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J Psychiatr Res. 2026 Feb 17;196:291-305. doi: 10.1016/j.jpsychires.2026.01.058. Online ahead of print.

ABSTRACT

In recent years, the use of functional Magnetic Resonance Imaging (fMRI) methods to predict treatment response in schizophrenia (SCZ) through statistical and machine learning (ML) algorithms has increased. We conducted a comprehensive literature review to assess the role of various fMRI measures in predicting pharmacological treatment response in psychosis. Literature available on PubMed from January 1990 to December 2023 was reviewed, and 21 fMRI studies were included. The results suggest that many studies have employed ML techniques, which may enhance the accuracy of treatment outcome predictions. Additionally, several studies utilizing resting-state fMRI have identified potential associations of functional connectivity patterns across multiple large-scale networks, including the default mode network (DMN), the salience network (SN), the central executive network (CEN), and sensory-motor circuits. These findings suggest that altered connectivity within and between these networks may be relevant for personalized treatment strategies in patients with psychosis, although further investigation is needed to confirm their predictive value. Future research should focus on developing robust and generalizable models to more reliably optimize treatment outcomes in psychosis.

PMID:41722425 | DOI:10.1016/j.jpsychires.2026.01.058

J Neuroimaging. 2026 Jan-Feb;36(1):e70129. doi: 10.1111/jon.70129.

ABSTRACT

BACKGROUND AND PURPOSE: Resting state functional connectivity can be measured using resting state functional MRI (fMRI), but also task-dependent fMRI in blocked designs. The latter has been demonstrated in healthy participants but not yet validated in clinical cohorts. Since functional connectivity of resting state networks (e.g., default mode network [DMN] and somatomotor network [SMN]) is altered in people with epilepsy, and the impact of the disease on the quality of the intermittent resting state data is unclear, we aimed to validate the method using a clinical fMRI in people with epilepsy.

METHODS: We compared functional connectivity derived from a standard resting state with rest periods of a clinical language fMRI (intermittent resting state) of 92 people with focal epilepsy. Both methods were analyzed across different aspects of functional connectivity: topography, within-network connectivity, and group-level comparisons. Therefore, we conducted independent component analyses (ICAs), similarity-, regions of interest (ROI)-to-ROI-, and second-level seed-based analyses.

RESULTS: Results indicated similar ICA-derived topography of DMN and SMN from both methods. Within-network connectivity also yielded comparable results. Seed-based analyses of left and right hippocampal connectivity in people with left and right temporal lobe epilepsy also revealed analogous results, with minor restrictions in right hippocampal connectivity.

CONCLUSION: The intermittent resting state method produces highly similar results to a standard resting state method in people with epilepsy across different aspects of functional connectivity. It is, therefore, an efficient approach to gain insights into functional connectivity networks in a clinical cohort without performing an additional resting state fMRI.

PMID:41721540 | DOI:10.1111/jon.70129

PLoS Comput Biol. 2026 Feb 20;22(2):e1013995. doi: 10.1371/journal.pcbi.1013995. Online ahead of print.

ABSTRACT

Resting state fMRI signals in mammals exhibit rich dynamics on a fast, frame-by-frame timescale of seconds, including the robust emergence of recurring fMRI co-activation patterns (CAPs). To understand how such dynamics emerges from the underlying anatomical cortico-cortical connectivity, we developed a whole-cortex model of resting-state fMRI signals in the mouse. Our model implemented neural input-output nonlinearities and excitatory-inhibitory interactions within cortical regions, as well as directed anatomical connectivity between regions inferred from the Allen mouse brain atlas. We found that, even if the model parameters were fitted to explain static properties of fMRI signals on the timescale of minutes, the model generated rich frame-by-frame attractor dynamics, with multiple stationary and oscillatory attractors. Guided by these theoretical predictions, we found that empirical mouse fMRI time series exhibited analogous signatures of attractor dynamics, and that model attractors recapitulated the topographical organization of empirical fMRI CAPs. The model established key relationships between attractor dynamics, CAPs and features of the directed cortico-cortical intra- and inter-hemispheric anatomical connectivity. Specifically, we found that neglecting fiber directionality severely affected the number of model's attractors and their ability to explain CAPs. Furthermore, modifying inter-hemispheric anatomical connectivity strength by decreasing or increasing it from the value of real mouse anatomical data, resulted in fewer attractors generated by cortico-cortical interactions and reduced non-homotopic features of the attractors generated by the model, which were important for better predicting empirical CAPs. These results offer novel theoretical insight into the dynamic organization of resting state fMRI in the mouse brain and suggest that the frame-wise BOLD activity captured by CAPs reflects an emerging property of cortical dynamics resulting from directed cortico-cortical interactions.

PMID:41719380 | DOI:10.1371/journal.pcbi.1013995

Aging Male. 2026 Dec 31;29(1):2632959. doi: 10.1080/13685538.2026.2632959. Epub 2026 Feb 19.

ABSTRACT

BACKGROUND: Integrating brain multimodal features (e.g. structural, functional, and cerebrospinal fluid metabolomic data) offers a promising approach to elucidate the neural mechanisms underlying erectile dysfunction (ED).

METHODS: Using two-sample Mendelian randomization, we assessed causal effects of 83 whole-brain morphological, 191 resting-state fMRI, and 338 cerebrospinal fluid metabolite phenotypes on ED. A p-value < 0.05 indicated statistical significance.

RESULTS: Reduced volumes in the left and right accumbens and enlarged volumes in the left pars opercularis and right putamen were associated with increased ED risk. Increased connectivity between occipital/precuneus and superior frontal gyrus (default/executive networks) elevated ED risk, while connectivity between postcentral/precentral and subcortical regions (motor/subcortical-cerebellum networks) reduced risk. Several metabolites were identified: elevated 4-Methylcatechol sulfate, adenine, gulonate, pyroglutamine, and thioproline increased ED risk, while higher cortisone, malate, phosphate, and X-21733 decreased risk.

CONCLUSION: We identified four morphological, two functional connectivity, and nine metabolic causal relationships with ED, enhancing understanding and suggesting novel therapeutic targets.

PMID:41715886 | DOI:10.1080/13685538.2026.2632959

Brain Stimul. 2026 Feb 17:103055. doi: 10.1016/j.brs.2026.103055. Online ahead of print.

ABSTRACT

BACKGROUND: This study aims to investigate whether intermittent theta-burst stimulation (iTBS) over the cerebellar vermis enhances balance recovery in subacute stroke, and examine the underlying neural mechanisms.

METHODS: Fifty-two patients with subacute stroke and balance impairment were randomized to receive either three weeks of iTBS (n = 26) or sham stimulation (n = 26). The primary outcome was the Berg Balance Scale (BBS). Secondary outcomes included additional motor function measures and surface electromyography (sEMG). Clinical assessments were conducted at baseline and at weeks 1, 2, 3, and 6 after treatment onset. sEMG and resting-state functional MRI were acquired before and after the intervention. Seed-based functional connectivity (FC) of the cerebellar vermis was analyzed using a 2 × 2 mixed-effects ANOVA. Associations between changes in BBS (ΔBBS) and FC alterations were examined using Pearson correlation analyses. Patients were further stratified into subgroups based on the direction of FC change (increase vs. decrease) to characterize distinct clinical and neural response patterns.

RESULTS: Compared with the sham group, patients receiving iTBS showed significantly greater improvements in balance, accompanied by increased sEMG activation of trunk and proximal lower-limb muscles, with effects sustained at follow-up. FC analyses revealed enhanced connectivity between Vermis X and bilateral occipitotemporal cortices, which was positively correlated with balance improvement (ΔBBS). Subgroup analyses identified distinct clinical and neural profiles: the FC-increase subgroup demonstrated sustained functional gains and enhanced cerebello-frontal connectivity, whereas the FC-decrease subgroup exhibited short-term improvement and reduced intracerebellar connectivity.

DISCUSSION: These findings indicate that cerebellar vermis-targeted iTBS facilitates balance recovery after subacute stroke through reorganization of cerebello-visual networks. Subgroup-specific patterns further highlight heterogeneous intracerebellar and cerebello-frontal plasticity, supporting the notion of patient-specific network pathways underlying the therapeutic effects of cerebellar stimulation.

PMID:41713679 | DOI:10.1016/j.brs.2026.103055

J Psychiatr Res. 2026 Feb 10;196:234-243. doi: 10.1016/j.jpsychires.2026.02.006. Online ahead of print.

ABSTRACT

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects normal brain development and results in impaired brain function. Most studies have focused on connectivity changes within the traditional low-frequency range, whereas the frequency-dependent nature of brain dynamics remains largely unexplored. This study employed whole-brain co-activation pattern (CAP) analysis to investigate the frequency-dependent spatiotemporal dynamics of spontaneous brain activity in ASD across three frequency bands: LFO (0.01-0.1 Hz), slow-5 (0.01-0.027 Hz), and slow-4 (0.027-0.073 Hz). Resting-state fMRI data were obtained from the NYU site of the ABIDE I database, comprising 52 individuals with ASD and 52 typical controls (TCs). Six CAPs were identified within each frequency band using k-means clustering. We then calculated and compared CAP dynamics, including the appearance frequency, duration, entry rate, and transition probability. Our results revealed that (1) CAPs across different frequency bands exhibited overall similar spatial patterns but showed significant differences in temporal evolution, with the slow-5 band demonstrating lower dynamic variability; (2) compared to the TC group, individuals with ASD exhibited abnormal brain dynamics in both the LFO and slow-4 bands, whereas no significant differences were observed in the slow-5 band; and (3) significant correlations were found between the dynamic metrics of CAPs in the LFO and slow-5 bands and the severity of restricted and repetitive behaviors (RRB) in individuals with ASD. These findings reveal frequency-specific abnormalities in brain dynamics in ASD, providing new insights into its time-varying neural mechanisms.

PMID:41713174 | DOI:10.1016/j.jpsychires.2026.02.006

IEEE J Biomed Health Inform. 2026 Feb 19;PP. doi: 10.1109/JBHI.2026.3666277. Online ahead of print.

ABSTRACT

Recent studies have demonstrated strong associations between the changes in dynamic functional connectivity (FC) and both behavioral and cognitive functions. The sliding window technique is the most widely used method for evaluating dynamic FC; however, it faces two key challenges: distributional shifts across windows and high dimensionality, as FC is computed across windows of the entire time series. To address these issues, we propose BRAINMAP (Bi-level Representation using Attention for INterpretability with Mamba-Aided Prediction) to model the dynamic FC of the brain. BRAINMAP employs the Optimal Transport technique to correct distributional shifts across sliding windows and leverages Graph Neural Networks (GNNs) in conjunction with a hybrid approach that integrates an attention mechanism and the Mamba block to effectively capture spatiotemporal features for functional MR images. Finally, we introduce a novel Top-K sliding window feature selection algorithm to induce sparsity in dynamic FC. We conducted an extensive evaluation of our model for diagnosing Attention Deficit Hyperactivity Disorder (ADHD) using three resting-state fMRI datasets: ADHD-200, UCLA, and CNI-TLC, which comprise a total of 447 subjects with ADHD and 845 typically developing controls. Our architecture outperformed existing state-of-the-art dynamic FC models in ADHD detection, achieving improvements ranging from 3% to 12% across the three datasets. We demonstrate that our proposed model produces robust biomarkers, most notably the connection between the dorsal attention network and the visual network. Using an association study, we further establish the clinical relevance of the identified biomarkers.

PMID:41712397 | DOI:10.1109/JBHI.2026.3666277

Brain Connect. 2026 Feb 18:21580014261420411. doi: 10.1177/21580014261420411. Online ahead of print.

ABSTRACT

BACKGROUND: Intracranial dural arteriovenous fistula (DAVF) disrupts cerebral hemodynamics and can lead to widespread alterations in brain network connectivity and cognitive function. This study aimed to evaluate spontaneous brain activity and cognitive changes in DAVF patients using resting-state functional MRI (rsfMRI) and neuropsychological assessment, with evaluations conducted at baseline, 1 month, and 1 year postembolization to capture dynamic recovery-related changes in brain function and cognition.

METHODS: Fifty DAVF patients and 50 age and sex-matched healthy controls underwent rsfMRI. Amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) metrics were computed at both whole-brain and network levels. Cognitive performance was assessed using Addenbrooke's Cognitive Examination (ACE). All patients underwent embolization, followed by rsfMRI and ACE evaluations at 1 month and 1 year. ACE scores were included as covariates to explore cognitive-network associations.

RESULTS: Compared with controls, DAVF patients showed significantly increased ALFF in cerebellar regions and decreased ALFF/fALFF in frontal, insular, and parietal areas, especially within the Default Mode Network (DMN) and Dorsal Attention Network (DAN). Postembolization, rsfMRI metrics showed normalization trends, especially in DMN and DAN, mirroring improvements in ACE scores. ACE-based covariate analysis revealed domain-specific correlations: memory scores correlated with ALFF in the DMN (r = 0.62), and visuospatial scores with DAN (r = 0.55).

CONCLUSIONS: This study provides longitudinal evidence that DAVF disrupts brain network integrity and cognition, with partial recovery following treatment. rsfMRI-derived ALFF and fALFF measures, particularly when analyzed alongside cognitive scores, may provide preliminary support for future clinical applications in DAVF prognosis and monitoring.

PMID:41709435 | DOI:10.1177/21580014261420411

Transl Psychiatry. 2026 Feb 18. doi: 10.1038/s41398-026-03837-6. Online ahead of print.

ABSTRACT

Internet gaming disorder (IGD) has been recognized as a serious mental illness and is often accompanied by depression (IGD-D). An ideal treatment strategy should have effects on both the conditions. Mindfulness meditation (MM) has attracted substantial attention for the treatment of psychiatric diseases; however, its effects on IGD-D and the underlying mechanisms remain unknown. A total of 70 patients with IGD-D were randomly divided into the MM and progressive muscle relaxation (PMR) groups. Of these patients, 61 completed the 1-month study (MM group, n = 34; PMR group, n = 27), including pre- and post-resting-state functional magnetic resonance imaging (fMRI) and 8 training sessions. Regional homogeneity and degree centrality were calculated, and overlapping brain regions were selected as seed points for functional connectivity (FC) analysis. The correlation of FC with behavioral data and neurotransmitters was subsequently evaluated. Compared with the PMR group, the MM group had less severe depression, addiction, and cravings. FC analysis showed that MM increased FC in the executive control, frontal-striatal, and default mode networks. FC was significantly correlated with 5-Hydroxytryptamine 1 A receptor, serotonin transporter, vesicular acetylcholine transporter and dopamine receptors D1 and D2. This study demonstrated that MM was effective in the treatment of IGD-D. MM altered the default mode network, enhanced top-down control, and emotion regulation, and disrupted negative reinforcement mechanisms. These phenomena were supported by the correlation between FC and behavioral as well as biochemical measures, suggesting that MM is a promising therapy for IGD-D.

PMID:41708591 | DOI:10.1038/s41398-026-03837-6

Neurosci Lett. 2026 Feb 16:138545. doi: 10.1016/j.neulet.2026.138545. Online ahead of print.

ABSTRACT

Disruptions in default mode network (DMN) connectivity are well documented in Alzheimer's disease (AD), yet their associations with specific cognitive domains remain unclear. This study examined relationships between anterior and posterior DMN functional connectivity and memory, executive function, and language performance across the AD continuum. We conducted a cross-sectional analysis of resting-state fMRI and composite cognitive scores from 154 participants (61 cognitively normal, 68 mild cognitive impairment [MCI], and 25 AD). DMN connectivity metrics were derived from region-of-interest-to-voxel correlations within anterior (aDMN) and posterior (pDMN) subdivisions. Associations between DMN measures and cognitive domains were assessed using multiple linear regression adjusted for age, sex, and years of education, with correction for multiple comparisons. No DMN measure was significantly associated with memory performance in any diagnostic group after correction. In the MCI group, executive and language performance were associated with anterior-posterior DMN connectivity, with weaker coupling linked to poorer performance across these domains. No significant DMN-cognition associations were observed in the cognitively normal or AD groups. After additional adjustment for white matter hyperintensities, only anterior-posterior DMN connectivity remained significantly associated with executive and language performance in the MCI group. Overall, DMN connectivity-cognition relationships were domain-specific and most evident in MCI, supporting the concept of a transitional stage in which network-level functional organization is related to cognitive performance.

PMID:41707903 | DOI:10.1016/j.neulet.2026.138545

J Affect Disord. 2026 Feb 16:121407. doi: 10.1016/j.jad.2026.121407. Online ahead of print.

ABSTRACT

OBJECTIVE: Suicide is a major cause of mortality in bipolar disorder (BD), yet its neural underpinnings remain insufficiently understood. Suicide risk is thought to involve impaired cognitive-emotional flexibility arising from fundamental disturbances in brain dynamics. This study aimed to test this hypothesis by characterizing the energetic and dynamical constraints underlying suicide vulnerability in BD.

METHODS: We applied energy landscape modeling to resting-state fMRI data from 123 individuals with BD (61 suicide attempters, 62 non-attempters) and 68 healthy controls. Brain activity was modeled as transitions between functional states, enabling quantification of neural rigidity. Group-level comparisons and correlation analyses were conducted to identify attractor stability, transition patterns, and their associations with clinical and cognitive measures.

RESULTS: Four dominant attractor basins were identified. Basins A and D showed progressively reduced appearance frequency and stability from healthy controls to non-attempters and suicide attempters. Increasing suicide risk was associated with greater neural rigidity, reflected in a more constrained transition architecture with shorter and more repetitive transition paths in suicide attempters. Lower stability of basin A was associated with higher suicide risk, with cognitive impairment statistically accounting for part of this association in mediation analyses.

CONCLUSION: Suicide vulnerability in BD is associated with entrenched functional brain states, reduced transition diversity, and elevated energetic constraints that may limit adaptive brain-state reconfiguration. These findings provide a mechanistic account of neural rigidity and suggest that altered brain-state dynamics may serve as a potential biomarker of suicide risk in BD.

PMID:41707727 | DOI:10.1016/j.jad.2026.121407

Neuroimage Clin. 2026 Feb 13;49:103965. doi: 10.1016/j.nicl.2026.103965. Online ahead of print.

ABSTRACT

Children, adolescents, and young adults with congenital heart disease (CHD) frequently experience disruptions in neurodevelopment affecting their executive functioning and other cognitive abilities, which in turn can impact academic performance, psychosocial adjustment, and overall quality of life. This exploratory study aims to investigate the impact of CHD on functional brain network connectivity and cognitive function, with a particular focus on executive functioning. Rather than relying on a single network construction method or arbitrary thresholds, our study methodically employed both weighted networks and binarized networks generated using absolute and proportional thresholding. This cross-method approach enables us to identify functional connectivity features that persist across heuristically and arbitrarily defined parameters, and to evaluate their association with neurocognition. Using resting-state fMRI data, we examined several network metrics across brain regions using three network construction types: weighted networks, absolute-threshold binarized networks, and proportional-threshold binarized networks. Regression models were then fit to neuropsychological test scores using metrics obtained from each network construction approach. Our results identified differences in network connectivity with a predilection for temporal, occipital, and subcortical regions, across both weighted and binarized networks. Furthermore, we identified distinct correlations between network metrics and cognitive performance, suggesting potential compensatory mechanisms within specific brain regions. These results provide an initial, methodologically transparent characterization of altered network organization in CHD and offer directions for future hypothesis-driven investigations.

PMID:41707454 | DOI:10.1016/j.nicl.2026.103965

EBioMedicine. 2025 Nov;121:105986. doi: 10.1016/j.ebiom.2025.105986. Epub 2025 Oct 25.

ABSTRACT

BACKGROUND: KCNQ2-developmental and epileptic encephalopathy (KCNQ2-DEE) is a severe neurodevelopmental disorder (NDD) characterised by early-life seizures but persistent cognitive impairment. The absence of early, quantifiable preclinical biomarkers for neurodevelopmental dysfunction limits the evaluation of new treatments. We hypothesise that key brain maturation processes are altered early in disease development and could serve as biomarkers for neurodevelopmental dysfunction.

METHODS: We performed longitudinal in-vivo brain imaging in 37 kcnq2Thr274Met/+ (KI) mice and 31 wild-type (WT) controls at three developmental stages: infancy (P14-21), juvenile (P32-42), and adulthood (P83-106). Resting-state functional MRI (rs-fMRI) assessed functional connectivity (FC), [18F]SynVesT-1 PET measured synaptic density, and diffusion tensor imaging (DTI) evaluated white and grey matter microstructure. Linear mixed models with Bonferroni correction were used to analyse genotype-by-age interactions across brain regions.

FINDINGS: At infant age, KI mice showed increased FC relative to WT, particularly within the default mode-like network (DMLN). During the juvenile stage, KI mice exhibited modest elevated synaptic density across brain regions, most notably in the cerebellum. By adulthood, KI mice displayed reduced FC, especially within the DMLN, compared to WT. No significant microstructural genotype-by-age interactions were found.

INTERPRETATION: KCNQ2-DEE disrupts neurodevelopment, with early hyperconnectivity and delayed synaptic pruning transitioning to adult hypoconnectivity. While this pattern is too subtle to use as a standalone biomarker, these findings establish a foundation for their use in longitudinal preclinical research targeting early therapeutic intervention.

FUNDING: Supported by the University of Antwerp, Fonds Wetenschappelijk Onderzoek, the Queen Elisabeth Medical Foundation, the European Joint Programme on Rare Disease, and Fondation Lejeune.

PMID:41705898 | DOI:10.1016/j.ebiom.2025.105986

Neuroimage Rep. 2026 Feb 9;6(1):100327. doi: 10.1016/j.ynirp.2026.100327. eCollection 2026 Mar.

ABSTRACT

INTRODUCTION: This study aims to identify early brain network changes in de novo Parkinson's disease (PD) using resting state-functional Magnetic Resonance Imaging (rs-fMRI), graph-theoretical analysis, and a functional brain network disruption index (k), applied here for the first time to de novo PD.

MATERIALS AND METHODS: The study enrolled untreated de novo PD patients and age- and sex-matched healthy controls. PD patients underwent comprehensive clinical assessments (MDS-UPDRS III, H&Y, MMSE, MoCA, NMSS). MRI data were acquired on a 3T system, including 3D T1-weighted MPRAGE and rs-fMRI. rs-fMRI data were pre-processed and analysed using graph theory.

RESULTS: The study included 30 de novo PD patients and 30 healthy controls. While global network metrics did not differ significantly, local metrics revealed a reduced disruption index k in de novo PD patients. The disruption index k was negatively correlated with MMSE scores and demonstrated strong discriminatory power between PD patients and healthy controls based on clustering coefficient metrics. Significant differences in hub regions were found, as some disappeared in PD patients while others emerged compared to healthy controls.

CONCLUSIONS: This study provides evidence of widespread functional alterations in the local brain networks of de novo Parkinson's disease (PD) patients, suggesting early reorganization of brain connectivity. The disruption index (k) demonstrated the ability to detect early and subtle changes in functional brain networks in de novo Parkinson patients.

SIGNIFICANCE: rs-fMRI can provide valuable insights into the early stages of PD pathophysiology helping to understand the complexity of PD.

PMID:41704899 | PMC:PMC12908061 | DOI:10.1016/j.ynirp.2026.100327

Neuroimage Rep. 2026 Feb 10;6(1):100326. doi: 10.1016/j.ynirp.2026.100326. eCollection 2026 Mar.

ABSTRACT

Autism spectrum disorder (ASD) is a neurodevelopmental condition, with ex vivo studies suggesting its neurobiological origin as early as the first and second trimester of pregnancy. Functional MRI studies using graph-theoretical approaches have isolated features in the global connectome architecture that distinguish toddlers with ASD from their typically developing peers. Additionally, functional connectivity patterns in the infant brain have shown to be predictive of later ASD diagnosis. An important yet unexplored question in the literature is whether graph-theoretical differences are evident prior to infancy, in the brain of fetuses who will later exhibit ASD traits in early childhood. In this study, we address this question using a sample of 88 children with both quality-assured fetal brain resting-state functional MRI data and standardized parent assessment of ASD traits including social-emotional and social communication skills and repetitive and restricted behaviors at age 3. Multiple regression analyses revealed no significant associations between fetal global graph features (e.g., network segregation, integration, and small-world architecture) and ASD traits at age 3 (p's > 0.1). Therefore, our findings do not provide support for prenatal emergence of global topographical differences of brain functional organization in fetuses who later develop ASD traits. However, this does not rule out the possibility of other neural signatures in the fetal functional connectome that may predict autistic traits and future ASD diagnosis.

PMID:41704898 | PMC:PMC12908067 | DOI:10.1016/j.ynirp.2026.100326

Front Clin Diabetes Healthc. 2026 Feb 2;7:1665837. doi: 10.3389/fcdhc.2026.1665837. eCollection 2026.

ABSTRACT

INTRODUCTION: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are widely used for type 2 diabetes mellitus (T2DM) and may influence reward-related pathways, suggesting potential effects on nicotine dependence and smoking-related outcomes. Randomized evidence in patients with T2DM remains limited. This trial evaluates the effects of GLP-1RAs on nicotine dependence and smoking exposure and explores potential neural mechanisms using functional MRI (fMRI).

METHODS AND ANALYSIS: This single-center, parallel-group randomized controlled trial will enroll 46 male adults with T2DM who are current smokers with Fagerström Test for Nicotine Dependence (FTND) score ≥4. Participants will be randomized (1:1) to receive a GLP-1RA or a dipeptidyl peptidase-4 inhibitor (DPP-4i) for 24 weeks as part of routine glucose-lowering therapy optimization. No structured smoking cessation counseling or smoking cessation pharmacotherapy will be provided by the research team. The primary endpoint is change in FTND score from baseline, assessed at weeks 1, 4, 8, 12, and 24. Secondary endpoints include changes in exhaled carbon monoxide (CO) and smoking cessation rate at weeks 12 and 24, and changes in metabolic parameters. Exploratory endpoints include changes in resting-state fMRI measures from baseline to week 24 and their associations with smoking- and metabolic-related outcomes.

TRIAL STATUS: Recruitment started in June 2025 and is ongoing.

CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov, identifier (NCT06924697).

PMID:41704542 | PMC:PMC12907205 | DOI:10.3389/fcdhc.2026.1665837

Eur J Neurosci. 2026 Feb;63(4):e70430. doi: 10.1111/ejn.70430.

ABSTRACT

Previous neuroimaging studies have revealed abnormal functional activity in multiple brain regions among individuals with alcohol use disorder (AUD). However, due to the heterogeneity in study designs, these findings lack consistency, leaving the core neuropathological mechanisms of AUD unclear to date. To address this, we conducted a quantitative whole-brain meta-analysis of relevant resting-state functional imaging data to identify persistent brain region characteristics in individuals with AUD. A systematic literature search was conducted across six databases from their inception to August 8, 2025. Subsequently, a meta-analysis employing the anomaly effect size-marked difference mapping (AES-SDM) method was performed to identify abnormal brain activity patterns in patients with AUD. This was supplemented by jackknife sensitivity analysis, heterogeneity testing, publication bias assessment, subgroup analysis, and meta-regression analysis. The results showed that a total of 16 articles (20 datasets) were included, involving 520 patients with AUD and 523 healthy controls (HCs). SDM meta-analysis revealed enhanced functional activity in the right pars opercularis of the inferior frontal gyrus of AUD patients compared to healthy controls, while reduced functional activity was observed in the bilateral postcentral gyrus and left precuneus. Sensitivity analyses and subgroup analyses demonstrated high robustness across all regions. Meta-regression analysis indicated that reduced activity in the left posterior central gyrus was significantly correlated with AUD severity and moderated by age. This study shows AUD patients have abnormal activity in brain regions linked to sensory processing, emotional regulation, and self-awareness, offering comprehensive insights into AUD's neuropathology.

PMID:41704208 | DOI:10.1111/ejn.70430

Schizophrenia (Heidelb). 2026 Feb 17. doi: 10.1038/s41537-026-00736-z. Online ahead of print.

ABSTRACT

Resting-state fMRI studies consistently demonstrate widespread dysconnectivity in schizophrenia (SZ), yet conventional analytic methods often fail to account for individual variability in functional brain organization. This study utilized individualized assessments of network size and topography to examine functional alterations in early psychosis. MRI data were drawn from the Human Connectome Project - Early Psychosis study (ages 18-34), including 86 individuals with non-affective psychosis (NAP) and 57 healthy controls (HC). Ten large-scale functional networks were delineated using template-matching procedures. Group differences in network size were evaluated using ANOVA, while network topography was examined with vertex-wise chi-square analyses and the Topographic Abnormality Index (TAI). Compared to controls, NAP participants showed significantly larger dorsal attention (DAN) and default mode (DMN) networks, along with a smaller sensorimotor-body (SBN) network (effect sizes d = 0.39-0.48). NAP also exhibited greater topographic abnormalities in the DAN, DMN, and cingulo-opercular (CON) networks. DMN size was inversely related to mania symptoms, antipsychotic treatment duration, and working memory performance, while smaller SBN size was also linked to reduced working memory. A k-means clustering revealed three psychosis biotypes. Biotype 1 had enlarged DAN and language network size, with higher antipsychotic exposure. Biotype 2 showed near-normal network profiles but elevated mood symptoms. Biotype 3 exhibited enlarged DMN/DAN and reduced frontoparietal network size, with prominent negative symptoms. Consistent with prior schizophrenia studies, DAN enlargement was present in early psychosis, suggesting stability across illness stages. Altered DAN and DMN organization may serve as early biomarkers to guide detection and intervention strategies.

PMID:41702896 | DOI:10.1038/s41537-026-00736-z

Soc Cogn Affect Neurosci. 2026 Feb 17:nsag010. doi: 10.1093/scan/nsag010. Online ahead of print.

ABSTRACT

This study investigates the relationship between gestational age (GA) and resting-state functional connectivity (rsFC) in a cohort of very preterm children at school age, and how these neural patterns relate to cognitive and theory of mind (ToM) performance. Resting-state functional magnetic resonance imaging (fMRI) data were collected from 52 children (GA < 32 weeks, birth weight <1500 g) and independent component analysis was applied to extract the resting-state networks. Results showed that GA was positively associated with rsFC of the precuneus and the paracentral region within the left posterior cerebellar network (lpCER), while negatively associated with rsFC of the insula and putamen within the anterior default mode network (DMN), and with rsFC of the postcentral gyrus within the right frontoparietal network (rFPN). Cognitive and neuropsychological assessments revealed that increased connectivity involving the lpCER correlated with better verbal comprehension, visuospatial ability, fluid reasoning, working memory, and ToM performance. Conversely, increased aDMN connectivity was associated with lower working memory and decreased rFPN connectivity was found associated with lower intelligence quotient. These results underscore the influence of GA on intrinsic brain networks supporting cognitive and socio-cognitive functions, and highlight potential neural markers that could inform targeted intervention strategies for preterm children.

PMID:41702370 | DOI:10.1093/scan/nsag010

IEEE J Biomed Health Inform. 2026 Feb 17;PP. doi: 10.1109/JBHI.2026.3665521. Online ahead of print.

ABSTRACT

Integrating resting-state functional magnetic resonance imaging (rs-fMRI) and phenotypic data is a promising way to build a comprehensive population graph for the prediction of brain disorders using graph neural networks (GNNs). However, existing GNN-based methods face two limitations: the complexity of relationships between subjects poses challenges in constructing a well-defined population graph, and the inherent node heterophily within the population graph is often overlooked. To address them, we propose a population graph with a phenotypic encoder, which leverages rs-fMRI and phenotypic data to model complex relationships between subjects and enables GNN to learn population-level features. We also design a heterophily-aware spectral graph convolution network that incorporates local similarity-based learning to assess node homophily and addresses the heterophily issue. Experiments demonstrate that our method performs well in classifying both Alzheimer's Disease and Autism Spectrum Disorder. In addition, it can distinguish between progressive and stable mild cognitive impairment, facilitating timely interventions for the diseases.

PMID:41701587 | DOI:10.1109/JBHI.2026.3665521