Objective: To evaluate the clinical impact of an automated neuroimaging-guided optimization algorithm (Illumina™, Boston Scientific) for programming in patients with suboptimal response to subthalamic nucleus Deep Brain Stimulation (STN-DBS).

Background: Clinical outcomes of STN-DBS in PD depend critically on optimal postoperative programming, which is becoming more complex and time-consuming with newer DBS systems due to the increasing number of programmable stimulation parameters. Neuroimaging-based tools enabling 3D reconstruction of electrode position and estimation of volume of tissue activated may support more targeted stimulation [1,2]. Automated optimization algorithms have been developed to maximize target stimulation while minimizing activation of regions associated with adverse effects [3].

Method: We conducted a prospective study of STN-DBS treated PD patients with suboptimal outcomes for gait, speech or tremor, persisting after 3 programming sessions following initial monopolar review. Preop MRI and postop CT were co-registered to reconstruct lead position/orientation and map the STN and surrounding regions. The Illumina™ algorithm was applied to maximize target stimulation while avoiding selected regions like the internal capsule. Clinical evaluations were performed at baseline and after two months using standard motor, non-motor and quality of life (QoL) scales.

Results: Twelve PD patients (7 men) with disease duration of 13.2 ± 2.2 years and STN-DBS duration of 32.8 ± 28.8 months were included. Neuroimaging-guided programming was applied bilaterally in 6 patients and unilaterally in 6, with minor adjustments of stimulation intensity or directionality in respect to parameters suggested by the algorithm. Ten patients reported improvement on the Patient Global Impression Scale (5 feeling much better and 5 moderately better). Parkinson’s Disease Questionnaire (PDQ-39) average score improved by 17.1% (p=0.034), MDS-UPDRS III by 26.2% (p=0.006), and the non-motor fluctuations score of the NOMOFA scale by 47% (p=0.011). Two patients showed no clinical or QoL benefit.

Conclusion: This first real-world experience suggests that neuroimaging-guided automated optimization may be useful to improve motor, non-motor and QoL outcomes in troubleshooting STN-DBS patients with suboptimal response.

References: [1] 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 Nov 8;12:785529. doi: 10.3389/fneur.2021.785529.
[2] 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 Jan 27;10(1):29. doi: 10.1038/s41531-024-00639-9.
[3] Malekmohammadi M, Mustakos R, Sheth S, et al. Automated optimization of deep brain stimulation parameters for modulating neuroimaging-based targets. J Neural Eng. 2022 Jul 20;19(4):10.1088/1741-2552/ac7e6c. doi: 10.1088/1741-2552/ac7e6c.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/neuroimaging-guided-automated-optimization-of-deep-brain-stimulation-in-parkinsons-disease-preliminary-results/