To start working with LEAD-DBS you either need:

  • Postoperative (and if available also preoperative) structural MRI series or
  • Postoperative CT and preoperative MRI images

LEAD-DBS works within Matlab >2014b and needs statistics & imaging toolboxes as well as SPM12 installed.

Development Team

Core Development

  • Andreas Horn, Harvard Medical School, USA
  • Ningfei Li, Technical University of Berlin, Germany
  • Siobhán Ewert, Charité, Germany
  • Andrea Kühn, Charité, Germany

Contributors (Code)

  • Ari Kappel, Mark Richardson (University of Pittsburg, USA)
  • Philip Plettig, Shelby Bachman, Rafael Serrano Sandoval (Charité, Germany)
  • Todd Herrington (Massachusetts General Hospital, Boston, USA)
  • Qianqian Fang (Northeastern University, Boston, USA)
  • Qinwan Rabbani (Ohio State University, Columbus, USA)
  • Sahbi Mallouli (Neurospin Paris, France)

Contributions of Atlases / Data from following authors

  • Ettore Accolla (University of Fribourg, Switzerland)
  • Cameron Craddock (Child Mind Institute, NYC)
  • Bogdan Draganski (University Lausanne)
  • Brian Edlow (Martinos Center / MGH, Boston, USA)
  • Lingzhong Fan (Chinese Academy of Sciences)
  • Birte Forstmann / Max Keuken (University Amsterdam)
  • Alexander Hammers (Imperial College London)
  • Marc Joliot / Natalie Tzourio-Mazoyer (University Bordeaux)
  • Arno Klein (Sage Bionetworks)
  • Antonio Meola (Brigham and Women’s Hospital, Boston, USA)
  • Janey Prodoehl (Midwestern University)
  • Anqi Qiu (University of Singapore)
  • Verena Rozanski (University of Munich)
  • Thomas Yeo (University of Singapore)
  • Dongyang Zhang (Washington University, St. Louis)


  • Pierre Deman, Astrid Kibleur (Grenoble Institut of Neurosciences, France)
  • Juan Carlos Baldermann (University Hospital Cologne, Germany)
  • Kristen Kanoff (Massachusetts General Hospital, USA)
  • Konrad Schumacher (University Hospital Freiburg, Germany)

Please feel free to join the team! Fork us on Github


DBS imaging involves a complex pipeline spanning over multiple scientific disciplines such as precise co-registration, multimodal image fusion, 3D image processing & localization, spatial statistics, surface & volumetric meshing as well as physical modeling using the finite element approach.

If incorporating connectivity measures, it expands to functional magnetic resonance imaging and diffusion-weighted imaging based tractography. Finally, electrophysiological measures such as local field potentials and electro-/magnetoencephalographic data may be incorporated to better understand DBS and enhance DBS modeling.

Thus, creating a “one-step solution” for DBS imaging is a non-trivial task. Although we aim at incorporating the best tools that are openly available for each sub-task into the Lead-DBS pipeline and constantly work hard to improve our tool, alternatives exist (with slight differences in focus) that may be better suited to specific subtasks of the whole collection. Below is a non-exhaustive list of alternatives we are aware of.


If you use the toolbox for research purposes, please cite the following publication:

Please also make sure to cite subcortical atlases and brain parcellations that you use with LEAD-DBS.

Please cite the fiber-connectome “Groupconnectome (Horn 2013)” supplied with LEAD if you use it.

Last but not least, Lead-DBS depends on a lot of shared libraries, a full list can be found here. Depending on your specific analysis, please cite the core contributors to it accordingly following general good scientific practice.