Geometric deep learning reveals a structuro-temporal understanding of healthy and pathologic brain aging.

ABSTRACT:

Background

Brain age has historically been investigated primarily at the whole brain level. The ability to deconstruct the brain into its composite parts and explore brain age at the sub-structure level offers unique advantages. These include the exploration of dynamic and interconnected relationships between different brain structures in healthy and pathologic aging. To achieve this, individual brain structures can be rendered as surface representations on which morphologic analysis is carried out. Combining the advantages of deep learning with the strengths of surface analysis, we investigate the aging process at the individual structure level with the hypothesis being that pathologic aging does not uniformly affect the aging process of individual structures.

Methods

MRI d

SUBMITTER: Besson P 

PROVIDER: S-EPMC9445244 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature