Machine learning-aided personalized DTI tractographic planning for deep brain stimulation of the superolateral medial forebrain bundle using HAMLET.

The approach allows overlying the results of patient-specific planning from two different approaches (manual deterministic and machine learning HAMLET). HAMLET shows the slMFB as a volume and thus serves as an objective tracking corridor. It helps to refine results from deterministic tracking in the surgical workspace without interfering with any part of the standard software solution. We have now included this workflow in our daily clinical experimental work on slMFB DBS for psychiatric indications.