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However, the underlying neuropsychological mechanisms remain poorly understood. This study aims to investigate the structural basis of self\u2010control and internet addiction. In a cohort of 96 internet gamers, we examined the relationships among grey matter volume and white matter integrity within the frontostriatal circuits and internet addiction severity, as well as self\u2010control measures. The results showed a significant and negative correlation between dACC grey matter volume and internet addiction severity (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001), but not with self\u2010control. Subsequent tractography from the dACC to the bilateral ventral striatum (VS) was conducted. The fractional anisotropy (FA) and radial diffusivity of dACC\u2010right VS pathway was negatively (<jats:italic>p<\/jats:italic>\u2009=\u20090.011) and positively (<jats:italic>p<\/jats:italic>\u2009=\u20090.020) correlated with internet addiction severity, respectively, and the FA was also positively correlated with self\u2010control (<jats:italic>p<\/jats:italic>\u2009=\u20090.036). These associations were not observed for the dACC\u2010left VS pathway. Further mediation analysis demonstrated a significant complete mediation effect of self\u2010control on the relationship between FA of the dACC\u2010right VS pathway and internet addiction severity. Our findings suggest that the dACC\u2010right VS pathway is a critical neural substrate for both internet addiction and self\u2010control. Deficits in this pathway may lead to impaired self\u2010regulation over internet usage, exacerbating the severity of internet addiction.<\/jats:p>","DOI":"10.1111\/adb.13399","type":"journal-article","created":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T04:39:10Z","timestamp":1715056750000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["White matter integrity of right frontostriatal circuit predicts internet addiction severity among internet gamers"],"prefix":"10.1111","volume":"29","author":[{"given":"Hui","family":"Zhou","sequence":"first","affiliation":[{"name":"The State Key Lab of Brain\u2010Machine Intelligence Zhejiang University  Hangzhou China"},{"name":"Department of Psychology and Behavioral Sciences Zhejiang University  Hangzhou 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