Introduction: Understanding the neural mechanisms that support successful smoking cessation is vital to the development of novel treatments for nicotine dependence. Method: To this end, we compared resting-state functional connectivity across three smoking groups: current, never, and former smokers. We used an independent component analysis (ICA) that allowed us to compare differences in intrinsic, large-scale networks across our groups. Using this technique, we were able to compare group differences across resting-state networks without the requirement of identifying coordinate-based regions of interest. Results: Overall, the ICA resulted in networks that were largely consistent with previous reports, including bilateral executive control networks, salience, and a default mode network. Group comparisons among the three groups revealed differences in three networks including sensorimotor, dorsal attention, and default mode networks, with differences localized to pre/postcentral gyrus, lateral occipital cortex, and superior parietal lobe. In all regions showing a difference, current smokers showed increased network amplitude compared to former and never smokers. Conclusion: Although some theoretical models of recovery have suggested an important role of frontal cortex and cognitive control, the current results seem to suggest that reductions in posterior regions including superior parietal lobe and somatosensory cortex may play a key role in maintaining long-term abstinence from cigarettes. Implications: The submitted research is a novel contribution to the study of successful nicotine abstinence, in part, because it includes individuals who have successfully overcome nicotine dependence. The use of ICA allowed for examination of large-scale resting-state networks throughout the brain without the need for specifying numerous regions of interest. This research supports the view that overcoming nicotine dependence may depend on reducing spontaneous activity in posterior regions of the brain rather than solely enhancing frontal control.
All Science Journal Classification (ASJC) codes
- Public Health, Environmental and Occupational Health