EEG rhythmic and arrhythmic spectral components and functional connectivity at resting state may predict the development of synucleinopathies in idiopathic REM sleep behavior disorder

STUDY OBJECTIVES

Idiopathic/isolated REM-sleep behavior disorder (iRBD) often precedes the onset of synucleinopathies. Here, we investigated whether baseline resting-state EEG advanced spectral power and functional connectivity differ between iRBD patients who converted towards a synucleinopathy at follow-up and those who did not.

METHODS

Eighty-one participants with iRBD (66.89±6.91 years) underwent a baseline resting-state EEG recording, a neuropsychological assessment and a neurological examination. We estimated EEG power spectral density using standard analyses and derived spectral estimates of rhythmic and arrhythmic components. Global and pairwise EEG functional connectivity analyses were computed using the weighted phase-lag index (wPLI). Pixel-based permutation tests were used to compare groups.

RESULTS

After a mean follow-up of 5.01±2.76 years, 34 patients were diagnosed with a synucleinopathy (67.81±7.34 years) and 47 remained disease-free (65.53±7.09 years). Among patients who converted, 22 were diagnosed with Parkinson's disease and 12 with dementia with Lewy bodies. As compared to patients who did not convert, patients who converted exhibited at baseline higher relative theta standard power, steeper slopes of the arrhythmic component and higher theta rhythmic power mostly in occipital regions. Furthermore, patients who converted showed higher beta global wPLI but lower alpha wPLI between left temporal and occipital regions.

CONCLUSION

Analyses of resting-state EEG rhythmic and arrhythmic components and functional connectivity suggest an imbalanced excitatory-to-inhibitory activity within large-scale networks, which is associated with later development of a synucleinopathy in iRBD patients.

Functional brain alterations in symptomatic dermographism patients-An exploratory magnetoencephalography study

Symptomatic dermographism (SD) is a common form of urticaria, which is triggered by stroking the skin. Brain involvement in its aetiology was investigated by means of magnetoencephalography (MEG) after provocation with histamine and dermography. Wheals were induced by histamine skin prick test and dermography in twelve SD patients and fourteen controls. Itch severity was scored on a Visual Analogue Scale (VAS). Relative power and functional connectivity (FC) were measured using a 306-channel whole-head MEG system at baseline and 10 min after histamine and dermography, and contrasted between groups and conditions. Furthermore, wheal diameter and itch scores after these procedures were correlated with the MEG values. SD patients had higher itch scores after histamine and dermography. No significant group-differences were observed in relative power or FC for any condition. In both groups, power decreases were mostly observed in the beta band, and power increases in the alpha bands, after provocation, with more regions involved in patients compared to controls. Increased FC was seen after histamine in patients, and after dermography in controls. In patients only, dermography and histamine wheal size correlated with the alpha2 power in the regions of interest that showed significant condition effects after these procedures. Our findings may be cautiously interpreted as aberrant itch processing, and suggest involvement of the central nervous system in the aetiology of SD.

Classification of autism spectrum disorder using electroencephalography in Chinese children: a cross-sectional retrospective study

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by diverse clinical features. EEG biomarkers such as spectral power and functional connectivity have emerged as potential tools for enhancing early diagnosis and understanding of the neural processes underlying ASD. However, existing studies yield conflicting results, necessitating a comprehensive, data-driven analysis. We conducted a retrospective cross-sectional study involving 246 children with ASD and 42 control children. EEG was collected, and diverse EEG features, including spectral power and spectral coherence were extracted. Statistical inference methods, coupled with machine learning models, were employed to identify differences in EEG features between ASD and control groups and develop classification models for diagnostic purposes. Our analysis revealed statistically significant differences in spectral coherence, particularly in gamma and beta frequency bands, indicating elevated long range functional connectivity between frontal and parietal regions in the ASD group. Machine learning models achieved modest classification performance of ROC-AUC at 0.65. While machine learning approaches offer some discriminative power classifying individuals with ASD from controls, they also indicate the need for further refinement.

Effect of popular songs from the reminiscence bump as autobiographical memory cues in aging: a preliminary study using EEG

Introduction

Music has the capacity to evoke emotions and memories. This capacity is influenced by whether or not the music is from the reminiscence bump (RB) period. However, research on the neural correlates of the processes of evoking autobiographical memories through songs is scant. The aim of this study was to analyze the differences at the level of frequency band activation in two situations: (1) whether or not the song is able to generate a memory; and (2) whether or not the song is from the RB period.

Methods

A total of 35 older adults (22 women, age range: 61-73 years) listened to 10 thirty-second musical clips that coincided with the period of their RB and 10 from the immediately subsequent 5 years (non-RB). To record the EEG signal, a brain-computer interface (BCI) with 14 channels was used. The signal was recorded during the 30-seconds of listening to each music clip.

Results

The results showed differences in the activation levels of the frequency bands in the frontal and temporal regions. It was also found that the non-retrieval of a memory in response to a song clip showed a greater activation of low frequency waves in the frontal region, compared to the trials that did generate a memory.

Discussion

These results suggest the importance of analyzing not only brain activation, but also neuronal functional connectivity at older ages, in order to better understand cognitive and emotional functions in aging.

The spectral slope as a marker of excitation/inhibition ratio and cognitive functioning in multiple sclerosis

Multiple sclerosis (MS) is a neurodegenerative disease characterized by neuronal and synaptic loss, resulting in an imbalance of excitatory and inhibitory synaptic transmission and potentially cognitive impairment. Current methods for measuring the excitation/inhibition (E/I) ratio are mostly invasive, but recent research combining neurocomputational modeling with measurements of local field potentials has indicated that the slope with which the power spectrum of neuronal activity captured by electro- and/or magnetoencephalography rolls off, is a non-invasive biomarker of the E/I ratio. A steeper roll-off is associated with a stronger inhibition. This novel method can be applied to assess the E/I ratio in people with multiple sclerosis (pwMS), detect the effect of medication such as benzodiazepines, and explore its utility as a biomarker for cognition. We recruited 44 healthy control subjects and 95 pwMS who underwent resting-state magnetoencephalographic recordings. The 1/f spectral slope of the neural power spectra was calculated for each subject and for each brain region. As expected, the spectral slope was significantly steeper in pwMS treated with benzodiazepines (BZDs) compared to pwMS not receiving BZDs (p = .01). In the sub-cohort of pwMS not treated with BZDs, we observed a steeper slope in cognitively impaired pwMS compared to cognitively preserved pwMS (p = .01) and healthy subjects (p = .02). Furthermore, we observed a significant correlation between 1/f spectral slope and verbal and spatial working memory functioning in the brain regions located in the prefrontal and parietal cortex. In this study, we highlighted the value of the spectral slope in MS by quantifying the effect of benzodiazepines and by putting it forward as a potential biomarker of cognitive deficits in pwMS.

Pubertal maturation and timing effects on resting state electroencephalography in autistic and comparison youth

Autistic and comparison individuals differ in resting-state electroencephalography (EEG), such that sex and age explain variability within and between groups. Pubertal maturation and timing may further explain variation, as previous work has suggested alterations in pubertal timing in autistic youth. In a sample from two studies of 181 autistic and 94 comparison youth (8 years to 17 years and 11 months), mixed-effects linear regressions were conducted to assess differences in EEG (midline power for theta, alpha, and beta frequency bands). Alpha power was analyzed as a mediator in the relation between pubertal maturation and timing with autistic traits in the autistic groups to understand the role of puberty in brain-based changes that contribute to functional outcomes. Individuals advanced in puberty exhibited decreased power in all bands. Those who experienced puberty relatively early showed decreased power in theta and beta bands, controlling for age, sex, and diagnosis. Autistic individuals further along in pubertal development exhibited lower social skills. Alpha mediated the relation between puberty and repetitive behaviors. Pubertal maturation and timing appear to play unique roles in the development of cognitive processes for autistic and comparison youth and should be considered in research on developmental variation in resting-state EEG.

Resting-state electroencephalographic characteristics related to mild cognitive impairments

Alzheimer's disease (AD) causes a rapid deterioration in cognitive and physical functions, including problem-solving, memory, language, and daily activities. Mild cognitive impairment (MCI) is considered a risk factor for AD, and early diagnosis and treatment of MCI may help slow the progression of AD. Electroencephalography (EEG) analysis has become an increasingly popular tool for developing biomarkers for MCI and AD diagnosis. Compared with healthy elderly, patients with AD showed very clear differences in EEG patterns, but it is inconclusive for MCI. This study aimed to investigate the resting-state EEG features of individuals with MCI (n = 12) and cognitively healthy controls (HC) (n = 13) with their eyes closed. EEG data were analyzed using spectral power, complexity, functional connectivity, and graph analysis. The results revealed no significant difference in EEG spectral power between the HC and MCI groups. However, we observed significant changes in brain complexity and networks in individuals with MCI compared with HC. Patients with MCI exhibited lower complexity in the middle temporal lobe, lower global efficiency in theta and alpha bands, higher local efficiency in the beta band, lower nodal efficiency in the frontal theta band, and less small-world network topology compared to the HC group. These observed differences may be related to underlying neuropathological alterations associated with MCI progression. The findings highlight the potential of network analysis as a promising tool for the diagnosis of MCI.

Resting-state EEG patterns of preschool-aged boys with autism spectrum disorder: A pilot study

Defective cognition development during preschool years is believed to be linked with core symptoms of autism spectrum disorder (ASD). Neurophysiological research on mechanisms underly the cognitive disabilities of preschool-aged children with ASD is scarce currently. This pilot study aimed to compare the resting spectral EEG power of preschool-aged boys with ASD with their matched typically developing peers. Children in the ASD group demonstrated reduced central and posterior absolute delta (1-4 Hz) and enhanced frontal absolute beta (12-30 Hz) and gamma (30-45 Hz). The relative power of the ASD group was elevated in delta, theta (4-8 Hz), alpha (8-12 Hz), beta, and gamma bands as compared to the controls. The theta/beta ratio decreased in the frontal regions and enhanced at Cz and Pz electrodes in the ASD group. Correlations between the inhibition and metacognition indices of the behavior rating inventory of executive function-preschool version (BRIEF-P) and the theta/beta ratio for children of both groups were significant. In conclusion, the present study revealed atypical resting spectral characteristics of boys with ASD at preschool ages. Future large-sampled studies for the generalization of our findings and a better understanding of the relationships between brain oscillations and phenotypes of ASD are warranted.

Associations between spontaneous electroencephalogram oscillations and oxygen saturation across normobaric and hypobaric hypoxia

High-altitude indoctrination (HAI) trains individuals to recognize symptoms of hypoxia by simulating high-altitude conditions using normobaric (NH) or hypobaric (HH) hypoxia. Previous studies suggest that despite equivalent inspired oxygen levels, physiological differences could exist between these conditions. In particular, differences in neurophysiological responses to these conditions are not clear. Our study aimed to investigate correlations between oxygen saturation (SpO₂ ) and neural responses in NH and HH. We recorded 5-min of resting-state eyes-open electroencephalogram (EEG) and SpO₂ during control, NH, and HH conditions from 13 participants. We applied a multivariate framework to characterize correlations between SpO₂ and EEG measures (spectral power and multiscale entropy [MSE]), within each participant and at the group level. Participants were desaturating during the first 150 s of NH versus steadily desaturated in HH. We considered the entire time interval, first and second half intervals, separately. All the conditions were characterized by statistically significant participant-specific patterns of EEG-SpO₂ correlations. However, at the group level, the desaturation period expressed a robust pattern of these correlations across frequencies and brain locations. Specifically, the first 150 s of NH during desaturation differed significantly from the other conditions with negative absolute alpha power-SpO₂ correlations and positive MSE-SpO₂ correlations. Once steadily desaturated, NH and HH had no significant differences in EEG-SpO₂ correlations. Our findings indicate that the desaturating phase of hypoxia is a critical period in HAI courses, which would require developing strategies for mitigating the hypoxic stimulus in a real-world situation.

Lumbar multiunit activity power spectrum during air-stepping in the spinal cat: evidence for a flexor dominated rostrocaudally distributed locomotor center

Clues about the organization of spinal networks responsible for rhythmic motor behaviors have come from examination of reflex circuitry, lesioning studies and single cell recordings. Recently, more attention has been paid to extracellularly recorded multiunit signals thought to represent the general activity of local cellular potentials. Focusing on the gross localization of spinal locomotor networks, we used multiunit signals of the lumbar cord to classify the activation and organization of those networks. We employed power spectral analysis to compare multiunit power across rhythmic conditions and locations and to infer patterns of activation based upon coherence and phase measures. We found greater multiunit power in mid-lumbar segments during stepping, supportive of previous lesioning studies isolating rhythm generating capabilities to these segments. We also found much greater multiunit power during the flexion phase of stepping than during the extension phase for all lumbar segments. Greater multiunit power at flexion indicates increased neural activity during this phase and is suggestive of previously reported asymmetries between flexor and extensor related interneuronal populations of the spinal rhythm generating network. Finally, the multiunit power showed no phase lag at coherent frequencies throughout the lumbar enlargement indicative of a longitudinal standing wave of neural activation. Our results suggest that the multiunit activity may be representative of the spinal rhythm generating activity that is distributed in a rostrocaudal gradient. Additionally, our results indicate that this multiunit activity may operate as a flexor dominant standing wave of activation that is synchronized throughout the rostrocaudal extent of the lumbar enlargement.

Effects of Early Nutrition Factors on Baseline Neurodevelopment during the First 6 Months of Life: An EEG Study

Throughout infancy, the brain undergoes rapid changes in structure and function that are sensitive to environmental influences, such as diet. Breastfed (BF) infants score higher on cognitive tests throughout infancy and into adolescence than formula fed (FF) infants, and these differences in neurocognitive development are reflected in higher concentrations of white and grey matter as measured by MRI. To further explore the effect diet has on cognitive development, electroencephalography (EEG) is used as a direct measure of neuronal activity and to assess specific frequency bands associated with cognitive processes. Task-free baseline EEGs were collected from infants fed with human milk (BF), dairy-based formula (MF), or soy-based formula (SF) at 2, 3, 4, 5, and 6 months of age to explore differences in frequency bands in both sensor and source space. Significant global differences in sensor space were seen in beta and gamma bands between BF and SF groups at ages 2 and 6 months, and these differences were further observed through volumetric modeling in source space. We conclude that BF infants exhibit earlier brain maturation reflected in greater power spectral density in these frequency bands.

Alternations of neuromagnetic activity across neurocognitive core networks among benign childhood epilepsy with centrotemporal spikes: A multi-frequency MEG study

Objective

We aimed to investigate the alternations of neuromagnetic activity across neurocognitive core networks among early untreated children having benign childhood epilepsy with centrotemporal spikes (BECTS).

Methods

We recorded the Magnetoencephalography (MEG) resting-state data from 48 untreated children having BECTS and 24 healthy children. The fourth edition of the Wechsler Intelligence Scale for Children (WISC-IV) was utilized to divide the children with BECTS into two groups: the cognitive impairment (CI) group with a full-scale intelligence quotient (FSIQ) of < 90 and the cognitive non-impairment (CNI) group with an FSIQ of > 90. We selected 26 bilateral cognitive-related regions of interest based on the triple network model. The neurocognitive core network spectral power was estimated using a minimum norm estimate (MNE).

Results

In the CNI group, the spectral power inside the bilateral anterior cingulate cortex (ACC) and the bilateral caudal middle frontal cortex (CMF) enhanced within the delta band and reduced within the alpha band. Both the CI and the CNI group demonstrated enhanced spectral power inside the bilateral posterior cingulate cortex (PCC), bilateral precuneus (PCu) region, bilateral superior and middle temporal cortex, bilateral inferior parietal lobe (IPL), and bilateral supramarginal cortex (SM) region in the delta band. Moreover, there was decreased spectral power in the alpha band. In addition, there were consistent changes in the high-frequency spectrum (> 90 Hz). The spectral power density within the insula cortex (IC), superior temporal cortex (ST), middle temporal cortex (MT), and parahippocampal cortex (PaH) also decreased. Therefore, studying high-frequency activity could lead to a new understanding of the pathogenesis of BECTS.

Conclusion

The alternations of spectral power among neurocognitive core networks could account for CI among early untreated children having BECTS. The dynamic properties of spectral power in different frequency bands could behave as biomarkers for diagnosing new BECTS.

Electroencephalography Theta/Beta Ratio Decreases in Patients with Severe Obstructive Sleep Apnea

Purpose

Accumulating evidence suggests that theta/beta ratio (TBR), an electroencephalographic (EEG) frequency band parameter, might serve as an objective marker of executive cognitive control in healthy adults. Obstructive sleep apnea (OSA) has a detrimental impact on patients' behavior and cognitive performance while whether TBR is different in OSA population has not been reported. This study aimed to explore the difference in relative EEG spectral power and TBR during sleep between patients with severe OSA and non-OSA groups.

Patients and Methods

142 participants with in-laboratory nocturnal PSG recording were included, among which 100 participants suffered severe OSA (apnea hypopnea index, AHI > 30 events/hour; OSA group) and 42 participants had no OSA (AHI ≤ 5 events/h; control group). The fast Fourier transformation was used to compute the EEG power spectrum for total sleep duration within contiguous 30-second epochs of sleep. The demographic and polysomnographic characteristics, relative EEG spectral power and TBR of the two groups were compared.

Results

It was found that the beta band power during NREM sleep and total sleep was significantly higher in the OSA group than controls (p < 0.001, p = 0.012, respectively), and the theta band power during NREM sleep and total sleep was significantly lower in the OSA group than controls (p = 0.019, p = 0.014, respectively). TBR during NREM sleep, REM sleep and total sleep was significantly lower in the OSA group compared to the control group (p < 0.001 for NREM sleep and total sleep, p = 0.015 for REM sleep). TBR was negatively correlated with AHI during NREM sleep (r=-0.324, p < 0.001) and total sleep (r=-0. 312, p < 0.001).

Conclusion

TBR was significantly decreased in severe OSA patients compared to the controls, which was attributed to both increased beta power and decreased theta power. TBR may be a stable EEG-biomarker of OSA patients, which may accurately and reliably identify phenotype of patients.

Temporal and Spectral Properties of the Auditory Mismatch Negativity and P3a Responses in Schizophrenia

The mismatch negativity (MMN) event-related potential (ERP) indexes relatively automatic detection of changes in sensory stimuli and is typically attenuated in individuals with schizophrenia. However, contributions of different frequencies of electroencephalographic (EEG) activity to the MMN and the later P3a attentional orienting response in schizophrenia are poorly understood and were the focus of the present study. Participants with a schizophrenia-spectrum disorder (n = 85) and non-psychiatric control participants (n = 74) completed a passive auditory oddball task containing 10% 50 ms "deviant" tones and 90% 100 ms "standard" tones. EEG data were analyzed using spatial principal component analysis (PCA) applied to wavelet-based time-frequency analysis and MMN and P3a ERPs. The schizophrenia group compared to the control group had smaller MMN amplitudes and lower deviant-minus-standard theta but not alpha event-related spectral perturbation (ERSP) after accounting for participant age and sex. Larger MMN and P3a amplitudes but not latencies were correlated with greater theta and alpha time-frequency activity. Multiple linear regression analyses revealed that control participants showed robust relationships between larger MMN amplitudes and greater deviant-minus-standard theta inter-trial coherence (ITC) and between larger P3a amplitudes and greater deviant-minus-standard theta ERSP, whereas these dynamic neural processes were less tightly coupled in participants with a schizophrenia-spectrum disorder. Study results help clarify frequency-based contributions of time-domain (ie, ERP) responses and indicate a potential disturbance in the neural dynamics of detecting change in sensory stimuli in schizophrenia. Overall, findings add to the growing body of evidence that psychotic illness is associated with widespread neural dysfunction in the theta frequency band.

Developmental trajectory of MEG resting-state oscillatory activity in children and adolescents: a longitudinal reliability study

Neural oscillations may be sensitive to aspects of brain maturation such as myelination and synaptic density changes. Better characterization of developmental trajectories and reliability is necessary for understanding typical and atypical neurodevelopment. Here, we examined reliability in 110 typically developing children and adolescents (aged 9-17 years) across 2.25 years. From 10 min of magnetoencephalography resting-state data, normalized source spectral power and intraclass correlation coefficients were calculated. We found sex-specific differences in global normalized power, with males showing age-related decreases in delta and theta, along with age-related increases in beta and gamma. Females had fewer significant age-related changes. Structural magnetic resonance imaging revealed that males had more total gray, subcortical gray, and cortical white matter volume. There were significant age-related changes in total gray matter volume with sex-specific and frequency-specific correlations to normalized power. In males, increased total gray matter volume correlated with increased theta and alpha, along with decreased gamma. Split-half reliability was excellent in all frequency bands and source regions. Test-retest reliability ranged from good (alpha) to fair (theta) to poor (remaining bands). While resting-state neural oscillations can have fingerprint-like quality in adults, we show here that neural oscillations continue to evolve in children and adolescents due to brain maturation and neurodevelopmental change.

Case Report: Prolonged Effects of Short-Term Transcranial Magnetic Stimulation on EEG Biomarkers, Spectral Power, and Seizure Frequency

Transcranial magnetic stimulation (TMS) is a non-invasive modality of focal brain stimulation in which a fluctuating magnetic field induces electrical currents within the cortex. It remains unclear to what extent TMS alters EEG biomarkers and how EEG biomarkers may guide treatment of focal epilepsy. We present a case of a 48-year-old man with focal epilepsy, refractory to multiple medication trials, who experienced a dramatic reduction in seizures after targeting the area of seizure onset within the left parietal-occipital region with low-frequency repetitive TMS (rTMS). Prior to treatment, he experienced focal seizures that impacted cognition including apraxia at least 50-60 times daily. MRI of the brain showed a large focal cortical dysplasia with contrast enhancement involving the left occipital-parietal junction. Stimulation for 5 consecutive days was well-tolerated and associated with a day-by-day reduction in seizure frequency. In addition, he was monitored with continuous video EEG, which showed continued and progressive changes in spectral power (decreased broadband power and increased infraslow delta activity) and a gradual reduction in seizure frequency and duration. One month after initial treatment, 2-day ambulatory EEG demonstrated seizure-freedom and MRI showed resolution of focal contrast enhancement. He continues to receive 2-3 days of rTMS every 2-4 months. He was seizure-free for 6 months, and at last follow-up of 17 months was experiencing auras approximately every 2 weeks without progression to disabling seizures. This case demonstrates that rTMS can be a well-tolerated and effective means of controlling medication-refractory seizures, and that EEG biomarkers change gradually in a fashion in association with seizure frequency. TMS influences cortical excitability, is a promising non-invasive means of treating focal epilepsy, and has measurable electrophysiologic effects.

Validation of Visually Identified Muscle Potentials during Human Sleep Using High Frequency/Low Frequency Spectral Power Ratios

Surface electromyography (EMG), typically recorded from muscle groups such as the mentalis (chin/mentum) and anterior tibialis (lower leg/crus), is often performed in human subjects undergoing overnight polysomnography. Such signals have great importance, not only in aiding in the definitions of normal sleep stages, but also in defining certain disease states with abnormal EMG activity during rapid eye movement (REM) sleep, e.g., REM sleep behavior disorder and parkinsonism. Gold standard approaches to evaluation of such EMG signals in the clinical realm are typically qualitative, and therefore burdensome and subject to individual interpretation. We originally developed a digitized, signal processing method using the ratio of high frequency to low frequency spectral power and validated this method against expert human scorer interpretation of transient muscle activation of the EMG signal. Herein, we further refine and validate our initial approach, applying this to EMG activity across 1,618,842 s of polysomnography recorded REM sleep acquired from 461 human participants. These data demonstrate a significant association between visual interpretation and the spectrally processed signals, indicating a highly accurate approach to detecting and quantifying abnormally high levels of EMG activity during REM sleep. Accordingly, our automated approach to EMG quantification during human sleep recording is practical, feasible, and may provide a much-needed clinical tool for the screening of REM sleep behavior disorder and parkinsonism.

Sleep Power Spectral Density and Spindles in PTSD and Their Relationship to Symptom Severity

Sleep disturbances are common in post-traumatic stress disorder (PTSD), although which sleep microarchitectural characteristics reliably classify those with and without PTSD remains equivocal. Here, we investigated sleep microarchitectural differences (i.e., spectral power, spindle activity) in trauma-exposed individuals that met (n = 45) or did not meet (n = 52) criteria for PTSD and how these differences relate to post-traumatic and related psychopathological symptoms. Using ecologically-relevant home sleep polysomnography recordings, we show that individuals with PTSD exhibit decreased beta spectral power during NREM sleep and increased fast sleep spindle peak frequencies. Contrary to prior reports, spectral power in the beta frequency range (20.31-29.88 Hz) was associated with reduced PTSD symptoms, reduced depression, anxiety and stress and greater subjective ability to regulate emotions. Increased fast frequency spindle activity was not associated with individual differences in psychopathology. Our findings may suggest an adaptive role for beta power during sleep in individuals exposed to a trauma, potentially conferring resilience. Further, we add to a growing body of evidence that spindle activity may be an important biomarker for studying PTSD pathophysiology.

EEG power spectral responses to wind farm compared with road traffic noise during sleep: A laboratory study

Wind turbine noise is dominated by low frequencies for which effects on sleep relative to more common environmental noise sources such as road traffic noise remain unknown. This study examined the effect of wind turbine noise compared with road traffic noise on sleep using quantitative electroencephalogram power spectral analysis. Twenty-three participants were exposed to 3-min samples of wind turbine noise and road traffic noise at three sound pressure levels (33, 38 and 43 dBA) in randomised order during established sleep. Acute (0-30 s) and more sustained (30-180 s) effects of noise presentations during N2 and N3 sleep were examined using spectral analysis of changes in electroencephalogram power frequency ranges across time in 5-s intervals. Both noise types produced time- and sound pressure level-dependent increases in electroencephalogram power, but with significant noise type by sound pressure level interactions in beta, alpha, theta and delta frequency bands (all p < 0.05). Wind turbine noise showed significantly lower delta, theta and beta activity immediately following noise onset compared with road traffic noise (all p < 0.05). However, alpha activity was higher for wind turbine noise played at lower sound pressure levels (33 dBA [p = 0.001] and 38 dBA [p = 0.003]) compared with traffic noise during N2 sleep. These findings support that spectral analyses show subtle effects of noise on sleep and that electroencephalogram changes following wind turbine noise and road traffic noise onset differ depending on sound pressure levels; however, these effects were mostly transient and had little impact on conventionally scored sleep. Further studies are needed to establish if electroencephalogram changes associated with modest environmental noise exposures have significant impacts on sleep quality and next-day functioning.

Mindfulness Training Associated With Resting-State Electroencephalograms Dynamics in Novice Practitioners via Mindful Breathing and Body-Scan

Objectives: Mindfulness-based stress reduction has been proven to improve mental health and quality of life. This study examined how mindfulness training and various types of mindfulness practices altered brain activity. Methods: Specifically, the spectral powers of scalp electroencephalography of the mindfulness-based stress reduction (MBSR) group (n=17) who underwent an 8-week MBSR training-including mindful breathing and body-scan-were evaluated and compared with those of the waitlist controls (n=14). Results: Empirical results indicated that the post-intervention effect of MBSR significantly elevated the resting-state beta powers and reduced resting-state delta powers in both practices; such changes were not observed in the waitlist control. Compared with mindful breathing, body-scanning resulted in an overall decline in electroencephalograms (EEG) spectral powers at both delta and low-gamma bands among trained participants. Conclusion: Together with our preliminary data of expert mediators, the aforementioned spectral changes were salient after intervention, but mitigated along with expertise. Additionally, after receiving training, the MBSR group's mindfulness and emotion regulation levels improved significantly, which were correlated with the EEG spectral changes in the theta, alpha, and low-beta bands. The results supported that MBSR might function as a unique internal processing tool that involves increased vigilant capability and induces alterations similar to other cognitive training.

Cortical Frontoparietal Network Dysfunction in CHMP2B-Frontotemporal Dementia

A rare cause of inherited frontotemporal dementia (FTD) is a mutation in the CHMP2B gene on chromosome 3 leading to the autosomal dominantly inherited FTD (CHMP2B-FTD). Since CHMP2B-FTD is clinically well-characterized, and patients show a distinct pattern of executive dysfunction, the condition offers possible insight in the early electroencephalographic (EEG) changes in the cortical networks. Specifically, EEG microstate analysis parses the EEG signals into topographies believed to represent discrete network activations. We investigated the EEG dynamics in patients with symptomatic CHMP2B-FTD (n = 5) as well as pre-symptomatic mutation carriers (n = 5) compared to non-carrier family members (n = 6). The data was parsed into four archetypal microstates and global power was calculated. A trend was found for lower occurrence in microstate D in CHMP2B-FTD (p-value = 0.177, F-value = 2.036). Patients with recent symptom onset (<1 year) showed an increased duration of microstate D, whereas patients who had been symptomatic for longer periods (>2 years) showed decreased duration. Patients with CHMP2B-FTD present with executive dysfunction, and microstate D has previously been shown to be associated with the fronto-parietal network. The biphasic pattern may represent the pathophysiological changes in brain dynamics during neurodegeneration, which may apply to other neurodegenerative diseases.

The Role of the Dorsal-Lateral Prefrontal Cortex in Reward Sensitivity During Approach-Avoidance Conflict

Approach-Avoidance conflict (AAC) arises from decisions with embedded positive and negative outcomes, such that approaching leads to reward and punishment and avoiding to neither. Despite its importance, the field lacks a mechanistic understanding of which regions are driving avoidance behavior during conflict. In the current task, we utilized transcranial magnetic stimulation (TMS) and drift-diffusion modeling to investigate the role of one of the most prominent regions relevant to AAC-the dorsolateral prefrontal cortex (dlPFC). The first experiment uses in-task disruption to examine the right dlPFC's (r-dlPFC) causal role in avoidance behavior. The second uses single TMS pulses to probe the excitability of the r-dlPFC, and downstream cortical activations, during avoidance behavior. Disrupting r-dlPFC during conflict decision-making reduced reward sensitivity. Further, r-dlPFC was engaged with a network of regions within the lateral and medial prefrontal, cingulate, and temporal cortices that associate with behavior during conflict. Together, these studies use TMS to demonstrate a role for the dlPFC in reward sensitivity during conflict and elucidate the r-dlPFC's network of cortical regions associated with avoidance behavior. By identifying r-dlPFC's mechanistic role in AAC behavior, contextualized within its conflict-specific downstream neural connectivity, we advance dlPFC as a potential neural target for psychiatric therapeutics.

Behavioral and Neural Variability of Naturalistic Arm Movements

Motor behaviors are central to many functions and dysfunctions of the brain, and understanding their neural basis has consequently been a major focus in neuroscience. However, most studies of motor behaviors have been restricted to artificial, repetitive paradigms, far removed from natural movements performed "in the wild." Here, we leveraged recent advances in machine learning and computer vision to analyze intracranial recordings from 12 human subjects during thousands of spontaneous, unstructured arm reach movements, observed over several days for each subject. These naturalistic movements elicited cortical spectral power patterns consistent with findings from controlled paradigms, but with considerable neural variability across subjects and events. We modeled interevent variability using 10 behavioral and environmental features; the most important features explaining this variability were reach angle and day of recording. Our work is among the first studies connecting behavioral and neural variability across cortex in humans during unstructured movements and contributes to our understanding of long-term naturalistic behavior.

Early Developmental EEG and Seizure Phenotypes in a Full Gene Deletion of Ubiquitin Protein Ligase E3A Rat Model of Angelman Syndrome

Angelman syndrome (AS) is a neurodevelopmental disorder with unique behavioral phenotypes, seizures, and distinctive electroencephalographic (EEG) patterns. Recent studies identified motor, social communication, and learning and memory deficits in a CRISPR engineered rat model with a complete maternal deletion of the Ube3a gene. It is unknown whether this model recapitulates other aspects of the clinical disorder. We report here the effect of Ube3a maternal deletion in the rat on epileptiform activity, seizure threshold, and quantitative EEG. Using video-synchronized EEG (vEEG) monitoring, we assessed spectral power and epileptiform activity early postnatally through adulthood. While EEG power was similar to wild-type (WT) at 1.5 weeks postnatally, at all other ages analyzed, our findings were similar to the AS phenotype in mice and humans with significantly increased δ power. Analysis of epileptiform activity in juvenile and adult rats showed increased time spent in epileptiform activity in AS compared with WT rats. We evaluated seizure threshold using pentylenetetrazol (PTZ), audiogenic stimulus, and hyperthermia to provoke febrile seizures (FSs). Behavioral seizure scoring following PTZ induction revealed no difference in seizure threshold in AS rats, however behavioral recovery from the PTZ-induced seizure was longer in the adult group with significantly increased hippocampal epileptiform activity during this phase. When exposed to hyperthermia, AS rat pups showed a significantly lower temperature threshold to first seizure than WT. Our findings highlight an age-dependence for the EEG and epileptiform phenotypes in a preclinical model of AS, and support the use of quantitative EEG and increased δ power as a potential biomarker of AS.

Generalized neural decoders for transfer learning across participants and recording modalities

OBJECTIVE

Advances in neural decoding have enabled brain-computer interfaces to perform increasingly complex and clinically-relevant tasks. However, such decoders are often tailored to specific participants, days, and recording sites, limiting their practical long-term usage. Therefore, a fundamental challenge is to develop neural decoders that can robustly train on pooled, multi-participant data and generalize to new participants.

APPROACH

We introduce a new decoder, HTNet, which uses a convolutional neural network with two innovations: (1) a Hilbert transform that computes spectral power at data-driven frequencies and (2) a layer that projects electrode-level data onto predefined brain regions. The projection layer critically enables applications with intracranial electrocorticography (ECoG), where electrode locations are not standardized and vary widely across participants. We trained HTNet to decode arm movements using pooled ECoG data from 11 of 12 participants and tested performance on unseen ECoG or electroencephalography (EEG) participants; these pretrained models were also subsequently fine-tuned to each test participant.

MAIN RESULTS

HTNet outperformed state-of-the-art decoders when tested on unseen participants, even when a different recording modality was used. By fine-tuning these generalized HTNet decoders, we achieved performance approaching the best tailored decoders with as few as 50 ECoG or 20 EEG events. We were also able to interpret HTNet's trained weights and demonstrate its ability to extract physiologically-relevant features.

SIGNIFICANCE

By generalizing to new participants and recording modalities, robustly handling variations in electrode placement, and allowing participant-specific fine-tuning with minimal data, HTNet is applicable across a broader range of neural decoding applications compared to current state-of-the-art decoders.

Spectral Power Analysis of Sleep Electroencephalography in Subjects with Different Severities of Obstructive Sleep Apnea and Healthy Controls

PURPOSE

Previous spectral analysis studies on obstructive sleep apnea (OSA) involved small samples, and the results were inconsistent. We performed a spectral analysis of sleep EEG based on different severities of OSA using the Sleep Heart Health Study data. This study aimed to determine the difference in EEG spectral power during sleep in the non-OSA group and with different severities of OSA in the general population.

PATIENTS AND METHODS

The participants (n = 5,804) underwent polysomnography, and they were classified into non-OSA, mild OSA, moderate OSA, and severe OSA groups. The fast Fourier transformation was used to compute the EEG power spectrum for total sleep duration within contiguous 30-second epochs of sleep. The EEG spectral powers of the groups were compared using 4,493 participants after adjusting potential confounding factors that could affect sleep EEG.

RESULTS

The power spectra differed significantly among the groups for all frequency bands (p corr < 0.001). We found that the quantitative EEG spectral powers in the beta and sigma bands of total sleep differed (p corr < 0.001) among the participants in the non-OSA group and with different severities of OSA, controlling for covariates. The beta power was higher and the sigma power was lower in the OSA groups than in the non-OSA group. The beta power decreased in the order of severe OSA, moderate OSA, mild OSA, and non-OSA.

CONCLUSION

This study suggests that there are differences between the microstructures of PSG-derived sleep EEG of non-OSA participants and those with different severities of OSA.

Change in EEG Activity is Associated with a Decrease in Tinnitus Awareness after rTMS

OBJECTIVE

To examine how 1Hz and 10Hz rTMS temporarily influence ratings of tinnitus loudness, annoyance, and awareness. The thalamocortical dysrhythmia (TCD) model of tinnitus was tested by examining changes in spectral power and coherence of resting state EEGs from baseline to each phase of treatment and correlating these data with change in tinnitus.

METHODS

Nineteen participants completed a double-blind, placebo (sham rTMS) controlled, within-subjects study with crossover between the two active rTMS treatment conditions. An imposed order effect, sham rTMS first, eliminated drift of active treatment into the placebo condition. The primary outcome measures were analogue ratings of tinnitus loudness, annoyance, and awareness, assessed repeatedly at baseline and during treatment, and 64 channel, resting state EEGs collected at baseline and the end of each treatment phase. Active rTMS consisted of 1800 pulses at 110% of motor threshold over temporal cortex delivered at 1Hz and 10Hz over four days. The research design also examined the effect of rTMS immediately following stimulation, regression to the mean in tinnitus ratings made over multiple days, and differences between treatment responders and non-responders.

RESULTS

There was no immediate effect of rTMS on tinnitus during a single rTMS session. Regression to the mean in tinnitus ratings occurred over three days of baseline and four days of treatment (both sham and active rTMS). After accounting for regression to the mean in the statistical model, 1Hz rTMS led to a significant decrease in tinnitus awareness from baseline and 10Hz rTMS trended in the same direction, whereas sham rTMS showed little change from baseline other than regression to the mean. Changes from baseline in spectral power of the resting state EEG provided partial support for predictions based on TCD model of tinnitus for active 1 and 10Hz rTMS but not sham rTMS. However, only an increase in beta coherence correlated significantly with a decrease in tinnitus awareness. Changes in the EEG were robust in treatment responders but absent among non-responders and during sham rTMS.

CONCLUSIONS

A positive response to rTMS for tinnitus is associated with an rTMS-induced change in beta coherence of the EEG. Increased beta coherence may be a biomarker of the rTMS effect; a "top-down" modulation of the EEG that promotes habituation to tinnitus. Participants whose tinnitus did not improve after rTMS did not show any changes in the EEG.

Exploring the Role of the Nucleus Accumbens in Adaptive Behavior Using Concurrent Intracranial and Extracranial Electrophysiological Recordings in Humans

Recent human electrophysiological evidence implicated θ-band communication between the nucleus accumbens (NAc) and frontal and parietal cortex in cognitive flexibility. Since the NAc is connected with the motor system, we tested whether phase and amplitude-based NAc-cortical connectivity and power modulation likewise underlie flexibility in motor action control. We combined concurrently recorded intracranial and extracranial electroencephalograms from seven psychiatric patients implanted with deep brain stimulation (DBS) electrodes who performed a stop signal task (SST). Inhibition success, as opposed to failure, was associated with greater prestimulus information flow from right NAc to medial frontal cortex through phase coupling of θ oscillations. Inhibition failure evoked θ power increases in the left NAc and medial frontal cortex, whereas parieto-occipital cortex showed an α power decrease. We conclude that NAc-to-frontal θ connectivity, possibly facilitating processing of task-relevant information, and α and θ power modulations, possibly reflecting post-error engagement of cognitive control, contribute to adaptive behavior pertaining motor control.

Universal spectral profile and dynamic evolution of muscle activation: a hallmark of muscle type and physiological state

The skeletal muscle is an integrated multicomponent system with complex dynamics of continuous myoelectrical activation of various muscle types across time scales to facilitate muscle coordination among units and adaptation to physiological states. To understand the multiscale dynamics of neuromuscular activity, we investigated spectral characteristics of different muscle types across time scales and their evolution with physiological states. We hypothesized that each muscle type is characterized by a specific spectral profile, reflecting muscle composition and function, that remains invariant over time scales and is universal across subjects. Furthermore, we hypothesized that the myoelectrical activation and corresponding spectral profile during certain movements exhibit an evolution path in time that is unique for each muscle type and reflects responses in muscle dynamics to exercise, fatigue, and aging. To probe the multiscale mechanism of neuromuscular regulation, we developed a novel protocol of repeated squat exercise segments, each performed until exhaustion, and we analyzed differentiated spectral power responses over a range of frequency bands for leg and back muscle activation in young and old subjects. We found that leg and back muscle activation is characterized by muscle-specific spectral profiles, with differentiated frequency band contribution, and a muscle-specific evolution path in response to fatigue and aging that is universal across subjects in each age group. The uncovered universality among subjects in the spectral profile of each muscle at a given physiological state, as well as the robustness in the evolution of these profiles over a range of time scales and states, reveals a previously unrecognized multiscale mechanism underlying the differentiated response of distinct muscle types to exercise-induced fatigue and aging.NEW & NOTEWORTHY To understand coordinated function of distinct fibers in a muscle, we investigated spectral dynamics of muscle activation during maximal exercise across a range of frequency bands and time scales of observation. We discovered a spectral profile that is specific for each muscle type, robust at short, intermediate, and large time scales, universal across subjects, and characterized by a muscle-specific evolution path with accumulation of fatigue and aging, indicating a previously unrecognized multiscale mechanism of muscle tone regulation.

Dreaming and Insomnia: Link between Physiological REM Parameters and Mentation Characteristics

(1) Background: An unresolved phenomenon of insomnia disorder is a discrepancy between objectively measured sleep and subjective complaints. It has been shown that rapid eye movement (REM) sleep might be especially vulnerable to an altered perception. The present work aimed to investigate the link between physiological REM parameters and mentation characteristics in REM sleep. (2) Methods: 22 patients with insomnia and 23 good sleepers indicating at least one REM mentation within an awakening study were included. Multivariate analyses of variance (MANOVAs) were calculated to examine group differences and effects of mentation characteristics on number of arousals, REM density, and spectral power prior to awakenings. (3) Results: Increased perceived wakefulness was related to lower delta, theta, and alpha power in the minute prior to the REM awakenings. Nevertheless, no group differences regarding spectral power were found. With respect to number of arousals and REM density, no significant effects of mentation characteristics and no group differences were found. (4) Conclusions: Our results suggest that spectral power in REM sleep is linked with altered sleep perception. Reduced delta, theta, and alpha power might be a signature of this modified REM sleep associated with a high level of perceived wakefulness. Future awakening studies are necessary to further explore the link between physiological REM parameters and sleep perception.

Neurophysiological Correlates of Adaptation and Interference during Asymmetrical Bimanual Movements

In this study, we investigated brain dynamics during interference between hands during bimanual movements. Participants performed a bimanual center-out reaching task in which a visuomotor rotation was applied to the right hand while the left hand did not receive visual feedback of its movements. This manipulation resulted in interference from the adapting right hand to the kinesthetically guided left hand. Electroencephalography (EEG) recordings during the task showed that spectral power in the high and low beta frequency bands was elevated early in exposure, but decreased throughout learning. This may be representative of error-based updating of internal models of movement. Additionally, coherence, a measure of neural functional connectivity, was elevated both within and between hemispheres in the beta frequencies during the initial presentation of the visuomotor rotation, and then decreased throughout adaptation. This suggests that beta oscillatory neural activity may be marker for transmission of conflicting motor information between hemispheres, which manifests in interference between the hands during asymmetrical bimanual movements.

Central Neuropathic Pain and Profiles of Quantitative Electroencephalography in Multiple Sclerosis Patients

Pain has a significant impact on the quality of life of patients with multiple sclerosis (MS). However, the neurophysiological mechanisms of central neuropathic pain in a MS course are not known. We hypothesized that changes in power spectral density (PSD) that take place in the electroencephalography (EEG) of MS patients with and without the central neuropathic pain (CNP) would differ. The study aimed to assess the features of quantitative EEG using the PSD indicator along with peak frequencies in the standard frequency bands in MS patients with and without CNP. We have analyzed the quantitative spectral content of the EEG at a resting state in 12 MS patients with CNP, 12 MS patients without CNP, and 12 gender- and age-matched healthy controls using fast Fourier transformation. Based on the ANOVA, at the group level, the theta band absolute and relative PSD showed an increase, whereas alpha band relative PSD showed a decrease in MS patients both with and without CNP. However, only in MS with CNP group, the absolute and relative PSD in the beta1 and beta2 bands increased and exceeded that in patients without pain. Only MS patients with CNP demonstrated the significantly increased absolute PSD for the theta, beta1, and beta2 frequency bands in most regions of interest. In the theta band, MS patients with CNP displayed the increase in absolute spectral power for the mid-temporal derivation of the right hemisphere and the increase in relative spectral power for the prefrontal derivation of this hemisphere. In the beta1 band, the increase in absolute spectral power was observed for the three temporal derivations of the right hemisphere, whereas in the beta2 band, for the occipital, parietal, and temporal lobes of both hemispheres. In the alpha band, only a relative spectral power decrease was revealed for the occipital lobes of both hemispheres and parietal lobe of the right hemisphere. In MS patients with CNP, the frequencies of the dominant spectral power (peak frequencies) in the high-frequency beta band were higher than in the healthy control in posterior areas of the left hemisphere. Data could represent central nervous system alterations related to central neuropathic pain in MS patients that lead to the disturbances in cortical communication.

Identification of Veterans With PTSD Based on EEG Features Collected During Sleep

Background: Previously, we identified sleep-electroencephalography (EEG) spectral power and synchrony features that differed significantly at a population-average level between subjects with and without posttraumatic stress disorder (PTSD). Here, we aimed to examine the extent to which a combination of such features could objectively identify individual subjects with PTSD. Methods: We analyzed EEG data recorded from 78 combat-exposed Veteran men with (n = 31) and without (n = 47) PTSD during two consecutive nights of sleep. To obviate the need for manual assessment of sleep staging and facilitate extraction of features from the EEG data, for each subject, we computed 780 stage-independent, whole-night features from the 10 most commonly used EEG channels. We performed feature selection and trained a logistic regression model using a training set consisting of the first 47 consecutive subjects (18 with PTSD) of the study. Then, we evaluated the model on a testing set consisting of the remaining 31 subjects (13 with PTSD). Results: Feature selection yielded three uncorrelated features that were consistent across the two consecutive nights and discriminative of PTSD. One feature was from the spectral power in the delta band (2-4 Hz) and the other two were from phase synchronies in the alpha (10-12 Hz) and gamma (32-40 Hz) bands. When we combined these features into a logistic regression model to predict the subjects in the testing set, the trained model yielded areas under the receiver operating characteristic curve of at least 0.80. Importantly, the model yielded a testing-set sensitivity of 0.85 and a positive predictive value (PPV) of 0.31. Conclusions: We identified robust stage-independent, whole-night features from EEG signals and combined them into a logistic regression model to discriminate subjects with and without PTSD. On the testing set, the model yielded a high sensitivity and a PPV that was twice the prevalence rate of PTSD in the U.S. Veteran population. We conclude that, using EEG signals collected during sleep, such a model can potentially serve as a means to objectively identify U.S. Veteran men with PTSD.

Electroencephalogram Profiles for Emotion Identification over the Brain Regions Using Spectral, Entropy and Temporal Biomarkers

Identifying emotions has become essential for comprehending varied human behavior during our daily lives. The electroencephalogram (EEG) has been adopted for eliciting information in terms of waveform distribution over the scalp. The rationale behind this work is twofold. First, it aims to propose spectral, entropy and temporal biomarkers for emotion identification. Second, it aims to integrate the spectral, entropy and temporal biomarkers as a means of developing spectro-spatial ( S S ) , entropy-spatial ( E S ) and temporo-spatial ( T S ) emotional profiles over the brain regions. The EEGs of 40 healthy volunteer students from the University of Vienna were recorded while they viewed seven brief emotional video clips. Features using spectral analysis, entropy method and temporal feature were computed. Three stages of two-way analysis of variance (ANOVA) were undertaken so as to identify the emotional biomarkers and Pearson's correlations were employed to determine the optimal explanatory profiles for emotional detection. The results evidence that the combination of applied spectral, entropy and temporal sets of features may provide and convey reliable biomarkers for identifying S S , E S and T S profiles relating to different emotional states over the brain areas. EEG biomarkers and profiles enable more comprehensive insights into various human behavior effects as an intervention on the brain.

Global EEG coherence as a marker for cognition in older adults at risk for dementia

Quantitative electroencephalography (EEG) provides useful information about neurophysiological health of the aging brain. Current studies investigating EEG coherence and power for specific brain areas and frequency bands have yielded inconsistent results. This study assessed EEG coherence and power indices at rest measured over the whole skull and for a wide frequency range as global EEG markers for cognition in a sample at risk for dementia. Since global markers are more reliable and less error-prone than region- and frequency-specific indices they might help to overcome previous inconsistencies. Global EEG coherence (1-30 Hz) and an EEG slowing score were assessed. The EEG slowing score was calculated by low-frequency power (1-8 Hz) divided by high-frequency power (9-30 Hz). In addition, the prognostic value of the two EEG indices for cognition and cognitive decline was assessed in a 5-year follow-up pilot study. Baseline global coherence correlated positively with cognition at baseline, but not with cognitive decline or with cognition at the 5-year follow-up. The EEG slowing ratio showed no significant association, neither with cognition at baseline or follow-up, nor with cognitive decline over a period of 5 years. The results indicate that the resting state global EEG coherence might be a useful and easy to assess electrophysiological correlate for neurocognitive health in older adults at risk for dementia. Because of the small statistical power for the follow-up analyses, the prognostic value of global coherence could not be determined in the present study. Future studies should assess its prognostic value with larger sample sizes.

Abnormalities of Alpha Activity in Frontocentral Region of the Brain as a Biomarker to Diagnose Adolescents With Bipolar Disorder

Objectives. To investigate brain abnormalities in adolescents with new-onset bipolar disorder (BD) during acute hypomanic and depressive episodes using electroencephalogram (EEG) analysis and to derive a computer-based method for diagnosis of the disorder. Methods. EEG spectral power and entropy of 21 adolescents with BD (included 11 patients in the hypomanic episode and 10 patients in the depressive episode) and 18 healthy adolescents were compared. Moreover, using significant differences and K-nearest-neighbors (KNN) classifier, it was attempted to distinguish the BD adolescents from normal ones. Results. The BD adolescents had higher values of spectral power in all frequency bands, particularly in the frontocentral, mid-temporal, and right parietal regions. Also, spectral entropy had significantly increased in delta, alpha, and gamma frequency bands for BD. A high accuracy of 95.8% was achieved by all significant differences in the alpha band in discriminating adolescents with BD. The depressive state showed higher values of spectral power and entropy in low-frequency bands (delta and theta) compared to the hypomanic state. Conclusion. Based on BD symptoms, especially inattention, increased alpha power is a rational finding which is associated with thalamus dysfunction. Thus, it seems that EEG alpha oscillation is the main source of abnormality in BD. Furthermore, EEG slowing in the depressive episode is related to inhibition of electrical activity and reduced cognitive functions.

EEG before and after total corpus callosotomy for pharmacoresistant infantile spasms: Fast oscillations and slow-wave connectivity in hypsarrhythmia

OBJECTIVE

We analyzed the features of fast oscillations (FOs) and connectivity in hypsarrhythmia to identify biomarkers for predicting seizure outcomes after total corpus callosotomy (TCC) in children with pharmacoresistant infantile spasms (IS). We hypothesize that the power of FOs and connectivity of slow waves in hypsarrhythmia would indicate the prognosis of IS.

METHOD

We retrospectively identified 42 children with pharmacoresistant IS who underwent TCC from 2009 to 2014 at Nagasaki Medical Center. We collected preoperative hypsarrhythmia for 200 seconds from each child. Children were categorized into three groups with interictal epileptic discharges on EEG at 6 months after TCC: group A, no epileptic discharge; group B, lateralized epileptic discharges; and group C; bilateral epileptic discharges. We analyzed spectral power and phase synchronization in preoperative hypsarrhythmia among the three groups.

RESULTS

We found 10 children in group A, 10 children in group B, and 22 children in group C. All group A and 1 in group B achieved seizure freedom after TCC. Six (67%) of 9 group B children who underwent further surgeries achieved seizure freedom. Ten (45%) of group C children had seizure reduction >50% after TCC, and 13 (87%) of 15 children who underwent further surgeries had residual seizures. The clinical profiles of the three groups did not differ significantly. The power of FOs (≥45 Hz) in hypsarrhythmia was significantly stronger in group C at the midline and temporal regions than in groups B and A (P = .014). The connectivity of theta (4-9 Hz) and FOs (29-70 Hz) tended to increase in group C, compared with the increased connectivity of 1-2 Hz in group A (P = .08).

SIGNIFICANCE

The increased power and connectivity of FOs in hypsarrhythmia may correlate with pharmacoresistant and surgically resistant seizures in IS. The existence and connectivity of FOs are associated with unilateral/bilateral cortical epileptogenicity in hypsarrhythmia. Prominent slow waves and connectivity without FOs might correlate with seizure freedom after TCC. Modulation of the callosal system with subcortical/cortical epileptic discharges might play a role in generating hypsarrhythmia and IS.

The Effects of Textured Insoles on Cortical Activity and Quiet Bipedal Standing With and Without Vision: An EEG Study

Wearing textured insoles (TIs) can reduce static postural sway, but the neurophysiological mechanisms by which these changes occur are not well understood. To address this issue, cortical activity was investigated in this study using electroencephalography (EEG) recordings from 19 scalp locations, in 15 healthy young adults (5 females; mean age = 27 ± 4.09 years) during quiet bipedal standing, under different insole conditions (textured versus smooth), with and without vision. Compared to smooth insoles (SIs), TIs significantly reduced postural sway in two measures; anterior-posterior range and standard deviation. In the EEG data, whole-head analyses showed cortical activity in the upper alpha power band was significantly reduced for textured compared to SIs. Exploratory analyses revealed this effect was significant both with and without vision, and was more pronounced over the parietal, compared to central regions, and over central compared to frontal regions. This trend was observed in low alpha and theta bands, but the effect of insole type was not significant. Textured insoles thus appear to affect not only balance outcomes but also cortical activity. The cortical activity adaptation may represent greater information becoming readily available at the cortical level, enhancing the representation of the body in space.

Quantitative Preterm EEG Analysis: The Need for Caution in Using Modern Data Science Techniques

Hemodynamic changes during neonatal transition increase the vulnerability of the preterm brain to injury. Real-time monitoring of brain function during this period would help identify the immediate impact of these changes on the brain. Neonatal EEG provides detailed real-time information about newborn brain function but can be difficult to interpret for non-experts; preterm neonatal EEG poses even greater challenges. An objective quantitative measure of preterm brain health would be invaluable during neonatal transition to help guide supportive care and ultimately protect the brain. Appropriate quantitative measures of preterm EEG must be calculated and care needs to be taken when applying the many techniques available for this task in the era of modern data science. This review provides valuable information about the factors that influence quantitative EEG analysis and describes the common pitfalls. Careful feature selection is required and attention must be paid to behavioral state given the variations encountered in newborn EEG during different states. Finally, the detrimental influence of artifacts on quantitative EEG analysis is illustrated.

EEG Data Collection in Children with ASD: The Role of State in Data Quality and Spectral Power

BACKGROUND

Electroencephalography can elucidate neurobiological mechanisms underlying heterogeneity in ASD. Studying the full range of children with ASD introduces methodological challenges stemming from participants' difficulties tolerating the data collection process, leading to diminished EEGdataretentionandincreasedvariabilityin participant 'state' during the recording. Quantifying state will improve data collection methods and aide in interpreting results.

OBJECTIVES

Observationally quantify participant state during the EEG recording; examine its relationship to child characteristics, data retention and spectral power.

METHODS

Participants included 5-11 year-old children with D (N=39) and age-matched TD children (N=16). Participants were acclimated to the EEG environment using behavioral strategies. EEG was recorded while participants watched a video of bubbles. Participant 'state' was rated using a Likert scale (Perceived State Rating: PSR).

RESULTS

Participants with ASD had more elevated PSR than TD participants. Less EEG data were retained in participants with higher PSR scores, but this was not related to age or IQ. TD participants had higher alpha power compared with the ASD group. Within the ASD group, participants with high PSR had decreased frontal alpha power.

CONCLUSIONS

Given supportive strategies, EEG data was collected from children with ASD across cognitive levels. Participant state influenced both EEG data retention and alpha spectral power. Alpha suppression is linked to attention and vigilance, suggesting that these participants were less 'at rest'. This highlights the importance of considering state when conducting EEG studies with challenging participants, both to increase data retention rates and to quantify the influence of state on EEG variables.

Thalamic reticular control of local sleep in mouse sensory cortex

Sleep affects brain activity globally, but many cortical sleep waves are spatially confined. Local rhythms serve cortical area-specific sleep needs and functions; however, mechanisms controlling locality are unclear. We identify the thalamic reticular nucleus (TRN) as a source for local, sensory-cortex-specific non-rapid-eye-movement sleep (NREMS) in mouse. Neurons in optogenetically identified sensory TRN sectors showed stronger repetitive burst discharge compared to non-sensory TRN cells due to higher activity of the low-threshold Ca²⁺ channel Ca_V3.3. Major NREMS rhythms in sensory but not non-sensory cortical areas were regulated in a Ca_V3.3-dependent manner. In particular, NREMS in somatosensory cortex was enriched in fast spindles, but switched to delta wave-dominated sleep when Ca_V3.3 channels were genetically eliminated or somatosensory TRN cells chemogenetically hyperpolarized. Our data indicate a previously unrecognized heterogeneity in a powerful forebrain oscillator that contributes to sensory-cortex-specific and dually regulated NREMS, enabling local sleep regulation according to use- and experience-dependence.

Falling asleep affects our behavior immediately and profoundly. During sleep, large electrical waves appear across the brain in areas responsible for consciousness, sensation and movement. In the cortex – the outer layer of the brain – sleep waves arise from networks that connect to the thalamus, a deeper structure within the brain. However, not all areas of the brain sleep equally. We know this intuitively because sensory stimuli, such as an alarm clock or a baby’s cry, can still wake us up. By contrast, we typically do not move much or take major decisions while we sleep. Therefore, the brain areas involved in sensation should not be expected to sleep in the same way as areas involved in movement or reasoning.

Neighboring brain areas generally show very different sleep waves. The brain regions that we use during the day can also affect how sleep varies from one area to the next. It is not well understood what determines these ‘local’ sleep properties.

By studying the brains of mice, Fernandez et al. now show that the networks between the cortex and thalamus are much more varied than previously thought, in particular regarding a thalamic nucleus that is relevant for sleep wave generation. These previously unrecognized differences deep within the brain are part of the origin of local sleep in the outer layer of the brain. Sleep wave activity differed depending on whether the networks were involved in sensory or non-sensory roles. The networks allow sensory areas to switch efficiently between different forms of local sleep. This might underlie how the brain’s sensory activity during the day can influence local sleep at night.

There is growing evidence that major sleep disorders are due to disturbances to local sleep. Techniques to modify or restore specific sleep waves locally in the brain could help to develop new sleep therapies. For example, having a detailed map of electrical waves within the sleep-disordered brain could help researchers to apply transcranial stimulation techniques in ways that might help to treat these debilitating disorders.

Changes in Mental Workload and Motor Performance Throughout Multiple Practice Sessions Under Various Levels of Task Difficulty

The allocation of mental workload is critical to maintain cognitive-motor performance under various demands. While mental workload has been investigated during performance, limited efforts have examined it during cognitive-motor learning, while none have concurrently manipulated task difficulty. It is reasonable to surmise that the difficulty level at which a skill is practiced would impact the rate of skill acquisition and also the rate at which mental workload is reduced during learning (relatively slowed for challenging compared to easier tasks). This study aimed to monitor mental workload by assessing cortical dynamics during a task practiced under two difficulty levels over four days while perceived task demand, performance, and electroencephalography (EEG) were collected. As expected, self-reported mental workload was reduced, greater working memory engagement via EEG theta synchrony was observed, and reduced cortical activation, as indexed by progressive EEG alpha synchrony was detected during practice. Task difficulty was positively related to the magnitude of alpha desynchrony and accompanied by elevations in the theta-alpha ratio. Counter to expectation, the absence of an interaction between task difficulty and practice days for both theta and alpha power indicates that the refinement of mental processes throughout learning occurred at a comparable rate for both levels of difficulty. Thus, the assessment of brain dynamics was sensitive to the rate of change of cognitive workload with practice, but not to the degree of difficulty. Future work should consider a broader range of task demands and additional measures of brain processes to further assess this phenomenon.

The impact of music on the bioelectrical oscillations of the brain

In this article we conclude the main scientific studies into the changes in the bioelectrical brainwave activity that occur while listening to music. A brainwave spectral analysis, derived from findings of electroencephalograms, is a powerful tool to obtain deep and objective insights into the effects of music on the brain. This capacity is being investigated in various contexts. Starting with a healthy population, studies also seek to determine the impact of music in such conditions as disorders of consciousness, psychiatric diseases, and chronic conditions, as well as to further explore the role of music for rehabilitation purposes. Supplemental investigations in this field are needed not only to deepen the knowledge of general neurophysiology of listening to music, but also to possibly open new perspectives for its broader use in clinical practices.

Electrophysiological changes in poststroke subjects with depressed mood: A quantitative EEG study

BACKGROUND

We aimed to explore the electrophysiological changes in poststroke subjects with depressed mood.

METHODS

Resting-state electroencephalogram (EEG) signals of 16 electrodes in 35 poststroke depressed, 24 poststroke nondepressed, and 35 age-matched healthy control subjects were analyzed by means of spectral power analysis, a quantitative EEG measurement of different frequency bands. The relationship among depressed mood, functional status, lesion side, and poststroke time was assessed by using variance and Spearman correlation analysis. Multiple analysis of variance was used to compare the differences among the 3 groups. Binary logistic regression analysis was used to establish a regression model to predict depressed mood in stroke subjects and to explore the association between depression and EEG band power. Receiver operating characteristic curves were used to estimate the ability of spectral power selected by binary logistic regression to indicate depressed mood in stroke subjects.

RESULTS

We found that the hemisphere in which the lesion was located and the time since stroke onset had no effect on depressed mood. Only the patient's functional status was related to emotional symptoms. Quantitative EEG analysis revealed increased delta, theta, and beta2 power in stroke subjects with depressed mood, particularly in temporal regions. The theta and beta2 power in the right temporal area were shown to be highly sensitive to depressed mood, and these parameters showed good discriminatory ability for depressed subjects following stroke.

CONCLUSION

Depressed mood after stroke is associated with functional status. Quantitative EEG parameters may be a useful tool in timely screening for depressed mood after stroke.

Quantitative Analysis of the Antiepileptogenic Effects of Low Frequency Stimulation Applied Prior or After Kindling Stimulation in Rats

Background and Objective: Developing quantitative measures based on spectral analysis of electroencephalograph (EEG) recordings of neural activities plays an important role in developing efficient treatments for epilepsy. Such biomarkers can be used for developing open or closed loop approaches for seizure prediction or prevention. This study aims to quantitatively evaluate antiepileptogenic effects of low frequency stimulation (LFS) applied immediately before or after kindling stimulations using spectral power analysis of extracellular EEG in rat. Methods: Nineteen adult rats were used: seven for kindle, six for LFS+Kindle (LFSK) and six for Kindle+LFS (KLFS). Four packages of LFS (1Hz) were applied immediately before or after rapid kindling stimulations. The power spectral densities of afterdischarge (AD) sections of EEG corresponding to different stages of kindling for delta (0-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-28 Hz), gamma (28-40 Hz) sub-bands, and theta/alpha ratio were comparatively investigated. Moreover, correlation between AD duration (ADD) and its different frequency components was calculated. Results: Both LFSK and KLFS significantly increased delta and reduced beta and gamma oscillations, compared with kindle group. However, just the reduction in LFSK group was significant. Both protocols increased theta/alpha ratio, but just LFSK showed significant increase (p < 0.05). Although LFSK enhanced theta/alpha ratio more than KLFS, the difference was not statistically significant. Furthermore, strong correlation between each frequency sub band and ADD was not observed in kindle and LFS treated groups (both LFSK and KLFS). Conclusion: Although behavioral assessments showed relatively the same level of antiepileptogenic effects for KLFS and LFSK, quantitative assessments showed more significant differences in the quantitative measures between the two protocols. Developing more quantitative EEG based measures correlated with LFS-induced effects can facilitate developing open or closed loop seizure prevention modalities.

Sleep Behavior and EEG Oscillations in Aged Dp(16)1Yey/+ Mice: A Down Syndrome Model

Down syndrome (DS) results from the triplication of genes located on human chromosome 21 (Hsa21). Though many cognitive and behavioral impairments are associated with DS, sleep disturbances remain poorly understood despite being a reported phenotype in approximately 60% of individuals diagnosed with DS. In this study, sleep and electroencephalography (EEG) oscillations were recorded from aged (12–14 mos.) Dp(16)1Yey/+ mice (Dp16), a mouse model of DS. We observed disrupted sleep demonstrated by increased activity during the dark phase and increased time awake at the expense of NREM sleep compared to wild-type mice. In addition, we found that Dp16 mice display significant differences in relative EEG power distribution among oscillation frequencies in both sleep and awake states. These results in Dp16 mice are consistent with sleep disturbances found in individuals with DS, and the abnormal EEG oscillations in aged Dp16 mice suggest a potential role for GABAergic activity in these sleep and EEG abnormalities. These sleep and EEG data reflect underlying differences in neuronal activity at the network level and thus are causative agents rather than merely symptoms of DS.

Abnormal Sleep Delta Rhythm and Interregional Phase Synchrony in Patients with Restless Legs Syndrome and Their Reversal by Dopamine Agonist Treatment

Background and Purpose

The purpose of this study was to characterize abnormal cortical activity during sleep in restless legs syndrome (RLS) patients and to determine the effects of treatment with a dopamine agonist. Based on whole-brain electroencephalograms, we attempted to verify alterations in the functional network as well as the spectral power of neural activities during sleep in RLS patients and to determine whether the changes are reversed by treatment with pramipexole.

Methods

Twelve drug-naïve RLS patients participated in the study. Overnight polysomnography was performed before and after treatment: the first recording was made immediately prior to administering the first dose of pramipexole, and the second recording was made 12–16 weeks after commencing pramipexole administration. Sixteen age-matched healthy participants served as a control group. The spectral power and interregional phase synchrony were analyzed in 30-s epochs. The functional characteristics of the cortical network were quantified using graph-theory measures.

Results

The delta-band power was significantly increased and the small-world network characteristics in the delta band were disrupted in RLS patients compared to the healthy controls. These abnormalities were successfully treated by dopaminergic medication. The delta-band power was significantly correlated with the RLS severity score in the RLS patients prior to treatment.

Conclusions

Our findings suggest that the spectral and functional network characteristics of neural activities during sleep become abnormal in RLS patients, and these abnormalities can be successfully treated by a dopamine agonist.

EEG Correlates of Ten Positive Emotions

Compared with the well documented neurophysiological findings on negative emotions, much less is known about positive emotions. In the present study, we explored the EEG correlates of ten different positive emotions (joy, gratitude, serenity, interest, hope, pride, amusement, inspiration, awe, and love). A group of 20 participants were invited to watch 30 short film clips with their EEGs simultaneously recorded. Distinct topographical patterns for different positive emotions were found for the correlation coefficients between the subjective ratings on the ten positive emotions per film clip and the corresponding EEG spectral powers in different frequency bands. Based on the similarities of the participants' ratings on the ten positive emotions, these emotions were further clustered into three representative clusters, as 'encouragement' for awe, gratitude, hope, inspiration, pride, 'playfulness' for amusement, joy, interest, and 'harmony' for love, serenity. Using the EEG spectral powers as features, both the binary classification on the higher and lower ratings on these positive emotions and the binary classification between the three positive emotion clusters, achieved accuracies of approximately 80% and above. To our knowledge, our study provides the first piece of evidence on the EEG correlates of different positive emotions.

Dynamic Changes in Spectral and Spatial Signatures of High Frequency Oscillations in Rat Hippocampi during Epileptogenesis in Acute and Chronic Stages

Objective

To analyze spectral and spatial signatures of high frequency oscillations (HFOs), which include ripples and fast ripples (FRs, >200 Hz) by quantitatively assessing average and peak spectral power in a rat model of different stages of epileptogenesis.

Methods

The lithium–pilocarpine model of temporal lobe epilepsy was used. The acute phase of epilepsy was assessed by recording intracranial electroencephalography (EEG) activity for 1 day after status epilepticus (SE). The chronic phase of epilepsy, including spontaneous recurrent seizures (SRSs), was assessed by recording EEG activity for 28 days after SE. Average and peak spectral power of five frequency bands of EEG signals in CA1, CA3, and DG regions of the hippocampus were analyzed with wavelet and digital filter.

Results

FRs occurred in the hippocampus in the animal model. Significant dynamic changes in the spectral power of FRS were identified in CA1 and CA3. The average spectral power of ripples increased at 20 min before SE (p < 0.05), peaked at 10 min before diazepam injection. It decreased at 10 min after diazepam (p < 0.05) and returned to baseline after 1 h. The average spectral power of FRs increased at 30 min before SE (p < 0.05) and peaked at 10 min before diazepam. It decreased at 10 min after diazepam (p < 0.05) and returned to baseline at 2 h after injection. The dynamic changes were similar between average and peak spectral power of FRs. Average and peak spectral power of both ripples and FRs in the chronic phase showed a gradual downward trend compared with normal rats 14 days after SE.

Significance

The spectral power of HFOs may be utilized to distinguish between normal and pathologic HFOs. Ictal average and peak spectral power of FRs were two parameters for predicting acute epileptic seizures, which could be used as a new quantitative biomarker and early warning marker of seizure. Changes in interictal HFOs power in the hippocampus at the chronic stage may be not related to seizure occurrence.

Altered Resting-State Cortical EEG Oscillations in Patients With Severe Asymptomatic Carotid Stenosis

Asymptomatic carotid stenosis is characterized by altered cerebral hemodynamics and cognitive impairment, but the underlying neurophysiological mechanism remains unclear. To elucidate the alterations of cortical activities, resting-state electrophysiological activities were recorded from patients with mild (<30%; n=10; age 57-85 years), moderate (30% to 50%; n=11; age 66-88 years), and severe (>50%; n=8; age 67-91 years) carotid stenosis. The current density and oscillatory power of the cortical sources were analyzed using the minimum norm estimates method combined with fast Fourier transform analysis. Our results indicate that the cortical current density among regions of the brain was similar, irrespective of the degree of carotid stenosis. With regard to the cortical oscillations, augmented theta activities in the bilateral parietal, left temporal, and left occipital regions and attenuated alpha activities in the bilateral frontal and right central regions were obtained in patients with severe asymptomatic carotid stenosis. We suggest that the source-based cortical oscillations at theta and alpha bands might reflect the alterations of the brain activities and characterize the altered neurophysiological mechanism of the brain with at least 50% occlusion of the carotid artery. Further longitudinal studies with larger populations are warranted to verify the present findings.