{"title":"利用数据驱动的疾病进展模型阐明重度抑郁症脑功能异常的发展轨迹","authors":"Yuhong Zheng, Peng Wang, Chi Yao, Jinghua Wang, Jinhui Wang, Shao-Wei Xue","doi":"10.1002/hbm.70249","DOIUrl":null,"url":null,"abstract":"<p>Concerns have arisen regarding the heterogeneity of patients with major depressive disorder (MDD), particularly when the varying disease progression trajectories among individuals are overlooked. Recognizing these distinct trajectories is crucial for personalized assessments and accurate disease progression predictions in MDD, posing a significant challenge in clinical practice. We utilized a data-driven subtype and stage inference (SuStaIn) model to infer trajectories based on cross-sectional amplitude of low-frequency fluctuations (ALFF) derived from resting-state functional magnetic resonance imaging data of 833 patients with MDD and 834 healthy controls. Based on distinct trajectories, two subtypes of MDD were identified: Subtype 1 showed declining ALFF from paracentral lobule (PCL) to thalamus to medial orbitofrontal cortex (OFCmed), with higher core depression scores and gray matter atrophy, whereas Subtype 2 had an opposing trajectory, with initial OFCmed ALFF decrease gradually extending to PCL. Our findings contribute to a better understanding of MDD heterogeneity and facilitate precise disease progression predictions.</p>","PeriodicalId":13019,"journal":{"name":"Human Brain Mapping","volume":"46 8","pages":""},"PeriodicalIF":3.3000,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70249","citationCount":"0","resultStr":"{\"title\":\"Elucidating Development Trajectories of Brain Functional Abnormalities in Major Depressive Disorder Utilizing a Data-Driven Disease Progression Model\",\"authors\":\"Yuhong Zheng, Peng Wang, Chi Yao, Jinghua Wang, Jinhui Wang, Shao-Wei Xue\",\"doi\":\"10.1002/hbm.70249\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Concerns have arisen regarding the heterogeneity of patients with major depressive disorder (MDD), particularly when the varying disease progression trajectories among individuals are overlooked. Recognizing these distinct trajectories is crucial for personalized assessments and accurate disease progression predictions in MDD, posing a significant challenge in clinical practice. We utilized a data-driven subtype and stage inference (SuStaIn) model to infer trajectories based on cross-sectional amplitude of low-frequency fluctuations (ALFF) derived from resting-state functional magnetic resonance imaging data of 833 patients with MDD and 834 healthy controls. Based on distinct trajectories, two subtypes of MDD were identified: Subtype 1 showed declining ALFF from paracentral lobule (PCL) to thalamus to medial orbitofrontal cortex (OFCmed), with higher core depression scores and gray matter atrophy, whereas Subtype 2 had an opposing trajectory, with initial OFCmed ALFF decrease gradually extending to PCL. Our findings contribute to a better understanding of MDD heterogeneity and facilitate precise disease progression predictions.</p>\",\"PeriodicalId\":13019,\"journal\":{\"name\":\"Human Brain Mapping\",\"volume\":\"46 8\",\"pages\":\"\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2025-06-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.70249\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Human Brain Mapping\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/hbm.70249\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"NEUROIMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human Brain Mapping","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/hbm.70249","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NEUROIMAGING","Score":null,"Total":0}
Elucidating Development Trajectories of Brain Functional Abnormalities in Major Depressive Disorder Utilizing a Data-Driven Disease Progression Model
Concerns have arisen regarding the heterogeneity of patients with major depressive disorder (MDD), particularly when the varying disease progression trajectories among individuals are overlooked. Recognizing these distinct trajectories is crucial for personalized assessments and accurate disease progression predictions in MDD, posing a significant challenge in clinical practice. We utilized a data-driven subtype and stage inference (SuStaIn) model to infer trajectories based on cross-sectional amplitude of low-frequency fluctuations (ALFF) derived from resting-state functional magnetic resonance imaging data of 833 patients with MDD and 834 healthy controls. Based on distinct trajectories, two subtypes of MDD were identified: Subtype 1 showed declining ALFF from paracentral lobule (PCL) to thalamus to medial orbitofrontal cortex (OFCmed), with higher core depression scores and gray matter atrophy, whereas Subtype 2 had an opposing trajectory, with initial OFCmed ALFF decrease gradually extending to PCL. Our findings contribute to a better understanding of MDD heterogeneity and facilitate precise disease progression predictions.
期刊介绍:
Human Brain Mapping publishes peer-reviewed basic, clinical, technical, and theoretical research in the interdisciplinary and rapidly expanding field of human brain mapping. The journal features research derived from non-invasive brain imaging modalities used to explore the spatial and temporal organization of the neural systems supporting human behavior. Imaging modalities of interest include positron emission tomography, event-related potentials, electro-and magnetoencephalography, magnetic resonance imaging, and single-photon emission tomography. Brain mapping research in both normal and clinical populations is encouraged.
Article formats include Research Articles, Review Articles, Clinical Case Studies, and Technique, as well as Technological Developments, Theoretical Articles, and Synthetic Reviews. Technical advances, such as novel brain imaging methods, analyses for detecting or localizing neural activity, synergistic uses of multiple imaging modalities, and strategies for the design of behavioral paradigms and neural-systems modeling are of particular interest. The journal endorses the propagation of methodological standards and encourages database development in the field of human brain mapping.