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Proceedings Paper

Embedded sparse representation of fMRI data via group-wise dictionary optimization
Author(s): Dajiang Zhu; Binbin Lin; Joshua Faskowitz; Jieping Ye; Paul M. Thompson
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Paper Abstract

Sparse learning enables dimension reduction and efficient modeling of high dimensional signals and images, but it may need to be tailored to best suit specific applications and datasets. Here we used sparse learning to efficiently represent functional magnetic resonance imaging (fMRI) data from the human brain. We propose a novel embedded sparse representation (ESR), to identify the most consistent dictionary atoms across different brain datasets via an iterative group-wise dictionary optimization procedure. In this framework, we introduced additional criteria to make the learned dictionary atoms more consistent across different subjects. We successfully identified four common dictionary atoms that follow the external task stimuli with very high accuracy. After projecting the corresponding coefficient vectors back into the 3-D brain volume space, the spatial patterns are also consistent with traditional fMRI analysis results. Our framework reveals common features of brain activation in a population, as a new, efficient fMRI analysis method.

Paper Details

Date Published: 21 March 2016
PDF: 7 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97841K (21 March 2016); doi: 10.1117/12.2217144
Show Author Affiliations
Dajiang Zhu, Univ. of Southern California (United States)
Binbin Lin, Univ. of Michigan (United States)
Joshua Faskowitz, Univ. of Southern California (United States)
Jieping Ye, Univ. of Michigan (United States)
Paul M. Thompson, Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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