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Recent studies have applied deep learning methods such as convolutionalrecurrent neural networks (CRNs) and Transformers to brain diseaseclassification based on dynamic functional connectivity networks (dFCNs), suchas Alzheimer's disease (AD), achieving better performance than traditionalmachine learning methods. However, in CRNs, the continuous convolutionoperations used to obtain high-order aggregation features may overlook thenon-linear correlation between different brain regions due to the essence ofconvolution being the linear weighted sum of local elements. Inspired by modernneuroscience on the research of covert attention in the nervous system, weintroduce the self-attention mechanism, a core module of Transformers, to modeldiversified covert attention patterns and apply these patterns to reconstructhigh-order sequence features of dFCNs in order to learn complex dynamic changesin brain information flow. Therefore, we propose a novel CRN method based ondiversified covert attention patterns, DCA-CRN, which combines the advantagesof CRNs in capturing local spatio-temporal features and sequence changepatterns, as well as Transformers in learning global and high-order correlationfeatures. Experimental results on the ADNI and ADHD-200 datasets demonstratethe prediction performance and generalization ability of our proposed method.

Reconstructing high-order sequence features of dynamic functional  connectivity networks based on diversified covert attention patterns for  Alzheimer's disease classification