Many brain morphometry studies have been performed in order to characterize the brain atrophy pattern of Alzheimer's disease (AD). The earliest studies focused on the volume of particular brain structures, such as hippocampus and entorhinal cortex. Even though volumetry is a powerful, robust and intuitive technique that has yielded a wealth of findings, more complex shape descriptors have been used to perform statistical shape analysis of particular brain structures. However, in shape analysis studies of brain structures the information of the relative pose between neighbor structures is typically disregarded. This work presents a framework to analyse pose information including the following approaches: similarity transformations with either pseudo-Riemannian or left-invariant Riemannian metric, and centered transformations with a bi-invariant Riemannian metric. As an illustration, an analysis of covariance (ANCOVA) and a discrimination analysis were performed on Alzheimer's Disease Neuroimaging Initiative (ADNI) data.
Bossa, M, Zacur, E & Olmos, S 2011, 'Statistical analysis of relative pose information of subcortical nuclei: Application on ADNI data', NeuroImage, vol. 55, no. 3, pp. 999-1008. https://doi.org/10.1016/j.neuroimage.2010.12.078
Bossa, M., Zacur, E., & Olmos, S. (2011). Statistical analysis of relative pose information of subcortical nuclei: Application on ADNI data. NeuroImage, 55(3), 999-1008. https://doi.org/10.1016/j.neuroimage.2010.12.078
@article{a2128d17eb864ed8af91999add3a6d41,
title = "Statistical analysis of relative pose information of subcortical nuclei: Application on ADNI data",
abstract = "Many brain morphometry studies have been performed in order to characterize the brain atrophy pattern of Alzheimer's disease (AD). The earliest studies focused on the volume of particular brain structures, such as hippocampus and entorhinal cortex. Even though volumetry is a powerful, robust and intuitive technique that has yielded a wealth of findings, more complex shape descriptors have been used to perform statistical shape analysis of particular brain structures. However, in shape analysis studies of brain structures the information of the relative pose between neighbor structures is typically disregarded. This work presents a framework to analyse pose information including the following approaches: similarity transformations with either pseudo-Riemannian or left-invariant Riemannian metric, and centered transformations with a bi-invariant Riemannian metric. As an illustration, an analysis of covariance (ANCOVA) and a discrimination analysis were performed on Alzheimer's Disease Neuroimaging Initiative (ADNI) data.",
keywords = "Alzheimer's disease, Pose information, Riemannian distance, Similarity transformations",
author = "Matias Bossa and Ernesto Zacur and Salvador Olmos",
year = "2011",
month = apr,
day = "1",
doi = "10.1016/j.neuroimage.2010.12.078",
language = "English",
volume = "55",
pages = "999--1008",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press Inc.",
number = "3",
}