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Evidence for the null hypothesis in functional magnetic resonance imaging using group-level Bayesian inference

View ORCID ProfileRuslan Masharipov, View ORCID ProfileYaroslav Nikolaev, View ORCID ProfileAlexander Korotkov, Michael Didur, Denis Cherednichenko, View ORCID ProfileMaxim Kireev
doi: https://doi.org/10.1101/2021.06.02.446711
Ruslan Masharipov
1N.P. Bechtereva Institute of the Human Brain, Russian Academy of Sciences, Academika Pavlova Street 9, St. Petersburg, 197376, Russia
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Yaroslav Nikolaev
1N.P. Bechtereva Institute of the Human Brain, Russian Academy of Sciences, Academika Pavlova Street 9, St. Petersburg, 197376, Russia
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Alexander Korotkov
1N.P. Bechtereva Institute of the Human Brain, Russian Academy of Sciences, Academika Pavlova Street 9, St. Petersburg, 197376, Russia
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Michael Didur
1N.P. Bechtereva Institute of the Human Brain, Russian Academy of Sciences, Academika Pavlova Street 9, St. Petersburg, 197376, Russia
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Denis Cherednichenko
1N.P. Bechtereva Institute of the Human Brain, Russian Academy of Sciences, Academika Pavlova Street 9, St. Petersburg, 197376, Russia
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Maxim Kireev
1N.P. Bechtereva Institute of the Human Brain, Russian Academy of Sciences, Academika Pavlova Street 9, St. Petersburg, 197376, Russia
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  • For correspondence: kireev@ihb.spb.ru
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Abstract

Classical null hypothesis significance testing is limited to the rejection of the point-null hypothesis; it does not allow the interpretation of non-significant results. Moreover, studies with a sufficiently large sample size will find statistically significant results even when the effect is negligible and may be considered practically equivalent to the ‘null effect’. This leads to a publication bias against the null hypothesis. There are two main approaches to assess ‘null effects’: shifting from the point-null to the interval-null hypothesis and considering the practical significance in the frequentist approach; using the Bayesian parameter inference based on posterior probabilities, or the Bayesian model inference based on Bayes factors. Herein, we discuss these statistical methods with particular focus on the application of the Bayesian parameter inference, as it is conceptually connected to both frequentist and Bayesian model inferences. Although Bayesian methods have been theoretically elaborated and implemented in commonly used neuroimaging software, they are not widely used for ‘null effect’ assessment. To demonstrate the advantages of using the Bayesian parameter inference, we compared it with classical null hypothesis significance testing for fMRI data group analysis. We also consider the problem of choosing a threshold for a practically significant effect and discuss possible applications of Bayesian parameter inference in fMRI studies. We argue that Bayesian inference, which directly provides evidence for both the null and alternative hypotheses, may be more intuitive and convenient for practical use than frequentist inference, which only provides evidence against the null hypothesis. Moreover, it may indicate that the obtained data are not sufficient to make a confident inference. Because interim analysis is easy to perform using Bayesian inference, one can evaluate the data as the sample size increases and decide to terminate the experiment if the obtained data are sufficient to make a confident inference. To facilitate the application of the Bayesian parameter inference to ‘null effect’ assessment, scripts with a simple GUI were developed.

Competing Interest Statement

The authors have declared no competing interest.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted June 02, 2021.
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Evidence for the null hypothesis in functional magnetic resonance imaging using group-level Bayesian inference
Ruslan Masharipov, Yaroslav Nikolaev, Alexander Korotkov, Michael Didur, Denis Cherednichenko, Maxim Kireev
bioRxiv 2021.06.02.446711; doi: https://doi.org/10.1101/2021.06.02.446711
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Evidence for the null hypothesis in functional magnetic resonance imaging using group-level Bayesian inference
Ruslan Masharipov, Yaroslav Nikolaev, Alexander Korotkov, Michael Didur, Denis Cherednichenko, Maxim Kireev
bioRxiv 2021.06.02.446711; doi: https://doi.org/10.1101/2021.06.02.446711

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