László Halász ^1,2* Bastian E. Sajonz ^3* Gabriella Miklós ^1,4,5* Gijs Van Elswijk ^5* Saman Hagh Gooie ^5* Bálint Várkuti ^5* Gertrud Tamas ¹ Volker A. Coenen ^3,6 Loránd Erōss ^1*

• ¹ Faculty of Medicine, Semmelweis University, Budapest, Hungary
• ² Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary
• ³ University of Freiburg Medical Center, Freiburg, Baden-Württemberg, Germany
• ⁴ János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Budapest, Hungary
• ⁵ CereGate GmbH, München, Germany
• ⁶ University of Freiburg, Freiburg, Baden-Wurttemberg, Germany

Introduction: 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. Methods: We used a dataset comprising 3359 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. Results: 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). Conclusion: 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.