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Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging
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Abstract
Deep brain stimulation (DBS) is a highly efficacious treatment option for movement disorders and a growing number of other indications are investigated in clinical trials. To ensure optimal treatment outcome, exact electrode placement is required. Moreover, to analyze the relationship between electrode location and clinical results, a precise reconstruction of electrode placement is required, posing specific challenges to the field of neuroimaging. Since 2014 the open source toolbox Lead-DBS is available, which aims at facilitating this process. The tool has since become a popular platform for DBS imaging. With support of a broad community of researchers worldwide, methods have been continuously updated and complemented by new tools for tasks such as multispectral nonlinear registration, structural/functional connectivity analyses, brain shift correction, reconstruction of microelectrode recordings and orientation detection of segmented DBS leads. The rapid development and emergence of these methods in DBS data analysis require us to revisit and revise the pipelines introduced in the original methods publication. Here we demonstrate the updated DBS and connectome pipelines of Lead-DBS using a single patient example with state-of-the-art high-field imaging as well as a retrospective cohort of patients scanned in a typical clinical setting at 1.5T. Imaging data of the 3T example patient is co-registered using five algorithms and nonlinearly warped into template space using ten approaches for comparative purposes. After reconstruction of DBS electrodes (which is possible using three methods and a specific refinement tool), the volume of tissue activated is calculated for two DBS settings using four distinct models and various parameters. Finally, four whole-brain tractography algorithms are applied to the patient's preoperative diffusion MRI data and structural as well as functional connectivity between the stimulation volume and other brain areas are estimated using a total of eight approaches and datasets. In addition, we demonstrate impact of selected preprocessing strategies on the retrospective sample of 51 PD patients. We compare the amount of variance in clinical improvement that can be explained by the computer model depending on the preprocessing method of choice. This work represents a multi-institutional collaborative effort to develop a comprehensive, open source pipeline for DBS imaging and connectomics, which has already empowered several studies, and may facilitate a variety of future studies in the field.
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“Historically, most targets discussed here have been identified pre-surgically in standard stereotactic anatomical space based on atlases [11,87].”
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Metadata
- Authors
- Horn, A., Li, N., Dembek, T. A., Kappel, A., Boulay, C., Ewert, S., Tietze, A., Husch, A., Perera, T., Neumann, W.-J., Reisert, M., Si, H., Oostenveld, R., Rorden, C., Yeh, F.-C., Fang, Q., Herrington, T. M., Vorwerk, J., & Kühn, A. A.
- Affiliations
- Stiftung Charité (DE)
- Charité – Universitätsmedizin Berlin (DE)
- University Hospital Cologne (DE)
- Wayne State University (US)
- Ottawa Hospital Research Institute (CA)
- University of Luxembourg (LU)
- Bionics Institute (AU)
- University of Freiburg (DE)
- Weierstrass Institute for Applied Analysis and Stochastics (DE)
- Radboud University Nijmegen (NL)
- University of Pittsburgh (US)
- Northeastern University (US)
- Harvard University (US)
- University of Utah (US)
- Corresponding author
- Horn, Andreas
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 184
- Pages
- 293-316
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC6286150
- OpenAlex
- W2950023299
- Cited by
- 739
- References
- 204
- Citations by year
- 2026: 57 · 2025: 108 · 2024: 101 · 2023: 101 · 2022: 113 · 2021: 115 · 2020: 99 · 2019: 43
- FWCI
- 43.89
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- NeuroImage (acceptedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Kölner Universitäts PublikationsServer (Universität zu Köln) (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- FreiDok plus (Universitätsbibliothek Freiburg) (submittedVersion)
- Open Repository and Bibliography (University of Luxembourg) (submittedVersion, other-oa)
- Radboud Repository (Radboud University) (submittedVersion)
- Funders
- American Brain Foundation
- National Institute of General Medical Sciences (R01-CA204443)
- American Academy of Neurology
- National Institute of Neurological Disorders and Stroke (K23NS099380)
- National Science Foundation (US IGNITE - 10037840)
- Deutsche Forschungsgemeinschaft (KFO 247, SPP 2041SPP 2041)
- National Institute of Dental and Craniofacial Research
- National Institutes of Health (R01-GM114365)
- State Government of Victoria
- Michael J. Fox Foundation for Parkinson's Research
- Colonial Foundation
- Medtronic
- National Cancer Institute
- National Institute of Mental Health
- Topics
- Health Sciences › Medicine › Clinical Neurology › Deep Brain Stimulation and Movement Disorders
- Social Sciences › Psychology › Clinical Psychology › Obsessive-Compulsive Spectrum Disorders
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- deep brain stimulation, brain shift correction, tractography, structural connectivity, functional connectivity, diffusion MRI, Parkinson's disease, tissue activation volume, connectomics
- MeSH terms
- Aged, Electrodes, Implanted, Female, Humans, Male, Middle Aged, Parkinson Disease, Software, Deep Brain Stimulation, Neuroimaging
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