Abstract #1117

Probabilistic Template Matching for Detection of Language Network with resting-state fMRI in Patients with Brain Tumors

Jian Ming Teo^1,2, Vinodh A. Kumar³, Jina Lee³, Rami W. Eldaya³, Ping Hou¹, Kyle R. Noll⁴, Sherise D. Ferguson⁵, Sujit S. Prabhu⁵, Max Wintermark⁵, and Ho-Ling Liu¹

¹Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ²Medical Physics, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, United States, ³Deparment of Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ⁴Neuro-Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, ⁵Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, United States

Synopsis

Keywords: fMRI Analysis, fMRI (resting state), Language Function

Motivation: Automated detection of resting-state language network with independent components analysis (ICA) of brain tumor patients is challenging.

Goal(s): Develop an algorithm to detect the language network with ICA guided by a probabilistic overlap map (POM).

Approach: POM was generated from sentence completion presurgical fMRI of 283 patients. Probabilistic template matching performs a direct search over probability thresholds and component numbers. Independent dataset of 28 patients was used for testing in comparison to an existing method.

Results: Recommended ICA components from our algorithm agreed better with tb-fMRI language localizations, demonstrating significantly higher Dice coefficients and Pearson correlation scores in left hemisphere primary language areas.

Impact: The proposed method can improve the accuracy of automated detection of rs-fMRI language network. This may benefit presurgical evaluation for patients whose tumors are adjacent to language areas but have limited tb-fMRI.

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