Psychoradiology最新文献

{"title":"Convergent and divergent spatial topographies of individualized brain functional networks and their developmental origins.","authors":"Jianlong Zhao, Yu Zhai, Yuehua Xu, Lianglong Sun, Tengda Zhao","doi":"10.1093/psyrad/kkag013","DOIUrl":"https://doi.org/10.1093/psyrad/kkag013","url":null,"abstract":"<p><strong>Background: </strong>The human brain is intrinsically organized as canonical functional networks with distinct spatial topographies. While precision functional mapping studies have delineated individualized topographies of single networks, the spatial coordination among these networks and its developmental origin remains largely unknown.</p><p><strong>Methods: </strong>Utilizing three well-established task-free functional magnetic resonance imaging (fMRI) datasets encompassing both conventional and densely sampled scans across neonatal and adult cohorts, we proposed functional topography covariance analysis (FOCA), a novel framework that quantifies convergent and divergent spatial alignments across individualized functional networks and further delineated their internetwork relationships, neurobiological basis, ontogenetic layouts, and cognitive outcomes.</p><p><strong>Results: </strong>In adults, FOCA consistently revealed self-clustered and gradient-distributed functional hierarchies characterized by convergent couplings within primary systems and divergent couplings in higher-order systems. Such pattern was well predicted by fundamental neurobiological attributes, especially aerobic glycolysis. In a large public neonatal cohort, FOCA matrix exhibited adult-inverted hierarchical couplings and prominent changes in auditory and action-mode networks, driven primarily by redistributions of negative couplings. Moreover, neonatal FOCA profiles in the primary visual system significantly predicted neurodevelopmental outcomes at 18 months. Finally, compared with conventional functional connectivity, FOCA demonstrated greater robustness to the global signal and higher sensitivity to the maturation of negative couplings.</p><p><strong>Conclusions: </strong>These findings highlight the critical role of negative functional connectivity and deepen our understanding of the cooperative-competitive interactions among functional systems and their developmental origins.</p>","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"6 ","pages":"kkag013"},"PeriodicalIF":2.9,"publicationDate":"2026-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13103294/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147791664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neurocomputational mechanisms of reward-based online mood regulation in adolescents with bipolar disorder and major depressive disorder.","authors":"Yu-Feng Xia, Yingyan Zhong, Zi-Jian Cheng, Enzhao Cong, Yifeng Xu, Ru-Yuan Zhang","doi":"10.1093/psyrad/kkag015","DOIUrl":"https://doi.org/10.1093/psyrad/kkag015","url":null,"abstract":"<p><strong>Background: </strong>The overlapping symptoms between bipolar disorder (BD) and major depressive disorder (MDD) pose a challenge in diagnosis and treatment. A prevailing hypothesis suggests that mood dysregulation may be linked to impairments in the reward system, but the neurocomputational differences between BD and MDD remain elusive. This study investigates whether atypical reward processing affects subjective mood in adolescents with BD and MDD. Our research aims to elucidate the behavioral and neural differences between the two groups, facilitating more accurate and timely diagnosis and intervention.</p><p><strong>Methods: </strong>Forty-five adolescents (aged ≤ 19 years) diagnosed with BD-II in depressed mood states (<i>N</i> = 25) or MDD (<i>N</i> = 20) completed a risky gambling task while their brain responses were recorded using functional magnetic resonance imaging (fMRI). Several computational models were constructed to uncover the associations between various reward components (e.g. reward prediction errors, RPE) and trial-wise fluctuations in subjective mood during the task.</p><p><strong>Results: </strong>Adolescents with BD exhibited a lower best choice rate and a higher uncertain choice rate compared to those with MDD. Computational modeling and mediation analysis suggested a tripartite mediating relationship between RPE-mood association, decision rationality, and symptom severity. Using fMRI, we observed significant RPE-related activation in the ventral striatum, which showed a slight positive correlation with the RPE-mood association. We also noted subtle differences in several brain regions (i.e. medial orbitofrontal cortex) between the BD and MDD groups. These differences were further associated with manic symptoms.</p><p><strong>Conclusion: </strong>Decision rationality mediated the association between RPE-mood association and symptom severity. Relative to adolescents with MDD, those with BD showed decreased decision rationality, along with modest but distinct reward-related neural patterns on fMRI. These findings highlight the crucial role of reward processing in mood regulation and provide preliminary neurocomputational evidence that may inform future diagnostic biomarker development.</p>","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"6 ","pages":"kkag015"},"PeriodicalIF":2.9,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13133896/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147824549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Alterations in self-related brain networks in autism spectrum disorder: a systematic review of functional and structural magnetic resonance imaging studies.","authors":"Liwei Zhou, Junrong Han, Hang Wu, Han Bao, Jiaying Wang, Pengmin Qin","doi":"10.1093/psyrad/kkag011","DOIUrl":"https://doi.org/10.1093/psyrad/kkag011","url":null,"abstract":"<p><p>Autism spectrum disorder (ASD) involves alterations in social communication and restricted, repetitive behaviors. Emerging evidence highlights atypical self-awareness as a key factor in ASD-related social impairments. However, the neural mechanisms underlying differences in self-processing remain fragmented. This systematic review synthesizes findings from 49 functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) studies (2000-2025) to explore self-related brain networks in ASD, based on the hierarchical self-model comprising interoceptive, exteroceptive, and mental levels. Across all three levels, consistent atypicalities were observed in ASD. The interoceptive level (insula, thalamus) showed altered functional connectivity (FC) and gray matter density, associated with atypical bodily and affective self-awareness. The exteroceptive level, which includes the medial prefrontal cortex (mPFC), temporoparietal junction (TPJ), and premotor cortex (PMC), exhibited reduced long-range FC and local coherence, potentially reflecting atypical self-other differentiation and communication. The mental level, involving the anterior and posterior cingulate cortices (ACC and PCC), revealed decreased FC and interhemispheric coherence, implicating atypical reflective self-processing. Disrupted cross-level interactions further suggest a breakdown in hierarchical self-integration. These findings emphasize the importance of self-related network alterations in ASD and support their inclusion in neurocognitive models of autism.</p>","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"6 ","pages":"kkag011"},"PeriodicalIF":2.9,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13092984/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147791702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine.","authors":"Mingxi Dang, Bing Liu, Yaojing Chen, Zhanjun Zhang","doi":"10.1093/psyrad/kkag007","DOIUrl":"10.1093/psyrad/kkag007","url":null,"abstract":"<p><p>Cognitive disorders, with dementia as a primary exemplar, present profound diagnostic and therapeutic challenges due to their complex pathologies and heterogeneous presentations. Artificial intelligence (AI), particularly when applied to multimodal neuroimaging and clinical data, offers a powerful approach to advancing precision medicine in this domain. This comprehensive review first examines foundational AI algorithms, including artificial neural networks for feature extraction, multimodal fusion strategies (e.g. early, intermediate, and late fusion) for data integration, and explainable AI (XAI) techniques to enhance clinical transparency. The core focus is on the application of these multimodal AI frameworks across the dementia care continuum, encompassing improved differential diagnosis, early detection through presymptomatic biomarkers, development of predictive models for disease progression, and optimization of patient stratification for clinical trials. Despite significant advances, persistent challenges remain, including limited generalizability across populations and protocols, data scarcity for non-Alzheimer's dementias and prodromal stages-exacerbated by demographic biases-and barriers to interpretability. We discuss solutions such as federated learning for privacy-preserving data sharing and advanced XAI techniques. Finally, we outline pivotal future directions, including intelligent sensor fusion for discovering novel early biomarkers, hybrid AI architectures combining generative and discriminative models, innovations for handling missing modalities, and robust multicenter data integration frameworks. By synthesizing these advances, this review highlights the role of multimodal AI in advancing precise diagnosis, early prediction, and therapeutic development for neurodegenerative and vascular cognitive disorders, while identifying key translational challenges for precision medicine.</p>","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"6 ","pages":"kkag007"},"PeriodicalIF":2.9,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13010822/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147517494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent developments in imaging transcriptomics for psychiatric disorders.","authors":"Juan Liu, Xian Ma, Wanze Xu, Xiaoyi Zhang, Feifei Liang, Jing Li, Yu Dou","doi":"10.1093/psyrad/kkag010","DOIUrl":"https://doi.org/10.1093/psyrad/kkag010","url":null,"abstract":"<p><p>Mental disorders refer to abnormal states that affect an individual's thinking, emotion, behavior, and perception, and are generally associated with dysregulation of brain function. They exhibit genetic heterogeneity as well as multi-level structural and functional abnormalities of the brain. Magnetic resonance imaging has been widely applied to detect macroscopic neurophenotypic alterations in patients with psychiatric disorders; however, it remains limited in directly revealing the underlying molecular and cellular mechanisms. Imaging transcriptomics, by integrating whole-brain gene expression atlases with neuroimaging features, offers a novel paradigm for exploring the associations between microscopic genetic expression and macroscopic neuroimaging phenotypes. This review systematically summarizes the methodological framework of imaging transcriptomic association studies and highlights recent advances in their application to psychiatric disorders such as depression. A growing body of evidence has revealed spatial coupling between structural and functional abnormalities in disease-related brain regions and gene expression in synaptic transmission, ion channels, neurodevelopment, and immune signaling. Imaging transcriptomics not only facilitates a multiscale understanding of the pathophysiological mechanisms of psychiatric disorders but also provides potential pathways for disease classification, targeted intervention, and precision diagnosis and treatment. Future research should further promote the integration of longitudinal imaging omics and spatial transcriptomic data to construct translatable multimodal models, thereby accelerating the translation of psychiatric neuroimaging from mechanistic research to clinical application.</p>","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"6 ","pages":"kkag010"},"PeriodicalIF":2.9,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13048277/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147624743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impaired connectivity between the thalamus and the visual pathway in schizophrenia: a multimodal magnetic resonance imaging study.","authors":"Changyue Hou, Meihua Yan, Sisi Jiang, Yuting Deng, Lang Zhang, Hechun Li, Mingjun Duan, Yafeng Wang, Gang Yao, Hui He, Roberto Rodríguez-Labrada, Dezhong Yao, Cheng Luo","doi":"10.1093/psyrad/kkag008","DOIUrl":"https://doi.org/10.1093/psyrad/kkag008","url":null,"abstract":"<p><strong>Background: </strong>Schizophrenia is a severe psychiatric disorder characterized by cognitive deficits as well as positive and negative symptoms. It is considered a disorder of widespread network dysconnectivity, including aberrant connectivity between the thalamus and the visual pathway. However, the relationships between the thalamus and various regions of the dorsal and ventral visual pathways in schizophrenia, and how the thalamus affects interactions among these visual regions, remain unclear.</p><p><strong>Methods: </strong>Resting-state functional magnetic resonance imaging, task-state functional magnetic resonance imaging, and diffusion tensor imaging data were acquired to examine the neural activity within the thalamus and the visual pathway, along with the relationships between them (i.e. functional connectivity, structural connectivity, and structure-function coupling). We also correlated the altered imaging parameters with clinical characteristics. Furthermore, based on previous molecular imaging in healthy controls, we explored the spatial associations between altered imaging parameters and receptor/transporter distributions.</p><p><strong>Results: </strong>We found significantly decreased neural activity and widespread altered thalamo-visual pathway connectivity in both dorsal and ventral pathways in schizophrenia patients. Moreover, schizophrenia patients exhibited altered mediation effects within the thalamo-dorsal visual pathway, involving MT, V1, V2, and V3. Abnormal neural activity and connectivity were related to disease duration and positive symptom severity. Altered neural activity of MT was correlated with the density of multiple neurotransmitters.</p><p><strong>Conclusions: </strong>Our findings further expand our understanding of thalamo-visual pathway dysconnectivity and primary information-processing deficits in schizophrenia, which may be related to clinical symptoms. Our findings may provide more potential insights for non-invasive intervention treatments.</p>","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"6 ","pages":"kkag008"},"PeriodicalIF":2.9,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13023046/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147576940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain entropy as a biomarker of major depression in adolescents and young adults: insights from multimodal resting-state functional magentic resonance imaging.","authors":"Ruoxi Lu, Jianyu Li, Yiran Li, Xinglin Zeng, Yan Guo, Danian Li, Ying Cui, Xinyu Liang, Hanyue Zhang, Jing Wang, Baohua Cheng, Yujie Liu, Ze Wang, Shijun Qiu","doi":"10.1093/psyrad/kkag009","DOIUrl":"https://doi.org/10.1093/psyrad/kkag009","url":null,"abstract":"<p><strong>Background: </strong>Major depressive disorder (MDD) in adolescents and young adults is increasingly prevalent, yet accurate diagnosis remains challenging due to the limitations of conventional neuroimaging metrics. Traditional resting-state functional magnetic resonance imaging (rs-fMRI) measures such as amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity density (FCD) primarily capture static aspects of brain activity and may overlook critical neural dynamics. Brain entropy (BEN), which quantifies temporal irregularity in rs-fMRI signals, may offer a complementary approach to better characterize neural alterations in MDD.</p><p><strong>Methods: </strong>We analyzed multimodal rs-fMRI data from 204 individuals aged 12-24 years (119 with MDD and 85 healthy controls). BEN was computed alongside ALFF, ReHo, and FCD to extract region-wise features across the brain. A support vector machine with recursive feature elimination (SVM-RFE) was used to classify MDD and healthy controls based on various feature combinations. Classification performance was evaluated using repeated cross-validation and permutation testing. Additionally, partial Spearman correlations were performed between selected brain features and clinical measures including depression severity, childhood trauma, sleep quality, and cognitive control.</p><p><strong>Results: </strong>Models incorporating BEN consistently outperformed those using traditional rs-fMRI features alone. The combination of BEN, ALFF, and FCD achieved the highest classification accuracy (AUC = 0.877, permutation test <i>P</i> < 0.001). The most frequently selected brain regions contributing to MDD classification included the putamen, paracentral lobule, cuneus, middle frontal gyrus, and rectus. BEN features also showed preliminary correlations with clinical variables such as childhood trauma and sleep quality, suggesting functional relevance.</p><p><strong>Conclusions: </strong>This study demonstrates that BEN provides complementary diagnostic information to traditional rs-fMRI features in classifying adolescent and young adult MDD. BEN-related alterations in brain activity may reflect underlying neurobiological disruptions and show potential as a functional neuroimaging biomarker for depression during a critical stage of brain development.</p>","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"6 ","pages":"kkag009"},"PeriodicalIF":2.9,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13092983/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147791660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Altered dynamic functional connectivity of orbitofrontal cortex underlies olfactory and cognitive impairment in late-life depression.","authors":"Ben Chen, Shuang Liang, Ting Su, Youxuan Zheng, Wei He, Huarong Zhou, Qiang Wang, Mingfeng Yang, Gaohong Lin, Danyan Xu, Yunheng Chen, Jiafu Li, Qin Liu, Kexin Yao, Zhangying Wu, Min Zhang, Le Hou, Yueyang Zhang, Xiaomei Zhong, Yuping Ning","doi":"10.1093/psyrad/kkag002","DOIUrl":"https://doi.org/10.1093/psyrad/kkag002","url":null,"abstract":"<p><strong>Background: </strong>Late-life depression (LLD) and odor identification (OI) dysfunction are risk factors for dementia, but the underlying neural mechanisms remain unclear. This study investigated dynamic functional connectivity (dFC) in olfactory brain regions of LLD patients with and without OI dysfunction and examined how dFC moderates the OI-cognition link.</p><p><strong>Methods: </strong>Resting-state functional magnetic resonance imaging data were acquired from 51 LLD patients with OI deficits (LLD-OID), 59 LLD patients without deficits (LLD-noOID), and 51 healthy controls (HC). A sliding-window approach (50 TR width, 1 TR step) was used to estimate dFC variability between the orbitofrontal cortex (OFC) and the whole brain. Bayesian regression and moderation analyses assessed associations among OFC dFC, OI scores, and cognitive measures. Results were robust across window sizes.</p><p><strong>Results: </strong>Compared to LLD-noOID and HC, LLD-OID showed decreased OFC-left inferior frontal gyrus dFC variability (<i>P</i> < 0.01) and increased OFC-right middle frontal gyrus (MFG) variability (<i>P</i> < 0.001). Higher OFC-MFG variability was associated with worse OI and cognitive performance and significantly moderated the OI-global cognition relationship (β = 1.06, <i>P</i> = 0.027, 95% CI [0.12, 2.0]). No group differences were found in primary olfactory regions.</p><p><strong>Conclusion: </strong>LLD patients with OI dysfunction exhibited more disrupted dFC in secondary olfactory regions compared with those without OI dysfunction. Dynamic OFC-MFG disconnectivity may index vulnerability to cognitive decline and dementia risk in LLD patients.</p>","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"6 ","pages":"kkag002"},"PeriodicalIF":2.9,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12916006/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146230149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive neural signature of internet gaming disorder severity revealed by cross-network connectivity.","authors":"Yi-Hao Hu, Meiting Wei, Xin Luo, Xuefeng Xu, Shuang Li, Anhang Jiang, Guang-Heng Dong","doi":"10.1093/psyrad/kkag006","DOIUrl":"https://doi.org/10.1093/psyrad/kkag006","url":null,"abstract":"<p><strong>Background: </strong>While internet gaming disorder (IGD) correlates with regional brain responses and functional connectivity, the brain network architecture underlying addiction severity remains poorly characterized.</p><p><strong>Methods: </strong>Using resting-state functional magentic resonance imaging data and addiction severity metrics from 586 participants (443 IGD, 143 recreational game users), we employed connectome-based predictive modeling (CPM) with leave-one-out cross-validation to identify neural networks predictive of IGD severity. The resulting network was evaluated for replicability in independent datasets, with key predictive networks and nodes further analyzed.</p><p><strong>Results: </strong>CPM identified a replicable addiction severity network. CPM significantly predicted individual gaming addiction scores (<i>r</i> = 0.19, <i>P</i> < 0.001), with features selected using a threshold of <i>P</i> < 0.01. Predictive power primarily derived from internetwork connectivity linking the subcortical, subvisual, and frontoparietal networks. Validation in independent data showed a directional trend (<i>r </i>= 0.17, <i>P </i>= 0.011).</p><p><strong>Conclusions: </strong>Individual variability in subcortical-subvisual-frontoparietal network connectivity predicts IGD addiction severity, highlighting these circuits as potential targets for neuromodulation interventions.</p>","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"6 ","pages":"kkag006"},"PeriodicalIF":2.9,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13103295/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147791715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain-predicted age difference is associated with the progression of subthreshold depression: evidence from the UK Biobank.","authors":"Haowei Dai, Lijing Niu, Qingzi Zhu, Yuanyuan Zeng, Yutong Ying, Xueping Yin, Xiangyi Liang, Xingqin Wang, Bihua Zhou, Qing Ma, Ruibin Zhang","doi":"10.1093/psyrad/kkag005","DOIUrl":"https://doi.org/10.1093/psyrad/kkag005","url":null,"abstract":"<p><strong>Background: </strong>Previous studies have reported accelerated brain aging in individuals with major depressive disorder (MDD) compared to healthy controls. However, these findings are based primarily on cross-sectional data, limiting dynamic association between brain aging and MDD. Here, we examined the relationship between brain aging and MDD progression by focusing on subthreshold depression, a prodromal stage of MDD, and aimed to determine whether quantitative markers of brain aging exhibit a stable association with disease progression.</p><p><strong>Methods: </strong>Using neuroimaging data from the UK Biobank and a support vector regression (SVR) model, we predicted brain age in individuals who exhibited subthreshold depressive symptoms at baseline but showed divergent mental status at follow-up, and then conducted between-group comparisons. Logistic regression was then applied to assess whether brain-predicted age difference (Brain-PAD) stably associates with the progression of subthreshold depression after adjusting for covariates.</p><p><strong>Results: </strong>Individuals with subthreshold depression showed a higher risk of progression to MDD compared to healthy controls. Those whose condition worsened from subthreshold depression to MDD exhibited greater brain aging than those who remained subthreshold or recovered. Importantly, Brain-PAD remained significantly and stably associated with this progression after controlling for sex, ethnicity, lifestyle, and socioeconomic factors.</p><p><strong>Conclusions: </strong>This study supports an association between brain aging and MDD progression and demonstrates a robust association between an increased Brain-PAD and the conversion from subthreshold depression to MDD. These findings enhance our understanding of MDD's developmental trajectory and suggest that Brain-PAD may facilitate early detection and intervention targeting brain aging.</p>","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"6 ","pages":"kkag005"},"PeriodicalIF":2.9,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12947158/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147328344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}