Most recent paper

NPJ Digit Med. 2026 Jul 20. doi: 10.1038/s41746-026-03011-8. Online ahead of print.

ABSTRACT

Major Depressive Disorder (MDD) is highly heterogeneous, limiting treatment efficacy. Despite efforts to delineate patient heterogeneity through subtyping, current approaches remain limited by noise, lack of clinical applicability, and insufficient external validation. Crucially, they focus on subtyping while neglecting staging information (e.g., illness duration). We developed BrainCVAE, a contrastive variational autoencoder, to disentangle MDD-specific neural features. Applying BrainCVAE to fALFF-derived resting-state fMRI from 1590 patients and 1308 controls identified two subtypes: Subtype 1 with hyperactivity in visual, attention, and default mode networks, and Subtype 2 with hypoactivity. Subtypes were validated in 1276 patients across independent centers. Subtype 1 showed superior responsiveness to pharmacological (SSRIs, SNRIs) and non-pharmacological (rTMS) interventions. Cross-sectional analyses revealed subtype-specific differences in DMN profiles across illness duration: Subtype 1 shifted from hyper- to hypoactivity, whereas Subtype 2 remained consistently hypoactive. In an independent dataset, illness duration correlated negatively with symptom reduction (r = -0.5565, 95% CI = (-0.8123, -0.1210), p = 0.0165). Datasets were ethically approved and registered on ClinicalTrials.gov: XJ_QG (NCT05577481, May 24, 2023), SAINT (NCT04653337, Oct 21, 2020), XJ_KG (NCT05544071, May 24, 2023). Integrating subtyping with illness staging bridges neurobiological heterogeneity and disease progression, providing a clinically actionable framework for precision treatment in MDD.

PMID:42477489 | DOI:10.1038/s41746-026-03011-8

Sci Data. 2026 Jul 20. doi: 10.1038/s41597-026-07914-9. Online ahead of print.

ABSTRACT

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that frequently affects the central nervous system. Publicly available SLE neuroimaging datasets remain limited in scale and modality. Here, we present a cross-sectional multicenter dataset comprising resting-state functional MRI (rs-fMRI) and diffusion tensor imaging (DTI) data from 631 participants (396 SLE patients, 235 healthy controls) across four Chinese clinical centers. The released dataset does not include individual-level raw DICOM or NIfTI images; instead, all shared imaging files are de-identified, processed ROI-level derivatives, including AAL-based BOLD time series and DTI-derived structural connectivity matrices weighted by fractional anisotropy and fiber number. ROI-level BOLD time series and DTI-derived structural connectivity matrices are paired in three centers. Data underwent rigorous quality control, including head-motion screening and outlier detection; cross-center comparability was further evaluated using ComBat harmonization of selected derived validation features, including regional ALFF and regional mean FA strength, for which no significant inter-site differences remained after harmonization. Clinical and neuropsychological assessments are also available. This dataset supports diverse computational neuroimaging analyses, including functional characterization, structural connectivity modeling, and structure-function coupling studies.

PMID:42477344 | DOI:10.1038/s41597-026-07914-9

Commun Biol. 2026 Jul 20. doi: 10.1038/s42003-026-10713-y. Online ahead of print.

ABSTRACT

Revealing how disrupted brain dynamics lead to altered consciousness levels remains a central challenge in understanding the neural mechanisms underlying consciousness. The brain criticality framework offers a promising perspective, in which optimal neural integration and information processing occur when the brain operates near a critical point, while also reflecting fundamental neural processes such as excitation/inhibition balance. Here, we combined resting-state functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) to systematically assess brain criticality in disorder of consciousness (DoC) patients. Our results revealed that patients in an unresponsive wakefulness state (UWS) exhibited significantly greater power-law scaling exponents in co-activation clusters, higher Ising energy, and lower phase synchronization compared to those in a minimally conscious state (MCS). These findings suggest a greater deviation from critical brain dynamics in UWS, reflecting diminished neural integration and increased disorder. The extent of these deviations correlated with metabolic deficits measured by PET, highlighting the functional relevance of altered neural dynamics. Importantly, critical metrics were significantly associated with clinical scores and outperformed PET in both diagnosis and prognosis. Together, our findings advance understanding of the neural mechanisms underlying consciousness and highlight the potential of criticality-based metrics for characterizing brain states and informing prognosis in DoC.

PMID:42477079 | DOI:10.1038/s42003-026-10713-y

Transl Psychiatry. 2026 Jul 21. doi: 10.1038/s41398-026-04284-z. Online ahead of print.

ABSTRACT

Emerging evidence links glymphatic dysfunction to depression, yet its regulatory mechanisms remain unclear. Through multimodal neuroimaging of two independent cohorts, we tested the hypothesis that the cholinergic basal forebrain (ChBF) modulates glymphatic function and thereby contributes to depression. Glymphatic function was indexed using two indirect MRI markers: the diffusion tensor image analysis along the perivascular space (DTI-ALPS) index and coupling between global blood-oxygen-level-dependent signals and cerebrospinal fluid signals (gBOLD-CSF coupling). In a stroke cohort, we explored ChBF-glymphatic-depression interactions through dual perspectives: acute lesion effects and longitudinal recovery. Baseline lesion network mapping analysis (n = 189) revealed that functional connectivity (FC) between ChBF and lesion predicted glymphatic impairment. Longitudinal resting-state fMRI analysis (n = 71) demonstrated that recovery of ChBF whole-brain FC correlated with glymphatic restoration. Glymphatic dysfunction mediated the association between ChBF damage and post-stroke depression severity. Subsequently, we validated these findings in non-stroke cohort (n = 38) receiving transcranial magnetic stimulation. FC between the stimulation site and ChBF predicted glymphatic improvement, with enhanced ChBF whole-brain FC mediating this association. Furthermore, increased ChBF whole-brain FC was correlated with antidepressant efficacy and glymphatic enhancement mediated this relationship. Our findings identify that ChBF play a regulatory role in glymphatic function, which in turn influences depression. This study provides a theoretical foundation for the development of glymphatic-based therapeutic strategies for depression.

PMID:42476965 | DOI:10.1038/s41398-026-04284-z

Acad Radiol. 2026 Jul 20:S1076-6332(26)00453-8. doi: 10.1016/j.acra.2026.06.040. Online ahead of print.

ABSTRACT

RATIONALE AND OBJECTIVES: Alzheimer's disease (AD) progression involves distinct spatiotemporal changes in functional connectivity (FC) within anterior-temporal (AT) and posterior-medial (PM) networks. This study characterizes FC alterations between medial temporal lobe (MTL) subregions and AT/PM networks across the AD continuum and examines their associations with tau/Aβ pathology and atrophy using second-generation tau-PET.

MATERIALS AND METHODS: Eighty-four participants, including 26 cognitively unimpaired Aβ negative(CU Aβ-), 19 cognitively unimpaired Aβ positive (CU Aβ+), 19 mild cognitive impairment Aβ positive(MCI Aβ+), 20 AD Aβ positive(AD Aβ+) underwent 3 T MRI, resting-state fMRI, [¹⁸F]florbetapir (Aβ), and [¹⁸F]MK6240 (tau) PET. MTL subregions were segmented via ASHS-T1, while AT/PM networks were defined using Harvard-Oxford Atlas. Group differences in FC, Aβ/tau standard uptake value ratio(SUVR), and gray matter volume (GMV) were assessed. Network SUVR values, GMV and intra-FC correlation analyses and correlation coefficient matrix plots were calculated.

RESULTS: Compared to CU Aβ-, CU Aβ+ exhibited increased intra-/inter-network FC (peak in anterior MTL: p < 0.001), while MCI/AD groups showed progressive FC declines. Posterior network FC reduction was pronounced in AD (p < 0.01). Left BA35 connectivity increased in CU Aβ+, whereas AD demonstrated FC reductions in bilateral posterior cingulate/hippocampus. Network-level A/T/N-FC correlations were integrated in CU Aβ+ but modular in AD, with strong tau-GMV anticorrelation.

CONCLUSION: Early AD stages feature compensatory AT/anterior MTL hyperconnectivity, transitioning to PM/posterior MTL disintegration as pathology advances. Multimodal biomarker-FC coupling reflects dynamic network reorganization, highlighting MTL-AT/PM connectivity as a biomarker of disease progression.

PMID:42476833 | DOI:10.1016/j.acra.2026.06.040

J Magn Reson Imaging. 2026 Jul 20. doi: 10.1002/jmri.70417. Online ahead of print.

ABSTRACT

BACKGROUND: Despite known brain alterations in insomnia-particularly prevalent in older females-how it affects sleep-dependent glymphatic clearance remains understudied due to in vivo human measurement challenges.

PURPOSE: To investigate altered neurofluid dynamics in women with insomnia using indirect neuroimaging markers.

STUDY TYPE: Prospective.

POPULATION: 46 healthy controls (HC; 56.3 ± 5.5 years) and 28 females with insomnia disorder (ID; 58.2 ± 4.9 years).

FIELD STRENGTH/SEQUENCE: 3.0 T, 3D T1-weighted magnetization-prepared rapid acquisition gradient echo, resting-state fMRI using gradient-echo echo-planar imaging, and multi-shell diffusion MRI using spin-echo-planar sequence.

ASSESSMENT: (1) Blood oxygen level dependent cerebral spinal fluid (BOLD-CSF) coupling measuring temporal coordination between neurovascular and CSF oscillations, (2) diffusion tensor image analysis along the perivascular space (DTI-ALPS) quantifying directional diffusivity in periventricular white matter, (3) choroid plexus (ChP) volume reflecting morphology of primary CSF-producing structures, and (4) nucleus basalis of Meynert (NBM) volume evaluating cholinergic system integrity potentially relevant to vascular regulation; (5) all participants completed self-reported sleep measures, including the Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), and Fatigue Severity Scale (FSS), and also underwent cognitive function testing.

STATISTICAL TESTS: Analysis of covariance evaluated between-group differences controlling for demographic and clinical covariates. Relationships with cognitive and sleep scores were assessed using partial correlations, stratified by group only when significant interaction effects were detected. Multiple comparisons were false discovery rate corrected (p < 0.05). Classification model performance was evaluated using the area under the receiver operating characteristic curve (AUC). Model comparisons were performed using DeLong's tests (ΔAUC) and stepwise likelihood ratio tests (LRT) to assess classification gain and the independent incremental contribution of each biomarker; all tests were two-sided with α = 0.05.

RESULTS: Compared to controls, insomnia patients showed significantly reduced BOLD-CSF coupling (-0.18 ± 0.20 vs. -0.32 ± 0.17), indicating altered temporal coordination between neurovascular and CSF dynamics. ChP volume was significantly enlarged in the insomnia group (1.72% ± 0.42% vs. 1.55% ± 0.39% of total intracranial volume), potentially reflecting compensatory CSF production upregulation, inflammatory changes, or vascular remodeling. NBM volume was significantly reduced in insomnia patients (201.75 ± 17.66 vs. 217.47 ± 21.04 mm3), suggesting cholinergic system alterations. In contrast, the DTI-ALPS index did not differ between groups (p = 0.85). BOLD-CSF coupling positively correlated with PSQI (r = 0.34), ISI (r = 0.41), and FSS (r = 0.40); ChP volume positively correlated with ISI (r = 0.32) and FSS (r = 0.35) (all FDR-corrected). A dataset consists of 74 participants (46 HC and 28 ID) were included, the four-marker classification model achieved moderate performance (AUC = 0.785, accuracy = 71.9%).

DATA CONCLUSION: Multiple indirect neuroimaging markers potentially related to neurofluid dynamics were altered in middle-aged and older women with chronic insomnia, except for DTI-ALPS. These findings include altered neurovascular-CSF coordination, ChP enlargement, and cholinergic system volume reduction.

EVIDENCE LEVEL: 2.

TECHNICAL EFFICACY: Stage 2.

PMID:42476764 | DOI:10.1002/jmri.70417

Curr Biol. 2026 Jul 20:S0960-9822(26)00806-7. doi: 10.1016/j.cub.2026.06.060. Online ahead of print.

ABSTRACT

Everyday interactions require not only knowing what objects are and where they are located in egocentric (body-centered) space but also understanding their internal spatial structure defined in allocentric (object-centered) space. Disruptions in allocentric computations have profound clinical consequences, yet the neural basis of such spatial coding mechanisms remains unclear. Classical models of visual cognition distinguish a ventral stream for object recognition, a dorsal stream for spatial processing, and a fronto-parietal network for attentional control. Here, we test the hypothesis that a recently identified region in the posterior infero-temporal cortex (PIT) may implement a key processing stage by integrating object and spatial information to support allocentric spatial representation. Using a novel behavioral paradigm combined with task-based and resting-state fMRI, we show that PIT is selectively engaged in allocentric processing yet exhibits a functional profile distinct from nearby visual category-selective cortex. Multivariate and representational similarity analyses reveal distinct allocentric and egocentric representations in PIT and posterior parietal cortex, with PIT supporting category-general allocentric representations across stimulus types. Connectivity analyses further show that PIT is stably coupled with the posterior parietal cortex across spatial demands but exhibits flexible, task-dependent interactions with ventral visual areas during allocentric (not egocentric) processing. These findings position PIT as a critical node at the interface of object and spatial systems, linking ventral stream representations with dorsal spatial processing in human visuospatial cognition.

PMID:42476138 | DOI:10.1016/j.cub.2026.06.060

Neural Plast. 2026;2026(1):e7203336. doi: 10.1155/np/7203336.

ABSTRACT

BACKGROUND: Irritable bowel syndrome (IBS) is a common disorder of brain-gut interaction frequently co-occurs with depressive symptoms (dIBS). The amygdala is a critical hub for emotion-visceral integration and exhibits functional heterogeneity across its subregions. However, subregion-specific network alterations in dIBS remain unclear.

METHODS: Forty-nine IBS patients and 36 demographically matched healthy controls (HCs) completed resting-state functional magnetic resonance imaging (rs-fMRI) and clinical assessments. Patients were stratified into dIBS (n = 28) and nondepressive IBS (ndIBS, n = 21) groups. Bilateral lateral amygdala (lAmyg) and medial amygdala (mAmyg) were selected as seeds for graph-theoretical nodal metrics and seed-based functional connectivity (FC) analyses. One-way analysis of covariance with post hoc comparisons was performed to assess intergroup differences. Correlation, mediation, and receiver operating characteristic (ROC) analyses were further conducted.

RESULTS: Compared with ndIBS patients, dIBS patients exhibited increased degree centrality (DC) and nodal efficiency (Ne) in the left lAmyg, which were positively associated with depressive symptom severity. In addition, dIBS patients showed widespread hypoconnectivity between amygdala subregions and prefrontal and sensorimotor regions, including the medial superior frontal gyrus, postcentral gyrus, and thalamus. These FC alterations were correlated with clinical symptoms severity. Notably, the mAmyg-related FC in superior frontal gyrus and thalamus showed mediation effects linking gastrointestinal symptoms and depressive symptoms and exhibited high accuracy in distinguishing dIBS from ndIBS (area under the curves, AUCs > 0.8).

CONCLUSION: Aberrant nodal properties and disrupted connectivity of amygdala subregions may reflect altered emotion-visceral integration in dIBS. These findings provide neuroimaging evidence for brain-gut interaction abnormalities in dIBS and suggest that amygdala subregional networks may serve as potential markers for symptom stratification and targeted interventions.

PMID:42473891 | DOI:10.1155/np/7203336

Alzheimers Dement (N Y). 2026 Jul 18;12(3):e70266. doi: 10.1002/trc2.70266. eCollection 2026 Jul-Sep.

ABSTRACT

INTRODUCTION: Dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) are neurocognitive disorders characterized by distinct but often overlapping pathological processes. These include α-synuclein, amyloid-beta 42 (Aβ42), and tau protein aggregation. While cerebrospinal fluid (CSF) biomarkers provide in vivo insight into these pathologies, their relationship with large-scale brain network dysfunction remains poorly understood. This study aimed to investigate the associations between CSF biomarker concentrations and resting-state functional connectivity in patients with DLB, AD, and mixed AD/DLB.

METHODS: Sixty-nine DLB patients, 17 AD patients, and 24 patients with mixed AD/DLB underwent clinical and neuropsychological evaluations, lumbar puncture for CSF biomarker analysis (total α-synuclein, Aβ42, pTau181, and tTau), and resting-state functional MRI. Patients were stratified by disease stage for subgroup analyses. Besides CSF total α-synuclein levels, α-synuclein seeding activity was assessed using real-time quaking-induced conversion (RT-QuIC) assays. ROI-to-ROI analyses were conducted using the CONN toolbox to explore associations between CSF biomarker levels and functional connectivity within and between major brain networks.

RESULTS: In DLB patients, lower CSF α-synuclein levels correlated with increased connectivity within the default mode network (DMN) (p FDR < 0.05). In dementia-stage DLB (d-DLB), lower Aβ42 levels correlated with reduced connectivity within the salience network (SN) (p FDR < 0.05). In AD, higher tTau levels correlated with decreased connectivity between the DMN and the SN (p FDR < 0.05). No significant associations were observed for CSF pTau181 or any RT-QuIC metric in any group, and the mixed AD/DLB group showed no biomarker-connectivity correlations at all.

DISCUSSION: We identified distinct patterns of DMN and SN connectivity changes associated with CSF α-synuclein and Aβ42 levels, respectively. These findings reflect key functional disruptions that may contribute to core clinical symptoms. They underscore the value of combining CSF biomarkers with functional MRI to elucidate DLB pathophysiology.

PMID:42473545 | PMC:PMC13380670 | DOI:10.1002/trc2.70266

Neuroscience. 2026 Jul 18:S0306-4522(26)00481-1. doi: 10.1016/j.neuroscience.2026.07.037. Online ahead of print.

ABSTRACT

Glioma surgery, while critical for tumor control, often disrupts brain network integrity, leading to postoperative neurological deficits. This study employed resting-state functional MRI (rs-fMRI) and diffusion tensor imaging (DTI) to assess functional and structural connectivity changes in 28 patients with supratentorial gliomas before and after surgical resection. We observed widespread reductions in functional connectivity (FC), regional homogeneity (ReHo), and amplitude of low-frequency fluctuations (ALFF) after surgery, primarily involving sensorimotor and language networks. DTI revealed significant decreases in white matter integrity (fractional anisotropy, FA) in tumor-adjacent tracts, with reduced structural connectivity between tumor-affected and contralateral regions. Notably, we identified compensatory FC enhancements in language- and cognitive-related regions, which positively correlated with postoperative Karnofsky Performance Status (KPS) scores. Inter group comparisons revealed that high-grade gliomas caused more severe network damage than low-grade tumors, and baseline network impairments were already evident preoperatively. These findings advance our understanding of glioma surgery-induced brain network disruption and highlight the role of neural plasticity in functional preservation. They also have important clinical implications for surgical planning, rehabilitation, and prognosis assessment.

PMID:42471179 | DOI:10.1016/j.neuroscience.2026.07.037

Psychiatry Res Neuroimaging. 2026 Jul 13;362:112285. doi: 10.1016/j.pscychresns.2026.112285. Online ahead of print.

ABSTRACT

BACKGROUND: Attention-Deficit/Hyperactivity Disorder (ADHD) often persists into adulthood and causes significant functional impairments. Resting-state functional MRI (rs-fMRI) offers valuable insights into intrinsic brain connectivity; however, connectivity (FC) alterations specific to adults with ADHD remain unclear. This systematic review aimed to synthesize rs-fMRI studies comparing adults with ADHD and healthy controls, identify consistent FC patterns, and assess methodological quality.

METHODS: Following PRISMA guidelines, PubMed, Embase, and Scopus were searched for rs-fMRI studies involving adults diagnosed with ADHD per DSM-IV/DSM-5 criteria. Eligible studies examined FC differences between adults with ADHD and healthy controls. Data extraction included study characteristics, imaging parameters, and FC methodologies. Study quality was assessed using the Newcastle-Ottawa Scale.

RESULTS: Eight studies (n = 529; 264 ADHD, 265 controls) met inclusion criteria. FC analyses employed Seed-Based Analysis (SBA), Independent Component Analysis (ICA), and graph theory methods. Despite methodological heterogeneity, consistent FC alterations were observed: increased FC in the default mode (DMN), visual (VN), and central executive (CEN) networks, and decreased FC in the ventral attention (VAN) and somatomotor (SMN) networks. Alterations were predominantly left-hemispheric.

CONCLUSION: Adults with ADHD exhibit distinct rs-FC disruptions, mainly involving DMN, VAN, CEN, and SMN. Standardized analytic approaches and subtype-specific analyses are needed for improved clinical relevance.

PMID:42470906 | DOI:10.1016/j.pscychresns.2026.112285

Psychiatry Res Neuroimaging. 2026 Jul 13;362:112286. doi: 10.1016/j.pscychresns.2026.112286. Online ahead of print.

ABSTRACT

Re-experiencing symptoms in posttraumatic stress disorder (PTSD) often involve vivid visual imagery, yet occipital resting-state functional connectivity (rsFC) remains understudied, particularly in women exposed to interpersonal violence. Clarifying these neural mechanisms may advance understanding of sensory and contextual processing disturbances that contribute to intrusive memories. Notably, occipital rsFC has not been evaluated in relation to re-experiencing using the Posttraumatic Stress Diagnostic Scale (PDS), a validated self-report measure that supports fine-grained symptom-level analyses. Sixty-three participants meeting DSM-IV criteria for PTSD completed clinical assessments and resting-state fMRI. PTSD severity was measured with the Clinician-Administered PTSD Scale (CAPS-IV) and the PDS. We conducted seed-based voxelwise rsFC analyses using bilateral visual cortex seeds (V1, V2, V3) and tested associations with re-experiencing symptoms. Greater V3-left cerebellar connectivity was positively associated with PDS re-experiencing subscores (t(61)=5.32, pFWE < .05). Item-level analyses suggested this effect was driven by endorsements of nightmares (t(61)=4.18, pFWE < .05) and physiological reactivity (t(61)=4.77, pFWE < .05). Increased V3-cerebellar connectivity may reflect heightened coupling between visual and sensorimotor/regulatory systems supporting PTSD-related re-experiencing. These interactions may relate more specifically to re experiencing symptoms than to global PTSD severity, suggesting a potential symptom targeted neural marker in trauma-exposed females.

PMID:42470905 | DOI:10.1016/j.pscychresns.2026.112286

Epilepsia. 2026 Jul 18. doi: 10.1002/epi.70390. Online ahead of print.

ABSTRACT

OBJECTIVE: Focal-to-bilateral tonic-clonic seizures (FBTCS) in temporal lobe epilepsy (TLE) involve thalamocortical networks, yet the functional integrity and role of specific thalamic subregions in seizure generalization remain unclear. In this cross-sectional study, we investigated whether thalamic subregion functional connectivity patterns distinguish TLE patients with and without FBTCS and tested for laterality-specific differences between right TLE (RTLE) and left TLE (LTLE.

METHODS: We analyzed resting-state functional magnetic resonance imaging (fMRI) data from 166 patients with TLE (71 RTLE, 95 LTLE; 120 FBTCS+, and 46 FBTCS-) and 119 healthy participants. Thalamic parcellation identified eight bilateral regions of interest. We computed graph theory measures of regional segregation, connection density, and integration, as well as intrinsic connectivity contrast to characterize thalamic subregion network topology. Sensitivity analyses controlled for pathology subgroup and anti-seizure medication burden.

RESULTS: TLE patients exhibited reduced cross-hemispheric functional connectivity between bilateral ventral anterior (VA) thalamic regions compared with controls. Compared with patients with LTLE, patients with RTLE showed more pronounced ipsilateral VA hypoconnectivity and more disrupted nodal topology (clustering coefficient, degree centrality, local efficiency) in the ipsilateral VAia. Within RTLE, FBTCS+ patients (vs FBTCS-) showed ipsilateral connectivity reductions and cross-hemispheric VA reductions that increased with illness duration. Within LTLE, FBTCS+ patients (vs FBTCS-) showed bilateral connectivity increases in the ventral posterior medial subregion. Sensitivity analyses confirmed that these findings were robust across pathology subtypes and independent of anti-seizure medication burden.

SIGNIFICANCE: These findings provide initial evidence that intra-thalamic functional connectivity, particularly within the bilateral VA complex, is associated with FBTCS history in TLE and varies with lateralization of seizureonset. The observations may represent a biomarker for FBTCS, but whether this is causal or an epiphenomenon of this seizure type remains to be determined.

PMID:42470665 | DOI:10.1002/epi.70390

J Affect Disord. 2026 Jul 17:122283. doi: 10.1016/j.jad.2026.122283. Online ahead of print.

ABSTRACT

Previous studies suggest that transcranial magnetic stimulation exerts antidepressant effects and is associated with alterations in functional connectivity (FC), but the neural correlates remain unclear. This exploratory sham-controlled trial investigated the effect of continuous theta-burst stimulation (cTBS) over the right dorsolateral prefrontal cortex (DLPFC) on FC in major depressive disorder (MDD). Seventy MDD patients were randomized to receive two-week treatment of personalized cTBS or sham stimulation. Resting-state fMRI was performed at baseline and post-treatment. Ultimately, 31 patients in the active cTBS group and 28 patients in the sham group passed imaging quality control and were included in the final analysis. To identify the FC that may have been influenced by cTBS treatment, two complementary FC analyses were conducted: (1) voxel-wise degree centrality (DC) followed by seed-based FC, and (2) an individual FC analysis based on the stimulation targets. Furthermore, correlations between FC changes and clinical symptoms improvement were examined. Both groups exhibited reductions of depression scores, with greater improvement in the active group. Compared to the sham group, active cTBS showed increased DC in the precuneus and elevated FC between the precuneus (within the para-cingulate network) and the right inferior parietal lobule (IPL) and DLPFC. Further stimulation target-based analysis revealed increased FC between stimulation targets and both the precuneus and visual regions following treatment. Our findings reveal neural changes associated with cTBS over the right DLPFC in MDD, notably involving the precuneus and its connectivity with the right IPL/DLPFC, suggesting alterations within the central executive network. TRIAL REGISTRATION: chictr.org.cn; ChiCTR2300068273.

PMID:42468853 | DOI:10.1016/j.jad.2026.122283

Neuroradiology. 2026 Jul 17. doi: 10.1007/s00234-026-04105-6. Online ahead of print.

ABSTRACT

PURPOSE: This study investigated longitudinal alterations in static functional connectivity (sFC) and dynamic functional connectivity (dFC) in patients with basal ganglia ischaemic stroke (BGIS) to elucidate their distinct roles in neurological recovery.

METHODS: Resting-state fMRI data from 29 BGIS patients and 34 healthy controls (HCs) were analysed at three time points (Days 1-7, 30, and 90). Using the right precentral gyrus as a seed, sFC and sliding window dFC analyses were performed. Whole-brain Gaussian random field correction was applied, and connectivity metrics were correlated with clinical motor and cognitive-emotional scores.

RESULTS: A total of 29 patients with BGIS and 34 HCs were included in this study. Compared with HCs, patients with BGIS had abnormal sFC and dFC in various other brain regions. Specifically, sFC was greater in the right angular and left precuneus regions, whereas dFC was reduced across the brain in BGIS patients (all p < 0.05). Longitudinal analyses revealed significant temporal variations in both sFC and dFC, primarily within the right middle temporal gyrus and left lingual gyrus (all p < 0.05). Preliminary correlations suggest that sFC changes may be related to motor recovery, whereas dFC changes are linked to cognitive-emotional outcomes.

CONCLUSION: This exploratory study provides preliminary insights into stage-specific alterations in sFC and dFC during BGIS recovery. Motor recovery might be related to alterations in sFC, whereas cognitive-emotional outcomes may correlate with temporal variations in dFC. These dissociations highlight the pivotal role of neural network flexibility and may provide preliminary clues to unpack the complex manifestations of neurological recovery after BGIS.

PMID:42467234 | DOI:10.1007/s00234-026-04105-6

Neuromodulation. 2026 Jun 23:S1094-7159(26)00625-2. doi: 10.1016/j.neurom.2026.06.465. Online ahead of print.

ABSTRACT

BACKGROUND: Concurrent transcranial magnetic stimulation and functional magnetic resonance imaging (TMS-fMRI) provides a mechanism for assessing the acute effects of transcranial magnetic stimulation (TMS) on functional connectivity (FC), allowing a unique perspective of how TMS induces antidepressant effects over the course of treatment. The aim of this secondary analysis of clinical trial data was to interrogate the relevance of the triple network theory in low-frequency TMS to the right dorsolateral prefrontal cortex (DLPFC) (low-frequency repetitive TMS [LFR]) by assessing perturbations in salience (SN), control (CN), and default mode (DMN) networks during TMS-fMRI.

MATERIALS AND METHODS: A total of 38 subjects with treatment-resistant depression underwent one session of concurrent TMS-fMRI at 1 Hz to the right DLPFC (LFR), with resting-state scans acquired immediately before and after. Patients subsequently underwent a four-week treatment course using the same protocol. Whole-brain FC was computed, as well as within- and between- network FC for the SN, CN, and DMN for each scan. FC modulation scores were computed to capture changes between resting-state and TMS-fMRI and were used to test for relationships between acute changes in FC during a single repetitive TMS treatment and clinical outcomes after a course of treatment.

RESULTS: Whole-brain FC decreased during the TMS-fMRI scan, as did within- and between-network FC for the SN, CN, and DMN. Resting-state scans acquired immediately before and after TMS-fMRI showed no differences in FC. After the four-week treatment course, eight subjects were classified as remitters (21%), eight subjects were responders but fell short of remission (21%), with the remaining 22 subjects (58%) showing nonresponse. FC modulation scores for the whole-brain were significantly associated with decreased depression scores at the end of treatment. Between-network FC modulation was initially correlated with clinical improvement, but these correlations did not persist when controlling for whole-brain FC modulation. When tested using predictive modeling, both whole-brain and network-level data significantly predicted treatment outcomes, with modulation involving SN predicting outcomes as effectively as whole-brain models.

CONCLUSIONS: LFR acutely disrupts FC at a global, whole-brain level that encompasses the triple networks. Modulation of the SN-CN and SN-DMN connectivity hold predictive value for clinical improvement comparable with that of global, whole-brain connectivity. This widespread global disruption may be an important mechanism through which TMS exerts antidepressant effects. Limitations include the use of atlas-based network definitions, small sample, and generalizability limited to LFR protocols only.

PMID:42467020 | DOI:10.1016/j.neurom.2026.06.465

Res Sq [Preprint]. 2026 Jul 10:rs.3.rs-9889932. doi: 10.21203/rs.3.rs-9889932/v1.

ABSTRACT

Opioid use disorder (OUD) is associated with persistent catecholaminergic dysfunction, but its impact on large-scale cortical organization remains unclear. We used pharmacological resting-state fMRI and functional gradient mapping to test whether OUD alters catecholaminergic modulation of cortical hierarchy. Fifty-three individuals with OUD and 40 healthy controls underwent resting-state fMRI after placebo and methylphenidate. Under placebo, principal and secondary cortical gradients showed canonical organization with no group differences. In controls, methylphenidate compressed the dynamic range of the principal gradient while preserving its spatial topology, indicating state-dependent modulation of the unimodal-transmodal hierarchy. This response was attenuated in OUD and further reduced among participants receiving methadone or buprenorphine. Methylphenidate improved visual attention in controls but not OUD, and gradient modulation predicted attentional benefit only in controls. Gradient features also predicted years of opioid use. These findings identify blunted catecholaminergic modulation of cortical hierarchy as a clinically relevant systems-level feature of OUD.

PMID:42466414 | PMC:PMC13370618 | DOI:10.21203/rs.3.rs-9889932/v1

Front Psychiatry. 2026 Jul 2;17:1841698. doi: 10.3389/fpsyt.2026.1841698. eCollection 2026.

ABSTRACT

PURPOSE: Autism is a common neurodevelopmental condition (NDC) that is characterized by restricted, repetitive behaviors and social communication differences that can impact the daily functioning of individuals. The clinical diagnosis of autism can be challenging, mainly due to its behavioral variability and frequent co-occurrence with other NDCs. This study investigates the ability of machine learning-based classification models trained using multimodal neuroimaging data combined with feature-importance analyses to identify development-specific brain characteristics associated with autism.

APPROACH: A total of 144 participants aged 5 to 18 years with structural MRI (sMRI), diffusion MRI (dMRI), and resting-state functional MRI (rs-fMRI) data available were obtained from the Autism Brain Imaging Data Exchange (ABIDE) database. Radiomic features were extracted from each MRI data modality and used to train support vector machine (SVM) classifiers to identify neuroimaging patterns associated with autism. Single MRI modality classifiers, as well as one combining all three modalities, were trained for comparison purposes. To investigate age-specific effects, the same approach was followed for three age sub-groups: younger children (5-11 years), adolescents (12-18 years), and the entire 5-18 years age cohort. Model performance was evaluated using leave-one-out cross-validation across 30 diagnosis-balanced data splits. Feature-importance analyses were conducted to identify the most important neuroimaging features for classification.

RESULTS: The classification accuracies of the unimodal models ranged from 68.3% to 75.3% for sMRI, from 69.3% to 77.6% for dMRI, and from 66.3% to 69.9% for rs-fMRI data across age groups. Among all single imaging modalities and age groups, dMRI showed the highest performance with a 77.6% accuracy in younger children (5-11 years). The multimodal approach improved classification performance when compared to the unimodal models in all age groups, achieving accuracies of 78.9%, 76.7%, and 70.5% in the younger, adolescent, and entire age cohorts, respectively. Our findings indicate that multimodal classifiers integrating complementary structural, microstructural, and functional imaging features result in a more comprehensive representation of brain features that strengthens model performance. The most informative brain regions for classification differed between children and adolescents while several diffusion-derived features significantly correlated with social responsiveness scores, emphasizing the clinical importance of studying white and gray matter microstructure in autism.

CONCLUSIONS: This study demonstrates the potential of multimodal neuroimaging-based machine learning models to identify development-specific biomarkers associated with autism. The results highlight the value of integrating age-stratified analyses of multimodal neuroimaging to better capture autism-associated developmental brain characteristics. The framework adopted in this study could be extended to explore other NDCs in the future.

PMID:42466188 | PMC:PMC13372782 | DOI:10.3389/fpsyt.2026.1841698

Front Neurosci. 2026 Jul 2;20:1859287. doi: 10.3389/fnins.2026.1859287. eCollection 2026.

ABSTRACT

INTRODUCTION: Inhibitory and excitatory neurotransmitter levels are linked to fast neuronal oscillations and infra-slow hemodynamic fluctuations, suggesting a shared excitation-inhibition (E/I) regulatory framework across measures. However, these relationships may differ in early development, when both excitatory and inhibitory cortical systems are undergoing substantial functional and structural maturation. Consequently, we hypothesize different functional coupling between neurochemical, electrophysiological, and hemodynamic proxies of E/I signaling in healthy full-term neonates compared to what has been observed in adults.

METHODS: Twenty-five healthy full-term neonates (mean postmenstrual age at study = 40.1 ± 1.4 weeks) underwent multimodal MRI and electroencephalography (EEG) recordings during natural resting-state to provide proxy measures of neural excitation and inhibition. These included frontal and occipital MRS measures of γ-aminobutyric acid (GABA+) and Glx (glutamate + glutamine) levels, and their ratio; EEG source-reconstructed power spectra decomposed into periodic beta (13-30 Hz) and gamma (30-45 Hz) features (center frequency and peak amplitude), relative to total band power and an aperiodic exponent; and infra-slow fMRI BOLD fluctuations (0.01-0.08 Hz) using amplitude of low-frequency fluctuations (mean and fractional ALFF). Crossmodal relationships were assessed using partial correlations controlling for age.

RESULTS: Occipital GABA+ was negatively correlated with beta relative power (r = -0.64, p = 0.01) and fractional ALFF (r = -0.55, p = 0.048), while mean ALFF was negatively correlated with gamma center frequency (r = -0.99, p = 0.02). These relationships were not observed in the frontal cortex. Instead, frontal Glx positively correlated with beta peak amplitude (r = 0.87, p < 0.01) and negatively correlated with beta (r = -0.78, p = 0.02) and gamma (r = -0.79, p = 0.02) relative power, potentially reflecting the existence of regionally distinct maturational trajectories.

DISCUSSION: Together, these preliminary findings suggest that commonly used neurochemical, oscillatory, and hemodynamic proxy measures of cortical excitatory and inhibitory processes may show only modest correspondence at birth, consistent with ongoing and hierarchal cortical development, leading to complex and asynchronous relationships between these measures.

PMID:42465728 | PMC:PMC13372886 | DOI:10.3389/fnins.2026.1859287

Front Aging Neurosci. 2026 Jul 2;18:1856371. doi: 10.3389/fnagi.2026.1856371. eCollection 2026.

ABSTRACT

Parkinson's disease (PD) is a common neurodegenerative disorder, and accurate diagnosis is crucial for timely intervention. Dynamic functional connectivity (DFC) analysis of resting-state functional magnetic resonance imaging (rs-fMRI) can capture the time-varying characteristics of brain networks, offering a new perspective for PD identification. However, traditional clustering-based DFC methods suffer from issues such as the loss of continuous temporal information. Existing machine learning approaches struggle to effectively integrate the temporal dynamics of DFC with inter-subject differences in functional connectivity patterns and are further limited by the poor generalizability and opaque decision-making processes inherent to the small sample sizes typical of neuroimaging data. Here, we propose a DFC analysis framework based on a multi-view graph convolutional network (GCN). This method independently constructs inter-subject similarity networks for each sliding time window, forming multi-view graph-structured inputs. It employs a shared-weight GCN to extract node embeddings from each window, which are then fused for classification. Furthermore, the gradient-weighted class activation mapping (Grad-CAM) algorithm is incorporated to provide visual interpretability of the model's decisions. Our method outperforms traditional static functional connectivity and clustering-based approaches in classification accuracy. Concurrently, it achieves superior classification performance compared to other benchmark models. Grad-CAM-based interpretability analysis further reveals that the frontal and parietal lobes contribute most significantly to the classification decisions, providing computational evidence for understanding the brain network mechanisms underlying PD.

PMID:42465696 | PMC:PMC13372981 | DOI:10.3389/fnagi.2026.1856371