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Determining epilepsy surgery targets through connectome-based computer simulations

Marcus Kaiser1*
  • 1 Newcastle University, School of Computing Science, United Kingdom

Our work on connectomics over the last 15 years has shown a small-world, modular, and hub architecture of brain networks (Martin et al. 2001; Sporns et al. 2004). Small-world features enable the brain to rapidly integrate and bind information while the modular architecture, present at different hierarchical levels, allows separate processing of various kinds of information (e.g. visual or auditory) while preventing a wide-scale spreading of activation (Kaiser et al. 2007a). Hub nodes play critical roles in information processing and are involved in many brain diseases (Kaiser et al. 2007b). Nonetheless, general observations of human brain connectivity, or of patients at the group-level, have so far had little impact on understanding cognition, or deficiencies in cognition, in individual subjects. As a result, human connectome information is not used as a biomarker for diagnosis or a predictor of the most suitable treatment strategy. After discussing the organisation of brain networks, we will show how connectivity can be used to determine treatment in individual patients. An important aspect of brain networks is their spatial and topological organisation (Kaiser et al. 2006). However, simply observing connectivity is insufficient as small changes in network organisation might lead to large changes in network behaviour (dynamics) (Kaiser, 2013). We therefore show how simulations can be applied to predict regions that are involved in pathological processes. For epilepsy, simulations show us which regions are involved (Hutchings et al. 2015), which treatment approach should be used, and whether a surgical intervention will be successful or not (Sinha et al. 2016). These are first steps towards using connectome-based computer simulations as a tool to understand normal and pathological processing in individuals and to model effects and side effects. While intervention for epilepsy involved surgical removal of parts of the brain, other diseases can be treated non-invasively through neuro-feedback or brain stimulation techniques. Developing models that are based on anatomical and functional information will be crucial for precision medicine of neural disorders (Wang et al. 2015).

Acknowledgements

This work was supported by the CANDO project (http://www.cando.ac.uk/) funded through the Wellcome Trust (102037) and EPSRC (NS/A000026/1) and the Human Brain Development project (https://biodynamo.web.cern.ch/ ) supported by EPSRC (EP/K026992/1), CERN Openlab and Intel.

References

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Kaiser, M., Görner, M., and Hilgetag, C.C. (2007a). Functional Criticality in Clustered
Networks without inhibition. New Journal of Physics 9, 110.

Kaiser, M., Martin, R., Andras, P., and Young, M.P. (2007b). Simulation of robustness against lesions of cortical networks. Eur J Neurosci 25, 3185-3192.

Kaiser, M., and Hilgetag, C.C. (2006). Nonoptimal Component Placement, but Short
Processing Paths, due to Long-Distance Projections in Neural Systems. PLoS
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Kaiser, M. (2013). The potential of the human connectome as a biomarker of brain
disease. Front Hum Neurosci 7, 484.


Martin, R., Kaiser, M., Andras, P., and Young, M.P. (2001). Is the Brain a Scale-Free Network? Paper presented at the Annual Conference of the Society for Neuroscience, San Diego, US.

Sinha, N., Dauwels, J., Wang, Y., Kaiser, M., Cash, S.S., Westover, M.B., Taylor, P.N. (2016). Predicting neurosurgical outcomes in focal epilepsy patients using computational modeling, under review.

Sporns, O., Chialvo, D.R., Kaiser, M., and Hilgetag, C.C. (2004). Organization,
development and function of complex brain networks. Trends Cogn Sci 8, 418-425.

Wang, Y., Hutchings, F., and Kaiser, M. (2015). Computational modeling of neurostimulation in brain diseases. In Progress in Brain Research (Elsevier).

Keywords: connectome, Neuroimaging, Computer Simulation, precision medicine, Epilepsy, brain connectivity, modelling, Translational Neuroscience, Brain Stimulation, Surgery, Computer-Assisted

Conference: Neuroinformatics 2016, Reading, United Kingdom, 3 Sep - 4 Sep, 2016.

Presentation Type: Investigator presentations

Topic: Clinical neuroscience

Citation: Kaiser M (2016). Determining epilepsy surgery targets through connectome-based computer simulations. Front. Neuroinform. Conference Abstract: Neuroinformatics 2016. doi: 10.3389/conf.fninf.2016.20.00015

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Received: 30 Apr 2016; Published Online: 18 Jul 2016.

* Correspondence: Prof. Marcus Kaiser, Newcastle University, School of Computing Science, Newcastle, NE1 7RU, United Kingdom, Marcus.kaiser@nottingham.ac.uk