Most recent paper

Brain Sci. 2025 Oct 23;15(11):1136. doi: 10.3390/brainsci15111136.

ABSTRACT

BACKGROUND: Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental condition for which accurate and automated diagnosis is crucial to enable timely intervention. Resting-state functional magnetic resonance imaging (rs-fMRI) serves as one of the key modalities for diagnosing ASD and elucidating its underlying mechanisms. Numerous existing studies using rs-fMRI data have achieved accurate diagnostic performance. However, these methods often rely on a single brain atlas for constructing brain networks and overlook the data heterogeneity caused by variations in imaging devices, acquisition parameters, and processing pipelines across multiple centers.

METHODS: To address these limitations, this paper proposes a multi-view, low-rank subspace graph structure learning method to integrate multi-atlas and multi-center data for automated ASD diagnosis, termed M3ASD. The proposed framework first constructs functional connectivity matrices from multi-center neuroimaging data using multiple brain atlases. Edge weight filtering is then applied to build multiple brain networks with diverse topological properties, forming several complementary views. Samples from different classes are separately projected into low-rank subspaces within each view to mitigate data heterogeneity. Multi-view consistency regularization is further incorporated to extract more consistent and discriminative features from the low-rank subspaces across views.

RESULTS: Experimental results on the ABIDE-I dataset demonstrate that our model achieves an accuracy of 83.21%, outperforming most existing methods and confirming its effectiveness.

CONCLUSIONS: The proposed method was validated using the publicly available Autism Brain Imaging Data Exchange (ABIDE) dataset. Experimental results demonstrate that the M3ASD method not only improves ASD diagnostic accuracy but also identifies common functional brain connections across atlases, thereby enhancing the interpretability of the method.

PMID:41300144 | DOI:10.3390/brainsci15111136

Brain Sci. 2025 Oct 22;15(11):1133. doi: 10.3390/brainsci15111133.

ABSTRACT

Background: Resting-state functional connectivity (RSFC) is widely used to identify abnormal brain function associated with depression. Resting-state functional magnetic resonance imaging (fMRI) scans have many potential confounds, and task-based FC might provide complementary information leading to better insight on brain function. Methods: We used MATLAB's (version 2024b) CONN toolbox (version 22a) to evaluate FC in 40 adults with and without major depressive disorder (MDD) (nMDD = 23, nHC = 17). fMRI acquisition was performed while participants were at rest and while performing the Selves Task, an individualized goal priming task. Seed-based analyses were performed using two seeds: medial prefrontal cortex (mPFC) and left hippocampus. Results: Both groups showed strong positive RSFC between the mPFC and other DMN regions, including the anterior cingulate cortex and precuneus, which had more focal positive FC to the mPFC during the task in both groups. Additionally, the MDD group had significantly lower RSFC between the mPFC and several regions, including the right inferior temporal gyrus. The left hippocampus seed-based analysis revealed a pattern of hypoconnectivity to multiple brain regions in MDD, including the cerebellum, which was present at rest and during the task. Conclusions: Our results indicated multiple FC differences between adults with and without MDD, as well as distinct FC patterns and contrast results in resting state and task-based analyses, including differential FC between mPFC-cerebellum and hippocampus-cerebellum. These results emphasize that resting-state and task-based fMRI capture distinct patterns of brain connectivity. Further investigation into combining resting-state and task-based FC could inform future neuroimaging research.

PMID:41300141 | DOI:10.3390/brainsci15111133

Sci Rep. 2025 Nov 27. doi: 10.1038/s41598-025-28507-3. Online ahead of print.

ABSTRACT

This study aims to explore the neural correlates of postoperative pain and its relationship with preoperative psychological issues in patients with rotator cuff tear (RCT). Functional MRI data were collected from 78 RCT patients and 48 healthy controls (HC). Voxel-wise comparisons assessed regional homogeneity (ReHo) differences between groups. Pearson correlation and mediation analyses investigated the links between clinical data and brain changes. Additionally, machine learning using support vector machines (SVM) classified RCT patients based on postoperative pain intensity. RCT patients showed functional alterations in brain areas such as the dorsal anterior cingulate cortex (dACC), primary somatosensory cortex (SI), precuneus, and cerebellum. Increased depression levels correlated positively (r² = 0.249, P < 0.001) with ReHo in the dACC. The relationship between depression and postoperative pain intensity was mediated by dACC ReHo (indirect effect: 0.22, CI: 0.01-0.26). The combined analysis of ReHo patterns and clinical data achieved a classification accuracy of 90.4% for distinguishing RCT patients with postoperative pain. Our findings indicate a notable link between depression and postoperative pain in RCT patients, potentially linked to functional abnormalities in the dACC. Neuroimaging markers may help identify individuals at higher risk for postoperative pain.

PMID:41298760 | DOI:10.1038/s41598-025-28507-3

J Affect Disord. 2025 Nov 24:120725. doi: 10.1016/j.jad.2025.120725. Online ahead of print.

ABSTRACT

BACKGROUND: Chronic stress impacts brain function and emotion regulation, increasing depression risk. How chronic stress shapes neural dynamics in response to acute stress remains unclear. This study investigates how chronic stress influences neural responses after acute stress, focusing on ventromedial prefrontal cortex (vmPFC)-amygdala and vmPFC-hippocampus functional connectivity (FC) and their relationship to depression symptoms.

METHODS: Eighty-seven adults underwent resting-state fMRI at baseline, during acute stress, and during recovery. Participants were divided into High and Low chronic stress groups based on perceived stress over the past 4 weeks. Depression symptoms were measured with the Symptom Checklist-90. Linear mixed-effect model and repeated-measures ANOVA were used to analyse neural dynamics and interaction effects. Recovery-related changes in FC were calculated as differences between acute stress and recovery.

RESULTS: Distinct neural dynamics patterns across stress phases emerged between groups. The Low group showed significant decreases in vmPFC-amygdala and vmPFC-hippocampus connectivity from acute stress to recovery, while the High group exhibited no changes. Chronic stress moderated the association between the recovery-related changes in vmPFC-amygdala connectivity and depression symptoms. In the High chronic stress group, greater decreases in FC from stress to recovery were associated with higher depression symptoms.

CONCLUSIONS: Chronic stress modulates neural dynamics during acute stress response and recovery, and their association with depression symptoms. Individuals with higher chronic stress exhibit blunted cortical-limbic circuit dynamics, potentially increasing depression vulnerability. Rapid disengagement of emotion regulation circuits may represent a maladaptive response supporting the allostatic load model. These findings clarify stress, brain, and depression relationships.

PMID:41297681 | DOI:10.1016/j.jad.2025.120725

Behav Brain Res. 2025 Nov 24:115962. doi: 10.1016/j.bbr.2025.115962. Online ahead of print.

ABSTRACT

OBJECTIVE: This study aimed to investigate the neural correlates of cognitive fusion (CF) in drug-naïve patients with obsessive-compulsive disorder (OCD) and to explore the potential involvement of neurotransmitter systems in these abnormalities.

METHODS: Following quality control, 54 first-episode, drug-naïve OCD patients and 56 matched healthy controls underwent resting-state functional magnetic resonance imaging (fMRI) scanning. The amplitude of low-frequency fluctuations (ALFF) and functional connectivity analyses were performed to examine differences in brain activity between the groups. Clinical assessments, including the Yale-Brown Obsessive Compulsive Scale, Beck Anxiety Inventory, Beck Depression Inventory, and CF questionnaire, were administered to measure the severity of obsessive-compulsive, anxiety, and depressive symptoms, as well as CF levels. Mediation and correlation analyses were conducted to explore the relationships between brain activity, CF, and OCD symptoms. Additionally, spatial correlation analyses were conducted to investigate the relationship between neural abnormalities and neurotransmitter systems.

RESULTS: OCD patients exhibited elevated ALFF in prefrontal regions. Crucially, the activity of the left dorsolateral superior frontal gyrus (SFGdl) mediated 45.11% of CF's effect on obsessive-compulsive symptoms (indirect effect = 0.060, 95%CI = [0.005,0.133]). Moreover, neurochemical analysis revealed significant negative correlations between regional ALFF in the left SFGdl and neurotransmitter systems, including dopamine, acetylcholine, and glutamate.

CONCLUSION: Our findings suggest that CF is associated with altered brain activity in prefrontal regions, which may contribute to the cognitive and emotional dysfunction observed in OCD. The negative correlations between these neural abnormalities and neurotransmitter systems provide further insight into the neurochemical mechanisms underlying OCD. These results offer novel perspectives on the pathophysiology of OCD and highlight potential targets for future therapeutic interventions.

PMID:41297562 | DOI:10.1016/j.bbr.2025.115962

Neuroimage Clin. 2025 Nov 22;48:103913. doi: 10.1016/j.nicl.2025.103913. Online ahead of print.

ABSTRACT

OBJECTIVE: Disruptions in resting-state functional brain networks are consistently observed in dementia, yet their underlying relationships remain incompletely understood. This study aimed to investigate potential associations between resting-state functional MRI (rs-fMRI) phenotypes and various dementia subtypes.

METHODS: We performed bidirectional two-sample Mendelian randomization (MR) analyses using summary statistics from 191 rs-fMRI phenotypes (n = 34,691) and five types of dementia (n = 6,618 to 373,159). Forward MR assessed the effects of rs-fMRI phenotypes on dementia risk, while reverse MR evaluated the impact of dementia on rs-fMRI phenotypes.

RESULTS: Forward MR analysis identified seven rs-fMRI phenotypes significantly associated with dementia risk. Enhanced dorsolateral superior frontal gyrus connectivity, part of the default mode network, was linked to reduced Alzheimer's disease risk (odds ratio (OR) = 0.52, 95 % confidence interval (CI): 0.41-0.66, P = 1.10 × 10-7). Increased connectivity within the default mode and central executive networks correlated with lower vascular dementia risk (OR = 0.60, 95 % CI: 0.48-0.75, P = 9.44 × 10-6). Reverse MR revealed significant associations between dementia subtypes and rs-fMRI phenotypes, including Alzheimer's disease-related increases in limbic connectivity and decreases in default mode and central executive networks. For Lewy body dementia, heightened connectivity in salience and sensorimotor networks and reduced default mode connectivity were observed.

INTERPRETATION: Our findings identify functional networks whose connectivity patterns may be associated with dementia risk and could provide potential insights for biomarker discovery or preventive research. However, these results are based on statistical inference and require further validation in longitudinal and experimental studies to confirm their clinical relevance and potential translational implications.

PMID:41297292 | DOI:10.1016/j.nicl.2025.103913

Sci Rep. 2025 Nov 26;15(1):42022. doi: 10.1038/s41598-025-29976-2.

ABSTRACT

Memory decline is a central cognitive symptom in Parkinson's Disease (PD). While task-fMRI studies link hippocampal activity (AHA) to poorer memory and olfactory performance, this relationship during rest remains understudied. The objectives of this study are to examine differences in resting-state hippocampal networks, explore the occurrence of reduced AHA within these networks, and investigate its impact on memory and olfaction in PD. Thirty-nine PD patients awaiting evaluation for device-aided Parkinson therapy and 46 healthy controls (HC) underwent resting-state fMRI (rs-fMRI). PD patients also completed a memory and olfactory assessment. Co-activation pattern (CAP) analysis was performed on the rs-fMRI data. Our results demonstrated reduced activity in two hippocampal networks in PD: Network 1, incorporating the visual cortex, cerebellum, superior parietal lobule, and precuneus, and Network 5, incorporating parts of the central executive network. PD subgroups with reduced AHA in Network 1 and 5 performed significantly worse on tests of auditory-verbal short-term, long-term and recognition memory, as well as odor identification. In conclusion, within specific resting-state hippocampal networks, reduced AHA in PD is linked to poorer auditory-verbal memory and odor identification.

PMID:41298806 | DOI:10.1038/s41598-025-29976-2

Biol Psychiatry Cogn Neurosci Neuroimaging. 2025 Nov 24:S2451-9022(25)00362-3. doi: 10.1016/j.bpsc.2025.11.005. Online ahead of print.

ABSTRACT

BACKGROUND: Between-subjects studies suggest that psychostimulants can shift whole-brain functional connectivity toward patterns linked to heightened sustained attention. In this study, we examined how a single dose of methamphetamine (MA, 20 mg) changes sustained attention and associated network-level functional organization in healthy adults.

METHODS: We conducted a within-subject study in which 76 healthy participants completed two fMRI scanning sessions after taking MA or placebo. We tested whether MA selectively affects behavioral and fMRI connectivity signatures of sustained attention and arousal.

RESULTS: Under MA, participants showed improved sustained attention task performance as well as functional connectivity signatures of higher sustained attention and arousal. These network changes emerged consistently across resting-state and task-based fMRI, indicating that MA influences attention- and arousal-related networks regardless of cognitive context. Furthermore, a support vector classifier distinguished functional connectivity patterns observed during the MA and placebo conditions, identifying connections overlapping with networks related to arousal.

CONCLUSIONS: Together, these findings align with prior work on other psychostimulants like methylphenidate, showing that MA modulates sustained attention and related large-scale brain networks. By revealing how MA modulates attention-relevant brain connectivity patterns, our results highlight the utility of psychostimulants as causal tools for probing the robustness, generalizability, and interpretability of brain-based biomarkers of behavior.

PMID:41297882 | DOI:10.1016/j.bpsc.2025.11.005

Brain Res Bull. 2025 Nov 24:111653. doi: 10.1016/j.brainresbull.2025.111653. Online ahead of print.

ABSTRACT

BACKGROUND: Migraine without aura (MWoA) is linked to abnormal subcortical/cortical network activity and neurotransmitter dysregulation. However, the alteration of functional integration and the information flow between brain networks participated in pain sensory pathway and the patterns of neurotransmitter dysregulation during the interictal period remain unclear.

METHODS: This cross-sectional study compared 53 interictal MWoA patients and 51 healthy controls using resting-state fMRI. Whole-brain functional integration (degree centrality, DC) and effective connectivity (EC) were analyzed. JuSpace toolbox mapped spatial correlation between functional alterations and neurotransmitter systems.

RESULTS: MWoA patients showed decreased DC in the left putamen and increased DC in the left angular gyrus. Altered EC from subcortical to cortical regions included pathways from the left putamen to right medial superior frontal gyrus, supramarginal gyrus, dorsolateral superior frontal gyrus, and postcentral gyrus, as well as bilateral caudate to left angular gyrus. Cortical-to-subcortical EC changes involved right dorsolateral superior frontal gyrus to left putamen and left angular gyrus to left caudate. EC from left putamen to right postcentral gyrus inversely correlated with headache frequency, while right caudate to left angular gyrus EC positively correlated with disease duration. Altered DC patterns spatially overlapped with serotonergic, dopaminergic, and glutamate pathways and correlated with quality-of-life impairments (MSQ scores).

CONCLUSION: MWoA involves disrupted functional integration and bidirectional subcortical-cortical connectivity during interictal periods, associated with headache severity and neurotransmitter system imbalances. These findings highlight network-level pathophysiology and neurochemical dysregulation underlying migraine.

PMID:41297797 | DOI:10.1016/j.brainresbull.2025.111653

IEEE J Biomed Health Inform. 2025 Nov 26;PP. doi: 10.1109/JBHI.2025.3637772. Online ahead of print.

ABSTRACT

Dynamic functional connectivity (dFC) analysis investigates how the functional interactions between brain regions change over time by identifying recurring connectivity patterns, known as dFC states, and tracking transitions between them. Non-negative matrix factorization (NMF) has been used in dFC analysis because it produces non-negative dFC states and coefficients, interpreting dFC states and their transitions straightforwardly. However, existing NMF-based methods are limited to processing dFC data with exclusively positive values, failing to align with the functional correlations and anti-correlations between brain regions. This paper proposes an orthogonal semi-nonnegative matrix factorization (OSemiNMF) method, extending NMF to directly handle mixed-sign dFC data. Furthermore, an orthogonality constraint on the bases (i.e., dFC states) is incorporated to enhance the uniqueness of dFC states. For 10 simulated datasets with varying properties, our method outperforms comparison methods, supporting its superior ability to capture dFC states and state transitions. Using four resting-state fMRI datasets consisting of 708 healthy controls (HCs) and 537 schizophrenia patients (SZs), our method identifies reproducible dFC states and state transitions across datasets. Further, our findings reveal that SZs spend less time in high-connectivity states compared to HCs. Our study identifies meaningful and reproducible biomarkers of schizophrenia, mainly involving the connectivity associated with the sub-cortical domain. In summary, the OSemiNMF method facilitates the dFC analysis for understanding brain dynamics.

PMID:41296953 | DOI:10.1109/JBHI.2025.3637772

Hum Brain Mapp. 2025 Dec 1;46(17):e70409. doi: 10.1002/hbm.70409.

ABSTRACT

Recent studies suggest that it is possible to predict an individual brain's spatial activation pattern in response to a paradigm from their functional connectivity at rest (rsFC). However, it is unclear whether this prediction works across the brain. We here aim to understand whether individual task activation can be best predicted in local regions that are highly specialised to the task at hand or whether there are domain-independent regions in the brain that carry most information about the individual. To answer this question, we used fMRI data from participants at rest and during an auditory oddball paradigm. We then predicted individual differences in brain responses to melodic deviants from their rsFC both across the whole brain and within the auditory cortices. Predictability was consistently higher in sensory association cortices: In the local (auditory cortex) parcellation, the best predicted area was the right superior temporal gyrus (STG), an auditory association area, while in the global parcellation, the best predicted network was the bilateral visual association cortex. Our results indicate that individual differences can be predicted in paradigm-relevant areas or general areas with high inter-individual variability. Predicting individual task activation from rsFC may be of clinical relevance in cases where patients are unable to carry out a certain task, such as, to inform surgical targets.

PMID:41293889 | DOI:10.1002/hbm.70409

Front Hum Neurosci. 2025 Nov 10;19:1669164. doi: 10.3389/fnhum.2025.1669164. eCollection 2025.

ABSTRACT

INTRODUCTION: Emotional well-being (EWB) is a multifaceted construct essential for human health, conceptualized as an umbrella term for related psychometric concepts such as psychological well-being (PWB), positive mental health, health-related quality of life, thriving, and subjective well-being (SWB). However, varying definitions have prompted calls for a consensus definition. Understanding the neural mechanisms of EWB is crucial for health and intervention efforts, yet findings remain inconsistent in both empirical studies and systematic reviews. The inconsistencies in prior systematic reviews may arise from diverse definitions, an emphasis on task-independent over task-dependent modalities, and biases introduced when statistical analyses are lacking.

METHODS: To address these gaps, this study presents the first preliminary systematic review and meta-analysis of the neural correlates of EWB using a consensus definition developed in 2023 by NIH EWB Research Network, which includes five domains: goal pursuit, life satisfaction, positive affect, quality of life, and sense of meaning. Importantly, we used a hypothesis-driven approach to separately examine task-dependent (task-based fMRI; n = 14) and task-independent modalities (resting-state fMRI and structural MRI; n = 7 each), clarifying their distinct and overlapping neural contributions of EWB.

RESULTS: The left pallidum as a key region associated with task-dependent modality, likely reflecting incentive and rewards processing, while task-independent findings implicate the right superior temporal gyrus (STG) and insula, suggesting roles in social cognition and interoceptive awareness. Across both modalities, frontoparietal regions emerge as shared substrates likely contributing to cognitive control processes central to EWB.

CONCLUSION: Despite limited sample sizes, this review provides a preliminary neural framework of EWB, highlighting distinct and shared contributions across modalities and lay an empirical foundation for future large-scale investigations.

SYSTEMATIC REVIEW REGISTRATION: https://osf.io/ymtb8/overview.

PMID:41293483 | PMC:PMC12640920 | DOI:10.3389/fnhum.2025.1669164

bioRxiv [Preprint]. 2025 Nov 12:2025.11.10.687633. doi: 10.1101/2025.11.10.687633.

ABSTRACT

Executive function (EF) develops rapidly during adolescence. However, deficits in EF also emerge in adolescence, representing a transdiagnostic symptom associated with many forms of psychopathology. To promote transdiagnostic research on EF during development, we introduce a new data resource - the Penn Longitudinal Executive functioning in Adolescent Development study (Penn LEAD) - that combines longitudinal multi-modal imaging data with rich clinical and cognitive phenotyping. These data include 225 imaging sessions from 132 individuals (8-16 years old at the time of enrollment) who are typically developing (27.3%), or meet criteria for attention-deficit hyperactivity disorder (20.5%) or the psychosis-spectrum (52.3%). In addition to phenotypic data from multiple cognitive tasks focused on EF, the study includes data from structural MRI, diffusion MRI, n -back task fMRI, resting-state fMRI, and arterial spin-labeled MRI. Notably, all raw data, fully-processed derived data, and detailed quality control recommendations are publicly shared on OpenNeuro. We anticipate that such analysis-ready data will accelerate research on EF development in psychiatry.

PMID:41292855 | PMC:PMC12642398 | DOI:10.1101/2025.11.10.687633

Sci Rep. 2025 Nov 25;15(1):41998. doi: 10.1038/s41598-025-26083-0.

ABSTRACT

Delayed encephalopathy after carbon monoxide poisoning (DEACMP) is the most severe and prevalent neurological sequela associated with carbon monoxide exposure. This study aims to investigate the time-varying characteristics of dynamic brain networks and their topological properties in DEACMP patients using resting-state functional magnetic resonance imaging (rs-fMRI). We conducted Functional MRI scans and clinical assessments for 25 DEACMP patients and 25 healthy controls (HCs). To capture the variability patterns of dynamic functional connectivity (dFC) between the two groups, we employed a sliding time window analysis method. Additionally, theoretical graph analysis was utilized to examine the variations in the topological properties of whole-brain functional networks. We found that DEACMP patients have two dFC states characterized by different connection patterns, State 1 and State2, and there were multiple inter-network and intra-network dynamic interactions in State2.Next, Abnormal dFC indicators were related to the MoCA scores. Finally, the dynamic brain network topological properties were variable. These findings may provide valuable insights into the disruptions in local information transmission and processing functions within the brain's functional networks in individuals with DEACMP.

PMID:41290957 | DOI:10.1038/s41598-025-26083-0

Sci Rep. 2025 Nov 25;15(1):41861. doi: 10.1038/s41598-025-25921-5.

ABSTRACT

Schizophrenia (SZ) is increasingly recognized as a network disorder marked by abnormal functional connectivity, yet the clinical utility of fMRI remains limited. Electroencephalography (EEG) provides a more practical alternative, though conventional complexity measures, such as sample entropy (SampEn), often fail to capture spatiotemporal network dynamics and yield inconsistent results. Here, we propose a novel EEG-based complexity network approach to investigate functional alterations in SZ subtypes, deficient (DS) and non-deficient (NDS), and to differentiate them from healthy controls (HCs). Resting-state EEG (64-channel, 500 Hz) was recorded from 19 DS patients, 19 NDS patients, and 30 HCs. Sample entropy and fuzzy entropy were computed, and complexity networks were constructed using Spearman and Pearson correlation coefficients. Key topological features, including global efficiency, local efficiency, and strength, were extracted and subjected to machine learning classification. While traditional SampEn differentiated groups only in the delta band, our network-based approach revealed distinct topological patterns: DS showed the highest local efficiency (δ, θ, α) but the lowest global efficiency (δ, α), whereas NDS exhibited lower global efficiency (θ) and higher local efficiency (β). SVM-based classification achieved an overall accuracy of 96.3%, with optimal performance in the δ and θ bands. These results underscore the utility of EEG complexity networks in distinguishing SZ subtypes from HCs and provide compelling evidence for aberrant connectivity in SZ. This method holds considerable promise for clinical applications, particularly in outpatient settings, though further validation in larger cohorts and task-based paradigms is warranted.

PMID:41290886 | DOI:10.1038/s41598-025-25921-5

Schizophrenia (Heidelb). 2025 Nov 25;11(1):142. doi: 10.1038/s41537-025-00687-x.

ABSTRACT

Understanding neurobiological similarities among individuals with psychosis risk symptoms can improve early identification and intervention strategies. We aimed to (i) identify neurobiologically similar psychosis risk subgroups by integrating resting-state functional connectivity and psychosis risk symptom data and (ii) discern discriminating symptom profiles and brain connectivity patterns in the identified sub-groups. Our sample (N = 922) was extracted from the Philadelphia Neurodevelopmental Cohort, a community group of individuals aged 12-21 years, with fMRI and self-reported psychopathology data. Analyses were conducted separately for youth and early adults. We constructed a two-layer network using pair-wise similarity distances between participants based on resting-state fMRI and psychosis risk symptoms measured with the PRIME screen. We then performed community detection via a multiplex stochastic block model to identify subject clusters. We identified 2 blocks or communities for both the youth (n = 458 and 179) and early adult (n = 173 and 112) groups. Connection parameter estimates of the neuroimaging layer were nearly identical between blocks for both age groups whereas there was significant variation for the symptom layer. Psychopathology symptom and brain system segregation profiles were consistent across age groups. The youth block (n = 458) with higher salience network segregation values had higher mean psychosis risk symptom scores while the early adult block (n = 173) with lower salience network segregation had higher mean psychosis risk symptom scores. By integrating global similarities in brain connectivity and psychosis risk symptoms, we identified distinct subgroups. These groups exhibit different symptom profiles and network segregation in youth and early adults, suggesting variations in developmental paths for psychosis spectrum.

PMID:41290706 | DOI:10.1038/s41537-025-00687-x

Diabetes Obes Metab. 2025 Nov 25. doi: 10.1111/dom.70309. Online ahead of print.

ABSTRACT

AIMS: This study investigates the relationship between multisensory (visual, somatosensory, and olfactory) dysfunction and cognitive decline in Type 2 diabetes (T2D), with a particular focus on the mediating role of olfactory dysfunction.

METHODS: We used resting-state fMRI to assess seed-based functional connectivity from the primary sensory cortices (visual, somatosensory, and olfactory) and whole-brain regional activity metrics in 152 patients with T2D and 50 controls. A Multisensory Dysfunction Index (MSDI) was constructed to quantify integrated sensory dysfunction, and moderated mediation analysis was performed to examine the impact of sensory complications on cognitive function.

RESULTS: The MSDI was correlated with sensory complication burden and associated with worse global cognitive performance (Montreal Cognitive Assessment, MoCA). Mediation analysis showed that odour identification mediated the relationship between MSDI and MoCA in T2D. This indirect effect was absent in diabetic peripheral neuropathy (DPN)+ individuals but remained significant in DPN- individuals. Additionally, olfactory dysfunction had both direct and indirect effects on cognition in DPN- patients.

CONCLUSIONS: Our findings highlight the central role of olfactory dysfunction in linking multisensory impairment to cognitive decline in T2D. The results emphasize the need for personalized management strategies based on sensory complications and suggest that preserving sensory network integrity may help maintain olfactory and cognitive health in T2D.

PMID:41287550 | DOI:10.1111/dom.70309

Eur J Neurosci. 2025 Nov;62(10):e70336. doi: 10.1111/ejn.70336.

ABSTRACT

Working memory (WM) is a core component of higher-order cognition, and its impairment is a common consequence of stroke. While traditional lesion-symptom mapping highlights focal damage, it often overlooks alterations in large-scale brain network dynamics. This study investigated WM deficits through functional connectivity (FC) analyses of frontoparietal networks in 34 patients with right hemisphere (RH) stroke and 35 healthy controls. Resting-state fMRI was used to examine region-of-interest and whole-brain seed-to-voxel FC in relation to WM performance on verbal and spatial N-back tasks. Compared to controls, stroke patients exhibited disrupted FC-WM associations, characterized by reduced intrahemispheric FC between anterior and posterior RH regions, which correlated with poorer WM performance. Notably, enhanced interhemispheric FC, particularly between the right middle and inferior frontal gyri and contralateral parietal cortices, was positively associated with WM accuracy, suggesting compensatory engagement of the intact hemisphere. No performance differences were observed between task modalities, supporting the involvement of domain-general WM mechanisms. These findings highlight the role of early network-level reorganization in shaping cognitive outcomes post-stroke. Specifically, WM deficits appear to result not solely from structural damage but from altered FC patterns, where reduced intrahemispheric connectivity may be mitigated by adaptive interhemispheric recruitment.

PMID:41285569 | DOI:10.1111/ejn.70336

Cereb Cortex. 2025 Nov 1;35(11):bhaf314. doi: 10.1093/cercor/bhaf314.

ABSTRACT

Insomnia disorder is the most common sleep disorder affecting millions of people. Brain research has linked insomnia to dysfunction in large-scale brain networks, not only during sleep but also in wakeful rest. Yet, the underlying brain dynamics remain little understood. In the present study, we directly addressed this using a data-driven framework for evaluating time-varying large-scale brain activity. We used functional magnetic imaging to compare participants with insomnia disorder to matched controls with no sleep complaints. Using Hidden Markov modeling (HMM) for a completely data-driven characterization of the brain dynamics of whole-brain activity, we found that insomnia disorder is characterized by significantly reduced switching rates between large-scale brain states. In particular, HMM was used to compare insomnia patients to controls, which showed that their brains spent significantly less time in two whole-brain states-the default mode network and a fronto-parietal network-complemented by increased time spent in a global activation state. Overall, the findings reveal the brain dynamics of insomnia to show that insomnia disorder is characterized by less flexible transitions between brain states at wakeful rest. This highlights the importance of evaluating the spatiotemporal dynamics of brain activity to advance the understanding of the neural underpinnings of insomnia disorder.

PMID:41283931 | DOI:10.1093/cercor/bhaf314

medRxiv [Preprint]. 2025 Oct 31:2025.10.29.25339059. doi: 10.1101/2025.10.29.25339059.

ABSTRACT

Migraine is a prevalent and disabling neurological disorder, characterized by difficulties in regulating headache activity, sensory processing, and cognitive-emotional states. Brain entropy quantifies the complexity of neural dynamics, where reduced entropy may reflect diminished neural adaptability, but its assessment with fMRI in migraine remains limited. Here, we examined alterations in brain entropy and their associations with clinical burden, attack timing, and symptomatology. Resting-state fMRI data were acquired from adults with episodic migraine, chronic migraine, and healthy controls. Following standard preprocessing, voxel-wise sample entropy was computed, and group differences were assessed using ANCOVA with age and sex as covariates. Associations with clinical burden and symptom measures were examined within affected regions. In chronic migraine, attack timing-related changes in entropy were further explored, and the largest Lyapunov exponent (LLE) was estimated to characterize chaotic dynamics underlying attack-related complexity changes. Migraine patients showed reduced entropy in sensory, attentional, and default mode regions compared to controls, most pronounced in chronic migraine. Lower entropy correlated with greater headache frequency and longer illness duration. In chronic migraine, entropy relatively increased during attacks in multisensory integration regions and was associated with positive and elevated LLEs, indicating partially restored complexity with weakly chaotic dynamics. Patients experiencing phonophobia and nausea also exhibited increased entropy in multisensory and default mode regions. Our findings demonstrate widespread reductions in brain entropy in migraine, reflecting impaired neural adaptability, whereas attacks may transiently restore complexity partially through chaotic dynamics. These results advance understanding of migraine pathophysiology and highlight potential targets for therapeutic intervention.

HIGHLIGHTS: Migraine is marked by reduced brain entropy across sensory, attentional and default mode regions, which correlates with disease burden.Reduced entropy reflects constrained neural adaptability within affected regions.Migraine attacks transiently restore entropy, suggesting partial recovery of neural adaptability.Positive and elevated Lyapunov exponents indicate a shift toward weakly chaotic dynamics during attacks.Symptoms such as phonophobia and nausea are linked to increased entropy in multisensory integration and default mode regions.

PMID:41282924 | PMC:PMC12636680 | DOI:10.1101/2025.10.29.25339059