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

Ming-Chou Ho^1,2, Hsin-An Shen³, Yi-Peng Eve Chang⁴

¹Department of Psychology, Chung Shan Medical University, Taichung 40201, Taiwan.

|July 2, 2021

Summary

Researchers developed machine learning models to identify betel quid (BQ) chewers using resting-state functional magnetic resonance imaging (rs-fMRI) brain scans. The models achieved 83% accuracy, offering a potential tool for tracking BQ use.

Keywords:

autoencoder betel quid logistic regression resting-state functional MRI (rs-fMRI)

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Area of Science:

Neuroimaging
Machine Learning
Public Health

Background:

Betel quid (BQ) is a widely consumed psychoactive substance in Asia and the Pacific.
Existing brain imaging techniques cannot reliably distinguish BQ chewers.
BQ use is associated with alterations in brain function.

Purpose of the Study:

To develop and validate autoencoder and machine learning models for identifying BQ chewers.
To discover brain alterations in BQ chewers using resting-state functional magnetic resonance imaging (rs-fMRI).

Main Methods:

rs-fMRI data were collected from 16 BQ chewers, 15 tobacco- and alcohol-user controls (TA), and 17 healthy controls (HC).
A convolutional neural network (CNN)-based autoencoder and logistic regression (LR) were employed for classification.
Leave-one-out-cross-validation (LOOCV) was used to assess model performance.

Main Results:

The logistic regression model achieved a highest accuracy of 83% in discriminating BQ chewers from TA and HC groups.
The models successfully identified BQ chewers based on rs-fMRI features.
Specific rs-fMRI feature sets improved classification accuracy.

Conclusions:

Autoencoder and machine learning models can effectively identify BQ chewers using rs-fMRI data.
This approach may offer a valuable method for monitoring BQ consumption in the future.
The findings highlight the potential of AI in neuroimaging for substance use research.