Identifying and validating subtypes within major psychiatric disorders based on frontal–posterior functional imbalance via deep learning

Miao Chang, Fay Y. Womer, Xiaohong Gong, Xi Chen, Lili Tang, Ruiqi Feng, Shuai Dong, Jia Duan, Yifan Chen, Ran Zhang, Yang Wang, Sihua Ren, Yi Wang, Jujiao Kang, Zhiyang Yin, Yange Wei, Shengnan Wei, Xiaowei Jiang, Ke Xu, Bo CaoYanbo Zhang, Weixiong Zhang, Yanqing Tang, Xizhe Zhang, Fei Wang

Research output: Contribution to journalArticlepeer-review

36 Scopus citations


Converging evidence increasingly implicates shared etiologic and pathophysiological characteristics among major psychiatric disorders (MPDs), such as schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). Examining the neurobiology of the psychotic-affective spectrum may greatly advance biological determination of psychiatric diagnosis, which is critical for the development of more effective treatments. In this study, ensemble clustering was developed to identify subtypes within a trans-diagnostic sample of MPDs. Whole brain amplitude of low-frequency fluctuations (ALFF) was used to extract the low-dimensional features for clustering in a total of 944 participants: 581 psychiatric patients (193 with SZ, 171 with BD, and 217 with MDD) and 363 healthy controls (HC). We identified two subtypes with differentiating patterns of functional imbalance between frontal and posterior brain regions, as compared to HC: (1) Archetypal MPDs (60% of MPDs) had increased frontal and decreased posterior ALFF, and decreased cortical thickness and white matter integrity in multiple brain regions that were associated with increased polygenic risk scores and enriched risk gene expression in brain tissues; (2) Atypical MPDs (40% of MPDs) had decreased frontal and increased posterior ALFF with no associated alterations in validity measures. Medicated Archetypal MPDs had lower symptom severity than their unmedicated counterparts; whereas medicated and unmedicated Atypical MPDs had no differences in symptom scores. Our findings suggest that frontal versus posterior functional imbalance as measured by ALFF is a novel putative trans-diagnostic biomarker differentiating subtypes of MPDs that could have implications for precision medicine.

Original languageEnglish
Pages (from-to)2991-3002
Number of pages12
JournalMolecular Psychiatry
Issue number7
StatePublished - Jul 2021


Dive into the research topics of 'Identifying and validating subtypes within major psychiatric disorders based on frontal–posterior functional imbalance via deep learning'. Together they form a unique fingerprint.

Cite this