Exploring tau protein and amyloid-beta propagation: A sensitivity analysis of mathematical models based on biological data
Year: 2024
Type: article
Abstract: Alzheimer’s disease is the most common dementia worldwide. Its pathological development is well known to be connected with the accumulation of two toxic proteins: tau protein and amyloid-β. Mathematical models and numerical simulations can predict the spreading patterns of misfolded proteins in this context. However, the calibration of the model parameters plays a crucial role in the final solution. In this work, we perform a sensitivity analysis of heterodimer and Fisher–Kolmogorov models to evaluate the impact of the equilibrium values of protein concentration on the solution patterns. We adopt advanced numerical methods such as the IMEX-DG method to accurately describe the propagating fronts in the propagation phenomena in a polygonal mesh of sagittal patient-specific brain geometry derived from magnetic resonance images. We calibrate the model parameters using biological measurements in the brain cortex for the tau protein and the amyloid-β in Alzheimer’s patients and controls. Finally, using the sensitivity analysis results, we discuss the applicability of both models in the correct simulation of the spreading of the two proteins. Statement of significance: Alzheimer’s disease is related to the accumulation of tau protein and amyloid-β. Mathematical models to predict the spreading patterns require accurate parameter calibration. In this work, we perform a sensitivity analysis of heterodimer and Fisher–Kolmogorov models to evaluate the impact of the equilibrium values of protein concentration on the solution patterns obtained with advanced numerical simulations on patient-specific brain geometry derived from magnetic resonance images. By using biological measurements in the brain cortex for the proteins in Alzheimer’s patients and controls, we use sensitivity analysis to discuss the applicability of models in simulating protein spreading.
Source: Brain Multiphysics
Author: Mattia Corti
Institution: Politecnico di Milano
Language: English
Subfield: Cellular and Molecular Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,740
Funders: Eli Lilly and Company, Istituto Nazionale di Alta Matematica "Francesco Severi", European Commission, National Institutes of Health, Avid Radiopharmaceuticals, Gruppo Nazionale per il Calcolo Scientifico, European Research Council, National Institute on Aging, National Institute of Biomedical Imaging and Bioengineering
Awards: G5377794632, DRIVING PERFORMANCE IN PRECLINICAL ALZHEIMER'S DISEASE, Healthy Aging and Senile Dementia, Washington University Institute of Clinical and Translational Sciences, G7831108753, G9000112288, G9000115080, Alzheimer's Disease Research Center, THE XNAT IMAGING INFORMATICS PLATFORM, Antecedent Biomarkers for Alzheimer Disease: The Adult Children Study
OpenAlex ID: W4395065645