干旱区生态环境知识资源中心(Arid): Spatio-temporal directed acyclic graph learning with attention mechanisms on brain functional time series and connectivity

Arid

DOI	10.1016/j.media.2022.102370
	Spatio-temporal directed acyclic graph learning with attention mechanisms on brain functional time series and connectivity
	Huang, Shih-Gu; Xia, Jing; Xu, Liyuan; Qiu, Anqi
通讯作者	Qiu, AQ
来源期刊	MEDICAL IMAGE ANALYSIS
ISSN	1361-8415
EISSN	1361-8423
出版年	2022
卷号	77
英文摘要	We develop a deep learning framework, spatio-temporal directed acyclic graph with attention mechanisms (ST-DAG-Att), to predict cognition and disease using functional magnetic resonance imaging (fMRI). This ST-DAG-Att framework comprises of two neural networks, (1) spatio-temporal graph convolutional network (ST-graph-conv) to learn the spatial and temporal information of functional time series at multiple temporal and spatial graph scales, where the graph is represented by the brain functional network, the spatial convolution is over the space of this graph, and the temporal convolution is over the time dimension; (2) functional connectivity convolutional network (FC-conv) to learn functional connectivity features, where the functional connectivity is derived from embedded multi-scale fMRI time series and the convolutional operation is applied along both edge and node dimensions of the brain functional network. This framework also consists of an attention component, i.e., functional connectivity-based spatial attention (FC-SAtt), that generates a spatial attention map through learning the local dependency among high-level features of functional connectivity and emphasizing meaningful brain regions. Moreover, both the ST-graph-conv and FC-conv networks are designed as feed-forward models structured as directed acyclic graphs (DAGs). Our experiments employ two large-scale datasets, Adolescent Brain Cognitive Development (ABCD, n = 7693 ) and Open Access Series of Imaging Study-3 (OASIS-3, n = 1786 ). Our results show that the ST-DAG-Att model is generalizable from cognition prediction to age prediction. It is robust to independent samples obtained from different sites of the ABCD study. It outperforms the existing machine learning techniques, including support vector regression (SVR), elastic net's mixture with random forest, spatio-temporal graph convolution, and BrainNetCNN. (c) 2022 The Author(s). Published by Elsevier B.V.
英文关键词	Brain functional network Directed acyclic graph Graph neural network Attention mechanism Graph pooling Multi-scale analysis
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:000912929700002
WOS关键词	INTELLIGENCE ; NETWORKS
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/2XILL650/393769
推荐引用方式 GB/T 7714	Huang, Shih-Gu,Xia, Jing,Xu, Liyuan,et al. Spatio-temporal directed acyclic graph learning with attention mechanisms on brain functional time series and connectivity[J],2022,77.
APA	Huang, Shih-Gu,Xia, Jing,Xu, Liyuan,&Qiu, Anqi.(2022).Spatio-temporal directed acyclic graph learning with attention mechanisms on brain functional time series and connectivity.MEDICAL IMAGE ANALYSIS,77.
MLA	Huang, Shih-Gu,et al."Spatio-temporal directed acyclic graph learning with attention mechanisms on brain functional time series and connectivity".MEDICAL IMAGE ANALYSIS 77(2022).