Researchers using task-fMRI data have access to a wide range of analysis tools to model brain activity. This diversity of analytical approaches has been shown to have substantial effects on neuroimaging results. Combined with selective reporting, this analytical flexibility can lead to an inflated rate of false positives and contributes to the irreproducibility of neuroimaging findings. Multiverse analyses are a way to systematically explore and integrate pipeline variation on a given dataset. We focus on the setting where multiple statistic maps are produced as an output of a set of analyses originating from a single datset. Meta-analysis is  a natural approach to extract consensus inferences from these maps, yet the traditional assumption of independence amongst input datasets does not hold. In this work we consider a suite of methods to conduct meta-analysis in the multiverse setting, accounting for inter-pipeline dependence among the results. The validity of these methods were assessed in a set of simulations and evaluated on a real world dataset from "NARPS", a multiverse analysis with 70 different statistic maps originating from the same data, and a multiverse analysis originating form the same HCP data. Our findings demonstrate the validity of our proposed same-data meta-analysis (SDMA) models under inter-pipeline dependence, and provide an array of options for the analysis multiverse data.