Subcortical surface morphometry in substance dependence: An ENIGMA addiction working group study.

While imaging studies have demonstrated volumetric differences in subcortical structures associated with dependence on various abused substances, findings to date have not been wholly consistent. Moreover, most studies have not compared brain morphology across those dependent on different substances of abuse to identify substance-specific and substance-general dependence effects. By pooling large multinational datasets from 33 imaging sites, this study examined subcortical surface morphology in 1628 nondependent controls and 2277 individuals with dependence on alcohol, nicotine, cocaine, methamphetamine, and/or cannabis. Subcortical structures were defined by FreeSurfer segmentation and converted to a mesh surface to extract two vertex-level metrics-the radial distance (RD) of the structure surface from a medial curve and the log of the Jacobian determinant (JD)-that, respectively, describe local thickness and surface area dilation/contraction. Mega-analyses were performed on measures of RD and JD to test for the main effect of substance dependence, controlling for age, sex, intracranial volume, and imaging site. Widespread differences between dependent users and nondependent controls were found across subcortical structures, driven primarily by users dependent on alcohol. Alcohol dependence was associated with localized lower RD and JD across most structures, with the strongest effects in the hippocampus, thalamus, putamen, and amygdala. Meanwhile, nicotine use was associated with greater RD and JD relative to nonsmokers in multiple regions, with the strongest effects in the bilateral hippocampus and right nucleus accumbens. By demonstrating subcortical morphological differences unique to alcohol and nicotine use, rather than dependence across all substances, results suggest substance-specific relationships with subcortical brain structures.

Behavioral and neural valuation of foods is driven by implicit knowledge of caloric content.

The factors that affect food choices are critical to understanding obesity. In the present study, healthy participants were shown pictures of foods to determine the impact of caloric content on food choice. Brain activity was then measured while participants bid for a chance to purchase and eat one item. True caloric density, but not individual estimates of calorie content, predicted how much participants were willing to pay for each item. Caloric density also correlated with the neural response to food pictures in the ventromedial prefrontal cortex, a brain area that encodes the value of stimuli and predicts immediate consumption. That same region exhibited functional connectivity with an appetitive brain network, and this connectivity was modulated by willingness to pay. Despite the fact that participants were poor at explicitly judging caloric content, their willingness to pay and brain activity both correlated with actual caloric density. This suggests that the reward value of a familiar food is dependent on implicit knowledge of its caloric content.

Genetic variation in CYP2A6 predicts neural reactivity to smoking cues as measured using fMRI.

Smoking cues trigger craving for cigarettes and relapse. Nicotine metabolism, mediated by the enzyme CYP2A6, also influences smoking behavior. In this study, we investigated how nicotine metabolism and genetic variation in CYP2A6 influence the neural response to smoking cues in humans using functional magnetic resonance imaging (fMRI). We hypothesized that individuals with faster rates of nicotine metabolism would have stronger conditioned responses to smoking cues because of closer coupling in everyday life between exposure to cigarettes and surges in blood nicotine concentration. In contrast, individuals with reduced rates of metabolism, who have relatively constant nicotine blood levels throughout the day, should be less likely to develop conditioned responses to cues. We screened 169 smokers for their rate of nicotine metabolism and CYP2A6 genotype, and selected 31 smokers with the fastest and slowest rates for fMRI, matched for daily cigarette intake. We measured their neural response to visual smoking and non-smoking cues using fMRI. As predicted, fast metabolizers, by phenotype or genotype, had significantly greater responses to visual cigarette cues than slow metabolizers in the amygdala, hippocampus, striatum, insula, and cingulate cortex. These results support the theory that drug cues are conditioned stimuli, and explain why fast metabolizers who smoke have lower cessation rates. They also provide insight into how genetics can shape human vulnerability to addiction, and have implications for tailoring smoking cessation programs based on individual genetics.