RT Journal Article
SR Electronic
T1 The ANTs Longitudinal Cortical Thickness Pipeline
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 170209
DO 10.1101/170209
A1 Nicholas J. Tustison
A1 Andrew J. Holbrook
A1 Brian B. Avants
A1 Jared M. Roberts
A1 Philip A. Cook
A1 Zachariah M. Reagh
A1 James R. Stone
A1 Daniel L. Gillen
A1 Michael A. Yassa
A1 for the Alzheimer’s Disease Neuroimaging Initiative
YR 2017
UL http://biorxiv.org/content/early/2017/07/30/170209.abstract
AB 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.