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38	38	== What can I find here? ==
39	39
40	40	* an IPython notebook that describes how to
41		-** use the TVB pipeline on one of the associated supercomputers using PyUnicore
42		-** upload model data to the supercomputer
	41	+** use the TVB processing pipeline on one of the associated supercomputers using PyUnicore
	42	+** upload MRI data to the supercomputer
43	43	** set up and run the pipeline
44	44	** download processing results
45	45
	46	+== What does the TVB processing pipeline do? ==
	47	+
	48	+After uploading MRI data to the supercomputer, the pipeline runs the three containers
	49	+
	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"]]
	53	+
	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.
	55	+
46	46	== How do I use it? ==
47	47
48	48	* the pipeline is implemented by three Docker containers (mrtrix3_connectome, fmriprep and tvb_converter)
...	...	@@ -99,8 +99,27 @@
99	99
100	100	* [[https:~~/~~/www.ncbi.nlm.nih.gov/pubmed/25837600>>url:https://www.ncbi.nlm.nih.gov/pubmed/25837600]]
101	101	* [[https:~~/~~/www.ncbi.nlm.nih.gov/pubmed/27480624>>url:https://www.ncbi.nlm.nih.gov/pubmed/27480624]]
	112	+
	113	+== Citing this work ==
	114	+
	115	+
	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).
	117	+
	118	+
	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]]
	120	+
	121	+
	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.
	123	+
	124	+
	125	+
	126	+Smith, R., & Connelly, A. (2019). MRtrix3_connectome: A BIDS Application for quantitative structural connectome construction. //OHBM//, W610.
	127	+
	128	+
	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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