Identifying commonality and specificity across psychosis sub-groups via classification based on features from dynamic connectivity analysis

• Type of Material: Article
• Authors: Yuhui Du, Shanxi University; Hui Hao, Shanxi University; Shuhua Wang, Emory University; Godfrey D. Pearlson, Yale University; Vince D. Calhoun, Emory University
• Language: English
• Date: 2020-01-01
• Publisher: Elsevier Inc.
• Publication Version: Final Published Version
• Copyright Statement: © 2020 The Authors.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.nicl.2020.102284
• Title of Journal or Parent Work: NeuroImage: Clinical
• Volume: 27
• Start Page: 102284
• End Page: 102284
• Grant/Funding Information: This work was supported by National Natural Science Foundation of China (Grant No. 61703253 to YHD), National Institutes of Health grants 5P20RR021938/P20GM103472 & R01EB020407 and National Science Foundation grant 1539067 (to VDC), and the 1331 Engineering Project of Shanxi Province, China.
• Keywords: Neuroimaging; Dynamic functional connectivity; Bipolar-schizophrenia network; Science & Technology; Life Sciences & Biomedicine; Identification; Framework; Functional magnetic resonance imaging; Bipolar disorder; Default mode network; Neurosciences & Neurology; Schizoaffective disorder; Imaging biomarkers; Independent component analysis; FMRI data; Functional connectivity; Schizophrenia; Phenotypes; ICA
• Research Categories: Biology, Neuroscience; Psychology, Psychobiology; Psychology, Clinical

Abstract

It is difficult to distinguish schizophrenia (SZ), schizoaffective disorder (SAD), and bipolar disorder with psychosis (BPP) as their clinical diagnoses rely on symptoms that overlap. In this paper, we investigate if there is biological evidence to support the symptom-based clinical categories by looking across the three disorders using dynamic connectivity measures, and provide meaningful characteristics on which brain functional connectivity measures are commonly or uniquely impaired. Large-sample functional magnetic resonance image (fMRI) datasets from 623 subjects including 238 healthy controls (HCs), 113 SZ patients, 132 SAD patients, and 140 BPP patients were analyzed. First, we computed whole-brain dynamic functional connectivity (DFC) using a sliding-window technique, and then extracted the individual connectivity states by applying our previously proposed decomposition-based DFC analysis method. Next, with the features from the dominant connectivity state, we assessed the clinical categories by performing both four-group (SZ, SAD, BPP and healthy control groups) and pair-wise classification using a support vector machine within cross-validation. Furthermore, we comprehensively summarized the shared and unique connectivity alterations among the disorders. In terms of the classification performance, our method achieved 69% in the four-group classification and >80% in the between-group classifications for the mean overall accuracy; and yielded 66% in the four-group classification and >80% in the between-group classifications for the mean balanced accuracy. Through summarizing the features that were automatically selected in the classifications, we found that among the three symptom-related disorders, their disorder-common impairments primarily included the decreased connectivity strength between thalamus and cerebellum and the increased strength between postcentral gyrus and thalamus. The disorder-unique changes included more various brain regions, mainly in the temporal and frontal gyrus. Our work demonstrates that dynamic functional connectivity provides biological evidence that both common and unique impairments exist in psychosis sub-groups.