What are we missing? What are we assuming? The need to foster feature discovery tools to improve statistical models

Statistical Parametric Mapping (SPM) has been profoundly influential to neuroimaging as it has fostered rigorous, statistically grounded structure for model-based inferences that have led to mechanistic insights about the human brain over the past 30 years. The statistical constructs shared with the world through SPM have been instrumental for deriving meaning from neuroimaging data; however, they require simplifying assumptions which can provide results that, while statistically sound, may not accurately reflect the mechanisms of brain function. A platform that fosters the exploration of the rich and varying neuronal and physiologic underpinnings of the measured signals and their associations to behavior and physiologic measures needs a different set of tools. These tools, typically developed outside of platforms, allow rapid, iterative hypotheses testing, and flexibility. Rather than primarily working to build tools that account for and normalize sources of variability, platforms would benefit from building more exploratory tools that enable the community to characterize and understand this non-random variability in the signal as it is likely to be brimming with features that could catalyze new directions, as well as enhance the reach of statistical models.