LESA: Longitudinal Elastic Shape Analysis of Brain Subcortical Structures

Abstract

Over the past 30 years, magnetic resonance imaging has become a ubiquitous tool for accurately visualizing the change and development of the brain’s subcortical structures (e.g., hippocampus). Although subcortical structures act as information hubs of the nervous system, their quantification is still in its infancy due to many challenges in shape extraction, representation, and modeling. Here, we develop a simple and efficient framework of longitudinal elastic shape analysis (LESA) for subcortical structures. Integrating ideas from elastic shape analysis of static surfaces and statistical modeling of sparse longitudinal data, LESA provides a set of tools for systematically quantifying changes of longitudinal subcortical surface shapes from raw structure MRI data. The key novelties of LESA include: (i) it can efficiently represent complex subcortical structures using a small number of basis functions and (ii) it can accurately delineate the spatiotemporal shape changes of the human subcortical structures. We applied LESA to analyze three longitudinal neuroimaging datasets and showcase its wide applications in estimating continuous shape trajectories, building life-span growth patterns, and comparing shape differences among different groups. In particular, with the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, we found that Alzheimer’s Disease (AD) can significantly speed the shape change of the lateral ventricle and the hippocampus from 60 to 75 years olds compared with normal aging. Supplementary materials for this article are available online.

Keywords:

• Alzheimer’s disease
• Elastic shape analysis
• Longitudinal shape trajectory
• Principal components analysis
• Subcortical structures

Supplementary Materials

The supplementary materials mainly contain results for subcortical structures on the right side of the brain, for example, right ventricle and hippocampus, and some additional results for the left ventricle and hippocampus.

Acknowledgments

The data used in the preparation of this article were obtained from three resources: the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu), the WU-Minn Human Connectome Project (HCP) consortium and the OpenPain Project (OPP). The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657), was funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. The funding for OPP (Principal Investigator: A. Vania Apkarian) was provided by the National Institute of Neurological Disorders and Stroke (NINDS) and National Institute of Drug Abuse (NIDA). OPP data are disseminated by the Apkarian Lab, Physiology Department at the Northwestern University, Chicago. This work was partially supported by U.S. NIH grants AG066970, MH120299, MH086633 and MH116527 and U.S. NSF grant 1953087. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or any other funding agency.

Additional information

Funding

This work was partially supported by U.S. NIH grants AG066970, MH120299, MH086633 and MH116527 and U.S. NSF grant 1953087. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or any other funding agency.