Spatial-Temporal Convolutional Attention for Mapping Functional Brain Networks

Recently, to overcome the shallow nature of the linear models, various of deep learning based methods have been Using functional magnetic resonance imaging (fMRI) and proposed to discover the FBNs. Most of these methods are deep learning to explore functional brain networks (FBNs) based on the autoencoders, they use different autoencoders has attracted many researchers. However, most of these to extract the sources in an self-supervised manner, and then studies are still based on the temporal correlation between use the generative linear model, such as LASSO to generate the sources and voxel signals, and lack of researches on the the FBNs [6, 7]. In general, these deep learning based methods dynamics of brain function. Due to the widespread local can indeed extract better encoder representations as the correlations in the volumes, FBNs can be generated directly sources than the classical methods, such as ICA and SDL, but in the spatial domain in a self-supervised manner by using still generate FBNs in a linear and independent manner, with spatial-wise attention (SA), and the resulting FBNs has the sources extraction and the FBNs generation as 2 separate a higher spatial similarity with templates compared to the steps. Generating the FBNs in such way is time-consuming classical method. Therefore, we proposed a novel Spatial-and does not fully utilize the advantages of deep learning, and Temporal Convolutional Attention (STCA) model to discover cannot directly generate the FBNs with deep learning.