Two subtypes of schizophrenia identified by an individual-level atypical pattern of tensor-based morphometric measurement
Weiyang Shi, Lingzhong Fan, Haiyan Wang, Bing Liu, Wen Li, Jin Li, Luqi Cheng, Congying Chu, Ming Song, Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, not Qingdao University of Science and Technology, please correct the current affiliation. Thank you, Na Luo, Yue Cui, Zhenwei Dong, Yuheng Lu, Yawei Ma, Liang Ma, Kaixin Li, Jun Chen, Yunchun Chen, Hua Guo, Peng Li, Lin Lü, Luxian Lv, Ping Wan, Huaning Wang, Huiling Wang, Hao Yan, Jun Yan, Yongfeng Yang, Hongxing Zhang, Dai Zhang, Tianzi Jiang
Abstract
Difficulties in parsing the multiaspect heterogeneity of schizophrenia (SCZ) based on current nosology highlight the need to subtype SCZ using objective biomarkers. Here, utilizing a large-scale multisite SCZ dataset, we identified and validated 2 neuroanatomical subtypes with individual-level abnormal patterns of the tensor-based morphometric measurement. Remarkably, compared with subtype 1, which showed moderate deficits of some subcortical nuclei and an enlarged striatum and cerebellum, subtype 2, which showed cerebellar atrophy and more severe subcortical nuclei atrophy, had a higher subscale score of negative symptoms, which is considered to be a core aspect of SCZ and is associated with functional outcome. Moreover, with the neuroimaging-clinic association analysis, we explored the detailed relationship between the heterogeneity of clinical symptoms and the heterogeneous abnormal neuroanatomical patterns with respect to the 2 subtypes. And the neuroimaging-transcription association analysis highlighted several potential heterogeneous biological factors that may underlie the subtypes. Our work provided an effective framework for investigating the heterogeneity of SCZ from multilevel aspects and may provide new insights for precision psychiatry.