Beta-band oscillations--signalling the status quo?

Frequency-specific network connectivity impairments linked to suicide attempts in major depressive disorder during the GO/NOGO task

BACKGROUND

Major depressive disorder (MDD) is a main risk factor of suicide, emphasizing the urgent need for understanding the neurobiological mechanisms underlying suicide attempts (SAs) in depressive patients. We hypothesized that aberrant frequency-specific functional connectivity patterns underlying an executive and inhibition task might be associated with SA in depression.

METHODS

The current study enrolled 143 subjects including 43 healthy controls and 87 patients with MDD (43 patients with SA and 44 without SA), who attended a GO/NOGO task during the magnetoencephalography recording. Time-frequency features in the whole-brain sensors and frequency-specific brain network connectivity patterns were estimated. Behavioral data was recorded during the tasks and neurocognitive assessments were conducted.

RESULTS

The SA group exhibited poorest behavioral and neurocognitive assessments performances. Decreased alpha/beta oscillations of the GO condition and increased alpha/beta oscillations of NOGO condition were observed in the SA group. Hypo-activated frontal-limbic connectivity in the alpha band and frontal-occipital connectivity in the beta band were observed in the SA group during the GO trials, meanwhile, hyper-activated frontal-temporal connectivity in the alpha band and frontal-parietal connectivity in the beta band were associated with SA during the NOGO trials. Frequency-specific features were correlated with the severity of suicide risk, neurocognitive assessments, and could be used to predict potential SAs.

CONCLUSIONS

Neuroimaging and neurocognitive evidences supported altered alpha/beta oscillations and connectivity patterns associated with SA in depression, suggesting that depressive patients with SA might exhibit impaired cognitive control functions.

Effect of Dual-Task Training on the Number of EEG Bands in Stroke Patients

BACKGROUND/OBJECTIVE

Dual-task training (DTT) positively impacts stroke recovery, but its effects on electroencephalography (EEG) using Fourier series analysis are under-researched. This study aimed to evaluate the effects of DTT on EEG in stroke patients by analyzing different EEG bands with fast Fourier transform (FFT).

METHODS

Five participants with unilateral ischemic stroke completed 12 sessions of 15-min DTT, three times a week for 4 weeks. EEG data were recorded before and after the intervention, and FFT analysis was conducted. Assessments of upper limb function, elbow flexor muscle tone, and daily living activities were also performed.

RESULTS

FFT analysis showed a reduction in delta, theta, alpha, and beta bands post-DTT, while their correlation between measurement times remained consistent. These changes were somewhat reflected in the participants' improved clinical outcomes.

CONCLUSION

The results suggest that DTT positively affects EEG band frequencies, with a consistent correlation between pre- and post-intervention measurements. This indicates that FFT analysis could be a useful tool for assessing DTT's impact on stroke recovery.

Inhibitory control in Bipolar Disorder disclosed by theta band modulation

BACKGROUND

Cognitive inhibition is key to cognitive control in healthy and psychiatric conditions. Bipolar Disorder (BD) individuals display a range of inhibitory deficits and high levels of impulsivity across all stages of the disease, including euthymia.

METHODS

We tested how the inhibition of heuristics in favor of analytical strategies influences the elaboration of sentences with logical quantifiers by means of a sentence-picture matching task in which the processing of quantified sentences containing the logical universal and particular quantifiers was required. Behavioral and brain oscillatory responses were assessed employing EEG recordings.

RESULTS

In Experiment 1, in a group of healthy volunteers, we demonstrated how the presence of a universal quantifier generates an inhibition, characterized by a high cognitive load, which is resolved at the expense of a poorer behavioral performance compared to a lower cognitive load and neutral control task. In Experiment 2, comparing healthy adults and BD patients, EEG time-frequency analysis showed a different modulation of the theta frequency band localized centrally in the medial frontal areas and representative of the different degrees of cognitive control between groups.

LIMITATIONS

Electrophysiological description should be interpreted with caution in light of the high signal-to-noise ratio determined by the complexity of the task.

CONCLUSIONS

Even in euthymia, BD limited availability of resources for cognitive inhibition impacts the functionality of a fronto-parietal cortical network, responsible for cognitive control, and orchestrated by the activity of frontal areas synchronized in theta and beta frequency.

Chronic pain and its association with cognitive decline and brain function abnormalities in older adults: Insights from EEG and neuropsychological assessment

Studies examining the interplay between chronic pain, cognitive function, and functional brain abnormalities in older adults are scarce. To address this gap, we administered a series of neuropsychological tests and recorded electroencephalography (EEG) data during resting-state conditions in 26 older adults with chronic pain (CPOA), 30 pain-free older adults (OA), and 31 younger adults (YA). CPOA demonstrated poorer performance compared to OA on the Stroop test, the Wisconsin Card Sorting Test (WCST) and Digit Span. Both groups of older adults exhibited higher beta activity compared to younger adults, with CPOA displaying particularly elevated beta-2 activity localized in the posterior cingulate cortex compared to OA. Correlational analyses indicated that in CPOA participants, heightened beta activity was linked to decreased performance on the WCST. Conversely, in OA, we observed a positive correlation between beta activity and performance on the WCST. Overall, our findings suggest that the cumulative impact of pain in aging would diminish the effectiveness of the functional compensatory mechanisms that occur during healthy aging, exacerbating cognitive decline.

How do foreign language learning experiences influence the self-reference effect?

People tend to display a processing bias towards information that is personally relevant, as opposed to information that is irrelevant to themselves. Such a bias can be influenced by long-term cultural experiences and temporary cultural priming tasks. However, the impact of the latter is transient, and it is unclear if an intermediate influence, more lasting than temporary priming tasks but less enduring than a cultural background, such as language learning, could induce a similar and stable processing bias. Given that language can shape people's mindset, this study aimed to investigate whether language learning experiences could affect participants' self-processing bias during a decision-making task. The findings showed that while behavioral results were not significant, ERP data indicated that advanced learners had more negative late N400 and P600 components for moderately self-relevant stimuli compared to highly self-relevant ones, mainly in the left or medial hemispheres. Beginners exhibited similar trends with marginal effects from fronto-central to parietal regions. Additionally, beginners displayed more negative N100 and early N400 responses and lacked a left-lateralized low-beta burst compared to advanced learners. These results suggest that the self-reference effect is present in both L2 beginners and advanced learners but is more pronounced in advanced learners. Notably, advanced learners with extensive English experience are more influenced by Western independent self-construal than beginners, leading them to focus more on highly self-related information and exhibit a stronger self-reference effect.

An experimental EEG study of brain activities underlying the Autonomous Sensory Meridian Response

Autonomous Sensory Meridian Response (ASMR) is an audio-visual phenomenon that has recently become popular. Many people have reported experiencing a tingling-like sensation through their body while watching audio/video clips known as ASMR clips. People capable of having such experiences have also reported improved overall well-being and feeling relaxed. However, the neural activity underlying this phenomenon is not yet well-studied. The present study aims to investigate this issue using electroencephalography (EEG) employing an exploratory approach. We recorded resting-state EEGs from twelve participants before and after watching an ASMR clip and a control video clip. We divided the participants into two groups capable of experiencing ASMR tingling (ASMR group) and not capable of experiencing ASMR tingling (Non-ASMR group), by performing "Jenks Natural Breaks" clustering method on the results of a self-report questionnaire. We calculated the spectral power of EEG recording and compared the resulting values between the groups and sessions. We demonstrated a decline in the power of EEG activities in the delta frequency band in all regions of the brain and an increase in alpha activity in the occipital area of the brain and increases in beta oscillations was noted over the left fronto-temporal region of the brain among ASMR group. We did not observe similar results among the Non-ASMRs participants or among ASMRs in the control group.

Altered theta band and theta/beta ratio in mismatch negativity associate with treatment effect in schizophrenia with auditory hallucinations

Evidence suggests that attenuated mismatch negative (MMN) waves have a close link to auditory verbal hallucinations (AVH) and their clinical outcomes, especially impaired neural oscillations such as θ, β representing attentional control. In current study, thirty patients with schizophrenia and AVH (SZ) and twenty-nine healthy controls (HC) underwent multi-feature MMN paradigm measurements including frequency and duration deviant stimuli (fMMN and dMMN). Clinical symptoms and MMN paradigm were followed up among SZ group after 8-week treatment. Results demonstrated that hallucinating patients exhibited attenuated dMMN amplitudes across Fz (p = 0.010), F1 (p = 0.020) and F2 (p = 0.014) electrodes, which were trendily recovered after treatment. Meanwhile, θ band and TBR at frontal fMMN and right temporal dMMN were significantly reduced in SZs. After treatment, SZs showed reduced scores of Hoffman's Auditory Hallucinations Rating Scale (AHRS), with a remarkable recovery in right temporal TBR of dMMN (p = 0.042) and a trending change in frontal TBR of fMMN (p = 0.090). The β band was decreased in dMMN (p = 0.035) by time. Additionally, P3 scores of Positive and Negative Syndrome Scale (PANSS) were negatively correlated with θ band of fMMN at baseline. Baseline scores of AHRS negatively predicted changes of dMMN amplitude after treatment, and changes of β band in left temporal dMMN predicted the reduction in scores of PANSS negative scale. These findings supported that deficits in θ oscillation and TBR during auditory attention process were crucial to clinical progression of schizophrenia with AVH.

Cortical dynamics underlying initiation of rapid steps with contrasting postural demands

Our ability to flexibly initiate rapid visually-guided stepping movements can be measured in the form of express visuomotor responses (EVRs), which are short-latency (∼100 ms), goal-directed bursts of lower-limb muscle activity. Interestingly, we previously demonstrated that recruitment of anticipatory postural adjustments (APAs) interacted with the subcortically-generated EVRs in the lower limb, suggesting context-dependent top-down modulation. We investigated the associated cortical dynamics prior to and during rapid step initiation towards a salient visual target in twenty-one young, healthy individuals while stepping under varying postural demands. We recorded high-density EEG, surface electromyography from gluteus medius and ground-reaction forces. Independent component analysis and time-frequency statistics revealed significant, yet relatively modest differences between conditions in preparatory cortical dynamics, most evidently in primary motor areas. Following target presentation, we observed stronger theta and alpha power enhancement in the supplementary motor area, and stronger alpha and beta power decrease in primary motor, parietal and occipital clusters during APA recruitment that preceded steps under high postural demands. Side-specific changes in motor cortex lagged the timing of EVR expression, supporting the EVR's purportedly subcortical origin. Together, our findings point towards greater cortical involvement in step initiation under high postural demands as compared to more reflexive, stimulus-driven steps. These findings may be particularly relevant for populations where postural control is impaired by age or disease, as more cortical resources may need to be allocated during stepping.

Unraveling the roles of spatial working memory sustained and selective neurons in prefrontal cortex

The heart of goal-directed behavior organization is working memory. Recent studies have emphasized the critical role of the prefrontal cortex (PFC) in working memory, highlighting elevated spiking levels in PFC neurons during working-memory delays. As a higher-order cortex, PFC contains various types of neurons with complex receptive fields, making it challenging to identify task-engaged neurons, particularly during the working memory periods when firing rates are lower compared to stimulus periods. While previous studies have primarily focused on neurons selective for sensory stimuli, there are also task-sustained neurons that are not selective for specific stimulus characteristics. In this study, we differentiate between working memory (WM)-sustained neurons, which show task-related activity without stimulus spatial selectivity, and working memory (WM)-selective neurons, which are selective for the location of the stimulus. To investigate their roles, we investigated the neural activities of the lateral PFC neurons in two macaque monkeys during a spatial working memory task. Fano factor analysis revealed that the neuronal variability of both WM-selective and WM-sustained neurons was similar and significantly higher than that of non-active neurons (neurons not modulated by the task). Moreover, the Fano factor of active neurons diminished during error trials compared to correct trials. The spike phase locking (SPL) value was measured to evaluate the coupling of local field potentials (LFPs) phases to spike times, considering neural network characteristics. SPL results indicated that both WM-selective neurons and WM-sustained neurons exhibited higher SPL in the alpha/beta-band compared to non-active neurons. Additionally, the alpha/beta-band SPL of working memory-active neurons decreased during error trials. In summary, despite the non-stimulus-specific activation of WM-sustained neurons, they may contribute to task performance alongside WM-selective neurons.

Predicting Placebo Responses Using EEG and Deep Convolutional Neural Networks: Correlations with Clinical Data Across Three Independent Datasets

Identifying likely placebo responders can help design more efficient clinical trials by stratifying participants, reducing sample size requirements, and enhancing the detection of true drug effects. In response to this need, we developed a deep convolutional neural network (DCNN) model using resting-state EEG data from the EMBARC study, achieving a balanced accuracy of 69% in predicting placebo responses in patients with major depressive disorder (MDD). We then applied this model to two additional datasets, LEMON and CAN-BIND-which did not include placebo groups-to investigate potential relationships between the model's predictions and various clinical features in independent samples. Notably, the model's predictions correlated with factors previously linked to placebo response in MDD, including age, extraversion, and cognitive processing speed. These findings highlight several factors associated with placebo susceptibility, offering insights that could guide more efficient clinical trial designs. Future research should explore the broader applicability of such predictive models across different medical conditions, and replicate the current EEG-based model of placebo response in independent samples.

The causal role of beta band desynchronization: Individualized high-definition transcranial alternating current stimulation improves bimanual motor control

OBJECTIVE

To unveil if 3 mA peak-to-peak high-definition β transcranial alternating current stimulation (tACS) applied over C4 -the area overlaying the right sensorimotor cortex-enhances bimanual motor control and affects movement-related β desynchronization (MRβD), thereby providing causal evidence for the polymorphic role of MRβD in motor control.

METHODS

In this sham-controlled, crossover study, 36 participants underwent 20 min of fixed 20 Hz tACS; tACS individualized to peak β activity during motor planning at baseline; and sham tACS randomized over three consecutive days. Each participant underwent all three conditions for a total of 108 sessions, ensuring within-subject comparisons. Before, during, and after tACS, participants performed a bimanual tracking task (BTT) and 64-channel electroencephalography (EEG) data was measured. Spatiotemporal and temporal clustering statistics with underlying linear mixed effect models were used to test our hypotheses.

RESULTS

Individualized tACS significantly improved bimanual motor control, both online and offline, and increased online MRβD during motor planning compared to fixed tACS. No offline effects of fixed and individualized tACS on MRβD were found compared to sham, although tACS effects did trend towards the hypothesized MRβD increase. Throughout the course of the study, MRβD and bimanual motor performance increased. Exclusively during motor planning, MRβD was positively associated to bimanual motor performance improvements, emphasizing the functionally polymorphic role of MRβD. tACS was well tolerated and no side-effects occurred.

CONCLUSION

Individualized β-tACS improves bimanual motor control and enhances motor planning MRβD online. These findings provide causal evidence for the importance of MRβD when planning complex motor behavior.

Electrophysiological Correlates of Lucid Dreaming: Sensor and Source Level Signatures

Lucid dreaming (LD) is a state of conscious awareness of the ongoing oneiric state, predominantly linked to REM sleep. Progress in understanding its neurobiological basis has been hindered by small sample sizes, diverse EEG setups, and artifacts like saccadic eye movements. To address these challenges in characterizing the electrophysiological correlates of LD, we introduced an adaptive multistage preprocessing pipeline, applied to human data (male and female) pooled across laboratories, allowing us to explore sensor- and source-level markers of LD. We observed that, while sensor-level differences between LD and nonlucid REM sleep were minimal, mixed-frequency analysis revealed broad low alpha to gamma power reductions during LD compared with wakefulness. Source-level analyses showed significant beta power (12-30 Hz) reductions in right central and parietal areas, including the temporoparietal junction, during LD. Moreover, functional connectivity in the alpha band (8-12 Hz) increased during LD compared with nonlucid REM sleep. During initial LD eye signaling compared with the baseline, source-level gamma1 power (30-36 Hz) increased in right temporo-occipital regions, including the right precuneus. Finally, functional connectivity analysis revealed increased interhemispheric and inter-regional gamma1 connectivity during LD, reflecting widespread network engagement. These results suggest that distinct source-level power and connectivity patterns characterize the dynamic neural processes underlying LD, including shifts in network communication and regional activation that may underlie the specific changes in perception, memory processing, self-awareness, and cognitive control. Taken together, these findings illuminate the electrophysiological correlates of LD, laying the groundwork for decoding the mechanisms of this intriguing state of consciousness.

Beta-band power modulation in the human amygdala during a Direct Reach arm reaching task

The human amygdala is primarily known for its involvement in processing emotional and fearful responses, but newer evidence has identified a role for this structure in motor processing. Our lab previously utilized an arm-reaching task and observed significant beta-band (13-30 Hz) modulation in the hippocampus. Given these results, we sought to characterize the role of beta-band modulation in the amygdala during movement execution in participants with stereoelectroencephalography (SEEG) depth electrodes in the amygdala for seizure localization. We show that 9 of 13 participants (69.2 %) showed decreased beta-band power in the amygdala during the Response (movement execution) phase of an arm-reaching task when compared to Fixation (baseline). Secondary analyses show that there are no statistically significant differences in beta-band modulation between ipsilateral and contralateral implanted electrodes, but there is a small difference between male and female participants. The decrease in beta-band power in the amygdala during the Response phase of a Direct Reach task is consistent with our previous findings in the hippocampus. Our study is the first to report beta-band modulation in the amygdala during motor processing and sets the stage for further studies into the involvement of the amygdala in motor control.

Characterizing the heterogeneity of neurodegenerative diseases through EEG normative modeling

Neurodegenerative diseases like Parkinson's (PD) and Alzheimer's (AD) exhibit considerable heterogeneity of functional brain features within patients, complicating diagnosis and treatment. Here, we use electroencephalography (EEG) and normative modeling to investigate neurophysiological mechanisms underpinning this heterogeneity. Resting-state EEG data from 14 clinical units included healthy adults (n = 499) and patients with PD (n = 237) and AD (n = 197), aged over 40. Spectral and source connectivity analyses provided features for normative modeling, revealing significant, frequency-dependent EEG deviations with high heterogeneity in PD and AD. Around 30% of patients exhibited spectral deviations, while ~80% showed functional source connectivity deviations. Notably, the spatial overlap of deviant features did not exceed 60% for spectral and 25% for connectivity analysis. Furthermore, patient-specific deviations correlated with clinical measures, with greater deviations linked to worse UPDRS for PD (⍴ = 0.24, p = 0.025) and MMSE for AD (⍴ = -0.26, p = 0.01). These results suggest that EEG deviations could enrich individualized clinical assessment in Precision Neurology.

Evaluating individual sensitivity to propofol through EEG complexity and information integration: from neural dynamics to precision anesthesia

Objective.Understanding the neural mechanisms underlying consciousness during anesthesia is critical for advancing anesthesiology and neuroscience. However, given the high variability in individual sensitivity to anesthetic agents, accurately elucidating the relationship between individual characteristics and drug responses is also crucial for ensuring clinical anesthesia safety.Approach.This study utilized high-density EEG data from 20 participants under various propofol-induced sedation states. We stratified participants into low- and high-sensitivity cohorts based on their behavioral responsiveness to standardized auditory stimuli during sedation. Then the metrics such as permutation entropy (PE), phase-lag entropy (PLE), and permutation cross mutual information (PCMI) were analyzed to evaluate neural complexity, the diversity of connectivity, and information integration. Machine learning models, including support vector machines (SVM), were applied to classify individual sensitivity to propofol, with SHapley Additive exPlanations (SHAP) analysis providing feature interpretability.Main results.Subjects were divided into high-performance (low-sensitivity) group and low-performance (high-sensitivity) group based on the accuracy of their responses to auditory stimuli. In the moderate sedation, the high-performance group exhibited elevated PE, increased PLE in alpha band and the decreased PLE in beta band, and decreased PCMI in alpha band. In the resting-state, we extracted 18 metrics that were significantly different between the two groups. Using these resting-state metrics as features, the SVM model achieved an accuracy of 87.5% ± 0.06% in classifying individuals into high- or low-sensitivity groups. SHAP analysis results indicated that the features, including the PLE value of temporal in alpha band (α-PLET) and the PCMI value of frontal-parietal in beta band (β-PCMIFP), were identified as robust predictors of propofol sensitivity, with high weights across various models.Significance.This study highlights the differential neural dynamics induced by propofol across performance groups. This study highlights that resting-state metrics can predict individual sensitivity to propofol. Our findings provide preliminary insights into the potential utility of pre-anesthesia brain state assessments in predicting individual propofol sensitivity, which may contribute to the development of more precise personalized anesthesia plans.

Amplitude coupling is altered in delirium of various etiologies: Results from a retrospective multi-center case-control EEG study

OBJECTIVE

Delirium manifests with comparable clinical presentations, regardless of its heterogeneous etiology. This suggests a final common pathway such as decreased electroencephalography (EEG) phase coupling. This study investigates if amplitude coupling, another mode of neural communication, is altered in delirium due to different etiologies.

METHODS

We analyzed EEGs of patients from three sites with either postoperative, poststroke or medical delirium and non-delirious control patients. Amplitude envelope correlation corrected for spatial leakage (AECc) was calculated and Mann-Whitney U-tests were used to compare patients with or without delirium. AECc differences among delirium types were compared using Kruskal-Wallis tests.

RESULTS

AECc was significantly increased in delirious (n = 173, age 79.2±9.3 years, 46 % female) as compared to non-delirious (n = 204, age 72.9±13.1 years, 45 % female) patients in the delta (median, effect size of difference: 0.16 vs. 0.12, r = 0.28, p < 0.01) and beta band (0.11 vs. 0.09, r = 0.14, p = 0.04). These changes did not differ among delirium types (p > 0.05).

CONCLUSIONS

We found modestly higher delta and beta band AECc in delirium compared to non-delirious control patients, regardless of the presumed etiology.

SIGNIFICANCE

This study provides evidence for altered amplitude coupling as mode of impaired neuronal communication in delirium, the role of which should be investigated in future studies of neural network pathophysiology.

Direct Comparison of EEG Resting State and Task Functional Connectivity Patterns for Predicting Working Memory Performance Using Connectome-Based Predictive Modeling

Background: The integration of machine learning with advanced neuroimaging has emerged as a powerful approach for uncovering the relationship between neuronal activity patterns and behavioral traits. While resting-state neuroimaging has significantly contributed to understanding the neural basis of cognition, recent fMRI studies suggest that task-based paradigms may offer superior predictive power for cognitive outcomes. However, this hypothesis has never been tested using electroencephalography (EEG) data. Methods: We conducted the first experimental comparison of predictive models built on high-density EEG data recorded during both resting-state and an auditory working memory task. Multiple data processing pipelines were employed to ensure robustness and reliability. Model performance was evaluated by computing the Pearson correlation coefficient between predicted and observed behavioral scores, supplemented by mean absolute error and root mean square error metrics for each model configuration. Results: Consistent with prior fMRI findings, task-based EEG data yielded slightly better modeling performance than resting-state data. Both conditions demonstrated high predictive accuracy, with peak correlations between observed and predicted values reaching r = 0.5. Alpha and beta band functional connectivity were the strongest predictors of working memory performance, followed by theta and gamma bands. Additionally, the choice of parcellation atlas and connectivity method significantly influenced results, highlighting the importance of methodological considerations. Conclusion: Our findings support the advantage of task-based EEG over resting-state data in predicting cognitive performance, aligning with. The study underscores the critical role of frequency-specific functional connectivity and methodological choices in model performance. These insights should guide future experimental designs in cognitive neuroscience. Impact Statement This study provides the first direct comparison of EEG-based functional connectivity during rest and task conditions for predicting working memory performance using connectome-based predictive modeling (CPM). It demonstrates that task-based EEG data slightly outperforms resting-state data, with alpha and beta bands being the most predictive. The findings highlight the critical influence of methodological choices, such as parcellation atlases and connectivity metrics, on model outcomes. By bridging gaps in EEG research and validating CPM's applicability, this work advances the optimization of neuroimaging protocols for cognitive assessment, offering insights for future studies in cognitive neuroscience.

Differentiation with electroencephalography microstate in temporal lobe epilepsy with and without cognitive decline

OBJECTIVE

Electroencephalography (EEG) microstate analysis is widely used in the study of various neurological and psychiatric disorders. We aim to assess whether EEG microstates are altered in TLE patients with and without cognitive decline.

METHODS

This study included a total of 47 temporal lobe epilepsy (TLE) patients with or without cognitive decline and 14 healthy controls (HCs). All participants underwent 64-channel EEG monitoring. Two-minute epochs of preprocessed eye closed awake EEG data were extracted for microstate analysis. Participants were divided into groups based on intelligence quotient (IQ) scores assessed by the Wechsler Intelligence Scale: normal cognition group (n = 23, IQ ≥ 90) and abnormal cognition group (n = 24, IQ < 90). We conducted frequency spectral analysis on each frequency sub-bands (delta, 1-4 Hz; theta, 4-8 Hz; alpha, 8-12 Hz; beta, 12-30 Hz) of the three groups. Then the following microstate parameters were extracted for analysis over the full frequency band (1-30 Hz) and frequency sub-bands: duration, coverage, occurrence, and transition probability. Statistical analysis using multivariate analysis of variance (MANOVA) with Bonferroni correction (α = 0.0025).

RESULT

Microstate analysis in the beta sub-band revealed significant differences among groups. TLE patients with abnormal cognition showed increased occurrence of Map-D; significantly higher transition probabilities from Map-A, Map-B, and Map-C to Map-D; and distinct microstate characteristics compared to patients with normal cognition and HCs. In frequency spectral analysis, the power across all frequency sub-bands showed no significant differences among TLE patients with normal cognition, TLE patients with abnormal cognition, and HCs.

CONCLUSION

Beta sub-band EEG microstates, particularly Map-D characteristics, may serve as potential neurophysiological markers for cognitive decline in TLE patients.

Beta-band power modulation in the human amygdala during a delayed reach task

INTRODUCTION

The amygdala is mostly known for its roles in emotional processing and social behavior. In recent years, it has been implicated in voluntary motor control due to its structural and functional connectivity with the motor cortex. By investigating whether the amygdala modulates during movement preparation, we can further examine its contributions to motor processing.

OBJECTIVE

We utilized a delayed reach task to measure beta-band (13-30 Hz) modulation in the amygdala during movement preparation. We hypothesized that we would see decreases in beta-band power during the Delay and Response phases of this task.

METHODS

Eleven subjects diagnosed with drug-resistant epilepsy (DRE), who were implanted with stereoelectroencephalographic (SEEG) electrodes, were recruited to this study. The beta-band power was recorded through a delayed reach task. We calculated the beta-band Power Spectral Density (PSD) using multi-taper spectral analysis and compared the trial-averaged PSD using a cluster-based permutation test to determine the significance of beta-band power differences between task phases.

RESULTS

100 % of participants and 44.8 % of gray matter contacts in the amygdala (n = 58) exhibited significantly decreased beta-band power during the Delay phase. During the Response phase, 90.9 % of participants and 58.6 % of gray matter contacts (n = 58) showed significantly decreased beta-band power. We also found a difference in the proportion of amygdala contacts showing beta-band modulation between those implanted in gray vs. white matter (p = 0.0035) but found no difference between contralateral vs. ipsilateral contacts (p = 0.17) and male vs. female participants (p = 0.34).

CONCLUSION

This study is the first to demonstrate beta-band power decreases in the amygdala during the Delay and Response phases of a delayed reach task. These findings demonstrate that the amygdala undergoes neural modulation prior to movement initiation and during movement execution.

Portable EEG for assessing attention in educational settings: A scoping review

BACKGROUND

Portable EEG provides the opportunity to capture neural correlates of attention in a more naturalistic environment. However, the field is still in its infancy, with varied research aims and methodologies. The current scoping review aims to clarify: (1) the research aims of the studies, (2) the portable EEG collection methodologies, and (3) the EEG measures of attention.

METHOD

The review followed the Preferred Reporting Items for Systematic Review and Meta-Analysis - Scoping Review extension. Two authors extracted data items from 45 eligible studies.

RESULTS

Three research aims were identified in previous studies: examining the effects of learning-related factors on attention captured by portable EEG (n = 23), developing attention classification algorithms (n = 7) and software for monitoring and promoting attention (n = 10), and verifying the signal quality of EEG derived from portable EEG in attentional tasks (n = 5). The testing sites and tasks were predominantly out-of-lab controlled settings and structured learning materials. To quantify attention, 8 studies employed a theory-driven approach, e.g., using EEG measures based on prior research correlating specific spectral power with attention. In contrast, 37 studies used data-driven approaches, e.g., using spectral power as input features for machine learning models to index attention.

DISCUSSION

Portable EEG has been a promising approach to measuring attention in educational settings. Meanwhile, there are challenges and opportunities related to the better translation of cognitive neuroscience research into practice.

Electrophysiological effects of deep brain stimulation in anorexia nervosa

OBJECTIVE

To study deep brain stimulation (DBS)-induced electrophysiological changes over time in patients with anorexia nervosa (AN).

METHODS

We performed EEG recordings on 4 AN patients treated with DBS at 3 time points, and on 8 age-matched controls. We extracted oscillatory power in the alpha and beta bands, connectivity and global network organization parameters based on graph theory.

RESULTS

We found strong significant within-subject changes in alpha and beta power over time. Nominally significant effects were observed for posterior left (L) alpha (p = 0.034) and anterior/posterior L scalp areas (p = 0.034 and p = 0.013, respectively), however, multiple testing indicated that the effects are heterogeneous across subjects. We found V-shaped curves over time for average functional connectivity. This was largely re-established at the final time-point. The graph-theoretical measures showed similar V-shaped effects consistent with an initially disordered network state.

CONCLUSION

Within-subject effects of stimulation were large, widespread over frequencies, and visible across wide brain areas and networks. Prolonged stimulation seemed to reinstate organization in the functional brain networks. Our results support the observations that effects of DBS are not merely local, but influence widespread pathological network activity and that, after an initial period of disorganisation, the brain adapts to the stimulation.

SIGNIFICANCE

A better understanding of the electrophysiological effects of DBS may allow us to personalize and optimize the intervention and thereby further improve effectiveness in AN.

Modulation of beta oscillatory dynamics in motor and frontal areas during physical fatigue

Beta-band oscillations have been suggested to promote the maintenance of the current motor (or cognitive) set, thus signaling the 'status quo' of the system. While this hypothesis has been reliably demonstrated in many studies, it fails to explain changes in beta-band activity due to the accumulation of physical fatigue. In the current study, we aimed to reconcile the functional role of beta oscillations during physical fatigue within the status quo theory. Using an innovative electroencephalography design, we identified two distinct beta-band power dynamics in the motor areas as fatigue rises: (i) an enhancement at rest, supposedly promoting the resting state, and (ii) a decrease during contraction, thought to reflect the increase in motor cortex activation necessary to cope with muscular fatigue. We then conducted effective connectivity analyses, which revealed that the modulations during contractions were driven by frontal areas. Finally, we implemented a biologically plausible model to replicate and characterize our results mechanistically. Together, our findings anchor the physical fatigue paradigm within the status quo theory, thus shedding light on the functional role of beta oscillations in physical fatigue. We further discuss a unified interpretation that might explain the conflicting evidence previously encountered in the physical fatigue literature.

Neurobiological mechanisms of sleep state misperception in insomnia disorder: A theoretical review

Sleep state misperception is core to pathophysiological models of insomnia, suggesting that it arises from a dysfunction in neurobiological mechanisms which result in sleep being misperceived as wake. The current review aims to synthesise the best available literature on the neurobiological mechanisms of sleep state misperception and the extent to which the existing literature supports this theory. Overall, findings suggest that cognitive and neurophysiological hyperarousal and dysfunctional sensory-gating mechanisms that insufficiently inhibit arousal may largely account for the phenomenon of sleep state misperception observed among patients with paradoxical insomnia. Most studies to date, however, have relied on comparing self-reports of sleep duration with polysomnography-derived sleep duration, limiting our ability to differentiate the effects of perception from retrospective-reporting bias. Therefore, more studies which use contemporaneous assessments of sleep-wake perception are required to directly test the hypothesis that subjective-objective discrepancies arise from altered perception of sleep states. We report here a research agenda to promote the further development of research in the field and propose several key empirical questions which remain to be explored.

Exoskeleton-guided passive movement elicits standardized EEG patterns for generalizable BCIs in stroke rehabilitation

BACKGROUND

Brain-computer interfaces (BCIs) hold significant potential for post-stroke motor recovery, yet active movement-based BCIs face limitations in generalization due to inter-subject variability. This study investigates passive movement-based BCIs, driven by exoskeleton-guided rehabilitation, to address these challenges by evaluating electroencephalogram (EEG) responses and algorithmic generalization in both healthy subjects and stroke patients.

METHODS

EEG signals were recorded from 20 healthy subjects and 10 stroke patients during voluntary and passive hand movements. Time and time-frequency domain analyses were performed to examine the event-related potential (ERP), event-related desynchronization (ERD), and synchronization (ERS) patterns. The performance of two BCI algorithms, Common Spatial Patterns (CSP) and EEGNet, was evaluated in both within-subject and cross-subject decoding tasks.

RESULTS

Time-domain and time-frequency analyses revealed that passive movements elicited stronger, more consistent ERPs in healthy subjects, particularly in bilateral motor cortices (contralateral: - 7.29 ± 4.51  μV; ipsilateral: - 4.33 ± 3.69  μV). Stroke patients exhibited impaired mu/beta ERD/ERS in the affected hemisphere during voluntary movements but demonstrated EEG patterns during passive movements resembling those of healthy subjects. Machine learning evaluation highlighted EEGNet's superior performance, achieving 84.19% accuracy in classifying affected vs. unaffected movements in patients, surpassing healthy subject left-right discrimination (58.38%). Cross-subject decoding further validated passive movement efficacy, with EEGNet attaining 86.00% (healthy) and 72.63% (stroke) accuracy, outperforming traditional CSP methods.

CONCLUSIONS

These findings underscore that passive movement elicits consistent neural responses, thereby enhancing the generalizability of decoding algorithms for stroke patients. By integrating exoskeleton-evoked proprioceptive feedback, this paradigm reduces inter-subject variability and improves clinical feasibility. Future work should explore the application of exoskeletons in the combination of active and passive movement for stroke rehabilitation.

Functional connectivity anomalies in medication-naive children with ADHD: Diagnostic potential, symptoms interpretation, and a mediation model

OBJECTIVE

To identify reliable electroencephalography (EEG) biomarkers for attention deficit/hyperactivity disorder (ADHD) by investigating anomalous functional connectivity patterns and their clinical relevance.

METHODS

Resting-state EEG data were collected from 74 children aged 6-12 (33 unmedicated ADHD; 41 typically developing). Functional connectivity was quantified using the imaginary part of coherency (ICOH). Machine learning (ML)-based support vector machine (SVM) modeling, regression, and mediation analyses linked connectivity features to symptom severity and diagnostic classification.

RESULTS

Children with ADHD exhibited beta (β) band hypo-connectivity in frontal regions (Fp2-F4, Fp1-Cz, F7-Cz) and theta (θ) band hyper-connectivity in left parietal-central networks (C3-P7, P3-P7, etc.). An SVM classifier achieved an average area under the curve of 0.89 using three connectivity features. Left parietal θ band hyper-connectivity (C3-P7) correlated with both inattention and hyperactivity/impulsivity and mediated their interrelationship.

CONCLUSIONS

ADHD is characterized by disrupted frontoparietal connectivity, with θ band hyper-connectivity in sensory-integration networks potentially compensating for impaired frontal regulation.

SIGNIFICANCE

These findings highlight C3-P7 θ band connectivity as both a diagnostic and mechanistic biomarker, providing novel target for neurofeedback therapies and enhancing the differential diagnosis in neurodevelopmental disorders.

Cross brain reshaping in congenital visual or hearing impairment: triple-network dysfunction

This research examines how congenital visual or hearing impairment reshapes brain function using EEG. The study involved 40 children with congenital visual impairment, 40 with hearing impairment and 42 age and gender-matched normal children as controls. The investigation included assessments of visual and auditory abilities, along with comprehensive EEG evaluations. Techniques such as source localization, functional connectivity and cross-frequency coupling were used to analyse variations in brain activity. Machine learning methods, specifically support vector machines, were utilized to identify key reshaping characteristics associated with congenital impairments. Results showed reduced activation in the visual cortex for visually impaired children and decreased activation in the auditory cortex for hearing-impaired children compared with the control group. Both impairment groups demonstrated significant reductions in functional connectivity across various brain regions, including the visual and auditory cortices, insula, parahippocampal gyrus, posterior cingulate gyrus and frontal cortex. The machine learning model highlighted aberrant connectivity between the visual/auditory cortex and the right insula, the medial prefrontal cortex and dorsolateral prefrontal cortex and the visual and auditory cortex in children with these impairments in the alpha frequency band. Spatially similar patterns of cross-frequency coupling of rhythmic activity were also observed. The study concludes that congenital visual and hearing impairments significantly impact brain development, identifying distinct functional characteristics and shared reshaping patterns. The consistent presence of dysrhythmic activity and reduced functional connectivity suggest the existence of a triple network anomaly.

Effects of Different Individuals and Verbal Tones on Neural Networks in the Brain of Children with Cerebral Palsy

Background/Objectives: Motivation is a key factor for improving motor function and cognitive control in patients. Motivation for rehabilitation is influenced by the relationship between the therapist and patient, wherein appropriate voice encouragement is necessary to increase motivation. Therefore, we examined the differences between mothers and other individuals, such as physical therapists (PTs), in their verbal interactions with children with cerebral palsy who have poor communication abilities, as well as the neurological and physiological effects of variations in the tone of their speech. Methods: The three participants were children with cerebral palsy (Participant A: boy, 3 years; Participant B: girl, 7 years; Participant C: girl, 9 years). Participants' mothers and the assigned PTs were asked to speak under three conditions. During this, the brain activity of the participants was measured using a 19-channel electroencephalogram. The results were further analyzed using Independent Component Analysis frequency analysis with exact Low-Resolution Brain Electromagnetic Tomography, allowing for the identification and visualization of neural activity in three-dimensional brain functional networks. Results: The results of the ICA frequency analysis for each participant revealed distinct patterns of brain activity in response to verbal encouragement from the mother and PT, with differences observed across the theta, alpha, and beta frequency bands. Conclusions: Our study suggests that the children were attentive to their mothers' inquiries and focused on their internal experiences. Furthermore, it was indicated that when addressed by the PT, the participants found it easier to grasp the meanings and intentions of the words.

Examining the role of novelty detection in 20- and 40-Hz auditory steady-state responses

An auditory steady-state response (ASSR) is an electrophysiological response to periodic stimuli that reflects the synchronization of endogenous oscillations. The 40-Hz ASSR is reduced in patients with schizophrenia, bipolar disorder, and autism spectrum disorder, making it a candidate biomarker for these psychiatric disorders. Previous studies have revealed that experimental conditions such as stimulus duration and inter-stimulus interval tend to affect ASSR, suggesting that novelty detection may play an important role in determining the magnitude of ASSR. The present study is the first to investigate the effect of novelty detection on 20- and 40-Hz ASSRs in healthy individuals. Magnetoencephalography recordings were obtained from 30 healthy adults exposed to auditory stimuli at 20 and 40 Hz. The stimuli were presented in three paradigms: 20- and 40-Hz repetitive presentations and random presentation, the latter being categorized by whether the preceding stimulus was the same (S trials) or different (D trials). The ASSR amplitude and inter-trial phase coherence (ITPC) were assessed via time-frequency analysis. The results revealed that the 20-Hz ASSR was suppressed with increased novelty, with the highest amplitude and ITPC observed during repetitive presentation. In contrast, the 40-Hz ASSR was enhanced by increased novelty, with the greatest measures observed during the D trials. These findings show that novelty detection modulates 20- and 40-Hz ASSRs in opposite directions, highlighting its critical role in shaping stimulus-induced oscillatory responses. This frequency-specific modulation pattern may provide a novel perspective for understanding ASSR abnormalities in psychiatric disorders.

Neurocomputational Mechanisms of Sense of Agency: Literature Review for Integrating Predictive Coding and Adaptive Control in Human-Machine Interfaces

BACKGROUND

The sense of agency (SoA)-the subjective experience of controlling one's own actions and their consequences-is a fundamental aspect of human cognition, volition, and motor control. Understanding how the SoA arises and is disrupted in neuropsychiatric disorders has significant implications for human-machine interface (HMI) design for neurorehabilitation. Traditional cognitive models of agency often fail to capture its full complexity, especially in dynamic and uncertain environments.

OBJECTIVE

This review synthesizes computational models-particularly predictive coding, Bayesian inference, and optimal control theories-to provide a unified framework for understanding the SoA in both healthy and dysfunctional brains. It aims to demonstrate how these models can inform the design of adaptive HMIs and therapeutic tools by aligning with the brain's own inference and control mechanisms.

METHODS

I reviewed the foundational and contemporary literature on predictive coding, Kalman filtering, the Linear-Quadratic-Gaussian (LQG) control framework, and active inference. I explored their integration with neurophysiological mechanisms, focusing on the somato-cognitive action network (SCAN) and its role in sensorimotor integration, intention encoding, and the judgment of agency. Case studies, simulations, and XR-based rehabilitation paradigms using robotic haptics were used to illustrate theoretical concepts.

RESULTS

The SoA emerges from hierarchical inference processes that combine top-down motor intentions with bottom-up sensory feedback. Predictive coding frameworks, especially when implemented via Kalman filters and LQG control, provide a mechanistic basis for modeling motor learning, error correction, and adaptive control. Disruptions in these inference processes underlie symptoms in disorders such as functional movement disorder. XR-based interventions using robotic interfaces can restore the SoA by modulating sensory precision and motor predictions through adaptive feedback and suggestion. Computer simulations demonstrate how internal models, and hypnotic suggestions influence state estimation, motor execution, and the recovery of agency.

CONCLUSIONS

Predictive coding and active inference offer a powerful computational framework for understanding and enhancing the SoA in health and disease. The SCAN system serves as a neural hub for integrating motor plans with cognitive and affective processes. Future work should explore the real-time modulation of agency via biofeedback, simulation, and SCAN-targeted non-invasive brain stimulation.

Spoken words affect visual object recognition via the modulation of alpha and beta oscillations

Hearing spoken words can enhance the recognition of visual object categories. Yet, the mechanisms that underpin this facilitation are incompletely understood. Recent proposals suggest that words can alter visual processes by activating category-specific representations in sensory regions. Here, we tested the hypothesis that neural oscillations serve as a mechanism to activate language-generated visual representations. Participants performed a cue-picture matching task where cues were either spoken words, in their native or second language, or natural sounds, while their EEG and reaction times were recorded. Behaviorally, we found that images cued by words were recognized faster than those cued by natural sounds. This indicates that language activates more accurate semantic representations compared to natural sounds. A time-frequency analysis of cue-target intervals revealed that this label-advantage effect was associated with enhanced power in posterior alpha (9-11 Hz) and beta oscillations (17-19 Hz), both of which were larger when the image was preceded by a word compared to a natural sound. These results suggest that alpha and beta rhythms may play distinct functional roles to support language-mediated visual object recognition: alpha might function to amplify sensory representations in posterior regions, while beta may (re)activate the network states elicited by the auditory cue.

EEG spectral analysis of nighttime sleep and daytime MSLTs and neurocognitive evaluations in subjects with co-morbid insomnia and OSA

BACKGROUND

Chronic insomnia and obstructive sleep apnea commonly co-occur. Few studies have explored the neurophysiological and neurocognitive characteristics of COMISA, which could help guide improving treatment diagnostic tools and determining novel therapeutic targets. This study aims to explore the neurophysiological and neurocognitive characteristics of COMISA using electroencephalographic (EEG) spectral analysis and subjective and objective neurocognitive measurements.

METHODS

Participants were from our community recruited OSA-insomnia-COMISA cohort with 206 included for our current analysis including 74 chronic insomniacs (CIs), 55 OSA patients and 77 COMISA patients. Standard polysomnography (PSG) and multiple sleep latency tests (MSLTs) were recorded and used to obtain relative EEG spectral power in each sleep stage during PSG and each session during MSLTs. A series of subjective and objective neurocognitive tests were conducted to evaluate executive function, attention, retrospective and prospective memory and meta-cognition.

RESULTS

In PSG and MSLTs, COMISA patients showed combined EEG power characteristics of both CIs and OSA. Specifically, COMISA patients exhibited similar EEG spectral characteristics to CIs, with decreased delta and increased alpha and beta power in NREM sleep stages, and increased beta power in REM and MSLTs. Similar to the EEG spectral power profile of OSA, COMISA patients showed increased delta power in REM and MSLTs. Compared to OSA patients, COMISA patients exhibited worse subjectively measured attention and meta-cognition related to negative beliefs about uncontrollability and danger of worry (NEG), which were positively associated with ISI scores.

CONCLUSIONS

The EEG spectral power characteristics of COMISA patients in overnight PSG and daytime MSLT appear to be the manifestation of elements of both CIs and OSA. However, the neurocognitive features of COMISA patients in subjectively measured attention and NEG meta-cognition were primarily affected by chronic insomnia.

Enhanced delta-gamma phase-amplitude coupling during phasic rapid eye movement sleep in isolated rapid eye movement sleep behavior disorder

STUDY OBJECTIVES

This study aims to analyze phase-amplitude coupling (PAC) patterns during rapid eye movement (REM) sleep in patients with isolated REM sleep behavior disorder (iRBD), compared with demography-matched healthy control (HC) participants.

METHODS

At baseline, electroencephalogram data from 13 iRBD patients and 10 HCs during REM sleep were analyzed. During follow-up, four patients (converters) later converted to alpha-synucleinopathies. Phasic and tonic REM states were determined by eye movement in 3 s epochs. PAC was compared between the groups, and correlations with clinical indicators were investigated. Additionally, the contribution of each electrode to PAC components was assessed.

RESULTS

Patients with iRBD exhibited increased delta (1-3 Hz)-gamma (30-50 Hz) PAC only during the phasic REM state, but not during the tonic state, compared to the HCs (p < .05). Elevated PAC in patients negatively correlated with the REM atonia index (p = 0.011) and olfactory function (p = 0.038). Increase PACs were predominent in the fronto-temporo-occipital regions (corrected p < .05). Furthermore, patients showed reduced gamma-amplitude contributions of the parietal region (corrected p < .05). This reduction exhibited a progressively decreasing trend from HC to nonconverters, and further to converters (p for trend = 0.044).

CONCLUSIONS

Our findings suggest PAC patterns during REM sleep could provide pathophysiological insights for iRBD. The widespread increase of PAC and reduced gamma-amplitude contribution in the parietal region suggest PAC during phasic REM sleep as potential biomarkers for disease progression in iRBD.

Developmental overproduction of cortical superficial neurons impairs adult auditory cortical processing

While evolutionary cortical expansion is thought to underlie the evolution of human cognitive capabilities, excessive developmental expansion can lead to megalencephaly, often found in neurodevelopmental disorders. Still, little is known about how the overproduction of cortical neurons during development affects cortical processing and behavior in later life. Here we show that developmental overproduction of cortical superficial neurons impairs auditory processing in adult mice. We applied XAV939 to overproduce cortical superficial excitatory neurons during development. XAV939-treated adult mice exhibited auditory behavioral deficits and abnormal auditory cortical processing. Furthermore, we found fewer functional monosynaptic connections between cortical putative excitatory neurons. Altogether, our results suggest that abnormal auditory cortical processing contributes to the atypical auditory detectability in XAV939-treated mice. Although the expansion of cortical size is evolutionarily advantageous, an abnormal expansion during development can result in detrimental effects on cortical processing and perceptual behavior in adulthood.

An active inference account of stuttering behavior

This paper presents an interpretation of stuttering behavior, based on the principles of the active inference framework. Stuttering is a neurodevelopmental disorder characterized by speech disfluencies such as repetitions, prolongations, and blocks. The principles of active inference, a theory of predictive processing and sentient behavior, can be used to conceptualize stuttering as a disruption in perception-action cycling underlying speech production. The theory proposed here posits that stuttering arises from aberrant sensory precision and prediction error dynamics, inhibiting syllable initiation. Relevant to this theory, two hypothesized mechanisms are proposed: (1) a mistiming in precision dynamics, and (2) excessive attentional focus. Both highlight the role of neural oscillations, prediction error, and hierarchical integration in speech production. This framework also explains the contextual variability of stuttering behaviors, including adaptation effects and fluency-inducing conditions. Reframing stuttering as a synaptopathy integrates neurobiological, psychological, and behavioral dimensions, suggesting disruptions in precision-weighting mediated by neuromodulatory systems. This active inference perspective provides a unified account of stuttering and sets the stage for innovative research and therapeutic approaches.

Working memory readout varies with frontal theta rhythms

Increasing evidence suggests that attention varies rhythmically, phase-locked to ongoing cortical oscillations. Here, we report that the phase of theta oscillations (3-6 Hz) in the frontal eye field (FEF) is associated with temporal and spatial variation of the read-out of information from working memory (WM). Non-human primates were briefly shown a sample array of colored squares. A short time later, they viewed a test array and were rewarded for identifying which square changed color (the target). Better performance (accuracy and reaction time) varied systematically with the phase of local field potential (LFP) theta at the time of test array onset as well as the target's location. This is consistent with theta "scanning" across the FEF and thus visual space from top to bottom. Theta was coupled, on opposing phases, to both spiking and beta (12-20 Hz). These results could be explained by a wave of activity that moves across the FEF, modulating the readout of information from WM.

Neural dynamics underlying the cue validity effect in target conflict resolution

Cue validity significantly influences attention guidance, either facilitating or hindering the ability for conflict resolution. Previous studies have demonstrated that the validity effect and conflict resolution are associated with better/worse behavioral performance and specific neural activations; however, the underlying neural mechanism of their interaction remains unclear. We hypothesized that the effect of cue validity might sustain specific sequences of neural activities until target occurrence and throughout the subsequent conflict resolution. In this study, we recorded the scalp electroencephalography during the Attention Network Test paradigm to investigate their interactions in neural dynamics. Specifically, we performed a cluster-level channel-time-frequency analysis to explore significant time-frequency neural activity patterns associated with these interactions, in scalp regions of interest determined by a data-driven strategy. Our results revealed a string of significant neural dynamics in the frontal and parietal regions, including initial broad-band (especially the gamma-band) activations and subsequent complex cognitive processes evoked/effected by the invalid cue, that were firstly elicited. Finally, the resolution of conflict was completed by the frontal behavior-related theta-band power reduction. In summary, our findings advanced the understanding of the temporal and spectral sequences of neural dynamics, with the key regions involved in the resolution of conflict after invalid cueing.

Investigating the Human Brain's Integration of Internal and External Reference Frames: The Role of the Alpha and Beta Bands in a Modified Temporal Order Judgment Task

The integration of the internal and external reference frames of the human brain is crucial for achieving accurate tactile spatial localization. However, the mechanisms underlying this integration have yet to be fully elucidated. This study adopted a modified temporal order judgment paradigm with an advanced weighted phase lag index method to investigate brain network interactions when the internal and external reference frames were integrated. We found that when the brain integrated internal and external reference frames, alpha oscillations decreased, beta oscillations increased, and inter-hemispheric connectivity increased. Specifically, compared with the match condition: first, the alpha band oscillation predominantly contributed to processing the internal reference frame mismatch; second, the alpha and late beta band oscillation predominantly contributed to processing the external reference frame mismatch; third, the early alpha and late beta band oscillation predominantly contributed to processing the internal and external reference frame mismatch. These findings suggest that the neural oscillation of the alpha and beta bands plays an essential role in tactile spatial localization.

Distinct age-related alterations in alpha-beta neural oscillatory activity during verbal working memory encoding in children and adolescents

Emerging imaging studies of working memory (WM) have identified significant WM-related oscillatory events that are unique to each phase of working memory (e.g. encoding, maintenance). Although many previous imaging studies have shown age-related changes within the frontoparietal network when performing a WM task, understanding of the age-related changes in the oscillatory dynamics underlying each phase of WM during development and their relationships to other cognitive function is still in its infancy. To this end, we enrolled a group of 74 typically-developing youths aged 7-15 years to perform a letter-based Sternberg WM task during magnetoencephalography. Trial-wise data were transformed into the time-frequency domain, and significant oscillatory responses during the encoding and maintenance phases of the task were independently imaged using beamforming. Our results revealed widespread age-related power differences in alpha-beta oscillatory activity during encoding throughout left frontal, parietal, temporal, occipital and cerebellar regions. By contrast, age-related differences in maintenance-related activity were limited to a small area in the superior temporal gyrus and parieto-occipital regions. Follow-up exploratory factor analysis of age-related encoding alpha-beta activity revealed two distinct factors, and these factors were each found to significantly mediate age-related improvements in both verbal and non-verbal cognitive ability. Additionally, late maintenance alpha activity was related to reaction time on the task. Taken together, our results indicate that the neural dynamics in the alpha and beta bands are uniquely sensitive to age-related changes throughout this developmental period and are related to both task performance and other aspects of cognitive development. KEY POINTS: Understanding of the age-related changes in neural oscillatory dynamics serving verbal working memory function is in its infancy. This study identified the age-related neural alterations during each phase of working memory processing in youths. Developmental differences during working memory processing were primarily isolated to alpha-beta activity during the encoding phase. Alpha-beta activity during encoding significantly mediated age-related improvements in both verbal and non-verbal ability. This study establishes new brain-behaviour relationships linking working memory function to other aspects of cognitive development.

Lifespan trajectories of motor control and neural oscillations: A systematic review of magnetoencephalography insights

Motor control (MC) evolves across the human lifespan, improving during childhood and adolescence, stabilizing in early adulthood, and declining in older age. These developmental and degenerative patterns are linked to neural oscillatory activity, which can be assessed via magnetoencephalography (MEG) to gain insights into motor planning, execution, termination, and command initiation. This review systematically examined age-related changes in MC and neural oscillations, centering on movement-related beta desynchronization (MRBD), post-movement beta rebound (PMBR), and movement-related gamma synchrony (MRGS). Following PRISMA guidelines, 17 cross-sectional studies were identified. The findings revealed significant enhancements in motor efficiency from childhood to adolescence, characterized by faster movement speed, shorter movement duration, weaker MRBD, and increased PMBR and MRGS. From adolescence to early adulthood, further improvements in motor performance were noted, accompanied by strengthened MRBD, PMBR, and a slight decline in MRGS. In older adults, motor performance deteriorates, presenting as slower movement and prolonged duration, alongside heightened resting beta power, elevated MRBD, and reduced PMBR. Alterations in MRGS remain insufficiently explored. Overall, MEG proves valuable for capturing neural dynamics underlying the development and decline of motor control across the lifespan. These findings underscore potential avenues for motor rehabilitation and cognitive interventions, particularly in aging populations.

Investigating automatic processing preference in high trait anxiety individuals: Behavioral and neuroelectrophysiological evidence

The Trait Anxiety Attention Control Theory suggests an imbalance in high trait anxiety individuals between bottom-up and top-down processing. To investigate this theory, we designed an experimental paradigm combining task-switching and Stroop tasks to investigate behavioral and neuroelectrophysiological features in trait anxiety. The results revealed a significant negative correlation between trait anxiety levels and switch costs, indicating a preference for automatic processing among those with high trait anxiety. Additionally, the EEG findings demonstrated that individuals with high trait anxiety exhibit a preference for automatic processing, as evidenced by the N4 latency and increased beta power during switch tasks. The consistency of these results across behavioral, ERPs, and time-frequency analyses suggests a propensity for automatic processing in high trait anxiety individuals, disrupting the equilibrium between top-down and bottom-up processes. This research offers empirical support for the Attention Control Theory, enhancing our comprehension of behavioral deviations and neural mechanisms in trait anxiety.

Exploring the neuromagnetic signatures of cognitive decline from mild cognitive impairment to Alzheimer's disease dementia

BACKGROUND

Developing non-invasive and affordable biomarkers to detect Alzheimer's disease (AD) at a prodromal stage is essential, particularly in the context of new disease-modifying therapies. Mild cognitive impairment (MCI) is a critical stage preceding dementia, but not all patients with MCI will progress to AD. This study explores the potential of magnetoencephalography (MEG) to predict cognitive decline from MCI to AD dementia.

METHODS

We analysed resting-state MEG data from the BioFIND dataset including 117 patients with MCI among whom 64 developed AD dementia (AD progression), while 53 remained cognitively stable (stable MCI), using spectral analysis. Logistic regression models estimated the additive explanation of selected clinical, MEG, and MRI variables for AD progression risk. We then built a high-dimensional classification model to combine all modalities and variables of interest.

FINDINGS

MEG 16-38Hz spectral power, particularly over parieto-occipital magnetometers, was significantly reduced in the AD progression group. In logistic regression models, decreased MEG 16-38Hz spectral power and reduced hippocampal volume/total grey matter ratio on MRI were independently linked to higher AD progression risk. The data-driven classification model confirmed, among other factors, the complementary information of MEG covariance (AUC = 0.74, SD = 0.13) and MRI cortical volumes (AUC = 0.77, SD = 0.14) to predict AD progression. Combining all inputs led to markedly improved classification scores (AUC = 0.81, SD = 0.12).

INTERPRETATION

These findings highlight the potential of spectral power and covariance as robust non-invasive electrophysiological biomarkers to predict AD progression, complementing other diagnostic measures, including cognitive scores and MRI.

FUNDING

This work was supported by: Fondation pour la Recherche Médicale (grant FDM202106013579).

Chronic dizziness in older adults: Disrupted sensorimotor EEG beta oscillations during postural instability

OBJECTIVE

Chronic dizziness is common in older adults, yet frequently occurs without a clear cause ('idiopathic dizziness'). Patients experience subjective unsteadiness with minimal objective imbalance, potentially related to small vessel disease. Here we examine the hypothesis that this syndrome is associated with disrupted cortical processing of postural instability.

METHODS

EEG and postural sway were recorded in 33 older adults with chronic, idiopathic dizziness (Age, Mean = 77.3 years, SD = 6.4, 61 % female) and 25 matched controls (Age, Mean = 76.9 years, SD = 6.0, 56 % female). EEG was time-locked to spontaneous instances of postural instability and analysed via time-frequency decomposition.

RESULTS

Significant between-group differences in EEG were observed during the early phase of postural instability (p < 0.05, cluster-corrected). Whilst controls exhibited broadband increase in EEG power across sensorimotor areas, dizzy patients displayed suppressed beta activity (19-24 Hz). Contrary to predictions, these differences did not relate to small vessel disease markers (rs < 0.05, ps > 0.720) but to fear of falling (r = -0.44, p = 0.001).

CONCLUSIONS

Previous work implies that suppressing cortical beta enhances the relay of sensory information. We therefore propose that the modulation in beta EEG observed in patients reflects an anxious, top-down strategy to increase sensitivity to instability, which paradoxically causes persistent feelings of subjective imbalance.

SIGNIFICANCE

These results identify associations between idiopathic dizziness and disrupted sensorimotor beta activation during postural instability. Cortical beta during imbalance may be a possible biomarker of chronic, idiopathic dizziness in older adults and/or fear of falling.

Cortical adaptations in regional activity and backbone network following short-term postural training with visual feedback for older adults

This study investigated cortical reorganization in older adults following short-term interactive balance training. Twenty participants aged 65-74 received training in stabilometer stance, visually aligning plate movement with a horizontal line on a monitor. Pre-test and post-test measured posture fluctuations and scalp EEG during stabilometer stance. Results showed a training-related decrease in root mean square (RMS) (p = 0.001) and an increase in mean frequency (p = 0.006) of posture fluctuations. Despite a decline in theta relative power in Fp1 (p = 0.027), stabilometer training led to a post-test increase in alpha relative power around electrodes of the ventral visual pathway (p = 0.002). Additionally, augmentations were noted in theta relative power in Tp8 (p = 0.033) and beta relative power in F7 (p = 0.039). Analysis of the minimum spanning tree (MST) of alpha inter-regional connectivity indicated a training-related decrease in leaf fraction (p = 0.011) and increase in average eccentricity (p = 0.041), respectively. Training-related changes in the RMS of posture fluctuation were positively correlated with changes in pooled alpha relative power in electrodes of the ventral visual pathway (r = 0.459, p = 0.042) and negatively correlated with changes in average eccentricity of the alpha MST network (r =  - 0.487, p = 0.029). In conclusion, short-term interactive training enhances balance by reorganizing regional and alpha-band network activities, which supports improved visual attention and prevents early visual processing idling during initial postural learning.

Single-Trial Electroencephalography Discrimination of Real, Regulated, Isometric Wrist Extension and Wrist Flexion

Improved interpretation of electroencephalography (EEG) associated with the neural control of essential hand movements, including wrist extension (WE) and wrist flexion (WF), could improve the performance of brain-computer interfaces (BCIs). These BCIs could control a prosthetic or orthotic hand to enable motor-impaired individuals to regain the performance of activities of daily living. This study investigated the interpretation of neural signal patterns associated with kinematic differences between real, regulated, isometric WE and WF movements from recorded EEG data. We used 128-channel EEG data recorded from 14 participants performing repetitions of the wrist movements, where the force, speed, and range of motion were regulated. The data were filtered into four frequency bands: delta and theta, mu and beta, low gamma, and high gamma. Within each frequency band, independent component analysis was used to isolate signals originating from seven cortical regions of interest. Features were extracted from these signals using a time-frequency algorithm and classified using Mahalanobis distance clustering. We successfully classified bilateral and unilateral WE and WF movements, with respective accuracies of 90.68% and 69.80%. The results also demonstrated that all frequency bands and regions of interest contained motor-related discriminatory information. Bilateral discrimination relied more on the mu and beta bands, while unilateral discrimination favoured the gamma bands. These results suggest that EEG-based BCIs could benefit from the extraction of features from multiple frequencies and cortical regions.

Classifying mental motor tasks from chronic ECoG-BCI recordings using phase-amplitude coupling features

Introduction

Phase-amplitude coupling (PAC), the modulation of high-frequency neural oscillations by the phase of slower oscillations, is increasingly recognized as a marker of goal-directed motor behavior. Despite this interest, its specific role and potential value in decoding attempted motor movements remain unclear.

Methods

This study investigates whether PAC-derived features can be leveraged to classify different motor behaviors from ECoG signals within Brain-Computer Interface (BCI) systems. ECoG data were collected using the WIMAGINE implant during BCI experiments with a tetraplegic patient performing mental motor tasks. The data underwent preprocessing to extract complex neural oscillation features (amplitude, phase) through spectral decomposition techniques. These features were then used to quantify PAC by calculating different coupling indices. PAC metrics served as input features in a machine learning pipeline to evaluate their effectiveness in predicting mental tasks (idle state, right-hand movement, left-hand movement) in both offline and pseudo-online modes.

Results

The PAC features demonstrated high accuracy in distinguishing among motor tasks, with key classification features highlighting the coupling of theta/low-gamma and beta/high-gamma frequency bands.

Discussion

These preliminary findings hold significant potential for advancing our understanding of motor behavior and for developing optimized BCI systems.

Beyond the neural underpinnings of action emulation in expert athletes: An EEG study

Athletes specializing in sports demanding rapid predictions and hand-eye coordination are highly trained in predicting the consequences of motor commands. This can be framed as highly efficient action emulation, but the neural underpinnings of this remain elusive. We examined the neural processes linked to the training effect of athletes (4000 h of training) by employing a continuous pursuit tracking task and EEG data. We manipulated feedback availability by intermittently occluding the cursor. As a performance measure, we used the distance between cursor and target (position error), the angle between the cursor and target movement direction (direction error) and the magnitude of cursor acceleration (acceleration error) to quantify movement strategy. In EEG data, we investigated beta, alpha, and theta frequency band oscillations. Athletes' position error is lower than non-athletes' when there is no feedback about the cursor location, but direction error is not. We found no quantitative power differences in the investigated frequency bands, but evidence that athletes and non-athletes accomplish action emulation through different functional neuroanatomical structures, especially when alpha and beta band activity is concerned. We surmise that non-athletes seemed to rely on top-down inhibitory control to predict guesses on cursor trajectories in the absence of cursor position feedback. In contrast, athletes might benefit from enhanced inhibitory gating mechanisms in the ventral stream and the integration of sensory and motor processes in the insular cortex, which could provide them with processing advantages in computing forward models. We further reflect that this advantage might be supported by alpha band activity in athletes' motor cortex, suggesting less inhibitory gating and a higher likelihood of executing integrated sensorimotor programs. We posit that current framings of neuroanatomical structures and neurophysiological processes in the action emulation framework must be revised to better capture superior motor performance.

Neural representation of consciously seen and unseen information

Machine learning (ML) techniques have steadily gained popularity in Neuroscience research, particularly when applied to the analysis of neuroimaging data. One of the most discussed topics in this field, the neural correlates of conscious (and unconscious) information, has also benefited from these approaches. Nevertheless, further research is still necessary to better understand the minimal neural mechanisms that are necessary and sufficient for experiencing any conscious percept, and which mechanisms are comparable and discernible between conscious and unconscious events. The aim of this study was two-fold. First, to explore whether it was possible to decode task-relevant features from electroencephalography (EEG) signals, particularly those related to perceptual awareness. Secondly, to test whether this decoding could be improved by using time-frequency representations instead of voltage. We employed a perceptual task in which participants were presented with near-threshold Gabor stimuli. They were asked to discriminate the orientation of the grating, and report whether they had perceived it or not. Participants' EEG signal was recorded while performing the task and was then analysed by using ML algorithms to decode distinctive task-related parameters. Results demonstrated the feasibility of decoding the presence/absence of the stimuli from EEG data, as well as participants' subjective perception, although the model failed to extract relevant information related to the orientation of the Gabor. Unconscious processing of unseen stimulation was observed both behaviourally and at the neural level. Moreover, contrary to conscious processing, unconscious representations were less stable across time, and only observed at early perceptual stages (~ 100 ms) and during response preparation. Furthermore, we conducted a comparative analysis of the performance of the classifier when employing either raw voltage signals or time-frequency representations, finding a substantial improvement when the latter was used to train the model, particularly in the theta and alpha bands. These findings underscore the significant potential of ML algorithms in decoding perceptual awareness from EEG data in consciousness research tasks.

Decoding the impact of negative physical self-perception on inhibitory control ability from theta and beta rhythms

Previous studies have found inhibitory control differences between obese individuals and those of normal weight. However, some normal-weight individuals with high negative physical self-perception on the fatness subscale show restrictive eating behaviors and attentional bias toward high-calorie food, potentially influencing these differences. We collected behavioral and electroencephalography data using a novel inhibitory control task. Results showed that individuals with high negative physical self-perception on the fatness subscale exhibited significantly greater restraint eating behavior compared to controls. Both theta and beta power differed between groups, with higher theta power in the high negative physical self-perception on the fatness subscale group than in the obese group and more negative beta power in the high negative physical self-perception on the fatness subscale group compared to both other groups. Theta power was greater in no-go than go conditions, while beta power was more negative in response to high-calorie versus low-calorie food stimuli. Importantly, theta power successfully decoded go/no-go conditions across all groups using multivariate pattern analysis, while beta power distinguished these conditions only in the negative physical self-perception on the fatness subscale and control groups. These findings suggest that theta and beta power, along with multivariate pattern analysis, can reliably distinguish inhibitory control ability among the three groups, highlighting the importance of considering negative physical self-perception on the fatness subscale when assessing inhibitory control differences between normal-weight and obese individuals.

Determining the effects of transcranial alternating current stimulation on corticomotor excitability and motor performance: A sham-controlled comparison of four frequencies

Transcranial alternating current stimulation (tACS) modulates brain oscillations and corticomotor plasticity. We examined the effects of four tACS frequencies (20 Hz, 40 Hz, 60 Hz, and 80 Hz) on motor cortex (M1) excitability and motor performance. In a randomised crossover design, 12 adults received 20-minute tACS sessions, with Sham as control. Corticomotor and intracortical excitability was measured up to 60-minutes post-tACS. Motor performance was evaluated using the Grooved Pegboard Test (GPT) and sensorimotor assessments. Our findings demonstrated frequency-dependent modulation of corticomotor excitability based on MEP amplitude. 20 Hz and 40 Hz tACS reduced MEPs, while 60 Hz and 80 Hz increased MEPs. Inhibition (cortical silent period, SP) was reduced across all tACS frequencies compared to Sham, with 20 Hz and 40 Hz showing consistent reductions, 60 Hz showing effects at post-0 and post-30, and 80 Hz at post-60. Furthermore, 60 Hz tACS decreased intracortical inhibition at post-0, while intracortical facilitation increased with 20 Hz and 60 Hz at post-0, and 40 Hz at post-60. Motor performance remained unaffected across frequencies. Regression analyses revealed that shorter SP at 60 min post 60 Hz tACS predicted faster reaction times, while greater MEP amplitudes at 60 min following 80 Hz tACS predicted improved hand dexterity. Overall, beta and gamma tACS frequencies modulate M1 excitability, with consistent effects on SP, suggesting potential use in conditions involving SP elongation, such as stroke and Huntington's disease. These findings highlight 60 Hz tACS as a potential tool for motor rehabilitation therapies.

M1 large-scale network dynamics support human motor resonance and its plastic reshaping

Motor resonance - the facilitation of corticospinal excitability during action observation - is considered a proxy of Action Observation Network (AON) recruitment in humans, with profound implications for social cognition and action understanding. Despite extensive research, the neural underpinnings supporting motor resonance emergence and rewriting remain unexplored. In this study, we investigated the role of sensorimotor associative learning in neural mechanisms underlying the motor resonance phenomenon. To this aim, we applied cross-systems paired associative stimulation (PAS) to induce novel visuomotor associations in the human brain. This protocol, which repeatedly pairs transcranial magnetic stimulation (TMS) pulses over the primary motor cortex (M1) with visual stimuli of actions, drives the emergence of an atypical, PAS-conditioned motor resonance response. Using TMS and electroencephalography (EEG) co-registration during action observation, we tracked the M1 functional connectivity profile during this process to map the inter-areal connectivity profiles associated with typical and PAS-induced motor resonance phenomena. Besides confirming, at the corticospinal level, the emergence of newly acquired motor resonance responses at the cost of typical ones after PAS administration, our results reveal dissociable aspects of motor resonance in M1 interregional communication. On the one side, typical motor resonance effects acquired through the lifespan are associated with prominent M1 alpha-band and reduced beta-band connectivity, which might facilitate the corticospinal output while integrating visuomotor information. Conversely, the atypical PAS-induced motor resonance is linked to M1 beta-band cortical connectivity modulations, only partially overlapping with interregional communication patterns related to the typical mirroring responses. This evidence suggests that beta-phase synchronization may be the critical mechanism supporting the formation of motor resonance by coordinating the activity of motor regions during action observation, which also involves alpha-band top-down control of frontal areas. These findings provide new insights into the neural dynamics underlying (typical and newly acquired) motor resonance, highlighting the role of large-scale interregional communication in sensorimotor associative learning within the AON.