Keywords: Elastography, Brain
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.
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