Data-driven analysis of functional MRI time-series using a region-growing approach

We present a data-driven method to analyze functional magnetic resonance imaging (fMRI) time-series where multiple hypotheses are generated for inferential methods from the data itself without any assumptions on the time-series. The method does not require the number of clusters to be defined a priori. Activation detection is based on region growing which specifically suits the spatiotemporal characteristics of fMRI data. Results presented for simulated as well as real fMRI data show that the proposed method efficiently segments fMRI data into regions of distinct functional activity.