Fellow Jackson Memorial Hospital Miami, Florida, United States
Disclosure(s):
Alexis A. Morell, MD: No financial relationships to disclose
Introduction: Large-scale brain networks and higher cognitive functions are frequently altered in patients with primary and secondary brain tumors.
Methods: We retrospectively included patients who underwent surgery for brain tumor resection at our Institution. Preoperative MRI imaging studies including T1-weighted and DTI sequences were uploaded into a machine-learning-based 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. Preoperative clinical data was also correlated with the brain network analysis.
Results: One hundred and ten patients were included in the study. The most affected network was the central executive network (49.09%), followed by the default mode network (42.72%) and dorsal attention network (31.82%). Patients with preoperative deficits showed a significantly higher number of altered networks before the surgery (3.25 vs 2.24, p < .001), compared to patients without deficits. Brain networks were more frequently affected in patients with primary brain tumors (30.09%) compared to secondary brain tumors (27.24%), but without statistical significance. Furthermore, we found that non-traditional eloquent areas were more frequently affected than traditional eloquent areas in all studied subpopulations.
Conclusion : Our results show that large-scale brain networks are frequently affected in patients with primary and secondary brain tumors, even in those 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.
