Statistical Analysis of Longitudinal Neuroimage Data with Linear Mixed Effects Models

By Jorge L. Bernal-Rusiel, Douglas N. Greve, Martin Reuter, Bruce Fischl, Mert Rory Sabuncu. NeuroImage, 2012

359 citations recorded by the source corpus.

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 evaluation of the performance of LME and competing alternatives popularly used in prior longitudinal structural MRI studies, namely repeated measures ANOVA and the analysis of annualized longitudinal change measures (e.g. atrophy rate). In our experiments, we analyzed MRI-derived longitudinal hippocampal volume and entorhinal cortex thickness measurements from a public dataset consisting of Alzheimer’s patients, subjects with mild cognitive impairment and healthy controls. Our results suggest that the LME approach offers superior statistical power in detecting longitudinal group differences.

Subfields: Cognitive Neuroscience, Psychiatry and Mental health, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/69288e8458022c27a06cacf469781f99dccf6dcf

Citations in the corpus

• Head motion during MRI acquisition reduces gray matter volume and thickness estimates (2015, neutral): “Analysis of the repeated measure data was performed with a linear mixed effects model for gray matter volume estimates [6] as well as via a spatio-temporal approach for FreeSurfer’s cortical thickness estimates [7].”
• Linear mixed-effects modeling approach to FMRI group analysis (2013, neutral): “The LMEmodeling strategy has recently been applied to simple cases such as the longitudinal volume changes of a brain region and cortex thickness (Bernal-Rusiel et al., 2012).”