Show simple item record

dc.contributor.authorLerma-Usabiaga, Garikoitz
dc.contributor.authorBenson, Noah
dc.contributor.authorWinawer, Jonathan
dc.contributor.authorWandell, Brian A.
dc.date.accessioned2020-07-27T10:25:18Z
dc.date.available2020-07-27T10:25:18Z
dc.date.issued2020
dc.identifier.citationLerma-Usabiaga G, Benson N, Winawer J, Wandell BA (2020) A validation framework for neuroimaging software: The case of population receptive fields. PLoS Comput Biol 16(6): e1007924. https://doi.org/10.1371/journal.pcbi.1007924es_ES
dc.identifier.issn1553-734X
dc.identifier.urihttp://hdl.handle.net/10810/45582
dc.descriptionPublished: June 25, 2020es_ES
dc.description.abstractNeuroimaging software methods are complex, making it a near certainty that some implementations will contain errors. Modern computational techniques (i.e., public code and data repositories, continuous integration, containerization) enable the reproducibility of the analyses and reduce coding errors, but they do not guarantee the scientific validity of the results. It is difficult, nay impossible, for researchers to check the accuracy of software by reading the source code; ground truth test datasets are needed. Computational reproducibility means providing software so that for the same input anyone obtains the same result, right or wrong. Computational validity means obtaining the right result for the ground-truth test data. We describe a framework for validating and sharing software implementations, and we illustrate its usage with an example application: population receptive field (pRF) methods for functional MRI data. The framework is composed of three main components implemented with containerization methods to guarantee computational reproducibility. In our example pRF application, those components are: (1) synthesis of fMRI time series from ground-truth pRF parameters, (2) implementation of four public pRF analysis tools and standardization of inputs and outputs, and (3) report creation to compare the results with the ground truth parameters. The framework was useful in identifying realistic conditions that lead to imperfect parameter recovery in all four pRF implementations, that would remain undetected using classic validation methods. We provide means to mitigate these problems in future experiments. A computational validation framework supports scientific rigor and creativity, as opposed to the oft-repeated suggestion that investigators rely upon a few agreed upon packages. We hope that the framework will be helpful to validate other critical neuroimaging algorithms, as having a validation framework helps (1) developers to build new software, (2) research scientists to verify the software’s accuracy, and (3) reviewers to evaluate the methods used in publications and grants.es_ES
dc.description.sponsorshipSupported by a Marie Sklodowska-Curie (https://ec.europa.eu/programmes/horizon2020/ en/h2020-section/marie-sklodowska-curie-actions) grant to G.L.-U. (H2020-MSCA-IF-2017-795807- ReCiModel) and National Institutes of Health (https://www.nih.gov/) grants supporting N.C.B. and J.W. (EY027401, EY027964, MH111417). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.es_ES
dc.language.isoenges_ES
dc.publisherPLOS COMPUTATIONAL BIOLOGYes_ES
dc.relationinfo:eu-repo/grantAgreement/EC/H2020- MSCA-IF-2017-795807es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectComputer softwarees_ES
dc.subjectNeuroimaginges_ES
dc.subjectFunctional magnetic resonance imaginges_ES
dc.subjectWhite noisees_ES
dc.subjectSoftware toolses_ES
dc.subjectalgorithmses_ES
dc.subjectRadiies_ES
dc.subjectReproducibilityes_ES
dc.titleA validation framework for neuroimaging software: The case of population receptive fieldses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder© 2020 Lerma-Usabiaga et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.es_ES
dc.relation.publisherversionhttps://journals.plos.org/ploscompbiol/es_ES
dc.identifier.doi10.1371/journal.pcbi.1007924


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record