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The application of machine learning methods to neuroimaging data has fundamentally altered the field of cognitive neuroscience. Future progress in understanding brain function using these methods will require addressing a number of key methodological and interpretive challenges. Because these challenges often remain unseen and metaphorically "haunt" our efforts to use these methods to understand the brain, we refer to them as "ghosts". In this paper, we describe three such ghosts, situate themdoi:10.1016/j.neuroimage.2017.08.019 pmid:28793239 fatcat:7nm43es45rcldjelaiqtpm3hma