Statistical Methods for Multilevel Multivariate Functional Studies

  • Ciprian Crainiceanu
  • NINDS R01 7/1/2017 6/30/2022

While imaging studies are widely used in clinical practice and research, the number of neuroimaging- based biomarkers is small. For example, in clinical trials of immunomodulatory therapies for MS, the only commonly used imaging biomarkers are the total lesion volume and the number of new and enhancing lesions. These biomarkers are essential, but do not capture the recovery process of lesions, which is thought to decline in more severe, progressive disease. The partial or complete recovery of lesions may depend both on the ability of the brain to heal and on external factors, such as treatment or environmental and behavioral exposures. In this proposal we take the natural next step of proposing imaging biomarkers for MS based on the formation and change of lesions as observed on multi-sequence structural MRIs. To solve this problem we propose to address several general methodological problems: 1) develop models and methods for the longitudinal analysis of several images of the same brain; 2) identify and estimate the length of history that is necessary to estimate recovery; 3) study the association with known biomarkers of the disease (in this case total volume and number of new and enhancing lesions); 4) develop methods that are robust to changes in imaging protocols that inevitably arise in longitudinal neuroimaging studies; and 5) develop the computational tools that allow for sophisticated methods to be implemented seamlessly in practice. While our scientiļ¬c problem is focused, the proposed statistical methods are general and can be applied to a wide variety of longitudinal neuroimaging studies. For example, there are many ongoing longitudinal neuroimaging studies, including the ADNI, AIBL, HBC, and MISTIE, where our methods could be used to study subtle or large changes in lesions or in white and gray matter intensities.