Deciphering the Network Effects of Deep Brain Stimulation in Parkinson's Disease

Gabriel Gonzalez-Escamilla; Nabin Koirala; Manuel Bange; Martin Glaser; Bogdan Pintea; Christian Dresel; Günther Deuschl; Muthuraman Muthuraman; Sergiu Groppa

doi:10.1007/s40120-021-00318-4

Deciphering the Network Effects of Deep Brain Stimulation in Parkinson's Disease

Neurol Ther. 2022 Mar;11(1):265-282. doi: 10.1007/s40120-021-00318-4. Epub 2022 Jan 9.

Authors

Affiliations

¹ Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131, Mainz, Germany. ggonzale@uni-mainz.de.
² Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131, Mainz, Germany.
³ Department of Neurosurgery, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131, Mainz, Germany.
⁴ Department of Neurosurgery, University Hospital Bergmannsheil, Bürkle de la Camp-Platz 1, 44789, Bochum, Germany.
⁵ Department of Neurology, Schleswig-Holstein University Hospital UKSH, Arnold-Heller-Straße 3, 24105, Kiel, Germany.
⁶ Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstrasse 1, 55131, Mainz, Germany. segroppa@uni-mainz.de.

^# Contributed equally.

Abstract

Introduction: Deep brain stimulation of the subthalamic nucleus (STN-DBS) is an established therapy for Parkinson's disease (PD). However, a more detailed characterization of the targeted network and its grey matter (GM) terminals that drive the clinical outcome is needed. In this direction, the use of MRI after DBS surgery is now possible due to recent advances in hardware, opening a window for the clarification of the association between the affected tissue, including white matter fiber pathways and modulated GM regions, and the DBS-related clinical outcome. Therefore, we present a computational framework for reconstruction of targeted networks on postoperative MRI.

Methods: We used a combination of preoperative whole-brain T1-weighted (T1w) and diffusion-weighted MRI data for morphometric integrity assessment and postoperative T1w MRI for electrode reconstruction and network reconstruction in 15 idiopathic PD patients. Within this framework, we made use of DBS lead artifact intensity profiles on postoperative MRI to determine DBS locations used as seeds for probabilistic tractography to cortical and subcortical targets within the motor circuitry. Lastly, we evaluated the relationship between brain microstructural characteristics of DBS-targeted brain network terminals and postoperative clinical outcomes.

Results: The proposed framework showed robust performance for identifying the DBS electrode positions. Connectivity profiles between the primary motor cortex (M1), supplementary motor area (SMA), and DBS locations were strongly associated with the stimulation intensity needed for the optimal clinical outcome. Local diffusion properties of the modulated pathways were related to DBS outcomes. STN-DBS motor symptom improvement was highly associated with cortical thickness in the middle frontal and superior frontal cortices, but not with subcortical volumetry.

Conclusion: These data suggest that STN-DBS outcomes largely rely on the modulatory interference from cortical areas, particularly M1 and SMA, to DBS locations.

Keywords: Cortical thickness; Deep brain stimulation; Motor circuit; Parkinson’s disease; Probabilistic tractography.