Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging

Kennedy, David N.; Ghosh, Satrajit; Gillespie, Tom; Goncalves, Mathias; Grethe, Jeffrey S.; Halchenko, Yaroslav O.; Hanke, Michael; Jarecka, Dorota; Kaczmarzyk, Jakub; Keator, David B.; Meyer, Kyle; Martone, Maryann E.; Poline, Jean‐Baptiste; Preuss, Nina; Abraham, Sanu Ann; Bates, Julianna F.; Crowley, A. B.; Haselgrove, Christian; Hodge, Steven M.; Padhy, Smruti; Sincomb, Troy

doi:10.31219/osf.io/u78a6

Cited by 14 publications

(21 citation statements)

References 7 publications

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“…We have provided an example “publication” with four supplementary files (Ghosh et al, 2017a), the: (1) data file, (2) workflow file, (3) execution environment specification, and (4) results. In this example, the data is from 24 publically accessible typically developing subjects between the ages of 10–15 that have a structural scan at 3 Tesla available from the 1000 Functional Connectomes Project at NITRC (doi 10.18116/C6C592; Kennedy, 2017). The workflow is a FSL-based (version 5.0.9) assessment of total brain, gray and white matter and subcortical structural volumes and is accessible at doi: 10.5281/zenodo.800758, (Ghosh et al, 2017b).…”

Section: Resultsmentioning

confidence: 99%

Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging

Kennedy

Abraham

Bates

et al. 2019

Front. Neuroinform.

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There has been a recent major upsurge in the concerns about reproducibility in many areas of science. Within the neuroimaging domain, one approach is to promote reproducibility is to target the re-executability of the publication. The information supporting such re-executability can enable the detailed examination of how an initial finding generalizes across changes in the processing approach, and sampled population, in a controlled scientific fashion. ReproNim: A Center for Reproducible Neuroimaging Computation is a recently funded initiative that seeks to facilitate the “last mile” implementations of core re-executability tools in order to reduce the accessibility barrier and increase adoption of standards and best practices at the neuroimaging research laboratory level. In this report, we summarize the overall approach and tools we have developed in this domain.

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Section: Resultsmentioning

confidence: 99%

Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging

Kennedy

Abraham

Bates

et al. 2019

Front. Neuroinform.

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“…We base our methods on the ideas of replication and generalization. Because these terms, along with reproducibility, re-execution, and robustness are used in various ways in the literature (Goodman et al, 2016;Kennedy et al, 2019;McNaught and Wilkinson, 1997;Patil et al, 2016;Plesser, 2017), we begin by explaining our usage. Scientific experimentalists typically set out to make a measurement that can be replicated.…”

Section: -Introductionmentioning

confidence: 99%

Replication and generalization in applied neuroimaging

et al. 2019

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There is much interest in translating neuroimaging findings into meaningful clinical diagnostics. The goal of scientific discoveries differs from clinical diagnostics. Scientific discoveries must replicate under a specific set of conditions; to translate to the clinic we must show that findings using purpose-built scientific instruments will be observable in clinical populations and instruments. Here we describe and evaluate data and computational methods designed to translate a scientific observation to a clinical setting. Using diffusion weighted imaging (DWI), Wahl et al., (2010) observed that across subjects the mean fractional anisotropy (FA) of homologous pairs of tracts is highly correlated. We hypothesize that this is a fundamental biological trait that should be present in most healthy participants, and deviations from this assessment may be a useful diagnostic metric. Using this metric as an illustration of our methods, we analyzed six pairs of homologous white matter tracts in nine different DWI datasets with 44 subjects each. Considering the original FA measurement as a baseline, we show that the new metric is between 2 and 4 times more precise when used in a clinical context. Our framework to translate research findings into clinical practice can be applied, in principle, to other neuroimaging results.

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“…Unlike Neurolex, as InterLex was developed after the FAIR principles became well known, it issues FAIR identifiers in the form of CURIES as well as full URIs. InterLex is being used as a vocabulary management system by several neuroscience-related projects in addition to SPARC, including ReproNIM (Kennedy et al, 2019) and the Open Data Commons for Spinal Cord Injury (Callahan et al, 2017). These latter two projects are also using InterLex to store common data elements and custom data elements and relate them to ontological terms.…”

Section: Adding and Extending Ontologiesmentioning

confidence: 99%

Extending and using anatomical vocabularies in the Stimulating Peripheral Activity to Relieve Conditions (SPARC) program

Surles-Zeigler

Sincomb

Gillespie

et al. 2021

Preprint

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The Stimulating Peripheral Activity to Relieve Conditions (SPARC) program is a US National Institutes of Health-funded effort to improve our understanding of the neural circuitry of the autonomic nervous system in support of bioelectronic medicine. As part of this effort, the SPARC project is generating multi-species, multimodal data, models, simulations, and anatomical maps supported by a comprehensive knowledge base of autonomic circuitry. To facilitate the organization of and integration across multi-faceted SPARC data and models, SPARC is implementing the FAIR data principles to ensure that all SPARC products are findable, accessible, interoperable, and reusable. We are therefore annotating and describing all products with a common FAIR vocabulary. The SPARC Vocabulary is built from a set of community ontologies covering major domains relevant to SPARC, including anatomy, physiology, experimental techniques, and molecules. The SPARC Vocabulary is incorporated into tools researchers use to segment and annotate their data, facilitating the application of these ontologies for annotation of research data. However, since investigators perform deep annotations on experimental data, not all terms and relationships are available in community ontologies. We therefore implemented a term management and vocabulary extension pipeline where SPARC researchers may extend the SPARC Vocabulary using InterLex, an online vocabulary management system. To ensure the quality of contributed terms, we have set up a curated term request and review pipeline specifically for anatomical terms involving expert review. Accepted terms are added to the SPARC Vocabulary and, when appropriate, contributed back to community ontologies to enhance autonomic nervous system coverage. Here, we provide an overview of the SPARC Vocabulary, the infrastructure and process for implementing the term management and review pipeline. In an analysis of > 300 anatomical contributed terms, the majority represented composite terms that necessitated combining terms within and across existing ontologies. Although these terms are not good candidates for community ontologies, they can be linked to structures contained within these ontologies. We conclude that the term request pipeline serves as a useful adjunct to community ontologies for annotating experimental data and increases the FAIRness of SPARC data.

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Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging

Cited by 14 publications

References 7 publications

Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging

Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging

Replication and generalization in applied neuroimaging

Extending and using anatomical vocabularies in the Stimulating Peripheral Activity to Relieve Conditions (SPARC) program

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