Most recent paper

Front Med (Lausanne). 2025 Nov 20;12:1585799. doi: 10.3389/fmed.2025.1585799. eCollection 2025.

ABSTRACT

BACKGROUND: Spinal diseases related pain represents a critical clinical issue that demands urgent resolution. Current treatment and assessment strategies predominantly focus on peripheral mechanisms. The application of functional magnetic resonance imaging (fMRI) offers a promising approach to identifying potential central targets for intervention.

METHODS: We retrospectively included 31 patients with spinal diseases related pain and 32 controls with non-spinal, orthopedic complaints (no chronic neurological or psychiatric disorders). All participants underwent resting-state brain fMRI (eyes closed, awake). We quantified amplitude of low-frequency fluctuations (ALFF) with mean normalization (mALFF) and z-transformation (zALFF), regional homogeneity (ReHo; 27-voxel neighborhood), seed-based functional connectivity (FC; pre/postcentral seeds), and degree centrality (DC; binary and weighted). Between group tests used voxel-wise two-sample t_tests with Gaussian random field (GRF) correction.

RESULTS: Patient group was associated with increased m/zALFF in right cerebellar lobule IX and right Superior Frontal Gyrus, medial part, and lower activity in bilateral postcentral gyri and the cuneus, decreased m/zALFF in bilateral postcentral gyri. ReHo analysis confirmed reduced local synchrony in postcentral regions, spatially overlapping with ALFF findings. FC analyses revealed enhanced cerebellar-thalamic connectivity (Crus1/2, thalamus) but reduced connectivity in sensorimotor and higher-order cortical networks. DC showed hyperconnectivity in left cerebellar Crus I with reduced Superior Frontal Orbital (Frontal_Sup_Orb). All findings survived GRF correction at the pre_specified thresholds.

CONCLUSION: Resting-state brain fMRI indicates a cerebello-thalamo-cortical alteration pattern in spinal diseases related pain featuring cerebellar involvement, prefrontal subspecialization, and multilevel sensorimotor disruption. These cross-sectional associations may inform hypothesis-generation for future neuromodulation studies and provide candidate biomarkers for monitoring, pending prospective validation.

PMID:41357497 | PMC:PMC12677010 | DOI:10.3389/fmed.2025.1585799

J Chin Med Assoc. 2025 Dec 8. doi: 10.1097/JCMA.0000000000001325. Online ahead of print.

ABSTRACT

Treatment-resistant depression (TRD) and alcohol use disorder (AUD) frequently coexist, complicating clinical management and contributing to poor outcomes. Despite their distinct clinical presentations, converging neuroimaging evidence indicates shared neural circuit dysfunctions. This review synthesizes resting-state functional magnetic resonance imaging (fMRI) findings, highlighting disruptions within and between core intrinsic brain networks-the default mode network (DMN), salience network (SN), and central executive network (CEN)-as well as subcortical-limbic circuitry. Both TRD and AUD feature reduced anterior-posterior DMN connectivity (mPFC-PCC), impaired CEN function (particularly within the DLPFC), and aberrant SN connectivity (anterior insula, ACC). Altered limbic interactions involving the amygdala, hippocampus, and striatum further reflect common mechanisms of heightened reward sensitivity and emotional dysregulation. Conventional pharmacotherapies demonstrate limited efficacy, underscoring the need for novel approaches. Neuromodulation, particularly deep transcranial magnetic stimulation (dTMS), has emerged as a promising intervention targeting these shared circuit abnormalities. While current evidence remains preliminary, integrating neuroimaging biomarkers, multimodal methods, and longitudinal designs will be crucial for refining treatment precision. This review highlights the translational potential of circuit-based interventions, offering a framework for personalized neuromodulation strategies to improve outcomes in patients with TRD, AUD, and their frequent comorbidity.

PMID:41355453 | DOI:10.1097/JCMA.0000000000001325

Brain Behav. 2025 Dec;15(12):e71039. doi: 10.1002/brb3.71039.

ABSTRACT

BACKGROUND: Early detection of subjective cognitive decline (SCD), a preclinical stage of Alzheimer's disease (AD), remains a clinical challenge due to its subtle manifestations. This study aims to address these challenges by introducing a novel approach to enhance the detection and analysis of SCD.

METHODS: A Frequency Self-Adaptive Wavelet Transform (FSAWT) model was developed and optimized for functional brain network (FBN) construction using resting-state functional MRI (rs-fMRI) data. The model dynamically selected "golden frequencies" to improve the accuracy and interpretability of brain connectivity patterns. FBNs from 240 participants (106 SCD, 134 controls) were analyzed and compared using traditional methods, pearson correlation (PC) and sparse representation (SR). Receiver operating characteristic-area under the curve (ROC-AUC) analysis validated the classification results.

RESULTS: Our findings demonstrate that individuals with SCD exhibit distinct functional connectivity alterations, including reversed parahippocampal gyrus-superior parietal gyrus connectivity-suggesting early DMN disintegration, weakened temporoparietal pathways linked to memory deficits, and enhanced fusiform gyrus-orbitofrontal connectivity. The frequency-optimized SRWT method achieved superior diagnostic performance (83.71% accuracy, AUC = 0.84) with 82.11% sensitivity and 85.71% specificity, significantly outperforming traditional approaches (61.93% accuracy for PC), highlighting its potential for early SCD detection through these network-based biomarkers.

CONCLUSIONS: The FSAWT model offers a robust framework for early SCD detection by integrating frequency-specific and cross-frequency dynamics. While these findings highlight potential contributions to precision diagnostics and personalized interventions for neurodegenerative disorders, such applications remain to be established in future studies. Future applications may also explore multimodal neuroimaging and broader cognitive impairments.

PMID:41355337 | DOI:10.1002/brb3.71039

Brain Stimul. 2025 Dec 4:102998. doi: 10.1016/j.brs.2025.102998. Online ahead of print.

ABSTRACT

BACKGROUND: Brain targets for transcranial magnetic stimulation (TMS) are often derived from anatomical landmarks or group-level neuroimaging data, which lack precision or personalization, contributing to the variability in TMS responses. Personalized functional magnetic resonance imaging (fMRI) may improve accuracy but remains resource-intensive and not widely accessible.

OBJECTIVE: To determine whether a new dual-site targeting method (dsGrid), based on dual-coil TMS mapping of motor connectivity, aligns with individual motor network connectivity derived from fMRI and validate its use.

METHODS: Forty-seven participants underwent resting-state and task-based fMRI, followed by dual-coil TMS during a goal-directed action task. Motor network connectivity was quantified using fMRI, both resting-state and task-based connectivity, and task-evoked BOLD activity was also quantified. The strength of fMRI-derived motor features was assessed at the TMS target sites identified by dsGrid and compared with the strength of these measures at a conventional group-based target (e.g., the P3 electrode from a 10-20 EEG system) and registered to individual anatomy.

RESULTS: Targets identified by dsGrid are more accurate in terms of individualized motor network connectivity and activation across all three fMRI modalities than group-based P3 coordinates mapped to individual MRI space.

CONCLUSION: dsGrid enables precise, individualized TMS targeting of functional motor circuits, supporting its use in both research and clinical neuromodulation.

PMID:41352727 | DOI:10.1016/j.brs.2025.102998

J Integr Neurosci. 2025 Nov 27;24(11):45003. doi: 10.31083/JIN45003.

ABSTRACT

BACKGROUND: Evidence suggests that subjective cognitive decline (SCD) involves abnormal structures and functional alterations in multiple brain networks, rather than a single brain region. Acupuncture has shown a positive therapeutic effect in treating SCD, although whether and how it can improve cognitive decline by altering large-scale brain network organization is unclear.

METHODS: We utilized resting-state functional magnetic resonance imaging (fMRI) data from 66 individuals with SCD (derived from a previous randomized controlled trial) and explored brain-wide network-level functional connectivity and topological property changes after 12 weeks of acupuncture intervention to examine its therapeutic mechanisms. The Auditory Verbal Learning Test-Huashan version (AVLT-H) test was used to measure objective memory performance. Neuroimaging outcomes included brain network functional connectivity and topological properties obtained from resting-state fMRI. A repeated-measures general linear model and mixed-effect analysis were used to examine group × time interaction effects on cognitive function and neuroimaging outcomes. Correlation analyses were used to examine the relationship between functional connections (FCs) and memory performance.

RESULTS: Compared with sham acupuncture, 12 weeks of acupuncture treatment significantly improved the objective memory performance of individuals with SCD. Five FCs within the sensorimotor network (SMN) and between the SMN and the cingulo-opercular network (CON) showed significant alterations after acupuncture. Two intrinsic SMN connections were enhanced by acupuncture, whereas inter-network FCs changed oppositely, negatively correlating with memory improvement. The topological properties of two regions within the SMN were also significantly modulated after acupuncture.

CONCLUSIONS: The results suggest that 12 weeks of acupuncture may improve objective memory performance in SCD, potentially by reducing FCs between the SMN and CON. Enhancing functional segregation of these networks may be a potential target for acupuncture treatment.

CLINICAL TRIAL REGISTRATION: No: NCT03444896. https://www.

CLINICALTRIALS: gov/study/NCT03444896.

PMID:41351443 | DOI:10.31083/JIN45003

Addict Biol. 2025 Dec;30(12):e70097. doi: 10.1111/adb.70097.

ABSTRACT

Protracted abstinence (PA) is the commonly implemented treatment of heroin-dependent individuals (HDIs) in China. However, the effect of abstinence duration on the brain function of HDIs during PA using resting-state functional magnetic resonance imaging (fMRI) remains unclear. Fourteen HDIs who had finished PA for about 6 months (PA6), 16 HDIs who had completed PA for about 11 months (PA11) and 15 demographically matched healthy controls (HC) underwent this fMRI study. We analysed the difference in amplitude of low-frequency fluctuation (ALFF) values among the three groups. Then we analysed the difference in functional connectivity (FC) based on the differential regions of ALFF. Additionally, we examined the relationship between FC of differential brain regions and abstinence duration. The differences in ALFF among the three groups were found to be significant in the bilateral putamen and left inferior parietal lobule (single voxel p < 0.001, cluster level p < 0.05 and GRF-corrected). Compared with the PA6 group, the PA11 group showed lower ALFF values of the differential regions with a tendency toward the HC group. Meanwhile, the PA11 group showed lower FC between the left putamen and left insula, between the right putamen and left insula and between the left inferior parietal lobule and bilateral inferior frontal gyrus (IFG), but higher FC between the left putamen and left inferior temporal gyrus. The above FC of HDIs negatively correlated with the abstinence duration, except for the left putamen-inferior temporal gyrus FC. The prolonged abstinence duration may be useful to restore the impaired brain function of HDIs to some extent, although more data are needed to validate this in future studies.

PMID:41351278 | DOI:10.1111/adb.70097

BMC Med Imaging. 2025 Dec 5;25(1):501. doi: 10.1186/s12880-025-02009-0.

ABSTRACT

OBJECTIVE: This study seeks to identify brain regions with atypical neural connectivity in individuals suffering from arthritis-related chronic pain, compared to healthy controls, using resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS: A seed-based connectivity analysis was conducted between the known pain-related regions of interest (ROIs), derived from the MNI (n = 76) and the Automated Anatomical Labeling (AAL) whole brain atlas (n = 116). We examined the connectivity differences in a cohort of 56 osteoarthritis patients and 20 healthy controls. Connectivity matrices were compared using permutation tests corrected for multiple comparisons, identifying statistically significant differences (p < 0.05). Subsequent network analysis resulted in hub scores, identifying the most central and influential brain regions within the altered connectivity network in patients experiencing pain.

RESULTS: The most significant atypical neural connections in osteoarthritis patients were identified in the cingulate gyrus, insula, inferior parietal lobe, and thalamus, with notable involvement of the occipital lobe, postcentral gyrus, inferior frontal gyrus, orbitofrontal cortex, temporal lobe, hippocampus, and basal ganglia. The thalamus, cingulate gyrus, and insula emerged as key hubs in the chronic pain network, reflecting disrupted sensory, emotional, and cognitive pain processing. No significant connectivity differences were found in the brainstem, cerebellum, superior parietal lobe, precentral gyrus, superior and middle frontal gyri, or amygdala.

CONCLUSION: Our data-driven approach reveals specific neural connectivity disruptions in OA, highlighting connections between the cingulate gyrus, temporal lobe, and thalamus. These findings identify specific network disruptions in OA-related pain, offering insight into altered brain connectivity and potential avenues for targeted interventions.

PMID:41350630 | DOI:10.1186/s12880-025-02009-0

Cogn Affect Behav Neurosci. 2025 Dec 5. doi: 10.3758/s13415-025-01365-2. Online ahead of print.

ABSTRACT

Loneliness is an unpleasant subjective experience associated with significant psychological and physical health problems. With increasing urbanization and aging populations, loneliness is becoming a global public health concern. Thus, understanding the neural correlates of loneliness is crucial for developing targeted intervention approaches. In the current study, we collected resting-state fMRI data from 238 young adults (ages 17-26; 59 males, 179 females) and used fractional amplitude of low-frequency fluctuations (fALFF) and functional connectivity (FC) analyses to investigate the neural correlates of loneliness. Results revealed that loneliness was negatively correlated with fALFF in the right posterior precuneus. Functional connectivity analyses showed that loneliness was positively correlated with connectivity between the right posterior precuneus and right superior frontal gyrus, and negatively correlated with connectivity between the right ventromedial prefrontal cortex and a network including the right cerebellum, left fusiform gyrus, and right superior occipital gyrus. These findings reveal neural correlates of loneliness, including distinct patterns of intrinsic activity in the posterior precuneus and specific functional connectivity patterns involving regions associated with social cognition and emotional regulation. The results provide neural evidence for understanding individual differences in loneliness and could potentially inform future research on neurostimulation and cognitive-behavioral interventions targeting these specific brain networks.

PMID:41350504 | DOI:10.3758/s13415-025-01365-2

Neural Netw. 2025 Nov 27;196:108379. doi: 10.1016/j.neunet.2025.108379. Online ahead of print.

ABSTRACT

Resting-state functional MRI (rs-fMRI) is widely used for diagnosing and analyzing brain disorders. However, existing fMRI studies have shown that learning-based approaches depend heavily on labeled training data, which is difficult to obtain due to the substantial time and effort required for annotation in clinical settings. To address these challenges, we propose GCSC-TA (Graph-level Contrastive Learning with Self-aware and Cross-sample Topology Augmentation) for brain disorder diagnosis and analysis using rs-fMRI. The proposed GCSC-TA generates two complementary augmented brain networks for each subject by introducing self-aware and cross-sample topology augmentations. This dual-view strategy enhances the identification of individual-specific features and also amplifies inter-subject functional heterogeneity. Moreover, we designed a min-max contrastive loss function to accommodate augmented brain networks, overcoming the limitations of traditional projection-based methods while performing graph-level contrastive learning on the original integrity of the brain topology structure. Extensive experiments on a private Major Depressive Disorder (MDD) dataset and the publicly available Autism Spectrum Disorder (ABIDE) dataset demonstrate the superior classification performance of GCSC-TA over several state-of-the-arts. Furthermore, GCSC-TA also identifies abnormal brain connectivity patterns associated with MDD and ASD, thereby advancing the interpretability and clinical utility of rs-fMRI for clinical diagnosis.

PMID:41349174 | DOI:10.1016/j.neunet.2025.108379

Front Psychiatry. 2025 Nov 19;16:1672148. doi: 10.3389/fpsyt.2025.1672148. eCollection 2025.

ABSTRACT

BACKGROUND: Gastrointestinal (GI) symptoms are a common and burdensome dimension of major depressive disorder (MDD), yet their neurobiological underpinnings are poorly understood. It is unclear how the brain's processing of visceral signals relates to the subjective experience of GI distress in depression. We aimed to identify a neural substrate for GI symptoms by examining functional connectivity (FC) between the insula and a network defined by gastric rhythms.

METHODS: We first identified a gastric-related seed in the posterior insula (GD-pINS) using a large normative dataset of 652 healthy adults. Subsequently, 100 MDD patients-stratified into groups with (GD; n=58) and without (NGD; n=42) GI symptoms-and 80 healthy controls (HCs) were recruited. Using resting-state fMRI, we analyzed FC between the GD-pINS and the gastric network (GN). Group differences, clinical correlations, and the utility of FC features for patient classification via a support vector machine (SVM) were assessed.

RESULTS: Compared to HCs, MDD patients as a whole showed reduced GD-pINS to GN connectivity. Paradoxically, GD patients exhibited relatively stronger connectivity than NGD patients. This symptom-specific enhancement was driven by pathways connecting the posterior insula to the secondary somatosensory cortex (SII). The strength of this insula-SII connection was positively correlated with GI symptom severity. An SVM classifier using these connectivity features distinguished between GD and NGD patients with high accuracy (AUC = 0.82).

CONCLUSIONS: Our findings reveal a distinct neural signature for GI distress in depression, characterized by aberrant connectivity within an insula-somatosensory circuit. This circuit, which shows relative enhancement in symptomatic patients against a backdrop of globally reduced connectivity, may reflect a mechanism of somatosensory amplification. It represents a potential biomarker for patient stratification and a novel target for therapeutic intervention.

PMID:41346640 | PMC:PMC12673926 | DOI:10.3389/fpsyt.2025.1672148

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

Addict Biol. 2025 Dec;30(12):e70105. doi: 10.1111/adb.70105.

ABSTRACT

Cannabis use disorder (CUD) affects ~22-million people globally and is characterised by difficulties in cutting down and quitting use, but the underlying neurobiology remains unclear. We examined resting-state functional connectivity (rsFC) between regions of interest (ROIs) of the addiction neurocircuitry and the rest of the brain in 65 individuals with moderate-to-severe CUD who reported attempts to cut down or quit, compared to 42 controls, and explored the association between rsFC and cannabis exposure and related problems, to elucidate potential drivers of rsFC alterations. The CUD group showed greater rsFC than controls between ROIs implicated in reward processing and habitual substance use (i.e., nucleus accumbens, putamen and pallidum) and occipito/parietal areas implicated in salience processing and disinhibition. Putamen-occipital rsFC correlated with levels of problematic cannabis use and depression symptoms. CUD appears to show neuroadaptations of the addiction neurocircuitry, previously demonstrated in other substance use disorders.

PMID:41339716 | DOI:10.1111/adb.70105