title: 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 (TCs) of the decomposition. Our results indicate the following: (1) at low levels of SM variability or when just one SM is varied, both GICA and IVA perform well, (2) at higher levels of SM variability or when more than one SMs are varied, IVA continues to perform well but GICA yields SM estimates that are composites of other SMs with errors in TCs, (3) both GICA and IVA remove spatial correlations of overlapping SMs and introduce artificial correlations in their TCs, (4) if number of SMs is over estimated, IVA continues to perform well but GICA introduces artifacts in the varying and extra SMs with artificial correlations in the TCs of extra components, and (5) in the absence or presence of SMs unique to one subject, GICA produces errors in TCs and IVA estimates are accurate. In summary, our simulation experiments (both simplistic and realistic) and our holistic analyses approach indicate that IVA produces results that are closer to ground truth and thereby better preserves subject variability. The improved version of IVA is now packaged into the GIFT toolbox (http://mialab.mrn.org/software/gift).
authors.affiliation: Autism and Developmental Medicine Institute, Geisinger Health System Danville, PA, USA ; The Mind Research Network and LBERI Albuquerque, NM, USA.
authors.forename: Andrew M
authors.initials: AM
authors.lastname: Michael
authors.affiliation: Department of Computer Science and Electrical Engineering, University of Maryland Baltimore, MD, USA.
authors.forename: Mathew
authors.initials: M
authors.lastname: Anderson
authors.affiliation: The Mind Research Network and LBERI Albuquerque, NM, USA.
authors.forename: Robyn L
authors.initials: RL
authors.lastname: Miller
authors.affiliation: Department of Computer Science and Electrical Engineering, University of Maryland Baltimore, MD, USA.
authors.forename: Tülay
authors.initials: T
authors.lastname: Adalı
authors.affiliation: The Mind Research Network and LBERI Albuquerque, NM, USA ; Department of Electrical and Computer Engineering, University of New Mexico Albuquerque, NM, USA.
authors.forename: Vince D
authors.initials: VD
authors.lastname: Calhoun
keywords: brain network analysis; fMRI; functional connectivity; group ICA method; independent vector analysis