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Brain Commun. 2025 Nov 21;7(6):fcaf461. doi: 10.1093/braincomms/fcaf461. eCollection 2025.

ABSTRACT

Resting-state functional connectivity has been linked to intelligence, and twin studies suggest that these associations may be influenced by genetic factors. To investigate this relationship, we analysed behavioural and resting-state functional magnetic resonance imaging data from young adult twins in the Human Connectome Project. General intelligence was assessed based on ten cognitive task performances. The results showed a positive correlation in both identical and fraternal twins, indicating a similarity of general intelligence among twin pairs. For the resting-state functional connectivity analysis, we conducted two approaches. In the first approach, twins were randomly assigned to two separate groups, ensuring that each pair was split between the groups. We then applied a connectome-based predictive method separately for identical and fraternal twins to predict general intelligence. Specifically, a predictive model was trained using one group's functional connectivity and then applied to its co-twin group to predict their general intelligence. Significant prediction was recorded in identical twins but not in fraternal twins, suggesting a high level of similarity of intelligence-related functional connectivity among identical twins. In the second approach, we aimed to quantify the intelligence similarity using the resting-state functional connectivity. To implement this, we generated models to predict the difference in general intelligence in twin pairs, where a smaller difference indicates a greater degree of similarity. The results showed that only the intelligence difference in identical twins was successfully predicted, where the default mode network showed a significant contribution, suggesting a higher neural basis for intelligence similarity in identical twins. Together, these findings demonstrate that functional connectivity patterns associated with intelligence extend across genetically identical twins. More broadly, they highlight the default mode network role in intelligence similarity and illustrate the utility of predictive modelling as a complementary framework to classical twin analyses.

PMID:41346464 | PMC:PMC12674170 | DOI:10.1093/braincomms/fcaf461

Front Aging Neurosci. 2025 Nov 19;17:1657723. doi: 10.3389/fnagi.2025.1657723. eCollection 2025.

ABSTRACT

BACKGROUND AND AIMS: Forecasting specific factors influencing cognitive impairment (CI) in Parkinson's disease (PD) patients can improve clinical outcomes. This study aims to identify brain areas vulnerable to vitamin D deficiency and assess functional integrity in PD patients with and without CI.

METHODS: Thirty-four PD patients [14 with CI (PD-CI), 20 with normal cognition (PD-NC)] and 21 healthy controls (HCs) underwent serum vitamin D testing, T1-weighted MRI, and resting-state functional MRI (rs-fMRI). Voxel-based morphometry (VBM) was used to compare gray matter volume (GMV) between PD patients and HCs. Whole-brain multiple regression analyses, adjusted for age and sex, identified GMV regions associated with vitamin D levels. Resting-state functional connectivity (FC) analyses were performed using vitamin D-related regions as seeds. Correlation and multivariate regression analyses, adjusted for Hoehn and Yahr stage and age, assessed relationships among FC, cognitive performance, and vitamin D levels.

RESULTS: Compared with HCs, PD patients exhibited significant GMV loss, affecting widespread brain regions including the middle frontal gyrus (MFG), superior frontal gyrus (SFG), and hippocampus. Region of interest (ROI)-based analysis revealed that vitamin D levels were associated with GMV in the bilateral MFG and SFG (r = -0.406, p = 0.021). These findings suggest that the MFG and SFG are vulnerable regions in PD patients linked to vitamin D levels. To assess the impact of abnormal vitamin D levels on relevant resting-state networks, clusters encompassing the bilateral SFG were used as ROIs. The intrinsic connectivity network of the vulnerable area, using the bilateral SFG as seed regions, revealed abnormal functional connectivity with several brain networks, including the visual network, the default mode network, the executive control network, the sensorimotor network, and the memory network. Abnormal FC values within the SFG functional network were associated with disease severity, cognitive dysfunction, and vitamin D levels (p < 0.05). Multi-model regression analyses revealed that connectivity in the left SFGmed network was negatively associated with CI in PD, with vitamin D levels showing a potential protective effect.

CONCLUSION: The SFG is associated with vitamin D levels in PD patients, and disruptions in its structural and functional connectivity may link to CI. Future longitudinal studies are necessary to confirm these associations and explore the potential impact of vitamin D supplementation on cognitive function in PD.

PMID:41346436 | PMC:PMC12673341 | DOI:10.3389/fnagi.2025.1657723

Front Aging Neurosci. 2025 Nov 19;17:1630826. doi: 10.3389/fnagi.2025.1630826. eCollection 2025.

ABSTRACT

OBJECTIVE: To characterize abnormal functional connectivity in dementia with Lewy bodies (DLB) and its association with cognitive impairment using resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS: Sixty-eight DLB patients and 38 age-, sex-, and education-matched healthy controls underwent neuropsychological assessments (MoCA, MMSE) and rs-fMRI. Imaging analyses included seed-based functional connectivity (sFC), independent component analysis (ICA), regional homogeneity (ReHo), fractional amplitude of low-frequency fluctuations (fALFF), and graph-theoretical network metrics (small-worldness, global/local efficiency).

RESULTS: DLB patients exhibited significantly reduced FC in the default mode network (DMN) and visual network, including PCC-AG (P < 0.001) and PCC-mPFC (P < 0.001). ReHo and fALFF indicated decreased local neural synchronization and low-frequency activity in the posterior occipital lobe (fALFF: P = 0.004), angular gyrus (fALFF: P = 0.001), left temporal pole (fALFF: P < 0.001), left parietal (ReHo: P < 0.001), and posterior cerebellar lobe (ReHo: P < 0.001). Graph theory revealed impaired global network topology in DLB, with decreased small-worldness (P < 0.001) and global efficiency (P < 0.001). PCC-AG connectivity positively correlated with the MoCA total score (r = 0.53, P < 0.001), attention (r = 0.46, P < 0.001), executive (r = 0.41, P < 0.001), and language function (r = 0.34, P < 0.001). Posterior occipital fALFF and left parietal ReHo showed significant positive correlations with multiple cognitive domains, including visuospatial ability (r = 0.34, P < 0.001 for fALFF; r = 0.42, P < 0.001 for ReHo) and memory (r = 0.45, P < 0.001 for fALFF; r = 0.27, P = 0.006 for ReHo). A combined model of PCC-AG connectivity, fALFF, and small-worldness predicted 42% of MoCA variance (R 2 = 0.42, P < 0.001).

CONCLUSION: DLB is characterized by DMN and visual network dysfunction, disrupted local neural activity, and impaired global network integration. These rs-fMRI metrics may serve as potential biomarkers for cognitive deficits in DLB.

PMID:41346435 | PMC:PMC12673660 | DOI:10.3389/fnagi.2025.1630826

Commun Biol. 2025 Dec 4. doi: 10.1038/s42003-025-09181-7. Online ahead of print.

ABSTRACT

The interplay between brain metabolism and function supports the brain's adaptive capacity in cognitively demanding processes. Prior work has linked glucose metabolism to resting-state fMRI activity, but often overlooks both hemodynamic confounders in the BOLD signal and the brain's dynamic nature. To address this, we employed a novel effective connectivity decomposition, separating symmetric partial covariance, capturing "true" statistical dependencies between regions, from antisymmetric differential covariance, reflecting directional brain flow. In 42 healthy subjects, we show that partial covariance corresponds to metabolic connectivity across regions, while node directionality relates to standardized uptake value ratio, a proxy for local glucose consumption. We subsequently tested the sensitivity of detected couplings in 43 glioma patients, identifying disruptions in both local and network-level effective-metabolic interactions that varied with tumor anatomical location. Our findings provide novel insights into the coupling between brain metabolism and functional dynamics at rest, advancing understanding of healthy and pathological brain states.

PMID:41345236 | DOI:10.1038/s42003-025-09181-7

Biol Psychol. 2025 Dec 2:109174. doi: 10.1016/j.biopsycho.2025.109174. Online ahead of print.

ABSTRACT

Depression constitutes a major global public health burden, with university students exhibiting a disproportionately high prevalence of depressive symptoms. Although Mindfulness-Based Stress Reduction (MBSR) has demonstrated efficacy in alleviating depressive symptomatology, its underlying neurobiological mechanisms remain incompletely understood. This randomized controlled trial investigated neural activity changes and functional connectivity alterations of MBSR's depression-reducing effects in university students using resting-state functional magnetic resonance imaging (rs-fMRI). Forty-two healthy university students were randomly assigned to either an 8-week MBSR intervention or a control group. Clinical outcomes were assessed using the Depression Anxiety Stress Scales-21, and rs-fMRI data were acquired to examine regional brain activity and functional connectivity. Results demonstrated that MBSR participants exhibited greater improvements in depression scores compared to the control group. Neuroimaging analyses indicated that MBSR intervention led to reduced Amplitude of Low-Frequency Fluctuations (ALFF), fractional ALFF, and Regional Homogeneity in the right middle cingulate cortex (MCC). Furthermore, seed-based functional connectivity analysis demonstrated decreased connectivity between the right MCC and regions involved in emotional regulation and self-referential processing, including the left hippocampus and bilateral precuneus, in the MBSR group relative to controls. Furthermore, changes in MCC-hippocampus and MCC-precuneus functional connectivity were negatively correlated with improvements in depression scores. These findings provide novel evidence that MBSR promotes adaptive neural reorganization, characterized by reduced activity and altered functional connectivity within the MCC-centric emotional regulation network, providing mechanistic insight into for its depression-reducing effects in subclinical populations and supporting the neural efficiency hypothesis.

PMID:41344637 | DOI:10.1016/j.biopsycho.2025.109174

Brain Res Bull. 2025 Dec 2:111669. doi: 10.1016/j.brainresbull.2025.111669. Online ahead of print.

ABSTRACT

OBJECTIVE: To characterize the dynamic functional network connectivity (dFNC) patterns in children with self-limited epilepsy with centrotemporal spikes (SeLECTS) and to uncover potential abnormalities in neural regulation and related functional impairments.

MATERIALS AND METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected from 61 children with SeLECTS and 69 healthy controls (HCs). Independent component analysis (ICA), the sliding window approach and hidden markov modeling (HMM) were employed to systematically investigate potential differences in dFNC properties between the two groups.

RESULTS: The dFNC analysis identified four dynamic states, with State 1 occurring most frequently. State 1 and State 3 represented two polarized connectivity patterns, with State 1 characterized by weak/negative connections and State 3 by widespread strong connections. In both states, children with SeLECTS showed significantly reduced connectivity within the dorsal attention network (DAN) compared with HCs (p < 0.001, FDR-corrected). In the connectivity-balanced State 2, children with SeLECTS showed significantly reduced fractional windows (p = 0.009) and mean dwell time (p = 0.018) compared with HCs, whereas no significant differences were observed in State 4. In addition, temporal variability of functional connectivity between the DAN and visual network (VIS) was significantly reduced in SeLECTS (p < 0.001, FDR-corrected), and this variability was positively correlated with full-scale intelligence quotient (FIQ) (p < 0.05). HMM results from another dynamic perspective further confirmed and echoed the above abnormalities.

CONCLUSION: This study revealed abnormal dynamic connectivity patterns of brain networks in children with SeLECTS from a multidimensional dynamic perspective. These macroscopic abnormalities may reflect an underlying excitation-inhibition imbalance in neural networks and provide new insights into brain functional reorganization and the potential neurobiological mechanisms of SeLECTS.

PMID:41344618 | DOI:10.1016/j.brainresbull.2025.111669

Prog Neuropsychopharmacol Biol Psychiatry. 2025 Dec 2:111573. doi: 10.1016/j.pnpbp.2025.111573. Online ahead of print.

ABSTRACT

BACKGROUND: Adolescent major depressive disorder (AMDD) emerges during a period of significant neurobiological reorganization, yet its specific pathophysiological mechanisms remain poorly understood. This study investigated structural-functional brain coupling (SC-FC coupling) in AMDD and its relationship with neurotransmitter systems and molecular profiles.

METHODS: We examined 107 adolescents with AMDD and 78 healthy controls. Participants underwent multimodal neuroimaging (DTI, resting-state fMRI), clinical assessment, and cognitive testing. We analyzed regional SC-FC coupling abnormalities and their associations with neurotransmitter distributions. Gene expression profiles underlying coupling alterations were examined through partial least squares regression with Allen Human Brain Atlas data. Cell-type enrichment analysis was performed using established transcriptomic references, and developmental expression trajectories were mapped using BrainSpan developmental transcriptome atlas through CSEA tool.

RESULTS: AMDD was characterized by decoupling in the default mode network and hypercoupling in somatomotor networks. These alterations demonstrated significant potential for diagnostic classification (AUC = 0.83-0.85) and correlated with clinical symptom severity. The spatial distribution of coupling alterations was significantly associated with multiple neurotransmitter systems, most robustly with dopaminergic and serotonergic markers. At the transcriptomic level, these alterations were correlated with distinct gene expression profiles, which were further linked to cell-type-specific signatures: genes associated with decoupled regions were enriched in neuronal lineages, while those associated with hypercoupled regions showed enrichment in glial cells.

CONCLUSIONS: These findings suggest that SC-FC alterations in AMDD are linked to neurotransmitter systems and cell-type-specific gene expression. These associations may reflect developmentally sensitive mechanisms that could inform age-appropriate intervention strategies for adolescent depression.

PMID:41344601 | DOI:10.1016/j.pnpbp.2025.111573

CNS Neurosci Ther. 2025 Dec;31(12):e70675. doi: 10.1002/cns.70675.

ABSTRACT

AIM: Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by significant heterogeneity in clinical symptoms and underlying neurobiology. This study aimed to identify distinct ASD biotypes and uncover their neurobiological underpinnings using a novel graph-based subtyping approach.

METHODS: Resting-state fMRI and clinical data from 443 males with ASD (17.22 ± 8.63 years) were analyzed. We proposed a population graph-based dual autoencoder for subtyping (PG-DAS), a deep clustering framework that integrates imaging data and nonimaging data to extract deep features for biotype identification. Statistical analyses were conducted to compare clinical scores and functional connectivity patterns between biotypes. Correlation analyses examined the associations between intra- and internetwork connectivity and clinical symptoms. Predictive modeling using support vector regression assessed the ability of network connectivity to predict clinical scores.

RESULTS: Two distinct ASD biotypes were identified. ASD1 exhibited significantly lower clinical scores and reduced network integration, characterized by weaker intra- and internetwork connectivity, particularly in core networks such as the cingulo-opercular network, linked to communication symptom scores. In contrast, ASD2 exhibited greater network segregation, with internetwork connectivity in sensorimotor-related networks correlating with total symptom scores. Predictive modeling further revealed biotype-specific brain-behavior associations, with ASD1 and ASD2 showing positive correlations with social and communication scores, respectively.

CONCLUSION: This study underscores the critical role of biotype-specific brain network patterns in understanding ASD heterogeneity. The proposed PG-DAS framework proved effective in ASD subtyping and holds promise for broader applications in exploring other neuroheterogeneous disorders.

PMID:41340232 | DOI:10.1002/cns.70675

BMC Med. 2025 Dec 3;23(1):675. doi: 10.1186/s12916-025-04556-3.

ABSTRACT

BACKGROUND: Bulimia nervosa (BN) is a severe psychiatric disorder characterized by dysregulated eating behaviors and impaired cognitive-emotional control. Despite increasing recognition of brain network dysfunction in BN, the interplay between structural connectivity (SC) and functional connectivity (FC), termed SC-FC coupling, remains poorly understood. This study aimed to comprehensively characterize SC-FC coupling alterations in BN using multimodal neuroimaging and to evaluate the predictive value for disordered eating behaviors.

METHODS: This study enrolled 79 patients with BN and 69 healthy controls who underwent high-resolution structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), and resting-state functional MRI (rs-fMRI). Functional and structural connectomes were constructed using the Schaefer-400 atlas. SC-FC coupling was quantified using eight biologically grounded similarity and communication metrics. A multivariate linear modeling framework was applied to estimate region-specific coupling profiles. Group comparisons and ridge regression-based leave-one-out cross-validation were used to identify altered coupling and predict symptom severity.

RESULTS: The global topological properties of the SC and FC networks were preserved in BN. However, patients exhibited significantly reduced degree centrality and nodal efficiency in the inferior frontal gyrus within the FC network. SC-FC coupling, quantified using the matching index (MI), showed widespread regional alterations in BN, particularly within the default mode, control, and attention networks. Seventeen brain parcels demonstrated significant group differences in MI-based coupling (false discovery rate (FDR)-corrected, p < 0.05), with both hypercoupling and hypocoupling observed. Findings were parcellation-robust (Glasser-360 replication; Dice = 0.93 vs. Schaefer-400). Moreover, coupling features moderately predicted binge-eating frequency (r = 0.24, p < 0.001), but not questionnaire-based emotional or behavioral scores.

CONCLUSIONS: In BN, macroscale white-matter organization is preserved, yet focal prefrontal functional decentralization and widespread, parcellation-robust SC-FC coupling changes invisible to single-modality analyses were observed. Multidimensional SC-FC coupling provides a sensitive neurobiological marker that explains clinically relevant variance in binge-eating behavior, highlighting its potential as a target for personalized diagnosis and intervention in BN.

PMID:41340129 | DOI:10.1186/s12916-025-04556-3