Evaluating the Prediction of Brain Maturity From Functional Connectivity After Motion Artifact Denoising.

ABSTRACT: The ability to make individual-level predictions from neuroanatomy has the potential to be particularly useful in child development. Previously, resting-state functional connectivity (RSFC) MRI has been used to successfully predict maturity and diagnosis of typically and atypically developing individuals. Unfortunately, submillimeter head motion in the scanner produces systematic, distance-dependent differences in RSFC and may contaminate, and potentially facilitate, these predictions. Here, we evaluated individual age prediction with RSFC after stringent motion denoising. Using multivariate machine learning, we found that 57% of the variance in individual RSFC after motion artifact denoising was explained by age, while 4% was explained by residual effects of head motion. When RSFC data we

SUBMITTER: Nielsen AN 

PROVIDER: S-EPMC6519700 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature