06/25/2014 at 9:45 AM #104
Please put posts regarding general discussion here.08/05/2014 at 9:35 AM #216PetraParticipant
is there any heuristic on how to determine the exact vertical position of the most ventral contact of the electrodes? On our CT scans the single contacts are sometimes not very clearly visible. The heuristic I am using now is: I shift the trajectory down until there is nothing visible anymore in the transversal view, and then go up a few steps again (usually about 4) until it is clearly visible but mostly still smaller in diameter than the dorsal contacts.
The diameter size seems to vary quite a lot and depend on the quality of the CT. Would you recommend to stick to one certain number of steps for each reconstruction?
I also noticed that saving the manual reconstruction does not work if the “Rotate 3D” button is activated. That was the reason why my manual corrections weren’t saved once before – it would be great if you could enable this, or otherwise point it out in the manual so that nobody else will stumble over it.
Petra08/05/2014 at 9:59 AM #217
this is a tough question since there is no real ground truth if you can’t see the contacts. You can try to adjust the contrast and brightness levels in the review window and try to see if you can determine the contacts. My experience is that one can see the single contacts better in mr images.
Another alternative is to double check in a different viewer after you have saved your reconstructions.
If you use Slicer in the version 3 (I have version 3 and 4 installed simultaneously on my computer), you can directly read in the .fcsv file Lead generates as fiducial points. You can overlay these points onto the image and adjust the contrast of the image better and more accurately in slicer than in lead. If you are lucky, you can see the single contacts there.
If you dont want to use slicer 3, you can also manually place some fiducial points in e.g. slicer 4 and enter the coordinates from the .fcsv or the ea_reconstruction.mat file manually.
I guess in general your heuristic is not quite wrong given the architecture of the electrodes and my own experience. I have analysed some patients that had both CT and MR postoperatively taken and could directly compare the results between MR and CT. Going ~4 steps up is at least a rough heuristic and the distances we talk about are probably quite small (i.e. within image resolution magnitude).
However, if you can’t really see all 4 contacts, you can also only guess how the spacing between the contacts changed due to the normalization procedure. If a patient’s anatomy is e.g. stretched a lot in z-direction to fit the template’s anatomy, the between-contact spacing of this patient within MNI-space should also be larger than the actual electrode spacing (e.g. 2 mm for most makes).
I hope this helps!08/05/2014 at 2:54 PM #218PetraParticipant
Thank you very much for the detailed answer! I will follow your advice and check some of the positions in slicer.03/08/2016 at 4:37 PM #817paoloParticipant
where can I find more informations on the specific steps of group analysis? I have already read the section on the web-manual.
Thank you03/08/2016 at 5:41 PM #821
I’m very sorry but lead_group is still widely undocumented. We just lack the man-power to do so but plan to update the manual soon and also add some more walkthrough videos. Lead group is also not really streamlined for a good user experience (also not so simple given there is not a real “one processing stream everybody should do”). Most of the few users that use it are probably in contact with me directly at the moment. There’s much more work to do on this and we certainly are working on it..
In general, there are probably the following goals that you can achieve with it:
1. Simple visualization in 2D and 3D of the whole group of electrodes
2. Tag certain electrodes with different colors to show a multi-group visualization (e.g. “responders” vs. “non-responders”).
-> I hope the above is more or less self-explaining, if not, please tell me. In 3D, you can render electrodes as solid or transparent or simply show a point-cloud of the contact centers. If you want to do the latter, you can specify only to show active or only passive contacts (or both). You can also highlight active contacts using all three visualizations. To do so, you need to…
3. Enter stimulation parameters. This largely changed with the new release, please use the newest version. The Mädler/Kuncel models implemented so far are designed for monopolar stimulations only. You can add up to 4 sources and for each specify one contact that you like to activate as anode (versus the case). There’s a novel VAT-model in the pipeline which will let you simulate more complex stimulations but that will take some months until release.
4. Determine which contacts are within a certain atlas structure. To do so, you need to calculate the “DBS stats” first. Choose an atlasset and press “calculate” in the “prepare DBS stats” panel. This will take a while. In the background, lead DBS will run 3D visualizations for each subject which also generates the stats. I think you need to have some stimulation parameters set for each patient for this to run. If you don’t know the real ones, you can just add fake ones here unless you also want to calculate connectivities (see below). If you just want to know which contact is e.g. within the STN, the stim params don’t matter.
After this is done, you can e.g. select the STN in the “Volume” list on the top and press “Generate target report”. This will generate a table with the distances from each electrode contact center to its closest atlas voxel. It also asks you for a threshold for this distance that will lead to “inside” or “outside” based on the distance. Please note that the distances never get to zero, even if the electrode is directly within the nucleus (since the nucleus voxels are mathematical points in this case). I feel that a distance between 0.5 and 1.5 is sensible, depending what you are analyzing. Of course, the distance used should be reported.
5. The “calculate DBS stats” button is very important, it basically calculates everything. The VAT for each patient, the stats of connectivities, etc. So for everything below, you probably need to run it once.
6. You can add regressors (top right). These can be basically anything. clinical variables (e.g. improvement in UPDRS scores), electrophysiological variables, whatever you can think of. You can enter regressors that have one scalar value for each patient, each hemisphere, each contact or each contact pair. With these regressors, you can do quite a few things:
6a. you can correlate the portion of VAT within an anatomical structure or the number of fibertracts between the VAT and a certain structure to the regressor (it needs to be a one per hemisphere or one per patient regressor). Again, select the anatomical structure in “Volumes” in the top (or the connectivity area in “Fibercounts” below that. Then press “Correlation between Regressors and Volume Intersections / Fibercounts”. It will give you some scatterplots. Please note that the raw output from this function will also be available in the Matlab workspace if you want to generate different figures/stats on your own with the data.
6b. if you have two groups, you can press “Group Comparison (Two-sample t-test)”. E.g. if you want to test whether your responders have significantly more portion of VAT inside the STN than your nonresponders or similar.
6c. You can tag the VATs of each patient with the regressor (again should be one per hemisphere or patient regressors). Select “Map regressor to VATs” and e.g. press Visualize 3D. It will generate a folder named “statvat_results” inside the lead_group directory.
6d. You can also map regressors to electrode contacts or contact pairs (e.g. electrophysiological variables recorded from an electrode pair). You can use any type of regressor here (if one-per-patient or -hemisphere, it will map it to the active contacts). This generates a pointcloud with values in 3D which is then being interpolated and exported as a probabilistic atlas.
More general functions:
1. Detach from single patient data means that after detaching, the lead_group file works “autonomously”. Good for sharing with collaborators or similar. If data is not detached, it is automatically updated e.g. if you change the reconstruction in a single patient folder.
2. Open Reconstruction GUI just opens lead with the selected patient. Can be handy some times.
3. LEAD Connectome Results are non-DBS functions. Interesting if you have a DTI and/or fMRI dataset of patients/subjects. I can tell you about these if interested.
Please feel free to ask if you have further specific questions. Please also note that lead_group is still quite buggy in terms of usability (there should be no errors concerning results). If you bump into errors, please just post them here, I’m usually quick in fixing stuff.
Best, Andy03/09/2016 at 4:16 PM #827paoloParticipant
Thank you very much. I’ll try to follow some of yours suggestions.08/22/2016 at 9:58 PM #1394ggilmoreParticipant
Hi LEAD-DBS team,
Will you be at SFN this year in San Diego?
Hoping to see you there!
Greydon08/22/2016 at 10:02 PM #1395
Two of us will be there, yes. Looking forward to meeting you there, too.10/13/2017 at 1:13 AM #3547
I would need some help regarding the output of the simulation of stimulation (example below):
1. I am trying to find exactly what these outputs are and I believe that the “vat_left.nii” and “vat_right.nii” are binary mask of the VTAs. Would that be a correct statement?
2. As a follow-up to my question above, it seems that I am getting errors when I try to use these binary mask and doing simple command such as adding the masks. For example, in FSL, if I use the fslmaths -add with the “vat_right.nii” I get the following error message:
Error message following fslmath (e.g., fslmaths 1802_left.nii -add 1803_left.nii sum_left.nii.gz)
WARNING:: Inconsistent orientations for individual images in pipeline!
Will use voxel-based orientation which is probably incorrect – *PLEASE CHECK*!
Would you have any idea why? Is there something I should do on the VTAs output from lead dbs before trying to add them?
Thank you!10/13/2017 at 4:40 PM #3553
To follow-up on my email, it seems that the s/q form is different amongst the VTA files. It was my understanding that when I used ANTs, the electrodes/VTAs would all be in ICBM 152 2009b Nonlinear Asymmetric. is there something I should do to the VTA files to homogenize them before trying to combine them?10/13/2017 at 6:23 PM #3556
vat_efield_right.nii is the efield (values in V/mm)
vat_efield_gauss.nii is a Gaussianized or zscored version of that image (can ignore)
vat_right.nii is a thresholded map at the seceted value in the GUI (0.2 by default).
Best, Andy10/13/2017 at 6:27 PM #3557
Re the image orientations, please use SPM tools at least for initial reslicing to be on the safe side (lead-DBS treats stuff as SPM does).
You can e.g. generate full-stack high-res 0.5 mm MNI files (which will be huge) by applying:
the ,0 means nn interpolations, so maps will remain binary. Can do 1 for trilinear.
will likewise reslice to the MNI space file you’re using.
Best, Andy10/13/2017 at 6:31 PM #3558
Thank you Andy.
Would you mind explaining what space are the VTAs output from lead dbs? I under the impression they were already in MNI space since the software draws them around the contact coordinates which is in MNI space? Sorry, just a little confused here as why we have to reslice.10/13/2017 at 6:46 PM #3559
Yes, world-space (mm) is in MNI, voxels are not. This is a general neuroimaging thing that most people find confusing in the beginning (and some never bother to learn).. The image itself is aways a matrix with numbers and the header affine matrix defines where each pixel/voxel ends up in. Now a simple example is that you could build the same MNI space with small or large bricks. In the large brick version (say with a voxel resolution of 3x3x3 mm) voxel number 10 could end up at a coordinate of 30 mm, whereas in the 2x2x2 mm version it would end up at 20 mm. Now the header matrix makes sure the correct voxels end up in the same space.
To attain a very high resolution but small file size we export the vtas in a very high-res spacing but define the data just exactly around the VTA itself.
So yes you’re correct, it’s in MNI, but the data has a completely different resolution. Good neuroimaging viewers wouldn’t care and just overlay them. AFAIK fsl forces users to have everything in the exact same resolution (at least fslview does).
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