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Identifying Internet Addiction and Evaluating the Efficacy of Treatment Based on Functional Connectivity Density: A Machine Learning Study

Although mounting neuroimaging studies have greatly improved our understanding of the neurobiological mechanism underlying internet addiction (IA), the results based on traditional group-level comparisons are insufficient in guiding individual clinical practice directly. Specific neuroimaging biomar...

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Autores principales: Wang, Yang, Qin, Yun, Li, Hui, Yao, Dezhong, Sun, Bo, Gong, Jinnan, Dai, Yu, Wen, Chao, Zhang, Lingrui, Zhang, Chenchen, Luo, Cheng, Zhu, Tianmin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8247769/
https://www.ncbi.nlm.nih.gov/pubmed/34220426
http://dx.doi.org/10.3389/fnins.2021.665578
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author Wang, Yang
Qin, Yun
Li, Hui
Yao, Dezhong
Sun, Bo
Gong, Jinnan
Dai, Yu
Wen, Chao
Zhang, Lingrui
Zhang, Chenchen
Luo, Cheng
Zhu, Tianmin
author_facet Wang, Yang
Qin, Yun
Li, Hui
Yao, Dezhong
Sun, Bo
Gong, Jinnan
Dai, Yu
Wen, Chao
Zhang, Lingrui
Zhang, Chenchen
Luo, Cheng
Zhu, Tianmin
author_sort Wang, Yang
collection PubMed
description Although mounting neuroimaging studies have greatly improved our understanding of the neurobiological mechanism underlying internet addiction (IA), the results based on traditional group-level comparisons are insufficient in guiding individual clinical practice directly. Specific neuroimaging biomarkers are urgently needed for IA diagnosis and the evaluation of therapy efficacy. Therefore, this study aimed to develop support vector machine (SVM) models to identify IA and assess the efficacy of cognitive behavior therapy (CBT) based on unbiased functional connectivity density (FCD). Resting-state fMRI data were acquired from 27 individuals with IA before and after 8-week CBT sessions and 30 demographically matched healthy controls (HCs). The discriminative FCDs were computed as the features of the support vector classification (SVC) model to identify individuals with IA from HCs, and the changes in these discriminative FCDs after treatment were further used as features of the support vector regression (SVR) model to evaluate the efficacy of CBT. Based on the informative FCDs, our SVC model successfully differentiated individuals with IA from HCs with an accuracy of 82.5% and an area under the curve (AUC) of 0.91. Our SVR model successfully evaluated the efficacy of CBT using the FCD change ratio with a correlation efficient of 0.59. The brain regions contributing to IA classification and CBT efficacy assessment were the left inferior frontal cortex (IFC), middle frontal cortex (MFC) and angular gyrus (AG), the right premotor cortex (PMC) and middle cingulate cortex (MCC), and the bilateral cerebellum, orbitofrontal cortex (OFC) and superior frontal cortex (SFC). These findings confirmed the FCDs of hyperactive impulsive habit system, hypoactive reflecting system and sensitive interoceptive reward awareness system as potential neuroimaging biomarkers for IA, which might provide objective indexes for the diagnosis and efficacy evaluation of IA.
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spelling pubmed-82477692021-07-02 Identifying Internet Addiction and Evaluating the Efficacy of Treatment Based on Functional Connectivity Density: A Machine Learning Study Wang, Yang Qin, Yun Li, Hui Yao, Dezhong Sun, Bo Gong, Jinnan Dai, Yu Wen, Chao Zhang, Lingrui Zhang, Chenchen Luo, Cheng Zhu, Tianmin Front Neurosci Neuroscience Although mounting neuroimaging studies have greatly improved our understanding of the neurobiological mechanism underlying internet addiction (IA), the results based on traditional group-level comparisons are insufficient in guiding individual clinical practice directly. Specific neuroimaging biomarkers are urgently needed for IA diagnosis and the evaluation of therapy efficacy. Therefore, this study aimed to develop support vector machine (SVM) models to identify IA and assess the efficacy of cognitive behavior therapy (CBT) based on unbiased functional connectivity density (FCD). Resting-state fMRI data were acquired from 27 individuals with IA before and after 8-week CBT sessions and 30 demographically matched healthy controls (HCs). The discriminative FCDs were computed as the features of the support vector classification (SVC) model to identify individuals with IA from HCs, and the changes in these discriminative FCDs after treatment were further used as features of the support vector regression (SVR) model to evaluate the efficacy of CBT. Based on the informative FCDs, our SVC model successfully differentiated individuals with IA from HCs with an accuracy of 82.5% and an area under the curve (AUC) of 0.91. Our SVR model successfully evaluated the efficacy of CBT using the FCD change ratio with a correlation efficient of 0.59. The brain regions contributing to IA classification and CBT efficacy assessment were the left inferior frontal cortex (IFC), middle frontal cortex (MFC) and angular gyrus (AG), the right premotor cortex (PMC) and middle cingulate cortex (MCC), and the bilateral cerebellum, orbitofrontal cortex (OFC) and superior frontal cortex (SFC). These findings confirmed the FCDs of hyperactive impulsive habit system, hypoactive reflecting system and sensitive interoceptive reward awareness system as potential neuroimaging biomarkers for IA, which might provide objective indexes for the diagnosis and efficacy evaluation of IA. Frontiers Media S.A. 2021-06-17 /pmc/articles/PMC8247769/ /pubmed/34220426 http://dx.doi.org/10.3389/fnins.2021.665578 Text en Copyright © 2021 Wang, Qin, Li, Yao, Sun, Gong, Dai, Wen, Zhang, Zhang, Luo and Zhu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Wang, Yang
Qin, Yun
Li, Hui
Yao, Dezhong
Sun, Bo
Gong, Jinnan
Dai, Yu
Wen, Chao
Zhang, Lingrui
Zhang, Chenchen
Luo, Cheng
Zhu, Tianmin
Identifying Internet Addiction and Evaluating the Efficacy of Treatment Based on Functional Connectivity Density: A Machine Learning Study
title Identifying Internet Addiction and Evaluating the Efficacy of Treatment Based on Functional Connectivity Density: A Machine Learning Study
title_full Identifying Internet Addiction and Evaluating the Efficacy of Treatment Based on Functional Connectivity Density: A Machine Learning Study
title_fullStr Identifying Internet Addiction and Evaluating the Efficacy of Treatment Based on Functional Connectivity Density: A Machine Learning Study
title_full_unstemmed Identifying Internet Addiction and Evaluating the Efficacy of Treatment Based on Functional Connectivity Density: A Machine Learning Study
title_short Identifying Internet Addiction and Evaluating the Efficacy of Treatment Based on Functional Connectivity Density: A Machine Learning Study
title_sort identifying internet addiction and evaluating the efficacy of treatment based on functional connectivity density: a machine learning study
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8247769/
https://www.ncbi.nlm.nih.gov/pubmed/34220426
http://dx.doi.org/10.3389/fnins.2021.665578
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