Open AccessClassification accuracy and functional difference prediction in different brain regions of drug abuser prefrontal lobe basing on machine-learning
Author(s) -
Banghua Yang,
Xiaochen Gu,
Shouwei Gao,
Ding Xu
Publication year - 2021
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2021288
Subject(s) - prefrontal cortex , ventrolateral prefrontal cortex , orbitofrontal cortex , dorsolateral prefrontal cortex , neuroscience , psychology , addiction , methamphetamine , consumer neuroscience , cognition , psychiatry
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.