Resting‐state connectome‐based support‐vector‐machine predictive modeling of internet gaming disorder
| Autor: | Ziliang Wang, Marc N. Potenza, Kun-Ru Song, Yuan-Wei Yao, Shan-Shan Ma, Jing Lan, Gaolang Gong, Lu-Lu Wu, Cui-Cui Xia, Linyuan Deng, Lu Liu, Jin-Tao Zhang, Xiaoyi Fang |
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| Rok vydání: | 2020 |
| Předmět: |
Adult
Male Support Vector Machine Medicine (miscellaneous) Executive Function Young Adult 03 medical and health sciences 0302 clinical medicine Neuroimaging Neural Pathways Connectome medicine Humans Default mode network Pharmacology Resting state fMRI medicine.diagnostic_test Brain Magnetic Resonance Imaging Regression 030227 psychiatry Behavior Addictive Support vector machine Psychiatry and Mental health Video Games Test score Psychology Functional magnetic resonance imaging human activities Neuroscience Internet Addiction Disorder 030217 neurology & neurosurgery |
| Zdroj: | Addiction Biology. 26 |
| ISSN: | 1369-1600 1355-6215 |
| DOI: | 10.1111/adb.12969 |
| Popis: | Internet gaming disorder (IGD), a worldwide mental health issue, has been widely studied using neuroimaging techniques during the last decade. Although dysfunctions in resting-state functional connectivity have been reported in IGD, mapping relationships from abnormal connectivity patterns to behavioral measures have not been fully investigated. Connectome-based predictive modeling (CPM)-a recently developed machine-learning approach-has been used to examine potential neural mechanisms in addictions and other psychiatric disorders. To identify the resting-state connections associated with IGD, we modified the CPM approach by replacing its core learning algorithm with a support vector machine. Resting-state functional magnetic resonance imaging (fMRI) data were acquired in 72 individuals with IGD and 41 healthy comparison participants. The modified CPM was conducted with respect to classification and regression. A comparison of whole-brain and network-based analyses showed that the default-mode network (DMN) is the most informative network in predicting IGD both in classification (individual identification accuracy = 78.76%) and regression (correspondence between predicted and actual psychometric scale score: r = 0.44, P < 0.001). To facilitate the characterization of the aberrant resting-state activity in the DMN, the identified networks have been mapped into a three-subsystem division of the DMN. Results suggest that individual differences in DMN function at rest could advance our understanding of IGD and variability in disorder etiology and intervention outcomes. |
| Databáze: | OpenAIRE |
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