Identification of Relevant Diffusion MRI Metrics Impacting Cognitive Functions Using a Novel Feature Selection Method

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. As exhaustive search is impractical, the key challenge is to identify relevant features over a large feature space with high accuracy within reasonable time-frame. 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 (improvement of r² in 8 of 9 cohorts and up to 0.08).

I. Introduction

Mild traumatic brain injury (mTBI) is a significant public health issue with millions of civilian, military, and sport-related injuries occurring every year [1] . Moreover, 20–30% of patients with mTBI develop persistent symptoms months to years after initial injury [2] . Cognitive complaints are important due to their significant impact on the quality of life. In this study, we examine the specific cognitive subdomain of working memory in relation to the underlying tissue microstructure by accessing diffusion MRI and predict performance on working memory. Defining specific imaging biomarkers related to cognitive dysfunction after mTBI would not only shed light on the underlying pathophysiology of injury leading to cognitive impairments, but also help to triage patients and offer a quantitative means to track recovery in the cognitive domain as well as track efficacy of targeted cognitive therapeutic strategies [3] . Tools to detect injury, predictive of symptoms are badly needed.