A systematic review of multimodal brain age studies: Uncovering a divergence between model accuracy and utility.
Patterns (N Y)
; 4(4): 100712, 2023 Apr 14.
Article
em En
| MEDLINE
| ID: mdl-37123443
ABSTRACT
Brain aging is a complex, multifaceted process that can be challenging to model in ways that are accurate and clinically useful. One of the most common approaches has been to apply machine learning to neuroimaging data with the goal of predicting age in a data-driven manner. Building on initial brain age studies that were derived solely from T1-weighted scans (i.e., unimodal), recent studies have incorporated features across multiple imaging modalities (i.e., "multimodal"). In this systematic review, we show that unimodal and multimodal models have distinct advantages. Multimodal models are the most accurate and sensitive to differences in chronic brain disorders. In contrast, unimodal models from functional magnetic resonance imaging were most sensitive to differences across a broad array of phenotypes. Altogether, multimodal imaging has provided us valuable insight for improving the accuracy of brain age models, but there is still much untapped potential with regard to achieving widespread clinical utility.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
/
Systematic_reviews
Idioma:
En
Revista:
Patterns (N Y)
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Estados Unidos