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Neuroimaging-based brain-age prediction of first-episode schizophrenia and the alteration of brain age after early medication

Published online by Cambridge University Press:  02 December 2021

Yi-Bin Xi
Affiliation:
Department of Radiology, Xi'an People's Hospital (Xi'an Fourth Hospital), China; and Department of Radiology, Xijing Hospital, Fourth Military Medical University, China
Xu-Sha Wu
Affiliation:
Department of Radiology, Xi'an People's Hospital (Xi'an Fourth Hospital), China; and School of Medical Technology, Shaanxi University of Chinese Medicine, China
Long-Biao Cui
Affiliation:
Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, China; and Department of Radiology, The Second Medical Center, Chinese PLA General Hospital, China
Li-Jun Bai
Affiliation:
The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, China
Shuo-Qiu Gan
Affiliation:
The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, China
Xiao-Yan Jia
Affiliation:
The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, China
Xuan Li
Affiliation:
The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, China
Yong-Qiang Xu
Affiliation:
Department of Radiology, Xijing Hospital, Fourth Military Medical University, China
Xiao-Wei Kang
Affiliation:
Department of Radiology, Xi'an People's Hospital (Xi'an Fourth Hospital), China
Fan Guo*
Affiliation:
Department of Radiology, Xijing Hospital, Fourth Military Medical University, China
Hong Yin*
Affiliation:
Department of Radiology, Xi'an People's Hospital (Xi'an Fourth Hospital), China; and Department of Radiology, Xijing Hospital, Fourth Military Medical University, China
*
Correspondence: Hong Yin. Email: yinnhong@163.com; Fan Guo. Email: guofan0602@hotmail.com
Correspondence: Hong Yin. Email: yinnhong@163.com; Fan Guo. Email: guofan0602@hotmail.com

Abstract

Background

Neuroimaging- and machine-learning-based brain-age prediction of schizophrenia is well established. However, the diagnostic significance and the effect of early medication on first-episode schizophrenia remains unclear.

Aims

To explore whether predicted brain age can be used as a biomarker for schizophrenia diagnosis, and the relationship between clinical characteristics and brain-predicted age difference (PAD), and the effects of early medication on predicted brain age.

Method

The predicted model was built on 523 diffusion tensor imaging magnetic resonance imaging scans from healthy controls. First, the brain-PAD of 60 patients with first-episode schizophrenia, 60 healthy controls and 21 follow-up patients from the principal data-set and 40 pairs of individuals in the replication data-set were calculated. Next, the brain-PAD between groups were compared and the correlations between brain-PAD and clinical measurements were analysed.

Results

The patients showed a significant increase in brain-PAD compared with healthy controls. After early medication, the brain-PAD of patients decreased significantly compared with baseline (P < 0.001). The fractional anisotropy value of 31/33 white matter tract features, which related to the brain-PAD scores, had significantly statistical differences before and after measurements (P < 0.05, false discovery rate corrected). Correlation analysis showed that the age gap was negatively associated with the positive score on the Positive and Negative Syndrome Scale in the principal data-set (r = −0.326, P = 0.014).

Conclusions

The brain age of patients with first-episode schizophrenia may be older than their chronological age. Early medication holds promise for improving the patient's brain ageing. Neuroimaging-based brain-age prediction can provide novel insights into the understanding of schizophrenia.

Type
Paper
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Royal College of Psychiatrists

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Footnotes

*

Joint first authors.

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