Providing Evidence for the Null Hypothesis in Functional Magnetic Resonance Imaging Using Group-Level Bayesian Inference

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. This leads to a bias against the null hypothesis. Herein, we discuss statistical approaches to ‘null effect’ assessment focusing o...

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Autores principales:	Ruslan Masharipov, Irina Knyazeva, Yaroslav Nikolaev, Alexander Korotkov, Michael Didur, Denis Cherednichenko, Maxim Kireev
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	null results fMRI Bayesian analyses human brain statistical parametric mapping Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/bb1ad68a0fa34742bb5a7a1707be1a02
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spelling	oai:doaj.org-article:bb1ad68a0fa34742bb5a7a1707be1a022021-12-02T07:30:16ZProviding Evidence for the Null Hypothesis in Functional Magnetic Resonance Imaging Using Group-Level Bayesian Inference1662-519610.3389/fninf.2021.738342https://doaj.org/article/bb1ad68a0fa34742bb5a7a1707be1a022021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fninf.2021.738342/fullhttps://doaj.org/toc/1662-5196Classical null hypothesis significance testing is limited to the rejection of the point-null hypothesis; it does not allow the interpretation of non-significant results. This leads to a bias against the null hypothesis. Herein, we discuss statistical approaches to ‘null effect’ assessment focusing on the Bayesian parameter inference (BPI). Although Bayesian methods have been theoretically elaborated and implemented in common neuroimaging software packages, they are not widely used for ‘null effect’ assessment. BPI considers the posterior probability of finding the effect within or outside the region of practical equivalence to the null value. It can be used to find both ‘activated/deactivated’ and ‘not activated’ voxels or to indicate that the obtained data are not sufficient using a single decision rule. It also allows to evaluate the data as the sample size increases and decide to stop the experiment if the obtained data are sufficient to make a confident inference. To demonstrate the advantages of using BPI for fMRI data group analysis, we compare it with classical null hypothesis significance testing on empirical data. We also use simulated data to show how BPI performs under different effect sizes, noise levels, noise distributions and sample sizes. Finally, we consider the problem of defining the region of practical equivalence for BPI and discuss possible applications of BPI in fMRI studies. To facilitate ‘null effect’ assessment for fMRI practitioners, we provide Statistical Parametric Mapping 12 based toolbox for Bayesian inference.Ruslan MasharipovIrina KnyazevaYaroslav NikolaevAlexander KorotkovMichael DidurDenis CherednichenkoMaxim KireevFrontiers Media S.A.articlenull resultsfMRIBayesian analyseshuman brainstatistical parametric mappingNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neuroinformatics, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	null results fMRI Bayesian analyses human brain statistical parametric mapping Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	null results fMRI Bayesian analyses human brain statistical parametric mapping Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Ruslan Masharipov Irina Knyazeva Yaroslav Nikolaev Alexander Korotkov Michael Didur Denis Cherednichenko Maxim Kireev Providing Evidence for the Null Hypothesis in Functional Magnetic Resonance Imaging Using Group-Level Bayesian Inference
description	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. This leads to a bias against the null hypothesis. Herein, we discuss statistical approaches to ‘null effect’ assessment focusing on the Bayesian parameter inference (BPI). Although Bayesian methods have been theoretically elaborated and implemented in common neuroimaging software packages, they are not widely used for ‘null effect’ assessment. BPI considers the posterior probability of finding the effect within or outside the region of practical equivalence to the null value. It can be used to find both ‘activated/deactivated’ and ‘not activated’ voxels or to indicate that the obtained data are not sufficient using a single decision rule. It also allows to evaluate the data as the sample size increases and decide to stop the experiment if the obtained data are sufficient to make a confident inference. To demonstrate the advantages of using BPI for fMRI data group analysis, we compare it with classical null hypothesis significance testing on empirical data. We also use simulated data to show how BPI performs under different effect sizes, noise levels, noise distributions and sample sizes. Finally, we consider the problem of defining the region of practical equivalence for BPI and discuss possible applications of BPI in fMRI studies. To facilitate ‘null effect’ assessment for fMRI practitioners, we provide Statistical Parametric Mapping 12 based toolbox for Bayesian inference.
format	article
author	Ruslan Masharipov Irina Knyazeva Yaroslav Nikolaev Alexander Korotkov Michael Didur Denis Cherednichenko Maxim Kireev
author_facet	Ruslan Masharipov Irina Knyazeva Yaroslav Nikolaev Alexander Korotkov Michael Didur Denis Cherednichenko Maxim Kireev
author_sort	Ruslan Masharipov
title	Providing Evidence for the Null Hypothesis in Functional Magnetic Resonance Imaging Using Group-Level Bayesian Inference
title_short	Providing Evidence for the Null Hypothesis in Functional Magnetic Resonance Imaging Using Group-Level Bayesian Inference
title_full	Providing Evidence for the Null Hypothesis in Functional Magnetic Resonance Imaging Using Group-Level Bayesian Inference
title_fullStr	Providing Evidence for the Null Hypothesis in Functional Magnetic Resonance Imaging Using Group-Level Bayesian Inference
title_full_unstemmed	Providing Evidence for the Null Hypothesis in Functional Magnetic Resonance Imaging Using Group-Level Bayesian Inference
title_sort	providing evidence for the null hypothesis in functional magnetic resonance imaging using group-level bayesian inference
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/bb1ad68a0fa34742bb5a7a1707be1a02
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Providing Evidence for the Null Hypothesis in Functional Magnetic Resonance Imaging Using Group-Level Bayesian Inference

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