2012 | OriginalPaper | Buchkapitel
Eigenanatomy Improves Detection Power for Longitudinal Cortical Change
verfasst von : Brian Avants, Paramveer Dhillon, Benjamin M. Kandel, Philip A. Cook, Corey T. McMillan, Murray Grossman, James C. Gee
Erschienen in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012
Verlag: Springer Berlin Heidelberg
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We contribute a novel and interpretable dimensionality reduction strategy,
eigenanatomy
, that is tuned for neuroimaging data. The method approximates the eigendecomposition of an image set with basis functions (the eigenanatomy vectors) that are
sparse
,
unsigned
and are
anatomically clustered
. We employ the eigenanatomy vectors as anatomical predictors to improve detection power in morphometry. Standard voxel-based morphometry (VBM) analyzes imaging data voxel-by-voxel—and follows this with cluster-based or voxel-wise multiple comparisons correction methods to determine significance. Eigenanatomy reverses the standard order of operations by first clustering the voxel data and then using standard linear regression in this reduced dimensionality space. As with traditional region-of-interest (ROI) analysis, this strategy can greatly improve detection power. Our results show that eigenanatomy provides a principled objective function that leads to localized, data-driven regions of interest. These regions improve our ability to quantify biologically plausible rates of cortical change in two distinct forms of neurodegeneration. We detail the algorithm and show experimental evidence of its efficacy.