Resting-state EEG Research Articles

Exploring foreign language anxiety and resting-state EEG alpha asymmetry.

Anxiety experienced when interacting in a foreign language hinders communication through detrimental behavioral, cognitive, and somatic effects. Despite its impact, there is limited research on how neural asymmetry relates to foreign language anxiety (FLA). While researchers have investigated FLA through brain imaging, there remains an absence of studies examining its correlation with frontal alpha asymmetry. Understanding FLA in the context of frontal alpha asymmetry is significant because it can reveal specific neural mechanisms underlying this anxiety. We investigated the associations between listening and speaking FLA - across behavioral, cognitive, and somatic domains - and participants' resting-state electroencephalography (EEG) signals prior to verbal interactions in a foreign language. The results revealed that significantly higher right-left frontal alpha asymmetry was associated with greater reported FLA in most listening and all of the speaking domains. This study offers insight into the neural processes in connection with FLA, highlighting the significance of frontal alpha asymmetry as a potential neural marker for understanding and addressing its unique challenges.

Time-Frequency functional connectivity alterations in Alzheimer's disease and frontotemporal dementia: An EEG analysis using machine learning.

Alzheimer's disease (AD) and frontotemporal dementia (FTD) are prevalent neurodegenerative diseases characterized by altered brain functional connectivity (FC), affecting over 100 million people worldwide. This study aims to identify distinct FC patterns as potential biomarkers for differential diagnosis. Resting-state EEG data from 36 AD patients, 23 FTD patients, and 29 healthy controls were analyzed using time-frequency and bandpass filtering FC metrics. These metrics were estimated through Pearson's correlations, mutual information, and phase lag index, and served as input features in a support vector machine (SVM) with Leave-One-Out Cross-Validation for group classification. Both AD and FTD exhibited significantly decreased FC in the theta band within the frontal lobe and increased FC in the beta band in the posterior regions. Additionally, a decreased FC in central regions at theta band was observed uniquely in AD, but not in FTD. SVM classification accuracies reached 95% for AD and 86% for FTD. High classification accuracies underscore the potential of these FC alterations as reliable biomarkers for AD and FTD. This is the first study to integrate time-frequency and bandpass filtering FC metrics to reveal brain network alterations in AD and FTD, providing new insights for diagnostics and neurodegenerative pathologies.

Significance of gender, brain region and EEG band complexity analysis for Parkinson's disease classification using recurrence plots and machine learning algorithms.

Parkinson Disease (PD) is a complex neurological disorder attributed by loss of neurons generating dopamine in the SN per compacta. Electroencephalogram (EEG) plays an important role in diagnosing PD as it offers a non-invasive continuous assessment of the disease progression and reflects these complex patterns. This study focuses on the non-linear analysis of resting state EEG signals in PD, with a gender-specific, brain region-specific, and EEG band-specific approach, utilizing recurrence plots (RPs) and machine learning (ML) algorithms for classification. For this an open EEG dataset consisting of 14 PD and 14 healthy (HC) subjects is utilized. Recurrence plots and cross-recurrence plots (CRPs) were constructed for each frequency band and brain region, extracting complexity measures such as determinism (DET) and entropy (ENT). The interpretability of the ML model decisions is investigated using explainability technique. The scattered distribution of points in RPs of male PD individuals reflects the complex and dynamic nature of abnormal brain function. Also, CRPs confirms the enhanced effect of Beta Gamma synchronization during PD in the Parietal region. Low DET and high ENT corresponds to the complex non-linear characteristics of EEG signals and brain neuronal circuits during PD condition in male subjects. The extracted recurrence features served as inputs to the ML models, which achieved high classification performance, across all the scenarios. This study demonstrates the potential of recurrence plot-based complexity analysis combined with machine learning for the gender-specific, region-specific, and band-specific assessment of EEG signals during resting state in Parkinson's disease.

Symmetry in Genetic Distance Metrics: Quantifying Variability in Neurological Disorders for Personalized Treatment of Alzheimer’s and Dementia

This paper aims to adapt and apply genetic distance metrics in biomedical signal processing to improve the classification and monitoring of neurological disorders, specifically Alzheimer’s disease and frontotemporal dementia. The primary objectives are: (1) to quantify the variability in EEG signal patterns among the distinct subtypes of neurodegenerative disorders and healthy individuals, and (2) to explore the potential of a novel genetic similarity metric in establishing correlations between brain signal dynamics and clinical progression. Using a dataset of resting-state EEG recordings (eyes closed) from 88 subjects (36 with Alzheimer’s disease, 23 with frontotemporal dementia, and 29 healthy individuals), a comparative analysis of brain activity patterns was conducted. Symmetry plays a critical role in the proposed genetic similarity metric, as it captures the balanced relationships between intra- and inter-group EEG signal patterns. Our findings demonstrate that this approach significantly improves disease subtype identification and highlights the potential of the genetic similarity metric to optimize the predictive models. Furthermore, this methodology supports the development of personalized therapeutic interventions tailored to individual patient profiles, making a novel contribution to the field of neurological signal analysis and advancing the application of EEG in personalized medicine.

MDD-SSTNet: detecting major depressive disorder by exploring spectral-spatial-temporal information on resting-state electroencephalography data based on deep neural network.

Major depressive disorder (MDD) is a psychiatric disorder characterized by persistent lethargy that can lead to suicide in severe cases. Hence, timely and accurate diagnosis and treatment are crucial. Previous neuroscience studies have demonstrated that major depressive disorder subjects exhibit topological brain network changes and different temporal electroencephalography (EEG) characteristics compared to healthy controls. Based on these phenomena, we proposed a novel model, termed as MDD-SSTNet, for detecting major depressive disorder by exploring spectral-spatial-temporal information from resting-state EEG with deep convolutional neural network. Firstly, MDD-SSTNet used the Sinc filter to obtain specific frequency band features from pre-processed EEG data. Secondly, two parallel branches were used to extract temporal and spatial features through convolution and other operations. Finally, the model was trained with a combined loss function of center loss and Binary Cross-Entropy Loss. Using leave-one-subject-out cross-validation on the HUSM dataset and MODMA dataset, the MDD-SSTNet model outperformed six baseline models, achieving average classification accuracies of 93.85% and 65.08%, respectively. These results indicate that MDD-SSTNet could effectively mine spatial-temporal difference information between major depressive disorder subjects and healthy control subjects, and it holds promise to provide an efficient approach for MDD detection with EEG data.

Alterations in resting-state EEG functional connectivity in patients with major depressive disorder receiving electroconvulsive therapy: A systematic review.

Electroconvulsive therapy (ECT) is highly efficacious for the treatment of major depressive disorder (MDD), but its mechanisms still require clarification. Even though depression is associated with alterations in functional connectivity (FC), EEG studies investigating effects of ECT on FC have not been systematically reviewed. Understanding these effects may help to identify the role of functional brain circuits in depression and its remission. This systematic review aimed to synthesize EEG studies investigating FC changes in ECT-treated patients with depression. A systematic literature search was conducted following PRISMA guidelines. Peer-reviewed studies on pre-to post-ECT resting-state EEG FC changes in adult patients with MDD were included. Three of 143 studies were included, of which two reported reduced FC in the alpha and beta frequency bands and increased theta band FC in patients with ECT-treated MDD. Changes in alpha band FC were associated with treatment outcomes. Patients with MDD exhibit increased electrophysiological resting-state alpha band FC, particularly frontally, compared with healthy subjects. Thus, ECT-induced decrease might indicate a trend toward normalization of oscillatory brain rhythms. As brain oscillations have been proposed to be involved in neuronal synchronization, which is important for communication between networks, the potential restoration in patients with depression and the association of FC changes with clinical improvement may indicate a potential mechanism of action of ECT. Understanding ECT's underlying mechanisms might ultimately enable treatment optimization, thus enhancing patient care. However, the number of studies is limited, with low-to-moderate EEG study quality, small sample sizes, and different electrophysiological FC measures.

Resting-State EEG Oscillations in Amyotrophic Lateral Sclerosis (ALS): Toward Mechanistic Insights and Clinical Markers.

Introduction: Amyotrophic lateral sclerosis (ALS) is a complex, progressive neurodegenerative disorder characterized by the degeneration of motor neurons in the brain, brainstem, and spinal cord. Several neuroimaging techniques can help reveal the pathophysiology of ALS. One of these is the electroencephalogram (EEG), a noninvasive and relatively inexpensive tool for examining electrical activity of the brain with excellent temporal precision. Methods: This mechanistic review examines the pattern of resting-state EEG activity. With a focus on publications published between January 1995 and October 2024, we carried out a comprehensive search in October 2024 across a number of databases, including PubMed/Medline, Research Gate, Google Scholar, and Cochrane. Results: The literature search yielded 17 studies included in this review. The studies varied significantly in their methodology and patient characteristics. Despite this, a common biomarker typical of ALS was found-reduced alpha power. Regarding other oscillations, the findings are less consistent and sometimes contradictory. As this is a mechanistic review, three possible explanations for this biomarker are provided. The main and most important one is increased cortical excitability. In addition, due to the limitations of the studies, recommendations for future research on this topic are outlined to enable a further and better understanding of EEG patterns in ALS. Conclusions: Most studies included in this review showed alpha power deficits in ALS patients, reflecting pathological hyperexcitability of the cerebral cortex. Future studies should address the methodological limitations identified in this review, including small sample sizes, inconsistent frequency-band definitions, and insufficient functional outcome measures, to solidify and extend current findings.

Theta transcranial alternating current stimulation over the prefrontal cortex enhances theta power and working memory performance.

Transcranial alternating current stimulation (tACS) is a promising tool for modulating brain oscillations. This study investigated whether 5 Hz tACS could modulate neural oscillations in the prefrontal cortex and how this modulation impacts performance in working memory (WM) tasks. In two sessions, 28 healthy participants received 5 Hz tACS or sham stimulation over the left dorsolateral prefrontal cortex (DLPFC) while performing tasks with high and low WM loads. Resting-state EEG was recorded before and after stimulations for 5 minutes. EEG power was measured at electrodes surrounding the stimulation site. The results showed that tACS significantly improved reaction time (RT) compared to sham stimulation. This effect was task-specific, as tACS improved RT for hit responses only in high WM load trials, with no impact on low-load trials. Moreover, tACS significantly increased EEG power at 5 Hz and in the theta band compared to pre-stimulation levels. These findings demonstrate that tACS applied over left DLPFC modulates post-stimulation brain oscillations at the stimulation sites - known as tACS after-effects. Furthermore, the results suggest that 5 Hz tACS enhances response speed by elevating task-related activity in the prefrontal cortex to an optimal level for task performance. In summary, the findings highlight the potential of tACS as a technique for modulating specific brain oscillations, with implications for research and therapeutic interventions.

Assessing the balance between excitation and inhibition in chronic pain through the aperiodic component of EEG

Chronic pain is a prevalent and debilitating condition whose neural mechanisms are incompletely understood. An imbalance of cerebral excitation and inhibition (E/I), particularly in the medial prefrontal cortex (mPFC), is believed to represent a crucial mechanism in the development and maintenance of chronic pain. Thus, identifying a non-invasive, scalable marker of E/I could provide valuable insights into the neural mechanisms of chronic pain and aid in developing clinically useful biomarkers. Recently, the aperiodic component of the electroencephalography (EEG) power spectrum has been proposed to represent a non-invasive proxy for E/I. We, therefore, assessed the aperiodic component in the mPFC of resting-state EEG recordings in 149 people with chronic pain and 115 healthy participants. We found robust evidence against differences in the aperiodic component in the mPFC between people with chronic pain and healthy participants, and no correlation between the aperiodic component and pain intensity. These findings were consistent across different subtypes of chronic pain and were similarly found in a whole-brain analysis. Their robustness was supported by preregistration and multiverse analyses across many different methodological choices. Together, our results suggest that the EEG aperiodic component does not differentiate between people with chronic pain and healthy individuals. These findings and the rigorous methodological approach can guide future studies investigating non-invasive, scalable markers of cerebral dysfunction in people with chronic pain and beyond.

Assessing the balance between excitation and inhibition in chronic pain through the aperiodic component of EEG.

Chronic pain is a prevalent and debilitating condition whose neural mechanisms are incompletely understood. An imbalance of cerebral excitation and inhibition (E/I), particularly in the medial prefrontal cortex (mPFC), is believed to represent a crucial mechanism in the development and maintenance of chronic pain. Thus, identifying a non-invasive, scalable marker of E/I could provide valuable insights into the neural mechanisms of chronic pain and aid in developing clinically useful biomarkers. Recently, the aperiodic component of the electroencephalography (EEG) power spectrum has been proposed to represent a non-invasive proxy for E/I. We, therefore, assessed the aperiodic component in the mPFC of resting-state EEG recordings in 149 people with chronic pain and 115 healthy participants. We found robust evidence against differences in the aperiodic component in the mPFC between people with chronic pain and healthy participants, and no correlation between the aperiodic component and pain intensity. These findings were consistent across different subtypes of chronic pain and were similarly found in a whole-brain analysis. Their robustness was supported by preregistration and multiverse analyses across many different methodological choices. Together, our results suggest that the EEG aperiodic component does not differentiate between people with chronic pain and healthy individuals. These findings and the rigorous methodological approach can guide future studies investigating non-invasive, scalable markers of cerebral dysfunction in people with chronic pain and beyond.

The level of cognitive functioning in school-aged children is predicted by resting EEG Directed Phase Lag Index

Quantifying cognitive potential relies on psychometric measures that do not directly reflect cortical activity. While the relationship between cognitive ability and resting state EEG signal dynamics has been extensively studied in children with below-average cognitive performances, there remains a paucity of research focusing on individuals with normal to above-average cognitive functioning. This study aimed to elucidate the resting EEG dynamics in children aged four to 12 years across normal to above-average cognitive potential. Our findings indicate that signal complexity, as measured by Multiscale Entropy (MSE), was not significantly predictive of the level of cognitive functioning. However, utilizing Directed Phase Lag Index (DPLI) as an effective connectivity measure, we observed consistent patterns of information flow between anterior and posterior regions. Fronto-parietal as well as local connectivity patterns were seen across most of the cognitive functions. Moreover, specific connectivity patterns were obtained for each intellectual quotient index (namely verbal comprehension, visuospatial, fluid reasoning, and processing speed indexes as well as full-scale intellectual quotient). These results underscore the presence of long-range connections and support fronto-parietal theories of cognitive abilities within the resting state brain dynamics of children.

Unveiling neural activity changes in mild cognitive impairment using microstate analysis and machine learning.

Mild cognitive impairment (MCI) is recognized as a condition that may increase the risk of developing Alzheimer's disease (AD). Understanding the neural correlates of MCI is crucial for elucidating its pathophysiology and developing effective interventions. Electroencephalogram (EEG) microstates, reflecting brain activity changes, have shown promise in MCI research. However, current approaches often lack comprehensive characterization of the complex neural dynamics associated with MCI. This study aims to investigate neurophysiological changes associated with MCI using a comprehensive set of microstate features, including traditional temporal features and entropy measures. Resting-state EEG data were collected from 69 MCI patients and healthy controls (HC). Microstate analysis was performed to extract conventional features (duration, coverage) and entropy measures. Statistical analysis, principal component analysis (PCA), and machine learning (ML) techniques were employed to evaluate neurophysiological patterns associated with MCI. MCI displayed altered microstate dynamics, with significantly longer coverage and duration in Microstate C but shorter in Microstates A, B, and D compared to HCs. PCA revealed two principal components, primarily composed of microstate dynamics and entropy measures, explaining over 75% of the variance. ML models achieved high accuracy in distinguishing MCI patterns. Our comprehensive analysis of EEG microstate features provides new insights into neurophysiological changes associated with MCI, highlighting the potential of EEG microstates for investigating complex neural changes in cognitive decline.

Characterizing PTSD Using Electrophysiology: Towards A Precision Medicine Approach

Objective. Resting-state EEG measures have shown potential in distinguishing individuals with PTSD from healthy controls. ERP components such as N2, P3, and late positive potential have been consistently linked to cognitive abnormalities in PTSD, especially in tasks involving emotional or trauma-related stimuli. However, meta-analyses have reported inconsistent findings. The understanding of biomarkers that can classify the varied symptoms of PTSD remains limited. This study aimed to develop a concise set of electrophysiological biomarkers, using neutral cognitive tasks, that could be applied across psychiatric conditions, and to identify biomarkers associated with the anxiety and depression dimensions of PTSD. Approach. Continuous simultaneous recordings of EEG and electrocardiogram (ECG) were obtained in veterans with PTSD (n = 29) and healthy controls (n = 62) during computerized tasks. EEG, ERP, and heart rate measures were evaluated in terms of their ability to discriminate between the groups or correlate with psychological measures. Results. The PTSD cohort exhibited faster alpha oscillations, reduced alpha power, and a flatter power spectrum. Furthermore, stronger reduction in alpha power was associated with higher trait anxiety, while a flatter slope was related to more severe depression symptoms in individuals with PTSD. In ERP tasks of visual memory and sustained attention, the PTSD cohort demonstrated delayed and exaggerated early components, along with attenuated LPP amplitudes. The three tasks revealed distinct and complementary EEG signatures PTSD. Significance. Multimodal individualized biomarkers based on EEG, cognitive ERPs, and ECG show promise as objective tools for assessing mood and anxiety disturbances within the PTSD spectrum.

VR Cognitive-based Intervention for Enhancing Cognitive Functions and Well-being in Older Adults with Mild Cognitive Impairment: Behavioral and EEG Evidence.

Objective: Mild cognitive impairment (MCI) has been recognized as a window of opportunity for therapeutic and preventive measures to slow cognitive decline. The current study investigated the efficacy of the virtual reality (VR) cognitive-based intervention on verbal and visuospatial short-term memory (STM), executive functions (EFs), and wellbeing among older adults with and without MCI. Method: The immersive VR cognitive-based intervention comprised eight 60-minute sessions, held twice a week over a span of 30 days. The participants consisted of 31 non-MCI older adults in the experimental group (mean age ± SD = 66.31 ± 3.12 years), 29 older adults with MCI in the experimental group (mean age ± SD = 68.19 ± 5.03 years), and 30 non-MCI older adults in the control group (mean age ± SD = 64.97 ± 3.35 years). The dependent variables were assessed by using a battery of computerized test, the well-being of older people questionnaire and resting-state EEG. A repeated-measures ANCOVA was employed to examine the effects of the developed VR intervention. Results: Significant improvements were observed in both STMs and EFs following the intervention, as indicated by behavioral and EEG findings, ranging from small to large effect sizes (i.e., = .05-.17). However, enhanced wellbeing was specifically observed among older adults with MCI in the experimental group, F(2, 87) = 6.78, p .01, = .11. Conclusions: The present findings lend support to the efficacy of VR cognitive-based interventions across clinical and non-clinical populations. These results underscore the immediate impact of the intervention across multimodal assessments, including neurophysiological changes, cognitive, and behavioral outcomes.

Resting state EEG source derived salience network theta connectivity mediates anxiety in community dwelling individuals reporting childhood trauma.

Childhood trauma (CT) has been consistently linked with etiology of anxiety and depression. Finding biomarkers that mediate the relationship between CT and psychopathology is important and electroencephalography (EEG) can be a useful and cost-effective tool serving this purpose. Hence, in the current research we investigated resting state EEG biomarkers associated with CT and how these may link to psychopathology of anxiety and depression in adults. A total of 324 community recruited participants (age range 15-93 years) completed standard self-report scales of CT, anxiety and depression, and also underwent an eyes-closed resting state EEG recording session. Based on several functional neuroimaging studies, which have shown that connectivity in the salience network with major nodes in the anterior cingulate cortex (ACC) and insula is modulated by CT, we derived salience network connectivity measures from resting state EEG source imaging. Also given that theta band (3-7 Hz) neural oscillations have been shown to have an ACC source, we specifically focused on theta band salience network connectivity. The results showed significant positive correlation between CT and both anxiety and depression. We also found that theta band salience network connectivity, but not network activity, had a significant inverse relationship with CT and specifically mediated the relationship between CT and anxiety, but not depression. Interrogating the subcomponents of CT, theta connectivity in the salience network mediated the relationship between anxiety and both emotional abuse and physical neglect. These results showcase the utility of a resting state EEG source imaging-based biomarker in understanding the mechanistic associations between CT and psychopathology in community dwelling individuals.

Resting-state microstate dynamics abnormalities in children with ADHD and co-occurring sleep problems.

Children with attention-deficit/hyperactivity disorder often experience sleep problems, exacerbating symptoms, and cognitive deficits. However, the neurophysiological mechanisms underlying such deficits remained unclear. This study aims to use resting-state microstate analysis to investigate the neurophysiological characteristics in children with ADHD and sleep problems and explore whether neurophysiological abnormalities are associated with sleep problems. Five-minute eyes-closed resting-state EEG data were collected in 34 children with both ADHD and sleep problems, 32 children with ADHD without sleep problems, and 22 healthy controls aged 6-12 years. Participants' parents completed the ADHD Rating Scale, Behavioral Rating Inventory of Executive Function, and Children's Sleep Habits Questionnaire. Five minutes of eyes-closed resting-state EEG data were collected, and agglomerative hierarchical clustering was used for microstate analysis. ANCOVAs, adjusted for sex and IQ, were used to compare ADHD symptoms, executive function, and microstate parameters across groups. Pearson partial correlations, controlling for sex and IQ, examined the association between microstate parameters and sleep problems. Children with both ADHD and sleep problems exhibited more severe inattentive (20.4±3.1 vs 18.0±3.4, p<0.05) and total symptoms (14.3±6.0 vs 13.0±5.3, p<0.05), along with greater deficits in emotional regulation (1.88±0.58 vs 1.60±0.42, p<0.05, organizational of materials 2.56±0.41 vs 2.20±0.55, p<0.05, behavioral regulation (1.88±0.40 vs 1.70±0.36, p<0.05), and global executive function (2.14±0.30 vs 1.95±0.30, p<0.05), compared to children with ADHD without sleep problems. Moreover, Both ADHD groups exhibited significantly reduced microstate D occurrence and lower transition probability from microstate C to D compared to healthy controls (all p<0.05). Additionally, no significant correlation was found between sleep problems and microstate parameters in all three groups after adjustment (all p>0.05). Children with both ADHD and sleep problems showed greater symptom severity and more pronounced executive function deficits compared to children with ADHD without sleep problems and healthy controls. Additionally, both ADHD groups showed overlapping atypical microstate parameters, suggesting children with co-occurring ADHD and sleep problems may share similar aberrant neurophysiological characteristics with children with ADHD alone. Resting-state EEG microstate parameters may serve as a sensitive tool for assessing sleep problems in children with ADHD, distinguishing them from typically developing children.

Individualized Spectral Features in First-episode and Drug-naïve Major Depressive Disorder: Insights from Periodic and Aperiodic EEG Analysis.

The detection of abnormal brain activity plays an important role in the early diagnosis and treatment of major depressive disorder (MDD). Recent studies have shown that the decomposition of the electroencephalography (EEG) spectrum into periodic and aperiodic components is useful for identifying the drivers of electrophysiologic abnormalities and avoiding individual differences. This study aimed to elucidate the pathologic changes in individualized periodic and aperiodic activities and their relationships with the symptoms of MDD. EEG data in the eyes-closed resting state were continuously recorded from 97 first-episode and drug-naïve patients with MDD and 90 healthy control (HC) participants. Both periodic oscillations and aperiodic components were obtained via the "fitting oscillations and one-over f" (FOOOF) algorithm and then used to compute individualized spectral features. MDD patients presented higher canonical alpha and beta band power but lower aperiodic-adjusted alpha and beta power. Furthermore, we found that alpha power was strongly correlated with the age of patients but not with disease symptoms. The aperiodic intercept was lower in the parietal‒occipital region and was positively correlated with the Hamilton Depression Rating Scale (HAMD) score after accounting for age and sex. In the asymmetry analysis, alpha activity appeared asymmetrical only in the HC group, whereas aperiodic activity was symmetrical in both groups. The findings of this study provide insights into the role of abnormal neural spiking activity and impaired neuroplasticity in MDD progression and suggest that the aperiodic intercept in resting-state EEG may be a potential biomarker of MDD.

Preliminary Evidence for Perturbation-Based tACS-EEG Biomarkers of Gamma Activity in Alzheimer's Disease.

Alzheimer's disease (AD) is characterized by impaired inhibitory circuitry and GABAergic dysfunction, which is associated with reduced fast brain oscillations in the gamma band (γ, 30-90Hz) in several animal models. Investigating such activity in human patients could lead to the identification of novel biomarkers of diagnostic and prognostic value. The current study aimed to test a multimodal "Perturbation-based" transcranial Alternating Current Stimulation-Electroencephalography (tACS)-EEG protocol to detect how responses to tACS in AD patients correlate with patients' clinical phenotype. Fourteen participants with mild to moderate dementia due to AD underwent a baseline assessment including cognitive status, peripheral neuroinflammation, and resting-state (rs)EEG. The tACS-EEG recordings included brief (6') tACS blocks of gamma (i.e., 40Hz) stimulation administered through 4 different montages, with Pre/Post 32-Channels EEG for each block. Changes in tACS-EEG and rsEEG γ band power with respect to baseline were adopted as a metric of induction and compared with cognitive scores and neuroinflammatory biomarkers. We found positive correlations between 40Hz-induced γ activity in fronto-central-parietal areas and patient cognitive status and negative ones with neuroinflammatory markers. Participants with greater cognitive impairment exhibited less γ induction and higher peripheral neuroinflammation. The same analysis performed with spectral power from baseline rsEEG resulted in no significant correlations, promoting the value of tACS-based perturbation for capturing individual differences in pathology-related brain features. Our work suggests a link between tACS-induced γ band spectral power and clinical severity, with weaker γ induction corresponding to more severe clinical/cognitive impairment. This study provides preliminary support for the development of novel physiological biomarkers and therapeutic targets based on disease severity.

EEG alpha activity as predictor for TBS-rTMS treatment outcome in depression.

Repetitive transcranial magnetic stimulation (rTMS) is an established psychiatric procedure for patients suffering from treatment-resistant depression (TRD). Biomarker identification to predict rTMS outcomes may assist the clinician in optimizing treatment selection. In recent years, different electrophysiological markers, in particular electroencephalographic (EEG) markers, were shown to yield discriminative power between responders and non-responders to various TRD treatments. However, so far, predictive markers for the Theta Burst Stimulation (TBS) protocol have remained scarce. The present study, therefore, aimed to identify such markers. Resting state EEGs of 10-15min were done in a group of 46 TRD patients prior to rTMS TBS treatment (600 stimuli over the left dorsolateral prefrontal cortex). Each patient underwent 19-21 sessions with 4-5 sessions per week. Depression was assessed using the Hamilton Depression Rating Scale and the Beck Depression Inventory II. Our study demonstrated that responders exhibited significantly lower FAA values in a baseline EEG indicating that left frontal alpha dominance was associated with a positive response to TBS-rTMS in TRD patients. FAA was independent of both gender and age. No other biomarker, including alpha peak frequency, or alpha power, showed a significant difference between responders and non-responders. Taken together, FAA observed in EEG readings is emerging as a promising indicator of treatment outcomes in patients with TRD. Given these findings, we suggest considering FAA as a predictive factor when assessing the effectiveness of therapeutic interventions. Further studies replicating these results in larger, diverse populations are needed to confirm FAA as a reliable biomarker of clinical outcome.

Abnormal dynamic features of cortical microstates for detecting early-stage Parkinson's disease by resting-state electroencephalography: Systematic analysis of the influence of eye condition.

Resting state electroencephalography (EEG) has proved useful in studying electrophysiological changes in neurodegenerative diseases. In many neuropathologies, microstate analysis of the eyes-closed (EC) scalp EEG is a robust and highly reproducible technique for assessing topological changes with high temporal resolution. However, scalp EEG microstate maps tend to underestimate the non-occipital or non-alpha-band networks, which can also be used to detect neuropathological changes. Recent evidence has shown that the source-space microstates can characterize distinct functional connectivity patterns but its clinical ability to detect neuropathological changes has not been demonstrated so far. It should also be remembered that the eye condition may play an important role in neural activity dynamics. The aim of this study was to systematically characterize the dynamic neuropathological features of sensor-space and source-space EEG microstates in PD patients with no cognitive impairment in both EC and EO conditions with the aim of identifying potential biomarkers that could be used as a complementary clinical screening method for early PD detection. We found that the dynamic features of the source-space microstates were more sensitive in detecting PD than the sensor-space microstates, while EO was able to detect neuropathological changes in PD patients better than EC. In EO, PD disease exhibited significantly higher occurrence and coverage in visual-network related source-space microstates and abnormally high duration in sensorimotor network-related microstates. Our results suggest that the source-space microstate analysis of resting-state EEG could provide robust biomarkers to detect early-stage PD, which would allow the development of patient-oriented strategies to prevent the disease and improve the patients' quality of life.