Most recent paper

PLoS Biol. 2026 Sep 1;24(9):e3003916. doi: 10.1371/journal.pbio.3003916. eCollection 2026 Sep.

ABSTRACT

Ongoing brain activity displays rich temporal variability associated with efficient cognition, with functional connectivity (FC) continually reconfiguring over time. The resulting functional connectivity dynamics (FCD) specifically show complex, fat-tailed statistics that alternate between persistent epochs and faster reconfiguration transients. While nonlinear whole-brain models tuned nearby a critical point have reproduced some aspects of FCD, they fall short of capturing its full temporal complexity. We propose that slow fluctuations in arousal offer a biologically plausible mechanism for exploring critical regimes in large-scale brain dynamics and thus enrich FCD. Using a connectome-based model of coupled cortical populations, we identified phase boundaries where system dynamics transition between regimes of faster or slower FCD. We then phenomenologically incorporated arousal changes, modeling them as stochastic fluctuations in key parameters such as cortical excitability, input gain, and noise amplitude. This explicitly time-dependent formulation enables the system to roam dynamically across regime boundaries, flexibly tuning its distance from critical transition lines and producing intermittent transitions that mirror the stochastic evolution observed in empirical FCD. Fitting these models to human resting-state fMRI and performing model comparison, we find that arousal-driven models more accurately reproduce the distinctive quantitative features of FCD, with the greatest improvements coming from the previously poorly accounted fat-tailed portions of the distributions. Together, these results suggest that arousal fluctuations-likely mediated by changes in neuromodulatory tone-shape the brain's attractor landscape over time, expanding the repertoire of accessible functional network states and providing a mechanistic basis for the complexity of spontaneous functional dynamics.

PMID:42678916 | DOI:10.1371/journal.pbio.3003916

CNS Neurosci Ther. 2026 Sep;32(9):e71129. doi: 10.1002/cns.71129.

ABSTRACT

BACKGROUND: Fluoxetine is an established pharmacological intervention for bulimia nervosa (BN); however, the macroscopic functional network remodeling underlying its efficacy in suppressing eating impulsivity remains poorly understood.

METHODS: Fifty-eight patients with BN and 58 healthy controls assessed at both baseline and follow-up underwent resting-state functional magnetic resonance imaging (rs-fMRI). Network-Based Statistic (NBS) analysis with edgewise paired comparisons was used to identify whole-brain connectivity changes in patients with BN. The resulting BN-derived component was subsequently examined in both groups using a Group × Time analysis. Exploratory brain-behavior associations within BN were examined using Spearman and partial Spearman correlations.

RESULTS: During follow-up, patients with BN showed significant improvements in depressive symptoms and externally driven eating behavior. Within-group NBS analysis identified a 23-edge component showing reduced functional connectivity in BN at follow-up, with network-level family-wise error correction (p < 0.05). This component primarily involved dorsal attention, ventral attention/salience, and somatomotor networks. Whole-brain NBS analysis in healthy controls did not identify a significant longitudinal component. When mean connectivity of the BN-derived component was extracted in both groups, connectivity decreased more strongly in BN than in healthy controls (Group × Time β = -0.053, p = 0.027).

CONCLUSIONS: The follow-up period in patients with BN was accompanied by clinical improvement and reduced functional connectivity among attention-, salience-, and sensorimotor-related systems. These findings provide preliminary evidence of treatment-period-associated functional network reconfiguration in BN.

TRIAL REGISTRATION: ChiCTR2200066885.

PMID:42678299 | DOI:10.1002/cns.71129

CNS Neurosci Ther. 2026 Sep;32(9):e71122. doi: 10.1002/cns.71122.

ABSTRACT

BACKGROUND: Emotional disorders (EDs), including anxiety disorders, major depression (MDD), and post-traumatic stress disorder (PTSD), show overlapping symptoms and neurobiological heterogeneity, limiting current diagnostic frameworks. Transdiagnostic biomarkers are critical for precision psychiatry.

METHODS: Resting-state functional connectivity (FC) from 261 ED patients and 201 healthy controls (HCs) was decomposed via nonnegative matrix factorization (NMF). Differential FC features defined latent disease factors, classifying patients into subtypes with distinct network/structural profiles.

RESULTS: The analysis identified five representative functional networks (RFNs) underlying the heterogeneity of emotional disorders. RFN1 reflected connectivity between sensorimotor and dorsal attention networks, while RFN2 was defined by theory-of-mind-related connectivity. RFN3, characterized by thalamocortical connections, was specifically linked to somatic sensation processing. RFN4 involved connectivity within the default mode network, and RFN5 captured multi-network-subcortical integration. These RFNs delineated five subtypes with distinct clinical profiles: ST1 (mixed anxiety/depression) exhibited RFN1-driven somatic vigilance and comorbid depressive symptoms; ST2 (mild symptoms) demonstrated resilience associated with RFN2; ST3 (somatic anxiety-dominant) showed RFN3-related elevations in somatic anxiety and panic symptoms; ST4 (anxiety-centric) was marked by RFN4-associated self-referential anxiety; and ST5 represented a depression-pure subgroup mapped to RFN5.

CONCLUSION: This study proposes a neurobiologically informed stratification for EDs, revealing transdiagnostic subtypes with distinct network signatures and clinical implications. The findings bridge symptom heterogeneity to circuit-level dysfunction, offering potential biomarkers for personalized interventions and advocating for transdiagnostic approaches in psychiatric research.

PMID:42676104 | DOI:10.1002/cns.71122

CNS Neurosci Ther. 2026 Sep;32(9):e71124. doi: 10.1002/cns.71124.

ABSTRACT

BACKGROUND: Cognitive impairment is a prevalent and debilitating feature in multiple sclerosis (MS), yet its pathophysiology remains incompletely understood. Dysfunction of the neurovascular unit (NVU) and impaired neurovascular coupling (NVC) may be related to cognitive performance in MS, but this pathway remains insufficiently studied. This study investigated whether NVC abnormalities are associated with cognitive performance in MS.

METHODS: Ninety-seven MS patients and 83 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI) and arterial spin labeling (ASL) perfusion MRI acquisition. The amplitude of low-frequency fluctuations (ALFF) was used as an index of regional spontaneous neuronal activity, and cerebral blood flow (CBF) was quantified. NVC was quantified globally (CBF-ALFF correlation) and regionally (CBF/ALFF ratio). We analyzed the correlations between regional NVC metrics and cognitive performance and Expanded Disability Status Scale (EDSS) scores. Sensitivity analyses tested robustness across alternative functional metrics, gray matter volume (GMV), T2 lesion volume, disability severity, treatment status, and the relapsing-remitting multiple sclerosis (RRMS) subgroup.

RESULTS: Compared with HCs, MS patients demonstrated (1) reduced CBF-ALFF coupling in the ALFF-based analysis, and (2) increased CBF/ALFF ratios in the bilateral medial prefrontal cortex (mPFC). The regional mPFC finding, particularly on the left, remained robust across sensitivity analyses and was associated with worse cognitive performance and higher EDSS scores.

CONCLUSION: These findings suggest altered NVC in MS, with regional prefrontal abnormalities representing the most robust and clinically relevant finding. Regional prefrontal NVC abnormalities may provide a potential imaging correlate of cognitive performance in MS.

PMID:42676004 | DOI:10.1002/cns.71124

Alzheimers Dement. 2026 Sep;22(9):e71744. doi: 10.1002/alz.71744.

ABSTRACT

INTRODUCTION: Intrinsic functional connectivity network abnormalities in C9orf72 hexanucleotide repeat expansion carriers emerge during the asymptomatic phase, yet longitudinal studies remain limited. We examined cross-sectional abnormalities and longitudinal connectivity changes across clinical stages.

METHODS: We analyzed task-free functional magnetic resonance imaging (fMRI) and structural MRI data in 36 asymptomatic (aSxC9), 17 prodromal (proC9), and 29 symptomatic (SxC9) carriers, and 107 healthy controls (HCs). Functional networks previously found altered in C9orf72, including salience, sensorimotor, default mode, and medial pulvinar thalamic networks, were examined. Associations between longitudinal connectivity and gray matter decline with baseline neurofilament light chain (NfL) concentrations and symptom severity were assessed.

RESULTS: aSxC9 and SxC9 showed longitudinal connectivity changes within specific networks. In aSxC9, connectivity changes correlated with baseline NfL. In proC9 and SxC9, changes in connectivity and gray matter were associated with baseline NfL and symptom severity.

DISCUSSION: C9orf72 expansion carriers demonstrate stage-specific network connectivity changes.

PMID:42671157 | DOI:10.1002/alz.71744

Neuroscience. 2026 Aug 30:S0306-4522(26)00592-0. doi: 10.1016/j.neuroscience.2026.08.048. Online ahead of print.

ABSTRACT

As traveling waves mediate neural information processing that depends on arousal states, we hypothesized that traveling waves would represent arousal states. To investigate this, we aimed to analyze brain-wide patterns of traveling waves during resting states using functional magnetic resonance imaging (rsfMRI). Specifically we focused on the temporal dynamics of the brain-wide patterns of rsfMRI traveling waves in association with arousal states. Using a publicly available dataset (N = 27), we extracted infra-slow (0.01-0.1 Hz) activity from blood oxygen level dependent (BOLD) signals. From infra-slow activity, we measured traveling waves via local phase gradient (LPG). Then, we clustered brain-wide LPGs through the k-means algorithm to extract brain-wide patterns of traveling waves, where each cluster represented each pattern. The optimal number of clusters was found to be 2. We analyzed the temporal dynamics of these clusters in relation to arousal states. During low-arousal states, brain-wide patterns of traveling waves featured in Cluster 1 appeared more often. During high-arousal states, those in Cluster 2 did so and lasted longer. As arousal increased, cluster dwell times shortened and transitions between clusters became more frequent, with the opposite pattern observed as arousal decreased. These findings suggest that brain-wide patterns of rsfMRI traveling waves reflect arousal states through their temporal dynamics, forming a syntax underlying arousal.

PMID:42669376 | DOI:10.1016/j.neuroscience.2026.08.048

CNS Neurosci Ther. 2026 Sep;32(9):e71113. doi: 10.1002/cns.71113.

ABSTRACT

BACKGROUND AND AIMS: How Crohn's disease alters large-scale brain network organization and interacts with affective symptoms remains poorly understood. We aimed to characterize disease-related disruptions in functional topology, rich-club organization, and hierarchical gradients, and to determine their associations with disease activity and anxiety.

METHODS: Ninety-seven Crohn's disease patients and 64 healthy controls underwent resting-state fMRI. Graph-theoretical metrics, rich-club analysis, functional gradients, and spectral dynamic causal modeling were applied to assess network properties, hierarchical architecture, and effective connectivity. Associations with the Crohn's Disease Activity Index and anxiety were examined via linear regression.

RESULTS: Crohn's disease patients showed widespread network disruptions modestly linked to disease activity, including reduced global and local efficiency, impaired rich-club connectivity, and compressed sensorimotor gradients. Decreased degree centrality in the insula, thalamus, and postcentral gyrus was accompanied by altered hierarchical organization and effective connectivity. Spectral dynamic causal modeling revealed anxiety-related modulation of insular excitation and thalamo-putaminal pathways.

CONCLUSIONS: Crohn's disease involves widespread functional network disruption and reorganization, particularly in interoceptive regions supporting sensory and emotional processing. Anxiety modulates core limbic circuitry, highlighting the interplay between disease activity and affective symptoms in shaping brain network.

PMID:42669140 | DOI:10.1002/cns.71113

J Neurol Sci. 2026 Aug 22;490:126151. doi: 10.1016/j.jns.2026.126151. Online ahead of print.

ABSTRACT

Decreased olfactory function occurs in over half of individuals aged 65-80 years, and in 62-80% of those over 80 years of age. Hyposmia is an early symptom and a potential marker for a number of neurodegenerative diseases, including Parkinson's disease (PD) and Alzheimer's disease (AD), and can predict the conversion from Mild Cognitive Impairment (MCI) to AD. Building on our previously published resting-state fMRI findings on olfactory network functional connectivity [10], this study examined the same 74 participants categorized as Cognitively Normal (CN), MCI, or PD with cognitive impairment (PD-CogImp) using the Brief Smell Identification Test (B-SIT®). Automated measurements of cortical thickness (CT) and volume for 34 bilateral cortical regions were obtained using FreeSurfer v7.3.2. A moderation analysis based on multiple linear regression was performed to investigate the relationship between B-SIT® scores and cortical measures, controlling for age, education, sex, APOE genotype, and estimated total intracranial volume (eTIV). A significant interaction effect of clinical diagnosis on the olfaction-cortical thickness relationship was identified in 3 out of 68 regions of interest (ROIs): the cuneus bilaterally and the right pericalcarine gyrus, all showing large effect sizes (η2 > 0.14). CN individuals showed a significant positive association between B-SIT® and CT in these regions, whereas MCI and PD-CogImp groups showed a negative trend. Cortical volume models yielded no significant findings after correction for multiple comparisons. These results highlight distinct diagnostic group-specific patterns in the olfaction-brain structure relationship, with implications for understanding early pathological aging and neurodegeneration.

PMID:42667825 | DOI:10.1016/j.jns.2026.126151

Neuroimage Clin. 2026 Aug 26;51:104051. doi: 10.1016/j.nicl.2026.104051. Online ahead of print.

ABSTRACT

OBJECTIVES: Parkinson's disease (PD) involves large-scale network disruptions, yet functional connectivity (FC) alterations specific to distinct primary motor cortex (M1) subregions and their subcortical targets remain poorly characterized. We aimed to investigate subregion-specific resting-state FC alterations in PD and their clinical associations.

METHODS: Resting-state fMRI data from 37 PD patients and 37 matched healthy controls were analyzed. Seed-to-voxel and expanded ROI-to-ROI analyses (incorporating subcortical nuclei) mapped FC patterns of two M1 subregions: inter-effector and effector-specific. A System Segregation index quantitatively assessed large-scale network boundaries.

RESULTS: PD patients exhibited local intra-M1 hyperconnectivity and subsystem-specific cortico-subcortical hyperconnectivity, particularly involving the putamen and thalamus for the effector-specific region. Seed-to-voxel analyses revealed divergent long-range dysconnectivity: the inter-effector region showed hypoconnectivity with sensorimotor and limbic areas, whereas the effector-specific region exhibited sensorimotor hypoconnectivity and a pathological loss of anti-correlation with cerebellar and frontoparietal networks. Quantitative evaluation confirmed a significant collapse in the System Segregation index, validating network boundary breakdown. Clinically, inter-effector hypoconnectivity to the right Rolandic operculum correlated with freezing of gait severity, while aberrant effector-specific-cerebellar positive FC correlated with tremor severity.

CONCLUSIONS: This study delineates a multilayered network pathology in PD, involving local hyperconnectivity, cortico-subcortical desegregation, and quantitatively verified impairment of large-scale network segregation. These subregion-specific alterations, particularly maladaptive cortico-cerebellar and cortico-striatal coupling, reframe PD as a disorder of network dedifferentiation, highlighting M1 subcircuits as targets for symptom-specific neuromodulation.

PMID:42667793 | DOI:10.1016/j.nicl.2026.104051

Int Forum Allergy Rhinol. 2026 Aug 29. doi: 10.1002/alr.70262. Online ahead of print.

ABSTRACT

BACKGROUND: Cognitive dysfunction is increasingly recognized as an extra-nasal manifestation of chronic rhinosinusitis (CRS). This scoping review characterizes the primary evidence on cognition in adult CRS, covering measures, treatment response, and incident dementia.

METHODS: We searched EMBASE, PubMed/MEDLINE, Web of Science, the Cochrane Library, CINAHL, and Global Index Medicus through May 2026 using a MeSH and free-text strategy. Inclusion criteria include primary studies of adult CRS with a formal clinical diagnosis (AAO-HNS, EPOS or ICD-coded) reporting at least one validated cognitive measure or recognized cognitive impairment. Two reviewers screened independently. Animal studies, reviews, conference abstracts, and non-CRS populations were excluded. PRISMA-ScR reporting was performed; heterogeneity precluded meta-analysis.

RESULTS: Twenty-four primary studies met inclusion criteria. Adults with CRS perform worse than controls on subjective (Cognitive Failures Questionnaire [CFQ], Neuro-QoL) and objective (Montreal Cognitive Assessment [MoCA], Automated Neuropsychological Assessment Metrics [ANAM], eye movement assessment, P300) cognitive instruments. Deficits correlate with disease-specific QoL severity (SNOT-22, Rhinosinusitis Disability Index [RSDI]). Cognitive measures improve after medical therapy or endoscopic sinus surgery. Two resting-state fMRI analyses identified altered orbitofrontal-precuneus connectivity scaling with sinonasal inflammation. Dementia results are mixed: a UK Biobank analysis (7176 CRS cases among 364,945 participants) reported increased Alzheimer disease risk (HR 1.33), while two Korean cohorts and a 2025 meta-analysis found no association.

CONCLUSION: Adults with CRS show measurable cognitive dysfunction that may improve with treatment, with a plausible neuroinflammatory pathophysiology. Whether this translates to incident dementia is contested. Outcome heterogeneity limits synthesis; therefore, development of a CRS-specific cognitive measurement set is recommended.

PMID:42667649 | DOI:10.1002/alr.70262

J Endocrinol Invest. 2026 Aug 29. doi: 10.1007/s40618-026-03036-6. Online ahead of print.

ABSTRACT

PURPOSE: Type 1 diabetes mellitus (T1DM) is associated with cognitive impairment, yet the underlying neurobiological mechanisms remain poorly understood. While resting-state functional magnetic resonance imaging (rs-fMRI) has revealed aberrant brain activity in T1DM, the spatial correlation between these functional alterations and normative neurotransmitter receptor/transporter distributions has not been systematically explored.

METHODS: We enrolled 43 T1DM patients and 52 well-matched healthy controls (HCs). All participants underwent rs-fMRI and comprehensive neuropsychological assessments. We analyzed spontaneous neural activity using the amplitude of low-frequency fluctuations (ALFF) and inter-hemispheric functional coordination using voxel-mirrored homotopic connectivity (VMHC). The spatial correlation between brain activity patterns and normative neurotransmitter receptor/transporter distributions was assessed using the JuSpace toolbox.

RESULTS: Compared to HCs, T1DM patients demonstrated poorer overall cognitive performance. Neuroimaging analyses revealed significantly decreased ALFF in the right thalamus and increased VMHC in the bilateral insula. More importantly, the ALFF reduction showed spatial overlap with the distributional regions of serotonergic (SERT), dopaminergic (D2, DAT), GABAergic (GABAa), opioid (MOR), glutamatergic (NMDA), and cholinergic (VAChT) systems. The VMHC increase showed spatial overlap with the distributional regions of the serotonergic system (SERT, 5-HT4). Most spatial associations were replicated across atlases, although the D2 and GABAa findings were not. Furthermore, right thalamic ALFF negatively correlated with HbA1c levels.

CONCLUSION: This study demonstrates spatial correspondences between T1DM-related brain functional alterations and normative distributions of multiple neurotransmitter receptors/transporters. The negative correlation between thalamic ALFF and HbA1c suggests an association between chronic hyperglycemia and local brain activity. These findings offer a novel perspective on the pathophysiology of cognitive impairment in T1DM.

PMID:42667349 | DOI:10.1007/s40618-026-03036-6

Imaging Neurosci (Camb). 2026 Aug 27;4:IMAG.a.1346. doi: 10.1162/IMAG.a.1346. eCollection 2026.

ABSTRACT

Accurate measurement of physiological signals such as respiration and cardiac activity is essential for modeling physiological confounds in BOLD-fMRI data. However, external monitoring devices such as respiratory belts and photoplethysmographs often suffer from signal loss, noise, or incomplete recordings, and many fMRI datasets lack physiological measurements altogether due to practical constraints. In this work, we propose a data-driven framework to reconstruct respiratory variation (RV) and heart rate variation (HRV) directly from resting-state fMRI data using a hybrid machine learning architecture that combines one-dimensional convolutional neural networks (1D-CNN) with gated recurrent units (GRUs). The model processes BOLD signals from 630 regions of interest (ROIs), spanning cortical and subcortical gray matter, white matter, and cerebrospinal fluid. For RV estimation, six head motion parameters were included as additional inputs to capture motion-related physiological information. The proposed method was trained and evaluated on three cohorts across the lifespan from the Human Connectome Project (HCP), including HCP in Development (HCP-D, ages 5-21 years), HCP in Young Adults (HCP-YA, ages 22-35 years), and HCP in Aging (HCP-A, ages 36-100 years). Age-specific architectural tuning was applied to accommodate developmental, neurovascular, and physiological variability across these populations. When compared against established CNN- and RNN-based baselines under matched preprocessing and native temporal resolution, the proposed architecture achieved consistent performance gains across all evaluation metrics. The largest relative improvements were observed for error-based and temporal alignment metrics, with MAE, MSE, and DTW reduced by approximately 7-10%, indicating lower absolute reconstruction error and improved temporal fidelity, while correlation-based improvements were more modest (approximately 5-7%). These results demonstrate the feasibility and effectiveness of reconstructing RV and HRV directly from fMRI time series using spatially and temporally enriched neural architectures. Building on earlier efforts in this domain, the proposed framework extends prior work through evaluation across multiple cohorts, broader anatomical coverage, and enhanced modeling capacity, offering a robust alternative for physiological modeling in the absence of external recordings.

PMID:42666759 | PMC:PMC13522955 | DOI:10.1162/IMAG.a.1346

J Neurosci Res. 2026 Sep;104(9):e70153. doi: 10.1002/jnr.70153.

ABSTRACT

Mild traumatic brain injury (mTBI) is associated with persistent physical, cognitive, and emotional symptoms, yet the functional brain changes that accompany recovery remain incompletely understood. This longitudinal study investigated brain entropy (BEN), a resting-state measure of irregularity or complexity of spontaneous BOLD activity derived from resting-state functional magnetic resonance imaging (rs-fMRI), during the first year following mTBI. Data of rs-fMRI were acquired from 48 patients with mTBI at the acute (< 14 days), 6-month, and 12-month post-injury visits, and 34 healthy controls (HCs). BEN was quantified using sample entropy. Patients with mTBI also underwent behavioral assessments evaluating quality of life and cognitive function. Behavioral assessments demonstrated progressive improvements in patient-reported quality of life and objective cognitive performance. Cross-sectional analyses revealed dynamic, time-dependent alterations in BEN. Compared with HCs, patients with mTBI exhibited higher BEN in precuneus, temporal, occipital, and sensorimotor regions during the acute and 6-month stages, whereas lower BEN was observed in sensorimotor, occipital, and frontoparietal regions at the 12-month follow-up. Longitudinal analyses demonstrated progressive regional reductions in BEN over time. Several associations between longitudinal changes in regional BEN and cognitive performance were nominally significant, particularly in the precuneus, but none survived multiple comparison corrections. These findings indicate that mTBI recovery is accompanied by dynamic, regionally specific changes in resting state BOLD irregularity. BEN may therefore provide a promising imaging biomarker for tracking the evolving neural dynamics underlying recovery following mTBI.

PMID:42666053 | DOI:10.1002/jnr.70153

IEEE Trans Neural Syst Rehabil Eng. 2026 Aug 28;PP. doi: 10.1109/TNSRE.2026.3728062. Online ahead of print.

ABSTRACT

Conventional neuroimaging tools for post-stroke motor function evaluation (e.g., EEG, fMRI) have some constraints. Conversely, functional near-infrared spectroscopy (fNIRS) offers a viable compromise. Nevertheless, few studies have yet quantitatively assessed the current motor function scores based on fNIRS data. This study proposed a Graph Convolutional Network (GCN) and Support Vector Regression (SVR) fusion model to fit Fugl-Meyer Assessment (FMA) scores by leveraging a multi-state integration of fNIRS metrics and clinical indicators. After preprocessing, brain network features and GCN features were extracted from the fNIRS data. Then a modified forward search method was used for SVR model training and feature selection from three feature sets: resting-state/task-state fNIRS feature sets and clinical feature set. Finally, the SVR model was employed to estimate the FMA scores. The coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE)were utilized to evaluate the models. The GCN-SVR fusion model demonstrated good goodness-of-fit, and exhibited limited variability. The multi-state model demonstrated better performance than both single-state models (P<0.001). For cortical cases, Aggregate measures over the nine sparsity levels confirmed both high accuracy and stable fitting (R²=0.8397±0.0487, RMSE=6.75±1.06, MAE=4.91±0.94), with R² consistently above 0.76 across sparsity levels. In subcortical patients, the multi-state model achieved a mean R² of 0.7562±0.0185, with RMSE=10.51±0.40 and MAE=7.74±0.53. The proposed GCN‑SVR fusion algorithm based on fNIRS data achieved high accuracy and stable performance in fitting FMA scores, while subset‑based sequential forward selection enhances multi-dataset feature selection.

PMID:42664099 | DOI:10.1109/TNSRE.2026.3728062

CNS Neurosci Ther. 2026 Aug;32(8):e71114. doi: 10.1002/cns.71114.

ABSTRACT

AIMS: To determine how lesion size and location shape longitudinal functional connectivity (FC) changes after stroke.

METHODS: Adult male mice underwent atlas-based resting-state fMRI at baseline and 1, 2, and 4 weeks after either a small photothrombotic cortical stroke (N = 25) or a larger transient cortico-striatal MCAO stroke (N = 6). FC was quantified across 98 atlas regions, focusing on sensorimotor cortex, striatum, and thalamus, including intra- and inter-hemispheric connectivity matrices and regional seed strength.

RESULTS: Cortical stroke caused widespread hyperconnectivity at Weeks 1-2, with about 90% of connections increased, followed by partial normalization by Week 4. This effect declined most strongly in the ischemic hemisphere and remained more sustained contralesionally. In contrast, cortico-striatal stroke induced global hypoconnectivity at Week 1, with more than 90% of connections decreased, a modest and heterogeneous shift toward baseline at Week 2, and widespread decreases persisting at Week 4. A subset of sensorimotor connections showing opposite changes in the two models robustly separated groups at all post-stroke time points. Regional lesion involvement scaled with the magnitude of baseline-referenced FC alterations.

CONCLUSION: Lesion topography drives distinct longitudinal FC trajectories after stroke and may help define network biomarkers and optimal windows for targeted interventions.

PMID:42661330 | DOI:10.1002/cns.71114

Schizophr Res. 2026 Aug 27;297:241-253. doi: 10.1016/j.schres.2026.08.013. Online ahead of print.

ABSTRACT

BACKGROUND: Disturbances in the self are central to schizophrenia-spectrum disorders (SZ) and may arise from abnormalities in emotional embodiment, or the representation of emotions through bodily sensations. The insular cortex plays a central role in interoception and emotional awareness, yet the relationship between emotional embodiment and insular functional connectivity (FC) remains unclear. We tested whether reduced differentiation of bodily emotion representations is associated with reduced differentiation of insular subregional FC profiles in SZ.

METHODS: Resting-state fMRI and emotional embodiment data were analyzed in 31 SZ and 40 healthy comparison (HC) participants. Emotional embodiment was assessed using the EMBODY task, in which participants mapped bodily sensation associated with a range of emotions. Seed-to-voxel FC analyses were performed for posterior, dorsal anterior, and ventral anterior insular subregions, and differentiation of their FC profiles was quantified using η2.

RESULTS: SZ participants exhibited significantly less differentiated bodily representations of emotions than HC participants. Insular subregional differentiation was statistically similar between groups, although SZ participants demonstrated reduced FC between the posterior insula and primary motor cortex and between the dorsal anterior insula and Broca's area. Lower differentiation of posterior and dorsal anterior insular FC profiles was associated with lower embodied activation for anger and depression.

CONCLUSIONS: Schizophrenia is characterized by less differentiated bodily representations of emotion, but these alterations showed only limited associations with insular subregional FC differentiation. These findings suggest that altered emotional embodiment in schizophrenia cannot be explained solely by reduced differentiation of insular functional connectivity and motivate further investigation using task-based neuroimaging paradigms.

PMID:42660028 | DOI:10.1016/j.schres.2026.08.013

Nat Commun. 2026 Jul 29;17(1):9207. doi: 10.1038/s41467-026-75959-w.

ABSTRACT

Traditional models of brain connectivity primarily focus on pairwise interactions, overlooking collective dynamics among multiple regions. Although many higher-order interaction (HOI) metrics have been proposed, their comparative properties and utility remain unclear. Here, we systematically compare information-theoretic and topological HOI metrics using resting-state and task fMRI data from 100 unrelated Human Connectome Project subjects. We identify a taxonomy of redundant, synergistic, and topological metrics, with the latter bridging the redundancy-synergy continuum. Despite methodological differences, all HOI metrics align with the brain's sensorimotor-to-association hierarchy, yet reveal distinct molecular signatures linking redundancy to metabolic profiles and synergy/topological scaffolds to receptor architecture. HOI metrics outperform pairwise connectivity in brain fingerprinting, whereas pairwise models remain competitive for task decoding. Multivariate analyses further identify topological descriptors as key links between brain function and behavioral variability. Overall, our findings provide a conceptual map of HOI methods for studying multi-regional brain interactions across cognitive and clinical neuroscience.

PMID:42660887 | DOI:10.1038/s41467-026-75959-w

Depress Anxiety. 2026 Aug 26;2026:4516539. doi: 10.1155/da/4516539. eCollection 2026.

ABSTRACT

BACKGROUND: Parenting significantly influences children's emotional well-being, but the specific nature and neural mechanisms of this influence remain unclear. This study investigated (1) the association between perceived maternal parenting and amygdala resting-state functional connectivity (rsFC) in childhood and (2) whether amygdala rsFC moderates the relation between perceived maternal parenting and subsequent depressive symptoms.

METHODS: A total of 220 children from the Growing Up in Singapore Towards Healthy Outcomes (GUSTO) cohort underwent resting-state fMRI at age 7.5 and assessments of perceived maternal parenting and depressive symptoms at ages 8.5-10.5 using the Children's Depression Inventory (CDI-2) and the child-reported Parental Bonding Instrument (PBI). Multiple regression analyses explored associations between maternal parenting (PBI subscales: overprotection and care) and rsFC of the whole amygdala and subnuclei. Moderation analyses evaluated whether amygdala rsFC moderated the relation between maternal parenting and subsequent depressive symptoms in children.

RESULTS: Two neural pathways were identified in relation to perceived maternal overprotection. One pathway showed a positive association between the rsFC of the right (R) basolateral amygdala (BLA) and the R angular gyrus (AG) as well as the left (L) inferior parietal gyrus (IPG). The other pathway showed a negative association with the L superficial amygdala (SFA)-anterior insula (INS) rsFC. The pathway from R amygdala-AG rsFC significantly moderated the strong positive association between perceived maternal overprotection and subsequent depressive symptoms. These associations were independent of maternal depressive symptoms.

CONCLUSIONS: This study provides insight into the neurobiological basis linking perceived maternal parenting to subsequent depressive symptoms. R amygdala-AG rsFC may serve as a neural marker of variability in children's responses to perceived maternal overprotection, shaping the nature of their depressive symptoms.

PMID:42656696 | PMC:PMC13507833 | DOI:10.1155/da/4516539

Front Neurosci. 2026 Aug 12;20:1859226. doi: 10.3389/fnins.2026.1859226. eCollection 2026.

ABSTRACT

BACKGROUND: Chronic fatigue syndrome (CFS) is a disabling multisystem disorder characterized by persistent fatigue, post-exertional malaise, unrefreshing sleep, and neurocognitive and gastrointestinal symptoms. Effective non-pharmacological treatments remain limited. Increasing evidence suggests that CFS involves not only peripheral metabolic and gastrointestinal abnormalities but also central nervous system dysfunction. Altered tryptophan metabolism and gut-brain axis dysfunction may contribute to its pathophysiology. Tuina, a manual therapy used in traditional Chinese medicine, has been hypothesized to influence somatosensory, autonomic, stress-related, and gastrointestinal pathways. However, randomized controlled trials evaluating its clinical effects and exploratory gut-brain correlates in CFS remain scarce.

METHODS: This multicenter, parallel-group, randomized controlled trial will enroll 166 participants with CFS, who will be allocated in a 1:1 ratio to a Tuina group or a standardized usual care group. The Tuina group will receive treatment twice weekly for 8 weeks, whereas the control group will receive standardized usual care consisting of health education and symptom-oriented management. The primary objective is to evaluate the effectiveness of Tuina on fatigue severity, assessed by the change in Fatigue Scale-14 (FS-14) score from baseline to week 8. Secondary outcomes include quality of life, sleep, pain, anxiety, depression, gastrointestinal symptoms, bowel function, and handgrip strength. Exploratory mechanistic outcomes include plasma tryptophan-pathway metabolites, gut microbiota profiling, resting-state functional magnetic resonance imaging, and magnetic resonance spectroscopy. Assessments will be conducted at baseline, weeks 4 and 8, and at 12 and 24 weeks after completion of treatment.

DISCUSSION: This study is designed to evaluate the clinical effects of Tuina in CFS while exploring a biologically informed gut-brain framework involving tryptophan metabolism, gut microbiota, and brain network alterations. To our knowledge, few randomized trials in CFS have prospectively integrated clinical, metabolomic, microbiome, and multimodal neuroimaging measures within a single protocol. The findings may help clarify whether a non-pharmacological intervention can modulate interconnected peripheral and central pathways relevant to fatigue and related symptoms.

CLINICAL TRIAL REGISTRATION: https://itmctr.ccebtcm.org.cn/mgt/project/view/1990257056896122880, Identifier ITMCTR2025002204.

PMID:42656510 | PMC:PMC13506880 | DOI:10.3389/fnins.2026.1859226

Front Mol Neurosci. 2026 Aug 12;19:1790428. doi: 10.3389/fnmol.2026.1790428. eCollection 2026.

ABSTRACT

OBJECTIVE: The central pathophysiology of chronic low back pain (CLBP) secondary to lumbar disc herniation (LDH) remains poorly understood, particularly the link between macroscale brain network dysfunction and its microscale biological underpinnings. This study aimed to bridge this critical gap by integrating resting-state fMRI, graph theory, and imaging transcriptomics.

METHODS: Resting-state fMRI data were acquired from 62 CLBP patients with LDH and 67 matched healthy controls (HCs). Whole-brain functional networks were constructed for each participant by calculating functional connectivity (FC) between 90 anatomical regions. Graph theoretical analysis was employed to derive global and nodal topological metrics. Group differences were identified and correlated with clinical variables. Crucially, these neuroimaging phenotypes were spatially mapped against the Allen Human Brain Atlas (AHBA) to uncover potential spatial molecular vulnerabilities.

RESULTS: At the connectional level, CLBP patients with LDH exhibited widespread hypoconnectivity across sensorimotor, ventral attention, and default mode networks. Further topological analysis revealed a profound focal degradation of hub function in the cingulate cortex, characterized by significantly reduced nodal degree, efficiency, and betweenness centrality (p < 0.001), alongside decreased nodal efficiency in the right supplementary motor area, while global network architecture was preserved. Critically, these deficits in cingulate hub topology were significantly correlated with pain intensity. Imaging transcriptomics linked this focal topological failure to genes enriched for neuroinflammatory and synaptic plasticity pathways.

CONCLUSION: These findings reveal multi-level functional network alterations in CLBP with LDH, linking focal hub failure to potential spatial molecular vulnerabilities associated with neuroinflammation and synaptic remodeling.

PMID:42656466 | PMC:PMC13506785 | DOI:10.3389/fnmol.2026.1790428