Neuroscientists are amassing the large-scale datasets needed to study individual differences and identify biomarkers. However, measurement reliability within individual samples is often suboptimal, thereby requiring unnecessarily large samples. We focus our comment on reliability in neuroimaging and provide examples of how the reliability can be increased.
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All code employed in this effort can be found on GitHub at https://github.com/TingsterX/power__reliability_sample_size
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We thank X. Castellanos, A. Franco, H. Kimball, A. Nikolaidis and X.-X. Xing for their helpful comments in the preparation of this commentary, as well as D. Klein for his guidance and encouragement of our focus on issues of reliability over the years, X. Castellanos for his support along the way, and all the contributors from CoRR and R3BRAIN for their enthusiasm on open neuroscience and data sharing. The two consortia are supported in part by the National Basic Research (973) Program (2015CB351702).
The authors declare no competing interests.
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Zuo, XN., Xu, T. & Milham, M.P. Harnessing reliability for neuroscience research. Nat Hum Behav 3, 768–771 (2019). https://doi.org/10.1038/s41562-019-0655-x
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