A method to standardize a reference of scalp EEG recordings to a point at infinity

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.

Resting-state EEG network variability predicts individual working memory behavior

Even during periods of rest, the brain exhibits spontaneous activity that dynamically fluctuates across spatially distributed regions in a globally coordinated manner, which has significant cognitive implications. However, the relationship between the temporal variability of resting-state networks and working memory (WM) remains largely unexplored. This study aims to address this gap by employing an EEG-based protocol combined with fuzzy entropy. First, we identified both flexible and robust patterns of dynamic resting-state networks. Subsequently, we observed a significant positive correlation between WM performance and network variability, particularly in connections associated with the frontal, right central, and right parietal lobes. Moreover, we found that the temporal variability of network properties was positively and significantly associated with WM performance. Additionally, distinct patterns of network variability were delineated, contributing to inter-individual differences in WM abilities, with these distinctions becoming more pronounced as task demands increased. Finally, using a multivariable predictive model based on these variability metrics, we effectively predicted individual WM performances. Notably, analogous analyses conducted in the source space validated the reproducibility of the temporal variability of resting-state networks in predicting individual WM behavior at higher spatial resolution, providing more precise anatomical localization of key brain regions. These results suggest that the temporal variability of resting-state networks reflects intrinsic dynamic changes in brain organization supporting WM and can serve as an objective predictor for individual WM behaviors.

Comparing MEG and EEG measurement set-ups for a brain-computer interface based on selective auditory attention

Auditory attention modulates auditory evoked responses to target vs. non-target sounds in electro- and magnetoencephalographic (EEG/MEG) recordings. Employing whole-scalp MEG recordings and offline classification algorithms has been shown to enable high accuracy in tracking the target of auditory attention. Here, we investigated the decrease in accuracy when moving from the whole-scalp MEG to lower channel count EEG recordings and when training the classifier only from the initial or middle part of the recording instead of extracting training trials throughout the recording. To this end, we recorded simultaneous MEG (306 channels) and EEG (64 channels) in 18 healthy volunteers while presented with concurrent streams of spoken "Yes"/"No" words and instructed to attend to one of them. We then trained support vector machine classifiers to predict the target of attention from unaveraged trials of MEG/EEG. Classifiers were trained on 204 MEG gradiometers or on EEG with 64, 30, nine or three channels with trials extracted randomly across or only from the beginning of the recording. The highest classification accuracy, 73.2% on average across the participants for one-second trials, was obtained with MEG when the training trials were randomly extracted throughout the recording. With EEG, the accuracy was 69%, 69%, 66%, and 61% when using 64, 30, nine, and three channels, respectively. When training the classifiers with the same amount of data but extracted only from the beginning of the recording, the accuracy dropped by 11%-units on average, causing the result from the three-channel EEG to fall below the chance level. The combination of five consecutive trials partially compensated for this drop such that it was one to 5%-units. Although moving from whole-scalp MEG to EEG reduces classification accuracy, usable auditory-attention-based brain-computer interfaces can be implemented with a small set of optimally placed EEG channels.

Estimating sensor-space EEG connectivity PART 1: Identifying best performing methods for functional connectivity in simulated data

OBJECTIVE

Functional brain connectivity (FC) can be estimated using electroencephalography (EEG). However, there is considerable variability across studies in the FC measures used and in data (pre-)processing methods, leading to difficulties comparing and amalgamating results between studies. Thus, standardisation of EEG (pre-)processing for the measurement and reporting of FC is needed.We aimed to assess differences in FC estimates produced by different settings across multiple EEG pre-processing steps, (including re-referencing and epoching) to validate a reliable methodological pipeline for assessing EEG-FC in simulated EEG data.

METHODS

We simulated EEG-FC data where the 'ground truth' of the connections is known and compared estimates of FC from this ground truth data across multiple FC measures and variations in multiple pre-processing steps.

RESULTS

Our results indicated that pre-processing steps that included segmenting the data into 40 or more epochs that were 6 s or more in length provided the most accurate estimation of the simulated FC. With regards to the data re-referencing, the Reference Electrode Standardization Technique or the common average re-referencing appeared best when used in conjunction with imaginary coherence and weighted phase lag index metrics. However, the magnitude-squared coherence FC measure performed best with the Current Source Density reference free techniques.

CONCLUSIONS & SIGNIFICANCE

Our paper provides an evidence-base for the influence of referencing, epoch length and number, controls for volume conduction, and different FC metrics on EEG-FC measurement. Using this evidence, we present an initial and promising account of the best performing (pre-)processing choices for robust EEG-FC assessment.

Estimating sensor-space EEG connectivity PART 2: Identifying optimal artifact reduction techniques for functional connectivity in real data

OBJECTIVES

Electroencephalography (EEG) can be used to assess functional brain connectivity (FC). However, there is considerable variability in the methods used for FC measurement across different studies, which may contribute to heterogeneity in research outcomes. We aimed to assess how different EEG pre-processing steps impact EEG-FC measurement when applied to real EEG data.

METHODS

Using the BrainClinics.com open-source EEG data repository we investigated how different pre-processing steps impacted the ability to detect age-related differences in alpha band FC and the test-retest reliability of FC measures. The pre-processing steps tested included artifact reduction techniques (Independent Component Analysis (ICA), wavelet-enhanced ICA (wICA), and Multi-channel Wiener Filters (MWF)), different epoch lengths (epochs that were 2 s versus 6 s in length), and different re-referencing montages (the common average reference (CAR) versus current source density (CSD) re-referencing). We also assessed different FC metrics including imaginary coherence (iCOH), real magnitude squared coherence (rMSC), and weighted phase lag index (wPLI) metrics.

RESULTS

The best performing pipeline at detecting age-related differences in alpha FC and providing high test-retest reliability included artifact reduction by ICA or wICA, data re-referenced using the CSD method, and FC measured by rMSC.

CONCLUSION & SIGNIFICANCE

This paper presents evidence for an EEG pre-processing pipeline that provides good detection of meaningful effects and high test-retest reliability for sensor space EEG alpha frequency FC.

EEG alpha power during creative ideation of graphic symbols

Graphic symbolic creation-transforming abstract concepts into visual forms-is a cognitively complex and uniquely human skill. Neurophysiological evidence suggests that oscillatory alpha activity is correlated with visual-figurative creative thinking. However, whether alpha oscillations play a functional role in generating graphic symbols remains unclear. To address this issue, we compared the EEG alpha power of 40 healthy adults while ideating creative and conventional graphic symbols representing an abstract concept's meaning (e.g., the word 'peace'). Our results revealed that the ideation of graphic symbols elicited alpha synchronization, with higher levels in the conventional compared to the creative condition, mainly over frontal-central, frontal-temporal, parietal-occipital, and occipital regions. Furthermore, we observed greater alpha synchronization in the right hemisphere than in the left across both conditions, particularly between temporal, central-parietal, and parietal electrodes. This asymmetry extended to central electrodes in the creative condition, while in the conventional condition, it was more pronounced over parietal-occipital regions. Finally, we also found that frontal and occipital alpha synchronization during the creative ideation phase predicted the subsequent originality scores of the graphic symbols produced. Together, these findings enhance our understanding of the dynamics of oscillatory alpha activity during graphic symbol creation, shedding light on how the interaction between inhibitory top-down control mechanisms and cognitive flexibility processes facilitates the transformation of abstract concepts into visual forms. These findings provide new insights into the neural processes underlying this uniquely human ability.

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.

The effect of diet on the development of EEG microstates in healthy infant throughout the first year of life

Electroencephalography (EEG) is used to directly measure neuronal activity and evaluate network dynamics with an excellent temporal resolution. These network dynamics in the form of EEG microstates - distinct yet transiently stable topographies captured at peaks of the global field power - are increasingly used as markers of disease, neurodegeneration, and neurodevelopment. However, few studies have evaluated EEG microstates throughout the first year of life, and currently none have examined the potential effects of infant diet. The current study seeks to investigate whether different diets impact EEG microstates throughout the first year of life. EEGs were collected from approximately 500 healthy infants who were fed a human milk, diary-, or soy-based formula at three, six, nine, and twelve months of age. Microstate classes and temporal characteristics were then calculated for each timepoint and diet. Microstates classes showed a clear developmental trajectory, with duration decreasing with age, and coverage, globally explained variance, and occurrence generally increasing with age. There were relatively few significant differences between infants fed different diets, indicating that diet potentially effects functional neurodevelopment more subtly than previously indicated in the literature. This study adds to the growing body of literature demonstrating that formula feeding does not have clear disadvantages in terms of infant functional neuronal development.

Emotional influences on remembering and forgetting explained by frontal and parietal dynamics

Based on item-method directed forgetting (DF) task, 60 participants were recruited to explore the influence of emotion (negative, neutral, and positive) on memory encoding processing, with all data referring to the encoding phase of the task. Behavioral results showed that participants were more successful at remembering negative pictures that needed to be forgotten, with both higher recognition rates and discrimination accuracy (Pr) compared with neutral pictures. In the brain, parietal activities reflected preferential processing during negative picture viewing through enhanced late parietal positive potentials (LPP) relative to neutral ones. In addition, "Remember" (R) instruction evoked a larger parietal P3 component, whereas "Forget" (F) instruction evoked a stronger frontal N2 component, each of which component was significantly associated with the DF effect (i.e., more recognized items of R-cue than that of F-cue), reflecting the fact that inhibitory control and selective rehearsal mechanisms were jointly responsible for the directed forgetting of emotional materials. Finally, we showed the presence of instruction-evoked low-frequency phase synchronization between frontal and parietal regions, and that these synchronization patterns differed between R-cue and F-cue in an emotion-dependent manner. Together, these findings reveal cognitive mechanisms and specific patterns of large-scale phase synchronization underlying active forgetting of emotional memories, deepening our comprehension of the interplay between cognition and emotion.NEW & NOTEWORTHY This study provides experimental evidence that emotional memories, especially negative ones, are more difficult to intentionally forget than neutral memories within the item-method directed forgetting paradigm. It explores the cognitive mechanisms underlying this process, highlighting the role of selective rehearsal and inhibitory control. In addition, it reveals emotion-dependent low-frequency phase synchronization between frontal and parietal regions, offering new insights into active forgetting of emotional memories.

Impact of EEG Reference Schemes on Event-Related Potential Outcomes: A Corollary Discharge Study Using a Talk/Listen Paradigm

The selection of an appropriate virtual reference schema is pivotal in determining the outcomes of event-related potential (ERP) studies, particularly within the widely utilized Talk/Listen ERP paradigm, which is employed to non-invasively explore the corollary discharge phenomenon in the speech-auditory system. This research centers on examining the effects of prevalent EEG reference schemas-linked mastoids (LM), common average reference (CAR), and reference electrode standardization technique (REST)-through statistical analysis, statistical parametric scalp mapping (SPSM), and source localization techniques. Our ANOVA findings indicate significant main effects for both the reference and the experimental condition on the amplitude of N1 ERPs. Depending on the reference used, the polarity and amplitude of the N1 ERPs demonstrate systematic variations: LM is associated with pronounced frontocentral activity, whereas both CAR and REST exhibit patterns of frontocentral and occipitotemporal activity. The significance of SPSM results is confined to regions exhibiting prominent N1 activity for each reference schema. Source analysis provides corroborative evidence more aligned with the SPSM results for CAR and REST than for LM, suggesting that results under CAR and REST are more objective and reliable. Therefore, the CAR and REST reference are recommended for future studies involving Talk/Listen ERP paradigms.

Multiscale neural dynamics in sleep transition volatility across age scales: a multimodal EEG-EMG-EOG analysis of temazepam effects

Recent advances in computational modeling techniques have facilitated a more nuanced understanding of sleep neural dynamics across the lifespan. In this study, we tensorize multiscale multimodal electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals and apply Generalized Autoregressive Conditional Heteroskedasticity (GARCH) modeling to quantify interactions between age scales and the use of pharmacological sleep aids on sleep stage transitions. Our cohort consists of 22 subjects in a crossover design study, where each subject received both a sleep aid and a placebo in different sessions. To understand these effects across the lifespan, three evenly distributed age groups were formed: 18-29, 30-49, and 50-66 years. The methodological framework implemented here employs tensor-based machine learning techniques to compute continuous wavelet transform time-frequency features and utilizes a GARCH model to quantify sleep signal volatility across age scales. Support Vector Machines are used for feature ranking, and our analysis captures interactions between signal entropy, age, and sleep aid status across frequency bands, sleep transitions, and sleep stages. GARCH model results reveal statistically significant volatility clustering in EEG, EMG, and EOG signals, particularly during transitions between REM and non-REM sleep. Notably, volatility was higher in the 50-66 age group compared to the 18-29 age group, with marked fluctuations during transitions from deep sleep to REM sleep (standard deviation of 0.35 in the older group vs. 0.30 in the 18-29 age group, p < 0.05). Statistical comparisons of volatility across frequency bands, age scales, and sleep stages highlight significant differences attributable to sleep aid use. Mean conditional volatility parameterization of the GARCH model reveals directional influences, with a causality index of 0.75 from frontal to occipital regions during REM sleep transition periods. Our methodological framework identifies distinct neural behavior patterns across age groups associated with each sleep stage and transition, offering insights into the development of targeted interventions for sleep regularity across the lifespan.

Electrical impedance tomography meets reduced order modelling: a framework for faster and more reliable electrical conductivity estimations

Objective.Inclusion of individualised electrical conductivities of head tissues is crucial for the accuracy of electrical source imaging techniques based on electro/magnetoencephalography and the efficacy of transcranial electrical stimulation. Parametric electrical impedance tomography (pEIT) is a method to cheaply and non-invasively estimate them using electrode arrays on the scalp to apply currents and measure the resulting potential distribution. Conductivities are then estimated by iteratively fitting a forward model to the measurements, incurring a prohibitive computational cost that is generally lowered at the expense of accuracy. Reducing the computational cost associated with the forward solutions would improve the accessibility of this method and unlock new capabilities.Approach.We introduce reduced order modelling (ROM) to massively speed up the calculations of these solutions for arbitrary conductivity values.Main results.We demonstrate this new ROM-pEIT framework using a realistic head model with six tissue compartments, with minimal errors in both the approximated numerical solutions and conductivity estimations. We show that the computational complexity required to reach a multi-parameter estimation with a negligible relative error is reduced by more than an order of magnitude when using this framework. Furthermore, we illustrate the benefits of this new framework in a number of practical cases, including its application to real pEIT data from three subjects.Significance.Results suggest that this framework can transform the use of pEIT for seeking personalised head conductivities, making it a valuable tool for researchers and clinicians.

The Practical Implications of Re-Referencing in ERP Studies: The Case of N400 in the Picture-Word Verification Task

Background: The selection of an optimal referencing method in event-related potential (ERP) research has been a long-standing debate, as it can significantly influence results and lead to data misinterpretation. Such misinterpretation can produce flawed scientific conclusions, like the inaccurate localization of neural processes, and in practical applications, such as using ERPs as biomarkers in medicine, it may result in incorrect diagnoses or ineffective treatments. In line with the development and advancement of good scientific practice (GSP) in ERP research, this study sought to address several questions regarding the most suitable digital reference for investigating the N400 ERP component. Methods: The study was conducted on 17 neurotypical participants. Based on previous research, the references evaluated included the common average reference (AVE), mean earlobe reference (EARS), left mastoid reference (L), mean mastoids reference (MM), neutral infinity reference (REST), and vertex reference (VERT). Results: The results showed that all digital references, except for VERT, successfully elicited the centroparietal N400 effect in the picture-word verification task. The AVE referencing method showed the most optimal set of metrics in terms of effect size and localization, although it also produced the smallest difference waves. The most similar topographic dynamics in the N400 window were observed between the AVE and REST referencing methods. Conclusions: As the most optimal regions of interest (ROI) for the picture-word elicited N400 effect, nine electrode sites spanning from superior frontocentral to parietal regions were identified, showing consistent effects across all referencing methods except VERT.

Effects of intermittent visual feedback on EEG characteristics during motor preparation and execution in a goal-directed task

Introduction

Visual feedback plays a crucial role in goal-directed tasks, facilitating movement preparation and execution by allowing individuals to adjust and optimize their movements. Enhanced movement preparation and execution help to increase neural activity in the brain. However, our understanding of the neurophysiological mechanisms underlying different types of visual feedback during task preparation and execution remains limited. Therefore, our study aims to investigate the impact of different types of visual feedback on movement-related brain activity in goal-directed tasks, in order to identify more effective forms of visual feedback in goal-directed tasks.

Methods

The electroencephalographic (EEG) data from 18 healthy subjects were collected under both continuous and intermittent visual feedback conditions during a goal-directed reaching task. We analyzed the EEG characteristics of the event-related potential (ERP), event-related synchronization/desynchronization (ERS/ERD) in all subjects during motor preparation and execution of the goal-directed reaching task.

Results

The results showed that, the amplitude of motor-related cortical potential (MRCP) in subjects was larger in the intermittent visual feedback condition compared to the continuous visual feedback condition during motor preparation, and the amplitude was largest at the CPz electrode. Additionally, mu-ERD was more pronounced during both motor preparation and execution under intermittent visual feedback condition.

Discussion

In conclusion, intermittent visual feedback enhanced the characteristics of subject's brain activation and cortical excitability in the time and time-frequency domains.

Leveraging mixed-effects location scale models to assess the ERP mismatch negativity's psychometric properties and trial-by-trial neural variability in toddler-mother dyads

Trial-by-trial neural variability, a measure of neural response stability, has been examined in relation to behavioral indicators using summary measures, but these methods do not characterize meaningful processes underlying variability. Mixed-effects location scale models (MELSMs) overcome these limitations by accounting for predictors and covariates of variability but have been rarely used in developmental studies. Here, we applied MELSMs to the ERP auditory mismatch negativity (MMN), a neural measure often related to language and psychopathology. 84 toddlers and 76 mothers completed a speech-syllable MMN paradigm. We extracted early and late MMN mean amplitudes from trial-level waveforms. We first characterized our sample's psychometric properties using MELSMs and found a wide range of subject-level internal consistency. Next, we examined the relation between toddler MMNs with theoretically relevant child behavioral and maternal variables. MELSMs offered better model fit than analyses that assumed constant variability. We found significant individual differences in trial-by-trial variability but no significant associations between toddler variability and their language, irritability, or mother variability indices. Overall, we illustrate how MELSMs can characterize psychometric properties and answer questions about individual differences in variability. We provide recommendations and resources as well as example code for analyzing trial-by-trial neural variability in future studies.

Resting-state EEG microstate analysis of internet gaming disorder and alcohol use disorder

INTRODUCTION

To investigate the neurophysiological aspects of addiction, the microstate characteristics of internet gaming disorder (IGD), alcohol use disorder (AUD), and healthy control (HC) groups were compared using resting-state electroencephalography (EEG).

METHODS

In total, 199 young adults (75 patients with IGD, 57 patients with AUD, and 67 HCs) participated in this study. We conducted EEG microstate analysis among the groups and also compared the obtained parameters with the results of psychological assessments.

RESULTS

The global explained variance, occurrence, and coverage of microstate C were significantly lower in the AUD group than in the IGD group. Additionally, rates of transition from microstates A, B, and D to C were significantly lower in the AUD group than in the IGD group, whereas rates of transition from microstate A to B were lower in the IGD group compared to HCs. Furthermore, the occurrence of microstate C and transition from microstate B to C were negatively correlated with the Alcohol Use Disorder Identification and Behavioural Inhibition Scale score.

CONCLUSION

There were significant differences in microstate characteristics among the groups, which correlated with the psychological scores. These findings suggest that microstate features can be used as neuromarkers in clinical settings to differentiate between addictive disorders and evaluate the pathophysiology of AUD and IGD.

Exploring the cognitive underpinnings of early hominin stone tool use through an experimental EEG approach

Technological innovation has been crucial in the evolution of our lineage, with tool use and production linked to complex cognitive processes. While previous research has examined the cognitive demands of early stone toolmaking, the neurocognitive aspects of early hominin tool use remain largely underexplored. This study relies on electroencephalography to investigate brain activation patterns associated with two distinct early hominin tool-using behaviors: forceful hammerstone percussion, practiced by both humans and non-human primates and linked to the earliest proposed stone tool industries, and precise flake cutting, an exclusive hominin behavior typically associated with the Oldowan. Our results show increased engagement of the frontoparietal regions during both tasks. Furthermore, we observed significantly increased beta power in the frontal and centroparietal areas when manipulating a cutting flake compared to a hammerstone, and increased beta activity over contralateral frontal areas during the aiming (planning) stage of the tool-using process. This original empirical evidence suggests that certain fundamental brain changes during early hominin evolution may be linked to precise stone tool use. These results offer new insights into the complex interplay between technology and human brain evolution and encourage further research on the neurocognitive underpinnings of hominin tool use.

Abnormal trial-to-trial variability in P300 time-varying directed eeg network of schizophrenia

Cognitive disturbance in identifying, processing, and responding to salient or novel stimuli are typical attributes of schizophrenia (SCH), and P300 has been proven to serve as a reliable psychosis endophenotype. The instability of neural processing across trials, i.e., trial-to-trial variability (TTV), is getting increasing attention in uncovering how the SCH "noisy" brain organizes during cognition processes. Nevertheless, the TTV in the brain network remains unrevealed, notably how it varies in different task stages. In this study, resorting to the time-varying directed electroencephalogram (EEG) network, we investigated the time-resolved TTV of the functional organizations subserving the evoking of P300. Results revealed anomalous TTV in time-varying networks across the delta, theta, alpha, beta1, and beta2 bands of SCH. The TTV of cross-band time-varying network properties can efficiently recognize SCH (accuracy: 83.39%, sensitivity: 89.22%, and specificity: 74.55%) and evaluate the psychiatric symptoms (i.e., Hamilton's depression scale-24, r = 0.430, p = 0.022, RMSE = 4.891; Hamilton's anxiety scale-14, r = 0.377, p = 0.048, RMSE = 4.575). Our study brings new insights into probing the time-resolved functional organization of the brain, and TTV in time-varying networks may provide a powerful tool for mining the substrates accounting for SCH and diagnostic evaluation of SCH.

Assessing consciousness in acute coma using name-evoked responses

Detecting consciousness in clinically unresponsive patients remains a significant challenge. Existing studies demonstrate that electroencephalography (EEG) can detect brain responses in behaviorally unresponsive patients, indicating potential for consciousness detection. However, most of this evidence is based on chronic patients, and there is a lack of studies focusing on acute coma cases. This study aims to detect signs of residual consciousness in patients with acute coma by using bedside EEG and electromyography (EMG) during an auditory oddball paradigm. We recruited patients with acute brain injury (either traumatic brain injury or cardiac arrest) who were admitted to the intensive care unit within two weeks after injury, with a Glasgow Coma Scale (GCS) score of 8 or below. Auditory stimuli included the patients' own names and other common names (referred to as standard names), spoken by the patients' relatives, delivered under two conditions: passive listening (where patients were instructed that sounds would be played) and active listening (where patients were asked to move hands when heard their own names). Brain and muscle activity were recorded using EEG and EMG during the auditory paradigm. Event-related potentials (ERP) and EMG spectra were analyzed and compared between responses to the subject's own name and other standard names in both passive and active listening conditions. A total of 22 patients were included in the final analysis. Subjects exhibited enhanced ERP responses when exposed to their own names, particularly during the active listening task. Compared to standard names or passive listening, distinct differences in brain network connectivity and increased EMG responses were detected during active listening to their own names. These findings suggest the presence of residual consciousness, offering the potential for assessing consciousness in behaviorally unresponsive patients.

Transforming of scalp EEGs with different channel locations by REST for comparative study

OBJECTIVE

The diversity of electrode placement systems brought the problem of channel location harmonization in large-scale electroencephalography (EEG) applications to the forefront. Therefore, our goal was to resolve this problem by introducing and assessing the reference electrode standardization technique (REST) to transform EEGs into a common electrode distribution with computational zero reference at infinity offline.

METHODS

Simulation and eye-closed resting-state EEG datasets were used to investigate the performance of REST for EEG signals and power configurations.

RESULTS

REST produced small errors (the root mean square error (RMSE): 0.2936-0.4583; absolute errors: 0.2343-0.3657) and high correlations (>0.9) between the estimated signals and true ones. The comparison of configuration similarities in power among various electrode distributions revealed that REST induced infinity reference could maintain a perfect performance similar (>0.9) to that of true one.

CONCLUSION

These results demonstrated that REST transformation could be adopted to resolve the channel location harmonization problem in large-scale EEG applications.

Structure of Spectral Composition and Synchronization in Human Sleep on the Whole Scalp: A Pilot Study

We used numerical methods to define the normative structure of the different stages of sleep and wake (W) in a pilot study of 19 participants without pathology (18-64 years old) using a double-banana bipolar montage. Artefact-free 120-240 s epoch lengths were visually identified and divided into 1 s windows with a 10% overlap. Differential channels were grouped into frontal, parieto-occipital, and temporal lobes. For every channel, the power spectrum (PS) was calculated via fast Fourier transform and used to compute the areas for the delta (0-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) bands, which were log-transformed. Furthermore, Pearson's correlation coefficient and coherence by bands were computed. Differences in logPS and synchronization from the whole scalp were observed between the sexes for specific stages. However, these differences vanished when specific lobes were considered. Considering the location and stages, the logPS and synchronization vary highly and specifically in a complex manner. Furthermore, the average spectra for every channel and stage were very well defined, with phase-specific features (e.g., the sigma band during N2 and N3, or the occipital alpha component during wakefulness), although the slow alpha component (8.0-8.5 Hz) persisted during NREM and REM sleep. The average spectra were symmetric between hemispheres. The properties of K-complexes and the sigma band (mainly due to sleep spindles-SSs) were deeply analyzed during the NREM N2 stage. The properties of the sigma band are directly related to the density of SSs. The average frequency of SSs in the frontal lobe was lower than that in the occipital lobe. In approximately 30% of the participants, SSs showed bimodal components in the anterior regions. qEEG can be easily and reliably used to study sleep in healthy participants and patients.

Functional connectivity of EEG motor rhythms after spinal cord injury

Spinal cord injury (SCI), which is the injury of the spinal cord site resulting in motor dysfunction, has prompted the use of motor imagery (MI)-based brain computer interface (BCI) systems for motor function reconstruction. However, analyzing electroencephalogram signals and brain function mechanisms for SCI patients is challenging. This is due to their low signal-to-noise ratio and high variability. We propose using the phase locking value (PLV) to construct the brain network in α and β rhythms for both SCI patients and healthy individuals. This approach aims to analyze the changes in brain network connectivity and brain function mechanisms following SCI. The results show that the connection strength of the α rhythm in the healthy control (HC) group is stronger than that in the SCI group, and the connection strength in the β rhythm of the SCI group is stronger than that in the HC group. Moreover, we extract the PLV with common spatial pattern (PLV-CSP) feature from the MI data of the SCI group. The experimental results for 12 SCI patients include that the peak classification accuracy is 100%, and the average accuracy of the ten-fold cross-verification is 95.6%. Our proposed approach can be used as a potential valuable method for SCI pathological studies and MI-based BCI rehabilitation systems.

Task-specific topology of brain networks supporting working memory and inhibition

Network neuroscience explores the brain's connectome, demonstrating that dynamic neural networks support cognitive functions. This study investigates how distinct cognitive abilities-working memory and cognitive inhibitory control-are supported by unique brain network configurations constructed by estimating whole-brain networks using mutual information. The study involved 195 participants who completed the Sternberg Item Recognition task and Flanker tasks while undergoing electroencephalography recording. A mixed-effects linear model analyzed the influence of network metrics on cognitive performance, considering individual differences and task-specific dynamics. The findings indicate that working memory and cognitive inhibitory control are associated with different network attributes, with working memory relying on distributed networks and cognitive inhibitory control on more segregated ones. Our analysis suggests that both strong and weak connections contribute to cognitive processes, with weak connections potentially leading to a more stable and support networks of memory and cognitive inhibitory control. The findings indirectly support the network neuroscience theory of intelligence, suggesting different functional topology of networks inherent to various cognitive functions. Nevertheless, we propose that understanding individual variations in cognitive abilities requires recognizing both shared and unique processes within the brain's network dynamics.

The influence of EEG channels and features significance on automatic detection of epileptic waves in MECT

Modified Electric Convulsive Therapy (MECT) is an efficacious physical therapy in treating mental disorders. The occurrence of epilepsy is a crucial benchmark for evaluating therapeutic effectiveness. However, the medical field still lacks relevant research on automatically detecting epileptic waves in MECT. Therefore, this article proposes a novel automatic detection method of epileptic waves in MECT. In this article, EEG local features (time, frequency, and time-frequency domains) and global features (Pearson correlation coefficient) are combined for epileptic wave detection with SVM (Support Vector Machine). We researched the system with 15 EEG detection channels. The dataset under investigation contains EEG data from 22 patients who received MECT and presented with epileptic seizures. The results revealed that LA (Logarithm of Activity) feature exhibits the best classification significance. When epileptic waves appear, there is a decrease in the power ratio of delta waves and an increase in the power ratio of theta waves. Additionally, the complexity of EEG decreases while the correlation between EEG channels increases. The Cz, F4, and P3 channels exhibit the highest classification significance among all EEG channels. Furthermore, based on the channel classification significance, the EEG detection channels number can be reduced to 8. Similarly, based on the feature classification significance, the local feature number can be reduced from 9 to 3. These conclusions can improve detection efficiency and reduce the cost for MECT. Moreover, the method we proposed can effectively detect epileptic waves in MECT. This work can provide physicians with a reference for evaluating the effectiveness of MECT.

Large-scale analysis of neural activity and connectivity from high-density electroencephalographic data

INTRODUCTION

High-density electroencephalography (hdEEG) is a technique used for the characterization of the neural activity and connectivity in the human brain. The analysis of EEG data involves several steps, including signal pre-processing, head modelling, source localization and activity/connectivity quantification. Visual check of the analysis steps is often necessary, making the process time- and resource-consuming and, therefore, not feasible for large datasets.

FINDINGS

Here we present the Noninvasive Electrophysiology Toolbox (NET), an open-source software for large-scale analysis of hdEEG data, running on the cross-platform MATLAB environment. NET combines all the tools required for a complete hdEEG analysis workflow, from raw signals to final measured values. By relying on reconstructed neural signals in the brain, NET can perform traditional analyses of time-locked neural responses, as well as more advanced functional connectivity and brain mapping analyses. The extracted quantitative neural data can be exported to provide broad compatibility with other software.

CONCLUSIONS

NET is freely available (https://github.com/bind-group-kul/net) under the GNU public license for non-commercial use and open-source development, together with a graphical user interface (GUI) and a user tutorial. While NET can be used interactively with the GUI, it is primarily aimed at unsupervised automation to process large hdEEG datasets efficiently. Its implementation creates indeed a highly customizable program suitable for analysis automation and tight integration into existing workflows.

Simultaneous EEG-fMRI Investigation of Rhythm-Dependent Thalamo-Cortical Circuits Alteration in Schizophrenia

Schizophrenia is accompanied by aberrant interactions of intrinsic brain networks. However, the modulatory effect of electroencephalography (EEG) rhythms on the functional connectivity (FC) in schizophrenia remains unclear. This study aims to provide new insight into network communication in schizophrenia by integrating FC and EEG rhythm information. After collecting simultaneous resting-state EEG-functional magnetic resonance imaging data, the effect of rhythm modulations on FC was explored using what we term "dynamic rhythm information." We also investigated the synergistic relationships among three networks under rhythm modulation conditions, where this relationship presents the coupling between two brain networks with other networks as the center by the rhythm modulation. This study found FC between the thalamus and cortical network regions was rhythm-specific. Further, the effects of the thalamus on the default mode network (DMN) and salience network (SN) were less similar under alpha rhythm modulation in schizophrenia patients than in controls ([Formula: see text]). However, the similarity between the effects of the central executive network (CEN) on the DMN and SN under gamma modulation was greater ([Formula: see text]), and the degree of coupling was negatively correlated with the duration of disease ([Formula: see text], [Formula: see text]). Moreover, schizophrenia patients exhibited less coupling with the thalamus as the center and greater coupling with the CEN as the center. These results indicate that modulations in dynamic rhythms might contribute to the disordered functional interactions seen in schizophrenia.

Correlation Between EEG Functional Connectivity and Fasting Blood Glucose in Healthy Subjects

Glucose metabolism is an important factor in human physiology and the main source of energy for the human brain. Low blood glucose concentration (hypoglycemia) will cause several neurological and cardiological symptoms. High concentration (hyperglycemia) is an indicator of a higher risk to diabetes. The current study aims to investigate the correlation between electroencephalographic signal (EEG) functional connectivity and fasting blood glucose concentration in healthy people with normal blood glucose concentration. The present study was carried out on a group of forty-four healthy volunteers. The resting-state eyes-closed 30-channel EEG was recorded for 6 minutes and blood samples were collected from the subjects on the same morning. To describe the functional connectivity, magnitude-squared coherence (MSC) was calculated between all channels in delta, theta, alpha, beta and gamma EEG frequency bands. The negative correlation between MSC and fasting blood glucose concentration was statistically significant in delta and gamma bands. The results of the study suggest that the variations in normal blood glucose concentration can affect brain functioning.Clinical Relevance- This is a step in the development of novel biomarkers for monitoring neurological and cognitive function, potentially aiding in the early detection and prevention of metabolic and neurocognitive disorders.

Functional Connectivity Analysis of Prej udice Among Colombian Armed Conflict Former Actors

Despite institutional efforts, reconciliation among former actors of the Colombian armed conflict has yet to be achieved, with prejudice being one direct driver of this drawback. We present an EEG-based functional connectivity study applied to four groups of former actors who completed an Implicit Association Test designed to measure prejudice toward victims or combatants. We analyzed seven measures of functional connectivity calculated in six different frequency bands and two experimental conditions. In the behavioral task, we found more prejudice toward victims from the same victims and more prejudice of civilians toward combatants. For the connectivity measures, we found differences in theta band among the victims' and ex-paramilitaries' groups concerning the civilians' and ex-guerrillas' groups, and differences in the beta2 band among the victims' and ex-guerrillas' groups concerning the ex-paramilitaries' group. The results help us design more effective socio-cognitive interventions to reduce prejudice.

Behavioral state-dependent associations between EEG temporal correlations and depressive symptoms

Previous studies have shown abnormal long-range temporal correlations in neuronal oscillations among individuals with Major Depressive Disorders, occurring during both resting states and transitions between resting and task states. However, the understanding of this effect in preclinical individuals with depression remains limited. This study investigated the association between temporal correlations of neuronal oscillations and depressive symptoms during resting and task states in preclinical individuals, specifically focusing on male action video gaming experts. Detrended fluctuation analysis (DFA), Lifetimes, and Waitingtimes were employed to explore temporal correlations across long-range and short-range scales. The results indicated widespread changes from the resting state to the task state across all frequency bands and temporal scales. Rest-task DFA changes in the alpha band exhibited a negative correlation with depressive scores at most electrodes. Significant positive correlations between DFA values and depressive scores were observed in the alpha band during the resting state but not in the task state. Similar patterns of results emerged concerning maladaptive negative emotion regulation strategies. Additionally, short-range temporal correlations in the alpha band echoed the DFA results. These findings underscore the state-dependent relationships between temporal correlations of neuronal oscillations and depressive symptoms, as well as maladaptive emotion regulation strategies, in preclinical individuals.

Bayesian estimation of group event-related potential components (BEGEP): testing a model for synthetic and real datasets

Objective.The spatial resolution of event-related potentials (ERPs) recorded on the head surface is quite low, since the sensors located on the scalp register mixtures of signals from several cortical sources. Bayesian models for multi-channel ERPs obtained from a group of subjects under multiple task conditions can aid in recovering signals from these sources.Approach.This study introduces a novel model that captures several important characteristics of ERP, including person-to-person variability in the magnitude and latency of source signals. Furthermore, the model takes into account that ERP noise, the main source of which is the background electroencephalogram, has the following properties: it is spatially correlated, spatially heterogeneous, and varies over time and from person to person. Bayesian inference algorithms have been developed to estimate the parameters of this model, and their performance has been evaluated through extensive experiments using synthetic data and real ERPs records in a large number of subjects (N= 351).Main results.The signal estimates obtained using these algorithms were compared with the results of the analysis of ERPs by conventional methods. This comparison showed that the use of this model is suitable for the analysis of ERPs and helps to reveal some features of source signals that are difficult to observe in their mixture signals recorded on the scalp.Significance.This study shown that the proposed method is a potentially useful tool for analyzing ERPs collected from groups of subjects in various cognitive neuroscience experiments.

Long-lasting negativity in the left motoric brain structures during word memory inhibition in the Think/No-Think paradigm

In this study, we investigated the electrical brain responses in a high-density EEG array (64 electrodes) elicited specifically by the word memory cue in the Think/No-Think paradigm in 46 participants. In a first step, we corroborated previous findings demonstrating sustained and reduced brain electrical frontal and parietal late potentials elicited by memory cues following the No-Think (NT) instructions as compared to the Think (T) instructions. The topographical analysis revealed that such reduction was significant 1000 ms after memory cue onset and that it was long-lasting for 1000 ms. In a second step, we estimated the underlying brain generators with a distributed method (swLORETA) which does not preconceive any localization in the gray matter. This method revealed that the cognitive process related to the inhibition of memory retrieval involved classical motoric cerebral structures with the left primary motor cortex (M1, BA4), thalamus, and premotor cortex (BA6). Also, the right frontal-polar cortex was involved in the T condition which we interpreted as an indication of its role in the maintaining of a cognitive set during remembering, by the selection of one cognitive mode of processing, Think, over the other, No-Think, across extended periods of time, as it might be necessary for the successful execution of the Think/No-Think task.

Multimodal Covariance Network Reflects Individual Cognitive Flexibility

Cognitive flexibility refers to the capacity to shift between patterns of mental function and relies on functional activity supported by anatomical structures. However, how the brain's structural-functional covarying is preconfigured in the resting state to facilitate cognitive flexibility under tasks remains unrevealed. Herein, we investigated the potential relationship between individual cognitive flexibility performance during the trail-making test (TMT) and structural-functional covariation of the large-scale multimodal covariance network (MCN) using magnetic resonance imaging (MRI) and electroencephalograph (EEG) datasets of 182 healthy participants. Results show that cognitive flexibility correlated significantly with the intra-subnetwork covariation of the visual network (VN) and somatomotor network (SMN) of MCN. Meanwhile, inter-subnetwork interactions across SMN and VN/default mode network/frontoparietal network (FPN), as well as across VN and ventral attention network (VAN)/dorsal attention network (DAN) were also found to be closely related to individual cognitive flexibility. After using resting-state MCN connectivity as representative features to train a multi-layer perceptron prediction model, we achieved a reliable prediction of individual cognitive flexibility performance. Collectively, this work offers new perspectives on the structural-functional coordination of cognitive flexibility and also provides neurobiological markers to predict individual cognitive flexibility.

Exploring resting-state EEG oscillations in patients with Neuromyelitis Optica Spectrum Disorder

BACKGROUND AND OBJECTIVE

Quantitative resting-state electroencephalography (rs-EEG) is a convenient method for characterizing the functional impairments and adaptations of the brain that has been shown to be valuable for assessing many neurological and psychiatric disorders, especially in monitoring disease status and assisting neuromodulation treatment. However, it has not yet been explored in patients with neuromyelitis optica spectrum disorder (NMOSD). This study aimed to investigate the rs-EEG features of NMOSD patients and explore the rs-EEG features related to disease characteristics and complications (such as anxiety, depression, and fatigue).

METHODS

A total of 32 NMOSD patients and 20 healthy controls (HCs) were recruited; their demographic and disease information were collected, and their anxiety, depression, and fatigue symptoms were evaluated. The rs-EEG power spectra of all the participants were obtained. After excluding the participants with low-quality rs-EEG data during processing, statistical analysis was conducted based on the clinical information and rs-EEG data of 29 patients and 19 HCs. The rs-EEG power (the mean spectral energy (MSE) of absolute power and relative power in all frequency bands, as well as the specific power for all electrode sites) of NMOSD patients and HCs was compared. Furthermore, correlation analyses were performed between rs-EEG power and other variables for NMOSD patients (including the disease characteristics and complications).

RESULTS

The distribution of the rs-EEG power spectra in NMOSD patients was similar to that in HCs. The dominant alpha-peaks shifted significantly towards a lower frequency for patients when compared to HCs. The delta and theta power was significantly increased in the NMOSD group compared to that in the HC group. The alpha oscillation power was found to be significantly negatively associated with the degree of anxiety (reflected by the anxiety subscore of hospital anxiety and depression scale (HADS)) and the degree of depression (reflected by the depression subscore of HADS). The gamma oscillation power was revealed to be significantly positively correlated with the fatigue severity scale (FSS) score, while further analysis indicated that the electrode sites of almost the whole brain region showing correlations with fatigue. Regarding the disease variables, no statistically significant rs-EEG features were related to the main disease features in NMOSD patients.

CONCLUSION

The results of this study suggest that the rs-EEG power spectra of NMOSD patients show increased slow oscillations and are potential biomarkers of widespread white matter microstructural damage in NMOSD. Moreover, this study revealed the rs-EEG features associated with anxiety, depression, and fatigue in NMOSD patients, which might help in the evaluation of these complications and the development of neuromodulation treatment. Quantitative rs-EEG analysis may play an important role in the management of NMOSD patients, and future studies are warranted to more comprehensively understand its application value.

The impact of degraded vision on emotional perception of audiovisual stimuli: An event-related potential study

Emotion recognition will be challenged for individuals when visual signals are degraded in real-life scenarios. Recently, researchers have conducted many studies on the distinct neural activity between clear and degraded audiovisual stimuli. These findings addressed the "how" question, but the precise stage of the distinct activity that occurred remains unknown. Therefore, it is crucial to use event-related potential (ERP) to explore the "when" question, just the time course of the neural activity of degraded audiovisual stimuli. In the present research, we established two conditions: clear auditory + degraded visual (AcVd) and clear auditory + clear visual (AcVc) multisensory conditions. We enlisted 31 participants to evaluate the emotional valence of audiovisual stimuli. The resulting data were analyzed using ERP in time domains and Microstate analysis. Current results suggest that degraded vision impairs the early-stage processing of audiovisual stimuli, with the superior parietal lobule (SPL) regulating audiovisual processing in a top-down fashion. Additionally, our findings indicate that negative and positive stimuli elicit greater EPN compared to neutral stimuli, pointing towards a subjective motivation-related attentional regulation. To sum up, in the early stage of emotional audiovisual processing, the degraded visual signal affected the perception of the physical attributes of audiovisual stimuli and had a further influence on emotion extraction processing, leading to the different regulation of top-down attention resources in the later stage.

EEG brain network variability is correlated with other pathophysiological indicators of critical patients in neurology intensive care unit

Continuous electroencephalogram (cEEG) plays a crucial role in monitoring and postoperative evaluation of critical patients with extensive EEG abnormalities. Recently, the temporal variability of dynamic resting-state functional connectivity has emerged as a novel approach to understanding the pathophysiological mechanisms underlying diseases. However, little is known about the underlying temporal variability of functional connections in critical patients admitted to neurology intensive care unit (NICU). Furthermore, considering the emerging field of network physiology that emphasizes the integrated nature of human organisms, we hypothesize that this temporal variability in brain activity may be potentially linked to other physiological functions. Therefore, this study aimed to investigate network variability using fuzzy entropy in 24-hour dynamic resting-state networks of critical patients in NICU, with an emphasis on exploring spatial topology changes over time. Our findings revealed both atypical flexible and robust architectures in critical patients. Specifically, the former exhibited denser functional connectivity across the left frontal and left parietal lobes, while the latter showed predominantly short-range connections within anterior regions. These patterns of network variability deviating from normality may underlie the altered network integrity leading to loss of consciousness and cognitive impairment observed in these patients. Additionally, we explored changes in 24-hour network properties and found simultaneous decreases in brain efficiency, heart rate, and blood pressure between approximately 1 pm and 5 pm. Moreover, we observed a close relationship between temporal variability of resting-state network properties and other physiological indicators including heart rate as well as liver and kidney function. These findings suggest that the application of a temporal variability-based cEEG analysis method offers valuable insights into underlying pathophysiological mechanisms of critical patients in NICU, and may present novel avenues for their condition monitoring, intervention, and treatment.

Defocused mode in depressed mood and its changes in time-frequency attention-related beta

Depressed mood has been proposed to possibly possess a unique mode of defocused attention. However, this argument needs to be supported by experimental evidence based on attentional performance. The present study used a perceptual load paradigm, combining factors of perceptual load, distractor-target compatibility, and eccentricity, to investigate the degree of attentional distraction in depressed mood. In addition, the mode of attentional distraction associated with depressed mood was explored with the time-frequency features of electroencephalography (EEG). The behavioral results showed that the high depressed mood (HD) group had significantly higher attentional distraction than the low depressed mood (LD) group. EEG results showed that 1) the beta power (especially beta-2, 18-30 Hz) of the two groups differed in the medio-late part of the attentional distraction, with significantly lower power in the HD group than in the LD group; 2) the results of the correlation between beta-2 power and depression scores revealed a significant negative correlation. These results imply that beta-2 is a potential marker that may be sensitive to depressed mood during attentional processing, which was further supported by the classification results of the support vector machine (SVM) with 80.65% accuracy between the HD and LD groups.

Feedback-related brain activity in individual decision: evidence from a gambling EEG study

In this study, based on scalp electroencephalogram (EEG), we conducted cortical source localization and functional network analyses to investigate the underlying mechanism explaining the decision processes when individuals anticipate maximizing gambling benefits, particularly in situations where the decision outcomes are inconsistent with the profit goals. The findings shed light on the feedback monitoring process, wherein incongruity between outcomes and gambling goals triggers a more pronounced medial frontal negativity and activates the frontal lobe. Moreover, long-range theta connectivity is implicated in processing surprise and uncertainty caused by inconsistent feedback conditions, while middle-range delta coupling reflects a more intricate evaluation of feedback outcomes, which subsequently modifies individual decision-making for optimizing future rewards. Collectively, these findings deepen our comprehension of decision-making under circumstances where the profit goals are compromised by decision outcomes and provide electrophysiological evidence supporting adaptive adjustments in individual decision strategies to achieve maximum benefit.

The Impact of Problematic Social Media Use on Inhibitory Control and the Role of Fear of Missing Out: Evidence from Event-Related Potentials

Introduction

The general deficit in inhibitory control of problematic social media users has received widespread attention. However, the neural correlates of problematic social media use (PSMU) and inhibitory control remain unclear. Additionally, the co-occurrence of the fear of missing out (FoMO) with social media use is common, yet its role in the relationship between PSMU and inhibitory control has not been investigated.

Methods

This study aimed to examine the electrophysiological correlates of PSMU and inhibitory control using a modified two-choice oddball task combined with event-related potentials (ERPs), and to explore the role of FoMO in this relationship. A total of 66 participants with varying degrees of PSMU were included in the analysis based on the Problematic Mobile Social Media Usage Questionnaire.

Results

The study found that PSMU could impact inhibitory control. Specifically, as the PSMU score increases, the N2 amplitude is greater for social media-related pictures, and the P3 amplitude is smaller, while no significant differences are observed for neutral pictures. This suggests that PSMU affects inhibitory control by consuming more cognitive resources in the early conflict detection stage and leading to insufficient cognitive resources in the later stages of the inhibitory process. Furthermore, FoMO played a mediating role between PSMU and inhibitory control. PSMU could further impact inhibitory control through FoMO.

Conclusion

This study provides electrophysiological evidence for deficits in inhibitory control in PSMU and suggests that FoMO may further reduce inhibitory control in PSMU individuals.

One out of ten independent components shows flipped polarity with poorer data quality: EEG database study

Independent component analysis (ICA) is widely used today for scalp-recorded EEG analysis. One of the limitations of ICA-based analysis is polarity indeterminacy. It is not easy to find detailed documentations that explains engineering solutions of how the polarity indeterminacy is addressed in a given implementation. We investigated how it is implemented in the case of EEGLAB and also the relation between the outcome of the polarity determination and classification of independent components (ICs) in terms of the estimated nature of the sources (brain, muscle, eye, etc.) using an open database of n = 212 EEG dataset of resting state recordings. We found that (1) about 91% of ICs showed positive-dominant IC scalp topographies; (2) positive-dominant ICs were more associated with brain-originated signals; (3) positive-dominant ICs showed more radial (peaked at 10-30 degrees deviations from the radial axis) dipolar projection pattern with less residual variance from fitting the equivalent current dipole. In conclusion, using the EEGLAB's default ICA algorithm, one out of 10 ICs results in flipping its polarity to negative, which is associated with non-radial dipole orientation with higher residual variance. Thus, we determined EEGLAB biases toward positive polarity in decomposing high-quality brain ICs.

The long-term effect of modulated acoustic stimulation on alteration in EEG brain network of chronic tinnitus patients: An exploratory study

Acoustic stimulation is one of the most influential techniques for distressing tinnitus, while how it functions to reverse neural changes associated with tinnitus remains undisclosed. In this study, our objective is to investigate alterations in brain networks to shed light on the enigma of acoustic intervention for tinnitus. We designed a 75-day long-term acoustic intervention experiment, during which chronic tinnitus patients received daily modulated acoustic stimulation with each session lasting 15 days. Every 15 days, professional tinnitus assessments were conducted, collecting both electroencephalogram (EEG) and tinnitus handicap inventory (THI) data from the patients. Thereafter, we investigated the changes in EEG network organizations during continuous acoustic stimulation and their progressive evolution throughout long-term therapy, alongside exploring the associations between the evolving changes of the network alterations and THI. Our current study findings reveal reorganization in alpha/beta long-range frontal-parietal-occipital connections as well as local frontal and parietal-occipital regions induced by acoustic stimulation. Furthermore, we observed a decrease in modulation effects as therapy sessions progressed. These alterations in brain networks reflect the reversal of tinnitus-related neural activities, particularly distress and perception; thus contributing to tinnitus rehabilitation through long-term modulation effects. This study provides unique insights into how long-term acoustic intervention affects the network organizations of tinnitus patients and deepens our understanding of the pathophysiological mechanisms underlying tinnitus rehabilitation.

Microstates of the cortical brain-heart axis

Electroencephalographic (EEG) microstates are brain states with quasi-stable scalp topography. Whether such states extend to the body level, that is, the peripheral autonomic nerves, remains unknown. We hypothesized that microstates extend at the brain-heart axis level as a functional state of the central autonomic network. Thus, we combined the EEG and heartbeat dynamics series to estimate the directional information transfer originating in the cortex targeting the sympathovagal and parasympathetic activity oscillations and vice versa for the afferent functional direction. Data were from two groups of participants: 36 healthy volunteers who were subjected to cognitive workload induced by mental arithmetic, and 26 participants who underwent physical stress induced by a cold pressure test. All participants were healthy at the time of the study. Based on statistical testing and goodness-of-fit evaluations, we demonstrated the existence of microstates of the functional brain-heart axis, with emphasis on the cerebral cortex, since the microstates are derived from EEG. Such nervous-system microstates are spatio-temporal quasi-stable states that exclusively refer to the efferent brain-to-heart direction. We demonstrated brain-heart microstates that could be associated with specific experimental conditions as well as brain-heart microstates that are non-specific to tasks.

Effects of fear of missing out on inhibitory control in social media context: evidence from event-related potentials

The present study aimed to investigate the impact of fear of missing out (FoMO) on inhibitory control in social media context. The present study used a two-choice oddball task combined with event-related potentials (ERPs) technology to measure inhibitory control. Based on the Fear of Missing Out Scale, participants with varying degrees of FoMO were recruited to complete two studies. A total of 78 participants in Study 1 completed a two-choice oddball task (stimuli "W" or "M"). The results showed that FoMO did not have a significant impact on general inhibitory control at both the behavioral and electrophysiological levels. To further examine the effect of FoMO in social media context. In Study 2, 72 participants completed a modified two-choice oddball task with three types of pictures (high and low social media-related and neutral). The behavioral results revealed that as FoMO scores increased, inhibitory control decreased. ERP analysis revealed that with higher FoMO scores, social media-related pictures elicited larger N2 amplitude and smaller P3 amplitude, but not for neutral pictures. This suggests that FoMO undermines inhibitory control by consuming more cognitive resources in the early conflict detection stage and leading to insufficient cognitive resources in the later stages of the inhibitory process. These findings suggest that FoMO can undermine inhibitory control in the social media context. Considering the indispensable use of social media in the digital age, addressing and understanding the influence of FoMO on inhibitory control could be essential for promoting healthy digital behaviors and cognitive functions.

EEG cortical network reveals the temporo-spatial mechanism of visual search

This study aims to explore a method based on brain networks for implicit attention by using wavelet coherence as feature to identify individual targets in the visual field, find the optimal classification rhythm and time window, and investigate the relationship between the optimal rhythm and N2pc event-related potential. The study uses a weighted minimum norm estimate to locate the sources of the scalp EEG and reconstructs the source time series. The functional connectivity between brain areas during the visual search process is evaluated using wavelet coherence analysis, and a lateral difference network is constructed based on the difference in coherence values between the left and right visual fields. A support vector machine classifier is trained based on the wavelet coherence network features to identify the target in the left or right visual field. We also extract N2pc from the source activity data of the parieto-occipital brain region and record the time period in which N2pc occurred. The study finds that the best classification performance is achieved in the theta rhythm from 200 to 400 ms and achieved an average classification accuracy of 87% (chance level: 51.07%) in a serial search task. And this time window corresponds to the time period when N2pc appeared. The results show that the use of wavelet coherence analysis to evaluate the functional connectivity between brain areas during the visual search process provides a new approach for analyzing brain activity. The study's findings regarding the relationship between the N2pc and theta rhythm and the effectiveness of using wavelet coherence network features based on the theta rhythm for visual search classification contribute to the understanding of the neural mechanisms underlying visual search.

One Definition to Join Them All: The N-Spherical Solution for the EEG Lead Field

Albeit its simplicity, the concentric spheres head model is widely used in EEG. The reason behind this is its simple mathematical definition, which allows for the calculation of lead fields with negligible computational cost, for example, for iterative approaches. Nevertheless, the literature shows contradictory formulations for the electrical solution of this head model. In this work, we study several different definitions for the electrical lead field of a four concentric spheres conduction model, finding that their results are contradictory. A thorough exploration of the mathematics used to build these formulations, provided in the original works, allowed for the identification of errors in some of the formulae, which proved to be the reason for the discrepancies. Moreover, this mathematical review revealed the iterative nature of some of these formulations, which allowed us to develop a formulation to solve the lead field in a head model built from an arbitrary number of concentric, homogeneous, and isotropic spheres.

Resting-state network predicts the decision-making behaviors of the proposer during the ultimatum game

Objective. The decision-making behavior of the proposer is a key factor in achieving effective and equitable maintenance of social resources, particularly in economic interactions, and thus understanding the neurocognitive basis of the proposer's decision-making is a crucial issue. Yet the neural substrate of the proposer's decision behavior, especially from the resting-state network perspective, remains unclear.Approach. In this study, we investigated the relationship between the resting-state network and decision proposals and further established a multivariable model to predict the proposers' unfair offer rates in the ultimatum game.Main results.The results indicated the unfair offer rates of proposers are significantly related to the resting-state frontal-occipital and frontal-parietal connectivity in the delta band, as well as the network properties. And compared to the conservative decision group (low unfair offer rate), the risk decision group (high unfair offer rate) exhibited stronger resting-state long-range linkages. Finally, the established multivariable model did accurately predict the unfair offer rates of the proposers, along with a correlation coefficient of 0.466 between the actual and predicted behaviors.Significance. Together, these findings demonstrated that related resting-state frontal-occipital and frontal-parietal connectivity may serve as a dispositional indicator of the risky behaviors for the proposers and subsequently predict a highly complex decision-making behavior, which contributed to the development of artificial intelligence decision-making system with biological characteristics as well.

Frequency-specific directed interactions between whole-brain regions during sentence processing using multimodal stimulus

Neural oscillations subserve a broad range of speech processing and language comprehension functions. Using an electroencephalogram (EEG), we investigated the frequency-specific directed interactions between whole-brain regions while the participants processed Chinese sentences using different modality stimuli (i.e., auditory, visual, and audio-visual). The results indicate that low-frequency responses correspond to the process of information flow aggregation in primary sensory cortices in different modalities. Information flow dominated by high-frequency responses exhibited characteristics of bottom-up flow from left posterior temporal to left frontal regions. The network pattern of top-down information flowing out of the left frontal lobe was presented by the joint dominance of low- and high-frequency rhythms. Overall, our results suggest that the brain may be modality-independent when processing higher-order language information.

Brain rhythmic abnormalities in convalescent patients with anti-NMDA receptor encephalitis: a resting-state EEG study

Objective

Anti-N-methyl-D-aspartate receptor encephalitis (anti-NMDARE) is autoimmune encephalitis with a characteristic neuropsychiatric syndrome and persistent cognition deficits even after clinical remission. The objective of this study was to uncover the potential noninvasive and quantified biomarkers related to residual brain distortions in convalescent anti-NMDARE patients.

Methods

Based on resting-state electroencephalograms (EEG), both power spectral density (PSD) and brain network analysis were performed to disclose the persistent distortions of brain rhythms in these patients. Potential biomarkers were then established to distinguish convalescent patients from healthy controls.

Results

Oppositely configured spatial patterns in PSD and network architecture within specific rhythms were identified, as the hyperactivated PSD spanning the middle and posterior regions obstructs the inter-regional information interactions in patients and thereby leads to attenuated frontoparietal and frontotemporal connectivity. Additionally, the EEG indexes within delta and theta rhythms were further clarified to be objective biomarkers that facilitated the noninvasive recognition of convalescent anti-NMDARE patients from healthy populations.

Conclusion

Current findings contributed to understanding the persistent and residual pathological states in convalescent anti-NMDARE patients, as well as informing clinical decisions of prognosis evaluation.

Rereferencing of clinical EEGs with nonunipolar mastoid reference to infinity reference by REST

OBJECTIVE

Conventional electroencephalography (EEG) offline subtraction rereferencing is invalid for many clinical practices when adopting a specific nonunipolar recording montage (e.g., the ipsilateral mastoid (IM) and contralateral mastoid (CM)). Further comparative analyses would thus be blocked due to the lack of a uniform offline reference. Therefore, our goal was to resolve this problem by introducing and assessing the reference electrode standardization technique (REST) to transform nonunipolar mastoid montages into a computational zero reference at infinity (IR) offline.

METHODS

For EEG signals and power/connectivity configurations, simulation and clinical schizophrenia resting-state EEG datasets were used to investigate the performance of REST.

RESULTS

REST produced small absolute errors (signal level: 1.21-1.26; power: 0.0057-0.021; connectivity: 0.066-0.088) and high correlations (>0.9) between the IM/CM-IR and true IR references. Using clinical data with the IM online reference, REST revealed valuable changes in spectral and connectivity (P < 0.05) in schizophrenia patients, consistent with previous studies.

CONCLUSIONS

These results demonstrated that REST transformation could be adopted to resolve the offline rereferencing of clinical EEGs with specific nonunipolar mastoid references.

SIGNIFICANCE

REST could be an effective and robust resolution for nonunipolar clinical EEGs and could therefore retrieve these data for further analysis by deriving a favorable offline reference IR.

A High-Density EEG Study Investigating the Neural Correlates of Continuity Editing Theory in VR Films

This paper presents a cognitive psychology experiment to explore the differences between 2D and virtual reality (VR) film editing techniques. We recruited sixteen volunteers to view a range of different display modes and edit types of experimental material. An electroencephalogram (EEG) was recorded simultaneously while the participants watched. Subjective results showed that the VR mode reflects higher load scores, particularly in the effort dimension. Different editing types have no effect on subjective immersion scores. The VR mode elicited stronger EEG energy, with differences concentrated in the occipital, parietal, and central regions. On the basis of this, visual evoked potential (VEP) analyses were conducted, and the results indicated that VR mode triggered greater spatial attention, while editing in 2D mode induced stronger semantic updating and active understanding. Furthermore, we found that while the effect of different edit types in both display modes is similar, cross-axis editing triggered greater cognitive violations than continuity editing, which could serve as scientific theoretical support for the development of future VR film editing techniques.

Therapeutic effect of tempo in Mozart's "Sonata for two pianos" (K. 448) in patients with epilepsy: An electroencephalographic study

BACKGROUND

Mozart's "Sonata for two pianos" (Köchel listing 448) has proven effective as music therapy for patients with epilepsy, but little is understood about the mechanism of which feature in it impacted therapeutic effect. This study explored whether tempo in that piece is important for its therapeutic effect.

METHODS

We measured the effects of tempo in Mozart's sonata on clinical and electroencephalographic parameters of 147 patients with epilepsy who listened to the music at slow, original, or accelerated speed. As a control, patients listened to Haydn's Symphony no. 94 at original speed.

RESULTS

Listening to Mozart's piece at original speed significantly reduced the number of interictal epileptic discharges. It decreased beta power in the frontal, parietal, and occipital regions, suggesting increased auditory attention and reduced visual attention. It also decreased functional connectivity among frontal, parietal, temporal, and occipital brain regions, also suggesting increased auditory attention and reduced visual attention. No such effects were observed after patients listened to the slow or fast version of Mozart's piece, or to Haydn's symphony at normal speed.

CONCLUSIONS

These results suggest that Mozart's "Sonata for two pianos" may exert therapeutic effects by regulating attention when played at its original tempo, but not slower or faster. These findings may help guide the design and optimization of music therapy against epilepsy.