Abstract
Spatial group independent component analysis (GICA) methods decompose multiple-subject functional magnetic resonance imaging (fMRI) data into a linear mixture of spatially independent components (ICs), some of which are subsequently characterized as brain functional networks. Group information guided independent component analysis (GIG-ICA) as a variant of GICA has been proposed to improve the accuracy of the subject-specific ICs estimation by optimizing their independence. Independent vector analysis (IVA) is another method which optimizes the independence among each subject's components and the dependence among corresponding components of different subjects. Both methods are promising in neuroimaging study and showed a better performance than the traditional GICA. However, the difference between IVA and GIG-ICA has not been well studied. A detailed comparison between them is demanded to provide guidance for functional network analyses. In this work, we employed multiple simulations to evaluate the performances of the two approaches in estimating subject-specific components and time courses under conditions of different data quality and quantity, varied number of sources generated and inaccurate number of components used in computation, as well as the presence of spatially subject-unique sources. We also compared the two methods using healthy subjects' test-retest resting-state fMRI data in terms of spatial functional networks and functional network connectivity (FNC). Results from simulations support that GIG-ICA showed better recovery accuracy of both components and time courses than IVA for those subject-common sources, and IVA outperformed GIG-ICA in component and time course estimation for the subject-unique sources. Results from real fMRI data suggest that GIG-ICA resulted in more reliable spatial functional networks and yielded higher and more robust modularity property of FNC, compared to IVA. Taken together, GIG-ICA is appropriate for estimating networks which are consistent across subjects, while IVA is able to estimate networks with great inter-subject variability or subject-unique property.
Highlights
There is a rapidly increasing interest in using functional magnetic resonance imaging data to characterize brain functional networks
Component and Time Course Accuracy Estimated from Independent vector analysis (IVA) and Group information guided independent component analysis (GIG-Independent component analysis (ICA)) in Experiment 1 Figure 3 shows the components/time courses (TCs) of one subject estimated by IVA and GIG-ICA from the simulated data with the contrast-to-noise ratio (CNR) = 1
Our results indicate the advantage of GIGICA in recovering subject-common sources than IVA, and GIGICA can yield components with higher accuracy even under the case of low quality and quantity of data
Summary
There is a rapidly increasing interest in using functional magnetic resonance imaging (fMRI) data to characterize brain functional networks. Spatial ICA (sICA) (McKeown et al, 1998), a popular method for analyzing fMRI data, decomposes fMRI data into a linear mixture of spatially independent components (ICs) some of which are subsequently identified as brain functional networks. The order of resulting ICs from individual-subject ICA is arbitrary, increasing the difficult to establish correspondence among ICs estimated from different subjects Another issue is that the estimated ICs include meaningful functional networks, and various artifact-related ICs resulting from imaging and non-neural physiological activity. The number of sources is unknown, so the number of ICs always needs to be estimated (Li et al, 2007); the numbers estimated using different criteria are varied (Zuo et al, 2010) These shortcomings of ICA bring difficulties to multiple-subject fMRI data analyses, especially when shared networks across subjects are expected for subsequent group analyses.
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