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Background: The complicated mental illness known as borderline personality disorder (BPD) is characterized by impulsivity, emotional instability, and interpersonal challenges. Measurement of brain electrical activity using electroencephalography (EEG) can yield helpful biomarkers for a range of psychiatric conditions. Objectives: The present study utilizes comprehensive EEG analysis to identify distinct neural patterns in BPD, providing insights and potential biomarkers for diagnosing and understanding BPD. Materials & Methods: In a cross-sectional study conducted on students of Guilan University (Iran), 534 students were evaluated through the Millon clinical multiaxial inventory from September 2018 to September 2020. Fourteen students were recruited into this case-control study: Seven students with BPD and seven healthy controls. EEG recording was performed by a standard 21-channel device. The EEG signals were processed with the EEGLAB toolbox in the MATLAB environment. The absolute and relative powers were calculated using the fast Fourier transform function. The power spectral density (PSD) and the coherence between different channels were evaluated, too. The statistical analysis was performed using GraphPad Prism software. Results: The results showed a significant increase in the delta band range, mainly in the frontotemporal region, and a reduction in the power of the alpha band in students with BPD. The frontal brain region of BPD patients also showed reduced alpha band coherence. Conclusion: Students with BPD exhibit distinct EEG patterns, including increased delta band power and reduced alpha band power and coherence. These findings suggest potential EEG biomarkers for BPD, highlighting the utility of EEG in understanding this complex disorder. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) wearable devices are particularly suitable for monitoring a subject's engagement while performing daily cognitive tasks. EEG information provided by wearable devices varies with the location of the electrodes, the suitable location of which can be obtained using standard multi-channel EEG recorders. Cognitive engagement can be assessed during working memory (WM) tasks, testing the mental ability to process information over a short period of time. WM could be impaired in patients with epilepsy. This study aims to evaluate the cognitive engagement of nine patients with epilepsy, coming from a public dataset by Boran et al., during a verbal WM task and to identify the most suitable location of the electrodes for this purpose. Cognitive engagement was evaluated by computing 37 engagement indexes based on the ratio of two or more EEG rhythms assessed by their spectral power. Results show that involvement index trends follow changes in cognitive engagement elicited by the WM task, and, overall, most changes appear most pronounced in the frontal regions, as observed in healthy subjects. Therefore, involvement indexes can reflect cognitive status changes, and frontal regions seem to be the ones to focus on when designing a wearable mental involvement monitoring EEG system, both in physiological and epileptic conditions. [ABSTRACT FROM AUTHOR]

The objectives of this machine-learning (ML) resting-state magnetoencephalography (rs-MEG) study involving children with mild traumatic brain injury (mTBI) and orthopedic injury (OI) controls were to define a neural injury signature of mTBI and to delineate the pattern(s) of neural injury that determine behavioral recovery. Children ages 8-15 years with mTBI (n = 59) and OI (n = 39) from consecutive admissions to an emergency department were studied prospectively for parent-rated post-concussion symptoms (PCS) at: 1) baseline (average of 3 weeks post-injury) to measure pre-injury symptoms and also concurrent symptoms; and 2) at 3-months post-injury. rs-MEG was conducted at the baseline assessment. The ML algorithm predicted cases of mTBI versus OI with sensitivity of 95.5 ± 1.6% and specificity of 90.2 ± 2.7% at 3-weeks post-injury for the combined delta-gamma frequencies. The sensitivity and specificity were significantly better (p

Electroencephalography (EEG) wearable devices are particularly suitable for monitoring a subject’s engagement while performing daily cognitive tasks. EEG information provided by wearable devices varies with the location of the electrodes, the suitable location of which can be obtained using standard multi-channel EEG recorders. Cognitive engagement can be assessed during working memory (WM) tasks, testing the mental ability to process information over a short period of time. WM could be impaired in patients with epilepsy. This study aims to evaluate the cognitive engagement of nine patients with epilepsy, coming from a public dataset by Boran et al., during a verbal WM task and to identify the most suitable location of the electrodes for this purpose. Cognitive engagement was evaluated by computing 37 engagement indexes based on the ratio of two or more EEG rhythms assessed by their spectral power. Results show that involvement index trends follow changes in cognitive engagement elicited by the WM task, and, overall, most changes appear most pronounced in the frontal regions, as observed in healthy subjects. Therefore, involvement indexes can reflect cognitive status changes, and frontal regions seem to be the ones to focus on when designing a wearable mental involvement monitoring EEG system, both in physiological and epileptic conditions.

Network oscillations are essential for all cognitive functions. Oscillatory deficits are well established in psychiatric diseases and are recapitulated in animal models. They are significantly and specifically affected by pharmacological interventions using psychoactive compounds. Dopamine D4 receptor (D4R) activation was shown to enhance gamma rhythm in freely moving rats and to specifically affect slow delta and theta oscillations in the urethane-anesthetized rat model. The goal of this study was to test the effect of D4R activation on slow network oscillations at delta and theta frequencies during wake states, potentially supporting enhanced functional connectivity during dopamine-induced attention and cognitive processing. Network activity was recorded in the prefrontal cortex (PFC), hippocampus (HC) and nucleus reuniens (RE) in control conditions and after injecting the D4R agonist A-412997 (3 and 5 mg/kg; systemic administration). We found that A-412997 elicited a lasting (~40 min) wake state and drastically enhanced narrow-band delta oscillations in the PFC and RE in a dose-dependent manner. It also preferentially enhanced delta synchrony over theta coupling within the PFC-RE-HC circuit, strongly strengthening PFC-RE coupling. Thus, our findings indicate that the D4R may contribute to cognitive processes, at least in part, through acting on wake delta oscillations and that the RE, providing an essential link between the PFC and HC, plays a prominent role in this mechanism. [ABSTRACT FROM AUTHOR]

ObjectiveAngelman syndrome (AS) is a severe neurodevelopmental disorder caused by loss of function of the maternally inherited UBE3A gene in neurons. Promising disease-modifying treatments to reinstate UBE3A expression are under development and an early measure of treatment response is critical to their deployment in clinical trials. Increased delta power in EEG recordings, reflecting abnormal neuronal synchrony, occurs in AS across species and correlates with genotype. Whether delta power provides a reliable biomarker for clinical symptoms remains unknown.MethodsWe analyzed combined EEG recordings and developmental assessments in a large cohort of individuals with AS (N = 82 subjects, 133 combined EEG and cognitive assessments, 1.08-28.16 years; 32F) and evaluated delta power as a biomarker for cognitive function, as measured by the Bayley Cognitive Score. We examined the robustness of this biomarker to varying states of consciousness, recording techniques and analysis procedures.ResultsDelta power predicted the Bayley Scale cognitive score (P  4) and predicted performance in expressive (P = 0.0209) and receptive communication (P

Combat-related mild traumatic brain injury (cmTBI) is a leading cause of sustained physical, cognitive, emotional, and behavioral disabilities in Veterans and active-duty military personnel. Accurate diagnosis of cmTBI is challenging since the symptom spectrum is broad and conventional neuroimaging techniques are insensitive to the underlying neuropathology. The present study developed a novel deep-learning neural network method, 3D-MEGNET, and applied it to resting-state magnetoencephalography (rs-MEG) source-magnitude imaging data from 59 symptomatic cmTBI individuals and 42 combat-deployed healthy controls (HCs). Analytic models of individual frequency bands and all bands together were tested. The All-frequency model, which combined delta-theta (1-7 Hz), alpha (8-12 Hz), beta (15-30 Hz), and gamma (30-80 Hz) frequency bands, outperformed models based on individual bands. The optimized 3D-MEGNET method distinguished cmTBI individuals from HCs with excellent sensitivity (99.9 ± 0.38%) and specificity (98.9 ± 1.54%). Receiver-operator-characteristic curve analysis showed that diagnostic accuracy was 0.99. The gamma and delta-theta band models outperformed alpha and beta band models. Among cmTBI individuals, but not controls, hyper delta-theta and gamma-band activity correlated with lower performance on neuropsychological tests, whereas hypo alpha and beta-band activity also correlated with lower neuropsychological test performance. This study provides an integrated framework for condensing large source-imaging variable sets into optimal combinations of regions and frequencies with high diagnostic accuracy and cognitive relevance in cmTBI. The all-frequency model offered more discriminative power than each frequency-band model alone. This approach offers an effective path for optimal characterization of behaviorally relevant neuroimaging features in neurological and psychiatric disorders.

ObjectiveTo determine the best of commonly used methods for computing the rate of decline in non-rapid eye movement (NREM) sleep EEG delta power overnight (Delta Decline) in terms of vulnerability to missing data and to evaluate whether this rate is slower in insomnia patients than healthy controls (HC).MethodsFifty-one insomnia patients and 53 HC underwent 6 nights of polysomnography. Four methods for estimating Delta Decline were compared (exponential and linear best-fit functions using NREM (1) episode mean, (2) peak, and (3) total delta power and (4) delta power for all available NREM epochs). The best method was applied to compare groups on linear and exponential rates of Delta Decline.ResultsBest-fit models using all available NREM epochs were significantly less vulnerable to deviation due to missing data than other methods. Insomnia patients displayed significantly slower linear and exponential Delta Decline than HC.ConclusionsComputing Delta Decline using all available NREM epochs was the best of the methods studied for minimizing the effects of missing data. Insomnia patients display slower Delta Decline, which is not explained by differences in total sleep time or wake after sleep onset.SignificanceThis study supports using all available NREM epochs in Delta Decline computation and suggests a slower rate in insomnia.

The functional consequences of ischemic stroke in the remote brain regions are not well characterized. The current study sought to determine changes in hippocampal oscillatory activity that may underlie the cognitive impairment observed following distal middle cerebral artery occlusion (dMCAO) without causing hippocampal structural damage. Local field potentials were recorded from the dorsal hippocampus and cortex in urethane-anesthetized rats with multichannel silicon probes during dMCAO and reperfusion, or mild ischemia induced by bilateral common carotid artery occlusion (CCAO). Bilateral change of brain state was evidenced by reduced theta/delta amplitude ratio and shortened high theta duration following acute dMCAO but not CCAO. An aberrant increase in the occurrence of sharp-wave-associated ripples (150-250 Hz), crucial for memory consolidation, was only detected after dMCAO reperfusion, coinciding with an increased occurrence of high-frequency discharges (250-450 Hz). dMCAO also significantly affected the modulation of gamma amplitude in the cortex coupled to hippocampal theta phase, although both hippocampal theta and gamma power were temporarily decreased during dMCAO. Our results suggest that MCAO may disrupt the balance between excitatory and inhibitory circuits in the hippocampus and alter the function of cortico-hippocampal network, providing a novel insight in how cortical stroke affects function in remote brain regions.

The objectives of this machine-learning (ML) resting-state magnetoencephalography (rs-MEG) study involving children with mild traumatic brain injury (mTBI) and orthopedic injury (OI) controls were to define a neural injury signature of mTBI and to delineate the pattern(s) of neural injury that determine behavioral recovery. Children ages 8-15 years with mTBI (n = 59) and OI (n = 39) from consecutive admissions to an emergency department were studied prospectively for parent-rated post-concussion symptoms (PCS) at: 1) baseline (average of 3 weeks post-injury) to measure pre-injury symptoms and also concurrent symptoms; and 2) at 3-months post-injury. rs-MEG was conducted at the baseline assessment. The ML algorithm predicted cases of mTBI versus OI with sensitivity of 95.5 ± 1.6% and specificity of 90.2 ± 2.7% at 3-weeks post-injury for the combined delta-gamma frequencies. The sensitivity and specificity were significantly better (p < 0.0001) for the combined delta-gamma frequencies compared with the delta-only and gamma-only frequencies. There were also spatial differences in rs-MEG activity between mTBI and OI groups in both delta and gamma bands in frontal and temporal lobe, as well as more widespread differences in the brain. The ML algorithm accounted for 84.5% of the variance in predicting recovery measured by PCS changes between 3 weeks and 3 months post-injury in the mTBI group, and this was significantly lower (p < 10−4) in the OI group (65.6%). Frontal lobe pole (higher) gamma activity was significantly (p < 0.001) associated with (worse) PCS recovery exclusively in the mTBI group. These findings demonstrate a neural injury signature of pediatric mTBI and patterns of mTBI-induced neural injury related to behavioral recovery. [ABSTRACT FROM AUTHOR]

Origin and functions of intermittent transitions among sleep stages, including short awakenings and arousals, constitute a challenge to the current homeostatic framework for sleep regulation, focusing on factors modulating sleep over large time scales. Here we propose that the complex micro-architecture characterizing the sleep-wake cycle results from an underlying non-equilibrium critical dynamics, bridging collective behaviors across spatio-temporal scales. We investigate θ and δ wave dynamics in control rats and in rats with lesions of sleep-promoting neurons in the parafacial zone. We demonstrate that intermittent bursts in θ and δ rhythms exhibit a complex temporal organization, with long-range power-law correlations and a robust duality of power law (θ-bursts, active phase) and exponential-like (δ-bursts, quiescent phase) duration distributions, typical features of non-equilibrium systems self-organizing at criticality. Crucially, such temporal organization relates to anti-correlated coupling between θ- and δ-bursts, and is independent of the dominant physiologic state and lesions, a solid indication of a basic principle in sleep dynamics.

Sleep has been implicated in both memory consolidation and forgetting of experiences. However, it is unclear what governs the balance between consolidation and forgetting. Here, we tested how activity-dependent processing during sleep might differentially regulate these two processes. We specifically examined how neural reactivations during non-rapid eye movement (NREM) sleep were causally linked to consolidation versus weakening of the neural correlates of neuroprosthetic skill. Strikingly, we found that slow oscillations (SOs) and delta (δ) waves have dissociable and competing roles in consolidation versus forgetting. By modulating cortical spiking linked to SOs or δ waves using closed-loop optogenetic methods, we could, respectively, weaken or strengthen consolidation and thereby bidirectionally modulate sleep-dependent performance gains. We further found that changes in the temporal coupling of spindles to SOs relative to δ waves could account for such effects. Thus, our results indicate that neural activity driven by SOs and δ waves have competing roles in sleep-dependent memory consolidation.

BACKGROUND:Angelman syndrome (AS) is a severe neurodevelopmental disorder caused by either disruptions of the gene UBE3A or deletion of chromosome 15 at 15q11-q13, which encompasses UBE3A and several other genes, including GABRB3, GABRA5, GABRG3, encoding gamma-aminobutyric acid type A receptor subunits (β3, α5, γ3). Individuals with deletions are generally more impaired than those with other genotypes, but the underlying pathophysiology remains largely unknown. Here, we used electroencephalography (EEG) to test the hypothesis that genes other than UBE3A located on 15q11-q13 cause differences in pathophysiology between AS genotypes. METHODS:We compared spectral power of clinical EEG recordings from children (1-18 years of age) with a deletion genotype (n = 37) or a nondeletion genotype (n = 21) and typically developing children without Angelman syndrome (n = 48). RESULTS:We found elevated theta power (peak frequency: 5.3 Hz) and diminished beta power (peak frequency: 23 Hz) in the deletion genotype compared with the nondeletion genotype as well as excess broadband EEG power (1-32 Hz) peaking in the delta frequency range (peak frequency: 2.8 Hz), shared by both genotypes but stronger for the deletion genotype at younger ages. CONCLUSIONS:Our results provide strong evidence for the contribution of non-UBE3A neuronal pathophysiology in deletion AS and suggest that hemizygosity of the GABRB3-GABRA5-GABRG3 gene cluster causes abnormal theta and beta EEG oscillations that may underlie the more severe clinical phenotype. Our work improves the understanding of AS pathophysiology and has direct implications for the development of AS treatments and biomarkers.

Genome-wide association studies have implicated many ion channels in schizophrenia pathophysiology. Although the functions of these channels are relatively well characterized by single-cell studies, the contributions of common variation in these channels to neurophysiological biomarkers and symptoms of schizophrenia remain elusive. Here, using computational modeling, we show that a common biomarker of schizophrenia, namely, an increase in delta-oscillation power, may be a direct consequence of altered expression or kinetics of voltage-gated ion channels or calcium transporters. Our model of a circuit of layer V pyramidal cells highlights multiple types of schizophrenia-related variants that contribute to altered dynamics in the delta-frequency band. Moreover, our model predicts that the same membrane mechanisms that increase the layer V pyramidal cell network gain and response to delta-frequency oscillations may also cause a deficit in a single-cell correlate of the prepulse inhibition, which is a behavioral biomarker highly associated with schizophrenia.

The vast majority of studies on hippocampal rhythms have been conducted on animals or humans in situations where their attention was focused on external stimuli or solving cognitive tasks. These studies formed the basis for the idea that rhythmical activity coordinates the work of neurons during information processing. However, at rest, when attention is not directed to external stimuli, brain rhythms do not disappear, although the parameters of oscillatory activity change. What is the functional load of rhythmical activity at rest? Hippocampal oscillatory activity during rest is called the non-theta state, as opposed to the theta state, a characteristic activity during active behavior.We dedicate our review to discussing the present state of the art in the research of the non-theta state. The key provisions of the review are as follows: (1) the non-theta state has its own characteristics of oscillatory and neuronal activity; (2) hippocampal non-theta state is possibly caused and maintained by change of rhythmicity of medial septal input under the influence of raphe nuclei; (3) there is no consensus in the literature about cognitive functions of the non-theta-non-ripple state; and (4) the antagonistic relationship between theta and delta rhythms observed in rodents is not always observed in humans. Most attention is paid to the non-theta-nonripple state, since this aspect of hippocampal activity has not been investigated properly and discussed in reviews. [ABSTRACT FROM AUTHOR]

The vast majority of studies on hippocampal rhythms have been conducted on animals or humans in situations where their attention was focused on external stimuli or solving cognitive tasks. These studies formed the basis for the idea that rhythmical activity coordinates the work of neurons during information processing. However, at rest, when attention is not directed to external stimuli, brain rhythms do not disappear, although the parameters of oscillatory activity change. What is the functional load of rhythmical activity at rest? Hippocampal oscillatory activity during rest is called the non-theta state, as opposed to the theta state, a characteristic activity during active behavior. We dedicate our review to discussing the present state of the art in the research of the non-theta state. The key provisions of the review are as follows: (1) the non-theta state has its own characteristics of oscillatory and neuronal activity; (2) hippocampal non-theta state is possibly caused and maintained by change of rhythmicity of medial septal input under the influence of raphe nuclei; (3) there is no consensus in the literature about cognitive functions of the non-theta-non-ripple state; and (4) the antagonistic relationship between theta and delta rhythms observed in rodents is not always observed in humans. Most attention is paid to the non-theta-non-ripple state, since this aspect of hippocampal activity has not been investigated properly and discussed in reviews.

Background:Angelman syndrome (AS) is a neurodevelopmental disorder characterized by intellectual disability, speech and motor impairments, epilepsy, abnormal sleep, and phenotypic overlap with autism. Individuals with AS display characteristic EEG patterns including high-amplitude rhythmic delta waves. Here, we sought to quantitatively explore EEG architecture in AS beyond known spectral power phenotypes. We were motivated by studies of functional connectivity and sleep spindles in autism to study these EEG readouts in children with AS. Methods:We analyzed retrospective wake and sleep EEGs from children with AS (age 4-11) and age-matched neurotypical controls. We assessed long-range and short-range functional connectivity by measuring coherence across multiple frequencies during wake and sleep. We quantified sleep spindles using automated and manual approaches. Results:During wakefulness, children with AS showed enhanced long-range EEG coherence across a wide range of frequencies. During sleep, children with AS showed increased long-range EEG coherence specifically in the gamma band. EEGs from children with AS contained fewer sleep spindles, and these spindles were shorter in duration than their neurotypical counterparts. Conclusions:We demonstrate two quantitative readouts of dysregulated sleep composition in children with AS-gamma coherence and spindles-and describe how functional connectivity patterns may be disrupted during wakefulness. Quantitative EEG phenotypes have potential as biomarkers and readouts of target engagement for future clinical trials and provide clues into how neural circuits are dysregulated in children with AS.

ObjectivePost-ictal EEG alterations have been identified in studies of intracranial recordings, but the clinical significance of post-ictal EEG activity is undetermined. The purpose of this study was to examine the relationship between peri-ictal EEG activity, surgical outcome, and extent of seizure propagation in a sample of pediatric epilepsy patients.MethodsIntracranial EEG recordings were obtained from 19 patients (mean age = 11.4 years, range = 3-20 years) with 57 seizures used for analysis (mean = 3.0 seizures per patient). For each seizure, 3-min segments were extracted from adjacent pre-ictal and post-ictal epochs. To compare physiology of the epileptic network between epochs, we calculated the relative delta power (Δ) using discrete Fourier transformation and constructed functional networks based on broadband connectivity (conn). We investigated differences between the pre-ictal (Δpre, connpre) and post-ictal (Δpost, connpost) segments in focal-network (i.e., confined to seizure onset zone) versus distributed-network (i.e., diffuse ictal propagation) seizures.ResultsDistributed-network (DN) seizures exhibited increased post-ictal delta power and global EEG connectivity compared to focal-network (FN) seizures. Following DN seizures, patients with seizure-free outcomes exhibited a 14.7% mean increase in delta power and an 8.3% mean increase in global connectivity compared to pre-ictal baseline, which was dramatically less than values observed among seizure-persistent patients (29.6% and 47.1%, respectively).SignificancePost-ictal differences between DN and FN seizures correlate with post-operative seizure persistence. We hypothesize that post-ictal deactivation of subcortical nuclei recruited during seizure propagation may account for this result while lending insights into mechanisms of post-operative seizure recurrence.

IMPORTANCE: Continuous electroencephalography (EEG) use in critically ill patients is expanding. There is no validated method to combine risk factors and guide clinicians in assessing seizure risk. OBJECTIVE: To use seizure risk factors from EEG and clinical history to create a simple scoring system associated with the probability of seizures in patients with acute illness. DESIGN, SETTING, AND PARTICIPANTS: We used a prospective multicenter (Emory University Hospital, Brigham and Womens Hospital, and Yale University Hospital) database containing clinical and electrographic variables on 5427 continuous EEG sessions from eligible patients if they had continuous EEG for clinical indications, excluding epilepsy monitoring unit admissions. We created a scoring system model to estimate seizure risk in acutely ill patients undergoing continuous EEG. The model was built using a new machine learning method (RiskSLIM) that is designed to produce accurate, risk-calibrated scoring systems with a limited number of variables and small integer weights. We validated the accuracy and risk calibration of our model using cross-validation and compared its performance with models built with state-of-the-art logistic regression methods. The database was developed by the Critical Care EEG Research Consortium and used data collected over 3 years. The EEG variables were interpreted using standardized terminology by certified reviewers. EXPOSURES: All patients had more than 6 hours of uninterrupted EEG recordings. MAIN OUTCOMES AND MEASURES: The main outcome was the average risk calibration error. RESULTS: There were 5427 continuous EEGs performed on 4772 participants (2868 men, 49.9%; median age, 61 years) performed at 3 institutions, without further demographic stratification. Our final model, 2HELPS2B, had an area under the curve of 0.819 and average calibration error of 2.7% (95% CI, 2.0%-3.6%). It included 6 variables with the following point assignments: (1) brief (ictal) rhythmic discharges (B[I]RDs) (2 points); (2) presence of lateralized periodic discharges, lateralized rhythmic delta activity, or bilateral independent periodic discharges (1 point); (3) prior seizure (1 point); (4) sporadic epileptiform discharges (1 point); (5) frequency greater than 2.0 Hz for any periodic or rhythmic pattern (1 point); and (6) presence of plus features (superimposed, rhythmic, sharp, or fast activity) (1 point). The probable seizure risk of each score was 5% for a score of 0, 12% for a score of 1, 27% for a score of 2, 50% for a score of 3, 73% for a score of 4, 88% for a score of 5, and greater than 95% for a score of 6 or 7. CONCLUSIONS AND RELEVANCE: The 2HELPS2B model is a quick accurate tool to aid clinical judgment of the risk of seizures in critically ill patients.

Common-phase synchronization of neuronal oscillations is a mechanism by which distributed brain regions can be integrated into transiently stable networks. Based on the hypothesis that schizophrenia is characterized by deficits in functional integration within neuronal networks, this study aimed to explore whether psychotic patients exhibit differences in brain regions involved in integrative mechanisms. We report an electroencephalography (EEG)-informed functional magnetic resonance imaging analysis of eyes-open resting-state data collected from patients and healthy controls at two study sites. Global field synchronization (GFS) was chosen as an EEG measure indicating common-phase synchronization across electrodes. Several brain clusters appeared to be coupled to GFS differently in patients and controls. Activation in brain areas belonging to the default mode network was negatively associated to GFS delta (1-3.5 Hz) and positively to GFS beta (13-30 Hz) bands in patients, whereas controls showed an opposite pattern for both GFS frequency bands in those regions; activation in the extrastriate visual cortex was inversely related to GFS alpha1 (8.5-10.5 Hz) band in healthy controls, while patients had a tendency toward a positive relationship. Taken together, the GFS measure might be useful for detecting additional aspects of deficient functional network integration in psychosis.

Objective: The objective of this study was to investigate the impact of smoking during pregnancy on the development of the child. While previous research has established its detrimental effects during early childhood, understanding potential long-term consequences into adulthood remains limited. This study specifically aimed to explore the influence of prenatal smoking exposure on brain activity and whether internalizing and externalizing symptoms are influenced by prenatal smoking exposure in a cohort of young adults., Methods: Utilizing data from 176 participants (mean age M = 24.68, SD = 0.49) and their mothers enrolled in a longitudinal risk study (MARS), we employed Generalized Additive Mixed Models (GAMMs) to analyze electroencephalography (EEG) power at rest and behavioral outcomes derived from the Young Adult-Self-Report (YASR) scales. Both covariate-unadjusted and -adjusted models were used, taking into account participant variables such as sex and age, as well as maternal factors like psychopathology and alcohol consumption, in addition to smoking and alcohol intake by the participants themselves., Results: The study revealed a significant impact of prenatal smoking on delta and theta band power, indicating decreased slower brain activity in prenatally exposed individuals compared to unexposed counterparts. Additionally, individuals exposed to prenatal smoking exhibited significantly higher levels of externalizing behavior. While this association was strongly influenced by maternal psychopathology, the child's gender, and the child's own substance use, the effect on delta power band remained after adjusting for covariates., Conclusion: The findings suggest that prenatal smoking exposure may have enduring effects on brain activity patterns in young adulthood. Conversely, the influence on externalizing behaviors depended on familial factors (maternal psychopathology) and the lifestyle of the individual (substance use)., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Tobias Banaschewski served in an advisory or consultancy role for Lundbeck, Medice, Neurim Pharmaceuticals, Oberberg GmbH, Shire. He received conference support or speaker’s fee by Lilly, Medice, Novartis and Shire. He has been involved in clinical trials conducted by Shire and Viforpharma. He received royalties from Hogrefe, Kohlhammer, CIP Medien, Oxford University Press. Daniel Brandeis serves as an unpaid scientific consultant for an EU-funded neurofeedback trial. The present work is unrelated to the above grants and relationships. The other authors report no biomedical financial interests or potential conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Our objective was to define the EEG features during sleep of children with neurodevelopmental disorders due to copy number gains of 15q11-q13 (Dup15q). We retrospectively reviewed continuous EEG recordings of 42 children with Dup15q (mean age: eight years, 32 with idic15), and data collected included background activity, interictal epileptiform discharges, sleep organization, and ictal activity. Three patterns were recognized: Pattern 1: Alpha–delta sleep was noted in 14 children (33%), not associated with any clinical changes. Pattern 2: Electrical status epilepticus in sleep was noted in 15 children (35%), all diagnosed with treatmentresistant epilepsy. Thirteen of the 15 children had clinical seizures. Pattern 3: Frequent bursts of high amplitude bifrontal predominant, paroxysmal fast activity (12–15 Hz) during non-REM sleep was noted in 15 children (35%). All 15 children had treatment-resistant epilepsy. This is the first report of electroencephalographic patterns during sleep of children with Dup15q reporting alpha-delta rhythms, CSWS, and high amplitude fast frequencies. Alpha-delta rhythms are described in children with dysautonomia and/or mood disorders and CSWS in children with developmental regression. The significance of these findings in cognitive function and epilepsy for the children in our cohort needs to be determined with follow-up studies.

The problem of rehabilitation therapy after ischemic strokes is one of the most relevant in current neurology [4, 7]. Neurophysiological research is currently leading in the evaluation and control of brain plasticity. Approximately 80% of all injuries are the injuries to the musculoskeletal system; almost half of them are upper limb traumas. Treatment efficacy depends on the quality of first aid. Conservative treatment methods are effective in 13%–60% of patients, whereas with surgical methods, the efficacy is between 10% and 70%. Moreover, patients with radius fractures usually lose their ability to work for 6 to 8 months; 10% of them have to change their profession after it; 6% to 17% of patients become disabled. Rehabilitation of such patients should include dynamic electroencephalographic (EEG) monitoring, since the information about the functional state of cerebral structures is necessary for organizing an effective rehabilitation program, predicting and assessing treatment outcomes.We have examined 26 patients with cerebral stroke (14 men and 12 women aged 42–65 years) in the early stages with the leading neurological syndrome manifesting as spastic hemiparesis and 27 patients with extension fractures of the distal radial physis (after manual reposition and plaster splint removal) at different stages of their rehabilitation with a week interval.We found that changes in brain electric activity (from similar leads of the contralateral hemisphere) were more significant in patients with acute stroke, which can be associated with specific changes in the cerebral rhythms after stroke, since deep location of the alteration focus results in more diffused EEG symptoms due to conducting paths. Positive dynamics in the reduction of slow-wave activity above the affected area in the late rehabilitation period was more pronounced in patients who performed dynamic and static exercises. Normalization of electrogenesis was more effective in patients performing static exercises than in those performing dynamic exercises.

Sleep is regulated by homeostatic processes, yet the biological basis of sleep pressure that accumulates during wakefulness, triggers sleep, and dissipates during sleep remains elusive. We explored a causal relationship between cellular synaptic strength and electroencephalography delta power indicating macro-level sleep pressure by developing a theoretical framework and a molecular tool to manipulate synaptic strength. The mathematical model predicted that increased synaptic strength promotes the neuronal "down state" and raises the delta power. Our molecular tool (synapse-targeted chemically induced translocation of Kalirin-7, SYNCit-K), which induces dendritic spine enlargement and synaptic potentiation through chemically induced translocation of protein Kalirin-7, demonstrated that synaptic potentiation of excitatory neurons in the prefrontal cortex (PFC) increases nonrapid eye movement sleep amounts and delta power. Thus, synaptic strength of PFC excitatory neurons dictates sleep pressure in mammals.

One of the major breakthroughs of neurobiology was the identification of distinct ranges of oscillatory activity in the neuronal network that were found to be responsible for specific biological functions, both physiological and pathological in nature. Astrocytes, physically coupled by gap junctions and possessing the ability to simultaneously modulate the functions of a large number of surrounding synapses, are perfectly positioned to introduce synchronised oscillatory activity into the neural network. However, astrocytic somatic calcium signalling has not been investigated to date in the frequency ranges of common neuronal oscillations, since astrocytes are generally considered to be slow responders in terms of Ca 2+ signalling. Using high-frequency two-photon imaging, we reveal fast Ca 2+ oscillations in the soma of astrocytes in the delta (0.5-4 Hz) and theta (4-8 Hz) frequency bands in vivo in the rat cortex under ketamine-xylazine anaesthesia, which is known to induce permanent slow-wave sleep. The high-frequency astrocytic Ca 2+ signals were not observed under fentanyl anaesthesia, excluding the possibility that the signals were introduced by motion artefacts. We also demonstrate that these fast astrocytic Ca 2+ signals, previously considered to be exclusive to neurons, are present in a large number of astrocytes and are phase synchronised at the astrocytic network level. We foresee that the disclosure of these high-frequency astrocytic signals may help with understanding the appearance of synchronised oscillatory signals and may open up new avenues of treatment for neurological conditions characterised by altered neuronal oscillations.

Background: Following the standardized nomenclature proposed by the American Clinical Neurophysiology Society (ACNS), rhythmic high-amplitude delta activity with superimposed spikes (RHADS) can be reported as an extreme delta brush (EDB). The clinical implications of similar electrographic patterns being reported as RHADS versus EDB are important to highlight. We aim to review the electrographic characteristics of RHADS, evaluate whether RHADS is seen in other neurological disorders, and identify the similar and unique characteristics between RHADS and EDB to ultimately determine the most accurate way to differentiate and report these patterns. We believe that the differentiation of RHADS and EDB is important as there is a vast difference in the diagnostic approach and the medical management of associated underlying etiologies., Data Source: We conducted an extensive search on MEDLINE and Pubmed utilizing various combinations of keywords. Searching for "gamma polymerase and EEG", or "RHADS" or "Alpers syndrome and EEG" or "EEG" AND "Alpers-Huttenlocher syndrome"., Results: Three articles were found to be focused on the description of "RHADS" pattern in Alpers Syndrome. No publication to date were found when searching for the terms "EDB" AND "children", AND "infant" AND "adolescent" excluding "encephalitis" and "neonate". Although RHADS and EDB appear as similar EEG patterns, meticulous analysis can differentiate them. RHADS is not exclusive to patients with Alpers-Huttenlocher syndrome and may manifest in regions beyond the posterior head region. Reactivity to eye-opening and response to anesthesia can be two other elements that help in the differentiation of these patterns., Conclusion: RHADS is not exclusive to patients with AHS and may manifest in regions beyond the posterior head region. Reactivity to eye-opening and response to anesthesia are features that help in the differentiation of these patterns., (© 2024. Children's Hospital, Zhejiang University School of Medicine.)

This study focused on detecting the reflections of healing and change in cortex activation in full-face transplantation and lesions patients on EEG activity. Face transplant patients have facial lesions before transplantation and, to identify pre-face transplant patients' brain activity in the absence of pre-transplant recordings, we used data obtained from pre-transplant facial lesion patients. Ten healthy, four facial lesion and three full-face transplant patients participated in this study. EEG data recorded for four different sensory stimuli (brush from the right face, right hand, left face, and left-hand regions) were analyzed using wavelet packet transform method. EEG waves were analyzed for standard bands. Our findings indicate significant change in the 2-4 Hz frequency range which may be a result of ongoing or previous cortical reorganization for face lesion and transplant patients. Alterations of the delta wave seen in patients with facial lesion and face transplant can also be explained by the intense central plasticity. Our findings show that the delta band differences might be used as a marker in the evaluation of post-transplant cortical plasticity in the future., (© 2024. The Author(s).)

This study was aimed at comparing the delta rhythm frequencies of electroencephalographic waves after inducing either with thiopentone or propofol intravenously in basset hound dogs. Frontopolar montage was used to obtain the electroencephalographic waves. The depth of anaesthesia was also assessed clinically by recording the clinical parameters viz., heart rate, oxygen saturation, endtidal carbon dioxide and blood pressure as well as by checking the reflexes. The animals induced with thiopentone had a lower frequency compared to the animals induced with propofol at all time periods. The frequency obtained immediately after inducing with thiopentone was 3.50 ± 2.40 Hz whereas the frequency obtained immediately after inducing with propofol was 3.94 ± 2.25 Hz. Whereas the frequency obtained at the 60th minute time period after thiopentone induction was 2.13 ± 0.49 Hz and after propofol induction was 2.72 ± 1.56 Hz. The reflexes were absent during the observation periods. [ABSTRACT FROM AUTHOR]

Understanding human sleep requires appropriate animal models. Sleep has been extensively studied in rodents, although rodent sleep differs substantially from human sleep. Here we investigate sleep in tree shrews, small diurnal mammals phylogenetically close to primates, and compare it to sleep in rats and humans using electrophysiological recordings from frontal cortex of each species. Tree shrews exhibited consolidated sleep, with a sleep bout duration parameter, τ, uncharacteristically high for a small mammal, and differing substantially from the sleep of rodents that is often punctuated by wakefulness. Two NREM sleep stages were observed in tree shrews: NREM, characterized by high delta waves and spindles, and an intermediate stage (IS-NREM) occurring on NREM to REM transitions and consisting of intermediate delta waves with concomitant theta-alpha activity. While IS-NREM activity was reliable in tree shrews, we could also detect it in human EEG data, on a subset of transitions. Finally, coupling events between sleep spindles and slow waves clustered near the beginning of the sleep period in tree shrews, paralleling humans, whereas they were more evenly distributed in rats. Our results suggest considerable homology of sleep structure between humans and tree shrews despite the large difference in body mass between these species. Dimanico et al investigated sleep in tree shrews using electrophysiological recordings and compared it to equivalent read-outs in rats and humans. They reported that there was considerable homology of sleep structure between humans and tree shrews despite the difference in body mass between these species. [ABSTRACT FROM AUTHOR]

ObjectiveThe Child Behavior Checklist-Dysregulation Profile (CBCL/DP) identifies youth at increased risk for significant psychopathology. Although the genetic architecture and several biological correlates of the CBCL/DP have been described, little work has elucidated its underlying neurobiology. We examined the potential utility of electroencephalography (EEG), along with behavioral and cognitive assessments, in differentiating individuals based on the CBCL/DP.MethodParticipants aged 7 to 14 years of age were categorized into 3 age- and sex-matched groups based on clinical assessment and CBCL/DP: typically developing controls without attention-deficit/hyperactivity disorder (ADHD) (n = 38), individuals with ADHD without the CBCL/DP (ADHD/DP-) (n = 38), and individuals with the CBCL/DP (CBCL/DP+) (n = 38). Groups were compared with EEG and measures of clinical phenomenology and cognition.ResultsADHD/DP- and CBCL/DP+ groups had increased inattention, but the CBCL/DP+ group had increased hyperactive/impulsive symptoms, disruptive behavior, mood, and anxiety comorbidities compared with the group with ADHD alone. Cognitive profiles suggested that ADHD/DP-participants had fast impulsive responses, whereas CBCL/DP+ participants were slow and inattentive. On EEG, CBCL/DP+ had a distinct profile of attenuated δ-band and elevated α-band spectral power in the central and parietal regions compared to ADHD/DP- and controls. The low-δ/high-α profile was correlated with measures of emotion and behavior problems and not with inattentive symptomatology or cognitive measures. There were no EEG differences between the ADHD/DP- and control groups.ConclusionsAn EEG/cognitive profile suggests a distinct pattern of underlying neural dysfunction with the CBCL/DP that might ultimately serve as a biosignature. Further work is required to identify potential relationships with clinically defined psychiatric disorders, particularly those of dysregulated mood.

Although visual input arrives continuously, sensory information is segmented into (quasi-)discrete events. Here, we investigated the neural correlates of spatiotemporal binding in humans with magnetoencephalography using two tasks where separate flashes were presented on each trial but were perceived, in a bistable way, as either a single or two separate events. The first task (two-flash fusion) involved judging one versus two flashes, whereas the second task (apparent motion: AM) involved judging coherent motion versus two stationary flashes. Results indicate two different functional networks underlying two unique aspects of temporal binding. In two-flash fusion trials, involving an integration window of ∼50 msec, evoked responses differed as a function of perceptual interpretation by ∼25 msec after stimuli offset. Multivariate decoding of subjective perception based on prestimulus oscillatory phase was significant for alpha-band activity in the right medial temporal (V5/MT) area, with the strength of prestimulus connectivity between early visual areas and V5/MT being predictive of performance. In contrast, the longer integration window (∼130 msec) for AM showed evoked field differences only ∼250 msec after stimuli offset. Phase decoding of the perceptual outcome in AM trials was significant for theta-band activity in the right intraparietal sulcus. Prestimulus theta-band connectivity between V5/MT and intraparietal sulcus best predicted AM perceptual outcome. For both tasks, phase effects found could not be accounted by concomitant variations in power. These results show a strong relationship between specific spatiotemporal binding windows and specific oscillations, linked to the information flow between different areas of the where and when visual pathways., (© 2023 Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.)

ABSTRACT Objective: To verify if, by three distinct quantifiers, the measured electroencephalographic signal at rest is different from the signal measured during a word reading situation, especially considering the faster rhythms, gamma and high-gamma, as it occurs in clinical rhythms (delta to beta). Methods: A total of 96 electroencephalographic signals measured from neurologically healthy volunteers were evaluated at two moments: resting and word reading. Each signal segment was measured by three quantifiers that separately assess normalized power, percent power, and right and left hemisphere coherence. The Mann-Whitney test was used to compare the results of the quantifiers in each brain range. Results: The gamma and high-gamma rhythms presented a more distinct behavior when comparing the analyzed moments (resting and reading) than the clinical rhythms. Conclusion: This finding contributes to the scarce literature on faster rhythms, which can contain information that is normally disregarded in neurological clinical practice.

People aged 90 years and older (oldest old), the fastest growing segment of the United States population, are known to have high rates of spells of all types, including strokes, transient ischemic attacks, and seizures. This study examined the prevalence of EEG abnormalities in 12 physically and cognitively healthy oldest old (mean age = 94 years) with no history of seizures or spells. Abnormalities were found in 83% of participants: temporal intermittent polymorphic slowing was seen in 67%, background slowing (alpha rhythm

• The FFA plays a preponderant role in discriminating confusable facial expressions. • Images of low confusion induce larger LPP amplitude than those of high confusion. • Images of low confusion induce stronger delta rhythm than those of high confusion. Although the recognition of facial expressions seems automatic and effortless, discrimination of expressions can still be error prone. Common errors are often due to visual similarities between some expressions (e.g., fear and surprise). However, little is known about the neural mechanisms underlying such a confusion effect. To address this question, we recorded the magnetoencephalography (MEG) while participants judged facial expressions that were either easily confused with or easily distinguished from other expressions. The results showed that the fusiform face area (FFA), rather than the posterior superior temporal sulcus (pSTS), played a preponderant role in discriminating confusable facial expressions. No difference between high confusion and low confusion conditions was observed on the M170 component in either the FFA or the pSTS, whilst a difference between two conditions started to emerge in the late positive potential (LPP), with the low confusion condition eliciting a larger LPP amplitude in the FFA. In addition, the power of delta was stronger in the time window of LPP component. This confusion effect was reflected in the FFA, which might be associated with the perceptual-to-conceptual shift. [ABSTRACT FROM AUTHOR]

Objective: To verify if, by three distinct quantifiers, the measured electroencephalographic signal at rest is different from the signal measured during a word reading situation, especially considering the faster rhythms, gamma and high-gamma, as it occurs in clinical rhythms (delta to beta). Methods: A total of 96 electroencephalographic signals measured from neurologically healthy volunteers were evaluated at two moments: resting and word reading. Each signal segment was measured by three quantifiers that separately assess normalized power, percent power, and right and left hemisphere coherence. The Mann-Whitney test was used to compare the results of the quantifiers in each brain range. Results: The gamma and high-gamma rhythms presented a more distinct behavior when comparing the analyzed moments (resting and reading) than the clinical rhythms. Conclusion: This finding contributes to the scarce literature on faster rhythms, which can contain information that is normally disregarded in neurological clinical practice. [ABSTRACT FROM AUTHOR]

Facial Expression (FE) recognition is a major marker of emotional ability. Behavioral studies show that FE recognition ability decreases with aging. Studying how event-related brain oscillations change with normal aging is important to better understand the underlying mechanisms of emotional processes. The aim of this study is to investigate changes in FE recognition due to normal aging using the EEG-Brain Oscillations approach. Fifteen young and fifteen elderly healthy subjects were included in the study. 15 photographs were used with 5 different FEs (angry, happy, neutral, sad, fearful). After each EEG recording session, subjects were asked to identify the FEs that were presented. Event-related delta, theta and, alpha phase-locking and frequency-band responses were analyzed. In the FE recognition part of the study, young subjects obtained better scores than the elderly subjects. There was a significant result regarding the locationXgroup comparison in the delta response; the young group had a higher delta response than the elderly group over the occipital area. There were significant locationXgroup differences in the theta and alpha phase locking values; the elderly group had higher theta and alpha phase locking values than the young group in the frontal area. Group differences were significant in the theta response and theta phase locking; the elderly individuals' theta response and phase locking values were higher compared to those of the young individuals. In elderly individuals, FE recognition impairment has been observed. It has been shown that the impairment may be characterized by decreased occipital delta responses and phase locking. This can be interpreted to mean that elderly individuals may have developed different brain dynamics as a compensating mechanism since they are not as efficient as young individuals in performing these functions. • Facial expressions (FE) recognition is a fundamental marker of emotional abilities. • Emotional abilities change with aging. • We have suggested electrophysiological neural correlates of the emotional aging. • Dynamic patterns of FE has been explored in the elderly via the brain oscillations. [ABSTRACT FROM AUTHOR]

Cross-modal perception allows olfactory information to integrate with other sensory modalities. Olfactory representations are processed by multisensory cortical pathways, where the aspects related to the haptic sensations are integrated. This complex reality allows the development of an integrated perception, where olfactory aspects compete with haptic and/or trigeminal activations. It is assumed that this integration involves both perceptive electrophysiological and metabolic/hemodynamic aspects, but there are no studies evaluating these activations in parallel. The aim of this study was to investigate brain dynamics during a cross-modal olfactory and haptic attention task, preceded by an exploratory session. The assessment of cross-modal dynamics was conducted through simultaneous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) recording, evaluating both electrophysiological and hemodynamic activities. The study consisted of two experimental sessions and was conducted with a sample of ten healthy subjects (mean age 25 ± 5.2 years). In Session 1, the subjects were trained to manipulate 3D haptic models (HC) and to smell different scents (SC). In Session 2, the subjects were tested during an attentive olfactory task, in order to investigate the olfactory event-related potentials (OERP) N1 and late positive component (LPC), and EEG rhythms associated with fNIRS components (oxy-Hb and deoxy-Hb). The main results of this study highlighted, in Task 1, a higher fNIRS oxy-Hb response during SC and a positive correlation with the delta rhythm in the central and parietal EEG region of interest. In Session 2, the N1 OERP highlighted a greater amplitude in SC. A negative correlation was found in HC for the deoxy-Hb parietal with frontal and central N1, and for the oxy-Hb frontal with N1 in the frontal, central and parietal regions of interests (ROIs). A negative correlation was found in parietal LPC amplitude with central deoxy-Hb. The data suggest that cross-modal valence modifies the attentional olfactory response and that the dorsal cortical/metabolic pathways are involved in these responses. This can be considered as an important starting point for understanding integrated cognition, as the subject could perceive in an ecological context.

Interactions between different cortical rhythms, such as slow and fast oscillations, have been hypothesized to underlie many cognitive functions. In patients diagnosed with schizophrenia, there is some evidence indicating that the interplay between slow and fast oscillations might be impaired or disrupted. In this study, we investigated multiple oscillatory interactions in schizophrenia using a novel approach based on information theory. This method allowed us to investigate interactions from a new perspective, where two or more rhythm interactions could be analyzed at the same time. We calculated the mutual information of multiple rhythms (MIMR) for EEG segments registered in resting state. Following previous studies, we focused on rhythm interactions between theta, alpha, and gamma. The results showed that, in general, MIMR was higher in patients than in controls for alpha-gamma and theta-gamma couplings. This finding of an increased coupling between slow and fast rhythms in schizophrenia may indicate complex interactions in the Default Mode Network (DMN) related to hyperactivation of internally guided cognition., (© 2023. The Author(s).)

Brain activities are a mixture of periodic and aperiodic components, manifesting in the power spectral density (PSD) as rhythmic oscillations with spectral peaks and broadband fluctuations. Periodic oscillatory properties of brain response to external stimulation are widely studied, while aperiodic component responses remain unclear. Here, we investigate spatiotemporal dynamics of periodic and aperiodic brain activity under peripheral nerve stimulation with acupuncture by parameterization of power spectra of EEG signals. Regarding periodic brain activity, spectral peak in delta band emerges in frontal and central brain regions indicates a typical phenomenon of neural entrainment, which is formed by coupling periodic brain activity to external rhythmic acupuncture stimulation. In addition, the statistical results show that alpha periodic power is an important indicator for characterizing the modulatory effects of acupuncture on periodic brain activity. As for aperiodic brain activity, broadband EEG spectral trend analysis demonstrates a steeper aperiodic slope in left parietal lobe and a stronger negative correlation with the aperiodic offset under acupuncture compared with resting state, with the absolute value of correlation coefficient increasing from 0.27 to 0.50. Based on the two parameters that can best characterize the acupuncture effect, alpha periodic power and aperiodic slope, the accurate decoding of acupuncture manipulation is realized with AUC = 0.87. This work shows the modulatory effect of peripheral nerve stimulation with acupuncture on the brain activity by characterizing the periodic and aperiodic spectrum features of EEG, providing new insights into the comprehensive understanding of the response processes of human brain to acupuncture stimulation.

Depressive disorder has been known to be associated with high variability in resting-state electroencephalography (EEG) signals. However, this phenomenon is often ignored in stimulus-related brain activities. This study proposed a new method to explore the EEG variability evoked by transcranial magnetic stimulation (TMS, TMS-EEG) in depressive disorder (DE) patients. The TMS-EEG data were collected from 34 DE patients and 36 healthy controls (HC). The maximum eigenvalue of the real binary correlation matrix, calculated between different trials using cross-correlation and surrogate methods, was extracted to assess trial-by-trial variability (TTV) of TMS-EEG. The new method was found to more sensitive and reliable than the standard deviation method. DE patients exhibited significantly smaller TTV in Gamma band and greater TTV in Delta band than HC. Furthermore, the HAMD-17 scores were negatively correlated with TTV values in Gamma band. This study represented the first investigation into the TTV in TMS-EEG data and revealed abnormal values in DE patients. Those findings enhance our understanding of TMS-EEG technology and provide valuable insights for studying the characteristics of DE.

Sleep is vital to our daily activity. Lack of proper sleep can impair functionality and overall health. While stress is known for its detrimental impact on sleep quality, the precise effect of pre-sleep stress on subsequent sleep structure remains unknown. This study introduced a novel approach to study the pre-sleep stress effect on sleep structure, specifically slow-wave sleep (SWS) deficiency. To achieve this, we selected forehead resting EEG immediately before and upon sleep onset to extract stress-related neurological markers through power spectra and entropy analysis. These markers include beta/delta correlation, alpha asymmetry, fuzzy entropy (FuzzEn) and spectral entropy (SpEn). Fifteen subjects were included in this study. Our results showed that subjects lacking SWS often exhibited signs of stress in EEG, such as an increased beta/delta correlation, higher alpha asymmetry, and increased FuzzEn in frontal EEG. Conversely, individuals with ample SWS displayed a weak beta/delta correlation and reduced FuzzEn. Finally, we employed several supervised learning models and found that the selected neurological markers can predict subsequent SWS deficiency. Our investigation demonstrated that the classifiers could effectively predict varying levels of slow-wave sleep (SWS) from pre-sleep EEG segments, achieving a mean balanced accuracy surpassing 0.75. The SMOTE-Tomek resampling method could improve the performance to 0.77. This study suggests that stress-related neurological markers derived from pre-sleep EEG can effectively predict SWS deficiency. Such information can be integrated with existing sleep-improving techniques to provide a personalized sleep forecasting and improvement solution.

Delta waves have been described as periods of generalized silence across the cortex, and their alternation with periods of endogenous activity results in the slow oscillation of slow-wave sleep. Despite evidence that delta waves are instrumental for memory consolidation, their specific role in reshaping cortical functional circuits remains puzzling. In a rat model, we found that delta waves are not periods of complete silence and that the residual activity is not mere neuronal noise. Instead, cortical cells involved in learning a spatial memory task subsequently formed cell assemblies during delta waves in response to transient reactivation of hippocampal ensembles during ripples. This process occurred selectively during endogenous or induced memory consolidation. Thus, delta waves represent isolated cortical computations tightly related to ongoing information processing underlying memory consolidation. [ABSTRACT FROM AUTHOR]

Abstract In this paper, the limiting behavior of Riemann solutions to the Euler equations for compressible fluids in power law is studied as the adiabatic exponent goes to zero. We show that the limit solution forms the delta wave of the zero pressure gas dynamics in the distribution sense. At the end of the paper, we also give some numerical results to present the phenomena of concentration. Highlights • Delta wave, Riemann solutions. • Formation of delta wave. • Pressureless Limit of Euler equations. • Zero pressure gas dynamics. • Numerical simulations. [ABSTRACT FROM AUTHOR]

Rhythmic activity in populations of neurons is associated with cognitive and motor function. Our understanding of the neuronal mechanisms underlying these core brain functions has benefitted from demonstrations of cellular, synaptic, and network phenomena, leading to the generation of discrete rhythms at the local network level. However, discrete frequencies of rhythmic activity rarely occur alone. Despite this, little is known about why multiple rhythms are generated together or what mechanisms underlie their interaction to promote brain function. One overarching theory is that different temporal scales of rhythmic activity correspond to communication between brain regions separated by different spatial scales. To test this, we quantified the cross-frequency interactions between two dominant rhythms--theta and delta activity--manifested during magnetoencephalography recordings of subjects performing a word-pair semantic decision task. Semantic processing has been suggested to involve the formation of functional links between anatomically disparate neuronal populations over a range of spatial scales, and a distributed network was manifest in the profile of theta-delta coupling seen. Furthermore, differences in the pattern of theta-delta coupling significantly correlated with semantic outcome. Using an established experimental model of concurrent delta and theta rhythms in neocortex, we show that these outcome-dependent dynamics could be reproduced in a manner determined by the strength of cholinergic neuromodulation. Theta-delta coupling correlated with discrete neuronal activity motifs segregated by the cortical layer, neuronal intrinsic properties, and long-range axonal targets. Thus, the model suggested that local, interlaminar neocortical theta-delta coupling may serve to coordinate both cortico-cortical and corticosubcortical computations during distributed network activity. [ABSTRACT FROM AUTHOR]

First attempts have demonstrated that the application of alpha/delta neurofeedback in the treatment of chronic tinnitus leads to a reduction of symptoms at the group level. However, recent research also suggests that chronic tinnitus is a decidedly heterogeneous phenomenon, one that requires treatment of distinct subgroups or even on an individual level. Thus, the purpose of this study was to evaluate an individually adjusted alpha/delta neurofeedback protocol. Following previous studies, the delta band fixed between 3 and 4 Hz was chosen as the frequency for inhibition. However, unlike the previous studies, the frequency range for the rewarded alpha band was not fixed between 8 and 12 Hz but rather individually determined according to each patient's specific alpha peak frequency (IAF). Twenty-six chronic tinnitus patients participated in 15 weekly neurofeedback training sessions and extensive pre- and post-tests, as well as follow-up testing 3 and 6 months after training. The main outcome measures were tinnitus-related distress measured with the Tinnitus Handicap Inventory (THI) and Tinnitus Questionnaire (TQ), tinnitus loudness, and pre- and post-training resting-state EEG activity in trained frequency bands. In Results, the neurofeedback protocol led to a significant reduction of tinnitus-related distress and tinnitus loudness. While distress remained on a low level even 6 months after the completion of training, loudness returned to baseline levels in the follow-up period. In addition, resting-state EEG activity showed an increase in the trained alpha/delta ratio over the course of the training. This ratio increase was related to training-induced changes of tinnitus-related distress as measured with TQ, mainly due to increases in the alpha frequency range. In sum, this study confirms the alpha/delta neurofeedback as a suitable option for the treatment of chronic tinnitus and represents a first step towards the development of individual neurofeedback protocols. This clinical trial was registered online at ClinicalTrials.gov (NCT02383147) and kofam.ch (SNCTP000001313). [ABSTRACT FROM AUTHOR]

Sleep is a physiological state that plays important role in the recovery of fatigue. However, the relationship between the physiological status of sleep and subjective fatigue remains unknown. In the present study, we hypothesized that the non-recovery of fatigue at wake time due to non-restorative sleep might be ascribed to changes in specific parameters of electroencephalography (EEG) and heart rate variability (HRV) in poor sleepers. Twenty healthy female shift-working nurses participated in the study. Subjective fatigue was assessed using the visual analog scale (VAS) at bedtime and wake time. During sleep on the night between 2 consecutive day shifts, the EEG powers at the frontal pole, HRV based on electrocardiograms, and distal-proximal gradient of skin temperature were recorded and analyzed. The results indicated that the subjects with high fatigue on the VAS at wake time exhibited (1) a decrease in deep non-rapid eye movement (NREM) (stageN3) sleep duration in the first sleep cycle; (2) a decrease in REM latency; (3) a decrease in ultra-slow and delta EEG powers, particularly from 30 to 65 min after sleep onset; (4) a decrease in the total power of HRV, particularly from 0 to 30 min after sleep onset; (5) an increase in the very low frequency component of HRV; and (6) a smaller increase in the distal-proximal gradient of skin temperature, than those of the subjects with low fatigue levels. The correlational and structural equation modeling analyses of these parameters suggested that an initial decrease in the total power of HRV from 0 to 30 min after sleep onset might inhibit the recovery from fatigue during sleep (i.e., increase the VAS score at wake time) via its effects on the ultra-slow and delta powers from 30 to 65 min after sleep onset, stageN3 duration in the first sleep cycle, REM latency, and distal-proximal gradient of skin temperature. These findings suggest an important role of these physiological factors in recovery from fatigue during sleep, and that interventions to modify these physiological factors might ameliorate fatigue at wake time. [ABSTRACT FROM AUTHOR]