Electroencephalography Data Research Articles

Continuity with caveats in anesthesia: state and response entropy of the EEG

The growing use of neuromonitoring in general anesthesia provides detailed insights into the effects of anesthetics on the brain. Our study focuses on the processed EEG indices State Entropy (SE), Response Entropy (RE), and Burst Suppression Ratio (BSR) of the GE EntropyTM Module, which serve as surrogate measures for estimating the level of anesthesia. While retrospectively analyzing SE and RE index values from patient records, we encountered a technical anomaly with a conspicuous distribution of index values. In this single-center, retrospective study, we analyzed processed intraoperative electroencephalographic (EEG) data from 15,608 patients who underwent general anesthesia. We employed various data visualization techniques, including histograms and heat maps, and fitted custom non-Gaussian curves. Individual patients’ anesthetic periods were evaluated in detail. To compare distributions, we utilized the Kolmogorov–Smirnov test and Kullback–Leibler divergence. The analysis also included the influence of the BSR on the distribution of SE and RE values. We identified distinct pillar indices for both SE and RE, i.e., index values with a higher probability of occurrence than others. These pillar index values were not age-dependent and followed a non-equidistant distribution pattern. This phenomenon occurs independently of the BSR distribution. SE and RE index values do not adhere to a continuous distribution, instead displaying prominent pillar indices with a consistent pattern of occurrence across all age groups. The specific features of the underlying algorithm responsible for this pattern remain elusive.

Resting-state electroencephalography delta and theta bands as compensatory oscillations in chronic neuropathic pain: a secondary data analysis.

Chronic neuropathic pain (CNP) remains a significant clinical challenge, with complex neurophysiological underpinnings that are not fully understood. Identifying specific neural oscillatory patterns related to pain perception and interference can enhance our understanding and management of CNP. To analyze resting electroencephalography data from individuals with chronic neuropathic pain to explore the possible neural signatures associated with pain intensity, pain interference, and specific neuropathic pain characteristics. We conducted a secondary analysis from a cross-sectional study using electroencephalography data from a previous study, and Brief Pain Inventory from 36 patients with chronic neuropathic pain. For statistical analysis, we modeled a linear or logistic regression by dependent variable for each model. As independent variables, we used electroencephalography data with such brain oscillations: as delta, theta, alpha, and beta, as well as the oscillations low alpha, high alpha, low beta, and high beta, for the central, frontal, and parietal regions. All models tested for confounding factors such as age and medication. There were no significant models for Pain interference in general activity, walking, work, relationships, sleep, and enjoyment of life. However, the model for pain intensity during the past four weeks showed decreased alpha oscillations, and increased delta and theta oscillations were associated with decreased levels of pain, especially in the central area. In terms of pain interference in mood, the model showed high oscillatory Alpha signals in the frontal and central regions correlated with mood impairment due to pain. Our models confirm recent findings proposing that lower oscillatory frequencies, likely related to subcortical pain sources, may be associated with brain compensatory mechanisms and thus may be associated with decreased pain levels. On the other hand, higher frequencies, including alpha oscillations, may disrupt top-down compensatory mechanisms.

38 Impacts of Racial Discrimination on Cognitive and Affective Processes and Drug-Cue Reactivity

OBJECTIVES/GOALS: Our overarching aim is to examine, in an African American population, cognitive, affective, and neurophysiological processes, as well as risk-taking behavior, in response to racial stigma cues. While accounting for individual differences, we aim to see how these processes and drug-cue reactivity are impacted or altered by exposure to racial cues. METHODS/STUDY POPULATION: Participants will be African Americans between 18 and 25 years of age, equally distributed across genders. We will recruit 75 participants in order to have adequate power to conduct our intended analyses—particularly pertaining to individual differences in risk behavior outcomes. Participants will be asked to complete demographic and self-report questionnaires. Participants will also be asked to complete computerized tasks while their physiological responses (heart rate, skin conductance, and electroencephalographic (EEG) data) are recorded. The tasks are as follow: resting, gambling, go/no-go, picture viewing (positive, negative, and neutral images), and a drug cue image set. These tasks will be repeated after the participant views a racial stigma image set to evaluate the impact of discrimination. RESULTS/ANTICIPATED RESULTS: Data from 18 participants has been collected. Data will be periodically preprocessed and validated (e.g., 1 participant was removed due to data recording errors, so the current valid Nis 17). Generally, we anticipate that behaviors and neural activity will be modulated across all tasks after viewing the racial stigma image set. Specifically, (a) cognitive and affective processing of singular events of racial stigma may indicate a stress response, (b) modulation from chronic experiences of racial stigma render neural systems increasingly sensitive to stigma cues, and thereby less equipped to regulate stress response, (c) the impact of these processes on altering risk behavior (may increase such behaviors), and (d) the impact of these modulations on altering drug-cue reactivity (may amplify reactivity). DISCUSSION/SIGNIFICANCE: The study will identify factors that contribute to stress and risk behavior among African Americans. A substantial gap continues to exist regarding the nature of risk behavior among African Americans, despite the fact that African Americans represent a health disparity population with unique vulnerabilities to health-relevant risk behavior.

Shallow Inception Domain Adaptation Network for EEG-Based Motor Imagery Classification

Electroencephalography (EEG) data across multiple individuals have a high variance. Directly using the data to train a deep learning model usually degrades the performance. To address this issue, we propose a shallow Inception domain adaptation framework to extract informative deep features from data of multiple subjects for accurate motor imagery (MI) recognition. To our best knowledge, the Inception architecture in deep learning is combined with domain adaptation (DA) scheme for the first time for the MI classification task. The approach contains two compact Inception blocks that decode temporal features in different scales. In addition, we jointly optimize a novel combined loss function to reduce both marginal and class conditional discrepancies caused by the multi-modal structure of EEG signals. The DA-based loss enables Inception blocks to take full advantage of their learning abilities to capture discriminative patterns of MI data from multiple subjects instead of relying on the target user only. To demonstrate the effectiveness of our approach, we conduct substantial experiments on two wellknown datasets, BCI competition IV-2a and IV-2b. Results show that our model achieves better performance than state-of-theart strategies. The proposed model is able to extract informative features from high-variant EEG data collected from different individuals and achieves accurate MI classifications.

Spatio-Temporal Image-Based Encoded Atlases for EEG Emotion Recognition.

Emotion recognition plays an essential role in human-human interaction since it is a key to understanding the emotional states and reactions of human beings when they are subject to events and engagements in everyday life. Moving towards human-computer interaction, the study of emotions becomes fundamental because it is at the basis of the design of advanced systems to support a broad spectrum of application areas, including forensic, rehabilitative, educational, and many others. An effective method for discriminating emotions is based on ElectroEncephaloGraphy (EEG) data analysis, which is used as input for classification systems. Collecting brain signals on several channels and for a wide range of emotions produces cumbersome datasets that are hard to manage, transmit, and use in varied applications. In this context, the paper introduces the Empátheia system, which explores a different EEG representation by encoding EEG signals into images prior to their classification. In particular, the proposed system extracts spatio-temporal image encodings, or atlases, from EEG data through the Processing and transfeR of Interaction States and Mappings through Image-based eNcoding (PRISMIN) framework, thus obtaining a compact representation of the input signals. The atlases are then classified through the Empátheia architecture, which comprises branches based on convolutional, recurrent, and transformer models designed and tuned to capture the spatial and temporal aspects of emotions. Extensive experiments were conducted on the Shanghai Jiao Tong University (SJTU) Emotion EEG Dataset (SEED) public dataset, where the proposed system significantly reduced its size while retaining high performance. The results obtained highlight the effectiveness of the proposed approach and suggest new avenues for data representation in emotion recognition from EEG signals.

Does artistic training affect color perception? A study of ERPs and EROs in experiencing colors of different brightness

Color is a visual cue that can convey emotions and attract attention, and there is no doubt that brightness is an important element of color differentiation. To examine the impact of art training on color perception, 44 participants were assigned to two groups—one for those with and one for those without art training—in an EEG experiment. While the participants had their electroencephalographic data recorded, they scored their emotional responses to color stimuli of different brightness levels based on the Munsell color system. The behavioral results revealed that in both groups, high-brightness colors were rated more positively than low-brightness colors. Furthermore, event-related potential results for the artist group showed that high-brightness colors enhanced P2 and P3 amplitudes. Moreover, non-artists had longer N2 latency than artists, and there was a significant Group × Brightness interaction separately for the N2 and P3 components. Simple effect analysis showed that N2 and P3 amplitudes were substantially higher for high-brightness stimuli than for lower-brightness stimuli in the artistic group, but this was not the case in the non-artist group. Additionally, evoked event-related oscillation results showed that in both groups, high-brightness stimuli also elicited large delta, theta, and alpha as well as low gamma responses. These results indicate that high-brightness color stimuli elicit more positive emotions and stronger neurological reactions and that artistic training may have a positive effect on top-down visual perception.

An EEG Dataset of Subject Pairs during Collaboration and Competition Tasks in Face-to-Face and Online Modalities

This dataset was acquired during collaboration and competition tasks performed by sixteen subject pairs (N = 32) of one female and one male under different (face-to-face and online) modalities. The collaborative task corresponds to cooperating to put together a 100-piece puzzle, while the competition task refers to playing against each other in a one-on-one classic 28-piece dominoes game. In the face-to-face modality, all interactions between the pair occurred in person. On the other hand, in the online modality, participants were physically separated, and interaction was only allowed through Zoom software with an active microphone and camera. Electroencephalography data of the two subjects were acquired simultaneously while performing the tasks. This article describes the experimental setup, the process of the data streams acquired during the tasks, and the assessment of data quality.

Individual-finger motor imagery classification: a data-driven approach with Shapley-informed augmentation

Objective. Classifying motor imagery (MI) tasks that involve fine motor control of the individual five fingers presents unique challenges when utilizing electroencephalography (EEG) data. In this paper, we systematically assess the classification of MI functions for the individual five fingers using single-trial time-domain EEG signals. This assessment encompasses both within-subject and cross-subject scenarios, supported by data-driven analysis that provides statistical validation of the neural correlate that could potentially discriminate between the five fingers. Approach. We present Shapley-informed augmentation, an informed approach to enhance within-subject classification accuracy. This method is rooted in insights gained from our data-driven analysis, which revealed inconsistent temporal features encoding the five fingers MI across sessions of the same subject. To evaluate its impact, we compare within-subject classification performance both before and after implementing this augmentation technique. Main results. Both the data-driven approach and the model explainability analysis revealed that the parietal cortex contains neural information that helps discriminate the individual five fingers’ MI apart. Shapley-informed augmentation successfully improved classification accuracy in sessions severely affected by inconsistent temporal features. The accuracy for sessions impacted by inconsistency in their temporal features increased by an average of 26.3%±6.70 , thereby enabling a broader range of subjects to benefit from brain–computer interaction (BCI) applications involving five-fingers MI classification. Conversely, non-impacted sessions experienced only a negligible average accuracy decrease of 2.01±5.44% . The average classification accuracy achieved is around 60.0% (within-session), 50.0% (within-subject) and 40.0% (leave-one-subject-out). Significance. This research offers data-driven evidence of neural correlates that could discriminate between the individual five fingers MI and introduces a novel Shapley-informed augmentation method to address temporal variability of features, ultimately contributing to the development of personalized systems.

Effects of non-invasive cervical spinal cord neuromodulation by trans-spinal electrical stimulation on cortico-muscular descending patterns in upper extremity of chronic stroke.

Background: Trans-spinal electrical stimulation (tsES) to the intact spinal cord poststroke may modulate the cortico-muscular control in stroke survivors with diverse lesions in the brain. This work aimed to investigate the immediate effects of tsES on the cortico-muscular descending patterns during voluntary upper extremity (UE) muscle contractions by analyzing cortico-muscular coherence (CMCoh) and electromyography (EMG) in people with chronic stroke. Methods: Twelve chronic stroke participants were recruited to perform wrist-hand extension and flexion tasks at submaximal levels of voluntary contraction for the corresponding agonist flexors and extensors. During the tasks, the tsES was delivered to the cervical spinal cord with rectangular biphasic pulses. Electroencephalography (EEG) data were collected from the sensorimotor cortex, and the EMG data were recorded from both distal and proximal UE muscles. The CMCoh, laterality index (LI) of the peak CMCoh, and EMG activation level parameters under both non-tsES and tsES conditions were compared to evaluate the immediate effects of tsES on the cortico-muscular descending pathway. Results: The CMCoh and LI of peak CMCoh in the agonist distal muscles showed significant increases (p < 0.05) during the wrist-hand extension and flexion tasks with the application of tsES. The EMG activation levels of the antagonist distal muscle during wrist-hand extension were significantly decreased (p < 0.05) with tsES. Additionally, the proximal UE muscles exhibited significant decreases (p < 0.05) in peak CMCoh and EMG activation levels by applying tsES. There was a significant increase (p < 0.05) in LI of peak CMCoh of proximal UE muscles during tsES. Conclusion: The cervical spinal cord neuromodulation via tsES enhanced the residual descending excitatory control, activated the local inhibitory circuits within the spinal cord, and reduced the cortical and proximal muscular compensatory effects. These results suggested the potential of tsES as a supplementary input for improving UE motor functions in stroke rehabilitation.

Reconstructing human gaze behavior from EEG using inverse reinforcement learning

Decoding eye movements from non-invasive electroencephalography (EEG) data is a challenging yet vital task for both scientific and practical purposes, especially for identifying neurodegenerative disorders like Alzheimer’s disease (AD). Our research tackles this complexity by adapting inverse reinforcement learning (IRL), a machine learning method, to infer decision-making strategies from observed behaviors. We implement this to understand the processes driving eye direction and movements during diverse cognitive tasks, providing new insights into this field. Our paper begins with a detailed description of the procedures for collecting and preprocessing EEG data related to gaze behavior. We then elaborate on the development of an IRL framework designed to predict the spatial and temporal dynamics of eye movements (scanpaths) in participants engaged in cognitive tasks of varying complexity. Our model is tailored to accommodate the complexities inherent in neural signals and the stochastic nature of human gaze patterns. Our research findings underscore IRL’s effectiveness in precisely forecasting gaze patterns based on a combination of EEG and image data. The correlation between the model’s predictions and the actual gaze behavior observed in controlled experiments reinforces the utility of IRL in cognitive neuroscience research. Notably, our IRL-EEG models demonstrated superior performance, especially in more complex cognitive tasks. We further delve into the implications of our results for enhancing the understanding of neural mechanisms that govern gaze behavior.

Connecting the dots: Exploring brain connectivity during responsibility recognition in construction contract negotiations

Despite recent advancements in monitoring brain activity, causal relationships within the brain during responsibility identification in construction contracts remain unexplored. We aimed to understand the neural mechanisms involved in the cognitive components and their interactions related to contract text reading by delving into the brain mechanisms of contract responsibility identification. This study investigated students’ brain connectivity using electroencephalography (EEG) data during a text-based contract responsibility-identification task. It employed an adaptive directed transfer function based on Granger causality to simulate directed and time-varying information flow in observed brain activity. We evaluated the EEG records of 18 participants under two reading conditions (involving or not involving contractor responsibility). During responsibility identification, the most substantial information exchange occurs in the somatosensory area of the brain. The results revealed a “top-down” cortical mechanism for responsibility identification, with the left parietal-occipital area (PO3) as the central hub promoting connectivity structures. These findings indicate that the perceptual processing of contract responsibility texts is associated with higher visual learning and memory quality. Contracts without contractor-responsibility clauses resulted in more substantial information flow output in the frontal cortex and consumed more cognitive resources. Our findings advance the understanding of cognitive processes involved in contract responsibility identification, providing a framework for investigating causal relationships within the brain and novel insights into cortical mechanisms. By identifying the neural basis of responsibility identification, stakeholders can develop effective training programs for negotiators and enhance their ability to interpret and implement construction contracts.

Multi-Algorithm Artifact Correction (MAAC) procedure part one: Algorithm and example

The Multi-Algorithm Artifact Correction (MAAC) procedure is presented for electroencephalographic (EEG) data, as made freely available in the open-source EP Toolkit (Dien, 2010). First the major EEG artifact correction methods (regression, spatial filters, principal components analysis, and independent components analysis) are reviewed. Contrary to the dominant approach of picking one method that is thought to be most effective, this review concludes that none are globally superior, but rather each has strengths and weaknesses. Then each of the major artifact types are reviewed (Blink, Corneo-Retinal Dipole, Saccadic Spike Potential, and Movement). For each one, it is proposed that one of the major correction methods is best matched to address it, resulting in the MAAC procedure. The MAAC itself is then presented, as implemented in the EP Toolkit, in order to provide a sense of the user experience. The primary goal of this present paper is to make the conceptual argument for the MAAC approach.

Improved emotion differentiation under reduced acoustic variability of speech in autism

BackgroundSocio-emotional impairments are among the diagnostic criteria for autism spectrum disorder (ASD), but the actual knowledge has substantiated both altered and intact emotional prosodies recognition. Here, a Bayesian framework of perception is considered suggesting that the oversampling of sensory evidence would impair perception within highly variable environments. However, reliable hierarchical structures for spectral and temporal cues would foster emotion discrimination by autistics.MethodsEvent-related spectral perturbations (ERSP) extracted from electroencephalographic (EEG) data indexed the perception of anger, disgust, fear, happiness, neutral, and sadness prosodies while listening to speech uttered by (a) human or (b) synthesized voices characterized by reduced volatility and variability of acoustic environments. The assessment of mechanisms for perception was extended to the visual domain by analyzing the behavioral accuracy within a non-social task in which dynamics of precision weighting between bottom-up evidence and top-down inferences were emphasized. Eighty children (mean 9.7 years old; standard deviation 1.8) volunteered including 40 autistics. The symptomatology was assessed at the time of the study via the Autism Diagnostic Observation Schedule, Second Edition, and parents’ responses on the Autism Spectrum Rating Scales. A mixed within-between analysis of variance was conducted to assess the effects of group (autism versus typical development), voice, emotions, and interaction between factors. A Bayesian analysis was implemented to quantify the evidence in favor of the null hypothesis in case of non-significance. Post hoc comparisons were corrected for multiple testing.ResultsAutistic children presented impaired emotion differentiation while listening to speech uttered by human voices, which was improved when the acoustic volatility and variability of voices were reduced. Divergent neural patterns were observed from neurotypicals to autistics, emphasizing different mechanisms for perception. Accordingly, behavioral measurements on the visual task were consistent with the over-precision ascribed to the environmental variability (sensory processing) that weakened performance. Unlike autistic children, neurotypicals could differentiate emotions induced by all voices.ConclusionsThis study outlines behavioral and neurophysiological mechanisms that underpin responses to sensory variability. Neurobiological insights into the processing of emotional prosodies emphasized the potential of acoustically modified emotional prosodies to improve emotion differentiation by autistics.Trial registrationBioMed Central ISRCTN Registry, ISRCTN18117434. Registered on September 20, 2020.

Combining detrended cross-correlation analysis with Riemannian geometry-based classification for improved brain-computer interface performance.

Riemannian geometry-based classification (RGBC) gained popularity in the field of brain-computer interfaces (BCIs) lately, due to its ability to deal with non-stationarities arising in electroencephalography (EEG) data. Domain adaptation, however, is most often performed on sample covariance matrices (SCMs) obtained from EEG data, and thus might not fully account for components affecting covariance estimation itself, such as regional trends. Detrended cross-correlation analysis (DCCA) can be utilized to estimate the covariance structure of such signals, yet it is computationally expensive in its original form. A recently proposed online implementation of DCCA, however, allows for its fast computation and thus makes it possible to employ DCCA in real-time applications. In this study we propose to replace the SCM with the DCCA matrix as input to RGBC and assess its effect on offline and online BCI performance. First we evaluated the proposed decoding pipeline offline on previously recorded EEG data from 18 individuals performing left and right hand motor imagery (MI), and benchmarked it against vanilla RGBC and popular MI-detection approaches. Subsequently, we recruited eight participants (with previous BCI experience) who operated an MI-based BCI (MI-BCI) online using the DCCA-enhanced Riemannian decoder. Finally, we tested the proposed method on a public, multi-class MI-BCI dataset. During offline evaluations the DCCA-based decoder consistently and significantly outperformed the other approaches. Online evaluation confirmed that the DCCA matrix could be computed in real-time even for 22-channel EEG, as well as subjects could control the MI-BCI with high command delivery (normalized Cohen's κ: 0.7409 ± 0.1515) and sample-wise MI detection (normalized Cohen's κ: 0.5200 ± 0.1610). Post-hoc analysis indicated characteristic connectivity patterns under both MI conditions, with stronger connectivity in the hemisphere contralateral to the MI task. Additionally, fractal scaling exponent of neural activity was found increased in the contralateral compared to the ipsilateral motor cortices (C4 and C3 for left and right MI, respectively) in both classes. Combining DCCA with Riemannian geometry-based decoding yields a robust and effective decoder, that not only improves upon the SCM-based approach but can also provide relevant information on the neurophysiological processes behind MI.

Previous motor task performance impacts phase-based EEG resting-state connectivity states

Abstract The resting human brain cycles through distinct states that can be analyzed using microstate analysis and electroencephalography (EEG) data. This approach classifies multichannel EEG data into spontaneously interchanging microstates based on topographic features. These microstates may be valuable biomarkers in neurodegenerative diseases since they reflect the resting brain’s state. However, microstates do not provide information about the active neural networks during the resting state. This article presents an alternative and complementary method for analyzing resting-state EEG data and demonstrates its reproducibility and reliability. This method considers cerebral connectivity states defined by phase synchronization and measured using the corrected imaginary phase-locking value (ciPLV) based on source-reconstructed EEG recordings. We analyzed resting-state EEG data from young, healthy participants acquired on five consecutive days before and after a motor task. We show that our data reproduce microstates previously reported. Further, we reveal four stable topographic patterns over the multiple recording sessions in the source connectivity space. While the classical microstates were unaffected by a preceding motor task, the connectivity states were altered, reflecting the suppression of frontal activity in the post-movement resting state.

Retrospective Multicenter Cohort Study on Safety and Electroencephalographic Response to Lacosamide for Neonatal Seizures

BackgroundThere is growing evidence supporting the safety and effectiveness of lacosamide in older children. However, minimal data are available for neonates. We aimed to determine the incidence of adverse events associated with lacosamide use and explore the electroencephalographic seizure response to lacosamide in neonates. MethodsA retrospective cohort study was conducted using data from seven pediatric hospitals from January 2009 to February 2020. For safety outcomes, neonates were followed for ≤30 days from index date. Electroencephalographic response of lacosamide was evaluated based on electroencephalographic reports for ≤3 days. ResultsAmong 47 neonates, 98% received the first lacosamide dose in the intensive care units. During the median follow-up of 12 days, 19% of neonates died, and the crude incidence rate per 1000 patient-days (95% confidence interval) of the adverse events by diagnostic categories ranged from 2.8 (0.3, 10.2) for blood or lymphatic system disorders and nervous system disorders to 10.5 (4.2, 21.6) for cardiac disorders. Electroencephalographic seizures were observed in 31 of 34 patients with available electroencephalographic data on the index date. There was seizure improvement in 29% of neonates on day 1 and also in 29% of neonates on day 2. On day 3, there was no change in 50% of neonates and unknown change in 50% of neonates. ConclusionsThe results are reassuring regarding the safety of lacosamide in neonates. Although some neonates had fewer seizures after lacosamide administration, the lack of a comparator arm and reliance on qualitative statements in electroencephalographic reports limit the preliminary efficacy results.

Neural attentional filters and behavioural outcome follow independent individual trajectories over the adult lifespan.

Preserved communication abilities promote healthy ageing. To this end, the age-typical loss of sensory acuity might in part be compensated for by an individual's preserved attentional neural filtering. Is such a compensatory brain-behaviour link longitudinally stable? Can it predict individual change in listening behaviour? We here show that individual listening behaviour and neural filtering ability follow largely independent developmental trajectories modelling electroencephalographic and behavioural data of N = 105 ageing individuals (39-82 y). First, despite the expected decline in hearing-threshold-derived sensory acuity, listening-task performance proved stable over 2 y. Second, neural filtering and behaviour were correlated only within each separate measurement timepoint (T1, T2). Longitudinally, however, our results raise caution on attention-guided neural filtering metrics as predictors of individual trajectories in listening behaviour: neither neural filtering at T1 nor its 2-year change could predict individual 2-year behavioural change, under a combination of modelling strategies.

Neural attentional filters and behavioural outcome follow independent individual trajectories over the adult lifespan

Preserved communication abilities promote healthy ageing. To this end, the age-typical loss of sensory acuity might in part be compensated for by an individual’s preserved attentional neural filtering. Is such a compensatory brain–behaviour link longitudinally stable? Can it predict individual change in listening behaviour? We here show that individual listening behaviour and neural filtering ability follow largely independent developmental trajectories modelling electroencephalographic and behavioural data of N = 105 ageing individuals (39–82 y). First, despite the expected decline in hearing-threshold-derived sensory acuity, listening-task performance proved stable over 2 y. Second, neural filtering and behaviour were correlated only within each separate measurement timepoint (T1, T2). Longitudinally, however, our results raise caution on attention-guided neural filtering metrics as predictors of individual trajectories in listening behaviour: neither neural filtering at T1 nor its 2-year change could predict individual 2-year behavioural change, under a combination of modelling strategies.

Developmental outcome of electroencephalographic findings in SYNGAP1 encephalopathy.

SYNGAP1 haploinsufficiency results in a developmental and epileptic encephalopathy (DEE) causing generalized epilepsies accompanied by a spectrum of neurodevelopmental symptoms. Concerning interictal epileptiform discharges (IEDs) in electroencephalograms (EEG), potential biomarkers have been postulated, including changes in background activity, fixation-off sensitivity (FOS) or eye closure sensitivity (ECS). In this study we clinically evaluate a new cohort of 36 SYNGAP1-DEE individuals. Standardized questionnaires were employed to collect clinical, electroencephalographic and genetic data. We investigated electroencephalographic findings, focusing on the cortical distribution of interictal abnormalities and their changes with age. Among the 36 SYNGAP1-DEE cases 18 presented variants in the SYNGAP1 gene that had never been previously reported. The mean age of diagnosis was 8 years and 8 months, ranging from 2 to 17years, with 55.9% being male. All subjects had global neurodevelopmental/language delay and behavioral abnormalities; 83.3% had moderate to profound intellectual disability (ID), 91.7% displayed autistic traits, 73% experienced sleep disorders and 86.1% suffered from epileptic seizures, mainly eyelid myoclonia with absences (55.3%). A total of 63 VEEGs were revised, observing a worsening of certain EEG findings with increasing age. A disorganized background was observed in all age ranges, yet this was more common among older cases. The main IEDs were bilateral synchronous and asynchronous posterior discharges, accounting for ≥50% in all age ranges. Generalized alterations with maximum amplitude in the anterior region showed as the second most frequent IED (≥15% in all age ranges) and were also more common with increasing age. Finally, diffuse fast activity was much more prevalent in cases with 6years or older. To the best of our knowledge, this is the first study to analyze EEG features across different age groups, revealing an increase in interictal abnormalities over infancy and adolescence. Our findings suggest that SYNGAP1 haploinsufficiency has complex effects in human brain development, some of which might unravel at different developmental stages. Furthermore, they highlight the potential of baseline EEG to identify candidate biomarkers and the importance of natural history studies to develop specialized therapies and clinical trials.

Five-week music therapy improves overall symptoms in schizophrenia by modulating theta and gamma oscillations.

Some clinical studies have shown that music therapy as an adjunctive therapy can improve overall symptoms in patients with schizophrenia. However, the neural mechanisms of this improvement remain unclear due to insufficient neuroimaging evidence. In this work, 17 patients with schizophrenia accepted a five-week music therapy (music group) that integrated listening, singing, and composing, and required patients to cooperate in a group to complete music therapy tasks. Meanwhile, 15 patients with schizophrenia received a five-week visual art intervention as the control group including handicraft and painting activities. We collected the Manchester Scale (MS) and Positive and Negative Symptom Scale (PANSS) scores and electroencephalography (EEG) data before and after intervention in two groups. Behavioral results showed that both interventions mentioned above can effectively help patients with schizophrenia relieve their overall symptoms, while a trend-level effect was observed in favor of music therapy. The EEG results indicated that music therapy can improve abnormal neural oscillations in schizophrenia which is reflected by a decrease in theta oscillation in the parietal lobe and an increase in gamma oscillation in the prefrontal lobe. In addition, correlation analysis showed that in the music group, both reductions in theta oscillations in the parietal lobe and increases in gamma oscillations in the prefrontal lobe were positively correlated with the improvement of overall symptoms. These findings help us to better understand the neural mechanisms by which music therapy improves overall symptoms in schizophrenia and provide more evidence for the application of music therapy in other psychiatric disorders.