Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models.

ABSTRACT: Longitudinal neuroimaging (LNI) studies are rapidly becoming more prevalent and growing in size. Today, no standardized computational tools exist for the analysis of LNI data and widely used methods are sub-optimal for the types of data encountered in real-life studies. Linear Mixed Effects (LME) modeling, a mature approach well known in the statistics community, offers a powerful and versatile framework for analyzing real-life LNI data. This article presents the theory behind LME models, contrasts it with other popular approaches in the context of LNI, and is accompanied with an array of computational tools that will be made freely available through FreeSurfer - a popular Magnetic Resonance Image (MRI) analysis software package. Our core contribution is to provide a quantitative empirical

SUBMITTER: Bernal-Rusiel JL 

PROVIDER: S-EPMC3586747 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature