Understanding large network datasets through embodied interaction in virtual reality

Abstract

The quantity of information we are producing is soaring: this generates large amounts of data that are frequently left unexplored. Novel tools are needed to stem this “data deluge”. We developed a system that enhances the understanding of large datasets through embodied navigation and natural gestures using the immersive mixed reality space called “eXperience Induction Machine” (XIM). One of the applications of our system is in the exploration of the human brain connectome: the network of nodes and connections that defines the information flow in the brain. We exposed participants to a connectome dataset using either our system or a state of the art software for visualization and analysis of connectomic data. We measured their understanding and visual memory of the connectome structure. Our results showed that participants retained more information about the structure of the network when using our system. Overall, our system constitutes a novel approach in the exploration and understanding of large network datasets.