Model order effects on independent vector analysis applied to complex-valued fMRI data

Abstract

Independent vector analysis (IVA) has exhibited promising applications to complex-valued fMRI data, however model order effects on complex-valued IVA have not yet been studied. As such, we investigate model order effects on IVA using 16 task-based complex-valued fMRI data sets. A noncircular fixed-point complex-valued IVA (non-FIVA) algorithm was utilized. The model orders were varied from 10 to 160. The ICASSO toolbox was modified for selecting the best spatial estimates across all runs to assess the IVA stability. Non-FIVA was compared to a complex-valued independent component analysis (ICA) algorithm as well as to real-valued IVA and ICA algorithms which analyzed magnitude-only fMRI data. The complex-valued analysis detected component splitting at higher model orders, but in a different way from the magnitude-only analysis in that a complete component and its sub-components exist simultaneously. This suggests that the incorporation of phase fMRI data may better preserve the integrity of the larger networks. Good stability was also achieved by non-FIVA with different orders.