Method and system for activating and analyzing the default mode network of a subject for neurophysiological data analysis, pattern identification and characterization

Abstract

The invention relates to a software-based method for activating and analyzing a neural network state of a subject for neurophysiological data analysis, pattern identification and characterization involving, the method comprising the steps of: (i) administering a resting-state condition during which the subject's brain activity is measured in the absence of rhythmic sensory stimulation or task; (ii) administering to the subject a sensory stimulation, possibly supplied with a cognitive task, selected from the group consisting of auditory stimulation, visual stimulation, haptic stimulation, electrical stimulation, and magnetic stimulation modalities such as transcranial alternating current stimulation, subauditory stimulation devices, functional electric stimulation, and skin-applied stimulation systems, or any combination thereof, at a frequency between at least 0.01 Hz and at most 120 Hz to induce rhythmic brain responses; (iii) measuring the subject's brain responses with one or more brain imaging or recording devices; (iv) analyzing the measured brain responses by performing a software analysis that generates either a brain topography or a network connectivity map in response to the sensory stimulation and a brain topography or connectivity map of the default mode network during the resting state; and (v) comparing the obtained brain topographies or functional connectivity maps to a database of normative data in order to identify abnormalities.

Description

[0001] METHOD AND SYSTEM FOR ACTIVATING AND ANALYZING THE DEFAULT MODE NETWORK OF A SUBJECT FOR NEUROPHYSIOLOGICAL DATA ANALYSIS, PATTERN IDENTIFICATION AND CHARACTERIZATION

[0002] FIELD OF THE INVENTION

[0003] The present invention relates to a method and system for analyzing brain activity of a subject. More particularly, this invention addresses advanced solutions for neurophysiological data analysis, pattern identification and characterization involving administering various forms of non-invasive sensory stimulation and measuring corresponding changes in brain activity through a variety of imaging or recording techniques.

[0004] BACKGROUND

[0005] Neurological disorders such as Alzheimer's disease are marked by changes in brain networks such as in the default mode network (DMN), a network of interacting brain regions that exhibits unique patterns of correlated activity, differentiating it from other task-focused or externally oriented brain networks. The term "default mode" reflects the observation that these interconnected regions demonstrate increased activation when individuals are not engaged in attention-demanding tasks or external stimuli, but rather when thinking inwardly or passively mind-wandering.

[0006] The DMN has been the subject of extensive neuroscientific research due to its strong association with core cognitive processes such as introspection, autobiographical memory, self- referential thought, future planning, and moral reasoning. Its robust involvement in high-level mental functions underscores its significance in both typical cognition and a variety of psychiatric and neurological conditions.

[0007] However, when the subject is not at rest, other brain networks such as the sensorimotor-, visual-, attention- and language networks, are activated depending on the task or applied endogenous stimuli. These paradigms require the active involvement of the subject and deviations in the activity and connectivity of these networks can be related to neurological disorders such as memory decline, aphasia, attention disorders, motor and cognitive impairments, etc. Composition of the Default Mode Network

[0008] The DMN comprises several distinct but interconnected brain regions:

[0009] 1. Medial Prefrontal Cortex (mPFC): This region is important for self- referential thought processes, social cognition, and aspects of decisionmaking. It contributes to the monitoring and evaluation of internal states and personal experiences.

[0010] 2. Posterior Cingulate Cortex (PCC) and Precuneus: These areas are linked to autobiographical memory, self-awareness, and the integration of emotional and sensory information into a cohesive experience. They appear to function as key integration hubs in the DMN.

[0011] 3. Angular Gyrus: Often implicated in language processing and episodic memory retrieval, the angular gyrus serves an integrative role by linking sensory and conceptual information, thus supporting reflective thought and language-based cognition.

[0012] 4. Hippocampus: Essential for the formation, consolidation, and retrieval of memories, the hippocampus underlies the DMN's role in remembering past experiences and imagining or planning future events.

[0013] Functional Roles of the DMN

[0014] The DMN is associated with several core functions that define our subjective sense of self and our ability to imagine hypothetical scenarios. First, it underpins self- referential thought, which emerges when individuals reflect upon their own emotions, traits, or personal narratives. Second, it plays a major role in autobiographical memory, enabling individuals to recall past personal events. Third, it is implicated in prospection, or future planning, by helping individuals envision and strategize about hypothetical or forthcoming scenarios. Fourth, the DMN participates in theory of mind, facilitating the ability to attribute mental states to others and to predict or interpret the motivations of those around us. Fifth, it also contributes to moral reasoning, affecting how individuals contemplate ethical or moral dilemmas, and to creative thinking by integrating memories, associations, and imaginative constructs. Finally, the DMN exhibits prominent activity during mind-wandering and daydreaming, when attention drifts away from the external environment. Relevance to Neurological and Psychiatric Conditions

[0015] Because of its pivotal function in memory, self-related thought, and planning, the DMN has been linked to many disorders:

[0016] 1. Alzheimer's Disease (AD): Characterized by progressive decline in memory and other cognitive domains, AD often shows early disruptions in brain network activity. Elevated amyloid-beta deposition in DMN hubs further indicates that the DMN may play a central role in disease progression.

[0017] 2. Mild Cognitive Impairment (MCI): A clinical syndrome characterized by cognitive decline greater than expected for age. Amyloid-beta positivity is used to distinguish MCI due to AD from other neurological causes such as Vascular Dementia and Parkinson's disease. It exhibits changes in DMN connectivity among that of other brain networks.

[0018] 3. Autism Spectrum Disorder (ASD): Studies have shown changes in large- scale brain networks, including disrupted connectivity, which may be linked to social cognition and empathic processing difficulties.

[0019] 4. Depression: Changes in multiple large-scale brain networks has been implicated in rumination and negative self-focus, common cognitive features in major depressive disorder.

[0020] 5. Schizophrenia: Changes in multiple large-scale brain networks have been related to psychotic symptoms, disorganized thinking, and deficits in selfrelated processing.

[0021] 6. Attention-Deficit / Hyperactivity Disorder (ADHD): disruptions in large- scale brain networks, especially disruptions in DMN connectivity during tasks requiring sustained attention, have been correlated with key ADHD symptoms.

[0022] Limitations in Current Assessment Methods

[0023] Despite the importance of brain networks such as DMN, existing techniques for measuring their activity or modulating them face significant limitations. Many standard approaches rely on passive resting-state fMRI scans or electroencephalography (EEG). These approaches are purely observational, lacking mechanisms to actively stimulate or entrain specific brain networks. Some more invasive methods, such as intracranial EEG or direct cortical stimulation, cannot be easily scaled for routine or at-home use. Therefore, a need exists for advanced, non- invasive techniques that can both activate targeted brain networks in a controlled manner and analyze changes in network connectivity or function, preferably in real time or with minimal delays.

[0024] Challenges and Unmet Needs

[0025] There is a strong clinical demand for methodologies that can achieve the following objectives:

[0026] 1. Early Detection and Diagnosis: Approaches that can identify subtle alterations in brain networks before severe clinical symptoms emerge would allow for earlier interventions in conditions like Alzheimer's disease or depression.

[0027] 2. Screening and Monitoring: Scalable techniques that could be used in routine check-ups or community screenings would be valuable for identifying at-risk individuals, particularly older adults or those with risk factors for AD dementia.

[0028] 3. Therapeutic Interventions: Non-invasive interventions that systematically and safely stimulate brain networks in specific frequency ranges (e.g., gamma band at approximately 30-80 Hz) might provide therapeutic benefits or at least slow disease progression.

[0029] 4. Continuous and Accessible Monitoring: Systems that can be deployed in clinical, home, or community-based environments, with user-friendly devices, would expand the potential for continuous or longitudinal tracking of changes in brain networks.

[0030] The present invention addresses these needs by proposing a combination of targeted sensory stimulation, measurement tools, and software-based analyses that yield high specificity and sensitivity for brain network activation and monitoring. It offers an accessible means for screening, diagnosing, or managing brain network-related pathologies and cognitive impairments, supporting both research and clinical objectives.

[0031] SUMMARY OF THE INVENTION

[0032] The present invention provides a novel method and system for activating and analyzing brain networks of a subject for neurophysiological data analysis, pattern identification and characterization. This invention aims to overcome existing drawbacks by combining non-invasive sensory stimulation (including auditory, visual, haptic, or other sensory modalities) at frequencies ranging from 0.01 Hz to 120 Hz, with concurrent measurement of brain activity using advanced imaging or recording techniques (such as EEG, fMRI, MEG, NIRS, PET, or SPECT). By employing specialized software analysis pipelines, the invention generates brain topographies and functional connectivity maps of the DMN, comparing these data to a curated database of normative and pathology-specific patterns to detect abnormalities or assess therapeutic efficacy.

[0033] First Aspect: Method

[0034] In a first aspect, the invention discloses a method comprising multiple key steps:

[0035] 1. Administration of Sensory Stimulation: Sensory inputs are delivered to the subject at adjustable frequencies, preferably in the gamma range around 40 Hz, but optionally as low as 0.01 Hz or as high as 120 Hz. The stimulation may be administered in modalities such as auditory, visual (e.g., LED flicker, VR-based stimuli), haptic (vibration), or a combination thereof.

[0036] 2. Resting state: between trials the subject is asked to keep eye closed during 1 minute.

[0037] 3. Measurement of Brain Responses: While the subject receives stimulation or is in resting state, one or more brain imaging or recording devices capture neural signals in real time.

[0038] 4. Software Analysis: The measured data undergo a software-based analysis that produces brain topographies or functional connectivity maps, specifically highlighting activation levels of DMN and possibly that of other brain networks.

[0039] 5. Comparison to a Database: The resulting maps are compared against a database of predetermined recordings that may include normative baselines or profiles of individuals with conditions such as Alzheimer's disease, autism, or ADHD. This comparison helps to determine whether the functioning of the subject's DMN and possibly other brain networks is atypical and may indicate early pathology or a specific disorder. 6. Optional Cognitive Task: In preferred embodiments, a cognitive challenge (e.g., an oddball paradigm) can be administered to enhance activation of certain brain networks and to enable richer assessments of cognitive capacity or deficits.

[0040] Second Aspect: System

[0041] In a second aspect, the invention introduces a system adapted for activating brain networks and analyzing the recordings according to the proposed method. This system includes:

[0042] 1. A Sensory Stimulation Device: The device is configurable for a wide range of frequencies (0.01 Hz to 120 Hz) and supports multiple modalities (e.g., LED arrays, headphones, tactile stimulators, VR headsets).

[0043] 2. Brain Imaging or Recording Devices: EEG caps, fMRI scanners, MEG arrays, NIRS sensors, or other modalities capture neural activity.

[0044] 3. A Processing Unit: This unit implements software algorithms that isolate the relevant signals (e.g., EEG waveforms or hemodynamic responses), generate topographic or connectivity maps, and identify subtle neuronal markers.

[0045] 4. Comparison Module: A database of normative data and known pathological patterns is integrated with machine learning or statistical tools. The comparison module identifies deviations indicative to identified populations and conditions, non-medical and medical, in assessing longitudinal effects of therapeutics and lifestyle changes.

[0046] Third Aspect: Use

[0047] In a third aspect, the invention relates to the use of the described method or system for activating and analyzing specific brain networks of a subject, enabling neurophysiological data analysis, pattern identification and characterization. This use facilitates the extraction of relevant features and patterns associated with altered or atypical neurophysiological activity, which may be relevant for support the assessment of conditions such as from mild cognitive impairment to major depression, ADHD, or schizophrenia. DETAILED DESCRIPTION OF THE INVENTION

[0048] Definitions and Interpretations

[0049] Unless otherwise defined, all terms used in describing the invention have their ordinary meanings as recognized by those skilled in the art. Technical and scientific terms adhere to conventional usage within cognitive neuroscience, electrophysiology, and medical device fields.

[0050] • "DMN" or "default mode network": A large-scale neural network active primarily in rest or inward-focused tasks, comprising regions such as the medial prefrontal cortex, posterior cingulate cortex, precuneus, angular gyrus, and hippocampus.

[0051] • Other brain networks: networks including but not restricted to those involved in sensorimotor-, visual-, auditory-, attention-, cognitive control-, working memory-, memory consolidation-, and language function.

[0052] • "Sensory stimulation": Stimulation that can be auditory, visual (e.g., flicker at specific frequencies), haptic, magnetic, electrical, or multi-modal, delivered at frequencies ranging between 0.01 Hz and 120 Hz to entrain or modulate neural oscillations.

[0053] • "Brain imaging or recording techniques": Methods including EEG, MEG, fMRI, NIRS, SPECT, PET, or ECoG that measure various correlates of brain activity.

[0054] • "Gamma frequency": A frequency band usually between 30 Hz and 80 Hz, often centered on ~40 Hz, associated with synchronous neuronal firing related to attention, working memory, and integrative cognitive processes.

[0055] The Method for Activating and Analyzing brain networks

[0056] The central objective of the method is to elucidate DMN and / or other network function by imposing a controlled sensory stimulation that engages or entrains relevant brain oscillations, while simultaneously measuring neural responses to evaluate whether the probed network exhibits typical or atypical connectivity patterns. Step 1: Administering Sensory Stimulation

[0057] A first step in the method involves administering sensory stimulation selected from the group consisting of auditory, visual, haptic, or combinations thereof. The stimulation frequency spans at least 0.01 Hz and at most 120 Hz, which ensures versatility for different experimental protocols. In many embodiments, the gamma frequency range of approximately 30-80 Hz, and especially ~40 Hz, is preferred due to numerous studies linking gamma oscillations to healthy cognitive processes, as well as to potential beneficial effects on neurodegenerative pathologies.

[0058] This sensory stimulation can be further adapted to the subject's comfort, age, or clinical condition. For example, neonates or pediatric populations might require milder intensities or shorter durations than adults. In some embodiments, older adults with suspected mild cognitive impairment may be offered prolonged gamma stimulation sessions (e.g., 30 minutes daily) over a span of weeks or months to enhance memory and cognitive functioning and to reduce disease progression.

[0059] Step 2: Measuring Brain Responses

[0060] After or during the resting state and the administration of sensory stimulation, it is essential to measure the subject's brain responses in real time. This is typically carried out using at least one brain imaging or recording modality, such as EEG, which offers excellent temporal resolution. The measurement period can range from a few seconds to multiple hours, depending on the experimental design. Some embodiments rely on motion-tolerant or portable systems that allow for in-home usage, broadening the invention's applicability to remote patient monitoring scenarios.

[0061] Step 3: Software Analysis

[0062] The data from the chosen imaging or recording device are fed into a software pipeline for real-time or offline processing. In advanced embodiments, the system leverages signal processing algorithms such as wavelet transforms, Fourier analysis, or independent component analysis (ICA) to remove artifacts (e.g., eye blinks, muscle noise), isolate specific frequency components, and identify patterns of neural synchronization. Machine learning models can be used to detect anomalies, classify neural states, or predict changes in connectivity related to cognitive load. Step 4: Generating Brain Topography and Functional Connectivity Maps

[0063] The software analysis produces a brain topography, which indicates the spatial distribution of neural activation. It may also yield a functional connectivity map revealing how different brain regions, including the DMN nodes, interact in response to the resting state or to the stimulation. The dynamic nature of these connectivity maps can be studied in various frequency bands, such as gamma, beta, or theta, depending on the protocol and the research or clinical questions at hand.

[0064] Step 5: Comparing to a Database

[0065] Once the connectivity maps are generated, they are compared to a database of normative data, disease-specific patterns, or population models (e.g., for individuals with AD, MCI, depression, or ADHD). This comparison employs statistical analyses, machine learning algorithms, or a combination thereof to ascertain whether the subject's patterns deviate from what is typically expected. Clinicians, researchers, or automated systems can then interpret these deviations to screen for possible impairments, detect early disease markers, or track the efficacy of ongoing treatments.

[0066] Optional Cognitive Task

[0067] In some embodiments, the invention includes an optional cognitive task administered concurrently with the sensory stimulation. The attention oddball paradigm (P300) or an n-back memory task can increase the engagement of targeted brain networks and provide a richer dataset, enhancing the classification sensitivity and specificity. By measuring event-related potentials (such as the amplitude and latency of the P300 wave), the method can evaluate the subject's attentional processes and memory capacity in tandem with targeted brain network activation.

[0068] The System for Activating and Analyzing the targeted brain networks

[0069] The second aspect of the invention discloses a system structured to implement the aforementioned method. This system typically includes:

[0070] 1. Sensory Stimulation Device: This may be an LED panel, a set of high- fidelity headphones, a vibrotactile feedback device, or any combination thereof. Its control module sets the stimulation frequency between 0.01 Hz and 120 Hz. 2. Brain Imaging or Recording Devices: These include EEG caps, MEG scanners, fMRI machines, or other technologies that provide the data needed for DMN assessment. In some embodiments, multiple imaging modalities operate concurrently.

[0071] 3. Processing Unit: This includes hardware (e.g., a CPU, GPU, or specialized Al accelerator) and software libraries to process and analyze the recorded signals, applying advanced algorithms to generate relevant metrics, topographies, and connectivity maps.

[0072] 4. Comparison Module: This software module uses statistical analysis or machine learning classifiers to compare the derived maps with a database containing normative or pathological data, identifying deviations amongst populations.

[0073] 5. Cognitive Task Module: Some system configurations include the ability to administer and monitor a cognitive challenge, such as an oddball task, for further classification refinement or enhancement.

[0074] Use of the Invention

[0075] The invention has applications in neurophysiological data analysis, pattern identification and characterization related to DMN or other brain network dysfunctions. Users may include clinicians seeking early detection of neurodegenerative diseases, researchers studying cognitive processes, and rehabilitation specialists designing novel interventions. Non-medical applications include improving attentional focus or memory performance in healthy individuals, as well as screening capabilities for safety-critical tasks such as piloting vehicles, flight control or handling sensitive machinery.

[0076] Predictive classification

[0077] The invention enables the similarity analysis of different groups in asymptomatic individuals by comparing their brain activity to patterns observed in different populations. For instance, an older adult presenting subtle deficits in memory may undergo daily gamma-frequency stimulation while wearing an EEG cap, with the recorded data compared to a normative database stratified by age and demographic factors. The system can flag early deviations that might warrant further clinical evaluation (e.g. amyloid-PET scan). Effective classification

[0078] For conditions such as Alzheimer's disease or ADHD, the system helps in establishing a more precise classification by detecting anomalies in brain activity and connectivity. In addition to classification, the system can function as a suggestion tool by scheduling repeated stimulation sessions in the gamma range to potentially improve cognitive function, sleep, and daily functioning. In the case of AD, it promotes glymphatic clearance, ventricular enlargement, functional connectivity, neuroinflammation, and engage glial cells, leading to neuroprotective effects.

[0079] At-Home or Clinical Monitoring

[0080] Because certain embodiments use portable or wearable devices, the invention can be brought into patients' homes for continuous monitoring. This arrangement is particularly beneficial for tracking disease progression in conditions like mild cognitive impairment or Alzheimer's disease, where frequent clinical visits might be difficult. The system's adaptive software can adjust stimulation frequency or duration based on observed progress, automatically tailoring stimulation for enhanced outcomes.

[0081] Other Non-Medical Applications

[0082] The proposed gamma stimulation may also be harnessed to improve mental wellbeing and cognitive performance in healthy individuals. The system can be used in meditative training, stress reduction, memory reinforcement, or skill learning, and may be adapted for gaming or e-learning platforms. In corporate or educational settings, gamma stimulation can be utilized to encourage creativity or to refine attentional capacity.

[0083] Explanation of Terms

[0084] The following terms are reiterated for clarity:

[0085] • "Comprise," "comprising," "comprises," or "comprised of" are intended to include the presence of elements or steps recited without excluding additional, unmentioned elements or steps.

[0086] • "Gamma frequency" or "gamma band" generally refers to the range of ~30 Hz to ~80 Hz. However, "about 40 Hz" is considered a particularly important subrange for potential therapeutic effects. • "Database of predetermined data" refers to any electronic or cloud-based repository containing normative data (from healthy individuals) and data from patients with specific conditions, enabling the comparison module to detect or classify atypical patterns in new subjects.

[0087] Examples

[0088] The invention is illustrated further by the following non-limiting examples. These examples are not intended to restrict the scope of the invention but rather to demonstrate how the claimed method and system can be used in practice.

[0089] Example 1: Visual Stimulation with an LED Panel

[0090] A proprietary 8 x 8 cm LED panel was designed to deliver visual flickering at precisely controlled frequencies in the gamma range (~40 Hz). The panel was mounted on an adjustable stand, ensuring that it remained at the correct height and angle for the participant. A central red LED served as a fixation point or to generate an oddball (infrequent but attended event) in the case of an attention task. EEG data gathered from subdural grids and depth electrodes indicated that rhythmic visual stimulation at 40 Hz resulted in detectable gamma-band synchronization in both the visual cortex and deeper structures such as the hippocampus which is often affected by Alzheimer's disease. This outcome highlights the ability of the system to generate robust neural entrainment with potential therapeutic benefits.

[0091] Example 2: Combined Visual and Auditory Stimulation in MCI

[0092] Researchers used a custom setup that combined LED flicker at 40 Hz with auditory clicks at the same frequency. Subjects with mild cognitive impairment (MCI) and amyloid positivity underwent daily sessions lasting 20 minutes. EEG topographies recorded at Fz, Cz, and Pz electrodes were analyzed, and software-based classification placed individuals into subgroups of mild, moderate, or advanced MCI risk. The results revealed that synchronizing both visual and auditory channels increased the amplitude of gamma oscillations in posterior cortical regions compared to single-channel stimulation, suggesting a possible enhancement for early detection or supportive therapy.

[0093] Example 3: Irregular vs. Regular Stimulation Patterns

[0094] A pilot study tested the difference between regular 40 Hz flicker and non-periodic flicker frequencies between 30 Hz and 50 Hz. Subjects undergoing the irregular stimulation showed absence of entrainment in gamma-band EEG power, while those exposed to the strict 40 Hz pattern (regular flickering) demonstrated a high signal- to-noise ratio at 40 Hz and thus strong entrainment. This was also the case during regular flickering with an oddball task present. When contrasting the scalp topographies of either resting state, regular flickering or regular flickering with an oddball task against irregular flickering, clear differences were revealed between cognitively unimpaired individuals with and without amyloid deposition.

[0095] Example 4: P300 Task to Differentiate AD vs. Healthy Controls

[0096] A group of older adults, half of whom were diagnosed with early-stage Alzheimer's disease, was recruited to assess whether the method could discern DMN dysfunction. An attention oddball paradigm was used in conjunction with 40 Hz auditory stimulation. Participants were instructed to press a button every time an infrequent "beep" occurred. The EEG data were analyzed for P300 amplitude and latency, and concurrent measures of DMN connectivity were obtained using fMRI. Alzheimer's disease patients showed lower P300 amplitude and abnormal DMN connectivity, particularly within the PCC-precuneus circuit. These findings validated the utility of the method for identifying or characterizing early-stage Alzheimer's disease.

[0097] Example 5: Application to ADHD

[0098] A sample of 40 children diagnosed with ADHD participated in a study involving daily 30-minute sessions of 40 Hz auditory stimulation combined with an n-back memory task. Baseline EEG scans revealed elevated DMN activity during tasks demanding external attention. However, after repeated sessions, children showed decreased DMN hyperconnectivity in task-relevant situations, correlating with better attentional performance and fewer behavioral issues. This example illustrated the potential for DMN-targeted stimulation to stabilize attentional processes in ADHD.

[0099] Example 6: Depression and DMN Hyperactivity

[0100] In a clinical trial, 25 adults with moderate depression underwent a daily 20-minute auditory stimulation session at 40 Hz while performing an oddball task. Baseline DMN connectivity was measured using fMRI, revealing extensive hyperactivity in mPFC and PCC regions during introspective or ruminative states. After a four-week intervention, follow-up measurements showed decreased DMN overactivity, improved mood as measured by standardized depression scales, and improved response times to oddball stimuli. The results suggested that gamma stimulation can reduce pathological DMN hyperactivation, potentially serving as an adjunct to pharmacotherapy or psychotherapy.

[0101] Example 7: Testing the Duration of Stimulation

[0102] In a separate study, healthy young adults received 1-second, 5-minute, 30-minute, and 1-hour exposures to 40 Hz flicker. EEG measurements confirmed that longer durations of stimulation produced more prominent and sustained gamma entrainment, although shorter bursts still elicited measurable responses. This finding supports the invention's flexibility in selecting an optimal duration for diverse objectives such as quick screenings, ongoing therapy, or in-depth research on longterm neural adaptations.

[0103] Example 8: Scalability and Home Use

[0104] Another embodiment tested a portable system that allowed older adults to receive daily gamma stimulation in their homes. A wearable EEG device linked to a tabletbased software module tracked daily usage, automatically adjusted the intensity and frequency of the flicker, and transmitted data to a cloud database. Results showed that participants adhered well to the routine, and clinicians were able to remotely monitor DMN activity changes over a three-month period, demonstrating a feasible model for decentralized, continuous cognitive monitoring.

[0105] Example 9: Criminal Rehabilitation and Empathy Enhancement

[0106] In an experimental setting, the system was used with incarcerated individuals who had shown remorse and willingness to participate in rehabilitation programs. The combination of gamma-frequency stimulation and empathy-oriented tasks (e.g., perspective-taking exercises) aimed to enhance the functional connectivity of DMN regions implicated in moral reasoning. Preliminary assessments indicated modest improvements in prosocial attitudes. These findings open a pathway for studying how targeted DMN stimulation may support social cognition and rehabilitation efforts.

[0107] Example 10: Non-Medical Creativity and Mindfulness Applications

[0108] A corporate pilot study attempted to leverage the invention for improving creativity and fostering mindfulness. Volunteers were exposed to 40 Hz audiovisual stimulation during guided mind-wandering sessions. Subjective reports indicated a heightened sense of clarity and focus, and objective measures showed short-term increases in functional connectivity within DMN hubs. While results are preliminary, they suggest a role for DMN-targeted entrainment in creative brainstorming, leadership development, or stress reduction protocols.

[0109] Advantages of the Invention

[0110] 1. Early Detection: By contrasting stimulation conditions, the method and system can discover pathological changes or dysfunctions at an earlier stage such the detection of amyloid accumulation in cognitively unimpaired individuals (preclinical AD) well before any clinical manifestation.

[0111] 2. Improved Sensitivity: The combination of resting state and precise sensory stimulation frequencies and real-time neural measurements yields highly sensitive indicators of abnormal activations and connectivity patterns.

[0112] 3. Non-Invasiveness: The invention emphasizes externally applied, non- invasive stimuli (visual, auditory, haptic), minimizing patient discomfort and expanding opportunities for at-home or widespread clinical usage.

[0113] 4. Adaptability: The frequency range (0.01-120 Hz), choice of modality, and optional cognitive tasks provide broad flexibility for multiple disorders and populations.

[0114] 5. Personalized analysis: The system can tailor stimulation parameters based on repeated measurements, optimizing interventions for each subject's unique profile or disease progression.

[0115] 6. Scalable Database Comparisons: The integrated database allows for large-scale normative references, disease-specific references, and crosscondition analytics.

[0116] Potential Modifications and Variations

[0117] The invention is not restricted to the specific embodiments described. Skilled persons may adopt variations in frequency ranges, types of stimulation (e.g., magnetic or electrical), or data processing methods while remaining within the scope of the claims. Further, the invention can be integrated with pharmacological interventions (e.g., cholinesterase inhibitors in Alzheimer's disease) or combined with behavioral therapies, offering multi-modal treatment approaches. Conclusion of the Detailed Description

[0118] From the foregoing, it is clear that the present invention satisfies longstanding needs for a technology that can simultaneously stimulate and measure activity of the targeted brain networks in a subject, providing highly detailed insights into cognitive and clinical states. By tailoring sensory stimulation parameters, measuring neural responses with advanced imaging modalities, and employing specialized software analysis to compare the resulting data to comprehensive databases, the invention enables effective neurophysiological data analysis, pattern identification and characterization. The invention is applicable in both medical and non-medical contexts, presenting numerous avenues for future development and optimization.

Claims

CLAIMS1. A software-based method for activating and analyzing a brain activity and network connectivity of a subject for neurophysiological data analysis, pattern identification and characterization, the method comprising the steps of:(i) administering a resting-state condition during which the subject's brain activity is measured in the absence of rhythmic sensory stimulation or task;(ii) administering to the subject a sensory stimulation selected from the group consisting of auditory stimulation, visual stimulation, haptic stimulation, electrical stimulation, and magnetic stimulation modalities such as transcranial alternating current stimulation, subauditory stimulation devices, functional electric stimulation, and skin-applied stimulation systems, or any combination thereof, at a frequency between at least 0.01 Hz and at most 120 Hz to induce rhythmic brain responses;(iii) measuring the subject's brain responses with one or more brain imaging or recording devices;(iv) analyzing the measured brain responses by performing a software analysis that generates either a brain topography or a functional connectivity map in response to the sensory stimulation and a brain topography or connectivity map during the resting state or sensory stimulation; and(v) comparing the obtained brain topographies or connectivity maps to a database of normative data in order to screen for abnormalities.

2. The software-based method according to claim 1, wherein the administered sensory stimulation is at a gamma frequency between about 30 Hz and about 80 Hz, and wherein the stimulation involves synchronized gamma oscillations within that frequency range.

3. The software-based method according to claim 1 or 2, wherein the method further comprises administering a cognitive task to the subject during the sensory stimulation, and wherein the cognitive task is preferably an attention task comprising infrequent stimuli to which the subject must respond.

4. The software-based method according to any of claims 1 to 3, wherein the cognitive task is an attention oddball paradigm that elicits a P300 response or an n-back memory task that elicits specific working memory-related responses.

5. The software-based method according to any of claims 1 to 4, wherein the database of predetermined data comprises population models or default mode network activity patterns associated with various cognitive conditions from different population groups, and wherein the database is used to classify deviations or similarities indicative of the subject's cognitive state.

6. The software-based method according to claim 5, wherein the database of activity patterns includes those associated with conditions selected from the group consisting of learning disorders, anxiety disorders, empathy disorders, Alzheimer's disease, autism spectrum disorder, depression, schizophrenia, attention-deficit / hyperactivity disorder, and mild cognitive impairment.

7. The software-based method according to any of claims 1 to 6, wherein the method comprises using the comparison results to provide a personalized output or recommendation for an intervention based on detected deviations in default mode network or other network activity and wherein the output is delivered to a neural entrainment device or a therapy module.

8. The software-based method according to any of claims 1 to 7, wherein the sensory stimulation is provided using a combination of visual, haptic, electrical, or magnetic stimulation modalities, including transcranial alternating current stimulation, subauditory stimulation devices, functional electric stimulation, and skin-applied stimulation systems, and wherein auditory stimulation at gamma frequencies is optionally included.

9. The software-based method according to any of claims 1 to 8, wherein the duration of sensory stimulation administered to the subject ranges from 1 second to 2 hours.

10. The software-based method according to any of claims 1 to 9, wherein the subject is a mammal, and wherein the subject is preferably a human.

11. A system for detection, neurophysiological data analysis, pattern identification and characterization, the system comprising: (i) a resting-state condition and a sensory stimulation device that is configured to administer sensory stimulation to a subject, wherein the sensory stimulation is selected from the group consisting of auditory stimulation, visual stimulation, haptic stimulation, electrical or magnetic stimulation modalities such as transcranial alternating current stimulation, subauditory stimulation devices, functional electric stimulation, and skin-applied stimulation systems, or any combination thereof, and wherein the device is configured to deliver stimulation at a frequency between at least 0.01 Hz and at most 120 Hz to induce rhythmic brain responses;(ii) one or more brain imaging or recording devices configured to measure the subject's brain responses during both the resting-state condition and the sensory stimulation;(iii) a processing unit configured to analyze the measured brain responses and generate a brain topography or functional connectivity map, wherein the processing unit is capable of extracting advanced signal processing features that include signal-to-noise ratio, power and entropy across frequency bands, area under the curve of power spectral density, phase offset, aperiodic components, phase slope index, phase lag index, phase latency index, and phase lock value, and wherein the processing unit can employ linear or nonlinear regression methods, filtering techniques, or machine-learning approaches to process the resulting datasets;(iv) a comparison module configured to compare the brain topography or network connectivity of both the sensory stimulation responses and the default mode network to a database of normative data in order to identify anomalous deviations and to assess the subject's cognitive state using machine learning or statistical pattern recognition; and wherein the system is adapted to provide outputs in a quantitative format for analysis of brain topography and network connectivity or abnormalities and is further adapted for detecting altered neural oscillations linked to beta amyloid or phosphorylated tau-protein-deposit-induced network dysfunction.

12. The system according to claim 11, wherein the brain imaging or recording devices are selected from the group consisting of electroencephalography, magnetoencephalography, functional magnetic resonance imaging, nearinfrared spectroscopy, electrocorticography, functional ultrasound imaging, positron emission tomography, or single photon emission computed tomography.

13. The system according to claim 11 or 12, wherein the system further comprises a cognitive task module that administers a cognitive task to the subject during the sensory stimulation, wherein the cognitive task module includes paradigms such as an attention oddball task that presents infrequent target stimuli interspersed with standard stimuli or an n-back workingmemory task, and wherein the cognitive task parameters dynamically adapt based on the subject's performance metrics.

14. The system according to any of claims 11 to 13, wherein the sensory stimulation device is chosen from a list consisting of light-emitting diode sources, high-fidelity headphones, vibrotactile feedback devices, visual displays, auditory transducers, tactile stimulators, wearable devices providing visual, auditory, or haptic feedback, virtual reality headsets, surround sound systems, haptic feedback gloves, stereo headphones, haptic armbands, projectors for visual stimuli, speaker systems for auditory stimuli, tactile stimulation pads, head-mounted displays, in-ear headphones, tactile feedback belts, augmented reality headsets, bone conduction headsets, wearable vibrotactile devices, and electrical or magnetic stimulation devices including transcranial alternating current stimulation or functional electric stimulation, or any combination thereof.

15. Use of the software- based method according to any of claims 1 to 10 or the system according to any of claims 11 to 14 for entraining brain activity in response to rhythmic sensory stimulation and for activating one or more targeted brain networks of a subject, including identifying significant network state variations for neurophysiological data analysis, pattern identification and characterization.