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, 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.
Highlights
Neuroimaging software methods are based on complex architectures, thousands of lines of code, and hundreds of configuration parameters
We describe a framework for validating and sharing software implementations, and we illustrate its usage with an example application: population receptive field methods for functional MRI data
We illustrate the framework with population receptive field tools because there are many: We found 11 public pRF implementations
Summary
Neuroimaging software methods are based on complex architectures, thousands of lines of code, and hundreds of configuration parameters. It is 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 the reduction of coding errors, but do not guarantee the scientific validity of the results. Computational reproducibility—enabling anyone to obtain the same result for the same input data and code—is one important component of scientific reproducibility [1,2]. Computational generalization means obtaining the same result for the same input data and different software implementations. Computational validity is the further test as to whether the result is correct. For reviewers and scientists alike, it is impossible to establish validity by just reading code
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