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Hi there,
you can base the MER on your postoperative lead in lead-dbs. Just open the MER tool to point fiducials.
Import from stealth station – never done that. No clue how it would work, probably, you’d need to write an importer yourself.Best, Andy
Hi there,
then easiest is to:
– process these subjects in lead connectome. No new normalization needed but just run “fibertracking” and normalize fibers
– then load the patients up in lead mapper -> select their stimulations as seed and patient specific connectivity in the dMRI source. Then run seed and you’ll get seedmaps from your VTA to other brain regions.
– After that, can use the scripts ea_Amap, ea_Rmap or ea_Cmap (see Horn 2017 AoN for details) to create maps that denote your behavioral changes as a function of connectivity across patients.Best, Andy
Mh, weird, I cannot reproduce it here. Maybe a clean install could be helpful.
In any case, made the ready atlas for you: https://www.dropbox.com/s/lc7fx7kijyvs7wo/mar_atlas.zip?dl=0Best, Andy
Hi mar,
no clue exactly what happened but it shouldn’t run into array size issues. What you can try:
– Don’t rename the atlas folder to something with a dash (‘ – copia’) but try to avoid blanks and dashes in filepaths where you can
– delete the gm_mask.nii and try again
– check out each nifti image in the generated atlas folder. Any of them looking suspicious? These are just normal nifti files, so you can look at them and compare them to niftis of other atlases.
– if not create the atlas manually (without the labeling2atlas script but e.g. only use selected items from the labeling file that you can e.g. export using the SPM imcalc.Best, Andy
Hi Monique,
I think what ningfei said may have been helpful but wasn’t exactly what you were looking for.
You can just use this snippet:
uipatdir={'/path/to/my/patientfolder1','/path/to/my/patientfolder2','/path/to/my/patientfolder3'}; cfg.xmm=-4; % x-coordinate cfg.ymm=-5; % y-coordinate cfg.zmm=10; % z-coordinate cfg.acmcpc=2; % relative to midcommissural point, AC would be 1, PC would be 3. fid=ea_mni2acpc(cfg,uipatdir);The first line would point to at least one patient normalized with Lead-DBS. Please do read the Horn 2017 NI paper to see why it may make a lot of sense to use a whole cohort to do this conversion. Instead, if you’re interested in the coordinate in ACPC in exactly one specific patient, then just using one patient is ideal.
The transformed coordinate will be fid.WarpedPointACPC;
AC, PC and MSP in native space will also be defined.
Then ea_acpc2mni would do the reverse.
Hope this helps!
Best, Andy
Hi Hao,
the MER tool does add bubbles e.g. when pressing the space bar. It only reacts when you’re in the main window (i.e. click on the main window first, then move around the trajectories and press space).
You need to activate keyboard control for one of the trajectories in the MER viewer, first, however.This being said, I think it’d be much easier for you to plot the points manually (e.g. using plot3() function). The Lead-DBS Viewer is nothing but a Matlab figure, so you can add anything you want to it using the normal Matlab commands.
Finally, the coordinates in the figure you sent were plotted with lead group (use color points by regressor, set the regressor to all ones and instead of plotting electrode trajectories, plot point clouds).Hope this helps!
Best, Andy
Hi Kailang,
unfortunately, the IXI database hasn’t been released.
You’d need to use a peer dataset of your own (e.g. DBS patients normalized with Lead-DBS of similar age of the cohort that was used to specify the coordinate).Best, Andy
…also note that most of the tools are heavily under development still and should be seen as “beta” state. IMHO the field of dbs connectomics is still developing and we predominantly put in code while we need it ourselves for specific projects. So bear with us if not everything works as intended..
Hi A51,
good name there!
Unfortunately I fear the documentation is scarce and we currently lack the resources to improve them.
I guess you already checked the manual? Then the walkthrough-video on the website is probably the best asset.
After you’ve been through that, I’d advise to join our slack channel and ask specific questions there.
Best to clarify what’s the state of your analysis (e.g. electrodes reconstructed, VTAs calculated?) and more importantly what exactly you wish to do. Running connectomics is a bit unspecific, I assume you first want to create connectivity maps from your VTAs? But if so, what’s the next step? I.e. is there a behavioral or clinical parameter that you want to address by means of connectivity to the VTA?
Would definitely need to have a pretty precise plan of what you want to answer to really help. Reason is that there are basically 4 regions in lead dbs where one can do one or the other (running seeds or connectivity matrices -> lead mapper; visualizing and analyzing single patient connectivity -> “convis” module directly in the lead dbs 3D viewer; running parcellation based connectivity stats from your vtas -> lead group; analyzing patient specific connectivity data -> lead connectome; group-wise analysis of such -> lead group connectome). I assume the latter two do not count here since you lack patient DTI. But just wanted to make the point that there are various tools for running connectomics in the lead suite..Hope this helps!
Best, Andy
Hi Monique,
I assume you’re referring to the reco.acpc coordinates inside the ea_reconstruction.mat file?
Indeed, I just checked and am not entirely sure if these are correct. In fact, the option to write out ACPC coordinates has been disabled by default a while ago but if you have an older installation (with older prefs), it would still do it in your case.There is an MNI to ACPC tool (inside the tools menu) in which you can map from MNI to ACPC based on selected patients. In there, it is unequivocal how coordinates are mapped and to which point they refer to. Please use this one instead.
The AC lead-dbs uses is at -0.5948, 2.1606, -3.2329 mm which I drew in per hand. However, it’s in 2009b NLIN ASYM space as everything in Lead-DBS, so not the 152 space Brett refers to (more on this here: http://www.lead-dbs.org/?p=1241).
Hope this helps!
Best, Andy
(will check and correct if necessary the acpc coord writeout in the next release).
Hi, what you could try is to make sure that all your seeds are really binary (i.e. only 0 and 1 in the images). Then it will be much faster and should also not consume as much memory. The lead mapper tool can use weighted seeds and these will be processed more elaborately given tracks need to be weighted for each voxel.
So if you break down a parcellation into single files, make sure to use nearest neighbor interpolation or do it manually making sure your nifti file only contains booleans (e.g. no values like 0.9999 or 0.000001 at bordering regions).Other than that no clue how to fix it easily.
Hi Larry,
the preprocessing basically follows the pipeline described in
https://www.ncbi.nlm.nih.gov/pubmed/24099851Motion params are regressed out, slice timing is not corrected for, no scrubbing. WM/CSF signal is regressed out and bandpass-filtering applied within a common window (see paper).
I’d say the rs-fMRI preprocessing pipeline currently is pretty old-school but works for most applications and is really easy to use. Then again also easy to e.g. use fMRIPREP or similar before using lead connectome.
Also we’re much looking for people interested in implementing some additional tools into the pipeline.
Contact me directly if that is interesting.Best, Andy
