A physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordings.

Cortical function emerges from the interactions of multi-scale networks that may be studied at a high level using neural mass models (NMM) that represent the mean activity of large numbers of neurons. Here, we provide first a new framework called laminar NMM, or LaNMM for short, where we combine conduction physics with NMMs to simulate electrophysiological measurements. Then, we employ this framework to infer the location of oscillatory generators from laminar-resolved data collected from the prefrontal cortex in the macaque monkey. We define a minimal model capable of generating coupled slow and fast oscillations, and we optimize LaNMM-specific parameters to fit multi-contact recordings. We rank the candidate models using an optimization function that evaluates the match between the functional connectivity (FC) of the model and data, where FC is defined by the covariance between bipolar voltage measurements at different cortical depths. The family of best solutions reproduces the FC of the observed electrophysiology by selecting locations of pyramidal cells and their synapses that result in the generation of fast activity at superficial layers and slow activity across most depths, in line with recent literature proposals. In closing, we discuss how this hybrid modeling framework can be more generally used to infer cortical circuitry.

Regional Thermal Radiation Characteristics of FY Satellite Remote Sensing Based on Big Data Analysis.

It is of great significance to study the thermal radiation anomalies of earthquake swarms in the same area in terms of selecting abnormal characteristic determination parameters, optimizing and determining the processing model, and understanding the abnormal machine. In this paper, we investigated short-term and long-term thermal radiation anomalies induced by earthquake swarms in Iran and Pakistan between 2007 and 2016. The anomalies were extracted from infrared remote sensing black body temperature data from the China Geostationary Meteorological Satellites (FY-2C/2E/2F/2G) using the multiscale time-frequency relative power spectrum (MS T-FRPS) method. By analyzing and summarizing the thermal radiation anomalies of series earthquake groups with consistency law through a stable and reliable MS T-FRPS method, we first obtained the relationship between anomalies and ShakeMaps from USGS and proposed the anomaly regional indicator (ARI) to determine seismic anomalies and the magnitude decision factor (MDF) to determine seismic magnitude. In addition, we explored the following discussions: earthquake impact on regional thermal radiation background and the relationship between thermal anomalies and earthquake magnitude and the like. Future research directions using the MS T-FRPS method to characterize regional thermal radiation anomalies induced by strong earthquakes could help improve the accuracy of earthquake magnitude determination.

Miner Fatigue Detection from Electroencephalogram-Based Relative Power Spectral Topography Using Convolutional Neural Network.

Fatigue of miners is caused by intensive workloads, long working hours, and shift-work schedules. It is one of the major factors increasing the risk of safety problems and work mistakes. Examining the detection of miner fatigue is important because it can potentially prevent work accidents and improve working efficiency in underground coal mines. Many previous studies have introduced feature-based machine-learning methods to estimate miner fatigue. This work proposes a method that uses electroencephalogram (EEG) signals to generate topographic maps containing frequency and spatial information. It utilizes a convolutional neural network (CNN) to classify the normal state, critical state, and fatigue state of miners. The topographic maps are generated from the EEG signals and contrasted using power spectral density (PSD) and relative power spectral density (RPSD). These two feature extraction methods were applied to feature recognition and four representative deep-learning methods. The results showthat RPSD achieves better performance than PSD in classification accuracy with all deep-learning methods. The CNN achieved superior results to the other deep-learning methods, with an accuracy of 94.5%, precision of 97.0%, sensitivity of 94.8%, and F1 score of 96.3%. Our results also show that the RPSD-CNN method outperforms the current state of the art. Thus, this method might be a useful and effective miner fatigue detection tool for coal companies in the near future.

Power Gaps Among Stakeholders in Israel's Primary Care and the Role of Primary Care Physicians' Relative Power in Their Intention to Use Video-Consultations with Patients.

Introduction: Although some correlates of primary care physicians (PCPs) telemedicine adoption have been studied, little is known about whether the intention to use video-consultations (VCs) relates to how PCPs view their power, relative to other stakeholder groups in primary care. The aim of this study was (1) to describe PCPs', patients', and policy makers' (PMs) views of their power and (2) to explore how PCPs views of power are associated with their intention to use VC. Methods: A convergent parallel mixed-methods design was used. Interviews were conducted with five focus groups that comprised 42 patients; five focus groups with 52 PCPs; and 24 individual interviews with PMs. A total of 508 patients, 311 PCPs, and 141 PMs completed the questionnaire, assessing intention to use VC and stakeholders' relative power. The qualitative data were analyzed using the thematic method; survey data were analyzed using quantitative methods. Results: All stakeholder groups rated PCPs' power as significantly lower, relative to that of patients and managers. PCPs' intention to use telemedicine was found to be significantly related to perceived power gaps between them and patients (r = -0.24, p < 0.001) and between them and managers (r = -0.45, p < 0.001). Themes revealed in the analysis describing how PCPs' low power influences their intention to use VC were as follows: PCPs' low-impact telemedicine-related decisions, increased work overload, "big brother" control, and Health Maintenance Organization demands for telemedicine mandatory usage. Conclusions: To successfully adopt VC, efforts should be made to increase PCPs' relative power, by strengthening their involvement in decision-making procedures and by increasing PCPs' control over their work environment.

[Improving college students sub-threshold depression by music neurofeedback].

Sub-threshold depression refers to a psychological sub-health state that fails to meet the diagnostic criteria for depression. Appropriate intervention can improve the state and reduce the risks of disease development. In this paper, we focus on music neurofeedback stimulation improving emotional state of sub-threshold depression college students.Twenty-four college students with sub-threshold depression participated in the experiment, 16 of whom were members of the experimental group. Decompression music based on spectrum classification was applied to 16 experimental group participants for 10 min/d music neural feedback stimulation with a period of 14 days, and no stimulation was applied to 8 control group participants. Three feature parameters of electroencephalogram (EEG) relative power, sample entropy and complexity were extracted for analysis. The results showed that the relative power of αã€ß and θ rhythm increased, while δ rhythm decreased after the stimulation of musical nerofeedback in the experimental group. The sample entropy and complexity were significantly increased after the stimulation, and the differences of these parameters pre and post stimulation were statistically significant ( P < 0.05), while the differences of all feature parameters in the control group were not statistically significant. In the experimental group, the scores of self-rating depression scale(SDS) decreased after the stimulation of musical nerofeedback, indicating that the depression was improved. The result of this study showed that music neurofeedback stimulation can improve sub-threshold depression and may provides an effective new way for college students to self-regulation of emotion.

Neurophysiological Characterization of a Non-Human Primate Model of Traumatic Spinal Cord Injury Utilizing Fine-Wire EMG Electrodes.

This study aims to characterize traumatic spinal cord injury (TSCI) neurophysiologically using an intramuscular fine-wire electromyography (EMG) electrode pair. EMG data were collected from an agonist-antagonist pair of tail muscles of Macaca fasicularis, pre- and post-lesion, and for a treatment and control group. The EMG signals were decomposed into multi-resolution subsets using wavelet transforms (WT), then the relative power (RP) was calculated for each individual reconstructed EMG sub-band. Linear mixed models were developed to test three hypotheses: (i) asymmetrical volitional activity of left and right side tail muscles (ii) the effect of the experimental TSCI on the frequency content of the EMG signal, (iii) and the effect of an experimental treatment. The results from the electrode pair data suggested that there is asymmetry in the EMG response of the left and right side muscles (p-value < 0.001). This is consistent with the construct of limb dominance. The results also suggest that the lesion resulted in clear changes in the EMG frequency distribution in the post-lesion period with a significant increment in the low-frequency sub-bands (D4, D6, and A6) of the left and right side, also a significant reduction in the high-frequency sub-bands (D1 and D2) of the right side (p-value < 0.001). The preliminary results suggest that using the RP of the EMG data, the fine-wire intramuscular EMG electrode pair are a suitable method of monitoring and measuring treatment effects of experimental treatments for spinal cord injury (SCI).

Real-Time ECG-Based Detection of Fatigue Driving Using Sample Entropy.

In present work, the heart rate variability (HRV) characteristics, calculated by sample entropy (SampEn), were used to analyze the driving fatigue state at successive driving stages. Combined with the relative power spectrum ratio ß/(θ + α), subjective questionnaire, and brain network parameters of electroencephalogram (EEG) signals, the relationships between the different characteristics for driving fatigue were discussed. Thus, it can conclude that the HRV characteristics (RR SampEn and R peaks SampEn), as well as the relative power spectrum ratio ß/(θ + α) of the channels (C3, C4, P3, P4), the subjective questionnaire, and the brain network parameters, can effectively detect driving fatigue at various driving stages. In addition, the method for collecting ECG signals from the palm part does not need patch electrodes, is convenient, and will be practical to use in actual driving situations in the future.

[Effects of low frequency repetitive transcranial magnetic stimulation on Electroencephalograph rhythm of children with autism].

Autism spectrum disorders (ASD) is a complex developmental disorder characterized by impairments in social communication and stereotyped behaviors. Electroencephalograph (EEG), which can measure neurological changes associated with cortical synaptic activity, has been proven to be a powerful tool for detecting neurological disorders. The main goal of this study is to explore the effects of repetitive transcranial magnetic stimulation (rTMS) on behavioral response and EEG. We enrolled 32 autistic children, rTMS group ( n = 16) and control group ( n = 16) and calculated the relative power of the δ, θ, α, ß rhythms in each brain area by fast Fourier transform and Welch's method. We also compared Autism Behavior Checklist (ABC) scores of the patients before and after rTMS. The results showed a significant decrease in the relative power of the δ band on right temporal region and parietal region and also a decreased coherence on frontal region after rTMS intervention. The study proves that rTMS could have positive effects on behavior of attention, execution ability, and language ability of children and could reduce their stereotyped behavior and radical behavior.

Correlating maternal and cord-blood inflammatory markers and BDNF with human fetal brain activity recorded by magnetoencephalography: An exploratory study.

Background: During gestation, the brain development of the fetus is affected by many biological markers, where inflammatory processes and neurotrophic factors have been of particular interest in the past decade. Aim: This exploratory study is the first attempt to explore the relationships between biomarker levels in maternal and cord-blood samples and human fetal brain activity measured with non-invasive fetal magnetoencephalography (fMEG). Method: Twenty-three women were enrolled in this study for collection of maternal serum and fMEG tracings immediately prior to their scheduled cesarean delivery. Twelve of these women had a preexisting diabetic condition. At the time of delivery, umbilical cord blood was also collected. Biomarker levels from both maternal and cord blood were measured and subsequently analyzed for correlations with fetal brain activity in four frequency bands extracted from fMEG power spectral densities. Results: Relative power in the delta, alpha, and beta frequency bands exhibited moderate-sized correlations with maternal BDNF and cord-blood CRP levels before and after adjusting for confounding diabetic status. These correlations were negative for the delta band, and positive for the alpha and beta bands. Maternal CRP and cord-blood BDNF and IL-6 exhibited negligible correlations with relative power in all four bands. Diabetes did not appear to be a strong confounding factor affecting the studied biomarkers. Conclusions: Maternal BDNF levels and cord-blood CRP levels appear to have a direct correlation to fetal brain activity. Our findings indicate the potential use of these biomarkers in conjunction with fetal brain electrophysiology to track fetal neurodevelopment.

High-definition transcranial direct current stimulation desynchronizes refractory status epilepticus.

Recently, we showed that high-definition transcranial direct current stimulation (hd-tDCS) can acutely reduce epileptic spike rates during and after stimulation in refractory status epilepticus (RSE), with a greater likelihood of patient discharge from the intensive care unit compared to historical controls. We investigate whether electroencephalographic (EEG) desynchronization during hd-tDCS can help account for observed anti-epileptic effects. Defining desynchronization as greater power in higher frequencies such as above 30 â€‹Hz ("gamma") and lesser power in frequency bands lower than 30 â€‹Hz, we analyzed 27 EEG sessions from 10 RSE patients who had received 20-minute session(s) of 2-milliamperes of transcranial direct current custom-targeted at the epileptic focus as previously determined by a clinical EEGer monitoring the EEG in real-time. During hd-tDCS, median relative power change over the EEG electrode chains in which power changes were maximal was +4.84%, -5.25%, -1.88%, -1.94%, and +4.99% for respective delta, theta, alpha, beta, and gamma frequency bands in the bipolar longitudinal montage (p â€‹= â€‹0.0001); and +4.13%, -5.44%, -1.81%, -3.23%, and +5.41% in the referential Laplacian montage (p â€‹= â€‹0.0012). After hd-tDCS, median relative power changes reversed over the EEG electrode chains in which power changes were maximal: -2.74%, +4.20%, +1.74%, +1.75%, and -4.68% for the respective delta, theta, alpha, beta, and gamma frequency bands in the bipolar longitudinal montage (p â€‹= â€‹0.0001); and +1.59%, +5.07%, +1.74%, +2.40%, and -5.12% in the referential Laplacian montage (p â€‹= â€‹0.0004). These findings are consistent with EEG desynchronization through theta-alpha-beta-gamma bands during hd-tDCS, helping account for the efficacy of hd-tDCS as an emerging novel anti-epileptic therapy against RSE.

Corrigendum: Relative Power Correlates With the Decoding Performance of Motor Imagery Both Across Time and Subjects.

[This corrects the article DOI: 10.3389/fnhum.2021.701091.].

High-Frequency Local Field Potential Oscillations May Modulate Aggressive Behaviors in Mice.

Aggressive behavior is one of congenital social behaviors in many species, which could be promoted by social neglect or isolation in the early stages of life. Many brain regions including the medial prefrontal cortex (mPFC), medial amygdala (MeA) and ventromedial hypothalamus (VMH) are demonstrated to relate to aggressive behavior; however, the dynamic patterns of neural activities during the occurrence of this behavior remain unclear. In this study, 21-day-old male CD-1 mice were reared in social isolation conditions and cohousing conditions for two weeks. Aggressive behaviors of each subject were estimated by the resident-intruder test. Simultaneously, the local field potentials of mPFC, MeA and VMH were recorded for exploring differences in the relative power spectra of different oscillations when aggressive behaviors occurred. The results showed that the following: (1) Compared with the cohousing mice, the socially isolated mice exhibited more aggression. (2) Regardless of "time condition" (pre-, during- and post- attack), the relative power spectra of beta band in the cohousing mice were significantly greater than those in the socially isolated mice, and inversely, the relative power spectra of gamma band in the cohousing mice were significantly smaller than those in the socially isolated mice. (3) The bilateral mPFC exhibited significantly smaller beta power spectra but greater gamma power spectra compared with other brain areas regardless of rearing patterns. (4) For the right VMH of the socially isolated mice, the relative power spectra of the gamma band during attacks were significantly greater than those before attack. These results suggest that aggressive behaviors in mice could be shaped by rearing patterns and that high-frequency oscillations (beta and gamma bands) may engage in mediating aggressive behaviors in mice.

Neurofeedback Training of Alpha Relative Power Improves the Performance of Motor Imagery Brain-Computer Interface.

Significant variation in performance in motor imagery (MI) tasks impedes their wide adoption for brain-computer interface (BCI) applications. Previous researchers have found that resting-state alpha-band power is positively correlated with MI-BCI performance. In this study, we designed a neurofeedback training (NFT) protocol based on the up-regulation of the alpha band relative power (RP) to investigate its effect on MI-BCI performance. The principal finding of this study is that alpha NFT could successfully help subjects increase alpha-rhythm power and improve their MI-BCI performance. An individual difference was also found in this study in that subjects who increased alpha power more had a better performance improvement. Additionally, the functional connectivity (FC) of the frontal-parietal (FP) network was found to be enhanced after alpha NFT. However, the enhancement failed to reach a significant level after multiple comparisons correction. These findings contribute to a better understanding of the neurophysiological mechanism of cognitive control through alpha regulation.

Corrigendum: Neurofeedback training of alpha relative power improves the performance of motor imagery brain-computer interface.

[This corrects the article DOI: 10.3389/fnhum.2022.831995.].

Sibling Relationships of Adolescents with Congenital Heart Disease.

Adolescents with congenital heart disease (CHD) continuously need family support because of their repeated follow ups, treatments, and complications. However, sibling relationships have not been well studied among adolescents with CHD. The purpose of the present study was to explore the relationships between adolescents with CHD and their siblings, and to examine these relationships according to birth order and age. Adolescents aged from 13 to 21 years who had been diagnosed with CHD and had siblings were included as participants. The Sibling Relationship Questionnaire (SRQ) was used. The SRQ consists of four factors: warmth/closeness, conflict, relative power/status, and rivalry. A univariate general linear model was conducted to identify the sibling relationship factors according to birth order and sibling ages. The score for relative power/status of participants who were the eldest sibling was higher than that of younger siblings. The score for rivalry increased as sibling age increased. Therefore, healthcare providers need to investigate sibling relationships and to explain the importance of self-identity and power balance between adolescents with CHD and their siblings to parents.

Relative Power Correlates With the Decoding Performance of Motor Imagery Both Across Time and Subjects.

One of the most significant challenges in the application of brain-computer interfaces (BCI) is the large performance variation, which often occurs over time or across users. Recent evidence suggests that the physiological states may explain this performance variation in BCI, however, the underlying neurophysiological mechanism is unclear. In this study, we conducted a seven-session motor-imagery (MI) experiment on 20 healthy subjects to investigate the neurophysiological mechanism on the performance variation. The classification accuracy was calculated offline by common spatial pattern (CSP) and support vector machine (SVM) algorithms to measure the MI performance of each subject and session. Relative Power (RP) values from different rhythms and task stages were used to reflect the physiological states and their correlation with the BCI performance was investigated. Results showed that the alpha band RP from the supplementary motor area (SMA) within a few seconds before MI was positively correlated with performance. Besides, the changes of RP between task and pre-task stage from theta, alpha, and gamma band were also found to be correlated with performance both across time and subjects. These findings reveal a neurophysiological manifestation of the performance variations, and would further provide a way to improve the BCI performance.

Electroencephalographic Activity in Patients with Claustrophobia: A Pilot Study.

BACKGROUND: Exposure to small confined spaces evokes physiological responses such as increased heart rate in claustrophobic patients. However, little is known about electrocortical activity while these people are functionally exposed to such phobic situations. The aim of this study was to examine possible changes in electrocortical activity in this population. METHOD: Two highly affected patients with claustrophobia and two healthy controls participated in this in vivo study during which electroencephalographic (EEG) activity was continuously recorded. Relative power spectral density (rPSD) was compared between two situations of being relaxed in a well-lit open area, and sitting in a relaxed chair in a small (90 cm × 180 cm × 155 cm) chamber with a dim light. This comparison of rPSDs in five frequency bands of EEG was intended to investigate possible patterns of change in electrical activity during fear-related situation. This possible change was also compared between claustrophobic patients and healthy controls in all cortical areas. RESULTS: Statistical models showed that there is a significant interaction between groups of participants and experimental situations in all frequency bands (P < 0.01). In other words, claustrophobic patients showed significantly different changes in electrical activity while going from rest to the test situation. Clear differences were observed in alpha and theta bands. In the theta band, while healthy controls showed an increase in rPSD, claustrophobic patients showed an opposite decrease in the power of electrical activity when entering the confined chamber. In alpha band, both groups showed an increase in rPSD, though this increase was significantly higher for claustrophobic patients. CONCLUSION: The effect of in vivo exposure to confined environments on EEG activity is different in claustrophobic patients than in healthy controls. Most of this contrast is observed in central and parietal areas of the cortex, and in the alpha and theta bands.

Sex-Based Performance Responses to an Acute Sprint Interval Cycling Training Session in Collegiate Athletes.

There are limited data pertaining to the effects of sex on sprint interval cycling (SIC) training session performance. Purpose: We investigated sex-based differences on sprint interval cycling (SIC) performance in collegiate soccer players. Methods: Twelve men and twelve women completed two identical lab trials, 7-14 days apart. The first lab session served as familiarization, "dry run," trial. Reported data were collected and analyzed during the second, "testing" SIC training trial. Each SIC training session was comprised of a warm-up, at 50 revolutions per min (RPM) with no resistance, and six repeated 30-s Wingate Anaerobic Tests (WAnT) separated by a 4-min recovery period between each sprint. Results: Significant (P ≤ .05) sex differences were observed in peak power (PP), peak power relative to body mass (RPP), mean power (MP), mean power relative to body mass (RMP) but not in peak power relative to fat free mass (FFMPP). When WAnT bouts 2-6 were expressed as %Δ of WAnT1, there were no significant (P > .05) differences between the sexes across all performance variables. Further, Cohen's d statistics demonstrated only trivial and small effect size between the groups. Average HR and RPE were not significantly (P > .05) different between the sexes. Correlational analysis revealed a significant (P ≤ .05) relationship between FFM, and PP and MP. Conclusion: Although overall performance may be affected by a number of physiological mechanisms, the results of the current study indicate that differences between men and women soccer players performing SIC training, are likely attributed to differences in body composition.

Decreased Alpha Peak Frequency Is Linked to Episodic Memory Impairment in Pathological Aging.

The Free and Cued Selective Reminding Test (FCSRT) is a largely validated neuropsychological test for the identification of amnestic syndrome from the early stage of Alzheimer's disease (AD). Previous electrophysiological data suggested a slowing down of the alpha rhythm in the AD-continuum as well as a key role of this rhythmic brain activity for episodic memory processes. This study therefore investigates the link between alpha brain activity and alterations in episodic memory as assessed by the FCSRT. For that purpose, 37 patients with altered FCSRT performance underwent a comprehensive neuropsychological assessment, supplemented by 18F-fluorodeoxyglucose positron emission tomography/structural magnetic resonance imaging (18FDG-PET/MR), and 10 min of resting-state magnetoencephalography (MEG). The individual alpha peak frequency (APF) in MEG resting-state data was positively correlated with patients' encoding efficiency as well as with the efficacy of semantic cues in facilitating patients' retrieval of previous stored word. The APF also correlated positively with patients' hippocampal volume and their regional glucose consumption in the posterior cingulate cortex. Overall, this study demonstrates that alterations in the ability to learn and store new information for a relatively short-term period are related to a slowing down of alpha rhythmic activity, possibly due to altered interactions in the extended mnemonic system. As such, a decreased APF may be considered as an electrophysiological correlate of short-term episodic memory dysfunction accompanying pathological aging.

Deep learning reveals personalized spatial spectral abnormalities of high delta and low alpha bands in EEG of patients with early Parkinson's disease.

Objective.Parkinson's disease (PD) is one of the most common neurodegenerative diseases, and early diagnosis is crucial to delay disease progression. The diagnosis of early PD has always been a difficult clinical problem due to the lack of reliable biomarkers. Electroencephalogram (EEG) is the most common clinical detection method, and studies have attempted to discover the EEG spectrum characteristics of early PD, but the reported conclusions are not uniform due to the heterogeneity of early PD patients. There is an urgent need for a more advanced algorithm to extract spectrum characteristics from EEG to satisfy the personalized requirements.Approach.The structured power spectral density with spatial distribution was used as the input of convolutional neural network (CNN). A visualization technique called gradient-weighted class activation mapping was used to extract the optimal frequency bands for identifying early PD. Based on the model visualization, we proposed a novel quantitative index of spectral characteristics, spatial-mapping relative power (SRP), to detect personalized abnormalities in the spatial spectral characteristics of EEG in early PD.Main results.We demonstrated the feasibility of applying CNN to identify the patients with early PD with an accuracy of 99.87% ± 0.03%. The models indicated the characteristic frequency bands (high-delta (3.5-4.5 Hz) and low-alpha (7.5-11 Hz) frequency bands) that are used to identify the early PD. The SRP of these two characteristic bands in early PD patients was significantly higher than that in the control group, and the abnormalities were consistent at the group and individual levels.Significance.This study provides a novel personalized detection algorithm based on deep learning to reveal the optimal frequency bands for identifying early PD and obtain the spatial frequency characteristics of early PD. The findings of this study will provide an effective reference for the auxiliary diagnosis of early PD in clinical practice.