Advanced brain ageing in Parkinson's disease is related to disease duration and individual impairment.

ABSTRACT: Machine learning can reliably predict individual age from MRI data, revealing that patients with neurodegenerative disorders show an elevated biological age. A surprising gap in the literature, however, pertains to Parkinson's disease. Here, we evaluate brain age in two cohorts of Parkinson's patients and investigated the relationship between individual brain age and clinical characteristics. We assessed 372 patients with idiopathic Parkinson's disease, newly diagnosed cases from the Parkinson's Progression Marker Initiative database and a more chronic local sample, as well as age- and sex-matched healthy controls. Following morphometric preprocessing and atlas-based compression, individual brain age was predicted using a multivariate machine learning model trained on an independent, multi

SUBMITTER: Eickhoff CR 

PROVIDER: S-EPMC8445399 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature