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

doi:doi.org/10.3390/brainsci11060809

中山醫學大學機構典藏 CSMUIR > 醫學科技學院 > 心理學系暨臨床心理學暨碩士班 > 期刊論文 > Item 310902500/21835

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题名:	A CNN-Based Autoencoder and Machine Learning Model for Identifying Betel-Quid Chewers Using Functional MRI Features
作者:	Ho Ming-Chou;Shen Hsin-An;Chang Yi-Peng Eve;Weng Jun-Cheng)
贡献者:	中山醫學大學;心理系
关键词:	betel quid;resting-state functional MRI (rs-fMRI);autoencoder;logistic regression
日期:	2021-06-18
上传时间:	2021-10-05T08:16:53Z (UTC)
出版者:	MDPI
摘要:	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.
URI:	https://ir.csmu.edu.tw:8080/handle/310902500/21835
關聯:	BRAIN SCIENCES, 11(6), 809
显示于类别:	[心理學系暨臨床心理學暨碩士班] 期刊論文

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