Abstract
Longitudinal studies of development and disease in the human brain have motivated the acquisition of large neuroimaging data sets and the concomitant development of robust methodological and statistical tools for quantifying neurostructural changes. Longitudinal-specific strategies for acquisition and processing have potentially significant benefits including more consistent estimates of intra-subject measurements while retaining predictive power. In this work, we introduce the open-source Advanced Normalization Tools (ANTs) cortical thickness longitudinal processing pipeline and its application on the first phase of the Alzheimer’s Disease Neuroimaging Initiative (ADNI-1) comprising over 600 subjects with multiple time points from baseline to 36 months. We demonstrate in these data that the single-subject template construction and native subject-space processing advantageously localizes data transformations and mitigates interpolation artifacts which results in a simultaneous minimization of within-subject variability and maximization of between-subject variability immediately estimable from a longitudinal mixed-effects modeling strategy. It is further shown that optimizing these dual criteria leads to greater scientific interpretability in terms of tighter confidence intervals in calculated mean trends, smaller prediction intervals, and narrower confidence intervals for determining cross-sectional effects. These concepts are first illustrated and explored in the entorhinal cortex. This evaluation strategy is then extended to the entire cortex, as defined by the Desikan-Killiany-Tourville labeling protocol, where comparisons are made with the popular crosssectional and longitudinal FreeSurfer processing streams.
