Determinants of early alcohol use in healthy adolescents: the differential contribution of neuroimaging and psychological factors

Abstract

Individual variation in reward sensitivity may have an important role in early substance use and subsequent development of substance abuse. This may be especially important during adolescence, a transition period marked by approach behavior and a propensity toward risk taking, novelty seeking and alteration of the social landscape. However, little is known about the relative contribution of personality, behavior, and brain responses for prediction of alcohol use in adolescents. In this study, we applied factor analyses and structural equation modeling to reward-related brain responses assessed by functional magnetic resonance imaging during a monetary incentive delay task. In addition, novelty seeking, sensation seeking, impulsivity, extraversion, and behavioral measures of risk taking were entered as predictors of early onset of drinking in a sample of 14-year-old healthy adolescents (N=324). Reward-associated behavior, personality, and brain responses all contributed to alcohol intake with personality explaining a higher proportion of the variance than behavior and brain responses. When only the ventral striatum was used, a small non-significant contribution to the prediction of early alcohol use was found. These data suggest that the role of reward-related brain activation may be more important in addiction than initiation of early drinking, where personality traits and reward-related behaviors were more significant. With up to 26% of explained variance, the interrelation of reward-related personality traits, behavior, and neural response patterns may convey risk for later alcohol abuse in adolescence, and thus may be identified as a vulnerability factor for the development of substance use disorders.

Figure 1

Monetary Incentive Delay (MID) task, adapted from Knutson et al (2001).

Figure 2

Brain activation in response to big win vs small win during the Monetary Incentive Delay (MID) anticipation phase (Appetitive and consummatory stages of reward processing could be assumed to involve qualitatively different affective phenomenology. As the anticipation of reward represents a period, during which subjects associate predictive cues with subsequent outcomes, resulting in a basis of reward processing and thus might be represent more adequately reward sensitivity. Thus, in the Results we will only focus on big vs small win during the MID anticipation phase.) (p<0.001, family wise-error (FWE) corrected, cluster >20 voxels, N=324).

Figure 3

Latent prediction model I based on Structural Equation Modeling (SEM) approach. (For this model, the goodness of fit index (GFI) was 0.91, the adjusted GFI (AGFI) was 0.88, and the parsimonious GFI (PGFI) had a value of 0.67. Furthermore, the non-normed fit index revealed 0.95 and the comparative fit index (CFI) 0.96. The largest standardized residual, another sorting index, was 0.044. The p-value of x² reached the 0.01 level; however, this is often seen in large samples exceeding a sample size of 100 subjects. The root mean squared error of approximation (RMSEA) value was close to 0, corresponding to a p-value of 0.9.)

Figure 4

Latent prediction II model based on Structural Equation Modeling (SEM) approach. (For this model, the goodness of fit index (GFI) was 0.95, the adjusted GFI (AGFI) was 0.87, and the parsimonious GFI (PGFI) was 0.67. Furthermore, the non-normed fit index revealed 0.94 and the comparative fit index (CFI) 0.95. The largest standardized residual, another sorting index, was 0.053. The p-value of x² reached the 1.0 level. The root mean squared error of approximation (RMSEA) value was close to 0, corresponding to a p-value of 0.9.)