Multimodal Magnetic Resonance Imaging Reveals Aberrant Brain Age Trajectory During Youth in Schizophrenia Patients

Jiayuan Huang, Pengfei Ke, Xiaoyi Chen, Shijia Li, Jing Zhou, Dongsheng Xiong, Yuanyuan Huang, Hehua Li, Yuping Ning, Xujun Duan, Xiaobo Li, Wensheng Zhang, Fengchun Wu, Kai Wu

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number823502
JournalFrontiers in Aging Neuroscience
Volume14
DOIs
StatePublished - Mar 3 2022

All Science Journal Classification (ASJC) codes

  • Aging
  • Cognitive Neuroscience

Keywords

  • accelerated brain aging
  • brain age gap
  • machine learning
  • multimodal magnetic resonance imaging
  • schizophrenia

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