ArticlePLoS computational biology2020
A validation framework for neuroimaging software: The case of population receptive fields.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 54 citations in OpenAlex.
- Quantification of planar cortical magnification with optimal transport and topological smoothing.NeuroImage · 2026Article
- The Population Spatial Frequency Toolbox.Journal of open research software · 2026Article
- Article
- Machine Learning Matches Human Performance at Segmenting the Human Visual Cortex.bioRxiv : the preprint server for biology · 2025Article
- Biases in Volumetric Versus Surface Analyses in Population Receptive Field Mapping.Human brain mapping · 2025Article
- Modeling neural contrast sensitivity functions in human visual cortex.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Sub-bundle based analysis reveals the role of human optic radiation in visual working memory.Human brain mapping · 2024Article
- Characterizing Spatiotemporal Population Receptive Fields in Human Visual Cortex with fMRI.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Normalization by orientation-tuned surround in human V1-V3.PLoS computational biology · 2023Article
- Comparing retinotopic maps of children and adults reveals a late-stage change in how V1 samples the visual field.Nature communications · 2023Article
- Comparison of Stimulus Types for Retinotopic Cortical Mapping of Macular Disease.Translational vision science & technology · 2023Article
- Characterizing visual cortical magnification with topological smoothing and optimal transportation.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Pitfalls in post hoc analyses of population receptive field data.NeuroImage · 2022Article
- What has vision science taught us about functional MRI?NeuroImage · 2022Article
- Visual timing-tuned responses in human association cortices and response dynamics in early visual cortex.Nature communications · 2022Article
- Article
- Cross-dataset reproducibility of human retinotopic maps.NeuroImage · 2021Article
- Article
- Divisive normalization unifies disparate response signatures throughout the human visual hierarchy.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Topology-preserving smoothing of retinotopic maps.PLoS computational biology · 2021Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.