Tree-dependent and topographic independent component analysis for fMRI analysis

Recently, a new paradigm in ICA emerged, that of finding "clusters" of dependent components. This striking philosophy found its implementation in two new ICA algorithms: tree-dependent and topographic ICA. Applied to fMRI, this leads to the unifying paradigm of combining two powerful exploratory data analysis methods, ICA and unsupervised clustering techniques. For the fMRI data, a comparative quantitative evaluation between the two methods, tree-dependent and topographic ICA was performed. The comparative results were evaluated based on (1) correlation and associated time-courses and (2) ROC study. It can be seen that topographic ICA outperforms all other ICA methods including tree-dependent ICA for 8 and 9 ICs. However, for 16 ICs topographic ICA is outperformed by both FastICA and tree-dependent ICA (KGV) using as an approximation of the mutual information the kernel generalized variance.