Resting-state Electroencephalography Research Articles

Preoperative resting-state electrophysiological signals predict acute but not chronic postoperative pain.

The prevalence of postoperative pain is notably high among the elderly population, which poses significant challenges for their postoperative recovery. In this study, we aimed to identify preoperative predictors for acute and chronic postoperative pain in patients undergoing lumbar spinal surgery through a longitudinal investigation. We recruited 75 patients (mean age 68.29 ± 5.60 years) and collected their resting-state electroencephalography (EEG) data two hours before the surgery. The aperiodic and periodic signal components were extracted from the resting-state EEG using the Fitting Oscillations and One-Over-F algorithm. We also collected the preoperative pain ratings, demographic information and the Hospital Anxiety and Depression Scale from all patients. The postoperative pain ratings were collected ten times from Day 1 to Week 12 after surgery. We observed a high incidence of postoperative acute and chronic pain among older patients. Preoperative pain and peak alpha frequency in resting-state EEG were the primary predictors of acute postoperative pain. Although age is a significant predictor of chronic postoperative pain, its predictive performance is poor. Overall, our study provides valuable insights into the complex pattern of preoperative EEG features, preoperative pain and age in predicting postoperative pain at different stages. Our findings highlight the significance of exploring preoperative features to identify patients who are at a higher risk of developing severe postoperative pain, which can aid in the development of more personalized and effective pain management strategies. The heightened occurrence of postoperative pain among the elderly presents formidable obstacles to their recuperation. This study delves into identifying preoperative factors influencing acute and chronic postoperative pain. Our findings indicate that preoperative pain and peak alpha frequency are crucial predictors of acute postoperative pain. However, the predictive performance for chronic postoperative pain is limited, although age was a significant predictor of chronic postoperative pain. These insights contribute to the identification of patients at elevated risk for severe acute and chronic postoperative pain, offering valuable guidance for pre-surgical risk assessment.

Modulation of neural networks and symptom correlated in fibromyalgia: A randomized double-blind multi-group explanatory clinical trial of home-based transcranial direct current stimulation.

Transcranial direct current stimulation (tDCS) might modulate neural activity and promote neural plasticity in patients with fibromyalgia (FM). This multi-group randomized clinical trial compared home-based active tDCS (HB-a-tDCS) on the left dorsolateral prefrontal cortex (l-DLPFC) or home-based sham tDCS (HB-s-tDCS), and HB-a-tDCS or HB-s-tDCS on the primary motor cortex (M1) in the connectivity analyses in eight regions of interest (ROIs) across eight resting-state electroencephalography (EEG) frequencies. We included 48 women with FM, aged 30 to 65, randomly assigned to 2:1:2:1 to receive 20 sessions during 20 minutes of HB-a-tDCS 2mA or HB-s-tDCS, over l-DLPFC or M1, respectively. EEG recordings were obtained before and after treatment with eyes open (EO) and eyes closed (EC). In the EC condition, comparing pre to post-treatment, the HB-a-tDCS on l-DLPFC decreased the lagged coherence connectivity in the delta frequency band between the right insula and left anterior cingulate cortex (ACC) (t = -3.542, p = .048). The l-DLPFC HB-a-tDCS compared to HB-s-tDCS decreased the lagged coherence connectivity in the delta frequency band between the right insula and left ACC (t = -4.000, p = .017). In the EO condition, the l-DLPFC HB-a-tDCS compared to M1 HB-s-tDCS increased the lagged coherence connectivity between the l-DLPFC and left ACC in the theta band (t = -4.059, p = .048). Regression analysis demonstrated that the HB-a-tDCS effect on the l-DLPFC was positively correlated with sleep quality. On the other hand, the HB-a-tDCS on l-DLPFC and HB-s-tDCS on M1 were positively correlated with pain catastrophizing. These results show that HB-a-tDCS affects the neural connectivity between parts of the brain that control pain's emotional and attentional aspects, which are most noticeable at lower EEG frequencies in a rest state. This effect on neural oscillations could serve as a neural marker associated with its efficacy in alleviating fibromyalgia symptoms. identifier [NCT03843203].

Modifiable dementia risk associated with smaller white matter volume and altered 1/f aperiodic brain activity: cross-sectional insights from the LEISURE study.

The rising prevalence of dementia necessitates identifying early neurobiological markers of dementia risk. Reduced cerebral white matter volume and flattening of the slope of the electrophysiological 1/f spectral power distribution provide neurobiological markers of brain ageing alongside cognitive decline. However, their association with modifiable dementia risk remains to be understood. A cross-sectional sample of 98 healthy older adults (79 females, mean age = 65.44) underwent structural magnetic resonance imaging (sMRI), resting-state electroencephalography (EEG), cognitive assessments and dementia risk scoring using the CogDrisk framework. Univariate and multivariate linear regression models were conducted to investigate the relationships between modifiable dementia risk and sMRI brain volumes, the exponent of EEG 1/f spectral power, and cognition, whilst controlling for non-modifiable factors. Smaller global white matter volume (F(1,87) = 6.884, R2 = 0.073, P = .010), and not grey (F(1,87) = 0.540, R2 = 0.006, P = .468) or ventricle volume (F(1,87) = 0.087, R2 = 0.001, P = .769), was associated with higher modifiable dementia risk. A lower exponent, reflecting a flatter 1/f spectral power distribution, was associated with higher dementia risk at frontal (F(1,92) = 4.096, R2 = 0.043, P = .046) but not temporal regions. No significant associations were found between cognitive performance and dementia risk. In multivariate analyses, both white matter volume and the exponent of the 1/f spectral power distribution independently associated with dementia risk. Structural and functional neurobiological markers of early brain ageing, but not cognitive function, are independently associated with modifiable dementia risk in healthy older adults.

Transient Increases in Alpha Power Relative to Healthy Reference Ranges in Awake Piglets After Repeated Rapid Head Rotations.

Background/Objectives: Sports-related concussions are a main cause of cognitive dysfunction and somatic complaints, particularly in youth. While the majority of concussion symptoms resolve within one week, cognitive effects may persist. In this study, we sought to study changes to cognition within this acute time frame. Methods: In this current study, we use an established swine model of traumatic brain injury (TBI) to study the effects of single and repeated head rotations on resting-state electroencephalography (rs-EEG) in awake piglets in the acute (within 7 days) time period after injury. We studied both healthy and experimental groups to (1) establish healthy reference ranges (RRs; N = 23) for one-minute rs-EEG in awake piglets, (2) compare the effects of single (N = 12) and repeated head rotations (N = 13) on rs-EEG, and (3) examine the acute time course (pre-injury and days 1, 4, and 7 post-injury) in animals administered single and repeated head rotations. EEG data were Fourier transformed, and total (1-30 Hz) and relative power in the alpha (8-12 Hz), beta (16.5-25 Hz), delta (1-4 Hz), and theta (4-7.5 Hz) bands were analyzed. Results: Total power and relative alpha, beta, delta, and theta power were consistent measures across days in healthy animals. We found a significant and transient increase in relative alpha power after repeated injury on day 1 in all regions and a rise above the healthy RR in the frontal and left temporal regions. Conclusions: Future studies will expand the study duration to investigate and inform clinical prognoses from acute measurements of rs-EEG.

EEG Oscillatory Activity and Resting-State Networks Associated with Neurocognitive Function in Mild Traumatic Brain Injury.

This study aimed to investigate the characteristics of resting-state electroencephalography (EEG) activity and brain networks in patients with mild traumatic brain injury (mTBI) and their association with neurocognitive function (NCF). We analyzed 26 patients with subacute mTBI and 21 healthy controls. The subacute mTBI group (9 females, 17 males) had a mean age of 29.9 ± 9.9 years, and the healthy controls (11 females, 10 males) had a mean age of 29.7 ± 11.5 years. Current source density, lagged phase synchronization, and resting-state network activity were analyzed using exact low-resolution electromagnetic tomography (eLORETA) with 60 s resting-state EEG data. In addition, a correlation analysis was performed between these EEG parameters and NCF in patients with mTBI. We used the statistical nonparametric mapping method in eLORETA to correct for multiple comparisons. There were no significant differences in EEG parameters between the patients with mTBI and healthy controls. However, in patients with mTBI, correlation analysis revealed negative correlations between theta activity in the anterior cingulate cortex and verbal short-term memory and between activity in the memory perception network and verbal memory. Our findings suggest that resting-state EEG may be clinically useful in investigating the mechanism of NCF decline in patients with mTBI.

Theta-gamma-coupling as predictor of working memory performance in young and elderly healthy people

The relationship between working memory (WM) and neuronal oscillations can be studied in detail using brain stimulation techniques, which provide a method for modulating these oscillations and thus influencing WM. The endogenous coupling between the amplitude of gamma oscillations and the phase of theta oscillations is crucial for cognitive control. Theta/gamma peak-coupled transcranial alternating current stimulation (TGCp-tACS) can modulate this coupling and thus influence WM performance. This study investigated the effects of TGCp-tACS on WM in older adults and compared their responses with those of younger participants from our previous work who underwent the same experimental design. Twenty-eight older subjects underwent both TGCp-tACS and sham stimulation sessions at least 72 h apart. Resting-state electroencephalography (EEG) was recorded before and after the interventions, and a WM task battery with five different WM tasks was performed during the interventions to assess various WM components. Outcomes measured included WM task performance (e.g., accuracy, reaction time (RT)) and changes in power spectral density (PSD) in different frequency bands. TGCp-tACS significantly decreased accuracy and RT on the 10- and 14-point Sternberg tasks and increased RT on the Digit Symbol Substitution Test in older adults. In contrast, younger participants showed a significant increase in accuracy only on the 14-item Sternberg task. Electrophysiological analysis revealed a decrease in delta and theta PSD and an increase in high gamma PSD in both younger and older participants after verum stimulation. In conclusion, theta-gamma coupling is essential for WM and modulation of this coupling affects WM performance. The effects of TGCp-tACS on WM vary with age due to natural brain changes. To better support older adults, the study suggests several strategies to improve cognitive function, including: Adjusting stimulation parameters, applying stimulation to two sites, conducting multiple sessions, and using brain imaging techniques for precise targeting.

Modulation of neural oscillations in escitalopram treatment: a Canadian biomarker integration network in depression study

Current pharmacological agents for depression have limited efficacy in achieving remission. Developing and validating new medications is challenging due to limited biological targets. This study aimed to link electrophysiological data and symptom improvement to better understand mechanisms underlying treatment response. Longitudinal changes in neural oscillations were assessed using resting-state electroencephalography (EEG) data from two Canadian Biomarker Integration Network in Depression studies, involving pharmacological and cognitive behavioral therapy (CBT) trials. Patients in the pharmacological trial received eight weeks of escitalopram, with treatment response defined as ≥ 50% decrease in Montgomery–Åsberg Depression Rating Scale (MADRS). Early (baseline to week 2) and late (baseline to week 8) changes in neural oscillation were investigated using relative power spectral measures. An association was found between an initial increase in theta and symptom improvement after 2 weeks. Additionally, late increases in delta and theta, along with a decrease in alpha, were linked to a reduction in MADRS after 8 weeks. These late changes were specifically observed in responders. To assess specificity, we extended our analysis to the independent CBT cohort. Responders exhibited an increase in delta and a decrease in alpha after 2 weeks. Furthermore, a late (baseline to week 16) decrease in alpha was associated with symptom improvement following CBT. Results suggest a common late decrease in alpha across both treatments, while modulatory effects in theta may be specific to escitalopram treatment. This study offers insights into electrophysiological markers indicating a favorable response to antidepressants, enhancing our comprehension of treatment response mechanisms in depression.

Correlation between physical exercise levels, depressive symptoms, and sleep quality in college students: Evidence from electroencephalography

BackgroundIn recent years, depression has increasingly become a major global public health issue. Among its common physical symptoms, sleep disturbances are prevalent in individuals with depression and are considered a risk factor for the progression of the disorder. Poor sleep quality may be a significant contributor to depression among college students. However, the EEG indicators that are commonly associated with depressive symptoms and sleep quality, as well as the effects of physical exercise on these EEG indicators, remain unclear. PurposeThis study aims to explore the relationship between physical activity levels, depressive symptoms, and sleep quality among college students, based on resting-state electroencephalography (EEG) data in a cross-sectional study. MethodsA total of 342 college students were recruited to assess physical activity levels, depressive symptoms, sleep quality, and EEG data. Pearson correlation coefficients were used to evaluate the relationships among these variables, and the PROCESS macro (Model 4) for SPSS was applied to examine the mediating role of sleep quality in the relationship between physical activity levels and depressive symptoms, with mediation effects tested using the bootstrap method. ResultsA significant difference was observed in the Pittsburgh Sleep Quality Index (PSQI) total scores between students with and without depressive symptoms (T = 9.746, P < 0.001). Students with depressive symptoms showed poorer sleep quality across various dimensions, including sleep duration, sleep onset latency, sleep disturbances, and daytime dysfunction. Depression symptom were positively correlated with Sleep Quality (r = 0.547, P < 0.001), indicating that higher depression symptoms are associated with poorer sleep quality. Correlations between depressive symptoms and EEG power values revealed significant associations with theta and beta2 frequencies in multiple brain regions (P < 0.05). Physical exercise showed a significant negative correlation with Sleep Quality scores (r = −0.158, P = 0.004), and with sleep duration (r = −0.141, P = 0.011) and daytime dysfunction (r = −0.142, P = 0.010). Additionally, physical exercise was negatively correlated with theta band power at F8 (P < 0.05). Mediation analysis demonstrated that physical exercise has a direct effect on depressive symptoms (β = −0.123, 95 % CI = −0.287 to −0.069) and an indirect effect through improved sleep quality (β = −0.074, 95 % CI = −0.089 to −0.016), accounting for 60.16 % and 40.65 % of the total effect, respectively. ConclusionThese findings provide a theoretical basis for developing precise exercise intervention programs to improve depressive symptoms and sleep quality among college students. The results indicate that moderate physical activity can help alleviate depressive symptoms and improve sleep quality, thereby enhancing the overall health of college students.

Altered cortical functional networks in Wilson's Disease: A resting-state electroencephalogram study

The neuropsychiatric symptoms are common in Wilson's disease (WD) patients. However, it remains unclear about the associated functional brain networks. In this study, source localization-based functional connectivity analysis of close-eye resting-state electroencephalography (EEG) were implemented to assess the characteristics of functional networks in 17 WD patients with neurological involvements and 17 healthy controls (HCs). The weighted phase-lag index (wPLI) was subsequently calculated in source space across five different frequency bands and the resulting connectivity matrix was transformed into a weighted graph whose structure was measured by five graphical analysis indicators, which were finally correlated with clinical scores. Compared to HCs, WD patients revealed disconnected sub-networks in delta, theta and alpha bands. Moreover, WD patients exhibited significantly reduced global clustering coefficients and small-worldness in all five frequency bands. In WD group, the severity of neurological symptoms and structural brain abnormalities were significantly correlated with disrupted functional networks. In conclusion, our study demonstrated that functional network deficits in WD can reflect the severity of their neurological symptoms and structural brain abnormalities. Resting-state EEG may be used as a marker of brain injury in WD.

Switching to Low Neurotoxic Antiretrovirals to Improve Neurocognition Among People Living With HIV-1-Associated Neurocognitive Disorder: The MARAND-X Randomized Clinical Trial

Background: The pathogenesis of HIV-associated neurocognitive (NC) impairment is multifactorial, and antiretroviral (ARV) neurotoxicity may contribute. However, interventional pharmacological studies are limited. Methods: Single-blind, randomized (1:1), controlled trial to assess the change of NC performance (Global Deficit Score, GDS, and domain scores) in PLWH with NC impairment randomized to continue their standard of care treatment or to switch to a less neurotoxic ARV regimen: darunavir/cobicistat, maraviroc, emtricitabine (MARAND-X). Participants had plasma and cerebrospinal fluid HIV RNA&lt; 50 copies/mL, R5-tropic HIV, and were on ARV regimens that did not include efavirenz and darunavir. The change of resting-state electroencephalography was also evaluated. The outcomes were assessed at week 24 of the intervention through tests for longitudinal paired data and mixed-effect models. Results: Thirty-eight participants were enrolled and 28 completed the follow-up. Global Deficit Score improved over time but with no difference between arms in longitudinal adjusted models. Perceptual functions improved in the MARAND-X, while long-term memory improved only in participants within the MARAND-X for whom the central nervous system penetration-effectiveness (CNS penetration effectiveness) score increased by ≥3. No significant changes in resting-state electroencephalography were observed. Conclusions: In this small but well-controlled study, the use of less neurotoxic ARV showed no major beneficial effect over an unchanged regimen. The beneficial effects on the memory domain of increasing CNS penetration effectiveness score suggest that ARV neuropenetration may have a role in cognitive function.

Support vector machine classification of patients with depression based on resting-state electroencephalography.

Depression is one of the most common mental disorders. Although depression is typically diagnosed by identifying specific symptoms and through history, no recognized standard for depression diagnosis exists. This assures the development of objective diagnostic tools for depression. We investigated the differences in the resting-state electroencephalograms (EEGs) of patients with depression and healthy controls (HCs) to distinguish patients from HCs by using a support vector machine (SVM) classifier with the following two feature selection approaches: t test and receiver operating characteristic analysis. We used the EEG data from the Patient Repository of EEG Data + Computational Tools; this study included 21 patients with depressive disorder (MDD) and 21 HCs. The relative frequency power, alpha interhemispheric asymmetry, left-right coherence, strength, clustering coefficient (CC), shortest path length, sample entropy (SampEn), multiscale entropy (MSE), and detrended fluctuation analysis (DFA) data were extracted to determine candidate EEG features associated with depression. With the t-test selection, the SVM classifier demonstrated the highest performance with the accuracy, sensitivity, and specificity of 96.66%, 95.93%, and 97.550% for the eye-open condition and 91.33%, 90.59%, and 91.81% for the eye-closed condition, respectively. For comparisons of features in the 2 selection approaches, the most influential features were relative frequency power and left-right coherence. Using this information to distinguish patients with MDD from HC subjects with the SVM classifier resulted in a mean accuracy over 90%. Although this result may not be robust enough for clinical applications, further exploration is necessary given the simplicity, objectivity, and efficiency of the classifier.

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.

Stable construction and analysis of MDD modular networks based on multi-center EEG data

BackgroundThe modular structure can reflect the activity pattern of the brain, and exploring it may help us understand the pathogenesis of major depressive disorder (MDD). However, little is known about how to build a stable modular structure in MDD patients and how modules are separated and integrated. MethodWe used four independent resting state Electroencephalography (EEG) datasets. Different coupling methods, window lengths, and optimized community detection algorithms were used to find a reliable and robust modular structure, and the module differences of MDD were analyzed from the perspectives of global module attributes and local topology in multiple frequency bands. ResultsThe combination of the Phase Lag Index (PLI) and the Louvain algorithm can achieve better results and can achieve stability at smaller window lengths. Compared with Healthy Controls (HC), MDD had higher Modularity (Q) values and the number of modules in low-frequency bands. In addition, MDD showed significant structural changes in the frontal and parietal-occipital lobes, which were confirmed by further correlation analysis. ConclusionOur results provided a reliable validation of the modular structure construction method in MDD patients and contributed strong evidence for the changes in emotional cognition and visual system function in MDD patients from a new perspective. These results would afford valuable insights for further exploration of the pathogenesis of MDD.

Classical, spaced, or accelerated transcranial magnetic stimulation of motor cortex for treating neuropathic pain: A 3-arm parallel non-inferiority study

BackgroundRepetitive transcranial magnetic stimulation (rTMS) of the primary motor cortex (M1) at high frequency (HF) is an effective treatment of neuropathic pain. The classical HF-rTMS protocol (CHF-rTMS) includes a daily session for one week as an induction phase of treatment followed by more spaced sessions. Another type of protocol without an induction phase and based solely on spaced sessions of HF-rTMS (SHF-rTMS) has also been shown to produce neuropathic pain relief. However, CHF-rTMS and SHF-rTMS of M1 have never been compared regarding their analgesic potential. Another type of rTMS paradigm, called accelerated intermittent theta burst stimulation (ACC-iTBS), has recently been proposed for the treatment of depression, the other clinical condition for which HF-rTMS is proposed as an effective therapeutic strategy. ACC-iTBS combines a high number of pulses delivered in short sessions grouped into a few days of stimulation. This type of protocol has never been applied to M1 for the treatment of pain. Methods/DesignThe objective of this single-centre randomized study is to compare the efficacy of three different rTMS protocols for the treatment of chronic neuropathic pain: CHF-rTMS, SHF-rTMS, and ACC-iTBS. The CHF-rTMS will consists of 10 stimulation sessions, including 5 daily sessions of 10Hz-rTMS (3,000 pulses per session) over one week, then one session per week for 5 weeks, for a total of 30,000 pulses delivered in 10 stimulation days. The SHF-rTMS protocol will only include 4 sessions of 20Hz-rTMS (1,600 pulses per session), one every 15 days, for a total of 6,400 pulses delivered in 4 stimulation days. The ACC-iTBS protocol will comprise 5 sessions of iTBS (600 pulses per session) completed in half a day for 2 consecutive days, repeated 5 weeks later, for a total of 30,000 pulses delivered in 4 stimulation days. Thus, CHF-rTMS and ACC-iTBS protocols will share a higher total number of TMS pulses (30,000 pulses) compared to SHF-rTMS protocol (6,400 pulses), while CHF-rTMS protocol will include a higher number of stimulation days (10 days) compared to ACC-iTBS and SHF-rTMS protocols (4 days). In all protocols, the M1 target will be defined in the same way and stimulated at the same intensity using a navigated rTMS (nTMS) procedure. The evaluation will be based on clinical outcomes with various scales and questionnaires assessed every week, from two weeks before the 7-week period of therapeutic stimulation until 4 weeks after. Additionally, three sets of neurophysiological outcomes (resting-state electroencephalography (EEG), nTMS-EEG recordings, and short intracortical inhibition measurement with threshold tracking method) will be assessed the week before and after the 7-week period of therapeutic stimulation. DiscussionThis study will make it possible to compare the analgesic efficacy of the CHF-rTMS and SHF-rTMS protocols and to appraise that of the ACC-iTBS protocol for the first time. This study will also make it possible to determine the respective influence of the total number of pulses and days of stimulation delivered to M1 on the extent of pain relief. Thus, if their analgesic efficacy is not inferior to that of CHF-rTMS, SHF-rTMS and especially the new ACC-iTBS protocol could be an optimal compromise of a more easy-to-perform rTMS protocol for the treatment of patients with chronic neuropathic pain.

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.

Mindfulness Training in High-Demand Cohorts Alters Resting-State Electroencephalography: An Exploratory Investigation of Individual Alpha Frequency, Aperiodic 1/f Activity, and Microstates

BackgroundMindfulness training (MT) programs have demonstrated utility as cognitive training tools, but there is little consensus on the neurophysiological processes that may underlie its benefits. It has been posited that intrinsic brain activity recorded at rest reflects the functional connectivity of large-scale brain networks and may provide insight into neuroplastic changes that support MT. In the current study, we indexed changes in several resting-state electroencephalography (EEG) parameters to investigate the neurophysiological underpinnings of MT. MethodsResting-state EEG data were collected from active-duty U.S. military personnel (N = 80) at 2 testing sessions: before (time [T] 1) and after (T2) engaging in an 8-week MT or active comparison intervention (positivity training). We examined longitudinal and/or groupwise differences in several EEG parameters through parameterization of power spectra (individual alpha frequency and 1/f activity) and microstate analysis. ResultsWhile no significant group × time differences were observed in individual alpha frequency, significant group × time effects were observed in several EEG parameters from T1 to T2. Compared with MT, positivity training was associated with a steepening of the 1/f slope and higher 1/f intercepts together with decreased duration and increased global field power of microstates. ConclusionsTaken together, these results suggest that the effects of interventions may be differentiated in resting-state brain activity in a sample of military personnel. Such findings provide insight into the neural underpinnings of MT-related brain changes, but more research is required to elucidate how these may relate to task-related neural and performance changes with MT and whether results generalize to other mindfulness interventions in alternative cohorts and contexts.

ComBat models for harmonization of resting-state EEG features in multisite studies

ObjectivePooling multisite resting-state electroencephalography (rsEEG) datasets may introduce bias due to batch effects (i.e., cross-site differences in the rsEEG related to scanner/sample characteristics). The Combining Batches (ComBat) models, introduced for microarray expression and adapted for neuroimaging, can control for batch effects while preserving the variability of biological covariates. We aim to evaluate four ComBat harmonization methods in a pooled sample from five independent rsEEG datasets of young and old adults. MethodsRsEEG signals (n = 374) were automatically preprocessed. Oscillatory and aperiodic rsEEG features were extracted in sensor space. Features were harmonized using neuroCombat (standard ComBat used in neuroimaging), neuroHarmonize (variant with nonlinear adjustment of covariates), OPNested-GMM (variant based on Gaussian Mixture Models to fit bimodal feature distributions), and HarmonizR (variant based on resampling to handle missing feature values). Relationships between rsEEG features and age were explored before and after harmonizing batch effects. ResultsBatch effects were identified in rsEEG features. All ComBat methods reduced batch effects and features’ dispersion; HarmonizR and OPNested-GMM ComBat achieved the greatest performance. Harmonized Beta power, individual Alpha peak frequency, Aperiodic exponent, and offset in posterior electrodes showed significant relations with age. All ComBat models maintained the direction of observed relationships while increasing the effect size. ConclusionsComBat models, particularly HarmonizeR and OPNested-GMM ComBat, effectively control for batch effects in rsEEG spectral features. SignificanceThis workflow can be used in multisite studies to harmonize batch effects in sensor-space rsEEG spectral features while preserving biological associations.

Differences and potential mechanisms of theta oscillation and temporoparietal and temporal-central networks in temporal lobe epilepsy patients with unilateral hippocampal sclerosis

BackgroundThere is a lack of further exploration of the epileptogenic network of specific types of epilepsy, such as unilateral hippocampal sclerosis (HS), and there is an urgent need to find exact evidence to confirm the consistency of its brain network changes.MethodsWe enrolled 22 mesial temporal lobe epilepsy with hippocampal sclerosis (mTLE-HS) patients to compare the differences in brain activity between 22 healthy controls (HCs) and them. Resting-state electroencephalography (EEG) was also measured. Then, we calculated the power spectral density and phase locking values in and between these electrodes.ResultsThe results showed the increased theta power was related to the high severity of epilepsy in the temporal, parietal, and central regions in mTLE-HS patients, and there were positive correlations between theta power in the contralateral temporal region and seizure frequency. Theta power in the ipsilateral parietal lobe is positively correlated with the number of anti-seizure medications (ASMs), but not with the usage of third-generation ASMs. Meanwhile, the temporal lobe of mTLE-HS patients had more connectivity with parietal lobe and central region.ConclusionsTheta power is an important EEG indicator of mTLE-HS, positively correlates with epilepsy severity and seizure frequency, and has network properties that can be observed outside the lesion. Moreover, the usage of third-generation ASMs did not affect the risk of increased theta power. Lastly, the temporoparietal and temporal-central networks are likely to be causative pathways in epilepsy patients with cognitive impairment. This study provides a potential guideline for the treatment of mTLE-HS in clinical practice.

Fractal Dimension Distributions of Resting-State Electroencephalography (EEG) Improve Detection of Dementia and Alzheimer’s Disease Compared to Traditional Fractal Analysis

Fractal Dimension Distributions of Resting-State Electroencephalography (EEG) Improve Detection of Dementia and Alzheimer’s Disease Compared to Traditional Fractal Analysis

Resting-state EEG features modulated by depressive state in healthy individuals: insights from theta PSD, theta-beta ratio, frontal-parietal PLV, and sLORETA.

Depressive states in both healthy individuals and those with major depressive disorder exhibit differences primarily in symptom severity rather than symptom type, suggesting that there is a spectrum of depressive symptoms. The increasing prevalence of mild depression carries lifelong implications, emphasizing its clinical and social significance, which parallels that of moderate depression. Early intervention and psychotherapy have shown effective outcomes in subthreshold depression. Electroencephalography serves as a non-invasive, powerful tool in depression research, with many studies employing it to discover biomarkers and explore underlying mechanisms for the identification and diagnosis of depression. However, the efficacy of these biomarkers in distinguishing various depressive states in healthy individuals and in understanding the associated mechanisms remains uncertain. In our study, we examined the power spectrum density and the region-based phase-locking value in healthy individuals with various depressive states during their resting state. We found significant differences in neural activity, even among healthy individuals. Participants were categorized into high, middle, and low depressive state groups based on their response to a questionnaire, and eyes-open resting-state electroencephalography was conducted. We observed significant differences among the different depressive state groups in theta- and beta-band power, as well as correlations in the theta-beta ratio in the frontal lobe and phase-locking connections in the frontal, parietal, and temporal lobes. Standardized low-resolution electromagnetic tomography analysis for source localization comparing the differences in resting-state networks among the three depressive state groups showed significant differences in the frontal and temporal lobes. We anticipate that our study will contribute to the development of effective biomarkers for the early detection and prevention of depression.