Keywords: White Matter, Machine Learning/Artificial Intelligence, Tractography & Fiber ModellingShape features of the arcuate fasciculi (AF) can be used for predicting language laterality by a machine-learning algorithm as determined by language task-based functional MRI (tb-fMRI) laterality index (LI) relatively accurately (AUC = 0.893, accuracy = 0.868) in a sample of 60 clinical preoperative patients with variable pathology. Constrained spherical deconvolution (CSD) tractograms seemed to give the best outcome of model training regardless of additional streamline filtering or anatomical constraint. The best-performing model appeared to prioritise bundle curl, irregularity and span over the more conventional measures of surface-area and volume.
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