Joint fMRI analysis and subject clustering using sparse dictionary learning

Seung-Jun Kim; Krishna K. Dontaraju

doi:10.1117/12.2273914

24 August 2017 Joint fMRI analysis and subject clustering using sparse dictionary learning

Seung-Jun Kim, Krishna K. Dontaraju

Proceedings Volume 10394, Wavelets and Sparsity XVII; 103940F (2017) https://doi.org/10.1117/12.2273914
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

Abstract

Multi-subject fMRI data analysis methods based on sparse dictionary learning are proposed. In addition to identifying the component spatial maps by exploiting the sparsity of the maps, clusters of the subjects are learned by postulating that the fMRI volumes admit a subspace clustering structure. Furthermore, in order to tune the associated hyper-parameters systematically, a cross-validation strategy is developed based on entry-wise sampling of the fMRI dataset. Efficient algorithms for solving the proposed constrained dictionary learning formulations are developed. Numerical tests performed on synthetic fMRI data show promising results and provides insights into the proposed technique.

Citation Download Citation

Seung-Jun Kim and Krishna K. Dontaraju "Joint fMRI analysis and subject clustering using sparse dictionary learning", Proc. SPIE 10394, Wavelets and Sparsity XVII, 103940F (24 August 2017); https://doi.org/10.1117/12.2273914

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