Estimating cortical thickness trajectories in children across different scanners using transfer learning from normative models.

ABSTRACT: This work illustrates the use of normative models in a longitudinal neuroimaging study of children aged 6-17 years and demonstrates how such models can be used to make meaningful comparisons in longitudinal studies, even when individuals are scanned with different scanners across successive study waves. More specifically, we first estimated a large-scale reference normative model using Hierarchical Bayesian Regression from N = 42,993 individuals across the lifespan and from dozens of sites. We then transfer these models to a longitudinal developmental cohort (N = 6285) with three measurement waves acquired on two different scanners that were unseen during estimation of the reference models. We show that the use of normative models provides individual deviation scores that are independent o

SUBMITTER: Gaiser C 

PROVIDER: S-EPMC10839740 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature