Recursive feature elimination (RFE), a machine learning technique, is used to sub-select node-based graph theoretical features that correlate with symptom severity in mTBI. Resting state functional connectivity was represented as a binary graph by thresholding correlation values computed between time courses of functional ROIs. Node-based graph theoretical metrics were computed and fed to the feature elimination model to regress on mTBI symptom scores. Using RFE we identified top features correlated to symptom severity in mTBI, which include eigen centrality and closeness of nodes within the salience and default-mode networks. Top features were analyzed for repeatability over multiple runs and multiple thresholds.