干旱区生态环境知识资源中心(Arid): TauFlowNet : Revealing latent propagation mechanism of tau aggregates using deep neural transport equations

Arid

DOI	10.1016/j.media.2024.103210
	TauFlowNet : Revealing latent propagation mechanism of tau aggregates using deep neural transport equations
	Dan, Tingting; Dere, Mustafa; Kim, Won Hwa; Kim, Minjeong; Wu, Guorong
通讯作者	Wu, GR
来源期刊	MEDICAL IMAGE ANALYSIS
ISSN	1361-8415
EISSN	1361-8423
出版年	2024
卷号	95
英文摘要	Mounting evidence shows that Alzheimer ' s disease (AD) is characterized by the propagation of tau aggregates throughout the brain in a prion -like manner. Since current pathology imaging technologies only provide a spatial mapping of tau accumulation, computational modeling becomes indispensable in analyzing the spatiotemporal propagation patterns of widespread tau aggregates from the longitudinal data. However, current state-of-the-art works focus on the longitudinal change of focal patterns, lacking a system-level understanding of the tau propagation mechanism that can explain and forecast the cascade of tau accumulation. To address this limitation, we conceptualize that the intercellular spreading of tau pathology forms a dynamic system where each node (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 scans. Specifically, we first translate the transport equation into a GNN (graph neural network) backbone, where the spreading flows are essentially driven by the potential energy of tau accumulation at each node. Conventional GNNs employ a l 2 -norm graph smoothness prior, resulting in nearly equal potential energies across nodes, leading to vanishing flows. Following this clue, we introduce the total variation (TV) into the graph transport model, 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-like) to characterize the TV-based spreading flow of tau aggregates, coined TauFlowNet. We evaluate our TauFlowNet on ADNI and OASIS datasets in terms of the prediction accuracy of future tau accumulation and explore the propagation mechanism of tau aggregates as the disease progresses. Compared to the current counterpart methods, our physics-informed deep model yields more accurate and interpretable results, demonstrating great potential in discovering novel neurobiological mechanisms through the lens of machine learning.
英文关键词	Partial differential equation Recurrent neural network System dynamic Neuroimaging
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001244896200001
WOS关键词	NETWORK DIFFUSION-MODEL ; PROGRESSION ; PATHOLOGY
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS研究方向	Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404899
推荐引用方式 GB/T 7714	Dan, Tingting,Dere, Mustafa,Kim, Won Hwa,et al. TauFlowNet : Revealing latent propagation mechanism of tau aggregates using deep neural transport equations[J],2024,95.
APA	Dan, Tingting,Dere, Mustafa,Kim, Won Hwa,Kim, Minjeong,&Wu, Guorong.(2024).TauFlowNet : Revealing latent propagation mechanism of tau aggregates using deep neural transport equations.MEDICAL IMAGE ANALYSIS,95.
MLA	Dan, Tingting,et al."TauFlowNet : Revealing latent propagation mechanism of tau aggregates using deep neural transport equations".MEDICAL IMAGE ANALYSIS 95(2024).