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

• 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
• Journal Title: SCHIZOPHRENIA RESEARCH
• Volume: Volume 245
• Publisher: ELSEVIER | 2022-07-01, Pages 141-150
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1016/j.schres.2021.02.007
• Subjects: Biology, Neuroscience; Computer Science
• Keywords: Science & Technology; Life Sciences & Biomedicine; Psychiatry; Deep learning; FMRI; Schizophrenia; Bipolar disorder; Schizoaffective disorder; INTERMEDIATE PHENOTYPES; GROUP ICA; CONNECTIVITY; BRAIN; PSYCHOSIS; NETWORK; CEREBELLUM; DEPRESSION; BIOMARKERS; ARTIFACT

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.