Most recent paper

Brain Commun. 2026 Jul 22;8(4):fcag290. doi: 10.1093/braincomms/fcag290. eCollection 2026.

ABSTRACT

Clinical trajectories in patients with functional neurological disorder (FND) are variable, and the neural mechanisms underlying this heterogeneity remain poorly understood. This longitudinal brain imaging study examined resting-state functional connectivity predictors and mechanisms of symptom change in FND. Thirty-two adults with FND (motor and/or seizure phenotypes) completed baseline questionnaires and functional MRI (fMRI), followed by naturalistic treatment for 6.8 ± 0.8 months. All participants completed follow-up questionnaires; 28 completed follow-up fMRI. At each timepoint, three graph-theory network metrics of resting-state functional connectivity were computed: whole-brain weighted-degree (centrality), cortical integration (between-network connectivity), and cortical segregation (within-network connectivity). All analyses adjusted for age, sex, antidepressants, head motion, time between sessions and baseline score of interest, with cluster-wise correction. Results were contextualized against 50 age-, sex-, and head motion-matched healthy controls (HCs). Based on patient-reported Clinical Global Impression of Improvement ratings, 59.4% improved, 31.3% were unchanged, and 9.3% worsened. Core FND symptom (i.e. Screening for Somatoform Symptoms-7 Subscale for Conversion Disorder) and non-core physical symptom (Patient Health Questionnaire-15) scores showed variable trajectories, with no group-level changes. For whole-brain weighted-degree analyses, baseline centrality in right middle frontal, precentral, and left cerebellar regions was positively associated with core FND symptom change; longitudinally, centrality decreases in right precentral, superior parietal, lateral occipital, and cerebellar regions were associated with symptom improvement. For cortical integration analyses, baseline between-network connectivity in ventral attention, frontoparietal, and default mode network regions was positively associated with core FND symptom change; longitudinally, decreases in between-network connectivity for regions of these same networks were associated with symptom improvement. For cortical segregation analyses, baseline within-network connectivity in frontoparietal network regions was positively associated with core FND symptom change; no regions showed longitudinal segregation changes associated with symptom change. The right anterior insula emerged as a convergent site across baseline and longitudinal integration analyses, with the most improved participants showing elevated baseline between-network connectivity relative to HCs that normalized at follow-up. More modest functional connectivity associations were observed with non-core physical symptom change, spanning baseline within-network connectivity in dorsal attention network regions and longitudinal between-network connectivity increases in visual network regions. Findings remained significant adjusting for FND phenotype, although several attenuated when accounting for baseline affective symptoms or trauma burden. In conclusion, this study identified baseline and longitudinal resting-state functional connectivity features linked to symptom change in FND, highlighting the potential of large-scale network interactions as prognostic markers and providing mechanistic insights that set the stage for novel, biologically informed interventions.

PMID:42553752 | PMC:PMC13435623 | DOI:10.1093/braincomms/fcag290

J Psychopharmacol. 2026 Aug 5:2698811261470456. doi: 10.1177/02698811261470456. Online ahead of print.

ABSTRACT

BACKGROUND: Cannabidiol (CBD) has emerged as a potential antipsychotic treatment, acting as a negative allosteric modulator of cannabinoid type-1 (CB1) receptors within the endocannabinoid system. Although previous neuroimaging studies have shown that CBD induces changes in aberrant brain activity and functional connectivity (FC) in psychosis, they have not directly integrated information about the spatial distribution of the molecular targets through which CBD may act.

AIM: In this study, we aimed to investigate whether a single dose of CBD may acutely modulate CB1 receptor-enriched FC in patients with early psychosis and to compare these effects with healthy controls (HC).

METHODS: Thirteen patients with early psychosis (PSY) and 14 age- and sex-matched HC underwent resting-state functional magnetic resonance imaging (fMRI). Patients participated in a randomised, double-blind, placebo-controlled crossover study receiving 600 mg CBD (PSY-CBD) or matched placebo (PSY-PLB). CB1 receptor-enriched FC was computed using Receptor-Enriched Analysis of functional Connectivity by Targets (REACT), integrating Positron Emission Tomography (PET)-derived CB1 receptor maps with fMRI data.

RESULTS: Three main findings emerged: (i) PSY-PLB showed increased CB1 receptor-enriched FC across multiple regions compared to HC; (ii) a single dose of CBD significantly reduced CB1 receptor-enriched FC of the right insular cortex relative to PLB; (iii) no differences were observed when CB1 receptor-enriched FC maps of PSY-CBD were compared with those of HC.

CONCLUSION: Our findings support the potential of CBD to regulate CB1 receptor-enriched FC in early psychosis, providing mechanistic insight into its antipsychotic effects and highlighting REACT as a valuable tool for integrating molecular and functional imaging in psychiatric research.

PMID:42552898 | DOI:10.1177/02698811261470456

BMC Psychiatry. 2026 Aug 4;26(1):588. doi: 10.1186/s12888-026-08336-y.

ABSTRACT

BACKGROUND: Major Depressive Disorder (MDD) is a prevalent mental health condition with significant societal impact. Although prior research has highlighted the brain changes modulated by antidepressant therapy, their efficacy and effectiveness are debated. The low rates of treatment response still existed in the pharmacological therapy of MDD. Exploring an optimal neurological predictor of symptom improvement caused by pharmacotherapy is urgently needed for improving response to treatment. Our purpose is to develop a predictive model for MDD therapy using machine learning techniques based on resting-state fMRI metrics.

METHODS: A total of 116 MDD patients underwent 3.0T resting-state magnetic resonance image scanning. Demographic data and the 24-item Hamilton Depression Rating Scale (HAMD-24) were collected from all participants. An additional independent cohort of 25 MDD patients was included for external model validation. Based on the reduction rate of HAMD-24 scores at different time points, the patients were divided into an early improvement group, an early response group, and a clinical response group: (1) Early improvement group: HAMD-24 reduction rate ≥ 20% after 1 week of treatment; (2) Early response group: HAMD-24 reduction rate ≥ 25% after 2 weeks of treatment; (3) Clinical response group: HAMD-24 reduction rate ≥ 50% after 4 weeks of treatment. For model construction, LASSO regression was applied for feature selection, integrating identified brain regions, HAMD-24 symptom clusters, and clinical variables. Five machine learning models were established based on features screened by the LASSO regression model to predict treatment response in MDD. All models were trained and assessed using 10-fold cross-validation, the most stable logistic regression model was selected for external validation in a temporally independent cohort (n = 25).

RESULTS: At 1 week, alterations were mainly observed in the frontal and sensorimotor regions. At 2 weeks, differences were found in the opercular, postcentral, orbitofrontal, temporal, and angular areas. At 4 weeks, additional abnormalities appeared in the frontal, occipital, and postcentral cortices. Moreover, right Postcentral gyru was found to be present in the differential brain regions observed at 1 week, 2 weeks, and 4 weeks comparisons. In addition, ten features related to the treatment response were identified using the LASSO regression model, including age, years of education, core depressive symptoms, and several brain region features primarily involving the postcentral gyrus. Among the constructed machine learning models, the Logistic Regression model based on LASSO-selected features (including age, education, core depressive symptoms, and the postcentral gyrus) demonstrated the most stable performance. It achieved an area under the curve (AUC) of 0.801 in the internal validation and maintained robust predictive performance in the independent external validation set, indicating promising generalizability. Following independent external validation, the results showed that the predictive performance of the Logistic regression model on the external validation set yielded an AUC value of 0.643 and an accuracy of 0.60. This indicates that the brain regions involved in the model-namely the right postcentral gyrus, right precentral gyrus, left superior frontal gyrus, left middle occipital gyrus, and the orbital part of the right inferior frontal gyrus-hold promise as exploratory neural correlates for predicting the efficacy of SSRIs in depression.

CONCLUSION: MDD exhibits early functional alterations in brain regions involved in emotion regulation, cognition, and sensorimotor processing. Integrating these multi-metric neural features with clinical variables via machine learning models suggests potential clinical utility for evaluating and predicting early SSRI treatment outcomes.

PMID:42552532 | DOI:10.1186/s12888-026-08336-y

Physiol Behav. 2026 Aug 4:115459. doi: 10.1016/j.physbeh.2026.115459. Online ahead of print.

ABSTRACT

OBJECTIVES: Although acute total sleep deprivation (TSD) is known to harm health and daily activities, how it specifically alters the link between distinct brain regions and emotional‑cognitive behaviors remains poorly understood. This study therefore investigated mood changes, cognitive performance, and functional connectivity in healthy adult males after 36 h of TSD.

METHODS: Sixty healthy adult males participated in a within‑subject protocol. Assessments were performed before and after 36 h of TSD using multiple measures: mood rating scales, behavioral tests of sustained attention and alertness, and resting‑state fMRI to evaluate functional connectivity across brain regions.

RESULTS: After 36 h of TSD, emotional responses were markedly impaired, and sustained attention/alertness declined significantly. Functional connectivity between the cerebellum and visual, auditory, and motor areas generally increased, reflecting a pattern of large-scale brain network reorganization induced by prolonged wakefulness.

CONCLUSION: Acute total sleep deprivation produces complex impairments in mood expression, sustained vigilance and large-scale intrinsic brain network coordination among healthy young males. The observed hyperconnectivity between the cerebellum and visual network may reflect multiple non-exclusive neural processes, with the underlying mechanism remaining to be clarified. These findings expand our understanding of brain functional reorganization induced by sleep deprivation and provide empirical evidence for subsequent investigations on sleep deprivation-related neural and behavioral disturbances.

PMID:42551724 | DOI:10.1016/j.physbeh.2026.115459

Artif Intell Med. 2026 Jul 27;181:103497. doi: 10.1016/j.artmed.2026.103497. Online ahead of print.

ABSTRACT

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder where early diagnosis is pivotal for effective intervention, yet it is hindered by subtle pathological feature differences among AD subtypes and severe class imbalance in medical imaging datasets. Existing structural magnetic resonance imaging (sMRI) and resting-state functional MRI (rs-fMRI) multimodal fusion methods for AD diagnosis mostly adopt simple concatenation or summation without fine-grained cross-modal alignment and interaction. To address these issues, we propose a Deep Cross-Branch Multi-Modal Fusion Network (DCMFNet) for early AD diagnosis. We first preprocess sMRI and rs-fMRI to extract ROI-based features, then perform dimension unification and normalization to realize cross-modal feature alignment. A novel Deep Cross-branch Multi-modal Feature Fusion (DCMF) module with three parallel branches and a dual-pathway cross-modal branch is designed to fully mine complementary and correlated cross-modal information, and the fused features are input into a Transformer encoder for classification. Moreover, we introduce the Logit Adjustment Cross-Entropy (LACE) loss to mitigate class imbalance by correcting decision boundaries based on class prior probabilities, enhancing the recognition of minor classes. The model is evaluated on a private clinical dataset. Experimental results show that DCMFNet outperforms traditional machine learning methods and state-of-the-art deep learning models in six binary AD subtype classification tasks, with the LACE loss and DCMF module effectively alleviating class imbalance and improving cross-modal feature representation. This work provides a reliable multimodal fusion framework for early AD diagnosis and reduces the diagnostic burden on healthcare professionals.

PMID:42551144 | DOI:10.1016/j.artmed.2026.103497

Brain Struct Funct. 2026 Aug 4;231(7):112. doi: 10.1007/s00429-026-03169-7.

ABSTRACT

Temporal lobe epilepsy (TLE) is increasingly recognized as a distributed network disorder extending beyond mesial temporal structures. Among the brain regions implicated in epileptic network organization, the thalamus has emerged as a key hub due to its extensive structural and functional connections with cortical, limbic, and subcortical systems.This integrative review aimed to synthesize current evidence regarding nucleus-specific thalamic abnormalities and thalamocortical connectivity alterations in TLE.A literature search was conducted following PRISMA guidelines using PubMed and Google Scholar databases. Twenty-eight studies were included in the qualitative synthesis, with nineteen studies providing detailed nucleus-specific structural, functional, or electrophysiological characterization of thalamic involvement.Across structural MRI, diffusion imaging, resting-state functional MRI, magnetoencephalography, and stereo-electroencephalography studies, convergent evidence indicated preferential involvement of the anterior thalamic nucleus (ANT), mediodorsal nucleus (MD), pulvinar, and intralaminar nuclei. TLE with mesial temporal sclerosis generally exhibited more extensive thalamic atrophy and thalamotemporal disconnection, whereas MRI-negative TLE showed more heterogeneous functional and network-level abnormalities. Electrophysiological studies demonstrated dynamic thalamic participation in seizure propagation and network synchronization, supporting an active role of thalamic nuclei in epileptic processes. Emerging longitudinal evidence further suggests that thalamocortical networks may undergo postoperative reorganization following successful epilepsy surgery.Overall, the available evidence supports a transition from a predominantly hippocampocentric framework toward a distributed thalamocortical network model of TLE. Nucleus-specific thalamic dysfunction appears to contribute to seizure propagation, cognitive impairment, and large-scale network dysregulation, highlighting the importance of multimodal approaches for understanding epileptic network organization.

PMID:42550279 | DOI:10.1007/s00429-026-03169-7

Nat Sci Sleep. 2026 Jul 30;18:606007. doi: 10.2147/NSS.S606007. eCollection 2026.

ABSTRACT

PURPOSE: Sleep disturbances are highly prevalent in patients with Parkinson's disease (PD). Bright light therapy (BLT) seems to be a promising non-pharmacological intervention for sleep disturbance in PD. However, the neural mechanisms underlying BLT remain unclear. In this pilot study, we aimed to explore the clinical effects of BLT on sleep and to explore potential accompanying changes in brain activity and functional connectivity.

PATIENTS AND METHODS: In this randomized, crossover, self-controlled pilot study, BLT was delivered using bright light at 10,000 lux, and dim light at 200 lux served as the control condition. Twenty-seven patients with PD were assigned to receive 30 days of BLT and 30 days of dim light therapy (DLT) in counterbalanced order, separated by a 30-day washout period. Clinical assessments of sleep, polysomnography (PSG) and resting-state functional magnetic resonance imaging (rs-fMRI) were performed at four time points: baseline, following the first intervention phase, following the washout period, and following the second intervention phase.

RESULTS: Compared with DLT, BLT was associated with a significantly greater reduction in Epworth Sleepiness Scale (ESS) scores (P=0.012) and tonic REM sleep without atonia (RWA) (P=0.002). In resting-state fMRI, no clusters showed significant alterations in fractional amplitude of low-frequency fluctuation (fALFF) or dynamic fALFF (dfALFF) after BLT compared to DLT. Functional connectivity (FC) analyses using the bilateral thalamus, bilateral calcarine cortex, and bilateral posterior cingulate cortex as seed ROIs revealed no clusters with significant between-intervention connectivity differences after multiple comparison correction.

CONCLUSION: BLT improved excessive daytime sleepiness and reduced tonic RWA relative to control light in PD, but did not lead to detectable alterations in intrinsic activity or FC within a priori hypothesized thalamo-cortical or visual pathways. These findings warrant further validation in larger, long-term studies.

PMID:42549269 | PMC:PMC13431437 | DOI:10.2147/NSS.S606007

Front Psychiatry. 2026 Jul 20;17:1846996. doi: 10.3389/fpsyt.2026.1846996. eCollection 2026.

ABSTRACT

INTRODUCTION: Early diagnosis of autism spectrum disorder (ASD) based on resting-state functional magnetic resonance imaging (rs-fMRI) is crucial for effective intervention and rehabilitation. Using rs-fMRI, functional brain networks (FBNs) are constructed to represent interactions among brain regions of interest (ROIs), and existing graph neural network-based methods, particularly Graph Attention Networks (GATs), have shown promise for FBN-based ASD diagnosis. However, most current approaches primarily aggregate ROIs through low-order pairwise interactions, while higher-order neighbors are incorporated only implicitly through increased network depth. This strategy often leads to over-smoothing of node representations and limits the capture of informative higher-order brain interactions.

METHODS: To address these challenges, we propose the Hierarchical Neighbor Integration Graph Attention Network (HiNIGAT), a general graph learning framework that explicitly models multi-order interactions in FBNs. Specifically, HiNIGAT introduces a multi-order attention mechanism that constrains each attention head to specialize in a distinct neighborhood order, enabling the model to capture brain interactions from local connectivity to global network structure. Furthermore, a bidirectional gated fusion strategy is proposed to adaptively integrate complementary information across multi-order features, facilitating effective local-global representation collaboration.

RESULTS: Experiments on the ABIDE-I dataset demonstrate the effectiveness of HiNIGAT.

DISCUSSION: The results highlight the importance of explicit multi-order integration for ASD diagnosis.

PMID:42548894 | PMC:PMC13430121 | DOI:10.3389/fpsyt.2026.1846996

J Oral Facial Pain Headache. 2026 Jul;40(4):120-129. doi: 10.22514/jofph.2026.054. Epub 2026 Jul 12.

ABSTRACT

BACKGROUND: Accumulating evidence indicates that the locus coeruleus (LC) plays a pivotal role in pain modulation and related network dysfunction in migraine. This study aimed to examine functional connectivity (FC) between the LC and other brain regions in patients with migraine without aura (MwoA) compared with healthy controls (HCs).

METHODS: A total of 55 patients with MwoA and 50 age-, sex-, and education-matched HCs underwent resting-state functional magnetic resonance imaging (fMRI). Seed-to-voxel whole-brain FC analysis was performed using the bilateral LC as seed regions. Different clinical and neuropsychological assessments are included. Pearson correlation analysis was used to determine the relationships between altered FC and clinical variables, and receiver operating characteristic (ROC) analysis was further performed to evaluate the discriminatory performance of significant FC measures.

RESULTS: Compared with HCs, patients with MwoA exhibited increased FC between the left LC and the right superior temporal gyrus (STG)/left cerebellar posterior lobe (CPL), as well as between the right LC and the left STG/left inferior occipital gyrus (IOG) (p < 0.05). Decreased FC was observed between the bilateral LC and the right superior frontal gyrus (SFG) (p < 0.05). Positive correlations were identified between disease duration and FC of the left LC-right STG connection (p = 0.012, r = 0.341), and between SDS scores and FC of the right LC-left IOG connection (p = 0.011, r = 0.344). ROC analysis demonstrated that FC between the right LC and right SFG had the best discriminatory performance (sensitivity 81.82%, specificity 68.00%).

CONCLUSIONS: Patients with MwoA exhibited altered LC-related resting-state FC involving brain regions associated with pain processing, sensory integration, and emotional regulation. By separately characterizing the whole-brain connectivity patterns of the bilateral LC, this study extends previous LC-related imaging findings in migraine and provides a more refined description of LC-centered network dysfunction in MwoA.

PMID:42548109 | DOI:10.22514/jofph.2026.054

Brain Res. 2026 Aug 3:150482. doi: 10.1016/j.brainres.2026.150482. Online ahead of print.

ABSTRACT

OBJECTIVE: Chronic Insomnia Disorder (CID) is characterized by maladaptive self-referential thinking linked to default mode network (DMN) dysfunction. Network Homogeneity (NH), a measure of local functional integration within networks, may capture this dysregulation but remains underexplored in CID and its response to treatment, particularly herbal medicine.

METHODS: This study employed resting-state functional magnetic resonance imaging (fMRI) to investigate DMN NH in 82 patients with CID (52 treated with Modified Suanzaoren Decoction [MSZRD], 30 with Estazolam) and 65 matched healthy controls (HCs). NH was calculated for the DMN. Baseline differences, longitudinal treatment effects (mixed-effects model), and correlations with clinical scores were examined. A support vector machine (SVM) classifier used baseline NH features for diagnostic discrimination.

RESULTS: At baseline, CID patients exhibited widespread abnormal NH in DMN nodes, including the temporal lobes, precuneus, and orbitofrontal cortex (OFC), compared to HCs. Post-treatment, a significant time effect (increased NH) was found in the right inferior frontal orbital gyrus (IFOG) and right superior temporal gyrus (STG) across both groups. Crucially, a group-by-time interaction was identified in the right medial frontal orbital gyrus (MFOG), where NH increased after MSZRD but decreased after Estazolam. Baseline NH in temporal regions correlated positively with insomnia and anxiety severity. The SVM classifier achieved significant accuracy in distinguishing patients from HCs.

CONCLUSIONS: CID is characterized by dysfunctional integration within the DMN. MSZRD and Estazolam demonstrate both convergent and divergent neuromodulatory effects, with MSZRD uniquely altering NH in a key prefrontal regulatory hub.

PMID:42546895 | DOI:10.1016/j.brainres.2026.150482

J Sex Med. 2026 Jul 3;23(8):qdag108. doi: 10.1093/jsxmed/qdag108.

ABSTRACT

BACKGROUND: Lifelong premature ejaculation (LPE) is a prevalent male sexual dysfunction with unclear neurobiological mechanisms. Despite its high prevalence, the etiology of LPE remains debated, often attributed to psychological or biological factors. Recent neuroimaging studies have highlighted the role of central nervous system dysregulation in sexual behavior.

AIM: This study investigates abnormal brain functions and altered network connectivity in LPE patients after visual sexual stimuli (VSS) using functional magnetic resonance imaging.

METHODS: Twenty-five LPE patients and 31 healthy controls (HCs) underwent resting-state and task-state functional magnetic resonance imaging (fMRI). Clinical data, including sexual history, self-reported intravaginal ejaculatory latency time, International Index of Erectile Function-5, the Chinese Index of Premature Ejaculation, anxiety/depression scores, and serum testosterone levels, were collected. Neuroimaging preprocessing and analysis focused on amplitude of low-frequency fluctuation, fractional ALFF, and regional homogeneity. Task-state fMRI compared brain activation patterns after VSS. Statistical analyses included voxel-based comparisons and network connectivity assessments using SPM12 and DPABI v3.0.

OUTCOMES: LPE patients demonstrate distinct neurofunctional abnormalities after VSS, particularly hyperactivation in the precuneus.

RESULTS: ‌Clinical Data‌: LPE patients exhibited significantly lower International Index of Erectile Function-5 scores and higher depression rates compared to HCs, with no differences in age, BMI, or testosterone levels. Brain Activation‌: During VSS, LPE patients showed relative signal decrease in the middle cingulate cortex and left precentral gyrus compared to HCs. Regional homogeneity analysis revealed hyperactivation in the precuneus and fusiform gyrus post-stimulus. Network Connectivity‌: Altered connectivity in premature ejaculation patients involved the fusiform gyrus (linked to posterior cingulate, hippocampus, parahippocampus, and supplementary motor areas) and the superior parietal lobule (connected to the angular gyrus).

CLINICAL IMPLICATIONS: These findings suggest that aberrant central nervous system processing of sexual stimuli contributes to premature ejaculation pathophysiology, offering potential targets for neuromodulatory therapies.

STRENGTHS AND LIMITATIONS: The study focuses on the different activation patterns of patients with LPE from the perspective of sexual arousal. The methodological aspects of research, such as the use of images or videos in sexual stimulation, remain controversial. The findings of our network analysis only demonstrated a limited number of altered functional connections, and no established network metrics were provided to substantiate the claim of extensive network disruption. These factors collectively represent important limitations of the present study.

CONCLUSION: The hyperactivity in this brain region observed in patients could represent a unique response to VSS among those with LPE, ultimately leading to alterations in their ejaculatory behavior.‌‌‌.

PMID:42544459 | DOI:10.1093/jsxmed/qdag108

bioRxiv [Preprint]. 2026 Jul 20:2026.07.19.739422. doi: 10.64898/2026.07.19.739422.

ABSTRACT

Connectome harmonics provide a spectral representation of structural brain connectivity that has emerged as a powerful framework for studying structure-function relationships. However, two fundamental questions remain unresolved: how should inter-individual variability in structural connectivity be incorporated into graph signal processing analyses, and at which spectral scale should connectome harmonics be interpreted? Here, using structural and functional MRI data from 875 participants, we show that subject-specific connectome harmonics reveal a reproducible hierarchical organization of the human connectome. Across the spectrum, harmonics exhibited orderly transitions in stability, graph support, sparsity and anatomical localization, and naturally organized into multiscale families sharing common structural properties. Sparse harmonic representations accurately reconstructed brain activity across resting-state and task paradigms, revealing frequency-dependent recruitment of this structural hierarchy during cognition. Finally, combining structural and functional harmonic organization enabled the prediction of individual cognitive performance, demonstrating that both representations capture complementary behaviorally relevant information. Together, our findings show that connectome harmonics should be viewed not simply as graph-frequency modes of structural connectivity, but as a hierarchically organized representation of the connectome that links brain structure, functional dynamics and cognition.

PMID:42539141 | PMC:PMC13419739 | DOI:10.64898/2026.07.19.739422

Prog Neuropsychopharmacol Biol Psychiatry. 2026 Aug 1:111873. doi: 10.1016/j.pnpbp.2026.111873. Online ahead of print.

ABSTRACT

High ventilation breathwork (HVB) is increasingly used in psychotherapeutic and contemplative contexts to induce altered states of consciousness (ASC), yet its neurobiological mechanisms remain poorly understood. Thirty experienced HVB practitioners (mean age 44.97 ± 11.80 years) underwent perfusion and resting-state fMRI to assess acute cerebrovascular and post-acute network changes. Cerebral blood flow (CBF) was measured using arterial spin labelling during baseline and the interval of maximal subjective effects. Acute psychotropic effects were rated using an online Likert scale, ASC intensity was assessed immediately after breathwork with the 5-Dimensional Altered States of Consciousness (5D-ASC) scale. HVB induced a 45.5% reduction in global grey matter cerebral blood flow (CBF; 29.81 ± 7.58 vs. 16.24 ± 4.35 ml/100 g/min; t(29) = -10.69, p < 0.001), accompanied by increased heart and respiratory rate. Default mode network (DMN) functional connectivity (FC) did not change significantly at the group level. In further exploratory analyses, greater decreases in DMN FC (pre - post breathwork) predicted higher 5D-ASC rating (β = -4.88, p < 0.05) in analyses using global signal regression, though this association was not significant without global signal regression (p = 0.75). CBF reductions were not associated with ΔDMN-FC or 5D-ASC ratings. HVB produces marked global cerebral hypoperfusion without systematic DMN FC changes, suggesting that hypoperfusion and network dynamics may represent partially dissociable mechanisms underlying breathwork-induced ASC. As a controllable, non-pharmacological means of inducing ASC, HVB may hold promise as a psychotherapeutic and psychedelic-adjacent intervention.

PMID:42542287 | DOI:10.1016/j.pnpbp.2026.111873

Epilepsia. 2026 Aug 1. doi: 10.1002/epi.70420. Online ahead of print.

ABSTRACT

OBJECTIVE: Memory impairment is a common comorbidity in temporal lobe epilepsy (TLE) and is thought to arise from hippocampal dysfunction and disrupted interactions within a distributed memory network involving medial temporal, frontal, and parietal cortical regions. However, the network-level mechanisms underlying memory impairment in TLE remain poorly understood. Leveraging complementary intracranial electroencephalography (iEEG) and resting-state functional magnetic resonance imaging (rsfMRI), this study aimed to investigate hippocampal network alterations associated with memory dysfunction in TLE and characterize hippocampal-cortical connectivity patterns linked to individual differences in memory performance.

METHODS: This retrospective study included 29 patients with TLE who underwent structural MRI and rsfMRI, 13 of whom also underwent interictal iEEG. For each participant, 94 subject-specific regions of interest (ROIs) were generated, including 92 cortical ROIs from individualized rsfMRI parcellation and bilateral hippocampal ROIs defined using FreeSurfer subcortical segmentation. iEEG signals were projected onto these ROIs for cross-modal alignment. Hippocampal-cortical connectivity was estimated using rsfMRI functional connectivity and iEEG imaginary coherence, with hemispheres standardized relative to the epileptogenic side. Memory-related connectivity within the hippocampal network was then identified through behavioral-connectivity correlation analysis followed by Elastic Net regularization.

RESULTS: Increased ipsilesional hippocampal-cortical alpha-band (8-12 Hz) connectivity derived from interictal iEEG was significantly correlated with worse Logical Memory performance. Consistently, rsfMRI analyses revealed a significant epileptogenic side (ipsilesional vs. contralesional) and connection type (within- vs. between-hemisphere) interaction, with stronger within-hemispheric hippocampal-cortical connectivity in the ipsilesional hemisphere. Behavioral-connectivity analyses indicate that immediate and delayed recall shared a core episodic memory architecture, but delayed recall relied more on large-scale network coordination supporting retrieval after a delay.

SIGNIFICANCE: Our multimodal findings suggest that increased hippocampal connectivity in the epileptogenic hemisphere may reflect pathological hypersynchronization that disrupts efficient memory network coordination in TLE and highlight hippocampal connectivity as a promising candidate biomarker for characterizing individual memory outcomes.

PMID:42541383 | DOI:10.1002/epi.70420

Biol Psychiatry Glob Open Sci. 2026 Jun 5;6(5):100767. doi: 10.1016/j.bpsgos.2026.100767. eCollection 2026 Sep.

ABSTRACT

BACKGROUND: Although trauma-focused psychotherapy remains the first-line treatment for posttraumatic stress disorder (PTSD), 14% to 35% of individuals with PTSD fail to achieve symptom remission. Transcranial magnetic stimulation (TMS) has shown promise as an adjunctive intervention, but its efficacy for PTSD remains inconsistent and may be better informed by the incorporation of brain-based signals to guide stimulation. To address this, we developed a connectivity-based modeling framework to identify functional networks that predict symptom severity and to estimate where stimulation would most effectively reduce PTSD symptoms.

METHODS: We used clinical, structural, and resting-state functional data from 350 veterans with and without PTSD. Principal component analysis (PCA) was applied to functional connectivity matrices to reduce dimensionality, and resulting component scores were entered into a least absolute shrinkage and selection operator regression to identify connections associated with symptom severity. To simulate TMS effects, we generated electric field models for 200 randomly distributed cortical sites per participant and used these distributions to estimate connectivity changes proportional to the modeled fields. The estimated changes were combined with PCA regression weights to predict stimulation-induced symptom changes at each site and were mapped across the cortex to create a whole-brain TMS targeting atlas.

RESULTS: The model identified 2 broad and opposing patterns. Stimulation expected to increase connectivity in the left prefrontal cortex was predicted to reduce symptoms, whereas stimulation expected to decrease connectivity in bilateral parietal and occipital regions was also predicted to reduce symptoms.

CONCLUSIONS: These findings provide a framework for individualized TMS targeting in PTSD and generate potential therapeutic targets for future clinical trials.

PMID:42540338 | PMC:PMC13425816 | DOI:10.1016/j.bpsgos.2026.100767

Front Psychol. 2026 Jul 17;17:1778839. doi: 10.3389/fpsyg.2026.1778839. eCollection 2026.

ABSTRACT

OBJECTIVES: Talent transfer (TT) from roller skating to speed skating is a recognized pathway for developing elite athletes. This study aimed to identify the sport-specific cerebral characteristics in these athletes by comparing their brain structure and function, thereby providing preliminary neuroimaging evidence relevant to "roller-to-ice" TT.

METHODS: We acquired structural and resting-state functional magnetic resonance imaging (fMRI) data from 10 national-level speed skaters and 14 roller skaters. Voxel-based morphometry (VBM) was used to assess whole-brain gray matter volume (GMV), and whole-brain voxel-wise resting-state analyses were conducted to calculate fractional amplitude of low-frequency fluctuations (fALFF) and degree centrality (DC).

RESULTS: Speed skaters demonstrated greater GMV in the bilateral occipital lobe, right orbitofrontal cortex, right medial temporal lobe, right insula, and cerebellum, but lower GMV in the bilateral precuneus, bilateral inferior temporal gyrus, right ventral frontal cortex, and left occipitotemporal junction compared to roller skaters (FWE corrected p < 0.05). Functionally, speed skaters exhibited higher DC in the left basal ganglia (caudate nucleus), while roller skaters showed higher fALFF in the right prefrontal cortex (FWE corrected p < 0.05).

CONCLUSION: The pronounced structural differences may reflect distinct long-term neuroplastic characteristics related to visuomotor integration, balance 2 control, and environmental predictability inherent to each sport. The relatively limited functional differences, coupled with the common engagement of motor planning networks, may indicate a common neural feature that underlies the potential for TT. These findings provide preliminary neurobiological evidence to inform talent identification and optimize training strategies for athletes transitioning from roller skating to speed skating.

PMID:42539737 | PMC:PMC13424456 | DOI:10.3389/fpsyg.2026.1778839

bioRxiv [Preprint]. 2026 Jul 25:2026.07.24.740635. doi: 10.64898/2026.07.24.740635.

ABSTRACT

Neural representations vary in their complexity across brain regions and tasks. How this variation emerges over human development remains poorly understood. We estimated intrinsic dimensionality in five naturalistic fMRI datasets (N = 781 unique participants, aged 3 months to 53 years) with T-PHATE -- a nonlinear manifold learning method robust to noisy, autocorrelated signals. In adults, brain regions relevant to a task had higher-dimensional activity than task-irrelevant regions across auditory, visual, and audiovisual stimuli. The modulation of representational complexity by tasks was absent in infants, emerged in early childhood, and strengthened logarithmically through adolescence. It reflected a selective collapse in dimensionality in task irrelevant regions, relative to a resting-state baseline, rather than an expansion of dimensionality in task-relevant regions. Such compression followed a trajectory from global and nonselective in infants to local and precise by adulthood. These results identify selective compression as a developmental engine of functional specialization: rather than adding complexity where it is needed, the brain dynamically pares it away where it is not.

PMID:42539279 | PMC:PMC13420943 | DOI:10.64898/2026.07.24.740635

bioRxiv [Preprint]. 2026 Jul 26:2026.07.24.740595. doi: 10.64898/2026.07.24.740595.

ABSTRACT

Neural activity is organized in low-dimensional structure, yet functional connectivity in fMRI typically represents each brain parcel by a single voxel-averaged time series. This scalar representation makes whole-brain connectivity tractable but discards potentially informative dimensions of within-parcel BOLD activity. Here, we represent each parcel by a low-dimensional temporal subspace derived from its principal-component time series and use the RV coefficient to quantify connectivity between regional subspaces. Across Human Connectome Project resting-state and working-memory data, progressively expanding these subspaces reveals reproducible connectivity regimes with distinct network and identifiability profiles. At rest, connectivity constructed from the first principal component identifies individuals more strongly than either voxel-averaged functional connectivity or higher-dimensional subspace representations. During working memory, identifiability instead peaks after secondary components are included, indicating that the distribution of individual-specific information across the regional PCA spectrum depends on cognitive state. These patterns replicate across independent samples and remain robust across multiple parcellation resolutions. Together, our findings show that within-parcel BOLD structure contains identity- and state-dependent information that is obscured by scalar regional summaries. Functional connectivity may therefore be better understood as a family of related connectomes indexed by the regional subspace retained, providing a general framework for mapping interactions between low-dimensional neural representations.

PMID:42539226 | PMC:PMC13419828 | DOI:10.64898/2026.07.24.740595

Br J Ophthalmol. 2026 Jul 31:bjo-2025-329114. doi: 10.1136/bjo-2025-329114. Online ahead of print.

ABSTRACT

BACKGROUND: Retinal degenerative diseases, including myopic macular degeneration (MMD) and retinitis pigmentosa (RP), lead to irreversible vision impairment, with limited treatment options. Transcranial magnetic stimulation (TMS), a non-invasive brain stimulation technique for rehabilitation, may hold potential for vision restoration. The pilot study explored whether TMS is associated with short-term functional visual improvements in patients with severe retinal degenerative diseases.

METHODS: This prospective, non-randomised, parallel-arm pilot study enrolled 98 patients with bilateral severe MMD or RP. Participants received either five consecutive days of theta-burst TMS or conventional conservative care. Primary outcomes were changes in best-corrected visual acuity (BCVA) and visual field from baseline to 4-week follow-up. Contrast sensitivity, macular sensitivity, fixation stability, Visual Function Index-14 (VF-14) and Visual Functioning Questionnaire-25 (VFQ-25) questionnaires were also evaluated. A subset of participants underwent resting-state functional magnetic resonance imaging (fMRI) to explore cortical functional changes.

RESULTS: The study analysed 174 eyes from 88 patients (mean age: 60.3±9.1 years; 52% female). Compared with controls, the TMS group showed greater improvements in BCVA (MMD: 0.22±0.28 logarithm of the minimum angle of resolution (logMAR); RP: 0.24±0.21 logMAR) and VF mean defect (MMD: 1.96±2.09 dB; RP: 1.08±1.28 dB). Contrast sensitivity, macular sensitivity and fixation stability also showed short-term gains. Patient-reported outcomes improved in several functional domains. fMRI demonstrated increased activity and functional connectivity in visual-related cortical regions following TMS.

CONCLUSIONS: In this exploratory pilot study, TMS treatment was associated with short-term functional visual improvements and cortical modulation in individuals with severe retinal degenerative diseases. These findings are hypothesis-generating and warrant validation through larger, randomised, masked clinical trials with longer follow-up.

PMID:42538141 | DOI:10.1136/bjo-2025-329114

Neuroimage Clin. 2026 Jul 29;51:104045. doi: 10.1016/j.nicl.2026.104045. Online ahead of print.

ABSTRACT

Bipolar disorder (BD) follows a neuro-progressive trajectory, yet current clinical staging models lack robust biological markers. Based on recent evidence, we hypothesized that BD stages are associated with alterations in the functional architecture of the Triple Network Model. We analyzed resting-state fMRI data from 123 individuals spanning five clinically defined BD stages. Using a hierarchical approach, we investigated how functional connectivity features characterize these stages, transitioning from coarse- to fine-grained analyses in both clinical classification (binary early-versus-late grouping to five-stage discrimination) and connectivity metrics (mean and standard deviation of connectivity to topological dynamic features). First, low-level functional connectivity features, combined with gene expression data, discriminated early- from late-stage illness, reaching higher performance than unimodal approaches. Next, evaluating the full staging spectrum revealed a non-linear trajectory in whole-brain connection density, which was lower at each successive stage from 0 to 3 but unexpectedly higher at stage 4. Network topology analysis contextualized this finding, revealing a reorganization of functional hubs in the final stage. Specifically, three hubs within the Salience Network exhibited decreased centrality despite the overall increase in connection density. This shift corresponded to a loss of temporal synchronization between these hubs and default-mode hubs, an axis of communication mediated by the salience network that is known to be important in healthy subjects and disrupted in psychiatric disorders. Overall, our findings suggest that late-stage BD is characterized by a subtle but significant reorganization of brain architecture, accompanied by alterations in the dynamics of the salience network which loses its coordinating role.

PMID:42537498 | DOI:10.1016/j.nicl.2026.104045