IEEE Transactions on Neural Systems and Rehabilitation Engineering (Jan 2024)

Neural Network Dynamics and Brain Oscillations Underlying Aberrant Inhibitory Control in Internet Addiction

  • Yi-Li Tseng,
  • Yu-Kai Su,
  • Wen-Jiun Chou,
  • Makoto Miyakoshi,
  • Ching-Shu Tsai,
  • Chia-Jung Li,
  • Sheng-Yu Lee,
  • Liang-Jen Wang

DOI
https://doi.org/10.1109/TNSRE.2024.3363756
Journal volume & issue
Vol. 32
pp. 946 – 955

Abstract

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Previous studies have reported a role of alterations in the brain’s inhibitory control mechanism in addiction. Mounting evidence from neuroimaging studies indicates that its key components can be evaluated with brain oscillations and connectivity during inhibitory control. In this study, we developed an internet-related stop-signal task with electroencephalography (EEG) signal recorded to investigate inhibitory control. Healthy controls and participants with Internet addiction were recruited to participate in the internet-related stop-signal task with 19-channel EEG signal recording, and the corresponding event-related potentials and spectral perturbations were analyzed. Brain effective connections were also evaluated using direct directed transfer function. The results showed that, relative to the healthy controls, participants with Internet addiction had increased Stop-P3 during inhibitory control, suggesting that they have an altered neural mechanism in impulsive control. Furthermore, participants with Internet addiction showed increased low-frequency synchronization and decreased alpha and beta desynchronization in the middle and right frontal regions compared to healthy controls. Aberrant brain effective connectivity was also observed, with increased occipital-parietal and intra-occipital connections, as well as decreased frontal-paracentral connection in participants with Internet addiction. These results suggest that physiological signals are essential in future implementations of cognitive assessment of Internet addiction to further investigate the underlying mechanisms and effective biomarkers.

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