A review of multivariate methods in brain imaging data fusion

Jing Sui; Tülay Adali; Yi-Ou Li; Honghui Yang; Vince D. Calhoun

doi:10.1117/12.843922

9 March 2010 A review of multivariate methods in brain imaging data fusion

Jing Sui, Tülay Adali, Yi-Ou Li, Honghui Yang, Vince D. Calhoun

Author Affiliations +

Proceedings Volume 7626, Medical Imaging 2010: Biomedical Applications in Molecular, Structural, and Functional Imaging; 76260D (2010) https://doi.org/10.1117/12.843922
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

On joint analysis of multi-task brain imaging data sets, a variety of multivariate methods have shown their strengths and been applied to achieve different purposes based on their respective assumptions. In this paper, we provide a comprehensive review on optimization assumptions of six data fusion models, including 1) four blind methods: joint independent component analysis (jICA), multimodal canonical correlation analysis (mCCA), CCA on blind source separation (sCCA) and partial least squares (PLS); 2) two semi-blind methods: parallel ICA and coefficient-constrained ICA (CC-ICA). We also propose a novel model for joint blind source separation (BSS) of two datasets using a combination of sCCA and jICA, i.e., 'CCA+ICA', which, compared with other joint BSS methods, can achieve higher decomposition accuracy as well as the correct automatic source link. Applications of the proposed model to real multitask fMRI data are compared to joint ICA and mCCA; CCA+ICA further shows its advantages in capturing both shared and distinct information, differentiating groups, and interpreting duration of illness in schizophrenia patients, hence promising applicability to a wide variety of medical imaging problems.

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Jing Sui, Tülay Adali, Yi-Ou Li, Honghui Yang, and Vince D. Calhoun "A review of multivariate methods in brain imaging data fusion", Proc. SPIE 7626, Medical Imaging 2010: Biomedical Applications in Molecular, Structural, and Functional Imaging, 76260D (9 March 2010); https://doi.org/10.1117/12.843922

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