University of Massachusetts-Amherst researchers have developed a computational model of addiction that combines a literature review of human and animal studies with experiments using virtual subjects.
The results suggest that higher cessation rates and lower relapse rates can be achieved when heavy smokers are treated with meditation plus pharmaceutical and cognitive behavioral therapy instead of drug-plus-talk therapy alone.
"Our higher-level conclusion is that a treatment based on meditation-like techniques can be helpful as a supplement to help someone get out of addiction," says lead study author Yariv Z. Levy.
The researchers sought to develop a computational hypothesis that examined effects from both the reward and anti-reward systems in the brain. They also followed subjects in three virtual case studies as part of a theoretical research approach that included 21 time-dependent biological processes and 71 parameters. All of the case studies featured virtual subjects who smoked cigarettes for the first time, became heavy users, and then wanted to quit smoking.
"Because it relies on the increasing amount of available data and knowledge, in silico research offers quick preliminary tests of rationally supported speculations...before full-scale experiments are launched with human patients or animals," the researchers note.
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