Characterizing Amyloid Pathogenic Spread in Alzheimer's Disease Through A Network Diffusion Model.

Area of Science:

• Neuroscience
• Computational Biology
• Medical Imaging

Background:

• Early amyloid-beta deposition is a key feature of Alzheimer's disease (AD).
• The precise mechanisms driving amyloid pathogenesis and spread remain incompletely understood.
• Understanding amyloid propagation is crucial for developing effective AD treatments.

Purpose of the Study:

• To develop and validate a network diffusion model simulating amyloid-beta (Aβ) spread in white matter brain networks.
• To investigate the temporal progression and seeding patterns of Aβ deposition across diagnostic groups (Healthy Control, Mild Cognitive Impairment, AD).
• To explore the role of structural brain networks in Aβ propagation and test the trans-synaptic spread hypothesis.

Main Methods:

• A network diffusion model was designed to simulate Aβ spread through white matter networks.
• The model was applied to diagnostic subpopulations: Healthy Control (HC), Mild Cognitive Impairment (MCI), and AD.
• Model outputs were validated against regional Aβ distributions observed via 18F-florbetapir positron emission tomography.

Main Results:

• The network diffusion model successfully simulated Aβ spread, correlating with observed PET imaging data (r=0.44-0.46, P<0.01).
• Optimal diffusion time (t) indicated temporal progression: HC (107.22 ± 16.67) < MCI (122.78 ± 19.63) < AD (136.20 ± 24.47).
• Consistent seeding regions (brainstem, orbitofrontal/lingual lobes) were identified across diagnostic groups, aligning with known amyloid staging.

Conclusions:

• The study validates a network diffusion model for simulating Alzheimer's disease amyloid spread.
• Findings support the trans-synaptic spread hypothesis, highlighting the importance of white matter networks in Aβ propagation.
• The model provides novel insights into AD pathogenesis and the temporal dynamics of amyloid deposition.