A major advantage of magnetic resonance imaging (MRI) over other neuroimaging methods is its capability to noninvasively locate a region of interest (ROI) in the human brain. For example, using functional MRI, we are able to pinpoint where in the brain a cognitive task elicits higher activation relative to a control. But just knowing the Cartesian coordinate of such a ROI is not useful if we cannot assign it a neuroanatomical label. For this reason, MRI images are usually normalized into a

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... n template space (Fonov et al., 2011) , where well-established atlases can be used to associate a given coordinate with the label of a brain region. Most major neuroimaging software packages provide some functionality to locate the main peaks of an ROI but this functionality is often restricted to a few atlases, frequently requires manual intervention, does not give the user much flexibility in the output creation process, and never considers the full extent of the ROI. To tackle those shortcomings, we created AtlasReader, a Python interface for generating coordinate tables and region labels from statistical MRI images. With AtlasReader, users can use any of the freely and publicly available neuroimaging atlases, without any restriction to their preferred software package, to create publication-ready output figures and tables that contain relevant information about the peaks and clusters extent of each ROI. To our knowledge, providing atlas information about the full extent of a cluster, i.e. over which atlas regions does a ROI extent, is a new feature that is not available in any other, comparable neuroimaging software package. Executing AtlasReader on an MRI image will create the following four outputs: 1. An overview figure showing all ROIs throughout the whole brain (Fig. 1) . 2. For each ROI, an informative figure showing the sagittal, coronal and transversal plane centered on the main peak of the ROI (Fig. 2) . 3. A table containing information about the main peaks in each ROI (Fig. 3) . 4. A table containing information about the cluster extent of each ROI (Fig. 4) . Users have many parameters available to guide the creation of these outputs. For example, with cluster_extent a user can specify the minimum number of contiguous voxels