Brain Effective Connectivity Estimation via Fourier Spatiotemporal Attention

Estimating brain effective connectivity (EC) from functional magnetic resonance imaging (fMRI) data can aid in comprehending the neural mechanisms underlying human behavior and cognition, providing a foundation for disease diagnosis. However, current spatiotemporal attention modules handle temporal and spatial attention separately, extracting temporal and spatial features either sequentially or in parallel. These approaches overlook the inherent spatiotemporal correlations present in real world fMRI data. Additionally, the presence of noise in fMRI data further limits the performance of existing methods. In this paper, we propose a novel brain effective connectivity estimation method based on Fourier spatiotemporal attention (FSTA-EC), which combines Fourier attention and spatiotemporal attention to simultaneously capture inter-series (spatial) dynamics and intra-series (temporal) dependencies from high-noise fMRI data. Specifically, Fourier attention is designed to convert the high-noise fMRI data to frequency domain, and map the denoised fMRI data back to physical domain, and spatiotemporal attention is crafted to simultaneously learn spatiotemporal dynamics. Furthermore, through a series of proofs, we demonstrate that incorporating learnable filter into fast Fourier transform and inverse fast Fourier transform processes is mathematically equivalent to performing cyclic convolution. The experimental results on simulated and real-resting-state fMRI datasets demonstrate that the proposed method exhibits superior performance when compared to state-of-the-art methods.

Two-stage Progressive Residual Dense Attention Network for Image Denoising

Deep convolutional neural networks (CNNs) for image denoising can effectively exploit rich hierarchical features and have achieved great success. However, many deep CNN-based denoising models equally utilize the hierarchical features of noisy images without paying attention to the more important and useful features, leading to relatively low performance. To address the issue, we design a new Two-stage Progressive Residual Dense Attention Network (TSP-RDANet) for image denoising, which divides the whole process of denoising into two sub-tasks to remove noise progressively. Two different attention mechanism-based denoising networks are designed for the two sequential sub-tasks: the residual dense attention module (RDAM) is designed for the first stage, and the hybrid dilated residual dense attention module (HDRDAM) is proposed for the second stage. The proposed attention modules are able to learn appropriate local features through dense connection between different convolutional layers, and the irrelevant features can also be suppressed. The two sub-networks are then connected by a long skip connection to retain the shallow feature to enhance the denoising performance. The experiments on seven benchmark datasets have verified that compared with many state-of-the-art methods, the proposed TSP-RDANet can obtain favorable results both on synthetic and real noisy image denoising. The code of our TSP-RDANet is available at https://github.com/WenCongWu/TSP-RDANet.