  Using deep learning models to recognize functional brain networks (FBNs) in
functional magnetic resonance imaging (fMRI) has been attracting increasing
interest recently. However, most existing work focuses on detecting static FBNs
from entire fMRI signals, such as correlation-based functional connectivity.
Sliding-window is a widely used strategy to capture the dynamics of FBNs, but
it is still limited in representing intrinsic functional interactive dynamics
at each time step. And the number of FBNs usually need to be set manually. More
over, due to the complexity of dynamic interactions in brain, traditional
linear and shallow models are insufficient in identifying complex and spatially
overlapped FBNs across each time step. In this paper, we propose a novel
Spatial and Channel-wise Attention Autoencoder (SCAAE) for discovering FBNs
dynamically. The core idea of SCAAE is to apply attention mechanism to FBNs
construction. Specifically, we designed two attention modules: 1) spatial-wise
attention (SA) module to discover FBNs in the spatial domain and 2) a
channel-wise attention (CA) module to weigh the channels for selecting the FBNs
automatically. We evaluated our approach on ADHD200 dataset and our results
indicate that the proposed SCAAE method can effectively recover the dynamic
changes of the FBNs at each fMRI time step, without using sliding windows. More
importantly, our proposed hybrid attention modules (SA and CA) do not enforce
assumptions of linearity and independence as previous methods, and thus provide
a novel approach to better understanding dynamic functional brain networks.
