Neural classification of internet gaming disorder and prediction of treatment response using a cue-reactivity fMRI task in young men

Zi-Liang Wang; Marc N Potenza; Kun-Ru Song; Xiao-Yi Fang; Lu Liu; Shan-Shan Ma; Cui-Cui Xia; Jing Lan; Yuan-Wei Yao; Jin-Tao Zhang

doi:10.1016/j.jpsychires.2020.11.014

Neural classification of internet gaming disorder and prediction of treatment response using a cue-reactivity fMRI task in young men

J Psychiatr Res. 2022 Jan:145:309-316. doi: 10.1016/j.jpsychires.2020.11.014. Epub 2020 Nov 7.

Authors

Zi-Liang Wang¹, Marc N Potenza², Kun-Ru Song¹, Xiao-Yi Fang³, Lu Liu⁴, Shan-Shan Ma⁵, Cui-Cui Xia⁶, Jing Lan⁷, Yuan-Wei Yao⁸, Jin-Tao Zhang⁹

Affiliations

¹ State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
² Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Child Study Center, Yale University School of Medicine, New Haven, CT, USA; Department of Neuroscience, Yale University School of Medicine, Connecticut Mental Health Center, New Haven, Connecticut Council on Problem Gambling, Wethersfield, CT, USA.
³ Institute of Developmental Psychology, Beijing Normal University, Beijing, 100875, China. Electronic address: fangxy@bnu.edu.cn.
⁴ Institute of Developmental Psychology, Beijing Normal University, Beijing, 100875, China; German Institute of Human Nutrition Potsdam-Rehbruecke, 14558, Nuthetal, Germany.
⁵ State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Institute of Developmental Psychology, Beijing Normal University, Beijing, 100875, China.
⁶ Psychological Counseling Center, Beijing Normal University, Beijing, 100875, China.
⁷ Institute of Developmental Psychology, Beijing Normal University, Beijing, 100875, China; The Family Institute at Northwestern University, Northwestern University, 60201, IL, USA.
⁸ State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Department of Education and Psychology, Freie Universität Berlin, Berlin, 14195, Germany.
⁹ State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Beijing Key Lab of Applied Experimental Psychology, School of Psychology, Beijing Normal University, Beijing, China. Electronic address: zhangjintao@bnu.edu.cn.

PMID: 33229034
DOI: 10.1016/j.jpsychires.2020.11.014

Abstract

Background: Neural mechanisms underlying internet gaming disorder (IGD) are important for diagnostic considerations and treatment development. However, neurobiological underpinnings of IGD remain relatively poorly understood.

Methods: We employed multi-voxel pattern analysis (MVPA), a machine-learning approach, to examine the potential of neural features to statistically predict IGD status and treatment outcome (percentage change in weekly gaming time) for IGD. Cue-reactivity fMRI-task data were collected from 40 male IGD subjects and 19 male healthy control (HC) subjects. 23 IGD subjects received 6 weeks of craving behavioral intervention (CBI) treatment. MVPA was applied to classify IGD subjects from HCs and statistically predict clinical outcomes.

Results: MVPA displayed a high (92.37%) accuracy (sensitivity of 90.00% and specificity of 94.74%) in the classification of IGD and HC subjects. The most discriminative brain regions that contribute to classification were the bilateral middle frontal gyrus, precuneus, and posterior lobe of the right cerebellum. MVPA statistically predicted clinical outcomes in the craving behavioral intervention (CBI) group (r = 0.48, p = 0.0032). The most strongly implicated brain regions in the prediction model were the right middle frontal gyrus, superior frontal gyrus, supramarginal gyrus, anterior/posterior lobes of the cerebellum and left postcentral gyrus.

Conclusions: The findings about cue-reactivity neural correlates could help identify IGD subjects and predict CBI-related treatment outcomes provide mechanistic insight into IGD and its treatment and may help promote treatment development efforts.

Keywords: Addictive behaviors; Behavior therapy; Craving; Internet; Multi-voxel pattern analysis; Video games.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Behavior, Addictive* / diagnostic imaging
Behavior, Addictive* / therapy
Brain / diagnostic imaging
Brain Mapping
Craving / physiology
Cues
Humans
Internet
Internet Addiction Disorder
Magnetic Resonance Imaging
Male
Video Games*