system can include a single TMS coil 800 that includes EEG 801 and electrical stimulation electrodes 802 disposed within the casing of the coil 800 of FIG. 8 shown in FIG. 8A. In some embodiments, the system can include a TMS coil 800, as shown in FIG. 8B, and can further include additional structure 805 covering other scalp regions to enable additional EEG 801 and electrical stimulation 802 of other brain regions and lobes 804, protruding from the casing of the TMS coil 800. In some embodiments, the system can include multiple TMS coils 800 configured to target multiple brain regions 804 and lobes 804, as shown in FIG. 8C, and can further include integrated additional EEG 801 and electrical stimulation electrodes 802.

Example

[0358] The embodiments of this specification are further illustrated by the following examples and detailed protocols. However, the examples are merely intended to illustrate the embodiments and should not be construed as limiting the scope of the technology described herein.

[0359] The examples demonstrate applying a perturbation-based method to (i) predict the response of AD patients to 6 months of treatment based on repetitive TMS and (ii) identify patients with AD from healthy individuals.

[0360] Example 1 Prediction of Response to Brain Stimulation Therapy in Patients with AD Data collected as part of a phase 2, double-blind, randomized, placebo-controlled clinical trial in AD patients demonstrate the safety, feasibility, and effectiveness of the proposed perturbation-based measure. The trial investigated the effectiveness of the long-term treatment process of repetitive TMS with clinical and cognitive effects captured over 24 weeks, based on a new approach targeting the functionally modified networks in AD, particularly the DMN and the precuneus region. After recruitment and baseline assessment, patients were randomly assigned in a 1:1 ratio to receive either real or sham rTMS over the precuneus muscle. All treatments were administered over 24 weeks without interruption. The trial included a 24-week treatment period with a 2-week intensive period (W1 and W2) during which rTMS of the precuneus was applied 5 times a week, Monday to Friday, followed by a maintenance phase (W3 - W24) during which the same stimulation was applied weekly for 22 weeks. A total of 51,200 pulses were delivered to each patient over the entire 24-week period. rTMS was performed using a magnetic stimulation device connected to the coil of FIG. 8.

[0361] The efficacy evaluation was conducted at baseline (W0) for the registered patients and caregivers, and repeated by blinded evaluators for the assigned groups at week 12 (W12) and week 24 (W24) (or at early termination). The principal investigator, patients, and their caregivers were also blinded. At each clinical visit (or at early termination), adverse events (AEs) were recorded, vital signs were measured, and physical and neurological examinations were performed. The Independent Data Monitoring Committee monitored the safety of the patients in accordance with the Data Monitoring Committee Charter.

[0362] Primary and Secondary Endpoints The primary endpoint was the change in the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) score at 24 weeks from baseline (the CDR-SB score ranges from 0 to 18, with higher scores indicating worse cognition and daily function). The intention-to-treat analysis set included all patients with efficacy data after baseline. The secondary endpoints were as follows: ● Change at week 24 from baseline in the Alzheimer's Disease Assessment Scale - Cognitive Subscale (ADAS-Cog) 11, ● Change at 24 weeks from baseline in the MMSE score, ● Change over 24 weeks from baseline in activities of daily living (ADCS-ADL).

[0363] Samples Eighty-six patients were screened and 50 patients were randomized. The mean age of the entire sample of patients was 73.7 years (SD = 6.6, range 62 - 84), and 52% were female. The patients had a mean raw MMSE score of 21.3 (SD = 2.5) at baseline. A total of 45 patients (90%) completed the treatment period.

[0364] Prediction of Response to rTMS Treatment Cortical physiology-based TMS measurements were selected by the principal investigator based on their knowledge and expertise regarding the pathophysiology of AD and used to predict response to treatment. This approach included three main single and paired perturbation measures: resting motor threshold (RMT), short afferent inhibition (SAI), and TMS-evoked potentials (TEP).

[0365] Resting motor threshold (RMT) RMT was defined as the minimum intensity producing MEP >50 μV in at least 5 out of 10 trials in the relaxed first dorsal interosseous (FDI) muscle of the right hand. RMT was evaluated over the optimal stimulation site for inducing MEP in the right FDI, called the "motor hot spot", and identified by placing the coil approximately over the central sulcus and moving it 0.5 cm at a time on the scalp over left M1.

[0366] Short afferent inhibition (SAI) Corticospinal assessment was performed using TMS-EMG, aiming to investigate whether short latency afferent inhibition (SAI) can predict clinical progression in AD patients in the real rTMS group. This measurement was based on the MEP amplitude evoked from the relaxed first dorsal interosseous (FDI) muscle of the contralateral hand to the stimulation. A posterior-anterior current was induced in the brain by placing the coil tangentially on the scalp at an angle of approximately 45° from the midline. The intensity of the single-pulse TMS stimulation was adjusted to evoke an MEP with a peak-to-peak amplitude of approximately 1 mV. SAI was measured with a conditioned electrical stimulus (200 μs) applied via bipolar electrodes to the right median nerve (cathode proximal) at the wrist, preceding single-pulse TMS at 16, 20, 24, and 28 ms. The intensity of the electrical stimulus was set just above the motor threshold to induce visible twitching of the abductor pollicis brevis muscle. To measure the SAI effect, the mean peak-to-peak amplitude of the conditioned MEP as a percentage of the mean peak-to-peak amplitude of the unconditioned MEP in that block was considered.

[0367] TMS-evoked potentials (TEP) The intensity of the stimulation was set at 90% of the RMT and tested on the muscle of the contralateral FDI at rest. Each session consisted of 80 single pulses of TMS applied at a random inter-stimulus interval (ISI) of 2 - 4 s over the DMN - precuneus, targeted using a neuronavigation system. Each participant wore a plug that played continuous white noise into the ear to reproduce a specific temporal jitter of the TMS clicks.

[0368] TMS - evoked EEG activity was recorded from the scalp using a TMS - compatible DC amplifier. The EEG, continuously recorded from 61 scalp sites, was arranged according to the 10 - 20 International System using TMS - compatible Ag / AgCl pellet electrodes attached to an elastic cap. The EEG signals were digitized at a sampling rate of 5 kHz. The skin / electrode impedance was maintained below 5 kΩ. Horizontal and vertical eye movements were detected by recording the electrooculogram (EOG) to offline exclude trials containing eye artifacts.

[0369] Physiological and TMS - related artifactual components were detected using INFOMAX - ICA and removed based on their scalp distribution, frequency, timing, and amplitude. The effects of rTMS were evaluated in three domains: in the temporal domain, by the TMS - evoked potential (TEP) from the stimulation site, in this case, the DMN - precuneus; in the oscillatory domain, by the TMS - related spectral perturbation (TRSP) from the stimulation site, i.e., the DMN - precuneus; and in the spatial domain, by source analysis performed over the entire scalp.

[0370] TEP was calculated considering a time window from 100 ms before to 300 ms after TMS, and baseline correction was from -100 to 0 ms (0 ms represents the time when TMS was applied). Frequency domain analysis was performed by using a time / frequency decomposition based on the Morlet wavelet (parameters c = 3, 41 linear 1 Hz steps from 4 to 45 Hz), and then calculating the TRSP. Power spectra were extracted for the theta (4 - 7 Hz), alpha (8 - 13 Hz), beta (14 - 30 Hz), and gamma bands (31 - 45 Hz), and averaged in a time window of 20 - 250 ms after TMS for the theta and alpha bands, and 20 - 70 ms after TMS for the beta and gamma bands.

[0371] TMS-EEG source reconstruction for prefrontal stimulation was performed using Brainstorm by fitting a distributed source model consisting of 15,000 elementary current dipoles. These dipole sources were distributed at each vertex of a mosaic cortical mesh template surface derived from the MNI standard 1 mm resolution brain (Colin27) as provided in the Brainstorm toolbox executed in MATLAB (R). First, a head model for source imaging was implemented according to the boundary element method (BEM). Based on this head model, the inverse problem was solved using a current density map.

[0372] Prediction of the effects of rTMS and responses to treatment Cognitive and clinical outcomes The mean baseline CDR-SB total score was 4.1 (SD = 1.8) in the real rTMS group and 4.6 (SD = 1.5) in the sham rTMS group. There was a significant difference (baseline vs. 24 weeks) in cognitive ability measured by the CDR-SB total score in the active rTMS group compared to the sham. The GLMM of repeated measures of the CDR-SB score showed significant results in terms of the difference between the interaction of group (p = 0.038) and time × group (p = 0.009), and patients treated with sham rTMS showed a general deterioration in patients' cognitive ability over time, which was not evident in the DMN-p-rTMS group. The GLMM estimated mean change (baseline - 24 weeks) of the CDR-SB score was -0.25 (95% confidence interval (CI) [-4.8, 4.3]) in DMN-p-rTMS and -1.42 (95% CI [-6.0, 3.3]) in the sham rTMS group. The proportion of responders defined as the percentage of patients showing a 1-point increase in the CDR-SB score was 68.2% in the DMN-p-rTMS group and 34.7% in the sham group.

[0373] The GLMM (adjusted for age and education) for repeated measures of the ADAS-Cog11 score showed significant results in terms of the interaction of time × group (p = 0.035). The GLMM estimated mean change (baseline - 24 weeks) of the ADAS-Cog11 score was -0.67 (95% confidence interval (CI) [-21.5, 20.2]) in DMN-p-rTMS and -4.2 (95% CI [-25.1, 16.6]) in the sham rTMS group.

[0374] The baseline mean of the ADCS-ADL total score was 58.6 (SD = 9.7) in the DMN-p-rTMS group and 58.3 (SD = 9.7) in the sham rTMS group. The estimated mean change (baseline - 24 weeks) of the ADCS-ADL score was -0.7 (95% CI [-27.2, 25.8]) in AD-DMN-p-rTMS and 7.5 (95% CI [-20.5, 35.5]) in the sham rTMS group, showing an improvement in DMN-p-rTMS compared to the sham rTMS group (interaction effect: p < 0.001).

[0375] Prediction of response to perturbation biomarker-based therapy To observe whether the neurophysiological measures at baseline level (T0) could predict the clinical progression of patients treated with rTMS, a multistage regression analysis was performed. Three neurophysiological measures were selected as predictors, and two of them, namely, RMT and SAI (at an ISI of 20 ms as this was the interval during which we observed the strongest inhibition), had been previously tested as predictors of clinical progression in AD, and the third measure was the TEP amplitude of the first peak, which is the response regulated by the rTMS protocol described herein. Since this was the main outcome, the CDR improvement between W1 and W24 was selected as the dependent variable. Pearson's correlation was performed with CDR, ADAS-cog, and ADCS-ADL to examine whether the TEP amplitude of the first peak was linearly correlated with clinical improvement.

[0376] The multistage regression analysis showed that the linear model significantly predicted clinical progression as tested with CDR when the model included all three predictors, namely, RMT, SAI, and TEP (Model 1: R2 = 0.461, p = 0.03), two predictors, namely, SAI and TEP (Model 2: R2 = 0.394, p = 0.023), and one predictor, namely, TEP (Model 3: R2 = 0.355, p = 0.009). The variable reduction method of coefficients revealed that the TEP amplitude of the first adjusted peak was the best predictor for clinical progression compared to SAI and RMT (all p < 0.05). Correlation analysis revealed a significant relationship between the TEP amplitude and CDR (r = -0.449, p = 0.021), ADAS-cog (r = -0.422, p = 0.018), and ADCS-ADL (r = 0.514, p = 0.009).

[0377] Observation Analysis of brain activity in response to TMS perturbations may enable prediction of response to brain stimulation therapy in patients with Alzheimer's disease. Fine-grained analysis of signal propagation may enable prediction of individual response rates and can be used to screen patients based on perturbation-based responses and assign them to specific treatments.

[0378] Example 2 Response to electromagnetic perturbations as a marker of disease pathology and altered synaptic plasticity in patients with Alzheimer's disease. Synaptic dysfunction is an important feature of the cognitive decline associated with Alzheimer's disease, but there is a lack of non-invasive in vivo approaches to reliably measure such dysfunction. In 50 patients with Alzheimer's disease, a new set of biomarkers of synaptic plasticity was identified and validated. TMS-induced EEG activity was collected after stimulation of specific hubs in the fronto-parietal network and default mode network involved in AD pathology. Specifically, the stimulation was delivered to the left dorsolateral prefrontal cortex (l-DLPFC), the precuneus (PC), and the left posterior parietal cortex (l-PPC).

[0379] Cortical oscillatory activity in the gamma range was significantly reduced in the frontal lobe of the patients, including activity induced locally by stimulating the dorsolateral prefrontal cortex or indirectly when stimulating the precuneus.

[0380] Cortical excitability, evaluated by TMS-evoked potentials (TEP), was higher in the same regions of AD patients compared to age-matched healthy volunteers (HV).

[0381] Comparison with other brain regions, including the parietal cortex, was the same across HV and patients with AD, and modification of gamma waves in the prefrontal lobe (DLPFC) was identified as a disease-specific marker of AD pathology. Furthermore, patients with more prominent reduction of DLPFC gamma waves showed stronger cognitive decline in subsequent follow-up evaluations conducted 24 weeks after the TMS-EEG session.

[0382] The details of the procedures and results are reported below.

[0383] Sample TMS-EEG data were obtained from patients with mild to moderate AD and analyzed according to the above procedures and methods. The mean age of the entire sample of AD patients was 69.2 years, 59% of whom were female, while the mean age of the HV group was 67.4 years. The AD patients had a mean baseline MMSE raw score of 22.7.

[0384] Patients were included in this study if they received a diagnosis established as likely to have mild to moderate AD according to the recommendations of the International Working Group.

[0385] TMS-EEG recording Neurophysiological characterization was performed by combining single-pulse TMS and EEG recording according to the above single-pulse TMS procedure. Throughout all TMS-EEG recordings, participants were seated in a comfortable armchair in a soundproof room in front of a computer screen. Participants were instructed to fixate on a white cross in the center of the screen and keep their arms in a relaxed position.

[0386] TMS was delivered over three target brain regions: the l-DLPFC, PC, and l-PPC. The order of stimulation of the regions was balanced across patients. The stimulation intensity of single-pulse TMS was individualized for each patient based on the corticospinal excitability level and brain anatomical structure.

[0387] Spatial / temporal domain analysis Figures 2A-2F show the spatiotemporal brain dynamics at the global and local levels after TMS. Figure 2A shows the TMS-evoked potential (TEP) 210 recorded over the entire scalp after TMS of the L-DLPFC for both the AD group and the HV group, and Figure 2B shows the corresponding TMS-evoked potentials 230, 231 at F3 recorded over the L-DLPFC. Figure 2C shows the overall-level TMS-evoked potential (TEP) 210 recorded over the entire scalp after TMS of the PC for both the AD group and the HV group, and Figure 2D shows the corresponding TMS-evoked potentials 240, 241 at Pz recorded over the PC. Figure 2E shows the TMS-evoked potential (TEP) 210 recorded over the entire scalp after TMS of the L-PPC for both the AD group and the HV group, and Figure 2F shows the corresponding TMS-evoked potentials 250, 251 at P3 recorded over the L-PPC. Error bars and shaded lines indicate the standard error. * indicates p < 0.05.

[0388] Figures 2A, 2C, and 2E show the spatio-temporal reconstruction of the TEP220 recorded over the entire scalp after stimulation of the L-DLPFC (Figure 2A), PC (Figure 2C), and L-PPC (Figure 2E). Regardless of the stimulation site, single-pulse TMS induced the well-known pattern of five main peaks (P1, P2, P3, P4, P5) of activity, lasting approximately 250 ms and having different latencies depending on the stimulation 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). The spatio-temporal reconstruction of the TEP220 showed similar spatial dynamics among three regions with prominent activity focused on the stimulation areas (P1, P2), then spreading within and between hemispheres (P3, P4), and finally resulting in centro-parietal positivity likely produced by the TMS-evoked auditory artifact (P5).

[0389] The analysis of TEP at the global levels 210, 220 was first performed to evaluate the differences in cortical excitability across the whole scalp between the AD and HV groups. When stimulating over the L-DLPFC (Figure 2A), AD patients showed higher cortical excitability over two clusters of left anterior electrodes (Monte Carlo p < 0.01) and a cluster of right posterior electrodes (all, Monte Carlo p < 0.01) between 20 - 40 ms after TMS. When stimulating over the PC (Figure 2C), AD patients showed higher cortical excitability over a cluster of four electrodes locally in the stimulated medial parietal region (all, Monte Carlo p < 0.01) between 30 - 50 ms after TMS, and then over one of the four electrodes over the stimulated area and one of the three frontal electrodes, a two-electrode cluster (all, Monte Carlo p < 0.01) in a temporal window between 50 - 90 ms. When the two groups were stimulated over the L-PPC, no differences were observed (Figure 2E) (all, p > 0.05).

[0390] To compare the reactivity of the stimulation regions between the AD group and the HV group, subsequent temporal analysis of the TEP induced at the local level was performed. Figures 2B, 2D, and 2F show the local TEP waveforms recorded at the electrodes closest to the stimulation regions: F3 (Figure 2B) for L-DLPFC stimulations 230, 231, Pz (Figure 2D) for PC stimulations 240, 241, and P3 (Figure 2F) for L-PPC stimulations 250, 251. When stimulating across the L-DLPFC, the AD group showed higher TEP amplitudes between 20 and 40 ms compared to the HV group (AD: 1.602 ± 0.286, HV: 1.137 ± 0.235; t(59.314) = 2.370, p = 0.021) (Figure 2B). When stimulating across the PC (Figure 2D), AD showed higher TEP amplitudes between 10 and 30 ms (AD: 2.671 ± 0.222, HV: 1.252 ± 0.368; t(79) = 3.263, p = 0.002), between 50 and 90 ms (AD: 1.671 ± 0.156, HV: 1.038 ± 0.182; t(79) = 2.208, p = 0.03), and between 90 and 160 ms (AD: 2.801 ± 0.201, HV: 1.637 ± 0.264; t(79) = 3.118, p = 0.003). When stimulating across the L-PPC, no difference was observed between the TEPs of the two groups (all, ps > 0.05) (Figure 2F).

[0391] Time / frequency domain analysis Figures 3A, 3C, and 3E show the overall time / frequency brain dynamics, and Figures 3B, 3D, and 3F show the corresponding local levels after TMS. Figure 3A shows the spectral perturbation (TRSP) 310 and scalp map 320 associated with the overall TMS of gamma waves after TMS of the L-DLPFC, and Figure 3B shows the corresponding TRSP recorded across the L-DLPFC when stimulated in the AD330 group and the HV group 332. Figure 3C shows the spectral perturbation (TRSP) 310 and scalp map 320 associated with the overall TMS of gamma waves after TMS of the PC, and Figure 3D shows a bar graph of the corresponding TRSP recorded across the PC when stimulated in the AD group 340 and the HV group 342. Figure 3E shows the spectral perturbation (TRSP) 310 and scalp map 320 associated with the overall TMS of gamma waves after TMS of the L-PPC, and Figure 3F shows the corresponding TRSP recorded across the L-PPC when stimulated in the AD group 350 and the HV group 352. Error bars indicate standard error. * indicates p < 0.05.

[0392] Figures 3A, 3C, and 3E show the spatio-temporal reconstruction of the TRSP310 recorded across the scalp after stimulation of the L-DLPFC (Figure 3A), PC (Figure 3C), and L-PPC (Figure 3E). Regardless of the site of stimulation, single-pulse TMS resulted in sustained oscillatory activity lasting approximately 250 ms, showing an initial prominent activation in the beta / gamma band lasting approximately 80 ms and subsequent activation in the alpha / theta band lasting approximately 250 ms. Analyzing at the local level, prominent oscillatory activity, e.g., the eigenfrequency, differed depending on the site stimulated.

[0393] The analysis of TRSP at the global level was performed to evaluate the differences in cortical oscillatory activities across the entire scalp between the AD group and the HV group. When stimulating over the L-DLPFC (Figure 3A), AD patients showed lower gamma frequency (30 - 50 Hz) activities induced from 100 - 150 ms after the TMS pulse in clusters of six bilateral frontal electrodes (Monte Carlo p < 0.01). When stimulating over the PC (Figure 3C), AD patients showed lower gamma frequency (30 - 50 Hz) activities induced from 100 - 150 ms after the TMS pulse in clusters of five bilateral frontal electrodes (Monte Carlo p < 0.01), as well as from 150 - 200 ms after the TMS pulse in two clusters of four frontal electrodes and two posterior medial electrodes (Monte Carlo p < 0.01). Stimulation of the L-PPC (Figure 3E) did not produce any differences in oscillatory activities between the two groups (all p s > 0.05).

[0394] The analysis of TRSP at the local level was performed to evaluate the local oscillatory activities of the stimulated regions (Figure 3B, 3D, and 3F) and measure their dominant frequencies. When stimulating over the L-DLPFC, the oscillatory activity chart at the local level at F3 331 showed that AD patients exhibited lower local gamma waves compared to the HV group (AD: 1.283 ± 0.143 dB, HV: 2.243 ± 0.267 dB; t(79) = -2.977, p = 0.004), while the other frequency bands showed no differences between the two groups (all p s > 0.05). When stimulating over the PC and the L-PPC, the corresponding local level oscillatory activity charts at Pz 341 and P3 351 showed that AD patients showed no differences in oscillatory activities compared to the HV group (all p s > 0.05).

[0395] Figure 4 shows the specificity of reduced gamma waves in the prefrontal cortex of AD patients. Spectral perturbations related to TMS are induced by TMS of L-DLPFC410, PC420, and L-PPC430. The first bar of each pair shows the power spectrum (4 - 50 Hz) in the AD brain, and the second bar shows the power spectrum induced in the HV brain. The light gray boxes indicate the characteristic frequencies of each region. Error bars indicate the standard error. * indicates p < 0.05.

[0396] Figure 4 shows the average TRSP values for each of the 46 frequency layers analyzed after stimulation of the three regions. Stimulation of L-DLPFC410 resulted in a characteristic frequency of 40.8 Hz for both groups. Compared to HV, the AD group showed lower local oscillatory activity at 35 - 46 Hz and thus also included the characteristic frequency of 40.8 Hz (mean p-value = 0.006). Stimulation of PC420 and L-PPC430 resulted in a characteristic frequency of 29.8 Hz for both groups. Compared to HV, the AD group showed no difference in local oscillatory activity in any of the layers considered (all ps > 0.05).

[0397] Linear relationship between clinical and neurophysiological scales Figure 5 is a scatter plot showing the linear relationship between the characteristic frequency of L-DLPFC (x-axis) and clinical scores, which changed 24 weeks after the first evaluation (y-axis), in the Clinical Dementia Rating (CDR)510, Neuropsychiatric Inventory (NPI)520, Alzheimer's Disease Cooperative Study - Activities of Daily Living (ADC-ADL)530, and Alzheimer's Disease Assessment Scale - Cognitive Subscale (ADAS-Cog)540.

[0398] Multivariate regression analysis showed that the stepwise algorithm with two predictors, namely the eigenfrequencies of the DLPFC and PC (CDR510: 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 eigenfrequency of the DLPFC (CDR510: 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), as tested with CDR510, showed that the linear model significantly predicted clinical progression (Figure 5). The variable reduction method of coefficients revealed that the eigenfrequency of the DLPFC was shown to be one of the best predictors for general clinical progression.

[0399] Example 3 Response to electromagnetic perturbation as a marker of protein clearance, neuroinflammation, and altered cognitive function in patients with Alzheimer's disease Background and theoretical basis Evidence from both animal and human studies suggests that enhanced neuronal excitability may represent an early characteristic feature of Alzheimer's disease. Hyperexcitability has been shown to even precede pathophysiological alterations underlying AD, such as the accumulation of amyloid plaques and inflammation. However, the relationship between altered cortical excitability, cognitive abilities, and biomarkers of AD pathology and inflammation remains largely unexplored. Using co-registration of transcranial magnetic stimulation (TMS) and TMS-induced evoked responses (TMS-EEG) via electroencephalogram recording, we quantified in vivo cortical excitability and brain oscillatory activity in patients with AD and examined the possibility of extracting TMS-EEG markers of cerebral neuroinflammation and protein clearance in these patients. TMS-EEG was performed in patients with AD together with amyloid (Aβ1-40 and Aβ1-42), p-tau, and inflammation (IL-6, IL-10, IL-17, and TNFα) biomarkers collected prior to the TMS-EEG session.

[0400] In this example, time and frequency domain analyses of the TMS-evoked potentials (TEPs) of 14 AD patients were performed to explore the relationships between TEPs, cognition, and blood biomarkers of neurodegeneration and inflammation. Stimulation was delivered over three different neuronavigated cortical sites: the left primary motor cortex (L-M1), right dorsolateral prefrontal cortex (R-DLPFC), and right occipital cortex (R-OC). Patients underwent an extensive neuropsychological evaluation at baseline. Blood amyloid (Aβ1-40 and Aβ1-42), p-tau, and inflammation (IL-6, IL-10, IL-17, and TNFα) biomarkers were collected prior to TMS-EEG registration.

[0401] Methods and Samples Patients with mild to moderate dementia due to AD were enrolled in the study. The inclusion criteria were signs or history of memory impairment, a Mini-Mental State Examination (MMSE) score of ≥18 (≥21 in mild AD and =18 - 20 in moderate AD), and a Clinical Dementia Rating (CDR) of ≥0.5. Participants underwent a baseline evaluation that was completed over 5 - 7 days. The baseline evaluation included i) neuropsychological assessment, ii) blood sample collection for the evaluation of neurodegenerative and neuroinflammatory biomarkers, iii) intermittent theta burst stimulation (iTBS) for the evaluation of LTP-like (long-term potentiation) plasticity, iv) resting-state EEG (rsEEG), and v) a session of TMS-EEG co-registration. The iTBS, rsEEG, and TMS-EEG sessions were conducted on three different days to avoid potential bias. All participants provided written informed consent.

[0402] Cognitive and Biomarker Assessments Participants underwent baseline cognitive evaluations to assess overall function [Alzheimer's Disease Assessment Scale - Cognitive Subscale - ADAS-Cog, Mini-Mental State Examination - MMSE, Montreal Cognitive Assessment - MoCA]; language / episodic memory and learning [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 to evaluate the peripheral concentrations of Aβ1-40, Aβ1-42, and p-tau. Plasma BDNF concentration and the levels of peripheral cytokines IL-6, IL-10, IL-17A, and TNFα were also evaluated.

[0403] TMS-EEG Assessments Using the coil of FIG. 8 oriented at an angle of 45 degrees from the midline, TMS was applied to three cortical sites. A T1-weighted MRI volume was used as a positional reference, and the coil was placed over the target area. Participants sat in a comfortable chair and were asked to remain still and attentive 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 a motor evoked potential (MEP) of at least 50 μV in at least 5 out of 10 trials. The stimulation intensity was set at 120% of the RMT using the coil of FIG. 8. Blocks of single TMS pulses were delivered at a stimulation interval of 1 - 4 seconds. EEG activity from the scalp was recorded during the TMS protocol using a TMS-compatible 64-channel (Ag / AgCl electrodes) EEG headset attached to an elastic cap.

[0404] Results Cognitive ability index FIG. 6 shows the results of linear regression between TMS-EEG characteristics and the cognitive ability of the patients. Univariate regression analysis of the time domain, overall TEP extraction characteristics, and cognitive ability index revealed a positive linear correlation between the ADAS-Cog score and the peak amplitude of the second TEP peak (P2) 610, and a negative correlation between the same TEP component after stimulation of the right DLPFC and the RAVLT score 620. Furthermore, the P4 peak amplitude across the right temporal lobe showed a positive correlation with the ADAS-Cog score and a negative correlation with the MoCA score 630 after stimulation. Finally, the RAVLT score showed a negative correlation with the left centro-parietal P2. Interestingly, all of the above results were statically significant after adjusting for the normalized induced electric field calculated via biophysical modeling. Frequency domain analysis of the TEP revealed a positive correlation between the overall theta power 640 and the ADAS-Cog score after stimulation.

[0405] Blood biomarkers of AD pathology Figure 7 shows the results of linear regression between TMS-EEG characteristics, amyloid-β, and inflammatory cytokine plasma levels. Linear regression of the AD pathology time domain, local TEP extraction characteristics, and blood biomarkers showed an inverse correlation between the plasma concentration of Aβ1-40 710 and the P3 peak amplitude over the left frontal lobe, right centro-parietal region, and temporal lobe after DLPFC stimulation. These findings remained significant after controlling for the induced electric field as a covariate in the regression model.

[0406] Blood biomarkers of neuroinflammation Some global and local TEP characteristics showed significant correlations with blood biomarkers of inflammation. More specifically, IL6 730 and TNFα 720 levels showed a positive correlation with the P2 peak amplitude over the left centro-parietal region and temporal lobe regions after stimulation, as shown in Figure 7. Similarly, IL-6, IL-10740, and TNFα showed a positive correlation with the peak amplitudes (P3, P4) of the latest TEP over both sides of the centro-parietal region and temporal lobe regions after stimulation in both the frontal and parietal regions (Figure 7). All of the above results were statically significant after adjusting for the normalized induced electric field.

[0407] Example 4 Combined transcranial magnetic stimulation and electrical stimulation for the evaluation and enhancement of long-term associative memory in patients with Alzheimer's disease Samples Forty-one young healthy participants were recruited. The sample size was estimated based on previous experiments conducted by the authors' group with a similar paradigm. Considering the possibility of dropout due to the large number of sessions required for each subject (i.e., 4), 24 participants were enrolled in Experiment 1. Ten participants were enrolled in Experiment 2 to replicate Experiment 1 and investigate long-term effects. Eighteen participants, including 10 who had already been recruited in Experiment 1, participated in both Experiments 3 and 4.

[0408] It fully complied with the safety guidelines and medical regulations of TMS and tACS. In addition to the criteria pointed out in the safety guidelines (such as history of seizures, metal implants in the head, implanted electronic devices, etc.), left-handedness, claustrophobia, taking antipsychotics, current or past mental disorders, or neuropathy were considered as exclusion criteria. The inclusion criteria included participants who were native speakers or fluent in Italian and had normal or corrected-to-normal vision and hearing.

[0409] Procedure Figure 9 shows an exemplary procedure for testing combined transcranial magnetic stimulation and electrical stimulation for the evaluation and enhancement of long-term associative memory in patients with Alzheimer's disease, including at least four experiments 910, 920, 930, and 940.

[0410] In Experiment 1 910, the behavioral effects of iTBS+γtACS co-stimulation were investigated by testing changes in memory ability in two memory tasks. Participants first underwent an MRI scan 909 to individualize the stimulation site to enable neuronavigation and then participated in a crossover design with three neuromodulation experimental sessions 904 separated by a washout week 905. Each session of the experimental session 904 corresponded to different balanced and randomized stimulation conditions (i.e., iTBS+γtACS, iTBS+sham tACS, sham iTBS+sham γtACS), followed immediately by two memory tasks (see the paragraph "Memory tasks"), namely, the face-name associative task (FNAT) (e.g., 906a - 906d), and the visual short-term memory binding test (STMB) 907.

[0411] Experiment 2. As shown in Experiment 1 910, the study of the behavioral effects investigating the long-term changes in memory ability was deepened by excluding the condition “sham iTBS + sham γtACS” that had no effect, and the experimental design was simplified to have two experimental sessions 904. In fact, the participants participated in a crossover design with two experimental sessions 904 corresponding to different balanced and randomized stimulation conditions (i.e., iTBS + γtACS, iTBS + sham tACS) divided by a washout week 905. In each session 904, the participants were required to attend three experimental test sessions. During the first test session (Day 1), the participants received a neuromodulation protocol 901 and then performed FNAT906a, 906b without a recognition phase and STMB907. In the second test session (Day 2), the participants performed FNAT playback using 906b with a 24-hour delay from the neuromodulation protocol 901, and in the third test session (Day 7), the participants performed FNAT playback 906d and recognition with a one-week delay from the neuromodulation protocol 901. The recognition test was performed only in the third session to avoid learning bias across cue playback on Day 2 and Day 7.

[0412] Experiment 3 930. The neurophysiological effects of the neuromodulation protocol 901 were investigated through the use of the combination of TMS-EEG908. Regarding Experiment 1 910, the participants received an MRI909 scan to individualize the stimulation site and enable neuronavigation. Then, the participants participated in two randomized and balanced experimental sessions 904 of neuromodulation (i.e., iTBS + γtACS, iTBS + sham tACS) divided by a washout week 905. TMS-EEG908 recordings were performed before (T0), immediately after (T1), and 20 minutes after (T2) the neuromodulation protocol 901 (Figure 9C).

[0413] Experiment 4. The functional connectivity effects of the neuromodulation protocol 901 were investigated via 940. fMRI scan 911. After the first MRI scan 909 required for neuronavigation, the participants underwent two randomized and balanced experimental sessions 904 of neuromodulation (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.

[0414] iTBS-γtACS neuromodulation protocol The co-stimulation consisted of a combination of iTBS and tACS delivered in the gamma band (γtACS), based on previous studies. The neuromodulation protocol 901 was delivered to each individual PC, based on their individual resting state anatomy 909 and functional MRI 911 (see "MRI data acquisition and preprocessing" paragraph) targeted with a stereotactic neuronavigation system. The active tACS electrode (anode) 903 was placed on the scalp over which the iTBS coil 902 was positioned, and the other tACS electrode (cathode) 912 was placed on the right shoulder muscle.

[0415] 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 at an intensity of 1 mA, for a total duration of 190 seconds.

[0416] iTBS was delivered via the coil 902 (70 mm) of FIG. 8. iTBS consisted of 10 bursts of 3 pulses lasting for 2 seconds at 50 Hz, repeated every 10 seconds with an 8-second pause between consecutive trainings, for a total of 600 pulses lasting for a total of 190 seconds. The stimulation intensity of iTBS was defined as the lowest intensity capable of generating a motor evoked potential (MEP) of approximately 200 μV in at least 5 out of 10 trials when the participants performed 10% of maximal contraction using visual feedback, and was set at 80% of the active motor threshold (AMT). The AMT was tested over the first dorsal interosseous hot spot of the primary motor cortex (M1) in the left dominant hemisphere using the tACS electrode 903 under the coil 902 to ensure the same scalp-coil distance as the neuromodulation protocol. Electromyographic activity was recorded from the contralateral FDI muscle using two Ag-AgCl surface cup electrodes (9 mm) in the belly tendon method.

[0417] To control for the individual contributions of technique and placebo effects, sham stimulation conditions were implemented. To give the participants a genuine sense of stimulation, a tACS sham condition was implemented by applying only a 2-second ramp-up and a 2-second ramp-down. The iTBS sham condition was performed by adding a wood layer under the coil.

[0418] Memory task Face-Name Association Task (FNAT). The first task, which was run in both Experiment 1 910 and Experiment 2 920, was the FNAT, a cross-modal associative memory test that required participants to pair photographs of unfamiliar faces with common names and occupations. For this test, FNAT906 was adapted to create three parallel formats by obtaining photographs from an online dataset (faces) and associating them with Italian names and occupations. Two versions of the task were used. The first version, implemented in Experiment 1 910, consisted of an immediate cue recall 906a and a 15-minute delayed cue recall 906b with recognition following the learning phase. The second version, implemented in Experiment 2 920, consisted of a learning phase on Day 1, an immediate cue recall 906a and a 15-minute delayed recall 906b, a 24-hour delayed cue recall 906c on Day 2, and a 1-week delayed cue recall 906d with recognition on Day 7.

[0419] The learning phase consisted of presenting 12 faces related to names and occupations on a PC screen for a certain period (e.g., up to 8 seconds or less than 8 seconds), asking the participants to read out the names and occupations aloud, ensuring that the participants' attention was focused on the items, and instructing them to memorize the face-name-occupation associations. Immediately after the learning phase occurred, cue recall was conducted, during which the participants were asked to recall the names and occupations of each face that was again presented for 8 seconds. The delayed cue recall consisted of presenting previously shown faces and asking the participants to say aloud the names and occupations of each face. Finally, during the recognition test, the participants first had to recognize the tested face from another distracting face with a matching age and gender. Then, for those names and / or occupations that were not correctly recalled in the previous delayed cue recall, the participants had to identify the correct name and / or occupation from among three alternative options each, and the distracting options were new names / occupations and names / occupations related to different faces.

[0420] For each task phase, three accuracy metrics were derived: immediate cue playback 906a of Experiment 1910, delayed cue playback 906b, and recognition; immediate cue playback 906a; delayed cue playback 906b (day 1); 24-hour delayed cue playback 906c (day 2); 1-week cue playback and recognition 906d (day 7): total of names played or recognized / 12 × 100; total of occupations played or recognized / 12 × 100; total of items fully played or recognized / 12 × 100 (i.e., both name and occupation were played / recognized).

[0421] Associative Test in Short-Term Memory (STMB). This test was conducted between the FNAT immediate cue playback 906a and the 15-minute delayed cue playback 906b. STMB907 is a recognition task that relies on a change detection paradigm, which requires participants to memorize a visual alignment of three black shapes (shape-only condition) or colored shapes (shape-color association condition) presented over a certain period (e.g., at least 2 seconds or less than 2 seconds) (learning phase). After a 1-second delay during which a blank screen is displayed (retention interval), a display containing the same or different items in new random positions on the screen is shown (test phase). Participants were asked to press the "1" button on the keyboard if the items shown in the learning and test phases were different (50% of the test), and "2" if they were the same. A total of 32 randomized tests were presented for each condition. The conditions were balanced between participants. Before starting the test, each participant underwent a perception test in which two alignments of shapes were displayed on the same screen to rule out perceptual impairments and train the participants to respond on the keyboard. Two accuracy metrics were derived for each of the shape-only condition and the shape-color association condition (total score for each condition / 32 × 100).

[0422] TMS-EEG Neurophysiological Evaluation Local cortical oscillations and connectivity changes were evaluated using single-pulse TMS during EEG recording. During TMS-EEG evaluation, participants sat in a comfortable armchair in a soundproof room and were instructed to stare at a black cross on the wall, and wore plugs in their ears that played continuous white noise to avoid potential responses related to auditory events. The intensity of the white noise was adjusted individually by increasing the volume (always below 90 decibels) until the participant was confident that they could no longer hear the TMS-evoked click sound. TMS for EEG recording was performed using the same stimulator as the neuromodulation protocol 901. The areas stimulated were the PC and the lPPC. The order of the stimulation areas was balanced across participants. Both the PC and the l-PPC were identified by individual resting-state architecture 909 and functional MRI 911 (see the "MRI Data Acquisition and Preprocessing" paragraph). The position of the coil 902 was continuously monitored using a neuronavigation system and oriented differently according to the area stimulated so that the direction of the current flow in the most effective (second) phase was in the posterior-anterior direction. To target the PC, the coil 902 was placed in an orientation parallel to the midline, while to target the l-PPC, the coil 902 was placed in an orientation 15 degrees from the midline. The single-pulse TMS intensity was set at 110% of the resting motor threshold (RMT), defined as the minimum intensity producing MEP > 50 μV in at least 5 out of 10 trials in the relaxed FDI muscle of the right hand. Each TMS-EEG block consisted of 90 single pulses with 2 - 42 randomized inter-stimulus intervals (ISIs).

[0423] EEG continuously recorded from 61 scalp sites was placed according to the 10 - 20 International System using TMS-compatible Ag / AgCl pellet electrodes attached to an elastic cap 913. Additional electrodes were used for ground and reference. The ground electrode was placed at Fpz and the reference was placed at the tip of the nose. The EEG signal was digitized at a sampling rate of 5 kHz. The skin / electrode impedance was maintained below 5 kΩ.

[0424] EEG processing. The TMS-EEG data was preprocessed offline. The data was segmented into epochs starting 1 second before and 12 after the TMS pulse. The TMS pulse artifacts were removed and replaced using cubic interpolation from 1 ms before to 10 ms after the pulse. Subsequently, the data was downsampled to 1,000 Hz and band-pass filtered at 1 - 80 Hz (Butterworth zero-phase filter). A 50 Hz notch filter was applied to reduce noise from the power supply. Then, all epochs were visually inspected and overly noisy EEG was excluded from the analysis. Independent component analysis (INFOMAX-ICA) was applied to the EEG signals 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 criteria. Finally, the signal was re-referenced to the average signal of all electrodes.

[0425] Cortical oscillations. The analysis of cortical oscillations was performed by carrying out time / frequency decomposition based on the Morlet wavelet (cycles: 5; frequency resolution: 1 Hz up to 4 - 90 Hz) and calculating the TMS-related spectral perturbation (TRSP) for the theta, alpha, beta, and gamma bands. To analyze the 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 stimulation region, the corresponding clusters were processed computationally and the TRSP values between 10 - 250 ms (see the "Results" paragraph as this is the average time window for spectral perturbation) were averaged according to the frequency bands of theta (4 - 7 Hz), alpha (8 - 13 Hz), beta (14 - 30 Hz), low gamma (31 - 40 Hz), and high gamma (41 - 90 Hz).

[0426] Connectivity. Connectivity analysis was performed by calculating the wavelet phase-locking value (PLV), a measure of phase synchrony in the range of 0 (complete lack of phase synchrony) to 1 (perfect phase synchrony). To evaluate the effect of the neuromodulation protocol on DMN connectivity, PLV was computed between the PC cluster (see the "Cortical Oscillations" section) and the frontomedial cluster (FM) composed of Fz, AFz, F1, and F2. To verify network effect specificity, PLV was also computed across the PC cluster and the l-PPC cluster. All computations were performed for each frequency band.

[0427] Cortical excitability was evaluated using transcranial magnetic stimulation-evoked potentials (TEPs), which were computed by averaging all time-locked EEG responses at each electrode from 100 ms before to 250 ms after the TMS pulse, with baseline correction at 100 ms before the TMS pulse. Pz-TEP was computed to evaluate the local effect of the neuromodulation protocol. Six peaks were determined through visual inspection of the TEP waveform: P1 at 10 - 25 ms, P2 at 26 - 40 ms, P3 at 41 - 55 ms, P4 at 56 - 65 ms, P5 at 66 - 85 ms, and P6 at 86 - 100 ms. The mean TEP amplitude was calculated within each time window.

[0428] Acquisition and preprocessing of MRI data MRI data were acquired to 1) identify and individualize the stimulation target prior to each experiment and 2) evaluate the effect of the neuromodulation protocol before and after the protocol (see the "Procedure" paragraph).

[0429] The structural imaging session was acquired using high-resolution T1-weighted (T1) anatomical images obtained via a 3D-T1w sequence (TR = 2500 ms, TE = 2 ms, TI = 1070 ms, flip angle (FA) = 8°, thickness = 1 mm, imaging matrix = 240×240). fMRI images were acquired using standard echo-planar blood oxygenation level-dependent (BOLD) imaging (TR = 3200 ms, TE = 25 ms, flip angle (FA) = 90°, thickness = 2.5 mm, gap = 2.5 mm, acquisition period: 6,46’’). Subjects were instructed to keep their eyes open, not to focus their thoughts on a particular topic, and not to cross their arms or legs.

[0430] For the individuation of the stimulation sites, the regions of interest (ROIs) of interest are represented by the PC node of the DMN and the L-PPC node of the fronto-parietal network (FPN). By computationally processing a seed-voxel correlation map 1200 for each participant, maps of positively and negatively correlated voxels representing the DMN and FPN respectively are obtained. MRI experts checked both the resting-state functional connectivity (rs-FC) map and the structural MRI data (i.e., T1-weighted images) to identify individual hotspots based on ad hoc criteria. In particular, the stimulation site is defined as being as close as possible to the local maximum of the rs-FC cluster identified as DMN-PC, and the FPN-PPC lies on the cortical gyrus and represents the shortest vertical path connecting the stimulating TMS coil 902 on the scalp and the cortex. Based on best judgment, the set of coordinates obtained was selected as the individual stimulation sites. The individual sets of coordinates were then transformed using a non-linear transformation to reconstruct the targets within the individual brain spaces. Finally, to ensure consistent in-session and between-session stimulation, the individualized targets were labeled on the anatomical MRI of the subject loaded into the neuronavigation system.

[0431] Data analysis Before undergoing parametric or non-parametric statistical processing, the assumption of the normal distribution of data residuals was evaluated by the Shapiro-Wilk test. The assumption of sphericity was verified by the Mauchly test, and the Huynh-Feldt correction was used if this test was significant. The significance level was set at α = 0.05.

[0432] To evaluate memory ability in Experiment 1 910, for each dependent variable, a repeated measures ANOVA was performed using the stimulation condition as a within-subject factor (i.e., iTBS+γtACS, iTBS+ sham tACS, sham iTBS+ sham tACS). Specifically, separate ANOVAs were conducted for each FNAT measurement (i.e., name, occupation, total) in each memory process (i.e., intermediate reproduction 906a, delayed reproduction 906b, and recognition). Also, separate ANOVAs were performed for STBM 907 accuracy and RT for both shape and association conditions.

[0433] To evaluate memory ability in Experiment 2 920, a repeated measures ANOVA was performed using the stimulation condition as a within-subject factor (iTBS+γtACS, iTBS+ sham tACS). Similar to Experiment 1 910, for each dependent variable, separate ANOVAs were conducted except for the FNAT delayed reproduction ability, which was analyzed by a repeated measures ANOVA using the stimulation condition (i.e., iTBS+γtACS, iTBS+ sham tACS) and time (i.e., day 1, day 2, day 7) as within-subject factors. Following the results obtained from this analysis (see the "Results" paragraph), a t-test with a corresponding adjustment was performed to examine the effect of the stimulation condition at each time point.

[0434] To evaluate changes in both cortical oscillations and connectivity in Experiment 3 930, a repeated measures ANOVA was calculated using the stimulation condition (iTBS+γtACS, iTBS+ sham tACS) and time T1-T0, T2-T0 (ΔT1, ΔT2) as within each frequency band factor. Also, in this case, a paired t-test was performed to examine the effect of the stimulation condition at each time point (T0, T1, T2).

[0435] To evaluate the cortical excitatory effects, repeated measures ANOVA was calculated using stimulation conditions (iTBS+γtACS, iTBS+ sham tACS), time (T0, T1, T2), and peaks (P1, P2, P3, P4, P5, P6) as within-subject factors. Post hoc comparisons were performed using paired t-tests corrected by the Bonferroni method.

[0436] To evaluate the potential predictive role of baseline levels of neurophysiological output (i.e., cortical oscillatory activity, connectivity, cortical excitability) on memory ability, stepwise linear regression was performed using a forward algorithm.

[0437] A general linear model (GLM) was used to test whether the stimulation conditions modulated the participants' resting-state functional connectivity (rsFC). Statistical analysis was performed considering stimulation conditions (i.e., iTBS+γtACS, iTBS+ sham tACS) and time points (i.e., before and after) as factors. Specifically, seed-voxel analysis considering the PC (i.e., the stimulation area) as a seed was computer-processed. The temporal correlation between this seed and all other voxels in the brain was calculated. Age, gender, and the order of stimulation were included as covariates in all analyses. The results were computer-processed by applying cluster size correction (false positive rate) and an alpha of 0.01.

[0438] Results All different stimulation protocols had good tolerance. No participants reported significant side effects related to the application of the neuromodulation protocol.

[0439] iTBS-γtACS for selectively improving long-term associative memory In Experiment 1910, the effects of three stimulation conditions (i.e., iTBS+γtACS, iTBS+ sham tACS, sham iTBS+ sham tACS) on memory ability were investigated. A main effect was found for the iTBS+γtACS condition on long-term associative memory ability investigated via FNAT906a, 906b. Specifically, the total FNAT, as illustrated by Graph 1010 (F2,38 = 6.81; p = 0.003; η 2 p = 0.264), was found to differ in both immediate-type FNAT recall 906a ability and, as illustrated by Graph 1020 (F2,38 = 6.35; p = 0.009; η 2 p = 0.25), delayed-type FNAT recall 906b ability. Post hoc analysis revealed that in iTBS+γtACS compared to iTBS+ sham tACS, higher total FNAT scores were evident in immediate recall (40.4±22.5% vs. 25.4±19%; post hoc p = 0.011) (Figure 10A), while an increase was seen in iTBS+γtACS delayed recall compared 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). Furthermore, an effect on FNAT name delayed recall 906b was seen (F2,38 = 3.46; p = 0.042; η 2 p = 0.154), with an increase in iTBS+γtACS compared to sham-iTBS+ sham tACS (42.9±21.5% vs. 33.8±19%; p = 0.048), and a trend was observed for immediate recall 906a (F2,38 = 3.2; p = 0.052; η 2 p = 0.144) (Figure 10). These stimulation condition effects were selective for long-term associative memory investigated via FNAT906a, 906b, and no effect was seen on visual short-term memory investigated via STMB RT1030 and accuracy 1040 (all, ps>0.05) (Figure 10B).

[0440] iTBS-γtACS has a lasting effect on associative memory To investigate the long-term effects of neuromodulation protocols on memory ability and reproduce the results of Experiment 1910, Experiment 2920 was conducted, in which delayed FNAT cues 906c were added 24 hours after the neuromodulation protocol 901 and 906d one week later. For Experiment 1910, as shown in the immediate reproduction result graph 1110 of Figure 11A, an effect of iTBS + γtACS on the memory ability of total immediate FNAT906a was seen, with an increase in ability after iTBS + γtACS compared to the control condition iTBS + sham tACS (26.7 ± 10.2% vs. 17.5 ± 8.3%; p = 0.024) (F1,9 = 7.31; p = 0.024; η 2 p = 0.448. More importantly, the stimulation condition was found to have a persistent effect on the total delayed FNAT ability (F1,9 = 8.433; p = 0.017; η 2 p = 0.484), as shown in the name result graph 1130, occupation result graph 1140, and total result graph 1050 of Figure 11B. Exploratory analysis revealed that such effects persisted up to one week after neuromodulation (Day 1906b: 25 ± 9.6% vs. 14.2 ± 9.7%; t9 = -2.751; p = 0.011; Day 2906c: 25.8 ± 10.7% vs. 15 ± 9.5%; t9 = -2.899; p = 0.009; Day 7906d: 23.3 ± 12.9% vs. 14.2 ± 12.5%; t9 = -2.400; p = 0.020).

[0441] Similar to Experiment 1910, no effects on FNAT906a, 906b recognition, and STMB907 accuracy, and RT (all ps > 0.05) were seen.

[0442] iTBS-γtACS increases gamma oscillations and the medial parietal-frontal connection Experiment 3930 aimed to investigate the effects of neuromodulation protocol 901 on cortical oscillations and connectivity by means of TRSP and PLV, respectively. Analysis of local cortical oscillations demonstrated the main effect of neuromodulation protocol 901 on gamma TRSP (F1, 13 = 5.073; p = 0.042; η 2 p = 0.281). Specifically, an increase in gamma-TRSP was observed after iTBS + γtACS compared to iTBS + sham tACS at both time points (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 stimulation of the PC, and indeed, no significant effect was seen across the lPPC (p > 0.05).

[0443] Analysis of connectivity demonstrated a significant effect of neuromodulation protocol 901 on PLV in the gamma frequency band (F1, 13 = 5.07; p = 0.042; η 2 p = 0.281), and in the alpha frequency band (F1, 13 = 13.32; p = 0.003; η 2 p = 0.506). Specifically, an increase in gamma PLV was observed 20 minutes after iTBS + γtACS compared to iTBS + sham tACS (ΔT2: 0.06 ± 0.11 vs. -0.03 ± 0.09; t13 = 2.53; p = 0.013), with a trend observed immediately afterwards (ΔT1: 0.07 ± 0.11 vs. 0.00 ± 0.11; t13 = 1.5; p = 0.07). On the other hand, alpha PLV increased both immediately after and 20 minutes after iTBS + γtACS compared to iTBS + sham tACS (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). Furthermore, a trend towards a stimulatory effect was seen across the computer-processed PLV indices in theta (F1, 13 = 4.309; p = 0.058; η 2 p = 0.249) and beta (F1, 13 = 4.583; p = 0.052; η 2 p = 0.261).

[0444] iTBS-γtACS increases functional connectivity In Experiment 4940, the effect of neuromodulation protocol 901 on functional connectivity was investigated by MRI analysis. Using the stimulated area (i.e., PC) as a seed, computer processing of seed-based functional connectivity analysis was performed. The analysis revealed a significant stimulus condition × time interaction on rs-FC between PC and bilateral insular cortex, cuneate cortex, prefrontal orbital cortex, and cingulate gyrus (Figure 12A). Furthermore, a main effect of stimulus condition was observed within PC and bilateral insular cortex, left cortex, and cingulate gyrus (Figure 12B). To further investigate the interaction effect, pairwise comparisons were performed to reveal the increase in rs-FC in the insular cortex, prefrontal orbital cortex, and cingulate gyrus after iTBS+γtACS when compared with the same condition before stimulation (Figure 12C). At the same time, a decrease in rs-FC in bilateral insular cortex and cuneate cortex after the iTBS+ sham tACS condition was observed when compared with pre-stimulus rs-FC (Figure 12D). No significant differences were found between groups before stimulation.

[0445] Figures 12A-D show the seed-voxel results of Experiment 4940 with significant results at p<0.02 (FDR corrected) shown on standard MNI maps for A) interaction between group and condition, B) main effect of condition, C) iTBS+γtACS: post>pre, D) iTBS+ sham tACS: post>pre. The color bar represents the F value or t value.

[0446] Computer system The present disclosure provides a computer system programmed to implement the methods of the present disclosure. FIG. 13 shows a computer system 1301 programmed or configured to perform one or more steps of the methods described herein and / or to control one or more aspects of the systems and methods described herein. The computer system 1301 can control various aspects of the present disclosure, such as, for example, applying a stimulus to a patient, recording a patient's biosignals, analyzing the recorded signals and / or one or more other inputs, and generating an output based on the recorded signals and signal analysis.

[0447] The computer system 1301 can be a user's electronic device or a computer system remotely located with respect to the electronic device. The electronic device can be a mobile electronic device.

[0448] The computer system 1301 includes a central processing unit (CPU, also referred to herein as "processor" and "computer processor") 1305, which can 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 device 1315 (e.g., hard disk), a communication interface 1320 (e.g., network adapter) for communicating with one or more other systems, and peripheral devices 1325 such as caches, other memories, data storage, and / or an electronic display adapter. The memory 1310, storage device 1315, interface 1320, and peripheral devices 1325 communicate with the CPU 1305 via a communication bus (solid lines), such as a motherboard. The storage device 1315 can be a data storage device (or data repository) for storing data. The computer system 1301 can be operably coupled to a computer network ("network") 1330 with the assistance of the communication interface 1320. The network 1330 can be the Internet, the Internet and / or an extranet, or an intranet and / or an extranet that communicates with the Internet. The network 1330 can be, in some cases, a telecommunications and / or data network. The network 1330 can include one or more computer servers that enable distributed computing, such as cloud computing. The network 1330 can, in some cases, implement a peer-to-peer network with the assistance of the computer system 1301, thereby enabling devices connected to the computer system 1301 to operate as clients or servers.

[0449] CPU 1305 can execute a sequence of machine-readable instructions that can be embodied in a program or software. The instructions can be stored in a memory location such as memory 1310. The instructions can be directed to CPU 1305 and then CPU 1305 can be programmed or configured to implement the methods of the present disclosure. Examples of operations performed by CPU 1305 can include fetch, decode, execute, and write-back.

[0450] CPU 1305 can be part of a circuit such as an integrated circuit. One or more other components of system 1301 can be provided within the circuit. In some cases, the circuit is an application specific integrated circuit (ASIC).

[0451] Storage device 1315 can store files such as drivers, libraries, and saved programs. Storage device 1315 can store user data, such as user preferences and user programs. In some cases, computer system 1301 can include one or more additional data storage devices external to computer system 1301, such as located on a remote server that communicates with computer system 1301 via an intranet or the Internet.

[0452] The computer system 1301 can communicate with one or more remote computer systems via the network 1330. For example, the computer system 1301 can communicate with a user's remote computer system. Examples of remote computer systems include personal computers (e.g., portable PCs), slates or tablet PCs (e.g., Apple® iPad®, Samsung® Galaxy Tab), telephones, smartphones (e.g., Apple® iPhone®, Android®-compatible devices, Blackberry®), or personal digital assistants. A user can access the computer system 1301 via the network 1330.

[0453] The methods described herein can be implemented, for example, by machine (e.g., computer processor) executable code stored in an electronic memory location of the computer system 1301 such as the memory 1310 or the electronic storage device 1315. The machine executable code or machine readable code can be provided in the form of software. In use, the code may be executed by the processor 1305. In some instances, the code can be retrieved from the storage device 1315 and stored in the memory 1310 for immediate access by the processor 1305. In some situations, the electronic storage device 1315 can be excluded and the machine executable instructions can be stored on the memory 1310.

[0454] The code can be pre-compiled and configured for use on a machine having a processor adapted to execute the code, or can be compiled at runtime. The code can be provided in a programming language selected to enable the code to be executed in a pre-compiled or compiled fashion.

[0455] Aspects of the systems and methods provided herein, such as computer system 1301, may be embodied in programming. Various aspects of the technology may typically be considered as a "product" or "article" in the form of machine (or processor) executable code and / or associated data carried or embodied in a type of machine-readable medium. The machine executable code can be stored on an electronic storage device such as a memory (e.g., read-only memory, random access memory, flash memory) or a hard disk. A "storage" type medium can comprise any or all of tangible memories such as computers, processors, or associated modules such as various semiconductor memories, tape drives, disk drives, etc., and can provide non-transitory storage at any time for software programming. All or part of the software can sometimes be communicated via the Internet or various other communication networks. Such communication can enable, for example, the loading of software from one computer or processor to another, such as from a management server or host computer to an application server computer platform. Thus, another type of medium that can carry software elements can be used, including light waves, radio waves, and electromagnetic waves, such as via a physical interface between local devices, via wired and optical landline networks, and via various air links. Physical elements that carry such waves, such as wired or wireless links, optical links, etc., can also be considered media for carrying software. As used herein, the term such as a computer or machine "readable medium" refers to any medium that participates in providing instructions to a processor for execution, unless limited to non-transitory tangible "storage" media.

[0456] Accordingly, machine-readable media such as computer-executable code can take many forms, including, but not limited to, tangible storage media, carrier wave media, or physical transmission media. Non-volatile memory media includes, for example, any optical or magnetic disk such as any computer storage device that can be used to implement, among other things, the databases shown in the drawings. Volatile memory media includes dynamic memory such as the main memory of such a computer platform. Tangible transmission media includes coaxial cables, copper wire, and fiber optics, including the wires that make up a bus within a computer system. Carrier wave transmission media can take the form of electrical or electromagnetic signals such as those generated during radio frequency (RF) and infrared (IR) data communications, or in the form of acoustic or light waves. Thus, common forms of computer-readable media include, for example, floppy (registered trademark) disks, flexible disks, hard disks, magnetic tape, any other magnetic media, CD-ROM, DVD or DVD-ROM, any other optical media, punch cards, paper tape, any other physical storage media with patterns of holes, RAM, ROM, PROM, and EPROM, FLASH-EPROM, any other memory chip or cartridge, carrier waves carrying data or instructions, cables or links carrying such carrier waves, or any other media from which a computer can read programming code and / or data. Many of these forms of computer-readable media can be involved in carrying one or more sequences of one or more of the instructions to a processor for execution.

[0457] Computer system 1301 includes, or can communicate with, an electronic display 1335 that includes a user interface (UI) 1340 for providing, for example, visual representations of stimulation parameters, graphical representations of recorded response signals, displays of non-invasive assessments of the state of a human brain, and the like. Examples of UIs include, but are not limited to, graphical user interfaces (GUIs) and web-based user interfaces.

[0458] The methods and systems of the present disclosure can be implemented by one or more algorithms. The algorithms can be implemented by software when executed by a central processing unit 1305. The algorithms can, for example, control a stimulation protocol, perform data analysis, and generate an output based on the data analysis.

[0459] Preferred embodiments of the present disclosure are shown and described herein, but it will be apparent to those skilled in the art that such embodiments are provided by way of example only. It is not intended that the present disclosure be limited by the specific examples provided within this specification. Although the present disclosure has been described with reference to the foregoing specification, the description and illustration of the embodiments herein are not intended to be construed in a limiting sense. Numerous variations, modifications, and substitutions can occur to those skilled in the art without departing from the scope of the present disclosure. Furthermore, it should be understood that all aspects of the present disclosure are not limited to the specific depictions, configurations, or relative proportions described herein, which depend on various conditions and variables. It should be understood that various alternatives to the embodiments described herein can be used in practicing the present disclosure. Accordingly, the present disclosure is also intended to cover any such alternatives, modifications, variations, or equivalents. The following claims define the scope of the invention, and it is intended that the methods and structures within the scope of these claims and their equivalents be covered thereby.

Claims

1. A system for use in a method of enhancing memory in a subject, comprising a transcranial magnetic stimulation (TMS) coil and transcranial alternating stimulation (tACS) electrodes, wherein the method is: Applying a combination of TMS and tACS to a first brain region within the target brain for a first period of time. Including, here, The first brain region mentioned above represents a functional brain network related to memory, The aforementioned tACS is applied using a target frequency in the gamma frequency range. Applying a combination of TMS and tACS to the first brain region in the target brain for a first period of time is During the first period, tACS is continuously applied as a sine wave, and Applying TMS as a set of stimulation pulse trains separated by non-stimulation periods. A system that includes this.

2. The system according to claim 1, wherein the application of TMS includes the application of intermittent theta burst stimulation (iTBS).

3. The method described above, Based on multiple evoked potentials, the stimulation location within the first brain region for stimulating the target is identified. It further includes, Applying the combination of TMS and tACS to the first brain region includes applying the combination of TMS and tACS at the stimulation site within the first brain region. The system according to claim 1.

4. The system according to claim 3, wherein applying the combination of TMS and tACS at the stimulation site in the first brain region includes using neuronavigation to track the stimulation site while applying the combination of TMS and tACS.

5. The system according to claim 1, wherein the target frequency is between 60 and 80 Hz.

6. The system according to claim 1, wherein applying TMS to the first brain region includes applying a set of bursts of magnetic pulses in the gamma frequency range.

7. The system according to claim 1, wherein the target frequency of the tACS stimulation is approximately 70 Hz.

8. The system according to claim 1, wherein the length of each of the non-stimulation periods is approximately four times longer than the length of each stimulation pulse in the set of stimulation pulses.

9. Applying a combination of TMS and tACS to a first brain region in the target brain for a first period of time, Placing tACS electrodes on the scalp of the subject; Arranging the coil shown in Figure 8 adjacent to the tACS electrode; and The tACS electrode and the controller connected to the coil in Figure 8 are used to control the stimulation transmitted through the tACS and the coil in Figure 8. The system according to claim 1, including the following:

10. The system according to claim 1, wherein the first brain region includes the precuneus region or the left posterior parietal cortex region.

11. The system according to claim 1, further comprising measuring the enhanced long-term memory capacity of the subject by a memory task performed by the subject after the application of the combination of TMS and tACS.

12. The system according to claim 11, wherein the memory task is a face-name association task or a short-term memory association test.

13. A memory augmentation system, Transcranial magnetic stimulation (TMS) coil; At least one transcranial alternating stimulator (tACS) electrode configured to be positioned on the scalp of the subject; and A controller configured to activate the TMS coil and the at least one tACS electrode in accordance with a stimulation protocol for applying stimulation to a first brain region in the target brain for a first period of time. Equipped with, The first brain region mentioned above represents a functional brain network related to memory, tACS is applied using a target frequency in the gamma frequency range. Applying the stimulus to the first brain region in the target brain for a first period of time is, During the first period, tACS is applied continuously as a sine wave, and Applying TMS as a set of stimulation pulse trains separated by non-stimulation periods. A memory enhancement system, including...

14. The memory enhancement system according to claim 13, wherein activating the TMS coil comprises activating the TMS coil to apply intermittent theta burst stimulation (iTBS) during the first period.

15. An electroencephalogram (EEG) recording system configured to record a plurality of evoked potentials from the brain of the subject, and A neuronavigation system configured to track the location of a stimulus during the application of the TMS and tACS during the first period. Furthermore, The controller, Based on the plurality of evoked potentials, the stimulation location within the first brain region for stimulating the target is identified. The TMS coil and the at least one tACS electrode are activated to apply stimulation to the stimulation location based at least partially on information provided by the neuronavigation system. The memory augmentation system according to claim 13, further configured as follows.

16. The memory enhancement system according to claim 13, wherein the length of each of the non-stimulation periods is approximately four times longer than the length of each stimulation pulse in the set of stimulation pulses.

17. The memory enhancement system according to claim 13, wherein the first brain region includes the precuneus region or the left posterior parietal cortex region.

18. The memory augmentation system according to claim 13, wherein the target frequency is between 60 and 80 Hz.

19. The memory augmentation system according to claim 13, wherein applying TMS to the first brain region includes applying a set of bursts of magnetic pulses in the gamma frequency range.

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