Longitudinal penalized functional regression for cognitive outcomes on neuronal tract measurements

We describe and analyse a longitudinal diffusion tensor imaging study relating changes in the microstructure of intracranial white matter tracts to cognitive disability in multiple-sclerosis patients. In this application the scalar outcome and the functional exposure are measured longitudinally. This data structure is new and raises challenges that cannot be addressed with current methods and software. To analyse the data, we introduce a penalized functional regression model and inferential tools designed specifically for these emerging types of data. Our proposed model extends the generalized linear mixed model by adding functional predictors; this method is computationally feasible and is applicable when the functional predictors are measured densely, sparsely or with error. On-line supplements compare two implementations, one likelihood based and the other Bayesian, and provide the software that is used in simulations; the likelihood-based implementation is included as the lpfr function in the R package refund that is available in the Comprehensive R Archive Network.