Most recent paper

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

bioRxiv [Preprint]. 2026 Jul 3:2026.07.02.736181. doi: 10.64898/2026.07.02.736181.

ABSTRACT

The basal ganglia and thalamus are key nodes in subcortico-cortical loops involved in sensory, motor, and cognitive function. In adults, posterior regions of the subcortex link to cortical sensorimotor networks and anterior regions link to association networks. Alterations in the size, strength, and selectivity of these subcortical regional network representations are implicated in several neuropsychiatric disorders, many of which originate early in development. However, the organization of these network representations at birth remains incompletely understood, limiting our ability to devise normative and atypical developmental models of subcortico-cortical interactions. Using resting-state fMRI, we characterized the size, strength, and selectivity of cortical network representations in the basal ganglia and thalamus in a set of neonates (n=261) and compared results to children (age range 9-11 years, n=69) and adults (n=120). We found that the broad anterior-posterior organization of the subcortex is present at birth, yet representations of somatomotor networks were larger at birth compared to children and adults (p<0.001). The strength and selectivity of subcortico-cortical functional connectivity (FC) exhibited interactions between age group and network (all p<0.001), such that subcortical representations of sensorimotor networks exhibited stronger FC and higher selectivity in neonates, while subcortical representations of association networks exhibited stronger FC and higher selectivity in older cohorts. In parallel, data-driven clustering revealed areas with integration of multiple networks in the neonatal subcortex. These results suggest that subcortico-cortical FC evolves over development largely in a sensorimotor-association manner and provide a baseline for normative and disordered subcortical development.

HIGHLIGHTS: The basic anterior-posterior layout of association to sensorimotor network representation in the basal ganglia and thalamus is present at term birth.Sensorimotor systems are overrepresented in neonates compared to children and adults.Sensorimotor representations exhibit greater functional connectivity strength and selectivity than association representations at birth, while association representations are stronger and more selective than sensorimotor representations in adults.The neonatal subcortex exhibits substantial integration of multiple cortical networks.

PMID:42427646 | PMC:PMC13344950 | DOI:10.64898/2026.07.02.736181

bioRxiv [Preprint]. 2026 Jul 2:2026.03.09.710457. doi: 10.64898/2026.03.09.710457.

ABSTRACT

Head motion systematically biases functional connectivity (FC) estimates in resting-state functional MRI (rs-fMRI). A common mitigation strategy is to censor high-motion volumes and discard high-motion runs. However, overly stringent censoring risks discarding signal alongside noise, potentially degrading FC estimates. Here, we test the efficacy of various censoring strategies on individual-specific cortical parcellations and personalized transcranial magnetic stimulation (TMS) target selection. Using precision-fMRI datasets comprising 50 individuals, we define individualized "ground-truth" references from ≥1 hour of low-motion data per participant. We then simulate 10-min or 20-min rs-fMRI sessions with varying motion levels from the remaining data, yielding final samples of 22 and 19 participants, respectively. Higher motion produces parcellations and TMS targets that deviate further from the ground-truth references. However, at any given motion level, lenient censoring produces higher quality parcellations and personalized TMS targets than strict censoring. The improvement is comparable to doubling scan duration from 20 to 40 min under strict censoring. With personalized connectome-guided TMS, a common dilemma is whether to re-scan patients with only high-motion runs. A mixed-motion session with one low-motion run and one high-motion run may often be considered usable after discarding the high-motion run and strict censoring. We find that lenient censoring of high-motion-only sessions yields TMS targets comparable to - or even better than - those derived from strictly censored mixed-motion sessions. Therefore, within the motion range and parcellation/TMS targeting frameworks evaluated here, patients may not need to be re-scanned solely because all runs exceed strict censoring criteria.

PMID:42427520 | PMC:PMC13345301 | DOI:10.64898/2026.03.09.710457

bioRxiv [Preprint]. 2026 Jul 1:2026.06.26.734900. doi: 10.64898/2026.06.26.734900.

ABSTRACT

The thalamus is a central hub that interfaces with widespread cortical and subcortical nodes. Thalamic deep brain stimulation (DBS) offers a principled strategy for distributed cortical modulation: since distinct thalamic nuclei project to spatially segregated cortical territories, stimulation at a single thalamic site can influence multiple cortical nodes. Realizing this potential requires accurate subject-specific estimates of directed thalamocortical effective connectivity (EC) and a computational framework for optimizing stimulation parameters that achieve desired cortical responses. Here, we address both challenges using Neural Perturbational Inference (NPI), a surrogate-brain approach that estimates EC by applying virtual perturbations to a nonlinear dynamical model fitted to resting-state fMRI data. We extend NPI to a high-resolution thalamocortical network comprising 360 cortical regions and 442 thalamic voxels spanning 12 nuclei. We introduce two innovations in training: (i) a temporal signal-to-noise ratio (tSNR)-weighted loss accounting for signal heterogeneity, and (ii) a multi-resolution, cross-scale consistency loss that regularizes model complexity. These strategies yield improved performance in synthetic benchmarks across varying tSNR regimes. Leveraging the inferred subject-specific EC, we further formulate a constrained linear control problem to identify sparse thalamic stimulation targets that achieve desired cortical activation patterns. We validate the inferred EC structure on two independent datasets: the MacStim dataset comprising two macaque monkeys with infrared neural stimulation on medial pulvinar, and the HumanTC resting-state fMRI dataset comprising twelve human subjects. Our results reveal site-specific thalamocortical EC profiles, producing interpretable predictions that align with known ground-truth structures. Together, this work establishes a computationally grounded pathway toward personalized optimization of thalamic DBS in both human and nonhuman primates.

PMID:42427509 | PMC:PMC13345149 | DOI:10.64898/2026.06.26.734900

Zhonghua Nei Ke Za Zhi. 2026 Jul 1;65(7):743-751. doi: 10.3760/cma.j.cn112138-20260305-00127.

ABSTRACT

Objective: To investigate the relationship between alterations in dynamic regional homogeneity (dReHo) and spatial navigation impairment in individuals with subjective cognitive decline (SCD) who exhibit a biased Traditional Chinese Medicine (TCM) constitution. Methods: A total of 63 participants with SCD were recruited from the Affiliated Drum Tower Hospital of Nanjing University Medical School between January 2024 and January 2026. The cohort comprised 30 individuals with a balanced constitution (mean age: 68±7 years; 17 males, 13 females) and 33 with a biased constitution (mean age: 68±6 years; 12 males, 21 females). All participants were assessed for spatial navigation ability, TCM constitution, and neuropsychological status. Resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired during the same period. The rs-fMRI time series was segmented using a sliding time window approach, and dReHo was subsequently calculated. Statistical analyses were performed using SPSS 23.0. Intergroup differences in dReHo variability were compared. Correlation analyses were performed between the dReHo values extracted from brain regions showing significant differences and both cognitive scale scores and spatial navigation behavioral metrics. Finally, logistic regression and receiver operating characteristic (ROC) curve analyses were conducted to evaluate the predictive value of spatial navigation behaviors, dReHo variability, and cognitive scales for distinguishing between balanced and biased TCM constitutions among participants with SCD. Results: Compared to SCD individuals with a balanced constitution, those with a biased constitution exhibited significantly lower scores on the MMSE (U=2.10, P=0.036), the Auditory Verbal Learning Test (AVLT) long-delayed recall (U=2.23, P=0.026), cued recall (U=2.08, P=0.037), and recognition (t=2.51, P=0.015). Conversely, the biased constitution group demonstrated significantly higher average error distances in egocentric- allocentric navigation (U=-2.24, P=0.025), egocentric navigation (U=-2.02, P=0.043), and delayed navigation (U=-2.16, P=0.031). Neuroimaging analysis revealed that individuals with a biased constitution displayed significantly increased dReHo variability in the right angular gyrus and bilateral supplementary motor areas (t=4.51, 3.05, respectively; all P<0.05, GRF-corrected). dReHo variability in these regions correlated positively with the average error distance in delayed spatial navigation (r=0.261, P=0.039 and r=0.286, P=0.023, respectively). Additionally, dReHo variability in the bilateral supplementary motor areas showed significant positive correlations with episodic memory and language function (r=0.271, P=0.032 and r=0.277, P=0.028, respectively), alongside a significant negative correlation with executive function (r=-0.259, P=0.040). A comprehensive prediction model integrating spatial navigation metrics, cognitive assessment scales, and dReHo variability demonstrated significant discriminative performance in distinguishing between biased and balanced TCM constitutions among individuals with SCD, yielding an area under the curve (AUC) of 0.875. Conclusion: Increased dReHo variability in the right angular gyrus and bilateral supplementary motor areas represents a potential neural mechanism underlying cognitive decline and spatial navigation deficits in individuals with SCD who display a biased constitution. Furthermore, the developed comprehensive model incorporating spatial navigation behaviors and dReHo variability exhibits high predictive efficacy in differentiating between balanced and biased TCM constitutions within this population.

PMID:42427047 | DOI:10.3760/cma.j.cn112138-20260305-00127

BMC Gastroenterol. 2026 Jul 9. doi: 10.1186/s12876-026-05097-6. Online ahead of print.

ABSTRACT

BACKGROUND: Symptoms of fatigue, depression or anxiety are frequent in Crohn's Disease (CD) and may relate to disturbed brain-gut interactions. While more prevalent in active disease, these symptoms are also experienced by many individuals with CD during remission. Little is known about neural networks underlying such extraintestinal symptoms in CD and their relationship with the current disease state. Using a data fusion approach for functional MRI, this study investigated spatiotemporal markers of resting-state brain activity and associations with neurotransmitter systems and symptoms of fatigue, depression or anxiety in an active disease state or remission.

METHODS: We examined n = 71 patients with CD in an active disease state (aCD; n = 47) or in remission (rCD; n = 24) and healthy controls (HC; n = 35). All participants underwent resting-state fMRI, completed symptom assessments for fatigue, depression and anxiety, and provided stool samples for analysis of faecal calprotectin (fCal; aCD and rCD only). Joint independent component analysis (jICA) of two resting-state brain activity parameters (temporal and spatial features) identified neural networks exhibiting disease-state-dependent alterations. Network connectivity strength was correlated with symptoms of fatigue, depression, and anxiety, as well as fecal calprotectin (fCal). We further explored associations of the networks with neurotransmitter receptor maps.

RESULTS: JICA revealed three networks differentiating between disease states and/or between patients and controls. One network comprising affective orbitofrontal and temporal brain regions, exhibited reduced connectivity in active disease (HC vs. aCD: p = 0.003, pFDR = 0.01; aCD vs. rCD: p < 0.001, pFDR < 0.001) and was linked to serotonergic/dopaminergic transmission, fatigue, and fCal. Another network comprised sensorimotor brain regions and showed diminished connectivity in patients in remission (HC vs. rCD: p = 0.034, pFDR = 0.06; aCD vs. rCD: p = 0.003, pFDR = 0.01), correlating with depression, anxiety, and dopaminergic activity. The third network reflected the default-mode network topography and distinguished patients irrespective of disease status from controls (HC vs. aCD: p = 0.013, pFDR = 0.01; HC vs. rCD: p = 0.037, pFDR = 0.06), but showed no associations with symptoms.

CONCLUSIONS: Resting-state brain network connectivity in patients with CD differed between active disease and remission, and was associated with symptoms of fatigue, depression, and anxiety. Alterations in sensorimotor networks were linked to depressive and anxiety symptoms, whereas affect-related networks were associated with fatigue. These observations suggest that distinct brain networks may contribute to specific neuropsychiatric symptom clusters in Crohn's disease and underscore the role of brain-gut axis mechanisms in these manifestations.

PMID:42426651 | DOI:10.1186/s12876-026-05097-6

J Affect Disord. 2026 Jul 9:122236. doi: 10.1016/j.jad.2026.122236. Online ahead of print.

ABSTRACT

BACKGROUND: The therapeutic strategies for bipolar disorder (BD) and unipolar depression (UD) are quite different. However, the majority of patients with BD often present with a depressive episode as their initial symptom and are misdiagnosed as UD. To date, no reliable tool has been able to accurately differentiate BD patients from UD patients.

METHODS: The spontaneous brain activity derived from functional MRI of 79 BD patients and 79 matched UD patients was used to establish machine learning (ML) models for distinguishing BD patients from UD patients. Furthermore, the imaging signatures obtained from the optimal model and statistically significant clinical characteristics were incorporated into the predictive nomogram. The performance of the nomogram was evaluated by calibration curve and decision curve analysis (DCA).

RESULTS: The ML model based on spontaneous brain activity of 10 brain regions with significant differences between BD patients and UD patients achieved optimal diagnostic performance, with an AUC of 0.894 in the validation dataset. Disease duration was identified as an independent clinical predictor. A nomogram integrating disease duration with the imaging signatures derived from the optimal model demonstrated good discriminative efficacy, with a C-index of 0.926. The calibration curve and DCA indicated excellent reliability and significant net clinical benefit.

CONCLUSIONS: Our study provides a preliminary proof-of-concept that a nomogram integrating spontaneous brain activity with clinical information may serve as a potential diagnostic tool for differentiating BD patients from UD patients.

PMID:42425243 | DOI:10.1016/j.jad.2026.122236

J Alzheimers Dis. 2026 Jul 9:13872877261467275. doi: 10.1177/13872877261467275. Online ahead of print.

ABSTRACT

BackgroundLow frequency oscillations in blood-oxygen-level-dependent signal (BOLD-LFOs) are generally considered nuisance signal in connectivity analysis and discarded. However, recent evidence suggests BOLD-LFOs shed light on cerebrovascular dysfunction and preclinical Alzheimer's disease, but the mechanisms remain unclear. No investigations have assessed the relationship between BOLD-LFOs and plasma pTau217, or how it differs in apolipoprotein ε4 (APOE4) carriers who are vulnerable to cerebrovascular dysfunction and genetically predisposed to AD.ObjectiveTo study the relationship between BOLD-LFOs and plasma p-Tau217 in APOE4 carriers compared to non-carriers.MethodsIndependently living older adults (N = 118) were recruited and underwent resting-state fMRI and venipuncture. BOLD-LFOs were quantified as signal power within the 0.01-0.10 Hz frequency range. Plasma pTau217 was assessed and linear regression quantified the interactive effect of APOE4 carrier status and BOLD-LFOs on plasma pTau217. 2×2 ANCOVA was used to compare BOLD-LFOs across APOE4 carrier and amyloid positivity statuses based on previously reported pTau217 cutoffs.ResultsThe interactive effect of APOE4 carrier status and BOLD-LFO power was significantly associated with plasma pTau217 (β = -0.78, p = 0.001). This relationship was driven by an inverse relationship between BOLD-LFOs and plasma pTau217 in APOE4 carriers (β = -0.57, p = 0.0007). Amyloid-β (+) APOE4 carriers displayed lower BOLD-LFOs than amyloid-β (-) APOE4 carriers (p = 0.008) and amyloid-β (+) non-carriers (p = 0.03). Models were adjusted for age, sex, vascular risk factors, and total intracranial volume.ConclusionsFindings suggests BOLD-LFOs are implicated in preclinical AD in an APOE4 dependent manner, adding support for the continued study of BOLD-LFOs in the context of cerebrovascular contributions to AD genetic risk.

PMID:42423522 | DOI:10.1177/13872877261467275

Front Comput Neurosci. 2026 Jun 24;20:1810942. doi: 10.3389/fncom.2026.1810942. eCollection 2026.

ABSTRACT

INTRODUCTION: Brain function emerges from coordinated activity across anatomically connected regions, where structural connectivity (SC)-the network of white matter pathways-provides the physical substrate for functional connectivity (FC), defined as the correlated activity between brain areas. While structural and functional networks exhibit substantial overlap, their relationship involves complex, indirect mechanisms, including the dynamic interplay of direct and indirect pathways. To systematically untangle how structural architecture shapes functional patterns, this work aims to establish a set of rules that decode how direct and indirect structural connections and motifs give rise to FC between brain regions.

METHODS: Specifically, using a generative linear model, we derive explicit rules that predict an individual's resting-state fMRI FC from diffusion-weighted imaging-derived SC, validated against topological null models.

RESULTS: Examining the rules reveals distinct classes of brain regions, with integrator hubs acting as structural linchpins promoting synchronization and mediator hubs serving as structural fulcrums orchestrating competing dynamics. Virtual lesion experiments further demonstrate how different cortical and subcortical systems distinctively contribute to global FC.

DISCUSSION: Together, by uncovering how structural architecture governs functional interactions, this framework enables us to predict how alterations in SC, resulting from disease or surgery, propagate through functional networks and contribute to cognitive and behavioral impairments.

PMID:42422232 | PMC:PMC13341858 | DOI:10.3389/fncom.2026.1810942