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Abstract #3501

A Data-Driven FMRI Analysis Using K-SVD Sparse Dictionary Learning

Kangjoo Lee1, Jong Chul Ye1

1Dept. Bio & Brain Engineering, KAIST, Daejon, Korea, Republic of

Statistical parametric mapping (SPM) is widely used for the statistical analysis of brain activity with fMRI. However, if the general linear model employs a fixed form of a canonical HRF, the ignorance of experimental and individual variance can lead to inaccurate detection of the real activation area. A variety of data-driven methods, which combine independent component analysis (ICA) with statistical analysis of fMRI dataset, were suggested to overcome the problem, such as the `HYBICAapproach and the unified `SPM-ICAmethod. However, recent study demonstrates that representation of the brain fMRI using sparse components is more promising rather than independent components. Also, the real brain fMRI signal may be regarded as a combination of small set of dynamic components, where each of them has different signal patterns and sparsely distributed in each voxel. Hence, we employ the K-SVD, a powerful sparse dictionary learning algorithm, to decompose the neural signal into dictionary atoms with specific local responses. Using the trained sparse dictionary as a design matrix in SPM, we extract which signal components contribute to the neural activation. We show the proposed method adapts the individual variation and extract the activation better than conventional methods.