Over the last two decades a vast technological, computational and societal infrastructure has emerged and transformed how information is collected and knowledge is gathered in all facets of science. Within the medical community, in response to numerous NIH data sharing initiatives and mandates, as well as through grassroots efforts, the community has succeeded in accumulating an extensive array of shared data. In the scientific process, methods should be reproducible. In the economics of science, data and methods should be maximally reusable in order to maximize the scientific return on the data acquisition investment. Data and analytic processing methods reuse have become a focal point in a growing concern about the replicability and power of many of today's scientific studies. The magnitude of this reproducibility issue indicates that a paradigm shift may be in order as to how we generate and report knowledge from our mounting public and private neuroimaging repositories. These factors impede scientific discovery and is ultimately a disservice to all the stakeholders, including the investigators themselves, their peers and colleagues, their institution, and their funding agencies. Our proposed BTRC resource, the Center for Reproducible Neuroimaging Computation (CRNC), seeks to implement a shift in the way neuroimaging research is performed. Through the development of technology that supports a comprehensive set of data management, analysis and utilization frameworks in support of both basic research and clinical activities, our overarching goal is 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 contains large numbers of erroneous conclusions (due to limited power, false positive, publication bias and occasionally mistakes). Given a neuroimaging study, it is exceedingly difficult to discern between false positive and true positive findings as data is hard to aggregate, and exact methods are hard to replicate. In order to advance the field in terms of analysis and publication in a way that embraces reproducibility, the overall Center will have the following aims: A) Deliver a reproducible analysis system comprised of components that include data and software discovery (TR&D 1), implementation of standardized workflow description and development of machine-readable markup and storage of the results of these workflows (TR&D 2) and development of execution options that facilitates operation in multiple computational environments and reduces barriers to scale and reliability (TR&D 3); B) Working with a community of collaborator and service users, deploy, test and validate the reproducible analysis system with a wide variety of use cases ranging from software developers to applied scientists that support the archiving and reuse of raw data and the archival and reuse of derived results to promote reproducible clinical research (and its publication) in multiple different application areas; and C) Provide training and education to the community to foster continued use and development of the reproducible framework in neuroimaging research.
A large investment in time and money is made to support neuroimaging research. However, much of this research, while very valuable, is performed on a relatively small number of subjects and cannot be replicated. Our proposed Center for Reproducible Neuroimaging Computation (CRNC), seeks to provide a completely reproducible framework for capturing data and performing neuroimaging analysis in support of improving all clinical and research studies using neuroimaging.
| 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 |
| 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 |
| Huntenburg, Julia M; Bazin, Pierre-Louis; Goulas, Alexandros et al. (2017) A Systematic Relationship Between Functional Connectivity and Intracortical Myelin in the Human Cerebral Cortex. Cereb Cortex 27:981-997 |
Showing the most recent 10 out of 19 publications
