Understanding multi-modal brain network data: An immersive 3D visualization approach

Understanding the human brain requires the incorporation of functional interaction patterns that depend on a variety of features like experimental setup, strength of directed connectedness or variability between several individuals or groups. In addition to these external factors, there are internal properties of the brain network as for example temporal propagation of connections, or connectivity patterns that only occur in a distinct frequency range of the signal. The visualization of detected networks covering all necessary information poses a substantial problem which is mainly due to the high number of features that have to be integrated within the same view in a natural spatial context. To address this problem, we propose a new tool that transfers the network into an anatomically arranged origin–destination view in a virtual visual analysis lab. This offers the user an opportunity to assess the temporal evolution of connectivity patterns and provides an intuitive and motivating way of exploring the corresponding features via navigation and interaction in virtual reality (VR). The approach was evaluated in a user study including participants with neuroscientific background as well as people working in the field of computer science. As a first proof of concept trial we used functional brain networks derived from time series of electroencephalography recordings evoked by visual stimuli. All participants gave a positive general feedback, notably they saw a benefit in using the VR view instead of the compared 2D desktop variant. This suggests that our application successfully fills a gap in the visualization of high-dimensional brain networks and that it is worthwhile to further follow and enhance the proposed representation method. © 2022 Elsevier Ltd