Multiscale brain age prediction reveals region-specific accelerated brain aging in Parkinson's disease

Yueh Sheng Chen, Chen Yuan Kuo, Cheng Hsien Lu, Yuan Wei Wang, Kun Hsien Chou*, Wei Che Lin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Brain biological age, which measures the aging process in the brain using neuroimaging data, has been used to assess advanced brain aging in neurodegenerative diseases, including Parkinson disease (PD). However, assuming that whole brain degeneration is uniform may not be sufficient for assessing the complex neurodegenerative processes in PD. In this study we constructed a multiscale brain age prediction models based on structural MRI of 1240 healthy participants. To assess the brain aging patterns using the brain age prediction model, 93 PD patients and 91 healthy controls matching for sex and age were included. We found increased global and regional brain age in PD patients. The advanced aging regions were predominantly noted in the frontal and temporal cortices, limbic system, basal ganglia, thalamus, and cerebellum. Furthermore, region-level rather than global brain age in PD patients was associated with disease severity. Our multiscale brain age prediction model could aid in the development of objective image-based biomarkers to detect advanced brain aging in neurodegenerative diseases.

Original languageEnglish
Pages (from-to)122-129
Number of pages8
JournalNeurobiology of Aging
Volume140
DOIs
StatePublished - Aug 2024

Keywords

  • Brain aging
  • Machine learning
  • Magnetic resonance imaging
  • Parkinson disease

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