Marcus Kaiser


Marcus Kaiser is Professor of Neuroinformatics in the School of Computing at Newcastle University and Visiting Professor at Shanghai Jiao Tong University. He is a Fellow of the Royal Society of Biology and is Chair of Neuroinformatics UK. 

Marcus is interested in complex networks, particularly in development, error-tolerance, structure, and function of biological networks. Therefore, he studies brain connectivity networks of cat and macaque as well as metabolic pathways and protein-protein interaction networks. This analysis of biological networks is covering various fields from neuroscience and biochemistry to numerical analysis and computer science.

He authored Changing Connectomes: Evolution, Development, and Dynamics in Network Neuroscience and his papers include Organization, development and function of complex brain networks, Nonoptimal Component Placement, but Short Processing Paths, due to Long-Distance Projections in Neural Systems, Clustered organization of cortical connectivity, Simulation of robustness against lesions of cortical networks, Spatial growth of real-world networks, A tutorial in connectome analysis: topological and spatial features of brain networks, and Temporal interactions between cortical rhythms. Read the full list of his publications!

Marcus studied biology at the Ruhr-University Bochum (Lab of Prof. Dr. K.P. Hoffmann) and computer science at the FernUni-Hagen (distance university). His master thesis (Diplomarbeit) under the supervision of Prof. Markus Lappe was about the visual localization of objects during saccadic eye movements. After that, he completed his Ph.D. studies at the Jacobs University Bremen in the group of Prof. Claus Hilgetag.

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  • In hierarchical brain networks Local-level connectivity within regions is a better predictor of epilepsy intervention outcomes than global connectivity between regions

    Invited Talks
    02-09-2022 - 10:00-10:30

    Xue Chen, Yanjiang Wang, Sebastian J. Kopetzky, Markus Butz-Ostendorf, Marcus Kaiser

    Finding clear connectome biomarkers for temporal lobe epilepsy (TLE) patients, in particular at early disease stages, remains a challenge. Currently, the whole-brain structural connectomes are analysed based on coarse parcellations (up to 1,000 nodes). However, such global parcellation-based connectomes may be unsuitable for detecting more localized changes in patients. Here, we use a high-resolution network (~50,000-nodes overall) to identify changes at the local level (within brain regions) and test its relation with duration and surgical outcome. Patients with TLE (n = 33) and age-, sex-matched healthy subjects (n = 36) underwent high-resolution (~50,000 nodes) structural network construction based on deterministic tracking of diffusion tensor imaging (DTI). Nodes were allocated to 68 cortical regions according to the Desikan-Killany (DK) atlas. The connectivity within regions was then used to predict surgical outcome. MRI processing, network reconstruction, and visualization of network changes were integrated into the NeuroXM(TM) Brain Science Suite. Lower clustering coefficient and higher edge density were found for local connectivity within regions in patients, but were absent for the global network between regions (68 cortical regions). Local connectivity changes, in terms of the number of changed regions and the magnitude of changes, increased with disease duration. Local connectivity yielded a better surgical outcome prediction (Mean value: 95.39% accuracy, 92.76% sensitivity, 100% specificity) than global connectivity. Connectivity within regions, compared to structural connectivity between brain regions, can be a more efficient biomarker for epilepsy assessment and surgery outcome prediction of medically intractable TLE.

    Figure 1. Constructing global (between-area) and local (within-area) structural networks.

    Figure 2. Changes in local connectivity, within regions, for shorter (fewer than 20 years) and longer (more than 20 years) duration of epilepsy.

Marcus Kaiser