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Distinguishing hypochondriasis and schizophrenia using regional homogeneity: a resting-state fMRI study and support vector machine analysis

Published online by Cambridge University Press:  05 April 2021

Kangyu Jin
Affiliation:
Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Mental Disorder’s Management of Zhejiang Province, Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, ZJ, People’s Republic of China
Dongrong Xu
Affiliation:
Columbia University & New York State Psychiatric Institute, New York, NY, USA
Zhe Shen
Affiliation:
Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Mental Disorder’s Management of Zhejiang Province, Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, ZJ, People’s Republic of China
Guoxun Feng
Affiliation:
Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Mental Disorder’s Management of Zhejiang Province, Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, ZJ, People’s Republic of China Department of Psychiatry, Ningbo Mental Health Center, Ningbo, ZJ, People’s Republic of China
Zhiyong Zhao
Affiliation:
Columbia University & New York State Psychiatric Institute, New York, NY, USA Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Department of Biomedical Engineering, Zhejiang University, Hangzhou, People’s Republic of China
Jing Lu
Affiliation:
Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Mental Disorder’s Management of Zhejiang Province, Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, ZJ, People’s Republic of China
Hailong Lyu
Affiliation:
Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Mental Disorder’s Management of Zhejiang Province, Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, ZJ, People’s Republic of China
Fen Pan
Affiliation:
Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Mental Disorder’s Management of Zhejiang Province, Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, ZJ, People’s Republic of China
Desheng Shang
Affiliation:
Department of Radiology, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, People’s Republic of China
Jingkai Chen
Affiliation:
Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Mental Disorder’s Management of Zhejiang Province, Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, ZJ, People’s Republic of China
Shaohua Hu
Affiliation:
Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Mental Disorder’s Management of Zhejiang Province, Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, ZJ, People’s Republic of China
Manli Huang*
Affiliation:
Department of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, The Key Laboratory of Mental Disorder’s Management of Zhejiang Province, Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, ZJ, People’s Republic of China
*
Author for correspondence: Manli Huang, Email: huangmanli@zju.edu.cn

Abstract

Objective:

A few former studies suggested that there are partial overlaps in abnormal brain structure and cognitive function between hypochondriasis (HS) and schizophrenia (SZ). But their differences in brain activity and cognitive function were unclear.

Methods:

Twenty-one HS patients, 23 SZ patients, and 24 healthy controls (HC) underwent resting-state functional magnetic resonance imaging (rs-fMRI) with the regional homogeneity analysis (ReHo), subsequently exploring the relationship between ReHo value and cognitive functions. The support vector machines (SVM) were used on effectiveness evaluation of ReHo for differentiating HS from SZ.

Results:

Compared with HC, HS showed significantly increased ReHo values in right middle temporal gyrus (MTG), left inferior parietal lobe (IPL), and right fusiform gyrus (FG), while SZ showed increased ReHo in left insula, decreased ReHo values in right paracentral lobule. Additionally, HS showed significantly higher ReHo values in FG, MTG, and left paracentral lobule, but lower in insula than SZ. The higher ReHo values in insula were associated with worse performance in MATRICS consensus cognitive battery (MCCB) in HS group. SVM analysis showed a combination of the ReHo values in insula and FG was able to satisfactorily distinguish the HS and SZ patients.

Conclusion:

Our results suggested that the altered default mode network (DMN), of which abnormal spontaneous neural activity occurs in multiple brain regions, might play a key role in the pathogenesis of HS, and the resting-state alterations of insula are closely related to cognitive dysfunction in HS. Furthermore, the combination of the ReHo in FG and insula was a relatively ideal indicator to distinguish HS from SZ.

Type
Original Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of Scandinavian College of Neuropsychopharmacology

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Footnotes

Dongrong Xu is co-first author.

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