Prediction of Single-Frequency 2D Viscoelastic Brain White Matter Properties Using an Ensemble of Forward Machine Learning Regression Models

Abstract Experimental characterization of brain white matter (BWM) using magnetic resonance elastography (MRE), diffusion tensor imaging (DTI), and numerical modeling is expensive, time-consuming, and constrained by computational limitations and model approximations. To address the scarcity of high-fidelity data, this study develops a machine learning (ML) workflow to predict single-frequency viscoelastic properties, specifically the homogenized storage modulus, of BWM. The dataset originates from a sensitivity study conducted in house where BWM was modeled as a two-dimensional (2D) triphasic composite of axons, myelin, and glial matrix. The triphasic unidirectional composite only considers 2D mechanics and diffusion in the transverse plane (perpendicular to axonal direction). Microstructural properties such as fiber volume fraction, intrinsic phase moduli, and axonal geometry were used as features for the ML model. Ensembles of regression and decision tree-based models, coupled with hyperparameter optimization, were explored, with model interpretation performed using SHapley Additive exPlanations (SHAP) analysis. Decision trees (DT) yielded the best predictive performance, with SHAP highlighting the importance of glial moduli and fiber volume fraction. This ML framework offers a surrogate too expensive in vivo characterization, provides insight into BWM mechanical dependencies, and can serve as a foundation for future inverse models aimed at understanding aging, dementia, and traumatic brain injury (TBI) mechanisms in neuroimaging studies.