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    Reproducibility and replicability in neuroimaging data analysis

    Files
    neurology_review_revised.pdf (969.2Kb)
    Links to Files
    https://journals.lww.com/co-neurology/Abstract/2022/08000/Reproducibility_and_replicability_in_neuroimaging.6.aspx
    Permanent Link
    https://doi.org/10.1097/WCO.0000000000001081
    http://hdl.handle.net/11603/25290
    Collections
    • UMBC Computer Science and Electrical Engineering Department
    • UMBC Faculty Collection
    Metadata
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    Author/Creator
    Adali, Tü̈lay
    Calhoun, Vince D.
    Author/Creator ORCID
    https://orcid.org/0000-0003-0594-2796
    Date
    2022-08
    Type of Work
    8 pages
    Text
    journal articles
    postprints
    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
    Rights
    This is not the final published version.
    Access to this item will begin on 9/1/2023.
    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.


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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3021


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.