Modeling brain dynamics after tumor resection using The Virtual Brain
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1
Department of Data Analysis, Ghent University, Belgium
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2
Florey Institute of Neuroscience and Mental Health, Australia
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3
Ghent University Hospital, Belgium
Brain tumor patients undergoing neurosurgery face significant uncertainty regarding the outcome of their surgery. Therefore, we aimed to investigate the potential of computational modeling to predict neurosurgical outcome after tumor resection. To this end, we first examined the stability of individual computational modelling parameters before and after patients’ tumor resection. To quantify a range of normal variability over time, equivalent procedures were performed on data acquired from control subjects at both time points. Subsequently, virtual tumor resection was performed on a subsample of patients’ pre-operative data, after which the correspondence with their empirical post-operative brain dynamics was evaluated that served as ground truth.
Large-scale brain dynamics were simulated in 18 brain tumor patients and 10 control subjects for which data was available before and after each patient’s tumor resection. Using The Virtual Brain, an open-access neuroinformatics platform (Sanz Leon et al., 2013), local and global parameters of the Reduced Wong-Wang model (Deco et al., 2014; Schirner, McIntosh, Jirsa, Deco, & Ritter, 2018) were individually optimized. Subsequently, possible differences in individually optimized model parameters were evaluated over time as well as between groups. In addition, the relationship between model parameters, structural network topology and cognitive performance was assessed. As a proof-of-concept, we further simulated post-operative brain dynamics in four glioma patients, by virtually lesioning their structural connectome to mimic the actual surgical procedure.
Results revealed relatively stable brain dynamics over time, both in control subjects as well as in meningioma and glioma patients who underwent tumor resection. Furthermore, several robust associations between individually optimized model parameters, structural network topology and cognitive performance were identified from pre- to post-operative assessment. Based on these findings, we performed the first proof-of-concept analyses to evaluate the potential of computational modelling to predict brain dynamics after tumor resection. Promising results were obtained in one glioma patient whose connectome could be successfully “virtualized”. In contrast, predictive accuracy of computational modeling after virtual neurosurgery was poor in the other three patients. Nevertheless, the virtually lesioned structural connectome of these three patients provided a remarkably good approximation of their post-operative functional connectivity.
Acknowledgements
This project has received funding from the Special Research Funds (BOF) of the University of Ghent (01MR0210 and 01J10715), Grant P7/11 from the Interuniversity Attraction Poles Program of the Belgian Federal Government, and the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2).
References
Deco, G., Ponce-Alvarez, A., Hagmann, P., Romani, G. L., Mantini, D., & Corbetta, M. (2014). How local excitation – inhibition ratio impacts the whole brain dynamics. The Journal of Neuroscience, 34(23), 7886–7898. doi:10.1523/JNEUROSCI.5068-13.2014
Sanz Leon, P., Knock, S. A., Woodman, M. M., Domide, L., Mersmann, J., McIntosh, A. R., & Jirsa, V. K. (2013). The Virtual Brain: a simulator of primate brain network dynamics. Frontiers in Neuroinformatics, 7, 10. doi:10.3389/fninf.2013.00010
Schirner, M., McIntosh, A. R., Jirsa, V. K., Deco, G., & Ritter, P. (2018). Inferring multi-scale neural mechanisms with brain network modelling. ELife, 7, e28927. doi:10.7554/eLife.28927
Keywords:
computational modeling,
brain tumor,
connectome,
diffusion imaging,
connectivity,
tumor resection
Conference:
13th National Congress of the Belgian Society for Neuroscience , Brussels, Belgium, 24 May - 24 May, 2019.
Presentation Type:
Poster presentation
Topic:
Behavioral/Systems Neuroscience
Citation:
Aerts
H,
Dhollander
T,
Achten
E and
Marinazzo
D
(2019). Modeling brain dynamics after tumor resection using The Virtual Brain.
Front. Neurosci.
Conference Abstract:
13th National Congress of the Belgian Society for Neuroscience .
doi: 10.3389/conf.fnins.2019.96.00069
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Received:
23 Apr 2019;
Published Online:
27 Sep 2019.
*
Correspondence:
Mx. Hannelore Aerts, Department of Data Analysis, Ghent University, Ghent, East Flanders, B-9000, Belgium, hannelore.aerts@ugent.be