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Sparse Classification for Significant Anatomy Detection in a Group Study

Banff International Research Station for Mathematical Innovation and Discovery

I will present a new framework for discriminative anatomy detection in high dimensional neuroimaging data. Current methods for identifying significant regions related to a group study typically use voxel-based mass univariate approaches. Those methods have limited ability to identify complex population differences because they do not take into account multivariate relationships in data. High dimensional pattern classification methods aim to optimally perform feature extraction and selection to find a set of features that differentiate the groups. However, they do not directly produce anatomically interpretable features. Following recent advances in sparse dimensionality reduction methods, we propose a sparse classification method that identifies anatomical regions that are both discriminative and clinically interpretable. Results on synthetic and real MRI data of multiple sclerosis patients and age- and gender-matched healthy controls show superior performance of our method in detecting stable and significant regions in a statistical group analysis when compared to a generative sparse method and to a voxel-based analysis method.

Mathematics; Statistics; Game theory, economics, social and behavioral sciences

video/mp4

Author affiliation: University of Alberta

2016-08-06

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/58701

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/