Use of multilayer network modularity and spatiotemporal network switching rate to explore changes of functional brain networks in Alzheimer's disease.

Abstract

Alzheimer's disease (AD) is progressive neurodegenerative disease. It is important to identify effective biomarkers to explore changes of complex functional brain networks in AD patients based on functional magnetic resonance imaging (fMRI). Recently, four fMRI brain network parameters were frequently used, including regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), fractional amplitude of low frequency fluctuations (f/ALFF) and degree centrality (DC). However, these parameters only present the changes of brain networks in a full time quantum, but ignore changes over a short period of time and lack space information. In this study we propose a new brain network parameter for fMRI, called multilayer network modularity and spatiotemporal network switching rate (stNSR). This parameter is calculated combing Pearson correlation sliding Hamming window and the Louvain algorithm. To verify the efficiency of stNSR, we selected 61 AD patients and 110 healthy controls (HC) from Xuanwu Hospital, Beijing, China. First, we used two-sample t test to identify regions of interest (ROI) between AD patients and HCs. Second, we calculated the stNSR values in these ROIs, and compared them with ReHo, ALFF, f/ALFF, and DC values between AD and HC groups. The results showed that, stNSR values in left calcimine fissure and surrounding cortex, left Lingual gyrus and left cerebellum inferior significantly increased, while stNSR values significantly decreased in left Para hippocampal gyrus, left temporal and superior temporal gyrus. As a comparison, changes in these ROIs could not be observed using ReHo, ALFF, f/ALFF, and DC. The results indicated that stNSR may reflect differences of brain networks between AD patients and HCs.