Stage-Specific Brain Aging in First-Episode Schizophrenia and Treatment-Resistant Schizophreniaopen access
- Authors
- Kim, Woo-Sung; Heo, Da-Woon; Shen, Jie; Tsogt, Uyanga; Odkhuu, Soyolsaikhan; Kim, Sung-Wan; Suk, Heung-Il; Ham, Byung-Joo; Rami, Fatima Zahra; Kang, Chae Yeong; Sui, Jing; Chung, Young-Chul
- Issue Date
- Mar-2023
- Publisher
- Cambridge University Press
- Keywords
- Brain age; sMRI; support vector regression; schizophrenia
- Citation
- International Journal of Neuropsychopharmacology, v.26, no.3, pp 207 - 216
- Pages
- 10
- Indexed
- SCIE
SCOPUS
- Journal Title
- International Journal of Neuropsychopharmacology
- Volume
- 26
- Number
- 3
- Start Page
- 207
- End Page
- 216
- URI
- https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/62598
- DOI
- 10.1093/ijnp/pyac080
- ISSN
- 1461-1457
1469-5111
- Abstract
- Background
Brain age is a popular brain-based biomarker that offers a powerful strategy for using neuroscience in clinical practice. We investigated the brain-predicted age difference (PAD) in patients with schizophrenia (SCZ), first-episode schizophrenia spectrum disorders (FE-SSDs), and treatment-resistant schizophrenia (TRS) using structural magnetic resonance imaging data. The association between brain-PAD and clinical parameters was also assessed.
Methods
We developed brain age prediction models for the association between 77 average structural brain measures and age in a training sample of controls (HCs) using ridge regression, support vector regression, and relevance vector regression. The trained models in the controls were applied to the test samples of the controls and 3 patient groups to obtain brain-based age estimates. The correlations were tested between the brain PAD and clinical measures in the patient groups.
Results
Model performance indicated that, regardless of the type of regression metric, the best model was support vector regression and the worst model was relevance vector regression for the training HCs. Accelerated brain aging was identified in patients with SCZ, FE-SSDs, and TRS compared with the HCs. A significant difference in brain PAD was observed between FE-SSDs and TRS using the ridge regression algorithm. Symptom severity, the Social and Occupational Functioning Assessment Scale, chlorpromazine equivalents, and cognitive function were correlated with the brain PAD in the patient groups.
Conclusions
These findings suggest additional progressive neuronal changes in the brain after SCZ onset. Therefore, pharmacological or psychosocial interventions targeting brain health should be developed and provided during the early course of SCZ.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - 2. Clinical Science > Department of Psychiatry > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.