Figure 1.

Flow chart of the network-based lesion-symptom mapping (NLSM)

(1) The spared fibers were extracted by removing the tracts that pass through the brain lesion mask of the patient based on the healthy atlas; (2) the spared network matrix was reconstructed by extracting the spared fibers between each pair of the 90 gray matter regions in the AAL90 atlas; (3) the spared network was weighted based on the mean FA value in the tract mask; (4) the objective function of the relationship between the global efficiency of the subnetwork and word reading performance was constructed; (5) the genetic algorithm (GA) procedure was calculated based on the objective function; (6) the optimal subnetwork in a GA procedure for a given n value was selected; (7) an incidence map of the 10 n values with the top mean R² values in the above subnetwork pool were generated; and (8) the reading subnetwork was identified according to the incidence map (see STAR Methods for details).