Data-Driven Visualization of Functional Brain Regions from Resting State fMRI Data

Functional parcellation of the human cortex plays an important role in the understanding of brain functions. Traditionally, functional areas are defined according to anatomical landmarks. Recently, new techniques were proposed that do not require a priori segmentation of the cortex. Such methods allow functional parcellation by functional information alone. We propose here a data-driven approach for the exploration of functional connectivity of the cortex. The method extends a known parcellation method, used in multichannel EEG analysis, to define and extract functional units (FUs), i.e., spatially connected brain regions that record highly correlated fMRI signals. We apply the method to the study of fMRI data and provide a visualization, inspired by the EEG case, that uses linked views to facilitate the understanding of both the location and the functional similarity of brain regions. Initial feedback on our approach was received from four domain experts, researchers in the field of neuroscience.

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