Capturing inter-subject variability with group independent component analysis of fMRI data: A simulation study

By Elena A. Allen, Erik B. Erhardt, Yonghua Wei, Tom Eichele, Vince D. Calhoun. 2012

251 citations recorded by the source corpus.

A key challenge in functional neuroimaging is the meaningful combination of results across subjects. Even in a sample of healthy participants, brain morphology and functional organization exhibit considerable variability, such that no two individuals have the same neural activation at the same location in response to the same stimulus. This inter-subject variability limits inferences at the group-level as average activation patterns may fail to represent the patterns seen in individuals. A promising approach to multi-subject analysis is group independent component analysis (GICA), which identifies group components and reconstructs activations at the individual level. GICA has gained considerable popularity, particularly in studies where temporal response models cannot be specified. However, a comprehensive understanding of the performance of GICA under realistic conditions of inter-subject variability is lacking. In this study we use simulated functional magnetic resonance imaging (fMRI) data to determine the capabilities and limitations of GICA under conditions of spatial, temporal, and amplitude variability. Simulations, generated with the SimTB toolbox, address questions that commonly arise in GICA studies, such as: (1) How well can individual subject activations be estimated and when will spatial variability preclude estimation? (2) Why does component splitting occur and how is it affected by model order? (3) How should we analyze component features to maximize sensitivity to inter-subject differences? Overall, our results indicate an excellent capability of GICA to capture between-subject differences and we make a number of recommendations regarding analytic choices for application to functional imaging data.

Subfields: Cognitive Neuroscience, Statistics and Probability, Neuroscience

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Citations in the corpus

Learning deep representations by mutual information estimation and maximization (2018, background): “13; Chen et al., 2018; Gonzalez-Garcia et al., 2018), or independently controllable (Thomas et al., 2017). As an example, independence is important in many neuroscience studies (Calhoun et al., 2008; Allen et al., 2012; Damaraju et al., 2014), where phrasing medical imaging data as a blind source se”
Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses (2017, background): “As such, while back-projection approaches may be tuneable to give good results in empirical studies using specific conditions in simulated data (Erhardt et al., 2011; Allen et al., 2012), we advocate the dual regression approach as being theoretically well-principled and practically “safer.”
Group information guided ICA for fMRI data analysis (2013, unknown): “9% data energy retained for each subject and the number of PCs set to no less than max ((the estimated number of ICs using MDL)+1, the number of ICs); parameter setting 4: data dimensionality reduction with 50 (half of the number of time points) PCs, which guaranteed near 100% energy retained for ea”
Multi-subject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery (2012, unknown): “The variability among subject can be considerable and the method works quite well in practice [5], [44], [49].”
Tracking whole-brain connectivity dynamics in the resting state. (2014, unknown): “…estimated using the GICA1 backreconstruction method based on PCA compression and projection (Calhoun et al. 2001; Erhardt et al. 2011), which is equivalent to least-squares based dual regression (Filippini et al. 2009) in the subject-specific PC-reduced space (see Appendix of Allen et al. 2012).”