Using machine learning to evaluate large-scale brain networks in patients with brain tumors: Traditional and non-traditional eloquent areas.

Background: 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. The objective of our study is to evaluate traditional and non-traditional eloquent areas in brain tumor patients using a machine-learning platform.

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. Network integrity was 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 (43%) and dorsal attention network (32%). Patients with preoperative deficits showed a significantly higher number of altered networks before the surgery (3.42 vs 2.19, < .001), compared to patients without deficits. Furthermore, we found that patients without neurologic deficits had an average 2.19 networks affected and 1.51 networks at-risk, with most of them being related to non-traditional eloquent areas ( < .001).

Conclusion: 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.