Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models.

ABSTRACT: The potential of normative modeling to make individualized predictions from neuroimaging data has enabled inferences that go beyond the case-control approach. However, site effects are often confounded with variables of interest in a complex manner and can bias estimates of normative models, which has impeded the application of normative models to large multi-site neuroimaging data sets. In this study, we suggest accommodating for these site effects by including them as random effects in a hierarchical Bayesian model. We compared the performance of a linear and a non-linear hierarchical Bayesian model in modeling the effect of age on cortical thickness. We used data of 570 healthy individuals from the ABIDE (autism brain imaging data exchange) data set in our experiments. In addition, we u

SUBMITTER: Bayer JMM 

PROVIDER: S-EPMC7614761 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature