  We present a method to estimate a multivariate Gaussian distribution of
diffusion tensor features in a set of brain regions based on a small sample of
healthy individuals, and use this distribution to identify imaging
abnormalities in subjects with mild traumatic brain injury. The multivariate
model receives a {\em apriori} knowledge in the form of a neighborhood graph
imposed on the precision matrix, which models brain region interactions, and an
additional $L_1$ sparsity constraint. The model is then estimated using the
graphical LASSO algorithm and the Mahalanobis distance of healthy and TBI
subjects to the distribution mean is used to evaluate the discriminatory power
of the model. Our experiments show that the addition of the {\em apriori}
neighborhood graph results in significant improvements in classification
performance compared to a model which does not take into account the brain
region interactions or one which uses a fully connected prior graph. In
addition, we describe a method, using our model, to detect the regions that
contribute the most to the overall abnormality of the DTI profile of a
subject's brain.
