Most recent paper

IEEE J Biomed Health Inform. 2026 Oct 6;PP. doi: 10.1109/JBHI.2026.3740889. Online ahead of print.

ABSTRACT

Resting-state functional magnetic resonance imaging (rs-fMRI) serves as a widely used non-invasive brain imaging tool for investigating neurological disorders through functional brain networks (FBN) modeling among brain regions of interest (ROIs). Deep learning frameworks, notably graph neural networks and Transformers, have advanced FBN analysis capabilities. However, existing research still faces two major challenges: (1) deep models require large labeled datasets, whereas rs-fMRI data are scarce and heterogeneous, and (2) most existing studies rely on a single brain atlas for FBN, ignoring complementary multi-scale information. To tackle these challenges, we propose MAFCF, a novel Multi-Atlas Feature Contrastive Fusion framework. Specifically, we construct multi-atlas FBN and design an adaptive graph augmentation strategy that perturbs both edges and node features to enhance the robustness of the representations. A hybrid GIN-Transformer encoder is then employed to jointly capture local subgraph patterns and global dependencies, while contrastive loss is used to guide representation learning. Furthermore, semantic preservation and cross-atlas alignment mechanisms are incorporated to ensure the consistency of feature representations. Extensive evaluations on two public datasets indicate that MAFCF consistently outperforms state-of-the-art methods in brain disorder diagnosis.

PMID:42837235 | DOI:10.1109/JBHI.2026.3740889

Cerebellum. 2026 Oct 6;25(5):150. doi: 10.1007/s12311-026-02095-y.

ABSTRACT

The cerebellum is a key node for detecting and adjusting social-prediction internal models. Yet social prediction has been scantly investigated in neurodegenerative cerebellar patients (CB). Thus, we examined CB social-prediction abilities and related cerebellar-cerebral networks, using tailored tasks and resting-state fMRI. Thirteen adult CB and twenty-six matched controls completed three computerized prediction tasks, where contextual priors activate specific internal models setting expectations which may influence recognition of ambiguous targets (emotion expressions, personality-traits, non-social objects). Accuracy and response times were analysed through 2 × 2 × 2 mixed-effects ANOVAs. A congruency index (CI) reflecting whether response was driven by contextual influence was derived from performance efficiency scores and analysed through a 2 × 2 mixed-effects ANOVA. Participants underwent 3T brain MRI, including T1-weighted and resting-state fMRI sequences. Posterior and vermian cerebellar lobules were chosen a-priori as regions of interest and used in a Seed-Based fMRI analysis examining cerebellar-cerebral functional connectivity (FC). FC values from patients' altered connectivity clusters were extracted and correlated with CIs. CB performed worse in the personality-traits task, showing higher reliance on contextual cues. Cerebellar-cerebral underconnectivity was found within networks involved in mentalizing and learning. In CB, higher CI in the personality-traits task, reflecting greater contextual influence and failure to revise behavioural responses, significantly correlated with right lobule VIII-right putamen underconnectivity. These findings indicate reduced flexibility in updating higher-order social predictions when contextual expectations conflict with incoming information, accompanied by altered cerebellar-cerebral FC. Exploratory findings further point to cerebellar-basal ganglia connectivity as a potentially relevant network component of predictive social cognition, warranting further investigation.

PMID:42836925 | DOI:10.1007/s12311-026-02095-y

Front Neurosci. 2026 Sep 21;20:1935362. doi: 10.3389/fnins.2026.1935362. eCollection 2026.

ABSTRACT

OBJECTIVES: We aimed to investigate the alterations of static and dynamic brain functional connectivity in dysthyroid optic neuropathy (DON) using resting-state functional MRI (rs-fMRI) with the voxel-mirrored homotopic connectivity (VMHC) and degree centrality (DC) methods.

MATERIALS AND METHODS: Seventy-five thyroid-associated ophthalmopathy (TAO) patients (29 DON and 46 non-DON) and 32 healthy controls (HCs) were prospectively recruited. Static and dynamic VMHC (sVMHC and dVMHC) and DC (sDC and dDC) values were measured and compared among groups. Support-vector machine (SVM) classification method was employed to examine the diagnostic performance of identified models in distinguishing DON patients from non-DON patients.

RESULTS: Compared to non-DON patients, DON patients showed decreased sVMHC in cerebellar lobule VI, inferior occipital gyrus (IOG) and superior occipital gyrus (SOG), alongside decreased dVMHC in IOG. DON patients also exhibited decreased sDC in bilateral middle occipital gyrus (MOG) in comparison to non-DON patients. Meanwhile, DON patients had lower sVMHC in lingual gyrus and MOG, together with decreased sDC in left MOG compared to HCs. Additionally, dVMHC in IOG of DON patients was positively correlated with the quality-of-life scores for appearance (r = 0.419, p = 0.033). When detecting DON, combined model (sVMHC+dVMHC+sDC) showed best diagnostic performance (AUC = 0.9528), followed by the dVMHC model (AUC = 0.9378), sDC model (AUC = 0.8598), and sVMHC model (AUC = 0.8193).

CONCLUSION: Brain functional connectivity differed between TAO patients with and without DON, which may reflect the underlying neural mechanism of disease. The dVMHC index showed promising discriminative potential for identifying DON. Combining static and dynamic metrics could further optimize diagnostic efficiency.

PMID:42835119 | PMC:PMC13635314 | DOI:10.3389/fnins.2026.1935362

Brain Inj. 2026 Oct 6:1-11. doi: 10.1080/02699052.2026.2743021. Online ahead of print.

ABSTRACT

PRIMARY OBJECTIVE: Zolpidem paradoxically restores behavioral responsiveness and communication in select patients with severe disorders of consciousness and profound motor deafferentation. While the anterior forebrain mesocircuit hypothesis attributes this awakening to central thalamic disinhibition, macroscopic network dynamics and lower brainstem gating remain under-characterized in locked-in syndrome (LIS).

METHODS AND PROCEDURES: A 45-year-old male with ventral pontine necrosis initially misdiagnosed as vegetative state/unresponsive wakefulness syndrome (VS/UWS) was formally identified with incomplete LIS through systematic ocular testing. Multimodal high-resolution MRI, deterministic diffusion tensor imaging (DTI) tractography, resting-state fMRI (before vs. after 10 mg oral zolpidem), dynamic sliding-window graph theory (45 s), infraslow BOLD spectral analysis (Slow-5: 0.010-0.027 Hz; Slow-4: 0.027-0.073 Hz), and seed-based thalamocortical coupling were acquired.

MAIN OUTCOMES AND RESULTS: DTI confirmed focal basis pontis transection with extensive preservation of supratentorial projection architecture. Baseline fMRI exhibited aberrant, unsegregated cross-network hyperconnectivity. Zolpidem restored canonical modular boundaries, pruned noisy long-range edges, induced focal BOLD reactivation in primary motor (M1) and precuneal cores, shifted power into the infraslow Slow-5 domain, and markedly augmented thalamocortical coupling (thalamo-precuneus: r = 0.18 to 0.61; striato-thalamus: r = 0.33 to 0.75).

CONCLUSIONS: Paradoxical awakening in incomplete LIS is underpinned by structural preservation of the supratentorial mantle. Beyond classical mesocircuit release, zolpidem engages brainstem gating structures, restoring modular segregation, infraslow frequency tuning, and cognitive-motor coupling.

PMID:42834796 | DOI:10.1080/02699052.2026.2743021

Rev Endocr Metab Disord. 2026 Oct 6. doi: 10.1007/s11154-026-10089-8. Online ahead of print.

ABSTRACT

Binge eating disorder (BED) is a prevalent public health concern associated with significant medical and psychosocial burden. To better understand the mechanisms underlying this concern, the present systematic review aimed to synthesize findings from resting-state and task-based functional magnetic resonance imaging (fMRI) studies of the neurobiology of BED. PubMed, MEDLINE, PsycInfo, Embase, and Web of Science were searched, the Joanna Briggs Institute Critical Appraisal Tools were used, and the PRISMA guidelines were followed. The review identified 24 studies, comprising approximately 1,689 non-independent participants from an estimated 17 unique cohorts. These included task-based fMRI studies (n = 14), resting-state fMRI studies (n = 6), and BED treatment-outcome fMRI studies (n = 4). Key findings of the review included: (i) differences in neural responsivity to palatable food cues involving reward- and salience-related circuitry (e.g., orbitofrontal cortex, ventral striatum, and insula), alongside reduced recruitment of inhibitory-control regions during cognitive-control tasks; (ii) evidence suggesting reward-related differences in BED may be more food-specific than generalized across reward types, although some findings indicate broader frontostriatal hyporesponsivity during monetary reward processing; and, (iii) findings indicating that BED may share neurobiological overlap with, but remain distinguishable from, obesity and bulimia nervosa. However, these findings/interpretations are preliminary, given limitations in the current evidence base, including small samples, methodological heterogeneity, and the underrepresentation of males. Future neuroimaging studies should prioritize large-scale, longitudinal, efforts that include more representative (e.g., greater inclusion of males) and developmental (e.g., more children and adolescents) samples and consider socioenvironmental contexts relevant to BED. Such efforts may help inform individual-level personalized interventions.

PMID:42834263 | DOI:10.1007/s11154-026-10089-8

Br J Anaesth. 2028 Aug 29:S0007-0912(26)00608-2. doi: 10.1016/j.bja.2026.08.010. Online ahead of print.

ABSTRACT

BACKGROUND: Chronic low back pain (cLBP) is the most prevalent chronic pain condition and the leading cause of disability worldwide. Acute movement-evoked pain strongly predicts cLBP exacerbation, reflecting maladaptive amplification of nociceptive input, yet the neural mechanisms supporting movement-evoked cLBP flares remain poorly characterised.

METHODS: A total of 114 participants with cLBP performed the cLBP-evoking leg raise test during perfusion-based arterial spin labelling functional MRI. Leg raise test-based pain intensity and unpleasantness ratings were assessed with an 11-point visual analogue scale (VAS; 0=no pain, 10=worst pain imaginable). The Brief Pain Inventory (BPI) measured chronic pain severity and interference. Whole-brain cerebral blood flow changes from supine to LRT and their relationship with VAS pain intensity were analysed. Analyses assessing default mode network-whole-brain resting-state functional connectivity supporting BPI severity were also conducted.

RESULTS: The LRT significantly increased pain intensity by 23% (P<0.001) and unpleasantness by 26% (P<0.001). Compared with supine, movement-evoked pain increased cerebral blood flow in the medial prefrontal and posterior cingulate cortices, core default mode network nodes, and nociceptive centres including the thalamus, primary motor cortex corresponding to the back, and parietal operculum. Higher leg raise test-evoked pain intensity ratings were associated with greater superior parietal lobule, supramarginal, and bilateral sensorimotor cortical activity. Higher BPI pain severity was associated with stronger default mode network-somatosensory cortex (back representation) resting-state functional connectivity.

CONCLUSIONS: Unlike the default mode network deactivation characteristically observed during experimental pain provocation, movement-evoked cLBP reflects maladaptive coupling between self-referential (default mode network) and somatosensory (nociceptive) networks. These findings provide mechanistic insight into the neurobiological processes supporting cLBP flares.

CLINICAL TRIAL REGISTRATION: NCT03354585.

PMID:42833965 | DOI:10.1016/j.bja.2026.08.010

Neuroimage. 2026 Oct 5:122239. doi: 10.1016/j.neuroimage.2026.122239. Online ahead of print.

ABSTRACT

Resting-state fMRI (RS-fMRI) has been known for decades as a promising method for evaluating cognitive and mental states, both in health and especially in disease, due to its ease of implementation as a short, standard MRI protocol. In clinical settings, a group of patients with a given disorder is typically compared to a group of healthy controls. This poses an inherent challenge of between-group comparison. We propose a new efficient model for characterizing changes to the temporal synchronization of brain activity measured using RS-fMRI between groups, summarized as individual correlation matrices. Our model posits that the between-group differences are the product of single-region effects describing the increase or decay of synchronization with the rest of the brain. This parsimonious model pools the correlation coefficients of each region with all others, and therefore can detect differences between groups even in small samples. Inference for this model accounts for the variability in individual correlation matrices, the within-group differences across individuals, and for the approximation error of the single-region model. This results in per-region estimates and confidence intervals for the parameters governing the difference between groups. In simulations, our model shows increased power to detect model-aligned alternatives compared with competing approaches. To demonstrate feasibility of the method in a clinical application, we use the model to analyze RS-fMRI correlation matrices in patients with transient global amnesia and healthy controls. Our model detects significant decreases in synchronization for the patient population in the amygdala after multiplicity correction as well as borderline decreases in memory-related brain regions that were not detected using mass-univariate tests without prior knowledge, suggesting its usefulness in the application of RS-fMRI in clinical settings.

PMID:42833462 | DOI:10.1016/j.neuroimage.2026.122239

Psychiatry Res Neuroimaging. 2026 Sep 28;364:112347. doi: 10.1016/j.pscychresns.2026.112347. Online ahead of print.

ABSTRACT

OBJECTIVE: Binge eating disorder (BED) commonly emerges during adolescence, yet little is known about intrinsic brain network function in this developmental period. This study examined resting-state functional connectivity (FC) in adolescents with BED.

METHOD: 19 adolescents with BED and 20 sex-, age-, and BMI Z-score-matched controls (12-18 years) underwent resting-state fMRI. FC was examined using an exploratory ROI-to-ROI analysis, and eating disorder symptoms were assessed using the Eating Disorder Examination Questionnaire (EDE-Q).

RESULTS: No between-group differences in ROI-to-ROI functional connectivity survived false discovery rate correction for multiple comparisons.

DISCUSSION: No robust alterations in resting-state FC were identified in adolescents with BED compared with healthy controls. However, given the modest sample size and limited temporal sampling, subtle connectivity differences cannot be excluded.

PMID:42833098 | DOI:10.1016/j.pscychresns.2026.112347

Psychoneuroendocrinology. 2026 Sep 26;194:108030. doi: 10.1016/j.psyneuen.2026.108030. Online ahead of print.

ABSTRACT

Posttraumatic stress disorder (PTSD) is a chronic psychiatric disorder associated with functional impairment and health burden. Evidence highlights the role of large-scale neural network disruptions, within and between the Default Mode Network (DMN) and Salience Network (SN), in PTSD pathophysiology, and early alterations in these networks may serve as biomarkers for predicting PTSD symptom trajectories and guiding early interventions. This longitudinal mega-analysis integrated resting-state functional connectivity (rsFC) data from two trauma-exposed cohorts (Tel Aviv and Toledo), collected within the first month posttrauma. Seed-based rsFC analyses focused on DMN regions (hippocampus, ventromedial prefrontal cortex [vmPFC], posterior cingulate cortex) and SN regions (amygdala, insula, dorsal anterior cingulate cortex [dACC]). PTSD symptoms were assessed at 12- to 14-month follow-up using the Clinician-Administered PTSD Scale (CAPS-5) and PTSD Checklist (PCL-5). Early posttrauma reductions in within-DMN connectivity, particularly between the hippocampus and rostral ACC/vmPFC, mPFC, and inferior parietal lobe, were associated with greater PTSD symptom severity at annual follow-up. Decreased connectivity between the DMN and visual cortex (calcarine) was also linked to more severe symptoms. Decreased DMN-SN connectivity, specifically between the amygdala and rostral ACC/vmPFC, was linked to more severe symptoms at follow-up, adding to a complex picture of the relationship between networks. Early disruptions in within-DMN connectivity emerged as the most consistent neural correlate of later PTSD symptom severity. These findings underscore a central role of within-DMN dysfunction in PTSD pathophysiology, reflecting deficits in context processing governed by hippocampus-mPFC circuitry. Identifying these early neural markers may guide targeted interventions to prevent chronic PTSD.

PMID:42833081 | DOI:10.1016/j.psyneuen.2026.108030

PLoS Comput Biol. 2026 Oct 5;22(10):e1014821. doi: 10.1371/journal.pcbi.1014821. Online ahead of print.

ABSTRACT

Ecological systems obey universal scaling laws such as Taylor's law, which links population variance to the sample mean. Whether other complex systems with integrated and segregated network topology, the human brain, in particular, follow analogous scaling laws, and how these laws behave under synchronized neural activity, remains unknown. To address this question, our initial attempt to implement the Taylor's law, has been hindered because neural signals at all measurement scales contain both positive and negative values, whereas Taylor's law was restricted to non-negative ecological data [1]. To circumvent this limitation, we extend spatial Taylor's law by replacing the sample mean with the root-mean-square (RMS) of the measurements, and apply this generalized law to detrended multivariate neural time series. The detrending operation has been performed as a necessary step to remove the trends and temporal mean from the data. Within this framework, we address our next question how synchronous or coordinated activity of the brain network influences the scaling law. We quantify synchrony using a dedicated metric of functional coordination and show analytically, and via numerical simulations using multivariate Poisson, negative binomial, uniform and gamma distributions, that the scaling exponent is inversely related to synchrony. Applying this approach to human fMRI data from three large cohorts (N = 840, ages 18-88 years) reveals distinct age-related trajectories of the scaling exponent, with healthy aging characterized by a substantial synchrony-induced reduction in this exponent. The synchrony-scaling relationship remains stable during resting-state activity but progressively shifts during naturalistic tasks across the lifespan, and is pronounced in limbic, subcortical, and cerebellar subnetworks, indicating preservation at subnetwork levels. Finally, individuals with ADHD exhibit altered synchrony-scaling coupling, highlighting the potential clinical and translational utility of this generalized scaling metric.

PMID:42832570 | DOI:10.1371/journal.pcbi.1014821

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