Because mild Traumatic Brain Injuries (mTBI) are heterogeneous, classification methods perform outlier detection from a model of healthy tissue. Such a model is challenging to construct. Instead, we utilize region-specific pairwise (person-to-person) comparisons. Each person-region is characterized by a distribution of Fractional Anisotropy and comparisons are made via Median, Mean, Bhattacharya and Kullback-Liebler distances. Additionally, we examine an ordinal decision rule which compares a subject's nth most atypical region to a healthy control's. Ordinal comparison is motivated by mTBI's heterogeneity; each mTBI has some set of damaged tissue which is not necessarily spatially consistent. These improvements correctly distinguish Persistent Post-Concussive Symptoms in a small dataset but achieve only a .74 AUC in identifying mTBI subjects with milder symptoms. Finally, we perform subject-specific simulations which characterize which injuries are detected and which are missed.