Outcomes after traumatic brain injury (TBI) are variable and only partially predicted by acute injury factors. With rich datasets, we can examine how numerous factors – cognitive scores, acute injury variables, demographic variables, and brain imaging variables – are interrelated and aid in outcome prediction. To help study this rich data, we applied CorEx, a novel method for unsupervised machine learning. CorEx decodes the hierarchical structure, identifying latent causes of dependence in the data. It groups predictor variables based on their joint information and inter-dependence. We examined 21 TBI patients 2-5 months post-injury along with healthy controls; both groups were assessed again 12 months later. Although we were limited in the number of participants, this tool for exploratory analysis found potential relationships between change in cognitive scores over the 12-month period and baseline brain volumes. Certain regional brain volumes measured post-injury could serve as predictors of patient recovery. As future planned analyses will examine greater sample sizes, we hope to perform follow-up statistical analysis of variables identified by CorEx in independent data.