Reproducibility and replicability in neuroimaging data analysis
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Author/Creator
Author/Creator ORCID
Date
2022-08
Type of Work
Department
Program
Citation of Original Publication
Adali, Tü̈laya; Calhoun, Vince D.b. Reproducibility and replicability in neuroimaging data analysis. Current Opinion in Neurology: August 2022 - Volume 35 - Issue 4 - p 475-481 doi: 10.1097/WCO.0000000000001081
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This is not the final published version.
Access to this item will begin on 9/1/2023.
Access to this item will begin on 9/1/2023.
Subjects
Abstract
Purpose of review
Machine learning solutions are being increasingly used in the analysis of neuroimaging (NI) data, and as a result, there is an increase in the emphasis of the reproducibility and replicability of these data-driven solutions. Although this is a very positive trend, related terminology is often not properly defined, and more importantly, (computational) reproducibility that refers to obtaining consistent results using the same data and the same code is often disregarded.
Recent findings
We review the findings of a recent paper on the topic along with other relevant literature, and present two examples that demonstrate the importance of accounting for reproducibility in widely used software for NI data.
Summary
We note that reproducibility should be a first step in all NI data analyses including those focusing on replicability, and introduce available solutions for assessing reproducibility. We add the cautionary remark that when not taken into account, lack of reproducibility can significantly bias all subsequent analysis stages.