Spatial-Temporal Convolutional Attention for Mapping Functional Brain Networks

Autor:	Liu, Yiheng, Ge, Enjie, Qiang, Ning, Liu, Tianming, Ge, Bao
Rok vydání:	2022
Předmět:	Quantitative Biology - Neurons and Cognition Computer Science - Computer Vision and Pattern Recognition Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Using functional magnetic resonance imaging (fMRI) and deep learning to explore functional brain networks (FBNs) has attracted many researchers. However, most of these studies are still based on the temporal correlation between the sources and voxel signals, and lack of researches on the dynamics of brain function. Due to the widespread local correlations in the volumes, FBNs can be generated directly in the spatial domain in a self-supervised manner by using spatial-wise attention (SA), and the resulting FBNs has a higher spatial similarity with templates compared to the classical method. Therefore, we proposed a novel Spatial-Temporal Convolutional Attention (STCA) model to discover the dynamic FBNs by using the sliding windows. To validate the performance of the proposed method, we evaluate the approach on HCP-rest dataset. The results indicate that STCA can be used to discover FBNs in a dynamic way which provide a novel approach to better understand human brain. Comment: 5 pages, 5 figures, submitted to 20th IEEE International Symposium on Biomedical Imaging (ISBI 2023)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.02315 Zobrazit plný text záznamu View this record from Arxiv