In neuroimaging studies, small sample sizes and the resultant reduced statistical power to detect effects that are not large, combined with inadequate analytic choices, concur to produce inflated or false-positive findings. To mitigate these issues, researchers often restrict analyses to specific brain areas, using the region of interest (ROI) approach. Crucially, ROI analysis assumes the a priori justified definition of the target region. Nonetheless, reports often lack details about where in the timeline, ranging from study conception to the data analysis and interpretation of findings, were ROIs selected. Frequently, the rationale for ROI selection is vague or inadequately founded on the existing literature. These shortcomings have important implications for ROI-based studies, augmenting the risk that observed effects are inflated or even false positives. Tools like preregistration and registered reports could address this problem, ensuring the validity of ROI-based studies. The benefits could be enhanced by additional practices such as selection of ROIs using quantitative methods (i.e., meta-analysis) and the sharing of whole-brain unthresholded maps of effect size, as well as of binary ROIs, in publicly accessible repositories.

The case for preregistering all region of interest (ROI) analyses in neuroimaging research

Cecchetti L;Handjaras G;Lettieri G;
2020

Abstract

In neuroimaging studies, small sample sizes and the resultant reduced statistical power to detect effects that are not large, combined with inadequate analytic choices, concur to produce inflated or false-positive findings. To mitigate these issues, researchers often restrict analyses to specific brain areas, using the region of interest (ROI) approach. Crucially, ROI analysis assumes the a priori justified definition of the target region. Nonetheless, reports often lack details about where in the timeline, ranging from study conception to the data analysis and interpretation of findings, were ROIs selected. Frequently, the rationale for ROI selection is vague or inadequately founded on the existing literature. These shortcomings have important implications for ROI-based studies, augmenting the risk that observed effects are inflated or even false positives. Tools like preregistration and registered reports could address this problem, ensuring the validity of ROI-based studies. The benefits could be enhanced by additional practices such as selection of ROIs using quantitative methods (i.e., meta-analysis) and the sharing of whole-brain unthresholded maps of effect size, as well as of binary ROIs, in publicly accessible repositories.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11771/16197
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