Most recent paper

Phytomedicine. 2026 Mar 27;155:158127. doi: 10.1016/j.phymed.2026.158127. Online ahead of print.

ABSTRACT

BACKGROUND: Circadian rhythm disruption and chronic sleep deprivation are increasingly recognized as key contributors to depression, largely through gut-brain axis dysregulation and neuroinflammatory activation. IL-17A, a pro-inflammatory cytokine primarily derived from intestinal Th17 cells, has emerged as a pivotal mediator linking gut immune imbalance to central nervous system dysfunction.

PURPOSE: This study aimed to elucidate the gut-derived IL-17A-STAT3/RORγt signaling mechanism underlying sleep-deprivation-induced depression and to determine whether Schisandrin B, a lignan from Schisandra chinensis, can alleviate depressive phenotypes by restoring gut-brain axis homeostasis.

METHODS: Clinical analyses of plasma cytokines and metabolites were integrated with a mouse model of sleep-deprivation-induced depression. Behavioral tests, resting-state fMRI, gut microbiota 16S rDNA sequencing, Western blotting, ELISA, and network pharmacology with molecular docking were employed to comprehensively investigate neuroimmune, microbial, and neurofunctional alterations.

RESULTS: Patients with circadian rhythm disorder-related depression exhibited elevated IL-17A and systemic inflammatory cytokines, accompanied by metabolic dysregulation. Sleep-deprived mice showed depressive-like behaviors, intestinal barrier disruption, Th17/IL-17A pathway activation, and abnormal RS-fMRI activity in mood-regulating brain regions. Schisandrin B treatment markedly reversed these changes-restoring gut microbial balance, enhancing barrier integrity, suppressing IL-17A-driven inflammation, and normalizing neural function. Mechanistically, Schisandrin B inhibited STAT3 phosphorylation and RORγt expression, while targeting MAPK1 and GSK3β as key regulatory nodes.

CONCLUSION: This study identifies gut-derived IL-17A-STAT3/RORγt signaling as a mechanistic bridge between sleep deprivation and neuroinflammation, providing direct evidence for the immunological basis of circadian rhythm-related depression. By integrating multi-omics and neuroimaging validation, we demonstrate for the first time that Schisandrin B exerts antidepressant-like effects via coordinated modulation of the gut-brain-immune network. These findings highlight Schisandrin B as a promising natural immunomodulatory candidate for the treatment of mood disorders associated with disrupted circadian rhythms.

PMID:41935463 | DOI:10.1016/j.phymed.2026.158127

Biol Sex Differ. 2026 Apr 4. doi: 10.1186/s13293-026-00891-z. Online ahead of print.

ABSTRACT

BACKGROUND: Understanding the impact of biological sex on the functional organization and dynamics of the brain is crucial for elucidating sex-specific differences in cognitive functions and neuropsychiatric disorders. Systems neuroscience often models the brain as a network of interconnected brain regions with functional connectivity (FC), i.e., the correlation between signal time courses, serving as a measure of connection strength. FC matrices, here derived from resting-state functional magnetic resonance imaging (rs-fMRI), define a network graph that can be characterized by its level of module segregation or, inversely, integration. Such parameters can be generated for the full length of the acquired data (static) or for short periods implying dynamically changing brain states. We recently made the interesting observation in a separate study (N = 63) that measures of brain integration and segregation based on dynamic functional connectivity (dFC) data differed between sexes, while graph-based measures based on static FC (sFC) did not, which we investigated in more detail in this study.

METHODS: We preregistered a replication of our analysis from the small sample in N = 501 subjects of the Human Connectome Project dataset. We performed cross-sectional comparisons between sexes of the static rs-fMRI graph parameters modularity and global efficiency, as well as the dFC parameters state prevalence, mean dwell time, mean inter-state transition time, and variability derived from a two-state model. Additionally, we explored whether sex differences in 66 cognitive and behavioral parameters are mediated by the FC integration measure with the strongest sex effect.

RESULTS: All static and dynamic measures of integration/segregation showed higher levels of functional integration in males, with effect sizes up to 0.60 for the dFC parameter prevalence. For three of the 66 explored cognitive and behavioral parameters, we observed that the prevalence of the integrated state mediated the sex difference: dexterity, agreeableness, and self-reported aggression.

CONCLUSION: We found consistent evidence across two datasets that rs-fMRI-based measures of brain integration are increased in males. An exploratory analysis, which requires replication, suggests that such differences mediate personality differences. This study highlights that biological sex differences in brain functional organization may contribute to sex-typical behaviors.

PMID:41935261 | DOI:10.1186/s13293-026-00891-z

Neuroimage. 2026 Mar 31:121904. doi: 10.1016/j.neuroimage.2026.121904. Online ahead of print.

ABSTRACT

PURPOSE: Alzheimer's disease is characterized by progressive accumulation of hyperphosphorylated tau protein, which propagates in a prion-like manner along connected neuronal pathways. However, it remains unclear whether functional connectivity between gray and white matter (FCGW) can predict tau spread. This study aimed to determine the association between FCGW and tau deposition and to evaluate its value in predicting longitudinal tau spread.

METHODS: We integrated resting-state fMRI with cross-sectional and longitudinal tau-PET data from two independent cohorts. We assessed baseline associations between FCGW and tau deposition and then constructed an individual-level spreading model to predict longitudinal tau accumulation.

RESULTS: In both cohorts, FCGW showed a positive correlation with tau deposition. Model-simulated white-matter tau deposition was associated with clinical scales and predicted cognitive decline. The spreading model, which incorporated baseline tau-PET and the top 10% of gray and white matter, yielded the highest predictive performance for future tau accumulation.

CONCLUSION: FCGW captures key network pathways underlying tau spread in AD and improves prediction of future tau accumulation. These findings highlight the importance of FCGW in understanding tau propagation and support development of network-targeted therapeutic strategies.

PMID:41933844 | DOI:10.1016/j.neuroimage.2026.121904

J Affect Disord. 2026 Mar 31:121719. doi: 10.1016/j.jad.2026.121719. Online ahead of print.

ABSTRACT

Depressive symptoms frequently co-occur in individuals with Mild Cognitive Impairment (MCI) and are thought to accelerate neurodegenerative progression. However, the underlying neural mechanisms of Depressed MCI (DMCI) remain largely unclear. This study employed a multimodal resting-state functional magnetic resonance imaging (rs-fMRI) approach combined with advanced machine learning techniques, to systematically examine spontaneous brain activity patterns and topological organization differences among DMCI, non-depressed MCI (nDMCI), and non-depressed cognitively normal controls (nDCN). The research analyzed amplitude-based rs-fMRI measures and graph-theoretical features. Voxel-wise analyses and connectivity comparisons were conducted between groups. Additionally, classification tasks were performed using classical machine learning models and a graph reinforcement learning (GRL) model. DMCI individuals exhibited increased activity in the right insula and decreased amplitude of low-frequency fluctuation (ALFF) in the left calcarine cortex, along with heightened fractional ALFF (fALFF) and percent amplitude of fluctuation (PerAF) in the precuneus and parahippocampal regions. Graph metrics revealed disrupted global and local efficiency in nDMCI compared to nDCN. Using differential matrices, machine learning achieved optimal accuracies of 0.82 ± 0.15 (DMCI vs. nDMCI) and 0.84 ± 0.15 (DMCI vs. nDCN). Conversely, the GRL model for nDMCI vs. nDCN peaked at 0.66 ± 0.02 using full matrices, dropping to 0.60 ± 0.04 with filtering, indicating deep graph models require complete topological data for subtle differences. Rs-fMRI and graph learning approaches offer promising avenues for subtype classification, highlighting the hyperactivity of the right insula and the integrity of the whole-brain functional connectivity matrix as crucial potential biomarkers of early pathological changes.

PMID:41933620 | DOI:10.1016/j.jad.2026.121719

Neuroimaging Clin N Am. 2026 May;36(2):367-377. doi: 10.1016/j.nic.2025.11.004. Epub 2026 Jan 23.

ABSTRACT

Functional MRI (fMRI) is a noninvasive imaging technique used to map areas of the brain important for language, motor, and visual function before surgery. Language lateralization with fMRI has proven useful as a surrogate for direct memory testing in patients with epilepsy to help predict postoperative morbidity, with Wada testing remaining useful if at risk for global amnesia. Task-based and resting-state fMRI can play a role in the evaluation of patients with generalized epilepsy before disconnective surgery or neuromodulation.

PMID:41932783 | DOI:10.1016/j.nic.2025.11.004

Biol Psychiatry Cogn Neurosci Neuroimaging. 2026 Apr 1:S2451-9022(26)00090-X. doi: 10.1016/j.bpsc.2026.03.017. Online ahead of print.

ABSTRACT

BACKGROUND: In Tourette Disorder (TD), tics are frequently associated with impulsivity, yet the mechanisms linking these dimensions and their evolution during adolescence remain unclear. We combined behavioral, clinical, and receptor-enriched by target (REACT) functional connectivity imaging to examine tic severity and impulsivity over time in TD adolescents.

METHODS: 64 TD adolescents and 56 healthy controls completed a saccadic motor waiting impulsivity (WI) task and resting-state fMRI at baseline. TD participants were reassessed 15 months later. REACT was used to examine dopamine transporter (DAT) - and serotonin 1B receptor (5-HT1B)-weighted functional connectivity within fronto-striatal motor and limbic networks. Longitudinal analyses focused on within-TD changes and their associations with clinical measures.

RESULTS: Behaviorally, no differences emerged between groups, but within TD, higher WI was associated with greater global tic severity (YGTSS/100). Longitudinally, both tics (YGTSS/50 and /100) and WI improved significantly. Compared to controls, TD showed a higher functional connectivity between caudate and anterior cingulate cortex (ACC) on the left. Longitudinally, tic reduction (YGTSS/50) was linked to increased connectivity between nucleus accumbens (NAcc) - ventral tegmental area on the left; reduced WI correlated with a higher left caudate - subgenual ACC and bilateral putamen-supplementary motor area (SMA) connectivity. In TD versus controls, REACT showed elevated both DAT-weighted connectivity between the NAcc-SMA and 5-HT1B-weighted connectivity between the raphe-SMA. Longitudinally in TD, while 5-HT1B-weighted connectivity declined DAT-weighted connectivity remained stable.

CONCLUSIONS: Changes in motor and limbic fronto-striatal functional connectivity were associated with longitudinal improvements in tics and WI in TD adolescents.

PMID:41932579 | DOI:10.1016/j.bpsc.2026.03.017

J Affect Disord. 2026 Apr 1:121718. doi: 10.1016/j.jad.2026.121718. Online ahead of print.

ABSTRACT

BACKGROUND: While brain network dysfunction characterizes attention-deficit/hyperactivity disorder (ADHD), surface-based connectivity patterns underlying its clinical heterogeneity remain underexplored. Herein, we investigated surface-based complex network architecture alterations in medication-naïve children with combined (ADHD-C) and inattentive (ADHD-I) subtypes.

METHODS: Children with ADHD-C (n = 43), ADHD-I (n = 35), and healthy controls (HCs; n = 31) were recruited for a series of clinical examinations and resting-state fMRI. We utilized surface-based graph theoretical analysis (GTA) and functional connectivity (FC) to assess network topology, correlating imaging indices with clinical variables.

RESULTS: Significant intra- and inter-network FC disruptions emerged in children with ADHD, particularly in default mode (DMN), ventral attentional (VAN), and somatosensory-motor (SMN) networks. In particular, the ADHD-C group (vs HC) exhibited more FC abnormalities involving SMN and DMN, whereas the ADHD-I group (vs HC) showed slightly more abnormal FC between VAN and dorsal attentional network (DAN). Crucially, ADHD-C patients demonstrated significantly weaker intra-SMN and SMN-DMN connectivity than the ADHD-I group. Generally, children with ADHD showed diminished global modularity, assortativity, and disrupted left lateral prefrontal cortex (PFCl1_L) nodal centrality. Additionally, higher FRCQ scores were significantly associated with increased assortativity in ADHD. The hypo-connectivity linking the DMN (Default-PFC7_L) and SMN (SomMot27_L) was correlated with both higher SNAP-IV Hyperactivity/Impulsivity and Total scores.

CONCLUSION: These findings elucidate neural substrates associated with sensorimotor and attentional deficits across ADHD subtypes. Surface-based network profiling underscores the disorder's biological heterogeneity and advances the mechanistic understanding of its complex neurodevelopment.

PMID:41932505 | DOI:10.1016/j.jad.2026.121718

Addict Behav. 2026 Apr 1;179:108700. doi: 10.1016/j.addbeh.2026.108700. Online ahead of print.

ABSTRACT

BACKGROUND: Alcohol Use Disorder (AUD) is increasingly understood as a disorder of connectomic dysregulation. However, node-centric models fail to capture the brain's overlapping functional architecture. We employed an edge-centric framework to quantify functional diversity from overlapping communities and investigated its neurobiological basis in AUD.

METHODS: We analyzed resting-state fMRI data from 93 individuals with AUD and 91 matched healthy controls. We quantified nodal functional diversity using normalized entropy derived from overlapping edge communities. In this context, high diversity (entropy approaching 1) reflects flexible, multi-network engagement, while low diversity (entropy approaching 0) reflects functional specialization. A Partial Least Squares Discriminant Analysis (PLS-DA) identified the whole-brain functional diversity pattern maximizing group separation. This pattern was then correlated with normative neurotransmitter receptor and gene expression data.

RESULTS: A PLS component significantly separated the groups (p < 0.001). This pattern was defined by decreased functional diversity in the nucleus accumbens and globus pallidus, and increased functional generalization in the insula and inferior frontal gyrus. This AUD-related pattern was negatively predicted by D1 and NMDA receptor distributions and positively by the 5-HTT transporter. Spatially, this pattern correlated with genes enriched for "synapse structure" and "cellular responses to stress".

CONCLUSION: Our edge-centric approach identified a bidirectional reorganization of functional diversity in AUD. This pattern, reflecting a specialized striatum and generalized insula, is spatially anchored to core dopaminergic/glutamatergic receptor maps and genetic pathways for synaptic plasticity and cellular stress, highlighting functional diversity as a novel, multilevel biomarker for AUD.

PMID:41932004 | DOI:10.1016/j.addbeh.2026.108700

Sci Rep. 2026 Apr 3. doi: 10.1038/s41598-026-43617-2. Online ahead of print.

ABSTRACT

Breathing rhythmically coordinates neural oscillations across the brain, yet how the breathing mode (nasal vs. oral) modulates large-scale functional networks over time remains unclear. Building on prior static connectivity findings, this study applied dynamic functional connectivity (dFC) analysis using a hidden Markov model (HMM) to resting-state fMRI data from 20 healthy adults during nasal and oral breathing, focusing on the 0.1-0.2 Hz frequency band. Three recurrent brain states were identified: (1) a weakly connected, segregated state; (2) a globally integrated state dominated by default mode, frontoparietal, salience, and limbic networks; and (3) a partially segregated intermediate state. Compared with oral breathing, nasal breathing stabilized the integrated state, increasing its lifetime (p-FDR = 0.03) and reducing switching rates (p-FDR = 0.002). Oral breathing showed greater fractional occupancy of the intermediate state (p-FDR = 0.03) and a higher probability of transitions from integration to fragmentation (p-FDR = 0.02). Graph-theoretic analysis also revealed that nasal breathing supported a configuration with higher efficiency and lower modularity. Taken together, this study provides the first respiration-entrained, HMM-based dFC analysis of resting-state fMRI, demonstrating that nasal breathing entrains a stable, globally coherent state, whereas oral breathing disrupts this stability and promotes fragmented network organization.

PMID:41932966 | DOI:10.1038/s41598-026-43617-2

Front Neurosci. 2026 Mar 18;20:1756997. doi: 10.3389/fnins.2026.1756997. eCollection 2026.

ABSTRACT

BACKGROUND: Migraine is a common neurological disorder involving network-level dysfunction. Increasing evidence suggests that migraine involves network-level dysfunction and is associated with altered resting-state functional connectivity. Traditional static functional connectivity analyses are limited in capturing the temporal dynamics of large-scale brain networks. The Hidden Markov Model (HMM) provides an advanced analytical framework to identify discrete, recurrent brain states and characterize their temporal properties without the constraints of arbitrary windowing assumptions.

OBJECTIVE: To characterize dynamic functional connectivity alterations in young patients with migraine without aura (MWoA) using HMM and examine associations between dynamic state metrics and clinical disability.

METHODS: Resting-state fMRI data were obtained from 200 participants (100 young MWoA patients and 100 matched healthy controls). Using the Dosenbach 160 ROI template (cerebellum excluded; N = 142), HMM identified recurring brain states. Group differences in fractional occupancy (FO), mean dwell time (MDT), and state transition probabilities were assessed. State-specific functional connectivity patterns were compared, and correlations with clinical indices (MIDAS, VAS, HIT-6) were evaluated.

RESULTS: Eleven robust dynamic brain states were identified. Compared with controls, migraine patients demonstrated increased FO and MDT in States 7 (dorsal attention network-dominant) and 9 (multisensory integration), alongside reduced values in sensorimotor states (States 3, 4, 8, 11). State 9 exhibited significant abnormalities in DMN-SC and DMN-VAN connectivity (FDR-corrected q < 0.05). Transition analyses revealed enhanced self-transitions and increased incoming transitions to States 7 and 9, whereas transitions among sensorimotor states were diminished. MDT in State 9 was positively correlated with MIDAS scores (r = 0.38, p < 0.05), indicating its association with functional disability.

CONCLUSIONS: Young MWoA patients exhibit a dual-mode dysfunction in brain dynamics: heightened external vigilance (State 7) and impaired segregation of internal-external processing (State 9), accompanied by instability in baseline sensorimotor configurations. Prolonged dwelling in State 9 and its correlation with disability highlight this multisensory integration state as a potential biomarker of migraine-related functional impairment. These findings provide new insights into neurobiological mechanisms and support dynamic network-based therapeutic strategies.

PMID:41929702 | PMC:PMC13038879 | DOI:10.3389/fnins.2026.1756997

bioRxiv [Preprint]. 2026 Mar 28:2026.03.25.714361. doi: 10.64898/2026.03.25.714361.

ABSTRACT

Compared with model-based and phenomenological descriptions of the spatiotemporal dynamics of resting-brain activity, statistical characterizations of resting-state fMRI (rs-fMRI) data remain relatively underexplored. Some sophisticated analysis techniques, such as Mapper-based topological data analysis (TDA) and innovation-driven coactivation pattern analysis (iCAP), can distinguish real data from phase-randomized (PR) surrogates, suggesting that rs-fMRI data are not as simple as stationary Gaussian processes. However, the exact statistical properties that distinguish real rs-fMRI data from PR surrogates have not yet been determined. In this study, we conducted system identification analysis and surrogate data analysis to specify key statistical properties that allow TDA and iCAP to discriminate real rs-fMRI data from PR surrogates. We first analyzed rs-fMRI data concatenated across scans using autoregressive (AR) modeling and found that the scan-concatenated rs-fMRI data were weakly non-Gaussian. However, non-Gaussianity alone was insufficient to reproduce realistic TDA and iCAP results because of non-stationarity across scans. AR modeling of single-scan data revealed that rs-fMRI data were statistically indistinguishable from a Gaussian distribution within a single scan, although TDA and iCAP results still differed between the real data and PR surrogates. A new surrogate dataset designed to preserve non-stationarity successfully reproduced realistic TDA and iCAP results, suggesting that TDA and iCAP likely capture the non-stationarity of rs-fMRI data to distinguish it from PR surrogates. Together, these results indicate approximate Gaussianity and non-stationarity in rs-fMRI data, providing a data-driven and statistical characterization of resting-state brain activity that can serve as a quantitative reference for whole brain simulations and generative models.

PMID:41929222 | PMC:PMC13041960 | DOI:10.64898/2026.03.25.714361

bioRxiv [Preprint]. 2026 Mar 26:2026.03.26.713939. doi: 10.64898/2026.03.26.713939.

ABSTRACT

Functional magnetic resonance imaging (fMRI) captures whole-brain activity fluctuations non-invasively in humans and animals. Beyond task/stimuli-locked responses, fMRI measures large-scale patterned activity during rest. An established method for identifying patterned activity in fMRI data, termed quasi-periodic pattern (QPP) analysis, identifies waves of activity which unfold over seconds and have consistent spatiotemporal characteristics. Notably, certain fMRI-QPPs are well-preserved across species and altered in various neuropsychiatric and neurodegenerative diseases. Yet, our collective understanding of their neural underpinnings is limited given the indirect nature of blood-oxygen-level dependent (BOLD) fMRI signals. Simultaneous measures of local field potentials have provided some affirmation that fMRI-QPPs have neural origins, but these point-measurements are limited to a handful of sites. Here, we use a unique multimodal implementation of simultaneous wide-field calcium (WF-Ca 2+ ) imaging and fMRI to investigate the neural origins of fMRI-QPPs.. We uncover a robust time-locked correlation between QPPs detected by cortex-wide fluorescent WF-Ca 2+ imaging of neural activity and QPPs of BOLD-fMRI. These data validate the hypothesis that BOLD QPPs derive from preceding slow waves of neural activity with regional and temporal precision.

PMID:41929218 | PMC:PMC13042064 | DOI:10.64898/2026.03.26.713939

Proc IEEE Int Symp Biomed Imaging. 2025 Apr;2025. doi: 10.1109/isbi60581.2025.10980924. Epub 2025 May 12.

ABSTRACT

This study examines how multi-scale intrinsic connectivity networks (ICNs) relate to cognitive and behavioral functions in adolescents, focusing on attention/vigilance, working memory, and behavioral regulation. Leveraging the NeuroMark 2.2 multi-scale ICN template obtained from over 100,000 subjects, we obtained multi-scale ICNs from baseline resting-state fMRI data from the ABCD Study. For this study, we are interested in "the fronto- thalamo-cerebellar (FTC) circuitry" and choose the subdomains of Neuromark 2.2 that cover it: Cerebellar (CB), Subcortical - Extended Thalamic (SC-ET), Higher Cognition - Insular Temporal (HC-IT), and Higher Cognition - Frontal (HC-FR), previously identified as relevant to cognitive and behavioral functions. Employing a multivariate approach combining principal component analysis (PCA) and canonical correlation analysis (CCA), we examined associations between these multi-scale ICNs and cognitive-behavioral outcomes. Our findings revealed significant associations, particularly for one of the estimated canonical components, linking multi-scale ICNs to cognitive and behavioral measures across both discovery and replication sets. This connectivity pattern may serve as a potential marker for attention, working memory, and behavioral regulation, offering new insights into a wide spectrum of neurodevelopmental disorders including Attention-Deficit/Hyperactivity Disorder (ADHD).

PMID:41928912 | PMC:PMC13042259 | DOI:10.1109/isbi60581.2025.10980924

Proc IEEE Int Symp Biomed Imaging. 2025 Apr;2025. doi: 10.1109/isbi60581.2025.10980889. Epub 2025 May 12.

ABSTRACT

Resting-state functional magnetic resonance imaging (rs-fMRI) offers valuable insights into the human brain's functional organization and is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this study, we introduce a novel attention mechanism for graphs with subnetworks, named Self Clustering Graph Transformer (SCGT), designed to handle the issue of uniform node updates in graph transformers. By using static functional connectivity (FC) correlation features as input to the transformer model, SCGT effectively captures the sub-network structure of the brain by performing cluster-specific updates to the nodes unlike uniform node updates like vanilla graph transformers, further allowing us to learn and interpret the subclusters. We validate our approach on the Adolescent Brain Cognitive Development (ABCD) dataset, comprising 7,957 participants, for the prediction of total cognitive score and gender classification. Our results demonstrate that SCGT outperforms the vanilla graph transformer method, and other recent models, offering a promising tool for modeling brain functional connectivity and interpreting the underlying subnetwork structures.

PMID:41928911 | PMC:PMC13042258 | DOI:10.1109/isbi60581.2025.10980889

J Headache Pain. 2026 Apr 2. doi: 10.1186/s10194-026-02351-w. Online ahead of print.

NO ABSTRACT

PMID:41928092 | DOI:10.1186/s10194-026-02351-w

Mol Psychiatry. 2026 Apr 2. doi: 10.1038/s41380-026-03589-1. Online ahead of print.

ABSTRACT

Impulsivity is a core symptom across multiple addictive disorders, including internet gaming disorder (IGD), yet its multidimensional nature-particularly the neural basis of state and trait impulsivity in IGD-remains poorly understood. We aimed to elucidate the neural correlates of both impulsivity dimensions and uncover how IGD interacts with the heightened impulsive tendencies. Here we conducted a fMRI study with 87 college students at risk for IGD, employing a modified card-guessing task to capture state impulsivity under a loss decision framework. Modified card-guessing paradigm was applied to assess subjects' state-impulsivity via the loss chasing behavior-the tendency to increase wagers to recover previous losses. Trait impulsivity was assessed using the UPPS-P scale. Another independent cohort (n = 84) with similar IGD profiles was further to validate our finding. Behavioral modelling indicated that state impulsivity, manifesting as the loss chasing behavior (i.e., higher wagers under the loss decision framework), dynamically increased as a function of the loss streaks. Neuroimaging analyses identified key brain regions-such as the right middle frontal gyrus, superior frontal gyrus, striatum, and insula-whose activation during loss feedback predicted subsequent impulsive decisions. These neural signatures of state impulsivity successfully distinguished high-risk IGD individuals. Crucially, resting-state functional connectivity (rs-FC) within these regions not only identified IGD risk but also predicted trait impulsivity. Mediation analysis further demonstrated that IGD's influence on trait impulsivity was indirectly mediated by rs-FC patterns linked to state impulsivity. Our findings elucidate the distinct yet interconnected neural representations of state and trait impulsivity in IGD, underscoring the neurobehavioral continuum linking transient impulsive states to enduring impulsive traits in IGD.

PMID:41927766 | DOI:10.1038/s41380-026-03589-1

Int J Dev Neurosci. 2026 Apr;86(2):e70120. doi: 10.1002/jdn.70120.

ABSTRACT

BACKGROUND: Structural white matter (WM) alterations are recognized in Autism Spectrum Disorder (ASD), yet the functional connectivity (FC) of WM networks and its clinical significance remain largely underexplored.

METHODS: This study aimed to investigate aberrant FC patterns within intra-WM (WM-WM) and WM-grey matter (WM-GM) networks in a large ASD cohort. Resting-state fMRI data from 272 ASD individuals and 368 typical controls (TC) from the ABIDE-II dataset were analysed. We constructed WM-WM and WM-GM FC networks using Pearson correlations between atlas-defined regions, applied ComBat harmonization and employed Network-Based Statistic (NBS) to identify group differences. Associations with clinical symptoms were assessed using the Social Responsiveness Scale (SRS) scores, and a CatBoost algorithm was used for diagnostic classification based on connectivity features.

RESULTS: NBS analyses revealed significantly increased connectivity in ASD for 116 WM-WM pairs and 58 WM-GM pairs (p < 0.05, FWER-corrected). Critically, the strength of these aberrant WM-WM functional connections exhibited a significant negative correlation with SRS total scores (r = -0.22, p < 0.001), whereas WM-GM connectivity showed no such significant association. The hybrid CatBoost classifier, integrating both WM-WM and WM-GM features, achieved moderate diagnostic discrimination (AUC = 0.669 ± 0.040).

CONCLUSION: These results offer novel insights into the aberrant functional architecture of WM-related networks in ASD, particularly linking intra-WM dysconnectivity to symptom severity, thereby enhancing our understanding of the neural substrates underlying social-communicative deficits.

PMID:41927508 | DOI:10.1002/jdn.70120

PLoS Biol. 2026 Apr 2;24(4):e3003666. doi: 10.1371/journal.pbio.3003666. eCollection 2026 Apr.

ABSTRACT

Emotion regulation is essential for well-being and mental health, yet individuals vary widely in their emotion regulation success. Why? Traditional neuroimaging studies of emotion regulation often focus on localized neural activity or isolated networks, overlooking how large-scale brain organization relates to the integration of distributed systems and sub-processes supporting regulatory success. Here, we applied a novel system-level framework based on spatial gradients of macroscale brain organization to study variance in emotion regulation success. Using two large functional magnetic resonance imaging (fMRI) datasets (n = 358, n = 263), we projected global activation patterns from a laboratory emotion regulation task onto principal gradients derived from independent resting-state fMRI data from the Human Connectome Project. These gradients capture low-dimensional patterns of neural variation, providing a topographical framework within which complex mental phenomena, such as emotion regulation, emerge. In both datasets, individual differences in regulation success were associated with systematic reconfiguration along Gradient 1-a principal axis differentiating unimodal and heteromodal brain areas. This gradient-based neural reconfiguration also associates with lower negative affect in daily life, as measured via smartphone-based experience sampling in a subset of participants (n = 55). Meta-analytic decoding via Neurosynth revealed that Gradient 1 and regulation success align with multiple psychological processes, including social cognition, memory, attention, and negative emotion, suggesting this gradient reflects diverse, integrative demands during effective emotion regulation. These findings introduce a gradient-based perspective on emotion regulation success that is biologically grounded in well-established large-scale brain organization and ecologically valid through its links with real-world emotional experience. Such gradient-based dynamics may serve as predictive biomarkers of regulatory success and inform targeted interventions in clinical populations.

PMID:41926350 | DOI:10.1371/journal.pbio.3003666

Neuropsychiatr Dis Treat. 2026 Mar 27;22:577735. doi: 10.2147/NDT.S577735. eCollection 2026.

ABSTRACT

OBJECTIVE: This study aimed to examine functional connectivity (FC) alterations in patients with major depressive disorder (MDD), specifically comparing those with and without anhedonia.

METHODS: 24 MDD patients with anhedonia (MDD-WA), 17 MDD patients without anhedonia (MDD-WoA), and 40 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI). Intrinsic brain function was assessed using resting-state FC and spatial associations with neurotransmitter receptor distribution analyses. The habenula (Hb) was selected as the region of interest (ROI), and the whole-brain FC of the Hb was compared across groups. Spatial correlations between inter-group FC differences and whole-brain neurotransmitter receptor/transporter expression templates, derived using the JuSpace tool, were analyzed. Additionally, FC values from differential brain regions were extracted and correlated with the scores on the clinical scale.

RESULTS: Compared to MDD-WoA patients, MDD-WA patients exhibited reduced FC between the Hb and the left middle frontal gyrus (MFG). Enhanced FC was observed between the Hb and bilateral putamen in MDD-WA patients compared to HCs. Additionally, in the MDD-WA group, changes in the Hb whole-brain FC demonstrated positive correlations with the spatial density distribution of specific neurotransmitter receptors and transporters. In contrast, MDD-WoA showed no significant Hb FC differences or neurotransmitter correlations compared to HCs. No significant correlations were found between the FC values of the intergroup-different brain regions and the total SHAPS-C, total HAMD-17, and all HAMD-17 factor scores (anxiety/somatization, weight loss, cognitive disturbance, retardation, and sleep disturbance).

CONCLUSION: In patients with MDD-WA, FC between the habenula and bilateral putamen, as well as between the habenula and left MFG, was altered. Given that the putamen is a core component of the striatum, all these findings suggest that habenula-prefrontal-striatal dysconnectivity may represent an anhedonia-specific biomarker in MDD. The FC patterns between the habenula and bilateral putamen/left middle frontal gyrus, along with the related neurotransmitter profiles identified in this study, may serve as objective indicators for monitoring therapeutic efficacy in the future.

PMID:41923921 | PMC:PMC13037516 | DOI:10.2147/NDT.S577735

Sci Rep. 2026 Apr 1. doi: 10.1038/s41598-026-44794-w. Online ahead of print.

ABSTRACT

Asthma involves not only airway inflammation but also aberrant central nervous system regulation. While static functional connectivity studies have revealed brain network abnormalities in asthma patients, the transient temporal dynamics of brain functional states remain largely unexplored. To investigate brain dynamic functional connectivity characteristics in asthma patients using Hidden Markov Models (HMM) and to identify potential neurobiological markers associated with clinical symptoms. Resting-state fMRI data were acquired from an initial pool of participants, with 120 age- and gender-matched individuals (60 asthma patients and 60 healthy controls) included after stringent quality control and head-motion scrubbing. HMM was applied to identify recurring brain states based on the Schaefer-142 parcellation. We compared groups on fractional occupancy (FO), mean dwell time (MDT), and transition probabilities. Exploratory correlation analyses were performed to evaluate the relationship between HMM-derived metrics and clinical scores (ACT and pulmonary function). HMM identified nine distinct functional states. Asthma patients exhibited a significantly increased MDT and FO in State 2 (characterized by somatomotor and dorsal attention network involvement) compared to healthy controls (p < 0.05). Exploratory analysis revealed a nominal positive correlation between the MDT of State 2 and Asthma Control Test (ACT) scores (r = 0.30, p < 0.05, uncorrected), suggesting a potential compensatory role of this state in symptom monitoring. Our findings reveal altered brain state dynamics in asthma, particularly the prolonged occupancy in a sensory-attention-related state. While the brain-clinical associations are exploratory, these dynamic metrics provide novel insights into the central mechanisms of asthma and may serve as preliminary neurobiological markers for symptom control.

PMID:41922478 | DOI:10.1038/s41598-026-44794-w