Most recent paper

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

Front Neuroendocrinol. 2026 Mar 30:101249. doi: 10.1016/j.yfrne.2026.101249. Online ahead of print.

ABSTRACT

A systematic review was conducted to investigate functional brain changes in type 1 diabetes mellitus (T1DM) assessed with functional magnetic resonance imaging (fMRI) between 2006 and 2025. A total of 27 eligible studies were included, involving 1072 participants with T1DM, with a mean age ranging from 20.4 to 51.5 years. The quality of these studies was evaluated using the NIH Quality Assessment Tool. Resting-state fMRI (n = 12) demonstrated 1) altered brain networks, especially in the default mode and salience networks, and 2) changes in subcortical regions and the frontal lobe. Task-based fMRI (n = 15) showed increased activity in the visual, salience, and thalamic networks, and decreased activity in the default mode network. The review highlights the complex relationship between T1DM and brain changes, presenting evidence of deviations from normal brain activity in specific areas involved in sensory-motor, limbic and cognitive regions that may reflect neurophysiological adaptations or consequences related to T1DM. Registration: PROSPERO (International Prospective Register of Systematic Reviews) under ID: CRD42023456789.

PMID:41921840 | DOI:10.1016/j.yfrne.2026.101249

J Int Neuropsychol Soc. 2026 Apr 1:1-12. doi: 10.1017/S135561772610191X. Online ahead of print.

ABSTRACT

OBJECTIVE: The quest for non-invasive and cost-effective biomarkers for mild cognitive impairment (MCI) and Alzheimer's disease (AD) has led to growing interest in resting-state functional magnetic resonance imaging (MRI). This study examined associations between whole-brain functional connectivity measures and cognitive performance across a spectrum of cognitive aging.

METHOD: A total of 108 older adults (mean age 74.1 ± 5.7 years), comprised of cognitively intact individuals, participants with amnestic MCI, and those with mild dementia due to probable AD, underwent high-resolution structural MRI and resting-state functional MRI scans and cognitive testing with the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Functional connectivity values were derived from a 17-network brain parcellation. Correlations were established between network connectivity values and RBANS Index scores.

RESULTS: Analyses revealed that lower RBANS Attention Index and Total Scale scores were significantly associated with increased connectivity between the ventral attention, central executive network, and limbic and default mode networks. Lower RBANS total scores were also associated with functional connectivity strength between the dorsal default mode networks and lateral frontoparietal regions of the central executive network, with increased connectivity observed across the dementia spectrum (Intact-MCI-AD).

CONCLUSIONS: These findings suggest that aberrant and potentially compensatory increases in functional connectivity may be linked to cognitive decline, supporting the utility of resting-state functional MRI as a promising biomarker for MCI and AD.

PMID:41919533 | DOI:10.1017/S135561772610191X

Acta Neuropsychiatr. 2026 Apr 1:1-20. doi: 10.1017/neu.2026.10073. Online ahead of print.

ABSTRACT

OBJECTIVE: There is a need for deeper understanding of neurological and psychological aspects of pedophilic disorder (PeD) to improve management of the disorder and thereby prevent child sexual abuse. Functional magnetic resonance imaging (fMRI) measures have been suggested as imaging biomarkers that may contribute towards this goal. A previous study using degarelix, a testosterone suppressing drug, showed promising results in decreasing the risk of committing child sexual abuse among individuals with PeD. In this study, we evaluate functional connectivity (FC) related to PeD and degarelix treatment.

METHODS: We used independent component analysis on resting state (rs)fMRI data acquired at baseline as well as two and ten weeks after injection of degarelix (or placebo) to evaluate FC alterations related to PeD and the degarelix treatment effect.

RESULTS: FC was altered in relation to several resting state networks in individuals with PeD compared to healthy controls at baseline. At follow-up time points, however, group comparisons were inconclusive and did after FDR correction not render statistically significant FC alterations when comparing patients to controls or related to degarelix treatment, child sexual abuse (CSA) dynamic risk scores or comorbidities.

CONCLUSION: We found FC alterations in PeD compared to healthy controls at baseline, however, no consistent, treatment specific FC signature of degarelix was demonstrated.

PMID:41918172 | DOI:10.1017/neu.2026.10073

Brain Behav. 2026 Apr;16(4):e71363. doi: 10.1002/brb3.71363.

ABSTRACT

PURPOSE: This study aimed to identify functional brain connectivity patterns associated with cognitive performance in Beta-thalassemia major (β-TM) children and to determine whether hematological factors influence cognition indirectly through alterations in connectivity.

METHOD: We recruited 25 children with β-TM and 35 age-matched healthy controls. Cognitive performance was assessed using the Wechsler Intelligence Scale (WIS). Resting-state functional MRI data were processed to construct whole-brain functional connectivity matrices. We applied network-based statistics (NBS) to compare connectivity differences between groups and connectome-based predictive modeling (CPM) with cross-validation to predict cognitive scores. Mediation analyses were further conducted to test whether hematological metrics (hemoglobin level, red blood cell distribution width) impacted cognition through functional connectivity.

FINDING: Compared to controls, β-TM children showed significantly reduced WIS scores and widespread disruptions in functional connectivity, particularly in cerebellar, motor, and temporal networks. The CPM approach identified a predictive network that largely overlapped with the NBS-derived network and robustly predicted WIS scores. Mediation analysis revealed that hemoglobin and red blood cell distribution width influenced cognitive scores indirectly through altered connectivity, indicating a full mediation effect.

CONCLUSION: This study provides evidence that hematological abnormalities in β-TM children impair cognitive performance via their impact on functional brain networks. Functional connectivity signatures derived from CPM may serve as early neuromarkers of cognitive vulnerability and could inform future monitoring and intervention strategies in this population.

PMID:41917757 | DOI:10.1002/brb3.71363

J Neural Transm (Vienna). 2026 Mar 31. doi: 10.1007/s00702-026-03148-6. Online ahead of print.

ABSTRACT

Brain connectivity dynamics in mild cognitive impairment with Lewy bodies (MCI-LB) is largely unknown. We aimed to identify brain connectivity dynamics related to cognitive performance in drug naïve MCI-LB. Healthy (55 participants, age 67.7 ± 6.4, 33 females) and MCI-LB subjects (30 participants, age 68.5 ± 6.0, 16 females) underwent cognitive testing and fMRI. The fast eigenvector centrality dynamic functional connectivity was employed to detect connectivity states. Mean state duration and occurrence were correlated to cognitive measures. In the MCI-LB group only, the occurrence and mean duration of the state characterized by between-network connectivity of the dorsal attention, sensorimotor, and visual networks correlated with visuospatial function (VSF) domain z-scores (r = 0.68, p = 0.003, and r = -0.68, p = 0.003, respectively). The VSF also correlated with the occurrence of the state characterized by intra-network connectivity of the frontoparietal control network (r = -0.55, p = 0.041). We identified specific mechanisms that seem to facilitate visuospatial performance in MCI-LB.

PMID:41915163 | DOI:10.1007/s00702-026-03148-6

Brain Behav. 2026 Apr;16(4):e71338. doi: 10.1002/brb3.71338.

ABSTRACT

BACKGROUND: Narcolepsy Type 1 (NT1) results from loss of hypothalamic orexin neurons, leading to unstable sleep-wake transitions. However, how this manifests as dynamic whole-brain functional state instability remains poorly characterized.

METHODS: We applied Hidden Markov Modeling (HMM) to resting-state functional magnetic resonance imaging (fMRI) data from 30 patients with NT1 and 30 age- and sex-matched healthy controls to identify recurrent brain states, quantify their fractional occupancy (FO), and examine associations with clinical symptoms-specifically excessive daytime sleepiness (Epworth Sleepiness Scale, ESS) and hallucinations.

RESULTS: Five distinct dynamic brain states were identified. Compared to controls, NT1 patients showed significantly reduced FO in State 1 (thalamocortical arousal/sensory gating; pFDR < 0.001) and elevated FO in State 4 (basal ganglia-limbic-sensorimotor integration; pFDR < 0.001) and State 5 (reward-introspection; pFDR = 0.036). Notably, within the NT1 group, patients with hallucinations exhibited higher FO in State 1 than those without (pFDR = 0.042), suggesting aberrant recruitment of this state during sleep-wake transitions. Additionally, State 4 FO showed a moderate positive correlation with ESS scores (Spearman's ρ = 0.38, pFDR = 0.078).

CONCLUSIONS: NT1 is associated with reduced stability of a thalamocortical alertness state and increased expression of REM-like limbic-subcortical network configurations. State 1 occupancy is relatively elevated in patients with hallucinations, while State 4 shows a positive association with excessive daytime sleepiness and features of REM dissociation. These findings support a dynamic, whole-brain systems-level framework for understanding symptom heterogeneity in NT1.

PMID:41914370 | DOI:10.1002/brb3.71338

Brain Behav. 2026 Apr;16(4):e71352. doi: 10.1002/brb3.71352.

ABSTRACT

BACKGROUND: Schizophrenia is a severe psychiatric disorder marked by specific cognitive and clinical disturbances, for which neuroimaging biomarkers remain elusive. Novel theoretical and computational frameworks, such as integrated information decomposition, offer promising approaches to provide interpretable biomarkers for neuroimaging alterations in schizophrenia, potentially capturing disruptions relevant to consciousness and self-experience.

METHODS: In this preliminary methodological exploration study, resting-state functional MRI (rsFMRI) data from 72 patients with schizophrenia and 74 healthy controls were retrieved and analyzed. Integrated information decomposition was leveraged to assess pairwise brain connectivity according to redundant, transferred, and synergistic components of information processing, as well as an overall metric of emergent consciousness/information integration: Φ. Clinical correlates with the Positive and Negative Syndrome Scale and the Wechsler Adult Intelligence Scale were assessed by partial Spearman correlations. Diagnostic accuracy was assessed through L1-regularized logistic regressions, after 5-fold cross-validation.

RESULTS: Redundancy was positively correlated with intelligence quotient (IQ) across both groups (rho = 0.187, p-value = 0.033). Within patients, information metrics were positively correlated with stereotyped thinking (min rho = 0.343, max p-value = 0.006) and preoccupation (min rho = 0.250, max p-value = 0.046). Positive symptoms were positively correlated with redundancy (min rho = 0.250, max p-value = 0.047). Promising diagnostic accuracy was reached with Φ (balanced accuracy = 64.38%, area under the curve = 70.89%), redundancy (balanced accuracy = 84.93%, area under the curve = 92.30%), and synergy (balanced accuracy = 65.75%, area under the curve = 70.93%).

CONCLUSIONS: These preliminary findings suggest that information metrics may offer clinically relevant, interpretable biomarkers for schizophrenia.

PMID:41913713 | DOI:10.1002/brb3.71352

Behav Brain Res. 2026 Mar 28:116181. doi: 10.1016/j.bbr.2026.116181. Online ahead of print.

ABSTRACT

BACKGROUND: Rumination is a transdiagnostic, persistent factor across many psychiatric conditions. Mapping its neural mechanisms may help differentiate ruminative profiles.

OBJECTIVE: To systematically review fMRI studies that evaluated rumination in psychiatric populations METHODS: We searched four databases (PubMed, EMBASE, Web of Science, Scopus) and included studies that integrated rumination measures with fMRI data.

RESULTS: Thirty-two articles met criteria; most were non-randomized (n = 31, 97%) and focused on depression (n = 24, 75%). Across disorders, clinical groups showed higher rumination than healthy controls, including in euthymic participants. In major depressive disorder, neural alterations implicated the default mode network, limbic and striatal regions, dorsolateral prefrontal cortex, and anterior cingulate cortex consistent with maladaptive ongoing processing. Neural signatures often persisted in remitted patients, suggesting possible trait markers of vulnerability. Intervention studies (n = 7) support using neuroimaging to track ruminative profiles and to inform more personalized interventions.

CONCLUSION: This review highlights the importance of examining rumination, here measured via trait questionnaires, state-induction paradigms, cognitive tasks, or resting-state fMRI, using more consistent conceptual and methodological approaches. Although rumination is transdiagnostic, implicated brain regions and networks may show disorder-specific patterns. Given heterogeneity in the literature, future work should prioritize controlled and interventional studies to clarify neural mechanisms and guide circuit-based, targeted treatments. PROSPERO REGISTRATION: #CRD532512.

PMID:41912094 | DOI:10.1016/j.bbr.2026.116181

Autism Res. 2026 Mar 30:e70241. doi: 10.1002/aur.70241. Online ahead of print.

ABSTRACT

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with atypical social communication as a core symptom. Variations in social information processing in individuals with ASD are associated with the social brain, which encompasses four specific subnetworks, that is, reward system, theory of mind network, mirror neuron system, and face perception network. However, the relationship between neural mechanisms of altered social functioning and modular integration of these subnetworks within the social brain remains unclear in ASD. With resting-state functional MRI (rs-fMRI) data from two large-scale datasets (ABIDE I and II), we computed the participation coefficient to explore the abnormal modular integration of the four subnetworks in 298 ASDs and 348 typically developing (TD) controls. Then, its associations with clinical symptoms, neurotransmitter systems, and transcriptional signatures were investigated. Additionally, the age effect on aberrant modular integration was estimated with linear regression models. Finally, we assessed the reproducibility of our results from a meta-perspective using other datasets. ASD participants exhibited increased integration of the reward system relative to TDs, which was correlated with Social Responsiveness Scale total score, the neurotransmitters such as 5HT1a and GABAa, and the disruption of the transcriptional signatures including cell proliferation and migration as well as tube and tissue morphogenesis. Additionally, the modular integration abnormality of the reward system was stable across development and replicated across datasets. We revealed a symptom-related, neurotransmitter- and transcriptional signature-associated, age-stable, and reproducible modular integration abnormality of the reward system in ASD. This hyper-integration was linked to reduced GABAa and serotonin receptor densities, providing neuroimaging and molecular evidence supporting the excitatory-inhibitory imbalance theory of ASD and insights into the mechanisms underlying social variations in ASD.

PMID:41912444 | DOI:10.1002/aur.70241