Band Power Research Articles

Distinct Longitudinal Changes in EEG Measures Reflecting Functional Network Disruption in ALS Cognitive Phenotypes

Amyotrophic lateral sclerosis (ALS) is characterised primarily by motor system degeneration, with clinical evidence of cognitive and behavioural change in up to 50% of cases. We have shown previously that resting-state EEG captures dysfunction in motor and cognitive networks in ALS. However, the longitudinal development of these dysfunctional patterns, especially in networks linked with cognitive-behavioural functions, remains unclear. Longitudinal studies on non-motor changes in ALS are essential to further develop our understanding of disease progression, improve care and enhance the evaluation of new treatments. To address this gap, we examined 124 ALS individuals with 128-channel resting-state EEG recordings, categorised by cognitive impairment (ALSci, n = 25), behavioural impairment (ALSbi, n = 58), or non-impaired (ALSncbi, n = 53), with 12 participants meeting the criteria for both ALSci and ALSbi. Using linear mixed-effects models, we characterised the general and phenotype-specific longitudinal changes in brain network, and their association with cognitive performance, behaviour changes, fine motor symptoms, and survival. Our findings revealed a significant decline in \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\ heta\\:$$\\end{document}-band spectral power over time in the temporal region along with increased \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\\gamma\\:}_{l}$$\\end{document}-band power in the fronto-temporal region in the ALS group. ALSncbi participants showed widespread β-band synchrony decrease, while ALSci participants exhibited increased co-modulation correlated with verbal fluency decline. Longitudinal network-level changes were specific of ALS subgroups and correlated with motor, cognitive, and behavioural decline, as well as with survival. Spectral EEG measures can longitudinally track abnormal network patterns, serving as a candidate stratification tool for clinical trials and personalised treatments in ALS.

A hierarchical random effects state-space model for modeling brain activities from electroencephalogram data.

Mental disorders present challenges in diagnosis and treatment due to their complex and heterogeneous nature. Electroencephalogram (EEG) has shown promise as a source of potential biomarkers for these disorders. However, existing methods for analyzing EEG signals have limitations in addressing heterogeneity and capturing complex brain activity patterns between regions. This paper proposes a novel random effects state-space model (RESSM) for analyzing large-scale multi-channel resting-state EEG signals, accounting for the heterogeneity of brain connectivities between groups and individual subjects. We incorporate multi-level random effects for temporal dynamical and spatial mapping matrices and address non-stationarity so that the brain connectivity patterns can vary over time. The model is fitted under a Bayesian hierarchical model framework coupled with a Gibbs sampler. Compared to previous mixed-effects state-space models, we directly model high-dimensional random effects matrices of interest without structural constraints and tackle the challenge of identifiability. Through extensive simulation studies, we demonstrate that our approach yields valid estimation and inference. We apply RESSM to a multi-site clinical trial of major depressive disorder (MDD). Our analysis uncovers significant differences in resting-state brain temporal dynamics among MDD patients compared to healthy individuals. In addition, we show the subject-level EEG features derived from RESSM exhibit a superior predictive value for the heterogeneous treatment effect compared to the EEG frequency band power, suggesting the potential of EEG as a valuable biomarker for MDD.

New System for Green EMI shielding: Organohydrogel with Multi-band Green Electromagnetic Shielding, Sensing, and Infrared-Stealth Capacity

Current research on green EMI shielding materials is often based on the misconception that absorption-dominated shielding is achieved when reflection loss (SER) exceeds absorption loss (SEA). Although this misconception has been corrected by a large body of literature, few studies have actually achieved absorbed power (A) greater than reflected power (R). In this study, PVA, glycerol and MXene were combined to form an organohydrogel (PMG) with oriented pores. The gel displays remarkable flexibility and strength, attributed to its robust network of hydrogen bond cross-links (the hysteresis return line remains stable under 1000 compression cycles). The PMG20-3 organohydrogel (0.78 wt% MXene) demonstrates a shielding performance of 42.34 dB (A/R=1.38) in the X-band and absorbs 99.9% of power in the terahertz band. This performance exceeds that of most previously reported systems and represents a new system for green electromagnetic shielding materials. Additionally, the PMG organohydrogel has flexible sensing and infrared stealth capabilities. These findings hold great promise for the development of green electromagnetic shielding multifunctional devices.

Neural decoding and feature selection methods for closed-loop control of avoidance behavior

Objective.Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as the foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors.Approach.We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance.Main results.Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low training/inference time and memory usage, requiring<310 ms for training,<0.051 ms for inference, and 16.6 kB of memory, using a single core of AMD Ryzen Threadripper PRO 5995WX CPU.Significance.Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.

Brain electrical activity in Newly diagnosed Generalized Anxiety Disorder: A Quantitative EEG analysis

Background: Generalized Anxiety Disorder (GAD) is one of the most common global mental disorders with negative impact on social life. Quantitative EEG can objectively estimate the anxiety status. Relatively increase beta activity along with decrease alpha activity is associated with GAD. Objectives:To evaluate power of all brain waves in female patients with GAD. Methods:This observational study was conducted on 20 female patients with GAD (age 20-40 years),For comparison, 20 healthy female volunteers of same age were enrolled as control. In this study, EEG was recorded for 3 minutes by placing electrodes on scalp according to 10-20 system. To assess brain electrical activities, the band power of delta, theta, alpha and beta brainwave was recorded from an EEG data acquisition device, EEG Traveler Brain Tech 32+ CMEEG -01, India. Power spectral analysis of EEG data was done by BT40 analysis software. Data were expressed as median (Inter Quartile Range). Statistical analysis was done by Mann-Whitney U test.Results: The significantly higher absolute power of beta was found in most of the electrodes of prefrontal, frontal and temporal region as well as significantly lower power of alpha, theta and delta werefound in all cortical regions in female GAD patients compared to healthy controls at baseline. Conclusion: This study concluded that GAD patients had high excitable state in most of cortical region associated with lack of relaxing ability. J Bangladesh Soc Physiol 2023;18(2): 78-88

Comparison of Brain Activity between Patients with Parkinson Disease Dementia and Patients Affected by Dementia with Lewy Body through EEG Analysis

Objective: Parkinson's disease dementia (PDD) and Dementia with Lewy bodies (DLB) are two syndromes categorized under synucleinopathy, sharing comparable symptoms. The identification of biomarkers would offer an accurate approach for improved diagnosis, treatment, and monitoring of treatment efficacy for these distinct forms of dementia. Method: This study utilized spectral analysis and nonlinear dynamic analysis to compare electroencephalogram (EEG) characteristics between PDD and DLB patients. EEG data was collected from 30 PDD patients, 36 DLB patients, and 36 healthy subjects at rest. Following a conditioning phase to minimize noise and eliminate artifacts, we derived spectral and complexity features using Welch's method and sample entropy. Analysis of variance with repeated measures was performed to compare spectral features and nonlinear dynamics of brain activity between the groups. Results: Post hoc comparison showed that in the control group, the power of delta and theta bands was lower and the power of alpha and beta bands was higher than in patients with PDD and DLB. (P &lt; 0.05). In the theta and alpha bands, the PDD group showed greater power than the DLB group (P &lt; 0.05). Furthermore, there was a significant main effect of diagnosis (F = 4.67, P = 0.007), and also the diagnosis by region interaction for complexity values (F = 4.58, P = 0.009). Post hoc analysis showed that the EEG complexity of the control group was significantly higher than that of the PDD and DLB groups in the frontal, central, temporal and parietal regions (P &lt; 0.05). Moreover, the EEG complexity of the PDD group was significantly higher than that of the DLB group in the central, temporal and parietal regions (P &lt; 0.05). Conclusion: Although both PDD and DLB had almost similar patterns compared to the control group, they showed differences in the EEG power spectrum and its nonlinear dynamics. Our findings indicated marked diffuse slowing and lower cortical complexity or activity in DLB patients compared to PDD in all regions, especially in the central, temporal and parietal areas.

Altered Resting-State Electroencephalogram Microstate Characteristics in Stroke Patients.

Stroke remains a leading cause of disability globally and movement impairment is the most common complication in stroke patients. Resting-state electroencephalography (EEG) microstate analysis is a non-invasive approach of whole-brain imaging based on the spatiotemporal pattern of the entire cerebral cortex. The present study aims to investigate microstate alterations in stroke patients. Resting-state EEG data collected from 24 stroke patients and 19 healthy controls matched by age and gender were subjected to microstate analysis. For four classic microstates labeled as class A, B, C and D, their temporal characteristics (duration, occurrence and coverage) and transition probabilities (TP) were extracted and compared between the two groups. Furthermore, we explored their correlations with clinical outcomes including the Fugl-Meyer assessment (FMA) and the action research arm test (ARAT) scores in stroke patients. Finally, we analyzed the relationship between the temporal characteristics and spectral power in frequency bands. False discovery rate (FDR) method was applied for correction of multiple comparisons. Microstate analysis revealed that the stroke group had lower occurrence of microstate A which was regarded as the sensorimotor network (SMN) compared with the control group (p = 0.003, adjusted p = 0.036, t = -2.959). The TP from microstate A to microstate D had a significant positive correlation with the Fugl-Meyer assessment of lower extremity (FMA-LE) scores (p = 0.049, r = 0.406), but this finding did not survive FDR adjustment (adjusted p = 0.432). Additionally, the occurrence and the coverage of microstate B were negatively correlated with the power of delta band in the stroke group, which did not pass adjustment (p = 0.033, adjusted p = 0.790, r = -0.436; p = 0.026, adjusted p = 0.790, r = -0.454, respectively). Our results confirm the abnormal temporal dynamics of brain activity in stroke patients. The study provides further electrophysiological evidence for understanding the mechanism of brain motor functional reorganization after stroke.

A study comparing brain wave patterns of fentanyl and ketamine in adult patients undergoing minimally invasive surgery.

This study aimed to investigate and compare the neurophysiological impacts of two widely used anesthetic agents, Fentanyl and Ketamine, on EEG power spectra during different stages of anesthesia in adult patients undergoing minimally invasive surgery. EEG data were collected from patients undergoing anesthesia with either Fentanyl or Ketamine. The data were analyzed for relative power spectrum and fast-to-slow wave power ratios, alongside Spectral Edge Frequency 95% (SEF95), at 3 key stages: pre-anesthesia, during stable anesthesia, and post-anesthesia. EEG Relative Power Spectrum: Initially, both groups exhibited similar EEG spectral profiles, establishing a uniform baseline (P > .05). Upon anesthesia induction, the Fentanyl group showed a substantial increase in delta band power (P < .05), suggesting deeper anesthesia, while the Ketamine group maintained higher alpha and beta band activity (P < .05), indicative of a lighter sedative effect. Fast and Slow Wave Power Ratios: The Fentanyl group exhibited a marked reduction in the fast-to-slow wave power ratio during anesthesia (P < .05), persisting post-anesthesia (P < .05) and indicating a lingering effect on brain activity. Conversely, the Ketamine group demonstrated a more stable ratio (P > .05), conducive to settings requiring rapid cognitive recovery. Spectral Edge Frequency 95% (SEF95): Analysis showed a significant decrease in SEF95 values for the Fentanyl group during anesthesia (P < .05), reflecting a shift towards lower frequency power. The Ketamine group experienced a less pronounced decrease (P > .05), maintaining a higher SEF95 value that suggested a lighter level of sedation. The study highlighted the distinct impacts of Fentanyl and Ketamine on EEG power spectra, with Fentanyl inducing deeper anesthesia as evidenced by shifts towards lower frequency activity and a significant decrease in SEF95 values. In contrast, Ketamine's preservation of higher frequency activity and more stable SEF95 values suggests a lighter, more dissociative anesthetic state. These findings emphasize the importance of EEG monitoring in anesthesia for tailoring anesthetic protocols to individual patient needs and optimizing postoperative outcomes.

Long-term effects of siponimod on cardiovascular and autonomic nervous system in secondary progressive multiple sclerosis.

Siponimod, a second-generation, selective sphingosine 1-phosphate receptor (S1PR) 1 and 5 modulator, represents an important therapeutic choice for active secondary progressive multiple sclerosis (SPMS). Besides the beneficial immunomodulatory effects, siponimod impacts cardiovascular function through S1PR1 modulation. Short-term vagomimetic effects on cardiac activity have proved to be mitigated by dose titration. However, long-term consequences are less known. This study aimed to investigate the long-term impact of siponimod on cardiac autonomic modulation in people with SPMS (pwSPMS). Heart rate variability (HRV) and vascular hemodynamic parameters were evaluated using Multiple Trigonometric Regressive Spectral analysis in 47 pwSPMS before siponimod therapy and after one, three, six and 12months of treatment. Autonomic activation tests (tilt test for the sympathetic and deep breathing test for the parasympathetic cardiac modulation) were performed at each examination. pwSPMS preserved regular cardiovascular modulation responses during the autonomic tests reflected in the variation of several HRV parameters, such as RMSSD, pNN50, total power of HRV, high-frequency and low-frequency bands of the spectral domain or hemodynamic vascular parameters (Cwk, Zao, TPR, MAP) and baroreflex sensitivity (BRS). In the long-term follow-up, RMSSD, pNN50, total power, BRS and CwK presented a significant decrease, underlining a reduction of the parasympathetic and a shift towards sympathetic predominance in cardiac autonomic modulation that tends to stabilise after 1year of treatment. Due to dose titration, the short-term effects of siponimod on cardiac autonomic modulation are mitigated. The long-term impact on cardiac autonomic modulation is similar to fingolimod. The autonomic activation tests showed normal cardiovascular responses during 1-year follow-up in pwSPMS, confirming the safety profile of siponimod. Further research on autonomic function could reveal whether the observed sympathetic activation is a compensatory response to S1P signaling intervention or a feature of the disease, while also shedding light on the role of S1PR modulation in MS.

Quantitative Electroencephalography for Predication of Neurological Dysfunction in Type A Aortic Dissection: A Prospective Observational Study.

Type A aortic dissection presents challenges with postoperative cerebral complications, and this study evaluates the predictive value of quantitative electroencephalography for perioperative brain function prognosis. Amplitude-integrated electroencephalography (aEEG) processes raw signals through filtering, amplitude integration, and time compression, displaying the data in a semilogarithmic format. Using this method, postoperative relative band power (post-RBP) α% and dynamic aEEG (ΔaEEG) grade were significantly associated with neurological dysfunction in univariate and multivariable analyses, with area under the receiver operating characteristic curve of 0.876 (95% CI, 0.825-0.926) for the combined model. Postoperative relative band power α% and ΔaEEG were significantly associated with adverse outcomes, with area under the receiver operating characteristic curve of 0.903 (95% CI, 0.835-0.971) for the combined model. Postoperative relative band power α% and ΔaEEG were significantly associated with transient neurological dysfunction and stroke, with areas under the receiver operating characteristic curve of 0.818 (95% CI, 0.760-0.876) and 0.868 (95% CI, 0.810-0.926) for transient neurological dysfunction, and 0.815 (95% CI, 0.743-0.886) and 0.831 (95% CI, 0.746-0.916) for stroke. Among 56 patients, the Alberta Stroke Program Early Computed Tomography score was superior to ΔaEEG in predicting neurological outcomes (area under the receiver operating characteristic curve of 0.872 versus 0.708 [95% CI, 0.633-0.783]; P<0.05). Perioperative quantitative electroencephalography monitoring offers valuable insights into brain function changes in patients with type A aortic dissection. ∆aEEG grades can aid in early detection of adverse outcomes, while postoperative relative band power and ∆aEEG grades predict transient neurological dysfunction. Quantitative electroencephalography can assist cardiac surgeons in assessing brain function and improving outcomes in patients with type A aortic dissection. URL: https://www.chictr.org.cn; Unique identifier: ChiCTR2200055980.

Neural Activity Differentiates Novel and Learned Event Boundaries.

People parse continuous experiences at natural breakpoints called event boundaries, which is important for understanding an environment's causal structure and for responding to uncertainty within it. However, it remains unclear how different forms of uncertainty affect the parsing of continuous experiences and how such uncertainty influences the brain's processing of ongoing events. We exposed human participants of both sexes (N = 34) to a continuous sequence of semantically meaningless images. We generated sequences from random walks through a graph that grouped images into temporal communities. After learning, we asked participants to segment another sequence at natural breakpoints (event boundaries). Participants segmented the sequence at learned transitions between communities, as well as at novel transitions, suggesting that people can segment temporally extended experiences into events based on learned structure as well as prediction error. Greater segmentation at novel boundaries was associated with enhanced parietal scalp electroencephalography (EEG) activity between 250 and 450 ms after the stimulus onset. Multivariate classification of EEG activity showed that novel and learned boundaries evoked distinct patterns of neural activity, particularly theta band power in posterior electrodes. Learning also led to distinct neural representations for stimuli within the temporal communities, while neural activity at learned boundary nodes showed predictive evidence for the adjacent community. The data show that people segment experiences at both learned and novel boundaries and suggest that learned event boundaries trigger retrieval of information about the upcoming community that could underlie anticipation of the next event in a sequence.

Machine learning based classification of presence utilizing psychophysiological signals in immersive virtual environments

In Virtual Reality (VR), a higher level of presence positively influences the experience and engagement of a user. There are several parameters that are responsible for generating different levels of presence in VR, including but not limited to, graphical fidelity, multi-sensory stimuli, and embodiment. However, standard methods of measuring presence, including self-reported questionnaires, are biased. This research focuses on developing a robust model, via machine learning, to detect different levels of presence in VR using multimodal neurological and physiological signals, including electroencephalography and electrodermal activity. An experiment has been undertaken whereby participants (N = 22) were each exposed to three different levels of presence (high, medium, and low) in a random order in VR. Four parameters within each level, including graphics fidelity, audio cues, latency, and embodiment with haptic feedback, were systematically manipulated to differentiate the levels. A number of multi-class classifiers were evaluated within a three-class classification problem, using a One-vs-Rest approach, including Support Vector Machine, k-Nearest Neighbour, Extra Gradient Boosting, Random Forest, Logistic Regression, and Multiple Layer Perceptron. Results demonstrated that the Multiple Layer Perceptron model obtained the highest macro average accuracy of 93±0.03%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$93\\pm 0.03\\%$$\\end{document}. Posthoc analysis revealed that relative band power, which is expressed as the ratio of power in a specific frequency band to the total baseline power, in both the frontal and parietal regions, including beta over theta and alpha ratio, and differential entropy were most significant in detecting different levels of presence.

Age-related breakdown in networks of inter-muscular coordination.

Assessing inter-muscular coordination in older adults is crucial, as it directly impacts an individual's ability for independent functioning, injury prevention, and active engagement in daily activities. However, the precise mechanisms by which distinct muscle fiber types synchronize their activity across muscles to generate coordinated movements in older adults remain unknown. Our objective is to investigate how distinct muscle groups dynamically synchronize with each other in young and older adults during exercise. Thirty-five young adults and nine older adults performed one bodyweight squat set until exhaustion. Simultaneous surface electromyography (sEMG) recordings were taken from the left and right vastus lateralis, and left and right erector spinae. To quantify inter-muscular coordination, we first obtained ten time series of sEMG band power for each muscle, representing the dynamics of different muscle fiber types. Next, we calculated the bivariate equal-time Pearson's cross-correlation for each pair of sEMG band power time series across all leg and back muscles. The main results show (i) an overall reduction in the degree of inter-muscular coordination, and (ii) increased stratification of the inter-muscular network in older adults compared to young adults. These findings suggest that as individuals age, the global inter-muscular network becomes less flexible and adaptable, hindering its ability to reorganize effectively in response to fatigue or other stimuli. This network approach opens new avenues for developing novel network-based markers to characterize multilevel inter-muscular interactions, which can help target functional deficits and potentially reduce the risk of falls and neuro-muscular injuries in older adults.

Investigation of Deficits in Auditory Emotional Content Recognition by Adult Cochlear Implant Users through the Study of Electroencephalographic Gamma and Alpha Asymmetry and Alexithymia Assessment.

Given the importance of emotion recognition for communication purposes, and the impairment for such skill in CI users despite impressive language performances, the aim of the present study was to investigate the neural correlates of emotion recognition skills, apart from language, in adult unilateral CI (UCI) users during a music in noise (happy/sad) recognition task. Furthermore, asymmetry was investigated through electroencephalographic (EEG) rhythm, given the traditional concept of hemispheric lateralization for emotional processing, and the intrinsic asymmetry due to the clinical UCI condition. Twenty adult UCI users and eight normal hearing (NH) controls were recruited. EEG gamma and alpha band power was assessed as there is evidence of a relationship between gamma and emotional response and between alpha asymmetry and tendency to approach or withdraw from stimuli. The TAS-20 questionnaire (alexithymia) was completed by the participants. The results showed no effect of background noise, while supporting that gamma activity related to emotion processing shows alterations in the UCI group compared to the NH group, and that these alterations are also modulated by the etiology of deafness. In particular, relative higher gamma activity in the CI side corresponds to positive processes, correlated with higher emotion recognition abilities, whereas gamma activity in the non-CI side may be related to positive processes inversely correlated with alexithymia and also inversely correlated with age; a correlation between TAS-20 scores and age was found only in the NH group. EEG gamma activity appears to be fundamental to the processing of the emotional aspect of music and also to the psychocognitive emotion-related component in adults with CI.

Enhanced network synchronization connectivity following transcranial direct current stimulation (tDCS) in bipolar depression: Effects on EEG oscillations and deep learning-based predictors of clinical remission

AimTo investigate oscillatory networks in bipolar depression, effects of a home-based tDCS treatment protocol, and potential predictors of clinical response. Methods20 participants (14 women) with bipolar disorder, mean age 50.75 ± 10.46 years, in a depressive episode of severe severity (mean Montgomery-Åsberg Rating Scale (MADRS) score 24.60 ± 2.87) received home-based transcranial direct current stimulation (tDCS) treatment for 6 weeks. Clinical remission defined as MADRS score < 10. Resting-state EEG data were acquired at baseline, prior to the start of treatment, and at the end of treatment, using a portable 4-channel EEG device (electrode positions: AF7, AF8, TP9, TP10). EEG band power was extracted for each electrode and phase locking value (PLV) was computed as a functional connectivity measure of phase synchronization. Deep learning was applied to pre-treatment PLV features to examine potential predictors of clinical remission. ResultsFollowing treatment, 11 participants (9 women) attained clinical remission. A significant positive correlation was observed with improvements in depressive symptoms and delta band PLV in frontal and temporoparietal regional channel pairs. An interaction effect in network synchronization was observed in beta band PLV in temporoparietal regions, in which participants who attained clinical remission showed increased synchronization following tDCS treatment, which was decreased in participants who did not achieve clinical remission. Main effects of clinical remission status were observed in several PLV bands: clinical remission following tDCS treatment was associated with increased PLV in frontal and temporal regions and in several frequency bands, including delta, theta, alpha and beta, as compared to participants who did not achieve clinical remission. The highest deep learning prediction accuracy 69.45 % (sensitivity 71.68 %, specificity 66.72 %) was obtained from PLV features combined from theta, beta, and gamma bands. ConclusionstDCS treatment enhances network synchronization, potentially increasing inhibitory control, which underscores improvement in depressive symptoms. Baseline EEG-based measures might aid predicting clinical response.

Preoperative biomarkers associated with delayed neurocognitive recovery.

To identify baseline biomarkers of delayed neurocognitive recovery (dNCR) using monitors commonly used in anesthesia. In this sub-study of observational prospective cohorts, we evaluated adult patients submitted to general anesthesia in a tertiary academic center in the United States. Electroencephalographic (EEG) features and cerebral oximetry were assessed in the perioperative period. The primary outcome was dNCR, defined as a decrease of 2 scores in the global Montreal Cognitive Assessment (MoCA) between the baseline and postoperative period. Forty-six adults (median [IQR] age, 65 [15]; 57% females; 65% American Society of Anesthesiologists (ASA) 3 were analyzed. Thirty-one patients developed dNCR (67%). Baseline higher EEG power in the lower alpha band (AUC = 0.73 (95% CI 0.48-0.93)) and lower alpha peak frequency (AUC = 0.83 (95% CI 0.48-1)), as well as lower cerebral oximetry (68 [5] vs 72 [3], p = 0.011) were associated with dNCR. Higher EEG power in the lower alpha band, lower alpha peak frequency, and lower cerebral oximetry values can be surrogates of baseline brain vulnerability.

Fronto-Central Changes in Multiple Frequency Bands in Active Tactile Width Discrimination Task.

The neural basis of tactile processing in humans has been extensively studied; however, the neurophysiological basis of human width discrimination remains relatively unexplored. In particular, the changes that occur in neural networks underlying active tactile width discrimination learning have yet to be described. Here, it is hypothesized that subjects learning to perform the active version of the width discrimination task would present changes in behavioral data and in the neurophysiological activity, specifically in networks of electrodes relevant for tactile and motor processing. The specific hypotheses tested here were that the performance and response latency of subjects would change between the first and the second blocks; the power of the different frequency bands would change between the first and the second blocks; electrode F4 would encode task performance and response latency through changes in the power of the delta, theta, alpha, beta, and low-gamma frequency bands; the relative power in the alpha and beta frequency bands in electrodes C3 and C4 (Interhemispheric Spectral Difference-ISD) would change because of learning between the first and the second blocks. To test this hypothesis, we recorded and analyzed electroencephalographic (EEG) activity while subjects performed a session where they were tested twice (i.e., two different blocks) in an active tactile width discrimination task using their right index finger. Subjects (n = 18) presented high performances (high discrimination accuracy) already in their first block, and therefore no significant improvements were found in the second block. Meanwhile, a reduction in response latency was observed between the two blocks. EEG recordings revealed an increase in power for the low-gamma frequency band (30-45 Hz) for electrodes F3 and C3 from the first to the second block. This change was correlated with neither performance nor latency. Analysis of the neural activity in electrode F4 revealed that the beta frequency band encoded the subjects' performance. Meanwhile, the delta frequency band in the same electrode revealed a complex pattern where blocks appeared clustered in two different patterns: an Upper Pattern (UP), where power and latency were highly correlated (Rho = 0.950), and a sparser and more uncorrelated Lower Pattern (LP). Blocks belonging to the UP or LP patterns did not differ in performance and were not specific to the first or the second block. However, blocks belonging to the LP presented an increase in response latency, increased variability in performance, and an increased ISD in alpha and beta frequency bands for the pair of electrodes C3-C4, suggesting that the LP may reflect a state related to increased cognitive load or task difficulty. These results suggest that changes in performance and latency in an active tactile width discrimination task are encoded in the delta, alpha, beta, and low-gamma frequency bands in a fronto-central network. The main contribution of this study is therefore related to the description of neural dynamics in frontal and central networks involved in the learning process of active tactile width discrimination.

Electroencephalography-based endogenous phenotype of diagnostic transition from major depressive disorder to bipolar disorder

The neuropathology of mood disorders, including the diagnostic transition from major depressive disorder (MDD) to bipolar disorder (BD), is poorly understood. This study investigated resting-state electroencephalography (EEG) activity in patients with MDD and those whose diagnosis changed from MDD to BD. Among sixty-eight enrolled patients with MDD, the diagnosis of 17 patients converted to BD during the study period. We applied machine learning techniques to differentiate the two groups using sensor- and source-level EEG features. At the sensor level, patients with BD showed higher theta band power at the AF3 channel and low-alpha band power at the FC5 channel compared to patients with MDD. At the source level, patients with BD showed higher theta band activity in the right anterior cingulate and low-alpha band activity in the left parahippocampal gyrus. These four EEG features were selected for discriminating between BD and MDD with the best classification performance showing an accuracy of 80.88%, a sensitivity of 76.47%, and a specificity of 82.35%. Our findings revealed distinct theta and low-alpha band activities in patients with BD and MDD. These differences could potentially serve as candidate neuromarkers for the diagnosis and diagnostic transition between the two distinct mood disorders.

Specific EEG resting state biomarkers in FXS and ASD

BackgroundFragile X syndrome (FXS) and autism spectrum disorder (ASD) are neurodevelopmental conditions that often have a substantial impact on daily functioning and quality of life. FXS is the most common cause of inherited intellectual disability (ID) and the most common monogenetic cause of ASD. Previous literature has shown that electrophysiological activity measured by electroencephalogram (EEG) during resting state is perturbated in FXS and ASD. However, whether electrophysiological profiles of participants with FXS and ASD are similar remains unclear. The aim of this study was to compare EEG alterations found in these two clinical populations presenting varying degrees of cognitive and behavioral impairments.MethodsResting state EEG signal complexity, alpha peak frequency (APF) and power spectral density (PSD) were compared between 47 participants with FXS (aged between 5–20), 49 participants with ASD (aged between 6–17), and 52 neurotypical (NT) controls with a similar age distribution using MANCOVAs with age as covariate when appropriate. MANCOVAs controlling for age, when appropriate, and nonverbal intelligence quotient (NVIQ) score were subsequently performed to determine the impact of cognitive functioning on EEG alterations.ResultsOur results showed that FXS participants manifested decreased signal complexity and APF compared to ASD participants and NT controls, as well as altered power in the theta, alpha and low gamma frequency bands. ASD participants showed exaggerated beta power compared to FXS participants and NT controls, as well as enhanced low and high gamma power compared to NT controls. However, ASD participants did not manifest altered signal complexity or APF. Furthermore, when controlling for NVIQ, results of decreased complexity in higher scales and lower APF in FXS participants compared to NT controls and ASD participants were not replicated.ConclusionsThese findings suggest that signal complexity and APF might reflect cognitive functioning, while altered power in the low gamma frequency band might be associated with neurodevelopmental conditions, particularly FXS and ASD.

The strength of anticipated distractors shapes EEG alpha and theta oscillations in a Working Memory task

Working Memory (WM) requires maintenance of task-relevant information and suppression of task-irrelevant/distracting information. Alpha and theta oscillations have been extensively investigated in relation to WM. However, studies that examine both theta and alpha bands in relation to distractors, encompassing not only power modulation but also connectivity modulation, remain scarce. Here, we depicted, at the EEG-source level, the increase in power and connectivity in theta and alpha bands induced by strong relative to weak distractors during a visual Sternberg-like WM task involving the encoding of verbal items. During retention, a strong or weak distractor was presented, predictable in time and nature. Analysis focused on the encoding and retention phases before distractor presentation. Theta and alpha power were computed in cortical regions of interest, and connectivity networks estimated via spectral Granger causality and synthetized using in/out degree indices. The following modulations were observed for strong vs. weak distractors. In theta band during encoding, the power in frontal regions increased, together with frontal-to-frontal and bottom-up occipital-to-temporal-to-frontal connectivity; even during retention, bottom-up theta connectivity increased. In alpha band during retention, but not during encoding, the power in temporal-occipital regions increased, together with top-down frontal-to-occipital and temporal-to-occipital connectivity. From our results, we postulate a proactive cooperation between theta and alpha mechanisms: the first would mediate enhancement of target representation both during encoding and retention, and the second would mediate increased inhibition of sensory areas during retention only, to suppress the processing of imminent distractor without interfering with the processing of ongoing target stimulus during encoding.