Open AccessClassification of schizophrenia by intersubject correlation in functional connectome
Author(s) -
Ji GongJun,
Chen Xingui,
Bai Tongjian,
Wang Lu,
Wei Qiang,
Gao Yaxiang,
Tao Longxiang,
He Kongliang,
Li Dandan,
Dong Yi,
Hu Panpan,
Yu Fengqiong,
Zhu Chunyan,
Tian Yanghua,
Yu Yongqiang,
Wang Kai
Publication year - 2019
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.24527
Subject(s) - connectome , schizophrenia (object oriented programming) , generalizability theory , functional magnetic resonance imaging , psychology , similarity (geometry) , resting state fmri , neuroscience , depression (economics) , correlation , psychiatry , functional connectivity , artificial intelligence , developmental psychology , computer science , mathematics , geometry , economics , image (mathematics) , macroeconomics
Abstract Functional connectomes have been suggested as fingerprinting for individual identification. Accordingly, we hypothesized that subjects in the same phenotypic group have similar functional connectome features, which could help to discriminate schizophrenia (SCH) patients from healthy controls (HCs) and from depression patients. To this end, we included resting‐state functional magnetic resonance imaging data of SCH, depression patients, and HCs from three centers. We first investigated the characteristics of connectome similarity between individuals, and found higher similarity between subjects belonging to the same group (i.e., SCH–SCH) than different groups (i.e., HC–SCH). These findings suggest that the average connectome within group (termed as g roup‐specific functional connectome [GFC]) may help in individual classification. Consistently, significant accuracy (75–77%) and area under curve (81–86%) were found in discriminating SCH from HC or depression patients by GFC‐based leave‐one‐out cross‐validation. Cross‐center classification further suggests a good generalizability of the GFC classification. We additionally included normal aging data (255 young and 242 old subjects with different scanning sequences) to show factors could be improved for better classification performance, and the findings emphasized the importance of increasing sample size but not temporal resolution during scanning. In conclusion, our findings suggest that the average functional connectome across subjects contained group‐specific biological features and may be helpful in clinical diagnosis for schizophrenia.