A while ago, Matthew Brett wrote an explanatory post and a review paper about the “MNI space”, its historical development and origins which largely focused on the differences between Talairach and MNI space. Also see his 2002 paper on this matter (1). Nowadays, the Talairach space doesn’t play a big role in modern neuroscience anymore, however, […]
We had a great time presenting a poster about LeadMD, a future certified version of Lead-DBS at the 20th International Congress of Parkinson’s Disease and Movement Disorders in Berlin on Monday. Thank you all for your helpful comments and suggestions regarding future work. The poster can be found here and the picture below show’s Philip Plettig, who is working on a connectivity based sub-structure segmentation algorithm, in front of the poster.
We are happy to announce that the core functionalities of Lead-DBS have been ported to support non-human primate (macaque) MRI and CT data.
Lead-DBS is a Matlab toolbox built to localize deep brain stimulation electrode placements based on postoperative imaging data. With the release of version 1.4.9 and the “macaque toolbox” (download here) you can start up Lead-DBS in macaque mode by entering
into the Matlab prompt.
Furthermore, the detailed INIA19 macaque atlas has been ported into MNI space and is available in preinstalled form within the Lead-DBS macaque toolbox.
Please do not hesitate to contact us in case of questions or suggestions.
We are happy to announce the first Lead-DBS workshop! The workshop will take place from Sunday, 27th to Monday, 28th of November 2016. In this hands-on workshop organized as a satellite event to the International DBS Symposium of the Clinical Research Group KFO 247, we will give an extensive overview over the possibilities and potential applications […]
We are happy to announce that since the initial publication of the methodological paper around Lead-DBS, there have been quite some studies that applied Lead DBS in a clinical research setting. Check out our publications page for more information.
We are happy to announce a major update of Lead-DBS (v1.4).
Here are some key features:
- Novel electrode format – now any lead design you can think of is possible. This specifically adds support for the novel Boston Scientific Vercise directed leads to the toolbox (see picture). Also, custom made electrodes can be added easily.
- Co-registration between pre-op MRIs and post-op CTs works much more robustly now using BRAINSFit.
- fMRI/dMRI-based connectomic results can now be explored using both a seed-based and graph-theoretical approach in the 3D-viewer. In fMRI, you can even loop across time using a sliding-window approach.
Even more importantly, I am very happy to mention that the development team has grown substantially: Welcome Ningfei Li, Philip Plettig & Siobhán Ewert to the team!
We will continue to improve especially the new features in the upcoming months and there are a few nice new features already in the pipeline. As always, suggestions, comments and help (fork us on github) are most welcome.
Let’s push the frontier a bit, shall we? Michael Okun recently published a NEJM-article entitled “Deep-Brain Stimulation — Entering the Era of Human Neural-Network Modulation” and Jamie Henderson wrote a perspective paper in 2012 titled “Connectomic Surgery”.
Thus, from now on, LEAD-DBS features a fully functional connectomic processing pipeline (DTI, rs-fMRI) that is simple to use and integrate to your work. In this way, you can now also use LEAD-DBS for patients who did not even undergo surgery (e.g. for connectomic analyses or visualizations).
We are happy to share the current NeuroImage cover art with you, which shows a 3D rendering made with our toolbox (volume 107, 15 February 2015).
The accompanying text for the image is the following:
Depicted is a three-dimensional reconstruction of deep brain stimulation electrode placements in a patient suffering from Parkinson’s disease. Using a novel open-source toolbox that is introduced in this issue of NeuroImage, the electrodes are modeled in their accurate position in the brain based on postoperative magnetic resonance imaging and using the accurate dimensions of the electrode lead model implanted in the patient’s brain.
Using such a reconstruction of the stimulation setting, it can be determined, whether deep brain stimulation electrodes are accurately placed within the target structure – in this case the sensorimotor functional zone of the subthalamic nucleus. Moreover, the volume of tissue activated by the stimulation can be modeled using the actual stimulation parameters applied in the patient’s treatment.
Lastly, based on the volume of activated tissue that has been modeled for a certain patient, diffusion weighted imaging based fiber-tracking may be used to analyze the influence of the stimulation on the whole structural (and functional) connectome of the patient’s brain. This combined approach allows the researcher to study local effects of the stimulation (associated with the placement of the electrode within the target structure), as well as distant effects effects (associated with the whole-brain network influenced by the stimulation).