@inproceedings{d8c14a2b36a04a52ab1041ebdc1d9fbc,
title = "A review of multivariate methods in brain imaging data fusion",
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.",
keywords = "Blind Source Separation (BSS), Brain imaging, CCA, CCA+ICA, Data fusion, Functional MRI, ICA, Multivariate methods",
author = "Jing Sui and T{\"u}lay Adali and Li, {Yi Ou} and Honghui Yang and Calhoun, {Vince D.}",
year = "2010",
doi = "10.1117/12.843922",
language = "English (US)",
isbn = "9780819480279",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
booktitle = "Medical Imaging 2010 - Biomedical Applications in Molecular, Structural, and Functional Imaging",
note = "Medical Imaging 2010 - Biomedical Applications in Molecular, Structural, and Functional Imaging ; Conference date: 14-02-2010 Through 16-02-2010",
}