Abstract
The importance of integrating research findings is incontrovertible and procedures for coordinate-based meta-analysis (CBMA) such as Activation Likelihood Estimation (ALE) have become a popular approach to combine results of fMRI studies when only peaks of activation are reported. As meta-analytical findings help building cumulative knowledge and guide future research, not only the quality of such analyses but also the way conclusions are drawn is extremely important. Like classical meta-analyses, coordinate-based meta-analyses can be subject to different forms of publication bias which may impact results and invalidate findings. The file drawer problem refers to the problem where studies fail to get published because they do not obtain anticipated results (e.g. due to lack of statistical significance). To enable assessing the stability of meta-analytical results and determine their robustness against the potential presence of the file drawer problem, we present an algorithm to determine the number of noise studies that can be added to an existing ALE fMRI meta-analysis before spatial convergence of reported activation peaks over studies in specific regions is no longer statistically significant. While methods to gain insight into the validity and limitations of results exist for other coordinate-based meta-analysis toolboxes, such as Galbraith plots for Multilevel Kernel Density Analysis (MKDA) and funnel plots and egger tests for seed-based d mapping, this procedure is the first to assess robustness against potential publication bias for the ALE algorithm. The method assists in interpreting meta-analytical results with the appropriate caution by looking how stable results remain in the presence of unreported information that may differ systematically from the information that is included. At the same time, the procedure provides further insight into the number of studies that drive the meta-analytical results. We illustrate the procedure through an example and test the effect of several parameters through extensive simulations. Code to generate noise studies is made freely available which enables users to easily use the algorithm when interpreting their results.
Highlights
Functional magnetic resonance imaging continues to contribute greatly to the knowledge about the location of cognitive functions in the brain [1]
We propose a measure for robustness against potential publication bias that is an adaptation of the classical Fail-Safe N
We proposed an algorithm that implements the principles of the Fail-Safe N (FSN) for Activation Likelihood Estimation (ALE) meta-analyses of Functional magnetic resonance imaging (fMRI) studies, providing a measure for assessing robustness of a statistically significant cluster
Summary
Functional magnetic resonance imaging (fMRI) continues to contribute greatly to the knowledge about the location of cognitive functions in the brain [1]. Neuroimaging studies are valuable sources of information for functional organization of the brain but often face substantial challenges. A huge variety in employed experimental conditions and analytical methods exist, possibly leading to inconsistencies across studies and paradigms. Several different implementations and task contrasts might be applied while exploring a certain paradigm, using different analysis toolboxes, pipelines and statistical thresholds. FMRI studies are relatively expensive which often limits the size of studies [2] leading to low statistical power to detect true activation [3]. Further progress in understanding human brain function will require integration of data across studies using meta-analyses, which can increase power to detect a true effect and allows to assess the replicability or consistency of activated regions across labs and tasks [4,5]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
