Prediction of Electrode Contacts for Clinically Effective Deep Brain Stimulation in Essential Tremor

Åström, Maria; Samuelsson, Jennifer; Roothans, Jonas; Fytagoridis, Anders; Рыжков, М. А.; Nijlunsing, Rutger; Blomstedt, Patric

doi:10.1159/000492230

Cited by 17 publications

(20 citation statements)

References 34 publications

(29 reference statements)

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“…Several attempts to predict postoperative tremor suppression focused on connectivity analysis or probabilistic stimulation mapping [15–17]. However, only Åström et al [18] focused on predicting the DBS contact to be chosen postoperatively. Based on probabilistic stimulation maps, the resulting software tool, showed that the predicted contact with rank 1 matched the clinically used contact in 60% of cases (rank 1-2 matched 83% of the cases).…”

Section: Discussionmentioning

confidence: 99%

Selecting the most effective DBS contact in essential tremor patients based on individual tractography

Petry‐Schmelzer

Dembek

Steffen

et al. 2020

Preprint

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Postoperative choice of the most effective DBS contact in patients with essential tremor (ET) so far relies on lengthy clinical testing. It was shown that postoperative effectiveness of DBS contacts depends on the distance to the dentatorubrothalamic tract (DRTT). Here, we investigated, whether the most effective DBS contact could be determined from the stimulation overlap with the individual DRTT. Seven ET patients with bilateral thalamic deep brain stimulation were included retrospectively. Tremor control was assessed contact-wise during test stimulation with 2mA. The individual DRTTs were identified from diffusion tensor imaging. Contacts were ranked by their overlap of the test stimulation with the respective DRTT in relation to their clinical effectiveness. A linear mixed-effects model was calculated to determine the influence of the DRTT-overlap on tremor control. In 92.9 % of the cases, the contact with the best clinical effect was the contact with the highest or second highest DRTT-overlap. On the group level, the DRTT-overlap explained 26.7% of variance of the clinical outcome (p<0.001). To conclude, the overlap with the DRTT based on individual tractography serves as a marker to determine the most effective DBS contact in ET patients and could help to reduce burdensome clinical testing in the future.

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Section: Discussionmentioning

confidence: 99%

Selecting the most effective DBS contact in essential tremor patients based on individual tractography

Petry‐Schmelzer

Dembek

Steffen

et al. 2020

Preprint

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“…This can lead to a lack of details of the small deep brain structures which are of high importance for DBS procedures ( Ou et al, 2014 ). For that reason, several studies have been based on group-specific templates from patients undergoing DBS ( Åström et al, 2018 ; Johnson et al, 2019 ).…”

Section: Deep Brain Anatomical Templates and Group Analysismentioning

confidence: 99%

“…To estimate the electrical effect of stimulation in a patient, several authors include the location and symptomatic effect of the contact of the electrode used for long-term stimulation ( Fytagoridis et al, 2013 ; Lalys et al, 2013 ). In contrast, a more precise estimation can be done by using in silico simulations of single patient distribution of the EF ( Åström et al, 2010 , 2018 ; Hemm et al, 2016 ; Akbarian-Tefaghi et al, 2017 ; Naesstrom et al, 2021 ). Implementation of this method can also be expanded to visualize induced improvements and adverse effects in patient-specific stimulation maps ( Shah et al, 2020 ).…”

Section: Deep Brain Anatomical Templates and Group Analysismentioning

confidence: 99%

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Deep Brain Stimulation: Emerging Tools for Simulation, Data Analysis, and Visualization

et al. 2022

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Deep brain stimulation (DBS) is a well-established neurosurgical procedure for movement disorders that is also being explored for treatment-resistant psychiatric conditions. This review highlights important consideration for DBS simulation and data analysis. The literature on DBS has expanded considerably in recent years, and this article aims to identify important trends in the field. During DBS planning, surgery, and follow up sessions, several large data sets are created for each patient, and it becomes clear that any group analysis of such data is a big data analysis problem and has to be handled with care. The aim of this review is to provide an update and overview from a neuroengineering perspective of the current DBS techniques, technical aids, and emerging tools with the focus on patient-specific electric field (EF) simulations, group analysis, and visualization in the DBS domain. Examples are given from the state-of-the-art literature including our own research. This work reviews different analysis methods for EF simulations, tractography, deep brain anatomical templates, and group analysis. Our analysis highlights that group analysis in DBS is a complex multi-level problem and selected parameters will highly influence the result. DBS analysis can only provide clinically relevant information if the EF simulations, tractography results, and derived brain atlases are based on as much patient-specific data as possible. A trend in DBS research is creation of more advanced and intuitive visualization of the complex analysis results suitable for the clinical environment.

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“…The goal of this session is to find optimal settings that stimulate the target region, while avoiding regions that cause side-effects. A detailed understanding of the spatial relationship between target structure, region of avoidance, and the implanted electrode, can provide valuable information for the time-consuming process of fine-tuning stimulation parameters [17,18,19].…”

Section: Introductionmentioning

confidence: 99%

DBSegment: Fast and robust segmentation of deep brain structures -- Evaluation of transportability across acquisition domains

Baniasadi¹,

Petersen²,

Gonçalves³

et al. 2021

Preprint

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Segmenting deep brain structures from magnetic resonance images is important for patient diagnosis, surgical planning, and research. Most current state-of-the-art solutions follow a segmentation-byregistration approach, where subject MRIs are mapped to a template with well-defined segmentations. However, registration-based pipelines are time-consuming, thus, limiting their clinical use. This paper uses deep learning to provide a robust and efficient deep brain segmentation solution. The method consists of a pre-processing step to conform all MRI images to the same orientation, followed by a convolutional neural network using the nnU-Net framework. We use a total of 14 datasets from both research and clinical collections. Of these, seven were used for training and validation and seven were retained for independent testing. We trained the network to segment 30 deep brain structures, as well as a brain mask, using labels generated from a registration-based approach. We evaluated the generalizability of the network by performing a leave-one-dataset-out cross-validation, and extensive testing on external datasets. Furthermore, we assessed cross-domain transportability by evaluating the results separately on different domains. We achieved an average DSC of 0.89 ± 0.04 on the independent testing datasets when compared to the registration-based gold standard. On our test system, the computation time decreased from 42 minutes for a reference registration-based pipeline to 1 minute. Our proposed method is fast, robust, and generalizes with high reliability. It can be extended to the segmentation of other brain structures. The method is publicly available on GitHub, as well as a pip package for convenient usage.

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Prediction of Electrode Contacts for Clinically Effective Deep Brain Stimulation in Essential Tremor

Cited by 17 publications

References 34 publications

Selecting the most effective DBS contact in essential tremor patients based on individual tractography

Selecting the most effective DBS contact in essential tremor patients based on individual tractography

Deep Brain Stimulation: Emerging Tools for Simulation, Data Analysis, and Visualization

DBSegment: Fast and robust segmentation of deep brain structures -- Evaluation of transportability across acquisition domains

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