Abstract
Deep brain stimulation (DBS) is an established therapy for the management of advanced Parkinson’s disease (PD). However, the coupled adjustment of pharmacologic therapy and stimulation parameter settings is a time-consuming process and treatment outcomes are not always optimal. In this study, we develop a linear function that relates the DBS parameters, the levodopa dosage, and patient-specific preoperative clinical data with the actual treatment motor outcomes. To this end, we incorporate image-based patient-specific computer models of the volume of tissue activated by DBS in a multi-linear regression analysis (6 PD patients; 60 follow up visits). The resulting predictor function was highly correlated with the actual motor outcomes (r = 0.76; p<0.05). These results demonstrate that the outcomes of a combined pharmacologic-DBS therapy can be predicted and may facilitate patient-specific treatment optimization for maximal benefits and minimal adverse effects.
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Keywords
- Deep Brain Stimulation
- Subthalamic Nucleus
- Clinical Decision Support System
- Deep Brain Stimulation Surgery
- Multiple Comparison Correction
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Shamir, R.R., Dolber, T., Noecker, A.M., Frankemolle, A.M., Walter, B.L., McIntyre, C.C. (2014). A Method for Predicting the Outcomes of Combined Pharmacologic and Deep Brain Stimulation Therapy for Parkinson’s Disease. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8674. Springer, Cham. https://doi.org/10.1007/978-3-319-10470-6_24
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DOI: https://doi.org/10.1007/978-3-319-10470-6_24
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