  Mild Traumatic Brain Injury (mTBI) is a significant public health problem.
The most troubling symptoms after mTBI are cognitive complaints. Studies show
measurable differences between patients with mTBI and healthy controls with
respect to tissue microstructure using diffusion MRI. However, it remains
unclear which diffusion measures are the most informative with regard to
cognitive functions in both the healthy state as well as after injury. In this
study, we use diffusion MRI to formulate a predictive model for performance on
working memory based on the most relevant MRI features. The key challenge is to
identify relevant features over a large feature space with high accuracy in an
efficient manner. To tackle this challenge, we propose a novel improvement of
the best first search approach with crossover operators inspired by genetic
algorithm. Compared against other heuristic feature selection algorithms, the
proposed method achieves significantly more accurate predictions and yields
clinically interpretable selected features.
