Huang Jiayuan, Ke Pengfei, Chen Xiaoyi, Li Shijia, Zhou Jing, Xiong Dongsheng, Huang Yuanyuan, Li Hehua, Ning Yuping, Duan Xujun, Li Xiaobo, Zhang Wensheng, Wu Fengchun, Wu Kai
Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China.
School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, China.
Front Aging Neurosci. 2022 Mar 3;14:823502. doi: 10.3389/fnagi.2022.823502. eCollection 2022.
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.
精神分裂症(SZ)患者中加速脑老化现象已被广泛报道。然而,利用三模态磁共振成像(MRI)数据对SZ患者的脑老化轨迹尚未有充分记录。在本研究中,纳入了138例年龄在20 - 60岁的精神分裂症患者和205名正常对照,并为每个个体采集了多模态MRI数据,包括结构MRI、静息态功能MRI和扩散张量成像。通过使用线性多元回归从多模态MRI数据中提取的特征来估计每个参与者的脑龄。SZ患者的脑龄差距与实际年龄之间的相关性最适合用一条正二次曲线拟合,峰值实际年龄为47.33岁。我们用这个峰值将受试者分为青年组和中年组。在正常对照中,青年组和中年组的脑龄与实际年龄匹配良好,但精神分裂症患者并非如此。更重要的是,精神分裂症患者在青年组中脑龄增加,而中年组中则不然。在本研究中,我们旨在利用多模态MRI数据研究SZ患者的脑老化轨迹,并揭示了年轻精神分裂症患者异常的脑龄轨迹,为精神分裂症的病理生理机制提供了新的见解。