Evaluating Large-Scale Brain Networks Patients With Brain Tumors: A Machine Learning Analysis of 100 Consecutive Patients

INTRODUCTION: Large-scale brain networks and higher cognitive functions are frequently altered in neuro-oncology patients, but comprehensive non-invasive brain mapping is difficult to achieve in the clinical setting. METHODS: We retrospectively included patients who underwent surgery for brain tumor resection at our Institution. Preoperative MRI with T1-weighted and DTI sequences were uploaded into the Quicktome platform. We categorized the integrity of nine large-scale brain networks: language, sensorimotor, visual, ventral attention, central executive, default mode, dorsal attention, salience and limbic. Networks were correlated with preoperative clinical data. RESULTS: One-hundred patients were included in the study. The most affected network was the central executive network (49%), followed by the default mode network (42%) and dorsal attention network (33%). Patients with preoperative deficits showed a significantly higher number of altered networks before the surgery (3.44 vs 2.16, p < .001), compared to patients without deficits. Furthermore, we found that patients without neurologic deficits had an average 2.16 networks affected and 1.56 networks at risk, with most of them being related to non-traditional eloquent areas (p = .014). CONCLUSIONS: Our results show that large-scale brain networks are frequently affected in patients with brain tumors, even when presenting without evident neurologic deficits. In our study, the most commonly affected brain networks were related to non-traditional eloquent areas. Integrating non-invasive brain mapping machine-learning techniques into the clinical setting may help elucidate how to preserve higher-order cognitive functions associated with those networks.