A general probabilistic model for group independent component analysis and its estimation methods

• Type of Material: Article
• Authors: Ying Guo, Emory University
• Language: English
• Date: 2011-12
• Publisher: Wiley: Biometrics
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2011, The International Biometric Society
• Final Published Version (URL): http://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2011.01601.x/abstract;jsessionid=53E16298847CD7DC55B59E4ACEEE5E55.d02t01
• Title of Journal or Parent Work: Biometrics
• ISSN: 0006-341X
• Volume: 67
• Issue: 4
• Start Page: 1532
• End Page: 1542
• Grant/Funding Information: This work was supported by a URC grant funded by Emory University Research Committee and NIH grant R01-MH079251.
• Keywords: Independent component analysis; Variational approximation; Multi-subject imaging data; Group inferences; EM algorithm; Maximum likelihood estimation; Functional magnetic resonance imaging (fMRI)
• Research Categories: Biology, Biostatistics

Abstract

SUMMARY Independent component analysis (ICA) has become an important tool for analyzing data from functional magnetic resonance imaging (fMRI) studies. ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a pre-specified group design matrix and the uncertainty in between-subjects variability in fMRI data. We present a general probabilistic ICA (PICA) model that can accommodate varying group structures of multi-subject spatio-temporal processes. An advantage of the proposed model is that it can flexibly model various types of group structures in different underlying neural source signals and under different experimental conditions in fMRI studies. A maximum likelihood method is used for estimating this general group ICA model. We propose two EM algorithms to obtain the ML estimates. The first method is an exact EM algorithm which provides an exact E-step and an explicit noniterative M-step. The second method is an variational approximation EM algorithm which is computationally more efficient than the exact EM. In simulation studies, we first compare the performance of the proposed general group PICA model and the existing probabilistic group ICA approach. We then compare the two proposed EM algorithms and show the variational approximation EM achieves comparable accuracy to the exact EM with significantly less computation time. An fMRI data example is used to illustrate application of the proposed methods.