Machine learning-based optimization of dual subthalamic nucleus and substantia nigra targeting in deep brain stimulation.

Overview

Authors: Dallas Leavitt^1,2, Farzin Negahbani¹, Alireza Gharabaghi^1,2,3,4,5,6

Institute for Neuromodulation and Neurotechnology (INN), University Hospital and University of Tübingen,Tübingen, Germany
Max Planck-University of Toronto Center for Neural Science & Technology (MPUTC),Toronto, ON Canada
Center for Digital Health (CDH), Tübingen, Germany
Cognitive Science Center (CSC), Tübingen, Germany
Center for Bionic Intelligence Tübingen Stuttgart (BITS), Tübingen, Germany
German Center for Mental Health (DZPG),Tübingen, Germany

Institutions: University of Tübingen (Germany); University of Toronto (Canada); Deutsches Zentrum für Psychische Gesundheit (Germany)

Journal: NPJ Parkinson's disease, volume 12, issue 1, article 124

Dates: received 8 May 2025; accepted 16 May 2026; published online 25 May 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1038/s41531-026-01406-8 · PMID 42185340 · PMCID PMC13201750 · OpenAlex W7162293732

Open access: gold, a free copy (OpenAlex)

Status: code on request

Categories: Parkinson's (population)

Methods: Machine learning

Keywords: Parkinson's disease, Movement disorders, Neurodegenerative diseases

Topic: Neurological disorders and treatments (Neurology, Medicine), according to OpenAlex

Funding: European Union’s Joint Program for Neurodegenerative Disease Research (01ED2309)

Citations: not cited yet (Europe PMC); 53 references in the paper

Abstract

Advances in deep brain stimulation lead technology have created new opportunities for multi-site network modulation, including applications for freezing of gait, but systematic strategies for trajectory planning are lacking. We evaluated trajectories targeting the subthalamic nucleus (STN) and the simultaneous engament of the substantia nigra (SN), specifically its pars reticulata (SNr), which is considered as a potential target in Parkinson’s disease. By analyzing 612 electrode trajectories implanted with standard protocols, we found that 61% of trajectories engaged the SNr; simulating larger array spans or deeper implantation depth increased SNr engagement to 76%. We then trained Gaussian Process Classifiers to predict successful SNr engagement. Targeting a point ≥1.5 mm lateral to the medial STN border along Bejjani’s line, with an AC-PC angle ≥55° was associated with a ≥ 95% probability of yielding an SNr trajectory. This framework demonstrates that machine learning-assisted data analysis can generate planning principles for precise dual-site stimulation approaches.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

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Data and code supporting the key findings of this study will be made available by the first author to researchers upon request following publication, in accordance with ethical and legal regulations.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 3 keywords, 1 funder, 50 references.

Cite

This paper

Leavitt, D., Negahbani, F., & Gharabaghi, A. (2026). Machine learning-based optimization of dual subthalamic nucleus and substantia nigra targeting in deep brain stimulation. NPJ Parkinson's disease, 12(1), 124. https://doi.org/10.1038/s41531-026-01406-8

BibTeX

@article{leavitt2026machine, author = {Leavitt, Dallas and Negahbani, Farzin and Gharabaghi, Alireza}, title = {{Machine learning-based optimization of dual subthalamic nucleus and substantia nigra targeting in deep brain stimulation}}, journal = {NPJ Parkinson's disease}, year = {2026}, month = may, volume = {12}, number = {1}, pages = {124}, publisher = {Nature Publishing Group}, issn = {2373-8057}, doi = {10.1038/s41531-026-01406-8}, url = {https://doi.org/10.1038/s41531-026-01406-8}, pmid = {42185340}, pmcid = {PMC13201750} }

RIS

TY - JOUR AU - Leavitt, Dallas AU - Negahbani, Farzin AU - Gharabaghi, Alireza TI - Machine learning-based optimization of dual subthalamic nucleus and substantia nigra targeting in deep brain stimulation T2 - NPJ Parkinson's disease J2 - NPJ Parkinsons Dis PY - 2026 DA - 2026/05/25 VL - 12 IS - 1 SP - 124 SN - 2373-8057 PB - Nature Publishing Group DO - 10.1038/s41531-026-01406-8 UR - https://doi.org/10.1038/s41531-026-01406-8 LA - en ER -

CSL-JSON

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