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Inter-brain synchrony occurs between individuals who feel connected socially, but how synchrony relates to felt connectedness under naturalistic social interaction has remained enigmatic. We hypothesized that inter-brain synchrony between naturally interacting individuals might be associated with the internalization of a social identity, a link between an individual's personal identity and the social group to which the individual belongs. A convenience sample of sixty participants were split into dyads and interacted naturalistically on a social task. Through mapping EEG oscillatory waveforms onto a conceptual model categorizing the formation of a social identity within a naturalistic conversation, greater inter-brain synchrony was observed in the emergent stage within the formation of a social identity compared to earlier stages, where a social identity was not present. We provide evidence for greater neural synchrony related to higher socio-psychological connectedness during the development of social identity under naturalistic social interaction., (© 2024 The Author(s). British Journal of Psychology published by John Wiley & Sons Ltd on behalf of The British Psychological Society.)

The purpose of this study was to investigate anticipatory functions in temporal cognition, identifying the presence of proactive brain processing specifically preceding a time discrimination task. To this aim, two discriminative response tasks (DRTs) were employed: a feature DRT and a temporal (T-DRT). While the F-DRT required discrimination among different geometrical shapes, the T-DRT required discrimination among different stimulus durations. Specifically, this study investigated the role of premotor and prefrontal cortices, and sensory visual areas in preparatory activity preceding time-processing by electroencephalographic methods and analyzing the event-related potential (ERP). ERP components associated with motor (the BP), cognitive (the pN), and sensory readiness (the vN) were analyzed on 21 participants completing the two DRTs. The results support the involvement of all considered brain areas in temporal cognition but extend this information by indicating that these areas can be engaged during the preparation phase before the stimulus is delivered. Furthermore, the T-DRT requires strong anticipatory activity in the PFC likely serving as a moderator of upcoming motor responses. Finally, visual areas were greatly engaged in the early phase of sensory readiness of the T-DRT probably to create top-down low-level representations of imminent events to facilitate perception., (Copyright © 2024 Elsevier Inc. All rights reserved.)

EEG involves recording electrical activity generated by the brain through electrodes placed on the scalp. Imagined speech classification has emerged as an essential area of research in brain-computer interfaces (BCIs). Despite significant advances, accurately classifying imagined speech signals remains challenging due to their complex and non-stationary nature. Existing approaches often struggle with low signal-to-noise ratios and high inter-subject variability. A proposed method named imagined speech functional connectivity graph (ISFCG) is implemented to deal with these issues. The functional connectivity graphs capture the complex relationships between brain regions during imagined speech tasks. These graphs are then used to extract features that serve as inputs to various machine-learning models. The ISFCG provides an alternative representation of imagined speech signals, focusing on brain connectivity features to enhance the analysis and classification process. Also, a convolutional neural network (CNN) is proposed to learn features from these complex graphs, leading to improved classification accuracy. Experimental results on a benchmark dataset demonstrate the effectiveness of our method., Competing Interests: Declarations. Conflict of interest: The authors declare no competing interests., (© 2025. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The brain attends to environmental rhythms by aligning the phase of internal oscillations. However, the factors underlying fluctuations in the strength of this phase entrainment remain largely unknown. In the present study, we examined whether the strength of low-frequency electroencephalography (EEG) phase entrainment to rhythmic stimulus sequences varied with the pupil size and posterior alpha-band power, thought to reflect the arousal level and excitability of posterior cortical brain areas, respectively. We recorded the pupil size and scalp EEG while participants carried out an intermodal selective attention task, in which they were instructed to attend to a rhythmic sequence of visual or auditory stimuli and ignore the other perceptual modality. As expected, intertrial phase coherence (ITC), a measure of entrainment strength, was larger for the task-relevant than for the task-irrelevant modality. Across the experiment, the pupil size and posterior alpha power were strongly linked with each other. Interestingly, ITC tracked both variables: larger pupil size was associated with a selective increase in entrainment to the task-relevant stimulus sequence, whereas larger posterior alpha power was associated with a decrease in phase entrainment to both the task-relevant and task-irrelevant stimulus sequences. Exploratory analyses showed that a temporal relation between ITC and posterior alpha power emerged in the time periods around pupil maxima and pupil minima. These results indicate that endogenous sources contribute distinctly to the fluctuations of EEG phase entrainment., Competing Interests: The authors declare no competing financial interests., (Copyright © 2025 Mäki-Marttunen et al.)

The cognitive neuroscience of human aging seeks to identify neural mechanisms behind the commonalities and individual differences in age-related behavioral changes. This goal has been pursued predominantly through structural or "task-free" resting-state functional neuroimaging. The former has elucidated the material foundations of behavioral decline, and the latter has provided key insight into how functional brain networks change with age. Crucially, however, neither is able to capture brain activity representing specific cognitive processes as they occur. In contrast, task-based functional imaging allows a direct probe into how aging affects real-time brain-behavior associations in any cognitive domain, from perception to higher-order cognition. Here, we outline why task-based functional neuroimaging must move center stage to better understand the neural bases of cognitive aging. In turn, we sketch a multi-modal, behavior-first research framework that is built upon cognitive experimentation and emphasizes the importance of theory and longitudinal design., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

The same object can be described at multiple levels of abstraction ("parka", "coat", "clothing"), yet human observers consistently name objects at a mid-level of specificity known as the basic level. Little is known about the temporal dynamics involved in retrieving neural representations that prioritize the basic level, nor how these dynamics change with evolving task demands. In this study, observers viewed 1080 objects arranged in a three-tier category taxonomy while 64-channel EEG was recorded. Observers performed a categorical one-back task in different recording sessions on the basic or subordinate levels. We used time-resolved multiple regression to assess the utility of superordinate-, basic-, and subordinate-level categories across the scalp. We found robust use of basic-level category information starting at about 50 ms after stimulus onset and moving from posterior electrodes (149 ms) through lateral (261 ms) to anterior sites (332 ms). Task differences were not evident in the first 200 ms of processing but were observed between 200-300 ms after stimulus presentation. Together, this work demonstrates that the object category representations prioritize the basic level and do so relatively early, congruent with results that show that basic-level categorization is an automatic and obligatory process., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Sleep deprivation (SD) is an experimental procedure to study the effects of sleep loss on the human brain. Neuroimaging techniques, such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), have been pivotal in studying these effects. The present chapter aims to retrace the state of the art regarding the literature that examines the SD effects on the brain through functional connectivity (FC) evaluated in fMRI and EEG settings, separately. Specifically, we focused on the cognitive domains mainly affected by sleep loss and the underlying brain connectivity alterations. SD disrupts homeostatic and circadian processes, negatively affecting cognitive and cerebral functionality explored through FC. All evidence confirms the detrimental role of SD on brain connectivity impacting several resting-state networks and resulting in cognitive impairments. To conclude, SD may offer useful insights into pathogenic mechanisms likely resembling those induced by chronic sleep loss that might find their application in the clinical context., (Copyright © 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.)

There is strong evidence proper nutrition is imperative for healthy infant neurodevelopment, providing the neural foundations for later cognition and behavior. Over the first years of life infants are supported by unique sources of nutrition (e.g., human milk, alternative milk sources). It is during this time that the brain undergoes its most drastic changes during postnatal development. Past research has examined associations between infant feeding and nutrition and morphological features of the brain, yet there remains a paucity of information on functional characteristics of neural activity during feeding. Within this article, we discuss how neuroimaging modalities can be optimized for researching the impacts of infant feeding and nutrition on brain function. We review past research utilizing EEG and fNIRS and describe our efforts to further develop neuroimaging approaches that allow for measurement of brain activity during active feeding with greater spatial resolution (e.g., fMRI and OPM-MEG). We also discuss current challenges, as well as the scientific and logistical limitations of each method. Once protocols have been optimized, these methods will provide the requisite insight into the underlying mechanisms of nutritional and feeding impacts on neurodevelopment, providing the missing piece in the field's efforts to understand this essential and ubiquitous part of early life., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Driving fatigue is a serious threat to driving safety. Therefore, it is of great significance to accurately detect driving fatigue. In this study, the generalized orthogonal partial directed coherence (gOPDC) algorithm, which measures the time-frequency domain interaction of electroencephalogram (EEG) signals, was used to accurately estimate the connectivity between cortical channels. The causal brain network of driver continuous driving is constructed. The results show that the clustering coefficient and global efficiency tend to decrease with the increase in driving time. Causal information flow in the left prefrontal, parietal, occipital regions and the right posterior frontal region increased significantly when subjects transitioned from awake to fatigued, while causal information flow in the right prefrontal, parietal, occipital regions and the left posterior frontal region decreased mutually significantly. Compared with the traditional driving fatigue algorithm, the accuracy of the method used in this paper is higher than the traditional methods., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Idiopathic REM sleep behavior disorder (iRBD) is recognized as a prodromal stage of neuro-degenerative disease. While brain network analysis is a well-documented approach for characterizing disease-related dysfunctions, the specific patterns in iRBD, particularly those related to hemispheric aberrations remain largely unexplored. To address this gap, this study investigated the topological abnormalities of multi-band EEG networks in patients with iRBD. Specifically, eyes-open resting-state EEG signals were collected from 30 iRBD patients and 30 matched health control (HC) participants. Graph theoretical analysis was then employed to explore network properties at the whole-brain and the hemispheric level. At the whole-brain level, we found aberrant increased local and global efficiency along with a distinct pattern of increased frontal and decreased parietal nodal efficiency in alpha band of iRBD patients. At the hemispheric level, iRBD networks displayed more efficient topological properties in the left hemisphere. Additionally, significant hemispheric asymmetry was observed in alpha-band iRBD network compared to that of HC. In sum, these findings provide novel insights into the disrupted network reorganization in iRBD and suggest aberrant hemispheric asymmetry as a potential neural biomarker for early diagnosis and monitoring of the disease., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

The infant brain undergoes rapid developmental changes in the first three years of life. Understanding these changes through the prediction of chronological age using neuroimaging can provide insights into typical and atypical brain development. We utilized 938 resting-state EEG recordings from 457 typically developing infants, 2 to 38 months old, to develop age prediction models. The multilayer perceptron model demonstrated the highest accuracy with an R 2 of 0.83 and a mean absolute error of 91.7 days. Feature importance analysis that combined hierarchical clustering and Shapley values identified two feature clusters describing periodic alpha and low beta activity as key predictors of age. Application of the model to EEG data from infants later diagnosed with autism or Down syndrome revealed significant underestimations of chronological age, supporting its potential as a clinical tool for early identification of alterations in brain development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Mental workload could affect human performance. An inappropriate workload level, whether too high or too low, leads to discomfort and decreased task performance. Auditory stimuli have been shown to act as an emotional medium to influence the workload. For example, the 'Mozart effect' has been shown to enhance performance in spatial reasoning tasks. However, the impact of auditory stimuli on task performance and brain activity remains unclear. This study examined the effects of three different environments-quiet, music, and white noise-on task performance and EEG activities. The N-back task was employed to induce mental workload, and the Psychomotor Vigilance Task assessed participants' alertness. We proposed a novel, statistically-based method to construct the brain functional network, avoiding issues associated with subjective threshold selection. This method systematically analyzed the connectivity patterns under different environments. Our analysis revealed that white noise negatively affected participants, primarily impacting brain activity in high-frequency ranges. This study provided deeper insights into the relationship between auditory stimuli and mental workload, offering a robust framework for future research on mental workload regulation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

The study reported herein attempts to understand the neural mechanisms engaged in the conscious control of breathing and breath-hold. The variations in the electroencephalogram (EEG) based functional connectivity (FC) of the human brain have been investigated during attentive breathing at 2 cycles per minute (cpm). The study presents its novelty through three main aspects. First, it explores the complex breathing circuitry beyond the brain stem, specifically examining how higher brain regions interact with respiratory cycles. Second, unlike previous studies that treated respiratory phases as a singular phenomenon, this research analyses inhalation, exhalation, and breath-holds separately, providing a deeper understanding of their individual dynamics and FC in the brain. Finally, the breathing protocol is designed to include inhale-hold and exhale-hold sessions alongside symmetric breathing, allowing for testing on healthy subjects rather than specialized cohorts, which were used in earlier studies. An experimental protocol involving equal durations of inhale, inhale-hold, exhale, and exhale-hold conditions, synchronized to a visual metronome, was designed and administered to 20 healthy subjects (9 females and 11 males, age: 32.0 ± 9.5 years (mean ± SD)). EEG data were collected during these sessions using the 64-channel eego™ mylab system from ANT Neuro. Further, FC was estimated for all possible pairs of EEG time series data, for 7 EEG bands. Feature selection using a genetic algorithm (GA) was performed to identify a subset of functional connections that would best distinguish the inhale, inhale-hold, exhale, and exhale-hold phases using a random committee classifier. The best accuracy of 95.056% was obtained when 403 theta-band functional connections were fed as input to the classifier, highlighting the efficacy of the theta-band functional connectome in distinguishing these phases of the respiratory cycle. This functional network was further characterized using graph measures, and observations illustrated a statistically significant difference in the efficiency of information exchange through the network during different respiratory phases., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Repetitive Transcranial Magnetic Stimulation (rTMS) is a promising intervention for Obsessive-Compulsive Disorder (OCD). However, understanding brain network changes following rTMS remains limited, despite its potential to enhance treatment efficacy. In this retrospective study, we investigated brain network reorganization in OCD patients after rTMS, using structural balance theory as a framework. We hypothesized that rTMS-induced functional plasticity would alter brain network topology, particularly affecting triadic associations, and leading to increased balance energy levels, indicative of a less stable network state. Brain functional networks were constructed from resting-state EEGs of OCD patients, with phase lag indexes calculated both before and after rTMS treatment. These networks were analyzed by comparing global parameters, including positive and negative links, triadic interactions (balanced/unbalanced), hub formation tendencies, and balance energy levels. We observed a significant decrease in weak-balanced triads and an increase in strong-unbalanced triads within the Beta І frequency band (12-15 Hz). Additionally, there was a notable reduction in the tendency of negative links to form hubs across certain frequency bands. These changes led to an increase in the network's balanced energy level, pushing it toward a less stable state. We hope these findings will refine rTMS strategies by facilitating brain network reorganization., Competing Interests: Declaration of competing interest The authors declare that there are no competing interests associated with this manuscript. Specifically: 1. The authors have no conflicts of interest to disclose. 2. None of the authors have any financial ties or relationships that could influence the submitted work. 3. The authors have received no support from any organization for the submitted work. 4. There are no financial relationships with any organizations that might have an interest in the submitted work. 5. There are no other relationships or activities that could appear to influence the submitted work., (Copyright © 2024. Published by Elsevier B.V.)

Objective. Cognition is achieved through communication between brain regions. Consequently, there is considerable interest in measuring effective connectivity. A promising effective connectivity metric is transcranial magnetic stimulation (TMS) evoked potentials (TEPs), an inflection in amplitude of the electroencephalogram recorded from one brain region as a result of TMS applied to another region. However, the TEP is confounded by multiple factors and there is a need for further investigation of the TEP as a measure of effective connectivity and to compare it to existing statistical measures of effective connectivity. Approach. To this end, we used a pre-existing experimental dataset to compare TEPs between a motor control task with and without visual feedback. We then used the results to compare our TEP-based measures of effective connectivity to established statistical measures of effective connectivity provided by multivariate auto-regressive modelling. Main results. Our results reveal significantly more negative TEPs when feedback is not presented from 40 ms to 100 ms post-TMS over frontal and central channels. We also see significantly more positive later TEPs from 280-400 ms on the contra-lateral hemisphere motor and parietal channels when no feedback is presented. These results suggest differences in effective connectivity are induced by visual feedback of movement. We further find that the variation in one of these early TEPs (the N40) is reliably related to directed coherence. Significance. Taken together, these results indicate components of the TEPs serve as a measure of effective connectivity. Furthermore, our results also support the idea that effective connectivity is a dynamic process and, importantly, support the further use of TEPs in delineating region-to-region maps of changes in effective connectivity as a result of motor control feedback., (Creative Commons Attribution license.)

Organizing the continuous stream of visual input into categories like places or faces is important for everyday function and social interactions. However, it is unknown when neural representations of these and other visual categories emerge. Here, we used steady-state evoked potential electroencephalography to measure cortical responses in infants at 3-4 months, 4-6 months, 6-8 months, and 12-15 months, when they viewed controlled, gray-level images of faces, limbs, corridors, characters, and cars. We found that distinct responses to these categories emerge at different ages. Reliable brain responses to faces emerge first, at 4-6 months, followed by limbs and places around 6-8 months. Between 6 and 15 months response patterns become more distinct, such that a classifier can decode what an infant is looking at from their brain responses. These findings have important implications for assessing typical and atypical cortical development as they not only suggest that category representations are learned, but also that representations of categories that may have innate substrates emerge at different times during infancy., Competing Interests: XY, ST, BF, YC, AN, KG No competing interests declared, (© 2024, Yan et al.)

The prediction of subject traits using brain data is an important goal in neuroscience, with relevant applications in clinical research, as well as in the study of differential psychology and cognition. While previous prediction work has predominantly been done on neuroimaging data, our focus is on electroencephalography (EEG), a relatively inexpensive, widely available and non-invasive data modality. However, EEG data is complex and needs some form of feature extraction for subsequent prediction. This process is sometimes done manually, risking biases and suboptimal decisions. Here we investigate the use of data-driven Kernel methods for prediction from single channels using the EEG spectrogram, which reflects macro-scale neural oscillations in the brain. Specifically, we introduce the idea of reinterpreting the spectrogram of each channel as a probability distribution, so that we can leverage advanced machine learning techniques that can handle probability distributions with mathematical rigour and without the need for manual feature extraction. We explore how the resulting technique, Kernel mean embedding regression, compares to a standard application of Kernel ridge regression as well as to a non-Kernelised approach. Overall, we found that the Kernel methods exhibit improved performance thanks to their capacity to handle nonlinearities in the relation between the EEG spectrogram and the trait of interest. We leveraged this method to predict biological age in a multinational EEG data set, HarMNqEEG, showing the method's capacity to generalise across experiments and acquisition setups., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

Not harming others is widely regarded as a fundamental tenet of human morality. Harm aversion based on the consequences of an action is called utilitarianism while focusing on the action itself is associated with deontology. This study investigated how interoceptive processing affects the neural processing of utilitarian and deontological moral decision-making. The study utilized the heartbeat-evoked potential (HEP), an averaged electrophysiological component from electroencephalogram (EEG) to gauge cardiac interoceptive processing. Twenty-seven participants were asked to make utilitarian and deontological decisions for personal and impersonal moral dilemmas (18 for each) with direct and indirect harm actions, respectively, while their EEG and electrocardiogram were being recorded. We found no difference in HEPs between personal and impersonal moral dilemmas. In contrast, differential HEPs were observed between utilitarian and deontological moral decision-making, regardless of type of dilemmas. Significant differences were observed over centro-posterior electrodes between 110 and 172 milliseconds after R-peaks during the Scenario Phase, and over right fronto-temporal electrodes between 314 and 404 milliseconds after R-peaks in the Decision Phase. We confirmed that these differences in HEP amplitude between deontological and utilitarian decisions did not stem from cardiac artifacts. These findings reveal that the brain utilizes interoceptive information to make subsequent moral decisions., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

Joint actions are defined as coordinated interactions of two or more agents toward a shared goal, often requiring different and complementary individual contributions. However, how humans can successfully act together without the interfering effects of observing incongruent movements is still largely unknown. It has been proposed that interpersonal predictive processes are at play to allow the formation of a Dyadic Motor Plan, encompassing both agents' shares. Yet, direct empirical support for such an integrated motor plan is still limited. In this study, we aimed at testing the properties of these anticipated representations. We collected electroencephalography data while human participants (N = 36; 27 females) drew shapes simultaneously to a virtual partner, in two social contexts: either they had to synchronize and act jointly or they performed the movements alongside, but independently. We adopted a multivariate approach to show that the social context influenced how the upcoming action of the partner is anticipated during the interval preceding the movement. We found evidence that acting jointly induces an encoding of the partner's action that is strongly intertwined with the participant's action, supporting the hypothesis of an integrative motor plan in joint but not in parallel actions., (© The Author(s) 2024. Published by Oxford University Press.)

Empathy is a critical component of social interaction that enables individuals to understand and share the emotions of others. We report a preregistered experiment in which 240 participants, including adolescents, young adults, and older adults, viewed images depicting hands and feet in physically or socially painful situations (versus nonpainful). Empathy was measured using imagined pain ratings and EEG mu suppression. Imagined pain was greater for physical versus social pain, with young adults showing particular sensitivity to social pain events compared to adolescents and older adults. Mu desynchronization was greater to pain versus no-pain situations, but the physical/social context did not modulate pain responses. Brain responses to painful situations increased linearly from adolescence to young and older adulthood. These findings highlight shared activity across the core empathy network for both physical and social pain contexts, and an empathic response that develops over the lifespan with accumulating social experience., (© The Author(s) 2024. Published by Oxford University Press.)

Objective. Prolonged engagement in tasks with varying attention demands is thought to elicit distinct forms of mental fatigue, potentially indicating variations in neural activity. This study aimed to investigate the association between mental fatigue and changes in electroencephalogram microstate dynamics during tasks with varying attention demands. Approach. In the present study, we employed a 2 × 2 repeated measures ANOVA to analyze the temporal parameters of four distinct microstates (A, B, C, and D) across different levels of attention demands (high vs. low) and mental fatigue (high vs. low) within a controlled flight simulation task involving 17 college students. Main results. Significant variations in mean durations were observed, with microstates A and B exhibiting shorter durations under high fatigue during low attention demands, while their durations increased under high attention demands. Microstate C showed increased occurrences with high fatigue under low attention demands and decreased occurrences under high attention demands. The duration and occurrence of the microstates exhibited different trends throughout the course of mental fatigue, potentially reflecting distinct fatigue-related processes. Significance. These findings establish a link between different types of mental fatigue and microstate dynamics, contributing to a comprehensive understanding of the neural processing mechanisms underlying mental fatigue., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

In auxiliary transportation within mines, accurately assessing the cognitive and response states of drivers is vital for ensuring safety and operational efficiency. This study investigates the effects of various vehicle interaction stimuli on the electroencephalography (EEG) signals of mine transport vehicle drivers, analyzing the cognitive and response states of drivers under different conditions to evaluate their impact on safety performance. Through experimental design, we simulate multiple scenarios encountered in real operations, including interactions with dynamic and static vehicles, personnel, and warning signs. EEG technology records brain signals during these scenarios, and data analysis reveals changes in the cognitive states and responses of drivers to different stimuli. The results indicate significant variations in EEG signals with interactions involving dynamic and static vehicles, personnel, and warning signs, reflecting shifts in the cognitive and response states of drivers. Additionally, the study examines the overall impact of different interaction objects and environments. The detailed analysis of EEG signals in different scenarios sheds light on changes in perception, attention, and responses related to drivers, which is critical for advancing safety and sustainability in mining operations.

Microglia, the resident immune cells in the brain, dynamically adapt their morphology based on their functional state. This study explored the relationship between microglial morphology and sleep-wake cycles in mice. Using Iba1 immunostaining to identify microglia, we quantified morphological changes in microglia at different timepoints in multiple brain regions (cortex, hippocampus, basal forebrain, hindbrain, and cerebellum) in B6 male mice using semi-automated 3D structural analysis. Simultaneously, in a separate group, we monitored wake and sleep stage-specific brain activity using EEG/EMG recordings. During natural sleep-wake cycles, we observed increased microglial complexity (enlarged volume, territorial coverage, and ramification) during wakefulness, characterized by high-frequency theta (8-12 Hz) and gamma activity (30-80 Hz). Conversely, during NREM sleep, which is dominated by delta activity (0.5-4 Hz), microglia displayed reduced complexity. Notably, this pattern was absent in brain regions lacking direct functional connections to areas generating vigilance stage-dependent thalamocortical oscillations. We then extended wakefulness to decouple circadian influence from sleep-wake-specific neuronal activity. This procedure attenuated the decrease in microglial complexity observed during natural sleep, suggesting a crucial role for neuronal activity. Subsequent recovery sleep restored microglial features, independent of the time of day (zeitgeber time). These findings reveal a dynamic interplay between vigilance stage-specific thalamocortical activity and microglial morphology across various brain regions. This suggests a potential role for microglia in sleep regulation and warrants further investigation to understand the underlying mechanisms., (© 2024 The Author(s). GLIA published by Wiley Periodicals LLC.)

In today's world, people suffer from many fatal maladies, and stress is one of them. Excessive stress can have deleterious effects on the health, brain, mind, and nervous system of humans. The goal of this paper is to design a deep learningbased human stress level measurement technique using electroencephalogram (EEG), and pulse rate. In this research, EEG signals and pulse rate of healthy subjects are recorded while they solve four different question sets of increasing complexity. It is assumed that the subjects undergo through four different stress levels, i.e., 'no stress', 'low stress', 'medium stress', and 'high stress', while solving these question sets. An attention mechanism-based CNN-TLSTM (convolutional neural network-tanh long short-term memory) model is proposed to detect the mental stress level of a person. An attention layer is incorporated into the designed TLSTM network to increase the classification accuracy of the CNN-TLSTM model. The CNN network is used for the automated extraction of intricate features from the EEG signals and pulse rate. Then TLSTM is used to classify the stress level of a person into four different categories using the CNNextracted features. The obtained average accuracy of the proposed CNN-TLSTM model is 97.86%. Experimentally, it is found that the designed stress level measurement technique is highly effective and outperforms most existing state-of-the-art techniques. In the future, functional Near-Infrared Spectroscopy (fNIRS), ECG, and Galvanic Skin Response (GSR) can be employed with EEG and pulse rate to increase the effectiveness of the designed stress level measurement technique.

Test article (TA)-induced seizures represent a major safety concern in drug development. Seizures (altered brain wave [electrophysiological] patterns) present clinically as abnormal consciousness with or without tonic/clonic convulsions (where "tonic" = stiffening and "clonic" = involuntary rhythmical movements). Neuropathological findings following seizures may be detected using many methods. Neuro-imaging may show a structural abnormality underlying seizures, such as focal cortical dysplasia or hippocampal sclerosis in patients with chronic epilepsy. Neural cell type-specific biomarkers in blood or cerebrospinal fluid may highlight neuronal damage and/or glial reactions but are not specific indicators of seizures while serum electrolyte and glucose imbalances may induce seizures. Gross observations and brain weights generally are unaffected by TAs with seizurogenic potential, but microscopic evaluation may reveal seizure-related neuron death in some brain regions (especially neocortex, hippocampus, and/or cerebellum). Current globally accepted best practices for neural sampling in nonclinical general toxicity studies provide a suitable screen for brain regions that are known sites of electrical disruption and/or display seizure-induced neural damage. Conventional nonclinical studies can afford an indication that a TA has a potential seizure liability (via in-life signs and/or microscopic evidence of neuron necrosis), but confirmation requires measuring brain electrical (electroencephalographic) activity in a nonclinical study., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Cognitive computational neuroscience has received broad attention in recent years as an emerging area integrating cognitive science, neuroscience, and artificial intelligence. At the heart of this field, approaches using encoding models allow for explaining brain activity from latent and high-dimensional features, including artificial neural networks. With the notable exception of temporal response function models that are applied to electroencephalography, most prior studies have focused on adult subjects, making it difficult to capture how brain representations change with learning and development. Here, we argue that future developmental cognitive neuroscience studies would benefit from approaches relying on encoding models. We provide an overview of encoding models used in adult functional magnetic resonance imaging research. This research has notably used data with a small number of subjects, but with a large number of samples per subject. Studies using encoding models also generally require task-based neuroimaging data. Though these represent challenges for developmental studies, we argue that these challenges may be overcome by using functional alignment techniques and naturalistic paradigms. These methods would facilitate encoding model analysis in developmental neuroimaging research, which may lead to important theoretical advances., Competing Interests: Declaration of Competing Interest The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

In 1924, at the University Hospital of Jena, Hans Berger first recorded an electrical brain signal in humans. This discovery revolutionized clinical neuroscience and neurotechnology, as it contributed to both electrophysiology and the development of the electroencephalogram (EEG). This article provides a historical overview of Hans Berger's seminal contributions, highlighting the importance of his early recordings, the motivations that drove him, and the scientific problems he had to initiate and solve, in a historical context of profoundly changing circumstances. He also faced low acceptance of his works initially, and only belatedly did they become accepted by the scientific community. Berger was known to be a humble but tenacious person who believed in his convictions to the core, and this strength of will is an example of passion for students and scholars of neuroscience. NEW & NOTEWORTHY In 1924, at the University Hospital in Jena, Hans Berger first recorded human brain electrical signals, revolutionizing clinical neurophysiology and neurotechnology. He developed the electroencephalogram (EEG) and identified alpha waves in the human scalp. Although initially met with skepticism, his work was later recognized as fundamental. Berger's perseverance and conviction in his research serve as an inspiring example of dedication for students and scientists in neuroscience.

Humans are highly efficient at recognising familiar faces. However, previous EEG/ERP research has given a partial and fragmented account of the neural basis of this remarkable ability. We argue that this is related to insufficient consideration of fundamental characteristics of familiar face recognition. These include image-independence (recognition across different pictures), levels of familiarity (familiar faces vary hugely in duration and intensity of our exposure to them), automaticity (we cannot voluntarily withhold from recognising a familiar face), and domain-selectivity (the degree to which face familiarity effects are selective). We review recent EEG/ERP work, combining uni- and multivariate methods, that has systematically targeted these shortcomings. We present a theoretical account of familiar face recognition, dividing it into early visual, domain-sensitive and domain-general phases, and integrating image-independence and levels of familiarity. Our account incorporates classic and more recent concepts, such as multi-dimensional face representation and course-to-fine processing. While several questions remain to be addressed, this new account represents a major step forward in our understanding of the neurophysiological basis of familiar face recognition., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Identifying relationships between structural and functional networks is crucial for understanding the large-scale organization of the human brain. The potential contribution of emerging techniques like functional near-infrared spectroscopy to investigate the structure-functional relationship has yet to be explored. In our study, using simultaneous Electroencephalography (EEG) and Functional near-infrared spectroscopy (fNIRS) recordings from 18 subjects, we characterize global and local structure-function coupling using source-reconstructed EEG and fNIRS signals in both resting state and motor imagery tasks, as this relationship during task periods remains underexplored. Employing the mathematical framework of graph signal processing, we investigate how this relationship varies across electrical and hemodynamic networks and different brain states. Results show that fNIRS structure-function coupling resembles slower-frequency EEG coupling at rest, with variations across brain states and oscillations. Locally, the relationship is heterogeneous, with greater coupling in the sensory cortex and increased decoupling in the association cortex, following the unimodal to transmodal gradient. Discrepancies between EEG and fNIRS are noted, particularly in the frontoparietal network. Cross-band representations of neural activity revealed lower correspondence between electrical and hemodynamic activity in the transmodal cortex, irrespective of brain state while showing specificity for the somatomotor network during a motor imagery task. Overall, these findings initiate a multimodal comprehension of structure-function relationship and brain organization when using affordable functional brain imaging., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

The progress in neuroimaging and electrophysiological techniques has shown substantial promise in improving the clinical assessment of disorders of consciousness (DOC). Through the examination of both stimulus-induced and spontaneous brain activity, numerous comprehensive investigations have explored variations in brain activity patterns among patients with DOC, yielding valuable insights for clinical diagnosis and prognostic purposes. Nonetheless, reaching a consensus on precise neuroimaging biomarkers for patients with DOC remains a challenge. Therefore, in this review, we begin by summarizing the empirical evidence related to neuroimaging biomarkers for DOC using various paradigms, including active, passive, and resting-state approaches, by employing task-based fMRI, resting-state fMRI (rs-fMRI), electroencephalography (EEG), and positron emission tomography (PET) techniques. Subsequently, we conducted a review of studies examining the neural correlates of consciousness in patients with DOC, with the findings holding potential value for the clinical application of DOC. Notably, previous research indicates that neuroimaging techniques have the potential to unveil covert awareness that conventional behavioral assessments might overlook. Furthermore, when integrated with various task paradigms or analytical approaches, this combination has the potential to significantly enhance the accuracy of both diagnosis and prognosis in DOC patients. Nonetheless, the stability of these neural biomarkers still needs additional validation, and future directions may entail integrating diagnostic and prognostic methods with big data and deep learning approaches., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Electroencephalography (EEG) microstates are canonical voltage topographies that reflect the temporal dynamics of brain networks on a millisecond time scale. Abnormalities in broadband microstate parameters have been observed in subjects with psychiatric symptoms, indicating their potential as clinical biomarkers. Considering distinct information provided by specific frequency bands of EEG, we hypothesized that microstates in decomposed frequency bands could provide a more detailed depiction of the underlying neuropathological mechanism. In this study, with a large open access resting-state dataset (n = 203), we examined the properties of frequency-specific microstates and their relationship with anxiety and depression symptoms. We conducted clustering on EEG topographies in decomposed frequency bands (delta, theta, alpha and beta), and determined the number of clusters with a meta-criterion. Microstate parameters, including global explained variance (GEV), duration, coverage, occurrence and transition probability, were calculated for eyes-open and eyes-closed states, respectively. Their ability to predict the severity of depression and anxiety symptoms were systematically identified by correlation, regression and classification analyses. Distinct microstate patterns were observed across different frequency bands. Microstate parameters in the alpha band held the best predictive power for emotional symptoms. Microstates B (GEV, coverage) and parieto-central maximum microstate E (coverage, occurrence, transitions from B to E) in the alpha band exhibited significant correlations with depression and anxiety, respectively. Microstate parameters of the alpha band achieved predictive R-square of 0.100 for anxiety scores, which is much higher than those of broadband (R-square = -0.026, p < 0.01). Similar results were found in classification of participants with high and low anxiety symptom scores (68% accuracy in alpha vs. 52% in broadband). These results suggested the value of frequency-specific microstates in predicting emotional symptoms., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Learning never stops. As we navigate life, we continuously acquire and update knowledge to optimize memory-guided action, with a gradual shift from the former to the latter as we master our environment. How are these learning dynamics expressed in the brain and in behavioral patterns? Here, we devised a spatiotemporal image learning task ("Memory Arena") in which participants learn a set of 50 items to criterion across repeated exposure blocks. Critically, brief task-free periods between successive image presentations allowed us to assess multivariate electroencephalogram (EEG) patterns representing the previous and/or upcoming image identity, as well as anticipatory eye movements toward the upcoming image location. As expected, participants eventually met the performance criterion, albeit with different learning rates. During task-free periods, we were able to readily decode representations of both previous and upcoming image identities. Importantly though, decoding strength followed opposing slopes for previous vs. upcoming images across time, with a gradual decline of evidence for the previous image and a gradual increase of evidence for the upcoming image. Moreover, the ratio of upcoming vs. previous image evidence directly followed behavioral learning rates. Finally, eye movement data revealed that participants increasingly used the task-free period to anticipate upcoming image locations, with target-precision slopes paralleling both behavioral performance measures as well as EEG decodability of the upcoming image across time. Together, these results unveil the neural and behavioral dynamics underlying the gradual transition from learning to memory-guided action., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Cognitive control enables us to translate our knowledge into actions, allowing us to flexibly adjust our behavior, according to environmental contexts, our internal goals, and future plans. Multimodal neuroimaging and neurostimulation techniques have proven essential for advancing our understanding of how cognitive control emerges from the coordination of distributed neuronal activities in the brain. In this review, we examine the literature on multimodal studies of cognitive control. We explore how these studies provide converging evidence for a novel, multiplexed model of cognitive control, in which neural oscillations support different levels of control processing along a functionally hierarchical organization of distinct frontoparietal networks., Competing Interests: Declaration of Competing Interest The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

When exposed to rhythmic patterns with temporal regularity, adults exhibit an inherent ability to extract and anticipate an underlying sequence of regularly spaced beats, which is internally constructed, as beats are experienced even when no events occur at beat positions (e.g., in the case of rests). Perception of rhythm and synchronization to periodicity is indispensable for development of cognitive functions, social interaction, and adaptive behavior. We evaluated neural oscillatory activity in premature newborns (n = 19, mean age, 32 ± 2.59 weeks gestational age) during exposure to an auditory rhythmic sequence, aiming to identify early traces of periodicity encoding and rhythm processing through entrainment of neural oscillations at this stage of neurodevelopment. The rhythmic sequence elicited a systematic modulation of alpha power, synchronized to expected beat locations coinciding with both tones and rests, and independent of whether the beat was preceded by tone or rest. In addition, the periodic alpha-band fluctuations reached maximal power slightly before the corresponding beat onset times. Together, our results show neural encoding of periodicity in the premature brain involving neural oscillations in the alpha range that are much faster than the beat tempo, through alignment of alpha power to the beat tempo, consistent with observations in adults on predictive processing of temporal regularities in auditory rhythms. RESEARCH HIGHLIGHTS: In response to the presented rhythmic pattern, systematic modulations of alpha power showed that the premature brain extracted the temporal regularity of the underlying beat. In contrast to evoked potentials, which are greatly reduced when there is no sounds event, the modulation of alpha power occurred for beats coinciding with both tones and rests in a predictive way. The findings provide the first evidence for the neural coding of periodicity in auditory rhythm perception before the age of term., (© 2024 The Author(s). Developmental Science published by John Wiley & Sons Ltd.)

The perception and recognition of objects around us empower environmental interaction. Harnessing the brain's signals to achieve this objective has consistently posed difficulties. Researchers are exploring whether the poor accuracy in this field is a result of the design of the temporal stimulation (block versus rapid event) or the inherent complexity of electroencephalogram (EEG) signals. Decoding perceptive signal responses in subjects has become increasingly complex due to high noise levels and the complex nature of brain activities. EEG signals have high temporal resolution and are non-stationary signals, i.e., their mean and variance vary overtime. This study aims to develop a deep learning model for the decoding of subjects' responses to rapid-event visual stimuli and highlights the major factors that contribute to low accuracy in the EEG visual classification task.The proposed multi-class, multi-channel model integrates feature fusion to handle complex, non-stationary signals. This model is applied to the largest publicly available EEG dataset for visual classification consisting of 40 object classes, with 1000 images in each class. Contemporary state-of-the-art studies in this area investigating a large number of object classes have achieved a maximum accuracy of 17.6%. In contrast, our approach, which integrates Multi-Class, Multi-Channel Feature Fusion (MCCFF), achieves a classification accuracy of 33.17% for 40 classes. These results demonstrate the potential of EEG signals in advancing EEG visual classification and offering potential for future applications in visual machine models.

With mounting evidence for the benefits of meditation, there has been a growing interest in measuring and quantifying meditative states. This study introduces the Inner Dereification Index (IDI), a class of personalized models designed to quantify the distance from non-meditative states such as mind wandering based on a single individual's neural activity. In addition to demonstrating high classification accuracy (median AUC: 0.996) at distinguishing meditation from thinking states moment by moment, IDI can accurately stratify meditator cohorts by experience, and correctly identify the practices most effective at training the dereification aspect of meditation (decentering from immersion with thoughts and perceptions and recognizing them as mental constructs). These results suggest that IDI models may be a useful real-time proxy for dereification and meditation progress, requiring only 1 min of mind wandering data (and no meditation data) during model training. Thus, they show promise for applications such as real-time meditation feedback, progress tracking, personalization of practices, and potential therapeutic applications of neurofeedback-assisted generation of positive states of consciousness., (© 2024. The Author(s).)

Identifying neural biomarkers of pain has long been a central theme in pain neuroscience. Here, we review the state-of-the-art candidates for neural biomarkers of acute and chronic pain. We classify these potential neural biomarkers into five categories based on the nature of their target variables, including neural biomarkers of (1) within-individual perception, (2) between-individual sensitivity, and (3) discriminability for acute pain, as well as (4) assessment and (5) prospective neural biomarkers for chronic pain. For each category, we provide a synthesized review of candidate biomarkers developed using neuroimaging techniques including functional magnetic resonance imaging (fMRI), structural magnetic resonance imaging (sMRI), and electroencephalography (EEG). We also discuss the conceptual and practical challenges in developing neural biomarkers of pain. Addressing these challenges, optimal biomarkers of pain can be developed to deepen our understanding of how the brain represents pain and ultimately help alleviate patients' suffering and improve their well-being., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Motion sickness is a common issue in electric vehicles, significantly impacting passenger comfort. This study aims to develop a functional brain network analysis model by integrating electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals to evaluate motion sickness symptoms. During real-world testing with the Feifan F7 series of new energy-electric vehicles from SAIC Motor Corp, data were collected from 32 participants. The EEG signals were divided into four frequency bands: delta-range, theta-range, alpha-range, and beta-range, and brain oxygenation variation was calculated from the fNIRS signals. Functional connectivity between brain regions was measured to construct functional brain network models for motion sickness analysis. A motion sickness detection model was developed using a graph convolutional network (GCN) to integrate EEG and fNIRS data. Our results show significant differences in brain functional connectivity between participants in motion and non-motion sickness states. The model that combined fNIRS data with high-frequency EEG signals achieved the best performance, improving the F1 score by 11.4% compared to using EEG data alone and by 8.2% compared to using fNIRS data alone. These results highlight the effectiveness of integrating EEG and fNIRS signals using GCN for motion sickness detection. They demonstrate the model's superiority over single-modality approaches, showcasing its potential for real-world applications in electric vehicles.

This research investigated the modulation of acupuncture at Quchi (LI11) on the brain activities in healthy individuals. Sub-bands power and EEG microstate analysis were carried out at pre-acupuncture, acupuncture, needle retaining and post-acupuncture periods in both the acupuncture group (n = 16) and control group (n = 18). Four microstate classes (A-D) were derived from the clustering procedure. Regression analysis was conducted, together with a two-way repeated measures ANOVA, which was then followed by Bonferroni correction. In the acupuncture group, we found the beta power during the acupuncture periods was significantly reduced. The channel-by-channel analysis revealed that acupuncture at LI11 mainly altered the power of delta, theta, and alpha waves in specific brain regions. The delta power increased predominantly in parietal, occipital, and central lobes, while theta and alpha power decreased predominantly in temporal, frontal, parietal, and occipital lobes. During the acupuncture period, participants in the acupuncture group showed a significant increase in both duration and contribution of microstate A, as well as the bidirectional transition probabilities A and B/D. Microstate analysis showed that acupuncture at LI11 significantly enhances the activity of microstate A and potentially strengthens the functional connectivity between the auditory network and either the visual network or the dorsal attention network. These correlational results indicate that acupuncture at LI11 mainly affects activities of the frontal, temporal, parietal, and occipital lobes. These findings highlight the potential of microstate as neuroimaging evidence and a specific index for elucidating the neuromodulatory effects of acupuncture at LI11., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Electroencephalography (EEG) has emerged as a primary non-invasive and mobile modality for understanding the complex workings of the human brain, providing invaluable insights into cognitive processes, neurological disorders, and brain-computer interfaces. Nevertheless, the volume of EEG data, the presence of artifacts, the selection of optimal channels, and the need for feature extraction from EEG data present considerable challenges in achieving meaningful and distinguishing outcomes for machine learning algorithms utilized to process EEG data. Consequently, the demand for sophisticated optimization techniques has become imperative to overcome these hurdles effectively. Evolutionary algorithms (EAs) and other nature-inspired metaheuristics have been applied as powerful design and optimization tools in recent years, showcasing their significance in addressing various design and optimization problems relevant to brain EEG-based applications. This paper presents a comprehensive survey highlighting the importance of EAs and other metaheuristics in EEG-based applications. The survey is organized according to the main areas where EAs have been applied, namely artifact mitigation, channel selection, feature extraction, feature selection, and signal classification. Finally, the current challenges and future aspects of EAs in the context of EEG-based applications are discussed., (Creative Commons Attribution license.)

Microstates represent brief periods of quasi-stable electroencephalography (EEG) scalp topography, offering insights into dynamic fluctuations in event-related potential (ERP) topographies. Despite this, there is a lack of a comprehensive systematic overview of microstate findings concerning cognitive face processing. This review aims to summarize ERP findings on face processing using microstate analyses and assess their effectiveness in characterizing face-related neural representations. A literature search was conducted for microstate ERP studies involving healthy individuals and psychiatric populations, utilizing PubMed, Google Scholar, Web of Science, PsychInfo, and Scopus databases. Twenty-two studies were identified, primarily focusing on healthy individuals (n = 16), with a smaller subset examining psychiatric populations (n = 6). The evidence reviewed in this study suggests that various microstates are consistently associated with distinct ERP stages involved in face processing, encompassing the processing of basic visual facial features to more complex functions such as analytical processing, facial recognition, and semantic representations. Furthermore, these studies shed light on atypical attentional neural mechanisms in Autism Spectrum Disorder (ASD), facial recognition deficits among emotional dysregulation disorders, and encoding and semantic dysfunctions in Post-Traumatic Stress Disorder (PTSD). In conclusion, this review underscores the practical utility of ERP microstate analyses in investigating face processing. Methodologies have evolved towards greater automation and data-driven approaches over time. Future research should aim to forecast clinical outcomes and conduct validation studies to directly demonstrate the efficacy of such analyses in inverse space., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Abnormalities in brain oscillatory patterns have long been observed in schizophrenia and psychotic disorders more broadly. However, far less is known about aperiodic neural activity in these disorders, which has been linked to excitation/inhibition balance and neuronal population spiking within the brain. Here, we analysed resting-state electroencephalographic (EEG) recordings from 43 first episode schizophrenia spectrum psychosis (FESSP) patients and 28 healthy controls to examine whether aperiodic activity is disrupted in FESSP. We further assessed potential associations between aperiodic activity in FESSP and clinical symptom severity using the Brief Psychiatric Rating Scale (BPRS), the Scale for the Assessment of Negative Symptoms (SANS), and the Scale for the Assessment of Positive Symptoms (SAPS). We found no significant differences in either the 1/f-like aperiodic exponent or the broadband aperiodic offset between the FESSP and healthy control groups when analysing the global neural signal averaged across all EEG electrodes. Bayesian analyses further supported these non-significant findings. However, additional non-parametric cluster-based permutation analyses did identify reduced aperiodic offset in the FESSP group, relative to controls across broad central, temporal, parietal and select frontal regions. No associations were found between either exponent or offset and clinical symptom severity when examining all FESSP participants, irrespective of antipsychotic medication status. However, offset was shown to predict BPRS and SANS scores in medication naive patients. In sum, this research presents an initial analysis of aperiodic neural activity in FESSP, offering preliminary evidence of altered aperiodic offset in this disorder. This contributes to a broader understanding of disrupted neural dynamics in early psychosis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Understanding the neurophysiological mechanisms of schizophrenia (SZ) is one of the challenges of neuroscience. Many anatomical and functional studies have pointed to problems in brain connectivity in SZ individuals. However, little is known about the relationships between specific brain regions and impairments in brain connectivity in SZ individuals. Herein we propose a new approach using time-varying graphs and the motif synchronization method to build dynamic brain functional networks (BFNs). Dynamic BFNs were constructed from resting-state electroencephalography (rs-EEG) of 14 schizophrenia (SZ) individuals and 14 healthy controls (HCs). BFNs were evaluated based on the percentage of synchronization importance between a pair of regions (considering external and internal interactions) over time. We found differences in the directed interaction between brain regions in SZ individuals compared to the control group. Our method revealed low bilaterally directed interactions between the temporal lobes in SZ individuals compared to HCs, indicating a potential link between altered brain connectivity and the characteristic symptoms of schizophrenia. From a clinical perspective, these results shed light on developing new therapeutic approaches targeting these specific neural interactions that are altered in individuals with SZ. This knowledge allows the application of better interventions focused on restoring or compensating for interrupted connectivity patterns., Competing Interests: Declaration of competing interest The authors declare no commercial or financial conflict of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Oligodendrocytes continue to differentiate from their precursor cells even in adulthood, a process that can be modulated by neuronal activity and experience. Previous work has indicated that conditional ablation of oligodendrogenesis in adult mice leads to learning and memory deficits in a range of behavioral tasks. The current study replicated and re-evaluated evidence for a role of oligodendrogenesis in motor learning, using a complex running wheel task. Further, we found that ablating oligodendrogenesis alters brain microstructure (ex vivo MRI) and brain activity (in vivo EEG) independent of experience with the task. This suggests a role for adult oligodendrocyte formation in the maintenance of brain function and indicates that task-independent changes due to oligodendrogenesis ablation need to be considered when interpreting learning and memory deficits in this model., (© 2024 The Author(s). GLIA published by Wiley Periodicals LLC.)

Preterm birth represents a public health problem, with prematurity being the leading cause of infant mortality. An objective brain maturation and oxygenation measurement are necessary. The objective has been To test the feasibility of EEG and NIRS combination in the assessment of physiological brain maturation and oxygenation in preterm and non-preterm babies. A systematic review in Pubmed, Web of Science, MEDLINE, Cochrane, Dialnet, CINAHL, Scopus, Lilacs and PEDro databases until December 2022 was developed. 598 registers were found, finally 5 of them reached the inclusion criteria. Two independent reviewers analyzed data and a third reviewer were available for discrepancies. All articles combined EEG and NIRS to assess brain oxygenation and maturation in healthy new-born babies. There is an agreement on the electrode's placement for EEG at P3 and P4; besides, these regions have been shown to be a development predictive area, as well as the frontoparietal region for the NIRS region and comparison between regions. There is little evidence about the physiological brain electrical activity and oxygenation without stimuli.Conclusion: EEG and NIRS have been useful to assess brain electrical activity and oxygenation in preterm and non-preterm. The combined measurement of these instruments could be essential in neurological disorders diagnosis or their sequels. Unfortunately, the heterogeneity of the results found prevents a consensus on which variables are the most appropriate for the assessment of this population. What is Known • Brain assessment could help clinicians to prevent sequels. • There is an agreement for EEG electrodes placement at P3 and P4 region. What is New • EEG and NIRS assessment are effective measurements for preterm babies. • P3 and P4 regions have shown to be a predictive area of development, as well as the frontoparietal region for NIRS assessment., (© 2024. The Author(s).)

Many everyday empathetic experiences arise within our social interactions and depend significantly on interpersonal closeness. However, the interbrain processes underlying social-oriented empathy by interpersonal closeness remain unclear. To address this gap, we conducted a dyadic social judgement task with dyads of friends and strangers, where targets received social evaluative feedback and empathizers observed the scenario in different experimental trials. Results showed that dyads of friends exhibited greater affect sharing than strangers when witnessing their partners being accepted or rejected. This was supported by the more pronounced event-related potential similarity in friends during the 340-840 ms post-feedback window, mediating the link between interpersonal closeness and affect sharing. Furthermore, witnessing emotional feedback elicited greater interbrain neural synchronization of brain α-oscillation between the empathizer's left prefrontal cortex and the target's left temporoparietal junction in dyads of friends compared with those of strangers. This empathy-related synchronization was associated with mutual affect sharing within dyads of friends but not within dyads of strangers. Our findings highlight the sensitivity of empathy to interpersonal closeness, which links to sustained attention and detailed evaluation in social scenarios, along with functional communication between brain regions for mentalizing and emotional regulation. These insights have therapeutic potential for improving social functioning and relationship satisfaction.

Background: Structural income inequality - the uneven income distribution across regions or countries - could affect brain structure and function, beyond individual differences. However, the impact of structural income inequality on the brain dynamics and the roles of demographics and cognition in these associations remains unexplored., Methods: Here, we assessed the impact of structural income inequality, as measured by the Gini coefficient on multiple EEG metrics, while considering the subject-level effects of demographic (age, sex, education) and cognitive factors. Resting-state EEG signals were collected from a diverse sample (countries = 10; healthy individuals = 1394 from Argentina, Brazil, Colombia, Chile, Cuba, Greece, Ireland, Italy, Turkey and United Kingdom). Complexity (fractal dimension, permutation entropy, Wiener entropy, spectral structure variability), power spectral and aperiodic components (1/f slope, knee, offset), as well as graph-theoretic measures were analysed., Findings: Despite variability in samples, data collection methods, and EEG acquisition parameters, structural inequality systematically predicted electrophysiological brain dynamics, proving to be a more crucial determinant of brain dynamics than individual-level factors. Complexity and aperiodic activity metrics captured better the effects of structural inequality on brain function. Following inequality, age and cognition emerged as the most influential predictors. The overall results provided convergent multimodal metrics of biologic embedding of structural income inequality characterised by less complex signals, increased random asynchronous neural activity, and reduced alpha and beta power, particularly over temporoposterior regions., Conclusion: These findings might challenge conventional neuroscience approaches that tend to overemphasise the influence of individual-level factors, while neglecting structural factors. Results pave the way for neuroscience-informed public policies aimed at tackling structural inequalities in diverse populations., (© 2024 The Author(s). Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.)

Attention Deficit Hyperactivity Disorder (ADHD) is a widespread neurobehavioral disorder affecting children and adolescents, requiring early detection for effective treatment. EEG connectivity measures can reveal the interdependencies between EEG recordings, highlighting brain network patterns and functional behavior that improve diagnostic accuracy. This study introduces a novel ADHD diagnostic method by combining linear and nonlinear brain connectivity maps with an attention-based convolutional neural network (Att-CNN). Pearson Correlation Coefficient (PCC) and Phase-Locking Value (PLV) are used to create fused connectivity maps (FCMs) from various EEG frequency subbands, which are then inputted into the Att-CNN. The attention module is strategically placed after the latest convolutional layer in the CNN. The performance of different optimizers (Adam and SGD) and learning rates are assessed. The suggested model obtained 98.88%, 98.41%, 98.19%, and 98.30% for accuracy, precision, recall, and F1 Score, respectively, using the SGD optimizer in the FCM of the theta band with a learning rate of 1e-1. With the use of FCM, Att-CNN, and advanced optimizers, the proposed technique has the potential to produce trustworthy instruments for the early diagnosis of ADHD, greatly enhancing both patient outcomes and diagnostic accuracy., Competing Interests: Declarations. Ethics Approval and Consent to Participate: Not applicable. Competing interest: The authors declare no competing interests. Declaration of using Generative AI and AI-assisted Technologies: During the preparation of this study, the authors used ChatGPT and Grammarly to improve readability and language. After using these tools, the authors reviewed and edited the content as needed and take full responsibility for the publication's content., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. Electroencephalography (EEG) is one of two brain imaging modalities central to the HBCD Study. EEG records electrical signals from the scalp that reflect electrical brain activity. In addition, the EEG signal can be synchronized to the presentation of discrete stimuli (auditory or visual) to measure specific cognitive processes with excellent temporal precision (e.g., event-related potentials; ERPs). EEG is particularly helpful for the HBCD Study as it can be used with awake, alert infants, and can be acquired continuously across development. The current paper reviews the HBCD Study's EEG/ERP protocol: (a) the selection and development of the tasks (Video Resting State, Visual Evoked Potential, Auditory Oddball, Face Processing); (b) the implementation of common cross-site acquisition parameters and hardware, site setup, training, and initial piloting; (c) the development of the preprocessing pipelines and creation of derivatives; and (d) the incorporation of equity and inclusion considerations. The paper also provides an overview of the functioning of the EEG Workgroup and the input from members across all steps of protocol development and piloting., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

How the human brain reconstructs, step-by-step, the core elements of past experiences is still unclear. Here, we map the spatiotemporal trajectories along which visual object memories are reconstructed during associative recall. Specifically, we inquire whether retrieval reinstates feature representations in a copy-like but reversed direction with respect to the initial perceptual experience, or alternatively, this reconstruction involves format transformations and regions beyond initial perception. Participants from two cohorts studied new associations between verbs and randomly paired object images, and subsequently recalled the objects when presented with the corresponding verb cue. We first analyze multivariate fMRI patterns to map where in the brain high- and low-level object features can be decoded during perception and retrieval, showing that retrieval is dominated by conceptual features, represented in comparatively late visual and parietal areas. A separately acquired EEG dataset is then used to track the temporal evolution of the reactivated patterns using similarity-based EEG-fMRI fusion. This fusion suggests that memory reconstruction proceeds from anterior frontotemporal to posterior occipital and parietal regions, in line with a conceptual-to-perceptual gradient but only partly following the same trajectories as during perception. Specifically, a linear regression statistically confirms that the sequential activation of ventral visual stream regions is reversed between image perception and retrieval. The fusion analysis also suggests an information relay to frontoparietal areas late during retrieval. Together, the results shed light onto the temporal dynamics of memory recall and the transformations that the information undergoes between the initial experience and its later reconstruction from memory., Competing Interests: The authors declare no competing financial interests., (Copyright © 2024 Lifanov-Carr et al.)