Learning Hierarchical-Order Functional Connectivity Networks for Mild Cognitive Impairment Diagnosis
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Functional connectivity network (FCN) extracted from resting-state fMRI has been widely used for brain disease diagnosis. However, previous FCN-based studies have at least two limitations: 1) The FCN construction procedure is often handcrafted and is not optimizable optimized for diagnosis tasks; 2) The connectivity is limited to be pair-wise (low-order) and not to capture the high-order collective interactions between groups of brain regions. Therefore, we propose a unified framework to learn both low- and high-order diseased-related FCNs. First, an encoder is designed to extract the disease-related features from fMRI signals, based on which disease-related low-order FCN (D-LOFCN, order k=1) is built. Then, by correlating the disease-related correlation profiles from D-LOFCN by the graph attention mechanism, we iteratively construct the disease-related high-order FCNs (D-HOFCNs) at k-order (k>1). Finally, both D-LOFCN and D-HOFCNs are forwarded into corresponding GNNs for producing the diagnosis. The experiments demonstrate that our method has higher effectiveness over other state-of-the-art methods on mild cognitive impairment diagnosis task.