The allostatic triage model of psychopathology (ATP Model): How reallocation of brain energetic resources under stress elicits psychiatric symptoms.

Combining static and dynamic brain network analysis with machine learning for enhanced diagnosis of major depressive disorder.

Early detection of subjective cognitive decline (SCD), a preclinical stage of Alzheimer's disease (AD), remains a clinical challenge due to its subtle manifestations. This study aims to address these challenges by introducing a novel approach to enhance the detection and analysis of SCD. A Frequency Self-Adaptive Wavelet Transform (FSAWT) model was developed and optimized for functional brain network (FBN) construction using resting-state functional MRI (rs-fMRI) data. The model dynamically selected "golden frequencies" to improve the accuracy and interpretability of brain connectivity patterns. FBNs from 240 participants (106 SCD, 134 controls) were analyzed and compared using traditional methods, pearson correlation (PC) and sparse representation (SR). Receiver operating characteristic-area under the curve (ROC-AUC) analysis validated the classification results. Our findings demonstrate that individuals with SCD exhibit distinct functional connectivity alterations, including reversed parahippocampal gyrus-superior parietal gyrus connectivity-suggesting early DMN disintegration, weakened temporoparietal pathways linked to memory deficits, and enhanced fusiform gyrus-orbitofrontal connectivity. The frequency-optimized SRWT method achieved superior diagnostic performance (83.71% accuracy, AUC = 0.84) with 82.11% sensitivity and 85.71% specificity, significantly outperforming traditional approaches (61.93% accuracy for PC), highlighting its potential for early SCD detection through these network-based biomarkers. The FSAWT model offers a robust framework for early SCD detection by integrating frequency-specific and cross-frequency dynamics. While these findings highlight potential contributions to precision diagnostics and personalized interventions for neurodegenerative disorders, such applications remain to be established in future studies. Future applications may also explore multimodal neuroimaging and broader cognitive impairments.

The functional brain networks related to procedural learning (PL) have never been explored in children with self-limited focal epilepsies of childhood (SeLFE), despite their role in the development of various sequence-related sensorimotor, language, and cognitive abilities that are impaired in this clinical population. Our study fills this gap by investigating PL and its interaction with the rapid reorganisation of resting-state functional connectivity (rsFC) in SeLFE. A serial reaction time task, preceded and followed by resting-state magnetoencephalography (MEG) recordings, was used to assess PL in 10 children with SeLFE and 28 age-, sex- and IQ-matched typically developing (TD) children. Pre- to post-learning rsFC changes were estimated using band-limited power envelope correlation, after regressing interictal epileptic discharges (IEDs) in SeLFE patients. rsFC maps were compared between groups and correlated with PL and IED frequency. Compared to TD peers, children with SeLFE showed atypical pre- to post-learning rsFC changes within widespread antero-posterior brain networks in theta, alpha and low beta bands, as well as reduced PL performance negatively correlated with sleep IED frequency. This MEG study is the first to demonstrate reduced PL abilities combined with atypical post-learning reorganisation of rsFC in children with SeLFE compared to TD peers. These results suggest that the pathophysiology of SeLFE, including the chronic repetition of IEDs during sleep across development, have a detrimental impact on the acquisition of PL brain-behaviour processes in these patients.

Task-specific effects of sleep deprivation on cognitive function and EEG brain network in night-shift nurses.

Does brain connectivity hold the key to safer roads? EEG-based fatigue detection in young drivers using interpretable deep learning.

A novel method for motor imagery electroencephalogram classification: cross tensor coupling decomposition based on diverse modality brain functional networks

Abstract Skilled reading arises from the coordinated activity of neural systems supporting word recognition, semantic processing, and executive control. While the structural architecture of white matter tracts involved in reading is well characterized, their functional contributions remain unclear. Here, we examined whether resting-state functional connectivity of the left uncinate fasciculus, a ventral frontal-temporal white matter tract implicated in semantic processing, modulates the relationship between word recognition and reading comprehension. Fifty-three participants (ages 10-14 years; 29 girls, 24 boys) completed resting-state functional MRI and standardized assessments of word recognition and reading comprehension. Functional connectivity between the left uncinate fasciculus and structurally connected gray matter regions, including the anterior and medial temporal lobes and the ventrolateral prefrontal cortex, was derived from BOLD signal correlations at rest. Regression analyses revealed that stronger uncinate fasciculus functional connectivity with semantic memory and control regions was associated with a weaker dependence of reading comprehension on word recognition skill. These findings suggest that semantic brain systems accessed via the uncinate fasciculus may support flexible meaning-based reading strategies, allowing comprehension to be sustained across varying levels of word recognition. By linking white matter functional connectivity to individual differences in reading performance, this study highlights the importance of semantic pathways in reading development and underscores the value of examining white matter-gray matter interactions in functional brain networks for reading.

Brain network connectivity and dementia risk: a bidirectional Mendelian randomisation perspective.

Background/Objectives: Amnestic mild cognitive impairment (aMCI) represents a transitional stage between normal aging and dementia, constituting a critical intervention window for Alzheimer's disease (AD). As a non-invasive intervention, neurofeedback training (NFT) has demonstrated potential in ameliorating cognitive deficits and clinical symptoms in aMCI patients; however, its mechanistic effects on functional brain connectivity remain inadequately elucidated. Methods: This study employed low- and high-order functional analytical approaches to comprehensively investigate the effects of NFT on dynamic brain functional networks in aMCI. Results: Our findings revealed that following NFT, aMCI patients exhibited enhanced connectivity strength, global efficiency, and nodal characteristics within the delta band, whereas connectivity was generally attenuated in the theta, alpha, and beta bands. Dynamic network analysis indicated increased entropy in short-time windows. Cognitive assessments showed a significant short-term improvement in MoCA scores among 92.9% of participants. Conclusions: These results suggest that NFT effectively remodels brain network activity patterns in aMCI patients, thereby facilitating cognitive improvement. These findings provide preliminary insights into the brain network mechanisms underlying NFT-mediated cognitive enhancement in aMCI.

High-order segregation of brain functional networks in major depressive disorder patients with and without suicide

INTRODUCTION. Cerebral palsy (CP) is the leading cause of motor impairment in childhood and calls for comprehensive rehabilitation approaches whose effectiveness must be objectively verified. Resting-state functional MRI (rs-fMRI) provides new opportunities to explore neuroplastic changes elicited by therapy. AIM . To evaluate the impact of a rehabilitation course employing a neuro-orthopedic suit on clinical outcomes and resting-state functional connectivity in children with spastic forms of CP. MATERIALS AND METHODS. Thirty children with spastic diplegia (Gross Motor Function Classification System, GMFCS levels II–III; mean age (8±3) years) were enrolled in a prospective study. All participants completed a 4-week rehabilitation program using the “Atlant” suit. Clinical assessments (GMFM-88, Modified Ashworth Scale, MACS, SATCo, goniometry) and rs-fMRI (Siemens 1.5 T) were performed before and after the course. Seed-based analysis focused on the sensorimotor network (SMN); intraand inter-network connectivity changes were examined. Statistics included paired t tests and Pearson correlation. RESULTS. After the 4-week intervention, significant improvements were observed on all principal clinical scales: total GMFM-88 scores increased, spasticity on the Modified Ashworth Scale decreased, manual ability (MACS) and trunk control (SATCo) improved, and joint range of motion increased on goniometry (all p<0.05). rs-fMRI revealed strengthened functional connectivity between key SMN nodes (primary motor cortex and supplementary motor area) and a reduction of pathological hyperconnectivity between the SMN and the default mode network (DMN). Enhancement of SMN connectivity correlated with motor gains (r=0.65, p<0.01). CONCLUSION . Use of the rehabilitation suit leads not only to clinical improvement but also to favorable reorganization of functional brain networks in children with CP. Resting-state network analysis is a sensitive tool for objectifying rehabilitation effects and elucidating mechanisms of neuroplasticity.

Virtual reality (VR) technologies can induce realistic emotions in controlled experimental settings, offering unprecedented opportunities to study how the human brain processes emotions under real-world conditions. The integration of VR experiences with electroencephalography (EEG) provides a promising potential for gaining novel insights into individual emotional states. However, the complex network dynamics underlying human emotions during VR experiences remain largely unexplored. To address this gap, we leveraged graph-theoretical approaches to investigate functional brain networks derived from EEG signals recorded during immersive VR experiments. We assessed key topological properties of functional brain networks across multiple frequency bands (delta, theta, alpha, beta, gamma, and high gamma) and compared network characteristics between different emotional states (negative, neutral, and positive). Furthermore, we evaluated whether these graph-based features could accurately distinguish between positive and negative emotions using machine learning approaches. Our findings revealed distinct network patterns associated with different emotional states. During negative emotional experiences, we observed two key neural signatures: increased high gamma band activity in the left central region and decreased theta band activity in the occipital region. Conversely, positive emotions were characterized by reduced activity across most frequency bands in the left frontal region. Our machine learning model achieved an average classification accuracy of 79% in differentiating positive and negative emotions using network features that combined graph-theoretical measures and connectivity weights across all frequency bands, with the high gamma band demonstrating particular importance for emotion processing. This study advances our understanding of how brain networks dynamically reorganize during VR-induced emotional experiences and establishes the potential of graph-based EEG features for robust emotion recognition, paving the way for personalized VR applications.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-23573-z.

Community detection provides a principled lens on mesoscale organization in functional brain networks, yet many widely used methods presume assortative structure and depend on arbitrary thresholding, which complicates the selection of the community countK. We conducted a systematic benchmark of three assumption lean approaches that operate directly on weighted functional connectivity matrices: the Weighted Stochastic Block Model, Spectral Clustering, and K-means. Performance was assessed on synthetic networks with known ground truth and on three neuroimaging cohorts spanning development, namely the Human Connectome Project, Washington University 120, and the Baby Connectome Project. We compared strategies for choosingK, including post hoc indices such as silhouette, Calinski–Harabasz, C index, modularity, variation of information, Normalized Mutual Information, and zRand, together with a likelihood-based criterion for the Weighted Stochastic Block Model that uses bootstrap confidence intervals for differences in log likelihood between successive values ofK. In simulations all methods recovered stable partitions, but the post hoc indices favored incorrect values ofKunder weak signal and nonassortative mixing. In adult datasets the indices do not yield a unique optimum, whereas the likelihood-based criterion selects a parsimonious range centered nearK= 11, which is consistent with established sensory and association systems. In infants and toddlers, the same procedure supports a largerKaround 15 and reveals developmentally distinct mesoscale architecture, including anterior and posterior subdivisions within default mode and fronto parietal systems. A consensus relabeling scheme based on Hungarian matching with Hamming distance further stabilizes solutions across runs and across values ofK. Overall, threshold free weighted methods mitigate assortative bias and the likelihood-based comparison provides a reproducible path to selectingK.

Each individual's complex, multidimensional environment, known as their 'exposome', plays an essential role in shaping cognitive neurodevelopment. Understanding the mechanisms whereby children's exposome influences their development is crucial to facilitate the design of interventions to foster positive developmental trajectories for all youth. Recent work has identified a general exposome factor associated with socio-economic inequality that is strongly related to cognition and individual differences in the spatial organization of functional brain networks in youth. Building on these findings, the current study explores whether alterations in functional connectivity may represent a potential mechanism linking variation in the exposome to cognitive performance. We apply a data-driven, cross-validated, whole-brain machine learning approach, connectome-based statistical inference, to identify patterns of functional connectivity associated with exposome scores among early adolescents enrolled in the Adolescent Brain Cognitive Development (ABCD) Study using data collected during three cognitive tasks and during rest. Additionally, we investigate whether the identified patterns of functional connectivity relate to individual differences in cognitive performance across three domains: General Cognition, Executive Functioning, and Learning/Memory. Models incorporating 10-fold cross-validation over 100 iterations identified consistent functional connections associated with the exposome across task and rest conditions (model performance: ns = 6,137-8,391, rs = 0.34 - 0.44, ps <.001). Results were robust across data collection sites and functional connections common across all significant models were associated with cognitive performance across domains (ps < 0.0009). Collectively, these findings reveal that multidimensional environmental exposures are reflected in patterns of functional connectivity and relate to cognitive functioning among youth.

ObjectiveThis study aimed to use electroencephalogram (EEG) microstate analysis to characterize transient topographic patterns and rapid brain network reorganization in anti-leucine-rich glioma-inactivated 1 (LGI1) antibody encephalitis (anti-LGI1-AE).MethodsEEG data were collected from fifteen patients with anti-LGI1-AE and eighteen age- and sex-matched controls. K-means clustering was used to extract microstate sequences, and temporal parameters were compared between groups. For microstates showing significant differences, weighted phase lag index matrices were computed across frequency bands, and network-based statistics were applied to identify functional connectivity differences.ResultsThe topographic pattern of Microstate A differed significantly between the anti-LGI1-AE group and the control group (p = 0.002). Patients exhibited a significantly higher occurrence of Microstates B and C (p = 0.015 and p = 0.001, respectively). Additionally, the mean global field power of Microstate C was reduced in the patient group (p = 0.007). The transition probability from Microstate A to B was increased in patients (p = 0.013), though this difference did not remain significant after false discovery rate (FDR) correction (pFDR = 0.161). EEG functional network analysis based on microstates with significant differences revealed that, during Microstate B, patients showed a widespread increase in whole-brain functional connectivity in the beta frequency band (all p < 0.001). During Microstate C, enhanced delta-band connectivity was observed with the left occipital region serving as a core hub (p = 0.002). Beta-band connectivity was also increased between the left posterior temporal region, midline structures, and left parietal regions (p = 0.037).ConclusionWidespread alterations in functional brain networks are present in anti-LGI1-AE. Changes in microstate temporal parameters and enhanced functional connectivity may reflect compensatory regulatory mechanisms or pathological hyperactivation, revealing functional brain changes that go beyond overt structural damage.

Abstract Alzheimer’s disease (AD) is characterized by progressive neural network degradation. In brain functional networks, overlapping module structures provide more accurate representations of brain function than nonoverlapping structures. Since the involvement of overlapping nodes in multiple modules can vary over time, investigating dynamic functional changes in the brain may provide deeper insights into the structural characteristics of these overlapping modules. However, the spatiotemporal dynamics of overlapping modular brain organization remain unclear. We employed resting-state fMRI to explore the overlapping modular organization and dynamic multilayer modules in 64 AD (Agemean = 74.04) and 61 healthy controls (HC, Agemean = 74.86) from the Alzheimer’s Disease Neuroimaging Initiative. Compared with HC, AD exhibited increased overlapping modules and decreased modularity, with altered nodal overlapping probability, particularly in the superior frontal cortex and hippocampus. Higher nodal overlapping probability correlated with greater flexibility and was associated with larger amyloid deposits. Lasso regression analysis further revealed strong correlations between overlapping nodal characteristics and cognitive performance. Our findings suggest that overlapping nodes are critical components in AD, demonstrating high amyloid deposition, significant functional flexibility, and strong associations to cognitive behavior. These alterations may enhance the understanding of AD pathology and contribute to the development of biomarkers for improved diagnosis and therapeutic strategies.

Cerebellar vermis and somatosensory-motor cortex differentially contribute to sex differences in acute pain perception in rats.

In Multiple Sclerosis, inflammation and neurodegeneration disrupt structural and functional brain networks. While the association between structural connectivity and disability is rather clear, functional connectivity changes are not yet characterised as a physiological response to the disease, as functionally meaningful adaptation or as a deceptive response. We explored the topology of brain networks of 65 Multiple Sclerosis patients over up to seven years in comparison to 59 controls. Connectomes based on probabilistic tractography from diffusion weighted imaging and resting-state MRI, were analysed with graph theory. The hub disruption index estimated connectivity perturbation in relation to the network hierarchy. In controls, we observed a transient increase in functional hub connectivity in the 5th and 6th age decade as a response to a subtle diffuse loss of structural connectivity, before structural and functional connectomes show a pronounced loss of hub connectivity. In Multiple Sclerosis, structural hub disruption was present from the disease onset while the transient upregulation of functional hub connectivity in the middle age was lacking. Patients seem to transition directly into an exhausted hub connectivity configuration. However, we observed the transient functional reorganisation of hubs in the first years after disease onset. Multiple Sclerosis patients present a probable physiological response to structural connectivity loss very early in the disease, potentially leading to an accelerated hub overload with accelerated neurodegeneration. The onset of chronic progression in the 5th age decade might be partially driven by the absence of the physiological increased hub connectivity observed in healthy individuals.

Background: Chemotherapy-related cognitive impairment (CRCI), commonly known as "chemobrain," frequently occurs during breast cancer treatment and has been linked to altered brain function. This resting-state functional magnetic resonance imaging study examined chemotherapy-related changes in functional brain activity, network connectivity, and associations with cognitive outcomes. Methods: Twenty-eight patients with breast cancer were assessed prechemotherapy (BB) and postchemotherapy (BBF), alongside 27 healthy controls of comparable age at baseline (BH) and follow-up (BHF). Mean fractional amplitude of low-frequency fluctuations (mfALFF) and mean regional homogeneity (mReHo) quantified functional brain activity. Graph theoretical analysis (GTA) assessed network topology; network-based statistics (NBS) evaluated interregional connectivity. Cognitive performance was evaluated through standardized assessments. Results: Postchemotherapy patients exhibited reduced anxiety and lower FACT-Cog scores. Voxel-wise analyses showed increased mfALFF in frontal regions and mReHo in superior temporal and inferior frontal gyri, alongside decreases in postcentral, lingual, and parahippocampal areas. Healthy controls showed increased activity in medial frontal and cingulate regions, with reductions in the temporal lobe and putamen. GTA revealed higher global efficiency and reduced modularity, path length, and network complexity in the BBF group compared with BHF. NBS showed weaker structural connectivity in motor and occipital regions prechemotherapy and decreased parietal and insular connectivity postchemotherapy. Multiple regression showed brain-behavior correlations: declines in FACT-Cog, Digit Symbol Substitution, and mood scores were linked to altered activity in frontal, parietal, cingulate, and occipital areas, while positive correlations suggested compensatory activation. Conclusions: Chemotherapy was associated with longitudinal alterations in brain activity, network organization, and connectivity in breast cancer survivors. Brain-behavior associations suggest disrupted neural networks may underlie CRCI.