Awareness and the EEG power spectrum: analysis of frequencies

Predicting Alzheimer's disease CSF core biomarkers: a multimodal Machine Learning approach

Introduction

Alzheimer's disease (AD) is a progressive neurodegenerative disorder. Current core cerebrospinal fluid (CSF) AD biomarkers, widely employed for diagnosis, require a lumbar puncture to be performed, making them impractical as screening tools. Considering the role of sleep disturbances in AD, recent research suggests quantitative sleep electroencephalography features as potential non-invasive biomarkers of AD pathology. However, quantitative analysis of comprehensive polysomnography (PSG) signals remains relatively understudied. PSG is a non-invasive test enabling qualitative and quantitative analysis of a wide range of parameters, offering additional insights alongside other biomarkers. Machine Learning (ML) gained interest for its ability to discern intricate patterns within complex datasets, offering promise in AD neuropathology detection. Therefore, this study aims to evaluate the effectiveness of a multimodal ML approach in predicting core AD CSF biomarkers.

Methods

Mild-moderate AD patients were prospectively recruited for PSG, followed by testing of CSF and blood samples for biomarkers. PSG signals underwent preprocessing to extract non-linear, time domain and frequency domain statistics quantitative features. Multiple ML algorithms were trained using four subsets of input features: clinical variables (CLINVAR), conventional PSG parameters (SLEEPVAR), quantitative PSG signal features (PSGVAR) and a combination of all subsets (ALL). Cross-validation techniques were employed to evaluate model performance and ensure generalizability. Regression models were developed to determine the most effective variable combinations for explaining variance in the biomarkers.

Results

On 49 subjects, Gradient Boosting Regressors achieved the best results in estimating biomarkers levels, using different loss functions for each biomarker: least absolute deviation (LAD) for the Aβ42, least squares (LS) for p-tau and Huber for t-tau. The ALL subset demonstrated the lowest training errors for all three biomarkers, albeit with varying test performance. Specifically, the SLEEPVAR subset yielded the best test performance in predicting Aβ42, while the ALL subset most accurately predicted p-tau and t-tau due to the lowest test errors.

Conclusions

Multimodal ML can help predict the outcome of CSF biomarkers in early AD by utilizing non-invasive and economically feasible variables. The integration of computational models into medical practice offers a promising tool for the screening of patients at risk of AD, potentially guiding clinical decisions.

Musical and electrical stimulation as intervention in disorder of consciousness (DOC) patients: A randomised cross-over trial

BACKGROUND

Disorders of consciousness (DOC), i.e., unresponsive wakefulness syndrome (UWS) or vegetative state (VS) and minimally conscious state (MCS), are conditions that can arise from severe brain injury, inducing widespread functional changes. Given the damaging implications resulting from these conditions, there is an increasing need for rehabilitation treatments aimed at enhancing the level of consciousness, the quality of life, and creating new recovery perspectives for the patients. Music may represent an additional rehabilitative tool in contexts where cognition and language are severely compromised, such as among DOC patients. A further type of rehabilitation strategies for DOC patients consists of Non-Invasive Brain Stimulation techniques (NIBS), including transcranial electrical stimulation (tES), affecting neural excitability and promoting brain plasticity.

OBJECTIVE

We here propose a novel rehabilitation protocol for DOC patients that combines music-based intervention and NIBS in neurological patients. The main objectives are (i) to assess the residual neuroplastic processes in DOC patients exposed to music, (ii) to determine the putative neural modulation and the clinical outcome in DOC patients of non-pharmacological strategies, i.e., tES(control condition), and music stimulation, and (iii) to evaluate the putative positive impact of this intervention on caregiver's burden and psychological distress.

METHODS

This is a randomised cross-over trial in which a total of 30 participants will be randomly allocated to one of three different combinations of conditions: (i) Music only, (ii) tES only (control condition), (iii) Music + tES. The music intervention will consist of listening to an individually tailored playlist including familiar and self-relevant music together with fixed songs; concerning NIBS, tES will be applied for 20 minutes every day, 5 times a week, for two weeks. After these stimulations two weeks of placebo treatments will follow, with sham stimulation combined with noise for other two weeks. The primary outcomes will be clinical, i.e., based on the differences in the scores obtained on the neuropsychological tests, such as Coma Recovery Scale-Revised, and neurophysiological measures as EEG, collected pre-intervention, post-intervention and post-placebo.

DISCUSSION

This study proposes a novel rehabilitation protocol for patients with DOC including a combined intervention of music and NIBS. Considering the need for rigorous longitudinal randomised controlled trials for people with severe brain injury disease, the results of this study will be highly informative for highlighting and implementing the putative beneficial role of music and NIBS in rehabilitation treatments.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT05706831, registered on January 30, 2023.

Quantitative analysis of visually normal EEG reveals spectral power abnormalities in temporal lobe epilepsy

OBJECTIVE

To compare quantitative spectral parameters of visually-normal EEG between Mesial Temporal Lobe Epilepsy (MTLE) patients and healthy controls (HC).

METHOD

We enrolled 26 MTLE patients and 26 HC. From each recording we calculated total power of all frequency bands and determined alpha-theta (ATR) and alpha-delta (ADR) power ratios in different brain regions. Group-wise differences between spectral parameters were investigated (p < 0.05). To test for associations between spectral-power and cognitive status, we evaluated correlations between neuropsychological tests and quantitative EEG (qEEG) metrics.

RESULTS

In all comparisons, ATR and ADR were significantly decreased in MTLE patients compared to HC, particularly over the hemisphere ipsilateral to epileptic activity. A positive correlation was seen in MTLE patients between ATR in ipsilateral temporal lobe, and results of neuropsychological tests of auditory verbal learning (RAVLT and RAVLT-D), short term verbal memory (Digit span backwards), and executive function (Weigl's sorting test). ADR values in the contralateral posterior region correlated positively with RAVLT-D and Digit span backwards tests.

DISCUSSION

Results confirmed that the power spectrum of qEEG is shifted towards lower frequencies in MTLE patients compared to HC.

CONCLUSION

Of note, our results were found in visually-normal recordings, providing further evidence of the value of qEEG for longitudinal monitoring of MTLE patients over time. Exploratory analysis of associations between qEEG and neuropsychological data suggest this could be useful for investigating effects of antiseizure medications on cognitive integrity in patients.

High-density cortical µECoG arrays concurrently track spreading depolarizations and long-term evolution of stroke in awake rats

Spreading depolarizations (SDs) are widely recognized as a major contributor to the progression of tissue damage from ischemic stroke even if blood flow can be restored. They are characterized by negative intracortical waveforms of up to -20 mV, propagation velocities of 3 - 6 mm/min, and massive disturbance of membrane ion homeostasis. High-density, micro-electrocorticographic (μECoG) epidural electrodes and custom, DC-coupled, multiplexed amplifiers, were used to continuously characterize and monitor SD and µECoG cortical signal evolution in awake, moving rats over days. This highly innovative approach can define these events over a large brain surface area (~ 3.4 × 3.4 mm), extending across the boundaries of the stroke, and offers sufficient electrode density (60 contacts total per array for a density of 5.7 electrodes / mm2) to measure and determine the origin of SDs in relation to the infarct boundaries. In addition, spontaneous ECoG activity can simultaneously be detected to further define cortical infarct regions. This technology allows us to understand dynamic stroke evolution and provides immediate cortical functional activity over days. Further translational development of this approach may facilitate improved treatment options for acute stroke patients.

Sex differences in body temperature and neural power spectra in response to repeated restraint stress

Repeated stress is associated with an increased risk of developing psychiatric illnesses such as post-traumatic stress disorder (PTSD), which is more common in women, yet the neurobiology behind this sex difference is unknown. Habituation to repeated stress is impaired in PTSD, and recent preclinical studies have shown that female rats do not habituate as fully as male rats to repeated stress, which leads to impairments in cognition and sleep. Further research should examine sex differences after repeated stress in other relevant measures, such as body temperature and neural activity. In this study, we analyzed core body temperature and EEG power spectra in adult male and female rats during restraint, as well as during sleep transitions following stress. We found that core body temperature of male rats habituated to repeated restraint more fully than female rats. Additionally, we found that females had a higher average beta band power than males on both days of restraint, indicating higher levels of arousal. Lastly, we observed that females had lower delta band power than males during sleep transitions on Day 1 of restraint, however, females demonstrated higher delta band power than males by Day 5 of restraint. This suggests that it may take females longer to initiate sleep recovery compared with males. These findings indicate that there are differences in the physiological and neural processes of males and females after repeated stress. Understanding the way that the stress response is regulated in both sexes can provide insight into individualized treatment for stress-related disorders.

Analyzing neural activity under prolonged mask usage through EEG

In recent COVID times, mask has been a compulsion at workplaces and institutes as a preventive measure against multiple viral diseases including coronavirus (COVID-19) disease. However, the effects of prolonged mask-wearing on humans' neural activity are not well known. This paper is to investigate the effect of prolonged mask usage on the human brain through electroencephalogram (EEG), which acquires neural activity and translates it into comprehensible electrical signals. The performances of 10 human subjects with and without mask were assessed on a random patterned alphabet game. Besides EEG, physiological parameters of oxygen saturation, heart rate, blood pressure, and body temperature were recorded. Spectral and statistical analysis were performed on the recorded entities along with linear discriminant analysis (LDA) on extracted spectral features. The mean EEG spectral power in alpha, beta, and gamma sub-bands of the subjects with mask was smaller than the subjects without mask. The performances on the task and the oxygen saturation level between the two groups differed significantly (p < 0.05). Whereas, the blood pressure, body temperature, and heart rate of both groups were similar. Based on the LDA analysis, the occipital and frontal lobes exhibited the greatest variability in channel measurements, with O1 and O2 channels in the occipital lobe demonstrating significant variations within the alpha band due to visual focus, while the F3, AF3, and F7 channels were found to be differentiating within the beta and gamma frequency bands due to the cognitive stimulating tasks. All other channels were observed to be non-discriminatory.

Medial Septal Glutamatergic Neurons Modulate States of Consciousness during Sevoflurane Anesthesia in Mice

BACKGROUND

Multiple neural structures involved in maintaining wakefulness have been found to promote arousal from general anesthesia. The medial septum is a critical region that modulates arousal behavior. This study hypothesized that glutamatergic neurons in the medial septum play a crucial role in regulating states of consciousness during sevoflurane general anesthesia.

METHODS

Adult male mice were used in this study. The effects of sevoflurane anesthesia on neuronal activity were determined by fiber photometry. Lesions and chemogenetic manipulations were used to study the effects of the altered activity of medial septal glutamatergic neurons on anesthesia induction, emergence, and sensitivity to sevoflurane. Optogenetic stimulation was used to observe the role of acute activation of medial septal glutamatergic neurons on cortical activity and behavioral changes during sevoflurane-induced continuous steady state of general anesthesia and burst suppression state.

RESULTS

The authors found that medial septal glutamatergic neuronal activity decreased during sevoflurane anesthesia induction and recovered in the early period of emergence. Chemogenetic activation of medial septal glutamatergic neurons prolonged the induction time (mean ± SD, hM3Dq-clozapine N-oxide vs. hM3Dq-saline, 297.5 ± 60.1 s vs. 229.4 ± 29.9 s, P < 0.001, n = 11) and decreased the emergence time (53.2 ± 11.8 s vs. 77.5 ± 33.5 s, P = 0.025, n = 11). Lesions or chemogenetic inhibition of these neurons produced the opposite effects. During steady state of general anesthesia and deep anesthesia-induced burst suppression state, acute optogenetic activation of medial septal glutamatergic neurons induced cortical activation and behavioral emergence.

CONCLUSIONS

The study findings reveal that activation of medial septal glutamatergic neurons has arousal-promoting effects during sevoflurane anesthesia in male mice. The activation of these neurons prolongs the induction and accelerates the emergence of anesthesia.

EDITOR’S PERSPECTIVE

The importance of REM sleep fragmentation in the effects of stress on sleep: Perspectives from preclinical studies

Psychological stress poses a risk for sleep disturbances. Importantly, trauma-exposed individuals who develop posttraumatic stress disorder (PTSD) frequently report insomnia and recurrent nightmares. Clinical studies have provided insight into the mechanisms of these sleep disturbances. We review polysomnographic findings in PTSD and identify analogous measures that have been made in animal models of PTSD. There is a rich empirical and theoretical literature on rapid eye movement sleep (REMS) substrates of insomnia and nightmares, with an emphasis on REMS fragmentation. For future investigations of stress-induced sleep changes, we recommend a focus on tonic, phasic and other microarchitectural REMS measures. Power spectral density analysis of the sleep EEG should also be utilized. Animal models with high construct validity can provide insight into gender and time following stressor exposure as moderating variables. Ultimately, preclinical studies with translational potential will lead to improved treatment for stress-related sleep disturbances.

Relationship between individual differences in pain empathy and task- and resting-state EEG

Pain empathy is a complex form of psychological inference that enables us to understand how others feel in the context of pain. Since pain empathy may be grounded in our own pain experiences, it exhibits huge inter-individual variability. However, the neural mechanisms behind the individual differences in pain empathy and its association with pain perception are still poorly understood. In this study, we aimed to characterize brain mechanisms associated with individual differences in pain empathy in adult participants (n = 24). The 32-channel electroencephalography (EEG) was recorded at rest and during a pain empathy task, and participants viewed static visual stimuli of the limbs submitted to painful and nonpainful stimulation to solicit empathy. The pain sensitivity of each participant was measured using a series of direct current stimulations. In our results, the N2 of Fz and the LPP of P3 and P4 were affected by painful pictures. We found that both delta and alpha bands in the frontal and parietal cortex were involved in the regulation of pain empathy. For the delta band, a close relationship was found between average power, either in the resting or task state, and individual differences in pain empathy. It suggested that the spectral power in Fz's delta band may reflect subjective pain empathy across individuals. For the alpha band, the functional connectivity between Fz and P3 under painful picture stimulation was correlated to individuals' pain sensitivity. It indicated that the alpha band may reflect individual differences in pain sensitivity and be involved in pain empathy processing. Our results suggested the distinct role of the delta and alpha bands of EEG signals in pain empathy processing and may deepen our understanding of the neural mechanisms underpinning pain empathy.

An overview of machine learning and deep learning techniques for predicting epileptic seizures

Epilepsy is a neurological disorder (the third most common, following stroke and migraines). A key aspect of its diagnosis is the presence of seizures that occur without a known cause and the potential for new seizures to occur. Machine learning has shown potential as a cost-effective alternative for rapid diagnosis. In this study, we review the current state of machine learning in the detection and prediction of epileptic seizures. The objective of this study is to portray the existing machine learning methods for seizure prediction. Internet bibliographical searches were conducted to identify relevant literature on the topic. Through cross-referencing from key articles, additional references were obtained to provide a comprehensive overview of the techniques. As the aim of this paper aims is not a pure bibliographical review of the subject, the publications here cited have been selected among many others based on their number of citations. To implement accurate diagnostic and treatment tools, it is necessary to achieve a balance between prediction time, sensitivity, and specificity. This balance can be achieved using deep learning algorithms. The best performance and results are often achieved by combining multiple techniques and features, but this approach can also increase computational requirements.

EEG dynamics induced by zolpidem forecast consciousness evolution in prolonged disorders of consciousness

OBJECTIVE

To explore whether the EEG dynamics induced by zolpidem can predict consciousness evolution in patients with prolonged disorders of consciousness (PDOC).

METHODS

We conducted a prospective explorative analysis on thirty-six patients with PDOC and eleven healthy controls. The EEG power spectrum was analyzed and categorized into 'ABCD' patterns at baseline and one hour after zolpidem administration at 10 mg. The clinical outcome was defined as consciousness improvement and no improvement six months after enrollment using the Coma Recovery Scale-Revised (CRS-R) score.

RESULTS

Zolpidem administration significantly increased the EEG power in the delta & theta bands and decreased EEG power in the beta bands in healthy controls. Further follow-up studies indicated that the increased EEG beta-band power induced by zolpidem can predict an improved consciousness six months after enrollment with an area under the receiver operating characteristic curve (AUC) of 0.829, the sensitivity of 94.38% and an accuracy of 81.48%.

CONCLUSIONS

Our work revealed that the specific EEG responses to zolpidem can predict consciousness recovery in PDOC patients.

SIGNIFICANCE

The zolpidem-induced specific EEG responses could potentially predict the recovery of PDOC patients, which may help clinicians and patients' families in their decision-making process.

Characteristics of changes in the functional status of the brain before and after 1,000 m all-out paddling for different levels of dragon boat athletes

Purposes

Dragon boat is a traditional sport in China, but the brain function characteristics of dragon boat athletes are still unclear. Our purpose is to explore the changing characteristics of brain function of dragon boat athletes at different levels before and after exercise by monitoring the changes of EEG power spectrum and microstate of athletes before and after rowing.

Methods

Twenty-four expert dragon boat athletes and 25 novice dragon boat athletes were selected as test subjects to perform the 1,000 m all-out paddling exercise on a dragon boat dynamometer. Their resting EEG data was collected pre- and post-exercise, and the EEG data was pre-processed and then analyzed using power spectrum and microstate based on Matlab software.

Results

Post-Exercise, the Heart Rate peak (HR peak), Percentage of Heart Rate max (HR max), Rating of Perceived Exertion (RPE), and Exercise duration of the novice group were significantly higher than expert group (p < 0.01). Pre-exercise, the power spectral density values in the δ, α1, α2, and β1 bands were significantly higher in the expert group compared to the novice group (p < 0.05). Post-exercise, the power spectral density values in the δ, θ, and α1 bands were significantly lower in the expert group compared to the novice group (p < 0.05), the power spectral density values of α2, β1, and β2 bands were significantly higher (p < 0.05). The results of microstate analysis showed that the duration and contribution of microstate class D were significantly higher in the pre-exercise expert group compared to the novice group (p < 0.05), the transition probabilities of A → D, C → D, and D → A were significantly higher (p < 0.05). Post-exercise, the duration, and contribution of microstate class C in the expert group decreased significantly compared to the novice group (p < 0.05), the occurrence of microstate classes A and D were significantly higher (p < 0.05), the transition probability of A → B was significantly higher (p < 0.05), and the transition probabilities of C → D and D → C were significantly lower (p < 0.05).

Conclusion

The functional brain state of dragon boat athletes was characterized by expert athletes with closer synaptic connections of brain neurons and higher activation of the dorsal attention network in the resting state pre-exercise. There still had higher activation of cortical neurons after paddling exercise. Expert athletes can better adapt to acute full-speed oar training.

Electroencephalogram based brain-computer interface: Applications, challenges, and opportunities

Brain-Computer Interfaces (BCI) is an exciting and emerging research area for researchers and scientists. It is a suitable combination of software and hardware to operate any device mentally. This review emphasizes the significant stages in the BCI domain, current problems, and state-of-the-art findings. This article also covers how current results can contribute to new knowledge about BCI, an overview of BCI from its early developments to recent advancements, BCI applications, challenges, and future directions. The authors pointed to unresolved issues and expressed how BCI is valuable for analyzing the human brain. Humans' dependence on machines has led humankind into a new future where BCI can play an essential role in improving this modern world.

A gut-secreted peptide suppresses arousability from sleep

Suppressing sensory arousal is critical for sleep, with deeper sleep requiring stronger sensory suppression. The mechanisms that enable sleeping animals to largely ignore their surroundings are not well understood. We show that the responsiveness of sleeping flies and mice to mechanical vibrations is better suppressed when the diet is protein rich. In flies, we describe a signaling pathway through which information about ingested proteins is conveyed from the gut to the brain to help suppress arousability. Higher protein concentration in the gut leads to increased activity of enteroendocrine cells that release the peptide CCHa1. CCHa1 signals to a small group of dopamine neurons in the brain to modulate their activity; the dopaminergic activity regulates the behavioral responsiveness of animals to vibrations. The CCHa1 pathway and dietary proteins do not influence responsiveness to all sensory inputs, showing that during sleep, different information streams can be gated through independent mechanisms.

Microstate analyses as an indicator of anesthesia-induced unconsciousness

OBJECTIVE

The objective of this study was to identify differences in electroencephalographic microstate topographies across three perioperative phases: anesthetic pre-induction, surgical anesthesia, and post-anesthesia care unit (PACU) admission.

METHODS

Whole-scalp 16-channel electroencephalographic recordings were taken throughout the perioperative period on n = 22 adult, non-cardiac surgical patients.

RESULTS

Several differences between perioperative periods were identified. Most notably, during surgical anesthesia, patients demonstrated increased mean duration and, consequently, a reduction in the occurrence of microstates when compared to both preoperative baseline and PACU admission. We also observed the presence of microstate F with propofol anesthesia during surgery, which had been previously identified with propofol infusion in laboratory settings using human volunteers. Finally, we observed inverse age effects with mean occurrence and duration of microstates, particularly during PACU recovery.

CONCLUSIONS

Microstate duration is significantly increased during surgery compared to both pre-induction and PACU recovery. These data suggest that microstate topographies may be useful in monitoring anesthetic depth.

SIGNIFICANCE

This work highlights the potential for microstate analysis in the perioperative setting. We identified distinct topographical signatures across perioperative periods and with increasing age, which is predictive of post-operative delirium.

Neurologic music therapy combined with EEG-tDCS for upper motor extremity performance in patients with corticobasal syndrome: Study protocol for a novel approach

BACKGROUND

Corticobasal syndrome (CBS) is an atypical parkinsonian disorder that involves degeneration of brain regions associated with motor coordination and sensory processing. Combining transcranial direct current stimulation (tDCS) with rehabilitation training has been shown to improve upper-limb performance in other disease models. Here, we describe the protocol investigating whether tDCS with neurologic music therapy (NMT) (patterned sensory enhancement and therapeutic instrumental music performance) enhances functional arm/hand performance in individuals with CBS.

METHODS

Study participants are randomly assigned to six 30-min sessions (twice per week for 3 weeks) of NMT + either sham tDCS or active tDCS. We aim to stimulate the frontoparietal cortex, which is associated with movement execution/coordination and sensory processing. The hemisphere contralateral to the more affected arm is stimulated (total stimulation current of 2 mA from 5 dime-sized electrodes). Individualized NMT sessions designed to exercise the upper limb are provided. Participants undergo gross/fine motor, cognitive and emotional assessments at baseline and follow-up (one month after the final session). To investigate the immediate effects of tDCS and NMT training, gross /fine motor, affective level, and kinematic parameter measurements using motion sensors are collected before and after each session. Electroencephalography is used to collect electrical neurophysiological responses before, during, and after tDCS+NMT sessions. The study participants, neurologic music therapist and outcome assessor are blinded to whether participants are in the sham or active tDCS group.

CONCLUSION

This noninvasive and patient-centered clinical trial for CBS may provide insight into rehabilitation options that are sorely lacking in this population.

Utilizing artificial intelligence and electroencephalography to assess expertise on a simulated neurosurgical task

Virtual reality surgical simulators have facilitated surgical education by providing a safe training environment. Electroencephalography (EEG) has been employed to assess neuroelectric activity during surgical performance. Machine learning (ML) has been applied to analyze EEG data split into frequency bands. Although EEG is widely used in fields requiring expert performance, it has yet been used to classify surgical expertise. Thus, the goals of this study were to (a) develop an ML model to accurately differentiate skilled and less-skilled performance using EEG data recorded during a simulated surgery, (b) explore the relative importance of each EEG bandwidth to expertise, and (c) analyze differences in EEG band powers between skilled and less-skilled individuals. We hypothesized that EEG recordings during a virtual reality surgery task would accurately predict the expertise level of the participant. Twenty-one participants performed three simulated brain tumor resection procedures on the NeuroVR™ platform (CAE Healthcare, Montreal, Canada) while EEG data was recorded. Participants were divided into 2 groups. The skilled group was composed of five neurosurgeons and five senior neurosurgical residents (PGY4-6), and the less-skilled group was composed of six junior residents (PGY1-3) and five medical students. A total of 13 metrics from EEG frequency bands and ratios (e.g., alpha, theta/beta ratio) were generated. Seven ML model types were trained using EEG activity to differentiate between skilled and less-skilled groups. The artificial neural network achieved the highest testing accuracy of 100% (AUROC = 1.0). Model interpretation via Shapley analysis identified low alpha (8-10 Hz) as the most important metric for classifying expertise. Skilled surgeons displayed higher (p = 0.044) low-alpha than the less-skilled group. Furthermore, skilled surgeons displayed significantly lower TBR (p = 0.048) and significantly higher beta (13-30 Hz, p = 0.049), beta 1 (15-18 Hz, p = 0.014), and beta 2 (19-22 Hz, p = 0.015), thus establishing these metrics as important markers of expertise. ACGME CORE COMPETENCIES: Practice-Based Learning and Improvement.

Electrophysiological trajectories of concussion recovery: From acute to prolonged stages in late teenagers

PURPOSE

Numerous studies have reported electrophysiological differences between concussed and non-concussed groups, but few studies have systematically explored recovery trajectories from acute concussion to symptom recovery and the transition from acute concussion to prolonged phases. Questions remain about recovery prognosis and the extent to which symptom resolution coincides with injury resolution. This study therefore investigated the electrophysiological differences in recoveries between simple and complex concussion.

METHODS

Student athletes with acute concussion from a previous study (19(2) years old) were tracked from pre-injury baseline, 24-48 hours after concussion, and through in-season recovery. The electroencephalography (EEG) with P300 evoked response trajectories from this acute study were compared to an age-matched population of 71 patients (18(2) years old) with prolonged post-concussive symptoms (PPCS), 61 (SD 31) days after concussion.

RESULTS

Acute, return-to-play, and PPCS groups all experienced a significant deficit in P300 amplitude compared to the pre-injury baseline group. The PPCS group, however, had significantly different EEG spectral and coherence patterns from every other group.

CONCLUSION

These data suggest that while the evoked response potentials deficits of simple concussion may persist in more prolonged stages, there are certain EEG measures unique to PPCS. These metrics are readily accessible to clinicians and may provide useful parameters to help predict trajectories, characterize injury (phenotype), and track the course of injury.

Exposure to nature is associated with decreased functional connectivity within the distress network: A resting state EEG study

Introduction

Despite the well-established evidence supporting the restorative potential of nature exposure, the neurophysiological underpinnings of the restorative cognitive/emotional effect of nature are not yet fully understood. The main purpose of the current study was to investigate the association between exposure to nature and electroencephalography (EEG) functional connectivity in the distress network.

Methods

Fifty-three individuals (11 men and 42 women; mean age 21.38 ± 1.54 years) were randomly assigned to two groups: (i) a green group and (ii) a gray group. A slideshow consisting of images depicting natural and urban scenarios were, respectively, presented to the green and the gray group. Before and after the slideshow, 5 min resting state (RS) EEG recordings were performed. The exact low-resolution electromagnetic tomography (eLORETA) software was used to execute all EEG analyses.

Results

Compared to the gray group, the green group showed a significant increase in positive emotions (F _{1; 50} = 9.50 p = 0.003) and in the subjective experience of being full of energy and alive (F _{1; 50} = 4.72 p = 0.035). Furthermore, as compared to urban pictures, the exposure to natural images was associated with a decrease of delta functional connectivity in the distress network, specifically between the left insula and left subgenual anterior cingulate cortex (T = -3.70, p = 0.023).

Discussion

Our results would seem to be in accordance with previous neurophysiological studies suggesting that experiencing natural environments is associated with brain functional dynamics linked to emotional restorative processes.

Feature Selection for Continuous within- and Cross-User EEG-Based Emotion Recognition

The monitoring of emotional state is important in the prevention and management of mental health problems and is increasingly being used to support affective computing. As such, researchers are exploring various modalities from which emotion can be inferred, such as through facial images or via electroencephalography (EEG) signals. Current research commonly investigates the performance of machine-learning-based emotion recognition systems by exposing users to stimuli that are assumed to elicit a single unchanging emotional response. Moreover, in order to demonstrate better results, many models are tested in evaluation frameworks that do not reflect realistic real-world implementations. Consequently, in this paper, we explore the design of EEG-based emotion recognition systems using longer, variable stimuli using the publicly available AMIGOS dataset. Feature engineering and selection results are evaluated across four different cross-validation frameworks, including versions of leave-one-movie-out (testing with a known user, but a previously unseen movie), leave-one-person-out (testing with a known movie, but a previously unseen person), and leave-one-person-and-movie-out (testing on both a new user and new movie). Results of feature selection lead to a 13% absolute improvement over comparable previously reported studies, and demonstrate the importance of evaluation framework on the design and performance of EEG-based emotion recognition systems.

Comparison of Electroencephalogram Power Spectrum Characteristics of Left and Right Dragon Boat Athletes after 1 km of Rowing

Purpose: This study aimed to detect differences in post-exercise brain activity between the left and right paddlers due to exercise by analyzing the resting-state electroencephalogram (EEG) power spectrum before and after exercise. Methods: Twenty-one right paddlers and twenty-two left paddlers completed a 1 km all-out test on a dragon boat ergometer, and their heart rate and exercise time were recorded. EEG signals were collected from superficial brain layers before and after exercise; then, the EEG power spectrum was extracted and compared in different frequency bands. In addition, the degree of lateralization in each brain region was assessed by the asymmetry index. Results: There was no significant difference in the power spectrum values and asymmetry indices between the left and right paddlers before rowing (p ˃ 0.05). However, after rowing, the left-paddlers group had significantly higher spectral power values in θ and α bands than the right-paddlers group (p < 0.05), and brain lateralization in both groups of athletes occurred mainly in the ipsilateral hemisphere of the frontal and central regions. Conclusion: The 1 km of rowing induced more brain activation in the left paddlers, and both left and right paddlers showed functional aggregation of hemispheric lateralization.

Stratified Multivariate Multiscale Dispersion Entropy for Physiological Signal Analysis

Multivariate entropy quantification algorithms are becoming a prominent tool for the extraction of information from multi-channel physiological time-series. However, in the analysis of physiological signals from heterogeneous organ systems, certain channels may overshadow the patterns of others, resulting in information loss. Here, we introduce the framework of Stratified Entropy to prioritize each channels' dynamics based on their allocation to respective strata, leading to a richer description of the multi-channel time-series. As an implementation of the framework, three algorithmic variations of the Stratified Multivariate Multiscale Dispersion Entropy are introduced. These variations and the original algorithm are applied to synthetic time-series, waveform physiological time-series, and derivative physiological data. Based on the synthetic time-series experiments, the variations successfully prioritize channels following their strata allocation while maintaining the low computation time of the original algorithm. In experiments on waveform physiological time-series and derivative physiological data, increased discrimination capacity was noted for multiple strata allocations in the variations when benchmarked to the original algorithm. This suggests improved physiological state monitoring by the variations. Furthermore, our variations can be modified to utilize a priori knowledge for the stratification of channels. Thus, our research provides a novel approach for the extraction of previously inaccessible information from multi-channel time series acquired from heterogeneous systems.

Size-Dependent Effects of Polystyrene Nanoparticles (PS-NPs) on Behaviors and Endogenous Neurochemicals in Zebrafish Larvae

Microplastics, small pieces of plastic derived from polystyrene, have recently become an ecological hazard due to their toxicity and widespread occurrence in aquatic ecosystems. In this study, we exposed zebrafish larvae to two types of fluorescent polystyrene nanoparticles (PS-NPs) to identify their size-dependent effects. PS-NPs of 50 nm, unlike 100 nm PS-NPs, were found to circulate in the blood vessels and accumulate in the brains of zebrafish larvae. Behavioral and electroencephalogram (EEG) analysis showed that 50 nm PS-NPs induce abnormal behavioral patterns and changes in EEG power spectral densities in zebrafish larvae. In addition, the quantification of endogenous neurochemicals in zebrafish larvae showed that 50 nm PS-NPs disturb dopaminergic metabolites, whereas 100 nm PS-NPs do not. Finally, we assessed the effect of PS-NPs on the permeability of the blood–brain barrier (BBB) using a microfluidic system. The results revealed that 50 nm PS-NPs have high BBB penetration compared with 100 nm PS-NPs. Taken together, we concluded that small nanoparticles disturb the nervous system, especially dopaminergic metabolites.

Transcutaneous Cervical Vagus Nerve Stimulation Induces Changes in the Electroencephalogram and Heart Rate Variability of Healthy Dogs, a Pilot Study

Transcutaneous cervical vagus nerve stimulation (tcVNS) has been used to treat epilepsy in people and dogs. Objective electroencephalographic (EEG) and heart rate variability (HRV) data associated with tcVNS have been reported in people. The question remained whether EEG and electrocardiography (ECG) would detect changes in brain activity and HRV, respectively, after tcVNS in dogs. Simultaneous EEG and Holter recordings, from 6 client-owned healthy dogs were compared for differences pre- and post- tcVNS in frequency band power analysis (EEG) and HRV. The feasibility and tolerance of the patients to the tcVNS were also noted. In a general linear mixed model, the average power per channel per frequency band was found to be significantly different pre- and post-stimulation in the theta (p = 0.02) and alpha bands (p = 0.04). The pooled power spectral analysis detected a significant decrease in the alpha (p < 0.01), theta (p = 0.01) and beta (p = 0.035) frequencies post-stimulation. No significant interaction was observed between dog, attitude, and stimulation in the multivariate model, neither within the same dog nor between individuals. There was a significant increase in the HRV measured by the standard deviation of the inter-beat (SDNN) index (p < 0.01) and a decrease in mean heart rate (p < 0.01) after tcVNS. The tcVNS was found to be well-tolerated. The results of this pilot study suggest that EEG and ECG can detect changes in brain activity and HRV associated with tcVNS in healthy dogs. Larger randomized controlled studies are required to confirm the results of this study and to assess tcVNS potential therapeutic value.

Power Spectrum and Connectivity Analysis in EEG Recording during Attention and Creativity Performance in Children

The present research aims at examining the power spectrum and exploring functional brain connectivity/disconnectivity during concentration performance, as measured by the d2 test of attention and creativity as measured by the CREA test in typically developing children. To this end, we examined brain connectivity by using phase synchrony (i.e., phase locking index (PLI) over the EEG signals acquired by the Emotiv EPOC neuroheadset in 15 children aged 9- to 12-years. Besides, as a complement, a power spectrum analysis of the acquired signals was performed. Our results indicated that, during d2 Test performance there was an increase in global gamma phase synchronization and there was a global alpha and theta band desynchronization. Conversely, during CREA task, power spectrum analysis showed a significant increase in the delta, beta, theta, and gamma bands. Connectivity analysis revealed marked synchronization in theta, alpha, and gamma. These findings are consistent with other neuroscience research indicating that multiple brain mechanisms are indeed involved in creativity. In addition, these results have important implications for the assessment of attention functions and creativity in clinical and research settings, as well as for neurofeedback interventions in children with typical and atypical development.

Quantitative Electroencephalography Findings in Patients With Diabetes Mellitus

Objective. Diabetes mellitus (DM) causes structural central nervous system (CNS) impairment, and this situation can be detected by quantitative electroencephalography (QEEG) findings before cognitive impairment is clinically observed. The main aim of this study is to uncover the effect of DM on brain function. Since QEEG reflects the CNS functioning, particularly in cognitive aspects, we expected electrophysiological clues to be found for prevention and follow-up in DM-related cognitive decline. Since a majority of the psychiatric population have cognitive dysfunction, we have given particular attention to those people. It was stated that a decrease was observed in the posterior cortical alpha power due to the hippocampal atrophy by several previous studies and we hypothesize that decreased alpha power will be observed also in DM. Methods. This study included 2094 psychiatric patients, 207 of whom were diagnosed with DM and 1887 of whom were not diagnosed with DM, and QEEG recordings were performed. Eyes-closed electroencephalography data were segmented into consecutive 2 s epochs. Fourier analysis was performed by averaging across 2 s epochs without artifacts. The absolute alpha power in the occipital regions (O1 and O2) of patients with and without DM was compared. Results. In the DM group, a decrease in the absolute alpha, alpha 1, and alpha 2 power in O1 and O2 was observed in comparison with the control group. It was determined that the type of psychiatric diagnosis did not affect QEEG findings. Conclusion. The decrease in absolute alpha power observed in patients diagnosed with DM may be related to the CNS impairment in DM. QEEG findings in DM can be useful while monitoring the CNS impairment, diagnosing DM-related dementia, in the follow-up of the cognitive process, constructing the protocols for electrophysiological interventions like neurofeedback and transcranial magnetic stimulation and monitoring the response to treatment.

Recent Developments in Diagnosis of Epilepsy: Scope of MicroRNA and Technological Advancements

Epilepsy is one of the most common neurological disorders, characterized by recurrent seizures, resulting from abnormally synchronized episodic neuronal discharges. Around 70 million people worldwide are suffering from epilepsy. The available antiepileptic medications are capable of controlling seizures in around 60-70% of patients, while the rest remain refractory. Poor seizure control is often associated with neuro-psychiatric comorbidities, mainly including memory impairment, depression, psychosis, neurodegeneration, motor impairment, neuroendocrine dysfunction, etc., resulting in poor prognosis. Effective treatment relies on early and correct detection of epileptic foci. Although there are currently a few well-established diagnostic techniques for epilepsy, they lack accuracy and cannot be applied to patients who are unsupportive or harbor metallic implants. Since a single test result from one of these techniques does not provide complete information about the epileptic foci, it is necessary to develop novel diagnostic tools. Herein, we provide a comprehensive overview of the current diagnostic tools of epilepsy, including electroencephalography (EEG) as well as structural and functional neuroimaging. We further discuss recent trends and advances in the diagnosis of epilepsy that will enable more effective diagnosis and clinical management of patients.

CNN-Based Prognosis of BCI Rehabilitation Using EEG From First Session BCI Training

Stroke is a world-leading disease for causing disability. Brain-computer interaction (BCI) training has been proved to be a promising method in facilitating motor recovery. However, due to differences in each patient's neural-clinical profile, the potential of recovery for different patients can vary significantly by conducting BCI training, which remains a major problem in clinical rehabilitation practice. To address this issue, the objective of this study is to prognosticate the outcome of BCI training using motor state electroencephalographic (EEG) collected during the first session of BCI tasks, with the aim of prescribing BCI training accordingly. A Convolution Neural Network (CNN) based prognosis model was developed to predict the outcome of 11 stroke patients' recovery following a 2-week rehabilitation training with BCI. In our study, functional connectivity and power spectrum have been evaluated and applied as the inputs of CNN to regress patients' recovery rate. A saliency map was used to identify the correlation between EEG channels with the recovery outcome. The performance of our model was assessed using the leave-one-out cross-validation. Overall, the proposed model predicted patients' recovery with R² 0.98 and MSE 0.89. According to the saliency map, the highest functional connectivity occurred in Fp2/Fpz-AF8, Fp2/F4/F8-P3, P1/PO7-PO5 and AF3-AF4. Our results demonstrated that deep learning method has the potential to predict the recovery rate of BCI training, which contributes to guiding individualized prescription in the early stage of clinical rehabilitation.

Effects of transcranial direct current stimulation (tDCS) and concurrent cognitive training on episodic memory in patients with traumatic brain injury: a double-blind, randomised, placebo-controlled study

INTRODUCTION

Deficits in episodic memory following traumatic brain injury (TBI) are common and affect independence in activities of daily living. Transcranial direct current stimulation (tDCS) and concurrent cognitive training may contribute to improve episodic memory in patients with TBI. Although previous studies have shown the potential of tDCS to improve cognition, the benefits of the tDCS applied simultaneously to cognitive training in participants with neurological disorders are inconsistent. This study aims to (1) investigate whether active tDCS combined with computer-assisted cognitive training enhances episodic memory compared with sham tDCS; (2) compare the differences between active tDCS applied over the left dorsolateral prefrontal cortex (lDLPFC) and bilateral temporal cortex (BTC) on episodic memory and; (3) investigate inter and intragroup changes on cortical activity measured by quantitative electroencephalogram (qEEG).

METHODS AND ANALYSIS

A randomised, parallel-group, double-blind placebo-controlled study is conducted. Thirty-six participants with chronic, moderate and severe closed TBI are being recruited and randomised into three groups (1:1:1) based on the placement of tDCS sponges and electrode activation (active or sham). TDCS is applied for 10 consecutive days for 20 min, combined with a computer-based cognitive training. Cognitive scores and qEEG are collected at baseline, on the last day of the stimulation session, and 3 months after the last tDCS session. We hypothesise that (1) the active tDCS group will improve episodic memory scores compared with the sham group; (2) differences on episodic memory scores will be shown between active BTC and lDLPFC and; (3) there will be significant delta reduction and an increase in alpha waves close to the location of the active electrodes compared with the sham group.

ETHICS AND DISSEMINATION

This study was approved by Hospital das Clínicas, University of São Paulo Ethical Institutional Review Border (CAAE: 87954518.0.0000.0068).

TRIAL REGISTRATION NUMBER

NCT04540783.

Treatment Effect of Exercise Intervention for Female College Students with Depression: Analysis of Electroencephalogram Microstates and Power Spectrum

This paper aims to assess the effect of exercise intervention on the improvement of college students with depression and to explore the change characteristics of microstates and the power spectrum in their resting-state electroencephalogram (EEG). Forty female college students with moderate depression were screened according to the Beck Depression Inventory-II (BDI-II) and Depression Self-Rating Scale (SDS) scores, and half of them received an exercise intervention for 18 weeks. The study utilized an EEG to define the resting-state networks, and the scores of all the participants were tracked during the intervention. Compared with those in the depression group, the power spectrum values in the θ and α bands were significantly decreased (p < 0.05), and the duration of microstate C increased significantly (p < 0.05), while the frequency of microstate B decreased significantly (p < 0.05) in the exercise intervention group. The transition probabilities showed that the exercise intervention group had a higher probability from B to D than those in the depression group (p < 0.01). In addition, the power of the δ and α bands were negatively correlated with the occurrence of microstate C (r = −0.842, p < 0.05 and r = −0.885, p < 0.01, respectively), and the power of the β band was positively correlated with the duration of microstate C (r = 0.900, p < 0.01) after exercise intervention. Our results suggest that the decreased duration of microstate C and the increased α power in depressed students are associated with reduced cognitive ability, emotional stability, and brain activity. Depression symptoms were notably improved after exercise intervention, thus providing a more scientific index for the research, rehabilitation mechanisms, and treatment of depression.

Quantitative EEG Findings in Patients With Psychogenic Nonepileptic Seizures

Objective. Psychogenic nonepileptic seizures (PNES), is one of the clinical manifestations of conversion disorder that epileptiform discharges do not accompany. Factors capable of increasing susceptibility to these seizures have not been adequately investigated yet. This study aims to investigate the quantitative electroencephalography (QEEG) findings for PNES by evaluating the resting EEG spectral power changes during the periods between seizures. Methods. Thirty-nine patients (29 females, 10 males) diagnosed with PNES (group 1) and 47 patients (23 females, 24 males) without any psychiatric diagnosis (group 2) were included in the study. The patients underwent a psychiatric examination at their first visit, were diagnosed and their EEGs were recorded. Using fast Fourier transformation (FFT), spectral power analysis was calculated for delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (15-30 Hz), high-beta (25-30 Hz), gamma-1 (31-40 Hz), gamma-2 (41-50 Hz), and gamma (30-80 Hz) frequency bands. Results. Six separate EEG band power, namely (C3-high beta, C3-gamma, C3-gamma-1, C3-gamma-2, P3-gamma, P3 gamma-1), were found to be higher in the patients diagnosed with PNES than in the control group. Conclusion. Our findings show that PNES correlate with high-frequency oscillations on central motor and somatosensory cortices.

EEG Cortical Activities and Networks Altered by Watching 2D/3D Virtual Reality Videos

Abstract. Virtual reality (VR), which can represent real-life events and situations, is being increasingly applied to many fields, such as education, entertainment, and medical rehabilitation. Correspondingly, the neural information processing of VR has attracted attention. However, the underlying neural mechanisms of VR environments have not yet been fully revealed. The purpose of this study was to examine the possible differences in brain activities and networks between the less immersive 2D and the fully immersive 3D VR environments. 3D VR videos and the same 2D scenes were presented to the participants and the scalp electroencephalogram (EEG) was recorded, respectively. Power spectral density (PSD) and the functional connectivity of these EEG signals were analyzed. The results showed that 3D VR videos significantly enhanced the PSD of θ rhythm (4–7 Hz) in the frontal lobe; decreased the PSD of α rhythm (8–13 Hz) in the parietal and the occipital lobes; increased the PSD of β rhythm (14–30 Hz) in the frontal, the parietal, the temporal, and the occipital lobes, relative to 2D VR watching. Furthermore, 3D versus 2D VR-induced alterations in the patterns of brain networks were similar to the patterns of PSD. Specifically, for the θ rhythm, 3D VR significantly enhanced the frontal and the temporal brain functional connectivity; for the α rhythm, 3D VR increased the parietal and the occipital networks; for the β rhythm, 3D VR remarkably increased the frontal, the occipital, the frontal-temporal and the frontal-occipital brain functional connectivity, relative to 2D VR. These significant differences between 3D and 2D VR video-watching suggest that the neural information processing of cortical activities and networks is correlated to the degree of immersion. The present results, collected with previous researches, implicate that some visual-related information processes, such as visual attention, visual perception, and visual immersion are more robust in 3D VR environments.

Assessment of Outliers and Detection of Artifactual Network Segments Using Univariate and Multivariate Dispersion Entropy on Physiological Signals

Network physiology has emerged as a promising paradigm for the extraction of clinically relevant information from physiological signals by moving from univariate to multivariate analysis, allowing for the inspection of interdependencies between organ systems. However, for its successful implementation, the disruptive effects of artifactual outliers, which are a common occurrence in physiological recordings, have to be studied, quantified, and addressed. Within the scope of this study, we utilize Dispersion Entropy (DisEn) to initially quantify the capacity of outlier samples to disrupt the values of univariate and multivariate features extracted with DisEn from physiological network segments consisting of synchronised, electroencephalogram, nasal respiratory, blood pressure, and electrocardiogram signals. The DisEn algorithm is selected due to its efficient computation and good performance in the detection of changes in signals for both univariate and multivariate time-series. The extracted features are then utilised for the training and testing of a logistic regression classifier in univariate and multivariate configurations in an effort to partially automate the detection of artifactual network segments. Our results indicate that outlier samples cause significant disruption in the values of extracted features with multivariate features displaying a certain level of robustness based on the number of signals formulating the network segments from which they are extracted. Furthermore, the deployed classifiers achieve noteworthy performance, where the percentage of correct network segment classification surpasses 95% in a number of experimental setups, with the effectiveness of each configuration being affected by the signal in which outliers are located. Finally, due to the increase in the number of features extracted within the framework of network physiology and the observed impact of artifactual samples in the accuracy of their values, the implementation of algorithmic steps capable of effective feature selection is highlighted as an important area for future research.

Effects of Cannabis Consumption on Sleep

Despite the fact that medical properties of Cannabis have been recognized for more than 5000 years, the use of Cannabis for medical purposes have recently reemerged and became more accessible. Cannabis is usually employed as a self-medication for the treatment of insomnia disorder. However, the effects of Cannabis on sleep depend on multiple factors such as metabolomic composition of the plant, dosage and route of administration. In the present chapter, we reviewed the main effect Cannabis on sleep. We focused on the effect of "crude or whole plant" Cannabis consumption (i.e., smoked, oral or vaporized) both in humans and experimental animal models.The data reviewed establish that Cannabis modifies sleep. Furthermore, a recent experimental study in animals suggests that vaporization (which is a recommended route for medical purposes) of Cannabis with high THC and negligible CBD, promotes NREM sleep. However, it is imperative to perform new clinical studies in order to confirm if the administration of Cannabis could be a beneficial therapy for the treatment of sleep disorders.

Collective almost synchronization-based model to extract and predict features of EEG signals

Understanding the brain is important in the fields of science, medicine, and engineering. A promising approach to better understand the brain is through computing models. These models were adjusted to reproduce data collected from the brain. One of the most commonly used types of data in neuroscience comes from electroencephalography (EEG), which records the tiny voltages generated when neurons in the brain are activated. In this study, we propose a model based on complex networks of weakly connected dynamical systems (Hindmarsh-Rose neurons or Kuramoto oscillators), set to operate in a dynamic regime recognized as Collective Almost Synchronization (CAS). Our model not only successfully reproduces EEG data from both healthy and epileptic EEG signals, but it also predicts EEG features, the Hurst exponent, and the power spectrum. The proposed model is able to forecast EEG signals 5.76 s in the future. The average forecasting error was 9.22%. The random Kuramoto model produced the outstanding result for forecasting seizure EEG with an error of 11.21%.

Effect of fixed dental prosthesis on the brain functions of partially edentulous patients - pilot study with power spectrum density analysis

Purpose:

This study was done to analyse the influence of fixed dental prosthesis (FDP) on brain function by analysing power spectral density of partially edentulous patients.

Materials and methods:

The study included unilateral missing mandibular molar replacement patients. The patients were restored with three-unit metal ceramic FDP restorations. The cognitive function was analysed with a mental state questionnaire. Power spectral density (PSD) analysis of EEG alpha waves was made pre- treatment, post treatment and 3 months after FDP treatment to analyse the brain function. The data in various phases were obtained before and after chewing. The results were statistically analysed.

Results:

The mean pre and post treatment PSD was 0.0175 (SD ±0.0132) and 0.0178 (SD ±0.0135). The mean post treatment PSD after three months was 0.024 (SD± 0.019). The results were analysed with repeated ANOVA and were statistically significant. (p<0.01).

Conclusion:

The study displayed improvement in brain function of partially edentulous patients with FDP rehabilitation.

Effect of depth of anesthesia on the phase lag entropy in patients undergoing general anesthesia by propofol: A STROBE-compliant study

The PLEM100 (Inbody Co., Ltd., Seoul, Korea) is a device for measuring phase lag entropy (PLE), a recently developed index for the quantification of consciousness during sedation and general anesthesia. In the present study, we assessed changes in PLE along with the level of consciousness during the induction of general anesthesia using propofol. PLE was compared with the bispectral index (BIS), which is currently the most commonly used index of consciousness.After obtaining Institutional Review Board approval and written informed consent, we enrolled 15 patients (8 men, 7 women; mean age: 37 ± 9 years; mean height: 168 ± 8 cm; mean weight; 68 ± 11 kg) undergoing nasal bone reduction. PLE and BIS sensors were attached simultaneously, and general anesthesia was induced via target-controlled infusion (TCI) of propofol. PLE and BIS scores were recorded when the calculated effect site concentration shown on the TCI pump was equal to the target concentrations of 1.5, 2.0, 2.5, 2.8, 3.0, 3.2, 3.4, and 3.5 μg/mL (and at each 0.1 μg/mL increase, thereafter). Observer's Assessment of Alertness/Sedation (OAA/S) scores were also recorded until unconsciousness was achieved. Throughout the anesthesia period, all pairs of PLE and BIS data were collected using data acquisition software.The partial correlation coefficients between OAA/S scores and PLE, and between OAA/S scores and BIS were 0.778 (P < .001) and 0.846 (P < .001), respectively. Throughout the period of anesthesia, PLE and BIS exhibited a significant positive correlation. The partial correlation coefficient prior to the loss of consciousness was 0.838 (P < .001), and 0.669 (P < .001) following the loss of consciousness. Intra-class correlation between the 2 indices was 0.889 (P < .001) and 0.791 (P < .001) prior and following the loss of consciousness, respectively.PLE exhibited a strong and predictable correlation with both BIS and OAA/S scores. These results suggest that PLE is reliable for assessing the level of consciousness during sedation and general anesthesia.

Inference for variance components in linear mixed-effect models with flexible random effect and error distributions

In many biomedical investigations, parameters of interest, such as the intraclass correlation coefficient, are functions of higher-order moments reflecting finer distributional characteristics. One popular method to make inference for such parameters is through postulating a parametric random effects model. We relax the standard normality assumptions for both the random effects and errors through the use of the Fleishman distribution, a flexible four-parameter distribution which accounts for the third and fourth cumulants. We propose a Fleishman bootstrap method to construct confidence intervals for correlated data and develop a normality test for the random effect and error distributions. Recognizing that the intraclass correlation coefficient may be heavily influenced by a few extreme observations, we propose a modified, quantile-normalized intraclass correlation coefficient. We evaluate our methods in simulation studies and apply these methods to the Childhood Adenotonsillectomy Trial sleep electroencephalogram data in quantifying wave-frequency correlation among different channels.

Augmentation of Dispersion Entropy for Handling Missing and Outlier Samples in Physiological Signal Monitoring

Entropy quantification algorithms are becoming a prominent tool for the physiological monitoring of individuals through the effective measurement of irregularity in biological signals. However, to ensure their effective adaptation in monitoring applications, the performance of these algorithms needs to be robust when analysing time-series containing missing and outlier samples, which are common occurrence in physiological monitoring setups such as wearable devices and intensive care units. This paper focuses on augmenting Dispersion Entropy (DisEn) by introducing novel variations of the algorithm for improved performance in such applications. The original algorithm and its variations are tested under different experimental setups that are replicated across heart rate interval, electroencephalogram, and respiratory impedance time-series. Our results indicate that the algorithmic variations of DisEn achieve considerable improvements in performance while our analysis signifies that, in consensus with previous research, outlier samples can have a major impact in the performance of entropy quantification algorithms. Consequently, the presented variations can aid the implementation of DisEn to physiological monitoring applications through the mitigation of the disruptive effect of missing and outlier samples.

DWT and CNN based multi-class motor imagery electroencephalographic signal recognition

OBJECTIVE

Brain computer interface (BCI) system allows humans to control external devices through motor imagery (MI) signals. However, many existing feature extraction algorithms cannot eliminate the influence of individual differences. This research proposed a new processing algorithm that can reduce the impact of individual differences on classification and improve the universality of the algorithm.

APPROACH

To select the optimal frequency band, the energy in each sub-band was calculated by the discrete wavelet transform. Power spectral density and visual geometric group network based convolutional neural network were used for feature extraction and classification respectively.

MAIN RESULTS

The test of the BCI Competition IV dataset IIa proved the superiority of the algorithm. In comparison with some commonly used methods, the proposed algorithm reduced classification calculation time while improving classification accuracy; the average classification accuracy rate reaches 96.21%, which is far exceeding the results obtained by the latest literature.

SIGNIFICANCE

The good classification performance of this research was rooted in the reduced number of parameters, the reduced consumption of computing resources, and the eliminated influence of individual differences. Therefore, the proposed algorithm can be applied to a real-time multi-class BCI system.

Brain activity during self-paced vs. fixed protocols in graded exercise testing

Electroencephalography research surrounding maximal exercise testing has been limited to male subjects. Additionally, studies have used open-looped protocols, meaning individuals do not know the exercise endpoint. Closed-loop protocols are often shown to result in optimal performance as self-pacing is permitted. The purpose of this study was to compare brain activity during open- and closed-loop maximal exercise protocols, and to determine if any sex differences are present. Twenty-seven subjects (12 males, ages 22.0 ± 2.5 years) participated in this study. A pre-assembled EEG sensor strip was used to collect brain activity from specific electrodes (F3/F4: dorsolateral prefrontal cortex, or dlPFC; and C3/Cz/C4: motor cortex, or MC). Alpha (8-12 Hz) and beta (12-30 Hz) frequency bands were analyzed. Subjects completed two maximal exercise tests on a cycle ergometer, separated by at least 48 h: a traditional, open-loop graded exercise test (GXT) and a closed-loop self-paced VO_2max (SPV) test. Mixed model ANOVAs were performed to compare power spectral density (PSD) between test protocols and sexes. A significant interaction of time and sex was shown in the dlPFC for males, during the GXT only (p = 001), where a peak was reached and then a decrease was shown. A continuous increase was shown in the SPV. Sex differences in brain activity during exercise could be associated with inhibitory control, which is a function of the dlPFC. Knowledge of an exercise endpoint could be influential towards cessation of exercise and changes in cortical brain activity.

Is resting state frontal alpha connectivity asymmetry a useful index to assess depressive symptoms? A preliminary investigation in a sample of university students

BACKGROUND

Frontal alpha asymmetry (FAA) has been widely investigated in depressive disorders (DDs) with contradictory and not conclusive results. The main aim of the current study was to explore the association between a new neurophysiological index, the so-called frontal alpha connectivity asymmetry index (FACA-I), and depressive symptoms.

METHODS

One hundred and thirteen participants (45 men and 68 women, mean age: 22.83 ± 2.26 years) were enrolled. Electroencephalographic (EEG) recordings were performed during 5 min of resting state (RS). FACA-I was computed by subtracting connectivity at left frontal regions from right frontal regions and dividing by their sum. RS FAA were also computed and compared to the FACA-I in all analyses.

RESULTS

After controlling for the presence of potential confounding variables (i.e., sex, age and anxiety symptoms), only FACA-I scores between medial prefrontal cortex and subgenual anterior cingulate cortex were negatively associated with both somatic and cognitive/affective depressive symptoms and were lower in individuals with significant level of depressive symptoms.

LIMITATIONS

We focused on a sample of university students with no formal diagnosis of depression and we did not assess FAA and FACA-I during cognitive and/or emotional tasks, which make our interpretation specific to the RS condition.

CONCLUSIONS

Taken together our data suggest that alpha connectivity asymmetry between medial prefrontal cortex and subgenual anterior cingulate cortex may be a useful neurophysiological index in the assessment of depressive symptoms.

EIQ: EEG based IQ test using wavelet packet transform and hierarchical extreme learning machine

BACKGROUND

The use of electroencephalography has been perpetually incrementing and has numerous applications such as clinical and psychiatric studies, social interactions, brain computer interface etc. Intelligence has baffled us for centuries, and we have attempted to quantify using EEG signals.

NEW METHOD

This paper aims at devising a novel non-invasive method of measuring human intelligence. A newly devised scoring scheme is used to ultimately generate a score for the subjects. Wavelet packet transform approach for feature extraction is applied to 5 channel EEG data. This approach uses db-8 as the mother wavelet. Hierarchical extreme learning machine is used for classification of the EEG signals.

RESULT

80.00% training accuracy and 73.33% testing accuracy was measured for the classifier. The average sensitivity and specificity across all three classes was measured to be 0.8133 and 0.8923 respectively. An aggregate score was determined from the classification of EEG data. The power spectral analysis of the EEG data was conducted and regions of the brain responsible for various activities was confirmed. In the memory test, theta and beta bands exhibit high power, for arithmetic test, alpha and beta bands are strong, whereas in linguistic test, theta, alpha and beta bands are equally strong.

COMPARISON

The traditional IQ test determines intelligence indirectly, based on the score obtained from Wechsler test. In this paper an attempt is made to measure intelligence based on various brain activities - memory, arithmetic, linguistic.

CONCLUSION

A new method to measure intelligence using direct approach by classifying the EEG signals is proposed.

Cortical Mapping in Conventional and High Dose Spinal Cord Stimulation: An Exploratory Power Spectrum and Functional Connectivity Analysis With Electroencephalography

OBJECTIVES

Spinal cord stimulation (SCS) is considered an effective pain-relieving treatment for patients with Failed Back Surgery Syndrome (FBSS). Despite the clinical effectiveness, it is unknown whether the altered functional connectivity in such patients, as compared to healthy persons, can be influenced by SCS. Therefore, the goal of this study is to evaluate whether brain connectivity assessed by EEG differs between baseline and SCS in patients with FBSS.

MATERIALS AND METHODS

Eight patients with FBSS underwent a resting-state EEG protocol before SCS, 1.5 months and 2.5 months after receiving SCS. At each frequency band, power spectrums were compared for no SCS, conventional (CON) SCS and High Dose (HD) SCS. Functional connectivity, with the aid of eConnectome was also calculated.

RESULTS

Significant differences in the average power density spectrum over the whole scalp were observed between no SCS, CON SCS and HD SCS in delta, theta and beta frequency bands (p < 0.01). The average power spectrum for CON SCS was significantly lower than the average power spectrum for HD SCS. Marked increases in strength of the information flow between electrode pair FC3-TP9 in the beta frequency band (p = 0.006) were found in favor of HD SCS.

CONCLUSIONS

The differences in power spectrum and connectivity between the three conditions lead to the hypothesis that HD SCS differs from CON SCS on average power spectrum, suggesting that HD SCS may have a higher contribution on the excitatory bottom-up pathway.

Information Dynamics of the Brain, Cardiovascular and Respiratory Network during Different Levels of Mental Stress

In this study, an analysis of brain, cardiovascular and respiratory dynamics was conducted combining information-theoretic measures with the Network Physiology paradigm during different levels of mental stress. Starting from low invasive recordings of electroencephalographic, electrocardiographic, respiratory, and blood volume pulse signals, the dynamical activity of seven physiological systems was probed with one-second time resolution measuring the time series of the δ, θ, α and β brain wave amplitudes, the cardiac period (RR interval), the respiratory amplitude, and the duration of blood pressure wave propagation (pulse arrival time, PAT). Synchronous 5-min windows of these time series, obtained from 18 subjects during resting wakefulness (REST), mental stress induced by mental arithmetic (MA) and sustained attention induced by serious game (SG), were taken to describe the dynamics of the nodes composing the observed physiological network. Network activity and connectivity were then assessed in the framework of information dynamics computing the new information generated by each node, the information dynamically stored in it, and the information transferred to it from the other network nodes. Moreover, the network topology was investigated using directed measures of conditional information transfer and assessing their statistical significance. We found that all network nodes dynamically produce and store significant amounts of information, with the new information being prevalent in the brain systems and the information storage being prevalent in the peripheral systems. The transition from REST to MA was associated with an increase of the new information produced by the respiratory signal time series (RESP), and that from MA to SG with a decrease of the new information produced by PAT. Each network node received a significant amount of information from the other nodes, with the highest amount transferred to RR and the lowest transferred to δ, θ, α and β. The topology of the physiological network underlying such information transfer was node- and state-dependent, with the peripheral subnetwork showing interactions from RR to PAT and between RESP and RR, PAT consistently across states, the brain subnetwork resulting more connected during MA, and the subnetwork of brain–peripheral interactions involving different brain rhythms in the three states and resulting primarily activated during MA. These results have both physiological relevance as regards the interpretation of central and autonomic effects on cardiovascular and respiratory variability, and practical relevance as regards the identification of features useful for the automatic distinction of different mental states.

The fundamental contribution of the electromyogram to a high bispectral index: a postoperative observational study

The electromyogram (EMG) activity has been reported to falsely increase BIS. Conversely, EMG seems necessary to constitute the high BIS indicative of an awake condition, and may play a fundamental role in calculating BIS, rather than distorting the appropriate BIS. However, exactly how EMG is associated with a high BIS remains unclear. We intended to clarify the respective contributions of EMG and various electroencephalogram (EEG) parameters to high BIS. In 79 courses of anaesthesia, BIS monitor-derived EMG parameters (EMGLOW), and other processed EEG parameters [SEF95 (spectral edge frequency 95%), SynchFastSlow (bispectral parameter), BetaRatio (frequency parameter), total power subtypes in five frequency range], were obtained simultaneously with BIS, every 3 s. These EEG parameters were used for receiver operating characteristic (ROC) analysis of detecting three BIS levels (BIS > 80, BIS > 70, and BIS > 60) to assess their diagnosabilities. A total of 218,418 data points derived from 79 cases were used for analysis. Area under the ROC curve (AUC) was calculated and optimal cut-off (threshold) was determined by Youden index. As the results, for detecting BIS > 80, the AUC of EMGLOW was 0.975 [0.974-0.977] (mean [95% confidence interval]), significantly higher than any other processed EEG parameters such as BetaRatio (0.832 [0.828-0.835]), SEF95 (0.821 [0.817-0.826]) and SynchFastSlow (0.769 [0.764-0.774]) (p < 0.05 each). The threshold of EMGLOW for detecting BIS > 80 was 35.7 dB, with high sensitivity (92.5%) and high specificity (96.5%). Our results suggest EMG contributes considerably to the diagnosis of high BIS, and is particularly essential for determining BIS > 80.

Automated seizure prediction

In the past two decades, significant advances have been made on automated electroencephalogram (EEG)-based diagnosis of epilepsy and seizure detection. A number of innovative algorithms have been introduced that can aid in epilepsy diagnosis with a high degree of accuracy. In recent years, the frontiers of computational epilepsy research have moved to seizure prediction, a more challenging problem. While antiepileptic medication can result in complete seizure freedom in many patients with epilepsy, up to one-third of patients living with epilepsy will have medically intractable epilepsy, where medications reduce seizure frequency but do not completely control seizures. If a seizure can be predicted prior to its clinical manifestation, then there is potential for abortive treatment to be given, either self-administered or via an implanted device administering medication or electrical stimulation. This will have a far-reaching impact on the treatment of epilepsy and patient's quality of life. This paper presents a state-of-the-art review of recent efforts and journal articles on seizure prediction. The technologies developed for epilepsy diagnosis and seizure detection are being adapted and extended for seizure prediction. The paper ends with some novel ideas for seizure prediction using the increasingly ubiquitous machine learning technology, particularly deep neural network machine learning.

Frontal Alpha Asymmetry and Theta Oscillations Associated With Information Sharing Intention

Social media has gained increasing importance in many aspects of everyday life, from building relationships to establishing collaborative networks between individuals worldwide. Sharing behavior is an essential part of maintaining these dynamic networks. However, the precise neural factors that could be related to sharing behavior in online communities remain unclear. In this study, we recorded electroencephalographic (EEG) oscillations of human subjects while they were watching short videos. The subjects were later asked to evaluate the videos based on how much they liked them and whether they would share them. We found that, at the population level, subjects watching videos that would not be shared had higher power spectral density (PSD) amplitudes in the theta band (4-8 Hz), primarily over the frontal and parietal sites of the right hemisphere, than subjects watching videos that would be shared. Previous studies have associated task disengagement with an increase in scalp-wide theta activation, which can be interpreted as a mind-wandering effect. This might suggest that the decision to not share the video may lead to a more automatic/effortless neural pattern. We also found that watching videos that would be shared was associated with lower PSD amplitudes in the alpha band (8-12 Hz) over the central and right frontal sites, and with more negative scores of frontal alpha asymmetry (FAA) index scores. These results may be related to previous work linking right-sided frontal EEG asymmetry to the pursuit of social conformity and avoidance of negative outcomes, such as social isolation. Finally, using support vector machine (SVM) algorithms, we show that these EEG parameters and preference rating scores can be used to improve the predictability of sharing information behavior. The information sharing-related EEG pattern described here could therefore improve our understanding of the neural markers associated with sharing behavior and contribute to studies about stimuli propagation.