Resting state fMRI analysis using a spatial regression mixture model

Functional MRI (fMRI) is one of the most important techniques to study the human brain. A relatively new problem to the analysis of fMRI data is the identification of brain networks when the brain is at rest i.e. no external stimulus is applied to the subject. In this work a method to find the Resting State Networks (RSNs), using fMRI time series, is proposed. To achieve that our method uses the Regression Mixtures Models (RMMs). RMMs are mixture models specifically design to cluster time series. Furthermore, our method takes into account the spatial correlations of fMRI data by using a new functional for the responsibilities of the mixture. Experimental results have showed the usefullness of the proposed approach compared to other methods of the field such as the k-means algorithm.