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• W4247236082 abstract "Event Abstract Back to Event Modeling brain dynamics in brain tumor patients using The Virtual Brain Hannelore Aerts1*, Michael Schirner2, Ben Jeurissen3, Dirk Van Roost4, Eric Achten5, Petra Ritter2 and Daniele Marinazzo1* 1 Department of Data Analysis, Ghent University, Belgium 2 Charité Universitätsmedizin Berlin, Germany 3 imec-Vision Lab, Department of Physics, Antwerp University, Belgium 4 Department of Neurosurgery, Ghent University Hospital, Belgium 5 Department of Neuroradiology, Ghent University Hospital, Belgium 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 (Figure 1); and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance. Layman summary Despite technological advances and clinical expertise, it remains difficult to predict how patients will do after tumor resection. “The Virtual Brain” holds great promise since it allows simulation of subject-specific brain activity. In the future, this could allow to virtually explore the effects of different surgical approaches on the patient’s brain and select the best one. Results of this study establish the basis for this purpose, by demonstrating that model parameters are unique for every person and that model parameters can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain. Figure 1 Keywords: brain tumor, connectome, computational modeling, graph theory, Cognition Conference: Belgian Brain Congress 2018 — Belgian Brain Council, LIEGE, Belgium, 19 Oct - 19 Oct, 2018. Presentation Type: e-posters Topic: NOVEL STRATEGIES FOR NEUROLOGICAL AND MENTAL DISORDERS: SCIENTIFIC BASIS AND VALUE FOR PATIENT-CENTERED CARE Citation: Aerts H, Schirner M, Jeurissen B, Van Roost D, Achten E, Ritter P and Marinazzo D (2019). Modeling brain dynamics in brain tumor patients using The Virtual Brain. Front. Neurosci. Conference Abstract: Belgian Brain Congress 2018 — Belgian Brain Council. doi: 10.3389/conf.fnins.2018.95.00030 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 13 Aug 2018; Published Online: 17 Jan 2019. * Correspondence: Miss. Hannelore Aerts, Department of Data Analysis, Ghent University, Ghent, East Flanders, B-9000, Belgium, hannelore.aerts@ugent.be Prof. Daniele Marinazzo, Department of Data Analysis, Ghent University, Ghent, East Flanders, B-9000, Belgium, daniele.marinazzo@ugent.be Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Hannelore Aerts Michael Schirner Ben Jeurissen Dirk Van Roost Eric Achten Petra Ritter Daniele Marinazzo Google Hannelore Aerts Michael Schirner Ben Jeurissen Dirk Van Roost Eric Achten Petra Ritter Daniele Marinazzo Google Scholar Hannelore Aerts Michael Schirner Ben Jeurissen Dirk Van Roost Eric Achten Petra Ritter Daniele Marinazzo PubMed Hannelore Aerts Michael Schirner Ben Jeurissen Dirk Van Roost Eric Achten Petra Ritter Daniele Marinazzo Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. 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• W4247236082 title "Modeling brain dynamics in brain tumor patients using The Virtual Brain" @default.
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