Can intra‐operative stimulation data predict post‐operative Deep Brain Stimulation outcomes? - A machine learning approach based on probabilistic maps

Introduction – Deep Brain Stimulation is an established treatment for movement disorders. Data-driven approaches like probabilistic mapping and predictive modeling are being increasingly used to optimize stimulation parameter selection. However, it remains unclear whether intra-operative test data alone can reliably inform such models. Clarifying the predictive value of intra-operative data is essential, as it may influence data collection strategies and surgical and programming protocols. Objective – This study examined whether post-operative DBS effects can be accurately predicted using intra-operative data alone or if post-operative information is required. Methods – A dataset comprising 1117 intra-operative and 1553 post-operative stimulation tests from 35 patients (14 Essential Tremor, 21 Parkinson's Disease) was analyzed. Volumes of tissue activated (VTAs) were simulated to generate probabilistic maps, from which mapping and target anatomy-related features were extracted to train predictive classification models (low and high improvement, side effects). Model performance was assessed across scenarios linking intra- and post-operative data. Results – Intra-operative data effectively identified regions associated with optimal stimulation, highlighting their utility in guiding parameter selection (predictive accuracy ~60%). However, the predictive relationship between VTAs, probabilistic maps, and clinical outcomes differed between intra- and post-operative contexts. When trained solely on intra-operative VTAs, the model performed at a near chance level (predictive accuracy ~35%). Discussion – The study demonstrates that while intra-operative data are useful for identifying optimal target regions, they are insufficient for accurately predicting post-operative DBS outcomes. Incorporating post-operative data remains crucial for reliable and clinically meaningful DBS effect prediction.

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Subject (DDC)

610 - Medizin und Gesundheit

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ISSN

2949-6691

Language

English

Created during FHNW affiliation

Yes

Strategic action fields FHNW

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Published

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peer-reviewed

Open access category

Gold

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Citation

Bucciarelli, V., Nordin, T., Stawiski, M., Wårdell, K., Coste, J., Lemaire, J.-J., Derost, P., Debilly, B., Guzman, R., Hemm-Ode, S., & Vogel, D. (2026). Can intra‐operative stimulation data predict post‐operative Deep Brain Stimulation outcomes? - A machine learning approach based on probabilistic maps. Deep Brain Stimulation, 15, 22–34. https://doi.org/10.1016/j.jdbs.2026.09.003

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