Pavlovian-To-Instrumental Transfer and Alcohol Consumption in Young Male Social Drinkers: Behavioral, Neural and Polygenic Correlates

In animals and humans, behavior can be influenced by irrelevant stimuli, a phenomenon called Pavlovian-to-instrumental transfer (PIT). In subjects with substance use disorder, PIT is even enhanced with functional activation in the nucleus accumbens (NAcc) and amygdala. While we observed enhanced behavioral and neural PIT effects in alcohol-dependent subjects, we here aimed to determine whether behavioral PIT is enhanced in young men with high-risk compared to low-risk drinking and subsequently related functional activation in an a-priori region of interest encompassing the NAcc and amygdala and related to polygenic risk for alcohol consumption. A representative sample of 18-year old men (n = 1937) was contacted: 445 were screened, 209 assessed: resulting in 191 valid behavioral, 139 imaging and 157 genetic datasets. None of the subjects fulfilled criteria for alcohol dependence according to the Diagnostic and Statistical Manual of Mental Disorders-IV-TextRevision (DSM-IV-TR). We measured how instrumental responding for rewards was influenced by background Pavlovian conditioned stimuli predicting action-independent rewards and losses. Behavioral PIT was enhanced in high-compared to low-risk drinkers (b = 0.09, SE = 0.03, z = 2.7, p < 0.009). Across all subjects, we observed PIT-related neural blood oxygen level-dependent (BOLD) signal in the right amygdala (t = 3.25, pSVC = 0.04, x = 26, y = −6, z = −12), but not in NAcc. The strength of the behavioral PIT effect was positively correlated with polygenic risk for alcohol consumption (rs = 0.17, p = 0.032). We conclude that behavioral PIT and polygenic risk for alcohol consumption might be a biomarker for a subclinical phenotype of risky alcohol consumption, even if no drug-related stimulus is present. The association between behavioral PIT effects and the amygdala might point to habitual processes related to out PIT task. In non-dependent young social drinkers, the amygdala rather than the NAcc is activated during PIT; possible different involvement in association with disease trajectory should be investigated in future studies.


Supplement
Representativeness of our study cohort Participants were descriptively comparable to similar cohorts drawn from the German general population regarding monthly per-capita income, DSM-IV nicotine dependence, alcohol consumption (except for an underrepresentation of alcohol-abstinent persons and an overrepresentation of persons drinking only small amounts of alcohol due to our inclusion criteria), DSM-IV alcohol abuse and BMI (see Table S1).

Measures of alcohol consumption
To characterize participants' drinking behavior and how this relates to polygenic risk, we calculated a sum score of drinking variables using information acquired with the CIDI interview 4, 5 : age of 1 st drink, age of 1 st time being drunk, estimated average alcohol consumption in past year (g alc/day), average alcohol consumption per drinking occasion in the past year (g alc), age of 1 st binge-drinking event, number of bingedrinking events lifetime, and average alcohol consumption per binge-drinking event in the past year (g alc). Binge-drinking was defined as the consumption of at least five drinks (≥60g alc) on one occasion. To increase reliability of the indicator of alcohol drinking behavior, we did not use the single CIDI items but calculated a sum score (drink score) from the z-scaled CIDI items with higher values indicating greater alcohol consumption 6 . Unlike the categorical high-versus low-risk drinker classification by the WHO, the resulting score is a continuous measure of alcohol intake that can be used for linear correlations with the polygenic risk score for alcohol consumption.
Moreover, the WHO suggests a second criterion for chronic harm of alcohol consumption based on the average alcohol intake per day 7 as assessed with the CIDI interview 4, 5 .
However, 95.8 % of our sample fell into the low-risk group according to this criterion rendering our data unsuitable to examine associations of chronic risk of alcohol intake with PIT. Given this distribution of chronic risk and the young age and relatively brief period of alcohol consumption (on average two years) of our participants, chronic risk was of secondary interest to us and, therefore, we focused on comparisons between participants with low-versus high-risk drinkers for acute alcohol-related problems 7 in our analyses as described in the main text.
Preprocessing of fMRI data FMRI data were pre-processed using Nipype 8 . Correction for differences in slice time acquisition was performed. Voxel-displacement maps were estimated based on acquired field maps. All images were realigned to correct for motion, distortion and their interaction. After coregistration of the individual structural image to the individual mean EPI, the structural image was spatially normalized and normalization parameters were applied to all EPIs. Finally, images were spatially smoothed with a Gaussian kernel of 8 mm full width at half maximum. During individual statistical analyses, data were high-pass filtered with a cut-off of 128 s. An event-related analysis was applied on two levels using the general linear model approach as implemented in SPM12.

Sample characteristics
In Table S2 the sample characteristics for the two groups of low-(n = 97) and high-risk drinkers (n = 94) are displayed. Behavioral PIT effect by group (high-versus low-risk drinkers according to WHO) We observed a stronger PIT effect in high-risk drinkers as indicated by a significant interaction effect of group (low-versus high-risk drinkers) and Pavlovian background value (ranging from -2€ to +2€) on number of button presses. Moreover, both groups learned the instrumental contingencies equally well as we found a significant main effect of instrumental condition (collect versus not-collect), but no interaction between instrumental condition and group. See Table S3 for all main and interaction effects of the regression model. Moreover, we controlled our results for smoking severity as measured by the FTND 13 and still observed a similar pattern (see Table S4). Moreover, we observed a main effect of Pavlovian background on the number of button presses (β = 0.12, SE = 0.02, p < .001), indicating a significant influence of background cues on instrumental response for the whole group (see Figure S1).

Neural CS effect
To explore the pure effect of CS (compound of fractal-like visual stimulus and tone in the background) during our PIT paradigm, we assessed on the second level the CS value across the whole group and again performed a small volume analysis using a ROI encompassing both the NAcc and the amygdala. We found no significant activation within the left (t = 0.79, pSVC = .82, x = -22, y = -8, z = -22) nor right amygdala (t = 0.9, pSVC = .79, x = 24, y = -8, z = -10). However, we found a CS value related activation in the right NAcc (t = 3.25, pSVC = .041, k = 1, x = 12, y = 10, z = -8, see Figure S2). This activation was elicited by passively viewing CSs without explicit task relevance for the subjects as instructed by us; thus, the effect is not directly comparable with alcohol-related cue-reactivity or monetary incentive delay tasks, where stimuli predict possible win or loss in accordance with fast responses. Figure S2. Pure CS effect during the PIT task. This effect was significant for the whole imaging group of n=139 subjects in the right NAcc only (p SVC = .041).

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Exploratory whole brain analysis of PIT effect The following Table S5 aims to give an overview of the exploratory whole brain analysis of the PIT-effect. We used a threshold of puncorr < .001 and cluster size k = 10.

Multi-level statistical approach including multi-modal data
We conducted an additional generalized linear mixed-effects analysis including multimodal data as additional predictors (behavioral PIT, alcohol drinking risk groups and genetic information) using the subjects that have full data sets (n = 115, see Table S6).
This analysis confirmed our results of enhanced PIT effects in high-compared to lowrisk drinking subjects (trend-wise due to lower statistical power in the smaller cohort, interaction effect between Pavlovian background value and group, p = .053). Moreover, we observed enhanced PIT effects with stronger amygdala activation (interaction effect between Pavlovian background value and amygdala activation, p < .001) and higher polygenic risk for alcohol consumption (interaction effect between Pavlovian background value and PRS, p = .019). All tests were conducted two-tailed.