TY - JOUR
T1 - Multimodal Magnetic Resonance Imaging Reveals Aberrant Brain Age Trajectory During Youth in Schizophrenia Patients
AU - Huang, Jiayuan
AU - Ke, Pengfei
AU - Chen, Xiaoyi
AU - Li, Shijia
AU - Zhou, Jing
AU - Xiong, Dongsheng
AU - Huang, Yuanyuan
AU - Li, Hehua
AU - Ning, Yuping
AU - Duan, Xujun
AU - Li, Xiaobo
AU - Zhang, Wensheng
AU - Wu, Fengchun
AU - Wu, Kai
N1 - Funding Information:
This work was supported by the National Key Research and Development Program of China (2020YFC2004300, 2020YFC2004301, 2019YFC0118800, 2019YFC0118802, 2019YFC0118804, 2019YFC0118805, 2021YFC2009400, and 2021YFC2009404), the National Natural Science Foundation of China (31771074, 81802230, and 72174082), Guangdong Basic and Applied Basic Research Foundation Outstanding Youth Project (2021B1515020064), the Key Research and Development Program of Guangdong (2018B030335001, 2020B0101130020, and 2020B0404010002), the Science and Technology Program of Guangzhou (201807010064, 201803010100, 201903010032, and 202103000032), Key Laboratory Program of Guangdong Provincial Education Department (2020KSYS001), and the Open Fund of the Guangdong Provincial Key Laboratory of Physical Activity and Health Promotion (2021B1212040014).
Publisher Copyright:
Copyright © 2022 Huang, Ke, Chen, Li, Zhou, Xiong, Huang, Li, Ning, Duan, Li, Zhang, Wu and Wu.
PY - 2022/3/3
Y1 - 2022/3/3
N2 - Accelerated brain aging had been widely reported in patients with schizophrenia (SZ). However, brain aging trajectories in SZ patients have not been well-documented using three-modal magnetic resonance imaging (MRI) data. In this study, 138 schizophrenia patients and 205 normal controls aged 20–60 were included and multimodal MRI data were acquired for each individual, including structural MRI, resting state-functional MRI and diffusion tensor imaging. The brain age of each participant was estimated by features extracted from multimodal MRI data using linear multiple regression. The correlation between the brain age gap and chronological age in SZ patients was best fitted by a positive quadratic curve with a peak chronological age of 47.33 years. We used the peak to divide the subjects into a youth group and a middle age group. In the normal controls, brain age matched chronological age well for both the youth and middle age groups, but this was not the case for schizophrenia patients. More importantly, schizophrenia patients exhibited increased brain age in the youth group but not in the middle age group. In this study, we aimed to investigate brain aging trajectories in SZ patients using multimodal MRI data and revealed an aberrant brain age trajectory in young schizophrenia patients, providing new insights into the pathophysiological mechanisms of schizophrenia.
AB - Accelerated brain aging had been widely reported in patients with schizophrenia (SZ). However, brain aging trajectories in SZ patients have not been well-documented using three-modal magnetic resonance imaging (MRI) data. In this study, 138 schizophrenia patients and 205 normal controls aged 20–60 were included and multimodal MRI data were acquired for each individual, including structural MRI, resting state-functional MRI and diffusion tensor imaging. The brain age of each participant was estimated by features extracted from multimodal MRI data using linear multiple regression. The correlation between the brain age gap and chronological age in SZ patients was best fitted by a positive quadratic curve with a peak chronological age of 47.33 years. We used the peak to divide the subjects into a youth group and a middle age group. In the normal controls, brain age matched chronological age well for both the youth and middle age groups, but this was not the case for schizophrenia patients. More importantly, schizophrenia patients exhibited increased brain age in the youth group but not in the middle age group. In this study, we aimed to investigate brain aging trajectories in SZ patients using multimodal MRI data and revealed an aberrant brain age trajectory in young schizophrenia patients, providing new insights into the pathophysiological mechanisms of schizophrenia.
KW - accelerated brain aging
KW - brain age gap
KW - machine learning
KW - multimodal magnetic resonance imaging
KW - schizophrenia
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UR - http://www.scopus.com/inward/citedby.url?scp=85127195683&partnerID=8YFLogxK
U2 - 10.3389/fnagi.2022.823502
DO - 10.3389/fnagi.2022.823502
M3 - Article
AN - SCOPUS:85127195683
SN - 1663-4365
VL - 14
JO - Frontiers in Aging Neuroscience
JF - Frontiers in Aging Neuroscience
M1 - 823502
ER -