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Activity Number: 451
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 3:05 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #321592
Title: Non-Gaussian Penalized PARAFAC Analysis of fMRI Data
Author(s): Jingsai Liang* and Don Hong
Companies: and Middle Tennessee State University
Keywords: ICA ; PARAFAC ; fMRI

Independent Component Analysis (ICA) method has been used successfully in fMRI data analysis. As an extension of the ICA, Tensorial Probabilistic ICA (TPICA) is used to decompose fMRI group data into three-mode of subject, temporal and spatial. But due to the independent constraint of the spatial components, TPICA is not very efficient in the presence of overlapping of active regions of different spatial components. Parallel Factor Analysis (PARAFAC) is another method used to process three-mode data and can be solved by alternating least-squares. PARAFAC may converge into some degenerate solutions if the matrix of one mode is collinear. However, it is reasonable to find significant collinear relationships within subject mode of two similar subjects in group fMRI data. Thus both TPICA and PARAFAC have unavoidable drawbacks. In this paper, we try to alleviate both overlapping and collinear issues by integrating the characters of PARAFAC and TPICA together, which imposes a non-Gaussian penalty term to each spatial component under the PARAFAC framework. The results of this proposed algorithm outperforms TPICA and PARAFAC on the simulation data and real fMRI data.

Authors who are presenting talks have a * after their name.

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