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Reconstructing high-order sequence features of dynamic functional connectivity networks based on diversified covert attention patterns for Alzheimer's disease classification
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.
Ordinal Pattern Kernel for Brain Connectivity Network Classification
Kernel-based method, such as graph kernel (i. e., kernel defined on graphs), has been proposed for measuring the similarity of brain networks, and yields the promising classification performance.
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