Abstract #1591

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

Sara Naghizadehkashani¹, Mahdi Alizadeh², Kiran Talekar¹, Devon Middleton¹, Zahra Sadeghi Adl¹, Omid Shoraka², Sahar Darabi Monadi³, Spandana Tammiraju⁴, Scott Faro⁴, and Feroze Mohamed⁴

¹Radiology, Thomas Jefferson University, philadelphia, PA, United States, ²neurosurgery, Thomas Jefferson University, philadelphia, PA, United States, ³Radiology, CHELSEA AND WESTMINSTER HOSPITAL NHS FOUNDATION TRUST, London, United Kingdom, ⁴Radiology, Thomas Jefferson University, Philadelphia, PA, United States

Synopsis

Keywords: fMRI Analysis, fMRI (resting state), Graph theory, Language, Tumor

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.

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