Machine learning identifies unaffected first-degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients

doi:10.1002/hbm.24678

CASIA OpenIR > 模式识别国家重点实验室

	Machine learning identifies unaffected first-degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients
	Jing, Rixing1,2 ; Li, Peng 3,4; Ding, Zengbo 5,6; Lin, Xiao3,4,7,8 ; Zhao, Rongjiang 9; Shi, Le 3,4; Yan, Hao 3,4; Liao, Jinmin 3,4; Zhuo, Chuanjun 10,11; Lu, Lin 3,4,5,6,7,8; Fan, Yong 12
发表期刊	HUMAN BRAIN MAPPING
ISSN	1065-9471
	2019-09-01
卷号	40 期号:13 页码:3930-3939
通讯作者	Fan, Yong(yong.fan@uphs.upenn.edu)
摘要	Schizophrenia (SCZ) patients and their unaffected first-degree relatives (FDRs) share similar functional neuroanatomy. However, it remains largely unknown to what extent unaffected FDRs with functional neuroanatomy patterns similar to patients can be identified at an individual level. In this study, we used a multivariate pattern classification method to learn informative large-scale functional networks (FNs) and build classifiers to distinguish 32 patients from 30 healthy controls and to classify 34 FDRs as with or without FNs similar to patients. Four informative FNs-the cerebellum, default mode network (DMN), ventral frontotemporal network, and posterior DMN with parahippocampal gyrus-were identified based on a training cohort and pattern classifiers built upon these FNs achieved a correct classification rate of 83.9% (sensitivity 87.5%, specificity 80.0%, and area under the receiver operating characteristic curve [AUC] 0.914) estimated based on leave-one-out cross-validation for the training cohort and a correct classification rate of 77.5% (sensitivity 72.5%, specificity 82.5%, and AUC 0.811) for an independent validation cohort. The classification scores of the FDRs and patients were negatively correlated with their measures of cognitive function. FDRs identified by the classifiers as having SCZ patterns were similar to the patients, but significantly different from the controls and FDRs with normal patterns in terms of their cognitive measures. These results demonstrate that the pattern classifiers built upon the informative FNs can serve as biomarkers for quantifying brain alterations in SCZ and help to identify FDRs with FN patterns and cognitive impairment similar to those of SCZ patients.
关键词	cognitive impairment functional networks machine learning pattern classification resting-state functional magnetic resonance imaging unaffected first-degree relatives
DOI	10.1002/hbm.24678
关键词[WOS]	ULTRA-HIGH-RISK ; WORKING-MEMORY ; PSYCHOSIS ; FMRI ; CONNECTIVITY ; DEFICITS ; SIBLINGS ; CLASSIFICATION ; INDIVIDUALS ; DYSFUNCTION
收录类别	SCI
语种	英语
资助项目	National Basic Research Program of China[2015CB856400] ; National Institutes of Health[EB022573] ; National Institutes of Health[MH112070] ; National Natural Science Foundation of China[81501158] ; National Natural Science Foundation of China[61473296] ; National Basic Research Program of China[2015CB856400] ; National Institutes of Health[EB022573] ; National Institutes of Health[MH112070] ; National Natural Science Foundation of China[81501158] ; National Natural Science Foundation of China[61473296]
项目资助者	National Basic Research Program of China ; National Institutes of Health ; National Natural Science Foundation of China
WOS研究方向	Neurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Neurosciences ; Neuroimaging ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000478645900016
出版者	WILEY
引用统计	被引频次：19[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/27755
专题	模式识别国家重点实验室
通讯作者	Fan, Yong
作者单位	1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Peking Univ, Inst Mental Hlth, Natl Clin Res Ctr Mental Disorders, Key Lab Mental Hlth, Beijing, Peoples R China 4.Peking Univ, Peking Univ Hosp 6, Beijing, Peoples R China 5.Peking Univ, Natl Inst Drug Dependence, Beijing, Peoples R China 6.Peking Univ, Beijing Key Lab Drug Dependence, Beijing, Peoples R China 7.Peking Univ, Peking Tsinghua Ctr Life Sci, Beijing, Peoples R China 8.Peking Univ, PKU IDG McGovern Inst Brain Res, Beijing, Peoples R China 9.Peking Univ, Beijing Hui Long Guan Hosp, Dept Alcohol & Drug Dependence, Beijing, Peoples R China 10.Nankai Univ, Affiliated Tianjin Anding Hosp, Tianjin Mental Hlth Ctr, Tianjin, Peoples R China 11.Tianjin Med Univ, Dept Psychiat, Tianjin, Peoples R China 12.Univ Penn, Dept Radiol, Perelman Sch Med, Philadelphia, PA 19104 USA
第一作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Jing, Rixing,Li, Peng,Ding, Zengbo,et al. Machine learning identifies unaffected first-degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients[J]. HUMAN BRAIN MAPPING,2019,40(13):3930-3939.
APA	Jing, Rixing.,Li, Peng.,Ding, Zengbo.,Lin, Xiao.,Zhao, Rongjiang.,...&Fan, Yong.(2019).Machine learning identifies unaffected first-degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients.HUMAN BRAIN MAPPING,40(13),3930-3939.
MLA	Jing, Rixing,et al."Machine learning identifies unaffected first-degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients".HUMAN BRAIN MAPPING 40.13(2019):3930-3939.