Previous research showed that employing results from meta-analyses of relevant previous fMRI studies can improve the performance of voxelwise Bayesian second-level fMRI analysis. In this process, prior distributions for Bayesian analysis can be determined by information acquired from the meta-analyses. However, only image-based meta-analysis, which is not widely accessible to fMRI researchers due to the lack of shared statistical images, was tested in the previous study, so the applicability of the prior determination method proposed by the previous study might be limited. In the present study, whether determining prior distributions based on coordinate-based meta-analysis, which is widely accessible to researchers, can also improve the performance of Bayesian analysis, was examined. Three different types of coordinate-based meta-analyses, BrainMap and Ginger ALE, and NeuroQuery, were tested as information sources for prior determination. Five different datasets addressing three task conditions, i.e., working memory, speech, and face processing, were analyzed via Bayesian analysis with a meta-analysis informed prior distribution, Bayesian analysis with a default Cauchy prior adjusted for multiple comparisons, and frequentist analysis with familywise error correction. The findings from the aforementioned analyses suggest that use of coordinate-based meta-analysis also significantly enhanced performance of Bayesian analysis as did image-based meta-analysis.


  • In fMRI analysis, how to threshold a statistical image resulting from the analysis has been a significant issue

  • The results are presented for each dataset, each type of meta-analysis used for prior determination, and each analysis type

  • As Han (2021a) only tested prior determination based on image-based meta-analysis in a previous study, in the present study, how the employment of coordinate-based meta-analysis, which has been widely used in the field, influenced the performance of Bayesian fMRI analysis was investigated

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In fMRI analysis, how to threshold a statistical image resulting from the analysis has been a significant issue. It would be necessary to consider and examine how to address potential issues and problems associated with multiple comparison correction and inflated false positives in fMRI analysis, which involves simultaneous multiple tests. In addition to the aforementioned issue associated with inflated false positives and thresholding, the interpretation of resultant p-values could be problematic in traditional frequentist fMRI analysis. Even if it is possible to control potential false positives through multiple comparison correction, it is still unclear whether resultant p-values can be used to examine whether a hypothesis of interest, instead of a null hypothesis, is supported by evidence [6]. Given that researchers are primarily interested in whether evidence supports their alternative hypothesis regarding the presence of a significant effect, this epistemological issue, associated with traditional frequentist fMRI analysis, shall be carefully considered


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