Dynamically weighted graph neural network for detection of early mild cognitive impairment

Abstract

Alzheimer’s disease (AD) is a prevalent neurodegenerative disease that primarily affects the elderly population. The early detection of mild cognitive impairment (MCI) holds significant clinical importance for prompt intervention and treatment of AD. Currently, functional connectivity (FC) networks-based diagnostic methods for early MCI (eMCI) detection are widely employed. FC considers the interaction patterns between brain regions as a topological structure. Recently, graph neural network (GNN) approaches have been utilized for disease diagnosis using FC networks, leveraging their ability to extract features from topological structures. However, existing methods typically treat FC features as GNN node attributes, disregarding the fact that FC features represent the weights of connection edges in FC networks with topological characteristics. In this paper, we propose a dynamically weighted GNN-based approach for early eMCI detection. Our method takes into account the topological structure and dynamic properties of FC networks by utilizing temporal FC features as the weighted adjacency matrix for dynamic GNNs. Moreover, the weighted graph local clustering coefficient of brain regions is employed as the node feature for GNNs. We extensively evaluated our approach using the ADNI database and achieved accuracies of 91.67% and 78.33% on low- and high-order FC networks, respectively. These results demonstrate the effectiveness and superiority of our proposed method compared to existing approaches.