# Prediction of subthalamic stimulation efficacy on isolated dystonia via support vector regression

## TL;DR

This study uses support vector regression to predict the effectiveness of subthalamic deep brain stimulation in treating isolated dystonia, finding that early improvement and specific brain regions are key indicators.

## Contribution

A novel support vector regression model is developed to predict STN-DBS outcomes in isolated dystonia patients.

## Key findings

- Patients showed an average 56% improvement after STN-DBS, influenced by symptom type and activated brain volumes.
- The SVR model predicted outcomes with 11% mean error, emphasizing the importance of dorsal posterior STN targeting.
- Early motor score improvement within one week strongly correlates with long-term outcomes.

## Abstract

Deep brain stimulation (DBS) of subthalamic nucleus (STN) has been well-established and increasingly applied in patients with isolated dystonia. Nevertheless, the surgical efficacy varies among patients. This study aims to explore the factors affecting clinical outcomes of STN-DBS on isolated dystonia and establish a well-performed prediction model.

In this prospective study, thirty-two dystonia patients were recruited and received bilateral STN-DBS at our center. Their baseline characteristics and up to one-year follow-up outcomes were assessed. Implanted electrodes of each subject were reconstructed with their contact coordinates and activated volumes calculated. We explored correlations between distinct clinical characteristics and surgical efficacy. Those features were then trained for the model in outcome prediction via support vector regression (SVR) algorithm and testified through cross-validation.

Patients demonstrated an average clinical improvement of 56 ± 25 % after STN-DBS, significantly affected by distinct symptom forms and activated volumes. The optimal targets and activated volumes were concentratedly located at the dorsal posterior region to STN. Most patients had a rapid response to STN-DBS, and their motor score improvement within one week was highly associated with long-term outcomes. The trained SVR model, contributed by distinct weights of features, could reach a maximum prediction accuracy with mean errors of 11 ± 7 %.

STN-DBS demonstrated significant and rapid therapeutic effects in patients with isolated dystonia, by possibly affecting the pallidofugal fibers. Early improvement highly indicates the ultimate outcomes. SVR proves valid in outcome prediction. Patients with predominant phasic and generalized symptoms, shorter disease duration, and younger onset age may be more favorable to STN-DBS in the long run.