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Lead-DBS

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Lead-DBS
Sample Lead-DBS session.
Original author(s)Andreas Horn
Developer(s)Mass General Brigham
Operating systemCross-platform
TypeNeuroimaging data analysis
LicenseGPL
Websitewww.lead-dbs.org
Electrode reconstruction generated with Lead-DBS. The picture shows two electrodes implanted into the subthalamic nucleus (orange) for treatment of Parkinson's disease. Other structures: Stimulation volumes (red), internal (green) and external (cyan) parts of the pallidum.

Lead-DBS is an open-source toolbox for reconstructions and modeling of Deep Brain Stimulation electrodes based on pre- and postoperative MRI & CT imaging.

Lead-DBS is available as a MATLAB toolbox or standalone binary for Windows, OS X and Linux. Besides MATLAB code, it contains a miniforge Python environment, as well as code modules that were compiled from Fortran and C. Parts of its code build upon other open-source tools available to the neuroimaging community, such as SPM, FSL, 3DSlicer, OSS-DBS, FreeSurfer, FieldTrip or Advanced Normalization tools. Lead-DBS was originally developed at the Charité Berlin beginning in 2012 by Andreas Horn and has been freely available for research use under the GNU General Public License since 2014. Since then, the toolbox has grown into an open-source project from an active development and user base at numerous institutions such as Mass General Brigham / Harvard Medical School, University of Cologne, University of Luxembourg and University of Melbourne. According to the toolbox website, the software has been downloaded over 65,000 times and has been used in over 500 scientific publications. Funding for continued development included an Emmy Noether award by the German Research Foundation as well as an R01 grant by the National Institute of Mental Health. Since 2014, Lead-DBS has been extended by the group analysis module Lead Group, the connectome processing tools Lead Connectome and Lead Mapper, the intraoperative module Lead-OR, as well as an interface with the biophysical modeling toolbox OSS-DBS. In 2018 and 2023, scientific articles describing versions 2 and 3 of the software have been published, respectively.

Notability and impact

According to Husch and colleagues, Lead-DBS is 'arguably the most established toolbox providing a semi-automatic framework for electrode localization' and Milchenko and colleagues described the tool as 'widely used'. Regarding the open-source nature of the software, Latorre and colleagues reported that 'A commitment of the community to open science will also democratize and increase the speed of advances with high uptake of currently available initiatives such as Lead-DBS'. The software has been used in a prospective clinical trial which showed that subthalamic stimulation settings in patients with Parkinson's disease which were generated with Lead-DBS were non-inferior to standard of care treatment. In 2022, the software was used to define optimal stimulation networks for DBS in Alzheimer's disease. In 2024, a new algorithm implemented with Lead-DBS was used to personalize DBS treatment in Parkinson's disease. Research carried out with Lead-DBS was featured at major news outlets, such as CNN and Fox News.

See also

References

  1. Horn, Andreas; Kühn, Andrea A. (February 2015). "Lead-DBS: A toolbox for deep brain stimulation electrode localizations and visualizations". NeuroImage. 107: 127–135. doi:10.1016/j.neuroimage.2014.12.002. PMID 25498389.
  2. "Publications – Lead-DBS". www.lead-dbs.org. Retrieved 2024-08-11.
  3. "DFG - GEPRIS - In Richtung netzwerkbasierter Hirnstimulation". gepris.dfg.de. Retrieved 2024-08-11.
  4. "RePORT ⟩ RePORTER". reporter.nih.gov. Retrieved 2024-08-11.
  5. ^ Treu, Svenja; Strange, Bryan; Oxenford, Simon; Neumann, Wolf-Julian; Kühn, Andrea; Li, Ningfei; Horn, Andreas (October 2020). "Deep brain stimulation: Imaging on a group level". NeuroImage. 219: 117018. doi:10.1016/j.neuroimage.2020.117018. PMID 32505698.
  6. Oxenford, Simón; Roediger, Jan; Neudorfer, Clemens; Milosevic, Luka; Güttler, Christopher; Spindler, Philipp; Vajkoczy, Peter; Neumann, Wolf-Julian; Kühn, Andrea; Horn, Andreas (2022-05-20). "Lead-OR: A multimodal platform for deep brain stimulation surgery". eLife. 11. doi:10.7554/eLife.72929. ISSN 2050-084X. PMC 9177150. PMID 35594135.
  7. Butenko, Konstantin; Bahls, Christian; Schröder, Max; Köhling, Rüdiger; van Rienen, Ursula (2020-07-06). Marinazzo, Daniele (ed.). "OSS-DBS: Open-source simulation platform for deep brain stimulation with a comprehensive automated modeling". PLOS Computational Biology. 16 (7): e1008023. Bibcode:2020PLSCB..16E8023B. doi:10.1371/journal.pcbi.1008023. ISSN 1553-7358. PMC 7384674. PMID 32628719.
  8. Horn, Andreas; Li, Ningfei; Dembek, Till A.; Kappel, Ari; Boulay, Chadwick; Ewert, Siobhan; Tietze, Anna; Husch, Andreas; Perera, Thushara; Neumann, Wolf-Julian; Reisert, Marco; Si, Hang; Oostenveld, Robert; Rorden, Christopher; Yeh, Fang-Cheng (January 2019). "Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging". NeuroImage. 184: 293–316. doi:10.1016/j.neuroimage.2018.08.068. PMC 6286150. PMID 30179717.
  9. Neudorfer, Clemens; Butenko, Konstantin; Oxenford, Simon; Rajamani, Nanditha; Achtzehn, Johannes; Goede, Lukas; Hollunder, Barbara; Ríos, Ana Sofía; Hart, Lauren; Tasserie, Jordy; Fernando, Kavisha B.; Nguyen, T. A. Khoa; Al-Fatly, Bassam; Vissani, Matteo; Fox, Michael (March 2023). "Lead-DBS v3.0: Mapping deep brain stimulation effects to local anatomy and global networks". NeuroImage. 268: 119862. doi:10.1016/j.neuroimage.2023.119862. PMC 10144063. PMID 36610682.
  10. Husch, Andreas; V. Petersen, Mikkel; Gemmar, Peter; Goncalves, Jorge; Hertel, Frank (2018). "PaCER - A fully automated method for electrode trajectory and contact reconstruction in deep brain stimulation". NeuroImage: Clinical. 17: 80–89. doi:10.1016/j.nicl.2017.10.004. PMC 5645007. PMID 29062684.
  11. Milchenko, Mikhail; Snyder, Abraham Z.; Campbell, Meghan C.; Dowling, Joshua L.; Rich, Keith M.; Brier, Lindsey M.; Perlmutter, Joel S.; Norris, Scott A. (October 2018). "ESM-CT: a precise method for localization of DBS electrodes in CT images". Journal of Neuroscience Methods. 308: 366–376. doi:10.1016/j.jneumeth.2018.09.009. PMC 6205293. PMID 30201271.
  12. Latorre, Anna; Rocchi, Lorenzo; Sadnicka, Anna (2021-05-14). "The Expanding Horizon of Neural Stimulation for Hyperkinetic Movement Disorders". Frontiers in Neurology. 12. doi:10.3389/fneur.2021.669690. ISSN 1664-2295. PMC 8160223. PMID 34054710.
  13. Roediger, Jan; Dembek, Till A; Achtzehn, Johannes; Busch, Johannes L; Krämer, Anna-Pauline; Faust, Katharina; Schneider, Gerd-Helge; Krause, Patricia; Horn, Andreas; Kühn, Andrea A (February 2023). "Automated deep brain stimulation programming based on electrode location: a randomised, crossover trial using a data-driven algorithm". The Lancet Digital Health. 5 (2): e59–e70. doi:10.1016/S2589-7500(22)00214-X. PMID 36528541.
  14. Berlin, Charité-Universitätsmedizin. "Press reports". www.charite.de. Retrieved 2024-08-11.
  15. "Researchers map deep brain stimulation target for Alzheimer's disease". EurekAlert!. Retrieved 2024-08-11.
  16. "New Deep Brain Stimulation Algorithm May Help Personalize Parkinson's Disease Treatment | Mass General Brigham". www.massgeneralbrigham.org. Retrieved 2024-08-11.
  17. Goodman, Brenda (2024-03-15). "Deep brain stimulation didn't work for a young OCD patient until new brain maps changed everything". CNN. Retrieved 2024-08-11.
  18. Rudy, Melissa (2024-03-04). "Researchers find sources of four brain disorders, which could lead to new treatments". Fox News. Retrieved 2024-08-11.

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