EEG Power Research Articles

Attenuation of homeostatic sleep response and rest-activity circadian rhythm in vitamin D deficient mice

ABSTRACT The link between vitamin D deficiency (VDD) and sleep disturbances has long been suggested. However, the direct causality between VDD, sleep disturbances, and circadian rhythm remains unclear. We aimed to characterize sleep-wake behavior and circadian rhythms in an animal model of VDD. VDD was induced by feeding vitamin D-deficient chow, and we analyzed sleep and circadian rhythm parameters. During light period, VDD mice exhibited reduced wake with more frequent wake bouts and increased NREM sleep time. However, during dark period, the wake EEG power spectrum peaked at theta band frequency, and slow-wave energy was suppressed in mice with VDD. Rest-activity analyses revealed increased circadian period, lower wheel counts, and more frequent and short activity bouts during VDD. Combining sleep and circadian data, we found significantly suppressed activities during the hours with a wake duration shorter than 30 minutes. Moreover, mice in VDD state exhibited a negative correlation between wake theta power and hourly wheel-running counts during dark period. Our data point to a direct link between VDD and disturbances in sleep and rest-activity circadian rhythm, featuring frequent wake bouts during the sleeping phase, reduced sleep pressure build-up in dark period, and reduced activity levels due to increased susceptibility to sleepiness.

Effectiveness assessment of repetitive transcranial alternating current stimulation with concurrent EEG and fNIRS measurement.

Transcranial alternating current stimulation (tACS) exhibits the capability to interact with endogenous brain oscillations using an external low-intensity sinusoidal current and influences cerebral function. Despite its potential benefits, the physiological mechanisms and effectiveness of tACS are currently a subject of debate and disagreement. The aims of our study are to (i) evaluate the neurological and behavioral impact of tACS by conducting repetitive sham-controlled experiments and (ii) propose criteria to evaluate effectiveness, which can serve as a benchmark to determine optimal individual-based tACS protocols. In this study, 15 healthy adults participated in the experiment over two visiting: sham and tACS (i.e., 5Hz, 1mA). During each visit, we used multimodal recordings of the participants' brain, including simultaneous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), along with a working memory (WM) score to quantify neurological effects and cognitive changes immediately after each repetitive sham/tACS session. Our results indicate increased WM scores, hemodynamic response strength, and EEG power in theta and delta bands both during and after the tACS period. Additionally, the observed effects do not increase with prolonged stimulation time, as the effects plateau towards the end of the experiment. In conclusion, our proposed closed-loop scheme offers a promising advance for evaluating the effectiveness of tACS during the stimulation session. Specifically, the assessment criteria use participant-specific brain-based signals along with a behavioral output. Moreover, we propose a feedback efficacy score that can aid in determining the optimal stimulation duration based on a participant-specific brain state, thereby preventing the risk of overstimulation.

Tenuigenin promotes non-rapid eye movement sleep via the GABAA receptor and exerts somnogenic effect in a MPTP mouse model of Parkinson's disease

Sleep disturbances are commonly non-motor symptoms in Parkinson's diseases (PD). However, standard dopamine replacement therapies for the treatment of motor symptoms often prove inadequate in combating sleep disturbances. Previous studies conducted by our research group have reported the neuroprotective effects of tenuigenin, a natural extract from Polygala tenuifolia root, which has been traditionally employed in treating insomnia. The objective of this study was to investigate the potential of tenuigenin in modulating sleep-wake behaviors and elucidate the underlying mechanisms. We employed EEG/EMG recordings to evaluate the impact of tenuigenin on sleep-wake profiles. Furthermore, we utilized c-Fos immunostaining, whole-cell patch clamping and local field potentials (LFP) recording to explore the mechanisms involved in sleep-promoting effects of tenuigenin. Additionally, we examined the effects of tenuigenin on sleep-promoting in MPTP PD mice. Here, we found tenuigenin demonstrated a significant increase in NREM sleep and a reduction in sleep latency in mice, without altering the EEG power density. Moreover, tenuigenin increased c-Fos expression in the ventrolateral preoptic area (VLPO) and stimulated sleep-promoting neurons in VLPO. The sleep-promoting effects of tenuigenin were abolished when mice were pretreated with flumazenil, an antagonist at the benzodiazepine site of the GABAA receptor. Furthermore, tenuigenin was found to ameliorate sleep disturbances in MPTP-induced mice. The results suggesting that tenuigenin facilitated a type of NREM sleep comparable to physiological NREM sleep through interaction with the GABAA receptor. Additionally, tenuigenin demonstrated improvements in sleep disturbances in MPTP-induced PD mice, suggesting its potential as a sleep-promoting substance, particularly for PD patients experiencing sleep disturbances.

Differences in the EEG Power Spectrum and Cross-Frequency Coupling Patterns between Young and Elderly Patients during Sevoflurane Anesthesia.

Electroencephalography (EEG) is widely used for monitoring the depth of anesthesia in surgical patients. Distinguishing age-related EEG features under general anesthesia will help to optimize anesthetic depth monitoring during surgery for elderly patients. This retrospective cohort study included 41 patients aged from 18 to 79 years undergoing noncardiac surgery under general anesthesia. We compared the power spectral signatures and phase-amplitude coupling patterns of the young and elderly groups under baseline and surgical anesthetic depth. General anesthesia by sevoflurane significantly increased the spectral power of delta, theta, alpha, and beta bands and strengthened the cross-frequency coupling both in young and elderly patients. However, the variation in EEG power spectral density and the modulation of alpha amplitudes on delta phases was relatively weaker in elderly patients. In conclusion, the EEG under general anesthesia using sevoflurane exhibited similar dynamic features between young and elderly patients, and the weakened alteration of spectral power and cross-frequency coupling patterns could be utilized to precisely quantify the depth of anesthesia in elderly patients.

Automatic Detection of Chronic Insomnia from Polysomnographic and Clinical Variables Using Machine Learning.

Insomnia is defined subjectively by the presence and frequency of specific clinical symptoms and an association with distress. Although sleep study data has shown some weak associations, no objective test can currently be used to predict insomnia. The purpose of this study was to use previously reported and relatively crafted insomnia-related polysomnographic variables in machine learning models to classify groups with and without insomnia. Demographics, diagnosed depression, Epworth Sleepiness Scale (ESS), and features derived from electroencephalography (EEG), arousals, and sleep stages from 3,407 sleep clinic patients (2,617 without insomnia and 790 insomnia patients based on responses to a set of questions) were included in this analysis. The number of features were reduced using pair-wise correlation and recursive feature elimination. Predictive value of three machine learning models (logistic regression, neural network, and support vector machine) was investigated, and the best performance was achieved with logistic regression, yielding a balanced accuracy of 71%. The most important features in predicting insomnia were depression, age, sex, duration of longest arousal, ESS score, and EEG power in theta and sigma bands across all sleep stages. Results indicate potential of machine learning-based screening for insomnia using clinical variables and EEG.

A Unified Framework for Investigating Aperiodic and Periodic Components in the Hearbeat Dynamics Spectrum: a Feasibility Study.

Heart Rate Variability (HRV) series is a widely used, non-invasive, and easy-to-acquire time-resolved signal for evaluating autonomic control on cardiovascular activity. Despite the recognition that heartbeat dynamics contains both periodic and aperiodic components, the majority of HRV modeling studies concentrate on only one component. On the one hand, there are models based on self-similarity and 1/f behavior that focus on the aperiodic component; on the other hand, there is the conventional division of the spectral domain into narrow-band oscillations, which considers HRV as a combination of periodic components. Taking inspiration from a recent parametrization of EEG power spectra, here we evaluate the applicability of a unified modeling framework to quantitatively assess heartbeat dynamics spectra as a mixture of aperiodic and periodic components. The proposed model is applied on publicly-available, real HRV series collected during postural changes from 10 healthy subjects. Results show that the proposed modeling effectively characterizes different experimental conditions and may complement HRV standard analysis defined in the frequency domain.

EEG Based Cortico-Muscular Connectivity During Standing Early Post Stroke.

In this exploratory study we studied brain activation and corticomuscular connectivity during standing in healthy individuals and persons with stroke within 40 days of cerebrovascular accident (CVA). EEG and EMG data were acquired during standing and analysis showed a trend of higher EEG power (hyper activation) in the stroke group. Direct corticomuscular connectivity between sensorimotor cortices and contralateral lower extremity muscles showed lower connectivity between affected motor, premotor, and sensory cortices, and contralateral lower extremity peripheral muscles with moderate effect size. The preliminary data in this paper suggest re-organization in left sensorimotor cortex role in controlling contralateral lower extremity muscles during standing. Correlational analysis in stroke group within 40 days of CVA showed a relationship between higher corticomuscular connectivity and better scores on balance assessments.Clinical Relevance- This study evaluates corticomuscular connectivity during standing in healthy controls and individuals with subacute stroke (within 40 days of injury). Better understanding of cortical control of standing post stroke is important to improve strategies used in mobility rehabilitation.

Depression diagnosis based on electroencephalography power ratios.

Depression is a common mental disorder that impacts millions of people across the world. However, its diagnosis is difficult due to the dependence on subjective testing. Although quantitative electroencephalography (EEG) has been investigated as a promising diagnostic tool for depression, the associated results have proven contradictory. The current study determines whether the alpha/beta (ABR), alpha/theta (ATR), and theta/beta (TBR) ratios can serve as biological markers of depression. We used open-access EEG data from OpenNeuro to investigate power ratios in the resting state of 46 patients with depression and 75 healthy controls. Spectral data were extracted by fast Fourier transform at the theta band (4-8Hz), alpha band (8-13Hz), and beta band (13-32Hz). Neural network, logistic regression, and receiver operating characteristic (ROC) curves were used to assess the diagnostic accuracies of each suggested index. Additionally, the cutoff point, sensitivity, specificity, positive predictive value, and negative predictive value at the maximized Youden index were compared for each variable. Decreased anterior frontal, frontal, central, parietal, occipital, and temporal ABR and decreased central and parietal TBR were observed in the depression group. The area under the curve of the ROC curves further revealed that these ratios could all effectively differentiate depression. In particular, the central, frontal, and parietal ABR exhibited high discrimination scores. Multiple logistic regression analysis demonstrated that the Beck Depression Inventory and Spielberger Trait Anxiety Inventory scores, as well as the probability of depression, increased with a decrease in the central ABR. Moreover, neural network analysis revealed that the global ABR was the most effective index for diagnosing depression among the three global EEG power ratios. The central, frontal, and parietal ABR represent potential biomarkers to differentiate patients with depression from healthy controls.

Neurophysiological signatures of sensory-processing sensitivity.

Sensory processing sensitivity is mainly captured based on questionnaires and it's neurophysiological basis is largely unknown. As hitherto no electroencephalography (EEG) study has been carried out, the aim of this work was to determine whether the self-reported level of SPS correlates with the EEG activity in different frequency bands. One hundred fifteen participants were measured with 64-channel EEG during a task-free resting state. After artifact correction, a power spectrum time series was calculated using the Fast Fourier Transform (FFT) for the following frequency bands: Delta: 1-3.5 Hz, theta: 4-7.5 Hz, alpha1: 8-10 Hz, alpha2: 10.5-12 Hz, beta1: 12.5-15 Hz, beta2: 15.5-25 Hz, gamma: 25.5-45 Hz, global: 1-45 Hz. Correlations with the 'Highly Sensitive Person Scale' (HSPS-G) scores were determined. Then, the lowest and the highest 30% of the cohort were contrasted as polar opposites. EEG features were compared between the two groups applying a paired two-tailed t-test. The HSPS-G scores correlated statistically significantly positive with beta 1 and 2, and global EEG power during resting with eyes open, but not during resting with eyes closed. The highly sensitive group revealed higher beta power (4.38 ± 0.32 vs. 4.21 ± 0.17, p = 0.014), higher gamma power (4.21 ± 0.37 vs. 4.00 ± 0.25, p = 0.010), and increased global EEG power (4.38 ± 0.29 vs. 4.25 ± 0.17, p = 0.041). The higher EEG activity in the HSP group was most pronounced in the central, parietal, and temporal region, whereas lower EEG activity was most present in occipital areas. For the first time, neurophysiological signatures associated with SPS during a task free resting state were demonstrated. Evidence is provided that neural processes differ between HSP and non-HSP. During resting with eyes open HSP exhibit higher EEG activity suggesting increased information processing. The findings could be of importance for the development of biomarkers for clinical diagnostics and intervention efficacy evaluation.

Support-vector classification of low-dose nitrous oxide administration with multi-channel EEG power spectra

Support-vector machines (SVMs) can potentially improve patient monitoring during nitrous oxide anaesthesia. By elucidating the effects of low-dose nitrous oxide on the power spectra of multi-channel EEG recordings, we quantified the degree to which these effects generalise across participants. In this single-blind, cross-over study, 32-channel EEG was recorded from 12 healthy participants exposed to 0, 20, 30 and 40% end-tidal nitrous oxide. Features of the delta-, theta-, alpha- and beta-band power were used within a 12-fold, participant-wise cross-validation framework to train and test two SVMs: (1) binary SVM classifying EEG during 0 or 40% exposure (chance = 50%); (2) multi-class SVM classifying EEG during 0, 20, 30 or 40% exposure (chance = 25%). Both the binary (accuracy 92%) and the multi-class (accuracy 52%) SVMs classified EEG recordings at rates significantly better than chance (p < 0.001 and p = 0.01, respectively). To determine the relative importance of frequency band features for classification accuracy, we systematically removed features before re-training and re-testing the SVMs. This showed the relative importance of decreased delta power and the frontal region. SVM classification identified that the most important effects of nitrous oxide were found in the delta band in the frontal electrodes that was consistent between participants. Furthermore, support-vector classification of nitrous oxide dosage is a promising method that might be used to improve patient monitoring during nitrous oxide anaesthesia.

Characteristics of EEG power spectra involved in the proficiency of motor learning.

Neuromodulation techniques for modulating brain activity can affect performance in a variety of behaviors. Techniques including transcranial alternating current stimulation and random noise stimulation can modulate neural oscillations. However, the intervention effect of neuromodulation approaches on motor learning is poor, partly because the electroencephalography (EEG) power spectra associated with the motor learning process has not yet been fully elucidated. Therefore, we investigated the characteristics of EEG power spectra in the process of motor learning in 15 right-handed healthy participants (5 females; mean age = 22.8 ± 3.0 years). The motor task was a ball-rotation task in which participants rotated two balls in the palm of their left hand. Participants performed a pre-test, the motor learning tasks, and a post-test. In the motor learning tasks, twenty 60 s trials were performed in the clockwise (CW) direction. Before and after the motor learning tasks, CW and counterclockwise (CCW; control condition) tasks were performed for 60 s each as pre- and post-tests. Therefore, CW direction was set as a motor learning task, while CCW was a test-only control task. EEG was recorded during the tests and tasks, and the power spectra in the alpha, beta, and gamma oscillations were calculated and compared between pre- and post-tests. The results showed that in the CW post-test, the power of the gamma band in the left parietal areas and the right frontal area was significantly higher than in the pre-test. In the CCW, there was no significant difference in each band at each area between the pre- and post-tests. Our findings reveal the characteristics of the EEG spectra related to the motor learning process. These results may help to establish more effective neuromodulation approaches to modifying neural oscillations in motor learning, including in rehabilitation fields.

Alpha and Theta Oscillations Are Causally Linked to Interference Inhibition: Evidence from High-Definition Transcranial Alternating Current Stimulation.

(1) Background: The Go/NoGo task and color-word Stroop task were used to investigate the effect of applying different frequency bands of neural oscillations to the lDLPFC on inhibitory control modulation. (2) Methods: Participants were randomly categorized into four groups and received HD-tACS at 6, 10, and 20 Hz or sham stimulation at 1.5 mA for 20 min. All participants performed a color-word Stroop task and Go/NoGo task before and immediately after the stimulation; closed-eye resting-state EEG signals were acquired for 3 min before and after the tasks. (3) Results: There were no significant differences in the Go/NoGo behavioral indices task across the four groups. In the color-word Stroop task, the Stroop effect of response time was significantly reduced by 6 and 10 Hz stimulations compared to sham stimulation, and the Stroop effect of accuracy was significantly reduced by 10 Hz stimulation. There were no significant differences in the frequency range-specific (delta, theta, alpha, beta, or gamma) resting EEG power before and after stimulation. (4) Conclusions: HD-tACS at 6 and 10 Hz effectively improved participants' performance on the color-word Stroop task, demonstrating the importance of the lDLPFC in interference inhibition and supporting a causal relationship between theta and alpha oscillations in interference inhibition.

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

ObjectiveTo explore whether the EEG dynamics induced by zolpidem can predict consciousness evolution in patients with prolonged disorders of consciousness (PDOC). MethodsWe 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. ResultsZolpidem 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%. ConclusionsOur work revealed that the specific EEG responses to zolpidem can predict consciousness recovery in PDOC patients. SignificanceThe 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.

Ictal EEG desynchronization during low-voltage fast activity for prediction of surgical outcomes in focal epilepsy.

The authors investigated alterations in functional connectivity (FC) and EEG power during ictal onset patterns of low-voltage fast activity (LVFA) in drug-resistant focal epilepsy. They hypothesized that such changes would be useful to classify epilepsy surgical outcomes. In a cohort of 79 patients with drug-resistant focal epilepsy who underwent stereoelectroencephalography (SEEG) evaluation as well as resective surgery, FC changes during the peri-LVFA period were measured using nonlinear regression (h2) and power spectral properties within/between three regions: the seizure onset zone (SOZ), early propagation zone (PZ), and noninvolved zone (NIZ). Desynchronization and power desynchronization h2 indices were calculated to assess the degree of EEG desynchronization during LVFA. Multivariate logistic regression was employed to control for confounding factors. Finally, receiver operating characteristic curves were generated to evaluate the performance of desynchronization indices in predicting surgical outcome. Fifty-three patients showed ictal LVFA and distinct zones of the SOZ, PZ, and NIZ. Among them, 39 patients (73.6%) achieved seizure freedom by the final follow-up. EEG desynchronization, measured by h2 analysis, was found in the seizure-free group during LVFA: FC decreased within the SOZ and between regions compared with the pre-LVFA and post-LVFA periods. In contrast, the non-seizure-free group showed no prominent EEG desynchronization. The h2 desynchronization index, but not the power desynchronization index, enabled classification of seizure-free versus non-seizure-free patients after resective surgery. EEG desynchronization during the peri-LVFA period, measured by within-zone and between-zone h2 analysis, may be helpful for identifying patients with favorable postsurgical outcomes and also may potentially improve epileptogenic zone identification in the future.

The maturation of aperiodic EEG activity across development reveals a progressive differentiation of wakefulness from sleep

During development, the brain undergoes radical structural and functional changes following a posterior-to-anterior gradient, associated with profound changes of cortical electrical activity during both wakefulness and sleep. However, a systematic assessment of the developmental effects on aperiodic EEG activity maturation across vigilance states is lacking, particularly regarding its topographical aspects. Here, in a population of 160 healthy infants, children and teenagers (from 2 to 17 years, 10 subjects for each year), we investigated the development of aperiodic EEG activity in wakefulness and sleep. Specifically, we parameterized the shape of the aperiodic background of the EEG Power Spectral Density (PSD) by means of the spectral exponent and offset; the exponent reflects the rate of exponential decay of power over increasing frequencies and the offset reflects an estimate of the y-intercept of the PSD.We found that sleep and development caused the EEG-PSD to rotate over opposite directions: during wakefulness the PSD showed a flatter decay and reduced offset over development, while during sleep it showed a steeper decay and a higher offset as sleep becomes deeper. During deep sleep (N2, N3) only the spectral offset decreased over age, indexing a broad-band voltage reduction. As a result, the difference between values in deep sleep and those in both light sleep (N1) and wakefulness increased with age, suggesting a progressive differentiation of wakefulness from sleep EEG activity, most prominent over the frontal regions, the latest to complete maturation. Notably, the broad-band spectral exponent values during deep sleep stages were entirely separated from wakefulness values, consistently across developmental ages and in line with previous findings in adults. Concerning topographical development, the location showing the steepest PSD decay and largest offset shifted from posterior to anterior regions with age. This shift, particularly evident during deep sleep, paralleled the migration of sleep slow wave activity and was consistent with neuroanatomical and cognitive development.Overall, aperiodic EEG activity distinguishes wakefulness from sleep regardless of age; while, during development, it reveals a postero-anterior topographical maturation and a progressive differentiation of wakefulness from sleep. Our study could help to interpret changes due to pathological conditions and may elucidate the neurophysiological processes underlying the development of wakefulness and sleep.

The Difference in Spectral Power Density in Sleep Electroencephalography According to the Estimation of Total Sleep Time

Objective: The discrepancy between self-reported subjective sleep time and objective sleep time varies from person to person, and the mechanism that can explain this discrepancy is unclear. This study aimed to investigate the difference in the spectral power of sleep electroencephalography (EEG) according to the estimation of total sleep time (TST).Methods: Of the 4,080 participants in the Sleep Heart Health Study, 2,363 participants with complete data from polysomnography, morning sleep survey, and spectral power of sleep EEG were included in the analysis. The participants were classified as normo-estimators (estimation of TST &lt;±60 min), under-estimators (underestimation of TST ≤60 min), or over-estimators (overestimation of TST ≥60 min). A fast Fourier transformation was used to calculate the EEG power spectrum for total sleep duration within contiguous 30-s epochs of sleep. The sleep EEG spectral power was compared among the groups after adjusting for potential confounding factors, such as age, sex, proportion of participants with insomnia, apnea-hypopnea index, and TST.Results: Of the 2,363 participants, 1,507 (63.8%), 412 (17.4%), and 444 (18.8%) were assigned to the normo-, under-, and over-estimator groups, respectively. The power spectra during total sleep differed significantly among the groups in the delta (p=0.008) and theta bands (p=0.017) after controlling for potential confounders.Conclusion: Higher delta and theta powers were found in the under-estimators than in the over-estimators. This study suggests that differences exist in the microstructures of polysomnography-derived sleep EEG between these two groups. This study suggests that differences exist in the microstructures of polysomnography-derived sleep EEG based on differences in sleep time estimation.

Comprehensive review of EEG data classification techniques for ADHD detection using machine learning and deep learning

Children who have Attention-Deficit/Hyperactivity Disorder (ADHD) have a chronic behavioral disease. Children with ADHD have a hard time focusing and controlling their actions. One of the most difficult problems in controlling and treating this condition is early detection. There is yet to be discovered a reliable professional procedure for early detection of this condition. The electroencephalogram (EEG) is a useful neuroimaging technique for researching ADHD; one of the key goals is to define the EEG of ADHD youngsters. Numerous methods based on EEG signals have been put out in the literature to address this issue since they are an effective neuroimaging approach for studying ADHD. The best recording formats and channels for diagnosing ADHD, however, have not been the subject of many research. Machine learning (ML) and Artificial Intelligence (AI) strategies for identifying ADHD using EEG-based tools are discussed in this paper. Although, in the case of ADHD, the utilization of ML and AI approaches is restricted. However, the data clearly imply that combining EEG technologies with ML/AI may be utilized to detect ADHD. For categorizing adult ADHD subtypes based on EEG power spectra, ML algorithms that incorporate several classifiers are presented. A widely used deep learning (DL) method is the convolutional neural network (CNN). The use of DL approaches in ADHD research, on the other hand, is currently restricted. EEG has been used in studies to look for ADHD neurological connections. Recent advances in deep learning algorithms, particularly CNN, are anticipated to overcome the issue.

The effect of sedation and time after cardiac arrest on coma outcome prognostication based on EEG power spectra.

Early prognostication of long-term outcome of comatose patients after cardiac arrest remains challenging. Electroencephalography-based power spectra after cardiac arrest have been shown to help with the identification of patients with favourable outcome during the first day of coma. Here, we aim at comparing the power spectra prognostic value during the first and second day after coma onset following cardiac arrest and to investigate the impact of sedation on prognostication. In this cohort observational study, we included comatose patients (N = 91) after cardiac arrest for whom resting-state electroencephalography was collected on the first and second day after cardiac arrest in four Swiss hospitals. We evaluated whether the average power spectra values at 4.6-15.2 Hz were predictive of patients' outcome based on the best cerebral performance category score at 3 months, with scores ranging from 1 to 5 and dichotomized as favourable (1-2) and unfavourable (3-5). We assessed the effect of sedation and its interaction with the electroencephalography-based power spectra on patient outcome prediction through a generalized linear mixed model. Power spectra values provided 100% positive predictive value (95% confidence intervals: 0.81-1.00) on the first day of coma, with correctly predicted 18 out of 45 favourable outcome patients. On the second day, power spectra values were not predictive of patients' outcome (positive predictive value: 0.46, 95% confidence intervals: 0.19-0.75). On the first day, we did not find evidence of any significant contribution of sedative infusion rates to the patient outcome prediction (P > 0.05). Comatose patients' outcome prediction based on electroencephalographic power spectra is higher on the first compared with the second day after cardiac arrest. Sedation does not appear to impact patient outcome prediction.

PET-validated EEG-machine learning algorithm predicts brain amyloid pathology in pre-dementia Alzheimer’s disease

Developing reliable biomarkers is important for screening Alzheimer’s disease (AD) and monitoring its progression. Although EEG is non-invasive direct measurement of brain neural activity and has potentials for various neurologic disorders, vulnerability to noise, difficulty in clinical interpretation and quantification of signal information have limited its clinical application. There have been many research about machine learning (ML) adoption with EEG, but the accuracy of detecting AD is not so high or not validated with Aβ PET scan. We developed EEG-ML algorithm to detect brain Aβ pathology among subjective cognitive decline (SCD) or mild cognitive impairment (MCI) population, and validated it with Aβ PET. 19-channel resting-state EEG and Aβ PET were collected from 311 subjects: 196 SCD(36 Aβ +, 160 Aβ −), 115 MCI(54 Aβ +, 61Aβ −). 235 EEG data were used for training ML, and 76 for validation. EEG features were standardized for age and sex. Multiple important features sets were selected by 6 statistics analysis. Then, we trained 8 multiple machine learning for each important features set. Meanwhile, we conducted paired t-test to find statistically different features between amyloid positive and negative group. The best model showed 90.9% sensitivity, 76.7% specificity and 82.9% accuracy in MCI + SCD (33 Aβ +, 43 Aβ −). Limited to SCD, 92.3% sensitivity, 75.0% specificity, 81.1% accuracy (13 Aβ +, 24 Aβ −). 90% sensitivity, 78.9% specificity and 84.6% accuracy for MCI (20 Aβ +, 19 Aβ −). Similar trends of EEG power have been observed from the group comparison between Aβ + and Aβ −, and between MCI and SCD: enhancement of frontal/ frontotemporal theta; attenuation of mid-beta in centroparietal areas. The present findings suggest that accurate classification for beta-amyloid accumulation in the brain based on QEEG alone could be possible, which implies that QEEG is a promising biomarker for beta-amyloid. Since QEEG is more accessible, cost-effective, and safer than amyloid PET, QEEG-based biomarkers may play an important role in the diagnosis and treatment of AD. We expect specific patterns in QEEG could play an important role to predict future progression of cognitive impairment in the preclinical stage of AD. Further feature engineering and validation with larger dataset is recommended.

Underlying pharmacological mechanisms of psilocin-induced broadband desynchronization and disconnection of EEG in rats.

Psilocybin is one of the most extensively studied psychedelic drugs with a broad therapeutic potential. Despite the fact that its psychoactivity is mainly attributed to the agonism at 5-HT2A receptors, it has high binding affinity also to 5-HT2C and 5-HT1A receptors and indirectly modulates the dopaminergic system. Psilocybin and its active metabolite psilocin, as well as other serotonergic psychedelics, induce broadband desynchronization and disconnection in EEG in humans as well as in animals. The contribution of serotonergic and dopaminergic mechanisms underlying these changes is not clear. The present study thus aims to elucidate the pharmacological mechanisms underlying psilocin-induced broadband desynchronization and disconnection in an animal model. Selective antagonists of serotonin receptors (5-HT1A WAY100635, 5-HT2A MDL100907, 5-HT2C SB242084) and antipsychotics haloperidol, a D2 antagonist, and clozapine, a mixed D2 and 5-HT receptor antagonist, were used in order to clarify the underlying pharmacology. Psilocin-induced broadband decrease in the mean absolute EEG power was normalized by all antagonists and antipsychotics used within the frequency range 1-25 Hz; however, decreases in 25-40 Hz were influenced only by clozapine. Psilocin-induced decrease in global functional connectivity and, specifically, fronto-temporal disconnection were reversed by the 5-HT2A antagonist while other drugs had no effect. These findings suggest the involvement of all three serotonergic receptors studied as well as the role of dopaminergic mechanisms in power spectra/current density with only the 5-HT2A receptor being effective in both studied metrics. This opens an important discussion on the role of other than 5-HT2A-dependent mechanisms underlying the neurobiology of psychedelics.