Open AccessDifferent types of drug abusers prefrontal cortex activation patterns and based on machine-learning classification
Author(s) -
Banghua Yang,
Xiaochen Gu,
Shouwei Gao,
Lin Yan,
Ding Xu,
Wen Wang
Publication year - 2022
Publication title -
journal of innovative optical health sciences/journal of innovation in optical health science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.421
H-Index - 24
eISSN - 1793-5458
pISSN - 1793-7205
DOI - 10.1142/s1793545822500122
Subject(s) - prefrontal cortex , orbitofrontal cortex , ventrolateral prefrontal cortex , addiction , dorsolateral prefrontal cortex , functional near infrared spectroscopy , neuroscience , methamphetamine , psychology , medicine , audiology , psychiatry , cognition
Drug addiction can cause abnormal brain activation changes, which are the root cause of drug craving and brain function errors. This study enrolled drug abusers to determine the effects of different drugs on brain activation. A functional near-infrared spectroscopy (fNIRS) device was used for the research. This study was designed with an experimental paradigm that included the induction of resting and drug addiction cravings. We collected the fNIRS data of 30 drug users, including 10 who used heroin, 10 who used Methamphetamine, and 10 who used mixed drugs. First, using Statistical Analysis, the study analyzed the activations of eight functional areas of the left and right hemispheres of the prefrontal cortex of drug addicts who respectively used heroin, Methamphetamine, and mixed drugs, including Left/Right-Dorsolateral prefrontal cortex (L/R-DLPFC), Left/Right-Ventrolateral prefrontal cortex (L/R-VLPFC), Left/Right-Frontopolar prefrontal cortex (L/R-FPC), and Left/Right Orbitofrontal Cortex (L/R-OFC). Second, referencing the degrees of activation of oxyhaemoglobin concentration (HbO[Formula: see text], the study made an analysis and got the specific activation patterns of each group of the addicts. Finally, after taking out data which are related to the addicts who recorded high degrees of activation among the three groups of addicts, and which had the same channel numbers, the paper classified the different drug abusers using the data as the input data for Convolutional Neural Networks (CNNs). The average three-class accuracy is 67.13%. It is of great significance for the analysis of brain function errors and personalized rehabilitation.