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Abstract ObjectiveNeurodegenerative 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. MethodsBrain 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). Effects of MS, MS disease course, disability, lesion burden, and DMT efficacy were assessed using linear mixed effects models. ResultsMS was associated with advanced predicted brain age cross‐sectionally and accelerated brain aging longitudinally in all techniques. While MS disease course (relapsing vs. progressive) did contribute to advanced brain age, disability was the primary correlate of advanced brain age. We found that advanced brain age at study enrollment predicted more disability accumulation longitudinally. Lastly, a more youthful appearing brain (predicted brain age less than actual age) was associated with decreased disability. InterpretationBrain age is a technically tractable and clinically relevant biomarker of disease pathology that correlates with and predicts increasing disability in MS. Advanced brain age predicts future disability accumulation.
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This content will become publicly available on January 22, 2026
Role of data-driven regional growth model in shaping brain folding patterns
The surface morphology of the developing mammalian brain is crucial for understanding brain function and dysfunction.
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- Award ID(s):
- 2011369
- PAR ID:
- 10645482
- Publisher / Repository:
- RSC
- Date Published:
- Journal Name:
- Soft Matter
- Volume:
- 21
- Issue:
- 4
- ISSN:
- 1744-683X
- Page Range / eLocation ID:
- 729 to 749
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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