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

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 c...

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Autores principales:	Banghua Yang, Xuelin Gu, Shouwei Gao, Ding Xu
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	drug addiction fnirs machine learning brodmann areas accuracy of brain regions differences in brain function Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/13bfe1e588184e88bc65d2e1fe0608da
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spelling	oai:doaj.org-article:13bfe1e588184e88bc65d2e1fe0608da2021-11-09T02:39:25ZClassification accuracy and functional difference prediction in different brain regions of drug abuser prefrontal lobe basing on machine-learning10.3934/mbe.20212881551-0018https://doaj.org/article/13bfe1e588184e88bc65d2e1fe0608da2021-06-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021288?viewType=HTMLhttps://doaj.org/toc/1551-0018Taking 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.Banghua YangXuelin GuShouwei GaoDing XuAIMS Pressarticledrug addictionfnirsmachine learningbrodmann areasaccuracy of brain regionsdifferences in brain functionBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 5, Pp 5692-5706 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	drug addiction fnirs machine learning brodmann areas accuracy of brain regions differences in brain function Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	drug addiction fnirs machine learning brodmann areas accuracy of brain regions differences in brain function Biotechnology TP248.13-248.65 Mathematics QA1-939 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
description	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.
format	article
author	Banghua Yang Xuelin Gu Shouwei Gao Ding Xu
author_facet	Banghua Yang Xuelin Gu Shouwei Gao Ding Xu
author_sort	Banghua Yang
title	Classification accuracy and functional difference prediction in different brain regions of drug abuser prefrontal lobe basing on machine-learning
title_short	Classification accuracy and functional difference prediction in different brain regions of drug abuser prefrontal lobe basing on machine-learning
title_full	Classification accuracy and functional difference prediction in different brain regions of drug abuser prefrontal lobe basing on machine-learning
title_fullStr	Classification accuracy and functional difference prediction in different brain regions of drug abuser prefrontal lobe basing on machine-learning
title_full_unstemmed	Classification accuracy and functional difference prediction in different brain regions of drug abuser prefrontal lobe basing on machine-learning
title_sort	classification accuracy and functional difference prediction in different brain regions of drug abuser prefrontal lobe basing on machine-learning
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/13bfe1e588184e88bc65d2e1fe0608da
work_keys_str_mv	AT banghuayang classificationaccuracyandfunctionaldifferencepredictionindifferentbrainregionsofdrugabuserprefrontallobebasingonmachinelearning AT xuelingu classificationaccuracyandfunctionaldifferencepredictionindifferentbrainregionsofdrugabuserprefrontallobebasingonmachinelearning AT shouweigao classificationaccuracyandfunctionaldifferencepredictionindifferentbrainregionsofdrugabuserprefrontallobebasingonmachinelearning AT dingxu classificationaccuracyandfunctionaldifferencepredictionindifferentbrainregionsofdrugabuserprefrontallobebasingonmachinelearning
_version_	1718441362445041664

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

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