Machine learning based prediction of single-frequency viscoelastic brain white matter - A data science framework

Characterizing brain white matter (BWM) using in vivo Magnetic Resonance Elastography (MRE) and Diffusion Tensor Imaging (DTI) is a costly, time-intensive process. Numerical modeling approaches, such as finite element models (FEMs), also face limitations in fidelity, computational resources, and accurately capturing the complex bio-physical behavior of brain tissues. To address the scarcity of experimental data, researchers are exploring machine learning (ML) as a surrogate for predicting the mechanical properties of brain tissues. Here in, an ML workflow is proposed for predicting the homogenized viscoelastic properties of BWM using FEM-derived data. The synthetic FE dataset originates from a sensitivity analysis, whereby a triphasic 2D composite model, consisting of axons, myelin, and glial matrix, was used to simulate transverse mechanical behavior under harmonic shear stress. This dataset is utilized to train and validate machine learning models aimed at predicting the frequency-dependent mechanical response. The proposed ML pipeline incorporates microstructural features such as fiber volume fraction, intrinsic phase moduli, and axonal geometry to build and train regression models. Feature selection and hyperparameter optimization were applied to improve prediction accuracy. Decision tree-based models outperformed other approaches, while SHAP interpretation revealed that glial moduli and fiber volume fraction significantly influenced the predictions. This framework offers a cost-effective alternative to in vivo characterization and computationally expensive physics based direct numerical simulation methods (FEM). It would also provide a basis for future ML-driven inverse models to explore the impact of various brain matter constituents on neuroimaging characteristics, potentially informing studies on aging, dementia, and traumatic brain injuries.