  Recent studies have applied deep learning methods such as convolutional
recurrent neural networks (CRNs) and Transformers to brain disease
classification based on dynamic functional connectivity networks (dFCNs), such
as Alzheimer's disease (AD), achieving better performance than traditional
machine learning methods. However, in CRNs, the continuous convolution
operations used to obtain high-order aggregation features may overlook the
non-linear correlation between different brain regions due to the essence of
convolution being the linear weighted sum of local elements. Inspired by modern
neuroscience on the research of covert attention in the nervous system, we
introduce the self-attention mechanism, a core module of Transformers, to model
diversified covert attention patterns and apply these patterns to reconstruct
high-order sequence features of dFCNs in order to learn complex dynamic changes
in brain information flow. Therefore, we propose a novel CRN method based on
diversified covert attention patterns, DCA-CRN, which combines the advantages
of CRNs in capturing local spatio-temporal features and sequence change
patterns, as well as Transformers in learning global and high-order correlation
features. Experimental results on the ADNI and ADHD-200 datasets demonstrate
the prediction performance and generalization ability of our proposed method.
