Prediction of cognitive performance differences in older age from multimodal neuroimaging data.

ABSTRACT: Differences in brain structure and functional and structural network architecture have been found to partly explain cognitive performance differences in older ages. Thus, they may serve as potential markers for these differences. Initial unimodal studies, however, have reported mixed prediction results of selective cognitive variables based on these brain features using machine learning (ML). Thus, the aim of the current study was to investigate the general validity of cognitive performance prediction from imaging data in healthy older adults. In particular, the focus was with examining whether (1) multimodal information, i.e., region-wise grey matter volume (GMV), resting-state functional connectivity (RSFC), and structural connectivity (SC) estimates, may improve predictability of cognit

SUBMITTER: Kramer C 

PROVIDER: S-EPMC10828156 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature