Abstract
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.
Highlights
Alzheimer’s disease (AD), the most common type of dementia, is a very serious and irreversible brain disorder [1], [2]
We propose to combine dynamic and static functional connectivity network (FCN) based on Pearson’s correlation coefficient (PCC) to classify mild cognitive impairment (MCI) stages
Our experiments show that the proposed method based on combined high-order network and graph convolutional network (GCN) leads to significant improvement of the classification performance superior to the-state-of-the-art methods
Summary
Alzheimer’s disease (AD), the most common type of dementia (accounting for 60%-80%), is a very serious and irreversible brain disorder [1], [2]. As the early stage of AD, mild cognitive impairment (MCI), has annual 10%-15% conversion rate and more than 50% conversion rate within 5 years turn to AD [3]. In MCI stages, with certain cognitive training and pharmacological treatment, the deterioration process can be delayed or stopped [4], [5]. MCI has developed to AD, there are no effective therapies so far. Due to high conversion rate and severity of deterioration, the diagnosis and classification of MCI are very important and have drawn much attention during recent years [6]–[8]
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