A lag functional linear model for prediction of magnetization transfer ratio in multiple sclerosis lesions

Journal Article (Journal Article)

We propose a lag functional linear model to predict a response using multiple functional predictors observed at discrete grids with noise. Two procedures are proposed to estimate the regression parameter functions: (1) an approach that ensures smoothness for each value of time using generalized cross-validation; and (2) a global smoothing approach using a restricted maximum likelihood framework. Numerical studies are presented to analyze predictive accuracy in many realistic scenarios. The methods are employed to estimate a magnetic resonance imaging (MRI)-based measure of tissue damage (the magnetization transfer ratio, or MTR) in multiple sclerosis (MS) lesions, a disease that causes damage to the myelin sheaths around axons in the central nervous system. Our method of estimation of MTR within lesions is useful retrospectively in research applications where MTR was not acquired, as well as in clinical practice settings where acquiring MTR is not currently part of the standard of care. The model facilitates the use of commonly acquired imaging modalities to estimate MTR within lesions, and outperforms cross-sectional models that do not account for temporal patterns of lesion development and repair.

Full Text

Duke Authors

Cited Authors

  • Pomann, GM; Staicu, AM; Lobaton, EJ; Mejia, AF; Dewey, BE; Reich, DS; Sweeney, EM; Shinohara, RT

Published Date

  • December 1, 2016

Published In

Volume / Issue

  • 10 / 4

Start / End Page

  • 2325 - 2348

Electronic International Standard Serial Number (EISSN)

  • 1941-7330

International Standard Serial Number (ISSN)

  • 1932-6157

Digital Object Identifier (DOI)

  • 10.1214/16-AOAS981

Citation Source

  • Scopus