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Review
. 2009 Mar;33(2):131-9.
doi: 10.1016/j.compmedimag.2008.10.011. Epub 2008 Dec 25.

Review of methods for functional brain connectivity detection using fMRI

Kaiming Li  1 , Lei Guo, Jingxin Nie, Gang Li, Tianming Liu
Affiliations
Review

Review of methods for functional brain connectivity detection using fMRI

Kaiming Li et al. Comput Med Imaging Graph. 2009 Mar.

Abstract

Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing attention of neuroscientists and computer scientists, since it opens a new window to explore functional network of human brain with relatively high resolution. A variety of methods for fcMRI study have been proposed. This paper intends to provide a technical review on computational methodologies developed for fcMRI analysis. From our perspective, these computational methods are classified into two general categories: model-driven methods and data-driven methods. Data-driven methods are a large family, and thus are further sub-classified into decomposition-based methods and clustering analysis methods. For each type of methods, principles, main contributors, and their advantages and drawbacks are discussed. Finally, potential applications of fcMRI are overviewed.

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Figures

Fig. 1
Fig. 1
Current methods developed for FcMRI Study.
Fig. 2
Fig. 2
Illustration for decomposition of a fMRI dataset X using SVD. Si is the singular value of X; Ui is the ith principal component; and Vi is the corresponding eigen map; p is the number of chosen components.
Fig. 3
Fig. 3
This figure illustrates the decomposition of a fMRI dataset using a certain type of ICA (spatial ICA to be exact. See the following several paragraphs for more information about sICA). Here, the independent components are spatially independent.
Fig. 4
Fig. 4
Illustration for independent component (IC) maps thresholding. IC map1 and IC map2 are 3D representation of IC 1 and IC 2 in Fig. 3.
Fig. 5
Fig. 5
Two illustrative signals that are highly correlated (the correlation coefficient is −0.8824). However, the intensity distance is considerable.
See this image and copyright information in PMC

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