Modeling brain dynamics after tumor resection using The Virtual Brain

Brain tumor patients scheduled for tumor resection often face significant uncertainty, as the outcome of neurosurgery is difficult to predict at the individual patient level. Recently, simulation of the activity of neural populations connected according to the white matter fibers, producing personal...

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Vydáno v:	NeuroImage (Orlando, Fla.) Ročník 213; s. 116738
Hlavní autoři:	Aerts, Hannelore, Schirner, Michael, Dhollander, Thijs, Jeurissen, Ben, Achten, Eric, Van Roost, Dirk, Ritter, Petra, Marinazzo, Daniele
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Inc 01.06.2020 Elsevier Limited Elsevier
Témata:	Adult Age Aged Brain - surgery Brain cancer Brain Neoplasms - surgery Brain tumor Brain tumors Computational neuroscience Computer Simulation Connectome Connectome - methods Convulsions & seizures Epilepsy Female Fibers Functional connectivity Graph theory Hospitals Humans Image Processing, Computer-Assisted - methods Machine learning Magnetic Resonance Imaging Male Middle Aged Mimicry Models, Neurological Nervous system Neural networks Neuroinformatics Neurosurgery Neurosurgical Procedures - methods Oncology Patients Substantia alba Surgery Surgical outcomes Tumors User-Computer Interface Brain tumor Functional connectivity Graph theory Connectome Computational neuroscience Neuroinformatics
ISSN:	1053-8119, 1095-9572, 1095-9572
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Popis
Shrnutí:	Brain tumor patients scheduled for tumor resection often face significant uncertainty, as the outcome of neurosurgery is difficult to predict at the individual patient level. Recently, simulation of the activity of neural populations connected according to the white matter fibers, producing personalized brain network models, has been introduced as a promising tool for this purpose. The Virtual Brain provides a robust open source framework to implement these models. However, brain network models first have to be validated, before they can be used to predict brain dynamics. In prior work, we optimized individual brain network model parameters to maximize the fit with empirical brain activity. In this study, we extend this line of research by examining the stability of fitted parameters before and after tumor resection, and compare it with baseline parameter variability using data from healthy control subjects. Based on these findings, we perform the first “virtual neurosurgery”, mimicking patient’s actual surgery by removing white matter fibers in the resection mask and simulating again neural activity on this new connectome. We find that brain network model parameters are relatively stable over time in brain tumor patients who underwent tumor resection, compared with baseline variability in healthy control subjects. Concerning the virtual neurosurgery analyses, use of the pre-surgery model implemented on the virtually resected structural connectome resulted in improved similarity with post-surgical empirical functional connectivity in some patients, but negligible improvement in others. These findings reveal interesting avenues for increasing interactions between computational neuroscience and neuro-oncology, as well as important limitations that warrant further investigation. •We build individual models of brain activity in brain tumor patients and controls.•Model parameters are stable at group level between pre- and post-operatory phase.•Global scaling parameter and efficiency of SC are negatively correlated.•Virtual neurosurgery (modelling using a resected individual SC matrix) is explored.•Variability in the results is evident; caution and more data are needed.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1053-8119 1095-9572 1095-9572
DOI:	10.1016/j.neuroimage.2020.116738