Most recent paper

Hum Brain Mapp. 2026 Aug;47(11):e70600. doi: 10.1002/hbm.70600.

ABSTRACT

Altered affective state dynamics are a characteristic feature of depression and can persist beyond symptomatic remission. Individuals with remitted major depressive disorder (rMDD) often show heightened reactivity to negative mood states and reduced efficiency in recovering from them, consistent with changes in affective dynamics after remission. These patterns may reflect alterations in the brain's capacity to flexibly shift between neural states that support distinct affective modes. Characterizing the dynamical mechanisms that govern transitions into and out of experimentally induced affective states is therefore essential for understanding vulnerability to recurrence and informing mechanistic interventions. We developed a data-driven framework combining dynamical system reconstruction (DSR) with model-based control to infer optimal control policies for transitions between resting and sad mood brain states using functional magnetic resonance imaging (fMRI) data. Nonlinear DSR models trained on individuals with rMDD and healthy controls (HC) yielded region-specific, state-dependent control strategies. Small regions (e.g., sgACC, NAcc) showed higher controllability, requiring less energy for state transitions. Notably, rMDD participants required less control energy than HC to shift both into and, to a more spatially restricted extent, out of sad mood states. Despite reaching the resting state target with similar proximity, however, they remained closer to the sad mood distribution when returning to rest, reflecting a residual bias toward the sad mood distribution. Elevated coupling in rMDD, especially toward the DLPFC, was linked to lower control energy, suggesting that stronger network coupling facilitates transitions. These findings indicate rMDD dynamics that ease entry into sad mood states but impede full disengagement. More broadly, they demonstrate how closed-loop control applied to data-driven dynamical models can provide mechanistic insight into brain state transitions and inform future hypotheses about cognitive vulnerability or compensatory processes.

PMID:42454569 | DOI:10.1002/hbm.70600

Front Psychiatry. 2026 Jun 30;17:1809961. doi: 10.3389/fpsyt.2026.1809961. eCollection 2026.

ABSTRACT

BACKGROUND: Bipolar disorder (BD) and major depressive disorder (MDD) exhibit overlapping clinical presentations, posing significant challenges for differential diagnosis and often leading to misidentification; therefore, elucidating the neural mechanisms that distinguish these two disorders is of critical importance.

METHODS: In this study, 122 adolescents (43 with BD, 39 with MDD, and 40 healthy controls) completed resting-state functional magnetic resonance imaging (rs-fMRI). Voxel-level seed-based functional connectivity (FC) analysis using default mode network (DMN) subregions and machine learning classification were applied.

RESULTS: Group-level analysis revealed that compared with healthy controls (HCs), patients with bipolar disorder (BD) exhibited significantly reduced FC between the anterior medial prefrontal cortex (aMPFC) and regions including the bilateral superior temporal gyrus (STG) and right temporal pole (TPOsup), as well as between the posterior inferior parietal lobule (pIPL) and the left middle temporal gyrus (MTG) and left STG. Relative to BD patients, patients with major depressive disorder (MDD) showed stronger FC between the aMPFC and left STG, and between the pIPL and right inferior frontal gyrus (IFG). No significant MDD-HC differences were detected in these circuits. Furthermore, no significant associations were found between altered DMN FC and clinical symptoms. Machine-learning analyses showed modest and unstable classification performance under nested 10-fold cross-validation, with a pooled out-of-fold AUC of 0.609, accuracy of 0.598, sensitivity of 0.564, and specificity of 0.628.

CONCLUSION: Our results indicate that patients with MDD and BD exhibit distinct patterns of DMN connectivity with regions subserving sensory and cognitive processing, which may provide a potential neurobiological marker worthy of further investigation for discriminating between these disorders.

PMID:42454329 | PMC:PMC13364920 | DOI:10.3389/fpsyt.2026.1809961

Front Med (Lausanne). 2026 Jun 30;13:1839975. doi: 10.3389/fmed.2026.1839975. eCollection 2026.

ABSTRACT

INTRODUCTION: Attention-Deficit/Hyperactivity Disorder (ADHD) is one of the most prevalent neurodevelopmental disorders, affecting approximately 5-7% of children and adolescents worldwide. Clinical diagnosis currently relies on behavioral assessments that are susceptible to subjectivity and inter-rater variability. Resting-state functional magnetic resonance imaging (rs-fMRI) offers a promising avenue for objective ADHD identification; however, most existing approaches depend on derivative feature representations, such as functional connectivity (FC), fractional amplitude of low-frequency fluctuations (fALFF), or regional homogeneity (ReHo), which substantially compress the original blood-oxygen-level-dependent (BOLD) signal prior to model training. Furthermore, limited cross-site generalizability and insufficient voxel-level interpretability remain barriers to clinical translation.

METHODS: We propose VoxSTNet (Voxel-level Spatiotemporal Network), an explainable and telepathology-ready framework that operates directly on four-dimensional rs-fMRI BOLD volumes. A two-stage processing pipeline preserves the complete raw BOLD signal while reducing computational burden through moderate compression. Subject-wise z-score normalization mitigates scanner-specific intensity variations without introducing fold leakage. A time-distributed three-dimensional convolutional neural network (3D-CNN) coupled with a gated recurrent unit (GRU) captures spatiotemporal representations, while HiResCAM provides voxel-level interpretability. Experiments were conducted on the ADHD-200 dataset comprising 760 subjects (300 ADHD and 460 controls) from six acquisition sites. Performance was evaluated using Leave-One-Site-Out (LOSO) cross-validation as the primary assessment and five-fold cross-validation as a secondary analysis.

RESULTS: Five-fold cross-validation achieved an accuracy of 98.7 ± 0.4%, sensitivity of 98.2%, specificity of 99.1%, and area under the receiver operating characteristic curve (AUC) of 99.4% (95% confidence interval [CI]: 97.9-99.5%). Under the more stringent LOSO protocol, the model achieved a mean accuracy of 78.4% (95% CI: 75.1-81.7%). A controlled data-selection analysis demonstrated that retaining raw voxel-level information improved performance relative to derivative-feature baselines. HiResCAM saliency maps consistently highlighted the right caudate nucleus across validation subjects (mean Dice coefficient = 0.61 ± 0.08; Wilcoxon p < 0.001).

DISCUSSION: VoxSTNet demonstrates that direct voxel-level modeling of rs-fMRI can achieve strong within-cohort performance while maintaining competitive cross-site generalizability. The identified saliency patterns align with established ADHD-related neurobiological findings, supporting the model's interpretability. Future work will focus on harmonization and domain-generalization strategies to further improve cross-site deployment performance.

PMID:42454147 | PMC:PMC13365325 | DOI:10.3389/fmed.2026.1839975

Transl Psychiatry. 2026 Jul 15;16(1):362. doi: 10.1038/s41398-026-04269-y.

ABSTRACT

First episodes of affective disorders often emerge during adolescence and young adulthood. Alterations in resting-state functional connectivity (RSFC) have been reported in affective disorders, yet findings are heterogeneous and associations of RSFC with subclinical affective symptoms in community samples remain limited. A better understanding of these underlying neurobiological mechanisms may aid in identifying early vulnerability markers of affective disorders. We examined associations between affective symptom severity and both static and dynamic RSFC using resting-state fMRI data from 512 adolescents and young adults (aged 14-23) drawn from an age- and sex-stratified population-based sample. Group independent component analysis was used to derive RSFC measures. Associations with depressive and manic symptom severity were assessed while controlling for age and sex. Dynamic RSFC was analyzed using a sliding-window approach. Static RSFC showed significant effects of age and sex but no associations with affective symptoms. Dynamic RSFC analysis identified four connectivity states. In one state, manic symptom severity and its interaction with depressive symptom severity were associated with connectivity between the postcentral gyrus and the right superior temporal gyrus. Additionally, the dynamic index fraction of time showed interactions of affective symptoms with age and sex. Overall, RSFC measures demonstrated limited sensitivity to subclinical affective symptom variation in community youth, with only a single state-specific association observed. These findings suggest that subtle alterations in somatomotor-default mode network connectivity may reflect early vulnerability-related processes, though replications and further research are required. Key limitations include the use of very brief symptom measures and developmental heterogeneity across the sample.

PMID:42449113 | DOI:10.1038/s41398-026-04269-y

Acad Radiol. 2026 Jul 14:S1076-6332(26)00456-3. doi: 10.1016/j.acra.2026.06.044. Online ahead of print.

ABSTRACT

RATIONALE AND OBJECTIVES: Cerebral small vessel disease (CSVD) affects white matter integrity and can alter the brain's network architecture. However, the relationship between structural and functional connectivity in CSVD remains underexplored. This study aims to investigate the differences in topological properties of structural and functional brain networks across CSVD burden groups.

MATERIALS AND METHODS: This cross-sectional study utilized data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu), concentrating on participants who had both DTI and resting-state fMRI data. Participants were classified into non-CSVD (CSVD-n), mild CSVD (CSVD-m), or severe CSVD (CSVD-s) groups based on a CSVD burden score ranging from zero to four. Structural networks were reconstructed from DTI data using deterministic tractography, while functional networks were derived from resting-state fMRI data through Pearson's correlation of regional time series. Graph theoretical analyses were conducted using GRETNA in MATLAB to calculate global and nodal metrics, as well as structure-function coupling indices. Group comparisons were performed using one-way ANOVA (one-way analysis of variance), with age, sex, and education as covariates. Statistical significance was assessed at a two-tailed p < 0.05, adjusted for multiple comparisons across nodes and networks using false discovery rate (FDR) correction.

RESULTS: The study included 280 participants (mean 75.0±7.9y, median 76y; 49.6% female). The participants were divided into groups: CSVD‑n (118, 42.1%), CSVD‑m (80, 28.6%), and CSVD‑s (82, 29.3%). Analysis of structural networks revealed that CSVD‑s exhibited reduced global/local efficiency (7.42±2.63 vs. 8.42±2.75, p = 0.03; 16.00±4.77 vs. 17.83±5.16, p = 0.02) and increased path length (0.15±0.04 vs. 0.13±0.04, p = 0.003). Nodal damage was observed in the left hippocampus in CSVD‑m (p = 0.0008) and was widespread in CSVD‑s (all p < 0.001). In terms of functional networks, no global differences were found (all p > 0.05); however, CSVD‑s exhibited nodal changes in the cingulate/Heschl gyrus (p < 0.001). Regarding coupling, CSVD‑m showed increased coupling in the insula/orbitofrontal regions (p = 0.002-0.041) and decreased coupling in the pallidum (p = 0.026). Conversely, CSVD‑s demonstrated increased coupling in the olfactory/occipital/temporal regions (p = 0.001-0.023) and decreased coupling in the rolandic operculum (p = 0.019) and hippocampus (compared to CSVD‑m, p = 0.009). Furthermore, left insular coupling was correlated with MoCA (r=-0.245, p = 0.015).

CONCLUSION: In the mild CSVD group, researchers observed increased coupling in regions such as the insula and orbitofrontal cortex. In contrast, the severe CSVD group exhibited widespread structural network differences, decreased coupling in the hippocampus and rolandic operculum, and functional nodal alterations. These cross-sectional comparisons among CSVD burden groups reveal a pattern of differences consistent with stage-like progression. However, causality cannot be inferred from this cross-sectional design.

PMID:42448482 | DOI:10.1016/j.acra.2026.06.044

Neuroimage. 2026 Jul 14:122128. doi: 10.1016/j.neuroimage.2026.122128. Online ahead of print.

ABSTRACT

BACKGROUND: Obsessive-compulsive disorder (OCD) has been associated with abnormal large-scale brain network dynamics, but its transient whole-brain co-activation states remain insufficiently characterized. Against this background, the current study investigated potential alterations in the dynamics of whole-brain co-activation pattern (CAP) in OCD.

METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) data were parcellated using the Brainnetome 246 atlas and analyzed with a CAP framework. CAP states were identified with k-means clustering, and temporal fraction, persistence, counts, and transition probabilities were compared between OCD and healthy controls.

RESULTS: Three recurrent CAP states were identified as the optimal clustering solution. CAP2 showed a distinct intermediate bipolar configuration, with positive weights mainly involving thalamic, inferior frontal, medial temporal, temporoparietal, and medial superior frontal regions and negative weights mainly involving auditory, primary sensorimotor and dorsal insular regions. Yeo-7 mapping further showed that CAP2 had positive weights in the default mode and negative weights in the somatomotor systems. Compared with healthy controls, patients with OCD showed increased CAP2 temporal fraction, persistence, and self-transition probability, together with reduced counts and reduced transition probabilities from CAP2 to both CAP1 and CAP3.

DISCUSSION: These findings suggest that OCD is associated with a reduced switching flexibility and an increased tendency to stay in a specific brain state configuration across time.

PMID:42448253 | DOI:10.1016/j.neuroimage.2026.122128

Imaging Neurosci (Camb). 2026 Jul 10;4:IMAG.a.1300. doi: 10.1162/IMAG.a.1300. eCollection 2026.

ABSTRACT

Naturalistic paradigms offer a powerful tool to investigate human brain function, but it remains difficult to link rich, continuous movie content to distributed brain activity in an interpretable way. In this study, I use a multimodal large language model (Gemini) as an automated "semantic annotator" to bridge naturalistic movie stimuli, brain responses, and cognitive performance. Using the Human Connectome Project movie-watching dataset, I segmented the film into 293 overlapping clips, prompting Gemini to rate each clip on 11 psychologically interpretable dimensions. Simultaneously, I extracted clip-wise BOLD activation patterns from the fMR images in 360 cortical ROIs. In this way, the AI and the brain effectively "watch" the same movies in parallel. For each brain ROI, I then fit linear regression models to predict clip-to-clip variation in movie-evoked responses from these features. Gemini-derived features robustly predicted movie-evoked responses in temporal, medial parietal, and lateral frontal association cortex, but explained little variance in unimodal somatosensory, dorsal parietal, insular, and piriform regions. Feature-weight maps reflected known functional specializations, and features with the largest global influence overlapped with the most explainable ROIs. Partial least squares analysis revealed that individual differences in resting-state connectivity strength and semantic explainability covaried along an asymmetric intrinsic axis: strongly integrated sensory-opercular systems at rest were associated with poorer AI predictability, whereas a smaller set of dorsal and medial association regions showed enhanced alignment. Finally, regional AI explainability in medial parietal and left perisylvian association areas was positively related to specific cognitive abilities. Together, these findings demonstrate that interpretable features from AI models provide a simple and scalable framework for quantifying AI-derived semantic predictability in naturalistic settings, offering a practical framework for utilizing artificial models as semantic references to probe human neural processing and individual differences.

PMID:42444710 | PMC:PMC13358718 | DOI:10.1162/IMAG.a.1300

Mol Autism. 2026 Jul 13. doi: 10.1186/s13229-026-00728-x. Online ahead of print.

ABSTRACT

BACKGROUND: Autism spectrum disorder (ASD) is characterized by profound clinical and biological heterogeneity. The neurodynamic profiles associated with divergent developmental trajectories of executive function (EF) during the critical transition from late childhood to early adolescence remain poorly understood. This study aimed to determine whether heterogeneity in EF development is associated with distinct neurodynamic profiles.

METHODS: In a longitudinal study, 68 children with ASD (aged 6-9 years) and 50 age-matched typically developing (TD) controls underwent baseline resting-state fMRI. The ASD group was followed for approximately 4 years. EF was assessed using the Behavior Rating Inventory of Executive Function (BRIEF), alongside follow-up depression, anxiety, and sleep outcomes. Longitudinal EF trajectory clusters were identified to characterize developmental heterogeneity. In parallel, baseline neurofunctional subtypes were derived from ALFF using a normative-deviation framework, with fALFF used for sensitivity analysis. CAP analysis was then applied to examine brain-state dynamics across these complementary stratification approaches.

RESULTS: EF declined longitudinally in the ASD group, particularly in behavioral regulation domains, and these changes were associated with depressive symptoms and sleep problems at follow-up. Three longitudinal EF trajectory clusters were identified, but baseline CAP dynamics showed minimal differences across these groups. In contrast, ALFF-derived neurofunctional subtypes exhibited distinct CAP dynamic profiles, with Subtype 1 showing greater engagement of visual-related states and Subtype 2 exhibiting enhanced transitions among DMN/FPN-related control states. fALFF-based analyses yielded similar subtype assignments, supporting the robustness of the neurofunctional stratification. Critically, similar EF deterioration was associated with distinct neurodynamic profiles, as reflected by subtype-specific state-transition patterns that showed opposite associations with EF changes.

LIMITATIONS: First, the TD group was not followed longitudinally, limiting precise quantification of deviation from normative developmental pathways. Second, the sample size and attrition during follow-up may affect the stability of subtype assignment. Finally, as inferences were based on resting-state fMRI, task-based or ecologically valid measures were not available to validate functional interpretations.

CONCLUSION: These findings provide evidence for neurodynamic heterogeneity in ASD, suggesting that similar clinical outcomes may be associated with divergent brain-state profiles. This work supports the move toward neuro-subtype-informed precision stratification and targeted intervention strategies.

PMID:42444012 | DOI:10.1186/s13229-026-00728-x

NPJ Parkinsons Dis. 2026 Jul 14. doi: 10.1038/s41531-026-01477-7. Online ahead of print.

ABSTRACT

This study aimed to develop and evaluate a spatiotemporal deep-neural-network (stDNN) using resting-state fMRI (rs-fMRI) data to identify brain biomarkers associated with isolated REM sleep behavior disorder (iRBD) and Parkinson's disease (PD) and to differentiate these conditions from controls. The final sample included 771 subjects, comprising 423 patients with PD, 144 with iRBD, and 204 healthy controls. stDNN model was applied to mean timeseries extracted for each subject from rs-fMRI data. By integrating spatio-temporal features, the network classified subjects based on distinct neural patterns. Model generalizability was assessed using subject-wise k-fold cross-validation. Explainable artificial intelligence (XAI) methods were applied. stDNN achieved balanced accuracy rates of 71.0% in distinguishing controls from PD and up to 71.9% in middle-stage PD cases. It also demonstrated over 80% accuracy in differentiating healthy controls from iRBD. XAI analysis highlighted the involvement of fronto-parietal and temporal regions, including the dorsolateral prefrontal cortex, and anterior temporal gyri, in distinguishing controls from PD. In the comparison with iRBD, key contributing areas included the bilateral superior and medial frontal gyri, dorsolateral prefrontal cortex, parietal and occipital regions (lingual gyri and cuneus). This study demonstrates the potential of stDNN to differentiate between iRBD, PD, and controls using rs-fMRI data.

PMID:42443195 | DOI:10.1038/s41531-026-01477-7

Acad Radiol. 2026 Jul 13:S1076-6332(26)00446-0. doi: 10.1016/j.acra.2026.06.031. Online ahead of print.

ABSTRACT

RATIONALE AND OBJECTIVES: MR-guided focused ultrasound (MRgFUS) thalamotomy is an emerging treatment for medication-refractory essential tremor (ET), but its effects on the motor network remain unclear. This study investigated static and dynamic functional connectivity (FC) within the motor network following MRgFUS in ET and explored correlations between changes in network properties and tremor improvement.

MATERIALS AND METHODS: Twenty-six ET patients who successfully underwent MRgFUS were included, with resting-state fMRI acquired before and one year post-procedure. Additionally, 26 healthy controls were recruited. Tremor severity was assessed using the clinical rating scale for tremor. Thirty ROIs related to the motor network were selected to construct FC matrices. Graph-theoretical analysis was used to assess topological properties, and network-based statistics were applied to evaluate FC. Dynamic FC was estimated using a sliding-window approach. Pearson correlation analysis was performed to assess the relationship between changes in network properties and tremor improvement.

RESULTS: ET patients showed a significant decrease in characteristic path length and significant increases in small-worldness (σ) and global efficiency within the motor network following MRgFUS. K-means clustering of the dFC matrices identified two distinct connectivity states: State 1 (weak connectivity) and State 2 (strong connectivity). In State 2, strong connectivity was mainly within the sensorimotor cortex and motor-related cerebellar regions. The occurrence of State 1 significantly decreased, while that of State 2 significantly increased in ET patients after MRgFUS. Changes in σ and state occurrence rates were associated with tremor improvement.

CONCLUSION: These findings suggest MRgFUS may alter both static network topology and dynamic connectivity in the motor network, reflecting changes in information transfer efficiency and functional integration.

PMID:42443007 | DOI:10.1016/j.acra.2026.06.031

J Affect Disord. 2026 Jul 13:122252. doi: 10.1016/j.jad.2026.122252. Online ahead of print.

ABSTRACT

Depression involves dysregulation across large-scale neural networks, yet substantial heterogeneity in resting-state functional connectivity (rsFC) across patients limits our understanding of treatment mechanisms. A key unresolved question is whether baseline network architecture differentially predicts response to pharmacological versus expectancy-driven treatment effects. In this mechanistic, hypothesis-generating study, 60 depressed participants completed resting-state fMRI at baseline and after 8 weeks of double-blind randomization to an SSRI or placebo. We tested whether connectivity between three networks - the dorsal attention (DAN), salience (SN), and default mode (DMN) networks - predicted treatment response as a function of drug assignment and treatment beliefs. We identified dissociable neural pathways for pharmacological and expectancy effects. Baseline connectivity between attention and salience networks, circuits involved in contextual processing, predicted response specifically among participants who developed placebo beliefs. Baseline connectivity between salience and default mode networks, circuits involved in mood regulation and internal state prediction, showed a double dissociation by drug assignment: higher connectivity predicted better outcomes with the SSRI, while lower connectivity predicted better outcomes with placebo. Network reorganization over treatment followed rather than predicted mood improvement and occurred only when drug assignment and belief aligned. These findings suggest that baseline connectivity patterns serve as trait-like neural markers differentiating pharmacological from expectancy-driven response pathways, and that network reorganization reflects a consequence of aligned pharmacological and psychological treatment effects. Replication in larger samples is warranted, but these results offer a novel framework for understanding and ultimately resolving heterogeneity in antidepressant treatment response.

PMID:42442664 | DOI:10.1016/j.jad.2026.122252

Prog Neuropsychopharmacol Biol Psychiatry. 2026 Jul 13:111839. doi: 10.1016/j.pnpbp.2026.111839. Online ahead of print.

ABSTRACT

BACKGROUND: Cannabis use among adolescents with depression is prevalent. Dysfunction of the reward system has been implicated in each condition separately, yet rarely examined together. Here, we investigated resting-state properties of reward networks in an adolescent sample with diverse internalizing psychopathology and cannabis use patterns.

METHODS: Clinicians interviewed adolescents and assessed depression severity dimensionally. Cannabis use was characterized by self-report and toxicology screens. Neuroimaging scans were acquired and processed using the Human Connectome Project pipelines. Participant-level resting state data were parcellated, and Reward Expectancy and Reward Attainment network masks derived from a reward task were applied. Graph theoretical metrics, including Strength Centrality (CStr), Eigenvector Centrality (CEig), and Local Efficiency (ELoc), were estimated within each network. Group-level clinical correlates of network properties were assessed with non-parametric analyses (10,000 permutations), adjusted for age, sex, and family-wise error (FWE) rates (pFWE < 0.05).

RESULTS: In the full sample (N = 131; 15.3 ± 2.2 years; 65.7% female), depression severity was associated with stronger ELoc of the ventral striatum within the Reward Attainment network. Among those who used cannabis (n = 38), heavier cannabis use was linked to weaker CStr of the anterior cingulate cortex and ELoc of the postcentral somatosensory area within the Reward Expectancy network. Exploratory analyses further suggested cannabis-related dorsolateral prefrontal and cerebellar dysconnectivity across the whole brain, as well as sex- and age-moderated effects.

CONCLUSION: Our findings provide preliminary evidence for adolescent cannabis use and depression being differentially associated with resting-state reward network properties. Additional research with larger cohort sizes is needed to corroborate findings.

PMID:42442474 | DOI:10.1016/j.pnpbp.2026.111839

Front Neurol. 2026 Jun 19;17:1764500. doi: 10.3389/fneur.2026.1764500. eCollection 2026.

ABSTRACT

BACKGROUND: Post-stroke dysphagia (PSD) remains a frequent and disabling sequela of ischemic stroke, and effective pharmacotherapy is still lacking. Acupuncture has demonstrated significant efficacy in improving swallowing function, yet its neural mechanisms remain unclear. Research into the functional reorganization of brain networks following acupuncture treatment remains insufficient.

OBJECTIVE: This protocol describes a randomized, single-blind, sham-controlled trial designed to evaluate the clinical effects and central neural mechanisms of Tiao Shen Li Yan acupuncture for PSD, using multimodal neuroimaging with resting-state functional MRI (rs-fMRI) and diffusion tensor imaging (DTI).

METHODS: Forty-six PSD patients will be randomized 1:1 to active or sham acupuncture (30 minutes sessions, 5 times per week for 6 weeks). The primary outcomes are rs-fMRI and DTI metrics reflecting functional and structural connectivity changes (ALFF, ReHo, FC, FA, MD). Secondary outcomes include Standardized Swallowing Assessment (SSA), videofluoroscopic swallowing study (VFSS)-based measures, Montreal Cognitive Assessment (MoCA), and Swallowing Quality-of-Life Questionnaire (SWAL-QOL). Twenty healthy volunteers will provide baseline neuroimaging reference data.

EXPECTED RESULTS: The study aims to clarify how Tiao Shen Li Yan acupuncture modulates structure-function coupling in swallowing-related brain networks and promotes functional reorganization of the brain, and to determine whether these imaging biomarkers correlate with clinical improvement.

DISCUSSION: By integrating clinical assessments with multimodal neuroimaging under a rigorous randomized design and by incorporating healthy controls to identify abnormal brain network sites in PSD, this study predefines structure-function coupling metrics and clinical endpoint measures to further explore the neural mechanisms of brain network reorganization underlying acupuncture treatment for PSD. This protocol may provide mechanistic evidence for acupuncture-induced neuroplasticity and support the development of treatment strategies for PSD.

TRIAL REGISTRATION: https://www.chictr.org.cn/hvshowproject.html?id=286791&v=1.1, identifier: ChiCTR2400086748.

PMID:42440940 | PMC:PMC13333810 | DOI:10.3389/fneur.2026.1764500

Front Psychiatry. 2026 Jun 22;17:1835586. doi: 10.3389/fpsyt.2026.1835586. eCollection 2026.

ABSTRACT

BACKGROUND: Post-stroke depression (PSD) is a common neuropsychiatric condition after stroke, but its resting-state functional imaging correlates remain incompletely characterized. This study examined multi-level resting-state functional differences between patients with PSD and healthy controls and evaluated whether interpretable machine learning could identify candidate imaging features associated with PSD.

METHODS: Fifty patients with PSD and 50 age- and sex-matched healthy controls underwent resting-state functional MRI. Four complementary imaging indices were extracted using the AAL atlas: amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), degree centrality (DC), and ROI-to-ROI functional connectivity (FC). Imaging features were adjusted for demographic variables, head motion, medication use, and stroke-related factors where appropriate. Candidate features with significant between-group differences were further reduced using LASSO regression. Nine machine-learning classifiers were trained and compared under the same feature-selection framework. Model performance was assessed primarily by ROC-AUC. SHapley Additive exPlanations (SHAP) were used to examine the contribution of individual features to the best-performing model.

RESULTS: Patients with PSD showed distributed resting-state functional differences involving cingulate, thalamic, prefrontal, insular, posterior default-mode, and visual-associated regions. Twenty-nine candidate features differed between groups, including 7 ReHo, 8 ALFF, 6 DC, and 8 FC features. LASSO retained 10 core features, with a cross-validated AUC of 0.878. Among the nine classifiers, the Extra Trees model achieved the highest independent test-set performance, with an AUC of 0.889. SHAP analysis indicated that the most influential features included DC in the left anterior cingulate and paracingulate gyri, ReHo in the left thalamus, FC between the left precuneus and left calcarine cortex, ALFF in the right precuneus, and ALFF in the left angular gyrus. Within the PSD group, moderate depression was associated with higher ALFF in the left insula and lower precuneus-calcarine connectivity compared with mild depression.

CONCLUSIONS: Patients with PSD showed multi-level resting-state functional differences compared with healthy controls. Interpretable machine learning identified a set of candidate rs-fMRI features with plausible neurobiological relevance. These findings require validation in larger longitudinal cohorts that include post-stroke patients without depression to clarify their specificity and clinical utility.

PMID:42440721 | PMC:PMC13333748 | DOI:10.3389/fpsyt.2026.1835586

Phys Act Nutr. 2026 Jun;30(2):14-22. doi: 10.20463/pan.2026.0015. Epub 2026 Jun 30.

ABSTRACT

PURPOSE: This narrative review synthesizes magnetic resonance imaging (MRI) evidence from exercise intervention studies in patients with Parkinson's disease (PD), focusing on structural and functional brain changes associated with motor and cognitive outcomes. We also propose an interpretative framework in which exercise-related neuroplasticity in PD is understood as region- and task-dependent reorganization across cognitive-motor networks.

METHODS: PubMed and Web of Science were searched for interventional studies in patients with PD that examined the effects of exercise-based training on motor and/or cognitive outcomes and included MRI-based assessments of brain structure or function. Studies were eligible if they used a randomized controlled or crossover design and were published in English.

RESULTS: Eighteen interventional trials met the inclusion criteria. Structural MRI findings were limited but suggested subtle region-specific changes in the cerebellum, putamen, and hippocampus rather than a uniform global structural response. Resting-state fMRI studies indicated distributed reorganization across both motor and extramotor networks, including the sensorimotor, salience, default mode, frontoparietal, attentional, and medial temporal systems. Task-based fMRI studies further suggested that exercise-related neural adaptation in PD is strongly shaped by task demand, with reduced prefrontal recruitment observed during complex gait-related conditions and increased recruitment of control-related regions during motor imagery or internally simulated tasks.

CONCLUSION: Exercise interventions in PD may influence motor and cognitive outcomes through neuroplastic changes that are better characterized by circuit-specific structural remodeling, context-dependent resting-state network retuning, and task-dependent functional reallocation rather than by a single uniform neural response.

PMID:42438840 | DOI:10.20463/pan.2026.0015

Hum Brain Mapp. 2026 Jul;47(10):e70606. doi: 10.1002/hbm.70606.

ABSTRACT

Adolescence is a critical period for brain development, impacting social, cognitive, and emotional functions. While hypothesis-driven studies have linked multiple person characteristics to brain development, the driving force behind differential brain development remains unclear. We applied unsupervised clustering to multimodal neuroimaging data from early adolescents in the Adolescent Brain Cognitive Development (ABCD) study. Clustering analyses were conducted separately for resting-state functional MRI (rs-fMRI), structural MRI (sMRI), and diffusion MRI (dMRI), and replicated in two independent samples of 2666 individuals each. Longitudinal trajectories over a two-year follow-up period were examined. Associations with sociodemographic and family-related factors were assessed. Two clusters were identified in rs-fMRI data across both independent samples. One cluster, comprising approximately 9%-10% of individuals, showed functional brain differences at baseline, along with altered neurodevelopmental trajectories over 2 years. These functional differences were associated with lower socioeconomic status, family instability, and stronger cultural/family values. In contrast, no significant clustering emerged from structural MRI and diffusion MRI data. Our findings suggest that sociodemographic factors are closely associated with early adolescent brain function and development, underscoring the need to consider social environment in neurodevelopmental models and prevention strategies.

PMID:42438098 | DOI:10.1002/hbm.70606

Hum Brain Mapp. 2026 Jul;47(10):e70599. doi: 10.1002/hbm.70599.

ABSTRACT

The integration of EEG and fMRI offers a powerful method for exploring the brain's spatial and temporal dynamics. However, existing approaches typically summarize both EEG and fMRI, often removing temporal information before combining the modalities. Our novel approach brings together group ICA of fMRI, group ICA of EEG, and joint ICA to propose a multimodal data fusion approach, named group joint ICA (gjICA), that links simultaneous EEG-fMRI data from multiple subjects. The proposed framework enables group-level mapping and provides single-subject estimates via back-reconstruction, facilitating a comprehensive picture of brain networks. The gjICA also introduces joint functional network connectivity (jFNC), which provides connectivity between fMRI networks as well as between EEG components, hence linking temporal and spatial information. When applied to concurrent resting EEG-fMRI data from 121 participants, we identified 63 multimodal brain components. The statistical analyses of these components further revealed that depression is characterized by widespread multimodal connectivity, with visual and higher cognition networks acting as hubs in these alterations. Further, joint histogram analysis revealed that depression is broadly associated with reduced EEG component expression with increased fMRI component expression in cerebellar, visual, subcortical, and sensorimotor networks, suggesting altered neurovascular coupling and modality-specific dysfunctions. Overall, the proposed gjICA approach provides a robust framework for fusing EEG and fMRI data while preserving the spatiotemporal information in both modalities, enabling the identification of a more comprehensive picture of multimodal neural relationships, enhancing our understanding of brain dynamics and providing new insights into complex brain disorders such as depression.

PMID:42438078 | DOI:10.1002/hbm.70599

J Pain. 2026 Jul 12:106347. doi: 10.1016/j.jpain.2026.106347. Online ahead of print.

ABSTRACT

Functional magnetic resonance imaging studies have demonstrated that back pain, particularly chronic back pain, is associated with altered functional brain connectivity, especially in regions involved in the modulation of pain and emotion regulation. Our study investigated dysconnectivity patterns in subacute back pain patients to distinguish those who develop chronic back pain from those who recover. This work utilized a publicly available longitudinal resting-state functional magnetic resonance imaging dataset from the OpenPain database, including clinical assessments collected across multiple sessions, and recorded pain scores. The dataset consists of 46 subacute back pain patients and 27 healthy controls. Based on longitudinal pain scores, patients were classified into recovery and chronic back pain groups. In contrast to prior analyses of the OpenPain dataset, which primarily relied on specific brain region's connectivity or predictive modeling, this study applies a voxel-wise dysconnectivity count framework combined with data-driven clustering to identify spatially precise, whole-brain signatures of abnormal functional connectivity. This approach enables detection of anatomically specific dysconnectivity patterns at a resolution not achieved in previous work. Our findings therefore extend the OpenPain literature by providing a fine-grained, network-wide characterization of early connectivity disturbances that distinguish recovery from back pain chronification. The results showed that the chronic back pain group exhibited greater dysconnectivity in regions linked to emotion regulation, pain processing, and attentional control. In contrast, the recovery group displayed connectivity deviations in sensorimotor, visual, and cognitive control regions. PERSPECTIVE: Baseline whole-brain dysconnectivity patterns differ between patients who recover and those who develop chronic pain. These findings highlight distributed network alterations associated with pain trajectories and provide insight into neural mechanisms underlying pain chronification.

PMID:42437575 | DOI:10.1016/j.jpain.2026.106347

Eur J Pain. 2026 Jul;30(6):e70331. doi: 10.1002/ejp.70331.

ABSTRACT

BACKGROUND AND OBJECTIVE: Fibromyalgia (FM) is a nociplastic chronic pain syndrome in which neuroimaging indicates central nervous system involvement, yet findings remain inconsistent across methods. We systematically reviewed structural and functional neuroimaging studies in adults with FM to identify the most reproducible cross-modal alterations.

DATABASES AND DATA TREATMENT: Following a PROSPERO-registered protocol (CRD420251234980) and PRISMA 2020, we searched PubMed, EMBASE, Web of Science and VHL/BVS for English-language studies published between 1 January 2010 and 16 October 2025. Eligible studies compared FM (any American College of Rheumatology criteria) with healthy controls and reported quantitative structural MRI, diffusion/structural connectivity or resting-state/task-based fMRI.

RESULTS: Ninety-one studies were included (24 morphometry, 8 diffusion/structural connectivity, 32 resting-state fMRI, 40 task-based fMRI). Morphometry most consistently showed reduced grey matter volume or cortical thickness in anterior cingulate, insular and prefrontal/orbitofrontal cortices, with recurrent reductions in temporal cortex, posterior cingulate, amygdala and hippocampus; thalamic increases were less frequent. Diffusion findings were sparse and bidirectional, yielding no stable white-matter alterations. Resting-state fMRI was highly heterogeneous, with contradictory default-mode, salience/insula and periaqueductal grey (PAG)-related connectivity changes. Task-based fMRI provided the clearest support for central sensitization, showing hyper-responsivity of nociceptive networks with dysregulated prefrontal engagement and weakened rostral anterior cingulate cortex (rACC)-PAG/brainstem descending modulation.

CONCLUSIONS: FM shows distributed brain alterations, with the most robust signals reflecting nociceptive amplification and impaired top-down control. Harmonized longitudinal multimodal studies are needed to establish reliable biomarkers.

SIGNIFICANCE STATEMENT: This systematic review shows that fibromyalgia neuroimaging findings are not best explained by a single biomarker, but by distributed alterations across pain-processing, modulatory and affective-cognitive systems. By comparing structural MRI, diffusion imaging, resting-state fMRI and task-based fMRI, the review identifies task-evoked nociceptive amplification and impaired descending modulation as the most coherent cross-study signal, while clarifying why resting-state and diffusion findings remain difficult to translate clinically.

PMID:42437468 | DOI:10.1002/ejp.70331

Clin Ophthalmol. 2026 Jul 7;20:613366. doi: 10.2147/OPTH.S613366. eCollection 2026.

ABSTRACT

BACKGROUND: Primary angle-closure glaucoma (PACG) is increasingly recognized as involving brain alterations beyond the visual pathway, but the dynamic organization of large-scale brain activity and its biological context remain unclear. Co-activation pattern (CAP) analysis can characterize transient brain states and may provide insight into state-specific functional reorganization in PACG.

METHODS: Resting-state fMRI data were collected from 44 PACG patients and 57 healthy controls. CAP analysis was performed across multiple frequency bands, and six CAP states were identified. Group differences in CAP temporal dynamics and transition profiles were examined. Spatial associations between CAP-related alterations and normative transcriptomic, cell-type, and neurotransmitter receptor maps were assessed using Allen Human Brain Atlas data, enrichment analyses, cell-type-specific profiling, and receptor/transporter density maps. CAP-derived features were further evaluated using support vector machine-recursive feature elimination and multiple machine-learning classifiers.

RESULTS: PACG patients showed state-specific alterations in CAP dynamics, with increased occurrence, dwell time, fractional occupancy, and self-transition probability of selected CAP states, alongside reduced engagement of complementary states. These altered states were organized into limbic-centered, spatially antithetical configurations involving attention, sensorimotor, and control networks. Imaging-transcriptomic analysis identified a dominant normative transcriptional gradient spatially associated with CAP alterations, involving genes enriched for neuronal excitability and synaptic regulation. Cell-type analyses indicated preferential enrichment in excitatory neurons, inhibitory neurons, and endothelial cells. CAP-related alterations also showed spatial associations with cholinergic, serotonergic, glutamatergic, and synaptic vesicle-related receptor systems. Machine-learning analyses demonstrated modest discriminative performance of CAP-derived features, with relatively high specificity but limited sensitivity.

CONCLUSION: PACG is associated with state-specific alterations in intrinsic brain dynamics that spatially align with normative molecular, cellular, and neuromodulatory architectures. These findings provide a multiscale, hypothesis-generating framework for understanding brain functional alterations in PACG.

PMID:42437301 | PMC:PMC13355844 | DOI:10.2147/OPTH.S613366