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. Author manuscript; available in PMC: 2019 Feb 2.
Published in final edited form as: Prog Neuropsychopharmacol Biol Psychiatry. 2017 Sep 20;81:284–296. doi: 10.1016/j.pnpbp.2017.09.010

Neural and Psychological Characteristics of College Students with Alcoholic Parents Differ Depending on Current Alcohol Use

Kathleen A Brown-Rice 1,2, Jamie L Scholl 2,3, Kelene A Fercho 2,3, Kami Pearson 1,2,3, Noah A Kallsen 4, Gareth E Davies 4, Erik A Ehli 4, Seth Olson 1, Amy Schweinle 1,2, Lee A Baugh 2,3, Gina L Forster 2,3
PMCID: PMC5690848  NIHMSID: NIHMS909474  PMID: 28939188

Abstract

A significant proportion of college students are adult children of an alcoholic parent (ACoA), which can confer greater risk of depression, poor self-esteem, alcohol and drug problems, and greater levels of college attrition. However, some ACoA are resilient to these negative outcomes. The goal of this study was to better understand the psychobiological factors that distinguish resilient and vulnerable college-aged ACoAs. To do so, scholastic performance and psychological health were measured in ACoA college students not engaged in hazardous alcohol use (resilient) and those currently engaged in hazardous alcohol use (vulnerable). Neural activity (as measured by functional magnetic resonance imaging) in response to performing working memory and emotion-based tasks were assessed. Furthermore, the frequency of polymorphisms in candidate genes associated with substance use, risk taking and stress reactivity were compared between the two ACoA groups. College ACoAs currently engaged in hazardous alcohol use reported more anxiety, depression and posttraumatic stress symptoms, and increased risky nicotine and marijuana use as compared to ACoAs resistant to problem alcohol use. ACoA college students with current problem alcohol showed greater activity of the middle frontal gyrus and reduced activation of the posterior cingulate in response to visual working memory and emotional processing tasks, which may relate to increased anxiety and problem alcohol and drug behaviors. Furthermore, polymorphisms of cholinergic receptor and the serotonin transporter genes also appear to contribute a role in problem alcohol use in ACoAs. Overall, findings point to several important psychobiological variables that distinguish ACoAs based on their current alcohol use that may be used in the future for early intervention.

Keywords: Alcohol, Anxiety, functional Magnetic Resonance Imaging, Single Nucleotide Polymorphism, Working Memory, Children of Alcoholics

Introduction

One-fourth to one-third of college students meet the criteria to be an adult child of an alcoholic parent (ACoA; Kelley et al., 2008). The impact of growing up with one or more alcoholic parent/s can be traumatic for a child (Mackrill & Hesse, 2011). In fact, ACoA status is correlated with increased occurrences of depression (Kelley et al., 2010; Klostermann et al., 2011), anxiety (Woodford et al., 2012), alcohol abuse (LaBrie, et al., 2007), low self-esteem (Neighbors et al., 2004), greater risk for developing substance use problems (Yoon et al., 2013; Eddie et al, 2015), and higher levels of college attrition (Kitsantas, et al. 2008). Specifically, ACoA have been reported to have greater difficulty in adjusting to college life during their freshman year than students from non-alcoholic homes (Porter & Pryor, 2007). The difficulties in college experienced by many ACoA students may be due to poor psychological and/or social functioning (Kelley et al., 2008; Hill et al., 2001), cognitive difficulties affecting scholastic performance (Schroeder & Kelley, 2008), or a combination of these factors, which are likely to be also compounded by the propensity for hazardous alcohol use in this population (Yoon et al., 2013; Eddie et al., 2015).

The majority of research investigating neural-behavioral correlates of ACoA relates to reward sensitivity, motivation, behavioral disinhibition/impulsivity and risky decision making, given that these are risk factors for alcohol use disorder (AUD) (Schweinsburg et al., 2004; Bjork et al., 2008; Acheson et al., 2009; Andrews et al., 2011; Cservenka & Nagel, 2012; Yau et al., 2012; Dager et al., 2013; Kareken et al., 2013; Weiland et al., 2013). Studies comparing adolescents with a familial history of AUD (1st or 2nd degree biological relative without necessarily being raised by this relative) to those without such history have provided some indication that family history alone alters neural functioning during emotional or cognitive processing. For example, the frontal cortex (medial frontal gyrus, dorsolateral frontal cortex, dorsal anterior cingulate) is necessary for executive functioning that is critical for day-to-day and scholastic performance of tasks like working memory, and is less active in adolescents with family history of AUD (Cservenka et al., 2012; Mackiewicz et al., 2013). Likewise, the activity of subcortical regions such as the amygdala, necessary for appropriate emotional processing and responding, is also reduced in young adults with a family history of AUD (Glen et al., 2007; Cacciaglia et al., 2013), suggesting reduced ability to engage appropriately with emotional stimuli. Thus, a family history of AUD may confer psychosocial, cognitive, and alcohol problems on offspring via poor frontal cortical-to-subcortical neural functioning.

Adult offspring of alcoholics have a 2.5 to 4.4 fold increase in the chance of developing AUD in their lifetime as compared to children of non-AUD parents, with greatest risk conferred by being raised by two alcoholic parents (Yoon et al., 2013). This risk is likely conferred by an interaction between genetic and environmental factors, given that genetic influences can contribute to alcohol use (Goate & Edenberg, 1998; Köhnke et al., 2008) and being raised by an alcoholic parent is associated with increased adverse childhood experiences, increased psychosocial behaviors associated with alcoholism in youth, and greater risk for adult AUD (Anda et al., 2002; Hussong et al., 2007). However, most research using youth or adult children of alcoholics normally involves individuals who are selected based on family history of AUD with no consideration for whether they were raised by an individual with AUD. Furthermore, participants in such studies are often without extensive current alcohol problems themselves, likely representing a resilient subpopulation of ACoA (Heitzeg et al., 2008). For example, previous research examining polymorphisms in genes of ACoA have shown relationships with brain derived neurotrophic factor polymorphisms and executive functioning (Benzerouk et al., 2013), and dopamine D2 receptor A1 allele expression with risk seeking (Ratsma et al., 2011). However, these prior studies used resilient, healthy ACoA so it is not clear whether genetic polymorphisms contribute to at-risk and resilient ACoA phenotypes.

In the few neural studies including adolescents with both low and high alcohol problems who have a family history of AUD, there appear to be effects on neural functioning that can be attributed to current alcohol use and other effects attributable to a family history of alcoholism (Heitzeg et al., 2008; 2010). For example, failure to deactivate the frontal cortex during response inhibition in a go/no-go task in adolescents with a positive family history of AUD is only apparent with current alcohol problems, whereas failure to deactivate the striatum is observed in adolescents with a family history of AUD regardless of current alcohol use (Heitzeg et al., 2010). Therefore, to effectively study underlying psychobiological factors that might lead to higher levels of problems in college students, the current study included ACoAs exhibiting risky or hazardous alcohol use (that is, a vulnerable subpopulation), in addition to those not currently engaged in hazardous alcohol use (a resilient population).

To better understand the psychobiological factors that distinguish outcomes for ACoAs, this study tested the hypothesis that ACoA college students engaged in hazardous alcohol use would show poorer scholastic performance and psychological health as compared to ACoAs without problem alcohol use, and this would be reflected in altered cortical activity during working memory performance and emotional processing. We also hypothesized that the frequency of polymorphisms in genes associated with increased risk for substance use, risk taking and stress reactivity would be higher in ACoA college students engaged in hazardous alcohol use, which might partially account for observed differences in neural and emotional reactivity between the two groups.

Materials and Methods

Participants

All experimental procedures received approval from the Institutional Review Board of the University of South Dakota. Forty-four right-handed college students were enrolled through advertisements on the campus of a Midwestern University (see Table 1 for demographics). Participants completed initial screening to determine whether they met criteria for an adult child of an alcoholic parent (ACoA) who was their caregiver, using the Children of Alcoholics Screening Test (CAST, Jones, 1983) to be included in the study. A score of 6 or above on the CAST indicated the participant was more than likely the child of an alcoholic/s and raised by this parent/s. Informed written consent was obtained at the start the initial screening and participants received $20 for this the initial screen.

Table 1.

Demographic and assessment measures for adult children of alcoholic parents based on current alcohol status.

Demographic Measure Non-Hazardous Hazardous
Gender (%) Male 36.36 42.11
Female 63.64 57.89
Age (range) 19–24 18–25
Race (%) White 81.82 94.74
Native American 0 5.26
Asian American 4.55 0
Multiracial 13.64 0
Assessment (Range) Scale Non-Hazardous Hazardous t Value p Value
CAST (0–30) 19.36±0.99 20.00±1.60 0.348 0.729
AUDIT (0–16+) 2.68±0.50 15.21±1.51 −8.337 <0.001
ASSIST * (0–27+) Tobacco 3.82±1.30 12.32±2.26 −3.372 0.002
Alcohol 6.50±0.92 22.53±2.56 −6.238 <0.001
Marijuana 2.64±0.73 7.42±1.52 −2.965 0.005
Cocaine 0.27±0.27 1.74±1.48 −1.041 0.304
Amphetamines 0.41±0.41 2.11±1.53 −1.142 0.13
Inhalants 0.00±0.00 0.58±0.33 1.907 0.064
Sedatives 0.14±0.14 4.63±1.98 −2.446 0.019
Hallucinogens 0.00±0.00 2.32±1.34 −2.316 0.07
Pain Medication/Opioids 0.27±0.27 1.68±0.79 −1.786 0.082
Duke Health Physical Health 73.64±4.03 64.74±4.48 1.48 0.147
Profile Mental Health 72.73±5.06 52.11±5.05 2.868 0.007
(0–100) Social Health 71.36±4.43 54.74±5.43 2.397 0.021
General Health 72.08±4.10 57.48±4.27 2.46 0.018
Perceived Health 77.27±5.43 50.00±8.55 2.767 0.009
Self Esteem 68.18±5.45 55.26±5.37 −1.677 0.102
Anxiety 34.84±5.12 49.46±5.13 −2.006 0.052
Depression 32.73±5.27 48.95±4.64 2.275 0.029
Anxiety and Depression 32.15±4.90 51.76±5.44 −2.686 0.011
Pain 29.55±6.29 47.37±8.09 1.762 0.086
Disability 0.00±0.00 15.79±5.48 −3.108 0.004
BDI–II (0–63) 11.5±1.94 19.53±3.08 −2.27 0.029
BAI (0–63) 8.09±1.57 13.68±2.15 −2.136 0.039
PCL (17–85) 36.64±3.25 47.95±3.77 −2.286 0.028
GPA (0–4) 3.45±0.10 3.15±0.20 −2.193 0.017
ACT (0–36) 23.85±0.83 24.10±0.99 0.168 0.868
SAT Composite (0–2400) 1475.5±113.22 1436±82.73 0.087 0.939
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Abbreviations: Children of Alcoholics Screening Test (CAST); Alcohol Use Disorders Identification Test (AUDIT); Alcohol, smoking and substance involvement screening test (ASSIST); Beck test for depression (BDI–II); Beck test for anxiety (BAI); PTSD Check List (PCL); Grade point average (GPA); American college testing (ACT); Scholastic assessment test (SAT).

*

Scores of 0–3 suggest low risk, scores of 4–26 suggest moderate risk, and scores > 27 suggest high risk of experiencing severe substance use issues.

Assessment of Alcohol and Substance Use, and Mental/Physical Health

The Alcohol Use Disorders Identification Test (AUDIT, World Health Organization, 1982) was used to assess hazardous alcohol use. Participants who met the criteria for the non-hazardous alcohol use group had a score below 8 (n = 23) and those in the hazardous alcohol use group had scores of 8 or greater (n = 21). The division of young adults into groups based on their current hazardous alcohol use is justified based on the observation that ACoAs who are not exhibiting problem alcohol problems at this age are likely to represent resilient individuals (Heitzeg et al., 2008).

Other measures collected included the Beck Anxiety Inventory (BAI; Beck & Steer, 1993), Beck Depression Inventory (BDI–II; Beck & Steer, 1996), the Posttraumatic Stress Disorder Checklist (PCL; Weathers et al., 2013), Alcohol, Smoking, and Substance Involvement Test (ASSIST, World Health Organization, 2010), and the Duke Health Profile, (The DUKE, Duke University, 2012). Participants self-reported their current grade point average (GPA) as well as college entrance exam scores (ACT/SAT), which were later confirmed for those undergoing the fMRI portion of the study with an unofficial transcript and a copy of their ACT/SAT exam score records.

fMRI Procedures and Blood Sampling

Screening

At the time of initial screening and interview, participants were also screened for contraindications to magnetic resonance imaging (MRI). Participants were excluded from the functional MRI (fMRI) portion of the study if they had contraindications to MRI or exhibited possible psychotic or other psychological symptoms that would make inclusion in the study potentially hazardous to them. This resulted in the removal of 16 of the 44 enrolled participants, leaving twenty-eight subjects participating in the fMRI and blood sampling portion of the study (14 each of the non-hazardous and hazardous groups). Participants were invited to complete the fMRI portion of the study within a week of their initial screen. Informed written consent was obtained immediately prior to participating in the fMRI portion of the study and participants received $75 to cover travel expenses to and from the scanning location.

Apparatus

Participants lay supine in the bore, with their head stabilized by noise-canceling headphones and foam padding. Images were presented on an MR-compatible 30″ LCD screen (Invivo, Gainesville, FL) that was placed at the head of the scanner and reflected with a rearward facing single reflection mirror box that was affixed to the top of the head-coil. Behavioral responses were collected utilizing an MR-compatible 4-button response box (Lumina LP-400, Cedrus Corporation, San Pedro, CA) placed at participant midline, just below the chest in order to minimize participant movement throughout the experiment. The response pad consisted of a keypad with four horizontally arranged buttons; participants were instructed to only press button one with their index finger to respond (or two buttons to move the scale bar to the left or right for the ranking emotion task) using their dominant (right) hand. To ensure the button box did not move throughout the experiment, it was affixed to the participant’s wrist via a Velcro strap. Stimulus presentation and data recording were accomplished using a dedicated PC running custom LabVIEW software (LabVIEW 2015, National Instruments, Austin, TX).

Blood Sampling and In-Scanner Tasks

Prior to the scanning session participants were given a breath analysis test to confirm a blood alcohol content of 0.00% using an Alco-Sensor FST (Intoximeters Inc.). Participants had a 5 ml blood sample collected in heparin-coated tubes prior to scanning as well as immediately after the imaging was completed, with samples kept on ice until processing. Participants were briefed on the tasks that they were to complete in the scanner, and were allowed to practice each task on a laptop computer until they felt comfortable (less than 10 minutes for all participants). Participants then were positioned into a 3-Tesla whole-body Siemens Skyra scanner, with a 20-channel birdcage RF coil (Siemens, AG). Participants completed three tasks in the scanner – the order randomized among participants. Tasks were all presented in an event-related design. It is important to note that the imaging tasks utilized did not incorporate a jittered (varying the timing of the TR relative to stimulus onset) design. By using a fixed inter-trial interval, we maximized our ability to detect changes in the amplitude of the hemodynamic response function (HRF). However, a drawback to this design is the implicit assumption that the shape of the HRF is equivalent in all conditions. As there were no a priori predictions that the shape of the HRF would vary as a function of condition, we favored the increased statistical power associated with a fixed inter-trial interval (Dale, 1998; Liu et al., 2001). After completion of the three tasks, T1-weighted anatomical images were collected.

Two working memory tasks were chosen to determine neural activity in response to both working memory alone (using geometric stimuli) and working memory with emotional load (using emotionally-valenced stimuli). Furthermore, a third task, was included that required participants to evaluate the valence of the stimulus to probe neural activity while evaluating emotional stimuli in the absence of working memory processes.

2-back Visual Working Memory Task

Participants were required to indicate by button press if the current image matched the image presented two steps earlier (Mackiewicz et al., 2013). Images were selected from a set of 30 geometric shapes (Figure 1A), and each image was presented for 500 msec with a 1500 msec inter-trial interval, 80 stimuli presented per run, three runs total. Response time, errors and correct responses were recorded.

Figure 1.

Figure 1

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Depiction of the tasks completed by participants during the fMRI portion of the study. (A) 2-back working memory task using geometric shapes. Participants must press a button when the image viewed matches that of the image shown 2 images previous. Measures include time to respond, and the number of errors. (B) 2-back working memory task with emotional load, utilizing International Affective Pictures Set (IAPS) images. Participants must press a button when the image viewed matches that of the image shown 2 images previous. Measures include time to respond, the number of false positive errors and the number of missed 2-backs. (C) Ranking emotion task where participants rate how happy or sad IAPS images made them feel. Participants use button responses to move the scale bar towards either the happy or sad face while viewing the image.

2-back Visual Working Memory Task with Emotional Load

Participants were also asked to perform a 2-back task with emotionally arousing images. Images were derived from the International Affective Picture System (IAPS, Bradley & Lang, 2007; Figure 1B), and conditions of neutral, positive and negative images (30 of each) were presented in a counter-balanced manner for 500 msec with a 1500 msec inter-trial interval, across nine runs (80 stimuli per run). Response time, errors, and correct responses were recorded.

Ranking Emotion Task

Participants were presented with 90 IAPS images with comparable valence ratings as those used in the 2-back working memory task with emotional load. The same image was not used in both 2-back and ranking tasks to avoid habituation to the image. Images were presented for 500 msec over three runs, and participants were allowed a further 5500 msec to use button press to move a scale bar towards a happy or sad face on the horizontal plane at the bottom of the image, with starting position at neutral (Figure 1C). Rankings were recorded on a scale of 0–7 with 0 being most positive and 7 being most negative.

Image Acquisition

All MR Imaging data was collected on a Siemens 3T Skyra scanner. The functional imaging data consisted of standard T2*-weighted pulse sequence which was aligned to T1-weighted anatomical images. Acquisition consisted of a 3D anatomical T1 weighted scan, where volumes were collected in the sagittal plane using an MP-RAGE sequence [TR, 1900ms; TE, 2.13 ms; FOV, 256 × 256 × 256; in-plane resolution, .9 mm × .9375mm × .9375mm voxels; flip angle, 9°]. Functional MRI volumes were collected using a T2*-weighted, single-shot, gradient-echo, echo-planar imaging acquisition sequence [TR, 2000ms; TE, 30ms; FOV, 220 × 220; slice thickness, 4 mm; gap thickness, 1mm; in-plane resolution, 3.44mm × 3.44mm; matrix size, 64 × 64 mm; flip angle, 90°].

Analysis of Single Nucleotide Polymorphisms (SNP)

DNA Extraction

DNA from 27 samples were extracted using the QIAsymphony® instrument coupled with the QIAsymphony® DSP DNA midi kit version 1.0 according to the manufacturer’s instructions (QIAGEN Inc, Germantown, MD). The extracted DNA was eluted into ATE buffer composed of 10 mM Tris-Cl pH 8.3, 0.1 mM EDTA, and 0.04% NaN3 (sodiumazide) with a final eluate volume of 200 μl.

SNP Genotyping

Forty-three different polymorphisms (42 single nucleotide polymorphisms (SNPs) and the SERT 5-HTTLPR; detailed in Table 2), putatively associated with addiction, risk taking behaviors, stress sensitivity or mental health, were assayed for each sample. SNPs that were chosen included those in the alcohol dehydrogenase family related to alcohol dependence and metabolism (Dick et al., 2008, MacGregor et al., 2009, Palmer et al., 2015), the monoamine systems which influence mental health and substance dependence (Dick et al., 2008, Le Strat et al., 2008, Cao et al., 2011, Duncan et al., 2012, Herman and Balogh 2012, Vereczkei et al., 2013, Clarke et al., 2014, Ching-Lopez et al., 2015,), the CRF system involving major depressive disorders (Kertes et al., 2011, Ching-Lopez et al., 2015) opioid and GABA receptors involving multidrug use and treatment outcomes/relapse (Dick et al., 2004, Anton et al., 2008, Lind et al., 2008, Kertes et al, 2011, Darpyak et al., 2013, Xu et al., 2013, Li et al., 2014) as well as several SNPs selected for their involvement in alcohol dependence and resiliency (Foroud et al., 2008, L Bevilacqua et al., 2009, Wetherill et al., 2008, Marcos et al., 2012). Extracted genomic DNA from each sample was normalized in DNA suspension buffer composed of 10 mM Tris and 0.1 mM EDTA to a working concentration of 20 ng/μl. For each SNP, normalized genomic DNA was added to a cocktail of TaqMan® Genotyping Master Mix and TaqMan® fluorescently labelled probes (Vic and Fam) in a ratio of 2 μl, 2.5 μl, and 1.5 μl respectively (Thermo Fisher Scientific, Waltham, MA). The DNA/PCR mix was loaded into a Bio-Rad 384 well plate (Bio-Rad, Hercules, CA) to undergo PCR amplification. PCR was performed on the Applied Biosystems ViiA 7 Real-Time PCR System (Thermo Fisher Scientific, Waltham, MA) with the following cycling parameters: 30 second pre-read stage at 60.0°C, 10 minute initial denaturing step at 95.0°C, PCR stage consisting of a 92.0°C denature step alternating with a 60.0°C annealing step for 40 cycles, followed by a 30 second post-read stage at 60.0°C. Genotype calls were made using TaqMan® Genotyper Software v1.3.1 by Life Technologies (Thermo Fisher Scientific, Waltham, MA).

Table 2.

Allele frequency for single nucleotide polymorphisms associated with risk taking, substance use and/or mental health.
Allele Frequency (%)
Polymorphism Gene p-Value Alleles Majora Non-Hazardous Major Allele Non-Hazardous Minor Allele Hazardous Major Allele Hazardous Minor Allele
rs10499934 ACN9 0.86 A:G A 75.00 25.00 80.77 19.23
rs1229984 ADH1B 1.00 A:G G 96.43 3.57 96.15 3.85
rs1614972 ADH1C 0.94 C:T C 60.71 39.29 65.38 34.62
rs3762894 ADH4 0.88 C:T T 75.00 25.00 73.08 26.92
rs1042713 ADRB2 0.92 A:G G 64.29 35.71 69.23 30.77
rs2238151 ALDH2 0.50 C:T T 60.71 39.29 73.08 26.92
rs1800497 ANKK1/DRD 2 0.73 C:T C 78.57 21.43 84.62 15.38
rs6265 BDNF 0.25 A:G G 78.57 21.43 92.31 7.69
rs1455858 CHRM2 0.07 A:G G 53.57 46.43 80.77 19.23
rs324650 CHRM2 0.92 A:T A 64.29 35.71 69.23 30.77
rs1051730 CHRNA3 0.09 C:T C 85.71 14.29 61.54 38.46
rs578776 CHRNA3 1.00 C:T C 17.86 82.14 15.38 84.62
rs16969968 CHRNA5 0.09 A:G G 85.71 14.29 61.54 38.46
rs1948 CHRNB4 0.09 C:T C 46.43 53.57 73.08 26.92
rs806368 CNR1 0.26 C:T T 67.86 32.14 84.62 15.38
rs4680 COMT 0.59 A:G A 57.14 42.86 46.15 53.85
rs3779250 CRFR2 0.18 A:G A 78.57 21.43 57.69 42.31
rs1715747 CRHBP 0.44 A:G A 71.43 28.57 57.69 42.31
rs242924 CRHR1 0.98 A:C C 42.86 57.14 46.15 53.85
rs1876831 CRHR1 0.61 A:G G 89.29 10.71 96.15 3.85
rs27048 DAT1 0.56 C:T C 50.00 50.00 61.54 38.46
rs1076560 DRD2 0.85 A:C C 28.57 71.43 34.62 65.38
rs6280 DRD3 0.90 C:T T 67.86 32.14 73.08 26.92
rs7724086 GABBR2 1.00 C:T C 85.71 14.29 88.46 11.54
rs279871 GABRA2 0.80 A:G A 53.57 46.43 53.85 46.15
rs1234292 GABRB3 0.88 A:G A 75.00 25.00 73.08 26.92
rs3097489 GABRG3 0.62 A:G G 64.29 35.71 53.85 46.15
rs6296 HTR1B 0.84 C:G G 35.71 64.29 34.62 65.38
rs6313 HTR2A 0.82 C:T C 57.14 42.86 57.69 42.31
rs77982984 HTR2B b C:T C 100.00 0.00 100.00 0.00
rs5906898 MAOA 0.62 A:G G 67.86 32.14 57.69 42.31
rs230530 NFKB1 0.99 C:T T 50.00 50.00 46.15 53.85
rs16139 NPY 1.00 A:G A 96.43 3.57 100.00 0.00
rs6989250 OPRK1 b C:G C 100.00 0.00 100.00 0.00
rs1799971 OPRM1 1.00 A:G A 82.14 17.86 84.62 15.38
rs2235751 PDYN 0.46 A:G A 89.29 10.71 80.77 19.23
rs3785157 SLC6A2 0.90 C:T C 67.86 32.14 73.08 26.92
rs6350 SLC6A3 1.00 C:T C 92.86 7.14 92.31 7.69
rs1042173 SLC6A4 0.56 G:T T 50.00 50.00 61.54 38.46
rs4274850 TACR3 0.89 A:C A 78.57 21.43 80.77 19.23
rs1799913 TPH1 0.96 A:C C 57.14 42.86 61.54 38.46
rs1352252 TPH2 0.25 A:G A 50.00 50.00 69.23 30.77
5-HTTLPR SLC6A4 0.03 LA:S/LG LA 71.42 28.57 50.00 50.00
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a

major and minor allele designations determined from the database of SNPs; https://www.ncbi.nlm.nih.gov/snp;

b

unable to test.

LA = long form, S = short form, LG = long form (Ehli et al., 2012).

SERT 5-HTTLPR

A promoter polymorphism in solute carrier family 6 member 4 – SLC6A4 (Serotonin transporter or SERT) was also genotyped. The serotonin-transporter-linked polymorphic region (5-HTTLPR) is a functional polymorphism present in the promoter region of the serotonin transporter gene and has received intense scrutiny with several studies being carried out on its role in a diverse range of psychiatric disorders and phenotypes. The 5-HTTLPR is a variable number tandem repeat (VNTR) consisting of varying 20–23bp imperfect repeat sequences. Within the VNTR loci, a SNP (rs25531) specific to the long allele is also present. Functionally, the S (14-repeat) allele variant reduces the transcriptional efficiency of the promoter, leading to reduced expression and decreased serotonin uptake in cell lines when compared to the L (16-repeat) allele (Lesch et al., 1996). For a number of years, the 5-HTTLPR was considered functionally biallelic (e.g. either L or S). However, more recent studies have shown the 5-HTTLPR to be triallelic (S, LA, and LG) (Hu et al., 2006). The characterization of an A>G SNP (rs25531) within the L allele of 5-HTTLPR creates a functional AP2 transcription factor binding site and subsequently attenuates mRNA expression which has led to the consideration for this loci to be triallelic (S, LA, and LG) (Hu et al., 2006; Kraft et al., 2005). Expression from the LG allele has been shown to be equivalent to the S allele (Ehli et al., 2012; Hu et al., 2006), so for purposes of this study we designate two separate alleles for analysis – S & LG in one group and LA alleles in the second group based on transcriptional efficiency. 5-HTTLPR and rs25331 was genotyped using restriction length fragment polymorphism (RFLP) with MspI. Primer sequences for 5-HTTLPR were forward primer (5′ –ATGCCAGCACCTAACCCCTAATGT-3′) and the reverse primer (5′-GGACCGCAAGGTGGGCGGGA-3′). This primer pair amplifies a 419 base pair product for the 16-repeat L allele and a 375 base pair product for the 14-repeat S allele. PCR reactions were performed using a PCR Master Mix (Promega, Madison, WI, USA) containing a final concentration of 1.5 mM MgCl2, 1× reaction buffer, 200μM of each dNTP, 40ng purified genomic DNA, 1.25 units Taq DNA polymerase, and 5pmols of each primer in a 25μl reaction. PCR cycling conditions consisted of an initial denaturation at 95°C for 15 minutes, 35 cycles each consisting of 30 s at 94°C, 30 s at 66°C, and 40 s at 72°C. Elongation was continued for 15 min at 72°C after the last cycle. To genotype the single nucleotide polymorphism (A>G), 10μl of the PCR product was subjected to restriction fragment length polymorphism analysis (RFLP) with an MspI restriction digest. The LG polymorphism specific to the long allele introduces an additional MspI restriction site within the PCR product. Fragments were separated on a 2% agarose gel supplemented with ethidium bromide (0.02%). The fragments used to discriminate each genotype are as follows: LA (326 and 93); and LG (152, 174, and 93 base pairs); S (283 and 83 base pairs).

Data Analysis

Demographic and assessment data, in-scanner behavioral data, and SNP data were analyzed with Sigma Plot for Windows v13.0 (Systat Software Inc., San Jose, California, USA). Significance levels for all statistical tests was set at p < 0.05. Assessment measures were compared among non-hazardous and hazardous groups by t-test (Table 1). For those assessment tools that had a cut-off score to indicate greater risk or psychopathology, the number of participants in each group below and above the cut-off were also compared using separate Chi-squared tests. To determine an effect of hazardous alcohol use on behavioral measures, separate two-way mixed design ANOVAs were used (group × stimulus condition), with significant interactions further analyzed by Student-Neuman-Keuls (SNK) post-hoc tests for multiple comparisons. Differences in allele frequency for the 43 SNPs analyzed were compared among the two groups with separate Chi-squared tests. Due to the low subject number for SNP identification, the Chi-squared tests used for the SNP analysis were not corrected for multiple comparisons. Therefore, SNP findings should be treated as preliminary and require confirmation with a larger data set. Those SNPs that approached significance were further analyzed by grouping participants based on genotype (e.g. Nugent et al., 2012) and comparing AUDIT and ASSIST scores across the three genotypes using one-way ANOVA, followed by SNK post-hoc tests for multiple comparisons.

Anatomical and functional images were processed and analyzed using Brain Voyager for Windows v20.2 (Rainer Goebel, Brain Innovation, Maastricht, The Netherlands). Preprocessing of functional data included slice scan time correction (cubic spline interpolation with an ascending/interleaved scanning order), 3D motion correction (trilinear/sinc interpolation with reference to the first volume of each run), and temporal filtering (high-pass GLM-Fourier filter with two sines/cosines). Both functional and isovoxelized anatomical data were then coregistered, adjusted to the ACPC plane, and normalized to Talairach space. Following coregistration between functional and anatomical data, spatial smoothing (Gaussian kernel; full-width at half maximum (FWHM) = 8 mm) was also performed. All whole-brain analyses (ANOVA, t-tests) were conducted in Brain Voyager with a cluster detection threshold of 300 voxels at p < 0.001, and significant clusters were subjected to Monte Carlo simulation to correct for multiple comparisons with a false discovery rate of p < 0.05. This cluster thresholding approach (Forman et al., 1995) is recommended as a method to reduce false positives, increase localization, and aide in the accurate interpretation of fMRI results (Woo, Krishnan, & Wager, 2014).

The first level of whole-brain analysis consisted of one-way ANOVA for the geometic shape 2-back working memory task (one factor of group) and separate two-way mixed design ANOVAs (group × stimulus condition) for the 2-back working memory task with emotional load and the rating of emotional images. Significant clusters were further analyzed by extracting β values and applying SNK post-hoc tests for multiple comparisons. The relationship between psychological measures (CAST, AUDIT, BDI, BAI and PCL) and brain activity (β weights) were determined using multiple linear regression analysis. The second level of whole-brain analysis was concerned with patterns of activity that would characterize hazardous and non-hazardous alcohol use within each of the three tasks to assess task/condition effects within each group. Thus, condition effects on blood-oxygen level dependent (BOLD) activity across the brain within each group were analyzed by separate repeated measure ANOVAs followed by pairwise t-test comparisons contrasting each condition. Peak voxels were used to determine p values, while all other measures including anatomical location were determined from the center of the volume of interest.

Results

Substance Use, Mental and Physical Health, and Scholastic Performance

Table 1 highlights similarities and differences between ACoA college student assessment measures, based on whether they currently exhibit non-hazardous or hazardous alcohol use. The two groups were similar in meaningful ways, ruling out some confounding variables. All of the CAST scores were well above the criterion cut-off of 6, and did not significantly differ (Table 1). The proportion of participants with one vs. two parents/caregivers with alcohol problems was equal among the two groups (data not shown), important given that previous research suggests that adults with two alcoholic parents are at two-fold greater life-time risk in developing AUD as compared to adults with one alcoholic parent (Yoon et al., 2013). Finally, ACT and SAT scores did not significantly differ (Table 1).

As would be expected, hazardous alcohol participants had significantly higher AUDIT scores (Table 1), an effect mirrored by higher scores on the alcohol scale of the ASSIST (Table 1). Hazardous-use participants also reported significantly higher use of sedatives and higher use and risk for abuse of other substances such as tobacco and marijuana (Table 1). Furthermore, hazardous alcohol use participants had a lower current GPA as compared to non-hazardous use participants (Table 1).

With regards to psychological and physical health, hazardous alcohol use participants reported significantly poorer mental, social and general health, as well as perceived health, and increased disability (Table 1) on the Duke Health Profile. Participants engaged in hazardous alcohol use also reported higher anxiety and depression scores on the Beck scales and higher posttraumatic stress symptoms on the PCL as compared to non-hazardous alcohol group (Table 1). Furthermore, the percent of participants reaching the diagnostic criteria of the Beck Depression Inventory (Beck & Steer, 1996) and Beck Anxiety Inventory (Beck & Steer, 1993) were significantly higher among hazardous alcohol participants (X2 (3) = 33.98, p <0.001; Figure 2A and X2 (3) = 25.88, p <0.001; Figure 2B, respectively). The diagnosis score for PTSD within a civilian population is > 30 on the PCL (Weathers et al, 2013). The majority of participants in both groups reported scores above this criterion but a significantly greater proportion of participants engaged in hazardous alcohol use scored above 30 on the PCL (X2 (1) = 5.25, p = 0.022; Figure 2C).

Figure 2.

Figure 2

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Percentage of participants meeting criteria for assessment measures. Non-hazardous and hazardous alcohol users self-reported responses to several measures including (A) the Beck Depression Inventory (Score categories: Minimal = 0–13; Mild = 14–19; Moderate = 20–28 and Severe = 29–63); (B) the Beck Anxiety Inventory (Score categories: Normal = 0–9; Mild = 10–18; Moderate = 19–29 and Severe = 30–63), and (C) the Posttraumatic stress disorder (PTSD) Checklist (PCL; Score greater than 30 for the PTSD category). All three measures showed significantly different proportion of non-hazardous to hazardous alcohol participants in the different categories (see text for statistics).

Performance on Working Memory and Ranking Emotion Tasks

For the 2-back working memory task comprised of geometric shapes (Figure 1A), no significant differences were found between non-hazardous and hazardous groups in response time or total errors (Table S1). Similarly, there were no significant differences (main effects or interactions) in response time or total errors, false positives and misses between hazardous and non-hazardous groups or across conditions for the 2-back working memory task with emotional load (Figure 1B; Table S1).

Within the ranking emotion task (Figure 1C), images were ranked similarly by nonhazardous and hazardous groups (F(1,50) = 0.0886, p = 0.767). However, there was a significant difference in ratings across the three emotional conditions (F(2,50) = 166.425, p < 0.001; Figure 3), but no interactions between group and condition were found (F(2,50) = 1.267, p = 0.296).

Figure 3.

Figure 3

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Ranking of the emotional images used for the 2-back working memory task. Participants ranked the images on a reverse scale (0–7) with 7 being most negative. Non-hazardous and hazardous alcohol users ranked the images similarly, with rankings across the three conditions significantly different. Data represent mean ± SEM. *significant difference between the 3 conditions (neutral, negative and positive, p < 0.001).

Changes in BOLD Activity during Working Memory and Ranking Emotion Tasks

An examination of movement in all 6-planes (translation in X, Y, and Z and rotation about X, Y, and Z) revealed one participant exceeded movement of 1mm of translation or 1-degree of rotation in the majority of runs/trials and this participant was removed from further analysis (hazardous group). However, there were no significant differences in movement between experimental groups. Primary whole-brain analysis comparing the two groups within each of the three tasks revealed no significant group or group × condition effects for the working memory tasks. However, a group × condition effect was observed for the left lateral middle frontal gyrus (BA10), a component of the frontopolar cortex (Figure 4A), during the ranking emotion task (group × condition F(2,40) = 6.640, p = 0.003). Non-hazardous participants showed less left BA10 activity in the negative vs. both neutral and positive conditions (p = 0.032 and 0.028 respectively) which was not observed in the hazardous alcohol participants (p > 0.05; Figure 4B). Thus, hazardous alcohol participants exhibited greater left BA10 activity in the negative condition when compared to non-hazardous participants (p = 0.027; Figure 4B). Furthermore, multiple linear regression analysis revealed that left BA10 activity in the negative condition could be predicted from a linear combination of AUDIT (alcohol use/misuse) and BAI (anxiety) scores (F(5,18) = 3.431, p = 0.025; r2 = 0.502). However, no such relationship existed for these or any other psychological measure for left BA10 activity in the positive (F(5,18) = 0.932, p = 0.483; r2 = 0.206) or neutral (F(5,18) = 0.813; p = 0.556 r2 = 0.184) conditions.

Figure 4.

Figure 4

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(A) Sagittal and coronal views depicting a significant group × condition interaction during the ranking emotion task, localized to the left middle frontal gyrus (BA10). Voxels are overlaid on an averaged T1-weighted anatomical scan created from all participants. (B) Beta weights extracted from left BA10 during the 3 conditions of the ranking emotion task. #significantly different compared to all other conditions in the same group. *significantly different from the hazardous group in the same condition.

A priori secondary analysis examined task effects within the two groups. BOLD activity during the 2-back working memory task are detailed in Table S2. Figure S1 highlights several patterns of activity that differed between the non-hazardous and hazardous alcohol groups (vs. fixation). The non-hazardous alcohol group exhibited increased activity in the left precentral gyrus (Brodman’s area [BA] 6), and left inferior temporal gyrus (BA37) (Figure S1A), decreased activity in the right middle frontal gyrus (BA10) and right precuneus (BA31) (Figure S1B) as well as increased activity in the right fusiform gyrus (BA19) (Figure S1C). In contrast, the hazardous alcohol use group exhibited increased bilateral activity in the dorsal anterior cingulate (BA32) and decreased activity in the left posterior cingulate (BA23) (Figure S1D) as well as decreased activity in the right parahippocampal gyrus (BA36) (Figure S1E) and increased activity in the left insula (BA13) (Figure S1F).

When emotional load was added to the 2-back working memory task, different patterns of activity were noted throughout the brain as compared to the 2-back working memory task comprised of geometric shapes. These findings are detailed in Table S3, and Figures S2–S3 highlight the patterns of activity that differ between non-hazardous and hazardous alcohol groups. Within the negative condition (vs. neutral), the non-hazardous group exhibited increased activity in the right and left posterior cingulate (BA23 &31; Figure S2A–B) as well as the fusiform gyrus (BA37; Figure S2C). The non-hazardous group also exhibited increased activity in the left interior frontal gyrus (BA9 & 13; Figure S2D–E) as well as the right superior temporal gyrus (BA38; Figure S2F). However, within the same negative condition, the hazardous alcohol use group only exhibited an increase in activity in the right superior temporal gyrus (BA39; Figure S3) in a more caudal location than that exhibited by the non-hazardous group (compare to Figure S2F).

When participants were asked to rate the emotional valence of images comparable to those used in the 2-back working memory task with emotional load, the patterns of neural activity across the brain were quite distinct from those elicited by the similar images in the 2-back task. Significant findings from whole-brain analyses performed for each group are detailed in Table S4 and differences in patterns of neural activation between hazardous and nonhazardous alcohol groups are illustrated by Figure S4. The non-hazardous alcohol group exhibited increased activity in the left middle frontal gyrus (BA10), and also showed increased activity in the left anterior cingulate (BA32) and the right lingual gyrus (BA18) when negative and positive conditions were contrasted (Figure S4A–C respectively). The hazardous alcohol use group exhibited decreased activity in the middle temporal gyrus (BA39; Figure S4D) and the left middle occipital gyrus (BA18; Figure S4E) in negative vs. positive conditions. In addition, the hazardous alcohol use group showed reduced activity in the left posterior cingulate gyrus (BA31; Figure S4F) when the positive condition was contrasted with neutral.

Single Nucleotide Polymorphisms

A total of 43 polymorphisms from candidate genes were genotyped that have been shown to be related to risk taking, substance use, or mental health and the allele frequencies for each in the study sample are detailed in Table 2. The hazardous alcohol group had a higher frequency of alleles shown to have lower transcriptional activity (S & LG) of the serotonin transporter (SLC6A4) compared to the non-hazardous alcohol group (X2 (1) = 4.783, p = 0.029). No other significant differences were found in allele frequencies between hazardous and non-hazardous alcohol use groups for any other polymorphism examined in this preliminary study, however, rs1455858 (CHRM2; cholinergic muscarinic receptor 2), rs1051730 (CHRNA3; cholinergic nicotinic receptor alpha 3), rs16969968 (CHRNA5; cholinergic nicotinic receptor alpha 5) and rs1948 (CHRNB4; cholinergic nicotinic receptor beta4 subunit) all showed trends towards significance (Table 2). To determine whether there was a ‘dose-response’ or additive effect of alleles on alcohol use, AUDIT scores were compared across the three possible genotypes for each of these five polymorphisms. As illustrated by Figure 5, AUDIT scores were significantly higher for T/T carriers of the rs1051730 for CHRNA3 (F(1,24) = 5.094, p = 0.014; SNK p < 0.05; Figure 5A), for C/C carriers of the rs1948 (CHRNB4) (F(1,24) = 4.788, p = 0.018; SNK p < 0.05; Figure 5B) and for A/A carriers of the rs16969968 for CHRNA5 (F(1,24) = 5.094, p = 0.014; SNK p < 0.05; Figure 5C). No significant difference in AUDIT scores across the three genotypes were observed for rs1455858 (CHRM2) (F(1,24) = 0.464, p = 0.502) or for 5-HTTLPR (SLC6A4) (F(1,24) = 1.193, p = 0.321) (Figures 5D–E). Furthermore, ASSIST scores for tobacco use did not significantly differ across the genotypes for any SNP (p > 0.05).

Figure 5.

Figure 5

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Alcohol Use Disorders Identification Test scores (AUDIT; mean ± SEM) across genotypes for the SNPs associated with four cholinergic receptor genes and the serotonin transporter gene. (A) rs1051730 CHRNA3, cholinergic nicotinic receptor alpha 3; (B) rs1948 CHRNB4, cholinergic nicotinic receptor beta4 subunit; (C) rs16969968 CHRNA5, cholinergic nicotinic receptor alpha 5; (D) rs1455858 CHRM2, cholinergic muscarinic receptor 2; and (E) rs25531 SLC6A4, serotonin transporter. LA = long form, S + LG = transcriptionally repressed variants (Ehli et al., 2012). *significantly different from heterozygous genotype; #significantly different from other homozygous genotype (p < 0.05).

Discussion

Given there is limited research comparing psychobiological factors of ACoAs with and without problem alcohol use and the difficulties in college experienced by many ACoA students (Kelley et al., 2008; Hill et al., 2001; Schroeder & Kelley, 2008), this current study provides multiple important findings. In line with the study hypothesis, several differences in self-reported physical and mental health as well as patterns of neural activity during cognitive and emotion ranking tasks were observed in college ACoAs currently engaged in hazardous alcohol use (vulnerable individuals). This was despite the similarities in experiences with alcoholic parents/caregivers among ACoAs with and without problem alcohol use. These differences included lower college GPA, increased symptoms of anxiety, depression and PTSD, reduced physical and social health, and increased risky tobacco, marijuana and sedative use for ACoAs currently engaged in hazardous alcohol use. This was paralleled by distinct patterns of neural activity during cognitive tasks, regardless of the emotional load of the task among ACoA groups with and without hazardous alcohol use, suggesting either vulnerability to alcoholic problems and/or current alcohol status affects cognitive and emotional processing.

Psychological Measures

Although there was no significant difference in reported experiences with an alcoholic parent (CAST) between the two groups, those exhibiting hazardous alcohol use also reported higher scores on anxiety, depression and PTSD scales. The current study did not measure current and perceived stress, which is an important factor for future research – however increased PTSD symptom severity in the hazardous alcohol group may be indicative of greater current stress levels that could promote drinking behavior. Certainly, PTSD symptom severity is positively related to alcohol consumption and problems (Gaher et al., 2014). Prior research has also demonstrated that both alcohol and marijuana have been used to reduce a person’s unpleasant feelings (Simons et al, 2000). Specifically, college students have been found to be particularly at risk for using substances to manage distressful emotions (Park & Levenson, 2002). The hazardous-use participants in the current study reported not only risky alcohol use but significantly more tobacco, sedative and marijuana use than the non-hazardous alcohol group. Therefore, the current findings suggest the possibility that ACoAs with problem alcohol use may also use multiple substances to manage their higher negative psychological symptoms, which should be directly tested in future work.

Further, ACoAs engaged in hazardous alcohol use reported significantly lower social health on the DUKE than the non-hazardous group members. Our findings lend support to previous findings that a sense of social belonging was the most significant indicator of an ACoA not engaging in risky alcohol use (Lee & Williams, 2013) and a decreased sense of belonging is highly correlated with depressive symptoms (McLaren, Gomez, Bailey, & Van Der Horst, 2007). In addition to negative mental health outcomes, being an ACoA is also associated with adverse physical health (Balsa, Homer, & French, 2009). Prior research has found that ACoAs have an increased number of serious physician-diagnosed health problems (e.g., hypertension, ulcers, back problems, bowel issues) than control participants (Hart, Fiissel, & McAleer, 2003). Our findings show that hazardous alcohol using ACoAs self-assess as having poorer physical and perceived physical health status than their resilient counterparts, suggesting that current alcohol use is an important factor in adverse physical health associated with being an ACoA.

Neural Responses During Emotional Evaluation and Working Memory

Participants in the hazardous alcohol group exhibited higher activity in the left lateral BA10 (middle frontal gyrus) when rating negative stimuli as compared to the non-hazardous group. Furthermore, hazardous alcohol use and anxiety scores were positively associated with left BA10 activity during the negative condition. Traditionally activation of the medial rather than lateral BA10 of the frontopolar cortex is observed with emotional conditions (Gilbert et al., 2006). Instead, the lateral BA10 is associated with executive function, particularly the simultaneous monitoring and integration of multiple cognitive operations, working memory and episodic memory (Owen et al., 2005; Gilbert et al., 2006). More specifically, the lateral BA10 is thought to be involved in processing internally-generated, introspective information (Christoff & Gabrieli, 2000). Therefore, it is not surprising that the lateral BA10 is implicated autobiographical memory, with the left lateral BA10 recruited when individuals elaborate on personal memories during autobiographical memory retrieval (Daselaar et al., 2008). Greater activity in the left lateral BA10 of individuals with hazardous alcohol use and higher anxiety, depression and PTSD scores is thus suggestive of introspective and potentially autobiographical processing of the negative images that elicited this activity.

Similar to the current study, activity in left lateral BA10 region of the frontopolar cortex has been positively associated with anxiety levels in schizophrenic patients during the negative condition of a social rejection paradigm (Lee et al., 2014). Furthermore, activity of this specific region is also implicated as an important indicator of exposure therapy-induced reductions in PTSD symptoms (Fonzo et al., 2017). The current and past findings combined suggest that left lateral BA10 activity may serve as a potential biomarker of altered processing of negatively-valenced stimuli, and in the case of the current study, even in the absence of differences in rating the negativity of such stimuli.

The BA10 is one of the last cortical regions to maturate (Ramani & Owen, 2004) and is thus likely to be susceptible to modifications in structure and function as a result of perturbations up until early adulthood. Therefore, hazardous alcohol use in the young adults of the current study may be a causal factor in the altered activity of this brain region, which warrants further investigation to determine causality. In turn, heightened left lateral BA10 activity is likely to promote and maintain problem substance use. Camchong et al. (2014) show that those with substance use disorder who eventually relapsed over the course of the longitudinal study exhibited greater functional connectivity between the left frontopolar cortex and the nucleus accumbens as compared to those that did not relapse. As suggested by Fonzo et al., (2017), the lateral BA10 may be a transdiagnostic target that is easily accessible by transcranial magnetic stimulation, and the current study suggests that exploring this idea in at-risk young adults of alcoholic parents could be warranted in the future.

While no direct group differences in neural activity were observed for the working memory tasks, decreased activity in the right middle frontal gyrus (BA10) within the nonhazardous alcohol group was observed when performing the 2-back task without emotional load. This is in line with that seen in adolescence, where youth not engaged in hazardous alcohol use but with family history of AUD show lower activation of BA10 while performing working memory tasks (Cservenka et al., 2012; Mackiewicz et al., 2013). These observations, combined with lower activity in the BA10 when rating negative images for non-hazardous participants as discussed above, is suggestive of lower BA10 activity across a variety of tasks as being characteristic of resilience to problem alcohol use in ACoAs.

In addition to the BA10, the posterior cingulate cortex was also revealed as an important region with distinct neural activity depending on hazardous alcohol use in ACoA across the different tasks. During performance of a 2-back task with no emotional load and also when rating negative images, ACoA engaged in hazardous alcohol use showed reduced activity in the posterior cingulate (BA31 and 23), similar to observations of adolescents with family history of AUD (Spadoni et al, 2008). When the 2-back was performed with emotionally-valenced stimuli, non-hazardous alcohol use participants exhibited greater activity along the axis of the posterior cingulate (BA31-23) within the negative condition of the 2-back, that was absent in the hazardous alcohol use group. Of relevance to the current study population, reduced activation of the posterior cingulate during a decision-making task predicts methamphetamine relapse in dependent adults (Paulus et al., 2005). Thus, deactivation or lack of activation of the posterior cingulate during cognitive challenges or emotional evaluation within ACOAs engaged in hazardous alcohol use may promote continued alcohol use despite negative consequences.

Candidate Gene Polymorphisms

One partial explanation for psychological differences between hazardous alcohol and nonhazardous alcohol use ACoAs is that those who engage in hazardous alcohol use have a higher frequency of 5-HTTLPR alleles (SERT) with a reduced transcriptional activity (S/LG) when compared to the resilient group. These variants reduce the expression of the serotonin transporter resulting in decreased serotonin uptake (Lesch et al., 1996). Furthermore, these functional variants interact with early-life adversity to alter cognitive and emotional processing, stress reactivity and alcohol preferences in non-human primates (Champoux et al., 2002; Barr et al., 2003, 2004). In humans, several (but not all) studies have demonstrated the transcriptionally repressed variants (S/LG) are associated with greater risk of depression, particularly after stressful life events (Canli & Lesch, 2007) and with alcohol dependence (Thompson & Kenna, 2016). Furthermore, significant SERT gene × life-stress interactions for these variants have been observed in activity of brain regions implicated in the current study, including the superior temporal gyrus, inferior frontal gyrus, insula and anterior cingulate (Canli & Lesch, 2007). Therefore, psychological health, alcohol use and brain activity engaged during cognitive and emotional processing may differ between ACoAs with and without problem alcohol use partially due to serotonin transporter function. This hypothesis warrants further investigation in a larger sample size given the potential predictive value of serotonin transporter genotype for ACoA outcomes, including treatment efficacy. This is because these reduced transcriptional efficiency variants are associated with reduced responsiveness to serotonin-based antidepressant and cognitive-behavioral therapy (Bryant et al., 2010; Mushtaq et al., 2012; Thompson and Kenna, 2016).

Several SNP polymorphisms related to cholinergic receptors approached significance when compared in the hazardous and non-hazardous groups, including the muscarinic 2 receptor (rs1455858), and the nicotinic receptor alpha 3 (rs1051730), alpha 5 (rs16969968) and beta 4 (rs1948) subunits. Secondary analysis demonstrated that alcohol use (but not tobacco use) was significantly higher with specific SNP genotypes for these nicotinic receptor subunits, in that allele effects were additive and/or dominant. This suggests that polymorphisms of alpha 3, alpha 5 and beta 4 and nicotinic receptor subunit genes may increase the risk of problem alcohol use in ACoAs. Certainly, SNPs associated with these three nicotinic receptor subunits have received attention in the past, with each SNP associated with nicotine or alcohol dependence (Grucza et al., 2008; Joslyn et al., 2008; Wang et al., 2009; 2016; Bjorngaard et al., 2013) and collectively conferring risk for early initiation of tobacco and alcohol use (Schlaepfer et al., 2008a,b). Therefore, larger sample sizes should be employed to test the prediction that nicotinic receptor SNPs contribute to increasing the risk that an ACoA will engage in problem alcohol use.

Summary and Conclusions

College ACoAs who are currently engaged in hazardous alcohol use and at-risk for AUD can be distinguished from ACoAs who appear resistant to problem alcohol use by higher negative psychological symptoms and distinct patterns of activity across the brain in response to working memory and emotional processing tasks. Specifically, increased activation of lateral BA10 and deactivation (or lack of activation) of the posterior cingulate observed in working memory and emotional processing tasks characterizes college ACoAs currently engaged in hazardous alcohol use, which may promote further substance use and relapse in these individuals and represent treatment targets in the future. Furthermore, polymorphisms in the serotonin transporter 5-HTTLPR region and cholinergic receptor genes and their interaction with current and perceived stress may play a role in risk for alcohol and substance misuse and reduced psychological health within ACoAs currently engaged in hazardous alcohol use. How these variants may contribute to altered neural activity characteristic of vulnerable young adults is an important future direction. Several limitations of this study, including low sample size for the primary SNP analysis and lack of a comparison sample without familial history of AUD, limit the conclusions that can be made regarding whether the biological and psychological outcomes are a result of current alcohol use or alternatively actually confer risk for alcohol problems. However, the findings from this study point to several important psychological and neurobiological variables that distinguish ACoAs based on their current alcohol use, providing specific direction for future studies in determining psychobiological markers and targets that may be used for early intervention.

Supplementary Material

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Highlights.

  • Adult children of alcoholic parents (ACoA) have increased risk for alcohol problems

  • ACoA with alcohol problems have poorer mental, physical and social health

  • Increased activity of middle frontal gyrus seen in ACoC with alcohol problems

  • Nicotinic subunit gene variants may increase risk for alcohol problems in ACoA

Acknowledgments

This work was funded by a pilot grant from the Center for Brain and Behavior Research at the University of South Dakota, a South Dakota Governor’s Team Development Grant and a Summer Program for Undergraduate Research in Addiction (SPURA) fellowship to KP (NIH grant number R25-DA033674). The authors thank Dawne Olson and Taylor Bosch for valuable assistance with these studies and thankfully recognize the assistance of all individuals and volunteers whose participation was essential in the successful completion of the study. The authors also gratefully acknowledge the work of radiology staff at Avera Sacred Heart Hospital in Yankton, SD, and the University of South Dakota Human Functional Imaging Core.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Financial Disclosures

All authors report no biomedical financial interests or potential conflicts of interest.

Authors Contribution

KB-R, GD, EE, AS, LB and GF were responsible for the study concept and design. KB-R and SO conducted and analyzed the assessment and interview portion of this study, while JS, KF, KP LB and GF contributed to the acquisition and analysis of neuroimaging data. JS, NK, GD and EE performed the genetic analysis.. AS and LB assisted with data analysis and interpretation of findings. KB-R, JS and GF drafted the manuscript. All authors provided critical revision of the manuscript and approved the final version for publication.

References

  1. Acheson A, Robinson JL, Glahn DC, Lovallo WR, Fox PT. Differential activation of the anterior cingulate cortex and caudate nucleus during a gambling simulation in persons with a family history of alcoholism: studies from the Oklahoma Family Health Patterns Project. Drug and Alcohol Dependence. 2009;100:17–23. doi: 10.1016/j.drugalcdep.2008.08.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Anda RF, Whitfield CL, Felitti VJ, Chapman D, Edwards VJ, Dube SR, Williamson DF. Adverse childhood experiences, alcoholic parents, and later risk of alcoholism and depression. Psychiatr Serv. 2002;53(8):1001–1009. doi: 10.1176/appi.ps.53.8.1001. [DOI] [PubMed] [Google Scholar]
  3. Andrews MM, Meda SA, Thomas AD, Potenza MN, Krystal JH, Worhunsky P, Stevens MC, O’Malley S, Book GA, Reynolds B, Pearlson GD. Individuals family history positive for alcoholism show functional magnetic resonance imaging differences in reward sensitivity that are related to impulsivity factors. Biological Psychiatry. 2011;69:675–683. doi: 10.1016/j.biopsych.2010.09.049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Anton RF, Oroszi G, O’Malley S, Couper D, Swift R, Pettinati H, et al. An evaluation of mu-opioid receptor (OPRM1) as a predictor of naltrexone response in the treatment of alcohol dependence: results from the combined pharmacotherapies and behavioral interventions for alcohol dependence (COMBINE) study. Arch Gen Psychiatry. 2008;65(2):135–144. doi: 10.1001/archpsyc.65.2.135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Balsa AI, Homer JF, French MT. The health effects of parental problem drinking on adult children. The Journal of Mental Health Policy and Economics. 2009;12:55–66. [PubMed] [Google Scholar]
  6. Barr CS, Newman TK, Becke ML, Champoux M, Lesch KP, Suomi SJ, Goldman D, Higley JD. Serotonin transporter gene variation is associated with alcohol sensitivity in rhesus macaques exposed to early-life stress. Alcohol Clin Exp Res. 2003;27(5):812–817. doi: 10.1097/01.ALC.0000067976.62827.ED. [DOI] [PubMed] [Google Scholar]
  7. Barr CS, Newman TK, Lindell S, Shannon C, Champoux M, Lesch KP, Suomi SJ, Goldman D, Higley JD. Interaction between serotonin transporter gene variation and rearing condition in alcohol preference and consumption in female primates. Arch Gen Psychiatry. 2004;61(11):1146–1152. doi: 10.1001/archpsyc.61.11.1146. [DOI] [PubMed] [Google Scholar]
  8. Beck AT, Steer RA. Beck Anxiety Inventory Manual. San Antonio, TX: Psychological Corporation; 1993. [Google Scholar]
  9. Beck AT, Steer RA, Brown GK. Manual for the Beck Depression Inventory-II. San Antonio, TX: Psychological Corporation; 1996. [Google Scholar]
  10. Benzerouk F, Gierski F, Gorwood P, Ramoz N, Stefaniak N, Hübsch B, Limosin F. Brain-derived neurotrophic factor (BDNF) Val66Met polymorphism and its implication in executive functions in adult offspring of alcohol-dependent probands. Alcohol. 2013;47(4):271–274. doi: 10.1016/j.alcohol.2013.03.001. [DOI] [PubMed] [Google Scholar]
  11. Bevilacqua L, Goldman D. Genes and addictions. Clin Pharmacol Ther. 2009;85(4):359–361. doi: 10.1038/clpt.2009.6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Bjork JM, Knutson B, Hommer DW. Incentive-elicited striatal activation in adolescent children of alcoholics. Addiction. 2008;103:1308–1319. doi: 10.1111/j.1360-0443.2008.02250.x. [DOI] [PubMed] [Google Scholar]
  13. Bjørngaard JH, Gunnell D, Elvestad MB, Smith GD, Skorpen F, Krokan H, Romundstad P. The causal role of smoking in anxiety and depression: a Mendelian randomization analysis of the HUNT study. Psychological Medicine. 2013;43:711–719. doi: 10.1017/S0033291712001274. [DOI] [PubMed] [Google Scholar]
  14. Bryant RA, Felmingham KL, Falconer EM, Pe Benito L, Dobson-Stone C, Pierce KD, Schofield PR. Peliminary evidence of the short allele of the serotonin transporter gene predicting poor response to cognitive behavior therapy in posttraumatic stress disorder. Biol Psychiatry. 2010;67(12):1217–1219. doi: 10.1016/j.biopsych.2010.03.016. [DOI] [PubMed] [Google Scholar]
  15. Bradley MM, Lang PJ. The International Affective Picture System (IAPS) in the study of emotion and attention. In: Coan JA, Allen JJB, editors. Handbook of Emotion Elicitation and Assessment. Oxford University Press; 2007. pp. 29–46. [Google Scholar]
  16. Cacciaglia R, Nees F, Pohlack ST, Flor H. A risk variant for alcoholism in the NMDA receptor affects amygdala activity during fear conditioning in humans. Biological Psychology. 2013;94:74–81. doi: 10.1016/j.biopsycho.2013.05.006. [DOI] [PubMed] [Google Scholar]
  17. Camchong J, Macdonald AW, 3rd, Mueller BA, Nelson B, Specker S, Slaymaker V, Lim KO. Changes in resting functional connectivity during abstinence in stimulant use disorder: a preliminary comparison of relapsers and abstainers. Drug Alcohol Depend. 2014;139:145–151. doi: 10.1016/j.drugalcdep.2014.03.024. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Canli T, Lesch KP. Long story short: the serotonin transporter in emotion regulation and social cognition. Nat Neurosci. 2007;10(9):1103–1109. doi: 10.1038/nn1964. [DOI] [PubMed] [Google Scholar]
  19. Cao JX, Hu J, Ye XM, Xia Y, Haile CA, Kosten TR, et al. Association between the 5-HTR1B gene polymorphisms and alcohol dependence in a Han Chinese population. Brain Research. 2011;1376:1–9. doi: 10.1016/j.brainres.2010.12.039. [DOI] [PubMed] [Google Scholar]
  20. Champoux M, Bennett A, Shannon C, Higley JD, Lesch KP, Suomi SJ. Serotonin transporter gene polymorphism, differential early rearing, and behavior in rhesus monkey neonates. Mol Psychiatry. 2002;7(10):1058–1063. doi: 10.1038/sj.mp.4001157. [DOI] [PubMed] [Google Scholar]
  21. Ching-López A, Cervilla J, Rivera M, Molina E, McKenney K, Ruiz-Perez I, et al. Epidemiological support for genetic variability at hypothalamic-pituitary-adrenal axis and serotonergic system as risk factors for major depression. Neuropsychiatric Disease and Treatment. 2015;11:2743–2754. doi: 10.2147/NDT.S90369. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Christoff K, Gabrieli JD. The frontopolar cortex and human cognition: evidence for a rostrocaudal hierarchical organization within the human frontal cortex. Psychobiology. 2000;28:165–186. [Google Scholar]
  23. Clarke TK, Weiss AR, Ferarro TN, Kampman KM, Dackis CA, Pettinati HM, et al. The dopamine receptor D2 (DRD2) SNP rs1076560 is associated with opioid addication. Ann Hum Genet. 2014;78(1):33–39. doi: 10.1111/ahg.12046. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Cservenka A, Nagel BJ. Risky Decision-Making: An fMRI Study of Youth at High Risk for Alcoholism. Alcoholism: Clinical and Experimental Research. 2012;36:604–615. doi: 10.1111/j.1530-0277.2011.01650.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Dager AD, Anderson BM, Stevens MC, Pearlson GD. Influence of alcohol use and family history of alcoholism on neural response to alcohol cues in college drinkers. Alcoholism: Clinical and Experimental Research. 2013;37:E161–E171. doi: 10.1111/j.1530-0277.2012.01879.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Dale AM. Optimal experimental design for event-related fMRI. Hum Brain Mapp. 1998:109–114. doi: 10.1002/(SICI)1097-0193(1999)8:2/3&#x0003c;109::AID-HBM7&#x0003e;3.0.CO;2-W. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Daselaar SM, Rice HJ, Greenberg DL, Cabeza R, LaBar KS, Rubin DC. The spatiotemporal dynamics of autobiographical memory: neural correlates of recall, emotional intensity, and reliving. Cereb Cortex. 2008;18(1):217–229. doi: 10.1093/cercor/bhm048. [DOI] [PubMed] [Google Scholar]
  28. Dick DM, Edenberg HJ, Xuei X, Goate A, Kuperman S, Schuckit M, et al. Association of GABRG3 with alcohol dependence. Alcoholism: Clinical & Experimental Research. 2004;28(1):4–9. doi: 10.1097/01.ALC.0000108645.54345.98. [DOI] [PubMed] [Google Scholar]
  29. Dick DM, Aliev F, Wang JC, Saccone S, Hinrichs A, Bertelsen S. A systematic single nucleotide polymorphism screen to fine-map alcohol dependence genes on chromosome 7 identifies a novel susceptibility gene ACN9. Biological Psychiatry. 2008;63:1047–1053. doi: 10.1016/j.biopsych.2007.11.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Duke University. Duke Health Profile. 2012 Available at: http://healthmeasures.mc.duke.edu/images/DukeForm.pdf.
  31. Duncan J, Owen AM. Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends in Neuroscience Journal. 2000;23:475–483. doi: 10.1016/s0166-2236(00)01633-7. [DOI] [PubMed] [Google Scholar]
  32. Eddie D, Hunter-Reel DA, Epstein EE, Cohn AM. Pathways to vulnerability for alcohol problem severity in a treatment-seeking sample. Addictive Disorders & Their Treatment. 2015;14:82–94. doi: 10.1097/ADT.0000000000000045. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Ehli EA, Hu Y, Lengyel-Nelson T, Hudziak JJ, Davies GE. Identification and functional characterization of three novel alleles for the serotonin transporter-linked polymorphic region. Molecular Psychiatry. 2012;17(2):185–192. doi: 10.1038/mp.2010.130. [DOI] [PubMed] [Google Scholar]
  34. Fonzo GA, Goodkind MS, Oathes DJ, Zaiko YV, Harvey M, Peng KK, et al. Selective Effects of Psychotherapy on Frontopolar Cortical Function in PTSD. American Journal of Psychiatry. 2017 doi: 10.1176/appi.ajp.2017.16091073. in press. appi.ajp.2017.16091073. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Foroud T, Wetherill LF, Kramer J, Tischfield JA, Nurnberger JI, Jr, Schuckit MA, et al. The tachykinin receptor 3 is associated with alcohol and cocaine dependence. Alcoholism: Clinical & Experimental Research. 2008;32(6):1023–1030. doi: 10.1111/j.1530-0277.2008.00663.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Gaher RM, Simons JS, Hahn AM, Hofman NL, Hansen J, Buchkoski J. An experience sampling study of PTSD and alcohol-related problems. Psychol Addict Behav. 2014;28(4):1013–1025. doi: 10.1037/a0037257. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Gilbert SJ, Spengler S, Simons JS, Steele JD, Lawrie SM, Frith CD, Burgess PW. Functional specialization within rostral prefrontal cortex (area 10): a meta-analysis. J Cogn Neurosci. 2006;18(6):932–948. doi: 10.1162/jocn.2006.18.6.932. [DOI] [PubMed] [Google Scholar]
  38. Glen DC, Lovallo WR, Fox PT. Reduced amygdala activation in young adults at high risk of alcoholism: Studies from the Oklahoma Family Health Patterns Project. Biological Psychiatry. 2007;61:1306–1309. doi: 10.1016/j.biopsych.2006.09.041. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Goate AM, Edenberg HJ. The genetics of alcoholism. Current Opinion in Genetics & Development. 1998;8:282–286. doi: 10.1016/s0959-437x(98)80082-8. [DOI] [PubMed] [Google Scholar]
  40. Grucza RA, Wang JC, Stitzel JA, Hinrichs AL, Saccone SF, Saccone NL, Bierut LJ. A risk allele for nicotine dependence in CHRNA5 is a protective allele for cocaine dependence. Biological Psychiatry. 2008;64:922–929. doi: 10.1016/j.biopsych.2008.04.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Hart KE, Fiissel DL, McAleer M. Do adult offspring of alcoholics suffer from poor medical health? A three-group comparison controlling for self-report bias. The Canadian Journal of Nursing Research = Revue Canadienne De Recherche En Sciences Infirmières. 2003;35:53–72. [PubMed] [Google Scholar]
  42. Heitzeg MM, Nigg JT, Yau WYW, Zubieta JK, Zucker RA. Affective Circuitry and Risk for Alcoholism in Late Adolescence: Differences in frontostriatal responses between vulnerable and resilient children of alcoholic parents. Alcoholism: Clinical and Experimental Research. 2008;32:414–426. doi: 10.1111/j.1530-0277.2007.00605.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Heitzeg MM, Nigg JT, Yau WYW, Zucker RA, Zubieta JK. Striatal dysfunction marks preexisting risk and medial prefrontal dysfunction is related to problem drinking in children of alcoholics. Biol Psychiatry. 2010;68:287–295. doi: 10.1016/j.biopsych.2010.02.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Herman A, Balogh KN. Polymorphisms of the serotonin transporter and receptor genes: susceptibility to substance abuse. Substance Abuse and Rehabilitations. 2012;3(1):49–57. doi: 10.2147/SAR.S25864. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Hill SY, De Bellis MD, Keshavan MS, Lowers L, Shen S, Hall J, Pitts T. Right amygdala volume in adolescent and young adult offspring from families at high risk for developing alcoholism. Biological Psychiatry. 2001;49:894–905. doi: 10.1016/s0006-3223(01)01088-5. [DOI] [PubMed] [Google Scholar]
  46. Hu XZ, Lipsky RH, Zhu G, Akhtar LA, Taubman J, Greenberg BD, et al. Serotonin transporter promoter gain-of-function genotypes are linked to obsessive-compulsive disorder. Am J Hum Genet. 2006;78(5):815–826. doi: 10.1086/503850. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Hussong AM, Wirth RJ, Edwards MC, Curran PJ, Chassin LA, Zucker RA. Externalizing symptoms among children of alcoholic parents: Entry points for an antisocial pathway to alcoholism. J Abnorm Psychol. 2007;116(3):529–542. doi: 10.1037/0021-843x.116.3.529. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Jones JW. Children of Alcoholics Screening Test, (CAST) Lindenhurst, IL: Camelot Unlimited; 1983. [Google Scholar]
  49. Joslyn G, Brush G, Robertson M, Smith TL, Kalmijn J, Schuckit M, White RL. Chromosome 15q25.1 genetic markers associated with level of response to alcohol in humans. Proc Natl Acad Sci USA. 2008;105(51):20368–20373. doi: 10.1073/pnas.0810970105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Kareken D, Dzemidzic M, Wetherill L, Eiler W, Oberlin B, Harezlak J, Wang Y, O’Connor S. Family history of alcoholism interacts with alcohol to affect brain regions involved in behavioral inhibition. Psychopharmacology. 2013;228:335–345. doi: 10.1007/s00213-013-3038-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Kelley ML, Braitman A, Henson JM, Schroeder V, Ladage J, Gumienny L. Relationships among depressive mood symptoms and parent and peer relations in collegiate children of alcoholics. American Journal of Orthopsychiatry. 2010;80:204–212. doi: 10.1111/j.1939-0025.2010.01024.x. [DOI] [PubMed] [Google Scholar]
  52. Kertes DA, Kalsi G, Prescott CA, Kuo PH, Patterson DG, Walsh C, et al. Neurotransmitter and neuromodulator genes associated with a history of depressive symptoms in individuals with alcohol dependence. Alcoholism: Clinical and Experimental Research. 2011;35(3):496–505. doi: 10.1111/j.1530-0277.2010.01366.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Kitsantas P, Kitsantas A, Anagnostopoulou T. A cross-cultural investigation of college student alcohol consumption: A classification tree analysis. Journal of Psychology. 2008;142:5–20. doi: 10.3200/JRLP.142.1.5-20. [DOI] [PubMed] [Google Scholar]
  54. Klostermann K, Chen R, Kelley ML, Schroeder VM, Braitman AL, Mignone T. Coping behavior and depressive symptoms in adult children of alcoholics. Substance Use & Misuse. 2011;46:1162–1168. doi: 10.3109/10826080903452546. [DOI] [PubMed] [Google Scholar]
  55. Köhnke MD. Approach to the genetics of alcoholism: A review based on pathophysiology. Biochemical Pharmacology. 2008;75:160–177. doi: 10.1016/j.bcp.2007.06.021. [DOI] [PubMed] [Google Scholar]
  56. Kraft JB, Slager SL, McGrath PJ, Hamilton Sequence analysis of the serotonin transporter and associations with antidepressant response. Biological Psychiatry. 2005;58(5):374–381. doi: 10.1016/j.biopsych.2005.04.048. [DOI] [PubMed] [Google Scholar]
  57. LaBrie JW, Hummer JF, Pederson ER. Reasons for drinking in the college student context: The differential role and risk of the social motivator. Journal of Studies on Alcohol and Drugs. 2007;68:393–398. doi: 10.15288/jsad.2007.68.393. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Le Strat Y, Ramoz N, Pickering P, Burger V, Boni C, Aubin HJ, et al. The 3′ part of the dopamine transporter gene DAT1/SLC6A3 is associated with withdrawal seizure in patients with alcohol dependence. Alcoholism: Clinical & Experimental Research. 2008;32(1):27–35. doi: 10.1111/j.1530-0277.2007.00552.x. [DOI] [PubMed] [Google Scholar]
  59. Lee H, Williams RA. Effects of parental alcoholism, sense of belonging, and resilience on depressive symptoms: a path model. Substance Use & Misuse. 2013;48:265–273. doi: 10.3109/10826084.2012.754899. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Lee H, Ku J, Kim J, Jang DP, Yoon KJ, Kim SI, Kim JJ. Aberrant neural responses to social rejection in patients with schizophrenia. Soc Neurosci. 2014;9(4):412–423. doi: 10.1080/17470919.2014.907202. [DOI] [PubMed] [Google Scholar]
  61. Lesch KP, Bengel D, Heils A, Sabol SZ, Greenberg BD, Petri S, et al. Association of anxiety-related traits with a polymorphism in the serotonin transporter gene regulatory region. Science. 1996;274(5292):1527–1531. doi: 10.1126/science.274.5292.1527. [DOI] [PubMed] [Google Scholar]
  62. Li D, Sulovari A, Cheng C, Zhao H, Kranzler HR, Gelernter J. Association of gamma-aminobutyric acid A receptor a2 gene (GABRA2) with alcohol use disorder. Neuropsychopharmacology. 2014;39(4):907–918. doi: 10.1038/npp.2013.291. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Lind PA, Macgregor S, Agrawal A, Montgomery GW, Heath AC, Martin NG, et al. The role of GABRA2 in alcohol dependence, smoking, and illicit drug use in an Australian population sample. Alcoholism: Clinical & Experimental Research. 2008;32(10):1721–1731. doi: 10.1111/j.1530-0277.2008.00768.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Liu TT, Frank LR, Wong EC, Buxton RB. Detection power, estimation efficiency, and predictability in event-related fMRI. Neuro-Image. 2001;13:759–773. doi: 10.1006/nimg.2000.0728. [DOI] [PubMed] [Google Scholar]
  65. Macgregor S, Lind PA, Bucholz KK, Hansell NK, Madden PA, Richter MM, et al. Associations of ADH and ALDH2 gene variation with self report alcohol reactions, consumption and dependence: an integrated analysis. Human Molecular Genetics. 2009;18(3):580–593. doi: 10.1093/hmg/ddn372. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Mackiewicz Seghete KL, Cservenka A, Herting MM, Nagel BJ. Atypical spatial working memory and task-general brain activity in adolescents with a family history of alcoholism. Alcoholism: Clinical & Experimental Research. 2013;37:390–398. doi: 10.1111/j.1530-0277.2012.01948.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Mackrill T, Hesse M. The Adult Children of Alcoholics Trauma Inventory. Substance Use & Misuse. 2011;46:1099–1104. doi: 10.3109/10826084.2011.561466. [DOI] [PubMed] [Google Scholar]
  68. Marcos M, Pastor I, de la Calle C, Barrio-Real L, Laso FJ, González-Sarmiento R. Cannabinoid receptor 1 gene is associated with alcohol dependence. Alcoholism: Clinical & Experimental Research. 2012;36(2):267–271. doi: 10.1111/j.1530-0277.2011.01623.x. [DOI] [PubMed] [Google Scholar]
  69. McLaren S, Gomez R, Bailey M, VanDerHorst RK. The association of depression and sense of belonging with suicidal ideation among older adults: Applicability of resiliency models. Suicide and Life-Threatening Behavior. 2007;37:89–102. doi: 10.1521/suli.2007.37.1.89. [DOI] [PubMed] [Google Scholar]
  70. Mushtaq DA, Ali A, Margoob MA, Murtaza I, Andrade C. Association between serotonin transporter gene promoter-region polymorphism and 4- and 12-week treatment response to sertraline in posttraumatic stress disorder. J Affect Disord. 2012;136(3):955–962. doi: 10.1016/j.jad.2011.08.033. [DOI] [PubMed] [Google Scholar]
  71. Neighbors C, Larimer ME, Geisner I, Knee C. Feeling controlled and drinking motives among college students: Contingent self-esteem as a mediator. Self & Identity. 2004;3:207–224. doi: 10.1080/13576500444000029. [DOI] [Google Scholar]
  72. Nugent NR, Lally MA, Brown L, Knopik VS, McGeary JE. OPRM1 and diagnosis-related posttraumatic stress disorder in binge-drinking patients living with HIV. AIDS Behav. 2012;16(8):2171–2180. doi: 10.1007/s10461-011-0095-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Owen AM, McMillan KM, Laird AR, Bullmore E. N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies. Human Brain Mapping. 2005;25:46–59. doi: 10.1002/hbm.20131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Palmer RH, Brick L, Nugent NR, Bidwell LC, McGeary JE, Knopik VS, et al. Examining the role of common genetic variants on alcohol, tobacco, cannabis and illicit drug dependence: genetics of vulnerability to drug dependence. Addiction. 2015;110(3):530–537. doi: 10.1111/add.12815. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Park CL, Levenson MR. Drinking to cope among college students: prevalence, problems and coping processes. Journal of Studies on Alcohol. 2002;63:486–497. doi: 10.15288/jsa.2002.63.486. [DOI] [PubMed] [Google Scholar]
  76. Paulus MP, Tapert SF, Schuckit MA. Neural activation patterns of methamphetamine dependent subjects during decision making predict relapse. Arch Gen Psychiatry. 2005;62:761–768. doi: 10.1001/archpsyc.62.7.761. [DOI] [PubMed] [Google Scholar]
  77. Porter SR, Pryor J. The effects of heavy episodic alcohol use on student engagement, academic performance, and time use. Journal of College Student Development. 2007;48:455–467. [Google Scholar]
  78. Ramnani N, Owen AM. Anterior prefrontal cortex: insights into function from anatomy and neuroimaging. Nat Rev Neurosci. 2004;5(3):184–194. doi: 10.1038/nrn1343. [DOI] [PubMed] [Google Scholar]
  79. Ratsma JE, van der Stelt O, Schoffelmeer AN, Westerveld And A, Boudewijn Gunning W. P3 event-related potential, dopamine D2 receptor A1 allele, and sensation-seeking in adult children of alcoholics. Alcoholism, Clinical and Experimental Research. 2001;25(7):960–967. [PubMed] [Google Scholar]
  80. Schlaepfer IR, Hoft NR, Ehringer MA. The genetic components of alcohol and nicotine co-addiction: from genes to behavior. Curr Drug Abuse Rev. 2008a;1:124–134. doi: 10.2174/1874473710801020124. [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Schlaepfer IR, Hoft NR, Collins AC, Corley RP, Hewitt JK, Hopfer CJ, et al. The CHRNA5/A3/B4 gene cluster variability as an important determinant of early alcohol and tobacco initiation in young adults. Biol Psychiatry. 2008b;63(11):1039–1046. doi: 10.1016/j.biopsych.2007.10.024. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Schroeder VM, Kelley ML. The influence of family factors on the executive functioning of adult children of alcoholics in college. Family Relations. 2008;57:404–414. doi: 10.1111/j.1741-3729.2008.00509.x. [DOI] [Google Scholar]
  83. Schweinsburg AD, Paulus MP, Barlett VC, Killeen LA, Caldwell LC, Pulido C, Brown SA, Tapert SF. An fMRI study of response inhibition in youths with a family history of alcoholism. Annals of the New York Academy of Sciences. 2004;1021:391–394. doi: 10.1196/annals.1308.050. [DOI] [PubMed] [Google Scholar]
  84. Sherva R, Kranzler HR, Yu R, Logue MW, Poling J, Arias AJ, Gelernter J. Variation in nicotinic acetylcholine receptor genes is associated with multiple substance dependence phenotypes. Neuropharmacology. 2010;35:1921–1931. doi: 10.1038/npp.2010.64. [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Shin LM, Whalen PJ, Pitman RK, Bush G, Macklin ML, Lasko NB, Orr SP, McInerney SC, Rauch SL. An fMRI study of anterior cingulate function in posttraumatic stress disorder. Biol Psychiatry. 2001;50:932–942. doi: 10.1016/s0006-3223(01)01215-x. [DOI] [PubMed] [Google Scholar]
  86. Simons J, Correia CJ, Carey KB. A comparison of motives for marijuana and alcohol use among experienced users. Addictive Behaviors. 2000;25:153–160. doi: 10.1016/s0306-4603(98)00104-x. [DOI] [PubMed] [Google Scholar]
  87. Spadoni AD, Norman AL, Schweinsburg AD, Tapert SF. Effects of family history of alcohol use disorders on spatial working memory BOLD response in adolescents. Alcoholism: Clinical and Experimental Research. 2008;32:1135–1145. doi: 10.1111/j.1530-0277.2008.00694.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Thompson MD, Kenna GA. Variation in the serotonin transporter gene and alcoholism: risk and response to pharmacotherapy. Alcohol. 2016;51(2):164–171. doi: 10.1093/alcalc/agv090. [DOI] [PMC free article] [PubMed] [Google Scholar]
  89. Vereczkei A, Demetrovics Z, Szekely A, Sarkozy P, Antal P, Szilagyi A, et al. Multivariate analysis of dopaminergic gene variants as risk factors of heroin dependence. PLoS ONE. 2013;8(6):1–11. doi: 10.1371/journal.pone.0066592. [DOI] [PMC free article] [PubMed] [Google Scholar]
  90. Wang JC, Grucza R, Cruchaga C, Hinrichs AL, Bertelsen S, Budde JP, Goate AM. Genetic variation in the CHRNA5 gene affects mRNA levels and is associated with risk for alcohol dependence. Molecular Psychiatry. 2009;14:501–510. doi: 10.1038/mp.2008.42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  91. Wang Q, Li S, Pan L, Li H, Yang X, Jiang F, et al. Association between variants in nicotinic acetylcholine receptor genes and smoking cessation in a Chinese rural population. Am J Addict. 2016;25(4):297–300. doi: 10.1111/ajad.12383. [DOI] [PubMed] [Google Scholar]
  92. Weathers FW, Litz BT, Keane TM, Palmieri PA, Marx BP, Schnurr PP. The PTSD Checklist for DSM-5 (PCL-5) 2013 Available at: www.ptsd.va.gov.
  93. Weiland BJ, Welsh RC, Yau WYW, Zucker RA, Zubieta JK, Heitzeg MM. Accumbens functional connectivity during reward mediates sensation-seeking and alcohol use in high-risk youth. Drug and Alcohol Dependence. 2013;128:130–139. doi: 10.1016/j.drugalcdep.2012.08.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  94. Wetherill L, Schuckit MA, Hesselbrock V, Xuei X, Liang T, Dick DM, et al. Neuropeptide Y receptor genes are associated with alcohol dependence, alcohol withdrawal phenotypes, and cocaine dependence. Alcoholism: Clinical & Experimental Research. 2008;32(12):2031–2040. doi: 10.1111/j.1530-0277.2008.00790.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  95. Whalen PJ, Bush G, McNally RJ, Wilhelm S, McInerney SC, Jenike MA, Rauch SL. The emotional counting Stroop paradigm: a functional magnetic resonance imaging probe of the anterior cingulate affective division. Biol Psychiatry. 1998;44:1219–1228. doi: 10.1016/s0006-3223(98)00251-0. [DOI] [PubMed] [Google Scholar]
  96. Woo CW, Krishnan A, Wager TD. Cluster-extent based thresholding in fMRI analyses: pitfalls and recommendations. Neuroimage. 2014;91:412–419. doi: 10.1016/j.neuroimage.2013.12.058. [DOI] [PMC free article] [PubMed] [Google Scholar]
  97. Woodford M, Buser JK, Riccobono L, Bartuska R. A collegiate children of alcoholics/addicts psychoeducational group. In: Fitch T, Marshall JL, Fitch T, Marshall JL, editors. Group work and outreach plans for college counselors. Alexandria, VA, US: American Counseling Association; 2011. pp. 161–169. [Google Scholar]
  98. World Health Organization. Alcohol, Smoking, and Substance Involvement Test. 2010 Available at: http://apps.who.int/iris/bitstream/10665/44320/1/9789241599382_eng.pdf.
  99. World Health Organization. Alcohol Use Disorders Identification Test. 1982 Available at: http://whqlibdoc.who.int/hq/2001/who_msd_msb_01.6a.pdf.
  100. Xu K, Seo D, Hodgkinson C, Hu Y, Goldman D, Sinha R. A variant on the kappa opioid receptor gene (OPRK1) is associated with stress response and related drug craving, limbic brain activation, and cocaine relapse risk. Transl Psychiatry. 2013;3(8):e292. doi: 10.1038/tp.2013.62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  101. Yau WYW, Zubieta JK, Weiland BJ, Samudra PG, Zucker RA, Heitzeg MM. Nucleus accumbens response to incentive stimuli anticipation in children of alcoholics: Relationships with precursive behavioral risk and lifetime alcohol use. Journal of Neuroscience. 2012;32:2544–2551. doi: 10.1523/JNEUROSCI.1390-11.2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  102. Yoon G, Westermeyer J, Kuskowski MA, Nesheim L. Impact of the number of parents with alcohol use disorder on alcohol use disorder in offspring: a population-based study. The Journal of Clinical Psychiatry. 2013;74:795–801. doi: 10.4088/JCP.13m08350. [DOI] [PubMed] [Google Scholar]

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Supplementary Materials

1
NIHMS909474-supplement-1.pdf (222.5KB, pdf)
2
NIHMS909474-supplement-2.pdf (294KB, pdf)
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NIHMS909474-supplement-3.pdf (91.2KB, pdf)
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NIHMS909474-supplement-4.pdf (281.1KB, pdf)
5
NIHMS909474-supplement-5.docx (17.2KB, docx)
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NIHMS909474-supplement-6.docx (16.3KB, docx)
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NIHMS909474-supplement-7.docx (17.8KB, docx)
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NIHMS909474-supplement-8.docx (15.9KB, docx)

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