Open AccessComparison of separation performance of independent component analysis algorithms for fMRI data
Author(s) -
Yogesh Kumar Sariya,
R. S. Anand
Publication year - 2018
Publication title -
journal of integrative neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.336
H-Index - 33
eISSN - 1757-448X
pISSN - 0219-6352
DOI - 10.3233/jin-170006
Subject(s) - independent component analysis , resting state fmri , computer science , infomax , artificial intelligence , pattern recognition (psychology) , closeness , principal component analysis , component (thermodynamics) , blind signal separation , mathematics , psychology , neuroscience , computer network , mathematical analysis , channel (broadcasting) , physics , thermodynamics
Independent component analysis, a data-driven analysis method, has found significant applications in task-based as well as resting state fMRI studies. There are numbers of independent component analysis algorithms available, but only a few of them have been used frequently so far for fMRI images. With a view that algorithms that are overlooked may outperform the most opted, a comparative study is taken up in this paper to analyze their abilities for the purpose of synthesis of fMRI images. In this paper, ten independent component algorithms: Fast ICA, INFOMAX, SIMBEC, JADE, ERICA, EVD, RADICAL, ICA-EBM, ERBM, and COMBI are compared. Their separation abilities are adjudged on both, synthetic and real fMRI images. Performance to decompose synthetic fMRI images is being monitored on the basis of spatial correlation coefficients, time elapsed to extract independent components and the visual appearance of independent components. Ranking of their performances on task-based real fMRI images are based on the closeness of time courses of identified independent components with model time course and the closeness of spatial maps of components with spatial templates while their competencies for resting state fMRI data are analyzed by examining how distinctly they decompose the data into the most consistent resting state networks. Sum of mutual information between all the permutations of decomposed components of resting state fMRI data are also calculated.