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Frontal midline theta power (FMT) has been associated with superior rifle shooting performance. Our experiment examined whether electroencephalographic-based training could increase FMT, shooting performance, and attentional focus in highly trained/elite biathletes. Participants (n = 28; age, M = 21.7, SD = 2.3) were assigned to a control group or an intervention group (with 3 hr of neurofeedback training). FMT increased from baseline during the neurofeedback training sessions (p ≤.05). However, there were no Group × Pre–post training (test) interactions for FMT or shooting performance (p >.05). There was a small Group × Test effect for attentional focus (p =.07, η p 2 =.12), indicating a potential benefit of neurofeedback training. Superior shooters were more proficient at increasing FMT during neurofeedback training, but this did not translate to greater improvements in shooting performance. Our findings suggest that the effects of neurofeedback training are transient and do not necessarily benefit performance. [ABSTRACT FROM AUTHOR]

Insomnia treatment among individuals with comorbid insomnia and obstructive sleep apnea is suboptimal. In a pilot randomized controlled trial, 19 individuals with comorbid insomnia and obstructive sleep apnea were allocated to one of two arms: EX + EX, consisting of two 8-week phases of exercise training (EX), or RE + CBTiEX, encompassing 8 weeks of relaxation training (RE) followed by 8 weeks of combined cognitive-behavioral therapy and exercise (CBTiEX). Outcomes included Insomnia Severity Index (ISI), polysomnography, and cardiorespiratory fitness measures. A mixed-model analysis of variance revealed a Group × Time interaction on peak oxygen consumption change, F(1, 14) = 10.1, p =.007, and EX increased peak oxygen consumption (p =.03, g′ = −0.41) and reduced ISI (p =.001, g′ = 0.82) compared with RE (p =.49, g = 0.16) post-8 weeks. Post-16 weeks, there was a significant Group × Time interaction (p =.014) driven by RE + CBTiEX yielding a larger improvement in ISI (p =.023, g′ = 1.48) than EX + EX (p =.88, g′ < 0.1). Objective sleep was unchanged. This study showed promising effects of regular EX alone and combined with cognitive-behavioral therapy for insomnia on ISI in comorbid insomnia and obstructive sleep apnea. [ABSTRACT FROM AUTHOR]

OBJECTIVE: ADHD is a prevalent neurodevelopmental disorder characterized by symptoms of inattention and hyperactivity-impulsivity. Impairments in executive functioning (EF) are central to models of ADHD, while alpha-band spectral power event-related decreases (ERD) have emerged as a putative electroencephalography (EEG) biomarker of EF in ADHD. Little is known about the roles of EF and alpha ERD and their interactions with symptoms of ADHD. METHOD: We estimated network models of ADHD symptoms and integrated alpha ERD measures into the symptom network. RESULTS: EF emerges as a bridge network node connecting alpha ERD and the hyperactivity/impulsivity and inattention symptoms. We found that EF most closely relates to a subset of symptoms, namely the motoric symptoms, seat (difficulty staying seated), and runs (running or climbing excessively). CONCLUSIONS: EF functions as a bridge node connecting alpha ERD and the ADHD symptom network. Motoric-type symptoms and EF deficits may constitute important nodes in the interplay between behavior/symptoms, cognition, and neurophysiological markers of ADHD.

Principal component analysis (PCA) has been widely employed for dimensionality reduction prior to multivariate pattern classification (decoding) in EEG research. The goal of the present study was to provide an evaluation of the effectiveness of PCA on decoding accuracy (using support vector machines) across a broad range of experimental paradigms. We evaluated several different PCA variations, including group-based and subject-based component decomposition and the application of Varimax rotation or no rotation. We also varied the numbers of PCs that were retained for the decoding analysis. We evaluated the resulting decoding accuracy for seven common event-related potential components (N170, mismatch negativity, N2pc, P3b, N400, lateralized readiness potential, and error-related negativity). We also examined more challenging decoding tasks, including decoding of face identity, facial expression, stimulus location, and stimulus orientation. The datasets also varied in the number and density of electrode sites. Our findings indicated that none of the PCA approaches consistently improved decoding performance related to no PCA, and the application of PCA frequently reduced decoding performance. Researchers should therefore be cautious about using PCA prior to decoding EEG data from similar experimental paradigms, populations, and recording setups.

A missense mutation in the transcription repressor Nucleus accumbens-associated 1 (NACC1) gene at c.892C>T (p.Arg298Trp) on chromosome 19 causes severe neurodevelopmental delay ( Schoch et al., 2017). To model this disorder, we engineered the first mouse model with the homologous mutation (Nacc1+/R284W ) and examined mice from E17.5 to 8 months. Both genders had delayed weight gain, epileptiform discharges and altered power spectral distribution in cortical electroencephalogram, behavioral seizures, and marked hindlimb clasping; females displayed thigmotaxis in an open field. In the cortex, NACC1 long isoform, which harbors the mutation, increased from 3 to 6 months, whereas the short isoform, which is not present in humans and lacks aaR284 in mice, rose steadily from postnatal day (P) 7. Nuclear NACC1 immunoreactivity increased in cortical pyramidal neurons and parvalbumin containing interneurons but not in nuclei of astrocytes or oligodendroglia. Glial fibrillary acidic protein staining in astrocytic processes was diminished. RNA-seq of P14 mutant mice cortex revealed over 1,000 differentially expressed genes (DEGs). Glial transcripts were downregulated and synaptic genes upregulated. Top gene ontology terms from upregulated DEGs relate to postsynapse and ion channel function, while downregulated DEGs enriched for terms relating to metabolic function, mitochondria, and ribosomes. Levels of synaptic proteins were changed, but number and length of synaptic contacts were unaltered at 3 months. Homozygosity worsened some phenotypes including postnatal survival, weight gain delay, and increase in nuclear NACC1. This mouse model simulates a rare form of autism and will be indispensable for assessing pathophysiology and targets for therapeutic intervention.

As the field of computational cognitive neuroscience continues to expand and generate new theories, there is a growing need for more advanced methods to test the hypothesis of brain-behavior relationships. Recent progress in Bayesian cognitive modeling has enabled the combination of neural and behavioral models into a single unifying framework. However, these approaches require manual feature extraction, and lack the capability to discover previously unknown neural features in more complex data. Consequently, this would hinder the expressiveness of the models. To address these challenges, we propose a Neurocognitive Variational Autoencoder (NCVA) to conjoin high-dimensional EEG with a cognitive model in both generative and predictive modeling analyses. Importantly, our NCVA enables both the prediction of EEG signals given behavioral data and the estimation of cognitive model parameters from EEG signals. This novel approach can allow for a more comprehensive understanding of the triplet relationship between behavior, brain activity, and cognitive processes.

Purpose: The aim of this study was to examine whether cortical activity changes during exercise with increasing cognitive demands in preadolescent children. Method: Twenty healthy children (8.75 [0.91] y) performed one movement game, which was conducted with lower and higher cognitive demands. During a baseline measurement and both exercise conditions, cortical activity was recorded using a 64-channel electroencephalographic system, and heart rate was assessed. Ratings of perceived excertion and perceived cognitive engagement were examined after each condition. To analyze power spectral density in the theta, alpha-1, and alpha-2 frequency bands, an adaptive mixture independent component analysis was used to determine the spatiotemporal sources of cortical activity, and brain components were clustered to identify spatial clusters. Results: One-way repeated-measures analyses of variance revealed significant main effects for condition on theta in the prefrontal cluster, on alpha-1 in the prefrontal, central, bilateral motor, bilateral parieto-occipital, and occipital clusters, and on alpha-2 in the left motor, central, and left parieto-occipital clusters. Compared with the lower cognitive demand exercise, cortical activity was significantly higher in theta power in the prefrontal cluster and in alpha-1 power in the occipital cluster during the higher cognitive demand exercise. Conclusion: The present study shows that exercise complexity seems to influence cortical processing as it increased with increasing cognitive demands. [ABSTRACT FROM AUTHOR]

Tinnitus is a prevalent hearing-loss deficit manifested as a phantom (internally generated by the brain) sound that is heard as a high-frequency tone in the majority of afflicted persons. Chronic tinnitus is debilitating, leading to distress, sleep deprivation, anxiety, and even suicidal thoughts. It has been theorized that, in the majority of afflicted persons, tinnitus can be attributed to the loss of high-frequency input from the cochlea to the auditory cortex, known as deafferentation. Deafferentation due to hearing loss develops with aging, which progressively causes tonotopic regions coding for the lost high-frequency coding to synchronize, leading to a phantom high-frequency sound sensation. Approaches to tinnitus remediation that demonstrated promise include inhibitory drugs, the use of tinnitus-specific frequency notching to increase lateral inhibition to the deafferented neurons, and multisensory approaches (auditory-motor and audiovisual) that work by coupling multisensory stimulation to the deafferented neural populations. The goal of this review is to put forward a theoretical framework of a multisensory approach to remedy tinnitus. Our theoretical framework posits that due to visions modulatory (inhibitory, excitatory) influence on the auditory pathway, a prolonged engagement in audiovisual activity, especially during daily discourse, as opposed to auditory-only activity/discourse, can progressively reorganize deafferented neural populations, resulting in the reduced synchrony of the deafferented neurons and a reduction in tinnitus severity over time.

Sleep disturbances are common features of neurodegenerative disorders including Huntingtons disease (HD). Sleep and circadian disruptions are recapitulated in animal models, providing the opportunity to evaluate the effectiveness of circadian interventions as countermeasures for neurodegenerative disease. For instance, time restricted feeding (TRF) successfully improved activity rhythms, sleep behavior and motor performance in mouse models of HD. Seeking to determine if these benefits extend to physiological measures of sleep, electroencephalography (EEG) was used to measure sleep/wake states and polysomnographic patterns in male and female wild-type (WT) and bacterial artificial chromosome transgenic (BACHD) adult mice, under TRF and ad lib feeding (ALF). Our findings show that male, but not female, BACHD mice exhibited significant changes in the temporal patterning of wake and non-rapid eye movement (NREM) sleep. The TRF intervention reduced the inappropriate early morning activity by increasing NREM sleep in the male BACHD mice. In addition, the scheduled feeding reduced sleep fragmentation (# bouts) in the male BACHD mice. The phase of the rhythm in rapid-eye movement (REM) sleep was significantly altered by the scheduled feeding in a sex-dependent manner. The treatment did impact the power spectral curves during the day in male but not female mice regardless of the genotype. Sleep homeostasis, as measured by the response to six hours of gentle handling, was not altered by the diet. Thus, TRF improves the temporal patterning and fragmentation of NREM sleep without impacting sleep homeostasis. This work adds critical support to the view that sleep is a modifiable risk factor in neurodegenerative diseases.

The spectral content of macroscopic neural activity evolves throughout development, yet how this maturation relates to underlying brain network formation and dynamics remains unknown. Here, we assess the developmental maturation of electroencephalogram spectra via Bayesian model inversion of the spectral graph model, a parsimonious whole-brain model of spatiospectral neural activity derived from linearized neural field models coupled by the structural connectome. Simulation-based inference was used to estimate age-varying spectral graph model parameter posterior distributions from electroencephalogram spectra spanning the developmental period. This model-fitting approach accurately captures observed developmental electroencephalogram spectral maturation via a neurobiologically consistent progression of key neural parameters: long-range coupling, axonal conduction speed, and excitatory:inhibitory balance. These results suggest that the spectral maturation of macroscopic neural activity observed during typical development is supported by age-dependent functional adaptations in localized neural dynamics and their long-range coupling across the macroscopic structural network.

BackgroundSleep disturbances are a prevalent and complex comorbidity in neurodevelopmental disorders (NDDs). Dup15q syndrome (duplications of 15q11.2-13.1) is a genetic disorder highly penetrant for NDDs such as autism and intellectual disability and it is frequently accompanied by significant disruptions in sleep patterns. The 15q critical region harbors genes crucial for brain development, notably UBE3A and a cluster of gamma-aminobutyric acid type A receptor (GABAAR) genes. We previously described an electrophysiological biomarker of the syndrome, marked by heightened beta oscillations (12-30 Hz) in individuals with Dup15q syndrome, akin to electroencephalogram (EEG) alterations induced by allosteric modulation of GABAARs. Those with Dup15q syndrome exhibited increased beta oscillations during the awake resting state and during sleep, and they showed profoundly abnormal NREM sleep. This study aims to assess the translational validity of these EEG signatures and to delve into their neurobiological underpinnings by quantifying sleep physiology in chromosome-engineered mice with maternal (matDp/ + mice) or paternal (patDp/ + mice) inheritance of the full 15q11.2-13.1-equivalent duplication, and mice with duplication of just the UBE3A gene (Ube3a overexpression mice; Ube3a OE mice) and comparing the sleep metrics with their respective wildtype (WT) littermate controls.MethodsWe collected 48-h EEG/EMG recordings from 35 (23 male, 12 female) 12-24-week-old matDp/ + , patDp/ + , Ube3a OE mice, and their WT littermate controls. We quantified baseline sleep, sleep fragmentation, spectral power dynamics during sleep states, and recovery following sleep deprivation. Within each group, distinctions between Dup15q mutant mice and WT littermate controls were evaluated using analysis of variance (ANOVA) and student's t-test. The impact of genotype and time was discerned through repeated measures ANOVA, and significance was established at p

BACKGROUND: Chronic pain is a leading cause of disability worldwide. Fibromyalgia is a particularly debilitating form of widespread chronic pain. Fibromyalgia remains poorly understood, and treatment options are limited or moderately effective at best. Here, we present a protocol for a mechanistic study investigating the effects of psychedelic-assisted-therapy in a fibromyalgia population. The principal focus of this trial is the central mechanism(s) of psilocybin-therapy i.e., in the brain and on associated mental schemata, primarily captured by electroencephalography (EEG) recordings of the acute psychedelic state, plus pre and post Magnetic Resonance Imaging (MRI). METHODS: Twenty participants with fibromyalgia will complete 8 study visits over 8 weeks. This will include two dosing sessions where participants will receive psilocybin at least once, with doses varying up to 25mg. Our primary outcomes are 1) Lempel-Ziv complexity (LZc) recorded acutely using EEG, and the 2) the (Brief Experiential Avoidance Questionnaire (BEAQ) measured at baseline and primary endpoint. Secondary outcomes will aim to capture broad aspects of the pain experience and related features through neuroimaging, self-report measures, behavioural paradigms, and qualitative interviews. Pain Symptomatology will be measured using the Brief Pain Inventory Interference Subscale (BPI-IS), physical and mental health-related function will be measured using the 36-Item Short Form Health Survey (SF-36). Further neurobiological investigations will include functional MRI (fMRI) and diffusion tensor imaging (changes from baseline to primary endpoint), and acute changes in pre- vs post-acute spontaneous brain activity - plus event-related potential functional plasticity markers, captured via EEG. DISCUSSION: The results of this study will provide valuable insight into the brain mechanisms involved in the action of psilocybin-therapy for fibromyalgia with potential implications for the therapeutic action of psychedelic-therapy more broadly. It will also deliver essential data to inform the design of a potential subsequent RCT.

• Study explored cognitive-motor interference as task difficulty increased in adults. • Cognitive errors rose with motor complexity during dual-task walking. • P3a amplitude increased, while P3b latency lengthened under task complexity. • P3 analysis revealed increased attentional demand with cognitive difficulty. • Results highlight need to balance attentional resources in cognitive-motor tasks. Dual-task paradigms, which involve performing cognitive and motor tasks simultaneously, are commonly used to study how attentional resources are allocated and managed under varying task demands. This study aimed to investigate cognitive-motor interferences (CMI) under different levels of cognitive and motor task difficulty without instruction on task prioritization. 17 healthy young adults performed an auditory oddball task with increasing cognitive and motor (walking vs. sitting) difficulty. Cognitive and motor performances, along with P3 (P3a and P3b) brainwave components, were analysed. Increasing cognitive difficulty resulted in more errors and increased P3a amplitude, reflecting enhanced attentional demand, while P3b remained unaffected. This suggests a threshold effect on attentional resources. Motor complexity lengthened P3a and P3b latencies without affecting amplitude, indicating delayed attentional resource recruitment. Additionally, walking with the most difficult cognitive task increased cognitive error, suggesting attentional resource limits. With increased motor and cognitive complexity, CMI emerged, leading to cognitive error increase and improved gait stability without amplitude changes in P3a and P3b. Two hypotheses were proposed: motor prioritization and motor facilitation. Our study suggests managing attentional resources to balance cognitive and motor tasks rather than linearly increasing task complexity. Viewing dual tasks as a new, integrated task is proposed, supported by previous neural network integration studies. Thus, understanding how the brain organizes tasks in response to constraints is crucial for comprehending complex task execution. [ABSTRACT FROM AUTHOR]

According to World Health Organization (WHO), it is estimated that approximately 50 million people have Epilepsy worldwide. 10 million people are effected in India by Epilepsy hardly very few come out with the disorder and undertake proper treatment rest risk their life without recognition of disease. Epilepsy is a chronic disease of the brain which is non-communicable. An epileptic seizure is the second most commonly occurring neurological disorder of the brain. It can be detected using Electroencephalography (EEG) signals, an effective diagnostic tool used to study brain anatomy. Technological advancements and modernization of analytical tools have improved the scope of validation and decision-making. It is important to predict and evaluate epilepsy in its initial stages to avoid the risk and complication. This review paper focuses on the methodology, techniques, and dataset used from different sources to classify and categorize epileptic seizures. The paper includes a detailed review of dataset sources, challenges, and ways forward in understanding epileptic seizures for futuristic learning and decision making. It also concentrates on the comparison of machine learning and deep learning methodology used with different datasets. [ABSTRACT FROM AUTHOR]

When the eye uses the brain and heart, the cardiovascular and nervous systems integrate and interact. Because changes in retinal microcirculation are independent predictors of cardiovascular events, the eye serves as a "display" to the cardiovascular system and brain. The eye, which has two circulatory systems and a rich vascular supply, is a prime candidate for this study and benefits from early damage to the target organ. Eye movements performed during the visual search pose a challenge in identifying critical points in the eye scene. Because it uses different brain pathways and relates to the cardiac cycle, humans' ability to spot anomalies under challenging circumstances means they are always needed for visual search. ECG (electrocardiogram), electroencephalogram (EEG), and eye tracking can improve visual search training and attention-tracking performance. EEG data can also be analyzed in real time using eye-tracking technology. Previous work has discussed the EEG or ECG concerning attraction during visual search. The eyeball's movement combined with the ECG in the previous investigation and introduced large electroencephalographic (EEG) artifacts. This assessment aims to (a) identify brain–heart coherent features influenced by the visual search task and (b) discover the behavior of EEG frequency bands and heart rate variability (HRV) features. EEG and ECG were used to analyze and predict inattention in individuals during a visual search task. The EEG determines human brain function and considers to detect the variability in the EEG frequency band. The work proposed a visual search task with EEG and ECG analysis. Five participants recorded EEG and ECG recordings in three different scenarios: rest, gaze tracking, and normal. Statistical evaluation was used to compare EEG and ECG characteristics and Pearson's correlation was employed for statistical analysis. Statistical ANOVA analysis revealed statistically significant (p > 0.05) differences between theta (F3) and alpha (F3) EEG and ECG features, as well as between theta (F4) and alpha (F4) EEG and ECG features. Additionally, alpha (F3) and theta (F3) were significant in the heart rate variability index (rMSSD), which monitored activity under eye tracking. There was also a significant difference between alpha (F3) and mean HR. Pearson's correlation between ECG and EEG shows that theta (O1) and alpha (O1) correlate with LF/HF and alpha (F3) and theta (F3) with rMSSD. Theta (F3) and mean heart rate were also correlated. Observing the above ECG and EEG characteristics can improve and control treatment options for conditions like neurovascular instability (NCVI), characterized by age-related changes in blood pressure and increased cerebral and cardiac leukoaraiosis. [ABSTRACT FROM AUTHOR]

Investigating social context effects and emotional modulation of attention in a laboratory setting is challenging. Electroencephalography (EEG) requires a controlled setting to avoid confounds, which goes against the nature of social interaction and emotional processing in real life. To bridge this gap, we developed a new paradigm to investigate the effects of social context and emotional expressions on attention in a laboratory setting. We co-registered eye-tracking and EEG to assess gaze behavior and brain activity while participants performed a discrimination task followed by feedback. Video clips of one second in which a confederate displayed either positive, neutral or negative expressions were presented as feedback to the discrimination task. Participants’ belief was manipulated by telling them that the videos were selected either by the computer (non-social condition) or by the experimenter in the adjacent room that observed them via videochat (social condition). We found that emotional expressions modulated late attention processing in the brain (EPN and LPC), but neither early processing (P1) nor saccade latency. Social context did not influence any of the variables studied. We conclude this new paradigm serves as a stepping stone to the development of new paradigms to study social interaction within EEG experiments. [ABSTRACT FROM AUTHOR]

Cochlear implants (CI) are one of the most successful treatments available to enable individuals with severe to profound hearing loss to regain access to the world of sound. This is accomplished through the electrical stimulation of the auditory nerve using electrodes implanted inside the cochlea. The use of subjective user feedback makes the process of fitting these devices much more challenging in cases where users are not able to actively or accurately report their experience (e.g. pediatrics), making an objective measurement that reflects the accuracy or effectiveness of a program quite attractive. We recorded one objective measure, the electrically-stimulated cortical auditory evoked potential (eCAEP), non-invasively using the CI in response to a simulated speech sound in seven adult participants and compared it to their eCAEP recorded using a scalp EEG set-up. The eCAEPs recorded with CI electrodes were comparable to scalp recorded eCAEPs (grand mean cross-correlation of r = 0.83, individual mean cross-correlations ranged from 0.13 to 0.70). Evoked potential peaks P1, N1 and P2 showed no significant latency difference based on if the eCAEP was recorded on the scalp or using the CI. The eCAEP waveforms recorded via the CI appear to converge in a distinct P1-N1-P2 waveform by as early as 130 sweeps. In conclusion, in this study we show the feasibility of recording the eCAEP directly through the CI system, which could potentially be used to guide CI fitting and track auditory cortex development in response to CI use. [ABSTRACT FROM AUTHOR]

Cognitive load is a critical internal state associated with learners' learning process and significantly influences learning outcomes. With the worldwide popularity of video‐based learning (VBL), tracking real‐time cognitive load variations becomes more and more important for the timely provision of adaptive learning support during the learning process. This study proposed and validated an electroencephalogram (EEG)‐supported approach to tracking real‐time cognitive load variations during continuous VBL. We recruited 108 healthy adult participants to watch a specially designed video lecture with a sequence of interconnected slides of equal length. EEG signals were continuously recorded throughout the session. The video lecture was designed with varying levels of content difficulty (ie, rated from 1 to 5) across slides and was narrated at three different speeds (ie, slow, normal and fast) to induce cognitive load variations. For each slide, the cognitive load was quantified using both subjective ratings (ie, self‐reported difficulty) and an EEG‐derived measure (ie, alpha power). Through linear mixed model analysis, we demonstrated the feasibility of using alpha power to track real‐time cognitive load variations during the continuous VBL process after controlling the effect of mental fatigue. This study provides a foundation for developing learning enhancement technologies that enable the timely provision of adaptive learning support in VBL. Practitioner notes What is already known about this topic Video‐based learning has become a prevailing learning method for the current generation. Tracking the internal learning state of learners is essential for the timely provision of adaptive learning support during the video‐based learning process. Cognitive load is a critical aspect of internal learning state. While EEG has been proven to be valuable in assessing average cognitive load of a task, few studies have investigated the feasibility of utilizing EEG to track real‐time cognitive load variations in a task. What this paper adds An EEG‐supported approach was proposed to track real‐time cognitive load variations in video‐based learning. A high consistency was found between subjective ratings and EEG‐derived measure of cognitive load. The presence of mental fatigue exerted a significant impact on EEG‐derived measure of cognitive load. Implications for practice and/or policy Generative AI can be leveraged to facilitate mass production of lectures required in the approach. Real‐time tracking of cognitive load variations in video‐based learning enables the timely provision of adaptive learning supports. Additional research is warranted to mitigate the effect of mental fatigue on real‐time tracking of cognitive load variations. What is already known about this topic Video‐based learning has become a prevailing learning method for the current generation. Tracking the internal learning state of learners is essential for the timely provision of adaptive learning support during the video‐based learning process. Cognitive load is a critical aspect of internal learning state. While EEG has been proven to be valuable in assessing average cognitive load of a task, few studies have investigated the feasibility of utilizing EEG to track real‐time cognitive load variations in a task. What this paper adds An EEG‐supported approach was proposed to track real‐time cognitive load variations in video‐based learning. A high consistency was found between subjective ratings and EEG‐derived measure of cognitive load. The presence of mental fatigue exerted a significant impact on EEG‐derived measure of cognitive load. Implications for practice and/or policy Generative AI can be leveraged to facilitate mass production of lectures required in the approach. Real‐time tracking of cognitive load variations in video‐based learning enables the timely provision of adaptive learning supports. Additional research is warranted to mitigate the effect of mental fatigue on real‐time tracking of cognitive load variations. [ABSTRACT FROM AUTHOR]

The detection and identification of epileptic seizures attracted considerable relevance for the neurophysiologists. In order to accomplish the detection of epileptic seizures or equivalently ictal EEG states, this paper proposes the use of nonlinear and chaos features not computed over the raw EEG signals as it was commonly experienced, but instead over intrinsic mode functions (IMFs) extracted subsequently to the application of newly time-frequency signal decomposition methods on the basis of empirical mode decomposition (EMD) and variational mode decomposition (VMD) methods. The first step within the proposed methodology is to excerpt the various components of the IMFs by EMD and VMD decomposition methods on time EEG segments. The Hjorth parameters, the Hurst exponent, the Recurrence Quantification Analysis (RQA), the detrended fluctuation analysis (DFA), the Largest Lyapunov Exponent (LLE), The Higuchi and Katz fractal dimensions (HFD and KFD), seven nonlinear and chaos features computed over the IMFs were investigated and their classification performances evaluated using the k-nearest neighbor (KNN) and the multilayer perceptron neural network (MLPNN) classifiers. Furthermore, the combination of the best nonlinear features has also been examined in terms of sensitivity, specificity and overall classification accuracy. The publicly available Bonn EEG dataset has been has been employed to validate the efficiency of the proposed method for detecting ictal EEG signals from normal or interictal EEG segments. Among the several experiments involved in the current study, the ultimate results establish that the overall classification accuracy can achieve 100%, 99.45%, 99.8%, 99.8%, 98.6% and 99.1% for six different epileptic seizure detection case problems studied, confirming the ability of the proposed methodology in helping the clinic practitioners in the epilepsy detection care units to classify seizure events with a great confidence. [ABSTRACT FROM AUTHOR]

This paper introduces a neural network‐based model designed for classifying emotional states by leveraging multimodal physiological signals. The model utilizes data from the AMIGOS and SEED‐V databases. The AMIGOS database integrates inputs from electroencephalogram (EEG), electrocardiogram (ECG), and galvanic skin response (GSR) to analyze emotional responses, while the SEED‐V database continuously updates EEG signals. We implemented a sequential neural network architecture featuring two hidden layers, which underwent substantial hyperparameter tuning to achieve optimal performance. Our model's effectiveness was tested through binary classification tasks focusing on arousal and valence, as well as a more complex four‐class classification that delineates emotional quadrants for the emotional tags: happy, sad, neutral, and disgust. In these varied scenarios, the model consistently demonstrated accuracy levels ranging from 79% to 86% in the AMIGOS database and up to 97% in SEED‐V. A notable aspect of our approach is the model's ability to accurately recognize emotions without the need for extensive signal preprocessing, a common challenge in multimodal emotion analysis. This feature enhances the practical applicability of our model in real‐world scenarios where rapid and efficient emotion recognition is essential. [ABSTRACT FROM AUTHOR]

Epilepsy is referred to as a neurological disorder, which is detected via examination and manual comprehension of Electroencephalogram (EEG) signals. In deep learning schemes, various enhancements have emerged to efficiently address complex issues by end-to-end learning. The major objective of this research is to propose a new seizure detection approach from EEG signals using a deep learning-based classification technique. The pre-processing is the initial stage, where denoising is performed using a Short-Time Fourier Transform (STFT). Subsequently, the statistical features, time-domain features and spectral features are extracted from the pre-processed signal. Finally, an efficient optimization approach, named Adadelta-Chameleon Swarm Algorithm (Adadelta-CSA), is proposed and employed to train Deep Neural Network (DNN) to carry out the precise seizure prediction. Here, the integration of the Adadelta concept in the Chameleon Swarm Algorithm (CSA) has resulted in Adadelta-CSA. At last, the performance of the Adadelta-CSA scheme-based DNN is compared with the existing techniques by considering accuracy, sensitivity and specificity, and it is found to produce better values of 0.951, 0.966, and 0.935, respectively. [ABSTRACT FROM AUTHOR]

[Display omitted] • N400 and P600 ERPs evoked in adolescents ignoring sentences with semantic errors. • Semantic processing is fundamentally different in passive versus active contexts. • We provide 22 control adolescent high-density electrophysiological BIDs datasets. Language comprehension requires semantic processing of individual words and their context within a sentence. Well-characterized event-related potential (ERP) components (the N400 and late positivity component (LPC/P600)) provide neuromarkers of semantic processing, and are robustly evoked when semantic errors are introduced into sentences. These measures are useful for evaluating semantic processing in clinical populations, but it is not known whether they can be evoked in more severe neurodevelopmental disorders where explicit attention to the sentence inputs cannot be objectively assessed (i.e., when sentences are passively listened to). We evaluated whether N400 and LPC/P600 could be detected in adolescents who were explicitly ignoring sentence inputs. Specifically, it was asked whether explicit attention to spoken inputs was required for semantic processing, or if a degree of automatic processing occurs when the focus of attention is directed elsewhere? High-density ERPs were acquired from twenty-two adolescents (12–17 years), under two experimental conditions: 1. individuals actively determined whether the final word in a sentence was congruent or incongruent with sentence context, or 2. passively listened to background sentences while watching a video. When sentences were ignored, N400 and LPC/P600 were robustly evoked to semantic errors, albeit with reduced amplitudes and protracted/delayed latencies. Statistically distinct topographic distributions during passive versus active paradigms pointed to distinct generator configurations for semantic processing as a function of attention. Covert semantic processing continues in neurotypical adolescents when explicit attention is withdrawn from sentence inputs. As such, this approach could be used to objectively investigate semantic processing in populations with communication deficits. [ABSTRACT FROM AUTHOR]

Introduction: According to reinforcement learning, humans adjust their behavior based on the difference between actual and anticipated outcomes (i.e., prediction error) with the main goal of maximizing rewards through their actions. Despite offering a strong theoretical framework to understand how we acquire motor skills, very few studies have investigated reinforcement learning predictions and its underlying mechanisms in motor skill acquisition. Methods: In the present study, we explored a 134-person dataset consisting of learners' feedback-evoked brain activity (reward positivity; RewP) and motor accuracy during the practice phase and delayed retention test to investigate whether these variables interacted according to reinforcement learning predictions. Results: Results showed a non-linear relationship between RewP and trial accuracy, which was moderated by the learners' performance level. Specifically, high-performing learners were more sensitive to violations in reward expectations compared to low-performing learners, likely because they developed a stronger representation of the skill and were able to rely on more stable outcome predictions. Furthermore, contrary to our prediction, the average RewP during acquisition did not predict performance on the delayed retention test. Discussion: Together, these findings support the use of reinforcement learning models to understand short-term behavior adaptation and highlight the complexity of the motor skill consolidation process, which would benefit from a multi-mechanistic approach to further our understanding of this phenomenon. [ABSTRACT FROM AUTHOR]

The most common manifestation of neurological disorders in children is the occurrence of epileptic seizures. In this study, we propose a multi-branch graph convolutional network (MGCNA) framework with a multi-head attention mechanism for detecting seizures in children. The MGCNA framework extracts effective and reliable features from high-dimensional data, particularly by exploring the relationships between EEG features and electrodes and considering the spatial and temporal dependencies in epileptic brains. This method incorporates three graph learning approaches to systematically assess the connectivity and synchronization of multi-channel EEG signals. The multi-branch graph convolutional network is employed to dynamically learn temporal correlations and spatial topological structures. Utilizing the multi-head attention mechanism to process multi-branch graph features further enhances the capability to handle local features. Experimental results demonstrate that the MGCNA exhibits superior performance on patient-specific and patient-independent experiments. Our end-to-end model for automatic detection of epileptic seizures could be employed to assist in clinical decision-making. [ABSTRACT FROM AUTHOR]

Background: Post-stroke depression (PSD) is a prevalent psychiatric complication among stroke survivors. The PSD researches focus on pathogenesis, new treatment methods and efficacy prediction. This study explored the electroencephalography (EEG) microstates in PSD and assessed their changes after acupuncture treatment, aiming to find the biological characteristics and the predictors of treatment efficacy of PSD. Methods: A 64-channel resting EEG data was collected from 70 PSD patients (PSD group) and 40 healthy controls (HC group) to explore the neuro-electrophysiological mechanism of PSD. The PSD patients received 6 weeks of acupuncture treatment. EEG data was collected from 60 PSD patients after acupuncture treatment (MA group) to verify whether acupuncture had a modulating effect on abnormal EEG microstates. Finally, the MA group was divided into two groups: the remission prediction group (RP group) and the non-remission prediction group (NRP group) according to the 24-Item Hamilton Depression Scale (HAMD-24) reduction rate. A prediction model for acupuncture treatment was established by baseline EEG microstates. Results: The duration of microstate D along with the occurrence and contribution of microstate C were reduced in PSD patients. Acupuncture treatment partially normalized abnormal EEG microstates in PSD patients. Baseline EEG microstates predicted the efficacy of acupuncture treatment with an area under the curve (AUC) of 0.964. Conclusion: This study provides a novel viewpoint on the neurophysiological mechanisms of PSD and emphasizes the potential of EEG microstates as a functional biomarker. Additionally, we anticipated the therapeutic outcomes of acupuncture by analyzing the baseline microstates, which holds significant practical implication for the PSD treatment. [ABSTRACT FROM AUTHOR]

Summary The transition from non‐rapid eye movement (NREM) to rapid eye movement (REM) sleep is considered a transitional or intermediate stage (IS), characterised by high amplitude spindles in the frontal cortex and theta activity in the occipital cortex. Early reports in rats showed an IS lasting from 1 to 5 s, but recent studies suggested a longer duration of this stage of up to 20 s. To further characterise the IS, we analysed its spectral characteristics on electrocorticogram (ECoG) recordings of the olfactory bulb (OB), primary motor (M1), primary somatosensory (S1), and secondary visual cortex (V2) in 12 Wistar male adult rats. By comparing the IS with consolidated NREM/REM epochs, our results reveal that the IS has specific power spectral patterns that fall out of the NREM and REM sleep state power distribution. Specifically, the main findings were that sigma (11–16 Hz) power in OB, M1, S1, and V2 increased during the IS compared with NREM and REM sleep, which started first in the frontal part of the brain (OB −54 s, M1 −53 s) prior to the last spindle occurrence. The beta band (17–30 Hz) power showed a similar pattern to that of the sigma band, starting −54 s before the last spindle occurrence in the M1 cortex. Notably, sigma infraslow coupling (~0.02 Hz) increased during the IS but occurred at a slower frequency (~0.01 Hz) compared with NREM sleep. Thus, we argue that the NREM to REM transition contains its own local spectral profile, in accordance with previous reports, and is more extended than described previously. [ABSTRACT FROM AUTHOR]

Key event-related potentials (ERPs) of perceptual decision-making such as centroparietal positivity (CPP) elucidate how evidence is accumulated toward a given choice. Furthermore, this accumulation can be impacted by visual target selection signals such as the N2 contralateral (N2c). How these underlying neural mechanisms of perceptual decision-making are influenced by the spatial congruence of distractors relative to target stimuli remains unclear. Here, we used electroencephalography (EEG) in humans of both sexes to investigate the effect of distractor spatial congruency (same vs different hemifield relative to targets) on perceptual decision-making.We confirmed that responses for perceptual decisions were slower for spatially incongruent versus congruent distractors of high salience. Similarly, markers of target selection (N2c peak amplitude) and evidence accumulation (CPP slope) were found to be lower when distractors were spatially incongruent versus congruent. To evaluate the effects of congruency further, we applied drift diffusion modeling to participant responses, which showed that larger amplitudes of both ERPs were correlated with shorter nondecision times when considering the effect of congruency. The modeling also suggested that congruency's effect on behavior occurred prior to and during evidence accumulation when considering the effects of the N2c peak and CPP slope. These findings point to spatially incongruent distractors, relative to congruent distractors, influencing decisions as early as the initial sensory processing phase and then continuing to exert an effect as evidence is accumulated throughout the decision-making process. Overall, our findings highlight how key electrophysiological signals of perceptual decision-making are influenced by the spatial congruence of target and distractor. [ABSTRACT FROM AUTHOR]

The subcortical visual pathway to the amygdala has long been considered a rapid and crude stream for processing emotionally salient information that is reliant on low spatial frequency (LSF) information. Recently, research has called this LSF dependency into question. To resolve this debate, we take advantage of an anatomical hemiretinal asymmetry, whereby the nasal hemiretina sends a higher proportion of information through the subcortical pathway than the temporal hemiretina. We recorded brain activity using electroencephalography (EEG) in human participants (N = 40) while they completed a monocular viewing paradigm. Pairs of faces (one fearful and one neutral, or both neutral) were projected simultaneously to the nasal and temporal hemiretina in three contrast-equated blocks; faces filtered to display only (i) LSF, (ii) high spatial frequency (HSF), or (iii) unfiltered information (broadband spatial frequency; BSF). BSF fearful faces were found to produce a greater naso-temporal asymmetry, with greater N170 amplitudes evoked by BSF faces in the nasal field, compared to HSF faces. Conversely, the naso-temporal asymmetry for LSF fearful faces did not differ between BSF and HSF. Collectively, these findings provide crucial evidence that the subcortical pathway carries combined spatial frequency visual signals, with a potential bias against HSF content. [ABSTRACT FROM AUTHOR]

Introduction: This study is the first to assess whether infants' developing social brains may be susceptible to the vicarious social experience of interparental positivity. Specifically, we explored whether infants' exposure to interparental positivity may vicariously shape their neural substrates of social development. Methods: In a sample of 45 infants (M AgeMonths = 11.01; 48.9% girls), infant left-frontal resting alpha electroencephalogram (EEG) asymmetry was derived as a reliable indicator of neural substrates linked to adaptive social development. Moreover, positive characteristics of the mother–father couple relationship were assessed both by means of observation and self-report by mother and father. Importantly, various relevant covariates were considered, including interparental negativity (observed and self-reported), as well as infants' direct caregiving experiences and duration of infant exposure to mother–father relationship-dynamics (parent-report). Results: Results indicated that higher levels of observed interparental positivity were associated with greater infant left-frontal alpha EEG asymmetry, even after accounting for covariates (β's > 0.422). Discussion: The current study's results are first to suggest that positive vicarious social experiences in infants' day-to-day lives play a significant role for early neural development. [ABSTRACT FROM AUTHOR]

Introduction: The normal transition to sleep is characterized by a reduction in higher frequency activity and an increase in lower frequency activity in frontal brain regions. In sleep onset insomnia these changes in activity are weaker and may prolong the transition to sleep. Methods: Using a wearable device, we compared 30min of short duration repetitive transcranial electric stimulation (SDR-tES) at 0.75Hz, prior to going to bed, with an active control at 25Hz in the same individuals. Results: Treatment with 0.75Hz significantly reduced sleep onset latency (SOL) by 53% when compared with pre-treatment baselines and was also significantly more effective than stimulation with 25Hz which reduced SOL by 30%. Reductions in SOL with 25Hz stimulation displayed order effects suggesting the possibility of placebo. No order effects were observed with 0.75Hz stimulation. The decrease in SOL with 0.75Hz treatment was proportional to an individual's baseline wherein those suffering from the longest pre-treated SOLs realized the greatest benefits. Changes in SOL were correlated with left/right frontal EEG signal coherence around the stimulation frequency, providing a possible mechanism and target for more focused treatment. Stimulation at both frequencies also decreased perceptions of insomnia symptoms measured with the Insomnia Severity Index, and comorbid anxiety measured with the State Trait Anxiety Index. Discussion: Our study identifies a new potential treatment for sleep onset insomnia that is comparably effective to current state-of-practice options including pharmacotherapy and cognitive behavioral therapy and is safe, effective, and can be delivered in the home. [ABSTRACT FROM AUTHOR]

Alpha oscillations are associated with various psychiatric disorders, with many studies focusing on the prefrontal cortex, where transcranial alternating current stimulation (TACS) is applied in the alpha frequency band. This approach often involves selecting individualized alpha frequencies to resonate with their endogenous alpha oscillations. While strong alpha oscillations (8–13 Hz) are typically induced when the eyes are closed, they can also occur during the resting state with eyes open. However, it remains unclear whether these alpha oscillations share a common neural generation mechanism. Exploring which of these alpha oscillations is more suitable as a stable alpha peak frequency is a question of significant interest. Therefore, to systematically study this issue, we specifically collected resting-state electroencephalographic (EEG) data from the prefrontal region of 40 individuals, under both eyes-open and closed- eye conditions, with multiple follow-ups extending up to nine months. Through spectral analysis on each person's entire dataset and averaging the results, we observed a significant positive correlation between the alpha-band power in the eyes-open and the eyes-closed states, in terms of both absolute power and relative power. Further analysis revealed that this correlation was primarily contributed by the periodic activity within the alpha band. Upon modelling the oscillatory components, we discovered distinct differences in the oscillatory characteristics-such as number of the alpha sub-oscillations between the eyes-open state and the eyes-closed state. Our study is the first to systematically explored the relationship between alpha oscillations in the prefrontal cortex in the eyes-open and eyes-closed states, identifying both shared part of the neural generation mechanism and some distinct neural mechanisms that are unique to each state. [ABSTRACT FROM AUTHOR]

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. [Display omitted] • Adaptive behavior requires a gradual switch from learning to memory-guided action • Brain activity patterns shift from encoding past stimuli to retrieving future stimuli • Eye movements indicate increasing anticipation of upcoming stimulus locations • Brain pattern and eye movement dynamics correlate with learning rates This study reveals how the brain dynamically shifts from learning to memory-guided behavior. Büchel et al. use electroencephalography (EEG) and eye tracking in a spatiotemporal learning task to show a gradual transition from encoding past stimuli to anticipating future ones, aligning neural and behavioral measures with learning rates. [ABSTRACT FROM AUTHOR]

Nowadays studies using Virtual Reality (VR) are gaining high popularity due to VR being a better approximation of the ecological environment for visual experiments than standard 2D display settings. VR technology has been already applied in medicine in the therapy of mental disorders, neurorehabilitation, and neurofeedback. However, its effectiveness compared to the standard 2D procedure is still not fully documented and limited information about the neurophysiological underpinnings of VR is provided. In this study, we tested participants' performance during several sessions of the computer game in two different environments, VR vs. 2D monitor display. Participants performed three 25 min gaming sessions of adapted Delay Match-To-Sample task during EEG recording. The results showed that the VR group outperformed the 2D display group in the first session and then maintained its performance level throughout the remaining two sessions while the 2D group increased performance in each session eventually leveling up in the last one. Also group differences in the EEG activity were most profound only in the first session. In this session, the VR group was characterized by stronger and more synchronized neuronal activity, especially in delta, theta, and gamma bands. The VR group was less impacted by visual arousals as indicated by the theta/beta2 ratio in parietal electrodes. [ABSTRACT FROM AUTHOR]

Introduction: Social anxiety disorder (SAD) is a prevalent psychiatric condition characterized by an intense fear of and avoidance of social situations. Traditional assessment methods for SAD primarily rely on subjective self-report questionnaires and clinical interviews, which can be prone to biases and inaccuracies. This study aims to explore the functional excitation-inhibition (fEI) ratio derived from EEG data as a potential objective biomarker for assessing SAD severity. Methods: Resting-state EEG data were collected from 20 control subjects and 60 individuals with varying degrees of SAD severity (mild, moderate, and severe). The fEI ratio was estimated across different EEG bands and analyzed, focusing on differences between control subjects and SAD groups. Results: Significantly higher fEI ratios were observed in the alpha and low beta EEG bands in individuals with SAD compared to controls, especially within the prefrontal cortex. Additionally, a positive correlation was found between the fEI ratio and the severity of social anxiety symptoms across SAD severity levels. Discussion: The findings indicate that the fEI ratio in the alpha and low beta bands may serve as a promising biomarker for assessing SAD severity. These results contribute to a deeper understanding of the neural mechanisms underlying social anxiety, offering a potentially more objective approach to SAD assessment compared to traditional methods. [ABSTRACT FROM AUTHOR]

Objective: Developmental and epileptic encephalopathies (DEEs) are neurological disorders characterized by developmental impairment and epilepsy. Our study aims to assess disease progression by comparing clinical findings, electroencephalography (EEG), and neuropsychological data from seizure onset to the last follow‐up evaluation. Methods: We retrospectively reviewed patients with genetic DEEs who were followed‐up at the epilepsy unit of Bambino Gesù Children's Hospital, Rome. We collected information regarding gender, family history, genetic variant, age at onset and at last follow‐up, neurological examination, type of seizure, drug resistance, occurrence of status epilepticus, and movement and cognitive and behavioral disorders. We compared EEG background activity, epileptiform abnormalities, and cognitive functions between seizure onset and the last follow‐up evaluation using the McNemar‐Bowker test (α = 5%). Results: A total of 160 patients (94 female) were included. Genetic analysis revealed a spectrum of pathogenic variants, with SCN1A being the most prevalent (25%). The median age at seizure onset and at the last follow‐up was 0.37 (interquartile range [IQR]: 0.09–0.75) and 8.54 years (IQR: 4.32–14.55), respectively. We documented a statistically significant difference in EEG background activity (p =.017) and cognitive impairment (p =.01) from seizure onset to the last follow‐up evaluation. No significant differences were detected for epileptiform abnormalities (p =.2). In addition, high prevalence rates were observed for drug resistance (81.9%), movement disorders (60.6%), behavioral and autism spectrum disorders (45%), neurological deficits (31.3%), and occurrence of status epilepticus (23.1%). Significance: Our study provides evidence that a clinical progression may appear in genetic DEEs, manifesting as development or worsening of cognitive impairment and disruption of EEG background activity. These results highlight the challenging clinical course and the importance of early intervention and personalized care in the management of patients with DEEs. [ABSTRACT FROM AUTHOR]

Objective: E2730, an uncompetitive γ‐aminobutyric acid (GABA) transporter‐1 (GAT‐1) inhibitor, has potent anti‐seizure effects in a rodent model of chronic temporal lobe epilepsy, the kainic acid status epilepticus (KASE) rat model. In this study, we examined purported neuroimaging and physiological surrogate biomarkers of the effect of E2730 on brain GABAergic function. Methods: We conducted a randomized cross‐over study, incorporating 1‐week treatments with E2730 (100 mg/kg/day subcutaneous infusion) or vehicle in epileptic post‐KASE rats. KASE rats underwent serial 9.4 T magnetic resonance spectroscopy (MRS) measuring GABA and other brain metabolites, [18F]Flumazenil positron emission tomography (PET) quantifying GABAA receptor availability, quantitative electroencephalography (qEEG) and transcranial magnetic stimulation (TMS)–mediated motor activity, as well as continuous video‐EEG recording to measure spontaneous seizures during each treatment. Age‐matched, healthy control animals treated with E2730 or vehicle were also studied. Results: E2730 treatment significantly reduced spontaneous seizures, with 8 of 11 animals becoming seizure‐free. MRS revealed that E2730‐treated animals had significantly reduced taurine levels. [18F]Flumazenil PET imaging revealed no changes in GABA receptor affinity or density during E2730 treatment. The power of gamma frequency oscillations in the EEG was decreased significantly in the auditory cortex and hippocampus of KASE and control rats during E2730 treatment. Auditory evoked gamma frequency power was enhanced by E2730 treatment in the auditory cortex of KASE and healthy controls, but only in the hippocampus of KASE rats. E2730 did not influence motor evoked potentials triggered by TMS. Significance: This study identified clinically relevant changes in multimodality imaging and functional purported biomarkers of GABAergic activity during E2730 treatment in epileptic and healthy control animals. These biomarkers could be utilized in clinical trials of E2730 and potentially other GABAergic drugs to provide surrogate endpoints, thereby reducing the cost of such trials. [ABSTRACT FROM AUTHOR]

Objective: We aimed to investigate sleep in children with drug‐resistant epilepsy (DRE), including developmental and epileptic encephalopathies (DEEs). Next, we examined differences in sleep macrostructure and microstructure and questionnaire outcomes between children with well‐controlled epilepsy (WCE) and children with DRE. Furthermore, we wanted to identify factors associated with poor sleep outcome in these children, as some factors might be targets to improve epilepsy and neurodevelopmental outcomes. Methods: A cross‐sectional study was conducted in children 4 to 18‐years‐old. Children without epilepsy, with WCE, and with DRE were included. Overnight electroencephalography (EEG), including chin electromyography and electrooculography, to allow sleep staging, was performed. Parents were asked to fill out a sleep questionnaire. Classical five‐stage sleep scoring was performed manually, spindles were automatically counted, and slow wave activity (SWA) in the first and last hour of slow wave sleep was calculated. Results: One hundred eighty‐two patients were included: 48 without epilepsy, 75 with WCE, and 59 with DRE. We found that children with DRE have significantly lower sleep efficiency (SE%), less time spent in rapid eye movement (REM) sleep, fewer sleep spindles, and a lower SWA decline over the night compared to children with WCE. Subjectively more severe sleep problems were reported by the caregivers and more daytime sleepiness was present in children with DRE. Least absolute shrinkage and selection operator (LASSO) regression showed that multifocal interictal epileptiform discharges (IEDs), benzodiazepine treatment, and longer duration of epilepsy were associated with lower SE% and lower REM sleep time. The presence of multifocal discharges and cerebral palsy was associated with fewer spindles. Benzodiazepine treatment, drug resistance, seizures during sleep, intellectual disability, and older age were associated with lower SWA decline. Significance: Both sleep macrostructure and microstructure are severely impacted in children with DRE, including those with DEEs. Epilepsy parameters play a distinct role in the disruption REM sleep, spindle count, and SWA decline. [ABSTRACT FROM AUTHOR]

Advances in brain–computer interfaces (BCIs) have enabled direct and functional connections between human brains and computing systems. Recent developments in artificial intelligence have also significantly improved the ability to detect brain activity patterns. In particular, using steady-state visual evoked potentials (SSVEPs) in BCIs has enabled noticeable advances in human activity monitoring and identification. However, the lack of publicly available electroencephalogram (EEG) datasets has limited the development of SSVEP-based BCI systems (SSVEP-BCIs) for human activity monitoring and assisted living. This study aims to provide an open-access multicategory EEG dataset created under the SSVEP-BCI paradigm, with participants performing forward, backward, left, and right movements to simulate directional control commands in a virtual environment developed in Unity. The purpose of these actions is to explore how the brain responds to visual stimuli of control commands. An SSVEP-BCI system is proposed to enable hands-free control of a virtual target in the virtual environment allowing participants to maneuver the virtual target using only their brain activity. This work demonstrates the feasibility of using SSVEP-BCIs in human activity monitoring and assessment. The preliminary experiment results indicate the effectiveness of the developed system with high accuracy, successfully classifying 89.88% of brainwave activity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study investigated differences in electroencephalography (EEG) activity within motor-related brain areas during three phases of a single-leg squat (SLS)—i.e., descending, holding, and ascending phases. Specifically, utilizing advanced magnetic resonance imaging guided EEG source localization techniques and markerless motion capture technology, we explored the interplay between concurrently recorded lower-extremity biomechanics and brain activity. Among the phases of a nondominant leg SLS, differences in contralateral brain activity (right hemisphere) were found in the activity of the precentral gyrus, the postcentral gyrus, and the sensory motor area. Alternatively, during the dominant SLS leg, differences among the three SLS phases in contralateral brain activity were fewer. Hemispheric dependent brain activity also significantly correlated with participants' knee valgus angle range of motion (right hemisphere) and peak knee valgus angles (left hemisphere). In addition to the novel brain and biomechanical findings, this study sheds light on the technical feasibility of recording EEG during complex multi-joint movements and its potential applications in understanding sensorimotor behavior. [ABSTRACT FROM AUTHOR]

Fragile X syndrome (FXS) is a rare neurodevelopmental disorder caused by a CGG repeat expansion ≥ 200 repeats in 5' untranslated region of the FMR1 gene, leading to intellectual disability and cognitive difficulties, including in the domain of communication. A recent phase 2a clinical trial testing BPN14770, a phosphodiesterase 4D inhibitor, showed improved cognition in 30 adult males with FXS on drug relative to placebo. The initial study found significant improvements in clinical measures assessing cognition, language, and daily functioning in addition to marginal improvements in electroencephalography (EEG) results for the amplitude of the N1 event-related potential (ERP) component. These EEG results suggest BPN14770 improved neural hyperexcitability in FXS. The current study investigated the relationship between BPN14770 pharmacokinetics and the amplitude of the N1 ERP component from the initial data. Consistent with the original group-level finding post-period 1 of the study, participants who received BPN14770 in period 1 showed a significant correlation between N1 amplitude and serum concentration of BPN14770 measured at the end of period 1. These findings strengthen the validity of the original result, indicating that BPN14770 improves cognitive performance by modulating neural hyperexcitability. This study represents the first report of a significant correlation between a reliably abnormal EEG marker and serum concentration of a novel pharmaceutical in FXS. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]