IntroductionMany e-cigarette users find the variety of e-cigarette flavors appealing. We examined whether preferences for e-liquid flavors and the total number of flavors preferred differed between samples of adolescent and adult e-cigarette users. We also examined whether these preferences were associated with e-cigarette use frequency for adolescents or adults, respectively.Materials and methodsThe analytic samples comprised 1) 396 adolescent, past-month e-cigarette users from 5 Connecticut high schools who completed an anonymous, school-based survey in Fall 2014 (56.1% male; 16.18 [1.18] years; 42.2% past-month smokers), and 2) 590 adult, past-month e-cigarette users who completed an anonymous, MTurk survey in Fall 2014 (53.7% male; 34.25 [9.89] years; 51.2% past-month smokers).ResultsCompared to adults, a larger proportion of adolescents preferred fruit, alcohol, and “other”-flavored e-liquids, whereas adults disproportionately preferred tobacco, menthol, mint, coffee, and spice-flavored e-liquids (p-values < .05). Adults also preferred a greater total number of flavors compared to adolescents and used e-cigarettes more frequently (p-values < .001). Flavor preferences uniquely were associated with frequency of e-cigarette use within the adolescent sample; the total number of flavors preferred was associated with more days of e-cigarette use (ηp2 = 0.04), as were preferences for fruit (ηp2 = 0.02), dessert (ηp2 = 0.02), and alcohol-flavored (ηp2 = 0.02) e-liquids.ConclusionsFlavor preferences differed between adolescent and adult samples. While youth reported less frequent e-cigarette use overall, their preferences for specific flavors and the total number of flavors preferred were associated with more days of e-cigarette use, indicating that flavor preferences may play an important role in adolescent e-cigarette use.

Over the past decade, there has been an abundance of research on the difference between age and age predicted using brain features, which is commonly referred to as the “brain age gap.” Researchers have identified that the brain age gap, as a linear transformation of an out‐of‐sample residual, is dependent on age. As such, any group differences on the brain age gap could simply be due to group differences on age. To mitigate the brain age gap's dependence on age, it has been proposed that age be regressed out of the brain age gap. If this modified brain age gap is treated as a corrected deviation from age, model accuracy statistics such as R2 will be artificially inflated to the extent that it is highly improbable that an R2 value below .85 will be obtained no matter the true model accuracy. Given the limitations of proposed brain age analyses, further theoretical work is warranted to determine the best way to quantify deviation from normality.

Clearly defining AO and AI using objective definitions that reflect specific amounts of alcohol (e.g., first sip; first standard drink; first binge) appears to outperform subjective definitions of alcohol use (e.g., first drunk).

The parieto-frontal integration theory (PFIT) identified a fronto-parietal network of regions where individual differences in brain parameters most strongly relate to cognitive performance. PFIT was supported and extended in adult samples, but not in youths or within single-scanner well-powered multimodal studies. We performed multimodal neuroimaging in 1601 youths age 8–22 on the same 3-Tesla scanner with contemporaneous neurocognitive assessment, measuring volume, gray matter density (GMD), mean diffusivity (MD), cerebral blood flow (CBF), resting-state functional magnetic resonance imaging measures of the amplitude of low frequency fluctuations (ALFFs) and regional homogeneity (ReHo), and activation to a working memory and a social cognition task. Across age and sex groups, better performance was associated with higher volumes, greater GMD, lower MD, lower CBF, higher ALFF and ReHo, and greater activation for the working memory task in PFIT regions. However, additional cortical, striatal, limbic, and cerebellar regions showed comparable effects, hence PFIT needs expansion into an extended PFIT (ExtPFIT) network incorporating nodes that support motivation and affect. Associations of brain parameters became stronger with advancing age group from childhood to adolescence to young adulthood, effects occurring earlier in females. This ExtPFIT network is developmentally fine-tuned, optimizing abundance and integrity of neural tissue while maintaining a low resting energy state.

Measures of medical cannabis (MC) use are lacking. This study details the development and psychometric evaluation of The Medical Cannabis Expectancy Questionnaire (MCEQ), a novel measure of positive and negative expectations associated with using combustible, vaporizable, and edible MC. 333 adult MC users completed a 30-minute online survey in Spring 2017 (64.0% female, 82.3% White, mean age 32.77[±10.19] years). Participants reported on demographics, product preference, MCEs, frequency of MC use, quality of life, and negative cannabis use consequences. Psychometric analyses included evaluations of latent factor structure, measurement invariance, between-groups differences in MCEs, and test-criterion relationships with MC outcomes. The 27-item MCEQ evidenced a 2-factor structure (positive/negative). MCEs were scalar invariant by product type, sex, and reasons for MC use (medical only vs medical/recreational). Participants held more positive MCEs for combustibles than for vaporizables or edibles and more negative MCEs for combustibles and edibles than for vaporizables. MCEs did not differ by sex. Participants who also used cannabis recreationally reported stronger positive MCEs for all MC products. MCEs also differed by product preference. Additionally, preference for and more positive MCEs associated with using a specific product were associated with more frequent use of that product. Positive MCEs for all products also were associated with increased quality of life, but these relationships failed to reach statistical significance after accounting for covariates. Finally, negative MCEs for combustibles and edibles were associated with more negative consequences. The MCEQ is the first psychometrically promising measure of MC expectancies, and it uniquely distinguishes among expectations associated with using combustible, vaporizable, and edible MC. As MC use continues to proliferate, having measures dedicated to MC (versus recreational cannabis) may better inform research and clinical efforts. Further, differentiating between product types is important given established differences among them (e.g., duration of effect onset).

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Over the past decade, there has been an abundance of research on the difference between age and age predicted using brain features, which is commonly referred to as the "brain age gap". Researchers have identified that the brain age gap, as a residual, is dependent on age. As such, any group differences on the brain age gap could simply be due to group differences on age. To mitigate the brain age gap's dependence on age, it has been proposed that age be regressed out of the brain age gap. If this modified brain age gap (MBAG) is treated as a corrected deviation from age, model accuracy statistics such as R 2 will be artificially inflated. Given the limitations of proposed brain age analyses, further theoretical work is warranted to determine the best way to quantify deviation from normality.