Probabilistic Subthalamic Nucleus Stimulation Sweet Spot Integration Into a Commercial Deep Brain Stimulation Programming Software Can Predict Effective Stimulation Parameters

Jaradat, Amer A.; Nowacki, Andreas; Montalbetti, Matteo; Debove, Ines; Petermann, Katrin; Schlaeppi, Janine-Ai; Lachenmayer, L.; Tinkhauser, Gerd; Krack, Paul; Nguyen, T. A. Khoa; Pollo, Claudio

doi:10.1016/j.neurom.2021.10.026

Cited by 6 publications

(7 citation statements)

References 32 publications

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“…While the sweet spot literature already suggested that the entire motor STN does not suffice as a target definition, the missing link between the sweet spot and motor outcome was unexpected [ 14, 15, 28 ]. We agree with Jaradat et al, who hypothesized that non-linear transformation from MNI to subject space might introduce detrimental inaccuracies, diminishing the predictive value of targets defined in a common space on a single subject level [ 46 ]. Correspondingly, we found that even though the dorsal border of the STN limited our VISUAL-A approach based on individual T2-contrast in subject space, the core region of the n-map in MNI space widely surpassed the dorsal border of the atlas STN with about 30% located in the extranuclear compartment.…”

Section: Discussionsupporting

confidence: 91%

“…Correspondingly, we found that even though the dorsal border of the STN limited our VISUAL-A approach based on individual T2-contrast in subject space, the core region of the n-map in MNI space widely surpassed the dorsal border of the atlas STN with about 30% located in the extranuclear compartment. Previous studies comparing the results of monopolar reviews and visual contact selection already reported higher levels of agreement for a visual approach compared to automatic sweet spot targeting, which might implicate a corrective impact of human interpretation during visual assessment [ 46, 47 ]. We conclude that further development beyond the applied methodology is necessary to translate insights from probabilistic sweet spots to the individual patient.…”

Section: Discussionmentioning

confidence: 99%

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A Retrospective Comparison of Multiple Approaches to Anatomically Informed Contact Selection in Subthalamic Deep Brain Stimulation for Parkinson’s Disease

Brandt,

Stopic,

van der Linden

et al. 2024

JPD

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Background: Conventional deep brain stimulation (DBS) programming via trial-and-error warrants improvement to ensure swift achievement of optimal outcomes. The definition of a sweet spot for subthalamic DBS in Parkinson’s disease (PD-STN-DBS) may offer such advancement. Objective: This investigation examines the association of long-term motor outcomes with contact selection during monopolar review and different strategies for anatomically informed contact selection in a retrospective real-life cohort of PD-STN-DBS. Methods: We compared contact selection based on a monopolar review (MPR) to multiple anatomically informed contact selection strategies in a cohort of 28 PD patients with STN-DBS. We employed a commercial software package for contact selection based on visual assessment of individual anatomy following two predefined strategies and two algorithmic approaches with automatic targeting of either the sensorimotor STN or our previously published sweet spot. Similarity indices between chronic stimulation and contact selection strategies were correlated to motor outcomes at 12 months follow-up. Results: Lateralized motor outcomes of chronic DBS were correlated to the similarity between chronic stimulation and visual contact selection targeting the dorsal part of the posterior STN (rho = 0.36, p = 0.007). Similar relationships could not be established for MPR or any of the other investigated strategies. Conclusions: Our data demonstrates that a visual contact selection following a predefined strategy can be linked to beneficial long-term motor outcomes in PD-STN-DBS. Since similar correlations could not be observed for the other approaches to anatomically informed contact selection, we conclude that clear definitions and prospective validation of any approach to imaging-based DBS-programming is warranted.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

A Retrospective Comparison of Multiple Approaches to Anatomically Informed Contact Selection in Subthalamic Deep Brain Stimulation for Parkinson’s Disease

Brandt,

Stopic,

van der Linden

et al. 2024

JPD

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“…Our suggestion performances for best level and best contact were lower than another sweet spot-guided study from our group ( Jaradat et al, 2022 ). In that study, the suggested best level matched the best clinical level in 72% of the leads compared to 56% in the present study.…”

Section: Discussioncontrasting

confidence: 86%

Programming of subthalamic nucleus deep brain stimulation for Parkinson’s disease with sweet spot-guided parameter suggestions

Nordenström

Petermann

Debove

et al. 2022

Front. Hum. Neurosci.

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Deep Brain Stimulation (DBS) is an effective treatment for advanced Parkinson’s disease. However, identifying stimulation parameters, such as contact and current amplitudes, is time-consuming based on trial and error. Directional leads add more stimulation options and render this process more challenging with a higher workload for neurologists and more discomfort for patients. In this study, a sweet spot-guided algorithm was developed that automatically suggested stimulation parameters. These suggestions were retrospectively compared to clinical monopolar reviews. A cohort of 24 Parkinson’s disease patients underwent bilateral DBS implantation in the subthalamic nucleus at our center. First, the DBS’ leads were reconstructed with the open-source toolbox Lead-DBS. Second, a sweet spot for rigidity reduction was set as the desired stimulation target for programming. This sweet spot and estimations of the volume of tissue activated were used to suggest (i) the best lead level, (ii) the best contact, and (iii) the effect thresholds for full therapeutic effect for each contact. To assess these sweet spot-guided suggestions, the clinical monopolar reviews were considered as ground truth. In addition, the sweet spot-guided suggestions for best lead level and best contact were compared against reconstruction-guided suggestions, which considered the lead location with respect to the subthalamic nucleus. Finally, a graphical user interface was developed as an add-on to Lead-DBS and is publicly available. With the interface, suggestions for all contacts of a lead can be generated in a few seconds. The accuracy for suggesting the best out of four lead levels was 56%. These sweet spot-guided suggestions were not significantly better than reconstruction-guided suggestions (p = 0.3). The accuracy for suggesting the best out of eight contacts was 41%. These sweet spot-guided suggestions were significantly better than reconstruction-guided suggestions (p < 0.001). The sweet spot-guided suggestions of each contact’s effect threshold had a mean error of 1.2 mA. On an individual lead level, the suggestions can vary more with mean errors ranging from 0.3 to 4.8 mA. Further analysis is warranted to improve the sweet spot-guided suggestions and to account for more symptoms and stimulation-induced side effects.

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“…Moreover, it needs to be considered that the implementation of brain‐sensing is not top priority for all different neurostimulator manufacturers and it may be limited by signal artifacts or other technical causes in individual subjects 77,78 . For these scenarios, a spectral topographic map may still be informative as a reference atlas tool to inform multicontact DBS programming, alone or in conjunction with imaging, modeling, or wearable tools 4,6,32,79,80 …”

Section: Discussionmentioning

confidence: 99%

“…77,78 For these scenarios, a spectral topographic map may still be informative as a reference atlas tool to inform multicontact DBS programming, alone or in conjunction with imaging, modeling, or wearable tools. 4,6,32,79,80…”

Section: Toward Automatization Of Dbs Programmingmentioning

confidence: 99%

Spectral Topography of the Subthalamic Nucleus to Inform Next‐Generation Deep Brain Stimulation

et al. 2023

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Background The landscape of neurophysiological symptoms and behavioral biomarkers in basal ganglia signals for movement disorders is expanding. The clinical translation of sensing‐based deep brain stimulation (DBS) also requires a thorough understanding of the anatomical organization of spectral biomarkers within the subthalamic nucleus (STN). Objectives The aims were to systematically investigate the spectral topography, including a wide range of sub‐bands in STN local field potentials (LFP) of Parkinson's disease (PD) patients, and to evaluate its predictive performance for clinical response to DBS. Methods STN‐LFPs were recorded from 70 PD patients (130 hemispheres) awake and at rest using multicontact DBS electrodes. A comprehensive spatial characterization, including hot spot localization and focality estimation, was performed for multiple sub‐bands (delta, theta, alpha, low‐beta, high‐beta, low‐gamma, high‐gamma, and fast‐gamma (FG) as well as low‐ and fast high‐frequency oscillations [HFO]) and compared to the clinical hot spot for rigidity response to DBS. A spectral biomarker map was established and used to predict the clinical response to DBS. Results The STN shows a heterogeneous topographic distribution of different spectral biomarkers, with the strongest segregation in the inferior‐superior axis. Relative to the superiorly localized beta hot spot, HFOs (FG, slow HFO) were localized up to 2 mm more inferiorly. Beta oscillations are spatially more spread compared to other sub‐bands. Both the spatial proximity of contacts to the beta hot spot and the distance to higher‐frequency hot spots were predictive for the best rigidity response to DBS. Conclusions The spatial segregation and properties of spectral biomarkers within the DBS target structure can additionally be informative for the implementation of next‐generation sensing‐based DBS. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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Probabilistic Subthalamic Nucleus Stimulation Sweet Spot Integration Into a Commercial Deep Brain Stimulation Programming Software Can Predict Effective Stimulation Parameters

Cited by 6 publications

References 32 publications

A Retrospective Comparison of Multiple Approaches to Anatomically Informed Contact Selection in Subthalamic Deep Brain Stimulation for Parkinson’s Disease

A Retrospective Comparison of Multiple Approaches to Anatomically Informed Contact Selection in Subthalamic Deep Brain Stimulation for Parkinson’s Disease

Programming of subthalamic nucleus deep brain stimulation for Parkinson’s disease with sweet spot-guided parameter suggestions

Spectral Topography of the Subthalamic Nucleus to Inform Next‐Generation Deep Brain Stimulation

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