Quantitative Expression of Latent Disease Factors in Individuals Associated with Psychopathology Dimensions and Treatment Response  

作　　者：Shaoling Zhao Qian Lv Ge Zhang Jiangtao Zhang Heqiu Wang Jianmin Zhang Meiyun Wang Zheng Wang 

机构地区：[1]Institute of Neuroscience,Center for Excellence in Brain Science and Intelligence Technology,State Key Laboratory of Neuroscience,Chinese Academy of Sciences,Shanghai 200031,China [2]University of Chinese Academy of Sciences,Beijing 100101,China [3]School of Psychological and Cognitive Sciences,Beijing Key Laboratory of Behavior and Mental Health,IDG/McGovern Institute for Brain Research,Peking-Tsinghua Center for Life Sciences,Peking University,Beijing 100871,China [4]Department of Medical Imaging,Henan Provincial People’s Hospital&the People’s Hospital of Zhengzhou University,Zhengzhou 450003,China [5]Tongde Hospital of Zhejiang Province(Zhejiang Mental Health Center),Zhejiang Office of Mental Health,Hangzhou 310012,China

出　　处：《Neuroscience Bulletin》2024年第11期1667-1680,共14页神经科学通报(英文版)

基　　金：supported by the National Natural Science Foundation(82151303);the National Key R&D Program of China(2021ZD0204002);the Key-Area Research and Development Program of Guangdong Province(2019B030335001);Shanghai Municipal Science and Technology Major Project(2018SHZDZX05);Strategic Priority Research Program of Chinese Academy of Science(XDB32000000);supported in part by the Postdoctoral Fellowship of the Peking-Tsinghua Center for Life Sciences.GSP data were provided by the Brain Genomics Superstruct Project of Harvard University and the Massachusetts General Hospital,(Principal Investigators:Randy Buckner,Joshua Roffman,and Jordan Smoller),with support from the Center for Brain Science Neuroinformatics Research Group,the Athinoula A.Martinos Center for Biomedical Imaging,and GSP Open Access Documentation the Center for Human Genetic Research.20 individual investigators at Harvard and MGH generously contributed data to the overall project.

摘　　要：Psychiatric comorbidity is common in symptombased diagnoses like autism spectrum disorder(ASD),attention/deficit hyper-activity disorder(ADHD),and obsessivecompulsive disorder(OCD).However,these co-occurring symptoms mediated by shared and/or distinct neural mechanisms are difficult to profile at the individual level.Capitalizing on unsupervised machine learning with a hierarchical Bayesian framework,we derived latent disease factors from resting-state functional connectivity data in a hybrid cohort of ASD and ADHD and delineated individual associations with dimensional symptoms based on canonical correlation analysis.Models based on the same factors generalized to previously unseen individuals in a subclinical cohort and one local OCD database with a subset of patients undergoing neurosurgical intervention.Four factors,identified as variably co-expressed in each patient,were significantly correlated with distinct symptom domains(r=–0.26–0.53,P<0.05):behavioral regulation(Factor-1),communication(Factor-2),anxiety(Factor-3),adaptive behaviors(Factor-4).Moreover,we demonstrated Factor-1 expressed in patients with OCD and Factor-3 expressed in participants with anxiety,at the degree to which factor expression was significantly predictive of individual symptom scores(r=0.18–0.5,P<0.01).Importantly,peri-intervention changes in Factor-1 of OCD were associated with variable treatment outcomes(r=0.39,P<0.05).Our results indicate that these data-derived latent disease factors quantify individual factor expression to inform dimensional symptom and treatment outcomes across cohorts,which may promote quantitative psychiatric diagnosis and personalized intervention.

关 键 词：Psychiatric comorbidity Latent disease factor Psychopathology dimension Treatment outcome Quantitative diagnosis 

分 类 号：R741[医药卫生—神经病学与精神病学]