Addict Behav. 2020 Feb 1;105:106334. doi: 10.1016/j.addbeh.2020.106334.
Horvath J1, Mundinger C1, Schmitgen MM1, Wolf ND1, Sambataro F2, Hirjak D3, Kubera KM1, Koenig J4, Christian Wolf R5.
Abstract
Popularity and availability of smartphones have dramatically increased in the past years. This trend is accompanied by increased concerns regarding potentially adverse effects of excessive smartphone use, particularly with respect to physical and mental health. Recently, the term “smartphone addiction” (SPA) has been introduced to describe smartphone-related addictive behavior and associated physical and psychosocial impairment. Here, we used structural and functional magnetic resonance imaging (MRI) at 3 T to investigate gray matter volume (GMV) and intrinsic neural activity in individuals with SPA (n = 22) compared to a control group (n = 26). SPA was assessed using the Smartphone Addiction Inventory (SPAI), GMV was investigated by means of voxel-based morphometry, and intrinsic neural activity was measured by the amplitude of low frequency fluctuations (ALFF). Compared to controls, individuals with SPA showed lower GMV in left anterior insula, inferior temporal and parahippocampal cortex (p < 0.001, uncorrected for height, followed by correction for spatial extent). Lower intrinsic activity in SPA was found in the right anterior cingulate cortex (ACC). A significant negative association was found between SPAI and both ACC volume and activity. In addition, a significant negative association between SPAI scores and left orbitofrontal GMV was found. This study provides first evidence for distinct structural and functional correlates of behavioral addiction in individuals meeting psychometric criteria for SPA. Given their widespread use and increasing popularity, the present study questions the harmlessness of smartphones, at least in individuals that may be at increased risk for developing smartphone-related addictive behaviors.
KEYWORDS: Addiction; Brain activity; Gray matter volume; Resting-state fMRI; Smartphone; Voxel-based morphometry
PMID: 32062336
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