A computational framework to predict the spreading of Alzheimer's disease.

Area of Science:

• Computational neuroscience
• Biomechanical modeling
• Neurodegenerative disease research

Background:

• Alzheimer's disease involves misfolded protein spread and brain structural changes.
• Translating microscopic protein dynamics to macroscopic degeneration is a key challenge.

Purpose of the Study:

• To develop a computational framework modeling Alzheimer's disease progression.
• To integrate multi-protein transport and brain tissue deformation in realistic geometries.

Main Methods:

• Utilized a 3D finite element framework with reaction-diffusion equations for tau and amyloid-beta protein propagation.
• Incorporated prion-like growth and anisotropic transport along white matter tracts.
• Modeled brain atrophy using a hyperelastic constitutive model driven by protein-dependent volume loss.

Main Results:

• The model reproduced key Alzheimer's disease morphological patterns.
• Simulations showed good quantitative agreement with longitudinal imaging measurements.
• The framework enables subject-specific simulations using medical imaging data.

Conclusions:

• The developed framework provides an extensible platform for studying Alzheimer's disease progression.
• The computational models and image processing tools are publicly available.
• This approach facilitates research into the bio-chemo-mechanical aspects of neurodegeneration.