Objective: To evaluate the imaging correlation between image-guided and neuroimaging-guided automated of bilateral STN stimulation in patients with PD.

Background: Subthalamic deep brain stimulation (STN-DBS) is a well-established surgical therapy for the treatment of advanced Parkinson’s disease (PD)¹. The efficacy of DBS depends on patient selection, electrode placement and programming of stimulation parameters². Tools such as image-guided programming (IGP) exist to improve the effectiveness and efficiency of the response to STN-DBS treatment^3,4. Using imaging data, physicians can simulate the volume of activated tissue (VTA) and adjust the stimulation settings more precisely^5,6. However, a new tool known as neuroimaging-guided automated programming (NGAP) has recently been introduced, which integrates imaging data with algorithm-based automation⁷. Currently, the correlation or overlap between the area stimulated by the clinician and the automated programming has not yet been determined.

Method: Guided reconstruction images were taken along with current programming data and the VTA of twelve (24 electrodes) PD patients who underwent bilateral STN-DBS and with an improvement response greater than 80% on the MDS-UPDRS-III scale. Subsequently, these same reconstruction images were loaded into the NGAP program, which automatically offered stimulation parameters through an algorithm. The VTA obtained from the IGP and NGAP were analyzed and the areas of overlap and the current used were evaluated

Results: Using IGP, the average VTA of the left STN was 0.09 cm³ and the average current was 2.8 mA. In the right STN the average VTA was 0.08 cm³ with an average current of 2.9 mA. When compared to NGAP we found differences in the VTAs of the right STN (NGAP VTA= 0.02cm³, p<0.001) and left STN (NGAP VTA= 0.04cm³, p<0.004), but not in the current (right STN mA= 2.31, p<0.067 neither left STN mA=2.28, p<0.374). The area of overlap between IGP and NGAP in the VTA was 47.5% in the left STN and 31.5% in the right STN.

Conclusion: Neuroimaging-guided automated programming is a tool that can assist in delineating the stimulated area by demonstrating a smaller VTA and overlapping regions comparable to those targeted by IGP. However, its clinical efficacy must be validated through clinical trials.

References: 1. Lozano AM. Deep Brain Stimulation for Parkinson ’ s Disease. Vol 7. Seventh Ed. Elsevier Inc.; 2001. doi:10.1016/B978-0-323-28782-1.00089-7
2. Nickl RC, Reich MM, Pozzi NG, et al. Rescuing suboptimal outcomes of subthalamic deep brain stimulation in Parkinson disease by surgical lead revision. Neurosurgery. 2019;85(2):E314-E321. doi:10.1093/neuros/nyz018
3. Lange F, Steigerwald F, Malzacher T, et al. Reduced Programming Time and Strong Symptom Control Even in Chronic Course Through Imaging-Based DBS Programming. Front Neurol. 2021;12. doi:10.3389/fneur.2021.785529
4. Torres V, Del Giudice K, Roldán P, et al. Image-guided programming deep brain stimulation improves clinical outcomes in patients with Parkinson’s disease. NPJ Parkinsons Dis. 2024;10(1). doi:10.1038/s41531-024-00639-9
5. Waldthaler J, Bopp M, Kühn N, et al. Imaging-based programming of subthalamic nucleus deep brain stimulation in Parkinson’s disease. Brain Stimul. 2021;14(5):1109-1117. doi:10.1016/j.brs.2021.07.064
6. Vilkhu G, Goas C, Miller JA, et al. Clinician vs. imaging-based subthalamic nucleus deep brain stimulation programming. Parkinsonism Relat Disord. 2023;106. doi:10.1016/j.parkreldis.2022.105241
7. Malekmohammadi M, Mustakos R, Sheth S, Pouratian N, McIntyre CC, Bijanki KR, et al. Automated optimization of deep brain stimulation parameters for modulating neuroimaging-based targets. J Neural Eng. 2022 Aug 1;19(4) DOI: 10.1088/1741-2552/ac7e6c

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MDS Abstracts - https://www.mdsabstracts.org/abstract/the-role-of-imaging-in-deep-brain-stimulation-programming-a-preliminary-comparative-study-of-image-guided-vs-neuroimaging-guided-automated-approaches-in-parkinsons-disease/