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Updated: Jul 20, 2026
09:22
In Vitro Aggregation Assays Using Hyperphosphorylated Tau Protein
Published on: January 2, 2015
A longitudinal model for tau aggregation in Alzheimer's disease based on structural connectivity
Fan Yang1,2, Samadrita Roy Chowdhury1,2, Heidi I L Jacobs2
1Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, USA.
Information Processing in Medical Imaging : Proceedings of the ... Conference
|June 4, 2019
Summary
This study introduces a network diffusion model to track tau spread in Alzheimer's disease (AD). The model uses brain connectivity to predict tau buildup, offering insights into disease progression.
Area of Science:
- Neuroscience
- Medical Imaging
- Computational Biology
Background:
- Tau tangles are key Alzheimer's disease (AD) markers, correlating with cognitive decline.
- Tau spread in the brain follows neural connections, not just physical proximity.
Purpose of the Study:
- To develop and validate a network diffusion model for tau aggregation in the aging brain.
- To identify mechanisms of tau buildup using neuroimaging data and network analysis.
Main Methods:
- Utilized longitudinal tau Positron Emission Tomography (PET) and structural connectivity Diffusion Tensor Imaging (DTI).
- Employed graph Laplacians as diffusion kernels and sparse source localization for model fitting.
- Validated the parametric model on an independent cohort to predict future tau buildup.
Main Results:
- Identified distinct propagative and generative patterns of tau buildup at a population level.
- Successfully predicted future tau accumulation with high accuracy.
- The model quantifies network-based tau spread and identifies seeding regions.
Conclusions:
- The network diffusion model accurately captures tau spread mechanisms in the aging brain.
- This approach provides a macroscopic view of tau pathology progression in Alzheimer's disease.
- The model successfully differentiates between network-driven spread and regional seeding.
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