"Brain age" predicts disability accumulation in multiple sclerosis.

ABSTRACT:

Objective

Neurodegenerative conditions often manifest radiologically with the appearance of premature aging. Multiple sclerosis (MS) biomarkers related to lesion burden are well developed, but measures of neurodegeneration are less well-developed. The appearance of premature aging quantified by machine learning applied to structural MRI assesses neurodegenerative pathology. We assess the explanatory and predictive power of "brain age" analysis on disability in MS using a large, real-world dataset.

Methods

Brain age analysis is predicated on the over-estimation of predicted brain age in patients with more advanced pathology. We compared the performance of three brain age algorithms in a large, longitudinal dataset (>13,000 imaging sessions from >6,000 individual MS patients).

SUBMITTER: Brier MR 

PROVIDER: S-EPMC10270248 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature