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Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging
Open accessPublished version of a preprint
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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Self-citationPost-operative deep brain stimulation assessment: Automatic data integration and report generation
Husch, A., Petersen, M. S., Gemmar, P., Gonçalves, J. M., Sunde, N. A., & Hertel, F. (2018) · Brain stimulation · doi:10.1016/j.brs.2018.01.031 · Cited by 17
Self-citationPersistence of Mania After Cessation of Stimulation Following Subthalamic Deep Brain Stimulation
Mosley, P. E., Marsh, R., Perry, A., Coyne, T. J., & Silburn, P. A. (2018) · Journal of Neuropsychiatry · doi:10.1176/appi.neuropsych.17060129 · Cited by 17
Konopleva, L. V., Il'yasov, K. A., Skibbe, H., Kiselev, V. G., Kellner, E., Dhital, B., & Reisert, M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.03.058 · Cited by 15
Self-citationA structural group-connectome in standard stereotactic (MNI) space
Horn, A. (2015) · Data in Brief · doi:10.1016/j.dib.2015.08.035 · Cited by 14
Self-citationBaldermann, J. C., Kohl, S., Visser-Vandewalle, V. E. R. M., Klehr, M., Huys, D., & Kuhn, J. (2016) · Brain stimulation · doi:10.1016/j.brs.2016.09.003 · Cited by 13
Peisker, C., Schüller, T., Peters, J., Wagner, B., Schilbach, L., Müller, U., Visser-Vandewalle, V. E. R. M., & Kuhn, J. (2018) · Brain Sciences · doi:10.3390/brainsci8020021 · Cited by 12
A 3D multimodal approach to precisely locate DBS electrodes in the basal ganglia brain region
da Silva, N. M., Rozanski, V. E., & Cunha, J. P. S. (2015) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2015.7146617 · Cited by 11
Reply: Oscillatory coupling of the subthalamic nucleus in obsessive compulsive disorder
Accolla, E. A., Horn, A., Ruiz, M. H., Neumann, W., & Kühn, A. A. (2017) · Brain · doi:10.1093/brain/awx165 · Cited by 10
Self-citationPrAGMATiC: a Probabilistic and Generative Model of Areas Tiling the CortexPreprint
Huth, A. G., Griffiths, T. L., Theunissen, F. E., & Gallant, J. L. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.03622 · Cited by 10
Hohlefeld, F. U., Ewald, A., Ehlen, F., Tiedt, H. O., Horn, A., Kühn, A. A., Curio, G., Klostermann, F., & Nikulin, V. V. (2017) · Clinical Neurophysiology · doi:10.1016/j.clinph.2016.12.026 · Cited by 7
Self-citationA High-Resolution Probabilistic In Vivo Atlas of Human Subcortical Brain NucleiPreprint
Pauli, W. M., Nili, A. N., & Tyszka, J. M. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/211201 · Cited by 7
A Virtual Patient Simulator Based on Human Connectome and 7 T MRI for Deep Brain Stimulation.
Bonmassar, G., Angelone, L. M., & Makris, N. (2014) · PubMed · Cited by 6
MNI T1 6thGen NLIN to MNI 2009b NLIN ANTs transform
Horn, A. (2016) · Figshare · doi:10.6084/m9.figshare.3502238.v1 · Cited by 6
Self-citationRobert, K. S., Rafay, A. Q., Natalie, R. H., Dara, B., Tuo, P. L., Samir, J. M., & Tang, C. (2014) · Cited by 6
Horn, A. (2017) · Figshare · doi:10.6084/m9.figshare.4644472.v2 · Cited by 4
Self-citationThe next step for imaging the subthalamic nucleus
Forstmann, B. U., Keuken, M. C., & Alkemade, A. (2016) · Brain · doi:10.1093/brain/aww245 · Cited by 3
Flagged first, then by citations
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Cited by
Sorted by citations
Technology of deep brain stimulation: current status and future directions
Krauss, J. K., Lipsman, N., Aziz, T. Z., Boutet, A., Brown, P., Chang, J. W., Davidson, B., Grill, W. M., Hariz, M. I., Horn, A., et al. (2020) · Nature Reviews Neurology · doi:10.1038/s41582-020-00426-z · Cited by 930
Self-citationA unified connectomic target for deep brain stimulation in obsessive-compulsive disorder
Li, N., Baldermann, J. C., Kibleur, A., Treu, S., Akram, H., Elias, G. J. B., Boutet, A., Lozano, A. M., Al‐Fatly, B., Strange, B. A., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-16734-3 · Cited by 317
Self-citation7 Tesla MRI of the ex vivo human brain at 100 micron resolution
Edlow, B. L., Mareyam, A., Horn, A., Polimeni, J. R., Witzel, T., Tisdall, M. D., Augustinack, J. C., Stockmann, J., Diamond, B. R., Stevens, A. A., et al. (2019) · Scientific Data · doi:10.1038/s41597-019-0254-8 · Cited by 309
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Dynamic coupling of whole-brain neuronal and neurotransmitter systems
Kringelbach, M. L., Cruzat, J., Cabral, J., Knudsen, G. M., Carhart-Harris, R. L., Whybrow, P. C., Logothetis, N. K., & Deco, G. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1921475117 · Cited by 284
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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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