Resting-State Functional Connectome in Patients with Brain Tumors Before and After Surgical Resection.

Abstract Purpose High-grade glioma (HGG) surgery has evolved around the principal belief that a safe maximal tumor resection improves symptoms, quality of life, and survival. Mapping brain function has been recently improved by resting state functional magnetic resonance imaging (rest-fMRI), a novel imaging technique that explores networks connectivity at “rest”. Methods This prospective study analyzed 10 patients with HGG in whom rest-fMRI connectivity was assessed both in single-subject and in group analysis before and after surgery. Seed-based functional connectivity analysis was performed with CONN toolbox. Network identification focused on 8 major functional connectivity networks. A voxel-wise ROI to ROI correlation maps to assess functional connectivity throughout the whole brain was computed from a priori seeds ROI in specific RSNs before and after surgical resection in each patient. Results Reliable topography of all 8 RSNs were successfully identified in each participant before surgical resection. Single-subject functional connectivity analysis showed functional disconnection for dorsal attention and salience networks, whereas the language network demonstrated functional connection either in the case of left temporal glioblastoma. Functional connectivity in group analysis showed wide variations of functional connectivity in the default mode, salience, and sensorimotor networks. However, salience and language networks, salience and default mode networks, and salience and sensorimotor networks showed a significant correlation (p-uncorrected Conclusions Resting-state fMRI can reliably detect common functional connectivity networks in patients with glioma and has the potential to anticipate network alterations after surgical resection.