UC Irvine

The linear model (LM) is typically used to analyze the relationship between imaging data and demographic/cognitive parameters. Each imaging measurement is considered as independent in this model. Recent neuroimaging studies collect time-series data, for which the assumption of independence is invalid. Instead, we use the linear mixed model (LMM) that gives us population effects and subject effects as regression coefficients. A downside of LMM is the computation burden. The purposes of this research are: (1) to develop the tools for analyzing large data, (2) to interpret results, and (3) to demonstrate how to use subject wise information.

We focused on a large dataset of diffusion weighted MR images. 730 images and demographic/cognitive tests were acquired in 176 subjects by the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our code can successfully analyze the 30GB dataset within one day. We found that the population effects coefficients of LMM are roughly similar to the coefficients estimated by LM, and the statistical significance of regressors in the LMM is typically lower than in the LM. We demonstrated that subject wise information can be used to determine an onset of deterioration for each healthy control. Results suggest that this parameter is helpful to model the general aging process.