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

• Published in: NeuroImage clinical Vol. 27; p. 102284
• Main Authors: Du, Yuhui, Hao, Hui, Wang, Shuhua, Pearlson, Godfrey D, Calhoun, Vince D.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.01.2020; Elsevier
• Subjects: Adult; Bipolar disorder; Bipolar Disorder - pathology; Bipolar Disorder - physiopathology; Brain - pathology; Brain - physiopathology; Brain Mapping - methods; Dynamic functional connectivity; Female; Functional magnetic resonance imaging; Humans; Independent component analysis; Magnetic Resonance Imaging - methods; Male; Middle Aged; Nerve Net - pathology; Nerve Net - physiopathology; Neural Pathways - pathology; Neural Pathways - physiopathology; Psychotic Disorders - pathology; Psychotic Disorders - physiopathology; Regular; Schizoaffective disorder; Schizophrenia; Schizophrenia - pathology; Schizophrenia - physiopathology; Functional magnetic resonance imaging; Schizophrenia; Bipolar disorder; Schizoaffective disorder; Dynamic functional connectivity; Independent component analysis
• ISSN: 2213-1582; 2213-1582
• DOI: 10.1016/j.nicl.2020.102284

•Identified the common and specific connectivity changes across psychosis sub-groups.•Proposed a classification framework using dynamic connectivity measures.•Yielded high classification accuracy for both the four- and pair-group classifications.•Revealed the disorder-common impairments in the thalamocortical connectivity. 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.