Most recent paper

Neuroreport. 2026 Jul 22. doi: 10.1097/WNR.0000000000002294. Online ahead of print.

ABSTRACT

OBJECTIVE: Resting-state functional MRI (rs-fMRI) was used to measure the differences in voxel-based indicators between patients with acute mild traumatic brain injury (mTBI) and healthy volunteers, and the correlation between the differences in these multimodal parameters and the cognitive performance of patients was analyzed.

METHODS: We collected rs-fMRI data from 35 mTBI patients within 7 days postinjury and 35 healthy volunteers. We employed amplitude of low-frequency fluctuations (ALFF), fractional ALFF, regional homogeneity, functional connectivity, and degree centrality to analyze the data and investigate the dysfunctional brain regions in acute mTBI. Neuropsychological assessments, including the Rivermead Post-Concussion Symptoms Questionnaire, Montreal Cognitive Assessment, Loewenstein Occupational Therapy Cognitive Assessment, and Trail Making Tests A and B, were administered to the 35 mTBI patients. Correlation analysis was performed between the graph theory parameters and the neuropsychological outcomes.

RESULTS: The ALFF in the insula of the mTBI group was significantly lower than that in the control group, and the degree centrality in the angular gyrus was also significantly reduced. Increased functional connectivity was observed between the cuneus and the middle occipital gyrus, whereas functional connectivity in the dorsolateral superior frontal gyrus was significantly decreased.

CONCLUSION: Correlation analysis between neuropsychological assessment scores and neural network function in the mTBI group suggested that multimodal rs-fMRI analysis may provide insights into dysfunctional brain regions in mTBI, though observed associations did not survive correction for multiple comparisons.

PMID:42485140 | DOI:10.1097/WNR.0000000000002294

Brain Commun. 2026 Jul 21;8(4):fcag205. doi: 10.1093/braincomms/fcag205. eCollection 2026.

ABSTRACT

The exact mechanisms underlying myoclonus-dystonia (M-D) remain unknown, although the basal ganglia-thalamo-cortical (BGTC) and cerebello-thalamo-cortical (CTC) networks are hypothesized to be involved. We aimed to investigate the static and dynamic features of networks related to motor control during rest in M-D patients using functional magnetic resonance imaging (fMRI). Resting-state fMRI data from 19 M-D patients and 19 healthy volunteers were analysed. Symptom severity was measured using the Clinical Global Impression-Severity Scale, anxiety and depression with the Hospital Anxiety and Depression Scale and cognitive impairment with the Montreal Cognitive Assessment. Independent component analysis was used to identify brain components corresponding to the BGTC and CTC networks. Static within-network (i.e. spatial contribution of voxels to the average network signal time course) and between-network (i.e. correlation between network time courses) functional connectivity were examined. We additionally performed a dynamic functional connectivity analysis focused on recurrent connectivity patterns (i.e. brain states) using k-means clustering of windowed functional connectivity correlation matrices, whereby we identified three prototypical dynamic connectivity states in the BGTC and CTC circuits. The following brain state summary measures were computed: fraction of time spent in a state, dwell time, number of state transitions and number of state visits. Static analysis revealed that patients with M-D showed increased functional connectivity (FC) of the left supramarginal gyrus within a cognitive control network (corresponding to the cortical part of BGTC and CTC circuits), suggesting stronger integration of this area in this network (within-network P fdr < 0.05). There were no significant group differences in between-network functional connectivity. Dynamic analysis revealed three dynamic connectivity states in BGTC and CTC circuits. Patients with M-D engaged less in a state characterized by high segregation of sensorimotor from basal ganglia and cerebellar domains, with high connectivity between networks within these domains. This finding may reflect reduced basal ganglia and cerebellar contributions during motor preparation in M-D. We found no relationships between fMRI findings and motor symptom severity in M-D patients. Our findings provide further evidence for disrupted brain network dynamics in BGTC and CTC circuits in M-D patients, supporting the hypothesis of compromised basal ganglia and cerebellar functioning. Particularly, the association of cerebellar and anterior parietal alterations is proposed to reflect impaired sensory prediction in feed-forward motor planning, potentially underlying irregular 'non-prepared movements'.

PMID:42482845 | PMC:PMC13386016 | DOI:10.1093/braincomms/fcag205

J Neurotrauma. 2026 Jul 21:8977151261466879. doi: 10.1177/08977151261466879. Online ahead of print.

ABSTRACT

Traumatic brain injury disrupts large-scale brain networks. Dynamic functional connectivity captures time-varying network interactions from functional MRI (fMRI) and provides insights into the brain's dynamic patterns of integration and segregation. Here, we investigate dynamic functional connectivity in subacute moderate-severe traumatic brain injury patients (10 days to 6 weeks post-injury) and explore the relationships with blood and imaging biomarkers of injury and propofol sedation. We hypothesized that traumatic brain injury patients would show less complex brain state dynamics that would be associated with greater injury severity measured by white matter integrity and blood biomarkers. Sixty-five subacute traumatic brain injury patients and 48 healthy controls underwent structural and resting-state fMRI. Patients were followed-up at 6 and 12 months post-injury. Plasma concentrations of neurofilament-light chain, microtubule-associated protein, glial fibrillary acidic protein, ubiquitin carboxyl-terminal hydrolase L1, and serum S100 calcium-binding protein B were measured. Fractional anisotropy (FA), a measure of white matter integrity, was derived from diffusion-weighted imaging for a set of white matter tracts. Dynamic functional connectivity analysis was performed using a sliding-window approach. Correlations between time courses of 19 regions of interest representing the default mode network, bilateral frontaloparietal networks, and the salience network were calculated, and k-means clustering was applied to these connectivity matrices. Temporal characteristics of the resulting brain states, including fraction time, dwell time, number of transitions, and entropy of state transitions, were calculated. Four distinct brain states were identified. Brief periods of anticorrelation between key large-scale networks that support cognitive control were a dominant feature. Traumatic brain injury resulted in reduced temporal flexibility, less anticorrelated activity, and fewer transitions. Reduced entropy of state transitions was significantly associated with elevated blood-based biomarkers and reduced white matter integrity. Propofol sedation markedly reduced entropy. Dominance analysis identified glial fibrillary acidic protein, an astroglial plasma marker, as the strongest predictor of entropy. Preserved entropy during the subacute period was a significant predictor of 12-month functional outcomes. Entropy normalization at 6 months was associated with changes in glial fibrillary acidic protein, ubiquitin carboxyl-terminal hydrolase L1, and microtubule-associated protein over the same time period. We show that dynamic functional connectivity is disrupted following moderate-to-severe traumatic brain injury and these effects are exacerbated by sedation. The observed reductions in brain state entropy indicate a loss of network segregation and a shift toward less complex and more predictable brain activity, with important implications for prognosis.

PMID:42482521 | DOI:10.1177/08977151261466879

CNS Neurosci Ther. 2026 Jul;32(7):e71023. doi: 10.1002/cns.71023.

ABSTRACT

AIM: Non-suicidal self-injury (NSSI) is a prevalent behavior among adolescents with major depressive disorder (MDD), yet the precise neural mechanisms remain unclear. This study aimed to investigate the temporal dynamics of brain connectivity associated with NSSI in adolescents with MDD using dynamic functional connectivity (dFC) analysis.

METHODS: Resting-state fMRI data from 204 adolescents (154 with NSSI, 50 without) were analyzed. dFC variability within the fronto-limbic network was assessed using a seed-based dynamic conditional correlation approach. Group differences in dFC variability were examined, and a machine-learning model was used to predict NSSI based on dFC features. Mediation analysis explored the dFC's role in the relationship between depressive symptoms and NSSI.

RESULTS: Adolescents with NSSI exhibited reduced dFC variability, which mediated the relationship between depressive severity and NSSI behavior (a*b = 0.144; p = 0.001). Key connections-insula, anterior cingulate cortex, orbitofrontal cortex, and hippocampus-were critical in distinguishing NSSI from non-NSSI groups. Machine learning models based on these connections achieved robust and stable performance with mean AUC of 0.84 and PR-AUC of 0.94 in predicting NSSI.

CONCLUSIONS: Altered dFC within the fronto-limbic network may underlie NSSI in adolescents with MDD, identifying preliminary neural features for targeted interventions and highlighting neurobiological heterogeneity associated with NSSI in adolescents with MDD.

PMID:42482506 | DOI:10.1002/cns.71023

BMC Psychiatry. 2026 Jul 21. doi: 10.1186/s12888-026-08282-9. Online ahead of print.

ABSTRACT

BACKGROUND: Schizophrenia is increasingly conceptualized as a disorder of large-scale brain network integration, yet how specific symptom phenotypes relate to reproducible resting-state functional connectivity (rsFC) signatures remains unclear.

METHODS: Resting-state fMRI data from 386 patients with schizophrenia and 212 healthy controls were analyzed to characterize large-scale functional connectivity patterns. Group-level connectivity differences were first identified (uncorrected P < 0.05, for exploratory feature selection), followed by within-patient analyses examining associations between altered connectivity and symptom subitems while adjusting for demographic factors. Multivariate models were then used to evaluate whether connectivity patterns showed systematic associations with individual symptom profiles.

RESULTS: Group comparisons revealed a dysconnectivity pattern characterized by reduced cross-network coupling between the visual network and higher-order systems, alongside selective increases in frontoparietal circuits. Within patients, connectivity alterations showed distinct association patterns across delusion, hallucination, and negative-symptom subitems. Multivariate analyses further indicated modest associations with several hallucination (e.g., H8, R²≈0.09) and delusion (e.g., D3, R²≈0.07) subitems, whereas associations with negative symptoms were minimal and showed limited generalization.

CONCLUSIONS: These findings support a hierarchical dysconnectivity profile centered on impaired perceptual-cognitive integration and suggest that specific positive-symptom phenotypes may be associated with partially consistent rsFC signatures. Overall effect sizes were modest, indicating that rsFC captures only a limited component of symptom variability.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:42481986 | DOI:10.1186/s12888-026-08282-9

BMC Med Imaging. 2026 Jul 21. doi: 10.1186/s12880-026-02602-x. Online ahead of print.

ABSTRACT

BACKGROUND: The "nose-brain axis" has been proposed as a key mechanism linking allergic rhinitis (AR) to central nervous system (CNS) dysfunction; however, alterations in functional connectivity (FC) gradients remain unexplored in AR. This cross-sectional study aimed to investigate the correlation between brain FC gradients and AR-related peripheral multi-omics profiles, as well as their clinical significance, through multimodal cross scale analysis.

METHODS: We enrolled cross-scale data from 22 AR patients and 20 healthy controls (HCs), including resting-state functional magnetic resonance imaging (rs-fMRI), serum proteomics and metabolomics, and clinical assessments. FC gradient analysis was employed to investigate hierarchical brain functional organization, with gradient values compared at global, regional, and network levels. Multivariate partial least squares (PLS) analysis was used to integrate the multidimensional associations among FC gradients, peripheral molecular signatures, and behavioral phenotypes.

RESULTS: No significant differences were observed at the global or Yeo's seven canonical networks. However, refined regional analysis revealed significant FC gradient alterations predominantly within the Default mode (DMN), Somatomotor (SMN), and Limbic (LN) networks, particularly in RH_Default_Temp_5 (p = 0.0031) and LH_Limbic_OFC_4 (p = 0.0034). These regional gradient abnormalities correlated significantly with AR severity and neuropsychiatric symptoms (p < 0.05), indicating "local fine-tuning" rather than "global collapse" in brain functional remodeling. Multi-omic profiling identified 121 differentially expressed proteins, 197 positive-ion-mode metabolites, 134 negative-ion-mode metabolites. The integrative analysis demonstrated significant associations between these peripheral molecular signatures and AR-related FC gradient alterations. These differentially expressed molecules were primarily enriched in immune response, complement activation, lipids and lipid-like molecules, and cholesterol metabolism pathways.

CONCLUSION: This study provides the first evidence of distinct FC gradient alterations in AR that are coupled to peripheral multi-omic shifts, offering novel insights into CNS mechanisms of AR and identifying potential molecular-neuroimaging biomarkers candidates for precision diagnosis and targeted therapy.

PMID:42481984 | DOI:10.1186/s12880-026-02602-x

Nat Commun. 2026 Jul 21;17(1):6678. doi: 10.1038/s41467-026-75585-6.

ABSTRACT

The network organization of the human brain dynamically reconfigures in response to changing environmental demands, an adaptive process that may be disrupted in a symptom-relevant manner across psychiatric illnesses. Here, in a transdiagnostic sample of participants with (n = 134) and without (n = 85) psychiatric diagnoses, functional connectomes from intrinsic (resting-state) and task-evoked fMRI were decomposed to identify constraints on brain network dynamics across six cognitive states. Hierarchical clustering of 110 clinical, behavioral, and cognitive measures identified participant-specific symptom profiles, revealing four core dimensions of functioning: internalizing, externalizing, cognitive, and social/reward. Brain network dynamics were flattened across cognitive states in individuals with psychiatric illness and could be used to accurately separate dimensional symptom profiles more robustly than both case/control status and primary diagnostic grouping. A key role of inhibitory cognitive control and frontoparietal network interactions was uncovered through systematic model comparison. We provide evidence that brain network dynamics can accurately differentiate the extent that psychiatrically-relevant dimensions of functioning are exhibited across health and disease.

PMID:42481484 | DOI:10.1038/s41467-026-75585-6

NPJ Parkinsons Dis. 2026 Jul 21;12(1):175. doi: 10.1038/s41531-026-01466-w.

ABSTRACT

Dopaminergic degeneration in Parkinson's disease disrupts large-scale brain networks, yet how dopamine loss and its treatment shape the brain's dynamic reconfiguration over time remains unknown. We combined resting-state fMRI with dopamine transporter scan in 136 drug-naive patients and 20 healthy controls from the PPMI cohort to determine how dopamine transporter availability relates to dynamic network reconfiguration, indexed by how brain regions switch communities over time. Patients showed reduced modular reconfiguration in the default-mode network. Dopamine transporter availability was differentially associated with reconfiguration, showing negative associations in visual and positive associations in limbic networks. Cognitive performance correlated with attention network reconfiguration, whereas motor impairment tracked dopamine loss. Longitudinal analyses in a subset with one year follow-up (n = 29) showed that network reconfiguration increased with dopaminergic decline, and medication modulated these dynamics toward the pattern seen in healthy controls. Our findings demonstrate that network reconfiguration captures dopamine-sensitive and cognition-relevant alterations in early Parkinson's disease.

PMID:42481479 | DOI:10.1038/s41531-026-01466-w

IEEE Trans Neural Syst Rehabil Eng. 2026 Jul 21;PP. doi: 10.1109/TNSRE.2026.3715571. Online ahead of print.

ABSTRACT

Accurate prognosis and treatment targeting for disorders of consciousness (DOC) remain challenging due to profound neurobiological heterogeneity. Current approaches to DOC state classification and prognostic prediction, which rely on resting-state functional magnetic resonance imaging (rs-fMRI)-based functional connectivity (FC) analysis, are limited by signal blurring from group-level averaging and cross-participant spatial variability. To overcome these limitations, we propose a novel framework integrating themulti-task learning-based sparse convex alternating structure optimization (MTL-sCASO). By jointly modeling multiple subjects within a unified optimization framework, MTL-sCASO reduces the confounding effects of spatial variability across participants, while decomposing each subject's rs-fMRI signals into individual-specific and shared FC components, thereby preserving subject-specific patterns that would otherwise be obscured. Leveraging these individualized FC alongside clinical data, we develop machine learning classifiers not only for DOC state discrimination and prediction of prognostic improvement, but also identify critical FC pairs and brain network features fundamental to both tasks. Our results demonstrate superior performance over conventional methods, achieving an accuracy of 81% in state classification and 77% in prognostic prediction. This framework advances precision diagnosis and reliable prognosis in DOC by shifting rs-fMRI analysis from group-level averaging to an individualized functional-connectivity perspective. This shift establishes an FC-based analytical paradigm that overcomes neurobiological heterogeneity and enables subject-tailored clinical decision-making.

PMID:42479508 | DOI:10.1109/TNSRE.2026.3715571

Sleep. 2026 Jul 21:zsag201. doi: 10.1093/sleep/zsag201. Online ahead of print.

ABSTRACT

STUDY OBJECTIVES: Sleep deprivation is known to impair cognitive performance, but individuals differ in their ability to maintain function under sleep loss. This study examined the neural mechanisms that support such resilience by tracking changes in resting-state functional connectivity during prolonged wakefulness.

METHODS: Six resting-state fMRI sessions obtained during approximately the first 32 hours of a 39-hour total sleep deprivation protocol were analyzed in sixteen healthy adults, enabling unusually dense longitudinal sampling of brain network dynamics during prolonged wakefulness. Functional connectivity changes were analyzed using network-based statistics as the primary approach for analyzing pairwise functional connectivity, with nodal strength used as a secondary node-level follow-up analysis in relation to a behavioral resilience index derived from psychomotor vigilance performance.

RESULTS: Two NBS-defined subnetworks, referred to here as the thalamocortical and perceptual-memory subnetworks, showed significant interactions between time awake and resilience, indicating that the trajectory of functional connectivity across prolonged wakefulness differed as a function of behavioral resilience. Participants with higher resilience showed less negative connectivity trajectories within these networks, particularly in the thalamus, globus pallidus, and visual cortex. In contrast, participants with lower resilience exhibited progressive declines in connectivity.

CONCLUSION: The ability to withstand the cognitive effects of sleep deprivation appears to be associated with less negative connectivity trajectories within thalamocortical and perceptual-memory subnetworks. These patterns may represent state-dependent neural correlates of resilience during sleep deprivation and may be relevant to future fatigue-state monitoring.

PMID:42477887 | DOI:10.1093/sleep/zsag201

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