Data visualization for inference in tomographic brain imaging

Tomographic imaging (i.e.magnetic resonance imaging [MRI], positron emission tomography [PET], X-ray computed tomography [CT]) offers a unique window in understanding structure-function relationships in the living brain.Nowadays, it is standard practice to acquire entire brain volumes and perform statistical analyses at each voxel, an approach known as statistical parametric mapping (Friston, Frith, Liddle, & Frackowiak, 1991).Because it is impossible to report the statistical results in every voxel, summary tables and figures are of importance.The choice made by authors to create such figures should however not be driven by aesthetic considerations alone but also driven by the message to convey (Rougier et al., 2014).This is not to say that beautiful figures should not be used, as more appealing figures might in fact help in remembering results (Borkin et al., 2013; Madan, 2015b).At the intersection of psychology, computer vision, graphic design and statistics, there is a field of research that looks at how to represent information and what features are beneficial or harmful in figure designs.For instance, Cleveland and McGill (1983) showed that changing the axis scaling in scatter plots can alter inference on associations between variables.Siegrist (1996) show that using perspective in pie charts, lead to falsely infer magnitude differences because the slices that are closer to the reader appear to be larger than those in the back.In general, there are recommendations for plotting the data rather than summary statistics as those summary values can be obtained with very different distributions which can preclude the use of some statistical tests (see e.g.Anscombe, 1973, for correlations or Weissgerber, Milic, Winham, & Garovic, 2015, for bar graphs).Here we discuss information provided in figures when presenting tomographic data results.Many proposals have already been made by others, what we offer is a principled way chose among those proposals and apply them.A review of articles using tomographic techniques published between January 2016 and June 2018 in the European Journal of Neuroscience (N = 30 -https ://github.com/ CPern et/MRI_FaceD ata_Wakem an-Henso n/blob/maste r/DataV iz/EJN_paper_review.csv) shows that four broad types of messages are obtained from statistical parametric ORCID

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