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 GUIhttp://bit.ly/1il2ItwIcons in the GUI are generated using the fontawesome typography
Visualization / Figure ExportMyaa – My Anti-Alias for Matlabhttp://bit.ly/1UqerKYAnders Brun
Create Video of Rotating 3D Plothttp://bit.ly/24LSOWWAlan Jennings
General file handlingTools for NIfTI and ANALYZE imagehttp://bit.ly/1ULDxTbJimmy Shen
stlwrite()http://bit.ly/1UUapt0Sven (ML Fileexchange user)
patch2plyhttp://bit.ly/29vPLBfJ. Xiao, 2013. Princeton Vision and Robotics Toolkit. Available from: http://bit.ly/29vPLBf
DICOM importdcm2niihttp://bit.ly/1U44FKFChris Rorden
Normalization into MNI space based on preoperative MR imagesSPM12 / DARTEL / New Segmenthttp://bit.ly/1YlReLeA manifold of them. E.g. see Ashburner, J. (2007). A fast diffeomorphic image registration algorithm, 38(1), 95–113. http://bit.ly/1WLv85y
Ashburner, J. (2012). SPM: a history.
Advanced Normalization Tools (ANTs) / SyN Registrationhttp://bit.ly/21i5B2vAvants, 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. http://bit.ly/1YnjEov
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. http://bit.ly/1YlRY2T
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. http://bit.ly/25Tbbio
BRAINSFit registrationhttp://bit.ly/1UNSukA
Rangesearching / DTI / VAT-ConnectivityFast Range Search through JIT (ver 2)http://bit.ly/1tmbSPEYi Cao
Inhullhttp://bit.ly/1VUWIwiJohn D’Errico
Intriangulation – which points are inside a 3d watertight triangulation?http://bit.ly/1ULDIxCJohannes Korsawe
dMRI (“DTI”) processing & fiber trackingUnring – tool for removal of the Gibbs ringing artefacthttp://bit.ly/1ULDGFXhttp://bit.ly/1U43HOO
Gibbstracker / DTI & Fibertools for SPMhttp://bit.ly/1U448IKReisert, M., Mader, I., Anastasopoulos, C., Weigel, M., Schnell, S., & Kiselev, V. (2011). Global fiber reconstruction becomes practical., 54(2), 955–962. http://bit.ly/1U44teA
Mesotracker / MesoFThttp://bit.ly/1YnjcqmReisert, 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. http://bit.ly/1XS5UDt
DSI Studio / Generalized Q-Ball Imaging (GQI)http://bit.ly/25TaPs5Yeh, F.-C., Wedeen, V. J., & Tseng, W.-Y. I. (2010). Generalized q-Sampling Imaging. IEEE Transactions on Medical Imaging, 29(9), 1626–1635. http://bit.ly/1rlFJ9r
Classical Tensor-Based DTI-Tractography http://bit.ly/1PoN5zdDirk-Jan Kroon
trk_read.m and trk_write.m (Part of along tract stats)http://bit.ly/1tmbIrsJohn Colby
fMRI processingResting-State fMRI Data Analysis Toolkit
(used for band-pass filtering)
http://bit.ly/290ThiSSong, 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. http://bit.ly/290T2on
Coordinate transpositionmap_coords.m, resize_img.mGed Ridgway
Normalizing Values / Generalized Linear Modelingnormal.mhttp://bit.ly/1PXp87Avan 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. http://bit.ly/1ZL2u2o
Estimating the Volume of Activated Tissue (VAT)SimBio (FEM-based field modeling) and FieldTrip (Mesh generation) toolboxeshttp://bit.ly/1rlJaNi
http://bit.ly/1UNVLjT
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. http://bit.ly/1rlJeN2
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)http://bit.ly/1UUbrVF
http://bit.ly/1tvbPRD
Zalesky, A., Fornito, A., & Bullmore, E. T. (2010). Network-based statistic: identifying differences in brain networks., 53(4), 1197–1207. http://bit.ly/1UoLPAq