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).
Highlights
Investigating macro-level brain circuitry using functional magnetic resonance imaging is of great interest to the neuroimaging community
The simulation results of this experiment are presented in Figure 2: where Sub1’s ground truth (GND) spatial maps (SMs) is in 1st row, Group ICA (GICA) SMs in 2nd row and Independent Vector Analysis (IVA) SMs in 3rd row
Our results indicate that SMs were very well estimated irrespective of variability in their amplitude
Summary
Investigating macro-level brain circuitry using functional magnetic resonance imaging (fMRI) is of great interest to the neuroimaging community. Functional brain connectivity maps are widely used to investigate healthy and diseased populations in order to identify aberrant networks patterns (Garrity et al, 2007; Greicius, 2008; Bassett and Bullmore, 2009). There has been increased interest to identify brain networks of resting state fMRI (rsMRI); data acquired while a subject is not performing a particular task. Approaches used to analyze resting state data include seed-based correlation analyses (Cohen et al, 2008) and data-driven approaches such as independent component analysis (ICA) (McKeown et al, 1998). Group ICA (GICA), a framework that includes ICA, is widely applied to group fMRI data (Calhoun et al, 2001; Calhoun and Adali, 2012). GICA provides a solution to the problem of permutation ambiguity of ICA by matching components across subjects; first estimating the group level components and estimating single subject spatial maps (SM)
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