Although stimulation-induced sensations are typically considered undesirable side
effects in clinical DBS therapy, there are emerging scenarios, such as computer-brain
interface applications, where these sensations may be intentionally created. The selection
of stimulation parameters, whether to avoid or induce sensations, is a challenging
task due to the vast parameter space involved. This study aims to streamline DBS parameter
selection by employing a machine learning model to predict the occurrence and somatic
location of paresthesias in response to thalamic DBS.We used a dataset comprising
3,359 paresthetic sensations collected from 18 thalamic DBS leads from 10 individuals
in two clinical centers. For each stimulation, we modeled the Volume of Tissue Activation
(VTA). We then used the stimulation parameters and the VTA information to train a
machine learning model to predict the occurrence of sensations and their corresponding
somatic areas.Our results show fair to substantial agreement with ground truth in
predicting the presence and somatic location of DBS-evoked paresthesias, with Kappa
values ranging from 0.31 to 0.72. We observed comparable performance in predicting
the presence of paresthesias for both seen and unseen cases (Kappa 0.72 vs. 0.60).
However, Kappa agreement for predicting specific somatic locations was significantly
lower for unseen cases (0.53 vs. 0.31).The results suggest that machine learning can
potentially be used to optimize DBS parameter selection, leading to faster and more
efficient postoperative management. Outcome predictions may be used to guide clinical
DBS programming or tuning of DBS based computer-brain interfaces.
