Sparse dictionary learning for resting-state fMRI analysis

Kangjoo Lee; Paul Kyu Han; Jong Chul Ye

doi:10.1117/12.894241

27 September 2011 Sparse dictionary learning for resting-state fMRI analysis

Kangjoo Lee, Paul Kyu Han, Jong Chul Ye

Proceedings Volume 8138, Wavelets and Sparsity XIV; 81381X (2011) https://doi.org/10.1117/12.894241
Event: SPIE Optical Engineering + Applications, 2011, San Diego, California, United States

Abstract

Recently, there has been increased interest in the usage of neuroimaging techniques to investigate what happens in the brain at rest. Functional imaging studies have revealed that the default-mode network activity is disrupted in Alzheimer's disease (AD). However, there is no consensus, as yet, on the choice of analysis method for the application of resting-state analysis for disease classification. This paper proposes a novel compressed sensing based resting-state fMRI analysis tool called Sparse-SPM. As the brain's functional systems has shown to have features of complex networks according to graph theoretical analysis, we apply a graph model to represent a sparse combination of information flows in complex network perspectives. In particular, a new concept of spatially adaptive design matrix has been proposed by implementing sparse dictionary learning based on sparsity. The proposed approach shows better performance compared to other conventional methods, such as independent component analysis (ICA) and seed-based approach, in classifying the AD patients from normal using resting-state analysis.

Citation Download Citation

Kangjoo Lee, Paul Kyu Han, and Jong Chul Ye "Sparse dictionary learning for resting-state fMRI analysis", Proc. SPIE 8138, Wavelets and Sparsity XIV, 81381X (27 September 2011); https://doi.org/10.1117/12.894241

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