Department of Sensor and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.
Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Hum Brain Mapp. 2021 Oct 1;42(14):4525-4537. doi: 10.1002/hbm.25562. Epub 2021 Jun 25.
Internet addiction refers to problematic patterns of internet use that continually alter the neural organization and brain networks that control impulsive behaviors and inhibitory functions. Individuals with elevated tendencies to develop internet addiction represent the transition between healthy and clinical conditions and may progress to behavioral addictive disorders. In this network neuroscience study, we used resting-state functional magnetic resonance imaging (rs-fMRI) to examine how and whether individual variations in the tendency of developing internet addiction rewire functional connectivity and diminish the amplitude of spontaneous low-frequency fluctuations in healthy brains. The influence of neurocognitive aging (aged over 60 years) on executive-cerebellar networks responsible for internet addictive behavior was also investigated. Our results revealed that individuals with an elevated tendency of developing internet addiction had disrupted executive-cerebellar networks but increased occipital-putamen connectivity, probably resulting from addiction-sensitive cognitive control processes and bottom-up sensory plasticity. Neurocognitive aging alleviated the effects of reduced mechanisms of prefrontal and cerebellar connectivity, suggesting age-related modulation of addiction-associated brain networks in response to compulsive internet use. Our findings highlight age-related and individual differences in altered functional connectivity and the brain networks of individuals at a high risk of developing internet addictive disorders. These results offer novel network-based preclinical markers of internet addictive behaviors for individuals of different ages.
网络成瘾是指持续改变控制冲动行为和抑制功能的神经组织和大脑网络的问题性互联网使用模式。具有较高网络成瘾倾向的个体代表了健康和临床状态之间的过渡,可能会发展为行为成瘾障碍。在这项网络神经科学研究中,我们使用静息态功能磁共振成像(rs-fMRI)来研究个体网络成瘾倾向如何以及是否会改变功能连接,并降低健康大脑中自发性低频波动的幅度。我们还研究了神经认知老化(年龄超过 60 岁)对负责网络成瘾行为的执行-小脑网络的影响。我们的结果表明,具有较高网络成瘾倾向的个体的执行-小脑网络受到干扰,但枕叶-壳核的连接增加,这可能是由于成瘾敏感的认知控制过程和自下而上的感觉可塑性。神经认知老化减轻了前额叶和小脑连接减少的影响,表明与强迫性互联网使用相关的大脑网络会随着年龄的增长而发生变化。我们的研究结果突出了具有不同年龄的个体的网络功能连接和大脑网络的与年龄相关的和个体差异,以及具有网络成瘾障碍高风险的个体。这些结果为不同年龄的个体提供了网络成瘾行为的新型临床前标志物。