BackgroundSelf-limited epilepsy with centrotemporal spikes (SeLECTS) is a common childhood epilepsy syndrome characterized by spontaneous seizure remission but frequent cognitive difficulties. Previous neuroimaging studies have reported cortical abnormalities in SeLECTS; however, findings remain heterogeneous and are often confounded by antiseizure medications exposure or by reliance on a single morphometric approach.MethodsWe conducted a multimodal structural MRI study on 30 drug-naïve children with SeLECTS and 30 age- and sex-matched healthy controls. Voxel-based morphometry was used to quantify gray matter volume, surface-based morphometry was employed to assess cortical thickness, gyrification, and sulcal depth, and a lateralization index was used to evaluate hemispheric asymmetry. Exploratory correlation analyses were performed between these results and clinical variables as well as scores from the Wechsler Intelligence Scale for Children-Revised.ResultsPatients showed increased bilateral pontine gray matter volume compared to controls. SBM identified widespread cortical thinning in frontoparietal and left temporal regions, increased gyrification in the right lateral orbitofrontal and left superior frontal gyri, and reduced right medial temporal sulcal depth. Atypical leftward lateralization was observed in the supramarginal, angular, and middle occipital gyri. Right pontine volume positively correlated with disease duration, while left superior frontal gyrification negatively correlated with verbal IQ.ConclusionDrug-naïve children with SeLECTS exhibit a complex pattern of cortical dysmaturation and subcortical structural variations. These findings suggest that the neuroanatomical signature of SeLECTS extends beyond the Rolandic cortex, involving subcortical nuclei and widespread developmental pruning pathways. While the mechanistic links to cognition remain speculative, these structural markers provide a framework for future longitudinal studies.

Social decision-making involves intricate and dynamic interactions between brains, yet prior hyperscanning research primarily concentrated on investigating the overall patterns of interbrain synchrony (IBS), leaving its fine-grained temporal dynamics unveiled. Here, after recording the electroencephalography of proposer-responder pairs who engaged in an iterated ultimatum game, time-varying IBS network architectures were explored by leveraging source-localized wavelet transform coherence and k-means clustering. Results revealed a sequence of temporally and functionally distinct IBS states along the response and feedback periods. Early states, occurring around stimulus onset, were dominated by a posterior parietal modular configuration, likely associated with shared attention and visual processing. In contrast, later states during the decision-feedback stage involved increased IBS in the frontal and temporoparietal regions, reflecting coordinated activity between interacting partners supporting decision execution and adaptive behavioral adjustments. Crucially, advantageous conditions (fair proposal or acceptance feedback) elicited more active and efficient dynamic IBS states than disadvantageous conditions (unfair proposal or rejection feedback), with greater IBS related to increased reciprocal behavior. These findings reveal recurring IBS patterns, suggesting that social decision-making is modulated not only by temporal fluctuations in IBS networks but also by flexible interbrain communication between key cortical regions.

Intellectual disability (ID) is one of the most serious developmental disorders of children, characterized by significant limitations in intellectual function and adaptive behaviors. However, topological alterations of functional brain networks in children with ID remain unclear. This study combined resting-state functional MRI (rs-fMRI) data and graph theory analysis to quantify the topological properties of functional brain networks in children with ID. Thirty-four children diagnosed with ID and twenty-eight sex-, age-, and education-matched healthy controls (HC) were included. Global, modular and nodal characteristics were calculated to investigate ID-related alterations in functional brain networks. Compared with HCs, decreased clustering coefficient and local efficiency were revealed in children with ID, indicating reduced functional segregation. At the nodal level, children with ID exhibited nodal alterations concentrated in the ventral attention network (VAN), particularly in the left middle cingulate gyrus and bilateral insula, together with nodes in the dorsal attention network (DAN) and the subcortical network (SubN). At an uncorrected threshold, modular connectivity analysis revealed exploratory decreases of subcortical-cortical connectivity between SubN and the somato-motor network (SMN), between the SubN and VAN, between the SubN and frontoparietal network (FPN), and between the SMN and VAN, as well as altered connector and provincial hub indices across the default mode, limbic, frontoparietal, visual and subcortical networks in the ID group. Together, our findings provided preliminary multi-level evidence of functional network reorganization spanning subcortical, sensory, attention, and control systems in children with ID, providing new insights into the topological organization of the cognitive dysfunction in ID.

Objective Current multimodal models often experience performance degradation when faced with incomplete data and lack effective fusion strategies for diverse data types, such as imaging, clinical, and electrophysiological data. This study aims to develop a novel model to improve the accuracy of early diagnosis of coronary artery disease. Materials and methods Clinical data, laboratory test results, coronary CTA images, and ECG data from consecutive patients who underwent coronary CTA examinations at the center between February 2022 and August 2023 were collected. Features were extracted using convolutional neural networks, and a multimodal prediction model was developed using a weighted fusion strategy. An internal validation was performed with a data split ratio of 7:1.5:1.5, and an independent external validation was conducted using an external dataset. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC-ROC), and model interpretability was assessed using the SHAP algorithm. Diagnostic consistency was evaluated using the Kappa coefficient. Result Compared to traditional diagnostic methods, the model has increased the diagnosis rate of coronary heart disease by 10.43% points in absolute terms and by 44.0% in relative terms. This improvement has greatly enhanced the ability to identify coronary heart disease at an early stage. For diagnosing cardiovascular diseases, the performance of the multimodal model is significantly superior to that of single imaging or clinical indicators. The area under the curve (AUC) was 0.89 in internal validation, and the model also demonstrated excellent performance in the external validation dataset, with an AUC of 0.83. Conclusion This model, by integrating three types of data for the first time and tolerating up to 50% missing data, significantly improves the detection rate of coronary artery disease and enhances diagnostic sensitivity and accuracy, thereby providing robust support for early clinical intervention and precise treatment.

Accurate prognosis assessment of comatose patients remains a significant challenge in neurocritical care. Growing evidence indicates that brain connectivity is integral to the maintenance of consciousness and may be linked to its recovery. In this study, we recorded bedside electroencephalography (EEG) from comatose patients during an auditory oddball name-calling task to investigate task-related dynamic causal modeling (DCM) connectivity and to examine whether connectivity strengths correlated with patients' functional recovery. Our findings reveal that a bidirectional model, incorporating reciprocal connectivity among the superior frontal gyri, superior parietal lobules, and primary auditory cortices, was significantly associated with the neural processing of name-calling stimuli in comatose patients. Furthermore, the strength of these DCM connections demonstrated a capacity to predict long-term prognostic outcomes, as evaluated via the Glasgow Outcome Scale-Extended scale. Together, these results provide evidence supporting the potential of DCM-derived biomarkers in evaluating functional prognosis in comatose patients. (ChiCTR2000033586).

Background and purpose:Alzheimer's disease (AD), the most common form of dementia worldwide, is characterized by progressive cognitive decline. Extensive evidence from dynamic functional connectivity (dFC) studies has demonstrated unstable functional states, reduced network flexibility, and impaired transitions between large-scale neurocognitive networks across the AD continuum. However, how these temporal abnormalities are embedded within the hierarchical spatial organization of brain networks, as captured by functional gradients (FG), and whether combined FG-dFC metrics can provide mechanistically interpretable and potentially sensitive imaging biomarkers, remain to be elucidated. Methods:This study enrolled 46 AD patients who were diagnosed according to the Amyloid/Tau/Neurodegeneration (ATN) biological diagnostic framework and 37 age- and sex-matched healthy controls (HC). All participants underwent resting-state fMRI. Functional gradients were derived using connectivity similarity matrices and diffusion embedding (aligned and standardized), while dFC was estimated with a sliding window approach and clustered into four recurrent states. Group differences were assessed with two-sample t-tests with Gaussian Random Field (GRF) correction. Correlation analyses included ATN biomarkers and cognitive scores. A linear support vector machine (SVM) with leave-one-out cross-validation evaluated classification performance based on significant FG features. Results:Compared to the healthy controls, AD patients exhibited widespread FG alterations between regions of the Default Mode Network (DMN) and the Sensorimotor Network (SMN). In the first gradient DMN, the left precuneus showed reduced gradient scores, whereas the right medial superior frontal gyrus and bilateral angular gyri were increased. In the first gradient of the SMN, the right supplementary motor area increased while bilateral superior temporal gyri decreased. Second-gradient reductions were confined to two regions: the left postcentral gyrus (SMN) and left middle occipital gyrus (visual network, VIS). The right medial superior frontal gyrus first-gradient score correlated negatively with T-Tau (r = -0.50, P = 0.006) and age (r = -0.36, P = 0.02); the right angular gyrus correlated negatively with age (r = -0.29, P = 0.04); the left precuneus correlated positively with age (r = 0.38, P = 0.009). dFC revealed four recurrent states (27.59, 17.67, 28.27, 26.47% of total occurrences). Relative to HC, AD showed higher FT and MDT in states 1-2 and lower scores in state 3, with NT unchanged, alongside state-dependent bidirectional connectivity changes (fronto-insular-sensorimotor increases; DMN-temporal and visuo-auditory decreases). The SVM achieved an AUC of 0.776, sensitivity 78.26%, specificity 67.57%, and accuracy 73.49%, with the right superior temporal gyrus within SMN first-gradient contributing most. Conclusion:AD is characterized by macro-scale hierarchical disorganization centered on the principal functional gradient, accompanied by reduced cross-state flexibility and state-dependent connectivity abnormalities. The combined functional gradient-dynamic functional connectivity (FG-dFC) analysis provides complementary spatiotemporal insights and reveals imaging features associated with T-Tau levels and age, offering new perspectives on the neuropathological mechanisms of AD and potential imaging biomarkers. Moreover, these network topology and dynamic connectivity metrics may prove useful for monitoring disease progression, evaluating treatment effects, and stratifying patients in future clinical and interventional studies.

Human social behaviors involve complex interactions between individuals, and understanding how interbrain neural activity reflects and predicts these interactions is critical for advancing social cognitive neuroscience. While electroencephalography (EEG) hyperscanning has been widely used to explore interpersonal neural dynamics, most studies focus on pairwise regional coupling, overlooking the brain's intrinsic network-level organization. Here, we propose a spatiotemporal network analysis framework that combines Bayesian non-negative matrix factorization with EEG source imaging to identify interpretable subnetworks with spatiotemporal information. Applying this framework to dyadic EEG datasets from interactive decision-making tasks identifies eight task-relevant subnetworks, including the default mode network (DMN), somatosensory-motor network (SMN), and visual network (VN). Effective interpersonal coordination was associated with enhanced network-level time-domain interbrain synchrony and spatial-domain inter-subject similarity, and the fusion of these metrics reliably predicted interactive behaviors. Notably, synchrony and similarity involving DMN, VN, and SMN emerge as robust predictors of interactive behaviors, with spatiotemporal coupling most prominent within these subnetworks. These findings reveal spatiotemporal network signatures underlying interpersonal neural synchronization and demonstrate the importance of distributed subnetworks and their temporal and spatial alignment in achieving effective social interactions. This framework provides a useful computational tool for probing the neurobiological basis of social behaviors.

Characterized by recurrent fluctuations in mood states, bipolar disorder (BD) is widely conceptualized as a disconnection syndrome associated with dysregulated brain dynamics. Nevertheless, the molecular mechanisms underlying this aberrant connectivity dynamics in BD remain elusive. Using resting-state electroencephalography (EEG) data from BD patients and healthy controls, this study first delineated the characteristic alterations in temporal variability of functional connectivity in BD and further elucidated their underlying molecular mechanisms and clinical relevance. Current findings revealed significantly reduced temporal variability within large-scale brain subnetworks, most notably in the dorsal attention, somatomotor, and visual networks. Importantly, these neurodynamic signatures effectively predicted the symptom severity in individuals with BD. Moreover, the spatial patterns of these dynamic alterations are associated with the expression of BD risk genes enriched in synaptic function and metabolic pathways, as well as with the spatial organizations of various neurotransmitter receptors, including CB1, mGluR5, H3, and MOR. Collectively, these results provide evidence for a multiscale pathophysiological framework that links genetic susceptibility and chemoarchitectural alterations to dynamic brain network instability, ultimately underpinning the core clinical manifestations in BD.

Cooperation and competition are fundamental to human social interaction. While recent hyperscanning studies have linked stronger interbrain synchrony (IBS) to successful cooperation, most have focused on dyadic interactions, leaving the underlying neural mechanisms of group-level social behavior largely unknown. Here, we employed EEG hyperscanning to investigate interbrain neural dynamics of triadic cooperative and competitive interactions. Distinct interbrain network patterns emerged in the delta and beta bands, with cooperation showing enhanced frontal-parietal IBS and more efficient network properties. Non-parametric cluster-based permutation tests further identified significant regional differences in a left-lateralized frontal-temporal-parietal cluster in both bands. Crucially, increased delta-band frontal-parietal IBS was closely associated with better group-level cooperative performance. Moreover, classification and prediction models based on delta-band interbrain metrics successfully distinguished interaction types and predicted cooperative outcomes. These findings uncover interbrain neurocognitive traits that reflect specific social behavioral contexts, highlighting the pivotal role of frontal-parietal synchrony and delta-band modulations in supporting group cooperation. Together, our results advance the understanding of the neural basis of triadic social interaction and underscore the potential of interbrain network signatures as biomarkers for decoding and predicting complex social behaviors.

Motor imagery-based brain-computer interface (MI-BCI) faces a critical challenge in achieving effective spatial-temporal feature modeling while maintaining a compact model parameterization. Herein, a lightweight model was proposed, termed as Dual-Attention-EEGNet (DA-EEGNet), which extends the EEGNet backbone by integrating a channel attention module and a depth attention module to selectively emphasize informative electrodes and temporally discriminative features. Two widely used MI benchmark datasets and three evaluation strategies, i.e. subject-dependent scenario, subject-independent scenario, and dataset-independent classification scenario, were utilized to verify the model's performance. Despite its compact design, DA-EEGNet contains merely 3.97[Formula: see text]k trainable parameters and achieves average classification accuracies of [Formula: see text] and [Formula: see text], outperforming or matching existing deep learning approaches that rely on substantially larger parameter counts. Ablation studies further confirm the complementary contributions of the channel and depth attention modules. In addition, visualization analyses, including temporal attention heatmaps and motor-area topographies, demonstrate that DA-EEGNet captures neurophysiologically meaningful spatial-temporal patterns consistent with MI-related brain activity. These results indicate that DA-EEGNet provides a favorable parameter-accuracy trade-off and serves as an efficient and interpretable baseline for MI-BCI applications.

Simultaneous EEG-fMRI acquisition integrates the complementary strengths of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), offering a more comprehensive understanding of brain dynamics. However, EEG data acquisition can sometimes be limited or compromised due to electrode issues, motion artifacts, or participant discomfort, underscoring the importance of methods to infer EEG spectral features from available fMRI recordings. In this study, we propose a deep learning framework that directly reconstructs EEG power spectral density (PSD) features from resting-state fMRI BOLD signals. By aggregating spatiotemporal information across 200 brain regions over consecutive time windows, our model effectively captures complex fMRI dynamics and maps them onto neural electrical activity. We evaluate our method on a simultaneous EEG-fMRI dataset comprising 24 healthy participants. PSD features are reconstructed across five canonical EEG frequency bands: Delta, Theta, Alpha, Beta, and Gamma. Experimental results demonstrate high intra-subject prediction performance, with average Pearson correlations exceeding 0.90 in the Gamma and Theta bands, and mean squared errors (MSE) as low as 0.0035. The highest band-specific correlation reaches 0.9886 in the Gamma band, highlighting a strong coupling between BOLD activity and high-frequency neural oscillations. Topographic analyses further confirm that the model accurately captures both subject-level and group-level EEG spatial patterns. To the best of our knowledge, this is the first study to directly decode EEG signals from fMRI signals, offering a pathway toward precise brain activity interpretation and advancing the integration of multimodal neuroimaging.

Schizophrenia (SCZ) is a highly disabling psychiatric disorder marked by compromised brain dynamic interactions. However, the underlying neuropathology of SCZ remains poorly elucidated in terms of its associated disruption of network-level and rhythm-specific dynamic fluctuations during the resting state. Herein, using a sample entropy-based temporal variability analysis framework, we investigate the complex fluctuation patterns of resting-state electroencephalogram networks as they transition over time for SCZ patients and their unaffected relatives (R-SCZ), as well as healthy controls (HC). Next, potential associations between variability networks and individual cognitive traits/clinical recordings were explored. Rhythm-specific abnormalities of baseline brain dynamics may disrupt the normal brain function of SCZ, particularly through the decoupling of frontal-temporal and temporal-parietal variability connectivity in the alpha and beta rhythms. The diminished variability differences observed between R-SCZ and HC suggest the possible existence of shared familial factors that influence specific traits related to network variability. Moreover, multidimensional representations of temporal variability networks can quantitatively characterize and even predict an individual's cognitive function (e.g. verbal memory) and clinical symptoms of SCZ patients. Current findings may offer new insights into comprehending the neuropathology and the potential role of familial susceptibility in SCZ, which may facilitate advancements in early diagnosis and intervention strategies.

Asymptomatic carotid stenosis (ACS) is associated with an increased risk of ischemic cerebrovascular events and may contribute to early cognitive decline. This study aimed to characterize dynamic alterations in brain function in ACS using resting-state functional magnetic resonance imaging (rs-fMRI) by combining dynamic functional connectivity (dFC) and dynamic amplitude of low-frequency fluctuations (dALFF), and to examine their relationships with cognitive performance. Patients with unilateral moderate-to-severe ACS (stenosis ≥ 50

Interpersonal negotiation is an essential component of social-economic interactions, yet the concept is not entirely gender-neutral. Male stereotypes - dominance, assertiveness, and rationality - help shape the negotiator role, giving men a perceived advantage in negotiations. This study adapts electroencephalogram hyperscanning and an iterated ultimatum game to investigate the gender differences in dyadic economic negotiation, focusing on event-related potentials and source-localized phase-locked interbrain synchronization (IBS). Behaviorally, dyads with male proposers (M-dyads) achieve better negotiation outcomes than dyads with female proposers (F-dyads) in goal-driven negotiation contexts, reflecting differences in negotiation strategies. Neurally, M-dyads exhibit lower P200 amplitude in the frontal cortex and significantly higher IBS within social brain networks, both of which are strongly associated with reciprocal negotiation behaviors. Notably, IBS between the temporoparietal junction (TPJ), medial prefrontal cortex (mPFC), and superior temporal sulcus (STS), alongside P200 amplitude, serves as a strong predictor of reciprocal behaviors, underscoring the role of TPJ, mPFC, and STS in interpersonal coordination. Moreover, significant mediation effects highlighted the bridging role of IBS between gender composition and negotiation outcomes. These findings provide neurobehavioral accounts of how dyad gender composition influences negotiation outcomes, suggesting that stronger IBS within key social brain regions underlies effective interpersonal coordination.

BACKGROUND:This study aimed to investigate functional brain network characteristics in pediatric tic disorder (TD) patients and explore the effects of aripiprazole treatment on the functional brain network. METHODS:Resting-state electroencephalogram (EEG) was recorded from 40 TD patients and 30 matched healthy controls to compare functional brain network differences between TD patients and healthy individuals. Additionally, 26 of these TD patients underwent a 12-week aripiprazole treatment and were evaluated for changes in their functional brain networks before and after treatment. Functional connectivity between brain regions was analyzed using the phase locking value (PLV), and global network topological properties (global efficiency, local efficiency, clustering coefficient, and characteristic path length) were quantified via graph theory. RESULTS:Compared with controls, TD patients exhibited reduced frontal functional connectivity (Fp2-Fz, Fp2-F4, Fz-F4) but compensatory enhanced connectivity in F7-F3 and C3-Cz. Tourette syndrome patients further exhibited Fp1-Fz hypo-connectivity. Aripiprazole treatment significantly increased global functional connectivity, elevated global and local efficiency and clustering coefficient, and shortened characteristic path length in TD patients. However, frontal hypo-connectivity persisted after treatment partly. CONCLUSIONS:Pediatric TD patients present frontal dysfunction with compensatory adaptations in frontal-temporal connection and sensorimotor networks. Aripiprazole modulates global brain network connectivity and topology but not does not normalize frontal deficits, providing neurophysiological evidence for TD pathophysiology and aripiprazole's therapeutic mechanism.

Schizophrenia (SCZ) and bipolar disorder (BD) are severe psychotic disorders with overlapping clinical manifestations, leading to high rates of misdiagnosis. This study aims to identify disorder-specific neurophysiological biomarkers using electroencephalography and contrastive machine learning to improve differential diagnosis. Resting-state electroencephalography was recorded from 52 patients with BD, 65 with SCZ, and 75 healthy controls. Temporal variability networks were constructed using sample entropy. Contrastive variational autoencoders decomposed these networks into components shared with healthy controls and components specific to each disorder. Based on disorder-specific components, predictive models for clinical symptoms were constructed. Additionally, spatial pattern network filters were implemented to extract discriminative features for the classification of BD and SCZ patients. Here we show pronounced differences in disorder-specific network components between SCZ and BD, especially in frontal-central/parietal connectivity, which were not discernible in the original or shared networks. These disorder-specific components correlate significantly with clinical assessment scores and support predictive modeling of symptom severity. By applying spatial pattern network filters to the disorder-specific components, we achieve 96.154% accuracy in distinguishing SCZ from BD, substantially surpassing conventional approaches. This integrative framework, combining dynamic network analysis with contrastive machine learning, provides a powerful methodology for extracting neurophysiological biomarkers and paves the way for biologically grounded diagnostics in psychotic disorders. Schizophrenia and bipolar disorder are serious mental illnesses that can look very similar, making them hard for doctors to tell apart. This study used an objective brain index (EEG) combined with an artificial intelligence method to find differences between the two conditions. The researchers analyzed brain activity patterns and isolated features unique to each disorder. They discovered that these unique patterns were linked to patients’ symptoms and could predict how severe those symptoms were. Most importantly, the method distinguished between the two disorders with over 96% accuracy. This work could lead to a more reliable, biology-based tool to help doctors diagnose patients correctly, reduce misdiagnosis, and guide more personalized treatment decisions in the future. Jiang, Ye et al. combine resting-state EEG and contrastive variational autoencoders to identify disorder-specific neurophysiological biomarkers in schizophrenia and bipolar disorder. The approach achieves over 96% accuracy in distinguishing the two disorders by revealing distinct frontal-central/parietal connectivity patterns.

Objective To characterize the dynamic functional network connectivity (dFNC) patterns in children with self-limited epilepsy with centrotemporal spikes (SeLECTS) and to uncover potential abnormalities in neural regulation and related functional impairments. Materials and Methods Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected from 61 children with SeLECTS and 69 healthy controls (HCs). Independent component analysis (ICA), the sliding window approach and hidden markov modeling (HMM) were employed to systematically investigate potential differences in dFNC properties between the two groups. Results The dFNC analysis identified four dynamic states, with State 1 occurring most frequently. State 1 and State 3 represented two polarized connectivity patterns, with State 1 characterized by weak/negative connections and State 3 by widespread strong connections. In both states, children with SeLECTS showed significantly reduced connectivity within the dorsal attention network (DAN) compared with HCs (p < 0.001, FDR-corrected). In the connectivity-balanced State 2, children with SeLECTS showed significantly reduced fractional windows (p = 0.009) and mean dwell time (p = 0.018) compared with HCs, whereas no significant differences were observed in State 4. In addition, temporal variability of functional connectivity between the DAN and visual network (VIS) was significantly reduced in SeLECTS (p < 0.001, FDR-corrected), and this variability was positively correlated with full-scale intelligence quotient (FIQ) (p < 0.05). HMM results from another dynamic perspective further confirmed and echoed the above abnormalities. Conclusion This study revealed abnormal dynamic connectivity patterns of brain networks in children with SeLECTS from a multidimensional dynamic perspective. These macroscopic abnormalities may reflect an underlying excitation–inhibition imbalance in neural networks and provide new insights into brain functional reorganization and the potential neurobiological mechanisms of SeLECTS.

The behavioral inhibition system (BIS), mediating responses to punishment cues and avoidance behaviors, is implicated in anxiety. However, the neural dynamics underpinning BIS, particularly regarding the temporal variability of brain network interactions, remain less explored. Using resting-state functional magnetic resonance imaging (rs-fMRI) of 181 healthy adults, this study investigated the association between BIS sensitivity and the temporal variability of functional connectivity within and between functional brain networks. This finding revealed a significant positive correlation between BIS scores and temporal variability, specifically in the connectivity involving subnetworks' sensory somatomotor hand network (SSHN)-ventral attention network (VAN), and sensory somatomotor mouth network (SSMN)-VAN. Notably, the high-BIS sensitivity group exhibited significantly greater temporal variability between VAN and SSMN/SSHN compared to the low-BIS sensitivity group. Furthermore, predicted BIS scores based on network variability showed a strong correlation with actual BIS scores (Pearson's [Formula: see text]). Moreover, significant mediation effects highlighted the bridging role of BIS scores between brain network variability and anxiety scale scores. This enhances the comprehension of the relationship between BIS, anxiety, and brain function, while also offering new insights into the pathogenesis of anxiety.

Uncovering the interbrain neural mechanisms underlying interpersonal negotiation offers insight into social decision-making dynamics in resource allocation. In this study, we used EEG hyperscanning alongside an iterated ultimatum game to investigate interbrain coupling and dyadic exchange behavior during negotiation. Frontal cortex event-related potentials (ERPs) revealed the distinct neural responses driven by partners' behavioral cues: the proposer's N200 differed significantly for fair versus unfair offers, and the responder's feedback-related negativity (FRN) showed a trend toward significance for the same contrast, while the proposer's N500 varied between acceptance and rejection feedback. Our analysis introduced a novel causal model based on directional phase transfer entropy (dPTE) and time-varying ERP amplitudes, illustrating directed neural processes driven by social exchange, where the proposer's brain activity initially exerts a causal impact on the responder's, whose feedback in turn influences the proposer, creating a closed-loop interaction that drives adaptive negotiation strategies. Additionally, our prediction model with autoregression with exogenous input, which incorporated these causal links between brains, demonstrated higher accuracy than single-brain or reverse causal models, underscoring the significance of dynamic interbrain coupling in interpersonal coordination. This causal model provides a mechanistic explanation of how proposer-responder pairs perceive and adapt to each other's decisions, facilitating shared attention and behavioral coordination in reciprocal, asymmetric negotiations. These findings offer a novel theoretical framework for studying complex social behaviors through interbrain dynamics and may inspire future applications in enhancing cooperative decision-making processes.

Motor imagery (MI) is a cognitive process that allows individuals to mentally simulate movements without physical execution. However, the exploration of functional connectivity (FC) and lateralization mechanisms under different MI actions remains insufficiently understood. In this work, the common orthogonal basis extraction (COBE) algorithm was employed to isolate action-specific components by removing shared background components from the raw FC of the MI process. We demonstrate that action-specific FC effectively captures the hemispheric statistical differences between left- and right-hand MI, outperforming traditional FC and temporal variability measures. And through a comprehensive analysis of network properties at three distinct levels, encompassing the whole-brain network properties, hemispherical properties, and individual nodal strength, complex lateralization patterns associated with diverse types of MI processes were successfully discerned. Furthermore, lateralization indices were further calculated to quantitatively reveal the degree of brain lateralization. Notably, the lateralization performance (LP) derived from action-specific FC exhibited a significant predictive capacity for MI performance, thereby suggesting its potential to evaluate individual MI capability. Collectively, these findings validate the action-specific FC patterns in characterizing neural mechanisms of MI processes and indicate that the LP could potentially be a useful tool to predict the MI performance of MI-based brain-computer inference (BCI), thereby contributing to the formulation of personalized therapeutic strategies for clinical rehabilitation from a new perspective.