Abstract
Alzheimer’s disease (AD) is characterized by the propagation of tau aggregates throughout the brain in a prion-like manner. Tremendous efforts have been made to analyze the spatiotemporal propagation patterns of widespread tau aggregates. However, current works focus on the change of focal patterns in lieu of a system-level understanding of the tau propagation mechanism that can explain and forecast the cascade of tau accumulation. To fill this gap, we conceptualize that the intercellular spreading of tau pathology forms a dynamic system where brain region is ubiquitously wired with other nodes while interacting with the build-up of pathological burdens. In this context, we formulate the biological process of tau spreading in a principled potential energy transport model (constrained by brain network topology), which allows us to develop an explainable neural network for uncovering the spatiotemporal dynamics of tau propagation from the longitudinal tau-PET images. We first translate the transport equation into a backbone of graph neural network (GNN), where the spreading flows are essentially driven by the potential energy of tau accumulation at each node. Further, we introduce the total variation (TV) into the graph transport model to prevent the flow vanishing caused by the \({\mathcal{l}}_{2}\)-norm regularization, where the nature of system’s Euler-Lagrange equations is to maximize the spreading flow while minimizing the overall potential energy. On top of this min-max optimization scenario, we design a generative adversarial network (GAN) to depict the TV-based spreading flow of tau aggregates, coined TauFlowNet. We evaluate TauFlowNet on ADNI dataset in terms of the prediction accuracy of future tau accumulation and explore the propagation mechanism of tau aggregates as the disease progresses. Compared to current methods, our physics-informed method yields more accurate and interpretable results, demonstrating great potential in discovering novel neurobiological mechanisms.
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References
Jack, C.R., et al.: NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 14(4), 535–562 (2018)
Al Mamun, A., et al.: Toxic tau: structural origins of tau aggregation in Alzheimer’s disease. Neural Regen. Res. 15(8), 1417 (2020)
Goedert, M., Eisenberg, D.S., Crowther, R.A.: Propagation of Tau aggregates and neurodegeneration. Annu. Rev. Neurosci. 40(1), 189–210 (2017)
Bassett, D.S., Sporns, O.: Network neuroscience. Nat. Neurosci. 20(3), 353–364 (2017)
Raj, A., Kuceyeski, A., Weiner, M.: A network diffusion model of disease progression in dementia. Neuron 73(6), 1204–1215 (2012)
Zhang, J., et al.: A network-guided reaction-diffusion model of AT [N] biomarkers in Alzheimer’s disease. In: 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE (2020)
Raj, A., et al.: Network diffusion model of progression predicts longitudinal patterns of atrophy and metabolism in Alzheimer’s disease. Cell Rep. 10(3), 359–369 (2015)
Raj, A., Powell, F.: Network model of pathology spread recapitulates neurodegeneration and selective vulnerability in Huntington’s disease. Neuroimage 235, 118008 (2021)
Vogel, J.W., et al.: Spread of pathological tau proteins through communicating neurons in human Alzheimer’s disease. Nat. Commun. 11(1), 2612 (2020)
Arnold, V.I.: Mathematical Methods of Classical Mechanics. Graduate Texts in Mathematics Mathematics. Springer, New York (1978). https://doi.org/10.1007/978-1-4757-1693-1
Zhou, J., et al.: Graph neural networks: a review of methods and applications. AI Open 1, 57–81 (2020)
Chamberlain, B., et al.: Grand: graph neural diffusion. In: International Conference on Machine Learning. PMLR (2021)
Matallah, H., Maouni, M., Lakhal, H.: Image restoration by a fractional reaction-diffusion process. Int. J. Anal. Appl. 19(5), 709–724 (2021)
Li, G., et al.: DeepGCNs: can GCNs go as deep as cnns? In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2019)
Xu, K., et al.: Representation learning on graphs with jumping knowledge networks. In: International Conference on Machine Learning. PMLR (2018)
Chen, M., et al.: Simple and deep graph convolutional networks. In: International Conference on Machine Learning. PMLR (2020)
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60(1), 259–268 (1992)
Chan, T., et al.: Total variation image restoration: overview and recent developments. In: Paragios, N., Chen, Y., Faugeras, O. (eds.) Handbook of Mathematical Models in Computer Vision, pp. 17–31. Springer, Boston (2006). https://doi.org/10.1007/0-387-28831-7_2
Zhao, J., Mathieu, M., LeCun, Y.: Energy-based generative adversarial network. arXiv preprint arXiv:1609.03126 (2016)
Destrieux, C., et al.: Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 53(1), 1–15 (2010)
Zhang, H., et al.: Semi-supervised classification of graph convolutional networks with Laplacian rank constraints. Neural Process. Lett. 1–12 (2021)
Riedmiller, M., Lernen, A.: Multi layer perceptron. Machine Learning Lab Special Lecture, pp. 7-24. University of Freiburg (2014)
Lee, W.J., et al.: Regional Aβ-tau interactions promote onset and acceleration of Alzheimer’s disease tau spreading. Neuron (2022)
Acknowledgment
This work was supported by Foundation of Hope, NIH R01AG068399, NIH R03AG073927. Won Hwa Kim was partially supported by IITP-2019-0-01906 (AI Graduate Program at POSTECH) funded by the Korean government (MSIT).
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Dan, T., Kim, M., Kim, W.H., Wu, G. (2023). TauFlowNet: Uncovering Propagation Mechanism of Tau Aggregates by Neural Transport Equation. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14222. Springer, Cham. https://doi.org/10.1007/978-3-031-43898-1_8
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