MaPPeRTrac: A Massively Parallel, Portable, and Reproducible Tractography Pipeline

Babuji, Y., Woodard, A., Li, Z., Katz, D.S., Clifford, B., Kumar, R., ..., & Chard, K. (2019). Parsl: Pervasive parallel programming in python. In The 28th ACM international symposium on high-performance parallel and distributed computing (hpdc). https://doi.org/10.1145/3307681.3325400

Basser, P. J., Mattiello, J., & LeBihan, D. (1994). MR diffusion tensor spectroscopy and imaging. Biophysical Journal, 66(1), 259–267.

Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

Behrens, T., Johansen-Berg, H., Jbabdi, S., Rushworth, M., & Woolrich, M. (2007). Probabilistic diffusion tractography with multiple fibre orientations. What can we gain? NeuroImage, 23, 144–155.

Article  Google Scholar 

Behrens, T., Woolrich, M., Jenkinson, M., Johansen-Berg, H., Nunes, R., Clare, S., ..., Smith, S. (2003). Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magnetic Resonance in Medicine, 50 (5), 1077–1088. https://doi.org/10.1002/mrm.10609

Bodien, Y.G., McCrea, M., Dikmen, S., Temkin, N., Boase, K., Machamer, J., ..., Investigators, T.R.A.C.K.-T.B.I. (2018). Optimizing outcome assessment in multicenter tbi trials: Perspectives from track-tbi and the tbi endpoints development initiative. The Journal of Head Trauma Rehabilitation, 33(3), 147–157. https://doi.org/10.1097/HTR.0000000000000367

Boettiger, C. (2015). An introduction to Docker for reproducible research. ACM SIGOPS Operating Systems Review, 49(1), 71–79.

Article  Google Scholar 

Conturo, T. E., Lori, N. F., Cull, T. S., Akbudak, E., Snyder, A. Z., Shimony, J. S., …, & Raichle, M. E. (1999). Tracking neuronal fiber pathways in the living human brain. Proceedings of the National Academy of Sciences, 96(18), 10422–10427.

Côté, M.-A., Girard, G., Boré, A., Garyfallidis, E., Houde, J.-C., & Descoteaux, M. (2013). Tractometer: Towards validation of tractography pipelines. Medical Image Analysis, 17(7), 844–857. https://doi.org/10.1016/j.media.2013.03.009

Cui, Z., Zhong, S., Xu, P., Gong, G., & He, Y. (2013). Panda: A pipeline toolbox for analyzing brain diffusion images. Frontiers in Human Neuroscience, 7, 42. https://doi.org/10.3389/fnhum.2013.00042

Article  ADS  PubMed  PubMed Central  Google Scholar 

Dagum, L., & Menon, R. (1998). OpenMP: An industry standard API for shared-memory programming. IEEE Computational Science and Engineering, 5(1), 46–55.

Article  Google Scholar 

Desai, N. (2005). Cobalt: an open source platform for hpc system software research. Edinburgh BG/L System Software Workshop (pp. 803–820).

Desikan, R.S., Ségonne, F., Fischl, B., Quinn, B.T., Dickerson, B.C., Blacker, D., ..., et al. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31(3), 968–980.

Fischl, B. (2012). FreeSurfer. Neuroimage, 62(2), 774–781.

Article  PubMed  Google Scholar 

Foster, M., & Deardorff, M. (2017). Open science framework (osf). Journal of the Medical Library Association, 105(2), 203. https://doi.org/10.5195/JMLA.2017.88

Gentzsch, W. (2001). Sun grid engine: Towards creating a compute power grid. Proceedings First IEEE/ACM International Symposium on Cluster Computing and the Grid (pp. 35–36).

Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S., Duff, E. P., ..., et al. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data, 3(1), 1–9.

Gorgolewski, K.J., Alfaro-Almagro, F., Auer, T., Bellec, P., Capot ̆a, M., Chakravarty, M. M., ..., et al. (2017). Bids apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. PLoS Computational Biology, 13(3), e1005209.

Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., & Smith, S. M. (2012). FSL. Neuroimage, 62(2), 782–790.

Article  PubMed  Google Scholar 

Karcher, N. R., & Barch, D. M. (2021). The ABCD study: understanding the development of risk for mental and physical health outcomes. Neuropsychopharmacol, 46, 131–142. https://doi.org/10.1038/s41386-020-0736-6

Article  Google Scholar 

Kiar, G., Bridgeford, E. W., Chandrashekhar, V., Mhembere, D., Burns, R., Gray Roncal, W. R., & Vogelstein, J. T. (2017). A comprehensive cloud framework for accurate and reliable human connectome estimation and meganalysis. BioRxiv. https://doi.org/10.1101/188706

Kurtzer, G. M., Sochat, V., & Bauer, M. W. (2017). Singularity: Scientific containers for mobility of compute. PloS One, 12(5), e0177459.

León, E. A., D’Hooge, T., Hanford, N., Karlin, I., Pankajakshan, R., Foraker, J., ... & Leininger, M. L. (2020). TOSS-2020: A commodity software stack for HPC. In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 1–15). IEEE.

LLNL. (2021). TOSS description. Retrieved 2021–04–30, from https://computing.llnl.gov/projects/toss-speeding-commodity-cluster-computing

Madhyastha, T. M., Koh, N., Day, T. K. M., Hernández-Fernández, M., Kelley, A., Peterson, D. J., ..., & Grabowski, T. J. (2017). Running neuroimaging applications on amazon web services: How, when, and at what cost? Frontiers in Neuroinformatics, 11, 63–63. https://doi.org/10.3389/fninf.2017.00063

Maximov, I. I., Alnæs, D., & Westlye, L. T. (2019). Towards an optimised processing pipeline for diffusion magnetic resonance imaging data: Effects of artefact corrections on diffusion metrics and their age associations in uk biobank. Human Brain Mapping, 40(14), 4146–4162. https://doi.org/10.1002/hbm.24691

Moon, J. Y., Bremer, P.-T., Mukherjee, P., Markowitz, A. J., Palacios, E. M., Cai, L. T., ..., the TRACK-TBI Consortium. (2020). Mappertrac: A massively parallel, portable, and reproducible tractography pipeline. BioRxiv, 2020-12.

Moon, J. Y., Mukherjee, P., Madduri, R. K., Markowitz, A. J., Cai, L. T., Palacios, E. M., ..., Bremer, P.-T. (2022). The case for optimized edge-centric tractography at scale. Frontiers in Neuroinformatics, 16, 752471.

Mukherjee, P., Berman, J. I., Chung, S. W., Hess, C. P., & Henry, R. G. (2008a). Diffusion tensor MR imaging and fiber tractography: Theoretic underpinnings. American Journal of Neuroradiology, 29(4), 632–641.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Mukherjee, P., Chung, S. W., Berman, J. I., Hess, C. P., & Henry, R. G. (2008b). Diffusion tensor MR imaging and fiber tractography: Technical considerations. American Journal of Neuroradiology, 29(5), 843–852.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Owen, J. P., Wang, M. B., & Mukherjee, P. (2016). Periventricular white matter is a nexus for network connectivity in the human brain. Brain Connectivity, 6(7), 548–557. https://doi.org/10.1089/brain.2016.0431

Article  PubMed  Google Scholar 

Owen, J. P., Chang, Y. S., & Mukherjee, P. (2015). Edge density imaging: Mapping the anatomic embedding of the structural connectome within the white matter of the human brain. NeuroImage, 109, 402–417. https://doi.org/10.1016/j.neuroimage.2015.01.007

Palacios, E. M., Yuh, E. L., Mac Donald, C. L., Bourla, I., Wren-Jarvis, J., Sun, X., ..., et al. (2022). Diffusion tensor imaging reveals elevated diffusivity of white matter microstructure that is independently associated with long-term outcome after mild traumatic brain injury: a TRACK-TBI study. Journal of Neurotrauma, 39(19–20), 1318–1328.

Payabvash, S., Palacios, E. M., Owen, J. P., Wang, M. B., Tavassoli, T., Gerdes, M., ..., Mukherjee, P. (2019). White matter connectome edge density in children with autism spectrum disorders: Potential imaging biomarkers using machine-learning models. Brain Connectivity, 9(2), 209–220, https://doi.org/10.1089/brain.2018.0658

Python Software Foundation. (n.d.). Python package index - pypi. Python Software Foundation. Retrieved from https://pypi.org/

Qi, X., & Arfanakis, K. (2021). Regionconnect: Rapidly extracting standardized brain connectivity information in voxel-wise neuroimaging studies. NeuroImage, 225, 117462. https://doi.org/10.1016/j.neuroimage.2020.117462

Raji, C. A., Wang, M. B., Nguyen, N., Owen, J. P., Palacios, E. M., Yuh, E. L., & Mukherjee, P. (2020). Connectome mapping with edge density imaging differentiates pediatric mild traumatic brain injury from typically developing controls: proof of concept. Pediatric Radiology, 50(11), 1594–1601. https://doi.org/10.1007/s00247-020-04743-9

Article  PubMed  PubMed Central  Google Scholar 

Reber, J., Hwang, K., Bowren, M., Bruss, J., Mukherjee, P., Tranel, D., & Boes, A.D. (2021). Cognitive impairment after focal brain lesions is better predicted by damage to structural than functional network hubs. Proceedings of the National Academy of Sciences, 118 (19). https://doi.org/10.1073/pnas.2018784118

Rex, D. E., Ma, J. Q., & Toga, A. W. (2003). The LONI pipeline processing environment. NeuroImage, 19(3), 1033–1048. https://doi.org/10.1016/S1053-8119(03)00185-X

Roine, T., Jeurissen, B., Perrone, D., Aelterman, J., Philips, W., Sijbers, J., & Leemans, A. (2019). Reproducibility and intercorrelation of graph theoretical measures in structural brain connectivity networks. Medical Image Analysis, 52, 56–67. https://doi.org/10.1016/j.media.2018.10.009

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. https://doi.org/10.1016/j.neuroimage.2015.03.055

Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E., Johansen-Berg, H., ..., et al. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23, S208–S219.

Sporns, O. (2013). The human connectome: Origins and challenges. NeuroImage, 80, 53–61.

Article  PubMed  Google Scholar 

Sporns, O., Tononi, G., & Kötter, R. (2005). The human connectome: a structural description of the human brain. PLoS Computational Biology, 1(4), e42.

Article  ADS  PubMed  PubMed Central  Google Scholar 

Tannenbaum, T., Wright, D., Miller, K., & Livny, M. (2001). Condor – a distributed job scheduler. In T. Sterling (Ed.), Beowulf cluster computing with Linux. MIT Press.

Google Scholar 

Thain, D., Tannenbaum, T., & Livny, M. (2005). Distributed computing in practice: The Condor experience. Concurrency - Practice and Experience, 17(2–4), 323–356.

Article  Google Scholar 

Theaud, G., Houde, J.-C., Boré, A., Rheault, F., Morency, F., & Descoteaux, M. (2020). Tractoflow: A robust, efficient and reproducible diffusion MRI pipeline leveraging Nextflow & Singularity. NeuroImage, 218, 116889. https://doi.org/10.1016/j.neuroimage.2020.116889

Top500. (2023). Top 500 supercomputer sites, release: November 2023. Retrieved from https://www.top500.org/

Tournier, J. D., Mori, S., & Leemans, A. (2011). Diffusion tensor imaging and beyond. Magnetic Resonance in Medicine, 65(6), 1532.

Article  PubMed  PubMed Central  Google Scholar 

Tournier, J.-D., Calamante, F., & Connelly, A. (2012). MRtrix: Diffusion tractography in crossing fiber regions. International Journal of Imaging Systems and Technology, 22(1), 53–66.

Article  Google Scholar 

Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., Ugurbil, K., & The WU-Minn HCP Consortium. (2013). The WU-Minn human connectome project: An overview. NeuroImage, 80, 62–79. https://doi.org/10.1016/j.neuroimage.2013.05.041

Vingelmann, P., Fitzek, F. H., NVIDIA. (2020). CUDA, release: 10.2.89. Retrieved from https://developer.nvidia.com/cuda-toolkit

Volkow, N. D., Koob, G. F., Croyle, R. T., Bianchi, D. W., Gordon, J. A., Koroshetz, W. J., ..., & Weiss, S. R. (2018). The conception of the ABCD study: From substance use to a broad NIH collaboration. Developmental Cognitive Neuroscience, 32, 4–7.

Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., ..., Mons, B. (2016). The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3(1), 160018. https://doi.org/10.1038/sdata.2016.18

Yoo, A.B., Jette, M.A., Grondona, M. (2003). Slurm: Simple linux utility for resource management. Workshop on Job Scheduling Strategies for Parallel Processing (pp. 44–60).

Yue, J. K., Vassar, M. J., Lingsma, H. F., Cooper, S. R., Okonkwo, D. O., Valadka, A. B., ..., & Sinha, T. K. (2013). Transforming research and clinical knowledge in traumatic brain injury pilot: multicenter implementation of the common data elements for traumatic brain injury. Journal of neurotrauma, 30(22), 1831–1844.

Yuh, E. L., Cooper, S. R., Mukherjee, P., Yue, J. K., Lingsma, H. F., Gordon, W. A., ..., Sinha, T. K. (2014). Diffusion tensor imaging for outcome prediction in mild traumatic brain injury: A TRACK-TBI study. Journal of Neurotrauma, 31(17), 1457–1477. https://doi.org/10.1089/neu.2013.3171

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