Psychoradiology最新文献

{"title":"Commentary: White matter functional connectome gradient dysfunction in major depressive disorder.","authors":"Shangping Cheng, Bing Chen, Jing Lu","doi":"10.1093/psyrad/kkag021","DOIUrl":"10.1093/psyrad/kkag021","url":null,"abstract":"","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"6 ","pages":"kkag021"},"PeriodicalIF":7.4,"publicationDate":"2026-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13262227/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148254880","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":"Effect of rTMS on cerebral functional hierarchical structure in schizophrenia.","authors":"Hanxi Wu, Hechun Li, Kewei Xiong, Sisi Jiang, Huan Huang, Changyue Hou, Jingyu Zhou, Roberto Rodriguez-Labrada, Dezhong Yao, Cheng Luo","doi":"10.1093/psyrad/kkag019","DOIUrl":"10.1093/psyrad/kkag019","url":null,"abstract":"<p><strong>Background: </strong>The hierarchical functional structure reflects the process of integrating primary information to form higher-order cognition in the human brain. Schizophrenia, as a severe chronic psychiatric illness, has been demonstrated to exhibit an abnormal cerebral functional hierarchical structure. Although repetitive transcranial magnetic stimulation (rTMS) of the dorsolateral prefrontal cortex has shown therapeutic efficacy on schizophrenia, the mechanism of rTMS in schizophrenia is still unclear. Thus, this study attempted to reveal the modulation of rTMS on the cerebral functional hierarchy and its potential molecular mechanism in schizophrenia.</p><p><strong>Methods: </strong>A longitudinal study was performed on 53 patients with schizophrenia (randomly assigned to the rTMS group or drug treatment group). Additionally, 24 age- and gender-matched healthy adults were recruited. Functional gradient analysis was executed to depict the hierarchical organization of schizophrenia. The alteration of the functional gradients at baseline and after 4 weeks of treatment was evaluated in schizophrenia to detect the effect of rTMS on the cerebral hierarchical structure.</p><p><strong>Results: </strong>We found rTMS alleviated the compression of the fronto-parietal network, the core node of the salience network, and the bilateral middle temporal areas in schizophrenia. Leveraging human brain gene expression data, we identified a spatial correlation between the expression of schizophrenia-related genes and impaired functional gradients, and further revealed a close association of the modulatory effects of rTMS with pathways related to neuroplasticity and neuroimmune processes. Furthermore, the alterations of gradient induced by rTMS were associated with the alleviation of clinical symptoms in schizophrenia, suggesting that the altered hierarchical structure may play an important role in the treatment of schizophrenia.</p><p><strong>Conclusions: </strong>Our findings indicate that the alteration of cerebral hierarchy induced by rTMS contributes to clinical symptom improvement in schizophrenia and provides new evidence for the efficacy of rTMS in the treatment of schizophrenia.</p>","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"6 ","pages":"kkag019"},"PeriodicalIF":7.4,"publicationDate":"2026-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13211985/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148044463","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":"Trends and challenges in imaging research of Parkinson's disease: a 10-year bibliometric analysis.","authors":"Junjie Lan, Limei Zhang, Lulu Gong, Kuan Lu, Jie Deng, Lihua Qiu","doi":"10.1093/psyrad/kkag018","DOIUrl":"10.1093/psyrad/kkag018","url":null,"abstract":"<p><strong>Background: </strong>In recent years, neuroimaging has emerged as a pivotal tool for investigating neurological and psychiatric disorders. Although numerous studies have demonstrated the diagnostic value of imaging in neurodegenerative diseases, including Parkinson's disease (PD), there remains a scarcity of comprehensive review articles that consolidate this expanding field. This study aims to address that gap by employing bibliometric analysis to outline current research trends and predict future directions.</p><p><strong>Methods: </strong>A total of 4794 publications issued between 2015 and 2024 were retrieved from the Web of Science Core Collection database. Using CiteSpace, we conducted quantitative analyses of publications, authors, institutions, journals, countries, keywords, citations, and references. The results were visualized via network maps.</p><p><strong>Results: </strong>The analysis included 4794 publications contributed by 631 authors from 430 institutions across 81 countries. China and the USA were the most productive countries, followed by Germany. University College London was the most prolific institution and served as a key hub in inter-institutional collaborations. Phil Hyu Lee was identified as the leading author, and <i>Movement Disorders</i> was the most influential journal, while <i>Parkinsonism & Related Disorders</i> published the highest number of articles within the dataset.</p><p><strong>Conclusion: </strong>Current research is increasingly focused on leveraging imaging techniques to detect early pathological changes in PD, such as dopaminergic neuron loss, abnormal protein deposition, and altered brain connectivity. These efforts aim to identify accurate diagnostic biomarkers and elucidate underlying disease mechanisms. This bibliometric analysis offers a comprehensive overview of the applications of neuroimaging in PD research over the past decade and may help inform future studies and foster international collaboration efforts.</p>","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"6 ","pages":"kkag018"},"PeriodicalIF":7.4,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13182252/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147977742","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":"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":"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":7.4,"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":"Commentary: γ-Aminobutyric acid and glutamate dysregulation in the dorsolateral prefrontal cortex of adolescents with first-episode major depressive disorder and the modulatory effects of repetitive transcranial magnetic stimulation.","authors":"Bin Qin, Shangping Cheng, Bing Chen, Jing Lu","doi":"10.1093/psyrad/kkag017","DOIUrl":"10.1093/psyrad/kkag017","url":null,"abstract":"","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"6 ","pages":"kkag017"},"PeriodicalIF":7.4,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13170795/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147944077","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":"Do cognitive reserve proxies capture a common neural signature? A systematic review and meta-analysis of task-based fMRI studies.","authors":"Annachiara Crocetta, Jordi Manuello, Donato Liloia, Sergio Duca, Tommaso Costa, Franco Cauda","doi":"10.1093/psyrad/kkag016","DOIUrl":"10.1093/psyrad/kkag016","url":null,"abstract":"<p><p>Cognitive reserve (CR) refers to the capacity of the brain to sustain cognitive performance despite age-related changes or pathophysiological conditions. Task-based functional magnetic resonance imaging (tb-fMRI) has been instrumental in exploring its neural correlates. CR is traditionally assessed through socio-behavioral proxies such as education, intelligence quotient, or composite indices. However, these proxies vary considerably in definition and application, posing challenges regarding their validity and consistency across experiments. This systematic review and meta-analysis examined whether these proxies are associated with consistent brain activation patterns in healthy adults. A literature search identified 12 eligible tb-fMRI experiments (<i>n</i> = 802 participants) reporting whole-brain CR-related activation. A coordinate-based meta-analysis using permutation of subject images-signed differential mapping (PSI-SDM) was conducted to assess consistent activation patterns across experiments, complemented by meta-regression analyses to examine whether differences in proxy type accounted for inter-study variability. The PSI-SDM meta-analysis yielded no significant clusters of activation. A qualitative synthesis of individual experiments further highlighted a lack of topographical consistency. These findings indicate that widely used socio-behavioral proxies are not associated with detectable, reproducible convergent patterns in tb-fMRI, revealing a measurement gap in current CR research. This null convergence likely reflects methodological heterogeneity and conceptual inconsistency in the literature, rather than the absence of CR-related neural mechanisms.</p>","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"6 ","pages":"kkag016"},"PeriodicalIF":7.4,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13244274/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148213725","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":"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":7.4,"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":"Investigating biotypes and neural characteristics among anxiety, depression, and comorbidity via a novel noisy label learning method.","authors":"Yuhui Du, Wei Huang, Yulong Wang, Vince D Calhoun","doi":"10.1093/psyrad/kkag014","DOIUrl":"10.1093/psyrad/kkag014","url":null,"abstract":"<p><strong>Background: </strong>Anxiety and depression share many symptoms, frequently leading to diagnostic uncertainty. They are also the most common co-occurring psychiatric disorders. Standard clinical evaluations typically diagnose each condition categorically rather than assessing their relative severity when both are present. Prior imaging studies largely concentrated on delineating similarities and distinctions between the two.</p><p><strong>Methods: </strong>To address this gap, we propose a novel noisy label learning method to investigate the biotypes and relevant neural characteristics among anxiety, depression, and their comorbidity. Our approach constructs a robust classification model to address potential diagnostic confusion among anxiety, depression, and comorbidity using a noisy label learning strategy, and employs the trained model to identify the anxiety and depression biotypes, as well as comorbidity biotypes characterized by differing degrees of anxiety and depression predominance.</p><p><strong>Results: </strong>Using brain functional network connectivity (FNC) from 502 depression patients, 245 anxiety patients, 177 comorbid patients with both anxiety and depression, and 500 healthy controls, we not only identify four distinct biotypes, but also reveal neural linkages between comorbid biotypes and depression and anxiety biotypes. Compared to the depression biotype (Biotype 1), the anxiety biotype (Biotype 2), and the comorbid biotype with an anxiety predominance (Biotype 3-2) demonstrate comparable alterations across seven FNCs, mainly including the connections between the cognitive control and visual domains. Relative to the anxiety biotype (Biotype 2), the depression biotype (Biotype 1), and the comorbid biotype with a depression predominance (Biotype 3-1) exhibit similar connectivity profiles across 16 FNCs, primarily involving connections within the cognitive control domain, as well as between the sensorimotor and visual domains. Furthermore, biotypes are characterized by distinct patterns of behavioral symptoms that are consistent with their underlying neural relationships.</p><p><strong>Conclusions: </strong>In summary, we propose a neuroimage-based model that addresses the diagnostic ambiguity between anxiety and depression and derive data-driven subtypes for promoting the precise diagnosis.</p>","PeriodicalId":93496,"journal":{"name":"Psychoradiology","volume":"6 ","pages":"kkag014"},"PeriodicalIF":7.4,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13192247/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148019189","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":"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":7.4,"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":7.4,"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}