Marcus Kaiser (Neuroinformatics School of Computing Science, Newcastle University, UK)

Abstract:

The human brain consists of connections between neurons at the local level and of connections between brain regions at the global level. The study of the entire network, the connectome, has become a recent focus in neuroscience research. Using routines from physics and the social sciences, neuronal networks were found to show properties of scale-free networks, making them robust towards random damage, and of small-world systems leading to better information integration. More importantly, developmental brain diseases such as epilepsy and schizophrenia show characteristic changes in these network properties. First, I will describe novel results concerning the hierarchical and modular organisation of neural networks. Second, I will report on the role of hierarchical modularity on activity spreading. Importantly, low connectivity between modules can provide bottlenecks for activity spreading. Limiting activity spreading is crucial for preventing epileptic seizures. Indeed, connectivity between modules is increased in epilepsy patients that could explain the rise of large-scale synchronization. Finally, I will discuss strategies for characterising neural networks using motifs, single node motifs, and motif fingerprints. As an example, I will show motif changes for brain networks at different ages and with different spatial resolutions.