# Identification of relevant diffusion MRI metrics impacting cognitive functions using a novel feature selection method

## TL;DR

This paper introduces a novel feature selection method for diffusion MRI data to identify key metrics that influence cognitive functions, improving prediction accuracy and interpretability in mTBI patients.

## Contribution

It presents a new genetic algorithm-inspired feature selection technique that enhances the identification of relevant MRI metrics for cognitive performance prediction.

## Key findings

- Proposed method outperforms existing feature selection algorithms in accuracy.
- Selected features are clinically interpretable and relevant.
- Improved prediction of working memory performance in mTBI.

## Abstract

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.