Self-attention based high order sequence feature reconstruction of
dynamic functional connectivity networks with rs-fMRI for brain disease
classification
Dynamic functional connectivity networks (dFCN) based on rs-fMRI have
demonstrated tremendous potential for brain function analysis and brain disease
classification. Recently, studies have applied deep learning techniques (i.e.,
convolutional neural network, CNN) to dFCN classification, and achieved better
performance than the traditional machine learning methods. Nevertheless,
previous deep learning methods usually perform successive convolutional
operations on the input dFCNs to obtain high-order brain network aggregation
features, extracting them from each sliding window using a series split, which
may neglect non-linear correlations among different regions and the
sequentiality of information. Thus, important high-order sequence information
of dFCNs, which could further improve the classification performance, is
ignored in these studies. Nowadays, inspired by the great success of
Transformer in natural language processing and computer vision, some latest
work has also emerged on the application of Transformer for brain disease
diagnosis based on rs-fMRI data. Although Transformer is capable of capturing
non-linear correlations, it lacks accounting for capturing local spatial
feature patterns and modelling the temporal dimension due to parallel
computing, even equipped with a positional encoding technique. To address these
issues, we propose a self-attention (SA) based convolutional recurrent network
(SA-CRN) learning framework for brain disease classification with rs-fMRI data.
The experimental results on a public dataset (i.e., ADNI) demonstrate the
effectiveness of our proposed SA-CRN method