MNE software for processing MEG and EEG data

Increased Global and Regional Connectivity in Propofol-induced Unconsciousness: Human Intracranial Electroencephalography Study

BACKGROUND

The conscious state is maintained through intact communication between brain regions. However, studies on global and regional connectivity changes in unconscious state have been inconsistent. These inconsistencies could arise from unclear definition of unconsciousness, spatial and temporal limitations of neuroimaging modalities, and estimating only single connectivity measure. This study investigated global and regional changes in amplitude and phase-based functional connectivity in propofol-induced unconsciousness, which is widely recognized as unconsciousness.

METHODS

Amplitude and phase-based functional connectivity was calculated using amplitude envelope correlation, weighted phase lag index, and magnitude squared coherence from intracranial electroencephalography data of 73 patients. Global changes in connectivity, complexity, and network efficiency were estimated. Regional connectivity changes between Brodmann areas, between seven cortical lobes, and between resting state networks were assessed across all frequency bands. Additionally, machine learning analysis was employed to identify specific regions in classifying conscious and unconscious states.

RESULTS

In the unconscious state, global connectivity increased across all frequency bands, while global complexity and efficiency decreased, accompanied by increased delta and decreased high gamma power spectral density. Regional connectivity increased between entire cortical regions across all frequency bands. Machine learning analysis revealed that posterior connectivity was the most influential in classifying consciousness. Amplitude-based connectivity predominantly increased in the delta and theta bands, while phase-based connectivity predominantly increased from the beta to high gamma bands.

CONCLUSIONS

Propofol anesthesia suppresses cortical activity and induces oscillatory changes characterized by increased delta power and decreased high gamma power. These changes are accompanied by increased functional connectivity and reduced network complexity and efficiency. These changes limit the brain's ability to generate a diverse repertoire of activity, ultimately leading to unconsciousness. Posterior connectivity, which showed high accuracy in predicting conscious states, would be crucial for sustaining consciousness.

From encoding to recognition: Exploring the shared neural signatures of visual memory

This study investigated the shared neural dynamics underlying encoding and recognition processes across diverse visual object stimulus types in short term experimental familiarization, using EEG-based representational similarity analysis and multivariate cross-classification. Building upon previous research, we extended our exploration to the encoding phase. We show early visual stimulus category effects around 150 ms post-stimulus onset and old/new effects around 400 to 600 ms. Notably, a divergence in neural responses for encoding, old, and new stimuli emerged around 300 ms, with items encountered during the study phase showing the highest differentiation from old items during the test phase. Cross-category classification demonstrated discernible memory-related effects as early as 150 ms. Anterior regions of interest, particularly in the right hemisphere, did not exhibit differentiation between experimental phases or between study and new items, hinting at similar processing for items first encountered, irrespective of experiment phase. While short-term experimental familiarity did not consistently adhere to the old >new pattern observed in long-term personal familiarity, statistically significant effects are observed specifically for experimentally familiarized faces, suggesting a potential unique phenomenon specific to facial stimuli. Further investigation is warranted to elucidate underlying mechanisms and determine the extent of face-specific effects. Lastly, our findings underscore the utility of multivariate cross-classification and cross-dataset classification as promising tools for probing abstraction and shared neural signatures of cognitive processing.

Pre-ictal causal connectivity reveals the epileptic network characteristics for deep brain stimulation

Deep brain stimulation of anterior nucleus of the thalamus (ANT-DBS) is an effective clinical treatment for drug-resistant focal epilepsy. However, the complex epileptic network characteristics underlying ANT-stimulation effectiveness remain unknown, owing to currently unclear connectivity between ANT and seizure-related cortex in pre-ictal periods. Here, we developed a novel individualized pre-ictal ANT-cortical tripartite connectivity network (PANT-CTCNet), aiming to reveal epileptic network characteristics using intracranial stereo-electroencephalography (sEEG) recordings in five patients with focal epilepsy for 90 trials. Each trial represented a pre-ictal or post-stimulus sEEG duration, which was used to construct the epileptic network. By employing conditional Granger causality, we constructed individualized ANT-cortical connectivity networks and found a common epileptic network centred on ANT closely connected with seizure-related cortex in pre-ictal periods. After ANT stimulation for clinical validation, strengthened pre-ictal connectivity between ANT and epileptogenic zones led to significant decline in the causal intensity of epileptic networks. The PANT-CTCNet can give a quantitative reference for clinical preoperative evaluation of patient suitability for ANT-DBS treatment. These findings regarding epileptic network characteristics provide theoretical basis in the selection of optimal surgical candidates for personalized ANT-DBS.

A cross-domain-based channel selection method for motor imagery

Selecting channels for motor imagery (MI)-based brain-computer interface (BCI) systems can not only enhance the portability of the systems, but also improve the decoding performance. Hence, we propose a cross-domain-based channel selection (CDCS) approach, which effectively minimizes the number of EEG channels used while maintaining high accuracy in MI recognition. The EEG source imaging (ESI) technique is employed to map scalp EEG into the cortical source domain. We divide the equivalent dipoles in the source domain into different regions by k-means clustering. Then, we calculate the band energy (5-40 Hz) of time series of dipoles in these regions by power spectral density (PSD), and the regions with the highest and lowest band energy are selected as the region of interests (ROIs) in the source domain. Subsequently, Pearson correlation coefficients between the dipole time series in ROIs and scalp EEG are used as the criterion for channel selection and a multi-trial-sorting-based channel selection strategy is proposed. Finally, we propose the CDCS-based MI classification framework, where common spatial pattern is applied to extract features and linear discriminant analysis is used to identify MI tasks. The CDCS method demonstrated significant improvement in decoding accuracy on two public datasets, achieving increases of 18.51% and 13.37% compared to all-channel method, and 10.74% and 3.43% compared to the three-channel method. The experimental results validated that CDCS is effective in selecting important channels.

Changes in Early Aperiodic EEG Activity Are Linked to Autism Diagnosis and Language Development in Infants With Family History of Autism

Delays in language often co-occur among toddlers diagnosed with autism. Despite the high prevalence of language delays, the neurobiology underlying such language challenges remains unclear. Prior research has shown reduced EEG power across multiple frequency bands in 3-to-6-month-old infants with an autistic sibling, followed by accelerated increases in power with age. In this study, we decompose the power spectra into aperiodic (broad band neural firing) and periodic (oscillations) activity to explore possible links between aperiodic changes in the first year of life and later language outcomes. Combining EEG data across two longitudinal studies of infants with and without autistic siblings, we assessed whether infants with an elevated familial likelihood (EFL) exhibit altered changes in both periodic and aperiodic EEG activity at 3 and 12 months of age, compared to those with a low likelihood (LL), and whether developmental change in activity is associated with language development. At 3 months of age (n = LL 59, EFL 57), we observed that EFL infants have significantly lower aperiodic activity from 6.7 to 55 Hz (p < 0.05). However, change in aperiodic activity from 3 to 12 months was significantly increased in infants with a later diagnosis of autism, compared to EFL infants without an autism diagnosis (n = LL-NoASD 41, EFL-noASD 16, EFL-ASD 16). In addition, greater increases in aperiodic offset and slope from 3 to 12 months were associated with worse language development measured at 18 months (n = 24). Findings suggest that early age-dependent changes in EEG aperiodic power may serve as potential indicators of autism and language development in infants with a family history of autism.

Neuroelectrical and behavioral correlates of constructed action recognition in Finnish Sign language

Language can be processed with varying levels of attentional involvement; consequently, the interplay between the language and attentional systems in the brain has been extensively studied in spoken languages. However, in signed languages (SLs), this interplay is less well understood. Here, we use Constructed Action (CA) - a meaning-making strategy based on enactment - as a window into the attentional mechanisms recruited in signed language comprehension. We explored the attentional processing of CA by identifying the sequence of processes involved and in which stage CA and its types might be processed differently. Finally, we investigated the associations between the brain mechanisms of CA detection and their behavioral manifestations, as well as with components of attention of the Attention Network Test (ANT). We also measured the electrophysiological correlates of performance on an oddball CA detection task in deaf and hearing L1 signers. We found that processes involved in all signers' active detection of CA involved automatic (indexed by N1 and P2) and attention-based processes (indexed by N2s and P3s). N2 posterior bilateral were also more negative for tokens of overt CA than for PT-only signs, while P3a was more positive for all types of CA than for PT. No significant results were found regarding the ANT. We conclude that specific attentional involvement in CA detection is triggered by the increasing enacting elements and saliency involved in CA. This study yielded new insights into the functional interaction between the neural mechanisms underlying attentional control and those mediating CA processing in SL.

Predictable motion is progressively extrapolated across temporally distinct processing stages in the human visual cortex

Neural processing of sensory information takes time. Consequently, to estimate the current state of the world, the brain must rely on predictive processes-for example, extrapolating the motion of a ball to determine its probable present position. Some evidence implicates early (pre-cortical) processing in extrapolation, but it remains unclear whether extrapolation continues during later-stage (cortical) processing, where further delays accumulate. Moreover, the majority of such evidence relies on invasive neurophysiological techniques in animals, with accurate characterization of extrapolation effects in the human brain currently lacking. Here, we address these issues by demonstrating how precise probabilistic maps can be constructed from human EEG recordings. Participants (N = 18, two sessions) viewed a stimulus moving along a circular trajectory while EEG was recorded. Using linear discriminant analysis (LDA) classification, we extracted maps of stimulus location over time and found evidence of a forwards temporal shift occurring across temporally distinct processing stages. This accelerated emergence of position representations indicates extrapolation occurring at multiple stages of processing, with representations progressively shifted closer to real-time. We further show evidence of representational overshoot during early-stage processing following unexpected changes to an object's trajectory, and demonstrate that the observed dynamics can emerge without supervision in a simulated neural network via spike-timing-dependent plasticity.

Learning Modulates Early Encephalographic Responses to Distracting Stimuli: A Combined SSVEP and ERP Study

Through experience, humans can learn to suppress locations that frequently contain distracting stimuli. However, the neural mechanism underlying learned suppression remains largely unknown. In this study, we combined steady-state visually evoked potentials (SSVEPs) with event-related potentials (ERPs) to investigate the mechanism behind statistically learned spatial suppression. Twenty-four male and female human participants performed a version of the additional singleton search task in which one location contained a distractor stimulus frequently. The search stimuli constantly flickered on-and-off the screen, resulting in steady-state entrainment. Prior to search onset, no differences in the SSVEP response were found, though a post hoc analysis did reveal proactive alpha lateralization. Following search onset, clear evoked differences in both the SSVEP and ERP signals emerged at the suppressed location relative to all other locations. Crucially, the early timing of these evoked modulations suggests that learned distractor suppression occurs at the initial stages of visual processing.

Timing of Speech in Brain and Glottis and the Feedback Delay Problem in Motor Control

To learn complex motor skills, an organism must be able to assign sensory feedback events to the actions that caused them. This matching problem would be simple if motor neuron output led to sensory feedback with a fixed, predictable lag. However, nonlinear dynamics in the brain and the body's periphery can decouple the timing of critical events from that of the motor output which caused them. During human speech production, for example, phonation from the glottis (a sound source for speech) begins suddenly when subglottal pressure and laryngeal tension cross a sharp threshold (i.e., a bifurcation). Only if the brain can predict the timing of these discrete peripheral events resulting from motor output, then, would it be possible to match sensory feedback to movements based on temporal coherence. We show that event onsets in the (male and female) human glottal waveform, measured using electroglottography, are reflected in the electroencephalogram during speech production, leading up to the time of the event itself. Conversely, glottal event times can be decoded from the electroencephalogram. After prolonged exposure to delayed auditory feedback, subjects recalibrate their behavioral threshold for detecting temporal auditory-motor mismatches and decoded event times decouple from actual movements. This suggests decoding performance is driven by plastic predictions of peripheral timing, providing a missing component for hindsight credit assignment, in which specific feedback events are associated with the neural activity that gave rise to movements. We discuss parallel findings from the birdsong system suggesting that results may generalize across vocal learning species.

Macroscale Traveling Waves Evoked by Single-Pulse Stimulation of the Human Brain

Understanding the spatiotemporal dynamics of neural signal propagation is fundamental to unraveling the complexities of brain function. Emerging evidence suggests that corticocortical-evoked potentials (CCEPs) resulting from single-pulse electrical stimulation (SPES) may be used to characterize the patterns of information flow between and within brain networks. At present, the basic spatiotemporal dynamics of CCEP propagation cortically and subcortically are incompletely understood. We hypothesized that SPES evokes neural traveling waves detectable in the three-dimensional space sampled by intracranial stereoelectroencephalography. Across a cohort of 21 adult males and females with intractable epilepsy, we delivered 17,631 stimulation pulses and recorded CCEP responses in 1,019 electrode contacts. The distance between each pair of electrode contacts was approximated using three different metrics (Euclidean distance, path length, and geodesic distance), representing direct, tractographic, and transcortical propagation, respectively. For each robust CCEP, we extracted amplitude-, spectral-, and phase-based features to identify traveling waves emanating from the site of stimulation. Many evoked responses to stimulation appear to propagate as traveling waves (∼14-28%, ∼5-19% with false discovery rate correction), despite sparse sampling throughout the brain. These stimulation-evoked traveling waves exhibited biologically plausible propagation velocities (range, 0.1-9.6 m/s). Our results reveal that direct electrical stimulation elicits neural activity with variable spatiotemporal dynamics that can be modeled as a traveling wave.

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.

Auditory evoked delta brushes involve stimulus-specific cortical networks in preterm infants

During the third trimester of gestation in humans, the auditory cortex displays spontaneous and auditory-evoked EEG patterns of intermittent local oscillatory activity nested in delta waves - delta brushes (DBs). To test whether the spatiotemporal dynamics of evoked DBs depends on stimulus type, we studied auditory evoked responses (AERs) to voice and "click" using 32-electrode EEG in 30 healthy neonates aged 30 to 38 post-menstrual weeks. Both stimuli elicited two peaks at approximately 250 ms and 600 ms, the second corresponding to the first principal components of the AER and the evoked DB. The DB showed stimulus-specific topography, temporal posterior and mid-temporal for "click", and mid-temporal and pre-central inferior for voice, and contained theta to gamma oscillations more widespread for the "click"response. Gamma oscillations increased with age. AERs predominated on the right but shifted toward the left with age for voice response. Auditory evoked DBs may therefore underlie specific auditory processing during fetal development.

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.

Neurophysiological features of dream recall and the phenomenology of dreams: Auditory stimulation impacts dream experiences

Studies on the electrophysiological and phenomenological aspects of dream experiences provide insight on consciousness during sleep. Whole night polysomnography (PSG) studies were conducted among 29 healthy young participants with high dream recall abilities. Dreams reports were collected during the second night by multiple awakening protocol. On the third night, participants were presented with an audiovisual task and during subsequent sleep, dream reports were collected following an auditory stimuli presentation. REM sleep dreams favor high dream recall rates when compared to N2 dreams. Enhanced EEG beta activity, functional connectivity across the brain structures of the default mode network (DMN) and activation of medial frontal cortex were observed during dream recall irrespective of the sleep states. Auditory stimulations influenced emotional dream experiences highlighting the possibility of target memory reactivation. The study highlights the potential role of dream states and dream experiences in understanding consciousness during sleep.

Characterizing Directional Dynamics of Semantic Prediction Based on Inter-regional Temporal Generalization

The event-related potential/field component N400(m) is a widely accepted neural index for semantic prediction. Top-down input from inferior frontal areas to perceptual brain regions is hypothesized to play a key role in generating the N400, but testing this has been challenging due to limitations of causal connectivity estimation. We here provide new evidence for a predictive model of speech comprehension in which IFG activity feeds back to shape subsequent activity in STG/MTG. Magnetoencephalography (MEG) data was obtained from 21 participants (10 men, 11 women) during a classic N400 paradigm where the semantic predictability of a fixed target noun was manipulated in simple German sentences through the preceding verb. To estimate causality, we implemented a novel approach, based on machine learning and temporal generalization, to test the effect of inferior frontal gyrus (IFG) on temporal regions. A support vector machine (SVM) classifier was trained on IFG activity to classify less predicted (LP) and highly predicted (HP) nouns and tested on superior/middle temporal gyri (STG/MTG) activity, time-point by time-point. The reverse procedure was then performed to establish spatiotemporal evidence for or against causality. Significant decoding results were found in our bottom-up model, which were trained at hierarchically lower level areas (STG/MTG) and tested at the hierarchically higher IFG areas. Most interestingly, decoding accuracy also significantly exceeded chance level when the classifier was trained on IFG activity and tested on successive activity in STG/MTG. Our findings indicate dynamic top-down and bottom-up flow of information between IFG and temporal areas when generating semantic predictions.

Classifying Obsessive-Compulsive Disorder from Resting-State EEG using Convolutional Neural Networks: A Pilot Study

Objective: Classifying obsessive-compulsive disorder (OCD) using brain data remains challenging. Resting-state electroencephalography (EEG) offers an affordable and noninvasive approach, but traditional machine learning methods have limited its predictive capability. We explored whether convolutional neural networks (CNNs) applied to minimally processed EEG time-frequency representations could offer a solution, effectively distinguishing individuals with OCD from healthy controls. Method: We collected resting-state EEG data from 20 unmedicated participants (10 OCD, 10 healthy controls). Clean, 4-second EEG segments were transformed into time-frequency representations using Morlet wavelets. In a two-step evaluation, we first used a 2D CNN classifier using leave-one-subject-out cross-validation and compared it to a traditional support vector machine (SVM) trained on spectral band power features. Second, using multimodal fusion, we examined whether adding clinical and demographic information improved classification. Results: The CNN achieved strong classification accuracy (82.0%, AUC: 0.86), significantly outperforming the chance-level SVM baseline (49.0%, AUC: 0.45). Most clinical variables did not improve performance beyond the EEG data alone (subject-level accuracy: 80.0%). However, incorporating education level boosted performance notably (accuracy: 85.0%, AUC: 0.89). Conclusion: CNNs applied to resting-state EEG show promise for diagnosing OCD, outperforming traditional machine learning methods. Despite sample size limitations, these findings highlight deep learning's potential in psychiatric applications. Education level emerged as a potentially complementary feature, warranting further investigation in larger, more diverse samples.

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.

Clinical, Electroencephalogram and Imaging Characteristics of Patients With Anti-LGI1 Antibody Encephalitis: A Multicenter Cohort Study

OBJECTIVES

To summarize the clinical, electroencephalogram (EEG), and imaging characteristics of patients with anti-leucine-rich glioma-inactivated 1 autoimmune encephalitis (LGI1-AE) and provide a reference for clinical diagnosis and treatment.

METHODS

We retrospectively analyzed 88 patients diagnosed with LGI1-AE between January 2018 and April 2024 in the Department of Neurology, Huashan Hospital, Fudan University, and the First Hospital of Jilin University.

RESULTS

This retrospective study analyzed 88 patients diagnosed with LGI1-AE. The initial clinical presentation predominantly featured rapidly progressive cognitive impairment (RPCI) (51.1%) and seizures (50%). Brain magnetic resonance imaging and 18 F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) indicated predominant lesion localization in the unilateral or bilateral temporal lobe and/or basal ganglia. Abnormal EEG was observed in 66 cases (79.5%). LGI1-AE cases had increased power in the low-frequency bands (δ and θ) compared to normal controls. Low-frequency band (δ and θ) power in T3 and Fz channels was positively correlated with LGI1 antibody titers in cerebrospinal fluid (CSF). Spearman correlation analysis showed that baseline modified Rankin Scale (mRS) scores were correlated with serum antibody titers and CSF antibody titers.

CONCLUSIONS

Baseline mRS scores and low-frequency power in the frontotemporal region showed a positive correlation with anti-LGI1 antibody titers, suggesting that antibody levels may reflect disease severity in LGI1 autoimmune encephalitis. Further studies are warranted to validate these associations in prospective multicenter cohorts.

Early peripheral nerve impairments in type 1 diabetes are associated with cortical inhibition of ankle joint proprioceptive afference

OBJECTIVE

Diabetic sensorimotor peripheral neuropathy (DSPN) is a common complication of type 1 diabetes mellitus (T1DM). However, it is still unclear how the cortical processing of proprioceptive afference is altered due to DSPN.

METHODS

Cortical responses to right and left ankle joint rotations were recorded with magnetoencephalography and pooled together in 20 T1DM participants and 20 healthy controls for source space comparisons. T1DM participants also underwent a lower limb nerve-conduction study to correlate peripheral nerve function with the cortical responses.

RESULTS

Primary sensorimotor (SM1) cortex activation was wider in T1DM patients during beta suppression, with no between-group differences in the response strength. However, stronger beta suppressions in T1DM patients were correlated with axon-loss in the peripheral sensory afferents (p < 0.05). Weaker beta rebounds and stronger SM1 evoked field amplitudes were associated with impaired conduction velocities in the mixed nerves (p < 0.05). Lastly, stronger SM1 beta power was associated with both demyelination and axon-loss in the lower limb sensory afferents (p < 0.05).

CONCLUSIONS

T1DM is accompanied with wider SM1 cortex activation to proprioceptive stimuli, and the early asymptomatic DSPN impairments are linked to increased levels of cortical inhibition.

SIGNIFICANCE

T1DM is associated with comprehensive central pathophysiology evident in early DSPN.

Adversarial testing of global neuronal workspace and integrated information theories of consciousness

Different theories explain how subjective experience arises from brain activity1,2. These theories have independently accrued evidence, but have not been directly compared3. Here we present an open science adversarial collaboration directly juxtaposing integrated information theory (IIT)4,5 and global neuronal workspace theory (GNWT)6-10 via a theory-neutral consortium11-13. The theory proponents and the consortium developed and preregistered the experimental design, divergent predictions, expected outcomes and interpretation thereof12. Human participants (n = 256) viewed suprathreshold stimuli for variable durations while neural activity was measured with functional magnetic resonance imaging, magnetoencephalography and intracranial electroencephalography. We found information about conscious content in visual, ventrotemporal and inferior frontal cortex, with sustained responses in occipital and lateral temporal cortex reflecting stimulus duration, and content-specific synchronization between frontal and early visual areas. These results align with some predictions of IIT and GNWT, while substantially challenging key tenets of both theories. For IIT, a lack of sustained synchronization within the posterior cortex contradicts the claim that network connectivity specifies consciousness. GNWT is challenged by the general lack of ignition at stimulus offset and limited representation of certain conscious dimensions in the prefrontal cortex. These challenges extend to other theories of consciousness that share some of the predictions tested here14-17. Beyond challenging the theories, we present an alternative approach to advance cognitive neuroscience through principled, theory-driven, collaborative research and highlight the need for a quantitative framework for systematic theory testing and building.

Simultaneous Eye Tracking and Cerebral Hemodynamic Monitoring in Infants: A Guide for Pediatric Outpatient Follow-Up

Simultaneous eye tracking and cerebral hemodynamic monitoring contribute to the understanding of neural responses to stimuli in infants. However, exploring the impact of complex socioeconomic and environmental adversities on neurodevelopment requires transitioning this tool from research laboratories into clinical practice to evaluate its feasibility in outpatient contexts.

BACKGROUND/OBJECTIVES

This study aimed to present a protocol for simultaneously integrating functional near-infrared spectroscopy (fNIRS) with eye tracking (ET) in infants at risk for neurodevelopmental disorders in a clinical setting with limited resources, during a cognitive task.

METHODS

The protocol was applied to infants in their first 12 months of life. The infants were exposed to tasks involving the processing of social and non-social stimuli, while their brain signals were monitored using fNIRS and their eyes were tracked with ET. The protocol included three main stages: (1) pre-collection, involving the preparation and habituation of the infants and equipment setup (fNIRS and ET); (2) cognitive function monitoring, using social and non-social stimuli to assess preferential processing via fNIRS and ET; and (3) post-collection, with guidelines for data pre-processing and analysis.

RESULTS

The application of the protocol allowed for the identification of technical challenges and the adaptation of procedures for clinical use. The main methodological challenges were difficulty using the conventional cap, excessive movement, synchronization issues between fNIRS and ET, and difficulties calibrating both devices across different age groups.

CONCLUSIONS

The standardization proposed in this protocol enables healthcare professionals to explore different neurocognitive aspects in pediatric clinical settings and expands the scope of neurodevelopmental assessments.

Optimization of OPM-MEG Layouts with a Limited Number of Sensors

Magnetoencephalography (MEG) is a non-invasive neuroimaging technique that measures weak magnetic fields generated by neural electrical activity in the brain. Traditional MEG systems use superconducting quantum interference device (SQUID) sensors, which require cryogenic cooling and employ a dense array of sensors to capture magnetic field maps (MFMs) around the head. Recent advancements have introduced optically pumped magnetometers (OPMs) as a promising alternative. Unlike SQUIDs, OPMs do not require cooling and can be placed closer to regions of interest (ROIs). This study aims to optimize the layout of OPM-MEG sensors, maximizing information capture with a limited number of sensors. We applied a sequential selection algorithm (SSA), originally developed for body surface potential mapping in electrocardiography, which requires a large database of full-head MFMs. While modern OPM-MEG systems offer full-head coverage, expected future clinical use will benefit from simplified procedures, where handling a lower number of sensors is easier and more efficient. To explore this, we converted full-head SQUID-MEG measurements of auditory-evoked fields (AEFs) into OPM-MEG layouts with 80 sensor sites. System conversion was done by calculating a current distribution on the brain surface using minimum norm estimation (MNE). We evaluated the SSA's performance under different protocols, for example, using measurements of single or combined OPM components. We assessed the quality of estimated MFMs using metrics, such as the correlation coefficient (CC), root-mean-square error, and relative error. Additionally, we performed source localization for the highest auditory response (M100) by fitting equivalent current dipoles. Our results show that the first 15 to 20 optimally selected sensors (CC > 0.95, localization error < 1 mm) capture most of the information contained in full-head MFMs. Our main finding is that for event-related fields, such as AEFs, which primarily originate from focal sources, a significantly smaller number of sensors than currently used in conventional MEG systems is sufficient to extract relevant information.

An Exploration on Aperiodic Activities and Transient Oscillations During Semantic Processing: A Study With Wearable MEG

The processing of semantic information is pivotal in language cognition. However, there is a scarcity of research exploring the semantic-related patterns associated with aperiodic and transient periodic brain activities. In this study, recently developed algorithms were employed to parameterize the time-frequency characteristics of neural activities captured with optically pumped magnetometers-based wearable Magnetoencephalography from participants engaged in a Chinese semantic-based task. This study elucidated the neural mechanisms during semantic processing, in relation to transient oscillations and aperiodic activity. Additionally, the results demonstrated that these parameterized features could serve as indicators for decoding semantics. These findings may offer novel contribution to analyzing the mechanism of semantic perception, which will be potential to rehabilitation of language disorders with OPM-MEG.

A multi-subject and multi-session EEG dataset for modelling human visual object recognition

We share a multi-subject and multi-session (MSS) dataset with 122-channel electroencephalographic (EEG) signals collected from 32 human participants. The data was obtained during serial visual presentation experiments in two paradigms. Dataset of first paradigm consists of around 800,000 trials presenting stimulus sequences at 5 Hz. Dataset of second paradigm comprises around 40,000 trials displaying each image for 1 second. Each participant completed between 1 to 5 sessions on different days, and each session lasted for approximately 1.5 hours of EEG recording. The stimulus set used in the experiments included 10,000 images, with 500 images per class, manually selected from PASCAL and ImageNet image databases. The MSS dataset can be useful for various studies, including but not limited to (1) exploring the characteristics of EEG visual response, (2) comparing the differences in EEG response of different visual paradigms, and (3) designing machine learning algorithms for cross-subject and cross-session brain-computer interfaces (BCIs) using EEG data from multiple subjects and sessions.

Test-retest reliability of functional near infrared spectroscopy during tasks of inhibitory control and working memory

Functional near-infrared spectroscopy (fNIRS) has become a well-established tool for neuroscience research and been suggested as a potential biomarker during clinical assessment in individuals with mental disorders. Biomarker need to be objective indications of biological processes which can be measured accurately and reproducibly. Despite various applications in clinical research, test-retest reliability of the fNIRS signal has not yet been evaluated sufficiently. To assess reliability of the fNIRS signal during tasks of executive functions, a group of 34 healthy subjects (13 male, 21 female) were tested twice for inhibitory control and working memory. On a group level results show a specific activation pattern throughout the two sessions, reflecting a task-related frontal network associated with the assessed cognitive functions. On the individual level the retest reliability of the activation patterns were considerably lower and differed strongly between participants. In conclusion, the interpretation of fNIRS signal on a single subject level is partially hampered by its low reliability. More studies are needed to optimize the retest reliability of fNIRS and to be applied on a routine basis in developmental research.

The power of pain: The temporal-spatial dynamics of empathy induced by body gestures and facial expressions

Two non-verbal pain representations, body gestures and facial expressions, can communicate pain to others and elicit our own empathic responses. However, the specific impact of these representations on neural responses of empathy, particularly in terms of temporal and spatial neural mechanisms, remains unclear. To address this issue, the present study developed a kinetic pain empathy paradigm comprising short animated videos depicting a protagonist's "real life" pain and no-pain experiences through body gestures and facial expressions. Electroencephalographic (EEG) recordings were conducted on 52 neurotypical adults; while they viewed the animations. Results from multivariate pattern, event-related potential, event-related spectrum perturbation, and source localization analyses revealed that pain expressed through facial expressions, but not body gestures, elicited increased N200 and P200 responses and activated various brain regions, i.e., the anterior cingulate cortex, insula, thalamus, ventromedial prefrontal cortex, temporal gyrus, cerebellum, and right supramarginal gyrus. Enhanced theta power with distinct spatial distributions were observed during early affective arousal and late cognitive reappraisal stages of the pain event. Multiple regression analyses showed a negative correlation between the N200 amplitude and pain catastrophizing, and a positive correlation between the P200 amplitude and autism traits. These findings demonstrate the temporal evolution of empathy evoked by dynamic pain display, highlighting the significant impact of facial expression and its association with individuals' unique psychological traits.

Current status of EEG microstate in psychiatric disorders: a systematic review and meta-analysis

BACKGROUND

EEG microstates are promising biomarkers for psychiatric conditions, though prior meta-analyses mainly focused on schizophrenia and mood disorders. This study expands the analysis to a wider range of mental disorders, examining microstate variations across the psychosis and mood spectra and assessing medication effects on schizophrenia.

METHODS

Following PRISMA guidelines, we conducted a comprehensive literature search, identifying 24 studies meeting inclusion criteria. Analyses were performed across two psychiatric subgroups: psychotic disorders and mood disorders. We further conducted a subgroup analysis within the schizophrenia spectrum to examine differences in microstate properties between medicated and unmedicated patients.

RESULTS

Microstate C demonstrated significant increase in coverage, and occurrence in patients with schizophrenia, first episode psychosis and high risk for psychosis, and increased duration in schizophrenia. The absence of increased occurrence in medicated schizophrenia patients suggests that this feature may be state-dependent or modulated by treatment. In contrast, microstate D exhibited significant decreases in duration and coverage in unmedicated schizophrenia patients, indicating potential links with acute psychotic states.

CONCLUSIONS

Our findings suggest that microstates C and D could serve as potential biomarkers in schizophrenia, with microstate D alterations linked to acute psychotic symptoms and microstate C potentially reflecting a chronic course or treatment effects. These results emphasize the clinical potential of microstate analysis in psychotic disorder diagnosis and treatment monitoring. The literature on microstate variations in neurodevelopmental and mood disorders is limited, highlighting the need for further research to determine their biomarker potential in these conditions.

Stimulation mapping and whole-brain modeling reveal gradients of excitability and recurrence in cortical networks

The human brain exhibits a modular and hierarchical structure, spanning low-order sensorimotor to high-order cognitive/affective systems. What is the mechanistic significance of this organization for brain dynamics and information processing properties? We investigated this question using rare simultaneous multimodal electrophysiology (stereotactic and scalp electroencephalography - EEG) recordings in 36 patients with drug-resistant focal epilepsy during presurgical intracerebral electrical stimulation (iES) (323 stimulation sessions). Our analyses revealed an anatomical gradient of excitability across the cortex, with stronger iES-evoked EEG responses in high-order compared to low-order regions. Mathematical modeling further showed that this variation in excitability levels results from a differential dependence on recurrent feedback from non-stimulated regions across the anatomical hierarchy, and could be extinguished by suppressing those connections in-silico. High-order brain regions/networks thus show an activity pattern characterized by more inter-network functional integration than low-order ones, which manifests as a spatial gradient of excitability that is emergent from, and causally dependent on, the underlying hierarchical network structure. These findings offer new insights into how hierarchical brain organization influences cognitive functions and could inform strategies for targeted neuromodulation therapies.

Reactivation of previous decisions repulsively biases sensory encoding but attractively biases decision-making

Automatic shaping of perception by past experiences is common in many cognitive functions, reflecting the exploitation of temporal regularities in environments. A striking example is serial dependence, i.e., current perception is biased by previous trials. However, the neural implementation of its operational circle in human brains remains unclear. In two experiments with electroencephalography (EEG)/magnetoencephalography (MEG) recordings and delayed-response tasks, we demonstrate a two-stage 'repulsive-then-attractive' past-present interaction mechanism underlying serial dependence. First, past-trial reports, instead of past stimuli, serve as a prior to be reactivated during both encoding and decision-making. Crucially, past reactivation interacts with current information processing in a two-stage manner: repelling and attracting the present during encoding and decision-making, and arising in the sensory cortex and prefrontal cortex, respectively. Finally, while the early stage occurs automatically, the late stage is modulated by task and predicts bias behavior. These findings might also illustrate general mechanisms of past-present influences in neural operations.

EEGDfus: A Conditional Diffusion Model for Fine-Grained EEG Denoising

Electroencephalogram (EEG) signals are vital in understanding brain activity, but their weak amplitude makes them susceptible to various artifacts. Accurate denoising of EEG data is crucial as a preprocessing step to ensure precise analysis and interpretation. In recent years, the diffusion model has garnered significant attention as a promising approach in generative modeling. This model effectively addresses the issue of over-smoothing in existing deep learning methods and thus has the potential to generate more refined denoised EEG signals. However, the generation process of the standard diffusion model is highly random, limiting its direct application to EEG denoising tasks. To address this limitation, we propose a conditional diffusion model specifically designed for EEG denoising. In this model, the standard diffusion model's denoising network is replaced by a novel dual-branch network, where noisy EEG information is used as a condition to guide the generation of corresponding clean EEG signals. This dual-branch structure leverages the complementary strengths of convolutional neural network (CNN) and Transformer architectures, integrating multi-scale features to comprehensively extract information from the signal. Extensive experiments demonstrate the remarkable performance of EEGDfus in EEG denoising. We tested it on two public datasets. Testing on two public datasets, EEGdenoiseNet and SSED, demonstrated that after denoising, the average correlation coefficient increased to 0.983 and 0.992 for EOG artifact removal, respectively. The proposed model outperforms commonly used baseline models, setting a new state-of-the-art benchmark in the field of EEG denoising.

Short report on a distinct electroencephalogram endophenotype for MTHFR gene variation co-occurring in autism spectrum disorder

Anecdotal reports link a distinct, bilateral, parieto-temporally generated 4.5-Hz rhythm on an electroencephalogram to a methylenetetrahydrofolate reductase gene variant co-occurring in autism spectrum disorder, but the validation of its precision is needed. The electroencephalograms of children with autism spectrum disorder showing the distinct bilateral parieto-temporally generated 4.5-Hz rhythm and their clinical chart report on polymerase chain reaction screening for methylenetetrahydrofolate reductase gene variants, 677C>T and 1298A>C, were retrieved from an outpatient clinic between February 2019 and April 2024. Twenty-five cases were identified. Patients were between 2 and 12 (7 ± 3) years old from Asian (n = 16, 64%), European (n = 5, 20%), African (n = 1, 4%) and mixed (n = 3, 12%) ethnicities. Twenty patients (80%) were positive for 677 C>Theterozygous (n = 3, 15%), 1298A>Cheterozygous (n = 8, 40%) or both (n = 9, 45%). The polymerase chain reaction testing detected neither variant in 5 (20%) patients. Therefore, the electroencephalogram-endophenotype showed 80% precision in identifying methylenetetrahydrofolate reductase gene variant within the sample. This preliminary data support the precision of the proposed distinct, bilateral, parieto-temporally generated 4.5-Hz rhythm in identifying methylenetetrahydrofolate reductase gene variants and its potential clinical applications as a valuable, non-invasive and objective measure within the population.Lay abstractMethylenetetrahydrofolate reductase mutations refer to genetic variations in the methylenetetrahydrofolate reductase enzyme, which plays an important role in folate metabolism. Folate is essential for neural development and signalling. Children with autism spectrum disorder have atypical neural signals compared with control. This study used a non-invasive method to identify a distinct neural signal that may be useful in future screening for methylenetetrahydrofolate reductase mutation in children with autism spectrum disorder. Given that the underlying causes of autism spectrum disorder have multiple genetic factors and often require subjective assessment, this study introduces a potential non-invasive screening method for methylenetetrahydrofolate reductase gene mutation. This method could provide valuable biomarkers for screening and personalised treatments, offering hope for improved risk stratification and bespoke nutritional support and supplements to mitigate the impact on affected individuals and their descendants.

Cortical beta modulation during active movement is highly reproducible in healthy adults

The rolandic beta (13-30 Hz) rhythm recorded over the sensorimotor cortices is known to be modified by movement execution and observation. Beta modulation has been considered as a biomarker of motor function in various neurological diseases, and active natural-like movements might offer a clinically feasible method to assess them. Although the stability of movement-related beta modulation has been addressed during passive and highly controlled active movements, the test-retest reliability of natural-like movements has not been established. We used magnetoencephalography (MEG) to evaluate the reproducibility of movement-related sensorimotor beta modulation longitudinally over 3 mo in a group of healthy adults (n = 22). We focused on the changes in beta activity both during active grasping movement (beta suppression) and after movement termination (beta rebound). The strengths of beta suppression and rebound were similar between the baseline and follow-up measurements; intraclass correlation coefficient values (0.76-0.96) demonstrated high reproducibility. Our results indicate that the beta modulation in response to an active hand-squeezing task has excellent test-retest reliability: the natural-like active movement paradigm is suitable for evaluating the functional state of the sensorimotor cortex and can be used as a biomarker in clinical follow-up studies.NEW & NOTEWORTHY This research demonstrates that the beta rhythm modulation related to active hand-squeezing task has an excellent test-retest reproducibility in healthy adults over a three-month follow-up period. This natural-like active movement is thus suitable for evaluating beta modulation to assess the functional state of the sensorimotor cortex and can be utilized as a biomarker, for example, in clinical longitudinal follow-up studies.

Traveling waves in the human visual cortex: An MEG-EEG model-based approach

Brain oscillations might be traveling waves propagating in cortex. Studying their propagation within single cortical areas has mostly been restricted to invasive measurements. Their investigation in healthy humans, however, requires non-invasive recordings, such as MEG or EEG. Identifying traveling waves with these techniques is challenging because source summation, volume conduction, and low signal-to-noise ratios make it difficult to localize cortical activity from sensor responses. The difficulty is compounded by the lack of a known ground truth in traveling wave experiments. Rather than source-localizing cortical responses from sensor activity, we developed a two-part model-based neuroimaging approach: (1) The putative neural sources of a propagating oscillation were modeled within primary visual cortex (V1) via retinotopic mapping from functional MRI recordings (encoding model); and (2) the modeled sources were projected onto MEG and EEG sensors to predict the resulting signal using a biophysical head model. We tested our model by comparing its predictions against the MEG-EEG signal obtained when participants viewed visual stimuli designed to elicit either fovea-to-periphery or periphery-to-fovea traveling waves or standing waves in V1, in which ground truth cortical waves could be reasonably assumed. Correlations on within-sensor phase and amplitude relations between predicted and measured data revealed good model performance. Crucially, the model predicted sensor data more accurately when the input to the model was a traveling wave going in the stimulus direction compared to when the input was a standing wave, or a traveling wave in a different direction. Furthermore, model accuracy peaked at the spatial and temporal frequency parameters of the visual stimulation. Together, our model successfully recovers traveling wave properties in cortex when they are induced by traveling waves in stimuli. This provides a sound basis for using MEG-EEG to study endogenous traveling waves in cortex and test hypotheses related with their role in cognition.

DLPFC Stimulation Suppresses High-Frequency Neural Activity in the Human sgACC

Transcranial magnetic stimulation (TMS) to the dorsolateral prefrontal cortex (DLPFC) is hypothesized to relieve symptoms of depression by inhibiting activity in the subgenual anterior cingulate cortex (sgACC). However, we have a limited understanding of how TMS influences neural activity in the sgACC, owing to its deep location within the brain. To better understand the mechanism of antidepressant response to TMS, we recruited two neurosurgical patients with indwelling electrodes and delivered TMS pulses to the DLPFC while simultaneously recording local field potentials from the sgACC. Spectral analysis revealed a decrease in high-frequency activity (HFA; 70-180 Hz) after each stimulation pulse, which was especially pronounced in the sgACC relative to other regions. TMS-evoked HFA power was generally anticorrelated between the DLPFC and sgACC, even while low-frequency phase locking between the two regions was enhanced. Together, these findings support the notion that TMS to the DLPFC can suppress neural firing in the sgACC, suggesting a possible mechanism by which this treatment regulates mood.

High-Definition MEG Source Estimation using the Reciprocal Boundary Element Fast Multipole Method

Magnetoencephalographic (MEG) source estimation relies on the computation of the gain (lead-field) matrix, which embodies the linear relationship between the amplitudes of the sources and the recorded signals. However, with a realistic forward model, the calculation of the gain matrix in a "direct" fashion is a computationally expensive task because the number of dipolar sources in standard MEG pipelines is often limited to ~10,000. We propose a fast approach based on the reciprocal relationship between MEG and transcranial magnetic stimulation (TMS). This approach couples naturally with the charge-based boundary element fast multipole method (BEM-FMM), which allows us to efficiently generate gain matrices for high-resolution multi-layer non-nested meshes involving source spaces of up to a ~1 million dipoles. We evaluate our approach by performing MEG source reconstruction against simulated data (at varying noise levels) obtained from the direct computation of MEG readings from 2000 different dipole positions over the cortical surface of 5 healthy subjects. Additionally, we test our methods with real MEG data from evoked somatosensory fields by right-hand median nerve stimulation in these same 5 subjects. We compare our experimental source reconstruction results against the standard MNE-Python source reconstruction pipeline.

Enhancing EEG-Based Emotion Detection with Hybrid Models: Insights from DEAP Dataset Applications

Emotion detection using electroencephalogram (EEG) signals is a rapidly evolving field with significant applications in mental health diagnostics, affective computing, and human-computer interaction. However, existing approaches often face challenges related to accuracy, interpretability, and real-time feasibility. This study leverages the DEAP dataset to explore and evaluate various machine learning and deep learning techniques for emotion recognition, aiming to address these challenges. To ensure reproducibility, we have made our code publicly available. Extensive experimentation was conducted using K-Nearest Neighbors (KNN), Support Vector Machines (SVMs), Decision Tree (DT), Random Forest (RF), Bidirectional Long Short-Term Memory (BiLSTM), Gated Recurrent Units (GRUs), Convolutional Neural Networks (CNNs), autoencoders, and transformers. Our hybrid approach achieved a peak accuracy of 85-95%, demonstrating the potential of advanced neural architectures in decoding emotional states from EEG signals. While this accuracy is slightly lower than some state-of-the-art methods, our approach offers advantages in computational efficiency and real-time applicability, making it suitable for practical deployment. Furthermore, we employed SHapley Additive exPlanations (SHAP) to enhance model interpretability, offering deeper insights into the contribution of individual features to classification decisions. A comparative analysis with existing methods highlights the novelty and advantages of our approach, particularly in terms of accuracy, interpretability, and computational efficiency. A key contribution of this study is the development of a real-time emotion detection system, which enables instantaneous classification of emotional states from EEG signals. We provide a detailed analysis of its computational efficiency and compare it with existing methods, demonstrating its feasibility for real-world applications. Our findings highlight the effectiveness of hybrid deep learning models in improving accuracy, interpretability, and real-time processing capabilities. These contributions have significant implications for applications in neurofeedback, mental health monitoring, and affective computing. Future work will focus on expanding the dataset, testing the system on a larger and more diverse participant pool, and further optimizing the system for broader clinical and industrial applications.

Neural Dynamics of the Processing of Speech Features: Evidence for a Progression of Features from Acoustic to Sentential Processing

When we listen to speech, our brain's neurophysiological responses "track" its acoustic features, but it is less well understood how these auditory responses are enhanced by linguistic content. Here, we recorded magnetoencephalography responses while subjects of both sexes listened to four types of continuous speechlike passages: speech envelope-modulated noise, English-like nonwords, scrambled words, and a narrative passage. Temporal response function (TRF) analysis provides strong neural evidence for the emergent features of speech processing in the cortex, from acoustics to higher-level linguistics, as incremental steps in neural speech processing. Critically, we show a stepwise hierarchical progression of progressively higher-order features over time, reflected in both bottom-up (early) and top-down (late) processing stages. Linguistically driven top-down mechanisms take the form of late N400-like responses, suggesting a central role of predictive coding mechanisms at multiple levels. As expected, the neural processing of lower-level acoustic feature responses is bilateral or right lateralized, with left lateralization emerging only for lexicosemantic features. Finally, our results identify potential neural markers, linguistic-level late responses, derived from TRF components modulated by linguistic content, suggesting that these markers are indicative of speech comprehension rather than mere speech perception.

A continuous approach to explain insomnia and subjective-objective sleep discrepancy

Understanding insomnia is crucial for improving its diagnosis and treatment. However, many subjective complaints about insomnia do not align with objective measures of sleep quality, as is the case in subjective-objective sleep discrepancy (SOSD). We address this discrepancy by measuring sleep intrusions and instability in polysomnographic recordings from a large clinical database. Using machine learning, we develop personalized models to infer hypnodensities-a continuous and probabilistic measure of sleep dynamics-, and analyze them via information theory to measure intrusions and instability in a principled way. We find that insomnia with SOSD involves sleep intrusions during intra-sleep wakefulness, while insomnia without SOSD shows wake intrusions during sleep, indicating distinct etiologies. By mapping these metrics to standard sleep features, we provide a continuous and interpretable framework for measuring sleep quality. This approach integrates and values subjective insomnia complaints with physiological data for a more accurate view of sleep quality and its disorders.

Extreme signal amplitude events in neuromagnetic oscillations reveal brain aging processing across adulthood

Introduction

Neurophysiological activity, as noninvasively captured by electro- and magnetoencephalography (EEG and MEG), demonstrates complex temporal fluctuations approximated by typical variations around the mean values and rare events with large amplitude. The statistical properties of these extreme and rare events in neurodynamics may reflect the limits or capacity of the brain as a complex system in information processing. However, the exact role of these extreme neurodynamic events in ageing, and their spectral and spatial patterns remain elusive. Our study hypothesized that ageing would be associated with frequency specific alterations in the brain's tendency to synchronize large ensembles of neurons and to produce extreme events.

Methods

To identify spatio-spectral patterns of these age-related changes in extreme neurodynamics, we examined resting-state MEG recordings from a large cohort of adults (n = 645), aged 18 to 89. We characterized extreme neurodynamics by computing sample skewness and kurtosis, and used Partial Least Squares to test for differences across age groups.

Results

Our findings revealed that each canonical frequency, from theta to lower gamma, displayed unique spatial patterns of either age-related increases, decreases, or both in the brain's tendency to produce extreme neuromagnetic events.

Discussion

Our study introduces a novel neuroimaging framework for understanding ageing through the extreme and rare events of the neurophysiological activity, offering more sensitivity than typical comparative approaches.

Hierarchical dynamic coding coordinates speech comprehension in the human brain

Speech comprehension involves transforming an acoustic waveform into meaning. To do so, the human brain generates a hierarchy of features that converts the sensory input into increasingly abstract language properties. However, little is known about how rapid incoming sequences of hierarchical features are continuously coordinated. Here, we propose that each language feature is supported by a dynamic neural code, which represents the sequence history of hierarchical features in parallel. To test this 'Hierarchical Dynamic Coding' (HDC) hypothesis, we use time-resolved decoding of brain activity to track the construction, maintenance, and update of a comprehensive hierarchy of language features spanning phonetic, word form, lexical-syntactic, syntactic and semantic representations. For this, we recorded 21 native English participants with magnetoencephalography (MEG), while they listened to two hours of short stories in English. Our analyses reveal three main findings. First, the brain represents and simultaneously maintains a sequence of hierarchical features. Second, the duration of these representations depends on their level in the language hierarchy. Third, each representation is maintained by a dynamic neural code, which evolves at a speed commensurate with its corresponding linguistic level. This HDC preserves the maintenance of information over time while limiting destructive interference between successive features. Overall, HDC reveals how the human brain maintains and updates the continuously unfolding language hierarchy during natural speech comprehension, thereby anchoring linguistic theories to their biological implementations.

Pulsed inhibition of corticospinal excitability by the thalamocortical sleep spindle

Thalamocortical sleep spindles, i.e., oscillatory bursts at ∼12-15 Hz of waxing and waning amplitude, are a hallmark feature of non-rapid eye movement (NREM) sleep and believed to play a key role in memory reactivation and consolidation. Generated in the thalamus and projecting to neocortex and hippocampus, they are phasically modulated by neocortical slow oscillations (<1 Hz) and in turn phasically modulate hippocampal sharp-wave ripples (>80 Hz). This hierarchical cross-frequency nesting, where slower oscillations group faster ones into certain excitability phases, may enable phase-dependent plasticity in the neocortex, and spindles have thus been considered windows of plasticity in the sleeping brain. However, the assumed phasic excitability modulation had not yet been demonstrated for spindles. Utilizing a recently developed real-time spindle detection algorithm, we applied spindle phase-triggered transcranial magnetic stimulation (TMS) to the primary motor cortex (M1) hand area to characterize the corticospinal excitability profile of spindles via motor evoked potentials (MEP). MEPs showed net suppression during spindles, driven by a "pulse of inhibition" during its falling flank with no inhibition or facilitation during its peak, rising flank, or trough. This unidirectional ("asymmetric") modulation occurred on top of the general sleep-related inhibition during spindle-free NREM sleep and did not extend into the refractory post-spindle periods. We conclude that spindles exert "asymmetric pulsed inhibition" on corticospinal excitability. These findings and the developed real-time spindle targeting methods enable future studies to investigate the causal role of spindles in phase-dependent synaptic plasticity and systems memory consolidation during sleep by repetitively targeting relevant spindle phases.

Phenome-wide associations of sleep characteristics in the Human Phenotype Project

Sleep tests commonly diagnose sleep disorders, but the diverse sleep-related biomarkers recorded by such tests can also provide broader health insights. In this study, we leveraged the uniquely comprehensive data from the Human Phenotype Project cohort, which includes 448 sleep characteristics collected from 16,812 nights of home sleep apnea test monitoring in 6,366 adults (3,043 male and 3,323 female participants), to study associations between sleep traits and body characteristics across 16 body systems. In this analysis, which identified thousands of significant associations, visceral adipose tissue (VAT) was the body characteristic that was most strongly correlated with the peripheral apnea-hypopnea index, as adjusted by sex, age and body mass index (BMI). Moreover, using sleep characteristics, we could predict over 15% of body characteristics, spanning 15 of the 16 body systems, in a held-out set of individuals. Notably, sleep characteristics contributed more to the prediction of certain insulin resistance, blood lipids (such as triglycerides) and cardiovascular measurements than to the characteristics of other body systems. This contribution was independent of VAT, as sleep characteristics outperformed age, BMI and VAT as predictors for these measurements in both male and female participants. Gut microbiome-related pathways and diet (especially for female participants) were notably predictive of clinical obstructive sleep apnea symptoms, particularly sleepiness, surpassing the prediction power of age, BMI and VAT on these symptoms. Together, lifestyle factors contributed to the prediction of over 50% of the sleep characteristics. This work lays the groundwork for exploring the associations of sleep traits with body characteristics and developing predictive models based on sleep monitoring.

EEG-Deformer: A Dense Convolutional Transformer for Brain-Computer Interfaces

Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is challenging yet essential for decoding brain activities using brain-computer interfaces (BCIs). Although Transformers are popular for their long-term sequential learning ability in the BCI field, most methods combining Transformers with convolutional neural networks (CNNs) fail to capture the coarse-to-fine temporal dynamics of EEG signals. To overcome this limitation, we introduce EEG-Deformer, which incorporates two main novel components into a CNN-Transformer: (1) a Hierarchical Coarse-to-Fine Transformer (HCT) block that integrates a Fine-grained Temporal Learning (FTL) branch into Transformers, effectively discerning coarse-to-fine temporal patterns; and (2) a Dense Information Purification (DIP) module, which utilizes multi-level, purified temporal information to enhance decoding accuracy. Comprehensive experiments on three representative cognitive tasksâcognitive attention, driving fatigue, and mental workload detectionâconsistently confirm the generalizability of our proposed EEG-Deformer, demonstrating that it either outperforms or performs comparably to existing state-of-the-art methods. Visualization results show that EEG-Deformer learns from neurophysiologically meaningful brain regions for the corresponding cognitive tasks.

Subthalamic Nucleus Deep Brain Stimulation in the Beta Frequency Range Boosts Cortical Beta Oscillations and Slows Down Movement

Recordings from Parkinson's disease (PD) patients show strong beta-band oscillations (13-35 Hz), which can be modulated by deep brain stimulation (DBS). While high-frequency DBS (>100 Hz) ameliorates motor symptoms and reduces beta activity in the basal ganglia and motor cortex, the effects of low-frequency DBS (<30 Hz) are less clear. Clarifying these effects is relevant for the debate about the role of beta oscillations in motor slowing, which might be causal or epiphenomenal. Here, we investigated how subthalamic nucleus (STN) beta-band DBS affects cortical beta oscillations and motor performance. We recorded the magnetoencephalogram of 14 PD patients (nine males) with DBS implants while on their usual medication. Following a baseline recording (DBS OFF), we applied bipolar DBS at beta frequencies (10, 16, 20, 26, and 30 Hz) via the left electrode in a cyclic fashion, turning stimulation on (5 s) and off (3 s) repeatedly. Cyclic stimulation was applied at rest and during right-hand finger tapping. In the baseline recording, we observed a negative correlation between the strength of hemispheric beta power lateralization and the tap rate. Importantly, beta-band DBS accentuated the lateralization and reduced the tap rate proportionally. The change in lateralization was specific to the alpha/beta range (8-26 Hz), outlasted stimulation, and did not depend on the stimulation frequency, suggesting a remote-induced response rather than entrainment. Our study demonstrates that cortical beta oscillations can be manipulated by STN beta-band DBS. This manipulation has consequences for motor performance, supporting a causal role of beta oscillations.

Cortical parcellation optimized for magnetoencephalography with a clustering technique

A typical approach to estimate connectivity from magnetoencephalographic (MEG) data consists of 1) computing a cortically-constrained, distributed source estimate, 2) dividing the cortex into parcels according to an anatomical atlas, 3) combining the source time courses within each parcel, and 4) computing a connectivity metric between these combined time courses. However, combining MEG signals to spatial mean activities of anatomically-defined parcels often leads to cancellation within and crosstalk between parcels. We present a method that divides the cortex into parcels whose activity can be faithfully represented by a single dipolar source while minimizing inter-parcel crosstalk. The method relies on unsupervised clustering of the MEG leadfields, also accounting for distances between the cortically-constrained sources to promote spatially contiguous parcels. The cluster each source point belongs to is determined by its k nearest-neighbour memberships. Inter-parcel crosstalk was minimized by assigning [Formula: see text] and a weight of 20%-40% to the spatial distances, leading to 60-120 parcels. Our approach, available through the Python package "megicparc", enables a compact yet anatomically-informed source-level representation of MEG data with a similar dimensionality as in the original sensor-level data. Such representation should enable significant improvements in source-space visualization of MEG features or in estimating functional connectivity.

Evaluating robotic actions: spatiotemporal brain dynamics of performance assessment in robot-assisted laparoscopic training

Introduction

Enhancing medical robot training traditionally relies on explicit feedback from physicians to identify optimal and suboptimal robotic actions during surgery. Passive brain-computer interfaces (BCIs) offer an emerging alternative by enabling implicit brain-based performance evaluations. However, effectively decoding these evaluations of robot performance requires a comprehensive understanding of the spatiotemporal brain dynamics identifying optimal and suboptimal robot actions within realistic settings.

Methods

We conducted an electroencephalographic study with 16 participants who mentally assessed the quality of robotic actions while observing simulated robot-assisted laparoscopic surgery scenarios designed to approximate real-world conditions. We aimed to identify key spatiotemporal dynamics using the surface Laplacian technique and two complementary data-driven methods: a mass-univariate permutation-based clustering and multivariate pattern analysis (MVPA)-based temporal decoding. A second goal was to identify the optimal time interval of evoked brain signatures for single-trial classification.

Results

Our analyses revealed three distinct spatiotemporal brain dynamics differentiating the quality assessment of optimal vs. suboptimal robotic actions during video-based laparoscopic training observations. Specifically, an enhanced left fronto-temporal current source, consistent with P300, LPP, and P600 components, indicated heightened attentional allocation and sustained evaluation processes during suboptimal robot actions. Additionally, amplified current sinks in right frontal and mid-occipito-parietal regions suggested prediction-based processing and conflict detection, consistent with the oERN and interaction-based ERN/N400. Both mass-univariate clustering and MVPA provided convergent evidence supporting these neural distinctions.

Discussion

The identified neural signatures propose that suboptimal robotic actions elicit enhanced, sustained brain dynamics linked to continuous attention allocation, action monitoring, conflict detection, and ongoing evaluative processing. The findings highlight the importance of prioritizing late evaluative brain signatures in BCIs to classify robotic actions reliably. These insights have significant implications for advancing machine-learning-based training paradigms.

Center frequency as optimal frequency of visual stimulation for spreading entrained gamma rhythms to other target brain regions in cognitively normal older adults

Gamma entrainment using 40 Hz sensory stimulation has shown promise in AD mouse models, but inconsistent results in AD patients, possibly due to interspecies and interindividual differences in center frequency (CF). This study aimed to investigate whether gamma rhythms entrained by visual stimulation at an individual's CF can spread better than those at other frequency conditions in older adults. We entrained gamma rhythms in 32 cognitively normal older participants using light flickering at 32 Hz, 34 Hz, 36 Hz, 38 Hz, and 40 Hz. We identified each individual's CF among these five frequencies and compared the spread, strength, and stability of gamma connectivity induced by light stimulation flickering at an individual's CF with those at other frequencies using generalized estimating equation and repeated measures ANOVA. In about two-thirds of the participants, 32 Hz (40.6%) and 34 Hz (28.1%) were identified as CF. The mean spread, strength, and stability of gamma connectivity involving the visual cortex (GCV-NV) were higher than those do not involve the visual cortex (GCNV-NV, p < 0.05). Between the visual cortex and other brain regions, FLS induced with frequencies of high event related spectral perturbation values, including CF and non-center frequency (NCF) 1, generally induced broader, stronger, and more stable gamma connectivity compared to most other NCFs (p < 0.001 when comparing NCFs with either CF and NCF1 for both strength and spread; p = 0.012 when comparing CF and NCF3 for stability). Gamma rhythms entrained by visual stimulation may better spread to other brain regions when its frequency matched to the individual's CF.

Neural Bases of Proactive and Predictive Processing of Meaningful Subword Units in Speech Comprehension

To comprehend speech, human brains identify meaningful units, like words, in the speech stream. But whereas the English 'She believed him.' has three words, the Arabic equivalent 'ṣaddaqathu' forms one word with three meaningful subword units, called morphemes: a verb stem ('ṣaddaqa'), a subject suffix ('-t-'), and a direct object pronoun ('-hu'). It remains unclear whether and how speech comprehension involves morpheme processing, above and beyond other language units. Here, we propose and test hierarchically nested encoding models of speech comprehension: a naïve model with word-, syllable-, and sound-level information; a bottom-up model with additional morpheme boundary information; and predictive models that process morphemes before these boundaries. We recorded MEG data as 27 participants (16 female) listened to Arabic sentences like 'ṣaddaqathu .' A temporal response function analysis revealed that in temporal and left inferior frontal regions, predictive models outperform the bottom-up model, which outperforms the naïve model. Moreover, verb stems were either length-ambiguous (e.g., 'ṣaddaqa' is initially mistakable for the shorter stem 'ṣadda', meaning 'blocked') or length-unambiguous (e.g., 'qayyama', meaning 'evaluated', cannot be mistaken for a shorter stem) but shared a uniqueness point, beyond which stem identity is disambiguated. Evoked analyses revealed differences between conditions before the uniqueness point, suggesting that, rather than await disambiguation, the brain employs proactive predictive strategies, processing accumulated input as soon as any possible stem is identifiable, even if not uniquely. These findings highlight the role of morphemes in speech and the importance of including morpheme-level information in neural and computational models of speech comprehension.

Novel machine learning-driven comparative analysis of CSP, STFT, and CSP-STFT fusion for EEG data classification across multiple meditation and non-meditation sessions in BCI pipeline

This study focuses on classifying multiple sessions of loving kindness meditation (LKM) and non-meditation electroencephalography (EEG) data. This novel study focuses on using multiple sessions of EEG data from a single individual to train a machine learning pipeline, and then using a new session data from the same individual for the classification. Here, two meditation techniques, LKM-Self and LKM-Others were compared with non-meditation EEG data for 12 participants. Among many tested, three BCI pipelines we built produced promising results, successfully detecting features in meditation/ non-meditation EEG data. While testing different feature extraction algorithms, a common neural network structure was used as the classification algorithm to compare the performance of the feature extraction algorithms. For two of those pipelines, Common Spatial Patterns (CSP) and Short Time Fourier Transform (STFT) were successfully used as feature extraction algorithms where both these algorithms are significantly new for meditation EEG. As a novel concept, the third BCI pipeline used a feature extraction algorithm that fused the features of CSP and STFT, achieving the highest classification accuracies among all tested pipelines. Analyses were conducted using EEG data of 3, 4 or 5 sessions, totaling 3960 tests on the entire dataset. At the end of the study, when considering all the tests, the overall classification accuracy using SCP alone was 67.1%, and it was 67.8% for STFT alone. The algorithm combining the features of CSP and STFT achieved an overall classification accuracy of 72.9% which is more than 5% higher than the other two pipelines. At the same time, the highest mean classification accuracy for the 12 participants was achieved using the pipeline with the combination of CSP STFT algorithm, reaching 75.5% for LKM-Self/ non-meditation for the case of 5 sessions of data. Additionally, the highest individual classification accuracy of 88.9% was obtained by the participant no. 14. Furthermore, the results showed that the classification accuracies for all three pipelines increased with the number of training sessions increased from 2 to 3 and then to 4. The study was successful in classifying a new session of EEG meditation/ non-meditation data after training machine learning algorithms using a different set of session data, and this achievement will be beneficial in the development of algorithms that support meditation.

Time to inhibit: P300 amplitude differences in individuals with high and low temporal efficiency

Temporal processing and inhibitory control are closely interconnected. This study investigated whether individuals of high and low temporal efficiency display different behavioral and neural patterns while performing an electrophysiological Go/No-Go task. Individuals with lower temporal processing had significantly poorer behavioral performance of the task-slower reaction times to Go stimuli, greater number of omissions, and lower stimulus detectability (lower d-prime value)-than the high temporal efficiency group. Additionally, participants with low temporal efficiency had significantly lower P300 response to correct inhibitions (No-Go stimuli) compared to those with high temporal efficiency. Furthermore, the greater amplitude of the difference wave (No-Go vs Go) in the high temporal efficiency group may suggest superior efficacy of response inhibition and attention control processes in comparison to the low temporal efficiency group. These findings highlight significant differences in inhibitory control at both behavioral and neural levels in young adults differing in temporal processing efficiency.