  Deep brain stimulation (DBS) has the potential to improve the quality of life
of people with a variety of neurological diseases. A key challenge in DBS is in
the placement of a stimulation electrode in the anatomical location that
maximizes efficacy and minimizes side effects. Pre-operative localization of
the optimal stimulation zone can reduce surgical times and morbidity. Current
methods of producing efficacy probability maps follow an anatomical guidance on
magnetic resonance imaging (MRI) to identify the areas with the highest
efficacy in a population. In this work, we propose to revisit this problem as a
classification problem, where each voxel in the MRI is a sample informed by the
surrounding anatomy. We use a patch-based convolutional neural network to
classify a stimulation coordinate as having a positive reduction in symptoms
during surgery. We use a cohort of 187 patients with a total of 2,869
stimulation coordinates, upon which 3D patches were extracted and associated
with an efficacy score. We compare our results with a registration-based method
of surgical planning. We show an improvement in the classification of
intraoperative stimulation coordinates as a positive response in reduction of
symptoms with AUC of 0.670 compared to a baseline registration-based approach,
which achieves an AUC of 0.627 (p < 0.01). Although additional validation is
needed, the proposed classification framework and deep learning method appear
well-suited for improving pre-surgical planning and personalize treatment
strategies.
