Mapping relationships among schizophrenia, bipolar and schizoaffective disorders: A deep classification and clustering framework using fMRI time series

• Type of Material: Article
• Authors: Weizheng Yan, Emory University; Min Zhao, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Zening Fu, Emory University; Godfrey D Pearlson, Yale University S; Jing Sui, Emory University; Vince D Calhoun, Emory University
• Language: English
• Date: 2022-07-01
• Publisher: ELSEVIER
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2024 Elsevier B.V
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.schres.2021.02.007
• Title of Journal or Parent Work: SCHIZOPHRENIA RESEARCH
• Volume: 245
• Start Page: 141
• End Page: 150
• Grant/Funding Information: This work was supported by Natural Science Foundation of China (82022035, 61773380), National Institute of Health (R01MH117107, R01MH118695 and R01EB020407), the NIMH support of the Bipolar-Schizophrenia Network for Intermediate Phenotypes (Grant R01MH077851, MH078113, MH077945, MH096942, and MH096957), and Beijing Municipal Science and Technology Commission (Z181100001518005).
• Keywords: BRAIN; Science & Technology; Deep learning; NETWORK; DEPRESSION; CEREBELLUM; Life Sciences & Biomedicine; BIOMARKERS; CONNECTIVITY; Schizoaffective disorder; ARTIFACT; GROUP ICA; INTERMEDIATE PHENOTYPES; Schizophrenia; PSYCHOSIS; Psychiatry; Bipolar disorder; FMRI
• Research Categories: Biology, Neuroscience; Computer Science

Abstract

Background: Psychiatric disorders are categorized using self-report and observational information rather than biological data. There is also considerable symptomatic overlap between different types of psychiatric disorders, which makes diagnostic categorization and multi-class classification challenging. Methods: In this work, we propose a unified framework for supervised classification and unsupervised clustering of psychotic disorders using brain imaging data. A new multi-scale recurrent neural network (MsRNN) model was developed and applied to fMRI time courses (TCs) for multi-class classification. The high-level representations of the original TCs were then submitted to a tSNE clustering model for visualizing the group differences between disorders. A leave-one-feature-out approach was used for disorder-related biomarker identification. Results: When studying fMRI from schizophrenia, psychotic bipolar disorder, schizoaffective disorder, and healthy individuals, the accuracy of a 4-class classification reached 46%, significantly above chance. The hippocampus, supplementary motor area and paracentral lobule were discovered as the most contributing regional TCs in the multi-class classification. Beyond this, visualization of the tSNE clustering suggested that the disease severity can be captured and schizoaffective disorder (SAD) may be separated into two subtypes. SAD cluster1 has significantly higher Positive And Negative Syndrome Scale (PANSS) scores than SAD cluster2 in PANSS negative2 (emotional withdrawal), general2 (anxiety), general3 (guilt feelings), general4 (tension). Conclusions: The proposed deep classification and clustering framework is not only able to identify psychiatric disorders with high accuracy, but also interpret the correlation between brain networks and specific psychiatric disorders, and reveal the relationship between them. This work provides a promising way to investigate a spectrum of similar disorders using neuroimaging-based measures.