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

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 lo...

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Autores principales: Du, Yuhui, Hao, Hui, Wang, Shuhua, Pearlson, Godfrey D, Calhoun, Vince D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7306624/
https://www.ncbi.nlm.nih.gov/pubmed/32563920
http://dx.doi.org/10.1016/j.nicl.2020.102284
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author Du, Yuhui
Hao, Hui
Wang, Shuhua
Pearlson, Godfrey D
Calhoun, Vince D.
author_facet Du, Yuhui
Hao, Hui
Wang, Shuhua
Pearlson, Godfrey D
Calhoun, Vince D.
author_sort Du, Yuhui
collection PubMed
description 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.
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spelling pubmed-73066242020-06-25 Identifying commonality and specificity across psychosis sub-groups via classification based on features from dynamic connectivity analysis Du, Yuhui Hao, Hui Wang, Shuhua Pearlson, Godfrey D Calhoun, Vince D. Neuroimage Clin Regular Article 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. Elsevier 2020-05-26 /pmc/articles/PMC7306624/ /pubmed/32563920 http://dx.doi.org/10.1016/j.nicl.2020.102284 Text en © 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Regular Article
Du, Yuhui
Hao, Hui
Wang, Shuhua
Pearlson, Godfrey D
Calhoun, Vince D.
Identifying commonality and specificity across psychosis sub-groups via classification based on features from dynamic connectivity analysis
title Identifying commonality and specificity across psychosis sub-groups via classification based on features from dynamic connectivity analysis
title_full Identifying commonality and specificity across psychosis sub-groups via classification based on features from dynamic connectivity analysis
title_fullStr Identifying commonality and specificity across psychosis sub-groups via classification based on features from dynamic connectivity analysis
title_full_unstemmed Identifying commonality and specificity across psychosis sub-groups via classification based on features from dynamic connectivity analysis
title_short Identifying commonality and specificity across psychosis sub-groups via classification based on features from dynamic connectivity analysis
title_sort identifying commonality and specificity across psychosis sub-groups via classification based on features from dynamic connectivity analysis
topic Regular Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7306624/
https://www.ncbi.nlm.nih.gov/pubmed/32563920
http://dx.doi.org/10.1016/j.nicl.2020.102284
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