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5		-= TVB Image Processing Pipeline With MINDS annotated Outputs =
	5	+= From MRI to Functional and Structural Connectivity =
6	6
	7	+Neuroimaging data preprocessing pipeline
	8	+
7	7	== [[Pipeline Video Tutorial>>https://drive.google.com/file/d/1VcXf3GX3KoihF4UzJQXzuGL4XWoqj5Jr/view||rel="noopener noreferrer" target="_blank"]] ==
8	8
9	9	== [[Direct link to notebook>>https://drive.ebrains.eu/#my-libs/lib/42ac27a5-981e-4cf6-8ec5-d67b0c24bf09/notebooks||rel="noopener noreferrer" target="_blank"]] ==
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36	36	== What can I find here? ==
37	37
38	38	* an IPython notebook that describes how to
39		-** use the TVB processing pipeline on one of the associated supercomputers using PyUnicore
40		-** upload MRI data to the supercomputer
	41	+** use the TVB pipeline on one of the associated supercomputers using PyUnicore
	42	+** upload model data to the supercomputer
41	41	** set up and run the pipeline
42	42	** download processing results
43	43
44		-== What does the TVB processing pipeline do? ==
45		-
46		-After uploading MRI data to the supercomputer, the pipeline runs the three containers
47		-
48		-* [[bids/mrtrix3_connectome>>https://hub.docker.com/r/bids/mrtrix3_connectome||rel="noopener noreferrer" target="_blank"]]
49		-* [[poldracklab/fmriprep>>https://hub.docker.com/r/poldracklab/fmriprep||rel="noopener noreferrer" target="_blank"]], and
50		-* [[thevirtualbrain/tvb_converter>>https://hub.docker.com/r/thevirtualbrain/tvb_converter||rel="noopener noreferrer" target="_blank"]]
51		-
52		-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.
53		-
54	54	== How do I use it? ==
55	55
56	56	* the pipeline is implemented by three Docker containers (mrtrix3_connectome, fmriprep and tvb_converter)
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107	107
108	108	* [[https:~~/~~/www.ncbi.nlm.nih.gov/pubmed/25837600>>url:https://www.ncbi.nlm.nih.gov/pubmed/25837600]]
109	109	* [[https:~~/~~/www.ncbi.nlm.nih.gov/pubmed/27480624>>url:https://www.ncbi.nlm.nih.gov/pubmed/27480624]]
110		-
111		-== Citing this work ==
112		-
113		-
114		-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).
115		-
116		-
117		-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]]
118		-
119		-
120		-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.
121		-
122		-
123		-Smith, R., & Connelly, A. (2019). MRtrix3_connectome: A BIDS Application for quantitative structural connectome construction. //OHBM//, W610.
124		-
125		-
126		-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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128	128
129		-== ==
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