| Summary: | Detection of early stages of Alzheimer's disease (AD) (i.e., mild cognitive impairment (MCI)) is important because it can delay or prevent progression to AD. The current researches of MCI classification are mainly based on static low-order functional connectivity network (FCN) and image information. However, static FCN cannot reflect time-varying dynamic behavior, low-order FCN overlooks inter-region interactions, and ignoring non-image information is not suitable especially when the size of dataset is small. In this paper, a method based on a combined high-order network and graph convolutional network (GCN) is proposed. The combined high-order network combines static, dynamic and high-level information to construct FCN while GCN is used to include non-image information to improve classifier's performance. Firstly, dynamic FCNs and static FCN are constructed by using a sliding window approach. Secondly, dynamic high-order FCNs and static high-order FCN based on the topographical similarity are then constructed. Thirdly, a novel combination method is proposed to utilize dynamic high-order FCNs and static high-order FCN to form a combined high-order FCN. Fourthly, features of the combined high-order FCNs are extracted by using a recursive feature elimination method. Lastly, after inputting extracted features into the GCN, in which MCI-graph establishes interactions between individuals and populations by using non-image information, the GCN outputs the binary classification results. Experimental results on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (adni.loni.ucla.edu) show that our framework has good performance.
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