CRNC - Training Core Project Abstract Our proposed BTRC resource, the Center for Reproducible Neuroimaging Computation (CRNC), aims to improve the way neuroimaging research is performed and reported. By developing and implementing technology to support a comprehensive set of data management, analysis and utilization frameworks for basic research and clinical activities, we seek to improve the reproducibility of neuroimaging science and extend the value of our national investment in neuroimaging research. Reproducibility is critical because the current literature is fraught with published ?results? that are false positives. This is mostly due to the lack of statistical power as well as analysis or software errors (occasionally misconduct). More importantly, given the current publication system, null findings are not published making it exceedingly difficult to discern between false positive and true positive finding as data is hard to aggregate, and exact methods are hard to replicate. In this Training and Dissemination Core, we provide training and education to the community to foster continued use and development of the reproducible framework in neuroimaging research. In neuroimaging, training is often focused on teaching the specificities of a particular tool, such as a single analysis software. Short workshops tend to be more a demonstration of a series of tools than the acquisition of in depth knowledge. We seek a new scalable strategy for training, by considering that core online courses focused on the CRNC reproducible research tools can be complemented by more foundational knowledge. To accomplish this, we will: 1) Provide specific training on the CRNC concepts, products and tools necessary for a full cycle of reproducible research; 2) Provide complementary training on the computational and statistical frameworks and techniques underlying our tools and train power users on the CRNC products. We will work in partnership with the other TR&D projects and the Collaborator and Service Project users to teach this reproducible analysis system and to foster the use of the reproducible framework and collaborations in the neuroimaging research community.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Biotechnology Resource Grants (P41)
Project #
1P41EB019936-01A1
Application #
8999841
Study Section
Special Emphasis Panel (ZEB1)
Project Start
Project End
Budget Start
2016-03-01
Budget End
2017-02-28
Support Year
1
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of Massachusetts Medical School Worcester
Department
Type
DUNS #
603847393
City
Worcester
State
MA
Country
United States
Zip Code
Solo, Victor; Poline, Jean-Baptiste; Lindquist, Martin A et al. (2018) Connectivity in fMRI: Blind Spots and Breakthroughs. IEEE Trans Med Imaging 37:1537-1550
Wimalaratne, Sarala M; Juty, Nick; Kunze, John et al. (2018) Uniform resolution of compact identifiers for biomedical data. Sci Data 5:180029
Kennedy, David N (2018) Neuroimaging Neuroinformatics: Sample Size and Other Evolutionary Topics. Neuroinformatics 16:149-150
Kim, Yang-Min; Poline, Jean-Baptiste; Dumas, Guillaume (2018) Experimenting with reproducibility: a case study of robustness in bioinformatics. Gigascience 7:
Guell, Xavier; Schmahmann, Jeremy D; Gabrieli, John DE et al. (2018) Functional gradients of the cerebellum. Elife 7:
Millman, K Jarrod; Brett, Matthew; Barnowski, Ross et al. (2018) Teaching Computational Reproducibility for Neuroimaging. Front Neurosci 12:727
James, Eric G; Leveille, Suzanne G; Hausdorff, Jeffrey M et al. (2017) Rhythmic Interlimb Coordination Impairments and the Risk for Developing Mobility Limitations. J Gerontol A Biol Sci Med Sci 72:1143-1148
Nichols, Thomas E; Das, Samir; Eickhoff, Simon B et al. (2017) Best practices in data analysis and sharing in neuroimaging using MRI. Nat Neurosci 20:299-303
Irimia, Andrei; Wei, Susan; Lu, Nanshu et al. (2017) Mobile Monitoring of Traumatic Brain Injury in Older Adults: Challenges and Opportunities. Neuroinformatics 15:227-230
Ghosh, Satrajit S; Poline, Jean-Baptiste; Keator, David B et al. (2017) A very simple, re-executable neuroimaging publication. F1000Res 6:124

Showing the most recent 10 out of 19 publications