Year: 2026
Type: article
Abstract: Introduction Positioning of deep brain stimulation (DBS) leads is paramount for optimizing therapeutic efficacy in Parkinson’s disease (PD) and dystonia. Quantitative determination of lead position remains essential; however, current atlas-based targeting and stereotactic coordinate methods, while informative, limit patient specificity afforded by high-resolution reconstruction and introduce subjective variability. Methods We developed a novel pipeline in Unity™ to ingest 7T MRI-based reconstructions of DBS leads within the globus pallidus internus (GPi). Using anatomical landmarks and structure-specific algorithms, the GPi was parcellated into 12 anatomically-based subregions in a semi-automated, reproducible manner. Active contact positions were localized relative to a novel coordinate system developed from a GPi-based bounding box. A novel distance-to-border metric remapped contacts onto a common atlas (PD25) for population comparison against the same contacts mapped onto a common left GPi space using mid-commissural point (MCP) coordinates (Schaltenbrand-Wahren atlas). Results Fifteen leads from 10 PD subjects were used for ellipsoid fitting of active contact locations, resulting in an elliptical volume of 38.94 mm 3 when using MCP coordinates, compared to a volume of 5.08 mm 3 with our GPi-specific coordinates. The mean distance-to-ellipse centroid was 3.45 ± 1.57 mm for MCP coordinates and 2.03 ± 0.82 for our GPi-specific coordinates. Our distance-to-border remapping metric yielded mean adjustments of 0.81 mm ( y -axis) and 1.61 mm ( z -axis). A subset of six GPi active contacts were plotted with post-DBS motor improvement scores, demonstrating the ability to link lead location with clinical outcomes. Conclusion Our novel software provides a quantifiable lead location with respect to the anatomical target, enhancing patient-specific lead localization by avoiding some of the pitfalls of either structure-to-atlas normalization or traditional stereotactic coordinates.
Source: Frontiers in Neuroanatomy
Institution: University of Minnesota
Language: English
Subfield: Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
Funder: National Institutes of Health
OpenAlex ID: W7131122280
