机译:机器学习识别不受影响的一级亲属,具有类似于精神分裂症患者的功能网络模式和认知障碍
National Laboratory of Pattern RecognitionInstitute of Automation Chinese Academy of;
Institute of Mental Health National Clinical Research Center for Mental Disorders Key Laboratory;
National Institute on Drug Dependence and Beijing Key laboratory of Drug DependencePeking;
Institute of Mental Health National Clinical Research Center for Mental Disorders Key Laboratory;
Department of Alcohol and Drug DependenceBeijing Hui‐Long‐Guan Hospital Peking UniversityBeijing;
Institute of Mental Health National Clinical Research Center for Mental Disorders Key Laboratory;
Institute of Mental Health National Clinical Research Center for Mental Disorders Key Laboratory;
Institute of Mental Health National Clinical Research Center for Mental Disorders Key Laboratory;
Tianjin Mental Health CenterNankai University Affiliated Tianjin Anding HospitalTianjin China;
Institute of Mental Health National Clinical Research Center for Mental Disorders Key Laboratory;
Department of RadiologyPerelman School of Medicine University of PennsylvaniaPhiladelphia;
cognitive impairment; functional networks; machine learning; pattern classification; resting‐state functional magnetic resonance imaging; unaffected first‐degree relatives;
机译:机器学习识别不受影响的一级亲属,具有类似于精神分裂症患者的功能网络模式和认知障碍
机译:精神分裂症和精神病性双极先证者及其未受影响的一级亲属之间的静止状态功能磁共振成像功能网络连通性的差异
机译:识别进行性轻度认知障碍患者脑功能连通性的改变模式:纵向全脑体素明智程度分析。
机译:精神分裂症未受影响的一级亲属的记忆功能的初步研究
机译:使用功能网络来识别精神分裂症患者并评估潜在缺陷的模型。
机译:机器学习可识别未受影响的一级亲属其功能网络模式和认知障碍类似于精神分裂症患者
机译:机器学习识别不受影响的一级亲属,具有与精神分裂症患者类似的功能网络模式和认知障碍