Paper Info
Title
Brain age gap as a potential biomarker for schizophrenia: A multi-site structural MRI study
Abstract
Gray matter atrophy in schizophrenia has been widely recognized; however, it remains controversial whether it reflects a neurodegenerative condition. Recent studies have suggested that the brain age gap (BAG) between the predicted and chronological ones may serve as a biomarker for early-stage neurodegeneration. Nevertheless, it is unknown its value for schizophrenia diagnosis and the potential meaning. We included structural MRI datasets from 8 independent sites in the current study, including 501 schizophrenia patients (SZ) and 512 healthy controls (HC). We first applied support vector regression (SVR) to train the age prediction model of the controls using the gray matter volume (GMV) and apply this model to predict the age of the SZ. Meta-analysis identified the SZ had significantly higher BAG than the HC (Cohen's d = 0.38, 95% confidence level = [0.19, 0.57]), and this trend was reliably repeated in each site. Furthermore, logistic regression demonstrated BAG can significantly discriminate the SZ from the HC (OR = 1. 07, P = 7.14 x 10(-8)). Finally, the linear regression study demonstrated a significant negative correlation between the BAG and gray matter volume in both groups, especially at the subcortical regions and prefrontal cortex (P<0.05, corrected using the family-wise error method).
Year
DOI
Venue
2021
10.1109/EMBC46164.2021.9631085
2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC)
DocType
Volume
ISSN
Conference
2021
1557-170X
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Weiqi Man100.34
Hao Ding200.34
Chao Chai300.34
Xingwei An42111.88
Feng Liu500.34
Wen Qin600.34
Chunshui Yu721219.19