Background: Public health concerns over the addictive potential of electronic cigarettes (e-cigs) have heightened in recent years. Brain function during e-cig use could provide an objective measure of the addictive potential of new vaping products to facilitate research; however, there are limited methods for delivering e-cig aerosols during functional magnetic resonance imaging (fMRI). The current study describes the development and feasibility testing of a prototype to deliver up to four different e-cig aerosols during fMRI. Methods: Standardized methods were used to test the devices’ air flow variability, nicotine yield, and free radical production. MRI scans were run with and without the device present to assess its safety and effects on MRI data quality. Five daily smokers were recruited to assess plasma nicotine absorption from e-liquids containing nicotine concentrations of 8, 11, 16, 24, and 36 mg/ml. Feedback was collected from participants through a semi-structured interview and computerized questionnaire to assess comfort and subjective experiences of inhaling aerosol from the device. Results: Nicotine yield captured from the aerosol produced by the device was highly correlated with the nicotine concentration of the e-liquids used (R2 = 0.965). Nicotine yield was reduced by a mean of 48% and free radical production by 17% after traveling through the device. The e-liquid containing the highest nicotine concentration tested (36 mg/ml) resulted in the highest plasma nicotine boost (6.6 ng/ml). Overall, participants reported that the device was comfortable to use and inhaling the e-cig aerosols was tolerable. The device was determined to be safe for use during fMRI and had insignificant effects on scan quality. Conclusions: With the current project, we were able to design a working prototype that safely and effectively delivers e-cig aerosols during fMRI. The device has the potential to be used to assess brain activation during e-cig use and to compare brain reactivity to varying flavors, nicotine concentrations, and other e-cig characteristics.
All Science Journal Classification (ASJC) codes
- Psychiatry and Mental health