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Ling-Jiao Wang

Researcher at McGovern Institute for Brain Research

Publications -  11
Citations -  665

Ling-Jiao Wang is an academic researcher from McGovern Institute for Brain Research. The author has contributed to research in topics: Cue reactivity & Insula. The author has an hindex of 11, co-authored 11 publications receiving 526 citations.

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Activation of the ventral and dorsal striatum during cue reactivity in Internet gaming disorder

TL;DR: It is suggested that a transition from ventral to dorsal striatal processing may occur among individuals with IGD, a condition without the impact of substance intake.
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Effects of craving behavioral intervention on neural substrates of cue-induced craving in Internet gaming disorder

TL;DR: Compared neural activation between 40 IGD and 19 healthy control subjects during an Internet-gaming cue-reactivity task and found that IGD subjects showed stronger activation in multiple brain areas, but lower activation in the posterior insula.
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Impaired decision-making under risk is associated with gaming-specific inhibition deficits among college students with Internet gaming disorder.

TL;DR: Individuals with IGD are impaired in some aspects of inhibition and decision-making functions, and that decision- making deficits under risk are linked to poor inhibition specifically related to gaming cues, which has implications for the development of novel intervention.
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Altered resting-state functional connectivity of the insula in young adults with Internet gaming disorder

TL;DR: A key role of the insula in manifestation of the core symptoms of IGD is highlighted and the importance to examine functional abnormalities of the anterior and posterior insula separately in IGDs is highlighted.
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Altered resting-state neural activity and changes following a craving behavioral intervention for Internet gaming disorder

TL;DR: The findings suggest that IGD is associated with abnormal resting-state neural activity in reward-related, default mode and executive control networks, and the CBI may exert effects by reducing interactions between regions within a reward- related network, and across the default Mode and Executive control networks.