Skip to main content
NCBI home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2026 Apr 22;50(4):e70300. doi: 10.1111/acer.70300

Intertemporal Decision‐Making, Nucleus Accumbens Activation, and Alcohol Use Trajectories in Young Adults With a Family History of Alcohol Use Disorder

Amanda Elton 1,2,3,4,✉, Jacqueline Aloumanis 2,3, JeeWon Cheong 3,5, J Hunter Allen 4, Jill A Star 1, Sara Jo Nixon 1,2,3
PMCID: PMC13101039  PMID: 42017396

ABSTRACT

Introduction

A family history of alcohol use disorder (AUD) is associated with increased personal risk for alcohol misuse and AUD. Family history of AUD is also related to increased impulsivity as measured by delay discounting tasks, representing a potential mechanistic link between family history and alcohol misuse. Delay discounting tasks assess individual differences in preferences for smaller, immediate versus larger, delayed rewards, the former being linked to substance misuse. Decision‐making on such tasks is underpinned by multiple neural systems, including those supporting reward valuation, cognitive control, and future‐oriented thinking. We hypothesized that family history of AUD would be associated with differences in one or more neural systems related to delay discounting, with differences relating to increases in alcohol misuse in young adulthood.

Methods

We tested 163 first‐year college students (105 females, ages 18–19) with varying levels of familial risk for AUD on a functional magnetic resonance imaging (fMRI) delay discounting task. Alcohol misuse was self‐reported at baseline and in 3‐yearly follow‐up surveys using the Alcohol Use Disorders Identification Test (AUDIT). Change in alcohol misuse was modeled using a latent growth model, and we examined mediation between family history and alcohol misuse trajectory (AUDIT intercept and slope) through functional activation of brain regions implicated in reward valuation (nucleus accumbens), cognitive control (middle frontal gyrus), and future‐oriented thinking (hippocampus).

Results

Family history of AUD was associated with greater nucleus accumbens activation (β = 0.286, SE = 0.117, p = 0.014), which in turn predicted a steeper AUDIT slope (β = 0.513, SE = 0.162, p = 0.002). No other mediators were significant.

Discussion

Our results demonstrate that nucleus accumbens function may be a key mechanism by which family history increases risk for alcohol misuse and AUD.

Keywords: alcohol use disorder, delay discounting, family history, fMRI, nucleus accumbens

We conducted an fMRI investigation of the effects of family history on neural correlates of delay discounting in 163 young adults. Structural equation modeling demonstrated that the effects of family history on nucleus accumbens activation prospectively predicted alcohol use trajectories over a 4‐year period.

graphic file with name ACER-50-0-g003.jpg

1. Introduction

Alcohol use disorder (AUD) frequently develops during late adolescence and young adulthood, affecting approximately 15% of individuals between ages 18 to 25 in the United States (Substance Abuse and Mental Health Services Administration 2021). One of the strongest predictors of developing AUD is a family history (FH) of AUD. Familial AUD increases an individual's risk for AUD by approximately 3–4‐fold (Anda et al. 2002; Kendler et al. 2012) through both heritable and environmental effects (Anda et al. 2002). Moreover, 22% of adults in the United States report having at least one biological parent with AUD (Yoon et al. 2013). Individuals with biological relatives affected by AUD are at elevated risk for earlier initiation and more severe trajectories of alcohol‐related problems across the lifespan (Kosty et al. 2020; Sher et al. 2005). However, the neurocognitive pathways through which familial risk is transmitted remain incompletely understood.

One mechanism hypothesized to underlie both FH and addiction, more generally, is delay discounting, or the tendency to discount delayed rewards, resulting in an exaggerated preference for immediate rewards. In laboratory delay discounting tasks, individuals choose between larger, delayed rewards and smaller, immediate (or less delayed) rewards to derive an individualized measure of impulsive decision‐making (Elton et al. 2017; Stanger et al. 2013). Steeper delay discounting is associated with greater substance use severity and worse treatment outcomes (Amlung et al. 2017; MacKillop et al. 2011). Moreover, higher rates of delay discounting prior to alcohol initiation predict adolescent and young adult drinking trajectories (Fröhner et al. 2022), suggesting this form of decision‐making relates to the development of alcohol misuse. Importantly, individuals with FH exhibit steeper discounting rates, suggesting an inherited or environmental vulnerability toward impulsive decision‐making (Herting et al. 2010; Mitchell 2011). In fact, the effects of environmental and familial risk factors on delay discounting behavior have been linked to future substance use (Kim‐Spoon et al. 2019; Liao et al. 2023). Thus, FH may relate to greater alcohol drinking trajectories through its effects on decision‐making processes involving immediate versus delayed rewards. Specifically, the association of FH with impulsive decision‐making and alcohol use may reflect functional differences in the brain regions that underlie intertemporal decision‐making.

Existing data indicate that decisions involving choices between immediate and delayed rewards engage separable neural systems that support valuation, future‐oriented thinking, and cognitive control (Ballard and Knutson 2009; Figner et al. 2010; Kable and Glimcher 2007; McClure et al. 2007, 2004; Peters and Büchel 2010). Reward valuation and motivation depend on nucleus accumbens (NAcc) activity (Ballard and Knutson 2009; Kable and Glimcher 2007), with greater activation relating to impulsive choices involving immediate rewards (McClure et al. 2007). The hippocampus contributes to episodic representation, including episodic prospection, or imaging future events and is thought to reduce discounting of delayed rewards through its role in future‐oriented thinking (Peters and Büchel 2010). Prefrontal cortical regions supporting cognitive control enable individuals to inhibit impulsive tendencies and are engaged during selection of larger, delayed rewards (Figner et al. 2010; McClure et al. 2004). These key systems are each associated with variation across individuals, leading to individual differences in choice behavior. Individual differences related to FH may be driven by alterations in one or more of these systems that promote impulsive decision‐making, ultimately increasing susceptibility to substance misuse and addiction.

Indeed, prior research suggests that FH is associated with altered function across neural systems underlying intertemporal decision‐making, even in substance‐naïve youth. For example, adolescents with FH have shown increased striatal reactivity to reward cues (Yau et al. 2012), altered connectivity in frontostriatal networks at rest (Cservenka et al. 2014), and larger NAcc volumes (Cservenka et al. 2015). FH among adolescents is also associated with greater engagement of prefrontal regions despite similar performance in a Stroop paradigm, suggesting less efficient functioning in these regions during explicit demands for cognitive control (Silveri et al. 2011). Alcohol‐naïve adolescents with a FH also exhibit patterns of altered hippocampal volume (Hanson et al. 2010), although studies of hippocampal functioning are currently lacking. There have been two smaller studies that previously examined fMRI delay discounting task differences related to FH. Butcher and colleagues identified heightened activation in posterior insula, thalamus, and parahippocampal gyrus in preadolescent youth with FH (Butcher et al. 2021), whereas Rodriguez‐Moreno and colleagues identified no brain differences in a sample of adolescents (Rodriguez‐Moreno et al. 2021). Taken together, the existing literature suggests that familial risk for AUD may be associated with early and persistent differences in brain regions supporting reward valuation and processing, episodic prospection, and cognitive control, indicating potential brain differences that could underpin the observed steeper discounting behavior in these individuals. However, whether young adults with FH exhibit differences in neural activation while making intertemporal decisions and how such differences relate to later alcohol use has not been investigated.

The current study aims to connect these prior associations by examining the delay discounting‐related neural mechanisms linking FH to alcohol use trajectories in early adulthood, specifically during college years, a developmental period marked by increased independence and alcohol consumption. Participants completed an fMRI delay discounting task at baseline to probe activation in regions associated with reward (NAcc), prospection (hippocampus), and cognitive control (prefrontal cortex). We applied latent growth mediation models to test the hypothesis that neural responses in these regions mediate the association between FH and alcohol use trajectory over 4 years. By integrating longitudinal alcohol use assessments with task‐based fMRI and a theory‐driven analytic approach, this study aimed to clarify the neural mechanisms by which FH contributes to the development of hazardous drinking in young adults.

2. Methods

The study included a baseline fMRI scan, baseline surveys, and 3‐yearly follow‐up surveys completed online.

2.1. Participants

A total of 165 18–19‐year‐old first‐year college students in 4‐year undergraduate degree programs were recruited from universities surrounding Chapel Hill, North Carolina. Exclusion criteria were MRI contraindications, self‐reported routine (e.g., daily or most days) psychoactive medication or substance use other than alcohol use (assuming they did not meet AUD criteria), neurological disorders, and psychiatric disorders other than past mood or anxiety disorders (current disorders excluded). Psychiatric disorders were assessed with a Mini‐International Neuropsychiatric Interview (M.I.N.I.) for DSM‐IV (Sheehan et al. 1998), with DSM‐5 criteria used to assess AUD and substance use disorders. Current and past AUD and substance use disorders were exclusionary at baseline. A five‐panel urine drug screen prior to the scan ensured no participants tested positive for cocaine, cannabis, opioids, amphetamines, or methamphetamine. An alcohol breathalyzer test similarly produced no positive results. To minimize confounding effects on brain activation, participants were instructed to refrain from any occasional or as‐needed psychoactive medication or other substance use for a period determined by the drug's half‐life (if medically permissible) leading up to the MRI scan. Participants provided written informed consent to participate in procedures, which were approved by the UNC Office of Human Research Ethics.

2.2. Self‐Report Data

Baseline surveys administered in REDCap assessed FH, childhood maltreatment histories, adolescent binge drinking frequency, and recent alcohol use.

FH was assessed with the Family History Assessment Module, measuring likely AUD among known first‐ and second‐degree biological relatives. A FH density composite score was calculated as a weighted total of the number of affected parents (0.5 for each), grandparents (0.25 for each), and maternal and paternal aunts and uncles (0.25/[total relatives in category] for each) (Stoltenberg et al. 1998).

The Childhood Trauma Questionnaire (CTQ) (Bernstein et al. 2003) was collected to assess childhood experiences of physical, emotional, and sexual abuse, and physical and emotional neglect, which were summed for a total score.

Adolescent binge drinking frequency was measured by an item assessing binge episodes prior to the age of 18 (Elton et al. 2021): “Before the age of 18, how often did you have 5 or more drinks (4 or more if you are female) containing any kind of alcohol within a 2‐h period?” Responses included, “Never,” “1–3 times,” “4–6 times,” “7–12 times,” “2–3 times/month,” “weekly,” and “>once/week.”

The Alcohol Use Disorders Identification Test (AUDIT) (Saunders et al. 1993) was administered at baseline and in yearly follow‐up surveys sent to participants' emails via REDCap. Scores of 8 or higher are associated with harmful alcohol use.

Two participants were missing baseline data and were excluded from all analyses. Thus, the final analytic sample was 163 participants.

Whereas baseline AUDIT data was available for all 163 participants, follow‐up data was available for 128, 94, and 87 participants at the first, second, and third follow‐ups, respectively. Baseline AUDIT scores did not significantly predict missingness at any time point, and AUDIT scores at follow‐ups did not predict missingness at subsequent follow‐ups. No other variables tested—sex, FH, CTQ, adolescent binge drinking, and delay discounting behavior—significantly related to missingness at any time point.

2.3. Delay Discounting Task

A 48‐trial pre‐scan delay‐discounting task using a rapid adjusting procedure was implemented outside the scanner to introduce the task and to provide individualized starting reward values at each delay (1 week, 1 month, 6 months, or 2 years) for the fMRI task (Koffarnus and Bickel 2014). The indifference points were used to calculate a model‐free, area‐under‐the‐curve behavioral measure of delay discounting for $100 and $1000, where larger area‐under‐the‐curve values reflect less discounting.

Participants next completed a 120‐trial delay discounting task during fMRI, divided into two runs of 60 trials. Two hypothetical choices were presented on the left and right sides of the screen: a smaller monetary reward available “TODAY” or a larger monetary reward available at 1 week, 1 month, 6 months, or 2 years. Larger rewards were either $100 or $1000, with smaller rewards adjusting based on prior choices to control decision difficulty. Each in‐scanner trial began with a 1.0 s cue to indicate the trial type: “WANT,” “SOONER,” and “LARGER.” There were 14 LARGER trials and 14 SOONER trials, representing control trials in which participants identified the larger reward or the reward available sooner, respectively. During the 80 WANT trials, participants selected their preferred reward. Cue text remained on the screen while the two monetary reward options were displayed for five additional seconds. Additionally, there were 12 null trials in which cues were presented without choices to enable deconvolution of cue and decision‐making trial components. Intertrial intervals ranged from 1 to 6 s. Trial types were pseudorandomly presented and balanced across runs.

Task fMRI data were missing for 15 subjects in the analytic sample related to incomplete scanning sessions due to time constraints (e.g., late arrivals, need for scanner reboot, or task malfunctioning) or data transfer errors. Task fMRI data were excluded for an additional five participants who did not make any impulsive choices during the task, affecting the validity of the results. Thus, 143 subjects within the final analytic sample of 163 contributed fMRI data. Of the 143 subjects contributing fMRI data, follow‐up data was available for 113, 83, and 80 participants at the first, second, and third follow‐ups respectively.

2.4. MRI Data Acquisition

Blood oxygenation level‐dependent (BOLD) fMRI data were collected with multiband echo‐planar imaging (EPI) on a Siemens 3 T Prisma scanner with a 32‐channel head coil.

The sequence included the following parameters: multiband factor = 8, TR = 800 ms, TE = 37 ms, flip angle = 52°, 2 mm isotropic voxels, 72 sagittal slices with interleaved acquisition, field of view (FOV) = 208 × 208, and bandwidth = 2290 Hz/pixel. A mid‐study scanner software update led to a minor sequence adjustment for 27 subjects: bandwidth = 2186 Hz/pixel and TE = 38.2 ms (Elton et al. 2023). The first run of the delay discounting task was acquired with anterior‐to‐posterior (AP) phase encoding, whereas a posterior‐to‐anterior (PA) phase encoding was used for the second run. Each run lasted 8 min for a total time of 16 min.

A magnetization‐prepared rapid gradient‐echo (MPRAGE) T1‐weighted image was acquired to assist with registration and tissue segmentation: TR = 2530 ms, TE = 2.3 ms, flip angle = 9°, 1 mm isotropic voxels, 176 sagittal slices, and FOV = 256 × 256.

2.5. MRI Data Preprocessing

Structural T1‐weighted (T1w) images and BOLD fMRI data were preprocessed using fMRIPrep (Esteban et al. 2019). Preprocessing of BOLD images included motion correction, slice‐timing correction, susceptibility distortion correction, co‐registration to anatomical images, spatial normalization, and estimation of confounding signals. Preprocessing for this study has been detailed previously (Elton et al. 2023).

2.6. First‐Level fMRI Analysis

A general linear model (GLM) (Friston et al. 1994) using 3dDeconvolve and 3dREML functions in AFNI (Cox 1996) evaluated activation of brain regions across trial types for each participant. Four trial types were modeled: all WANT trials with a $100 delayed reward, all WANT trials with a $1000 delayed reward, SOONER trials, and LARGER trials. Choices of immediate and delayed reward trials were not modeled separately due to their common patterns of brain activation during decision‐making when subjective values for delayed and immediate rewards are closely matched (Bickel et al. 2009; Butcher et al. 2021; Stanger et al. 2013) and to maintain an adequate number of trials for reliable estimates of each event type. Confounding signals related to six motion parameters, average WM and CSF time series, as well as the derivatives, squared values, and square of the derivatives for each of these measures were modeled as nuisance regressors. Additionally, 10 components derived using aCompCor from a combined WM and CSF mask were modeled as nuisance regressors (Muschelli et al. 2014). To further reduce motion effects, we censored time points where the calculated framewise displacement was > 0.5 mm.

The contrast of interest was the difference in estimated activation between WANT trials and control trials, for example, (0.5 × WANT for $100 + 0.5 × WANT for $1000)−(0.5 × SOONER +0.5 × LARGER) to isolate brain activation related to subjective decision‐making.

2.7. Region‐Of‐Interest (ROI) Approach

ROIs were selected from the Desikan‐Killiany atlas based on neural processes known to underlie delay discounting behavior, specifically valuation, prospection, and cognitive control. In line with existing data‐supported theory (Lempert et al. 2019; Peters and Büchel 2011), we selected the bilateral NAcc to represent valuation, the bilateral hippocampus to represent prospection, and the bilateral caudal middle frontal gyrus to represent cognitive control (Figure 1). The selected regions closely map onto the implicated processes based on prior literature (Figner et al. 2010; McClure et al. 2007, 2004; Peters and Büchel 2010). For each participant, their average contrast weight among voxels within each ROI for the WANT>Control contrast was calculated. Sample distributions of contrast values in these regions were examined, and values greater or less than three standard deviations from the mean were replaced with the value equal to three standard deviations from the mean to reduce the influence of extreme values.

FIGURE 1.

FIGURE 1

Open in a new tab

Regions‐of‐interest representing delay discounting neural processes. Nucleus accumbens activation represents reward valuation (orange). Hippocampal activation is involved in imagining the future or prospection (yellow). The middle frontal gyrus is involved in cognitive control (red). Regions were defined from the Desikan‐Killiany atlas.

2.8. Latent Growth Mediation Model

Structural equation modeling in Mplus 8.11 (Muthén and Muthén 2017) was used to estimate a latent growth mediation model (Figure 2) to examine the delay‐discounting neural mechanisms that link FH to alcohol use. As previously described, the FH predictor variable was modeled using a weighted density score of parents' and second‐degree relatives' AUD‐related behaviors. Alcohol use trajectory was modeled using AUDIT scores collected annually over 4 years. The latent intercept was specified to represent baseline AUDIT scores, and the latent slope captured change over 4 years. To capture a primarily linear trajectory while allowing flexibility at the year 4 assessment, slope factor loadings were fixed to 0, 1, and 2, with the loading for the final time point freely estimated to improve model fit and facilitate model convergence. The model was estimated using MLR in Mplus, which yields robust standard errors under non‐normality and handles missing data under the assumption of missing at random (MAR).

FIGURE 2.

FIGURE 2

Open in a new tab

Latent growth mediation model testing family history effects on alcohol use trajectory through neural activation. * p < 0.05, **p < 0.01,. AUC, area under the curve; AUDIT, Alcohol Use Disorders Identification Test; cMFG, caudal middle frontal gyrus; Hipp, hippocampus; NAcc, nucleus accumbens; L, left; R, right.

Sex assigned at birth (1 = female, 0 = male) was a covariate. To control for higher rates of childhood maltreatment experienced by individuals with FH (Dube et al. 2001), we included CTQ total scores (Bernstein et al. 2003) as a covariate. We controlled for adolescent binge alcohol use with a binary variable in which reports of “Never” binge drinking were coded as a 0, and all other responses were coded as 1 (Liao et al. 2023). Additionally, five subjects demonstrated no selection of immediate choices throughout the task, suggesting that the task failed to provide the same level of decision difficulty for this subgroup compared to other participants. Therefore, we excluded these participants' fMRI data from analyses but included their non‐imaging data.

To examine mediation, reward, prospection, and cognitive control latent variables were constructed from ROI activation in the bilateral NAcc, hippocampus, and caudal middle frontal gyrus, respectively, and were included as mediator variables. Additionally, behavioral delay discounting (area‐under‐the‐curve) was included as a behavioral mediator, represented by a latent variable indicated by $100 and $1000 choice options. The indirect path from FH to the latent slope through each mediator was calculated.

2.9. Sensitivity Analyses

Due to known associations of FH with childhood maltreatment (Dube et al. 2001), and known effects of childhood maltreatment on regions under investigation, particularly the hippocampus (Teicher et al. 2012), our primary analysis included CTQ scores as a covariate. To explore the potential influence of this decision on results, we additionally estimated the model without CTQ scores as a covariate.

Secondly, to account for potential influences of anxiety and depressive symptoms, we tested baseline total scores from the Beck Depression Inventory (Beck et al. 1961) and State–Trait Anxiety Inventory (Spielberger et al. 1980) (trait scores) as covariates.

Finally, because cognitive control engages regions across the cortex, we ran additional tests with a broader cognitive control network that demonstrated significant loading on a latent cognitive control factor (bilateral caudal middle frontal gyrus, rostral middle frontal gyrus, and inferior parietal lobe) to test the influence of ROI selection on results.

3. Results

3.1. Self‐Report Data

Participant self‐report and behavioral data are presented in Table 1. The sample distribution of FH density is displayed in Figure S1.

TABLE 1.

Descriptive statistics (means and standard deviations) for self‐report and behavioral data.

Variables Mean SD Median Min Max
Sex, n (% Female) 105 (64%)
Binge drinking prior to age 18, n (%) 41 (25%)
Childhood Trauma Questionnaire 36.0 10.8 33 25 91
Family history density 0.41 0.42 0.29 0.0 1.6
AUDIT baseline 2.7 2.9 2 0 12
AUDIT follow‐up 1 3.6 3.7 3 0 17
AUDIT follow‐up 2 4.0 3.8 3 0 22
AUDIT follow‐up 3 4.2 3.6 3 0 18
Delay discounting AUC $100 55.0 27.8 52.2 2.6 98.4
Delay discounting AUC $1000 612 283 606 36 984
Open in a new tab

Abbreviations: AUC, area under the curve; AUDIT, Alcohol Use Disorders Identification Test.

3.2. Alcohol Misuse Trajectories

The unconditional latent growth model to characterize the AUDIT trajectory shape demonstrated an acceptable model fit: X 2(5) = 4.571, p = 0.471; CFI = 1.000; TLI = 1.000; RMSEA = 0.000 [90% CI: 0.000, 0.104]; SRMR = 0.042. The model indicated that mean AUDIT scores began at 2.719, with significant variability in baseline levels (Var = 5.821, SE = 1.311, p < 0.001). The average slope was positive (M = 0.751, SE = 0.169, p < 0.001), suggesting that AUDIT scores increased over time, with significant individual differences in rates of change (Var = 1.452, SE = 0.531, p = 0.006). The freely estimated slope loading for the final time point was 2.436, indicating that the increase in AUDIT scores from year 3 to year 4 was smaller than expected under strict linear growth, consistent with a leveling‐off of alcohol use in the later period. Figure 3 displays individual values of AUDIT scores over time.

FIGURE 3.

FIGURE 3

Open in a new tab

Alcohol use trajectories over 4 years. (A) Longitudinal time series plot of AUDIT scores over time with each line representing a single participant (n = 163) and the black line representing average AUDIT score across all participants. (B) AUDIT score histograms indicating AUDIT frequencies at each time point.

3.3. Delay Discounting Mediation of Family History and Alcohol Misuse

A latent growth mediation model of delay discounting‐related neural mechanisms linking FH to patterns of alcohol use in early adulthood (Figure 2) also demonstrated an acceptable model fit: X 2(73) = 76.626, p = 0.247; CFI = 0.987; TLI = 0.979; RMSEA = 0.026 [90% CI: 0.000, 0.054]; SRMR = 0.045. All factor loadings on the mediator variables were significant (p < 0.001), indicating a strong measurement of the latent constructs.

Standardized coefficients of FH predicting neural mediators (Table 2) showed that FH significantly predicted activation in the NAcc (β = 0.286, SE = 0.117, p = 0.014). FH was not significantly associated with hippocampus (β = −0.013, SE = 0.111, p = 0.905) or middle frontal gyrus (β = −0.0115, SE = 0.100, p = 0.248) and did not significantly predict lower area under the curve, that is, more impulsive delay discounting behavior (β = −0.123, SE = 0.080, p = 0.128). There were effects of CTQ scores (β = −0.260, SE = 0.095, p = 0.006) and adolescent alcohol misuse scores (β = −0.189, SE = 0.086, p = 0.028) on reduced activation in the hippocampus.

TABLE 2.

Standardized model results for effects of family history and covariates on latent mediators.

Predictors Mediators
β SE p
Nucleus accumbens
Family history 0.286 0.117 0.014
Sex −0.027 0.215 0.900
Adolescent binge drinking −0.328 0.289 0.256
Childhood trauma 0.019 0.143 0.895
Middle frontal gyrus
Family history −0.115 0.100 0.248
Sex 0.182 0.248 0.463
Adolescent binge drinking 0.140 0.222 0.527
Childhood trauma 0.089 0.092 0.338
Hippocampus
Family history −0.013 0.111 0.905
Sex 0.261 0.179 0.144
Adolescent binge drinking −0.436 0.197 0.027
Childhood trauma −0.260 0.095 0.006
Delay discounting
Family history −0.123 0.080 0.128
Sex 0.462 0.166 0.005
Adolescent binge drinking −0.196 0.199 0.326
Childhood trauma 0.033 0.091 0.719
Open in a new tab

Note: Latent mediators were the bilateral nucleus accumbens to represent reward processing, bilateral caudal middle frontal gyrus to represent cognitive control, and bilateral hippocampus to represent prospection. Delay discounting was comprised of the area‐under‐the‐curve behavior for $100 and $1000 choices. Significant associations are indicated with bolded text.

The slope of alcohol use trajectory was significantly predicted by the NAcc mediator variable (β = 0.513, SE = 0.162, p = 0.002). Detailed standardized model results for the latent intercept and slope are in Table 3.

TABLE 3.

Standardized model results for effects of predictor variables and covariates on latent intercept and slope of AUDIT scores.

Predictors Intercept Slope
β SE p β SE p
Nucleus accumbens 0.052 0.171 0.761 0.513 0.162 0.002
Middle frontal gyrus 0.064 0.150 0.670 0.144 0.194 0.456
Hippocampus 0.032 0.143 0.820 0.055 0.167 0.741
Delay discounting −0.119 0.091 0.193 0.008 0.124 0.948
Family history 0.117 0.101 0.246 −0.160 0.128 0.212
Sex 0.203 0.173 0.242 −0.091 0.267 0.732
Adolescent binge drinking 1.567 0.154 0.000 −0.174 0.107 0.103
Childhood trauma 0.004 0.080 0.958 0.155 0.152 0.308
Open in a new tab

Note: Latent mediators were the bilateral nucleus accumbens to represent reward processing, bilateral caudal middle frontal gyrus to represent cognitive control, and bilateral hippocampus to represent prospection. Delay discounting was comprised of the area‐under‐the‐curve behavior for $100 and $1000 options. Significant associations are indicated with bolded text.

There was a significant indirect effect of FH on AUDIT slope through the NAcc latent mediator (β = 0.147, SE = 0.073, p = 0.044).

3.4. Sensitivity Analyses

Results from sensitivity analyses indicated that removing CTQ scores from the model did not alter interpretation of the primary findings (Tables S1 and S2), and the indirect effect of FH through the NAcc remained significant (β = 0.156, SE = 0.079, p = 0.048). ROIs selected to represent cognitive control did not substantially affect results and interpretation (Tables S3 and S4), and again, the indirect effect of FH through the NAcc remained significant (β = 0.132, SE = 0.064, p = 0.040) but the indirect effect through cognitive control‐related ROIs was not (β = 0.002, SE = 0.023, p = 0.927). Additionally, there were no significant effects of depression or anxiety scores, and previous associations remained significant when including these covariates (Tables S5 and S6), including the indirect effect of FH through the NAcc (β = 0.155, SE = 0.073, p = 0.035).

4. Discussion

This study yielded several novel findings. First, we identified the effects of FH—a major risk factor for AUD—on the neural correlates of delay discounting, revealing significantly elevated activation in the NAcc. Secondly, although the role of delay discounting as a mechanism risk for addiction has long been appreciated (Mitchell 2011), studies identifying the neural mechanisms linking this vulnerability to substance misuse have been limited. In the current study, we found that NAcc activation during delay discounting prospectively predicted future alcohol use. Finally, using mediation analysis in a structural equation modeling framework, we identified the neural mechanisms linking FH to patterns of alcohol use in young adulthood: greater activation of the NAcc mediated the association between FH and steeper alcohol use trajectories in young adulthood. These results support theoretical models of the NAcc and reward circuit functioning as a neurobiological mechanism of risk for addiction (Volkow and Morales 2015; Volkow et al. 2011) and indicate this neurobiological mechanism may be a major pathway through which FH promotes alcohol misuse and AUD.

The NAcc, a core region of the mesolimbic dopamine system, has been implicated in reward valuation and the motivational salience of appetitive cues (Koob and Volkow 2016; Volkow and Morales 2015). Increased activity in this region is observed in individuals with substance use disorders in response to conditioned drug cues (MacNiven et al. 2018). Supporting its possible role in promoting risk for addiction, alterations in NAcc structure and task‐free fMRI measures of NAcc function have also been observed in youth with AUD FH prior to alcohol initiation (Cservenka et al. 2014, 2015). Other studies have demonstrated blunted NAcc activation to reward‐predicting cues in monetary incentive delay tasks among FH‐positive youth (Martz et al. 2022; Yau et al. 2012). The current findings add to this growing evidence of effects of FH on NAcc reward function and expand this observation to intertemporal decision‐making for rewards, suggesting that altered reward‐related functioning may extend across multiple types of tasks and behaviors.

Investigations of the effects of FH on neural correlates of delay discounting have thus far been limited. A prior study of 125 adolescents with FH of substance use disorders found no effects of FH on fMRI activation during a delay‐discounting task despite the detection of behavioral differences (Rodriguez‐Moreno et al. 2021). However, that study included whole‐brain analyses, with no clusters surviving correction for multiple comparison, as well as underpowered main effects of task in the striatum, suggesting the discrepancy could stem from differences in methodology and/or power. Another fMRI study found that substance‐naïve preadolescent youth with FH (n = 35) had greater activation in the posterior insula, thalamus, and parahippocampal gyrus compared with controls (n = 24) (Butcher et al. 2021). A study of 33 adolescents linked FH to changes in white matter structure, which related to slower response times during a delay discounting task (Herting et al. 2010). Results from the current study yielded significant effects of FH based on our ROI approach, and further suggest these effects relate to future drinking behavior.

While prior studies have shown steeper delay discounting in individuals with FH, our study identifies the underlying neural activation patterns associated with this behavioral expression and links it to longitudinal alcohol use trajectories. The fact that this mediation occurred via neural, rather than behavioral markers suggests added explanatory power of neuroimaging in identifying mechanisms of risk. In fact, although FH was associated with trends in steeper behavioral discounting in the expected direction in the current sample, this behavior did not significantly mediate effects on drinking. Although discounting behavior is observable and predictive, patterns of activation across multiple brain systems likely contribute to similar behaviors due to their relative roles in supporting immediate versus delayed choices. Thus, NAcc function may be a more predictive marker of risk for alcohol misuse in young adults with FH than delay discounting behavior.

Candidate mediators related to prospection (hippocampus) and cognitive control (prefrontal cortex) were not significant neural predictors of alcohol use trajectories in this college sample. Other brain systems involved in delay discounting may be more predictive of alcohol use in other contexts or age groups. For example, in a sample of adolescents (ages 12–18) in an outpatient treatment program, substance use during the treatment period was related to neural activation during delay discounting in a medial temporal lobe “limbic” network (greater activation related to greater substance use), but not a network centered in the NAcc (Stanger et al. 2013). Network functional connectivity in the default‐mode network, which supports future‐oriented thinking, related to longer term, posttreatment substance use. Qualitative differences between these treatment‐associated findings and the findings of the current study suggest neural processes supporting treatment response may differ from those predicting alcohol use in a nontreatment‐seeking college sample. Another longitudinal study using IMAGEN data identified correlational trends for lower AUDIT scores at ages 16 and 18 in participants with greater fMRI activation in the dorsolateral prefrontal cortex during delay discounting at those time points, but there was no significant association of NAcc with alcohol use when examined as an ROI. The role of prefrontal regions in alcohol misuse in adolescence may stem from high interindividual variation due to rapid development of cognitive control during that period (Stevens et al. 2007), whereas the NAcc may play a larger role in young adulthood. In the current study, the FH effect on the dorsolateral prefrontal and the extended cognitive control network ROIs were not significant, indicating FH did not affect these regions during decision‐making in this sample. However, it is also possible the study was underpowered to detect these effects, as standardized βs of FH effects indicated small effects in the expected direction on the dorsolateral prefrontal (β = −0.115) and the extended cognitive control network (β = −0.125). Overall, the current findings suggest reward sensitivity may represent a proximal neurobiological link between FH and young adult drinking behavior. Other cognitive processes may exert a greater influence in other developmental periods, in other stages of AUD development (e.g., initiation or transition to compulsive use), or in different contexts (e.g., during abstinence or treatment). Although the current approach only identified a mediating effect of FH on alcohol use through the NAcc, the potential role of additional brain systems may be revealed by future studies in other samples and contexts.

The findings of this study have implications for early intervention among young adults. Reducing hazardous alcohol use, especially on college campuses, remains an important goal of intervention research. Although current NIAAA recommendations for individual‐level strategies for college students include targeting high‐risk student groups (e.g., Greek organizations), none currently utilize a precision‐medicine approach based on neurocognitive features of the at‐risk individual. Thus, an improved understanding of the individual‐level factors that promote alcohol misuse and AUD among young adults, including their neural and behavioral mechanisms, represents a crucial step toward reducing the personal and economic burden of hazardous drinking. For example, enhanced reward sensitivity could serve as a screening marker in at‐risk individuals, assisting with the implementation of tailored prevention strategies. Young adults with heightened reward‐related reactivity might also benefit from targeted strategies to modulate reward sensitivity and/or enhance the salience of non‐drug rewards. In fact, the mesolimbic dopamine system, with the ventral tegmental area as a key hub, has been implicated in the ability of natural rewards to protect against the development of AUD (Zheng et al. 2026). Given the association of NAcc activation with FH, such interventions may be particularly effective in young adults with familial risk.

4.1. Limitations

Some study limitations should be noted. This was a college‐educated, nontreatment‐seeking sample from a narrow geographic region, and alcohol use may be driven by different neural mechanisms in other age groups or contexts. Second, there was substantial attrition during the final time points. Although observed variables did not predict missingness—for example, AUDIT scores at one time point did not predict missingness at the next time point—it is possible that results could be affected by missingness. Third, the theory‐driven ROI‐based approach may result in Type II error, as FH may promote alcohol use through other brain regions not included in the analysis. Additionally, we focused our hypothesis on an fMRI contrast of decision‐making across delays, reward magnitude, and choice selection to isolate brain activation that is not focused on one particular element of the decision‐making process. However, other studies have modeled separate contrasts related to reward magnitude, delay, or level of difficulty to explicitly examine the decision‐making processes separately, which could be an alternative approach for future work in this area. Additionally, the assessment of family history was dependent on participants' familiarity with their biological relatives and knowledge of their drinking, which could contribute to inaccurate reports. Finally, there may be sex differences in the role of FH and the brain in alcohol drinking that this study was not powered to examine (Elton et al. 2023).

5. Conclusions

Given the high prevalence of FH (Yoon et al. 2013) and the negative outcomes associated with this risk factor (Kosty et al. 2020; Sher et al. 2005), the current study sought to uncover the neurobiological mechanisms by which FH increases risk for AUD in first‐year college students. The findings contribute to mounting evidence that the NAcc is altered in individuals with high familial risk for AUD and suggest that differences in reward circuitry function confer vulnerability for AUD by biasing individuals toward immediate rewards. This novel insight highlights the reward system as a potential neural target of intervention in at‐risk individuals.

Funding

This work was supported by the National Institute on Alcohol Abuse and Alcoholism, K01AA026334.

Disclosure

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Figure S1: FH Density Composite Score frequency across the analytic sample (n = 163). FH density composite score was calculated as a weighted total of the number of affected biological parents (0.5 for each), grandparents (0.25 for each), and maternal and paternal aunts and uncles (0.25/[total relatives in category] for each).

Table S1: Standardized model results for effects of family history and covariates on latent mediators without Childhood Trauma Questionnaire scores as a predictor.

Table S2: Standardized model results for effects of predictor variables and covariates on latent intercept and slope without Childhood Trauma Questionnaire scores as a predictor.

Table S3: Standardized model results for effects of family history and covariates on latent mediators including extended middle frontal gyrus network as a cognitive control predictor variable

Table S4: Standardized model results for effects of predictor variables and covariates on latent intercept and slope including extended cognitive control predictor variable.

Table S5: Standardized model results for effects of predictor variables and covariates on latent mediators including depression and anxiety severity covariates.

Table S6: Standardized model results for effects of predictor variables and covariates on latent intercept and slope including depression and anxiety severity covariates.

ACER-50-0-s001.docx (64.8KB, docx)

Acknowledgments

This work was supported by NIH grant K01AA026334 to A.E. We posthumously acknowledge the contributions of Charlotte Ann Boettiger, PhD, and thank her for her mentorship on this project.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Amlung, M. , Vedelago L., Acker J., Balodis I., and MacKillop J.. 2017. “Steep Delay Discounting and Addictive Behavior: A Meta‐Analysis of Continuous Associations.” Addiction 112: 51–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Anda, R. F. , Whitfield C. L., Felitti V. J., et al. 2002. “Adverse Childhood Experiences, Alcoholic Parents, and Later Risk of Alcoholism and Depression.” Psychiatric Services 53: 1001–1009. [DOI] [PubMed] [Google Scholar]
  3. Ballard, K. , and Knutson B.. 2009. “Dissociable Neural Representations of Future Reward Magnitude and Delay During Temporal Discounting.” NeuroImage 45: 143–150. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Beck, A. T. , Ward C. H., Mendelson M., Mock J., and Erbaugh J.. 1961. “An Inventory for Measuring Depression.” Archives of General Psychiatry 4: 561–571. [DOI] [PubMed] [Google Scholar]
  5. Bernstein, D. P. , Stein J. A., Newcomb M. D., et al. 2003. “Development and Validation of a Brief Screening Version of the Childhood Trauma Questionnaire.” Child Abuse & Neglect 27: 169–190. [DOI] [PubMed] [Google Scholar]
  6. Bickel, W. K. , Pitcock J. A., Yi R., and Angtuaco E. J. C.. 2009. “Congruence of BOLD Response Across Intertemporal Choice Conditions: Fictive and Real Money Gains and Losses.” Journal of Neuroscience 29: 8839–8846. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Butcher, T. J. , Dzemidzic M., Harezlak J., Hulvershorn L. A., and Oberlin B. G.. 2021. “Brain Responses During Delay Discounting in Youth at High‐Risk for Substance Use Disorders.” NeuroImage: Clinical 32: 102772. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Cox, R. W. 1996. “AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages.” Computers and Biomedical Research 29: 162–173. [DOI] [PubMed] [Google Scholar]
  9. Cservenka, A. , Casimo K., Fair D. A., and Nagel B. J.. 2014. “Resting State Functional Connectivity of the Nucleus Accumbens in Youth With a Family History of Alcoholism.” Psychiatry Research 221: 210–219. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Cservenka, A. , Gillespie A. J., Michael P. G., and Nagel B. J.. 2015. “Family History Density of Alcoholism Relates to Left Nucleus Accumbens Volume in Adolescent Girls.” Journal of Studies on Alcohol and Drugs 76: 47–56. [PMC free article] [PubMed] [Google Scholar]
  11. Dube, S. R. , Anda R. F., Felitti V. J., Croft J. B., Edwards V. J., and Giles W. H.. 2001. “Growing Up With Parental Alcohol Abuse: Exposure to Childhood Abuse, Neglect, and Household Dysfunction.” Child Abuse & Neglect 25: 1627–1640. [DOI] [PubMed] [Google Scholar]
  12. Elton, A. , Allen J. H., Yorke M., Khan F., Xu P., and Boettiger C. A.. 2023. “Sex Moderates Family History of Alcohol Use Disorder and Childhood Maltreatment Effects on an fMRI Stop‐Signal Task.” Human Brain Mapping 44: 2436–2450. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Elton, A. , Faulkner M., Robinson D., and Boettiger C.. 2021. “Acute Depletion of Dopamine Precursors in the Human Brain: Effects on Functional Connectivity and Alcohol Attentional Bias.” Neuropsychopharmacology 46, no. 8: 1421–1431. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Elton, A. , Smith C. T., Parrish M. H., and Boettiger C. A.. 2017. “Neural Systems Underlying Individual Differences in Intertemporal Decision‐Making.” Journal of Cognitive Neuroscience 29, no. 3: 467–479. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Esteban, O. , Markiewicz C. J., Blair R. W., et al. 2019. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” Nature Methods 16: 111–116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Figner, B. , Knoch D., Johnson E. J., et al. 2010. “Lateral Prefrontal Cortex and Self‐Control in Intertemporal Choice.” Nature Neuroscience 13: 538–539. [DOI] [PubMed] [Google Scholar]
  17. Friston, K. J. , Holmes A. P., Worsley K. J., Poline J. P., Frith C. D., and Frackowiak R. S. J.. 1994. “Statistical Parametric Maps in Functional Imaging: A General Linear Approach.” Human Brain Mapping 2: 189–210. [Google Scholar]
  18. Fröhner, J. H. , Ripke S., Jurk S., et al. 2022. “Associations of Delay Discounting and Drinking Trajectories From Ages 14 to 22.” Alcoholism, Clinical and Experimental Research 46: 667–681. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Hanson, K. L. , Medina K. L., Nagel B. J., Spadoni A. D., Gorlick A., and Tapert S. F.. 2010. “Hippocampal Volumes in Adolescents With and Without a Family History of Alcoholism.” American Journal of Drug and Alcohol Abuse 36: 161–167. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Herting, M. M. , Schwartz D., Mitchell S. H., and Nagel B. J.. 2010. “Delay Discounting Behavior and White Matter Microstructure Abnormalities in Youth With a Family History of Alcoholism.” Alcoholism, Clinical and Experimental Research 34: 1590–1602. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Kable, J. W. , and Glimcher P. W.. 2007. “The Neural Correlates of Subjective Value During Intertemporal Choice.” Nature Neuroscience 10: 1625–1633. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Kendler, K. S. , Sundquist K., Ohlsson H., et al. 2012. “Genetic and Familial Environmental Influences on the Risk for Drug Abuse: A National Swedish Adoption Study.” Archives of General Psychiatry 69: 690–697. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Kim‐Spoon, J. , Lauharatanahirun N., Peviani K., et al. 2019. “Longitudinal Pathways Linking Family Risk, Neural Risk Processing, Delay Discounting, and Adolescent Substance Use.” Journal of Child Psychology and Psychiatry 60: 655–664. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Koffarnus, M. N. , and Bickel W. K.. 2014. “A 5‐Trial Adjusting Delay Discounting Task: Accurate Discount Rates in Less Than One Minute.” Experimental and Clinical Psychopharmacology 22: 222–228. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Koob, G. F. , and Volkow N. D.. 2016. “Neurobiology of Addiction: A Neurocircuitry Analysis.” Lancet Psychiatry 3: 760–773. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Kosty, D. B. , Farmer R. F., Seeley J. R., et al. 2020. “The Number of Biological Parents With Alcohol Use Disorder Histories and Risk to Offspring Through Age 30.” Addictive Behaviors 102: 106196. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Lempert, K. M. , Steinglass J. E., Pinto A., Kable J. W., and Simpson H. B.. 2019. “Can Delay Discounting Deliver on the Promise of RDoC?” Psychological Medicine 49: 190–199. [DOI] [PubMed] [Google Scholar]
  28. Liao, J. , Allen J. H., Yorke M., Boettiger C. A., and Elton A.. 2023. “Family History, Childhood Maltreatment, and Adolescent Binge Drinking Exert Synergistic Effects on Delay Discounting and Future Alcohol Use.” American Journal of Drug and Alcohol Abuse 49: 652–663. [DOI] [PubMed] [Google Scholar]
  29. MacKillop, J. , Amlung M. T., Few L. R., Ray L. A., Sweet L. H., and Munafo M. R.. 2011. “Delayed Reward Discounting and Addictive Behavior: A Meta‐Analysis.” Psychopharmacology 216: 305–321. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. MacNiven, K. H. , Jensen E. L. S., Borg N., Padula C. B., Humphreys K., and Knutson B.. 2018. “Association of Neural Responses to Drug Cues With Subsequent Relapse to Stimulant Use.” JAMA Network Open 1: e186466. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Martz, M. E. , Hardee J. E., Cope L. M., et al. 2022. “Nucleus Accumbens Response to Reward Among Children With a Family History of Alcohol Use Problems: Convergent Findings From the ABCD Study and Michigan Longitudinal Study.” Brain Sciences 12: 913. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. McClure, S. M. , Ericson K. M., Laibson D. I., Loewenstein G., and Cohen J. D.. 2007. “Time Discounting for Primary Rewards.” Journal of Neuroscience 27: 5796–5804. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. McClure, S. M. , Laibson D. I., Loewenstein G., and Cohen J. D.. 2004. “Separate Neural Systems Value Immediate and Delayed Monetary Rewards.” Science 306: 503–507. [DOI] [PubMed] [Google Scholar]
  34. Mitchell, S. H. 2011. “The Genetic Basis of Delay Discounting and Its Genetic Relationship to Alcohol Dependence.” Behavioural Processes 87: 10–17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Muschelli, J. , Nebel M. B., Caffo B. S., Barber A. D., Pekar J. J., and Mostofsky S. H.. 2014. “Reduction of Motion‐Related Artifacts in Resting State fMRI Using aCompCor.” NeuroImage 96: 22–35. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Muthén, B. , and Muthén L.. 2017. Mplus, Handbook of Item Response Theory, 507–518. Chapman and Hall/CRC. [Google Scholar]
  37. Peters, J. , and Büchel C.. 2010. “Episodic Future Thinking Reduces Reward Delay Discounting Through an Enhancement of Prefrontal‐Mediotemporal Interactions.” Neuron 66: 138–148. [DOI] [PubMed] [Google Scholar]
  38. Peters, J. , and Büchel C.. 2011. “The Neural Mechanisms of Inter‐Temporal Decision‐Making: Understanding Variability.” Trends in Cognitive Sciences 15: 227–239. [DOI] [PubMed] [Google Scholar]
  39. Rodriguez‐Moreno, D. V. , Cycowicz Y. M., Figner B., et al. 2021. “Delay Discounting and Neurocognitive Correlates Among Inner City Adolescents With and Without Family History of Substance Use Disorder.” Developmental Cognitive Neuroscience 48: 100942. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Saunders, J. B. , Aasland O. G., Babor T. F., de la Fuente J. R., and Grant M.. 1993. “Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO Collaborative Project on Early Detection of Persons With Harmful Alcohol Consumption—II.” Addiction 88: 791–804. [DOI] [PubMed] [Google Scholar]
  41. Sheehan, D. V. , Lecrubier Y., Sheehan K. H., et al. 1998. “The Mini‐International Neuropsychiatric Interview (M.I.N.I.): The Development and Validation of a Structured Diagnostic Psychiatric Interview for DSM‐IV and ICD‐10.” Journal of Clinical Psychiatry 59: 22–33. [PubMed] [Google Scholar]
  42. Sher, K. J. , Grekin E. R., and Williams N. A.. 2005. “The Development of Alcohol Use Disorders.” Annual Review of Clinical Psychology 1: 493–523. [DOI] [PubMed] [Google Scholar]
  43. Silveri, M. , Rogowska J., McCaffrey A., and Yurgelun‐Todd D.. 2011. “Adolescents at Risk for Alcohol Abuse Demonstrate Altered Frontal Lobe Activation During Stroop Performance.” Alcoholism, Clinical and Experimental Research 35: 218–228. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Spielberger, C. D. , Vagg P. R., Barker L. R., Donham G. W., and Westberry L. G.. 1980. “The Factor Structure of the State‐Trait Anxiety Inventory.” Stress and Anxiety 7: 95–109. [Google Scholar]
  45. Stanger, C. , Elton A., Ryan S. R., James G. A., Budney A. J., and Kilts C. D.. 2013. “Neuroeconomics and Adolescent Substance Abuse: Individual Differences in Neural Networks and Delay Discounting.” Journal of the American Academy of Child and Adolescent Psychiatry 52: 747–755. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Stevens, M. C. , Kiehl K. A., Pearlson G. D., and Calhoun V. D.. 2007. “Functional Neural Networks Underlying Response Inhibition in Adolescents and Adults.” Behavioural Brain Research 181: 12–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Stoltenberg, S. F. , Mudd S. A., Blow F. C., and Hill E. M.. 1998. “Evaluating Measures of Family History of Alcoholism: Density Versus Dichotomy.” Addiction 93: 1511–1520. [DOI] [PubMed] [Google Scholar]
  48. Substance Abuse and Mental Health Services Administration . 2021. “Key Substance Use and Mental Health Indicators in the United States: Results From the 2020 National Survey on Drug Use and Health.” In Series Key substance use and mental health indicators in the United States: Results from the 2020 National Survey on Drug Use and Health, National Survey on Drug Use and Health (Center for Behavioral Health Statistics and Quality SAaMHSA ed, Rockville, MD).
  49. Teicher, M. H. , Anderson C. M., and Polcari A.. 2012. “Childhood Maltreatment Is Associated With Reduced Volume in the Hippocampal Subfields CA3, Dentate Gyrus, and Subiculum.” Proceedings of the National Academy of Sciences of the United States of America 109: E563–E572. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Volkow, N. D. , and Morales M.. 2015. “The Brain on Drugs: From Reward to Addiction.” Cell 162: 712–725. [DOI] [PubMed] [Google Scholar]
  51. Volkow, N. D. , Wang G. J., Fowler J. S., Tomasi D., and Telang F.. 2011. “Addiction: Beyond Dopamine Reward Circuitry.” Proceedings of the National Academy of Sciences of the United States of America 108: 15037–15042. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Yau, W. Y. , Zubieta J. K., Weiland B. J., Samudra P. G., Zucker R. A., and Heitzeg M. M.. 2012. “Nucleus Accumbens Response to Incentive Stimuli Anticipation in Children of Alcoholics: Relationships With Precursive Behavioral Risk and Lifetime Alcohol Use.” Journal of Neuroscience 32: 2544–2551. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Yoon, G. , Westermeyer J., Kuskowski M. A., and Nesheim L.. 2013. “Impact of the Number of Parents With Alcohol Use Disorder on Alcohol Use Disorder in Offspring: A Population‐Based Study.” Journal of Clinical Psychiatry 74: 795–801. [DOI] [PubMed] [Google Scholar]
  54. Zheng, W. , Liu X., Lu T., et al. 2026. “Effect of Natural Rewards on Substance Use Disorder: An Incentive Sensitization Perspective.” Biological Psychiatry 99: 436–445. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1: FH Density Composite Score frequency across the analytic sample (n = 163). FH density composite score was calculated as a weighted total of the number of affected biological parents (0.5 for each), grandparents (0.25 for each), and maternal and paternal aunts and uncles (0.25/[total relatives in category] for each).

Table S1: Standardized model results for effects of family history and covariates on latent mediators without Childhood Trauma Questionnaire scores as a predictor.

Table S2: Standardized model results for effects of predictor variables and covariates on latent intercept and slope without Childhood Trauma Questionnaire scores as a predictor.

Table S3: Standardized model results for effects of family history and covariates on latent mediators including extended middle frontal gyrus network as a cognitive control predictor variable

Table S4: Standardized model results for effects of predictor variables and covariates on latent intercept and slope including extended cognitive control predictor variable.

Table S5: Standardized model results for effects of predictor variables and covariates on latent mediators including depression and anxiety severity covariates.

Table S6: Standardized model results for effects of predictor variables and covariates on latent intercept and slope including depression and anxiety severity covariates.

ACER-50-0-s001.docx (64.8KB, docx)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Articles from Alcohol, Clinical & Experimental Research are provided here courtesy of Wiley

RESOURCES