Exploring Neural Alterations in Patients with Tumor-related Language Deficits using Graph Theory Measures; a resting-state fMRI study

Motivation: while conventional functional MRI methods can provide pre-surgical language brain mapping in brain tumor patients, further neural reorganizations have not been fully explored using advanced fMRI analysis methods in patients with tumor-induced language deficit. Goal(s): To investigate neural reorganizations in patients with tumor-induced language deficit utilizing graph theory analysis on resting-state (rs) fMRI. Approach: Patients with left temporal tumors with/without language deficit(LD) underwent rs-fMRI. Image pre-processing and ROI-to-ROI analysis followed by Graph theory measurements were done and then compared between between LD patients and patients with normal speech. Results: Three regions of interest showed significant differences between groups across seven graph theory measures. Impact: Our results suggest that regions beyond traditional language-eloquent areas may help preserve language-related connections and compensate for tumor-related language impairments. Using more advanced fMRI analysis techniques could positively impact pre-surgical planning for these patients.