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Proceedings Paper

Robust biological parametric mapping: an improved technique for multimodal brain image analysis
Author(s): Xue Yang; Lori Beason-Held; Susan M. Resnick; Bennett A. Landman
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Paper Abstract

Mapping the quantitative relationship between structure and function in the human brain is an important and challenging problem. Numerous volumetric, surface, region of interest and voxelwise image processing techniques have been developed to statistically assess potential correlations between imaging and non-imaging metrics. Recently, biological parametric mapping has extended the widely popular statistical parametric approach to enable application of the general linear model to multiple image modalities (both for regressors and regressands) along with scalar valued observations. This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities. However, as presented, the biological parametric mapping approach is not robust to outliers and may lead to invalid inferences (e.g., artifactual low p-values) due to slight mis-registration or variation in anatomy between subjects. To enable widespread application of this approach, we introduce robust regression and robust inference in the neuroimaging context of application of the general linear model. Through simulation and empirical studies, we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power. The robust approach and associated software package provides a reliable way to quantitatively assess voxelwise correlations between structural and functional neuroimaging modalities.

Paper Details

Date Published: 14 March 2011
PDF: 8 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79623X (14 March 2011); doi: 10.1117/12.877593
Show Author Affiliations
Xue Yang, Vanderbilt Univ. (United States)
Lori Beason-Held, National Institutes of Health (United States)
Susan M. Resnick, National Institutes of Health (United States)
Bennett A. Landman, Vanderbilt Univ. (United States)
The John Hopkins Univ. (United States)

Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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