Most recent paper

J Psychopharmacol. 2026 Jul 24:2698811261464981. doi: 10.1177/02698811261464981. Online ahead of print.

ABSTRACT

Adolescence is a critical neurodevelopmental stage marked by heightened plasticity and vulnerability to environmental influences such as nicotine. Nicotine disrupts neural function via nicotinic acetylcholine receptors, leading to long-term impairments in reward processing, cognition, and emotional regulation. While links between adolescent nicotine exposure and adult psychiatric or cognitive deficits are established, the influence of genetics and sex remains unclear. We examined the long-term effects of adolescent nicotine exposure on adult behavior and neural circuitry in male and female C57BL/6J and DBA/2J mice. Nicotine (24 mg/kg/day) or saline was administered via subcutaneous osmotic minipumps from postnatal day 37 for 12 days. Behavioral assessments 4 weeks later included the elevated plus maze for anxiety-related behavior and the open field test for locomotor activity after acute nicotine (0.81 mg/kg). Resting-state functional magnetic resonance imaging evaluated brain connectivity changes. Nicotine exposure altered both behavior and functional connectivity, depending on strain and sex. Adult female C57BL/6J mice exposed to nicotine during adolescence had lower anxiety-like behavior and higher locomotor activity, along with increased striatal-hippocampal connectivity, suggesting adaptations either directly related to nicotine exposure or compensatory adaptations. In contrast, adult female DBA/2J mice exposed to nicotine during adolescence showed widespread disruptions in cortico-striatal-thalamic, polymodal association, and hippocampal networks critical for cognitive and emotional regulation. Overall, greater effects were seen in female mice. Findings reveal that adolescent nicotine exposure drives enduring, strain- and sex-dependent changes in adult brain connectivity and behavior. Considering genetics and sex is essential for tailoring interventions for nicotine addiction and its neuropsychiatric consequences.

PMID:42495975 | DOI:10.1177/02698811261464981

Front Neurosci. 2026 Jul 9;20:1877959. doi: 10.3389/fnins.2026.1877959. eCollection 2026.

ABSTRACT

OBJECTIVE: To explore abnormal changes in brain entropy (BEN) and resting-state functional connectivity (RSFC) in peritoneal dialysis (PD) patients and their associations with cognitive impairment (CI).

METHODS: Fifty-three PD patients and 49 age-, gender-, and education-matched healthy controls (HCs) were enrolled. Resting-state functional magnetic resonance imaging (rs-fMRI) was performed to calculate BEN and RSFC. Neuropsychological assessments and clinical indicator collection were conducted. PD patients were divided into mild cognitive impairment (MCI) and non-cognitive impairment (NCI) groups using Montreal Cognitive Assessment (MoCA) scores. Correlation analyses were performed between BEN/RSFC values and neuropsychological/clinical indicators.

RESULTS: PD patients exhibited significantly poorer performance in multiple cognitive scales than HCs (all p < 0.001). Compared with HCs, PD patients had decreased BEN in the right middle occipital gyrus and left caudate nucleus, and increased BEN in the left middle temporal gyrus and right fusiform gyrus. Reduced RSFC was found between the right middle occipital gyrus and the right fusiform gyrus, right middle frontal gyrus, and right precuneus in PD patients. BEN and RSFC values were correlated with emotional scale scores, cognitive scale subscores, and clinical indicators (e.g., glycosylated hemoglobin, transferrin saturation).

CONCLUSION: Patients with end-stage kidney disease undergoing peritoneal dialysis present abnormal brain entropy and functional connectivity patterns. These alterations are associated with systemic metabolic disorders, long-term dialysis treatment, and cognitive/emotional impairment.

PMID:42495268 | PMC:PMC13391916 | DOI:10.3389/fnins.2026.1877959

J Alzheimers Dis. 2026 Jul 23:13872877261469189. doi: 10.1177/13872877261469189. Online ahead of print.

ABSTRACT

BackgroundThe mechanisms of early brain damage in cerebral amyloid angiopathy (CAA), a highly prevalent comorbid condition in Alzheimer's disease, are not completely understood. While current CAA diagnosis relies on late-stage MRI markers, impaired neurovascular coupling (NVC) has emerged as a promising early marker.ObjectiveTo investigate whether early vascular changes in CAA are associated with functional brain consequences, we examined the association between NVC and various functional connectivity metrics.MethodsWe analyzed MRI data from 93 older adults (71 ± 9 years old). A subgroup meeting Boston criteria v2.0 for possible or probable CAA (n = 46) was analyzed separately to investigate associations in confirmed CAA-related pathology. NVC (time to peak, time to baseline, and BOLD amplitude) was assessed in the occipital cortex using a visual stimulation task. Functional connectivity was assessed at multiple scales using resting-state fMRI, including within ten standard networks, between individual brain regions (edgewise analysis), and across the whole brain (graph theory).ResultsGeneral linear models, adjusted for age, sex, and clinical diagnosis, showed no significant associations between NVC and global connectivity metrics (networks and graph theory). Edgewise analyses revealed limited significant localized functional connections for each NVC measure. Findings were consistent across the total sample and the CAA subgroup.ConclusionsEarly CAA-related pathology is associated with localized rather than global functional connectivity. While large-scale connectivity remains preserved, edgewise analyses indicate subtle localized functional alterations. This suggests that functional connectivity disruption may originate as small-scale deficits that only progress into widespread, global impairment in advanced disease stages.

PMID:42489441 | DOI:10.1177/13872877261469189

Front Pharmacol. 2026 Jul 8;17:1840628. doi: 10.3389/fphar.2026.1840628. eCollection 2026.

ABSTRACT

BACKGROUND: Bronchopulmonary dysplasia (BPD) in preterm infants is frequently accompanied by neurodevelopmental impairment, yet the central neuropharmacological actions of dexamethasone (DEX), a commonly used therapy for severe or evolving BPD, remain incompletely understood. In particular, whether DEX exerts timing-dependent neuroprotection in the developing brain and the mechanisms underlying such effects are unclear.

METHODS: We investigated the neuroprotective effects of DEX in a neonatal rat double-hit model combining prenatal maternal lipopolysaccharide exposure with postnatal hyperoxia. A tapered DEX regimen was initiated on postnatal day (P)1, P3, or P8 to evaluate the therapeutic window. Lung pathology, survival, hippocampal injury, microglial reactivity, behavioral outcomes, resting-state functional magnetic resonance imaging (rs-fMRI), targeted metabolomics, and microglia-neuron coculture experiments were used to characterize pharmacological efficacy and mechanism.

RESULTS: Among the tested regimens, DEX initiated at P3 produced the most consistent protective effects, improving alveolar structure, survival, hippocampal pathology, and microglial reactivity. P3-initiated DEX also improved recognition memory, exploratory/anxiety-related behavior, spatial memory retention, and motor coordination, and was associated with partial restoration of hippocampal functional connectivity. At the molecular level, DEX partially restored hippocampal glutamate/GABA balance, reduced Synapsin I phosphorylation, and normalized VGLUT1/VGAT associated synaptic abnormalities. Mechanistically, microglia-derived IL-1β promoted neuronal ERK/Syn1 activation, whereas DEX interrupted this inflammatory signaling axis in a microglia-neuron coculture system. Targeted metabolomics and perturbation experiments further showed that DEX increased Acod1-dependent itaconate reprogramming under inflammatory priming, thereby suppressing microglial IL-1β and downstream neuronal P-Syn1/Syn1 signaling.

CONCLUSION: These findings identify a developmentally sensitive therapeutic window for DEX neuroprotection in neonatal BPD-associated brain injury and suggest that microglial Acod1-itaconate-dependent regulation of IL-1β/ERK/Syn1 signaling contributes to its central protective effects. This study expands the pharmacological interpretation of DEX beyond pulmonary benefit and supports an immunometabolic framework for understanding corticosteroid actions in the developing brain.

PMID:42488574 | PMC:PMC13388472 | DOI:10.3389/fphar.2026.1840628

Hum Brain Mapp. 2026 Aug;47(11):e70615. doi: 10.1002/hbm.70615.

ABSTRACT

Transcutaneous auricular vagus nerve stimulation (taVNS) has shown promise in enhancing cognitive and emotional functions, yet its neural mechanisms remain unclear largely because existing analytical methods cannot characterize multiscale functional connectivity nor reliably infer causal interactions between brain regions in the presence of hemodynamic delays in fMRI signals. To address these limitations, we propose a Multiscale Spatiotemporal Causal Mapping (MSTCM) algorithm that integrates community-aware multiscale functional connectivity with delay-compensated causal inference. This design enables MSTCM to characterize multiscale connectivity structure and infer directed information flow with enhanced robustness. In evaluations using simulated fMRI data, MSTCM significantly outperformed seven existing causal inference algorithms across multiple evaluation metrics, including precision, sensitivity, Matthews correlation coefficient (MCC), and area under the receiver operating characteristic curve (AUC). Applied to resting-state fMRI across four predefined large-scale cortical networks before and after taVNS, MSTCM revealed that taVNS reduced functional coupling between the left lateral sensorimotor cortex (L-LSMC) and the right intraparietal sulcus (R-IPS), increased global efficiency, enhanced causal integration within the salience network (SN), weakened causal connectivity within the dorsal attention network (DAN), and strengthened information flow from DAN to SN. These findings suggest that taVNS may enhance cognitive flexibility and emotional regulation by shifting information processing from exteroceptive toward interoceptive pathways and improving large-scale network efficiency. Consequently, this study provides not only a novel methodological approach but also new neuroimaging evidence supporting the clinical potential of taVNS.

PMID:42487269 | DOI:10.1002/hbm.70615

BMC Psychiatry. 2026 Jul 22;26(1):559. doi: 10.1186/s12888-026-08409-y.

ABSTRACT

BACKGROUND: The gut microbiota is intricately implicated in the pathogenesis of Major Depressive Disorder (MDD), with the vagus nerve serving as a key regulatory bridge. Transcutaneous Auricular Vagus Nerve Stimulation (taVNS) has emerged as a promising non-invasive therapeutic strategy for MDD by modulating the gut-brain axis, yet the precise brain-gut interaction mechanisms underlying its antidepressant effects remain poorly characterized. This study is a registered clinical trial (ChiCTR2200059591; Registered 4 May 2022; https://www.chictr.org.cn ).

OBJECTIVE/HYPOTHESIS: This study aimed to verify the clinical efficacy of taVNS for MDD and elucidate the underlying brain-gut crosstalk mechanisms, by integrating comprehensive clinical assessments, resting-state functional magnetic resonance imaging (rs-fMRI) neuroimaging data and gut metagenomic profiling.

METHODS: Ninety-five patients diagnosed with MDD were randomly allocated at a 1:1 ratio to either the active taVNS group (auricular concha stimulation) or the sham taVNS group (superior concha of mid-helix stimulation). Eighty patients (40 per group) completed the entire intervention course and were included in the final statistical analysis. All participants underwent 30-minute stimulation twice daily (4/20 Hz, 3-8 mA) for 8 consecutive weeks (5 days per week). Standardized clinical assessments were administered at baseline and post-intervention, including the 17-item Hamilton Depression Rating Scale (HAMD-17), 14-item Hamilton Anxiety Rating Scale (HAMA-14), and Gastrointestinal Symptom Rating Scale (GSRS). Rs-fMRI was performed to quantify core neural activity metrics, including amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), and degree centrality (DC); fecal samples were collected for high-throughput metagenomic analysis. Spearman correlation analysis and mediation analysis were further conducted to dissect the interactive relationships between brain neural activity and gut microbiota.

RESULTS: The active taVNS group achieved significantly superior clinical efficacy relative to the sham group, with a HAMD-17 response rate of 62.50% and remission rate of 35.00%, versus 30.00% and 2.50% in the sham group (all P < 0.05). Rs-fMRI analyses revealed significant group×time interaction effects on neural activity: decreased ALFF in the right calcarine sulcus; altered fALFF in the right inferior temporal gyrus, left cuneus, right superior frontal gyrus (SFG) and right angular gyrus; reduced ReHo in the right calcarine sulcus and bilateral insula; and increased DC in the right caudate nucleus and left anterior cingulate gyrus. Gut microbiota profiling identified anaerobic butyrate-producing bacteria and Faecalibacterium prausnitzii as potential biomarkers linked to taVNS therapeutic effects. HAMD-17 scores were negatively correlated with Faecalibacterium prausnitzii abundance (r=-0.566, P < 0.01) and positively correlated with anaerobic butyrate-producing bacteria abundance (r = 0.406, P < 0.01). Mediation analysis suggested that fALFF values in the right SFG may indirectly modulate depressive symptoms via regulating Faecalibacterium prausnitzii abundance (indirect effect 95% CI: 0.3039-2.4466), with a significant partial mediation effect observed, though future studies controlling for dietary and other confounding variables are needed to confirm this relationship.

CONCLUSION: taVNS effectively alleviates depressive symptoms in MDD patients via dual complementary pathways: directly modulating neural activity in the right SFG to regulate depression-related brain function, and indirectly maintaining gut microbiota homeostasis by enriching beneficial taxa such as Faecalibacterium prausnitzii. These findings provide novel mechanistic insights into the brain-gut interaction underlying the antidepressant effects of taVNS, laying a theoretical foundation for its clinical application in MDD management.

PMID:42487113 | DOI:10.1186/s12888-026-08409-y

Mol Psychiatry. 2026 Jul 22. doi: 10.1038/s41380-026-03779-x. Online ahead of print.

ABSTRACT

The STRATIFY (Brain Network-Based Stratification of Reinforcement-Related Disorders) and ESTRA (Eating Disorders Stratification) studies were established as harmonised "sibling" cohorts to develop a mechanistically informed framework for stratifying psychiatric disorders. Here, we describe the study design, methodology, and cohort characteristics. Both studies investigate how network properties of brain structure and function, together with biological markers derived from blood-based genomics, epigenetics, and proteomics, relate to reinforcement-related behaviours that cut across major depressive disorder, alcohol use disorder, psychosis, and eating disorders. A further objective is to identify discriminative multimodal features that predict disease onset, symptom course, and functional outcomes, thereby supporting the development of targeted interventions. STRATIFY and ESTRA recruited 674 patients and 70 healthy controls aged 18-30 years (76% females), supplemented by 199 age- and sex-matched healthy controls from the population-based IMAGEN cohort assessed at the same sites using harmonised protocols. Multimodal assessment included structured clinical interviews, self-report measures, cognitive testing, biosamples for molecular analyses, and multimodal MRI (structural, diffusion, resting-state, and task-based fMRI). ESTRA participants additionally completed longitudinal follow-up, and all cohorts were assessed during the COVID-19 pandemic. STRATIFY and ESTRA together constitute a large-scale, open-science resource integrating multimodal brain, behavioural, and biological data across transdiagnostic patient cohorts in early adulthood. The anonymised dataset is available to the research community through managed access, supporting international collaboration and accelerating the development of mechanistically informed classification systems and predictive tools in psychiatry.

PMID:42486943 | DOI:10.1038/s41380-026-03779-x

Prog Neuropsychopharmacol Biol Psychiatry. 2026 Jul 22:111849. doi: 10.1016/j.pnpbp.2026.111849. Online ahead of print.

ABSTRACT

BACKGROUND: The dynamics of large-scale brain networks in tobacco use disorder (TUD) remain poorly understood. Dynamic functional connectivity (dFC) captures time-varying interactions among brain regions and provides a framework for examining network-level alterations in TUD. This study investigated dFC states and their topological properties to identify neural correlates of nicotine dependence.

METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) data were obtained from 46 individuals with TUD and 48 healthy controls (HCs). Regional time series were extracted using the Dosenbach-160 atlas and assigned to six large-scale functional networks. dFC states were identified using a sliding-window approach with k-means clustering, with the optimal number of clusters (k = 2) determined by the elbow criterion. State-specific graph-theoretical metrics were calculated, and inter-network connectivity differences were assessed using Network-Based Statistics (NBS). Multivariate support vector regression (SVR) with leave-one-out cross-validation (LOOCV) was used to predict nicotine dependence severity, measured by the Fagerström Test for Nicotine Dependence (FTND). Exploratory analyses examined associations between dynamic network measures and Reasons for Smoking Questionnaire (RRSQ) scores.

RESULTS: Two dFC states were identified: a relatively weakly connected State 1 and a relatively strongly connected State 2. Compared with HCs, individuals with TUD spent more time in State 1, showed longer dwell time, and exhibited fewer state transitions. In State 1, TUD patients showed a more randomized network topology, reflected by reduced clustering coefficient and increased global efficiency. NBS analysis revealed reduced connectivity within the sensorimotor network and weakened coupling between sensorimotor, cognitive control, and default mode networks. SVR analysis demonstrated that State 1 topological metrics significantly predicted FTND scores (r = 0.507, permutation p = 0.030). Furthermore, correlation analyses showed that the fractional occupancy of State 1 was positively associated with the pharmacological dimension of the RRSQ (PFDR = 0.028), suggesting a link between physiological nicotine dependence and increased occupancy of this weakly connected state.

CONCLUSION: TUD is characterized by constrained brain network dynamics and prolonged occupancy of a hyper-integrated, metabolically costly baseline state. This maladaptive pattern is associated with the pharmacological dimension of smoking motivation, and its state-specific topological features predict individual nicotine dependence severity. Taken together, these findings link network efficiency imbalance to physiological dependence and suggest that dynamic network metrics may serve as potential neuroimaging biomarkers for assessing mechanisms and clinical severity in TUD.

PMID:42486386 | DOI:10.1016/j.pnpbp.2026.111849

J Neurosci Methods. 2026 Jul 22:110865. doi: 10.1016/j.jneumeth.2026.110865. Online ahead of print.

ABSTRACT

BACKGROUND: In the ongoing search for prognostic tools in neonates suffering perinatal asphyxia, we assessed the feasibility of resting-state functional magnetic resonance imaging (RS-fMRI). The aim of our study was to determine both the intra-session and inter-subject reproducibility of resting-state networks (RSNs), as well as factors affecting these.

NEW METHOD: We conducted a prospective multicenter study in 21 asphyxiated term newborns (mean gestational age = 39.2 weeks, mean 5-minute Apgar score = 3) after treatment with controlled hypothermia at a level III neonatal intensive care unit. RSNs were assessed twice with RS-fMRI at 1.5T using both group MELODIC ICA and single-subject ICA. Intraclass correlation coefficients were calculated on all networks identified from the group MELODIC. Feasibility of the RS-fMRI technique was determined by assessing the ability to discriminate common RSNs, the presence of motion artifacts and the temporal signal to noise ratio.

RESULTS: Seven RSNs could be identified, anterior default mode network aDMN), default mode network (DMN), left frontoparietal network (lFPN), right frontoparietal network (rFPN), auditory network (AN), sensorimotor network (SMN), parietal network (ParietalN). Two networks (DMN and rFPN) demonstrated moderate reproducibility (ICC 0.62 and 0.69, respectively). The other networks demonstrated poor reproducibility (ICC <0.5).

CONCLUSION: Following perinatal asphyxia, reproducibility was moderate for DMN and rFPN and poor for the other RSNs.

PMID:42486234 | DOI:10.1016/j.jneumeth.2026.110865

J Neural Eng. 2026 Jul 22. doi: 10.1088/1741-2552/ae8eb1. Online ahead of print.

ABSTRACT

Machine learning (ML) and deep learning (DL) models are increasingly being&#xD;explored for individualized epilepsy risk prediction after a first unprovoked seizure (UFS) and&#xD;after acute brain insults such as stroke or traumatic brain injury. We systematically evaluated&#xD;their predictive performance, input modalities, validation strategies, methodological quality,&#xD;and translational readiness across these two clinical pathways.&#xD;Approach. PubMed, Scopus, IEEE Xplore, and Web of Science were searched for Englishlanguage human studies published between January 2005 and October 2025. Eligible studies&#xD;used ML or DL to predict seizure recurrence after UFS or epilepsy development after&#xD;acute brain insult using clinical, neuroimaging, electrophysiological, electronic-health-record,&#xD;or multimodal data. Two reviewers performed blinded duplicate screening, followed by&#xD;duplicate data extraction using a CHARMS-aligned form. Risk of bias and applicability were&#xD;independently assessed using PROBAST+AI across the Participants, Predictors, Outcome,&#xD;and Analysis domains.&#xD;Main results. Thirteen studies met the eligibility criteria: six addressed UFS and seven&#xD;addressed post-insult epilepsy. Reported AUCs for the best-performing models ranged from&#xD;0.60 to 0.93, with the highest discrimination observed in models using high-dimensional&#xD;neuroimaging, unstructured clinical text, or multimodal data. These inputs included MRI&#xD;morphometric asymmetry, clinical free text, EEG, diffusion MRI, resting-state fMRI, and&#xD;multimodal fusion. In the three studies that directly compared modality combinations,&#xD;multimodal models improved AUC by approximately 0.04-0.10 over the best single-modality&#xD;counterpart. Model credibility was strongest when independent validation, transparent&#xD;feature handling, and calibration assessment were reported.&#xD;Significance. ML/DL approaches show clear potential for earlier, individualized epilepsy&#xD;risk stratification, particularly when complementary clinical, electrophysiological, and&#xD;neuroimaging data are integrated. Future studies should prioritize prospective multi-site&#xD;validation, standardized EEG/MRI data structures, transparent multi-metric reporting, and&#xD;reproducible model documentation aligned with TRIPOD+AI and PROBAST+AI.

PMID:42486148 | DOI:10.1088/1741-2552/ae8eb1

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