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.
Highlights
Schizophrenia (SZ) is one of the costliest mental disorders in terms of human suffering and societal expenditure, with a 1% lifetime risk, chronicity, severity, and an impaired quality of life
The Multiple linear regression (MLR) in the normal control (NC) group had the best accuracy based on different model magnetic resonance imaging (MRI) combinations (Supplementary Table 1), in which the Pearson correlation, mean absolute error (MAE), coefficient of determination and root mean squared error (rMSE) of the MLR were 0.88, 3.24 years, 0.77 and 4.14 years respectively and significant in the permutation test
When the same analysis was performed on the NC group, no significant differences were found in the youth group or in the middle age group
Summary
Schizophrenia (SZ) is one of the costliest mental disorders in terms of human suffering and societal expenditure, with a 1% lifetime risk, chronicity, severity, and an impaired quality of life (van Os and Kapur, 2009; Cocchi et al, 2011; Charlson et al, 2018). Recent studies have found structural abnormalities in SZ patients, including decreased fractional anisotropy, gray matter volume (GMV) and hippocampal volume (Ellison-Wright and Bullmore, 2009; Bois et al, 2016; Wu et al, 2018; Duan et al, 2021), but brain volume changes are not constant throughout the course of the illness (van Haren et al, 2008). Functional magnetic resonance imaging (MRI) studies have shown similar abnormalities in the brains of SZ patients, such as a decrease in the amplitude of low-frequency fluctuations (Huang et al, 2010), an increase in functional connectivity within the default mode network (He et al, 2013), and changes in network homogeneity (Guo et al, 2014). Structural and functional abnormalities result in different brain aging trajectories (Mitelman et al, 2009; Mandl et al, 2010). Several recent studies have revealed that some of the changes observed in SZ patients are similar to those seen in physiological aging (Douaud et al, 2009; Nenadic et al, 2012)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
