  Independent component analysis (ICA), as a data driven method, has shown to
be a powerful tool for functional magnetic resonance imaging (fMRI) data
analysis. One drawback of this multivariate approach is, that it is not
compatible to the analysis of group data in general. Therefore various
techniques have been proposed in order to overcome this limitation of ICA. In
this paper a novel ICA-based work-flow for extracting resting state networks
from fMRI group studies is proposed. An empirical mode decomposition (EMD) is
used to generate reference signals in a data driven manner, which can be
incorporated into a constrained version of ICA (cICA), what helps to eliminate
the inherent ambiguities of ICA. The results of the proposed workflow are then
compared to those obtained by a widely used group ICA approach for fMRI
analysis. In this paper it is demonstrated that intrinsic modes, extracted by
EMD, are suitable to serve as references for cICA to obtain typical resting
state patterns, which are consistent over subjects. By introducing these
reference signals into the ICA, our processing pipeline makes it transparent
for the user, how comparable activity patterns across subjects emerge. This
additionally allows adapting the trade-off between enforcing similarity across
subjects and preserving individual subject features.
