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48	48	After uploading MRI data to the supercomputer, the pipeline runs the three containers
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50		-* [[bids/mrtrix3_connectome>>https://hub.docker.com/r/bids/mrtrix3_connectome||rel="noopener noreferrer" target="_blank"]]
51		-* [[poldracklab/fmriprep>>https://hub.docker.com/r/poldracklab/fmriprep||rel="noopener noreferrer" target="_blank"]], and
52		-* [[thevirtualbrain/tvb_converter>>https://hub.docker.com/r/thevirtualbrain/tvb_converter||rel="noopener noreferrer" target="_blank"]]
	50	+* [[bids/mrtrix3_connectome>>https://hub.docker.com/r/bids/mrtrix3_connectome||rel=" noopener noreferrer" target="_blank"]]
	51	+* [[poldracklab/fmriprep>>https://hub.docker.com/r/poldracklab/fmriprep||rel=" noopener noreferrer" target="_blank"]], and
	52	+* [[thevirtualbrain/tvb_converter>>https://hub.docker.com/r/thevirtualbrain/tvb_converter||rel=" noopener noreferrer" target="_blank"]]
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54	54	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.
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110	110	* [[https:~~/~~/www.ncbi.nlm.nih.gov/pubmed/25837600>>url:https://www.ncbi.nlm.nih.gov/pubmed/25837600]]
111	111	* [[https:~~/~~/www.ncbi.nlm.nih.gov/pubmed/27480624>>url:https://www.ncbi.nlm.nih.gov/pubmed/27480624]]
112		-
113		-== Citing this work ==
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116		-When using this pipeline for published work, please acknowledge MRtrix3, MRtrix3_connectome (R. Smith & Connelly, 2019; Tournier et al., 2019) and fmriprep (Esteban et al., 2019).
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119		-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>>https://doi.org/10.1038/s41592-018-0235-4]]
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122		-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.
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126		-Smith, R., & Connelly, A. (2019). MRtrix3_connectome: A BIDS Application for quantitative structural connectome construction. //OHBM//, W610.
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129		-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
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132		-== ==
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