Journal:Neuroimaging, genetics, and clinical data sharing in Python using the CubicWeb framework
| Full article title | Neuroimaging, genetics, and clinical data sharing in Python using the CubicWeb framework |
|---|---|
| Journal | Frontiers in Neuroinformatics |
| Author(s) | Grigis, Antoine; Goyard, David; Cherbonnier, Robin; Gareau, Thomas; Papadopoulos Orfanos, Dimitri; Chauvat, Nicolas; Di Mascio, Adrien; Schumann, Gunter; Spooren, Will; Murphy, Declan; Frouin, Vincent |
| Author affiliation(s) | Université Paris-Saclay, Logilab, King’s College London, F. Hoffmann-La Roche Pharmaceuticals |
| Primary contact | Email: antoine dot grigis at cea dot fr |
| Editors | Marcus, Daniel |
| Year published | 2017 |
| Volume and issue | 11 |
| Page(s) | 18 |
| DOI | 10.3389/fninf.2017.00018 |
| ISSN | 1662-5196 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | http://journal.frontiersin.org/article/10.3389/fninf.2017.00018/full |
| Download | http://journal.frontiersin.org/article/10.3389/fninf.2017.00018/pdf (PDF) |
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Abstract
In neurosciences or psychiatry, the emergence of large multi-center population imaging studies raises numerous technological challenges. From distributed data collection, across different institutions and countries, to final data publication service, one must handle the massive, heterogeneous, and complex data from genetics, imaging, demographics, or clinical scores. These data must be both efficiently obtained and downloadable. We present a Python solution, based on the CubicWeb open-source semantic framework, aimed at building population imaging study repositories. In addition, we focus on the tools developed around this framework to overcome the challenges associated with data sharing and collaborative requirements. We describe a set of three highly adaptive web services that transform the CubicWeb framework into a (1) multi-center upload platform, (2) collaborative quality assessment platform, and (3) publication platform endowed with massive-download capabilities. Two major European projects, IMAGEN and EU-AIMS, are currently supported by the described framework. We also present a Python package that enables end users to remotely query neuroimaging, genetics, and clinical data from scripts.
Keywords: web service, data sharing, database, neuroimaging, genetics, medical informatics, Python
Introduction
Health research strategies using neuroimaging have shifted in recent years: the focus has moved from patient care only, to a combination of patient care and prevention. In the case of neurodegenerative and psychiatric diseases, this drives the creation of increasingly numerous massive imaging studies, also known as population imaging (PI) surveys.[1][2] It should be noticed that PI studies no longer consist of image data only. The recent wide availability of high-throughput genomics has augmented the subject data with genetics, epigenetics, and functional genomics. Likewise, the standardization of personality, demographics, and deficit tests in psychiatry facilitates the acquisition of clinical/behavioral records to enrich the subject data in large population studies. Moreover, PI studies now classically encompass more than one single imaging session per subject and cover multiple-time point heterogeneous experiments. Ultimately, these studies with complex imaging and extended data (PIx) require multi-center acquisitions to build a large target population.
A regular PIx infrastructure has to cover the following three main topics: (1) data collection, (2) quality control (QC) with data processing, and (3) data indexing and publication with controlled data sharing mechanisms. Furthermore, PIx infrastructures must evolve during the life cycle of a population imaging project, and they must also be resilient to extreme evolutions of the data content and management. In the projects we manage, we experience several extreme evolutions. The first kind of evolution may affect the published dataset such as adding a new modality for all subjects, a new time point or a new subcohort. Second, the amount of data requested evolves dramatically as the project consortium gets enlarged.[3] Finally, internal ontologies have to evolve constantly in order to match the ongoing initiatives on interoperability.[4][5]
References
- ↑ Hurko, O.; Black, S.E.; Doody, R. et al. (2012). "The ADNI Publication Policy: Commensurate recognition of critical contributors who are not authors". NeuroImage 59 (4): 4196–4200. doi:10.1016/j.neuroimage.2011.10.085. PMC PMC3676932. PMID 22100665. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3676932.
- ↑ Poldrack, R.A.; Gorgolewski, K.J. (2014). "Making big data open: Data sharing in neuroimaging". Nature Neuroscience 17 (11): 1510–7. doi:10.1038/nn.3818. PMID 25349916.
- ↑ Gorgolewski, K.J.; Varoquaux, G.; Rivera, G. et al. (2015). "NeuroVault.org: A web-based repository for collecting and sharing unthresholded statistical maps of the human brain". Frontiers in Neuroinformatics 9: 8. doi:10.3389/fninf.2015.00008. PMC PMC4392315. PMID 25914639. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4392315.
- ↑ Scheufele, E.; Aronzon, D.; Coopersmith, R. et al. (2014). "tranSMART: An Open Source Knowledge Management and High Content Data Analytics Platform". AMIA Joint Summits on Translational Science 2014: 96–101. PMC PMC4333702. PMID 25717408. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4333702.
- ↑ Gorgolewski, K.J.; Auer, T.; Calhoun, V.D. et al. (2016). "The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments". Scientific Data 3: 160044. doi:10.1038/sdata.2016.44. PMC PMC4978148. PMID 27326542. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4978148.
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added. References are in order of appearance rather than alphabetical order (as the original was) due to the way this wiki works.
