Experimental Comparisons of Sparse Dictionary Learning and Independent Component Analysis for Brain Network Inference from fMRI Data.

IEEE Trans Biomed Eng. 2018 May 17;:

Authors: Zhang W, Lv J, Li X, Zhu D, Jiang X, Zhang S, Zhao Y, Guo L, Ye J, Hu D, Liu T

Abstract
Independent Component Analysis (ICA) has been one of the most popular methods for inferring functional brain networks from fMRI data. Recently, sparse dictionary learning (SDL) has been shown to be an alternative, effective approach to inferring functional networks based on fMRI data. However, there have been little experimental comparisons between ICA and SDL in the literature so far. In this work, we conduct comprehensive comparisons between four variants of ICA methods and three variants of SDL methods by using synthesized fMRI data with ground-truth. Our results showed that ICA methods perform very well and slightly better than SDL methods when functional networks' spatial overlaps are minor, but ICA methods have difficulty in differentiating functional networks with moderate or significant spatial overlaps. In contrast, the SDL algorithms perform consistently well no matter how functional networks spatially overlap, and importantly, SDL methods are significantly better than ICA methods when spatial overlaps between networks are moderate or severe. This work offers empirical better understanding of ICA and SDL algorithms in inferring functional networks from fMRI data, and provides new guidelines and caveats when constructing and interpreting functional networks in the era of fMRI-based connectomics.

PMID: 29993466 [PubMed - as supplied by publisher]