MATERIALS AND METHODS: A scoping review was performed to elaborate on the research regarding resting-state EEG and task-based EEG, particularly for Go/No-go paradigms pertaining to subjects with IAD or specifically IGD. The role of EEG was identified in its diagnostic capability to identify the salient changes that occurred in the response to reward network and the executive control network, using restingstate and task-based EEG. The implication of using EEG in monitoring the therapy for IAD and IGD was also reviewed.
RESULTS: EEG generally revealed reduced beta waves and increased theta waves in addicts. IGD with depression demonstrated increased theta and decreased alpha waves. Whereas increased P300, a late cognitive ERP component, was frequently associated with impaired excessive allocation of attentional resources of the IAD towards addiction-specific cues. IGD had increased whole brain delta waves at baseline, which showed significant reduction post therapy.
CONCLUSION: EEG can identify distinct neurophysiological changes among Internet Addiction Disorder and Internet Gaming Disorder that are akin to substance abuse disorders.
METHOD: A prospective cross-sectional study was conducted using the validated Smartphone Addiction Scale-Malay version (SAS-M) questionnaire. One-way ANOVA was used to determine the correlation between the PSU among the students categorised by their ethnicity, hand dominance and by their field of study. MLR analysis was applied to predict PSU based on socio-demographic data, usage patterns, psychological factors and fields of study.
RESULTS: A total of 1060 students completed the questionnaire. Most students had some degree of problematic usage of the smartphone. Students used smartphones predominantly to access SNAs, namely Instagram. Longer duration on the smartphone per day, younger age at first using a smartphone and underlying depression carried higher risk of developing PSU, whereas the field of study (science vs. humanities based) did not contribute to an increased risk of developing PSU.
CONCLUSION: Findings from this study can help better inform university administrators about at- risk groups of undergraduate students who may benefit from targeted intervention designed to reduce their addictive behaviour patterns.
Aims: The primary objective was to evaluate whether addiction-specific cues compared with neutral cues, i.e., negative emotional valence cues vs. positive emotional valence cues, would elicit activation of the dopaminergic reward network (i.e., precuneus, nucleus accumbens, and amygdala) and consecutive deactivation of the executive control network [i.e., medial prefrontal cortex (mPFC) and dorsolateral prefrontal cortex (dlPFC)], in the PIGU subjects.
Method: An fMRI cue-induced reactivity study was performed using negative emotional valence, positive emotional valence, and truly neutral cues, using Instagram themes. Thirty subjects were divided into PIGU and healthy control (HC) groups, based on a set of diagnostic criteria using behavioral tests, including the Modified Instagram Addiction Test (IGAT), to assess the severity of PIGU. In-scanner recordings of the subjects' responses to the images and regional activity of the neural addiction pathways were recorded.
Results: Negative emotional valence > positive emotional valence cues elicited increased activations in the precuneus in the PIGU group. A negative and moderate correlation was observed between PSC at the right mPFC with the IGAT scores of the PIGU subjects when corrected for multiple comparisons [r = -0.777, (p < 0.004, two-tailed)].
Conclusion: Addiction-specific Instagram-themed cues identify the neurobiological underpinnings of Instagram addiction. Activations of the dopaminergic reward system and deactivation of the executive control network indicate converging neuropathological pathways between Instagram addiction and other types of addictions.