A constrained ICA-EMD Model for Group Level fMRI Analysis
This is an incremental improvement for researchers analyzing fMRI group studies, enabling more transparent and adaptable extraction of resting state networks.
The paper tackled the problem of applying independent component analysis (ICA) to group-level fMRI data by proposing a novel workflow that uses empirical mode decomposition (EMD) to generate reference signals for constrained ICA (cICA), resulting in consistent resting state patterns across subjects and allowing user control over similarity versus individual features.
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.