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• W2968553640 abstract "Over the past decades, neuroscientists are increasingly becoming aware of the limited reproducibility of neuroimaging results. More particularly, functional Magnetic Resonance Imaging (fMRI) data is characterized by a low signal-to-noise ratio with a high cost to scan participants. This in turn leads to a low observed power in the literature to detect reasonable effect sizes (Poldrack et al., 2017). Several solutions have been proposed, from which one is to systematically aggregate published studies to improve the control of type I and II errors. In this contribution, we investigate two approaches of pooling fMRI data across studies. A first method is to extend the hierarchical two-stage model typically used in an fMRI data analysis to aggregate trials and subjects within individual studies. For every spatial location in the brain (i.e. every voxel), a General Linear Model (GLM) is fitted with a random effects term associated with studies. We use current software from the neuroimaging literature to estimate all parameters from the model. Note that we need a brain image per study containing the parameter estimates for the GLM at every brain location. And we need an additional map containing its variance estimates.A second approach is to rely on methods for meta-analysis using a random effects model. At every brain location, we transform the test-statistics associated with each study to Hedges’ g and calculate its variance. Then we estimate between-study heterogeneity using the method of moments estimator (DerSimonian and Laird, 1986). Finally, we calculate a weighted average with the weights being the inverse of the sum of within- and between-study variability. An added benefit of this approach is that we only need the sample size and one brain image per study containing the test-statistic at each location. We calculate for both methods the average standardized bias, length and coverage of confidence intervals using Monte-Carlo simulations and on resting state fMRI. The latter is obtained by instructing participants to do nothing in a scanner. The result is an image without any signal related to the design of a random experiment. Furthermore, it reflects realistic fMRI data under the null hypothesis of no activation (i.e. noise). Our results indicate that the three-level GLM model does not outperform meta-analysis techniques. Specifically, we observe more liberal empirical coverages associated with the three-level GLM model." @default.
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• W2968553640 date "2018-01-01" @default.
• W2968553640 modified "2023-09-27" @default.
• W2968553640 title "Evaluating statistical hierarchical models to pool fMRI results across studies" @default.
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