Most recent paper

iScience. 2026 Jun 4;29(6):116173. doi: 10.1016/j.isci.2026.116173. eCollection 2026 Jun 19.

ABSTRACT

Brain graph classification from resting-state fMRI (rs-fMRI) can support the identification of neurological conditions and inform personalized analysis. Here, we present a hierarchical sparse spatiotemporal graph neural network (STGNN)-GLNSTGNN-to address sparse feature selection in spatiotemporal brain graph classification. We evaluated GLNSTGNN on two rs-fMRI datasets comprising 1,956 participants with 200 regions of interest (ROIs) and 12 subnetworks after standardized preprocessing. GLNSTGNN applies GroupLassoNet-based hierarchical sparsity to select informative features, while combining spatial graph convolution on a fixed functional connectivity adjacency with temporal convolution on time-varying BOLD signals to capture spatial dependencies and temporal dynamics. Across multiple baselines, GLNSTGNN showed improved discriminative performance and consistent ROI selection, supporting interpretable subnetwork-level patterns. These results suggest that integrating hierarchical sparsity with spatiotemporal graph learning can provide a practical framework for robust and interpretable brain graph classification.

PMID:42305596 | PMC:PMC13266135 | DOI:10.1016/j.isci.2026.116173

Prog Neuropsychopharmacol Biol Psychiatry. 2026 Jun 16:111789. doi: 10.1016/j.pnpbp.2026.111789. Online ahead of print.

ABSTRACT

BACKGROUND: Previous research on brain network topology in Tobacco Use Disorder (TUD) has been inconsistent, likely due to overlooking the heterogeneity of addiction severity. Consequently, how these topological alterations manifest across different smoking severities is not fully understood.

METHODS: Resting-state functional magnetic resonance imaging (fMRI) and clinical data were collected from 102 males (24 heavy smokers, 36 light smokers, 42 healthy controls). Based on the fMRI data, we computed global graph metrics and applied Network-Based Statistics (NBS) analysis to identify abnormal subnetworks, and performed correlation analyses between global graph metrics and clinical scales. A Support Vector Machine (SVM) classifier was constructed using functional connectivity (FC) strength, graph metrics, and multi-scale fusion features to discriminate the severity of smoking at the individual level within a rigorous repeated stratified nested cross-validation framework.

RESULTS: Graph-theory analysis revealed that patients with Tobacco Use Disorder (TUD) exhibited significant large-scale topological reorganization, characterized by increased global integration, indexed by higher global efficiency (Eglob), and reduced local segregation, indexed by a lower clustering coefficient (Cp). Network-Based Statistics (NBS) identified two distinct subnetworks showing reduced connectivity in the TUD group, primarily involving the default mode, limbic, and sensorimotor networks. Subgroup analyses further demonstrated a severity-dependent pattern of network alterations. Light smokers showed increased Eglob with relatively preserved Cp, whereas heavy smokers exhibited a significant reduction in Cp accompanied by more extensive disruption of connectivity across distributed higher-order networks. In contrast, no significant subnetworks were detected in the light smoker group. Importantly, correlation analysis revealed a significant negative association between Cp and Fagerström Test for Nicotine Dependence (FTND) scores. Finally, a multi-scale machine learning model integrating network features and evaluated using 10 × 5 repeated stratified nested cross-validation achieved optimal classification of smoking severity (ensemble AUC = 0.8380), outperforming models based on single-modality features.

CONCLUSION: TUD involves a severity-dependent pathophysiological gradient, which may reflect a hypothesized shift from compensatory strengthening of global integration in light smokers to advanced-severity weakening of local network organization in heavy smokers. The severity-dependent reduction in local segregation, together with widespread hypoconnectivity, suggests multi-scale network disruption. These multi-scale functional network features provide preliminary evidence supporting their potential utility for severity stratification in TUD.

PMID:42303076 | DOI:10.1016/j.pnpbp.2026.111789

Behav Brain Res. 2026 Jun 16:116316. doi: 10.1016/j.bbr.2026.116316. Online ahead of print.

ABSTRACT

Non-suicidal self-injury (NSSI) is highly prevalent in adolescents with major depressive disorder (MDD), elevating suicide risk and indicating poor prognosis. While neuroimaging studies have examined NSSI and MDD separately, systematic reviews focusing on major depressive disorder with non-suicidal self-injury (nsMDD) remain scarce. In accordance with PRISMA guidelines, this study systematically reviewed 24 neuroimaging studies (2013 - 2025) involving 2,379 participants (mean age ≤23 years) from PubMed, PsycInfo, and Embase. Using the Newcastle-Ottawa scale for quality assessment, we found 20 functional magnetic resonance imaging (resting-state fMRI and task fMRI) and 4 structural magnetic resonance imaging (MRI) studies. Key findings identified distinct neural signatures specific to nsMDD that differentiate it from depression without NSSI, including: suppression of the frontal gyrus, reduced volume of the putamen, abnormal activation of the lingual gyrus, midline cortical structures, and the prefrontal-limbic-mesencephalic circuit. In terms of neural networks, the default mode network exhibits enhanced connectivity with other neural networks, while the frontoparietal network shows abnormal suppression. Despite limitations including cross-sectional designs, gender imbalance, and scarce multimodal data, these findings provide a basis for targeted intervention that require longitudinal validation of their clinical utility.

PMID:42302888 | DOI:10.1016/j.bbr.2026.116316

Neuroimage. 2026 Jun 16:122055. doi: 10.1016/j.neuroimage.2026.122055. Online ahead of print.

ABSTRACT

Functional connectivity changes in neurodegeneration involve not only regional disconnection but also scale-specific reorganization of brain networks. We introduce the Multiscale Spectral Gaussian Filtering (MSGauF) framework, which transforms each subject's Laplacian spectrum into a data-driven coordinate system for connectivity analysis. MSGauF defines adaptive frequency bands from spectral changepoints, and within each band, normalized similarity measures enable sign-separated, cluster-level permutation testing without requiring eigenvector alignment. Simulations show improved precision when connectivity changes are confined to specific spectral ranges. Applied to resting-state fMRI from Alzheimer's and Parkinson's cohorts, the method revealed distinct spectral signatures of disease. In Alzheimer's disease, connectivity shifted from large-scale attenuation in mild impairment to fine-scale polarity reversal, reflecting disrupted long-range inhibition and local hyperexcitability. In Parkinson's disease, polarity was preserved but spectrally compressed, indicating reduced flexibility and rigid synchronization of local circuits. These findings highlight the advantages of frequency-resolved analysis in revealing structured, multiscale connectivity changes that are missed by conventional broadband approaches.

PMID:42302882 | DOI:10.1016/j.neuroimage.2026.122055

Eur Child Adolesc Psychiatry. 2026 Jun 16. doi: 10.1007/s00787-026-03089-1. Online ahead of print.

ABSTRACT

Communication deficits in Autism Spectrum Disorder (ASD) involve impairments in both verbal and nonverbal domains, potentially associated with altered brain network connectivity related to language, attention, and social cognition systems. This study investigated functional connectivity patterns among the Default Mode Network (DMN), Salience Network (SN), Dorsal Attention Network (DAN), and Language Network (LN) in male children with ASD using resting-state fMRI data and Principal Component Analysis (PCA) to define sample-specific regions of interest. The sample included 53 males with ASD and 27 typically developing controls aged 5 to 12 years. Group comparisons revealed underconnectivity between the SN and LN (p = 0.003, beta = -0.49; p = 0.04, beta = -0.38) and overconnectivity between the DMN and LN in the ASD group (p = 0.003, beta = 0.5; p = 0.02, beta = 0.4). Crucially, further analyses showed that impairments in verbal (p = 0.016, beta = -0.45; p = 0.02, beta = -0.46) and nonverbal (p = 0.03; beta = -0.42) communication in ASD were associated with reduced connectivity within the DAN and between the DAN and SN, rather than with the LN. These findings suggest that communication difficulties in ASD have a stronger attentional basis linked to disruptions in sustained and switching attention mechanisms, as opposed to isolated language network dysfunctions. Our results underscore the importance of considering the integrated functioning of attentional and social brain networks to better understand the neural substrates of communication challenges in ASD.

PMID:42301279 | DOI:10.1007/s00787-026-03089-1

Diabetes Metab Syndr Obes. 2026 Jun 10;19:592789. doi: 10.2147/DMSO.S592789. eCollection 2026.

ABSTRACT

PURPOSE: Obesity can exacerbate metabolic dysfunction in patients with type 2 diabetes mellitus (T2DM), and the degree of various injuries to our body cannot be fully controlled. However, research on early brain function changes in overweight/obese patients with T2DM is not yet perfect. Herein, we studied the topological structure of brain networks and cognitive function alterations in newly-onset overweight/obese T2DM patients.

METHODS: To investigate the changes in topological structure, we collected clinical data, cognitive scales, and resting-state functional magnetic resonance imaging (fMRI) data from 32 patients with newly-onset overweight/obese T2DM and 27 normal controls (NCs). The topological structure of the constructed network, derived from preprocessed fMRI data, was extracted and analyzed using graph theory methods by GRETNA. The relationships between changes in topological structure and clinical data as well as neurocognitive test scores in patients with newly-onset overweight/obese T2DM were analyzed.

RESULTS: Within the set threshold range, both newly-onset T2DM and NC groups had global small-world (δ) values >1.1, but the T2DM group showed lower δ values at each threshold. Compared to NC, T2DM had decreased δ (P = 0.0423, T = -2.079), global efficiency (Eglob) (P = 0.0312, T = -2.212), and increased characteristic path length (Lp) (P = 0.0253, T = 2.301). After multiple comparison corrections, local topological metrics showed no significant inter-group differences. For the sake of prudence, we employed a significance reference threshold of p < 0.005 for uncorrected analyses. Through exploratory investigation, we identified that, without multiple comparison corrections, there were inter-group nodal differences in specific brain regions for the metrics including nodal degree centrality (Dc), nodal clustering coefficient (NCp), and nodal local efficiency (NLe). Network-based statistic found no aberrant connections (P > 0.05). In the T2DN group, a positive correlation was observed between the Dc of left inferior parietal angular gyrus and BMI (P < 0.05).

CONCLUSION: The functional networks of patients with newly-onset T2DM have changes in network efficiency and brain region function. Brain function damage may exist in the early stage of overweight/obese T2DM. This study provides a new thought for the neurobiological mechanism of early brain function damage in overweight/obese T2DM. It is worth noting that the findings of this study suggest that the combined metabolic burden resulting from the coexistence of T2DM and overweight/obesity may jointly participate in mediating alterations in brain networks. However, constrained by the study design, this study failed to independently distinguish the specific effects of obesity. In the future, it is still necessary to conduct refined analyses stratified by BMI to further disentangle the independent and interactive effects of obesity and hyperglycemia on brain structure and function.

PMID:42299365 | PMC:PMC13264985 | DOI:10.2147/DMSO.S592789

Zhonghua Yi Xue Za Zhi. 2026 Jun 16;106(22):2250-2258. doi: 10.3760/cma.j.cn112137-20260410-00976.

ABSTRACT

Objective: To explore the brain functional and cognitive characteristics of schizophrenia (SZ) patients with predominant positive or negative symptoms using resting-state functional magnetic resonance imaging (rs-fMRI). Methods: SZ patients who visited the Second Affiliated Hospital of Henan Medical University from March 2021 to June 2024 were prospectively included and evaluated with the Positive and Negative Syndrome Scale (PANSS). They were then divided into the positive symptom-dominant SZ group (SZ-Ⅰ group) and the negative symptom-dominant SZ group (SZ-Ⅱ group) based on the results. Healthy individuals were recruited from the surrounding communities as the control group. The cognitive function of SZ patients was assessed using the MATRICS Consensus Cognitive Battery (MCCB). Whole-brain rs-fMRI data was collected from all subjects, and low-frequency amplitude (ALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo) were calculated. Brain regions with significant differences among the three groups were identified through inter-group comparisons and used as regions of interest (ROI) to calculate the whole-brain functional connectivity (FC) and perform inter-group comparisons. Partial correlation analysis was conducted to explore the correlations between rs-fMRI and MCCB subscale scores among the three groups. Results: The SZ-Ⅰ group included 18 males and 20 females, with an average age of (28.2±7.8) years; the SZ-Ⅱ group included 19 males and 18 females, with an average age of (30.9±6.4) years; and the control group included 16 males and 20 females, with an average age of (32.6±9.3) years. There were no significant differences in gender and age among the three groups (all P>0.05). The fALFF value of the right caudate nucleus showed significant differences among the three groups, with the SZ-Ⅰ group having a higher fALFF value than the SZ-Ⅱ group, and both SZ groups having higher fALFF values than the control group (all P<0.05, FDR corrected). The FC values from the right caudate nucleus to the right angular gyrus and the right caudate nucleus to the right inferior parietal lobule were higher in the SZ-Ⅱ group than in the SZ-Ⅰ group (all P<0.05, GRF corrected). In the SZ-Ⅰ group, the fALFF value of the right caudate nucleus was potentially negatively correlated with the maze test score (r=-0.388, PFDR=0.340), and in the SZ-Ⅱ group, the fALFF value of the right caudate nucleus was potentially negatively correlated with the spatial span test (r=-0.479, PFDR=0.100). Conclusions: There are differences in the brain functional activities and connections related to the right caudate nucleus in SZ patients with predominant positive or negative symptoms. The abnormal brain functional activities are associated with the specific cognitive impairments in SZ patients with different symptom characteristics, suggesting that the right caudate nucleus can be a potential functional imaging biomarker for differentiating SZ patients with different symptom characteristics.

PMID:42297586 | DOI:10.3760/cma.j.cn112137-20260410-00976

Psychiatry Res Neuroimaging. 2026 Jun 1;362:112255. doi: 10.1016/j.pscychresns.2026.112255. Online ahead of print.

ABSTRACT

Resting-state fMRI has been used to study aberrant functional connectivity properties in patients with anorexia nervosa (AN) at several stages of the illness. One popular way to extract these metrics is to use graph theory to showcase aberrant brain connectivity between patients with AN versus controls. However, most studies use classic analyses to investigate these differences, which could limit the number and choices of features used in one model. Instead, machine learning models have proven to be a promising tool in studying the functional connectivity of various disorders. In this study, we employ a combination of local graph metrics and a support vector machine to distinguish between first-onset AN (N = 56) cases and controls (N = 64). We replicate and extend prior work evaluating the predictive value of an existing machine learning approaches in detecting functional connectivity differences in patients with AN. Our method achieves an average classification accuracy of 65% with cross-validation evaluation. We further demonstrate that the results are driven mainly by the participation index of the nodes that are implicated in distinguishing the two groups. Our findings contribute to the growing body of evidence supporting the predictive value of resting-state fMRI in the study of anorexia nervosa.

PMID:42296672 | DOI:10.1016/j.pscychresns.2026.112255

Front Oncol. 2026 May 29;16:1850713. doi: 10.3389/fonc.2026.1850713. eCollection 2026.

ABSTRACT

BACKGROUND: Modern linguistic theories propose that language processing is supported by widely distributed large-scale modules that flexibly interact with domain-general networks. Progressive gliomas tend to reshape the language-associated systems causing dynamic reorganization that can be characterized by resting-state and task-based fMRI, thereby informing surgical decision-making. However, the interplay of neuropathological factors influencing reorganization patterns remains unclear, with ambiguity regarding the recruitment of cognitive resources across various levels of linguistic complexity.

METHODS: We included 100 patients with gliomas and 127 matched normal controls in this study. Activation analyses were performed on task fMRI data acquired during a picture-naming paradigm. Spectral DCM and connectome analyses were combined to examine state-dependent shifts between rest and task conditions. ANOVAs were used to assess relationships between clinicopathological factors and compensatory mechanisms. Mediation analyses were used to explore the mediated pathways among clinicopathological factors, topological indicators, and language performance.

RESULTS: First, we observed widely distributed activations associated with glioma-induced perilesional and remote reorganization patterns, exhibiting predominantly right-hemispheric lateralization in domain-general networks. VLSM analyses further showed that the spatial distribution of these activation clusters was significantly associated with tumor location and grade. Second, spectral DCM analyses indicated that task demands were associated with a greater number of positive effective connections across the reorganized language network, interactions among which were inherently dynamic varying with exogenous linguistic complexity and endogenous functional integrity. Third, ANOVAs suggested that alterations in language network topology were accompanied by flexible engagement of domain-general network components, which were associated with a partial restoration of the balance between integration and segregation under task conditions. Finally, we identified associations and potential mediation pathways among clinicopathological factors, topological properties, and language performance, consistent with the concept of glioma-related network metaplasticity.

CONCLUSION: Our findings highlight that the dynamics of language reorganization depend on clinicopathological factors of gliomas and may thus open new perspectives for personalized surgical strategies for functional protection in the era of network neurosurgery. These group-level findings provide a modeling framework that may guide future research toward individualized network assessment and surgical planning.

PMID:42294334 | PMC:PMC13259869 | DOI:10.3389/fonc.2026.1850713

Front Hum Neurosci. 2026 May 29;20:1859875. doi: 10.3389/fnhum.2026.1859875. eCollection 2026.

ABSTRACT

OBJECTIVE: Non-pharmacological somatic interventions can modulate brain function via body-brain communication, yet the underlying neurobiological mechanisms remain unclear. This study used resting-state fMRI and JuSpace spatial correlation to investigate how meridian-sinew therapy affects regional brain function and its spatial coupling with neurotransmitter systems in healthy volunteers.

METHODS: Forty-two healthy volunteers underwent resting-state fMRI and a 2-back working memory task before and after a 50 min meridian-sinew therapy session. We quantified changes in local brain activity using ALFF and ReHo. JuSpace was applied to examine spatial correlations between functional alteration maps and neurotransmitter receptor/transporter density atlases.

RESULTS: Post-intervention reaction time in the 2-back task was significantly reduced. Neuroimaging revealed increased ALFF and ReHo in the prefrontal cortex, and decreased ReHo in the left putamen and insula. Spatial correlation analyses showed that ALFF changes were significantly correlated with 5-HT2a, CB1, mGluR5, DAT, NAT, NMDA, SERT, and VAChT distributions, while ReHo changes correlated only with SERT.

CONCLUSION: Meridian-sinew therapy modulates regional brain physiology by enhancing prefrontal neural activity and suppressing subcortical interoceptive processing, accompanied by improved working memory efficiency. These functional changes show consistent spatial associations with multiple neurotransmitter systems. Our findings provide multimodal neuroimaging evidence for understanding how somatic interventions shape brain function through body-brain communication pathways.

PMID:42294096 | PMC:PMC13260330 | DOI:10.3389/fnhum.2026.1859875

Depress Anxiety. 2026 Jun 11;2026:4701907. doi: 10.1155/da/4701907. eCollection 2026.

ABSTRACT

BACKGROUND: This study aimed to investigate the neural basis of individual differences in antidepressant efficacy using an 8-week longitudinal multitime point resting-state functional magnetic resonance imaging (fMRI) design.

METHODS: Forty-eight patients with major depressive disorder (MDD) completed two or three scans, and 44 healthy controls (HCs) underwent a baseline scan. Patients were categorized into remission (MDDr) and nonremission (MDDnr) groups based on treatment outcomes. Group differences in resting-state functional connectivity (rsFC) of the amygdala subregions at baseline were examined among MDDr, MDDnr, and HCs. Longitudinal changes in the identified rsFC were compared between the MDDr and MDDnr groups. Correlation analyses were conducted to explore the relationship between baseline rsFC or its longitudinal changes and depressive symptom severity or improvement.

RESULTS: At baseline, rsFC between the right basolateral (BL) amygdala and the right supplementary motor area (SMA) was lower in the MDDr group but higher in the MDDnr group compared with HCs, although this effect did not survive multiple comparisons correction across amygdala subregions. The trajectory of this rsFC differed between the two patient groups during treatment, with normalization observed at 2 and 8 weeks posttreatment. Correlation analyses indicated that baseline rsFC was associated with treatment response and that longitudinal changes in rsFC were aligned with symptom improvement, although some associations did not survive multiple comparisons correction.

CONCLUSIONS: Our findings provide novel and valuable insights into the neural mechanisms underlying antidepressant response and highlight the role of amygdala subregional connectivity in explaining interindividual variability in treatment efficacy.

TRIAL REGISTRATION: ClinicalTrials.gov identifier: ChiCTR2400093823.

PMID:42292915 | PMC:PMC13254815 | DOI:10.1155/da/4701907

J Biomed Phys Eng. 2026 Jun 1;16(3):205-218. doi: 10.31661/jbpe.v0i0.2505-1930. eCollection 2026 Jun.

ABSTRACT

BACKGROUND: Functional Magnetic Resonance Imaging (fMRI) is a powerful modality for investigating changes in healthy brains and those with disorders. Anosmia, an olfactory disorder, is commonly associated with traumatic brain injury, particularly in patients suffering from severe trauma.

OBJECTIVE: In this study, we aimed to utilize Resting-State fMRI (rs-fMRI) to examine changes in Functional Connectivity (FC) networks between Healthy Controls (HCs) and patients with Post-Traumatic Anosmia (PTA).

MATERIAL AND METHODS: In this retrospective study, we performed rs-fMRI on forty-four PTA patients and forty-three HCs. The Sniffin' Sticks test was used to assess olfactory function. Seed-based Analysis (SBA) and Independent Component Analysis (ICA) were conducted using MATLAB-based imaging software.

RESULTS: PTA patients showed lower Threshold-Discrimination-Identification (TDI) scores compared to HCs. SBA revealed increased FC correlations in the anterior cingulate cortex, piriform, insular cortex, and prefrontal area in PTA patients. Using ICA on the whole brain network, we found increased FC in the right frontal pole, cerebellum, right putamen, anterior cingulate cortex, postcentral gyrus, orbitofrontal cortex, and amygdala in PTA compared to HCs. In PTA patients, global efficiency of the entire brain network showed a significant association with olfactory performance.

CONCLUSION: This study suggests that neural-level olfactory deficits following head trauma are most accurately characterized through SBA and ICA analyses in higher-order regions outside the primary olfactory cortex.

PMID:42292679 | PMC:PMC13263385 | DOI:10.31661/jbpe.v0i0.2505-1930

Front Neurosci. 2026 May 29;20:1803897. doi: 10.3389/fnins.2026.1803897. eCollection 2026.

ABSTRACT

INTRODUCTION: Neural network effects of transcranial direct current stimulation (tDCS) are poorly understood. Here, we introduce a prospective, empirically informed, multimodal functional magnetic resonance imaging (fMRI) framework for guiding target selection and hypothesis-based analysis in future focal tDCS-fMRI studies.

METHODS: We illustrate our approach by using data of 37 healthy individuals (19 females; mean age ± SD = 25.8 ± 5.9) recruited from two tDCS-fMRI studies that were acquired at the same scanner and with placebo-tDCS. Participants completed two resting-state (RS) sessions and two task-fMRI sessions (object-location memory, OLM, or associative picture-pseudoword learning, APPL, experiments). Seed-based RS analysis identified functional networks originating from target regions for focal tDCS (right occipito-temporal cortex, rOTC; left ventral IFG, lvIFG) and established their test-retest reliability (TRR), using intraclass correlation coefficients (ICC). Dice coefficients quantified overlap between seeded RS networks and task-evoked activity to identify task-active regions potentially affected by downstream network effects from the target regions.

RESULTS: Seed-based analyses identified highly reliable ventral visual-limbic (rOTC) and language-related networks (lvIFG), with 72-77% of voxels showing good-to-excellent TRR (ICC ≥ 0.75). Only a subset of network voxels identified by the RS analyses overlapped with activity elicited by the experimental paradigms (ranging from 7.5-55%), with larger correspondence for the OLM (Dice: 0.249-0.349; APPL 0.065-0.106). Therefore, the degree of potential tDCS network effects varied substantially depending on the target region, the extent of its functional network and task-specific activity patterns. Degree of correspondence was further mediated by the selected contrasts-of-interest in the task-based analyses, with more conservative control conditions resulting in reduced overlap.

CONCLUSION: In sum, we established a principled multimodal fMRI framework bridging a critical gap in neuromodulation research. By integrating reliable intrinsic connectivity maps with task-evoked activity patterns, we provide a method to prospectively identify network-level targets for focal brain stimulation and generate hypotheses for tDCS-fMRI analyses. This approach shifts the rationale from stimulating isolated brain regions to strategically targeting key nodes within a predefined functional pathway.

PMID:42292341 | PMC:PMC13260067 | DOI:10.3389/fnins.2026.1803897

Transl Psychiatry. 2026 Jun 15. doi: 10.1038/s41398-026-04142-y. Online ahead of print.

ABSTRACT

Anxiety is highly comorbid with disorders of gut-brain interaction, including irritable bowel syndrome (IBS). The gut microbiota is implicated in both conditions, yet the underlying mechanisms remain unclear. This study aimed to elucidate the neuropathological basis of anxiety in diarrhea-predominant IBS (IBS-D) and identify potential microbiota-based therapeutic targets. Here, Mendelian randomization analysis established a bidirectional causal relationship between IBS and anxiety. In our clinical cohort, 35.85% of IBS-D patients presented with comorbid anxiety, and GAD-7 anxiety scores correlated significantly with IBS symptom severity. Resting-state functional magnetic resonance imaging (rs-fMRI) revealed that IBS-D patients with comorbid anxiety exhibited significantly altered regional homogeneity (ReHo) in nine brain regions, with the most pronounced reductions observed in the bilateral amygdala. Moreover, bilateral amygdala ReHo could effectively differentiate these patients, with receiver operating characteristic (ROC) analysis yielding area under curve (AUC) of 0.746. To investigate the underlying pathology, we established a water avoidance stress (WAS) mouse model that successfully recapitulated the anxiety-like behaviors and visceral hypersensitivity observed clinically. Critically, these phenotypes were transmissible, as recipient mice colonized with microbiota from the WAS-induced IBS-D group also developed visceral hypersensitivity and anxiety-like behaviors. Phocaeicola vulgatus was determined as a key bacterial strain significantly depleted in both our IBS-D patient cohort and the WAS-induced IBS-D mice, with its abundance negatively correlating with anxiety levels. As anticipated, IBS-D patients with lower abundance of P. vulgatus exhibited reduced amygdala ReHo and Mendelian randomization analysis identified P. vulgatus as a protective factor against anxiety. Correspondingly, therapeutic supplementation with P. vulgatus ameliorated anxiety-like behaviors in WAS-induced IBS-D mice by attenuating neuroinflammation and restoring neuronal morphology in the amygdala. In conclusion, this study provides the first evidence that P. vulgatus can alleviate anxiety in IBS by targeting amygdala-centered neuropathology, presenting a novel psychobiotic strategy for treating emotional comorbidities in disorders of gut-brain interaction.

PMID:42289401 | DOI:10.1038/s41398-026-04142-y

Addict Behav. 2026 Jun 11;182:108775. doi: 10.1016/j.addbeh.2026.108775. Online ahead of print.

ABSTRACT

BACKGROUND: Tobacco use disorder (TUD) is a major public health issue with significant individual differences. A deeper understanding of its neurobiology and reliable biomarkers is needed. This study investigated whether inter-subject variability in resting-state brain functional connectivity (IVFC) could serve as such a marker for TUD.

METHODS: Resting-state fMRI data from 123 male TUD patients and 123 healthy controls (HCs) were collected and analyzed. IVFC was computed within seven major brain lobes. Five machine learning models (random forest, gradient boosting, extra trees, multi-layer perceptron, and support vector machine) were trained to classify the groups based on IVFC features. Univariate regression was conducted with each lobe's IVFC predicting clinical scores. Multivariate stepwise regression was then performed to identify the best combination of IVFC predictors for dependence severity.

RESULTS: TUD showed significantly altered IVFC in six brain lobes compared to controls, with the largest difference in the insular lobe. The machine learning models, particularly the extra trees classifier, achieved high accuracy (up to 88 %) in classifying TUD, primarily utilizing features from the temporal and limbic lobes. Univariate regression showed that higher IVFC in frontal, insular, limbic, and temporal lobes was associated with lower nicotine dependence severity. Multivariate stepwise regression identified insular lobe IVFC as the sole independent predictor.

CONCLUSIONS: These findings suggest that widespread alterations in IVFC may characterize TUD and relate to its clinical severity, indicating the potential value of this measure as a neurobiological marker. The combination of IVFC analysis with machine learning appears to offer a promising approach for distinguishing TUD, which could contribute to advancing our understanding of its underlying brain mechanisms.

PMID:42289141 | DOI:10.1016/j.addbeh.2026.108775

BMC Med Imaging. 2026 Jun 13. doi: 10.1186/s12880-026-02503-z. Online ahead of print.

ABSTRACT

BACKGROUND: Cerebrovascular reactivity(CVR), a key indicator of cerebrovascular reserve, is crucial for evaluating cerebrovascular pathophysiology. This study employed resting-state MRI (rs-MRI) to assess CVR alteration in patients with unilateral middle cerebral artery stenosis or occlusion (MCA-S) under non-hypercapnic conditions, comparing them with healthy controls.

METHODS: A total of 41 patients with unilateral MCA-S and 50 age-, sex-, and education-matched normal controls (NC). All underwent rs-MRI and neuropsychological assessments. CVR was derived from rs-fMRI frequency band signals, and t-test was conducted to obtain the CVR-differentiated brain regions. Exploratory seed-based FC analysis was further performed to characterize the network context of regions showing altered CVR. Partial correlation analyses explored relationships between these differential brain regions and both neuropsychological assessments and clinical indicators. Discriminative performance of the CVR-related metric between MCA-S and controls was evaluated using receiver operating characteristic (ROC) curves.

RESULTS: Compared with the NC group, patients with MCA-S exhibited increased CVR in the contralesional Cerebellum Crus1 (CC1) and decreased iCVR in the ipsilesional postcentral gyrus (PoCG). When contralesional CC1 served as regions of interest (ROIs), increased FC was observed in the ipsilesional middle frontal gyrus (MFG) and the contralesional precuneus of MCA-S patients. The partial correlation analysis indicated a positive correlation between the FC of the ipsilesional MFG and anxiety scores (r = 0.404, (95%CI: 0.106, 0.631), P = 0.012, P-FDR = 0.030). Using ipsilesional PoCG as the ROI, MCA-S patients showed significantly decreased FC in ipsilesional PoCG, contralesional precentral gyrus (PreCG), and ipsilesional supplementary motor area. The FC of the contralesional PreCG showed a positive correlation with anxiety scores (r = 0.436, (95%CI: 0.142, 0.658), P = 0.006, P-FDR = 0.030). ROC analysis demonstrated strong diagnostic accuracy for CVR in CC1 (AUC = 0.809) and PoCG (AUC = 0.787), with a combined AUC of 0.866.

CONCLUSION: Non-hypercapnic rs-MRI effectively evaluates CVR alterations in MCA-S patients and may serve as a complementary physiological biomarker for characterizing hemodynamic alteration.

PMID:42288826 | DOI:10.1186/s12880-026-02503-z

Brain Res Bull. 2026 Jun 13:112008. doi: 10.1016/j.brainresbull.2026.112008. Online ahead of print.

ABSTRACT

OBJECTIVE: This study uses resting-state functional magnetic resonance imaging (fMRI) to understand and compare the effects of hypoxic conditions at high and ultra-high altitudes.

METHODS: Regional homogeneity (ReHo) and degree centrality (DC) values were calculated and compared between 47 low-altitude (LA, <500m), 39 high-altitude (HA, 1520m), and 34 ultra-high-altitude (UHA, 3650m) healthy adults. Correlations with heart rate and blood oxygen saturation (SpO₂) were analyzed.

RESULTS: Compared to the LA group, the UHA/HA group had significantly lower ReHo values in the bilateral basal ganglia, prefrontal lobes (left/right), left paracentral lobule, and these were positively correlated with SpO₂. Conversely, ReHo values were significantly higher in the bilateral posterior occipital and left superior parietal lobes, and were negatively correlated with SpO₂. DC values were significantly lower in the left orbitofrontal cortex, bilateral pallidum and left inferior frontal gyrus, and were positively correlated with SpO₂. Synchronous decreases in ReHo and DC were found in the left prefrontal cortex, bilateral pallidum and putamen.

CONCLUSION: In high-altitude environments, functional activity is decreased in the basal ganglia, prefrontal cortex, and hippocampus, is accompanied by a compensatory increase in the occipital and superior parietal lobes. Concurrent reductions in DC and ReHo within the left prefrontal cortex, bilateral pallidum and putamen might serve as biomarkers for high-altitude hypoxic functional alterations and aid early detection and intervention of hypoxia-induced brain damage.

PMID:42288178 | DOI:10.1016/j.brainresbull.2026.112008

Brain Inform. 2026 Jun 13. doi: 10.1186/s40708-026-00312-2. Online ahead of print.

ABSTRACT

Deficits in neural information integration are hypothesized to underlie diverse psychiatric symptoms, yet the specific patterns of alteration across different disorders remain unclear. In this study, we decomposed information dynamics between brain regions into synergistic and redundant components using a recent information-theoretic approach based on Partial Information Decomposition and applied to resting-state fMRI data from individuals with schizophrenia (SZ), autism spectrum disorder (ASD), and attention-deficit/hyperactivity disorder (ADHD). Our analysis revealed distinct disorder-specific profiles: SZ and ASD exhibited a widespread reduction in synergy, whereas ADHD showed a contrasting increase. Furthermore, ASD was uniquely characterized by a significant reduction in redundancy. Meta-analytic functional annotation using NeuroSynth associated synergy with higher-order cognitive functions and redundancy with lower-level sensorimotor processing. To investigate multivariate organization of these patterns that distinguish psychiatric diagnoses, we employed Linear Discriminant Analysis (LDA). This analysis demonstrated that synergy and redundancy partially capture distinct dimensions of network variation, exhibiting substantial complementarity in their multivariate structure. While redundancy overlapped considerably with correlation-based connectivity, synergy reflected additional structure not fully represented by conventional measures. Together, these findings indicate that decomposing information dynamics provides complementary perspectives on large-scale network organization, offering a refined framework for characterizing psychiatric and neurodevelopmental disorders.

PMID:42287597 | DOI:10.1186/s40708-026-00312-2

Brain Connect. 2026 Jun 13:21580014261456366. doi: 10.1177/21580014261456366. Online ahead of print.

ABSTRACT

BACKGROUND: Investigation of the neural substrates of post-traumatic stress disorder (PTSD) in military personnel using whole-brain approaches remains scarce, hindering the development of circuit-based neuromodulatory interventions.

OBJECTIVES: This study aimed to identify potential associations between clinical symptoms and whole-brain resting-state functional connectivity with magnetic resonance imaging in military personnel with adulthood-onset war-related PTSD.

METHODS: Thirty-seven soldiers from the Canadian Armed Forces with moderate to severe treatment-resistant PTSD participated in this study. We assessed PTSD, anxiety and depressive symptoms, quality of life, and time since trauma. We characterized the whole-brain functional connectome using independent component analysis and regions of interest (ROI)-to-ROI connectivity, as well as its topology using graph theory.

RESULTS: Greater severity of PTSD and anxiety symptoms was associated with lower connectivity (r < 0) between the default mode network (DMN) and frontoparietal network. Greater severity of PTSD symptoms was also associated with a higher nodal clustering coefficient of the inferior parietal lobule from the DMN. Greater severity of anxiety symptoms and longer time since trauma was the only clinical variables that correlated with higher connectivity patterns, all involving the visual networks (the frontoparietal-visual, the visual-DMN, and within-visual networks).

CONCLUSIONS: This work contributes to identifying brain targets for the development of personalized neuromodulatory interventions. In particular, the DMN may be a promising target to alleviate PTSD symptoms, and the visual network may be a target to treat comorbid anxiety symptoms.

PMID:42287083 | DOI:10.1177/21580014261456366

J Neuroeng Rehabil. 2026 Jun 12. doi: 10.1186/s12984-026-02048-w. Online ahead of print.

ABSTRACT

BACKGROUND: Motor recovery after ischemic stroke involves complex functional reorganization, yet the underlying molecular and cellular mechanisms remain poorly understood. This study integrated longitudinal neuroimaging and brain-wide transcriptomic data to characterize the functional dynamics and their gene-expression correlates during motor recovery following subcortical ischemic stroke.

METHODS: We recruited 34 patients with acute right subcortical ischemic stroke and 32 age- and sex-matched healthy controls. All participants underwent baseline resting-state functional magnetic resonance imaging, with 22 patients completing a 3-month follow-up scan. Spontaneous neural activity was assessed using the amplitude of low-frequency fluctuations (ALFF), followed by seed-based whole-brain functional connectivity (FC) analysis from regions with longitudinal ALFF differences. We then applied partial least squares (PLS) regression to spatially correlate longitudinal ALFF changes with transcriptomic data from the Allen Human Brain Atlas, identifying a gene expression profile spatially associated with these ALFF changes. These genes were subsequently subjected to functional enrichment and cell-type specificity analyses.

RESULTS: Compared with healthy controls, acute-stage stroke patients showed significantly decreased ALFF in the contralesional precentral gyrus. At 3-month follow-up, ALFF in this region significantly increased, accompanied by strengthened interhemispheric FC with its ipsilesional homologue. Critically, these longitudinal changes in ALFF and interhemispheric FC were significantly correlated with motor recovery. Based on PLS regression, we further identified a specific gene expression profile spatially correlated with the observed ALFF changes. This gene set was specifically enriched in excitatory and inhibitory neurons and was primarily involved in synaptic structure and signaling.

CONCLUSIONS: By linking macroscale imaging dynamics with microscale molecular features, this study demonstrates that the contralesional precentral gyrus plays a supportive role in motor recovery during the subacute phase of subcortical ischemic stroke, with neuronal synaptic plasticity as a potential mechanism. Collectively, these findings inform stage-specific strategies to target interhemispheric inhibition.

PMID:42286663 | DOI:10.1186/s12984-026-02048-w