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Lead-DBS is a scientific toolbox facilitating deep brain stimulation electrode locations and connectomic imaging.

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LEAD-DBS

LEAD-DBS is NOT intended for clinical use!

About Lead-DBS

LEAD-DBS is a MATLAB toolbox facilitating the:

  • reconstruction of deep-brain-stimulation (DBS) electrodes in the human brain on basis of postoperative MRI and/or CT imaging
  • the visualization of localization results in 2D/3D
  • a group-analysis of DBS-electrode placement results and their effects on clinical results
  • simulation of DBS stimulations (calculation of volume of activated tissue – VAT)
  • diffusion tensor imaging (DTI) based connectivity estimates and fiber-tracking from the VAT to other brain regions (connectomic surgery)

Installation

Prerequisites

  • Recommended RAM size: 16GB or more
  • MATLAB version: R2017b (MATLAB 9.3) or later
  • MATLAB Image Processing Toolbox
  • MATLAB Signal Processing Toolbox
  • SPM12

Normal installation

Lead-DBS can be downloaded from our website (www.lead-dbs.org) in fully functional form.

Development installation

Alternatively, especially in case you wish to modify and contribute to Lead-DBS, you can

  • Clone the Lead-DBS repository from github.
  • Download the necessary data and unzip it into the cloned git repository.

We’d love to implement your improvements into Lead-DBS – please contact us for direct push access to Github or feel free to add pull-requests to the Lead-DBS repository.

Getting started

You can run Lead-DBS by typing "lead demo" into the Matlab prompt. This will open up the main GUI and a 3D viewer with an example patient. But there's much more to explore. Head over to https://www.lead-dbs.org/ to see a walkthrough tutorial, a manual, some more screenshots and other ressources. There's also a helpline in form of a Slack channel. We would love to hear from you.

Questions

If you have questions/problems when using Lead-DBS, you can checkout our:

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Lead-DBS is a scientific toolbox facilitating deep brain stimulation electrode locations and connectomic imaging.

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  • MATLAB 50.9%
  • Python 45.3%
  • C++ 1.8%
  • Mathematica 0.7%
  • C 0.6%
  • M 0.3%
  • Other 0.4%