Abstract #1693

Resting-state functional activity and brain network abnormalities in betel nut chewers

Yu-Syuan Chou¹, Ming-Chou Ho², and Jun-Cheng Weng^1,3

¹Department of Medical Imaging and Radiological Sciences, Chung Shan Medical University, Taichung, Taiwan, ²Department of Psychology, Chung Shan Medical University, Taichung, Taiwan, ³Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung, Taiwan

Betel nut, also known as areca, is the fourth most commonly used drug worldwide after tobacco, alcohol, and caffeine and also a stimulant and addictive substance. Previously, CM Chen et al. probed into the influence of religious affiliation on heavy betel nut chewing, and studied on the relationship between health risk perception and betel nut chewing. Feng Chen et al. analyzed gray matter abnormalities between betel nut chewers and healthy subjects with voxel-based morphometry (VBM). However, there were few studies mentioned about the functional activity and brain network changes in betel nut chewers using functional magnetic resonance imaging (fMRI). Therefore, our aim was to use resting-state fMRI (rs-fMRI) to investigate the functional differences between betel nut chewers and healthy participants with amplitude of low frequency fluctuations (ALFF) and regional homogeneity (ReHo). The graph theoretical and network-based statistic (NBS) analyses were also used to find the network difference between two groups. Our results revealed different topological organization and poor global integration of the brain network in the betel nut chewers.

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