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

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.