Quick links

• TVB on EBRAINS (Short Version)
• TVB on EBRAINS (Long Version)
• TVB on EBRAINS (Mini Videos)
• User Story

What can I find here?

• the code underlying the pipeline including the orchestrator that manages processing on the supercomputer
• an IPython notebook that describes how to
  • use the TVB processing pipeline on one of the associated supercomputers using PyUnicore
  • upload MRI data to the supercomputer
  • set up and run the pipeline
  • download processing results

What does the TVB processing pipeline do?

After uploading MRI data to the supercomputer, the pipeline runs the three containers

• bids/mrtrix3_connectome
• poldracklab/fmriprep, and
• thevirtualbrain/tvb_converter

The TVB Processing Pipeline takes multimodal MRI data sets (anatomical, functional and diffusion-weighted MRI) as input and generates SCs, region-average fMRI time series, FCs, brain surfaces, electrode positions, lead field matrices, and atlas parcellations as output. The pipeline performs preprocessing and distortion-correction on MRI data as well as white matter fiber bundle tractography on diffusion data. Outputs are formatted according to two data standards: a TVB-ready data set that can be directly used to simulate brain network models and the same output in BIDS format.

How do I use it?

• the pipeline is implemented by three Docker containers (mrtrix3_connectome, fmriprep and tvb_converter)
• the containers can be executed on supercomputers and operated via IPython notebooks 
• Find the notebook by clicking on "Drive" in the left menu. The notebook is located in the folder "notebooks"
• Direct link to notebook: https://drive.ebrains.eu/d/5e2fbc74575c47e88780/
• To use the notebook, download it onto your local filesystem, create a new Collab and upload it there.
• Then, head over to https://lab.ebrains.eu/ to edit and run your notebook.
• Depending on whether you created a public or a private notebook it will be accessible in either "drive/Shared with all" or "drive/Shared with groups'"

Metadata

Publications on TVB/brain model preprocessing

• https://www.ncbi.nlm.nih.gov/pubmed/25837600
• https://www.ncbi.nlm.nih.gov/pubmed/27480624

Citing this work

When using this pipeline for published work, please acknowledge Schirner et al. 2015, Schirner et al. 2022, MRtrix3, MRtrix3_connectome (R. Smith & Connelly, 2019; Tournier et al., 2019) and fmriprep (Esteban et al., 2019).

Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., Oya, H., Ghosh, S. S., Wright, J., Durnez, J., Poldrack, R. A., & Gorgolewski, K. J. (2019). fMRIPrep: a robust preprocessing pipeline for functional MRI. Nature Methods. https://doi.org/10.1038/s41592-018-0235-4

Schirner, M., Rothmeier, S., Jirsa, V. K., McIntosh, A. R., & Ritter, P. (2015). An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data. NeuroImage, 117, 343-357.   

Schirner, Domide, Perdikis, Triebkorn, Stefanovski, Pai, Prodan, Valean, Palmer, Langford, Blickensdörfer, van der Vlag, Diaz-Pier, Peyser, Woodman, Zehl, Fousek, Petkoski, Kusch, Hashemi, Marinazzo, Mangin, Flöel, Akintoye, Stahl, Deco, McIntosh, Hilgetag, Morgan, Schuller, Upton, McMurtrie, Dickscheid, Bjaalie, Amunts, Mersmann, Jirsa, Ritter (2022). Brain Simulation as a Cloud Service: The Virtual Brain on the European Research Platform EBRAINS. Neuroimage. https://doi.org/10.1016/j.neuroimage.2022.118973

Smith, R., & Connelly, A. (2019). MRtrix3_connectome: A BIDS Application for quantitative structural connectome construction. OHBM, W610.

Tournier, J. D., Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., Christiaens, D., Jeurissen, B., Yeh, C. H., & Connelly, A. (2019). MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. In NeuroImage. https://doi.org/10.1016/j.neuroimage.2019.116137