Visualising the Transition of Large Networks via Dimensionality Reduction to Illustrate the Evolution of the Human Brain (Unknown language)

Advances in high-throughput imaging techniques enable the creation of networks depicting spatio-temporal biological and neurophysiological processes with unprecedented size and magnitude. These networks involve thousands of nodes, which can not be compared over time by traditional methods due to complexity and clutter. When investigating networks over multiple time steps, a crucial question for the visualisation research community becomes apparent: How to visually trace changes of the connectivity over several transitions? Therefore, we developed an easy-to-use method that maps multiple networks to a common embedding space. Visualising the distribution of node-clusters of interest (e.g. brain regions) enables their tracing over time. We demonstrate this approach by visualizing spatial co-evolution networks of different evolutionary timepoints as small multiples to investigate how the human brain genetically and functionally evolved over the mammalian lineage.