Beyond linearity in neuroimaging: Capturing nonlinear relationships with application to longitudinal studies.

ABSTRACT: The ubiquitous adoption of linearity for quantitative predictors in statistical modeling is likely attributable to its advantages of straightforward interpretation and computational feasibility. The linearity assumption may be a reasonable approximation especially when the variable is confined within a narrow range, but it can be problematic when the variable's effect is non-monotonic or complex. Furthermore, visualization and model assessment of a linear fit are usually omitted because of challenges at the whole brain level in neuroimaging. By adopting a principle of learning from the data in the presence of uncertainty to resolve the problematic aspects of conventional polynomial fitting, we introduce a flexible and adaptive approach of multilevel smoothing splines (MSS) to capture any n

SUBMITTER: Chen G 

PROVIDER: S-EPMC8284193 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature