Morphometric Integrated Classification Index: A Multisite Model-Based, Interpretable, Shareable and Evolvable Biomarker for Schizophrenia

作者：Yingying Xie, Hao Ding, Xiaotong Du, Chao Chai, Xiaotong Wei, Jie Sun, Chuanjun Zhuo, Lina Wang, Jie Li, Hongjun Tian, Meng Liang, Shijie Zhang, Chunshui Yu, Wen Qin · 发表于：Schizophrenia Bulletin · 年份：2022 · DOI：10.1093/schbul/sbac096 · 被引用次数：9 · 研究领域：Schizophrenia research and treatment、Dementia and Cognitive Impairment Research、Functional Brain Connectivity Studies

BACKGROUND AND HYPOTHESIS: Multisite massive schizophrenia neuroimaging data sharing is becoming critical in understanding the pathophysiological mechanism and making an objective diagnosis of schizophrenia; it remains challenging to obtain a generalizable and interpretable, shareable, and evolvable neuroimaging biomarker for schizophrenia diagnosis. STUDY DESIGN: A Morphometric Integrated Classification Index (MICI) was proposed as a potential biomarker for schizophrenia diagnosis based on structural magnetic resonance imaging data of 1270 subjects from 10 sites (588 schizophrenia patients and 682 normal controls). An optimal XGBoost classifier plus sample-weighted SHapley Additive explanation algorithms were used to construct the MICI measure. STUDY RESULTS: The MICI measure achieved comparable performance with the sample-weighted ensembling model and merged model based on raw data (Delong test, P > 0.82) while outperformed the single-site models (Delong test, P < 0.05) in either the independent-sample testing datasets from the 9 sites or the independent-site dataset (generalizable). Besides, when new sites were embedded in, the performance of this measure was gradually increasing (evolvable). Finally, MICI was strongly associated with the severity of schizophrenia brain structural abnormality, with the patients' positive and negative symptoms, and with the brain expression profiles of schizophrenia risk genes (interpretable). CONCLUSIONS: In summary, the proposed MICI biom...