Most recent paper

PeerJ. 2026 Sep 30;14:e21439. doi: 10.7717/peerj.21439. eCollection 2026.

ABSTRACT

Resting-state functional magnetic resonance imaging (rs-fMRI) captures spontaneous neural activity and has become a valuable tool for investigating functional alterations in brain disorders. A widely used strategy is to estimate functional connectivity (FC) between brain regions based on blood-oxygen-level-dependent (BOLD) signals, which can be further represented as functional brain graphs (FBGs). Despite promising results, existing methods typically rely on static FBGs derived from a single brain atlas, which limits their ability to capture the temporal variability of brain activity and integrate complementary anatomical information. Moreover, the modular organization of brain networks-an important feature linked to cognitive function and disease-has often been overlooked in model design. To address these limitations, we propose a modularity-constrained dynamic FBG learning framework for brain disorder classification. The proposed method integrates FBGs constructed from multiple brain atlases and captures dynamic spatio-temporal patterns using a sliding window strategy and a Transformer encoder. We employ a stochastic block model (SBM) to detect modular structures, and further incorporate modularity constraints into the training objective to enhance model interpretability and regularize learning. Experiments on the Autism Brain lmaging Data Exchange (ABIDE) and the REST-meta-MDD Consortium (REST-MDD) datasets demonstrate that our method achieves classification accuracies of 68.5% and 65.3%, respectively, outperforming conventional and competitive deep learning methods. This framework provides a novel perspective for dynamic modeling of brain functional connectivity and demonstrates potential for clinical application.

PMID:42829711 | PMC:PMC13633691 | DOI:10.7717/peerj.21439

J Voice. 2026 Oct 3:S0892-1997(26)00508-4. doi: 10.1016/j.jvoice.2026.09.032. Online ahead of print.

ABSTRACT

OBJECTIVES: Laryngeal dystonia (LD) is a voice motor disorder involving multilevel central networks. Existing LD functional magnetic resonance imaging (fMRI) studies use heterogeneous region-of-interest (ROI) definitions and parcellation schemes, limiting comparability and reproducibility. To provide a more consistent framework for ROI definition and whole-brain network-level analysis, we aimed to develop and technically validate a standardized, small-structure-preserving whole-brain ROI atlas for LD fMRI research.

METHODS: We integrated Automated Anatomical Labeling version 1 (AAL1)-derived cerebral and selected subcortical regions, the Spatially Unbiased Infratentorial Template (SUIT) cerebellar atlas, the Najdenovska thalamic nuclei atlas, and Brainstem Navigator-derived diencephalic and brainstem regions. Atlases were harmonized to Montreal Neurological Institute (MNI) space with nearest-neighbor interpolation and small-structure-prioritized merging, generating a 1-mm reference atlas (LD-208) and a 2-mm fMRI-ready atlas (LD-205). Quality control included ROI continuity, zero-voxel checks, voxel counts, connected components, and source tracing. LD-205 was tested in resting-state fMRI data from 10 healthy participants.

RESULTS: LD-208 and LD-205 contained 208 and 205 ROIs, respectively. Compared with LD-208, LD-205 did not retain three extremely small brainstem ROIs on the 2-mm grid. Both atlases had continuous ROI numbering and no zero-voxel ROIs. All LD-205 ROIs yielded valid voxels, mean blood oxygen level-dependent (BOLD) time series, and complete 205 × 205 connectivity matrices in every participant.

CONCLUSIONS: LD-208 and LD-205 provide traceable and quality-controlled whole-brain ROI frameworks for LD-oriented fMRI research. The technical validation demonstrates the feasibility of applying LD-205 to conventional resting-state fMRI data and supports its use as a standardized ROI framework for future disease-specific studies.

PMID:42829319 | DOI:10.1016/j.jvoice.2026.09.032

Neurosci Res. 2026 Oct 2:105127. doi: 10.1016/j.neures.2026.105127. Online ahead of print.

ABSTRACT

Vector autoregressive modeling (VAR) is a powerful method for estimating dynamic functional connectivity (dFC) from resting-state functional magnetic resonance imaging (fMRI) data. However, VAR-based dFC can become unstable when only limited data are available. We compared multiple regularization approaches (PLS, PCA, Ridge, and Lasso) to examine whether they can stabilize VAR-based dFC without substantially compromising behaviorally relevant information. All regularization methods improved stability. Behavioral measure prediction improved and reached a level comparable to Pearson correlation-based FC, except Lasso. These findings suggest that regularized VAR-based dFC can provide behaviorally relevant information about temporal dynamics not represented in Pearson correlation-based FC.

PMID:42826884 | DOI:10.1016/j.neures.2026.105127

Neurotherapeutics. 2026 Oct 2;23(6):e01091. doi: 10.1016/j.neurot.2026.e01091. Online ahead of print.

ABSTRACT

Treatment-resistant depression (TRD) is characterized by persistent symptoms and poor response to conventional therapies, yet its dynamic neural mechanisms and treatment-related changes remain incompletely understood. This study applied a hidden Markov model (HMM) combined with neurotransmitter atlas mapping to investigate whole-brain spatiotemporal dynamics in TRD and their modulation by electroconvulsive therapy (ECT). Resting-state fMRI data were collected from 72 TRD patients (52 completing follow-up) and 63 healthy controls. HMM identified six recurring brain states defined by distinct patterns of functional connectivity and averaged functional activity. At baseline, TRD patients showed reduced occupancy and stability of globally coordinated states and increased engagement of subcortical-dominant states, indicating impaired global stability and disrupted cortical-subcortical balance. After ECT, clinical symptoms improved significantly, accompanied by increased occupancy of states characterized by higher activity within the default mode and frontoparietal networks, as well as a highly integrated whole-brain state. These changes suggest improved overall brain coordination and cortical engagement rather than full normalization of baseline abnormalities. In contrast, subcortical-dominant states persisted after treatment, consistent with trait-like features of TRD. Neurotransmitter mapping showed that treatment-responsive states were associated with serotonergic, dopaminergic, and glutamatergic systems, whereas persistent subcortical states were linked to noradrenergic and cholinergic markers. Overall, these findings suggest that ECT improves symptoms by shifting brain dynamics toward more adaptive and integrated states, rather than fully restoring baseline function. This dynamic perspective provides new insight into the neural mechanisms of TRD and its treatment.

PMID:42826578 | DOI:10.1016/j.neurot.2026.e01091

J Neurosurg. 2026 Oct 2:1-12. doi: 10.3171/2026.4.JNS26181. Online ahead of print.

ABSTRACT

OBJECTIVE: The objective of this study was to investigate the mechanisms of motor compensation in glioma patients by quantifying both the structural decoupling index (SDI) value and the integrity of transcallosal white matter tracts. The authors aimed to evaluate their individual and combined utility in predicting long-term postoperative motor outcomes.

METHODS: Seventy patients with motor area gliomas and 23 healthy controls were retrospectively included. All participants underwent preoperative 3T resting-state fMRI and diffusion MRI. The SDI value was computed using a graph signal processing approach to quantify the decoupling between functional activity and structural connectivity. White matter integrity and disconnection severity of the corpus callosum were assessed using TractSeg and tractography. Motor function was evaluated using the Medical Research Council scale preoperatively and at the 3-month follow-up. Statistical analyses included group comparisons, correlation analyses, and multivariate logistic regression to identify predictors of severe motor impairment (SMI).

RESULTS: Glioma patients exhibited altered SDI values in motor-related regions, including the precentral gyrus and thalamus. A higher SDI value in the contralateral precentral gyrus was associated with better motor recovery (p = 0.00025), while greater disconnection severity in the anterior midbody of the corpus callosum (CC4) correlated with poorer motor function (p = 0.00041). Multivariate analysis identified IDH-wildtype status, higher CC4 disconnection severity, and lower contralateral precentral gyrus SDI value as independent predictors of SMI. A nomogram integrating these factors showed excellent predictive accuracy (C-index = 0.914).

CONCLUSIONS: The SDI captures adaptive functional decoupling in the contralateral motor cortex, which may be associated with compensatory mechanisms in glioma patients. Combined with callosal integrity, the SDI enhances the prediction of postoperative motor outcomes, offering a novel biomarker for preoperative risk stratification and personalized rehabilitation planning.

PMID:42826412 | DOI:10.3171/2026.4.JNS26181

Front Neurosci. 2026 Sep 17;20:1771767. doi: 10.3389/fnins.2026.1771767. eCollection 2026.

ABSTRACT

RATIONALE: Concussion disrupts brain connectivity, and effective treatments remain limited. Mindfulness-based interventions (MBIs) may influence functional connectivity (FC) of networks affected by concussion, but pediatric trials are lacking. This study investigated whether an app-delivered MBI produced greater FC changes than a sham condition across the default mode (DMN), salience (SN), and central executive (CEN) networks.

METHODS: This randomized clinical trial (NCT05105802) recruited participants aged 12.00-17.99 years from the emergency department or interdisciplinary concussion clinic, then randomly assigned them to MBI or cognitive sham. Brain imaging sessions occurred at 72 h and 4 weeks post-concussion. Region-of-interest and seed-to-voxel analyses examined Group × Time interactions in FC across DMN, SN, and CEN (p < 0.050 FDR ).

RESULTS: Thirty-six participants were included (MBI: n = 16, median [IQR] age = 14.23 [13.52-15.65] years, 31% female; Sham: n = 20, median [IQR] age = 14.05 [13.13-16.26] years, 45% female). Intention-to-treat analyses revealed significant Group × Time interactions across networks. DMN-CEN FC patterns differed, with MBI participants showing diminished FC. Within DMN, four clusters showed significant interactions (p < 0.050 FDR ), three exhibiting larger FC reductions at 4 weeks in MBI participants. MBI participants showed greater FC increase in one SN cluster (p = 0.040 FDR ) and larger FC decrease in one CEN cluster (p = 0.024 FDR ) at 4 weeks. Per-protocol analyses identified one significant DMN cluster (p < 0.001 FDR ) where MBI participants exhibited greater FC increases.

CONCLUSION: Mindfulness-based intervention training led to changes in FC in networks implicated in concussion recovery. Larger studies are needed to support these findings.

CLINICAL TRIAL REGISTRATION: https://clinicaltrials.gov/study/NCT05105802, identifier NCT05105802.

PMID:42824171 | PMC:PMC13627364 | DOI:10.3389/fnins.2026.1771767

Hum Brain Mapp. 2026 Oct 1;47(14):e70652. doi: 10.1002/hbm.70652.

ABSTRACT

Abundant research from early neurocognitive models to recent mega-analyses has highlighted the central role of frontostriatal circuits in attention deficit hyperactivity disorder (ADHD). However, it remains unclear which specific aspects of functionally organised frontostriatal loops are affected by ADHD and how these disruptions differ across individuals in light of the disorder's marked heterogeneity. We investigated frontostriatal connectivity in individualised circuits in 40 adults with ADHD and 36 neurotypical controls using a multimodal dataset that included diffusion-weighted magnetic resonance imaging (MRI), as well as functional MRI during working memory updating, resting state, and a naturalistic attention task. Compared with the neurotypical controls, the adults with ADHD exhibited reduced structural connectivity strength in a tract originating from left orbital prefrontal areas. However, structural results did not reproduce in control analyses using different thresholds in individual formation of regions of interest. Functional data revealed weaker connectivity in the ADHD group during verbal and visuospatial working memory tasks in circuits connecting the striatum with the dorsolateral prefrontal cortex, motor regions, and orbitofrontal cortex, while no group differences were found during resting state or the naturalistic task. Comparisons with atlas-based analyses highlighted the precision of individualised connectivity mapping. Together, these findings suggest that frontostriatal functional connectivity aberrancies in ADHD are driven by striatum-dependent cognitive processes such as working memory, and that precise mapping of frontostriatal pathways can be achieved through individualised connectivity mapping.

PMID:42823824 | DOI:10.1002/hbm.70652

J Headache Pain. 2026 Aug 17;27(1):232. doi: 10.1186/s10194-026-02492-y.

ABSTRACT

BACKGROUND: Classical trigeminal neuralgia (CTN) is characterized by transient, stimulus-evoked pain attacks, but post-stimulation changes in local brain synchronization remain unclear. We characterized static and dynamic local functional synchronization at baseline and during resting-state scans initiated after a single trigger-zone stimulation.

METHODS: Baseline analyses included 44 healthy controls (HC), 80 CTN patients with stimulation-evoked pain (CTN_pain), and 68 CTN patients without evoked pain (CTN_no_pain). Longitudinal analyses included 39 HC, 75 CTN_pain patients, and 64 CTN_no_pain patients completed all the three resting-state fMRI sessions: baseline, a scan initiated within 5 seconds after stimulation, and a scan initiated 30 minutes later. The first 20 seconds of each time series were discarded. Regional homogeneity, coherence-based regional homogeneity, and their dynamic measures (ReHo, CoReHo, dReHo, and dCoReHo) were assessed using voxel-wise group/time analyses; exploratory region of interest (ROI)-level mixed-effects models tested group × time interactions.

RESULTS: At baseline, group differences were observed in Frontal_Med_Orb_R, Insula_L/R, and Hippocampus_R/L. Insula_R dReHo and Frontal_Med_Orb_R ReHo differed between CTN_pain and CTN_no_pain patients. Within-group post-stimulation changes were limited in HC and broader in both CTN subgroups. Exploratory ROI-level sensitivity analyses identified seven group × time interactions that remained significant after metric-specific Bonferroni correction. Exploratory imaging-clinical analyses indicated that imaging measures associations with pain scores in CTN_pain patients and quality of life, anxiety and depression scores in CTN_no_pain patients.

CONCLUSIONS: CTN subgroups defined by the observed pain-evocation outcome differed in baseline and temporal measures of resting-state local synchronization. Baseline Insula_R dReHo was the subgroup-associated measure but should not be interpreted as causal or predictive. Exploratory group × time interactions and imaging-clinical correlations provide preliminary evidence that requires independent replication. These findings reflect post-stimulation resting-state alterations rather than acute activation.

PMID:42823765 | DOI:10.1186/s10194-026-02492-y

bioRxiv [Preprint]. 2026 Sep 25:2026.09.20.753026. doi: 10.64898/2026.09.20.753026.

ABSTRACT

Multi-echo functional magnetic resonance imaging (fMRI) acquires signals with distinct contrast profiles across echo times (TEs). Empirical evidence suggests that different TEs capture distinct signal contributions with varying sensitivity to BOLD and non-BOLD processes from long to short TEs (Stroman, 2002; Krishnamurthy, 2023; Dong, 2024). Previous literature has not yet examined how brain states extracted from temporal dynamic functional connectivity (dFNC) evolve across TEs. Here we present a methodological and empirical study of TE-dependent evolution of dynamic connectivity states, with a phenomenological two-component model for interpretation. Multiple echoes were acquired with an echo planar time-resolved imaging (EPTI) sequence. TE-specific components were extracted via atlas-based group ICA (Neuromark 1.0), and brain connectivity states were generated via windowed dFNC and spatial ICA clustering of windows. Within-subject and group-level states evolved across TEs in network organization and variance. States exhibited TE-dependent variance profiles, with distinct peak echo times; and group-level points of state transition were independently mapped to TE-dependent transitions in variance. We modified a bi-exponential model to simulate and model changes in the dFNC time-course across TEs. The bi-exponential model revealed high consistency with subject states (R2 fit=0.86, res. error=0.55, 86% model convergence rate), suggesting a two-component signal system contributing to dynamic TE-dependent connectivity changes in state. A significant correlation between relative model terms was estimated on a cell-wise basis across states (r(24) = -0.43, p=0.03, 95% CI [-0.70, -0.04]). This suggests that specific bi-exponential signal parameters (i.e., short FNC1,TE=0, long FNC2,TE=0, and long FNC2,decay) were associated with the observed TE-dependent variations in connectivity states across TEs. In sum, our analyses provide downstream implications for TE-aware state analysis dependent on connectivity magnitude (FNCTE=0) and rate of connectivity decay (FNCdecay), in a two-component signal model, with future implications for TE-dependent state biomarkers.

PMID:42817992 | PMC:PMC13622153 | DOI:10.64898/2026.09.20.753026

Psychiatry Res Neuroimaging. 2026 Sep 18;364:112335. doi: 10.1016/j.pscychresns.2026.112335. Online ahead of print.

ABSTRACT

BACKGROUND: Obsessive-compulsive disorder (OCD) is clinically heterogeneous, and traditional threat-based models do not fully explain this diversity. Intolerance of uncertainty (IU) and not-just-right experiences (NJREs) have emerged as clinically relevant features of OCD, but their associations with spontaneous brain activity remain unclear.

METHODS: Fifty-seven patients with OCD and 38 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging. Regional spontaneous activity was assessed using the amplitude of low-frequency fluctuations (ALFF). Clinical measures included the Intolerance of Uncertainty Scale, the Not-Just-Right Experiences Questionnaire-Revised, the Yale-Brown Obsessive Compulsive Scale, and the State Anxiety Inventory. Group differences in ALFF were examined, and partial correlations were performed within the OCD group after controlling for age, sex, education, mean framewise displacement, state anxiety, and medication status.

RESULTS: Compared with HCs, patients with OCD showed increased ALFF in the bilateral orbital middle frontal gyrus and decreased ALFF in the left precuneus and right superior parietal gyrus. They also exhibited significantly higher IU scores. Within the OCD group, greater IU was associated with more severe NJREs and obsessive-compulsive symptoms (both Bonferroni-corrected p < 0.0125). Although IU was not associated with regional ALFF, greater NJRE severity was associated with both higher symptom severity and increased ALFF in the right orbital middle frontal gyrus (r = 0.380, p = 0.006).

CONCLUSIONS: OCD is characterized by altered frontoparietal spontaneous activity and elevated IU. The observed associations among IU, NJREs, symptom severity, and orbitofrontal ALFF suggest that uncertainty-related vulnerability, incompleteness-related experiences, and altered intrinsic brain activity are interconnected clinical features of OCD.

PMID:42822272 | DOI:10.1016/j.pscychresns.2026.112335

Med Image Anal. 2026 Sep 29;115:104346. doi: 10.1016/j.media.2026.104346. Online ahead of print.

ABSTRACT

Major depressive disorder (MDD) is associated with distributed disruptions in functional brain networks, yet current connectome-based biomarkers often rely on a single parcellation and show limited cross-site robustness and biological interpretability. We propose a Hierarchical Multi-Atlas Fusion Network (HMAF-Net) for robust and interpretable cross-site analysis of functional connectomes in MDD. HMAF-Net reconciles heterogeneous anatomical and functional atlases by learning a small set of shared fusion nodes that serve as latent anchors for cross-atlas semantic alignment. For each atlas, it extracts node-wise descriptors and uses Transformer-based sparse soft assignment with a structure-aware contrastive objective to align atlas nodes in a common semantic space while retaining atlas-specific information. The resulting fusion-node embeddings support subject-level classification and spatial mapping for biomarker localization and imaging-transcriptomic association. We evaluated HMAF-Net on the multi-site REST-meta-MDD cohort (N = 1,611). Under 10-fold cross-validation, HMAF-Net achieved 72.28% accuracy and 71.49% AUC, showing the highest mean accuracy among representative atlas-fusion and graph-based baselines under identical splits. Under leave-one-site-out evaluation, it reached 77.48% mean accuracy, with site-wise analyses indicating residual variability associated with cohort size and head motion. External validation on two independent MDD datasets further supported cross-dataset generalizability. Fusion-node involvement shifts revealed reproducible fronto-limbic and default-mode-salience alterations. These spatial profiles were associated with Allen Human Brain Atlas expression gradients and enriched for synaptic and neurotransmission pathways, providing a molecularly grounded axis for interpretation. By integrating cross-atlas alignment, cross-site evaluation, and transcriptomic interpretation, HMAF-Net yields compact connectomic embeddings for reproducible and mechanistically informed MDD biomarker development.

PMID:42822187 | DOI:10.1016/j.media.2026.104346

Neurophotonics. 2026 Jul;13(3):035013. doi: 10.1117/1.NPh.13.3.035013. Epub 2026 Sep 30.

ABSTRACT

SIGNIFICANCE: Resting-state functional connectivity (RSFC) is an important measure in advancing our understanding of brain function and development as well as various neurological and mental disorders. Studying RSFC with functional near-infrared spectroscopy (fNIRS) offers several advantages over functional magnetic resonance imaging (fMRI), especially for clinical and pediatric populations. However, the optimal strategy to estimate RSFC based on fNIRS, particularly in identifying reliable connectivity patterns across chromophores, remains unclear. Establishing robust analysis approaches is essential for reliable and clinically meaningful applications.

AIM: This study systematically evaluated commonly used analysis methods regarding their effectiveness to detect RSFC patterns within the motor network using both oxygenated (HbO) and deoxygenated (HbR) hemoglobin signals.

APPROACH: Near whole-head resting-state fNIRS data were analyzed from 38 participants. RSFC was estimated with five analytical approaches: three seed-based methods (SBA-GLM, SBA-GLM with respiratory regression, and SBA-Correlation) and two independent component analysis (ICA) approaches using two different contrast functions. Performance was assessed via receiver operating characteristic analyses based on both anatomical and functional definitions of motor-related connectivity. Areas under the curves (AUCs) were statistically compared with DeLong's test, and the spatial similarity between HbO and HbR RSFC was quantified by correlating RSFC patterns from the two chromophores.

RESULTS: Across reference definitions and chromophores, ICA consistently achieved higher performance ( AUC = 0.82 to 0.96) in detecting motor-related RSFC than SBA ( AUC = 0.63 to 0.86). Significant differences emerged when functionally defined connectivity references were used, with ICA outperforming SBA across chromophores. Under certain conditions, correlational SBA ( AUC = 0.66 to 0.86) significantly outperformed GLM-based methods ( AUC = 0.63 to 0.85). Finally, ICA results demonstrated greater spatial similarity between obtained HbO and HbR RSFC patterns ( r = 0.90 to 0.92) than SBA ( r = 0.84 to 0.86), indicating cross-chromophore consistency of the resulting RSFC maps.

CONCLUSIONS: ICA provides a robust and consistent framework for estimating fNIRS-based RSFC across both HbO and HbR, outperforming SBA in accuracy and cross-chromophore consistency. While correlational SBA offers a computationally efficient alternative and outperforms GLM-based methods, ICA should be preferred when reliable and chromophore-consistent RSFC estimates are required. Importantly, these findings demonstrate that HbR contains RSFC information comparable to HbO and highlights the critical role of analytical strategy and reference definition in RSFC evaluation. Collectively, these results contribute to the methodological standardization of fNIRS-based RSFC and support its use in future neuroscientific and clinical applications.

PMID:42819603 | PMC:PMC13626422 | DOI:10.1117/1.NPh.13.3.035013

Front Neurol. 2026 Sep 16;17:1915567. doi: 10.3389/fneur.2026.1915567. eCollection 2026.

ABSTRACT

OBJECTIVE: This study investigated the macroscopic neural mechanisms of cognitive impairment after Deep Nuclei Intracerebral Hemorrhage (DNICH) from the dimensions of brain activity and brain networks, and further identified characteristic fingerprints of cognitive representation.

METHOD: Patients with cognitive impairment after DNICH and healthy controls (HC) were prospectively and consecutively enrolled. Resting-state fMRI (rs-fMRI) data and the cognitive scale Mini-Mental State Examination (MMSE) were collected. Spontaneous brain activity measured by fractional Amplitude of Low-Frequency Fluctuations (fALFF) was used to identify hub brain regions of cognitive representation, which were then used as Regions of Interest (ROI) for Functional Connectivity (FC) analysis, in order to investigate the potential mechanisms after DNICH cognitive impairment and identify characteristic fingerprints for distinguishing patients with dementia (dementia was defined as MMSE ≤ 17).

RESULT: This study enrolled 139 patients with DNICH and 100 HC. There were no significant differences in baseline characteristics including age, gender, years of education, height, and weight between the two groups. Hematomas were predominantly located in the bilateral basal ganglia, more commonly on the left side, with varying degrees of thalamic and white matter involvement. Whole-brain zfALFF analysis showed that MMSE scores were significantly correlated with zfALFF values in brain regions including right dorsolateral prefrontal cortex (R-DLPFC) and posterior cingulate cortex (PCC), with R-DLPFC positively correlated and PCC negatively correlated with cognition (AlphaSim correction, p < 0.01; cluster size >19). In addition, FC within the executive control network (ECN) and default mode network (DMN) was abnormally altered in patients with cognitive impairment and was closely associated with cognitive function (r = -0.29-0.51). FC fingerprints demonstrated stable predictive ability for dementia status (AUC = 0.70-0.73).

CONCLUSION: Abnormal coordination of intra-network and inter-network FC of ECN and DMN may represent a shared neural mechanism of cognitive impairment after DNICH. Modulating FC between these hub brain regions (R-DLPFC and PCC) may provide new therapeutic strategies for cognitive recovery after DNICH.

PMID:42818636 | PMC:PMC13623672 | DOI:10.3389/fneur.2026.1915567

bioRxiv [Preprint]. 2026 Sep 11:2026.09.04.749545. doi: 10.64898/2026.09.04.749545.

ABSTRACT

Psychotic disorders are severe mental conditions whose biological underpinnings remain elusive. This is in part due to marked heterogeneity, which may have precluded advances in precise understanding and treatment of the disorders. Previous work has tried to elucidate this heterogeneity; nevertheless, sex, as an important biological variable, has not been well accounted for despite established sex differences. Using resting-state fMRI data from the BSNIP consortium (N=1753; 64.3% probands with psychosis include 571 females and 556 males; 35.7% of controls includes 369 females and 257 males), we extracted multiscale functional network connectivity and employed unsupervised learning methods to identify potential subgroups with distinct neurobiological profiles, characterized by an overrepresentation of males or females (termed here as sex-dominant Psychosis Imaging Neurosubtypes, PINs). Four sex-dominant neurosubtypes emerged, two female-dominant (fPIN-1, fPIN-2) and two male-dominant (mPIN-1, mPIN-2). Each PIN exhibited distinct, replicable dysconnectivity patterns that uniquely contributed to cognitive deficits. Brain-Predicted Cognitive performance derived from these PIN-specific dysconnectivity, prominent among the triple network (default mode and salience), subcortical-basal ganglia, higher cognition-insular temporal, and visual-occipitotemporal subdomains, correlated with measured cognitive performance and showed PIN-specific reductions relative to controls, confirming neurosubtype-congruent associations. Moreover, fPIN-2's distinct dysconnectivity also predicted positive and negative symptom scores. Our findings lend support for sex-dominant neurobiological heterogeneity, with sex-dominant PINs presenting unique dysconnectivity patterns that contribute to cognitive and symptom outcomes, even though neurosubtypes appear clinically similar. These results establish sex as a critical biological variable in deconstructing psychosis heterogeneity, which holds promise in revealing more precise biomarkers for disease characterization and guiding personalized treatment strategies.

PMID:42818538 | PMC:PMC13622486 | DOI:10.64898/2026.09.04.749545

ArXiv [Preprint]. 2026 Aug 7:arXiv:2608.07287v1.

ABSTRACT

Headache is the most common neurological disorder in children and substantially affects quality of life. We investigated whether resting-state functional MRI (rs-fMRI) can support pediatric headache classification using machine learning. We encoded rs-fMRI data using NeuroSTORM, a recent foundation model, and fine-tuned it to distinguish healthy controls from children with headache and subsequently classify headache subtypes. We compared NeuroSTORM with a standard neuroscience approach using functional-connectivity (FC) matrices derived from brain activity as predictors. Using 189 rs-fMRI scans from 110 individuals collected across two visits (prevalence of any headache: 74%), NeuroSTORM achieved an area under the receiver operating characteristic curve (AUROC) of 0.82 (95% CI, 0.82-0.82) and an area under the precision-recall curve (AUPRC) of 0.93 (95% CI, 0.93-0.94) for discriminating headache from non-headache. In contrast, models trained on FC matrices showed lower performance (AUROC, 0.67 [95% CI, 0.67-0.67]; AUPRC, 0.85 [95% CI, 0.85-0.85]). In multiclass classification of healthy controls, chronic migraine, and non-chronic headaches (e.g., post-viral headache, new daily persistent headache, post-traumatic headache), NeuroSTORM achieved a macro-AUROC of 0.69 (95% CI, 0.68-0.69). Results suggest that the approach can distinguish chronic migraine but has difficulty differentiating other headache subtypes from chronic migraine. Overall, under limited-data conditions, NeuroSTORM appears to capture latent rs-fMRI representations that transfer to headache-related tasks without relying on FC features. These findings provide proof of concept for fMRI-based prediction of pediatric headache and highlight potential future utility for subtype identification and individualized treatment strategies.

PMID:42818527 | PMC:PMC13622986

medRxiv [Preprint]. 2026 Sep 21:2026.02.04.26345538. doi: 10.64898/2026.02.04.26345538.

ABSTRACT

Bullying is an adverse childhood experience affecting up to one-third of the global population and linked to psychosis-like experiences (PLEs), which increase the risk of psychotic disorders. This study aimed to investigate the association between the severity and persistence of bullying and PLEs, including non-paranoid, hallucination-like experiences and their distress, and whether multiscale brain functional network connectivity (msFNC) mediates the pathway from bullying to PLEs.

METHODS: We used data from the ABCD Study, a large, multisite, population-based prospective cohort study following U.S. adolescents. We included adolescents with complete bullying and PLEs assessments at the 2- and 3-year follow-ups (T1: n=10,939, 47% females; T2: n=10,102). We examined the associations between bullying severity and temporal exposure and PLEs using linear mixed-effects models. In a 2-year resting-state fMRI subsample (n=5,280), we used a Neuromark framework to analyze whether msFNC statistically mediated the pathway from bullying to PLEs.

RESULTS: Higher PLEs were associated with the presence and severity of bullying (non-bullied vs. mild bullying: d=-0.24, CI:-0.30 to -0.19, p<0.0001; moderate vs. severe bullying: d=-0.62, CI:-0.69 to -0.56, p<0.0001). When bullying ceased, PLEs returned to non-bullied levels, whereas persistence over two years led to greater elevations (d=-0.36, CI:-0.43 to -0.29, p<0.0001), with similar patterns for non-paranoid and hallucination-like experiences and their distress. msFNC in paralimbic, default-mode, central executive, somatomotor, temporoparietal, insulotemporal, and frontal networks statistically mediated the association.

CONCLUSIONS: Bullying is time- and dose-dependently associated with PLEs. msFNC may provide convergent evidence for distributed neurobiological correlates of the association between bullying exposure and PLEs.

PMID:42818432 | PMC:PMC13622310 | DOI:10.64898/2026.02.04.26345538

medRxiv [Preprint]. 2026 Sep 23:2026.09.18.26363166. doi: 10.64898/2026.09.18.26363166.

ABSTRACT

Trauma-related intrusive memories (TRIMs) are a core symptom of posttraumatic stress disorder and cause significant distress and impairment for trauma-exposed individuals due to their vivid sensory features that intrude on ongoing thought. These phenomenological properties point to a neuropathology that spans autobiographical memory, spontaneous thought, and sensory perception. The hierarchical organization of the cortex maximally segregates the default mode network (DMN), which supports autobiographical memory and ongoing thought, from the sensory cortices (SC). Accruing evidence implicates altered interactions between the DMN and SC in PTSD and TRIMs, suggesting a breakdown in this segregation. We sought to test whether diminished DMN-SC segregation, indexed by a shorter functional distance between the DMN and SC within cortical connectivity gradients, was associated with the vividness and intrusiveness of TRIMs. One-hundred eight trauma-exposed adults completed two weeks of ecological momentary assessments (EMAs) of TRIM properties followed by resting-state fMRI. Gradient analysis of cortical functional connectivity was performed to compute the Euclidean distance between resting-state networks. Pearson correlations revealed an association between a shorter DMN-SC distance and greater TRIM vividness and intrusiveness, with multivariate models indicating a specificity for intrusiveness. Importantly, the DMN-SC distance was not associated with clinician-rated symptom severity and explained unique variance above and beyond conventional functional connectivity measures. These findings suggest that a loss of functional segregation between the DMN and SC - a core organizational feature of the cortical hierarchy - is associated with the intrusive sensory vividness of TRIMs, offering insights into candidate mechanisms of and neurotherapeutic targets for intrusive trauma memories. .

PMID:42818426 | PMC:PMC13622322 | DOI:10.64898/2026.09.18.26363166

bioRxiv [Preprint]. 2026 Sep 24:2026.09.17.752468. doi: 10.64898/2026.09.17.752468.

ABSTRACT

INTRODUCTION: White matter (WM) functional activity, quantified as slow-4 fractional amplitude of low-frequency fluctuations (fALFF; 0.027-0.073 Hz), may reflect Alzheimer's disease (AD) pathology, but its utility relative to established structural imaging markers remains poorly characterized.

METHODS: We analyzed 369 ADNI-3 participants with baseline resting-state fMRI. Global WM slow-4 fALFF (JHU-20 atlas) was compared against hippocampal volume, entorhinal cortical thickness, FA, and MD across 11 outcomes: plasma biomarkers (pTau217, Aβ42/40, NfL, GFAP; n = 108), PET (amyloid Centiloid, tau SUVR, FDG; n = 66-207), and cognition (MEM, EF, mPACC, MMSE; n ≈ 222-225) using head-to-head benchmarking and variance decomposition.

RESULTS: WM fALFF was significantly associated with GFAP (partial RZ = 5.35%), amyloid PET (partial RZ = 2.17%), tau PET (partial RZ = 3.52%), executive function (partial RZ = 2.61%), and mPACC (partial RZ = 3.53%). In trimmed models, fALFF explained unique variance in NfL and GFAP beyond FA and MD (unique ΔRZ: 3.65% and 4.45%) - nearly 8-fold greater than DTI's unique contribution for NfL. Gray matter markers showed larger associations with cognition and PET pathology. The incremental value of fALFF was modality-specific: it persisted beyond DTI markers (NfL, GFAP, amyloid PET, executive function, mPACC) and beyond gray matter markers (NfL, GFAP), but in every domain - plasma included - fALFF was no longer significant when all five imaging markers were entered simultaneously.

DISCUSSION: WM slow-4 fALFF captures neurodegeneration-related variance - particularly astroglial reactivity and axonal injury - incremental to DTI microstructure and partially independent of gray matter atrophy. These findings position WM functional activity as a complementary, non-invasive imaging marker in the multimodal AD biomarker landscape.

RESEARCH IN CONTEXT: Evidence before this study: White matter (WM) resting-state BOLD signals carry physiologically valid functional information. WM slow-4 fALFF is reduced in preclinical Alzheimer's disease (AD) and correlates with amyloid burden and cognition. Direct benchmarking against established structural imaging markers within matched samples has remained scarce.Added value of this study: In 369 ADNI-3 participants, we conducted a direct same-sample head-to-head comparison of global WM slow-4 fALFF versus hippocampal volume, entorhinal thickness, global FA, and MD across 11 pre-specified AD biomarker and cognitive outcomes. WM fALFF showed the largest covariate-adjusted association with GFAP (partial RZ = 5.35%) and uniquely explained ∼8× more NfL variance than DTI in trimmed models, contributing non-redundant functional information.Implications of all the available evidence: Global WM slow-4 fALFF, derived from standard resting-state fMRI, is a non-invasive complement to structural imaging that showed associations with markers of astroglial reactivity and neuroaxonal injury, and could augment multimodal AD biomarker panels without additional scanning cost.

PMID:42818102 | PMC:PMC13622072 | DOI:10.64898/2026.09.17.752468

CNS Neurosci Ther. 2026 Oct;32(10):e71159. doi: 10.1002/cns.71159.

ABSTRACT

BACKGROUND: Adolescents with non-suicidal self-injury (NSSI) frequently exhibit severe sleep disturbances and heterogeneous cognitive impairments, yet the underlying neurophysiological basis remains unclear. The glymphatic system, a sleep-dependent macroscopic clearance network, is critical for metabolic waste removal and neurocognitive homeostasis. Therefore, this study aimed to investigate the physiological role of glymphatic function in linking sleep burden to distinct cognitive performances in adolescents with NSSI.

METHODS: We assessed 44 NSSI adolescents and 33 healthy controls (HCs). Using resting-state functional magnetic resonance imaging (rs-fMRI), glymphatic function was quantified by calculating the coupling strength between global blood-oxygen-level-dependent (gBOLD) signals and cerebrospinal fluid (CSF) flow. To capture the heterogeneity in their cognitive profiles, we evaluated "hot" affective decision-making via the Iowa Gambling Task (IGT) and "cold" executive function via the Wisconsin Card Sorting Test (WCST). Subjective sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI).

RESULTS: In the NSSI group, poorer sleep quality was positively correlated with stronger gBOLD-CSF coupling (r = 0.317, p = 0.036), and stronger gBOLD-CSF coupling predicted better IGT performance, especially during the high-load strategy phase (Block 3) (r = 0.447, p = 0.002). Furthermore, glymphatic function fully mediated the relationship between sleep quality and affective decision-making performance (indirect effect = 0.682, 95% CI [0.062, 1.515]). Interaction analyses confirmed this physiological-behavioral coupling was disease-specific (present only in NSSI; interaction p = 0.011) and domain-specific (supporting "hot" affective cognition rather than "cold" executive function).

CONCLUSIONS: In NSSI adolescents, sleep-associated glymphatic upregulation reflects a stress-induced compensatory response. This mechanism domain-specifically preserves high-load affective decision-making against severe psychopathological stress. These findings highlight brain metabolic clearance as a novel physiological target for bolstering cognitive resilience in high-risk youth.

PMID:42817812 | DOI:10.1002/cns.71159

CNS Neurosci Ther. 2026 Oct;32(10):e71178. doi: 10.1002/cns.71178.

ABSTRACT

AIMS: Compound Ciwujia Granules (CWJ) have shown antidepressant-like effects, but the neural and molecular mechanisms underlying these effects remain unclear. This study aimed to investigate the mechanisms by which CWJ ameliorates anxiety- and depression-like behaviors.

METHODS: A 6-week chronic unpredictable mild stress (CUMS) model was established in rats, and behavioral tests were performed to assess the antidepressant-like effects of CWJ. Resting-state functional magnetic resonance imaging (rs-fMRI), in vivo fiber photometry, and chemogenetic manipulation were used to identify the brain region and neuronal population involved in the effects of CWJ. Transcriptomic analysis combined with network pharmacology was used to predict candidate molecular pathways, which were further validated by quantitative real-time polymerase chain reaction, immunofluorescence, western blotting, and pharmacological interventions.

RESULTS: CWJ increased the open-arm ratio in the EPM, reduced FST immobility, and lowered serum corticosterone (CORT) levels in CUMS rats. Convergent rs-fMRI, c-Fos immunostaining, and fiber photometry identified that the dorsomedial prefrontal cortex (dmPFC) was a key brain region involved in the antidepressant-like effects of CWJ. CWJ increased glutamatergic neuronal activity and reduced neuroinflammatory responses associated with TNF-related inflammatory signaling in the dmPFC. Moreover, pharmacological activation of TNF-related inflammatory signaling partially attenuated the behavioral benefits of CWJ, supporting the involvement of TNF-related inflammatory signaling in mediating its antidepressant effects.

CONCLUSIONS: These findings suggest that CWJ attenuated CUMS-induced anxiety- and depression-like behaviors by restoring dmPFC glutamatergic neuronal activity and suppressing TNF-related neuroinflammation in the dmPFC.

PMID:42817052 | DOI:10.1002/cns.71178