Abstract
Scientific data generation in the world is continuous. However, scientific studies once published do not take advantage of new data. In order to leverage this incoming flow of data, we present Neuro-DISK, an end-to-end framework to continuously process neuroscience data and update the assessment of a given hypothesis as new data become available. Our scope is within the ENIGMA consortium, a large international collaboration for neuro-imaging and genetics whose goal is to understand brain structure and function. Neuro-DISK includes an ontology and framework to organize datasets, cohorts, researchers, tools, working groups and organizations participating in multi-site studies, such as those of ENIGMA, and an automated discovery framework to continuously test hypotheses through the execution of scientific workflows. We illustrate the usefulness of our approach with an implemented example.
D. Garijo and S. Fakhraei—Co-first author.
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Acknowledgments
We are grateful to the KAVLI foundation for their support of ENIGMA Informatics (PIs: Jahanshad and Gil). We also acknowledge support from the National Science Foundation under awards IIS-1344272 (PI: Gil), ICER-1541029 (Co-PI: Gil), and IIS-1344272 (PI: Gil), and from the National Institutes of Health’s Big Data to Knowledge Grant U54EB020403 for support for ENIGMA (PI: Thompson) and High resolution mapping of the genetic risk for disease in the aging brain grant R01AG059874 (PI: Jahanshad). Data used in preparing this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu), phases both 1 and 2. As such, many investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators is available online (http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf). We also used the DLBS (http://fcon_1000.projects.nitrc.org/indi/retro/dlbs.html) dataset and the UK Biobank in this study. This research was conducted using the UK Biobank Resource under Application Number ‘11559’.
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Garijo, D. et al. (2019). Towards Automated Hypothesis Testing in Neuroscience. In: Gadepally, V., et al. Heterogeneous Data Management, Polystores, and Analytics for Healthcare. DMAH Poly 2019 2019. Lecture Notes in Computer Science(), vol 11721. Springer, Cham. https://doi.org/10.1007/978-3-030-33752-0_18
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