Towards data-driven group inferences of resting-state fMRI data in rodents: comparison of Group ICA, GIG-ICA, and IVA-GL

Abstract Background A trend in the development of resting-state fMRI (rsfMRI) data analysis is the drive towards more data-driven methods. Group Independent Component Analysis (GICA) is a well-proven data-driven method for performing fMRI group analysis, though not without issues, especially the back-reconstruction from group-level independent components to individual-level components. Group information-guided ICA (GIG-ICA) and Independent Vector Analysis (IVA) are recent extensions of GICA that were shown to outperform GICA in the identification of unique rsfMRI biomarkers in psychiatric conditions. New Method In this work, GICA, GIG-ICA, and IVA-GL analysis methods were applied to rsfMRI data acquired from 9 mice under different doses of medetomidine (0.1 – 0.3 mg/kg/h) in the before and after forepaw stimulation, and their performance was compared to determine whether GIG-ICA and IVA-GL outperform GICA in identifying robust and reliable resting-state networks in the rodent brain. Results Our results showed IVA-GL method had certain desirable performance characteristics over the other two methods under minimal data pre-processing and data-driven assumptions in application to analysis of mouse resting-state functional MRI. Comparison with existing methods: IVA-GL provides better stability towards detecting group differences at different model order assumptions and performed better at separating data well-defined and functionally reasonable components in mouse resting-state fMRI. At higher model order and more likely functional component assumptions, GIG-ICA and IVA-GL were found to have greater sensitivity at detecting functional connectivity changes due to physiological challenges compared to GICA. Conclusions This study indicates that IVA-GL yields better detection of resting-state networks in the rodent brain compared to other ICA methods and a promising data-driven analysis method for rodent rsfMRI.