LESA: Longitudinal Elastic Shape Analysis of Brain Subcortical Structures.

Area of Science:

• Neuroimaging
• Biomedical Engineering
• Computational Anatomy

Background:

• Subcortical structures are vital information hubs, but their quantification remains challenging.
• Existing methods struggle with shape extraction, representation, and modeling for longitudinal data.
• Magnetic Resonance Imaging (MRI) is crucial for visualizing brain development.

Purpose of the Study:

• To develop a novel framework, Longitudinal Elastic Shape Analysis (LESA), for quantifying longitudinal subcortical structure changes.
• To address challenges in shape extraction, representation, and modeling of complex brain structures.
• To enable systematic quantification of shape changes from raw MRI data.

Main Methods:

• LESA integrates elastic shape analysis of static surfaces with statistical modeling of sparse longitudinal data.
• It employs basis functions for efficient representation of complex subcortical structures.
• The framework accurately delineates spatiotemporal shape changes in human subcortical structures.

Main Results:

• LESA was applied to three longitudinal neuroimaging datasets, demonstrating its versatility.
• Applications include estimating continuous shape trajectories and building life-span growth patterns.
• Analysis of Alzheimer's Disease Neuroimaging Initiative (ADNI) data revealed accelerated ventricle and hippocampus shape changes in Alzheimer's Disease (AD) patients aged 60-75.

Conclusions:

• LESA offers an efficient and accurate method for analyzing longitudinal changes in subcortical brain structures.
• The framework facilitates understanding of normal aging and disease-related shape alterations.
• Findings highlight accelerated shape changes in ventricles and hippocampi in individuals with Alzheimer's Disease.