New Directions in Statistical Learning for Meaningful and Reproducible fMRI Analysis

Statistical learning methods have become mainstream in the analysis of Functional Magnetic Resonance Imaging (fMRI) data, spurred on by a growing consensus that meaningful neuro-scientific models built from fMRI data should be capable of accurate predictions of behavior or neural functioning. These approaches have convinced most neuroscientists that there is tremendous potential in the decoding of brain states using statistical learning. Along with this realization, though, has come a growing recognition of the limitations inherent in using black-box prediction methods for drawing neuro-scientific interpretations. The primary challenge now is how best to exploit statistical learning to answer scientific questions by incorporating domain knowledge and embodying hypotheses about cognitive processes into our models. Further advances will require resolution of many open questions, including: 1) Variability/Robustness: to what extent do patterns in fMRI replicate across trials, subjects, tasks, and studies? To what extent are processes that are observable through the fMRI BOLD response truly replicable across these different conditions? How similar is the neural functioning of one subject to another? 2) Representation: the most common data representation continues to consider voxels as static and independent, and examples are i.i.d.; however, activation patterns almost surely do not lie in voxel space. What are the true, modular activation structures? What is the relationship between similarity in cognitive state space and similarity in fMRI activation space? Can causality be inferred from fMRI? This workshop will engage leaders in the field in a debate about these issues while providing an opportunity for presentation of cutting-edge research addressing these questions.