Most recent paper

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

Mol Psychiatry. 2026 Jul 11. doi: 10.1038/s41380-026-03748-4. Online ahead of print.

ABSTRACT

Alcohol Use Disorder (AUD) is associated with vast clinical, behavioral, and neurobiological heterogeneity that hinder both the efficacy of current interventions and future treatment development. While recent efforts have focused on stratifying psychiatric disorders based on underlying neurobiological markers, such approaches remain largely unexplored in AUD. To address this, we empirically-derived neurobiological subtypes of AUD and evaluated their reproducibility and clinical relevance. Participants with complete resting-state functional MRI (rs-fMRI), phenotypic and clinical data from the Human Connectome Project were included (N = 668; 58% Female; 22% AUD [mild AUD: n = 109; moderate-severe AUD: n = 38], 40% Female within AUD). Neurobiological subtypes of AUD were identified using a semi-supervised clustering approach based on graph theory metrics derived from whole-brain rs-fMRI. Subtypes were extensively validated for reproducibility using resampling, permutation testing, and temporal stability, and for clinical relevance using comprehensive assessments of multi-modal behaviors and clinical characteristics. Two robust and distinct neurobiological subtypes of AUD with unique clinical profiles emerged. Subtype 1 showed greater sensory-motor integration, but lower frontoparietal/salience integration, with elevated externalizing behaviors. Subtype 2 demonstrated the opposite pattern: greater integration in frontoparietal, salience, and default-mode networks, and lower sensory-motor integration, characterized primarily by internalizing behaviors. Subtypes did not differ by AUD symptoms or use characteristics. Importantly, the subtype structure and phenotypic profiles remained robust when including AUD individuals with comorbid cannabis dependence, supporting generalizability and translational relevance to clinically comorbid populations. In summary, we identified neurobiological subtypes of AUD with distinct phenotypic and clinical profiles, capturing greater nuance than previous phenotypically-derived subgroups, while remaining interpretable and clinically meaningful. These findings provide a robust framework for understanding AUD's underlying mechanisms and underscore their potential for a neurobiologically informed approach to nosology, treatment allocation, and personalized interventions in AUD.

PMID:42436260 | DOI:10.1038/s41380-026-03748-4

Eur J Radiol. 2026 Jul 2;203:113047. doi: 10.1016/j.ejrad.2026.113047. Online ahead of print.

ABSTRACT

Assessing muscle activity is essential for diagnosis and treatment of movement disorders such as dystonia and spasticity. While task-based muscle functional magnetic resonance imaging (m-fMRI) enables non-invasive imaging of muscle activation, conventional methods rely on comparisons between rest and activity, which are unsuitable for patients with sustained muscle contractions. This pilot study introduces a resting-state muscle fMRI (rs-m-fMRI) approach based on regional homogeneity (ReHo) to evaluate muscle activity from spontaneous BOLD fluctuations during sustained isometric contraction without block-design contrasts. Eight healthy male participants performed separate isometric plantar and dorsal foot flexion tasks during 3 T MRI scanning. rs-m-fMRI data were analyzed using ReHo to assess local synchronization of BOLD signal. Calf muscle activation was quantified as the percentage of suprathreshold z-transformed ReHo voxels within each segmented muscle and activation thresholds were derived via ROC analysis. ROC analysis demonstrated moderate discrimination between expected active and inactive muscle regions (AUC = 0.63), with sensitivity of 0.57 and specificity of 0.61 at the selected threshold. Consistent condition-related differences were observed between active and inactive muscles during both conditions, with a higher percentage of suprathreshold zReHo voxels in voluntarily contracted muscles. This pilot study demonstrates the feasibility of detecting contraction-related ReHo differences using rs-m-fMRI from a single continuous acquisition. The activation threshold was internally calibrated using expected agonist and antagonist muscle groups and therefore does not represent an externally validated classifier. Further studies incorporating independent physiological validation, reproducibility assessment and larger patient cohorts are required before clinical translation.

PMID:42435570 | DOI:10.1016/j.ejrad.2026.113047

Med Biol Eng Comput. 2026 Jul 11. doi: 10.1007/s11517-026-03619-8. Online ahead of print.

ABSTRACT

BACKGROUND: Resting-state functional MRI (rs-fMRI) has been applied to investigate cognitive impairment (CI) in Parkinson's disease (PD). Nevertheless, reported functional connectivity (FC) alterations remain heterogeneous, partly due to reliance on linear analytical approaches and limited validation across datasets.

OBJECTIVE: To develop a machine-learning framework for identifying generalizable FC markers of CI in PD.

METHODS: Rs-fMRI data were obtained from an online cohort (for model training) and an independent local cohort (for external validation) of individuals with PD. Subjects were stratified according to the presence of CI. All images were preprocessed using an identical pipeline to derive whole-brain FC. A coarse-to-fine feature selection strategy was implemented, combining a genetic algorithm for global feature reduction with sequential feature selection using leave-one-out cross-validation.

RESULTS: In the training dataset (n = 181), genetic algorithm-based selection reduced 13,366 ROI-pair features to 229, achieving an accuracy of 0.83. Subsequent sequential selection further reduced the feature set to 10 ROI pairs, improving accuracy to 0.92. In the validation dataset (n = 32), the classification accuracy was 0.88, with FC patterns showing lateralized cortical and cerebellar involvement.

CONCLUSION: The proposed framework identifies interpretable signatures of rs-fMRI-based markers associated with CI in PD and demonstrates the generalizability.

PMID:42435273 | DOI:10.1007/s11517-026-03619-8

Data Brief. 2026 Jul 2;67:113056. doi: 10.1016/j.dib.2026.113056. eCollection 2026 Aug.

ABSTRACT

Functional MRI (fMRI) remains one of the primary tools for non-invasive studies of brain activity and organization in humans. Recently, multi-echo imaging has generated interest due to the improved signal-to-noise and potential for superior denoising. In parallel, developments in MRI processing techniques, such as the use of phase data and advanced denoising methods, have continued to advance the field. An important step towards adopting these methodologies is directly comparing them to existing techniques using real-world data. We present a neuroimaging dataset that allows for within-subject comparison of single-echo vs multi-echo imaging with cutting-edge imaging acquisition parameters such as magnitude and phase reconstruction and no-excitation (noise only) volumes. The sample includes test-retest data from eight young adults. Each session includes a T1-weighted anatomical scan, four resting-state functional MRI scans (two each for single-echo and complex multi-echo), and a multi-echo task-based functional MRI scan from a fractal n-back working memory task. Raw imaging files are released, as well as derivatives from our open-source processing pipelines. These rich data provide opportunities for direct comparison of single-echo and multi-echo methodologies. Moving forward, the data facilitate studies of evaluating how advanced image acquisition and processing impact test-retest reliability.

PMID:42434500 | PMC:PMC13351147 | DOI:10.1016/j.dib.2026.113056

Quant Imaging Med Surg. 2026 Jul 1;16(7):546. doi: 10.21037/qims-2026-1-0340. Epub 2026 Jun 5.

ABSTRACT

BACKGROUND: Pontine infarction (PI) accounts for 7% of ischemic strokes, yet motor recovery varies significantly despite comparable lesion topography. The neural mechanisms underlying this heterogeneity remain unclear. This study aimed to investigate lesion laterality-dependent functional reorganization within the motor execution network following PI through the use of region of interest (ROI)-based resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS: A total of 31 patients with acute unilateral PI [19 with left PI (LPI) and 12 with right PI (RPI)] and 31 matched controls underwent rs-fMRI. Seed-based functional connectivity (FC) analysis of the motor execution network was performed with spherical ROIs (6-mm radius). Group differences in FC were tested with one-way analysis of variance (ANOVA) and post hoc Bonferroni correction [cluster-level family-wise error (FWE)-corrected P<0.05]. Correlations between FC, infarct volume, and National Institute of Health Stroke Scale (NIHSS) scores were assessed.

RESULTS: No significant intergroup differences were observed in age, education years, head motion parameters, or gender distribution (P>0.05). Lesion volumes during the acute phase and NIHSS scores did not differ significantly between the LPI and RPI groups (P>0.05). In patients with LPI, FC was enhanced between the right dorsolateral prefrontal cortex (DLPFC) and the thalamus/basal ganglia, between the right supplementary motor area (SMA)/precentral gyrus and ipsilateral basal ganglia, and between the thalamus and bilateral cerebellum (all cluster-level FWE-corrected P values <0.05). In contrast, FC was reduced within the ipsilateral motor cortex and between the ventral premotor cortex and sensory cortex (all cluster-level FWE-corrected P values <0.05). In patients with RPI, FC was enhanced between the posterior cingulate cortex (PCC)/precuneus and thalamus and between the left SMA and contralateral basal ganglia (all cluster-level FWE-corrected P values <0.05). Within the LPI group, the FC between the right DLPFC and thalamus correlated positively with infarct volume (r=0.575; P=0.012). However, no significant correlations were observed between FC alterations and NIHSS scores or the NIHSS motor subscores (upper and lower extremity items), respectively, in either the LPI or RPI groups (all P values >0.05).

CONCLUSIONS: Motor recovery post-PI may be influenced by dynamic imbalances in multinetwork early alterations and potentially shaped by lesion laterality: left-sided lesions primarily show recruitment of contralateral prefrontal cognitive resources, whereas right-sided lesions appear to engage default mode network (DMN)-mediated spatial remapping. The thalamo-basal ganglia hub may orchestrate transhemispheric integration, and its FC alterations, such as those within the DLPFC-thalamic pathway, might reflect compensatory potential, potentially offering insights into rehabilitation strategies. However, longitudinal studies are needed to validate these preliminary findings.

PMID:42433535 | PMC:PMC13349987 | DOI:10.21037/qims-2026-1-0340

Quant Imaging Med Surg. 2026 Jul 1;16(7):524. doi: 10.21037/qims-2026-1-0015. Epub 2026 May 18.

ABSTRACT

BACKGROUND: Resting-state functional magnetic resonance imaging (rs-fMRI) method was employed to investigate the abnormal patterns of functional connectivity (FC) between hippocampus and whole brain in patients with post-stroke cognitive impairment (PSCI). The aim was to explore imaging biomarkers, providing imaging evidence for understanding neurocognitive mechanisms of PSCI and formulating targeted intervention strategies.

METHODS: A total of 42 patients with acute ischemic stroke (AIS) were recruited from The First Affiliated Hospital of Xinjiang Medical University between June 2025 to October 2025 in this retrospective cross-sectional study. Concurrently, 40 healthy controls (HCs) without a history of cognitive impairment (CI) were recruited from the community. All participants underwent structural magnetic resonance imaging (MRI) and rs-fMRI scans. Cognitive function in PSCI patients was assessed longitudinally at 3 months post-stroke using standardized neuropsychological instruments, including Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE). Seed-based FC analysis was performed using bilateral hippocampus as regions of interest (ROIs) to compute whole brain connectivity maps. Pearson partial correlation analyses were conducted to examine the associations between altered FC strengths and cognitive scores, controlling for potential confounding variables.

RESULTS: Compared with the HC group, PSCI patients exhibited significantly lower MoCA (Z=0.203, P<0.001) and MMSE (Z=0.129, P<0.001) scores. Enhanced FC was observed in PSCI patients between left hippocampus and left superior temporal pole (t=4.435, P<0.001), right frontal inferior opercular (t=5.079, P<0.001), left inferior parietal (t=4.310, P<0.001), and right superior frontal gyrus (t=3.870, P<0.001). Additionally, enhanced connectivity was found between right hippocampus and right frontal inferior opercular (t=4.246, P<0.001) as well as the right supramarginal gyrus (t=3.794, P=0.001). Pearson correlation analyses revealed that enhanced FC between right hippocampus and right frontal inferior opercular was positively correlated with both MMSE (r=0.467, P=0.001) and MoCA (r=0.434, P=0.003) scores.

CONCLUSIONS: The FC between hippocampus and cerebral cortex is enhanced in PSCI patients. Abnormal connection between hippocampus and right frontal inferior opercular can be used as a potential imaging biomarker to reveal the neurocognitive mechanism of PSCI. In the future, cognitive function can be optimized by regulating these regions.

PMID:42433513 | PMC:PMC13350004 | DOI:10.21037/qims-2026-1-0015

BMC Med Imaging. 2026 Jul 10. doi: 10.1186/s12880-026-02567-x. Online ahead of print.

ABSTRACT

BACKGROUND: Migraine chronification remains difficult to characterize at the individual level, and robust baseline imaging markers associated with subsequent conversion from episodic migraine (EM) to chronic migraine (CM) are lacking. Resting-state functional MRI (rs-fMRI) dynamic functional connectivity (dFC) captures transient brain network interactions, but conventional approaches provide limited interpretability for clinical translation.

METHODS: We enrolled 195 participants at baseline, including 95 healthy controls (HC) and 100 patients with episodic migraine at baseline. All migraine patients underwent baseline rs-fMRI during the interictal phase and were followed for 12 months; 70 remained EM (non-converters) and 30 converted to CM. Sliding-window dFC matrices (55-TR window, 2-TR step) were constructed from 142 regions of interest and represented as temporal graph sequences. We developed an interpretable temporal graph neural network integrating a two-layer graph isomorphism network, a gated recurrent unit, and dual spatial-temporal attention mechanisms to quantify node-level ("where") and window-level ("when") importance. A multilayer perceptron performed three-class discrimination among HC, EM non-converters, and CM converters. Model performance was evaluated using stratified ten-fold cross-validation. Edge-wise ANCOVA controlling for age, sex, and mean framewise displacement with false discovery rate correction was applied to dFC matrices at key temporal-attention windows and within k-means-derived dFC states.

RESULTS: Four recurrent dFC states were identified. State 3 showed the highest fractional occupancy and mean dwell time and was the only state with significant between-group edge-wise differences after correction. Compared with HC, migraine groups exhibited altered connectivity involving sensory, attention, default-mode, and subcortical systems. CM converters showed additional baseline abnormalities involving sensorimotor-visual, default-mode, ventral attention, and limbic-related circuits. Spatial attention shifted from occipital-frontoparietal hubs in HC to frontal-insular prominence in EM non-converters and a frontoparietal-limbic configuration in CM converters. Temporal attention peaked at window 23, where chronification-related abnormalities were most evident. The model achieved AUCs of 0.817(HC), 0.832(EM non-converters), and 0.874(CM converters), with a macro-AUC of 0.841 (95% CI 0.77-0.91). Findings were robust across alternative window lengths and step sizes.

CONCLUSIONS: This rs-fMRI dFC-based temporal graph learning framework identified baseline spatiotemporal network patterns associated with subsequent migraine chronification and generated individualized time-resolved importance maps. Although the model showed promising internal discriminative performance, these findings should be interpreted as risk-related pattern identification rather than as evidence of a clinically validated prediction tool. External prospective multicenter validation is required before clinical translation.

PMID:42432514 | DOI:10.1186/s12880-026-02567-x

BMJ Open. 2026 Jul 10;16(7):e116849. doi: 10.1136/bmjopen-2026-116849.

ABSTRACT

INTRODUCTION: Impaired inhibitory control is a core neurocognitive deficit in children with attention deficit hyperactivity disorder (ADHD). Physical exercise and mindfulness training are each promising non-pharmacological approaches for ADHD; however, whether combining mindfulness with different exercise modalities, particularly team-based and individual-based open-skill exercise versus closed-skill exercise, yields differential effects on inhibitory control and its neural substrates remains unknown. This study aims to compare these intervention approaches in children with ADHD while exploring associated neural correlates.

METHODS AND ANALYSIS: This is a single-centre, assessor-blinded and statistician-blinded, three-arm parallel-group randomised controlled trial. 60 children with ADHD aged 6-8 years will be randomly assigned (1:1:1) to one of three 8-week intervention groups: (1) team-based open-skill exercise (basketball) combined with mindfulness training, (2) individual-based open-skill exercise (badminton) combined with mindfulness training or (3) closed-skill exercise (structured aerobic activity) combined with mindfulness training. The co-primary outcomes are behavioural inhibitory control, assessed using the Go/No-Go task and Stroop Colour-Word Test at baseline, postintervention and 3-month follow-up. Secondary outcomes include attention, ADHD symptom severity, executive function, health-related quality of life and resting-state functional MRI indices.

ETHICS AND DISSEMINATION: This trial has received ethical approval from the Ethics Committee of the Affiliated Changzhou Children's Hospital of Nantong University (Approval No.: 2024-029; Approval Date: 2 January 2025) and will be conducted in accordance with the Declaration of Helsinki (2024 revision). The protocol has been prospectively registered with the Chinese Clinical Trial Registry. Findings will be disseminated through peer-reviewed publications and academic conferences.

TRIAL REGISTRATION NUMBER: ChiCTR2500108809.

PMID:42431671 | DOI:10.1136/bmjopen-2026-116849

Brain Organoid Syst Neurosci J. 2025 Dec;3:96-106. doi: 10.1016/j.bosn.2025.04.008. Epub 2025 May 1.

ABSTRACT

Neuroimaging studies of major depressive disorder (MDD) report widespread disease-attributed abnormalities of brain structure and function. However, reports from mass univariate-driven studies are inconsistent. The objective of this study was to determine if a neuroimaging-based biomarker of MDD, which can reliably distinguish patients from healthy controls, can be generated using multivariate measures. Multivariate modeling of MDD was achieved through generation of a meta-analytic node-and-edge network model of MDD in which disease impacted brain regions (nodes) and their covariances (edges) were quantified with structural equation modeling (SEM). SEM assessment and voxel-based morphometry (VBM) analysis in primary datasets served to test our hypothesis that multivariate analyses of MDD provide improved signal over mass univariate methods. Brain areas reliably impacted by MDD (nodes) and their covariances (edges) were informed by previously published coordinate-based meta-analysis activation/anatomical likelihood estimation (CBMA-ALE) by our group. Meta-analytic model was then fit in primary structural (T1) magnetic resonance imaging (MRI) data and resting-state functional MRI (rs-fMRI) data. Primary datasets were derived from two previously recruited cohorts. Outcome measures (testing for differences between MDD and controls) from standardized SEM included: a) model goodness of fit assessment, and b) individual edge strength. SEM measures were assessed in heterogeneous MDD patient groups, and subsequently re-tested in 7 clinical subgroups of MDD patients. Meta-analytically generated MDD network model yielded 9 nodes with 6 edges among the regions. Model goodness of fit in meta-analytic datasets were good to exceptional. Model goodness of fit in regionally sampled gray matter density in primary T1 data was exceptional in clinical subgroups of MDD, poor in clinically heterogeneous subgroups of MDD, and poor in healthy control subjects. VBM analysis of the same T1 datasets yielded sparse results. Model goodness did not distinguish MDD from controls in regionally sampled primary rs-fMRI. These findings support our hypothesis of improved multivariate signal in MDD compared to findings derived from mass univariate analyses, however this effect was only detectable in T1 data (groupwise). Improved SEM goodness of fit in clinical subgroups of MDD patients supports our hypothesis of detectable neuroimaging effects of clinical heterogeneity in MDD.

PMID:42428155 | PMC:PMC13347272 | DOI:10.1016/j.bosn.2025.04.008

Res Sq [Preprint]. 2026 Jul 3:rs.3.rs-9544648. doi: 10.21203/rs.3.rs-9544648/v1.

ABSTRACT

Decreased connectivity within the default mode network (DMN) has been consistently implicated in post traumatic stress disorder (PTSD), but critical nodes through which traumatic stress changes DMN connectivity and DMN connectivity patterns that are linked to specific traumatic stress effects are not well understood. To address this, resting-state functional connectivity (rs-FConn) within the DMN was analyzed using modular and graph theory tools to characterize DMN connectivity changes brought on by single prolonged stress (SPS); a rat traumatic stress model. Results identified a set of negative edges that connect anterior and posterior DMN nodes. We refer to these as A-P edges and the anterior cingulate cortex (ACC) and rostral retrosplenial cortex (rRSC) were nodes that had the largest number of these edges. A-P edge frequency and graph connectivity measures decreased with a second fMRI scan in control rats and these decreases, and rRSC A-P edge frequency, were disrupted by SPS. Traumatic stress leads to deficits in extinction retention and to examine how DMN connectivity changed during extinction, we used correlated c-Fos levels among select DMN nodes to approximate DMN connectivity during fear/threat conditioning, and extinction learning and memory. Results suggest that SPS decreased DMN connectivity under most conditions, but enhanced DMN connectivity during extinction testing. Overall, the results of this study raise the possibility that while SPS does decrease DMN connectivity and disrupts changes in DMN connectivity brought on by a second fMRI scan, certain aspects of DMN connectivity are enhanced with SPS.

PMID:42427863 | PMC:PMC13345523 | DOI:10.21203/rs.3.rs-9544648/v1