Data-Driven Analysis of Brain Tissue Mechanics using Magnetic Resonance Elastography and Non-Negative Matrix Factorization

Motivation: There exists a need for a comprehensive method to analyze regional brain tissue mechanics that accounts for variability across subject populations. Goal(s): Here we aimed to implement a multivariate data-driven technique to capture brain mechanical properties across a wide population while preserving small-scale differences between subjects. Approach: Non-negative matrix factorization was used to reduce mechanical properties derived from magnetic resonance elastography (MRE) into a low-dimensional form to generate unconfined regions of the brain that demonstrate high covariance across all subjects. Results: This technique was able to capture recognizable anatomical regions in the brain without structural input to determine weightings on the population average. Impact: This low-dimensional representation of brain tissue mechanics acquired from non-negative matrix factorization and MRE will help define baseline properties that accurately represent a wide range of subject populations while minimizing variability across imaging studies and contributing to improved statistical models.