Identification of a large-scale functional network in functional magnetic resonance imaging

In functional magnetic resonance imaging (fMRI), cerebral activity has been increasingly considered as the consequence of a network activation. Selecting the brain regions relevant for the network has thus become a key issue. We propose to define the so-called large-scale functional network involved in a particular task as a set of regions exhibiting strong intrinsic homogeneity, as well as at least one strong long-distance inter-regional interaction. We develop a method to identify such a network, and we validate it on a real dataset, in a context where the existence of a distributed network has already been demonstrated. Our results are compatible with previous studies. This new tool is thus promising for selecting regions when analyzing functional connectivity in fMRI.