Classification accuracy and functional difference prediction in different brain regions of drug abuser prefrontal lobe basing on machine-learning

Banghua Yang; Xuelin Gu; Shouwei Gao; Ding Xu; Banghua Yang; Xuelin Gu; Shouwei Gao; Ding Xu

doi:10.3934/mbe.2021288

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 5: 5692-5706. doi: 10.3934/mbe.2021288

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Research article

Classification accuracy and functional difference prediction in different brain regions of drug abuser prefrontal lobe basing on machine-learning

1.
School of Mechanical and Electrical Engineering and Automation, Shanghai University, Shanghai 200444, China
2.
Shanghai Drug Rehabilitation Administration Bureau, Shanghai 200080, China

Received: 25 April 2021 Accepted: 15 June 2021 Published: 23 June 2021

Taking different types of addictive drugs such as methamphetamine, heroin, and mixed drugs causes brain functional Changes. Based on the prefrontal functional near-infrared spectroscopy, this study was designed with an experimental paradigm that included the induction of resting and drug addiction cravings. Hemoglobin concentrations of 30 drug users (10 on methamphetamine, 10 on heroin, and 10 on mixed type) were collected. For these three types of individuals, the convolutional neural networks (CNN) was designed to classify eight Brodmann areas and the entire prefrontal area, and the average accuracy of the three classifications on each functional area was obtained. As a result, the classification accuracy was lower on the left side than on the right in the dorsolateral prefrontal cortex (DLPFC) of the drug users, while it was higher on the left than on the right in the ventrolateral prefrontal cortex (VLPFC), frontopolar prefrontal cortex (FPC), and orbitofrontal cortex (OFC). Then the differences in eight functional areas between the three types of individuals were statistically analyzed, and results showed significant differences in the right VLPFC and right OFC.
- drug addiction,
- fNIRS,
- machine learning,
- Brodmann areas,
- accuracy of brain regions,
- differences in brain function
Citation: Banghua Yang, Xuelin Gu, Shouwei Gao, Ding Xu. Classification accuracy and functional difference prediction in different brain regions of drug abuser prefrontal lobe basing on machine-learning[J]. Mathematical Biosciences and Engineering, 2021, 18(5): 5692-5706. doi: 10.3934/mbe.2021288

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Abstract

Taking different types of addictive drugs such as methamphetamine, heroin, and mixed drugs causes brain functional Changes. Based on the prefrontal functional near-infrared spectroscopy, this study was designed with an experimental paradigm that included the induction of resting and drug addiction cravings. Hemoglobin concentrations of 30 drug users (10 on methamphetamine, 10 on heroin, and 10 on mixed type) were collected. For these three types of individuals, the convolutional neural networks (CNN) was designed to classify eight Brodmann areas and the entire prefrontal area, and the average accuracy of the three classifications on each functional area was obtained. As a result, the classification accuracy was lower on the left side than on the right in the dorsolateral prefrontal cortex (DLPFC) of the drug users, while it was higher on the left than on the right in the ventrolateral prefrontal cortex (VLPFC), frontopolar prefrontal cortex (FPC), and orbitofrontal cortex (OFC). Then the differences in eight functional areas between the three types of individuals were statistically analyzed, and results showed significant differences in the right VLPFC and right OFC.

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