Functional connectome gradient of prefrontal cortex as biomarkers of high risk for internet gaming disorder
Xinwen Wen,
Lirong Yue,
Zhe Du,
Jiahao Zhao,
Mengjiao Ge,
Cunfeng Yuan,
Hongmei Wang,
Qinghua He,
Kai Yuan
Affiliations
Xinwen Wen
School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
Lirong Yue
School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
Zhe Du
School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
Jiahao Zhao
School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
Mengjiao Ge
Faculty of Psychology, MOE Key Laboratory of Cognition and Personality, Southwest University, Chongqing 400715, China
Cunfeng Yuan
Drug Rehabilitation Administration of the Ministry of Justice, Beijing, China
Hongmei Wang
Department of Medical Imaging, Inner Mongolia People's Hospital, Hohhot 010017, China
Qinghua He
Faculty of Psychology, MOE Key Laboratory of Cognition and Personality, Southwest University, Chongqing 400715, China; Corresponding authors.
Kai Yuan
School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China; Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China; Engineering Research Center of Molecular and Neuro Imaging Ministry of Education, Xi'an, Shaanxi 710071, China; Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China; Corresponding authors.
Adolescents and young adults are considered a high-risk group for internet gaming disorder (IGD). Early screening for high-risk individuals with IGD and exploring the underlying neural mechanisms is an effective strategy to reduce the harm of IGD. We recruited 219 non-internet gaming addicted college students and evaluated them with magnetic resonance imaging, followed by a two-year longitudinal follow-up. We used functional connectome gradient (FCG) to capture the macroscopic hierarchical organization of human brain. Canonical correlation analysis was employed to identify components mapping relationships between FCG and behavioral scores. Consequently, K-means clustering was used to define distinct subtypes. The risk of developing IGD and FCG patterns were compared among the subtypes. Three subtypes were identified and subtype 3 exhibited the highest risk for developing IGD according to the occurrence rates of IGD two years later: (1) subtype 1 (5.3 %, 4 participants), (2) subtype 2 (10.8 %, 9 participants), (3) subtype 3 (20 %, 12 participants). The abnormal FCG in the inferior frontal gyrus and posterior cingulate cortex at baseline were observed in subtype 3, which were correlated with impulsivity. These findings advanced understanding of the biological and behavioral heterogeneity associated with developing of IGD, and represented a promising step toward the prediction of high-risk individuals.
