The list below is giving credit to various open source software libraries or packages that are used within Lead-DBS. If you use Lead-DBS for your research, please cite the according libraries that you use properly.

About dependencies

Lead-DBS can be seen as a collection of useful tools that have been gathered together for the purpose of DBS electrode reconstructions and related processing. The core job of our development team is to find and plug in the best neuroimaging tools available within the open-source community and write bits of pieces of own code where no good code can be found. 3D-visualizations and electrode reconstruction algorithms (TRAC/CORE) have been built by ourselves. As for other parts of the toolbox, we aim at incorporating the best tools available. For instance, normalizations into MNI space can be done from within Lead-DBS by either DARTEL or ANTs, both of which have shown superior to all 12 competing algorithms in Klein 2009. As for Fibertracking, Lead-DBS among other routines uses the Gibbs’ tracking algorithm, which showed superior to all 9 competing algorithms in Fillard 2011. Thus, Lead-DBS heavily depends on shared libraries and other tools that should be referenced properly if used for research projects.

Part of processing pipeline of Lead-DBS in which the software is usedSoftware nameReference LinkPublication / Author
AppearanceIcons in the GUI in the GUI are generated using the fontawesome typography
Visualization / Figure ExportMyaa – My Anti-Alias for Matlab Brun
Create Video of Rotating 3D Plot Jennings
General file handlingTools for NIfTI and ANALYZE image Shen
stlwrite() (ML Fileexchange user)
patch2ply Xiao, 2013. Princeton Vision and Robotics Toolkit. Available from:
DICOM importdcm2nii Rorden
Normalization into MNI space based on preoperative MR imagesSPM12 / DARTEL / New Segment manifold of them. E.g. see Ashburner, J. (2007). A fast diffeomorphic image registration algorithm, 38(1), 95–113.
Ashburner, J. (2012). SPM: a history.
Advanced Normalization Tools (ANTs) / SyN Registration, B. B., Epstein, C. L., Grossman, M., & Gee, J. C. (2008). Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 12(1), 26–41.
Avants, B. B., Tustison, N. J., Song, G., Cook, P. A., Klein, A., & Gee, J. C. (2011). A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage, 54(3), 2033–2044.
Zhang, H., Avants, B. B., Yushkevich, P. A., Woo, J. H., Wang, S., McCluskey, L. F., et al. (2007). High-dimensional spatial normalization of diffusion tensor images improves the detection of white matter differences: an example study using amyotrophic lateral sclerosis. IEEE Transactions on Medical Imaging, 26(11), 1585–1597.
BRAINSFit registration
Rangesearching / DTI / VAT-ConnectivityFast Range Search through JIT (ver 2) Cao
Inhull D’Errico
Intriangulation – which points are inside a 3d watertight triangulation? Korsawe
dMRI (“DTI”) processing & fiber trackingUnring – tool for removal of the Gibbs ringing artefact
Gibbstracker / DTI & Fibertools for SPM, M., Mader, I., Anastasopoulos, C., Weigel, M., Schnell, S., & Kiselev, V. (2011). Global fiber reconstruction becomes practical., 54(2), 955–962.
Mesotracker / MesoFT, M., Kiselev, V. G., Dihtal, B., Kellner, E., & Novikov, D. S. (2014). MesoFT: Unifying Diffusion Modelling and Fiber Tracking. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 (Vol. 8675, pp. 201–208). Cham: Springer International Publishing.
DSI Studio / Generalized Q-Ball Imaging (GQI), F.-C., Wedeen, V. J., & Tseng, W.-Y. I. (2010). Generalized q-Sampling Imaging. IEEE Transactions on Medical Imaging, 29(9), 1626–1635.
Classical Tensor-Based DTI-Tractography Kroon
trk_read.m and trk_write.m (Part of along tract stats) Colby
fMRI processingResting-State fMRI Data Analysis Toolkit
(used for band-pass filtering), X.-W., Dong, Z.-Y., Long, X.-Y., Li, S.-F., Zuo, X.-N., Zhu, C.-Z., et al. (2011). REST: A Toolkit for Resting-State Functional Magnetic Resonance Imaging Data Processing. PLoS ONE, 6(9), e25031.
Coordinate transpositionmap_coords.m, resize_img.mGed Ridgway
Normalizing Values / Generalized Linear Modelingnormal.m Albada, S. J., & Robinson, P. A. (2007). Transformation of arbitrary distributions to the normal distribution with application to EEG test-retest reliability. Journal of Neuroscience Methods, 161(2), 205–211.
Estimating the Volume of Activated Tissue (VAT)SimBio (FEM-based field modeling) and FieldTrip (Mesh generation) toolboxes
Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J.-M. (2010). FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data. Computational Intelligence and Neuroscience, 2011(1), 1–9.
Wolters, C. H., Anwander, A., & Tricoche, X. (2005). Influence of local and remote white matter conductivity anisotropy for a thalamic source on EEG/MEG field and return current computation.
Network statsNetwork based statistics (NBS)
Zalesky, A., Fornito, A., & Bullmore, E. T. (2010). Network-based statistic: identifying differences in brain networks., 53(4), 1197–1207.