Abstract
Modeling brain dynamics after tumor resection using The Virtual Brain
Highlights
Many brain tumor patients undergoing neurosurgery face significant uncertainty regarding the outcome of surgery
Results from our study reveal that model parameters describing brain dynamics are relatively stable over time in brain tumor patients who underwent tumor resection, relative to baseline variability levels observed in healthy control subjects
Our study is the first investigation of potential changes in model parameters describing brain dynamics after brain tumor resection using large-scale brain network modeling
Summary
Many brain tumor patients undergoing neurosurgery face significant uncertainty regarding the outcome of surgery. Average neurosurgical outcomes for patient cohorts can be predicted with a varying degree of accuracy (Emblem et al, 2015; Senders et al, 2017); the heterogeneity of brain tumors complicates predictions on an individual patient level. Several studies have addressed this limitation by applying graph theoretical and machine learning approaches to infer neurosurgical outcome at the individual patient level (for a review see Senders et al, 2018). Others have evaluated machine learning strategies designed to predict survival in glioma (Emblem et al, 2009, 2015) and traumatic brain injury patients (Rughani et al, 2010). One study found that graph measures derived from the pre-surgical functional connectome of patients with temporal lobe epilepsy were able to predict post-surgical cognitive performance scores across different domains (Doucet et al, 2015)
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