  Independent component analysis (ICA) is now a widely used solution for the
analysis of multi-subject functional magnetic resonance imaging (fMRI) data.
Independent vector analysis (IVA) generalizes ICA to multiple datasets, i.e.,
to multi-subject data, and in addition to higher-order statistical information
in ICA, it leverages the statistical dependence across the datasets as an
additional type of statistical diversity. As such, it preserves variability in
the estimation of single-subject maps but its performance might suffer when the
number of datasets increases. Constrained IVA is an effective way to bypass
computational issues and improve the quality of separation by incorporating
available prior information. Existing constrained IVA approaches often rely on
user-defined threshold values to define the constraints. However, an improperly
selected threshold can have a negative impact on the final results. This paper
proposes two novel methods for constrained IVA: one using an adaptive-reverse
scheme to select variable thresholds for the constraints and a second one based
on a threshold-free formulation by leveraging the unique structure of IVA. We
demonstrate that our solutions provide an attractive solution to multi-subject
fMRI analysis both by simulations and through analysis of resting state fMRI
data collected from 98 subjects -- the highest number of subjects ever used by
IVA algorithms. Our results show that both proposed approaches obtain
significantly better separation quality and model match while providing
computationally efficient and highly reproducible solutions.
