Changes in the Objective Measures of Sleep in Association with Menses Among Female Athletes with Poor Subjective Sleep Quality: Female Athletes with Poor Subjective Sleep Quality Have More Sleep Arousals During Menses

Purpose

Female athletes with menstrual abnormalities have poor sleep quality. However, whether female athletes with poor sleep quality based on subjective assessment have distinctive changes in objective measures of sleep in association with menses remains unclear. This study aimed to compare changes in objective sleep measurements during and following menses between collegiate female athletes with and without poor subjective sleep quality.

Patients and Methods

Female collegiate athletes (age range/mean ± standard deviation: 18-22/ 22.2±1.1) with regular menstrual cycles were recruited. The participants underwent home electroencephalogram monitoring during the first and second nights after the onset of menses and one night between the seventh and 10th nights after menses onset (mid-follicular phase). The Pittsburgh Sleep Quality Index (PSQI) was used to assess the subjective sleep quality. Interactions between the presence of poor subjective sleep quality (ie, PSQI ≥6) and changes in objective measures of sleep in association with menses were analyzed.

Results

Data of 45 athletes, including 13 with poor subjective sleep quality, showed that changes in arousal index in athletes with poor subjective sleep quality were distinctive from those in athletes without poor subjective sleep quality (p = 0.036 for interaction). In athletes with poor subjective sleep quality, the arousal index was significantly increased in menses (p for analysis of variance, 0.015), especially on the first night after the onset of menses compared with during the mid-follicular phase (p = 0.016).

Conclusion

Collegiate female athletes with regular menstrual cycles are likely to have poor subjective sleep quality in association with more frequent arousal during the first night after the onset of menses than during the mid-follicular phase.

Distinct lateral hypothalamic CaMKIIα neuronal populations regulate wakefulness and locomotor activity

For nearly a century, evidence has accumulated indicating that the lateral hypothalamus (LH) contains neurons essential to sustain wakefulness. While lesion or inactivation of LH neurons produces a profound increase in sleep, stimulation of inhibitory LH neurons promotes wakefulness. To date, the primary wake-promoting cells that have been identified in the LH are the hypocretin/orexin (Hcrt) neurons, yet these neurons have little impact on total sleep or wake duration across the 24-h period. Recently, we and others have identified other LH populations that increase wakefulness. In the present study, we conducted microendoscopic calcium imaging in the LH concomitant with EEG and locomotor activity (LMA) recordings and found that a subset of LH neurons that express Ca2+/calmodulin-dependent protein kinase IIα (CaMKIIα) are preferentially active during wakefulness. Chemogenetic activation of these neurons induced sustained wakefulness and greatly increased LMA even in the absence of Hcrt signaling. Few LH CaMKIIα-expressing neurons are hypocretinergic or histaminergic while a small but significant proportion are GABAergic. Ablation of LH inhibitory neurons followed by activation of the remaining LH CaMKIIα neurons induced similar levels of wakefulness but blunted the LMA increase. Ablated animals showed no significant changes in sleep architecture but both spontaneous LMA and high theta (8 to 10 Hz) power during wakefulness were reduced. Together, these findings indicate the existence of two subpopulations of LH CaMKIIα neurons: an inhibitory population that promotes locomotion without affecting sleep architecture and an excitatory population that promotes prolonged wakefulness even in the absence of Hcrt signaling.

Evoked oscillatory cortical activity during acute pain: Probing brain in pain by transcranial magnetic stimulation combined with electroencephalogram

Temporal dynamics of local cortical rhythms during acute pain remain largely unknown. The current study used a novel approach based on transcranial magnetic stimulation combined with electroencephalogram (TMS-EEG) to investigate evoked-oscillatory cortical activity during acute pain. Motor (M1) and dorsolateral prefrontal cortex (DLPFC) were probed by TMS, respectively, to record oscillatory power (event-related spectral perturbation and relative spectral power) and phase synchronization (inter-trial coherence) by 63 EEG channels during experimentally induced acute heat pain in 24 healthy participants. TMS-EEG was recorded before, during, and after noxious heat (acute pain condition) and non-noxious warm (Control condition), delivered in a randomized sequence. The main frequency bands (α, β1, and β2) of TMS-evoked potentials after M1 and DLPFC stimulation were recorded close to the TMS coil and remotely. Cold and heat pain thresholds were measured before TMS-EEG. Over M1, acute pain decreased α-band oscillatory power locally and α-band phase synchronization remotely in parietal-occipital clusters compared with non-noxious warm (all p < .05). The remote (parietal-occipital) decrease in α-band phase synchronization during acute pain correlated with the cold (p = .001) and heat pain thresholds (p = .023) and to local (M1) α-band oscillatory power decrease (p = .024). Over DLPFC, acute pain only decreased β1-band power locally compared with non-noxious warm (p = .015). Thus, evoked-oscillatory cortical activity to M1 stimulation is reduced by acute pain in central and parietal-occipital regions and correlated with pain sensitivity, in contrast to DLPFC, which had only local effects. This finding expands the significance of α and β band oscillations and may have relevance for pain therapies.

Instantaneous estimation of momentary affective responses using neurophysiological signals and a spatiotemporal emotional intensity regression network

Previous studies in affective computing often use a fixed emotional label to train an emotion classifier with electroencephalography (EEG) from individuals experiencing an affective stimulus. However, EEGs encode emotional dynamics that include varying intensities within a given emotional category. To investigate these variations in emotional intensity, we propose a framework that obtains momentary affective labels for fine-grained segments of EEGs with human feedback. We then model these labeled segments using a novel spatiotemporal emotional intensity regression network (STEIR-Net). It integrates temporal EEG patterns from nine predefined cortical regions to provide a continuous estimation of emotional intensity. We demonstrate that the STEIR-Net outperforms classical regression models by reducing the root mean square error (RMSE) by an average of 4∼9 % and 2∼4 % for the SEED and SEED-IV databases, respectively. We find that the frontal and temporal cortical regions contribute significantly to the affective intensity's variation. Higher absolute values of the Spearman correlation coefficient between the model estimation and momentary affective labels under happiness (0.2114) and fear (0.2072) compared to neutral (0.1694) and sad (0.1895) emotions were observed. Besides, increasing the input length of the EEG segments from 4 to 20 s further reduces the RMSE from 1.3548 to 1.3188.

Sleep and the sleep electroencephalogram in C57BL/6 and C3H/HeN mice

Different mouse strains used in biomedical research show different phenotypes associated with their genotypes. Two mouse strains commonly used in biomedical sleep research are C57Bl/6 and C3H/He, the strains differ in numerous aspects, including their ability to secrete melatonin as well as the expression of several sleep-related genes. However, sleep regulation has only limitedly been compared between C3H/HeN and C57Bl/6 mice. We therefore compared sleep-wake behaviour and EEG-measured spectral brain activity for C57bl/6 and C3H/HeN mice during a 12:12 h light: dark baseline and during and after a 6 h sleep deprivation. The C3H mice spent more time in NREM sleep around the light-dark transition and more time in REM sleep during the dark phase compared with C57bl/6 mice. The C3H mice also showed more EEG activity in the 4.5-7.5 Hz range during all stages and a stronger 24 h modulation of EEG power density in almost all EEG frequencies during NREM sleep. After the sleep deprivation, C3H mice showed a stronger recovery response, which was expressed in both a larger increase in EEG slow wave activity (SWA) and more time spent in NREM sleep. We show large differences regarding sleep architecture and EEG activity between C3H and C57bl/6 mice. These differences include the amount of waking during the late dark phase, the 24 h amplitude in EEG power density, and the amount of REM sleep during the dark phase. We conclude that differences between mouse strains should be considered when selecting a model strain to improve the generalisability of studies investigating biomedical parameters related to sleep and circadian rhythms.

Multimodal non-invasive evaluation in MRI-negative epilepsy patients

Presurgical evaluation is still challenging for MRI-negative epilepsy patients. As non-invasive modalities are the easiest acceptable and economic methods in determining the epileptogenic zone, we analyzed the localization value of common non-invasive methods in MRI-negative epilepsy patients. In this study, we included epilepsy patients undergoing presurgical evaluation with presurgical negative MRI. MRI post-processing was performed using a Morphometric Analysis Program (MAP) on T1-weighted volumetric MRI. The relationship between MAP, magnetoencephalography (MEG), scalp electroencephalogram (EEG), and seizure outcomes was analyzed to figure out the localization value of different non-invasive methods. Eighty-six patients were included in this study. Complete resection of the MAP-positive regions or the MEG-positive regions was positively associated with seizure freedom (p = 0.028 and 0.007, respectively). When an area is co-localized by MAP and MEG, the resection of the area was significantly associated with seizure freedom (p = 0.006). However, neither the EEG lateralization nor the EEG localization showed statistical association with the surgical outcome (p = 0.683 and 0.505, respectively). In conclusion, scalp EEG had a limited role in presurgical localization and predicting seizure outcome, combining MAP and MEG results can significantly improve the localization of epileptogenic lesions and have a positive association with seizure-free outcome. PLAIN LANGUAGE SUMMARY: Due to the lack of obvious structure abnormalities on neuroimaging examinations, the identification of epilepsy lesions in MRI-negative epilepsy patients can be difficult. In this study, we intended to use non-invasive examinations to explore the potential epileptic lesions in MRI-negative epilepsy patients and to determine the results accuracy by comparing the neuroimaging results with the epilepsy surgery outcomes. A total of 86 epilepsy patients without obvious structure lesions on MRI were included, and we found that the combinations of different non-invasive examinations and neuroimaging post-processing methods are significantly associated with the seizure freedom results of epilepsy surgery.

Cerebral asymmetry representation learning-based deep subdomain adaptation network for electroencephalogram-based emotion recognition

Objective.Extracting discriminative spatial information from multiple electrodes is a crucial and challenging problem for electroencephalogram (EEG)-based emotion recognition. Additionally, the domain shift caused by the individual differences degrades the performance of cross-subject EEG classification.Approach.To deal with the above problems, we propose the cerebral asymmetry representation learning-based deep subdomain adaptation network (CARL-DSAN) to enhance cross-subject EEG-based emotion recognition. Specifically, the CARL module is inspired by the neuroscience findings that asymmetrical activations of the left and right brain hemispheres occur during cognitive and affective processes. In the CARL module, we introduce a novel two-step strategy for extracting discriminative features through intra-hemisphere spatial learning and asymmetry representation learning. Moreover, the transformer encoders within the CARL module can emphasize the contributive electrodes and electrode pairs. Subsequently, the DSAN module, known for its superior performance over global domain adaptation, is adopted to mitigate domain shift and further improve the cross-subject performance by aligning relevant subdomains that share the same class samples.Main Results.To validate the effectiveness of the CARL-DSAN, we conduct subject-independent experiments on the DEAP database, achieving accuracies of 68.67% and 67.11% for arousal and valence classification, respectively, and corresponding accuracies of 67.70% and 67.18% on the MAHNOB-HCI database.Significance.The results demonstrate that CARL-DSAN can achieve an outstanding cross-subject performance in both arousal and valence classification.

Abnormal theta and alpha oscillations in children and adolescents with first-episode psychosis and clinical high-risk psychosis

BACKGROUND

Cognitive control deficits are one of the main symptoms of psychosis. The basic neural oscillation patterns associated with cognitive control are already present in early adolescence. However, as previous studies have focused on adults with psychosis, it is unclear whether neurobiological impairments in cognitive control are present in children and adolescents with first-episode psychosis (FEP) or clinical high-risk (CHR) state for psychosis.

AIMS

To explore the deficits of electroencephalogram related to cognitive control tasks in children and adolescents with FEP and CHR.

METHOD

Electroencephalogram was recorded in untreated 48 patients with FEP, 24 patients with CHR and 42 healthy controls aged 10-17 years, while performing the visual oddball task. The N2 amplitude, theta and alpha oscillations were then analysed and compared between groups.

RESULTS

There was no significant group difference in N2 amplitude (P = 0.099). All groups showed increased theta and alpha oscillations relative to baseline before the stimulus in the frontal, central, left fronto-central and right fronto-central areas. These changes differed significantly between groups, with the FEP group showing significantly smaller theta (P < 0.001) and alpha (P < 0.01) oscillation than healthy controls. Theta and alpha oscillations in the CHR group did not differ significantly from the FEP group and healthy controls.

CONCLUSIONS

These results suggest that neural damage has already occurred in the early stage of psychosis, and that abnormal rhythmic activity of neurons may constitute the pathophysiological mechanism of cognitive dysfunction related to early-onset psychosis.

A Novel Framework for Epileptic Seizure Detection Using Electroencephalogram Signals Based on the Bat Feature Selection Algorithm

The precise electroencephalogram (EEG) signal classification with the highest possible accuracy is a key goal in the brain-computer interface (BCI). Considering the complexity and nonstationary nature of the EEG signals, there is an urgent need for effective feature extraction and data mining techniques. Here, we introduce a novel pipeline based on Bat and genetic algorithms for feature construction and dimension reduction of EEG signals. After wavelet extraction and segmentation, the Bat algorithm identifies the most relevant features. We use these features and a genetic algorithm combined with a neural network method to automatically classify the segments of the epilepsy EEG signals. We also use available classification methods based on k-Nearest Neighbors or naïve Bayes for comparison purposes. The code distinguishes individual signals within various combinations of data obtained from healthy volunteers with open or closed eyes and patients suffering from epilepsy disorders during seizure-free periods or seizure activities. Compared to the previously introduced methods, our proposed framework demonstrates a superior balance of high accuracy and short runtime. The minimum achieved accuracies for balanced and unbalanced classes are 100% and 75.9%, respectively. This approach has the potential for direct applications in clinics, enabling accurate and rapid analysis of the epilepsy EEG signals obtained from patients.

Factors Associated with Early and Late Seizure Related to Aneurysmal Subarachnoid Hemorrhage

Post-stroke epilepsy may occur after aneurysmal subarachnoid hemorrhage (aSAH). Both early and late seizures could cause severe neurocognitive deficits if administration of appropriate antiseizure medication is delayed. Therefore, it is important to elucidate the risk factors for early and late seizures, which could be shared with medical teams to promptly manage seizures. There are aspects of both hemorrhage and ischemia in aSAH, and thus, numerous risk factors are considered for early and late seizures. We examined factors associated with aSAH-related early and late seizures. Among 297 patients who had aSAH and underwent direct or endovascular surgery, 25 had early seizures and 20 had late seizures. Patients who did not experience any seizures in at least 2-years of follow-up (n = 81) were used as the control group. Early seizures were associated with older age and acute severe nonneurological infection, whereas late seizures were associated with intraparenchymal lesion volume >10 mL and shunt placement. In patients with late seizures, consistency was frequently observed between electroencephalogram and the presence of intraparenchymal lesions. The frontopolar electrode on electroencephalogram was highly sensitive to abnormality in early seizures. Early seizures were induced by the patient's systemic factors, which may lower the threshold for cortical excitability. Patients with intraparenchymal lesions who undergo shunt placement should be carefully followed up for late seizures.

A study on EEG differences between active counting and focused breathing tasks for more sensitive detection of consciousness

Introduction

In studies on consciousness detection for patients with disorders of consciousness, difference comparison of EEG responses based on active and passive task modes is difficult to sensitively detect patients' consciousness, while a single potential analysis of EEG responses cannot comprehensively and accurately determine patients' consciousness status. Therefore, in this paper, we designed a new consciousness detection paradigm based on a multi-stage cognitive task that could induce a series of event-related potentials and ERD/ERS phenomena reflecting different consciousness contents. A simple and direct task of paying attention to breathing was designed, and a comprehensive evaluation of consciousness level was conducted using multi-feature joint analysis.

Methods

We recorded the EEG responses of 20 healthy subjects in three modes and reported the consciousness-related mean event-related potential amplitude, ERD/ERS phenomena, and the classification accuracy, sensitivity, and specificity of the EEG responses under different conditions.

Results

The results showed that the EEG responses of the subjects under different conditions were significantly different in the time domain and time-frequency domain. Compared with the passive mode, the amplitudes of the event-related potentials in the breathing mode were further reduced, and the theta-ERS and alpha-ERD phenomena in the frontal region were further weakened. The breathing mode showed greater distinguishability from the active mode in machine learning-based classification.

Discussion

By analyzing multiple features of EEG responses in different modes and stimuli, it is expected to achieve more sensitive and accurate consciousness detection. This study can provide a new idea for the design of consciousness detection methods.

Effect of biofeedback intervention on neurological characteristics of children with attention deficit hyperactivity disorder

OBJECTIVE

To evaluate the effects of biofeedback intervention on the neurological characteristics of children with attention deficit hyperactivity disorder (ADHD).

METHODS

150 children aged 6-12 years with ADHD were divided into two groups according to the treatment method. The atomoxetine group received atomoxetine drug treatment, and the combined treatment group received EEG biofeedback therapy. Continuous performance test (CPT), SNAP-IV and WFIRS-P were used to assess attention and behavior, and functional magnetic resonance imaging (fMRI) was used to observe changes in brain activity.

RESULTS

The response time, error times, error response rate and attention fluctuation index of CPT in the combined treatment group were significantly lower than those in the atomoxetine group (p < 0.05), and the correct response rate was higher than that in the atomoxetine group (p < 0.05). After intervention, SNAP-IV and WFIRS-P scores in the combined treatment group were significantly lower than those in the atomoxetine group (p < 0.001). fMRI results showed that the activity of the prefrontal, parietal, amygdala and hippocampus in the combined treatment group was significantly higher than that in the atomoxetine group (p < 0.001).

CONCLUSION

Biofeedback intervention can significantly improve the attention and behavior of ADHD children and positively regulate the neural activity in related brain areas on the basis of drug treatment, suggesting that biofeedback therapy can be considered as a potential effective nondrug treatment option for ADHD children.

Research on Ocular Artifacts Removal from Single-Channel Electroencephalogram Signals in Obstructive Sleep Apnea Patients Based on Support Vector Machine, Improved Variational Mode Decomposition, and Second-Order Blind Identification

The electroencephalogram (EEG) has recently emerged as a pivotal tool in brain imaging analysis, playing a crucial role in accurately interpreting brain functions and states. To address the problem that the presence of ocular artifacts in the EEG signals of patients with obstructive sleep apnea syndrome (OSAS) severely affects the accuracy of sleep staging recognition, we propose a method that integrates a support vector machine (SVM) with genetic algorithm (GA)-optimized variational mode decomposition (VMD) and second-order blind identification (SOBI) for the removal of ocular artifacts from single-channel EEG signals. The SVM is utilized to identify artifact-contaminated segments within preprocessed single-channel EEG signals. Subsequently, these signals are decomposed into variational modal components across different frequency bands using the GA-optimized VMD algorithm. These components undergo further decomposition via the SOBI algorithm, followed by the computation of their approximate entropy. An approximate entropy threshold is set to identify and remove components laden with ocular artifacts. Finally, the signal is reconstructed using the inverse SOBI and VMD algorithms. To validate the efficacy of our proposed method, we conducted experiments utilizing both simulated data and real OSAS sleep EEG data. The experimental results demonstrate that our algorithm not only effectively mitigates the presence of ocular artifacts but also minimizes EEG signal distortion, thereby enhancing the precision of sleep staging recognition based on the EEG signals of OSAS patients.

14 and 6 Hz like spike wave activity is a common finding in in young patients with Prader-Willi syndrome

STUDY OBJECTIVES

Our aim was to characterize the 14 and 6 like spike wave activity seen on electroencephalogram (EEG) in children with Prader-Willi syndrome (PWS) undergoing polysomnogram (PSG).

METHODS

We performed a retrospective review of children with PWS and healthy controls who underwent diagnostic PSGs between January 1, 2007 to December 31, 2020 at SickKids, Toronto, Canada. EEGs from the PSGs were reviewed for the presence of the 14 and 6 like spike wave activity and its characteristics. Clinical correlation of the EEG variant with sleep disordered breathing indices from the PSG was also evaluated.

RESULTS

94 children with PWS and 50 healthy controls were included. The age, median (IQR) for the cohort was 1.42 (0.6, 4.2) years. There were 50 (53.2%) males in the PWS cohort. The EEG variant prevalence in this cohort was 51.0% (n=48) in children with PWS and 0% for the healthy controls. 14 and 6 Hz like spike wave activity was bilateral in 52% (25/48) children with PWS. The waves had a negative deflection in almost all patients 44/48 (92%) with PWS. It was predominantly located in the frontal leads for children with PWS, 23/48 (47.9%). It most frequently occurred during NREM stage 2 sleep for children with PWS, 25/48 (52.0%). The mean (SD) frequency was 6.8 (0.97) Hz. The median (IQR) length of the waves was 1.1 (0.8, 1.4) seconds in children with PWS. There was no correlation between the presence of the EEG variant and sleep disordered breathing indices in children with PWS.

CONCLUSIONS

14 and 6 Hz like spike wave activity EEG variant was present in more than 50% of a pediatric cohort of PWS as compared to 0% in healthy children. This EEG variant did not appear to be associated with sleep disordered breathing indices in children with PWS and is of unknown clinical significance.

Dissociations between spontaneous electroencephalographic features and the perturbational complexity index in the minimally conscious state

The analysis of spontaneous electroencephalogram (EEG) is a cornerstone in the assessment of patients with disorders of consciousness (DoC). Although preserved EEG patterns are highly suggestive of consciousness even in unresponsive patients, moderately or severely abnormal patterns are difficult to interpret. Indeed, growing evidence shows that consciousness can be present despite either large delta or reduced alpha activity in spontaneous EEG. Quantifying the complexity of EEG responses to direct cortical perturbations (perturbational complexity index [PCI]) may complement the observational approach and provide a reliable assessment of consciousness even when spontaneous EEG features are inconclusive. To seek empirical evidence of this hypothesis, we compared PCI with EEG spectral measures in the same population of minimally conscious state (MCS) patients (n = 40) hospitalized in rehabilitation facilities. We found a remarkable variability in spontaneous EEG features across MCS patients as compared with healthy controls: in particular, a pattern of predominant delta and highly reduced alpha power-more often observed in vegetative state/unresponsive wakefulness syndrome (VS/UWS) patients-was found in a non-negligible number of MCS patients. Conversely, PCI values invariably fell above an externally validated empirical cutoff for consciousness in all MCS patients, consistent with the presence of clearly discernible, albeit fleeting, behavioural signs of awareness. These results confirm that, in some MCS patients, spontaneous EEG rhythms may be inconclusive about the actual capacity for consciousness and suggest that a perturbational approach can effectively compensate for this pitfall with practical implications for the individual patient's stratification and tailored rehabilitation.

The Prevalence and Risk Factors of Electrical Status Epilepticus During Slow-Wave Sleep in Self-Limited Epilepsy With Centrotemporal Spikes

Objective. To investigate the prevalence and risk factors for electrical status epilepticus during slow-wave sleep (ESES) in patients with self-limited epilepsy with centrotemporal spikes (SeLECTS). Methods. The clinical and follow-up data of children with SeLECTS were collected between 2017 and 2021. Patients were divided into typical ESES, atypical ESES, and non-ESES groups according to spike-wave indices (SWI). Clinical and electroencephalography characteristics were retrospectively analyzed. Logistic regression was used to identify risk factors for ESES. Results. A total of 95 patients with SeLECTS were enrolled. Seven patients (7.4%) developed typical ESES, 30 (31.6%) developed atypical ESES, 25 (26.3%) developed ESES at the first visit, and 12 (12.6%) developed ESES during treatment and follow-up. Multivariate logistic regression analysis showed that the risk factors for SeLECTS combined with ESES were Rolandic double or multiple spikes (OR = 8.626, 95% CI: 2.644-28.147, P < .001) and Rolandic slow waves (OR = 53.550, 95% CI: 6.339-452.368, P < .001). There were no significant differences in seizure characteristics, electroencephalogram (EEG) findings, or cognitive impairment between the atypical and typical ESES groups. Conclusion. More than one-third of the SeLECTS patients combined with ESES. Both atypical and typical ESES scores can affect cognitive function. On electroencephalography, interictal Rolandic double/multiple spikes and slow-wave abnormalities may indicate SeLECTS with ESES.

Biomimetic Deep Learning Networks With Applications to Epileptic Spasms and Seizure Prediction

OBJECTIVE

In this study, we present a novel biomimetic deep learning network for epileptic spasms and seizure prediction and compare its performance with state-of-the-art conventional machine learning models.

METHODS

Our proposed model incorporates modular Volterra kernel convolutional networks and bidirectional recurrent networks in combination with the phase amplitude cross-frequency coupling features derived from scalp EEG. They are applied to the standard CHB-MIT dataset containing focal epilepsy episodes as well as two other datasets from the Montefiore Medical Center and the University of California Los Angeles that provide data of patients experiencing infantile spasm (IS) syndrome.

RESULTS

Overall, in this study, the networks can produce accurate predictions (100%) and significant detection latencies (10 min). Furthermore, the biomimetic network outperforms conventional ones by producing no false positives.

SIGNIFICANCE

Biomimetic neural networks utilize extensive knowledge about processing and learning in the electrical networks of the brain. Predicting seizures in adults can improve their quality of life. Epileptic spasms in infants are part of a particular seizure type that needs identifying when suspicious behaviors are noticed in babies. Predicting epileptic spasms within a given time frame (the prediction horizon) suggests their existence and allows an epileptologist to flag an EEG trace for future review.

The effects of neurofeedback training for children with cerebral palsy and co-occurring attention deficits: A pilot study

BACKGROUND

Limited research exists regarding the effectiveness of electroencephalogram (EEG) neurofeedback training for children with cerebral palsy (CP) and co-occurring attention deficits (ADs), despite the increasing prevalence of these dual conditions. This study aimed to fill this gap by examining the impact of neurofeedback training on the attention levels of children with CP and AD.

METHODS

Nineteen children with both CP and co-occurring ADs were randomly assigned to either a neurofeedback or control group. The neurofeedback group received 20 sessions of training, lasting approximately 1 h per day, twice a week. Theta/beta ratios of the quantitative electroencephalography (QEEG) recordings were measured pre-training and post-training in the resting state. The Continuous Performance Test (CPT), the Test of Visual Perceptual Skills-3rd Version (TVPS-3) and the Conners' Parent Rating Scale (CPRS) were measured at pre- and post-training.

RESULTS

The neurofeedback group showed both decreased theta/beta ratios compared with control group (p = 0.04) at post-training and a within-group improvement during training (p = 0.02). Additionally, the neurofeedback group had a trend of decreased omission rates of the CPT (p = 0.08) and the visual sequential memory and the visual closure subscores in the TVPS-3, compared with the control group (p = 0.02 and p = 0.01, respectively).

CONCLUSIONS

The results suggested that children with CP and co-occurring AD may benefit from neurofeedback training in their attention level. Further research is needed to explore long-term effects and expand its application in this population.

Antiseizure Medication-Induced Nystagmus During Eye Closure Identified by Electroencephalography

Nystagmus is a well-known side effect of antiseizure medicines (ASMs), but it is often underestimated and overlooked. Here, we describe a case in which nystagmus during eye closure was identified early using routine electroencephalography (EEG). A 34-year-old man developed focal epilepsy after head trauma at the age of 25 years. The patient was treated with carbamazepine but liver dysfunction was observed; therefore, treatment was attempted with lacosamide (LCM) and lamotrigine. With an increase in the LCM dose, steep potential changes suggestive of horizontal nystagmus were observed in the electrooculogram, F7, and F8 on EEG, and the patient complained of eye shaking during eye closure. These symptoms and EEG findings improved with LCM dose reduction. If the presence of nystagmus is identified on EEG coincidentally and a patient's subjective symptoms with ASM are confirmed, it is advisable to taper and/or discontinue the causative agent.

Validation of a sleep staging classification model for healthy adults based on two combinations of a single-channel EEG headband and wrist actigraphy

STUDY OBJECTIVES

The aim of this study was to develop a sleep staging classification model capable of accurately performing on different wearable devices.

METHODS

Twenty-three healthy subjects underwent a full-night type I polysomnography and used two devices' combinations: (A) flexible single-channel electroencephalogram headband+actigraphy (N=12) and (B) rigid single-channel electroencephalogram headband+actigraphy (N=11). The signals were segmented into 30-second epochs according to polysomnographic stages (scored by a board-certified sleep technologist) (model ground truth) and 18 frequency and time features were extracted. The model consisted of an ensemble of bagged decision trees. Bagging refers to bootstrap aggregation to reduce overfitting and improve generalization. To evaluate the model, a training dataset under 5-fold cross-validation and an 80-20% dataset split was used. The headbands were also evaluated without the actigraphy feature. Subjects also completed a usability evaluation (comfort, pain while sleeping, and sleep disturbance).

RESULTS

Combination A had an F1-score of 98.4% and the flexible headband alone of 97.7% (error rate for N1: combination A=9.8%; flexible headband alone=15.7%). Combination B had an F1-score of 96.9% and the rigid headband alone of 95.3% (error rate for N1: combination B=17.0%; rigid headband alone=27.7%); in both, N1 was more confounded with N2.

CONCLUSIONS

We developed an accurate sleep classification model based on a single-channel EEG device, and actigraphy was not an important feature of the model. Both headbands were found to be useful, with the rigid one being more disruptive to sleep. Future research can improve our results by applying the developed model in a population with sleep disorders.

CLINICAL TRIAL REGISTRATION

Actigraphy, Wearable EEG Band and Smartphone for Sleep Staging (ID NCT04943562).

Periodic and aperiodic changes to cortical EEG in response to pharmacological manipulation

Cortical electroencephalograms (EEGs) may help understanding of neuropsychiatric illness and new treatment mechanisms. The aperiodic component (1/f) of EEG power spectra is often treated as noise, but recent studies suggest that changes to the aperiodic exponent of power spectra may reflect changes in excitation/inhibition balance, a concept linked to antidepressant effects, epilepsy, autism, and other clinical conditions. One confound of previous studies is behavioral state, because factors associated with behavioral state other than excitation/inhibition ratio may alter EEG parameters. Thus, to test the robustness of the aperiodic exponent as a predictor of excitation/inhibition ratio, we analyzed video-EEG during active exploration in mice of both sexes during various pharmacological manipulations with the fitting oscillations and one over f (FOOOF) algorithm. We found that GABAA receptor (GABAAR)-positive allosteric modulators increased the aperiodic exponent, consistent with the hypothesis that an increased exponent signals enhanced cortical inhibition, but other drugs (ketamine and GABAAR antagonists at subconvulsive doses) did not follow the prediction. To tilt excitation/inhibition ratio more selectively toward excitation, we suppressed the activity of parvalbumin-positive interneurons with Designer Receptors Exclusively Activated by Designer Drugs (DREADDs). Contrary to our expectations, circuit disinhibition with the DREADD increased the aperiodic exponent. We conclude that the aperiodic exponent of EEG power spectra does not yield a universally reliable marker of cortical excitation/inhibition ratio.NEW & NOTEWORTHY Neuropsychiatric illness may be associated with altered excitation/inhibition balance. A single electroencephalogram (EEG) parameter, the aperiodic exponent of power spectra, may predict the ratio between excitation and inhibition. Here, we use cortical EEGs in mice to evaluate this hypothesis, using pharmacological manipulations of known mechanism. We show that the aperiodic exponent of EEG power spectra is not a reliable marker of excitation/inhibition ratio. Thus, alternative markers of this ratio must be sought.

Prevalence of epileptiform electroencephalographic abnormalities in people without a history of seizures: A systematic review and meta-analysis

Abnormal patterns identified on electroencephalogram (EEG) are one of the primary diagnostic tests for epilepsy. However, epidemiological studies have established that both benign and epileptiform abnormalities (EAs) occur on the EEG of nonepileptic, seizure-free people as well. The reported rates of EAs in nonepileptic, seizure-free populations vary, and the true prevalence is unknown. The primary objective of this systematic review and meta-analysis was to estimate the overall prevalence of EAs in the EEG of people without a history of seizures. Secondary aims were to characterize (1) the cortical localization of focal abnormalities, (2) the proportion of findings that occurred during standard EEG stimulation protocols, and (3) the persistence and implications of abnormalities at follow-up. A comprehensive electronic search of six bibliographic databases was completed: Embase, MEDLINE, PsycInfo, Cumulative Index of Nursing and Allied Health Literature, Cochrane Central Register for Controlled Trials, and Web of Science. No search date restrictions were applied. Overall effect size was calculated using a generalized linear mixed-effects model. Fifty-three studies, totaling 73 990 individuals, met our inclusion criteria. The overall point prevalence of EAs was 1.74% (95% confidence interval [CI] = 1.13-2.67). Due to the risk of bias in the literature, especially from participant selection, we believe this to be an overestimate of the true prevalence. Prevalence of EAs was greater in children (2.45%, 95% CI = 1.41-4.21) and the elderly (5.96%, 95% CI = 1.39-22.13) compared with adults (.93%, 95% CI = .48-1.80). Reports of developing epilepsy after an EA-positive EEG were rare. The likelihood of subsequent positive findings on follow-up EEG may be as high as 50%. Our study has limitations in that males were overrepresented in the study samples, there is substantial heterogeneity among studies, and many studies provided insufficient detail about their exclusion criteria. Nonetheless, our estimates provide benchmark data for future studies examining EAs in clinical populations, particularly behavioral and psychiatric populations.

Comparison of power spectra from overnight electroencephalography between patients with Down syndrome and matched control subjects

Electroencephalograms can capture brain oscillatory activities during sleep as a form of electrophysiological signals. We analysed electroencephalogram recordings from full-night in-laboratory polysomnography from 100 patients with Down syndrome, and 100 age- and sex-matched controls. The ages of patients with Down syndrome spanned 1 month to 31 years (median 4.4 years); 84 were younger than 12 years, and 54 were male. From each electroencephalogram, we extracted relative power in six frequency bands or rhythms (delta, theta, alpha, slow sigma, fast sigma, and beta) from six channels (frontal F3 and F4, central C3 and C4, and occipital O1 and O2) during five sleep stages (N3, N2, N1, R and W)-180 features in all. We examined differences in relative power between Down syndrome and control electroencephalograms for each feature separately. During wake and N1 sleep stages, alpha rhythms (8.0-10.5 Hz) had significantly lower power in patients with Down syndrome than controls. Moreover, the rate of increase in alpha power with age during rapid eye movement sleep was significantly slower in Down syndrome than control subjects. During wake and N1 sleep, delta rhythms (0.25-4.5 Hz) had higher power in patients with Down syndrome than controls. During N2 sleep, slow sigma rhythms (10.5-12.5 Hz) had lower power in patients with DS than controls. These findings extend previous research from routine electroencephalogram studies demonstrating that patients with Down syndrome had reduced circadian amplitude-the difference between wake alpha power and deep sleep delta power was smaller in Down syndrome than control subjects. We envision that these brain oscillatory activities may be used as surrogate markers for clinical trials for patients with Down syndrome.

Effect of Phase Clustering Bias on Phase-Amplitude Coupling for Emotional EEG

BACKGROUND

Emotions are thought to be related to distinct patterns of neural oscillations, but the interactions among multi-frequency neural oscillations during different emotional states lack full exploration. Phase-amplitude coupling is a promising tool for understanding the complexity of the neurophysiological system, thereby playing a crucial role in revealing the physiological mechanisms underlying emotional electroencephalogram (EEG). However, the non-sinusoidal characteristics of EEG lead to the non-uniform distribution of phase angles, which could potentially affect the analysis of phase-amplitude coupling. Removing phase clustering bias (PCB) can uniform the distribution of phase angles, but the effect of this approach is unknown on emotional EEG phase-amplitude coupling. This study aims to explore the effect of PCB on cross-frequency phase-amplitude coupling for emotional EEG.

METHODS

The technique of removing PCB was implemented on a publicly accessible emotional EEG dataset to calculate debiased phase-amplitude coupling. Statistical analysis and classification were conducted to compare the difference in emotional EEG phase-amplitude coupling prior to and post the removal of PCB.

RESULTS

Emotional EEG phase-amplitude coupling values are overestimated due to PCB. Removing PCB enhances the difference in coupling strength between fear and happy emotions in the frontal lobe. Comparable emotion recognition performance was achieved with fewer features after removing PCB.

CONCLUSIONS

These findings suggest that removing PCB enhances the difference in emotional EEG phase-amplitude coupling patterns and generates features that contain more emotional information. Removing PCB may be advantageous for analyzing emotional EEG phase-amplitude coupling and recognizing human emotions.

Restoration of postictal cortical activity after electroconvulsive therapy relates to recovery of orientation in person, place, and time

BACKGROUND

Most patients show temporary impairments in clinical orientation after electroconvulsive therapy (ECT)-induced seizures. It is unclear how postictal reorientation relates to electroencephalography (EEG) restoration. This relationship may provide additional measures to quantify postictal recovery and shed light on neurophysiological aspects of reorientation after ECT.

METHODS

We analyzed prospectively collected clinical and continuous ictal and postictal EEG data from ECT patients. Postictal EEG restoration up to 1 h was estimated by the evolution of the normalized alpha-delta ratio (ADR). Times to reorientation in the cognitive domains of person, place, and time were assessed postictally. In each cognitive domain, a linear mixed model was fitted to investigate the relationships between time to reorientation and postictal EEG restoration.

RESULTS

In total, 272 pairs of ictal-postictal EEG and reorientation times of 32 patients were included. In all domains, longer time to reorientation was associated with slower postictal EEG recovery. Longer seizure duration and postictal administration of midazolam were related to longer time to reorientation in all domains. At 1-hour post-seizure, most patients were clinically reoriented, while their EEG had only partly restored.

CONCLUSIONS

We show a relationship between postictal EEG restoration and clinical reorientation after ECT-induced seizures. EEG was more sensitive than reorientation time in all domains to detect postictal recovery beyond 1-hour post-seizure. Our findings indicate that clinical reorientation probably depends on gradual cortical synaptic recovery, with longer seizure duration leading to longer postsynaptic suppression after ECT seizures.

Distinct Effects of Brain Activation Using tDCS and Observational Practice: Implications for Motor Rehabilitation

Complex motor skills can be acquired while observing a model without physical practice. Transcranial direct-current stimulation (tDCS) applied to the primary motor cortex (M1) also facilitates motor learning. However, the effectiveness of observational practice for bimanual coordination skills is debated. We compared the behavioural and brain causal connectivity patterns following three interventions: primary motor cortex stimulation (M1-tDCS), action-observation (AO) and a combined group (AO+M1-tDCS) when acquiring a bimanual, two-ball juggling skill. Thirty healthy young adults with no juggling experience were randomly assigned to either video observation of a skilled juggler, anodal M1-tDCS or video observation combined with M1-tDCS. Thirty trials of juggling were performed and scored after the intervention. Resting-state EEG data were collected before and after the intervention. Information flow rate was applied to EEG source data to measure causal connectivity. The two observation groups were more accurate than the tDCS alone group. In the AO condition, there was strong information exchange from (L) parietal to (R) parietal regions, strong bidirectional information exchange between (R) parietal and (R) occipital regions and an extensive network of activity that was (L) lateralized. The M1-tDCS condition was characterized by bilateral long-range connections with the strongest information exchange from the (R) occipital region to the (R) temporal and (L) occipital regions. AO+M1-tDCS induced strong bidirectional information exchange in occipital and temporal regions in both hemispheres. Uniquely, it was the only condition that was characterized by information exchange between the (R) frontal and central regions. This study provides new results about the distinct network dynamics of stimulating the brain for skill acquisition, providing insights for motor rehabilitation.

A design and implementation of multi-character classification scheme based on motor imagery EEG signals

In the field of brain-to-text communication, it is difficult to finish highly dexterous behaviors of writing multi-character by motor-imagery-based brain-computer interface (MI-BCI), setting a barrier to restore communication in people who have lost the ability to move and speak. In this paper, we design and implement a multi-character classification scheme based on 29 characters of motor imagery (MI) electroencephalogram (EEG) signals, which contains 26 English letters and 3 punctuation marks. Firstly, we design a novel experimental paradigm to increase the variety of BCI inputs by asking subjects to imagine the movement of writing 29 characters instead of gross motor skills such as reaching or grasping. Secondly, because of the high dimension of EEG signals, we adopt power spectral density (PSD), principal components analysis (PCA), kernel principal components analysis (KPCA) respectively to decompose EEG signals and extract feature, and then test the results with pearson product-moment correlation coefficient (PCCs). Thirdly, we respectively employ k-nearest neighbor (kNN), support vector machine (SVM), extreme learning machine (ELM) and light gradient boosting machine (LightGBM) to classify 29 characters and compare the results. We have implemented a complete scheme, including paradigm design, signal acquisition, feature extraction and classification, which can effectively classify 29 characters. The experimental results show that the KPCA has the best feature extraction effect and the kNN has the highest classification accuracy, with the final classification accuracy reaching 96.2%, which is better than other studies.

The role of the sleep K-complex on the conversion from mild cognitive impairment to Alzheimer's disease

The present literature points to an alteration of the human K-complex during non-rapid eye movement sleep in Alzheimer's disease. Nevertheless, the few findings on the K-complex changes in mild cognitive impairment and their possible predictive role on the Alzheimer's disease conversion show mixed findings, lack of replication, and a main interest for the frontal region. The aim of the present study was to assess K-complex measures in amnesic mild cognitive impairment subsequently converted in Alzheimer's disease over different cortical regions, comparing them with healthy controls and stable amnesic mild cognitive impairment. We assessed baseline K-complex density, amplitude, area under the curve and overnight changes in frontal, central and parietal midline derivations of 12 amnesic mild cognitive impairment subsequently converted in Alzheimer's disease, 12 stable amnesic mild cognitive impairment and 12 healthy controls. We also assessed delta electroencephalogram power, to determine if K-complex alterations in amnesic mild cognitive impairment occur with modification of the electroencephalogram power in the frequency range of the slow-wave activity. We found a reduced parietal K-complex density in amnesic mild cognitive impairment subsequently converted in Alzheimer's disease compared with stable amnesic mild cognitive impairment and healthy controls, without changes in K-complex morphology and overnight modulation. Both amnesic mild cognitive impairment groups showed decreased slow-wave sleep percentage compared with healthy controls. No differences between groups were observed in slow-wave activity power. Our findings suggest that K-complex alterations in mild cognitive impairment may be observed earlier in parietal regions, likely mirroring the topographical progression of Alzheimer's disease-related brain pathology, and express a frontal predominance only in a full-blown phase of Alzheimer's disease. Consistently with previous results, such K-complex modification occurs in the absence of significant electroencephalogram power changes in the slow oscillations range.

Multiplex dynamic networks in the newborn brain disclose latent links with neurobehavioral phenotypes

The higher brain functions arise from coordinated neural activity between distinct brain regions, but the spatial, temporal, and spectral complexity of these functional connectivity networks (FCNs) has challenged the identification of correlates with neurobehavioral phenotypes. Characterizing behavioral correlates of early life FCNs is important to understand the activity dependent emergence of neurodevelopmental performance and for improving health outcomes. Here, we develop an analysis pipeline for identifying multiplex dynamic FCNs that combine spectral and spatiotemporal characteristics of the newborn cortical activity. This data-driven approach automatically uncovers latent networks that show robust neurobehavioral correlations and consistent effects by in utero drug exposure. Altogether, the proposed pipeline provides a robust end-to-end solution for an objective assessment and quantitation of neurobehaviorally meaningful network constellations in the highly dynamic cortical functions.

Analysing the behaviour change of brain regions of methamphetamine abusers using electroencephalogram signals: Hope to design a decision support system

Long-term use of methamphetamine (meth) causes cognitive and neuropsychological impairments. Analysing the impact of this substance on the human brain can aid prevention and treatment efforts. In this study, the electroencephalogram (EEG) signals of meth abusers in the abstinence period and healthy subjects were recorded during eyes-closed and eyes-opened states to distinguish the brain regions that meth can significantly influence. In addition, a decision support system (DSS) was introduced as a complementary method to recognize substance users accompanied by biochemical tests. According to these goals, the recorded EEG signals were pre-processed and decomposed into frequency bands using the discrete wavelet transform (DWT) method. For each frequency band, energy, KS entropy, Higuchi and Katz fractal dimensions of signals were calculated. Then, statistical analysis was applied to select features whose channels contain a p-value less than 0.05. These features between two groups were compared, and the location of channels containing more features was specified as discriminative brain areas. Due to evaluating the performance of features and distinguishing the two groups in each frequency band, features were fed into a k-nearest neighbour (KNN), support vector machine (SVM), multilayer perceptron neural networks (MLP) and linear discriminant analysis (LDA) classifiers. The results indicated that prolonged consumption of meth has a considerable impact on the brain areas responsible for working memory, motor function, attention, visual interpretation, and speech processing. Furthermore, the best classification accuracy, almost 95.8%, was attained in the gamma band during the eyes-closed state.

A Retrospective Study of the Patient State Index During General Anesthesia in Infants and Young Children

This study described electroencephalogram (EEG) parameters in children under general anesthesia, which could monitor patient-specific brain responses to anesthetics and assess the effects of anesthesia. The objective was to detect the patient state index (PSI) and associated factors. We analyzed EEG parameters in patients in the age range 1 to 36 months. Patients were stratified into 2 groups as those aged 1 to 12 months and 13 to 36 months. Sixty-two patients were involved. Spectral edge frequency (SEF), PSI, and blood pressure were lower, and burst suppression rate (BSR) and heart rate were higher in the 1 to 12 months group. The SEF was associated with PSI in both groups. Age and blood pressure were positively associated with PSI, and BSR was negatively related to PSI in children under 1 year of age. Blood pressure was not associated with PSI in the 13 to 36 months age group. We found that the PSI levels did not accurately assess the depth of anesthesia in children under 1 year of age.

The first-night effect and the consistency of short sleep in insomnia disorder

The nature and degree of objective sleep impairments in insomnia disorder remain unclear. This issue is complicated further by potential changes in sleep architecture on the first compared with subsequent nights in the laboratory. Evidence regarding differential first-night effects in people with insomnia disorder and controls is mixed. Here, we aimed to further characterize insomnia- and night-related differences in sleep architecture. A comprehensive set of 26 sleep variables was derived from two consecutive nights of polysomnography in 61 age-matched patients with insomnia and 61 good sleeper controls. People with insomnia expressed consistently poorer sleep than controls on several variables during both nights. While poorer sleep during the first night was observed in both groups, there were qualitative differences regarding the specific sleep variables expressing a first-night effect. Short sleep (total sleep time < 6 hr) was more likely during the first night and in insomnia, although approximately 40% of patients with insomnia presenting with short sleep on night 1 no longer met this criterion on night 2, which is important given the notion of short-sleeping insomnia as a robust subtype.

Is Visual Prediction Impaired in Adolescents with Autism spectrum Disorder? Event-Related Potentials in a Cued Visual GO/NOGO Task

Aim: Deviant visual processing has been observed in autism spectrum disorder (ASD), manifesting as decreased P1 and P2 components of visual event-related potentials (ERPs). Alterations have been attributed to a failure of Bayesian inference, characterized by hypo-activation of top-down predictive abilities. To test this hypothesis, we measured the visual negativity (vN) as an ERP index of visual preparation hypothesized to mirror predictive brain activity. Method: ERPs in a cued visual GO/NOGO task in 63 adolescents with ASD (IQ > 70, attention-deficit hyperactivity disorder excluded) were compared with ERPs in a sex- and age-matched group of 60 typically developing (TD) controls. Results: The behavioral variables (omissions, commissions, reaction time, and reaction time variability), as well as ERP components reflecting, among other processes, cognitive control (contingent negative variation, P3 GO, P3 NOGO, N2 NOGO) did not differ between the groups. There were group differences in visually based ERPs. Besides P1 and P2 differences, the vN component differentiated the 2 groups with the highest effect size (d  =  0.74).Conclusion: This ERP study lends support to the hypothesis suggesting that a Bayesian hypo-prediction could underlie unique perceptual experiences in individuals with ASD. This could lead to a predisposition to perceive the world with reduced influence and modulation from contextual cues, prior experiences, and pre-existing expectations.

Flexible Dry Electrode Based on a Wrinkled Surface That Uses Carbon Nanotube/Polymer Composites for Recording Electroencephalograms

Electroencephalography (EEG) captures minute electrical signals emanating from the brain. These signals are vulnerable to interference from external noise and dynamic artifacts; hence, accurately recording such signals is challenging. Although dry electrodes are convenient, their signals are of limited quality; consequently, wet electrodes are predominantly used in EEG. Therefore, developing dry electrodes for accurately and stably recording EEG signals is crucial. In this study, we developed flexible dry electrodes using polydimethylsiloxane (PDMS)/carbon-nanotube (CNT) composites with isotropically wrinkled surfaces that effectively combine the advantages of wet and dry electrodes. Adjusting the PDMS crosslinker ratio led to good adhesion, resulting in a highly adhesive CNT/PDMS composite with a low Young's modulus that exhibited excellent electrical and mechanical properties owing to its ability to conformally contact skin. The isotropically wrinkled surface also effectively controls dynamic artifacts during EEG signal detection and ensures accurate signal analysis. The results of this study demonstrate that dry electrodes based on flexible CNT/PDMS composites and corrugated structures can outperform wet electrodes. The introduction of such electrodes is expected to enable the accurate analysis and monitoring of EEG signals in various scenarios, including clinical trials.

Bidirectional feature pyramid attention-based temporal convolutional network model for motor imagery electroencephalogram classification

Introduction

As an interactive method gaining popularity, brain-computer interfaces (BCIs) aim to facilitate communication between the brain and external devices. Among the various research topics in BCIs, the classification of motor imagery using electroencephalography (EEG) signals has the potential to greatly improve the quality of life for people with disabilities.

Methods

This technology assists them in controlling computers or other devices like prosthetic limbs, wheelchairs, and drones. However, the current performance of EEG signal decoding is not sufficient for real-world applications based on Motor Imagery EEG (MI-EEG). To address this issue, this study proposes an attention-based bidirectional feature pyramid temporal convolutional network model for the classification task of MI-EEG. The model incorporates a multi-head self-attention mechanism to weigh significant features in the MI-EEG signals. It also utilizes a temporal convolution network (TCN) to separate high-level temporal features. The signals are enhanced using the sliding-window technique, and channel and time-domain information of the MI-EEG signals is extracted through convolution.

Results

Additionally, a bidirectional feature pyramid structure is employed to implement attention mechanisms across different scales and multiple frequency bands of the MI-EEG signals. The performance of our model is evaluated on the BCI Competition IV-2a dataset and the BCI Competition IV-2b dataset, and the results showed that our model outperformed the state-of-the-art baseline model, with an accuracy of 87.5 and 86.3% for the subject-dependent, respectively.

Discussion

In conclusion, the BFATCNet model offers a novel approach for EEG-based motor imagery classification in BCIs, effectively capturing relevant features through attention mechanisms and temporal convolutional networks. Its superior performance on the BCI Competition IV-2a and IV-2b datasets highlights its potential for real-world applications. However, its performance on other datasets may vary, necessitating further research on data augmentation techniques and integration with multiple modalities to enhance interpretability and generalization. Additionally, reducing computational complexity for real-time applications is an important area for future work.

EEG generation mechanism of lower limb active movement intention and its virtual reality induction enhancement: a preliminary study

Introduction

Active rehabilitation requires active neurological participation when users use rehabilitation equipment. A brain-computer interface (BCI) is a direct communication channel for detecting changes in the nervous system. Individuals with dyskinesia have unclear intentions to initiate movement due to physical or psychological factors, which is not conducive to detection. Virtual reality (VR) technology can be a potential tool to enhance the movement intention from pre-movement neural signals in clinical exercise therapy. However, its effect on electroencephalogram (EEG) signals is not yet known. Therefore, the objective of this paper is to construct a model of the EEG signal generation mechanism of lower limb active movement intention and then investigate whether VR induction could improve movement intention detection based on EEG.

Methods

Firstly, a neural dynamic model of lower limb active movement intention generation was established from the perspective of signal transmission and information processing. Secondly, the movement-related EEG signal was calculated based on the model, and the effect of VR induction was simulated. Movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were extracted to analyze the enhancement of movement intention. Finally, we recorded EEG signals of 12 subjects in normal and VR environments to verify the effectiveness and feasibility of the above model and VR induction enhancement of lower limb active movement intention for individuals with dyskinesia.

Results

Simulation and experimental results show that VR induction can effectively enhance the EEG features of subjects and improve the detectability of movement intention.

Discussion

The proposed model can simulate the EEG signal of lower limb active movement intention, and VR induction can enhance the early and accurate detectability of lower limb active movement intention. It lays the foundation for further robot control based on the actual needs of users.

The effect of music tempo on movement flow

There has been much controversy over the effects of music tempo on movement flow. In this study, a single-factor repeated-measurement design was used to explore the effect of music tempo (fast, slow, and no music control) on movement flow by measuring both subjective experiences and objective electroencephalographic (EEG) characteristics during brisk walking. In the experiment, 20 college students walked briskly on a treadmill using EEG equipment. Each participant walked for 10 min on three different days. Their brain waves were recorded during brisk walking on a treadmill. After each walk, the participants completed a form of short flow state scale-2 (S FSS-2), which covered nine major aspects of flow. The results showed that music tempo had a significant effect on subjective experiences and objective physiological characteristics; that is, a higher subjective flow level for fast-tempo music in brisk walking and a significant enhancement of mean power values in the subconscious brain waves of the delta, theta, alpha, and beta bands for fast tempo music were observed. A fast tempo facilitated the movement flow. The findings of this study can be instructive for the use of music in exercises to improve sports training outcomes.

Developmental Dyslexia: Insights from EEG-Based Findings and Molecular Signatures-A Pilot Study

Developmental dyslexia (DD) is a learning disorder. Although risk genes have been identified, environmental factors, and particularly stress arising from constant difficulties, have been associated with the occurrence of DD by affecting brain plasticity and function, especially during critical neurodevelopmental stages. In this work, electroencephalogram (EEG) findings were coupled with the genetic and epigenetic molecular signatures of individuals with DD and matched controls. Specifically, we investigated the genetic and epigenetic correlates of key stress-associated genes (NR3C1, NR3C2, FKBP5, GILZ, SLC6A4) with psychological characteristics (depression, anxiety, and stress) often included in DD diagnostic criteria, as well as with brain EEG findings. We paired the observed brain rhythms with the expression levels of stress-related genes, investigated the epigenetic profile of the stress regulator glucocorticoid receptor (GR) and correlated such indices with demographic findings. This study presents a new interdisciplinary approach and findings that support the idea that stress, attributed to the demands of the school environment, may act as a contributing factor in the occurrence of the DD phenotype.

EEG in Down Syndrome-A Review and Insights into Potential Neural Mechanisms

Introduction: Down syndrome (DS) stands out as one of the most prevalent genetic disorders, imposing a significant burden on both society and the healthcare system. Scientists are making efforts to understand the neural mechanisms behind the pathophysiology of this disorder. Among the valuable methods for studying these mechanisms is electroencephalography (EEG), a non-invasive technique that measures the brain's electrical activity, characterised by its excellent temporal resolution. This review aims to consolidate studies examining EEG usage in individuals with DS. The objective was to identify shared elements of disrupted EEG activity and, crucially, to elucidate the neural mechanisms underpinning these deviations. Searches were conducted on Pubmed/Medline, Research Gate, and Cochrane databases. Results: The literature search yielded 17 relevant articles. Despite the significant time span, small sample size, and overall heterogeneity of the included studies, three common features of aberrant EEG activity in people with DS were found. Potential mechanisms for this altered activity were delineated. Conclusions: The studies included in this review show altered EEG activity in people with DS compared to the control group. To bolster these current findings, future investigations with larger sample sizes are imperative.

Epileptic seizure clustering and accumulation at transition from activity to rest in GAERS rats

Knowing when seizures occur may help patients and can also provide insight into epileptogenesis mechanisms. We recorded seizures over periods of several days in the Genetic Absence Epileptic Rat from Strasbourg (GAERS) model of absence epilepsy, while we monitored behavioral activity with a combined head accelerometer (ACCEL), neck electromyogram (EMG), and electrooculogram (EOG). The three markers consistently discriminated between states of behavioral activity and rest. Both GAERS and control Wistar rats spent more time in rest (55-66%) than in activity (34-45%), yet GAERS showed prolonged continuous episodes of activity (23 vs. 18 min) and rest (34 vs. 30 min). On average, seizures lasted 13 s and were separated by 3.2 min. Isolated seizures were associated with a decrease in the power of the activity markers from steep for ACCEL to moderate for EMG and weak for EOG, with ACCEL and EMG power changes starting before seizure onset. Seizures tended to occur in bursts, with the probability of seizing significantly increasing around a seizure in a window of ±4 min. Furthermore, the seizure rate was strongly increased for several minutes when transitioning from activity to rest. These results point to mechanisms that control behavioral states as determining factors of seizure occurrence.

Comparing movement-related cortical potential between real and simulated movement tasks from an ecological validity perspective

Introduction

Concerns regarding the ecological validity of movement-related cortical potential (MRCP) experimental tasks that are related to motor learning have recently been growing. Therefore, we compared MRCP during real movement task (RMT) and simulated movement task (SMT) from an ecological validity perspective.

Methods

The participants performed both RMT and SMT, and MRCP were measured using electroencephalogram (EEG). EEG was based on the 10-20 method, with electrodes placed in the motor cortex (C3 and C4) and supplementary motor cortex (FCz [between Fz and Cz] and Cz) areas. This experiment examined the MRCP using Bereitschaftspotential (BP) and negative slope (NS') onset times, and BP, NS', and motor potential (MP) amplitudes during the task.

Results

The results revealed that the SMT exhibited later BP and NS' onset times and smaller BP, NS', and MP amplitudes than the RMT. Furthermore, in RMT, the onset time of MRCP was delayed, and the amplitude of MRCP was smaller in the second half of the 200 times task than in the first half, whereas in SMT, there was no change in onset time and amplitude. The SMT showed a different MRCP than the RMT, suggesting that the ecological validity of the task should be fully considered when investigating the cortical activity associated with motor skill learning using MRCP.

Conclusion

Ecological validity of the study should be fully considered when investigating the cortical activity associated with motor skill learning using MRCP. Moreover, it is important to understand the differences between the two methods when applied clinically.

Modulating Consciousness through Awareness Training Program and Its Impacts on Psychological Stress and Age-Related Gamma Waves

Aging often leads to awareness decline and psychological stress. Meditation, a method of modulating consciousness, may help individuals improve overall awareness and increase emotional resilience toward stress. This study explored the potential influence of the Awareness Training Program (ATP), a form of consciousness modulation, on age-related brain wave changes and psychological stress in middle-aged adults. Eighty-five participants with mild stress were recruited and randomly assigned to ATP (45.00 ± 8.00 yr) or control (46.67 ± 7.80 yr) groups, matched by age and gender. Ten-minute resting-state EEG data, obtained while the participants' eyes were closed, were collected using a 128-channel EEG system (EGI). A strong positive Pearson correlation was found between fast-wave (beta wave, 12-25 Hz; gamma wave, 25-40 Hz) EEG and age. However, after the 7-week ATP intervention, this correlation became insignificant in the ATP group. Furthermore, there was a significant reduction in stress levels, as measured by the Chinese version of the 10 item Perceived Stress Scale (PSS-10), in the ATP group. These results suggest that ATP may help modulate age-related effects on fast brain waves, as evidenced by the reduced correlation magnitude between age and gamma waves, and lower psychological stress. This suggests that ATP, as a form of consciousness modulation, may improve stress resilience and modulate age-related gamma wave changes.

Response to experimental cold-induced pain discloses a resistant category among endurance athletes, with a distinct profile of pain-related behavior and GABAergic EEG markers: a case-control preliminary study

Pain is a major public health problem worldwide, with a high rate of treatment failure. Among promising non-pharmacological therapies, physical exercise is an attractive, cheap, accessible and innocuous method; beyond other health benefits. However, its highly variable therapeutic effect and incompletely understood underlying mechanisms (plausibly involving the GABAergic neurotransmission) require further research. This case-control study aimed to investigate the impact of long-lasting intensive endurance sport practice (≥7 h/week for the last 6 months at the time of the experiment) on the response to experimental cold-induced pain (as a suitable chronic pain model), assuming that highly trained individual would better resist to pain, develop advantageous pain-copying strategies and enhance their GABAergic signaling. For this purpose, clinical pain-related data, response to a cold-pressor test and high-density EEG high (Hβ) and low beta (Lβ) oscillations were documented. Among 27 athletes and 27 age-adjusted non-trained controls (right-handed males), a category of highly pain-resistant participants (mostly athletes, 48.1%) was identified, displaying lower fear of pain, compared to non-resistant non-athletes. Furthermore, they tolerated longer cold-water immersion and perceived lower maximal sensory pain. However, while having similar Hβ and Lβ powers at baseline, they exhibited a reduction between cold and pain perceptions and between pain threshold and tolerance (respectively -60% and - 6.6%; -179.5% and - 5.9%; normalized differences), in contrast to the increase noticed in non-resistant non-athletes (+21% and + 14%; +23.3% and + 13.6% respectively). Our results suggest a beneficial effect of long-lasting physical exercise on resistance to pain and pain-related behaviors, and a modification in brain GABAergic signaling. In light of the current knowledge, we propose that the GABAergic neurotransmission could display multifaceted changes to be differently interpreted, depending on the training profile and on the homeostatic setting (e.g., in pain-free versus chronic pain conditions). Despite limitations related to the sample size and to absence of direct observations under acute physical exercise, this precursory study brings into light the unique profile of resistant individuals (probably favored by training) allowing highly informative observation on physical exercise-induced analgesia and paving the way for future clinical translation. Further characterizing pain-resistant individuals would open avenues for a targeted and physiologically informed pain management.

EEG microstates analysis after TMS in patients with subacute stroke during the resting state

To investigate whether intermittent theta burst stimulation over the cerebellum induces changes in resting-state electroencephalography microstates in patients with subacute stroke and its correlation with cognitive and emotional function. Twenty-four stroke patients and 17 healthy controls were included in this study. Patients and healthy controls were assessed at baseline, including resting-state electroencephalography and neuropsychological scales. Fifteen patients received lateral cerebellar intermittent theta burst stimulation as well as routine rehabilitation training (intermittent theta burst stimulation-RRT group), whereas 9 patients received only conventional rehabilitation training (routine rehabilitation training group). After 2 wk, baseline data were recorded again in both groups. Stroke patients exhibited reduced parameters in microstate D and increased parameters in microstate C compared with healthy controls. However, after the administration of intermittent theta burst stimulation over the lateral cerebellum, significant alterations were observed in the majority of metrics for both microstates D and C. Lateral cerebellar intermittent theta burst stimulation combined with conventional rehabilitation has a stronger tendency to improve emotional and cognitive function in patients with subacute stroke than conventional rehabilitation. The improvement of mood and cognitive function was significantly associated with microstates C and D. We identified electroencephalography microstate spatiotemporal dynamics associated with clinical improvement following a course of intermittent theta burst stimulation therapy.

Divergent electroencephalogram resting-state functional network alterations in subgroups of autism spectrum disorder: a symptom-based clustering analysis

Autism spectrum disorder (ASD) is characterized by etiological and phenotypic heterogeneity. Despite efforts to categorize ASD into subtypes, research on specific functional connectivity changes within ASD subgroups based on clinical presentations is limited. This study proposed a symptom-based clustering approach to identify subgroups of ASD based on multiple clinical rating scales and investigate their distinct Electroencephalogram (EEG) functional connectivity patterns. Eyes-opened resting-state EEG data were collected from 72 children with ASD and 63 typically developing (TD) children. A data-driven clustering approach based on Social Responsiveness Scales-Second Edition and Vinland-3 scores was used to identify subgroups. EEG functional connectivity and topological characteristics in four frequency bands were assessed. Two subgroups were identified: mild ASD (mASD, n = 37) and severe ASD (sASD, n = 35). Compared to TD, mASD showed increased functional connectivity in the beta band, while sASD exhibited decreased connectivity in the alpha band. Significant between-group differences in global and regional topological abnormalities were found in both alpha and beta bands. The proposed symptom-based clustering approach revealed the divergent functional connectivity patterns in the ASD subgroups that was not observed in typical ASD studies. Our study thus provides a new perspective to address the heterogeneity in ASD research.

Components of event-related potentials and borderline personality disorder: a meta-analysis

Background: Borderline personality disorder (BPD) is characterized by symptoms associated with difficulties in emotion regulation, altered self-image, impulsivity, and instability in personal relationships. A relationship has been found between BPD symptoms and altered neuropsychological processes. Studies of event-related potentials (ERP) measured with electroencephalogram (EEG) have found neural correlates related to BPD symptoms. Of note is the P300 component, considered a potential mental health biomarker for trauma-associated disorders. However, no meta-analysis has been found to demonstrate this relationship.Objectives: To evaluate the relationship between the P300 component and BPD symptoms. To evaluate the relationship of other ERP components with BPD symptoms.Methods: The method and procedure were adjusted to the PRISMA checklist. The search was performed in three databases: WOS, Scopus and PubMed. A Random Effects Model was used to perform the analysis of the studies. In addition, a meta-regression was performed with % women, Gini and GDP. Finally, a descriptive analysis of the main results found between P300, other ERP components (LPP, P100 and ERN/Ne) and BPD symptoms was performed.Results: From a review of 485 articles, a meta-analysis was performed with six articles that met the inclusion criteria. A moderate, positive relationship was found between the P300 component and BPD symptoms (REM = .489; p < .001). It was not possible to perform meta-analyses for other ERP components (LPP, P100 and ERN/Ne) due to the low number of articles found.Conclusion: The idea that P300 could be considered for use as a biomarker to identify altered neural correlates in BPD is reinforced. In addition, a moderating effect of inequality (Gini) was detected.

Gamma music: a new acoustic stimulus for gamma-frequency auditory steady-state response

A frequency range exceeding approximately 30 Hz, denoted as the gamma frequency range, is associated with various cognitive functions, consciousness, sensory integration, short-term memory, working memory, encoding and maintenance of episodic memory, and retrieval processes. In this study, we proposed a new form of gamma stimulation, called gamma music, combining 40 Hz auditory stimuli and music. This gamma music consists of drums, bass, and keyboard sounds, each containing a 40 Hz frequency oscillation. Since 40 Hz stimuli are known to induce an auditory steady-state response (ASSR), we used the 40 Hz power and phase locking index (PLI) as indices of neural activity during sound stimulation. We also recorded subjective ratings of each sound through a questionnaire using a visual analog scale. The gamma music, gamma drums, gamma bass, and gamma keyboard sounds showed significantly higher values in 40 Hz power and PLI compared to the control music without a 40 Hz oscillation. Particularly, the gamma keyboard sound showed a potential to induce strong ASSR, showing high values in these indices. In the subjective ratings, the gamma music, especially the gamma keyboard sound, received more relaxed, comfortable, preferred, pleasant, and natural impressions compared to the control music with conventional gamma stimulation. These results indicate that our proposed gamma music has potential as a new method for inducing ASSR. Particularly, the gamma keyboard sound proved to be an effective acoustic source for inducing a strong ASSR while preserving the comfortable and pleasant sensation of listening to music. Our developed gamma music, characterized by its pleasantness to the human ear, offers a significant advantage for the long-term use of gamma stimulation. The utilization of this music could potentially reduce the physical and psychological burden on participants compared to conventional 40 Hz stimuli. This music is not only expected to contribute to fundamental neuroscience research utilizing ASSR but also to facilitate the implementation of gamma music-based interventions aimed at enhancing human cognitive functions in everyday life.

Brain-behavior analysis of transcranial direct current stimulation effects on a complex surgical motor task

Transcranial Direct Current Stimulation (tDCS) has demonstrated its potential in enhancing surgical training and performance compared to sham tDCS. However, optimizing its efficacy requires the selection of appropriate brain targets informed by neuroimaging and mechanistic understanding. Previous studies have established the feasibility of using portable brain imaging, combining functional near-infrared spectroscopy (fNIRS) with tDCS during Fundamentals of Laparoscopic Surgery (FLS) tasks. This allows concurrent monitoring of cortical activations. Building on these foundations, our study aimed to explore the multi-modal imaging of the brain response using fNIRS and electroencephalogram (EEG) to tDCS targeting the right cerebellar (CER) and left ventrolateral prefrontal cortex (PFC) during a challenging FLS suturing with intracorporeal knot tying task. Involving twelve novices with a medical/premedical background (age: 22-28 years, two males, 10 females with one female with left-hand dominance), our investigation sought mechanistic insights into tDCS effects on brain areas related to error-based learning, a fundamental skill acquisition mechanism. The results revealed that right CER tDCS applied to the posterior lobe elicited a statistically significant (q < 0.05) brain response in bilateral prefrontal areas at the onset of the FLS task, surpassing the response seen with sham tDCS. Additionally, right CER tDCS led to a significant (p < 0.05) improvement in FLS scores compared to sham tDCS. Conversely, the left PFC tDCS did not yield a statistically significant brain response or improvement in FLS performance. In conclusion, right CER tDCS demonstrated the activation of bilateral prefrontal brain areas, providing valuable mechanistic insights into the effects of CER tDCS on FLS peformance. These insights motivate future investigations into the effects of CER tDCS on error-related perception-action coupling through directed functional connectivity studies.

Hyperventilation Induced Seizures in Focal Epilepsy: Two Cases and a Review of Literature

We report two cases of temporo-perisylvian epilepsy with habitual seizures consistently inducible by hyperventilation (HV). One case was non-lesional, while the other was a lesional temporo-perisylvian epilepsy. Both underwent surgical resection and were seizure-free or nearly seizure-free thereafter. We discuss the pathophysiological changes evoked by HV in healthy brains, and those with generalized and focal epilepsy. We provide a comprehensive and critical review of the literature on the role of HV in focal epilepsy. We suggest HV should be considered an activation method for patients with focal epilepsy during epilepsy monitoring unit admissions and may help in the localization of the epileptogenic network/zone.

Juvenile idiopathic epilepsy in Egyptian Arabian foals, a potential animal model of self-limited epilepsy in children

BACKGROUND

Juvenile idiopathic epilepsy (JIE) is categorized as a generalized epilepsy. Epilepsy classification entails electrocortical characterization and localization of epileptic discharges (ED) using electroencephalography (EEG).

HYPOTHESIS/OBJECTIVES

Characterize epilepsy in Egyptian Arabian foals with JIE using EEG.

ANIMALS

Sixty-nine foals (JIE, 48; controls, 21).

METHODS

Retrospective study. Inclusion criteria consisted of Egyptian Arabian foals: (1) JIE group diagnosed based on witnessed or recorded seizures, and neurological and EEG findings, and (2) control group of healthy nonepileptic age-matched foals. Clinical data were obtained in 48 foals. Electroencephalography with photic stimulation was performed under standing sedation in 37 JIE foals and 21 controls.

RESULTS

Abnormalities on EEG were found in 95% of epileptic foals (35 of 37) and in 3 of 21 control asymptomatic foals with affected siblings. Focal ED were detected predominantly in the central vertex with diffusion into the centroparietal or frontocentral regions (n = 35). Generalization of ED occurred in 14 JIE foals. Epileptic discharges commonly were seen during wakefulness (n = 27/37 JIE foals) and sedated sleep (n = 35/37 JIE foals; 3/21 controls). Photic stimulation triggered focal central ED in 15 of 21 JIE foals.

CONCLUSIONS AND CLINICAL IMPORTANCE

Juvenile idiopathic epilepsy has a focal onset of ED at the central vertex with spread resulting in clinical generalized tonic-clonic seizures with facial motor activity and loss of consciousness. Electroencephalography with photic stimulation contributes to accurate phenotyping of epilepsy. Foals with this benign self-limiting disorder might serve as a naturally occurring animal model for self-limited epilepsy in children.