dc.contributor.author | BOKDE, ARUN | |
dc.date.accessioned | 2019-10-16T13:20:08Z | |
dc.date.available | 2019-10-16T13:20:08Z | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018 | en |
dc.identifier.citation | Bossier, H., Seurinck, R., Kuhn, S., Banaschewski, T., Barker, G.J., Bokde, A.L.W., Martinot, J.-L., Lemaitre, H., Paus, T., Millenet, S., Moerkerke, B., The influence of study-level inference models and study set size on coordinate-based fMRI meta-analyses, Frontiers in Neuroscience, 2018, 11, 745 | en |
dc.identifier.other | Y | |
dc.identifier.uri | http://hdl.handle.net/2262/89760 | |
dc.description.abstract | Given the increasing amount of neuroimaging studies, there is a growing need to summarize published results. Coordinate-based meta-analyses use the locations of statistically significant local maxima with possibly the associated effect sizes to aggregate studies. In this paper, we investigate the influence of key characteristics of a coordinate-based meta-analysis on (1) the balance between false and true positives and (2) the activation reliability of the outcome from a coordinate-based meta-analysis.More particularly, we consider the influence of the chosen group level model at the study level [fixed effects, ordinary least squares (OLS), or mixed effects models], the type of coordinate-based meta-analysis [Activation Likelihood Estimation (ALE) that only uses peak locations, fixed effects, and random effects meta-analysis that take into account both peak location and height] and the amount of studies included in the analysis (from 10to 35). To do this, we apply a resampling scheme on a large dataset (N=1,400) to create a test condition and compare this with an independent evaluation condition. The test condition corresponds to subsampling participants into studies and combine these using meta-analyses. The evaluation condition corresponds to a high-powered group analysis.We observe the best performance when using mixed effects models in individual studies combined with a random effects meta-analysis. Moreover the performance increases with the number of studies included in the meta-analysis. When peak height is not taken into consideration, we show that the popular ALE procedure is a good alternative in terms of the balance between type I and II errors. However, it requires more studies compared to other procedures in terms of activation reliability. Finally, we discuss the differences,interpretations, and limitations of our results | en |
dc.format.extent | 00745 | en |
dc.language.iso | en | en |
dc.publisher | Frontiers Media | en |
dc.relation.ispartofseries | Frontiers in Neuroscience; | |
dc.relation.ispartofseries | 11; | |
dc.relation.ispartofseries | JAN; | |
dc.rights | Y | en |
dc.subject | Coordinate-based meta-analysis | en |
dc.subject | fMRI | en |
dc.subject | Group modeling | en |
dc.subject | Mixed effects models | en |
dc.subject | Random effects models | en |
dc.subject | Reliability | en |
dc.title | The influence of study-level inference models and study set size on coordinate-based fMRI meta-analyses | en |
dc.type | Journal Article | en |
dc.type.supercollection | scholarly_publications | en |
dc.type.supercollection | refereed_publications | en |
dc.identifier.peoplefinderurl | http://people.tcd.ie/bokdea | |
dc.identifier.rssinternalid | 186842 | |
dc.identifier.doi | http://dx.doi.org/10.3389/fnins.2017.00745 | |
dc.rights.ecaccessrights | openAccess | |
dc.identifier.orcid_id | 0000-0003-0114-4914 | |