Embedded sparse representation of fMRI data via group-wise dictionary optimization

Dajiang Zhu; Binbin Lin; Joshua Faskowitz; Jieping Ye; Paul M. Thompson

doi:10.1117/12.2217144

21 March 2016 Embedded sparse representation of fMRI data via group-wise dictionary optimization

Dajiang Zhu, Binbin Lin, Joshua Faskowitz, Jieping Ye, Paul M. Thompson

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 97841K (2016) https://doi.org/10.1117/12.2217144
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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.

Citation Download Citation

Dajiang Zhu, Binbin Lin, Joshua Faskowitz, Jieping Ye, and Paul M. Thompson "Embedded sparse representation of fMRI data via group-wise dictionary optimization", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97841K (21 March 2016); https://doi.org/10.1117/12.2217144

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