Preserving subject variability in group fMRI analysis: performance evaluation of GICA vs. IVA.

ABSTRACT: Independent component analysis (ICA) is a widely applied technique to derive functionally connected brain networks from fMRI data. Group ICA (GICA) and Independent Vector Analysis (IVA) are extensions of ICA that enable users to perform group fMRI analyses; however a full comparison of the performance limits of GICA and IVA has not been investigated. Recent interest in resting state fMRI data with potentially higher degree of subject variability makes the evaluation of the above techniques important. In this paper we compare component estimation accuracies of GICA and an improved version of IVA using simulated fMRI datasets. We systematically change the degree of inter-subject spatial variability of components and evaluate estimation accuracy over all spatial maps (SMs) and time courses (T

SUBMITTER: Michael AM 

PROVIDER: S-EPMC4071815 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature