Abstract
Internet gaming disorder (IGD) is often diagnosed on the basis of nine underlying criteria from the latest version of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Here, we examined whether such symptom-based categorization could be translated into computation-based classification. Structural MRI (sMRI) and diffusion-weighted MRI (dMRI) data were acquired in 38 gamers diagnosed with IGD, 68 normal gamers diagnosed as not having IGD, and 37 healthy non-gamers. We generated 108 features of gray matter (GM) and white matter (WM) structure from the MRI data. When regularized logistic regression was applied to the 108 neuroanatomical features to select important ones for the distinction between the groups, the disordered and normal gamers were represented in terms of 43 and 21 features, respectively, in relation to the healthy non-gamers, whereas the disordered gamers were represented in terms of 11 features in relation to the normal gamers. In support vector machines (SVM) using the sparse neuroanatomical features as predictors, the disordered and normal gamers were discriminated successfully, with accuracy exceeding 98%, from the healthy non-gamers, but the classification between the disordered and normal gamers was relatively challenging. These findings suggest that pathological and non-pathological gamers as categorized with the criteria from the DSM-5 could be represented by sparse neuroanatomical features, especially in the context of discriminating those from non-gaming healthy individuals.
Highlights
Having been suggested as pathological addiction for decades [1], it is only recently that Internet gaming disorder (IGD) was listed in the Diagnostic and Statistical Manual of Mental Disorders (DSM)
In the discrimination of the disordered gamers from the healthy non-gamers, 43 features selected at λMinErr comprised the thickness of 24 gray matter (GM) regions and the fractional anisotropy (FA) of 19 white matter (WM) tracts, and 34 features selected at λ1SE comprised the thickness of 15 GM regions and the FA of 19 WM tracts
In the distinction of the normal gamers from the healthy non-gamers, 21 features selected at λMinErr comprised the thickness of 12 GM regions and the radial diffusivity (RD) of 9 WM tracts, and 12 features selected at λ1SE comprised the thickness of 6 GM regions and the RD of 6 WM tracts
Summary
Having been suggested as pathological addiction for decades [1], it is only recently that Internet gaming disorder (IGD) was listed in the Diagnostic and Statistical Manual of Mental Disorders (DSM). A substantial body of work has shown that IGD is associated with structural alterations in the brain: shrinkage of gray matter (GM) volume [4,5,6], reduction in cortical thickness [7], and loss of white matter (WM) integrity [8, 9] have been typically demonstrated. These neuroanatomical changes related to IGD suggest that such brain imaging parameters can serve as biomarkers to distinguish individuals with IGD from other individuals. These attempts may be in line with efforts to move beyond descriptive diagnosis by employing computational approaches to psychiatry [10], datadriven approaches based on machine learning (ML) to tackle the diagnosis of mental illness [11]
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