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BackgroundAlthough recent studies have clearly demonstrated functional and structural abnormalities in adolescents with internet gaming addiction (IGA), less is known about how IGA affects perfusion in the human brain. We used pseudocontinuous arterial spin-labeling (ASL) perfusion functional magnetic resonance imaging (fMRI) to measure the effects of IGA on resting brain functions by comparing resting cerebral blood flow in adolescents with IGA and normal subjects.MethodsFifteen adolescents with IGA and 18 matched normal adolescents underwent structural and perfusion fMRI in the resting state. Direct subtraction, voxel-wise general linear modeling was performed to compare resting cerebral blood flow (CBF) between the 2 groups. Correlations were calculated between the mean CBF value in all clusters that survived AlphaSim correction and the Chen Internet Addiction Scale (CIAS) scores, Barratt Impulsiveness Scale-11 (BIS-11) scores, or hours of Internet use per week (hours) in the 15 subjects with IGA.ResultsCompared with control subjects, adolescents with IGA showed significantly higher global CBF in the left inferior temporal lobe/fusiform gyrus, left parahippocampal gyrus/amygdala, right medial frontal lobe/anterior cingulate cortex, left insula, right insula, right middle temporal gyrus, right precentral gyrus, left supplementary motor area, left cingulate gyrus, and right inferior parietal lobe. Lower CBF was found in the left middle temporal gyrus, left middle occipital gyrus, and right cingulate gyrus. There were no significant correlations between mean CBF values in all clusters that survived AlphaSim correction and CIAS or BIS-11 scores or hours of Internet use per week.ConclusionsIn this study, we used ASL perfusion fMRI and noninvasively quantified resting CBF to demonstrate that IGA alters the CBF distribution in the adolescent brain. The results support the hypothesis that IGA is a behavioral addiction that may share similar neurobiological abnormalities with other addictive disorders.

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This paper proposes a novel approach for constructing effective personalized policies when the observed data lacks counter-factual information, is biased and possesses many features. The approach is applicable in a wide variety of settings from healthcare to advertising to education to finance. These settings have in common that the decision maker can observe, for each previous instance, an array of features of the instance, the action taken in that instance, and the reward realized -but not the rewards of actions that were not taken: the counterfactual information. Learning in such settings is made even more difficult because the observed data is typically biased by the existing policy (that generated the data) and because the array of features that might affect the reward in a particular instance -and hence should be taken into account in deciding on an action in each particular instance -is often vast. The approach presented here estimates propensity scores for the observed data, infers counterfactuals, identifies a (relatively small) number of features that are (most) relevant for each possible action and instance, and prescribes a policy to be followed. Comparison of the proposed algorithm against state-of-art algorithms on actual datasets demonstrates that the proposed algorithm achieves a significant improvement in performance.

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