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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.

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.

Humans categorize objects not only based on perceptual features (e.g. red, rounded), but also function (e.g. used to transport people). Category membership can be communicated via labelling (e.g. 'apple', 'vehicle'). While it is well established that even preverbal infants rely on labels to learn categories, it remains unclear what is the nature of those categories: whether they simply contain sets of visual features diagnostic of category membership, or whether they additionally contain abstract category markers or symbols (e.g. linguistic in the form of category labels or non-linguistic). To address this question, we first used labelling to teach two novel object categories, each composed of unfamiliar visually unrelated objects, to adults and nine-month-olds. Then, we assessed categorization in an electroencephalography category-oddball task. Both adults and infants displayed stronger neural responses to the infrequent category, which, in the absence of visual features shared by all category members, indicates that the categories they set up contained feature-independent category markers. Well before language production starts, labels help infants to discover categories without relying on perceptual similarities across objects and build category representations with summary elements that may be critical for the development of abstract thought. [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]

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]

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]

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]

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]

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]

Effectively regulating anger driving has become critical in ensuring road safety. The existing research lacks a feasible exploration of anger-driving regulation. This paper delves into the effect and neural mechanisms of emotional olfactory stimuli (EOS) on regulating anger driving based on EEG. First, this study designed an angry driving regulation experiment based on EOS to record EEG signals. Second, brain activation patterns under various EOS conditions are explored by analyzing functional brain networks (FBNs). Additionally, the paper analyzes dynamic alterations in anger-related characteristics to explore the intensity and persistence of regulating anger driving under different EOS. Finally, the paper studies the frequency energy of EEG changes under EOS through time–frequency analysis. The results indicate that EOS can effectively regulate a driver's anger emotions, especially with the banana odor showing superior effects. Under banana odor stimulus, synchronization between the parietal and temporal lobes significantly decreased. Notably, the regulatory effect of banana odor is optimal and exhibits sustained efficacy. The regulatory effect of banana odor on anger emotions is persistent. Furthermore, the impact of banana odor significantly reduces the distribution of high-energy activation states in the parietal lobe region. Our findings provide new insights into the dynamic characterization of functional connectivity during anger-driving regulation and demonstrate the potential of using EOS as a reliable tool for regulating angry driving. [ABSTRACT FROM AUTHOR]

The increasing integration of brain–computer interfaces (BCIs) with augmented reality (AR) presents new possibilities for immersive and interactive environments, particularly through the use of head-mounted displays (HMDs). Despite the growing interest, a comprehensive understanding of BCI-AR systems is still emerging. This systematic review aims to synthesize existing research on the use of BCIs for controlling AR environments via HMDs, highlighting the technological advancements and challenges in this domain. An extensive search across electronic databases, including IEEEXplore, PubMed, and Scopus, was conducted following the PRISMA guidelines, resulting in 41 studies eligible for analysis. This review identifies key areas for future research, potential limitations, and offers insights into the evolving trends in BCI-AR systems, contributing to the development of more robust and user-friendly applications. [ABSTRACT FROM AUTHOR]

This study presents a novel approach to electroencephalography (EEG) biometric authentication using eye blink artifacts. Unlike traditional methods that rely on imagination and mental tasks, which are susceptible to emotional and physical variations, this approach leverages the consistent effects of eye blinks on brainwaves for authentication. Brainwaves were recorded using the NeuroSky Mindwave Mobile 2 headset, and eye blinks were extracted via NeuroSky's blink detection algorithm. An authentication algorithm was developed based on blink strength, time, and frequency. The proposed method demonstrated high performance with an accuracy (ACC) of 97%, a false acceptance rate (FAR) of 5%, and a false rejection rate (FRR) of 1%. This study also explored the impact of emotions and physical exercise on the authentication process, confirming the method's robustness under varying conditions. These findings suggest that eye blink artifacts offer a reliable and practical biometric trait for EEG-based authentication systems, providing a secure alternative to traditional biometric methods. The substantial contribution of this research lies in demonstrating the superior stability and usability of eye blink-based EEG authentication under diverse conditions, compared to existing EEG authentication methods that often require mental tasks or multi-channel recordings. [ABSTRACT FROM AUTHOR]

Learning disability is a condition usual amongst most populace due to poor phonological capability in humans making them impaired. One such neurological disorder is developmental dyslexia, a lack of reading and writing skills leading to difficulty in school education. The essential causes of developmental dyslexia are the consumption of more drug treatments during pregnancy, the over-the-counter purchase of medicines for minor ailments without the recommendation of physicians, and uncared-for head accidents during early life. The occurrence of this trouble is acute in India. Attempts were made by many to detect dyslexic children to reduce the intensity of this hassle. In this proposed effort, machine learning is used to locate significant styles characterizing people using EEG samples. A dataset is used for examination of developmental dyslexia, and classification is done using K nearest neighbor (KNN), decision tree, linear discriminant analysis (LDA), and support vector machine (SVM) to evaluate the performance. This piece of research work is done on MATLAB to provide results on simulation with classification accuracy of 90.76% for SVM, sensitivity of 89% for SVM, and LDA with 91.89% specificity for SVM providing optimum yield. [ABSTRACT FROM AUTHOR]

The alpha rhythm in human electroencephalography (EEG) is known to decrease in frequency with age. Previous study has shown that elderly individuals with dementia exhibit higher S values (spatial variability) and SD values (temporal variability) in the triple correlation of the occipital region (P3, P4, Oz) compared to healthy elderly individuals. The objective of this research is to examine changes in S and SD values of the alpha band with aging in healthy individuals using triple correlation values from the frontal region. The subjects were 50 healthy elderly subjects (mean age 73.0 ± 5.1 years), 34 healthy younger subjects (mean age 28.1 ± 4.6 years), and 21 dementia patients (mean age 70.1 ± 9.1 years). The methodology involved recording EEG for 5 min during rest with closed eyes, and then calculating S and SD values of the alpha band (8-13 Hz) using three electrodes in the frontal region (F3, F4, Fpz). The findings indicated that the S values of young individuals were significantly higher than those of elderly individuals (p < 0.01), whereas the SD values of young individuals tended to be lower than those of elderly individuals. The elevated S values in young individuals imply greater spatial variability akin to individuals with dementia, whereas the reduced SD values in young individuals suggest lower temporal variability unlike individuals with dementia. The discrepancy between the S value and SD value in healthy young individuals suggests that the normal cortical dipole in the frontal regions might be more abundant in them compared to healthy elderly individuals. [ABSTRACT FROM AUTHOR]

Introduction: Human color vision exhibits significant diversity that cannot be fully explained by categorical classifications. Understanding how individuals with different color vision phenotypes perceive, recognize, and react to the same physical stimuli provides valuable insights into sensory characteristics. This study aimed to identify behavioral and neural differences between different color visions, primarily classified as typical trichromats and anomalous trichromats, in response to two chromatic stimuli, blue-green and red, during an attention demanding oddball task. Methods: We analyzed the P3 component of event-related potentials (ERPs), associated with attention, and conducted a broad spatiotemporal exploration of neural differences. Behavioral responses were also analyzed to complement neural data. Participants included typical trichromats (n = 13) and anomalous trichromats (n = 5), and the chromatic stimuli were presented in an oddball paradigm. Results: Typical trichromats exhibited faster potentiation from the occipital to parietal regions in response to the more salient red stimulus, particularly in the area overlapping with the P3 component. In contrast, anomalous trichromats revealed faster potentiation to the expected more salient blue-green stimulus in the occipital to parietal regions, with no other significant neural differences between stimuli. Comparisons between the color vision types showed no significant overall neural differences. Discussion: The large variability in red-green sensitivity among anomalous trichromats, along with neural variability not fully explained by this sensitivity, likely contributed to the absence of clear neural distinctions based on color saliency. While reaction times were influenced by red-green sensitivity, neural signals showed ambiguity regarding saliency differences. These findings suggest that factors beyond red-green sensitivity influenced neural activity related to color perception and cognition in minority color vision phenotypes. Further research with larger sample sizes is needed to more comprehensively explore these neural dynamics and their broader implications. [ABSTRACT FROM AUTHOR]

Introduction: We discuss event-related power differences (ERPDs) in low- and broadband-γ oscillations as the embedded-clause edge is processed in wh-dependencies such as Which decision regarding/about him/her did Paul say that Lydie rejected without hesitation? in first (L1) and second language (L2) French speakers. Methods: The experimental conditions manipulated whether pronouns appeared in modifiers (Mods; regarding him/her) or in noun complements (Comps; about him/ her) and whether they matched or mismatched a matrix-clause subject in gender. Results: Across L1 and L2 speakers, we found that anaphora-linked ERPDs for Mods vs. Comps in evoked power first arose in low γ and then in broadband γ. Referential elements first seem to be retrieved from working memory by narrowband processes in low γ and then referential identification seems to be computed in broadband-γ output. Interactions between discourse- and syntax-based referential processes for the Mods vs. Comps in these ERPDs furthermore suggest that multidomain γ-band processing enables a range of elementary operations for discourse and semantic interpretation. Discussion: We argue that a multidomain mechanism enabling operations conditioned by the syntactic and semantic nature of the elements processed interacts with local brain microcircuits representing features and feature sets that have been established in L1 or L2 acquisition, accounting for a single language epistemology across learning contexts. [ABSTRACT FROM AUTHOR]

Background: To gain a comprehensive understanding of the altered sensory processing in patients with migraine, in this study, we developed an electroencephalography (EEG) protocol for examining brainstem and cortical responses to sensory stimulation. Furthermore, machine learning techniques were employed to identify neural signatures from evoked brainstem–cortex activation and their interactions, facilitating the identification of the presence and subtype of migraine. Methods: This study analysed 1,000-epoch-averaged somatosensory evoked responses from 342 participants, comprising 113 healthy controls (HCs), 106 patients with chronic migraine (CM), and 123 patients with episodic migraine (EM). Activation amplitude and effective connectivity were obtained using weighted minimum norm estimates with spectral Granger causality analysis. This study used support vector machine algorithms to develop classification models; multimodal data (amplitude, connectivity, and scores of psychometric assessments) were applied to assess the reliability and generalisability of the identification results from the classification models. Results: The findings revealed that patients with migraine exhibited reduced amplitudes for responses in both the brainstem and cortical regions and increased effective connectivity between these regions in the gamma and high-gamma frequency bands. The classification model with characteristic features performed well in distinguishing patients with CM from HCs, achieving an accuracy of 81.8% and an area under the curve (AUC) of 0.86 during training and an accuracy of 76.2% and an AUC of 0.89 during independent testing. Similarly, the model effectively identified patients with EM, with an accuracy of 77.5% and an AUC of 0.84 during training and an accuracy of 87% and an AUC of 0.88 during independent testing. Additionally, the model successfully differentiated patients with CM from patients with EM, with an accuracy of 70.5% and an AUC of 0.73 during training and an accuracy of 72.7% and an AUC of 0.74 during independent testing. Conclusion: Altered brainstem-cortex activation and interaction are characteristic of the abnormal sensory processing in migraine. Combining evoked activity analysis with machine learning offers a reliable and generalisable tool for identifying patients with migraine and for assessing the severity of their condition. Thus, this approach is an effective and rapid diagnostic tool for clinicians. [ABSTRACT FROM AUTHOR]

Background: Preterm birth is a principal reason for perinatal morbidity and mortality increasing the incidence of severe neurodevelopmental deficits. There is growing proof that early postnatal amplitude-integrated electroencephalography (aEEG) has a prognostic value for neurodevelopmental consequence in preterm born neonates. Furthermore, MRI has been widely utilized to enhance comprehension of the brain substrate responsible for neurodevelopmental abnormalities. Thus, this study aims at evaluating the role of early postnatal aEEG and brain MRI in forecasting neurodevelopmental consequence in preterm infants at the age of 1 year. Methods: A cohort study performed in the neonatal intensive care unit of a tertiary hospital during the duration from October 2021 to June 2023 including 60 preterm neonates < 32 weeks of gestation. All cases were monitored by aEEG within the 1st 72 h of life for at least 4 h, and then brain MRI and aEEG were done at term equivalent age (TEA) of 40 weeks. Regarding the neurodevelopmental outcome, our cases were assessed by Bayley scale III screening test at the age of 1 year. Results: Of the study participants, 41.7% were males, and 58.3% were females with a mean gestational age of 30.40 ± 0.94 weeks and mean weight of 1.36 ± 0.17 kg. aEEG showed that 83.3% of the cases had continuous normal background activity at TEA, and MRI showed that 75% of the cases were normal. Comparing between non-affected and affected groups as categorized by Bayley scale regarding aEEG and MRI findings, there was greatly statistically significant difference between the two groups (P < 0.001). Brain MRI showed higher sensitivity and accuracy than aEEG. Conclusion: Brain MRI at TEA is more sensitive and accurate than aEEG to predict the neurodevelopmental outcome. aEEG at TEA is more predictor for neurodevelopmental outcome than at birth. The combination of both aEEG and brain MRI at TEA gives more prediction about the degree of affection in neurodevelopmental outcome in preterm infants. [ABSTRACT FROM AUTHOR]

A concern in the field of autism electroencephalography (EEG) biomarker discovery is their lack of reproducibility. In the present study, we considered the problem of learning reproducible associations between multiple features of resting state (RS) neural activity and autism, using EEG data collected during a RS paradigm from 36 to 96 month-old children diagnosed with autism (N = 224) and neurotypical children (N = 69). Specifically, EEG spectral power and functional connectivity features were used as inputs to a regularized generalized linear model trained to predict diagnostic group (autism versus neurotypical). To evaluate our model, we proposed a procedure that quantified both the predictive generalization and reproducibility of learned associations produced by the model. When prioritizing both model predictive performance and reproducibility of associations, a highly reproducible profile of associations emerged. This profile revealed a distinct pattern of increased gamma power and connectivity in occipital and posterior midline regions associated with an autism diagnosis. Conversely, model selection based on predictive performance alone resulted in non-robust associations. Finally, we built a custom machine learning model that further empirically improved robustness of learned associations. Our results highlight the need for model selection criteria that maximize the scientific utility provided by reproducibility instead of predictive performance. [ABSTRACT FROM AUTHOR]

Introduction: EEG-based emotion recognition has gradually become a new research direction, known as affective Brain-Computer Interface (aBCI), which has huge application potential in human-computer interaction and neuroscience. However, how to extract spatio-temporal fusion features from complex EEG signals and build learning method with high recognition accuracy and strong interpretability is still challenging. Methods: In this paper, we propose a hybrid attention spatio-temporal feature fusion network for EEG-based emotion recognition. First, we designed a spatial attention feature extractor capable of merging shallow and deep features to extract spatial information and adaptively select crucial features under different emotional states. Then, the temporal feature extractor based on the multi-head attention mechanism is integrated to perform spatio-temporal feature fusion to achieve emotion recognition. Finally, we visualize the extracted spatial attention features using feature maps, further analyzing key channels corresponding to different emotions and subjects. Results: Our method outperforms the current state-of-the-art methods on two public datasets, SEED and DEAP. The recognition accuracy are 99.12% ± 1.25% (SEED), 98.93% ± 1.45% (DEAP-arousal), and 98.57% ± 2.60% (DEAP-valence). We also conduct ablation experiments, using statistical methods to analyze the impact of each module on the final result. The spatial attention features reveal that emotion-related neural patterns indeed exist, which is consistent with conclusions in the field of neurology. Discussion: The experimental results show that our method can effectively extract and fuse spatial and temporal information. It has excellent recognition performance, and also possesses strong robustness, performing stably across different datasets and experimental environments for emotion recognition. [ABSTRACT FROM AUTHOR]

Deep Learning (DL) has the potential to enhance patient outcomes in healthcare by implementing proficient systems for disease detection and diagnosis. However, the complexity and lack of interpretability impede their widespread adoption in critical high-stakes predictions in healthcare. Incorporating uncertainty estimations in DL systems can increase trustworthiness, providing valuable insights into the model's confidence and improving the explanation of predictions. Additionally, introducing explainability measures, recognized and embraced by healthcare experts, can help address this challenge. In this study, we investigate DL models' ability to predict sex directly from electroencephalography (EEG) data. While sex prediction have limited direct clinical application, its binary nature makes it a valuable benchmark for optimizing deep learning techniques in EEG data analysis. Furthermore, we explore the use of DL ensembles to improve performance over single models and as an approach to increase interpretability and performance through uncertainty estimation. Lastly, we use a data-driven approach to evaluate the relationship between frequency bands and sex prediction, offering insights into their relative importance. InceptionNetwork, a single DL model, achieved 90.7% accuracy and an AUC of 0.947, and the best-performing ensemble, combining variations of InceptionNetwork and EEGNet, achieved 91.1% accuracy in predicting sex from EEG data using five-fold cross-validation. Uncertainty estimation through deep ensembles led to increased prediction performance, and the models were able to classify sex in all frequency bands, indicating sex-specific features across all bands. [ABSTRACT FROM AUTHOR]

During the last four decades there has been a significant growth of interest in mindfulness-based practices and their potential to foster improvements in health, wellbeing and human functioning in a variety of clinical and nonclinical populations. With this growth has come a renewed interest in understanding the psychological processes involved as well as the neuropsychological mechanisms by which such practices operate and effect transformative personal experiences and positive change. The current perspective paper (i) presents a basic taxonomy of meditation types and the structure and function of the processes believed to be involved, (ii) describes these components in terms of key neuroanatomical regions of interest, and (iii) critically appraises current findings regarding EEG measures as they relate to different aspects of meditation, functional activity and connectivity across regions of interest. The correlates between mindfulness and EEG are well described in terms of attentional and interoceptive processes and neuroanatomical regions of interest. To a lesser extent, there is also a growing understanding of such correlates for meditation techniques centred on compassion and loving-kindness meditation. However, the same does not apply to wisdom-based and null-state meditation practices where consistent associations between neuropsychological processes and EEG characteristics have proven elusive. These latter practices are viewed by many as key to fostering the deeper transformative experiences underlying psychological and spiritual development, and although studies of null-state meditation have yielded promising theoretical developments, more research is required. Future research could also benefit from better standardisation of EEG measures and analytic techniques to allow more robust metanalyses, and greater consistency of terminology regarding the fundamental components of meditation practice. [ABSTRACT FROM AUTHOR]

Introduction: The aim of the study was to assess the efficacy of In-Dex sedation in comparison to oral melatonin and hydroxyzine in individuals with Autism Spectrum Disorder (ASD) undergoing EEG recording and 15 determine which categories of patients exhibit the most favorable response to In-Dex sedation. Methods: This retrospective observational study involved pediatric patients with ASD who underwent sleep-EEG recording across two periods, before (biennium 2018-19) and after (biennium 2021-22) the routine implementation of In-Dex sedation. Clinical, EEG, and sedation data were stored in a database. A logistic multiple regression model was employed, with the failure of EEG serving as the dependent variable. Results: In the first period 203 EEGs were performed with a rate of failure of 10.8%, while in the second one 177 EEGs were recorded with a percentage of failure of 7.3% (8.3% with MH 23 sedation and 5.8% with In-Dex sedation). No significant adverse events were reported in either period. Multivariate logistic analysis demonstrated that In-Dex decreased the probability of failure (OR=0.25, 25 (0.61-0.88)), while the presence of behavioral disturbances (OR=3.65((1.54-8.85)) and the use of antipsychotic drugs (OR=2.76, (1.09-6.95)) increased it. Discussion: In the light of these results, we can state that In-Dex sedation is safe and reduce EEG failure rate compared to the use of melatonin and hydroxyzine alone, particularly in patients with severe behavioral issues. [ABSTRACT FROM AUTHOR]

Comprehending speech in noise (SiN) poses a challenge for older hearing-impaired listeners, requiring auditory and working memory resources. Visual speech cues provide additional sensory information supporting speech understanding, while the extent of such visual benefit is characterized by large variability, which might be accounted for by individual differences in working memory capacity (WMC). In the current study, we investigated behavioral and neurofunctional (i.e., neural speech tracking) correlates of auditory and audio-visual speech comprehension in babble noise and the associations with WMC. Healthy older adults with hearing impairment quantified by pure-tone hearing loss (threshold average: 31.85–57 dB, N = 67) listened to sentences in babble noise in audio-only, visual-only and audio-visual speech modality and performed a pattern matching and a comprehension task, while electroencephalography (EEG) was recorded. Behaviorally, no significant difference in task performance was observed across modalities. However, we did find a significant association between individual working memory capacity and task performance, suggesting a more complex interplay between audio-visual speech cues, working memory capacity and real-world listening tasks. Furthermore, we found that the visual speech presentation was accompanied by increased cortical tracking of the speech envelope, particularly in a right-hemispheric auditory topographical cluster. Post-hoc, we investigated the potential relationships between the behavioral performance and neural speech tracking but were not able to establish a significant association. Overall, our results show an increase in neurofunctional correlates of speech associated with congruent visual speech cues, specifically in a right auditory cluster, suggesting multisensory integration. [ABSTRACT FROM AUTHOR]

Introduction: Event-Related Potentials (ERPs) are valuable for studying brain activity withmillisecond-level temporal resolution. While the temporal resolution of this technique is excellent, the spatial resolution is limited. Source localization aims to identify the brain regions generating the EEG data, thus increasing the spatial resolution, but its accuracy depends heavily on the headmodel used. This study compares the performance of subject-specific and template-based head models in both simulated and real-world ERP localization tasks. Methods: Simulated data mimicking realistic ERPs was created to evaluate the impact of head model choice systematically, after which subject-specific and template-based head models were used for the reconstruction of the data. The different modeling approaches were also applied to a face recognition dataset. Results: The results indicate that the template models capture the simulated activity less accurately, producing more spurious sources and identifying less true sources correctly. Furthermore, the results show that while creating more accurate and detailed head models is beneficial for the localization accuracy when using subject-specific head models, this is less the case for template head models. The main N170 source of the face recognition dataset was correctly localized to the fusiformgyrus, a known face processing area, using the subject-specificmodels. Apart fromthe fusiformgyrus, the templatemodels also reconstructed several other sources, illustrating the localization inaccuracies. Discussion: While template models allow researchers to investigate the neural generators of ERP components when no subject-specific MRIs are available, it could lead to misinterpretations. Therefore, it is important to consider a priori knowledge and hypotheses when interpreting results obtained with template head models, acknowledging potential localization errors. [ABSTRACT FROM AUTHOR]

This brief report revisits the earliest known hyperscanning study published in 1965, which examined simultaneous EEG recordings of identical twins separated by six meters. The original study's findings suggested that eye closure in one twin elicited alpha activity in the other, despite physical separation. Leveraging contemporary signal processing techniques, we reanalyzed the digitized data to validate their findings. Spectral analysis confirmed alpha activity in the twins' EEG signals, aligning with the original observations. Multitapering along with background noise subtraction also revealed the alpha activity in the unrelated subject, which could not be observed by visual inspection alone. Coherence analysis revealed significant alpha band synchrony between a twin and an unrelated subject, differing from the initial study's conclusions. Our findings indicate that even historical data can yield new insights when revisited with contemporary data analysis tools and highlight the potential for future large-scale studies using advanced techniques to explore nonlocal brain interactions. [ABSTRACT FROM AUTHOR]

Computer-based learning has gained popularity in recent years, providing learners greater flexibility and freedom. However, these learning environments do not consider the learner's mental state in real-time, resulting in less optimized learning experiences. This research aimed to explore the effect on the learning experience of a novel EEG-based Brain-Computer Interface (BCI) that adjusts the speed of information presentation in real-time during a learning task according to the learner's cognitive load. We also explored how motivation moderated these effects. In accordance with three experimental groups (non-adaptive, adaptive, and adaptive with motivation), participants performed a calibration task (n-back), followed by a memory-based learning task concerning astrological constellations. Learning gains were assessed based on performance on the learning task. Self-perceived mental workload, cognitive absorption and satisfaction were assessed using a post-test questionnaire. Between-group analyses using Mann-Whitney tests suggested that combining BCI and motivational factors led to more significant learning gains and an improved learning experience. No significant difference existed between the BCI without motivational factor and regular non-adaptive interface for overall learning gains, self-perceived mental workload, and cognitive absorption. However, participants who undertook the experiment with an imposed learning pace reported higher overall satisfaction with their learning experience and a higher level of temporal stress. Our findings suggest BCI's potential applicability and feasibility in improving memorization-based learning experiences. Further work should seek to optimize the BCI adaptive index and explore generalizability to other learning contexts. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]