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Machine learning based prediction of single-frequency viscoelastic brain white matter - A data science framework
M Agarwal1, Assimina A Pelegri1
1Mechanical and Aerospace Engineering, Rutgers University-New Brunswick, Piscataway, NJ, 08854, USA; Advanced Materials & Structures Laboratory, Rutgers University-New Brunswick, Piscataway, NJ, 08854, USA.
Computers in Biology and Medicine
|October 5, 2025
Summary
Machine learning models can now predict brain white matter mechanical properties, offering a faster, cheaper alternative to traditional imaging and complex simulations. This approach uses microstructural features for accurate predictions, aiding research into brain conditions.
Area of Science:
- Neuroscience
- Biophysics
- Computational Biology
Background:
- In vivo Magnetic Resonance Elastography (MRE) and Diffusion Tensor Imaging (DTI) are costly and time-consuming for brain white matter (BWM) characterization.
- Existing numerical modeling approaches like finite element models (FEMs) have limitations in accuracy and computational resources for complex brain tissue behavior.
Purpose of the Study:
- To develop a cost-effective machine learning (ML) workflow for predicting the homogenized viscoelastic properties of BWM.
- To create a surrogate model using FEM-derived data to overcome the scarcity of experimental data.
Main Methods:
- A triphasic 2D composite model simulating BWM under shear stress was used to generate a synthetic FEM dataset.
- Machine learning regression models were trained using microstructural features (fiber volume fraction, moduli, axonal geometry).
- Feature selection and hyperparameter optimization were employed to enhance prediction accuracy, with decision tree-based models showing superior performance.
Main Results:
- Machine learning models successfully predicted the frequency-dependent mechanical response of BWM.
- SHAP interpretation identified glial moduli and fiber volume fraction as key predictors of mechanical properties.
- Decision tree-based ML models demonstrated high accuracy in predicting viscoelastic properties.
Conclusions:
- The proposed ML framework provides a computationally efficient and cost-effective alternative to in vivo MRE/DTI and direct FEM simulations for BWM characterization.
- This approach lays the groundwork for future ML-driven inverse models to link brain tissue constituents to neuroimaging characteristics.
- Potential applications include informing studies on neurodegenerative diseases like aging, dementia, and traumatic brain injuries.
