Acute Effect of Betel Quid Chewing on Brain Network Dynamics: A Resting-State Functional Magnetic Resonance Imaging Study

Betel quid (BQ) is one of the most popular addictive substances in the world. However, the neurophysiological mechanism underlying BQ addiction remains unclear. This study aimed to investigate whether and how BQ chewing would affect brain function in the framework of a dynamic brain network model. Resting-state functional magnetic resonance imaging scans were collected from 24 male BQ-dependent individuals and 26 male non-addictive healthy individuals before and promptly after chewing BQ. Switching rate, a measure of temporal stability of functional brain networks, was calculated at both global and local levels for each scan. The results showed that BQ-dependent and healthy groups did not significantly differ on switching rate before BQ chewing (F = 0.784, p = 0.381, analysis of covariance controlling for age, education, and head motion). After chewing BQ, both BQ-dependent (t = 2.674, p = 0.014, paired t-test) and healthy (t = 2.313, p = 0.029, paired t-test) individuals showed a significantly increased global switching rate compared to those before chewing BQ. Significant corresponding local-level effects were observed within the occipital areas for both groups, and within the cingulo-opercular, fronto-parietal, and cerebellum regions for BQ-dependent individuals. Moreover, in BQ-dependent individuals, switching rate was significantly correlated with the severity of BQ addiction assessed by the Betel Quid Dependence Scale scores (Spearman's rho = 0.471, p = 0.020) before BQ chewing. Our study provides preliminary evidence for the acute effects of BQ chewing on brain functional dynamism. These findings may provide insights into the neural mechanisms of substance addictions.

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Best Window Width Determination and Glioma Analysis Application of Dynamic Brain Network Measure on Resting-State Functional Magnetic Resonance Imaging

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2016.1881 ◽

2016 ◽

Vol 6 (7) ◽

pp. 1735-1740 ◽

Cited By ~ 3

Author(s):

Ziyi Wang ◽

Xiaojie Zhang ◽

Weibei Dou ◽

Mingyu Zhang ◽

Hongyan Chen ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Functional Magnetic Resonance Imaging ◽

Resting State ◽

Brain Network ◽

Functional Magnetic Resonance ◽

Resonance Imaging ◽

Window Width ◽

Network Measure ◽

Analysis Application

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A CNN-Based Autoencoder and Machine Learning Model for Identifying Betel-Quid Chewers Using Functional MRI Features

Brain Sciences ◽

10.3390/brainsci11060809 ◽

2021 ◽

Vol 11 (6) ◽

pp. 809

Author(s):

Ming-Chou Ho ◽

Hsin-An Shen ◽

Yi-Peng Eve Chang ◽

Jun-Cheng Weng

Keyword(s):

Magnetic Resonance Imaging ◽

Machine Learning ◽

Magnetic Resonance ◽

Functional Magnetic Resonance Imaging ◽

Resting State ◽

Learning Model ◽

Betel Quid ◽

Functional Magnetic Resonance ◽

Resonance Imaging ◽

Machine Learning Model

Betel quid (BQ) is one of the most commonly used psychoactive substances in some parts of Asia and the Pacific. Although some studies have shown brain function alterations in BQ chewers, it is virtually impossible for radiologists’ to visually distinguish MRI maps of BQ chewers from others. In this study, we aimed to construct autoencoder and machine-learning models to discover brain alterations in BQ chewers based on the features of resting-state functional magnetic resonance imaging. Resting-state functional magnetic resonance imaging (rs-fMRI) was obtained from 16 BQ chewers, 15 tobacco- and alcohol-user controls (TA), and 17 healthy controls (HC). We used an autoencoder and machine learning model to identify BQ chewers among the three groups. A convolutional neural network (CNN)-based autoencoder model and supervised machine learning algorithm logistic regression (LR) were used to discriminate BQ chewers from TA and HC. Classifying the brain MRIs of HC, TA controls, and BQ chewers by conducting leave-one-out-cross-validation (LOOCV) resulted in the highest accuracy of 83%, which was attained by LR with two rs-fMRI feature sets. In our research, we constructed an autoencoder and machine-learning model that was able to identify BQ chewers from among TA controls and HC, which were based on data from rs-fMRI, and this might provide a helpful approach for tracking BQ chewers in the future.

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