Automated optimization of deep brain stimulation parameters for modulating neuroimaging-based targets

Abstract

Objective. Therapeutic efficacy of deep brain stimulation (DBS) in both established and emerging indications, is highly dependent on accurate lead placement and optimized clinical programming. The latter relies on clinicians’ experience to search among available sets of stimulation parameters and can be limited by the time constraints of clinical practice. Recent innovations in device technology have expanded the number of possible electrode configurations and parameter sets available to clinicians, amplifying the challenge of time constraints. We hypothesize that patient specific neuroimaging data can effectively assist the clinical programming using automated algorithms. Approach. This paper introduces the DBS Illumina 3D algorithm as a tool which uses patient-specific imaging to find stimulation settings that optimizes activating a target area while minimizing the stimulation of areas outside the target that could result in unknown or undesired side effects. This approach utilizes preoperative neuroimaging data paired with the postoperative reconstruction of the lead trajectory to search the available stimulation space and identify optimized stimulation parameters. We describe the application of this algorithm in three patients with treatment-resistant depression who underwent bilateral implantation of DBS in subcallosal cingulate cortex and ventral capsule/ventral striatum using tractography optimized targeting with an imaging defined target previously described. Main results. Compared to the stimulation settings selected by the clinicians (informed by anatomy), stimulation settings produced by the algorithm that achieved similar or greater target coverage, produced a significantly smaller stimulation area that spilled outside the target (P = 0.002). Significance. The DBS Illumina 3D algorithm is seamlessly integrated with the clinician programmer software and effectively and rapidly assists clinicians with the analysis of image based anatomy, and provides a starting point to search the highly complex stimulation parameter space and arrive at the stimulation settings that optimize activating a target area.

Towards biomarker-based optimization of deep brain stimulation in Parkinson’s disease patients

“…This approach involves visualization of the lead and different contacts in relation to the relevant nuclei. Studies have shown that image-guided programming can be significantly less time-consuming whilst still leading to non-inferior motor improvements compared to conventional programming ( Pourfar et al, 2015 ; Lange et al, 2021 ; Malekmohammadi et al, 2022 ). More recently, image-guided approaches also visualize the DBS-induced spread of electrical stimulation to give the programmer a clearer theoretical indication of the stimulated area, such as the electric field (EF), to guide and improve stimulation effects ( Hemm et al, 2005 ; Åström et al, 2009 ; Nguyen et al, 2019 ).…”

An optimization framework for targeted spinal cord stimulation

“…In general, the utilization of optimization routines for programming SCS and other neurostimulation approaches is limited. Many optimization studies in the neurostimulation field focus on DBS [57][58][59][60] with some of them focusing on retinal prostheses [61][62][63][64][65]. These studies use analytic methods [57] or heuristic approaches [58].…”

Proceedings of the 10th annual deep brain stimulation think tank: Advances in cutting edge technologies, artificial intelligence, neuromodulation, neuroethics, interventional psychiatry, and women in neuromodulation

“…Platform supporting this vision, developed in collaboration with Brainlab Inc., include commercially available GUIDE XT and STIMVIEW XT which integrate BSN’s Stimulation Field Models (SFMs) with automatic detection of lead location and orientation, and places these models in an auto-segmented, patient specific anatomy ( Lange et al, 2021 ; Waldthaler et al, 2021 ). More recent advances include the DBS Illumina 3D algorithm which is an optimization algorithm that synthesizes imaging-based information to optimize SFM size, shape, and location to accelerate Image Guided Programming ( Malekmohammadi et al, 2022 ). BSN is working to make this algorithm commercially available in the future.…”