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Independent component analysis (ICA), as a data driven method, has shown tobe a powerful tool for functional magnetic resonance imaging (fMRI) dataanalysis. One drawback of this multivariate approach is, that it is notcompatible to the analysis of group data in general. Therefore varioustechniques have been proposed in order to overcome this limitation of ICA. Inthis paper a novel ICA-based work-flow for extracting resting state networksfrom fMRI group studies is proposed. An empirical mode decomposition (EMD) isused to generate reference signals in a data driven manner, which can beincorporated into a constrained version of ICA (cICA), what helps to eliminatethe inherent ambiguities of ICA. The results of the proposed workflow are thencompared to those obtained by a widely used group ICA approach for fMRIanalysis. In this paper it is demonstrated that intrinsic modes, extracted byEMD, are suitable to serve as references for cICA to obtain typical restingstate patterns, which are consistent over subjects. By introducing thesereference signals into the ICA, our processing pipeline makes it transparentfor the user, how comparable activity patterns across subjects emerge. Thisadditionally allows adapting the trade-off between enforcing similarity acrosssubjects and preserving individual subject features.

A constrained ICA-EMD Model for Group Level fMRI Analysis