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Modeling brain dynamics in brain tumor patients using The Virtual Brain

View ORCID ProfileHannelore Aerts, View ORCID ProfileMichael Schirner, View ORCID ProfileBen Jeurissen, View ORCID ProfileDirk Van Roost, View ORCID ProfileRik Achten, View ORCID ProfilePetra Ritter, View ORCID ProfileDaniele Marinazzo
doi: https://doi.org/10.1101/265637
Hannelore Aerts
1Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Belgium
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  • For correspondence: hannelore.aerts@ugent.be daniele.marinazzo@ugent.be
Michael Schirner
2Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Dept. of Neurology, Germany
3Bernstein Focus State Dependencies of Learning & Bernstein Center for Computational Neuroscience, Berlin, Germany
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Ben Jeurissen
4imec - Vision Lab, Department of Physics, University of Antwerp, Belgium
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Dirk Van Roost
5Department of Neurosurgery, Ghent University Hospital, Belgium
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Rik Achten
6Department of Neuroradiology, Ghent University Hospital, Belgium
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Petra Ritter
2Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Dept. of Neurology, Germany
3Bernstein Focus State Dependencies of Learning & Bernstein Center for Computational Neuroscience, Berlin, Germany
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Daniele Marinazzo
1Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Belgium
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  • For correspondence: hannelore.aerts@ugent.be daniele.marinazzo@ugent.be
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Abstract

Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, non-invasive neuroimaging techniques such as functional MRI and diffusion weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex non-linear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics.

As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong-Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed.

Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 07, 2018.
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Modeling brain dynamics in brain tumor patients using The Virtual Brain
Hannelore Aerts, Michael Schirner, Ben Jeurissen, Dirk Van Roost, Rik Achten, Petra Ritter, Daniele Marinazzo
bioRxiv 265637; doi: https://doi.org/10.1101/265637
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Modeling brain dynamics in brain tumor patients using The Virtual Brain
Hannelore Aerts, Michael Schirner, Ben Jeurissen, Dirk Van Roost, Rik Achten, Petra Ritter, Daniele Marinazzo
bioRxiv 265637; doi: https://doi.org/10.1101/265637

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