Unlocking interbrain neural signatures differences during triadic cooperation and competition: Evidence from EEG hyperscanning.

We aimed to compare network properties between focal-onset nonconvulsive status epilepticus (NCSE) and toxic/metabolic encephalopathy (TME) during periods of periodic discharge using graph theoretical analysis, and to evaluate the applicability of graph measures as markers for the differential diagnosis between focal-onset NCSE and TME, using machine learning algorithms. Electroencephalography (EEG) data from 50 focal-onset NCSE and 44 TMEs were analyzed. Epochs with nonictal periodic discharges were selected, and the coherence in each frequency band was analyzed. Graph theoretical analysis was performed to compare brain network properties between the groups. Eight different traditional machine learning methods were implemented to evaluate the utility of graph theoretical measures as input features to discriminate between the two conditions. The average degree (in delta, alpha, beta, and gamma bands), strength (in delta band), global efficiency (in delta and alpha bands), local efficiency (in delta band), clustering coefficient (in delta band), and transitivity (in delta band) were higher in TME than in NCSE. TME showed lower modularity (in delta band) and assortativity (in alpha, beta, and gamma bands) than NCSE. Machine learning algorithms based on EEG global graph measures classified NCSE and TME with high accuracy, and gradient boosting was the most accurate classification model with an area under the receiver operating characteristics curve of 0.904. Our findings on differences in network properties may provide novel insights that graph measures reflecting the network properties could be quantitative markers for the differential diagnosis between focal-onset NCSE and TME.

Human listeners can focus on one speech stream out of several concurrent ones. The present study aimed to assess the whole-brain functional networks underlying a) the process of focusing attention on a single speech stream vs. dividing attention between two streams and 2) speech processing on different time-scales and depth. Two spoken narratives were presented simultaneously while listeners were instructed to a) track and memorize the contents of a speech stream and b) detect the presence of numerals or syntactic violations in the same (“focused attended condition”) or in the parallel stream (“divided attended condition”). Speech content tracking was found to be associated with stronger connectivity in lower frequency bands (delta band- 0,5–4 Hz), whereas the detection tasks were linked with networks operating in the faster alpha (8–10 Hz) and beta (13–30 Hz) bands. These results suggest that the oscillation frequencies of the dominant brain networks during speech processing may be related to the duration of the time window within which information is integrated. We also found that focusing attention on a single speaker compared to dividing attention between two concurrent speakers was predominantly associated with connections involving the frontal cortices in the delta (0.5–4 Hz), alpha (8–10 Hz), and beta bands (13–30 Hz), whereas dividing attention between two parallel speech streams was linked with stronger connectivity involving the parietal cortices in the delta and beta frequency bands. Overall, connections strengthened by focused attention may reflect control over information selection, whereas connections strengthened by divided attention may reflect the need for maintaining two streams in parallel and the related control processes necessary for performing the tasks.

Neuromodulation of resting state brain network topography by heterolateral prefrontal transcranial photobiomodulation.

The early diagnosis of Parkinson’s disease (PD) has always been a difficult problem to be solved clinically. At present, there is no clinical auxiliary diagnostic index for reference. We attempted to extract potential biomarkers for early PD from the currently used scalp EEG detection methods in clinical practice. We calculated the phase synchronization index to quantify the synchrony of EEG channels in various frequency bands (delta, theta, alpha and beta bands) of early PD. The results showed that the synchronization of early PD in the delta band was significantly lower than the healthy level, and the brain region reflecting the lower synchronization was located in the temporal lobe, the posterior temporal lobe, the parietal lobe (the posterior center) and the occipital lobe. Moreover, this lower synchronicity is consistent with weaker brain functional connections. Besides, by constructing functional brain network, the graph theoretic topological features of each frequency band of early PD are presented. We have found that early PD has characteristics of small world network in the delta and beta bands, and functional integration and separation characteristics of brain network in early PD are significantly abnormal in the delta, theta, alpha and beta bands. These results indicate that early PD has significant pathological changes from the perspective of brain function network analysis, and its characteristics can be described by multiple features, which may provide auxiliary guidance for the clinical diagnosis of early PD, and also provide theoretical support for the brain function changes of early PD.

Lack of sleep causes central fatigue in the body, which in turn affects brain function, and similarly, intense exercise causes both central and peripheral fatigue. This study aims to characterize the brain state, and in particular the functional changes in the relevant brain regions, after intense exercise in sleep-deprived conditions by detecting EEG signals. Thirty healthy adolescents were screened to participate in the trial, a sleep-deprivation model was developed, and a running exercise was performed the following morning. Meanwhile, pre-exercise and post-exercise Electroencephalogram (EEG) data were collected from the subjects using a 32-conductor electroencephalogram acquisition system (Neuroscan), and the data were analyzed using MATLAB (2013b) to process the data and analyzed Phase Lag Index (PLI) and graph theory metrics for different brain connections. Compared with the control group, the pre-exercise sleep-deprivation group showed significantly lower functional brain connectivity in the central and right temporal lobes in the Delta band (p < 0.05), significantly lower functional brain connectivity in the parietal and occipital regions in the Theta band (p < 0.05), and significantly higher functional brain connectivity in the left temporal and right parietal regions in the Beta2 band (p < 0.05). In the post-exercise sleep-deprivation group, functional brain connectivity was significantly lower in the central to right occipital and central regions in the Delta band (p < 0.05), significantly higher in the whole brain regions in the Theta, Alpha2, and Beta1 bands (p < 0.05 and 0.001), significantly higher in the right central, right parietal, and right temporal regions in the Alpha1 band (p < 0.05), and in the Beta2 band, the functional brain connections from the left frontal region to the right parietal region were significantly lower (p < 0.05). The results of the brain functional network properties showed that the clustering coefficients in the Delta band were significantly lower in the pre-exercise sleep-deprivation group compared to the control group (p < 0.05); the characteristic path length and global efficiency in the Theta band were significantly lower (p < 0.05 and 0.001). The post-exercise sleep-deprivation group showed significantly higher clustering coefficients, input lengths, and local efficiencies (p < 0.001), and significantly lower global efficiencies in the Delta and Theta bands (p < 0.001), and significantly higher clustering coefficients and local efficiencies (p < 0.001) and significantly lower input lengths and global efficiencies in the Alpha1 band compared with the control group (p < 0.001). After sleep deprivation, the pre-exercise resting state reduces the rate of information transfer in the functional networks of the adolescent brain, slowing the transfer of information between brain regions. After performing strenuous exercise, sleep deprivation leads to decreased athletic performance in adolescents. After a prolonged period of intense exercise, brain activity is gradually suppressed, resulting in even slower work efficiency and, eventually, increased information transfer in adolescents.

Electroencephalographic patterns observed during increasing depth anesthesia with thiopental sodium-ether, with ketamine hydrochloride-ether and with pentobarbital sodium were correlated with concentration of anesthetic agents in the blood. Analysis of electroencephalogram was made by histogram method.The results are as follows:1) Before anesthesia phase; Beta band of 20-25 Hz waves were shown 60-70% following alpha band was 30%.2) Induction phase; Delta band and theta band were increasing about 40% in place of beta band, but case of ketamine hydrochloride intervenous were increased beta band.3) Maintenance phase; 20-30% ether gas inhalation 5 minutes, a few beta band increased when this stage ether concentration in blood was about 60 mg/dl. Ether concentration in blood was over 70 mg/dl increasing delta band and theta band appearance ratio.4) Ether inhalation 30 minutes stage; Ether concentration in blood of 110 mg/dl when it was delta band only appearance ratio over about 50%.It was suggested that this method of analysis is adaptable to the quantitative study of contribution of each anesthetic agents when used simultaneously, provided that a significant change in EEG patterns and frequency band (delta, theta, alpha and beta waves bands), subject to classification, is produced by the agents considered.From the results of the present work, it was concluded that the changes EEG patterns were closely related to the depth of anesthesia and pharmacokinetics in the dog anesthetized with ether.

Wavelet spectra of visual evoked potentials: Time course of delta, theta, alpha and beta bands

Relationship between Delta, Sigma, Beta, and Gamma EEG bands at REM sleep onset and REM sleep end

ObjectiveThis study aimed to elucidate the distributions of abnormal activities, as well as the functional connectivity and topological properties of brain networks, in patients diagnosed with anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis.MethodFrom February 2016 to February 2025, acute-phase magnetoencephalography (MEG) data were successfully acquired from 16 patients diagnosed with anti-NMDAR encephalitis at the Affiliated Brain Hospital of Nanjing Medical University. MEG was employed to evaluate the power spectral density (PSD) across multiple frequency bands. Further analysis concentrated on functional connectivity and the topological characteristics of brain networks in order to identify distinctive neurophysiological features associated with the condition.ResultsDuring the acute phase, the PSD in the delta band (1–3 Hz) showed greater power in posterior regions and lower power in anterior regions, with the highest energy concentrated bilaterally in the occipitoparietal and temporal areas. In the theta band (5–7 Hz), the PSD was predominantly localized to the bilateral occipitoparietal regions. The beta1 band (13–20 Hz) was primarily distributed in the right temporo-occipitoparietal regions, while the beta2 band (20–30 Hz) was predominantly distributed in the left temporal, occipitoparietal, and certain frontal regions. Functional connectivity analysis revealed enhanced connections between the left caudal anterior cingulate (CAC_L) and the left superior parietal lobe in the delta and theta bands. Increased connectivity was also observed between the left frontal pole and Precuneus_L, CAC_L, and the left superior temporal gyrus (STG_L)in the theta and beta2 bands. Furthermore, enhanced connectivity between STG_L and Pericalcarine_R was observed in the theta, beta2, and gamma bands. Patients with anti-NMDAR encephalitis demonstrated significantly reduced global efficiency and notable increases in average path length, local efficiency, and clustering coefficient in multiple bands, suggesting local clustering of brain networks during the acute phase.ConclusionAlterations in PSD distribution and brain networks across different frequency bands may provide valuable insights into the electrophysiological changes observed in the brains of anti-NMDAR encephalitis patients. Furthermore, these findings may offer valuable mechanistic insights that could contribute to the development of future diagnostic strategies.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00415-025-13302-x.

EEG has a central role in the diagnosis of epileptiform abnormalities helpful in diagnosing epilepsy. Since irregularities are random and sporadic events, easily activated in the initial phase of sleep but difficult to observe in a standard EEG examination, sleep deprivation is a frequent condition to be used. Thus, in this study the EEG monitoring of 44 subjects, 14 without epilepsy and 30 with epilepsy, afferent to the IRCCS Centro Neurolesi “Bonino Pulejo” of Messina were examined after sleep deprivation the day before performing the registration. EEGs were recorded according to the international setting system using nineteen channels. The normalized power spectral densities in delta (2–4 Hz), theta (4–8 Hz), alpha (8–13 Hz) and beta (13–30 Hz) band were computed and the non-linear parameters such as beta exponent, fractal dimension and zero crossing were considered. The differences between the sleep and awake were significant in almost all the channels in the beta band and in posterior areas for beta exponent, fractal dimension and zero crossing in normal subjects. In epileptic patients they were significant in all the channels in the delta band and for the non-linear parameters, and in several ones in theta and beta bands. Even if in posterior areas all the spectral and the non-linear parameters showed different values between epileptic and healthy subjects, no significant differences were found. The results suggest that analysis of spectral power as well as of complexity, obtained by non-linear parameters, could be used to identify differences between healthy and epileptic patients.

This study seeks to investigate the electrophysiological mechanisms associated with mild cognitive impairment (MCI) in elderly patients with coronary artery disease (CAD) through the application of source-reconstructed EEG in conjunction with machine learning methodologies. We retrospectively analyzed clinical data and resting-state 64-channel EEG recorded during hospitalization at The First Hospital of Changsha. Participants included primary hypertension without CAD (n = 53) and CAD with primary hypertension (n = 117), with CAD stratified by Montreal Cognitive Assessment (MoCA) into MCI (n = 49) and cognitively normal (n = 68). EEG sources were reconstructed using an ICBM152-based head model and BEM forward modeling, yielding 82 Brodmann-atlas ROIs; functional connectivity was quantified using lagged phase synchronization (LPS) in delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) bands. Group comparisons applied false discovery rate correction. For MCI classification among patients with CAD, the dataset was randomly split into training and testing sets (7:3). Feature selection was performed in the training set using an independent-samples t-test followed by L1-penalized logistic regression. Subsequently, eight machine-learning classifiers were trained using the selected LPS features, with hyperparameters optimized by grid search under five-fold cross-validation. Model interpretability was assessed using SHAP. Baseline demographics and vascular comorbidities were comparable across groups; MoCA scores were lower in the MCI subgroup. Relative to hypertensive controls without CAD, cognitively normal CAD patients showed reduced frontal connectivity, including decreased alpha-band LPS (BA8L-46R) and beta-band LPS (BA44L-44R). Compared with cognitively normal CAD, CAD with MCI exhibited broader multi-band dysconnectivity across alpha, beta, theta, and delta bands, with mixed delta-band changes. In the test set, the Gradient Boosting model achieved the best performance for identifying MCI within CAD (AUC = 0.895). SHAP highlighted the most influential features, led by decreased alpha-band BA8L-46R connectivity, alongside delta- and beta-band alterations. Coronary artery disease is associated with frontal network disruption, which becomes more extensive and frequency-diverse as MCI progresses. Interpretable machine learning further highlights a small set of connectivity abnormalities-particularly within premotor-prefrontal pathways-as candidate markers for MCI classification within a CAD cohort, supporting a vascular-relevant interpretation, which warrants further validation.

Changes in high-order interaction measures of synergy and redundancy during non-ordinary states of consciousness induced by meditation, hypnosis, and auto-induced cognitive trance

ObjectiveThe study aimed to find the difference in functional network topology on interictal electroencephalographic (EEG) between patients with drug-resistant epilepsy (DRE) and healthy people.MethodsWe retrospectively analyzed the medical records as well as EEG data of ten patients with DRE and recruited five sex-age-matched healthy controls (HC group). Each participant remained awake while undergoing video-electroencephalography (vEEG) monitoring. After excluding data that contained abnormal discharges, we screened EEG segments that were free of artifacts and put them together into 20-min segments. The screened data was bandpass filtered to different frequency bands (delta, theta, alpha, beta, and gamma). The weighted phase lag index (wPLI) and the network properties were calculated to evaluate changes in the topology of the functional network. Finally, the results were statistically analyzed, and the false discovery rate (FDR) was used to correct for differences after multiple comparisons.ResultsIn the full frequency band (0.5–45 Hz), the functional connectivity in the DRE group during the interictal period was significantly lower than that in the HC group (p < 0.05). Compared to the HC group, in the full frequency band, the DRE group exhibited significantly decreased clustering coefficient (CC), node degree (D), and global efficiency (GE), while the characteristic path length (CPL) significantly increased (p < 0.05). In the sub-frequency bands, the functional connectivity of the DRE group was significantly lower than that of the HC group in the delta band but higher in the alpha, beta, and gamma bands (p < 0.05). The statistical results of network properties revealed that in the delta band, the DRE group had significantly decreased values for D, CC, and GE, but in the alpha, beta, and gamma bands, these values were significantly increased (p < 0.05). Additionally, the CPL of the DRE group significantly increased in the delta and theta bands but significantly decreased in the alpha, beta, and gamma bands (p < 0.05).ConclusionThe topology structure of the functional network in DRE patients was significantly changed compared with healthy people, which was reflected in different frequency bands. It provided a theoretical basis for understanding the pathological network alterations of DRE.

Directed information flow between brain regions might be disrupted in children with Attention Deficit Hyperactivity Disorder (ADHD) which is related to the behavioral characteristics of ADHD. This paper aims to investigate the different information pathways of brain networks in children with ADHD in comparison with healthy subjects. EEG recordings were obtained from 61 children with ADHD and 60 healthy children without neurological disorders during attentional visual task. Effective connectivity among all scalp channels was calculated using directed phase transfer entropy (dPTE) for delta, theta, alpha, beta, and lower-gamma frequency bands. Group differences were evaluated using permutation tests in connectivity between regions. Significant posterior to anterior patterns of information flow in theta frequency bands were found in healthy subjects (p-value < 0.05), while disrupted pattern flow, in an opposite way, was found in ADHD children. In the beta band, information flow in pathways between anterior regions was significantly higher in healthy individuals than in the ADHD group. These differences are more indicated in connectivity that leads from frontal and central regions to the right frontal regions of the brain (F8 electrode). Furthermore, connections from central and lateral parietal areas to Pz electrode areas are statistically significant and higher in healthy children in this band. In the delta band, internal connections in the anterior region show a significant difference between the two groups, as this amount is higher in the ADHD group. Our analysis may provide new insights into information flow in brain regions of ADHD children in comparison with healthy children.

Asymmetrically impaired standing control is a prevalent disability among stroke patients; however, most of the neuromuscular characteristics are unclear. Therefore, the main purpose of this study was to investigate between-limb differences in intermuscular coherence during quiet standing. Consequently, 15 patients who had sub-acute stroke performed a quiet standing task without assistive devices, and electromyography was measured on the bilateral tibialis anterior (TA), soleus (SL), and medial gastrocnemius (MG). The intermuscular coherence of the unilateral synergistic (SL–MG) pair and unilateral antagonist (TA–SL and TA–MG) pairs in the delta (0–5 Hz) and beta (15–35 Hz) bands were calculated and compared between the paretic and non-paretic limbs. The unilateral synergistic SL–MG coherence in the beta band was significantly greater in the non-paretic limb than in the paretic limb (p = 0.017), while unilateral antagonist TA–MG coherence in the delta band was significantly greater in the paretic limb than in the non-paretic limb (p < 0.01). During quiet standing, stroke patients showed asymmetry in the cortical control of the plantar flexor muscles, and synchronous control between the antagonistic muscles was characteristic of the paretic limb. This study identified abnormal muscle activity patterns and asymmetrical cortical control underlying impaired standing balance in patients with sub-acute stroke using an intermuscular coherence analysis.