A validation framework for neuroimaging software: the case of population receptive fields

Abstract

Neuroimaging 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. We apply the framework to an 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: (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. We identified realistic conditions that lead to imperfect parameter recovery in all four implementations, and we provide means to reduce this problem. The computational validity framework supports scientific rigor and creativity, as opposed to the oft-repeated suggestion that investigators rely upon a few agreed upon packages. The framework and methods can be extended to other critical neuroimaging algorithms. Having validation frameworks help (1) developers to build new software, (2) research scientists to verify the softwares accuracy, and (3) reviewers to evaluate the methods used in publications and grants. Author SummaryComputer science provides powerful tools and techniques for implementing and deploying software. These techniques support software collaboration, reduce coding errors and enable reproducibility of the analyses. A further question is whether the software estimates are correct (valid). We describe a framework for validating and sharing software implementations based on ground-truth testing. We applied the framework to four separate applications that implemented population receptive field (pRF) estimates for functional MRI data. We quantified the validity, and we also documented limitations with these applications. Finally, we provide ways to mitigate these limitations. Implementing a software validation framework along with sharing and reproducibility is an important step for the complex methods used in neuroscience. Validation will help developers to build new software, researchers verify that the results are valid, and reviewers to evaluate the precision of methods in publications and grants.