Identifying Methamphetamine Dependence Using Regional Homogeneity in BOLD Signals
Methamphetamine is a highly addictive drug of abuse, which will cause a series of abnormal consequences mentally and physically. This paper is aimed at studying whether the abnormalities of regional homogeneity (ReHo) could be effective features to distinguish individuals with methamphetamine depend...
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doaj-87b8f41a32e24c97b0c311ae8b7916f82020-11-25T02:36:39ZengHindawi LimitedComputational and Mathematical Methods in Medicine1748-670X1748-67182020-01-01202010.1155/2020/32679493267949Identifying Methamphetamine Dependence Using Regional Homogeneity in BOLD SignalsHufei Yu0Shucai Huang1Xiaojie Zhang2Qiuping Huang3Jun Liu4Hongxian Chen5Yan Tang6School of Computer Science and Engineering, Central South University, Changsha, Hunan 410000, ChinaDepartment of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, ChinaDepartment of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, ChinaDepartment of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, ChinaDepartment of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, ChinaDepartment of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410000, ChinaSchool of Computer Science and Engineering, Central South University, Changsha, Hunan 410000, ChinaMethamphetamine is a highly addictive drug of abuse, which will cause a series of abnormal consequences mentally and physically. This paper is aimed at studying whether the abnormalities of regional homogeneity (ReHo) could be effective features to distinguish individuals with methamphetamine dependence (MAD) from control subjects using machine-learning methods. We made use of resting-state fMRI to measure the regional homogeneity of 41 individuals with MAD and 42 age- and sex-matched control subjects and found that compared with control subjects, individuals with MAD have lower ReHo values in the right medial superior frontal gyrus but higher ReHo values in the right temporal inferior fusiform. In addition, AdaBoost classifier, a pretty effective ensemble learning of machine learning, was employed to classify individuals with MAD from control subjects with abnormal ReHo values. By utilizing the leave-one-out cross-validation method, we got the accuracy more than 84.3%, which means we can almost distinguish individuals with MAD from the control subjects in ReHo values via machine-learning approaches. In a word, our research results suggested that the AdaBoost classifier-neuroimaging approach may be a promising way to find whether a person has been addicted to methamphetamine, and also, this paper shows that resting-state fMRI should be considered as a biomarker, a noninvasive and effective assistant tool for evaluating MAD.http://dx.doi.org/10.1155/2020/3267949 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hufei Yu Shucai Huang Xiaojie Zhang Qiuping Huang Jun Liu Hongxian Chen Yan Tang |
spellingShingle |
Hufei Yu Shucai Huang Xiaojie Zhang Qiuping Huang Jun Liu Hongxian Chen Yan Tang Identifying Methamphetamine Dependence Using Regional Homogeneity in BOLD Signals Computational and Mathematical Methods in Medicine |
author_facet |
Hufei Yu Shucai Huang Xiaojie Zhang Qiuping Huang Jun Liu Hongxian Chen Yan Tang |
author_sort |
Hufei Yu |
title |
Identifying Methamphetamine Dependence Using Regional Homogeneity in BOLD Signals |
title_short |
Identifying Methamphetamine Dependence Using Regional Homogeneity in BOLD Signals |
title_full |
Identifying Methamphetamine Dependence Using Regional Homogeneity in BOLD Signals |
title_fullStr |
Identifying Methamphetamine Dependence Using Regional Homogeneity in BOLD Signals |
title_full_unstemmed |
Identifying Methamphetamine Dependence Using Regional Homogeneity in BOLD Signals |
title_sort |
identifying methamphetamine dependence using regional homogeneity in bold signals |
publisher |
Hindawi Limited |
series |
Computational and Mathematical Methods in Medicine |
issn |
1748-670X 1748-6718 |
publishDate |
2020-01-01 |
description |
Methamphetamine is a highly addictive drug of abuse, which will cause a series of abnormal consequences mentally and physically. This paper is aimed at studying whether the abnormalities of regional homogeneity (ReHo) could be effective features to distinguish individuals with methamphetamine dependence (MAD) from control subjects using machine-learning methods. We made use of resting-state fMRI to measure the regional homogeneity of 41 individuals with MAD and 42 age- and sex-matched control subjects and found that compared with control subjects, individuals with MAD have lower ReHo values in the right medial superior frontal gyrus but higher ReHo values in the right temporal inferior fusiform. In addition, AdaBoost classifier, a pretty effective ensemble learning of machine learning, was employed to classify individuals with MAD from control subjects with abnormal ReHo values. By utilizing the leave-one-out cross-validation method, we got the accuracy more than 84.3%, which means we can almost distinguish individuals with MAD from the control subjects in ReHo values via machine-learning approaches. In a word, our research results suggested that the AdaBoost classifier-neuroimaging approach may be a promising way to find whether a person has been addicted to methamphetamine, and also, this paper shows that resting-state fMRI should be considered as a biomarker, a noninvasive and effective assistant tool for evaluating MAD. |
url |
http://dx.doi.org/10.1155/2020/3267949 |
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