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Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2025 Sept 28;50(9):1579-1589. doi: 10.11817/j.issn.1672-7347.2025.250392.

ABSTRACT

OBJECTIVES: Major depressive disorder (MDD) is a common affective disorder with complex etiologies and largely unclear pathophysiological mechanisms. The nucleus accumbens (NAc) plays a central role in reward processing, motivational regulation, and emotional integration. Neuroimaging studies suggest that structural and functional abnormalities of the NAc are key contributors to the pathogenesis of MDD. However, the alterations in structure-function coupling (SFC) of the NAc in MDD remain poorly understood. This study aims to systematically investigate abnormal functional connectivity (FC) and SFC of the NAc in patients with MDD by integrating functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) techniques.

METHODS: A case-control design was adopted. Patients who met diagnostic criteria for a current depressive episode of MDD and had a 17-item Hamilton Rating Scale for Depression (HAMD-17) total score ≥17 were enrolled as the MDD group, while age-, sex-, and education-matched healthy controls (HCs) were included as the HC group. All participants underwent high-resolution T1-weighted structural imaging, resting-state fMRI, and DTI scanning using a 3.0T MR system. fMRI data preprocessing was performed using SPM12 (Statistical Parametric Mapping 12) and DPARSF (Data Processing Assistant for Resting-State fMRI), while DTI preprocessing was conducted using FSL (FMRIB Software Library). Based on the Brainnetome Atlas, the cerebral cortex was parcellated into 246 regions. FC values between bilateral NAc and the whole brain and the strength of structural connectivity (sSC) derived from probabilistic tractography were calculated. SFC values of bilateral NAc were computed using region-wise Spearman correlations between sSC and FC (ρ). A multiple linear regression model was constructed using FC as the dependent variable and age, gender, years of education, and head motion parameters as covariates, and corrected FC values were extracted from the regression residuals. Group differences in corrected FC values were assessed using independent-sample t-tests with false discovery rate (FDR) correction at a significance level of P<0.1. Analysis of covariance was used to compare SFC values between groups, controlling for age, gender, and years of education (a significance level of P<0.05). FC values showing significant intergroup differences and SFC values of bilateral NAc were correlated with HAMD-17 total scores using Spearman correlation analysis.

RESULTS: There were no significant differences between the MDD and the HC groups in gender (χ2=0.792, P=0.373), age (t=-0.930, P=0.292), or years of education (t=0.003, P=0.059). Compared with HCs, patients with MDD exhibited significantly increased FC in the following connections: BG.L.3 (left NAc)-IPL.R.4 (right inferior parietal lobule), BG.R.3 (right NAc)-IPL.R.4, BG.R.3-Tha.R.8 (right lateral prefrontal thalamus), and BG.R.3- MFG.R.4 (right middle frontal gyrus) (all FDR-corrected P<0.1). The SFC values of bilateral NAc were significantly reduced in the MDD group compared with the HC group (left: F=11.768, P=0.001; right: F=4.386, P=0.047). Spearman correlation analyses showed no significant associations between altered FC or bilateral NAc SFC values and HAMD-17 total scores in the MDD group (all P>0.05).

CONCLUSIONS: Patients with MDD exhibit enhanced NAc FC, predominantly between the NAc and cognition-related regions such as the inferior parietal lobule and middle frontal gyrus, suggesting imbalance between the reward circuit and cognitive regulatory networks. Moreover, the significantly reduced SFC of bilateral NAc indicated impaired structural-functional integration in MDD. These findings provide potential neuroimaging evidence supporting the involvement of the NAc in the pathophysiological mechanisms of MDD.

PMID:41492742 | PMC:PMC12740730 | DOI:10.11817/j.issn.1672-7347.2025.250392

Aging Brain. 2025 Dec 12;9:100155. doi: 10.1016/j.nbas.2025.100155. eCollection 2026.

ABSTRACT

Subjective Cognitive Decline (SCD) is the perception of a persistent decline in cognitive function and self-reported concerns over cognitive ability in older adults with normal objective cognitive performance. SCD is associated with increased Alzheimer's Disease (AD) risk and early AD pathology. The neurobiological underpinnings of SCD and cognitive or neural circuit alterations during SCD remain unclear. This study aimed to identify patterns of brain network functional connectivity that are associated with quantitative measures of cognitive concerns, and to examine how these functional patterns are related to performance in the cognitive domains of visual-spatial processing, attentional control, and working memory. This analysis combined data from three studies of cognitively healthy older adults which included a quantified assessment of cognitive concern severity, resting-state fMRI, and cognitive testing in the above domains. We examined brain network-to-network functional connectivity associated with self-rated cognitive concern severity, and then how the identified patterns relate to cognitive performance. Results showed that greater cognitive concern severity was associated with unique patterns of functional connectivity between the Default Mode Network and the Language and Salience Networks in older adults without objective cognitive impairment. While greater cognitive concern severity alone was associated with slower processing reaction time, these functional connectivity patterns were associated with faster processing reaction time. This suggests that these functional connectivity patterns may alter the relationship between cognitive concern severity and psychomotor slowing. These findings support that despite the perception of cognitive changes in older adults, normal cognitive performance may be maintained through functional connectivity changes in brain networks important to directing visual-spatial attention and processing.

PMID:41492384 | PMC:PMC12764441 | DOI:10.1016/j.nbas.2025.100155

Psychogeriatrics. 2026 Jan;26(1):e70126. doi: 10.1111/psyg.70126.

ABSTRACT

BACKGROUND: Observational investigations have reported correlations between brain imaging-derived phenotypes (IDPs) and dementia, as well as dysfunctions in brain resting-state functional networks in dementia patients. However, the causal nature of these relationships remains largely unknown.

METHODS: Herein we applied bidirectional two-sample Mendelian randomisation analysis to infer the causal relationships between 587 IDPs (N = 33 224) and 191 brain resting-state functional networks (n = 34 691) with dementia and its sub-types (AD, PDD, FTD and DLB; n = 3024-216 771) using genetic variants-single nucleotide polymorphism (SNPs) as instrumental variables.

RESULTS: The forward MR identified 14 IDP phenotypes that are causally related to the risk of dementia, including frontotemporal dementia (FTD) and Lewy body dementia (DLB). For example, a decrease in the thickness of the right rostral middle frontal cortex was strongly associated with an increased risk of dementia. The reverse MR analysis revealed significant associations between 153 IDP phenotypes and the risk of FTD and DLB and between 73 rs-fMRI phenotypes and the risk of dementia and AD. For instance, a higher risk of DLB was associated with a decrease in FA in the right posterior thalamic radiation. Additionally, the risk of Alzheimer's disease dementia is causally associated with reduced connectivity in the default mode and salience networks.

CONCLUSIONS: We identified 14 IDPs causally associated with dementia or its subtypes. We also identified potential causal effects of FTD and DLB on 153 IDPs and dementia and AD on 73 rs-fMRI phenotypes. Our findings provide insights into the aetiology of dementia and highlight structural brain changes and functional network impairments throughout the disease process. Furthermore, these results contribute to the identification of potential imaging-based predictors and therapeutic targets for dementia.

PMID:41492205 | DOI:10.1111/psyg.70126

J Neurooncol. 2026 Jan 5;176(2):151. doi: 10.1007/s11060-025-05361-x.

ABSTRACT

PURPOSE: Glioblastoma (GBM) is the most prevalent and lethal primary brain tumor. Non-invasive presurgical biomarkers are urgently needed to predict patients’ overall survival (OS). Here we demonstrated a nuanced prognostic tool using sample entropy to assess Blood-Oxygen-Level-Dependent (BOLD) complexity and predict survival outcome, which is computationally efficient, reproducible, robust to noise, and readily transferable across cohorts.

METHODS: Resting-state fMRI from 205 treatment-naïve GBM patients and 1148 cognitively stable healthy controls were evaluated. Sample entropy (SampEn), a complexity metric, was evaluated in relation to OS at four levels: whole brain voxel-wise, 15 resting state networks (RSNs), a 64-feature autoencoded latent space, and complexity dynamics along contrast-enhancing (CE) boundary.

RESULTS: GBM patients showed a significant reduction in global SampEn versus controls (p < 0.001). Among RSNs, medial temporal lobe (MTL) and basal ganglia (BGA) SampEn correlated inversely with OS (R² = 0.033 and 0.034; p = 0.008 and 0.006). The latent-space-dependent Cox risk score stratifies patients into high and low survival populations (p < 0.001). The number of SampEn peaks at the CE boundary also correlated negatively with OS (R² = 0.020, p = 0.037).

CONCLUSIONS: Voxel-wise SampEn revealed widespread loss of BOLD complexity in GBM. It identifies influences at RSNs and tumor-edge, characterizing survival. Latent space analysis revealed whole-brain SampEn characteristics, which provide a compact, data-driven biomarker that augments conventional Cox modelling and stratifies the patient survival. These findings show fMRI-derived SampEn measures are efficient and robust for risk stratification and mechanistic insight in glioblastoma.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11060-025-05361-x.

PMID:41491449 | PMC:PMC12769512 | DOI:10.1007/s11060-025-05361-x

iScience. 2025 Dec 1;29(1):114282. doi: 10.1016/j.isci.2025.114282. eCollection 2026 Jan 16.

ABSTRACT

The accurate identification of individuals from functional connectomes (FCs) is central to individualized neuro/psychiatric assessment. Traditional metrics (Pearson and Euclidean) fail to capture the non-Euclidean geometry of FCs, and geodesic metrics (affine-invariant and Log-Euclidean) require task- and scale-specific regularization and degrade in high-dimensional settings. To address these challenges, we propose the Alpha-Z Bures-Wasserstein divergence, a geometry-aware divergence for FC comparison that operates effectively without meticulous parameter tuning. Across Human Connectome Project tasks, scan lengths, and spatial resolutions, we benchmark Alpha-Z against classical and state-of-the-art manifold-based distances and quantify how varying regularization influences geodesic performance. Alpha-Z yields consistently higher identification rates, with pronounced advantages in rank-deficient regimes, and preserves performance across parcellations and conditions. We further verify generalization across resting-state and task fMRI under multiple parcellation schemes. These results position Alpha-Z as a reliable, robust, and scalable framework for functional connectivity analysis, improving sensitivity to cognitive and behavioral patterns and offering strong potential for individualized clinical neuroscience.

PMID:41488781 | PMC:PMC12757632 | DOI:10.1016/j.isci.2025.114282

Front Psychiatry. 2025 Dec 18;16:1695846. doi: 10.3389/fpsyt.2025.1695846. eCollection 2025.

ABSTRACT

INTRODUCTION: Emotion regulation is a key domain of social cognition, and its impairment contributes to poor psychosocial functioning in schizophrenia (SZ). The "Managing Emotions" (ME) branch of the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) is widely used to assess this ability, yet its neural correlates remain unclear.

METHODS: We examined resting-state functional connectivity (rsFC) associated with MSCEIT-ME performance in 56 patients with schizophrenia and 56 healthy controls matched for age, sex, and years of education. Seed-based correlation analyses focused on three large-scale networks previously implicated in emotion regulation: the salience network (SN), the language network (LN), and the ventral attention network (VAN). Between-group differences and brain-behavior relationships were tested while controlling for IQ scores on the Wechsler Abbreviated Scale of Intelligence (WASI). False discovery rate Benjamini-Yekutieli (FDR-BY) correction was applied to all analyses.

RESULTS: Patients with SZ scored significantly lower on the MSCEIT-ME compared to healthy subjects (HCs). Moreover, SZ patients exhibited reduced left-lateralized rsFC between SN and LN regions relative to HCs. These findings indicate altered language-salience connectivity in schizophrenia and show that, while connectivity is associated with emotion regulation ability in healthy individuals, no significant brain-behavior association was detected in patients. Therefore, the neural mechanisms underlying emotion regulation deficits in schizophrenia remain to be clarified.

CONCLUSION: Schizophrenia was characterized by altered left-lateralized language-salience connectivity. However, because no significant brain-behavior associations were found in patients, the neural basis of emotion-regulation deficits in schizophrenia remains unresolved, highlighting the need for network-level investigations in larger samples.

PMID:41488561 | PMC:PMC12756178 | DOI:10.3389/fpsyt.2025.1695846

Front Neurosci. 2025 Dec 18;19:1725420. doi: 10.3389/fnins.2025.1725420. eCollection 2025.

ABSTRACT

INTRODUCTION: In this study, we propose a data-driven approach that integrates behavioral diagnosis with neuroimaging features to identify representative UWS and MCS patients from a large inpatient cohort.

METHODS: Clinical information was extracted using a subset of UWS patients with CRS-R scores ≤ 5. Neuroimaging biomarkers were established as the increased and decreased functional connectivity indices of anatomically defined regions covering the whole brain. The algorithm was implemented through an iterative refinement process that converged on a division of UWS and MCS patients into representative and excluded (or nonrepresentative) patient groups.

RESULTS: Thirty-one out of 58 UWS patients and 23 out of 30 MCS patients were identified as representative, with an average classification accuracy of 90.2% in differentiating between the two groups. In contrast, differentiating between excluded UWS patients (n = 27) and representative MCS patients (n = 23) and between all UWS (n = 58) and MCS (n = 30) patients produced average classification accuracies of 50.9 and 64.3%, respectively. Furthermore, altered DMN functional connectivity between representative UWS and MCS patients revealed a consistent pattern as shown in prior studies, while comparisons involving excluded patients did not.

DISCUSSION: These results highlight the value of integrating behavioral scores and neural connectivity features for DOC classification, providing a more coherent basis for downstream analysis and machine-learning applications in DOC classification.

PMID:41488324 | PMC:PMC12756502 | DOI:10.3389/fnins.2025.1725420

Innov Aging. 2025 Nov 14;10(1):igaf129. doi: 10.1093/geroni/igaf129. eCollection 2026.

ABSTRACT

BACKGROUND AND OBJECTIVES: Subjective cognitive decline (SCD) is a preclinical stage of mild cognitive impairment (MCI). Although dance training has been shown to be beneficial for mental health, cognitive function, and neural activity in older adults with MCI, its effect on SCD remains unclear. This study aimed to examine the effects of dance training on the aforementioned factors and on oxytocin secretion in older adults with SCD.

RESEARCH DESIGN AND METHODS: Participants (aged 65-84 years) were assigned to either the intervention group (n = 22) with a 12-week dance training program or the control group without any alternative training (n = 22). Apathy, depression, Montreal Cognitive Assessment scores, urinary oxytocin levels, and resting-state functional magnetic resonance imaging indices, including amplitude of low-frequency fluctuations (ALFF) and functional connectivity (FC), were evaluated pre- and post-intervention.

RESULTS: Compared to the control group, the intervention group exhibited significantly higher urinary oxytocin levels and significantly higher ALFF in the left medial orbitofrontal cortex post-intervention. Moreover, the intervention group showed more enhanced FC between the left medial orbitofrontal cortex and the left precuneus post-intervention than the control group. However, mental health or cognitive performance was not significantly different between the groups.

DISCUSSION AND IMPLICATIONS: Our results are particularly important in light of previous findings that older adults with SCD show a reduced FC between the medial orbitofrontal cortex and the precuneus, and that oxytocin levels are positively associated with the prefrontal-amygdala oxytocinergic circuit in socioemotional processing. Thus, dance training may contribute to socioemotional resilience-related neural and molecular adaptations in SCD.

PMID:41487488 | PMC:PMC12759060 | DOI:10.1093/geroni/igaf129

J Affect Disord. 2026 Jan 1:121110. doi: 10.1016/j.jad.2025.121110. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Dysfunction in the cortical-striatal-thalamo-cortical circuit is considered a core pathological mechanism of obsessive-compulsive disorder (OCD) and may contribute to abnormalities in the sensorimotor network (SMN). Although altered SMN patterns in OCD have been reported using resting-state fMRI, SMN alterations remain underexplored in resting-state EEG (rsEEG). This study aimed to identify frequency-specific SMN alterations in patients with OCD compared to healthy controls (HCs) using rsEEG.

METHODS: Eyes-closed rsEEG were collected from 41 patients with OCD and 41 HCs. SMN was constructed by eight cortical regions and functional connectivity (FC) with the weighted phase-lag index across six frequency bands. Group differences in FC and strength were assessed using permutation testing. Correlation analysis was conducted between significantly altered measures and Yale-Brown Obsessive Compulsive Scale (Y-BOCS). Machine learning-based classification was applied to assess the potential of SMN features as biomarkers for OCD.

RESULTS: In the theta band, FC between the left primary somatosensory cortex (S1) and the left supplementary motor area was significantly increased in OCD relative to HC. In the high alpha band, FCs between the left S1 and right primary motor cortex (M1), and between the left S1 and right premotor cortex (PMC), as well as local strength in the right PMC, were significantly increased in OCD. FCs between left S1 and right M1 in the high alpha band positively correlated with Y-BOCS. Classification accuracy was achieved at 78.05 %.

CONCLUSION: These findings suggest that rsEEG-derived SMN alterations may reflect neurophysiological mechanisms of OCD and serve as candidate biomarkers.

PMID:41483883 | DOI:10.1016/j.jad.2025.121110

Psychiatry Res Neuroimaging. 2025 Dec 24;357:112116. doi: 10.1016/j.pscychresns.2025.112116. Online ahead of print.

ABSTRACT

This study investigated resting-state functional connectivity (rsFC) patterns in individuals with opioid use disorder (OUD), stimulant use disorder (StUD) and healthy controls (HC). Using seed-based analysis of key subcortical regions, we found distinct connectivity profiles associated with each substance type. OUD showed reduced connectivity between limbic/basal ganglia structures and sensorimotor regions, along with increased pallidum-angular gyrus connectivity compared to HC. StUD exhibited decreased striatal-default mode network and limbic-prefrontal connectivity relative to HC. Direct comparison between OUD and StUD revealed widespread corticostriatal, striato-cerebellar, and prefrontal-limbic hyperconnectivity in OUD compared to StUD. These substance-specific alterations in intrinsic brain organization may reflect differential neuroadaptations underlying the cognitive and behavioral manifestations of opioid versus stimulant use disorders. Our findings highlight the potential of rsFC patterns as a biomarker for distinguishing among different subtypes of addiction and informing targeted interventions.

PMID:41483578 | DOI:10.1016/j.pscychresns.2025.112116

J Affect Disord. 2025 Dec 31:120969. doi: 10.1016/j.jad.2025.120969. Online ahead of print.

ABSTRACT

BACKGROUND: The increasing incidence of adolescent depression represents a serious public health concern. Despite clear diagnostic criteria, the wide range of symptoms and their overlap with other psychiatric disorders make it difficult to provide effective personalized treatment in adolescents. The integration of resting-state functional magnetic resonance imaging (rs-fMRI) and machine learning has shown promise in identifying diagnostic biomarkers and shedding light on personalized treatments in adult depression. However, equivalent studies in adolescent depression are lacking. Therefore, the present study aimed to identify diagnostic rs-fMRI biomarkers for adolescent depression.

METHODS: Phenotypic and rs-fMRI data of 127 adolescents (64 adolescents with depression; 63 healthy controls) were acquired from the Boston Adolescent Neuroimaging of Depression and Anxiety dataset. Partial correlation was used to compute the functional connectome of the whole brain. Repeated nested cross validation with Boruta feature selection and support vector machine was employed to build a classification model to discriminate adolescents with depression from healthy controls.

RESULTS: The classification model identified 46 fine-scale connectivity features of the functional connectome as co-biomarkers in adolescent depression. The connectivity between the right medial/superior temporal gyrus and left pars triangularis/rostral middle frontal gyrus, as well as between the right medial orbitofrontal/ rostral anterior cingulate cortex and right precuneus/isthmus cingulate gyrus were identified as the most important features in adolescent depression.

CONCLUSIONS: The identification of a novel neuroimaging composite-biomarker panel here sheds light on depression diagnosis in adolescence. The retention of anatomical resolution within these composite biomarkers may facilitate the development of individualized neuromodulation treatment strategies.

PMID:41482270 | DOI:10.1016/j.jad.2025.120969

Int J Rheum Dis. 2026 Jan;29(1):e70531. doi: 10.1111/1756-185x.70531.

ABSTRACT

OBJECTIVES: The subdivisions of the anterior cingulate cortex (ACC) are involved in distinct functions in the processing of chronic pain and regulation of emotions. However, the specific impact of each ACC subdivision on fibromyalgia (FM) remains unclear. This study aimed to systematically investigate the abnormal resting-state functional connectivity (rsFC) patterns between the ACC (and its subregions) and other chronic-pain-related limbic cortices and subcortical nuclei in patients with FM.

METHODS: Resting-state functional magnetic resonance imaging (fMRI) was conducted in 31 patients diagnosed with fibromyalgia (FM) and 32 demographically matched healthy controls (HCs). Using subdivisions of the anterior cingulate cortex (ACC) as regions of interest, we employed a seed-based resting-state functional connectivity (rsFC) approach to identify alterations in connectivity between limbic cortex and subcortical nuclei. A two-sample t-test was applied to compare functional connectivity differences between the two groups. Additionally, Pearson correlation analysis was performed to examine the relationships between rsFC alterations and measures of executive function and clinical symptom severity.

RESULTS: Patients with FM demonstrated aberrant rsFC of the dorsal ACC (dACC) with the limbic system, notably the amygdala (t = 2.840, SE = 0.942, p = 0.007), parahippocampal gyrus (t = 2.340, SE = 0.905, p = 0.024), and insula (t = 2.159, SE = 0.835, p = 0.036). Subregion analyses further revealed heightened connectivity of the anterior midcingulate cortex (aMCC) with the parahippocampal gyrus (t = 2.737, SE = 1.064, p = 0.009), and increased connectivity of the superior anterior cingulate cortex (supACC) with the insula (t = 2.596, SE = 0.706, p = 0.013) and amygdala (t = 2.398, SE = 0.812, p = 0.021), which were significantly associated with pain severity and depressive symptoms in FM.

CONCLUSION: This study revealed specific abnormalities in the rsFC between the dACC and the limbic cortices and subcortical nuclei in FM patients. The heightened connectivity of the aMCC with the parahippocampal gyrus and of the supACC with the insula and amygdala was closely associated with the regulation of emotion and processing of chronic pain.

PMID:41480821 | PMC:PMC12757978 | DOI:10.1111/1756-185x.70531

medRxiv [Preprint]. 2025 Dec 22:2025.12.19.25342404. doi: 10.64898/2025.12.19.25342404.

ABSTRACT

BACKGROUND: Cerebellar transcranial magnetic stimulation (TMS) may serve as an adjuvant therapy for psychosis symptoms, most recently we have shown improvements in negative symptoms. Historically, cerebellum TMS has not utilized functional neuroanatomy for targeting, and the precision of TMS to the cerebellum is unclear. A classical view of the cerebellum as solely involved in motor computations has been updated with the discovery of rich non-motor connectivity including the default, dorsal attention, frontoparietal control and ventral attention networks. We sought to assess cerebellar TMS magnetic field effect within individually defined networks of the cerebellum.

METHODS: Imaging data from schizophrenia and schizoaffective participants (n=27) in a double-blinded trial of cerebellar TMS ( NCT05343598 ) was used. Individualized resting-state connectivity fMRI maps of the cerebellum was computed for 7 canonical networks (Yeo et al 2011; Buckner et al 2011). Individualized TMS simulations were computed in SimNIBS with real-world participant-specific coil placement and intensity determination.

RESULTS: The peak stimulation effect (99th percentile) for each network in each participant was computed. The electric field induced by cerebellar TMS predominantly engaged specific functional networks more than others (p<0.001), indicating selective targeting of these networks. The strongest effects were found on default (44.4%), limbic (37%) and frontoparietal control (11.1%) networks. Cerebellar brain network organization was found to be similar in the patient sample to previously published large-sample organization.

CONCLUSIONS: For personalized TMS, it is important to consider the targeted network, as well as the potential off-target network effects. Our findings demonstrate that cerebellar TMS has the strongest field effect on non-motor, cognitive and affective networks within the cerebellum. These results suggest cerebellar TMS may be ideal for schizophrenia symptoms unaddressed by pharmacological treatments, and effects may vary by individual network topology.

PMID:41480026 | PMC:PMC12755297 | DOI:10.64898/2025.12.19.25342404

Front Psychol. 2025 Dec 17;16:1734803. doi: 10.3389/fpsyg.2025.1734803. eCollection 2025.

ABSTRACT

BACKGROUND: Tobacco smoking continues to be a leading cause of preventable morbidity and mortality globally, with the success rate of unaided cessation remaining consistently low. Understanding the neurobiological mechanisms of smoking cessation is crucial for improving quit rates. However, there has been a lack of studies examining brain network changes associated with smoking cessation over time. In this study, we aimed to investigate longitudinal changes in the functional connectivity (FC) of large-scale brain networks underlying smoking cessation outcomes using resting-state functional magnetic resonance imaging (fMRI).

METHODS: A total of 98 treatment-seeking smokers participated in a 5-week cessation program and underwent resting-state fMRI scans before and after the intervention. Independent component analysis identified the salience network (SN), executive control network (ECN), and default mode network (DMN) components, and region of interest (ROI)-to-ROI FC was compared between successful and unsuccessful quitters using a group × time mixed-effects model. Correlations with smoking-related measures were explored.

RESULTS: Significant group-by-time interaction effects were found in FC, particularly involving connections between SN and ECN, as well as between the SN and DMN. Specifically, successful quitters exhibited greater baseline FC in the SN-ECN and SN-DMN circuits, which tended to decrease and converge toward levels observed in unsuccessful quitters during the cessation process. Exploratory correlational analyses revealed trends suggesting that stronger pre-quit connectivity between the SN and ECN was associated with greater withdrawal severity and longer smoking history in successful quitters.

CONCLUSION: Taken together, the reduction of initially elevated pre-quit FC in SN-ECN and SN-DMN circuits may reflect an adaptive neural process that supports successful withdrawal management and attentional reallocation during cessation. The identification of these neural substrates not only enhances our mechanistic understanding of smoking cessation over time but also underscores the need for targeted interventions that focus on these neural circuits to enhance quit outcomes.

PMID:41479984 | PMC:PMC12753871 | DOI:10.3389/fpsyg.2025.1734803

J Prev Alzheimers Dis. 2026 Jan 1:100462. doi: 10.1016/j.tjpad.2025.100462. Online ahead of print.

ABSTRACT

BACKGROUND: Predicting the transition from subjective cognitive decline (SCD) to mild cognitive impairment (MCI) is critical for dementia prevention.

OBJECTIVE: Comprehensive assessment of MRI-based macro-/micro-structural and functional brain changes in SCD to develop an individualized model predicting transition to MCI.

DESIGN, SETTING, AND PARTICIPANTS: Patients with SCD were screened from the ADNI, NACC, and OASIS-3 databases. 89 patients met the inclusion criteria and underwent structural magnetic resonance imaging (sMRI) and resting-state functional MRI (rs-fMRI). Over a 10-year follow-up, 49 patients progressed to MCI, while 40 remained stable.

MEASUREMENTS: The VB-net automated brain segmentation, extracting hippocampal radiomics and whole brain subregion volume features. Brain functional features were extracted based on rs-fMRI. Cox regression was used to develop predictive models, which were independently validated with the testing set. The nomogram was constructed to estimate the probability of transition to MCI at 5-/7-/10-year. The nomogram's accuracy was assessed using calibration curves and concordance index (C-index), and clinical utility was evaluated through decision curve analysis.

RESULTS: The model incorporating age, brain volume, functional, and radiomics features demonstrated the highest predictive performance for SCD progression in training (C-index: 0.962; 95 % CI: 0.95-0.98) and testing (C-index: 0.911; 95 % CI: 0.861-0.968) sets. A nomogram comprising 10 predictors was constructed to estimate individualized risk of progression to MCI at 5-/7-/10-year. The calibration curve showed good agreement between predicted and observed values. Decision curve analysis demonstrated the nomogram had substantial clinical value.

CONCLUSIONS: This multivariate model and nomogram could accurately predict the individual progression from SCD to MCI.

PMID:41478831 | DOI:10.1016/j.tjpad.2025.100462

Behav Brain Res. 2025 Dec 30;501:116008. doi: 10.1016/j.bbr.2025.116008. Online ahead of print.

ABSTRACT

Adolescence is a period of heightened sensation-seeking, risk-taking, and reward sensitivity, characterized by structural and functional changes in the brain. Developmental changes in functional connectivity between cortical and subcortical regions may refine communication within reward-related circuitry, influencing learning and decision-making. Here, we compared reinforcement learning behavior and its relationship to resting-state functional connectivity in reward-related circuits in adolescents and adults. Fifty-eight healthy participants (32 adolescents aged 13-16; 26 adults aged 30-40) completed a probabilistic two-armed bandit task and resting-state functional magnetic resonance imaging (fMRI). The learning-related parameters learning rate (α) and inverse temperature (β, an index of the randomness of choices) and their relationship to functional connectivity were modeled from behavioral data using Q learning in a hierarchical Bayesian framework. In the whole sample, learning rate was associated with functional connectivity in several cortico-subcortical pathways, particularly involving the anterior cingulate cortex. Adolescents exhibited lower learning rate and inverse temperature values than adults and had a stronger association between learning rate and fronto-striatal connectivity. Adolescents also showed less tendency to stay with winning options in the task, defined as the proportion of trials where participants repeated the previous choice after a reward. These findings highlight the involvement of the ACC in reward learning and indicate that behavior in a reinforcement learning context is characterized by reduced feedback-driven learning and more variable choice behavior or greater exploration in adolescents compared to adults, and suggest that adolescents rely more on fronto-striatal connectivity during learning.

PMID:41478440 | DOI:10.1016/j.bbr.2025.116008

Psychophysiology. 2026 Jan;63(1):e70223. doi: 10.1111/psyp.70223.

ABSTRACT

Alexithymia is a multidimensional construct characterized by difficulties in identifying and describing feelings and reduced ability to engage in abstract thinking. Although often co-occurring with other psychological and neurodevelopmental conditions such as anxiety, depression and autism spectrum disorders, alexithymia is believed to be associated with unique alterations within the socio-emotional brain networks. With the semantic and neuromodulatory brainstem systems playing a key role in social and affective cognition, the current work aimed to study their contributions to alexithymia in unprecedented detail. First, we attempted to identify resting-state functional connectivity patterns of the social semantic hubs (superior anterior temporal lobe) and monoamine-producing regions (dorsal raphe, ventral tegmental area and locus coeruleus) linked to each alexithymia domain. Secondly, by deploying tractography and graph analysis of the associated structural network, we intended to identify their potential anatomical correlates. Alexithymia was strongly associated with dysconnectivity within the semantic network, and altered functional connectivity between the neuromodulatory brainstem regions and cortical areas crucial for social cognition and emotion regulation, including medial prefrontal cortex and inferior parietal lobule. On the anatomical level, these findings were paralleled by negative links with network modularity, suggestive of less specialized neural processing, and decreased clustering coefficient of the semantic node in the left posterior middle temporal gyrus. Despite observing associations with trait-anxiety and emotion suppression for some of the highlighted findings, these phenomena did not mediate the effects of alexithymia. Therefore, the current work highlights the existence of functional and structural alterations within socio-emotional networks as neural markers of alexithymia.

PMID:41476431 | DOI:10.1111/psyp.70223

Br J Anaesth. 2025 Dec 30:S0007-0912(25)00844-X. doi: 10.1016/j.bja.2025.11.036. Online ahead of print.

ABSTRACT

BACKGROUND: Postoperative delirium is associated with increased morbidity, mortality, future cognitive decline, or dementia. Understanding the neural mechanisms that differentiate individual brain vulnerabilities is critical for future therapeutic development and prevention of postoperative delirium. We investigated the hypothesis that impaired resting state functional connectivity indicates predisposition to delirium.

METHODS: Preoperative blood oxygen level-dependent functional MRI data were collected from 120 participants (>65 yr, 52 female) undergoing major elective non-intracranial surgery. Denoised blood oxygen level-dependent signal time-series for 400 cortical regions were used to calculate resting state functional connectivity within and between canonical resting state networks. We used a support vector machine to determine whether resting state functional connectivity across higher-order cortical networks was predictive of postoperative delirium.

RESULTS: Group comparisons revealed significantly decreased within-network connectivity in salience-ventral attention, cognitive control, and default mode network in participants with postoperative delirium (n=31) compared with non-delirious participants (n=89; non-parametric permutation test, 1000 iterations, P<0.05). We found overall weaker connectivity within the default mode network and specific differences across the sub-networks of the default mode which overlap with higher-order cognitive processing. Supervised machine learning identified that the visual and salience-ventral attentional networks predicted postoperative delirium incidence with an accuracy of 68%.

CONCLUSIONS: Resting state functional connectivity is a neural correlate of vulnerability to postoperative delirium. Disrupted resting state connectivity within higher-order cognitive association areas, including the default mode network, salience attention, and cognitive control networks, was specifically correlated with delirium.

CLINICAL TRIAL REGISTRATION: NCT01980511 and NCT03124303.

PMID:41475933 | DOI:10.1016/j.bja.2025.11.036

BMJ Open. 2025 Dec 31;15(12):e108722. doi: 10.1136/bmjopen-2025-108722.

ABSTRACT

INTRODUCTION: Postoperative ileus (POI) is a prevalent complication following abdominal surgeries, significantly compromising patients' quality of life and imposing a socioeconomic burden. Electroacupuncture (EA), a widely used therapeutic approach in China, has shown promise as an effective intervention for POI. However, the neural mechanism underlying its therapeutic effects remains unclear. Thus, this study aims to evaluate the efficacy of EA treatment for POI and investigate its central mechanism by functional MRI (fMRI).

METHODS AND ANALYSIS: This randomised controlled clinical trial will be conducted across three hospitals in China. A total of 50 eligible patients with colorectal cancer scheduled for elective laparoscopic surgery will be randomly assigned to either the EA or sham electroacupuncture (SA) group in a 1:1 ratio. All patients will undergo 5 sessions of 30 min EA or SA over 5 consecutive days post-surgery (once daily). Resting-state fMRI (rs-fMRI) scans will be performed at baseline and the end of treatment to examine brain functional changes related to EA treatment. The primary outcome is the time to first defecation. Secondary outcomes include the time to first flatus, ambulation, tolerability of semiliquid and solid food; length of postoperative hospital stay; severity of postoperative pain, abdominal distension and nausea; frequency of postoperative nausea and vomiting episodes; rate of readmission. Postoperative complications will be monitored and documented throughout the trial duration. Credibility and expectancy evaluation, along with blinding assessment, will be conducted after the first treatment session. Pearson/Spearman correlation analysis will be performed to determine the relationship between clinical variables and rs-fMRI metrics.

ETHICS AND DISSEMINATION: This protocol has been approved by the ethics committees of Beijing University of Chinese Medicine (number 2024BZYLL0113), Cancer Hospital Chinese Academy of Medical Sciences (number 24/323-4603), Beijing Friendship Hospital Affiliated to Capital Medical University (number 2024-P2-081-01) and Beijing Chaoyang Huanxing Cancer Hospital (number 2024-011-02). Participants will sign the paper-based informed consent form before enrolment. The results will be disseminated through peer-reviewed publications.

TRIAL REGISTRATION NUMBER: ITMCTR2024000504.

PMID:41475813 | PMC:PMC12766759 | DOI:10.1136/bmjopen-2025-108722

Prog Neuropsychopharmacol Biol Psychiatry. 2025 Dec 29:111602. doi: 10.1016/j.pnpbp.2025.111602. Online ahead of print.

ABSTRACT

BACKGROUND: Recent studies have identified the orbitofrontal cortex (OFC) as a potential target for alleviating negative symptoms in schizophrenia. However, the neurobiological mechanisms underlying repetitive transcranial magnetic stimulation (rTMS) delivered to the OFC remain unclear.

METHODS: In this randomized controlled trial, seventy first-episode, drug-naïve patients with schizophrenia were assigned to receive either 20 sessions of active 1 Hz rTMS over the right lateral OFC (N = 36) or sham stimulation (N = 34). Clinical outcomes were measured using the Positive and Negative Syndrome Scale (PANSS). Resting-state functional MRI data were collected before and after treatment to assess changes in regional brain activity and functional connectivity, using fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and seed-based connectivity analyses.

RESULTS: Compared to sham stimulation, active OFC-rTMS led to significantly greater reductions in PANSS scores (total: 22.7 vs. 14.3, p = 0.003, Cohen's d = 0.733; negative: 6.2 vs. 4.0, p = 0.037, Cohen's d = 0.510). Neuroimaging analyses revealed increased spontaneous activity (fALFF and ReHo) in the right OFC and bilateral inferior parietal lobule (IPL), along with enhanced functional connectivity between the OFC and IPL in the active rTMS group. Importantly, IPL-related functional reorganization was significantly associated with symptom improvement, particularly in negative and general domains.

CONCLUSIONS: These findings suggest that rTMS targeting the OFC exerts therapeutic effects in schizophrenia by modulating IPL function and OFC-IPL connectivity. The IPL may serve as a critical downstream node mediating the clinical benefits of OFC-rTMS, offering novel insights into network-based neuromodulation strategies for negative symptoms.

PMID:41475556 | DOI:10.1016/j.pnpbp.2025.111602