Lead-DBS
  • Home
  • About
    • Quickstart Primer (Post-OP MR-Scans)
    • Data & Code inclusion philosophy
    • Quickstart Primer (Post-OP CT-Scans)
    • Deep Brain Stimulation
    • Lead Connectome
    • Publications
    • List of Lead-DBS dependencies
    • Citing Lead-DBS
  • News
  • Contact
  • Help/Support
    • Manual
    • Knowledge Base
      • Atlases/Resources
        • Subcortical Atlases (MNI-Space)
        • Cortical Atlas Parcellations (MNI-Space)
        • Macaque Atlases (MNI-Space)
        • The DISTAL atlas
        • FEM-based VTA model
        • Normative Connectomes
      • Lead-DBS Methods
        • Subcortical Electrophysiology Mapping (SEM)
        • AC/PC to MNI conversion
        • Connectivity Benefit Mapping
      • Other Videos
      • Screenshots
      • Walkthrough-Videos
    • Slack User Channel
    • Forum
  • Workshops
    • Past workshops
      • Berlin 2016
      • Shanghai September 2018
      • Hamburg February 2019
    • Berlin September 2019
    • Machine Learning – Berlin September 2019
    • Brisbane February 2020
  • Download
  • Lead-Connectome
  • Search
  • Menu Menu

Normative connectomes and cortical lesion maps (stroke)

You are here: Home1 / Forums2 / Support forum ARCHIVED – Please use Slack Channel instead3 / Normative connectomes and cortical lesion maps (stroke)4

Tagged: cortical, lesion, Normative connectome, stoke

Viewing 11 posts - 1 through 11 (of 11 total)
  • Author
    Posts
  • 05/16/2018 at 5:11 AM #4680
    ljh
    Participant

    Hi Andreas and co.,
    I’d like to use the ‘normative connectomes’ feature of LEAD DBS in a stroke patient cohort. I’m hoping to i) draw tracts from the lesion site (a 3d nifti file) and ii) create a “disconnectivity“ matrix from existing parcellations.
    I have successfully downloaded lead-dbs and the normative connectomes I plan to use (Horn, 2013).
    Using Lead Connectome Mapper, I do the following:
     No patient directory chosen
     1. Seed: a 3d lesion map (where 1 denotes the seed region)
     2. Include structural connectivity: select Horn 2013 connectome – export in 2mm.
     3. Connectivity map from seed
     Run!

    I get an error that suggests I haven’t placed an anatomy file in the subject folder (error pasted below). However, I haven’t actually specified a patient folder, and I don’t have individual level anatomical data – just lesion maps that are already normalized etc., Any suggestions on the best way forward here?

    Best,
    Luke

    *************
    Running structural connectivity…
    Command: seed
    Iterating voxels (1/1): 100%
    Iterating fibers (1/1): 100%
    Done.
    Warning: No anatomy information found! Please put either
    anat_t1.nii, anat_t2.nii or anat_pd.nii into subject folder.

    05/16/2018 at 6:59 AM #4683
    andreashorn
    Keymaster

    Hi Luke,

    as a general rule of thumb, always ignore warnings in lead-dbs (this is just a warning, not an error).
    The result files should already be there.

    Best, Andy

    05/17/2018 at 12:56 AM #4698
    ljh
    Participant

    Hi Andy,
    Thank you for the reply – you are indeed correct! The output looks fine.

    As a follow up question – I want to investigate the structural connectivity between a seed roi (a lesion) and a common parcellation (rather than at the voxel level). To do this I have entered a the lesion as a seed roi amongst 246 other parcellation seeds and selected the ‘connectivity matrix’ option. This produces a 247 x 247 matrix which I assume denotes the number of streamlines between each of the regions I have entered into the seeds (246 parcellation rois and one lesion) – is that correct? (and if so – why does the diagonal contain values?)

    Thanks again for the help – lead dbs is a fantastic tool!

    Best,
    Luke

    05/17/2018 at 6:44 AM #4701
    andreashorn
    Keymaster

    Hi, correct – the diagonals are just streamlines going through each region alone. Can ignore it or use it to normalize connections or similar.

    05/18/2018 at 12:03 PM #4724
    ljh
    Participant

    Thanks again Andy – I had another question (sorry!). Is the above approach what you would suggest for creating a normative connectome in common parcellations (i.e., loading each ROI of a parcellation separately as a seed and then using the ‘connectivity matrix’ option)?

    While this works well in the smaller datasets, it seems to use a lot of memory in the NKI 169 atlas. For example, in a 512 parcellation of the cortex is uses over 128 GB of memory which is quite problematic. I thought perhaps there might be a more efficient option, or some extra pre-processing I could do to the dataset to reduce the memory load (although I could not find any documentation).

    Thanks again for the help, I promise to stop bothering you soon!

    05/18/2018 at 1:05 PM #4725
    andreashorn
    Keymaster

    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.

    05/31/2018 at 5:50 AM #4806
    area51
    Participant

    Hi. Totally new to this but interested in normative connectomics. Is there an online tutorial on how to do this in Lead? I have some limbic DBS subjects that do not have pre-operative DTI. I would like to run connectomics on the VAT from the lead-dbs electrode contact reconstructions.

    Thanks,
    A51

    05/31/2018 at 10:38 AM #4807
    andreashorn
    Keymaster

    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

    05/31/2018 at 10:40 AM #4808
    andreashorn
    Keymaster

    …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..

    07/10/2018 at 1:38 AM #5062
    area51
    Participant

    Thanks Andy. To answer you’re questions. I would like to look at the individual patient connectivity from the VTA (electrodes and VTA already constructed with LeadDBS). We have behavioral stimulation mapping data where a behavior occurs only at certain voltages and we would like to map behavior to tracts stimulated.

    -A51

    07/10/2018 at 7:21 AM #5063
    andreashorn
    Keymaster

    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

  • Author
    Posts
Viewing 11 posts - 1 through 11 (of 11 total)
  • The forum ‘Support forum ARCHIVED – Please use Slack Channel instead’ is closed to new topics and replies.

Your Account

Log In
Register

Forum Statistics

Registered Users
132
Forums
1
Topics
185
Replies
607
Topic Tags
81

Subscribe to our newsletter

Latest Tweets

  • Tweet Avatar
    leaddbs
    @leaddbs
    Nice study led by @DrAlfonsoFasano on GPi-DBS for pediatric dystonia. Voxel cluster most associated with clinical i… https://t.co/IScNqVQX94

    19h
  • Tweet Avatar
    leaddbs
    @leaddbs
    This is probably the oldest easteregg in @leaddbs, surprising that it survived until now from the very first releas… https://t.co/qqcUBw3nW1

    5d
  • Tweet Avatar
    leaddbs
    @leaddbs
    RT @Chencheng_Zhang: Our study demonstrates the beneficial effects of GPi-DBS for treating camptocormia in PD patients in the short term https://t.co/eq03g2oKb2

    5d

Recent Posts

  • Lead-DBS on the cover of NeuroImage
  • Lead-DBS on the cover of Biological Psychiatry
  • Lead-DBS on the cover of Annals of Neurology
  • Crucial methodological updates for Lead-DBS
  • 2nd Lead-DBS Workshop in Shanghai, China

Archives

  • September 2020
  • June 2019
  • November 2018
  • September 2018
  • August 2018
  • March 2018
  • November 2017
  • September 2017
  • July 2017
  • April 2017
  • July 2016
  • June 2016
  • May 2016
  • March 2016
  • December 2015
  • November 2015
  • April 2015
  • January 2015
  • December 2014
  • October 2014
  • September 2014
  • July 2014
  • June 2014

Impressum & Datenschutzerklärung

Scroll to top