The brains of "internet-addicted" teenagers may differ significantly from those of non-addicted teens, a small study suggests.
MRI scans of their brains appear to show damage to white matter as well as the fibres that connect it, suggesting that heavy internet use, like alcoholism and gambling, may be linked with cognitive impairment.
Internet addiction, though not officially recognized by health-care authorities, has been defined in several studies as an impulse-control disorder. It has been characterized by an overwhelming desire to stay online and impairs an individual’s ability to function on a day-to-day basis. Going offline leads the addicted individual to experience withdrawal symptoms similar to those of alcohol and drugs.
The study of 17 adolescents apparently addicted to the internet and 16 controls was conducted by Chinese researchers and published in the Wednesday issue of the journal PLOS One.
The researchers used a technique called fractional anisotropy (FA) to measure the organization of the brain, which is greatly influenced by the number and location of white matter fibres. Those study participants who had displayed addiction symptoms showed lower FA values in a variety of regions of the brain, such as as the orbito-frontal white matter, corpus callosum, cingulum, inferior fronto-occipital fasciculus and corona radiation. Lower FA values indicate that the nerve fibres are not functioning properly.
"Overall, our findings indicate that internet addiction disorder has abnormal white matter integrity in brain regions involved in emotional generation and processing, executive attention, decision making and cognitive control," write the authors. "The results also suggest that IAD may share psychological and neural mechanisms with other types of substance addiction and impulse control disorders."
The researchers theorize that the myelin, a protective sheath around nerve fibres, is disrupted in a variety of regions of the brain in people with IAD. They also believe that fractional anisotropy may eventually become an effective way of detecting the severity of internet addiction.