Towards robust probabilistic maps in Deep Brain Stimulation: exploring the impact of patient number, stimulation counts, and statistical approaches
The proposed PSS stability boundary provides a practical reference for designing DBS studies and stimulation screening protocols aimed at probabilistic mapping. The Bayesian <i>t</i>-test emerged as a reliable method across both cohorts, supporting its potential in studies with limited sample sizes and scenarios where the method needs to be readily generalized to varying symptoms. These findings underscore the importance of considering both cohort size and stimulation count in probabilistic DBS mapping and call for further investigation into method-specific sensitivities to clinical and procedural factors.
Authors
- Patric C. Blomstedt (ORCID: https://orcid.org/0000-0001-9413-6950)
- Teresa Nordin (ORCID: https://orcid.org/0000-0002-1641-9848)
- Raphaël Guzman (ORCID: https://orcid.org/0000-0001-9620-0581)
- Jean‐Jacques Lemaire (ORCID: https://orcid.org/0000-0003-4395-5328)
- Karin K Wårdell
- Simone Hemm
- Jérôme Coste
- Dorian Vogel
- Vittoria Bucciarelli
Institutions
- Linköping University (SE)
- University of Basel (CH)
- University Hospital of Basel (CH)
- FHNW University of Applied Sciences and Arts Northwestern Switzerland (CH)
- Sigma Clermont (FR)
- Umeå University (SE)
Publication Details
- Journal
- Frontiers in Computational Neuroscience
- Published
- 2026-01-21
- DOI
- https://doi.org/10.3389/fncom.2025.1699192
- Primary Topic
- Neurological disorders and treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- Parkinson Research Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Linköpings Universitet