Neurophysiological Correlates and Differential Drug Response in Subjects With a Family History of an Alcohol Use Disorder

Abstract

A family history of an alcohol use disorder (AUD) has been shown to increase one’s risk of developing an AUD. Additionally, a positive family history of AUD (family history positive (FHP)) has neurobiological and neuropsychopharmacological consequences, and this review summarizes differential drug response as well as neuroanatomical and neurocognitive correlates. FHP status is related to altered responses to a number of drugs, including substances with abuse liability like alcohol, opioids, amphetamines, and ketamine. FHP individuals demonstrate fewer aversive effects and more rewarding response to both alcohol and subanesthetic dose ketamine. Ketamine is a rapid-acting antidepressant, and several studies have reported that ketamine is more effective for FHP treatment-resistant depressed individuals. In short, the reviewed neurophysiological differences may contribute to ketamine’s enhanced antidepressant efficacy in FHP patients. Volumetric differences in the amygdala, nucleus accumbens, neocortex, and cerebellum are commonly reported. Furthermore, FHP has also been associated with altered neurocognitive performance, e.g., increased impulsivity. The imaging and psychological literature supports a neurodevelopmental lag hypothesis in FHP youth. The review will further discuss these findings in depth.

Keywords

family history of alcohol dependence familial alcoholism ketamine neuroimaging treatment-resistant depression

Introduction

The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition defines alcohol use disorder (AUD) as the presence of at least 2 of 11 possible symptoms including alcohol tolerance, withdrawal symptoms, alcohol craving, unsuccessful attempts to stop or reduce drinking, and frequently drinking more than intended.¹ AUDs are highly prevalent disorders, with a 12-month prevalence rate of 12.7% among adults in the United States,² with men being more likely to have an AUD than women.^1,2 Of the global population, the estimated 12-month prevalence rate of AUD is 3.6% (of people aged 15–64), indicating AUDs are more common in the United States than other countries.¹ AUDs are associated with negative outcomes such as work absences, low work productivity, homelessness, suicide, work and vehicular accidents, and violence. Worldwide, it is estimated that 3.8% of all deaths are alcohol-related.¹ Additionally, chronic alcohol use is related to a number of neurocognitive impairments³ and changes in brain structure and connectivity.⁴ Several factors lead to the development of AUDs, with one of the most influential factors being genetics.

AUDs are highly heritable.^5–8 Heritability estimates range from 30% to 60%^1,6,8,9 with shared environment with someone affected with an AUD explaining an additional 10% of risk.⁸ Having a family history of an AUD (family history positive (FHP)) is also linked to differences in brain structure, cognitive functioning, and social outcomes.¹⁰ FHP is frequently studied as an at-risk biomarker, with newer research examining the genetic and neurobiological mechanisms by which heritable risk for AUD is transmitted, as well as FHP in relation to response to substances.^10,11

Operational definitions of FHP status vary from study to study. Early researchers identified FHP individuals as sons of alcoholic fathers.^12,13 Many studies have looked at family history density, i.e., quantity and/or relatedness of affected family members, and categorized subjects as either high risk (HR) or low risk (LR) for AUD, rather than dichotomizing presence or absence of family history.^14,15 With that said, researchers have typically defined FHP as having at least one first-degree relative with an AUD and/or at least one or two second-degree relatives with an AUD. As corollary, family history negative (FHN) individuals have been defined as all first (and sometimes also second)-degree relatives are without an AUD.^16,17 Methods for determining family history have varied as well. For example, many studies use family history questionnaires delivered to the participant, others ask informants knowledgeable of participant family history (often relatives), and others have identified individuals who meet AUD criteria and then studied their relatives/offspring.¹⁷

Variety in operational definitions aside, several differences regarding FHP individuals are identified in the literature. This review summarizes these findings. After a brief discussion of stress and psychosocial impairment, we review drug response in FHP subjects with a particular emphasis on responsiveness to alcohol and ketamine. We then review neuroanatomical and neurocognitive correlates of FHP.

Increased Stress and Psychosocial Impairment Associated With FHP

FHP is linked to a number of stressful life experiences. In a qualitative study of adult FHP experiences with their alcoholic parents, participants described violence and increased abuse towards spouses, siblings, and self when their AUD parent was intoxicated as well as parentification, where the child is placed into the caregiver role for their parent(s) and/or siblings.¹⁸ FHP subjects described poor relationships with the alcoholic parent and other family members.¹⁸ FHP youth demonstrate increased externalizing and internalizing problems, poorer academic performance, increased mental disorders, and lower social competence.¹⁹ Externalizing behaviors and poorer self-regulation have been reported as young as one year old.¹⁹ These negative outcomes continue to manifest into adulthood. FHP adults report continued difficulties with decreased self-esteem,^18,19relationships,¹⁸ interpersonal anxiety,¹⁹ adjustment skills,¹⁸ and poor academic performance.¹⁹

Another notable psychosocial impairment associated with FHP is increased rates of substance use disorders (SUD). In probands with AUD, 8.3% of first-degree relatives had a drug use disorder (not including AUD or nicotine use disorder) and 35.5% had AUD. In control probands, only 3.5% of relatives had a drug use disorder and 14.9% had AUD.⁷ FHP adults also drink more alcohol in response to stress compared to their FHN counterparts.²⁰ This increase rate of alcohol consumption may be due to differential responsiveness, which will now be discussed.

FHP and Differential Drug Response

Alcohol

It comes as no surprise that FHP individuals respond differently to alcohol and alcohol-related cues. FHP subjects appear to be immediately more sensitive to alcohol consumption but quickly develop acute tolerance to its intoxicating effects.²¹ Specifically, Morzorati et al.²¹ utilized the alcohol clamp method, which entails administering intravenous (IV) alcohol to subjects until desired blood alcohol content (BAC) is reached. Once target BAC is achieved, researchers continually acquire breathalyzer readings and adjust the alcohol infusion rates accordingly to maintain (or “clamp”) desired BAC. FHP participants reported feeling more intoxicated during the ramp phase compared to FHN participants. However, as the clamp was maintained, FHP participants seemed to become more tolerant, i.e., reported feeling less “ascending limb” intoxication effects like euphoria and disinhibition, compared to FHN participants.²¹ This acute tolerance may be another mechanism by which the risk of alcoholism leads to increased consumption, as FHP subjects required progressively greater amounts of alcohol to maintain rewarding intoxicating effects.

As alluded to above, there is an accepted biphasic model of alcohol response: the “ascending limb,” which occurs as BAC rises, and descending limb, which occurs as BAC falls. Subjective stimulant effects of alcohol are more common in the ascending limb, while subjective sedative effects are more common in the descending limb.²² FHP individuals endorse more stimulant effects in response to alcohol.^23,24 When examined in light versus heavy drinkers, heavy drinkers reported stimulant effects shortly after consuming alcohol and, as the BAC fell, experienced less sedative effects than other groups.²⁵

Schuckit and coworkers^26–28 have coined the term “low level of response” (LLR) primarily to describe FHP individuals having attenuated responsivity to alcohol challenge (and greater risk of progression to AUD). For example, those subjects that demonstrated LLR as measured by subjective reports of intoxication, body sway, hormone levels, and electrophysiological/myological testing were more likely to be FHP and subsequently develop AUDs.^27,28 In sum, with the notable exception of the Morzorati et al.²¹ study finding that FHP individuals have a higher level of response in the initial phase of the alcohol clamp, these studies provide compelling evidence linking family history of AUD, LLR, and alcohol dependence.

Further, alcohol expectancies have been shown to correlate with their reported effects. In one study, FHN participants were generally more sensitive to alcohol than FHP counterparts; however, expectancies of alcohol predicted FHP participants’ reports of intoxication, while BAC predicted self-reported intoxication of the FHN participants.²⁹ Furthermore, FHP young adults had fewer negative associations with alcohol than their FHN counterparts; however, the two groups did not differ on perceived positive consequences of alcohol use.³⁰ FHP individuals also respond differently to alcohol-related cues. Specifically, FHP female social drinkers have increased salivary response,³¹ and FHP individuals have increased activity in medial prefrontal cortex (PFC), posterior cingulate, orbitofrontal, and inferior temporal cortices,^32,33 fusiform gyrus,³⁴ and hippocampus³⁴ in response to a variety of alcohol-related cues. Even in college samples, where FHP and FHN participants’ rates of alcohol consumption are similar, FHP participants experienced more AUD symptoms, drug involvement, hangovers, and other alcohol-related problems.³⁵ Taken together, the familial transmission of AUD may be partly driven by FHP individuals’ alcohol-related expectancies and altered responsivity to its biphasic effects.

Marijuana

In cannabis use disorder probands, 36% of first-degree relatives had AUD, which is remarkably similar to the rate of AUDs among relatives of probands with an AUD (35.5%).⁷ This is in contrast to probands with opioid, cocaine, or without drug use disorders, for whom 14.5% to 18.1% of relatives had AUDs. These findings indicate that these two SUDs are more strongly correlated than other SUDs.⁷ Additionally, when FHP subjects were challenged with IV Δ⁹-tetrahydrocannabinol (THC), they reported feeling more “high” than did subjects in the FHN group. However, FHP participants did not exhibit increased cognitive or physical effects in response to cannabis.³⁶ This study suggests that the cannabinoid receptor (CB1R), through which Δ9-ΤΗC produces its central effects, may be linked to the heritability of AUDs.³⁶ Further research examining the relationship between cannabis response and family history of AUD is warranted.

Other substances

FHP adults with AUD may respond better to the mu-opioid receptor antagonist naltrexone, a United States Food and Drug Administration (FDA)-approved treatment for AUD.³⁷ In FHP nontreatment seeking, alcohol-dependent adult males, pretreatment with one dose of naltrexone (100 mg) reduced the number of drinks selected during a task in which participants were given the choice to select money or alcohol. This suggests altered opioid receptor responsivity in FHP males.³⁷ Additionally, FHP adults reported more negative subjective effects such as anxiety, distrust, and dizziness in response to IV amphetamine challenge.¹⁰ Because amphetamines produce euphoric effects primarily through dopaminergic neurotransmission, increased dopamine receptor availability in FHP individuals may contribute to this increase in unpleasant effects with amphetamines.¹⁰ Even preferences for sugar have been linked to family history of AUD. FHP participants are more than twice as likely to have a “sweet tooth” than FHN individuals,³⁸ and FHP participants demonstrate greater activation in the right amygdala in response to oral sucrose.³⁹

Ketamine

Ketamine is a glutamate modulator and an FDA-approved dissociative anesthetic,⁴⁰ and the FDA has recently approved esketamine for the treatment of resistant depression (TRD).⁴¹ Ketamine is a derivative of phencyclidine (PCP), which was synthetically developed to provide a less psychotomimetic alternative for anesthesia.⁴² Ketamine and its metabolites are central nervous system penetrant, and the parent racemic compound has a relatively short half-life. Ketamine is metabolized in the liver by cytochromes (CYP) 3A and 2B6.⁴² Because of extensive first-pass metabolism by the liver, oral ketamine has poor bioavailability. Thus, nonparenteral—intranasal, intramuscular, and IV—forms are the preferred means of administration. Ketamine is a noncompetitive antagonist of N-methyl-D-aspartate (NMDA) receptors.⁴² Interestingly, subanesthetic doses of ketamine do not have an affinity for gamma-aminobutyric acid receptors in the human brain, which is unlike most other analgesics.⁴² In higher doses, ketamine produces a trance-like dissociative state and acts as an anesthetic. In lower doses, it can relieve pain, is sedative, and can have mild psychotomimetic effects. Because of its transient hallucinogenic and tranquilizing effects, ketamine is often abused recreationally.^42,43 In studies that used subanesthetic doses of ketamine for TRD participants, shortly after one dose of ketamine, norketamine, dehydronorketamine, and hydroxynorketamine (HNK) were the primary metabolites present in blood plasma.⁴³ In mice studies, ketamine, norketamine, and HNK metabolites reached peak concentrations in the brain minutes after IV ketamine infusion.⁴³ These metabolites and their quick penetration of the blood–brain barrier may be responsible for ketamine’s neuropsychiatric effects.

Not everyone has the same response to ketamine. Specifically, FHP individuals display a differential response to ketamine (Table 1) as well as other NMDA receptor antagonists such as memantine.^44,45 FHP participants exhibited decreased psychotomimetic effects in response to ketamine, whereas FHN participants reported greater dysphoria, negative symptoms (e.g. psychomotor retardation and blunted affect), and dissociation.⁴⁴ Because both alcohol and ketamine are weak NMDA receptor antagonists, these findings suggest that aberrant NMDA receptor functioning may have a role in the transmission and development of AUDs.⁴⁴ Ketamine also has rapid-acting antidepressant effects in TRD and treatment-resistant bipolar disorder when administered at subanesthetic doses. Based on the differential profile described above, family history of AUD was studied as a potential predictor of treatment response with consistent demonstration of greater magnitude and maintenance of antidepressant efficacy in FHP unipolar and bipolar TRD subjects.^46–51 Table 1 provides an overview of research findings regarding differential response to ketamine in FHP individuals.

Table 1.

Differential effects of ketamine based on family history of alcohol use disorders.

	Study	Population	Findings
Psychotomimetic and dissociative effects	Petrakis et al. (2004)⁴⁴	Healthy subjects	FHP experienced ↓ perceptual alterations.
Psychotomimetic and dissociative effects	Luckenbaugh et al. (2012)⁴⁶	TR BD	FHP experienced ↓ dissociative effects.
Reward valence	Petrakis et al. (2004)⁴⁴	Healthy subjects	FHP experienced ↓ dysphoric symptoms.
	Phelps et al. (2009)⁴⁷	TR MDD	FHP experienced ↓ dysphoric symptoms.
	Yoon et al. (2016)⁹⁵	Healthy subjects	FHP reported ↑ ratio stimulant (rewarding): sedative (aversive) affects.
Antidepressant response	Phelps et al. (2009)⁴⁷	TR MDD	FHP had ↑ improvement of depressive symptoms 230-minutes post-infusion.
	Luckenbaugh et al. (2012)⁴⁶	TR BD	FHP had ↑ improvement of depressive symptoms for three days post-infusion.
	Permoda-Osip et al. (2014)⁵¹	TR BD	FHP had ↑ improvement of depressive symptoms 14 days post-infusion; 76% of FHP were responders versus 36% of FHN.
	Niciu et al. (2014)⁴⁸	TR BD and MDD	FHP had ↑ improvement of depressive symptoms one and seven days post-infusion.
	Niciu et al. (2014)⁴⁹	TR MDD	FHP had ↑ depression improvement and decreased relapse for up to four weeks.
	Pennybaker et al. (2017)⁵⁰	TR BD and MDD	All extended responders (four weeks post infusion) were FHP.
	Rong et al. (2018)⁹⁶ (systematic review)	TR BD and MDD	FHP status was the most replicated predictor of antidepressant response.

Note. FHP: family history positive; TR: treatment resistant; BD: bipolar disorder/depression; MDD: major depressive disorder, FHN: family history negative.

Our group is currently using a multimodal approach (including pharmacological imaging) to identify alcohol-sensitive biomarkers of antidepressant response to ketamine (Clinicaltrials.gov ID: NCT02122562). Many FHP neuroanatomical and neurocognitive correlates have been identified to date, and, we hypothesize, some may relate to antidepressant response to ketamine. We will now turn to these structural and functional differences.

FHP Neuroanatomical and Neurocognitive Correlates

Tessner and Hill⁵² reviewed the neural circuitry associated with risk factors for developing AUD, which they conceived as consisting of both internalizing and externalizing pathways. The externalizing pathway consists of altered cerebellothalamocortical circuitry and is characterized by behavioral disinhibition. The internalizing pathway, on the other hand, is characterized by aberrant reward sensitivity. The amygdala, nucleus accumbens (NAcc), hypothalamus, cingulate, and orbitofrontal cortex are implicated in the internalizing pathway.⁵² Our review supports these pathways, as altered amygdala, NAcc, cortical, and cerebellar structure and function are frequently reported in FHP participants. Additionally, neurocognitive differences associated with FHP, such as altered reward-sensitivity, impulsivity, and executive functioning, are typically associated with altered activation in these brain areas. We will now review structural brain differences in FHP individuals.

Neuroanatomical Differences

Magnetic resonance imaging (MRI) and functional MRI (fMRI) are commonly used methods for studying structural and functional brain differences, respectively, between FHP and FHN individuals. These neuroimaging modalities have identified differences in several brain structures including the amygdala, basal ganglia, neocortex, cerebellum, and hippocampus (Table 2).

Table 2.

Neuroanatomical and molecular differences based on family history of alcohol use disorders.

Structure	Studies	Findings
Amygdala	Hill et al. (2013)⁵³; Dager et al. (2014)⁹	FHP adults and adolescents have ↓ amygdala volumes.
	Benegal et al. (2007)⁵⁴; Hill et al. (2001)¹⁵	FHP adults and children have ↓ right amygdala volumes.
	Sjoerds et al. (2013)⁵⁶	No volume differences in adult sample.
	McHugh et al. (2010)⁵⁵	FHP children with short allele 5-HTTLPR have smallest amygdala.
Basal ganglia	Cservenka et al. (2015)⁵⁸	↑ FH density was related to ↑ NAcc volumes in adolescent females, not in males.
	Squeglia et al. (2014)¹⁷	No differences in NAcc volume in youth sample.
	Dager et al. (2014)⁹; Hill et al. (2013)⁵³; Benegal et al. (2007)⁵⁴	No volume differences in the putamen, caudate, or globus pallidus.
	Alvanzo et al. (2017)¹⁰	FHP adults have ↑ dopamine receptor availability in posterior caudate, anterior putamen, and right ventral striatum.
Neocortex	Benegal et al. (2007)⁵⁴	FHP adolescents had ↓ cingulate and superior frontal gyrus volumes.
	Sjoerds et al. (2013)⁵⁶; Dager et al. (2014)⁹	No volume differences were found in adult sample.
	Cohen-Gilbert et al. (2015)⁶¹	FHP adolescents had ↑ glutamine/glutamate ratio in anterior cingulate cortex. This was not found in adults.
	Jones and Nagel (2019)⁶⁵	FHP youth had ↓ FA in prefrontal cortex and ↓ MD in thalamus. This affect dissipated by late adolescence.
	Acheson et al. (2014)⁶⁴	FHP youths had ↓ FA in frontocortical areas.
Cerebellum	Hill et al. (2007)⁶⁶; Benegal et al. (2007)⁵⁴	FHP male youth and adults had ↑ gray matter volumes in cerebellum than FHN males.
	Hill et al. (2011)⁶⁷; Hill et al. (2016)⁶⁸	FHP adolescents and adults had ↑ cerebellar volumes.
	Cardenas et al. (2005)⁷⁰	FHP heavy drinkers lost ↓ gray and white matter volume due to chronic alcohol use than FHN heavy drinkers.
Hippocampus	Benegal et al. (2007)⁵⁴	FHP participants had ↓ hippocampal volume.
	Benegal et al. (2007)⁵⁴; Sjoerds et al. (2013)⁵⁶	FHP participants had ↓ parahippocampal gyrus volume.
	Hanson et al. (2010)⁷¹	FHP adolescent males had ↑ left hippocampal volume than FHN males. No differences in adolescent females.
	Dager et al. (2014)⁹; Hill et al. (2001)¹⁵	No volume differences.

Note. FHP: family history positive; FH: family history; NAcc: nucleus accumbens; FA: fractional anisotropy; MD: mean diffusivity; FHN: family history negative.

Amygdala

One of the most well-replicated neuroimaging findings in FHP individuals is abnormal amygdala volume. Several studies have found that FHP individuals have smaller amygdala than FHN individuals,^9,53 whereas other studies found this difference only in the right amygdala.^15,54 Smaller amygdala have been detected as early as childhood⁵⁴ and persist after matching for IQ and socioeconomic status.¹⁵ Genetic research links family history with amygdala volume. HR children who carry the short allele of 5-HTTLPR, a serotonin transporter gene implicated in alcohol dependence risk,⁵⁵ had the smallest amygdala volumes compared to long allele and LR counterparts.⁵³ The authors hypothesized that smaller amygdala volumes are a result of stressful childhood experiences, as family cohesion predicted larger amygdalas, and that those who had short alleles were most vulnerable to environmental influences.⁵³ However, some evidence suggests that smaller amygdala volumes are the result of a developmental lag rather than a static difference between FHP and FHN individuals. Sjoerds et al.⁵⁶ found no differences in amygdala volumes in an adult sample, and Benegal et al.⁵⁴ found that family history-related brain differences were most prominent in the younger participants in the study. Given that most studies that identified differences in amygdala volume examined adolescents and emerging adults,^15,53,54 longitudinal research is needed to determine if the amygdala shows a different pattern of development in FHP versus FHN individuals and whether stressful experiences, rather than FHP, are a better predictor of amygdala volumes. Smaller amygdalas are related to increased aggression in adulthood⁵⁷; however, it is unclear if this applies to children. Task-related fMRI research suggests the altered amygdala may lead to increased risk-taking, which will be discussed later.

NAcc and other basal ganglia structures

Several structures within the basal ganglia have been examined in relation to family history of AUD, with NAcc being of particular interest. Greater FH density has been linked to increased NAcc volumes in adolescent, alcohol-naive females but not males;⁵⁸ however, other studies have demonstrated no differences in NAcc volumes.¹⁷ Because the NAcc is associated with reward reactivity, possible volumetric differences in females may indicate altered responsivity to rewards. While volumetric differences in other basal ganglia structures such as the caudate,^9,53,54 globus pallidus,⁹ or putamen⁹ have not been found, differences in dopamine receptor availability between FHP and FHN individuals have been identified. Specifically, FHP nonalcohol-dependent adult drinkers have more dopamine (D₂/D₃) receptor availability in the right ventral striatum, posterior caudate, and anterior putamen (areas associated with reward and motivation) than their FHN counterparts.¹⁰ Because these results were obtained from an adult sample of drinkers who had not developed an AUD despite elevated risk, the increased dopamine (D₂/D₃) receptor availability in basal ganglia structures may be a protective factor against developing an AUD for FHP individuals.

Neocortex

Cortical differences between FHP and FHN individuals have been examined using a variety of neuroimaging techniques including MRI, fMRI, and magnetic resonance spectroscopy (MRS). A number of cortical regions are smaller in FHP youth, i.e., smaller cingulate, superior frontal gyrus (SFG), and thalamic volumes,⁵⁴ but not FHP adults.^9,56 Furthermore, FHP alcohol-naive adolescents had thinner cortices in frontal and parietal lobes when compared to FHN adolescents, which was more pronounced at younger ages.⁵⁹ Reduced volumes in cortical areas are associated with increased externalizing symptoms, which may contribute to the development of AUD and other negative outcomes.⁵⁴ These findings in FHP youths, which have not been replicated with adults, could again support a developmental lag hypothesis, which is described in great detail below. It is also possible that many older FHP adolescents with significant alcohol use were excluded from this study, resulting in a biased group of older FHPs.⁵⁹

MRS utilizes the signal shift from hydrogen ions (protons) to determine concentrations of metabolites in a specific tissue, including the brain.⁶⁰ Commonly examined neurometabolites include N-acetyl aspartate, lipids, lactate, and glutamine/glutamate.⁶⁰ Using MRS, researchers have found that FHP adolescents, but not FHP adults, have elevated glutamine/glutamate ratios in the anterior cingulate cortex compared to FHN adolescents.⁶¹ Interestingly, FHP adolescents had glutamine/glutamate ratios that were more similar to both FHN and FHP adults.⁶¹ In FHP adults, increased motor impulsivity was associated with lower glutamine/glutamate ratios, whereas in FHN adolescents, increased impulsivity was associated with higher glutamine/glutamate ratios.⁶¹ These results suggest that altered glutamine/glutamate ratios may be related to impulsivity in FHP participants.

Another neuroimaging technique, diffusion tensor imaging, allows researchers to measure white matter integrity by measuring mean diffusivity (MD), fractional anisotropy (FA), axial diffusivity, and radial diffusivity.⁶² FA measures directionality of water diffusion. Greater FA indicates more organized and greater white matter integrity.⁶³ MD measures magnitude of water diffusion, which also provides insights into the integrity of white matter microstructure.⁶³ FHP youths have been reported to have lower FA in frontocortical areas, indicating poorer white matter integrity,⁶⁴ and increased impulsivity due to decreased top-down control of subcortical brain regions, i.e., amygdala. However, another study found that FHP youth have higher FA in 19 white matter regions.¹⁷ A recent longitudinal study followed FHP individuals through adolescence and compared those who went on to become binge drinkers versus those who did not.⁶⁵ FHP was related to reduced FA in the PFC and reduced MD in the thalamus,⁶⁵ but these differences dissipated by late adolescence.⁶⁵ This indicates white matter integrity may also be affected by a proposed developmental delay. They also found that FHP adolescents who went on to binge-drink had lower MD in the SFG before the onset of binge drinking, compared to FHP counterparts that did not go on to binge-drink.⁶⁵ This indicates that lower MD in cortical areas in FHP adolescents is related to greater risk of problematic alcohol use, although this effect may not persist into adulthood.

Cerebellum

Several studies have examined the cerebellum as it relates to family history of AUD, with variable results. For example, HR male adolescents and young adults have greater gray matter volumes in the right cerebellum compared to their LR counterparts.⁶⁶ A subsequent study from the same group reported that HR adolescents and young adults had greater gray matter volume and total volume in the cerebellum but found no significant differences in white matter volumes.⁶⁷ A more recent study found that HR adolescents and young adults had greater gray matter, white matter, and total cerebellar volumes.⁶⁸ In contrast, Benegal et al.⁵⁴ reported that HR male youth (ages 8–24) had smaller gray matter volumes compared to FHN individuals. Notably, there are discrepancies between the Hill et al.^66,67 studies and Benegal et al.’s study that may explain these contrasting results. Namely, Benegal et al.⁵⁴ used an alcohol-naive sample, whereas the Hill et al.^66–68 studies included participants that were not alcohol-naïve, with some participants meeting criteria for an AUD or other SUDs. While it is unclear the nature of the relationship between cerebellar volumes and FHP youth, abnormalities may translate to altered motor abilities.

Ultimately, it may be difficult to tease out the effects of familial risk versus alcohol consumption in young adult and older populations, as alcohol consumption is also related to decreased cerebellar volume,⁶⁹ and family history of AUD is associated with greater alcohol consumption and increased risk of AUD. Specifically, FHP heavy drinkers had lower cerebrospinal fluid volumes than FHN heavy drinkers, indicating FHP heavy drinkers had lost less gray and white matter volume due to chronic alcohol abuse than did FHN heavy drinkers. In short, FHP status was a protective factor against gray and white matter losses due to chronic alcohol abuse.⁷⁰ As a result, age of first drink, past and/or recent alcohol consumption, AUD diagnosis, and potentially other drinking-related variables should be entered as covariates in future studies of the effects of family history of AUD on cerebellar volume.

Hippocampus

The hippocampus has also been a region of interest when examining differences between FHP and FHN individuals, and, as in the cerebellum, results have been variable. FHP participants had smaller hippocampi⁵⁴ and smaller surrounding structures, i.e., smaller parahippocampal gyrus, than their FHN counterparts.^54,56 However, other studies have found that FHP adolescent males had larger left hippocampi than their male FHN counterparts with no hippocampal volume differences observed in adolescent females.⁷¹ Other studies found no differences in hippocampal volumes between FHP and FHN participants.^9,15

While further research is needed to verify the relationship between FHP and the hippocampus, altered volume in this area may relate to altered behavioral inhibition and visual-spatial functioning related to FHP, which will be discussed in depth later in the paper. While several studies have linked alcohol use to reduced hippocampal volumes,⁷² further research is necessary to determine how much reduced volume, if any, can be attributed to family history of AUD.

In sum, numerous neuroimaging modalities have been used to study and identify structural, functional, and neurochemical differences between FHP and FHN individuals. Structural differences between FHP and FHN seem to be more common in youth than in adults. This alludes to the proposed neurodevelopmental lag in FHP individuals, i.e., brain structure and/or function is developmentally aberrant (“behind”) in childhood and/or adolescence, but, by the time FHP individuals reach adulthood, these differences are no longer apparent (“caught up”) compared to their age-matched FHN counterparts. However, in order to separate the effects of alcohol use from effects of familial risk, many studies include only alcohol-naïve or nonalcohol-dependent participants. In contrast to the neurodevelopmental lag hypothesis, it may also be possible that, by adulthood, many FHP individuals have succumbed to AUD and are excluded from these studies, thereby contributing to sampling bias, i.e., resilient FHP participants who more closely resemble FHN subjects in adulthood.

Next, even in the most commonly studied brain regions, there are contradictory neuroanatomical findings. In addition to possible sampling biases and age-related effects, many studies have small samples that are underpowered to detect small-to-moderate effects. The use of disparate neuroimaging platforms, processing pipelines, and result interpretation may also muddle the water. Nevertheless, given the number of structural and functional brain differences in FHP and FHN groups that have been reported, one would also anticipate altered neurocognitive performance and traits. We will now review the research examining neurocognitive differences based on family history of AUD.

Neurocognitive Differences

Many studies have examined family history of AUD in relation to neurocognitive performance using a variety of self-reported and task performance measures (Table 3). While many aspects of neurocognition have been studied, executive functioning differences between FHP and FHN individuals have been the most extensively studied. Executive function includes a number of higher order cognitive tasks including inhibition, motivation, working memory, spatial performance, emotional regulation, and attention-shifting, which will each be described sequentially.⁷³

Table 3.

Neurocognitive differences based on family history of alcohol use disorders.

Structure	Studies	Findings
Inhibition	Acheson et al. (2014)⁸⁰	FHP youths had ↑ activation in posterior cingulate/precuneus, bilateral middle and superior temporal gyrus, and medial superior FG during Go/NoGo task. FHP youths had slower reaction times.
	DeVito et al. (2013)⁸¹	FHP adults had ↑ activation in left anterior insula and inferior FG during Go/NoGo task.
	Henderson et al. (2018)⁵⁹	FHP adolescents had ↑ scores on delay discounting task.
	Gierski et al. (2013)⁷⁵	FHP adults had ↓ performance on Wisconsin Card Sorting Task and Stroop test. FHP adults rated themselves as more impulsive.
	Acheson et al. (2011)⁷⁶	FHP adults had ↓ performance on GoStop task.
Motivation	Andrews et al. (2011)⁷⁷	FHP participants had ↑ activity in caudate at prospect of reward, ↓ activity in NAcc, insula, and orbitofrontal cortex when anticipating reward, and ↓ activity in NAcc and amygdala after loss.
	Acheson et al. (2009)⁷⁹	FHP participants had ↑ activity in the left dorsal anterior cingulate cortex and left caudate nucleus during Iowa Gambling Task.
	Cservenka and Nagel (2012)⁸⁵	FHP adolescents had ↓ activation in the right cerebellum and right dorsolateral PFC when making risky decisions on a task.
	Glahn et al. (2007)⁸³	FHP participants did not demonstrate amygdala activation in response to fearful faces, whereas FHN participants did.
	Yarosh et al. (2014)⁷⁴	FHP participants rated themselves as ↑ reward-sensitive.
Working Memory and Spatial Performance	Cservenka et al. (2012)⁸⁶	FHP youth had slower reaction times on a verbal working memory task. They had ↓ activation in the right anterior and dorsolateral PFCs, right cingulate gyrus, and right inferior FG during said task.
	Henderson et al. (2018)⁵⁹	FHP adolescents had ↓ performance on visual spatial sequence task.
	Acheson et al. (2011)⁷⁶	FHP adults had ↓ performance on immediate memory task.
	Corral et al. (2003)⁹⁰	FHP subjects ages 7 to 15 had ↓ scores on block design and digit span. These effects dissipated by the time participants were ages 11 to 17.
	Garland et al. (1993)¹⁶	FHP males took more trials to learn how to perform a spatial task.

Note. FHP: family history positive; FG: frontal gyrus; NAcc: nucleus accumbens; PFC: prefrontal cortex; FHN: family history negative.

Inhibition

Family history of AUD is related to increased impulsivity^59,74–76 based on task performance and self-report; however, as is the case in the neuroanatomy literature, not every study has replicated these findings.^77–79 Although participants did not perform or rate themselves differently on impulsiveness, FHP participants displayed greater activation in the posterior cingulate/precuneus, bilateral middle/superior temporal gyrus, and medial SFG while performing impulsivity tasks,⁸⁰ and FHP males displayed greater activation in the left insula and interior frontal gyrus during a similar impulsivity task.⁸¹ This indicates that, while there may not always be observable differences in behavior, inhibitory control may require more cognitive effort for FHP individuals. FHP individuals requiring more cognitive effort may suggest some sort of detrimental deficit. However, increased activation in the dorsolateral PFC during correct No-Go trials on a Go/No-Go task predicted resilience against substance use for FHP youth.⁸² More research is necessary in order to determine what differences in activation might mean for long-term outcomes.

Motivation

FHP individuals are more consistently motivated by rewards.^59,76,74 FHP participants had greater activation in the left dorsal anterior cingulate cortex and left caudate nucleus during a gambling task.⁷⁹ FHP participants demonstrated greater activation in the caudate at the prospect of a monetary reward, less activation in the insula, orbitofrontal cortex, and NAcc⁷⁷ at the anticipation of reward, and decreased activation in the NAcc and amygdala⁷⁷ when failing to win money, thereby indicating that FHP participants may be less sensitive to monetary loss. Additionally, FHP young adults have decreased amygdala activation in response to fearful faces compared to their FHN counterparts, indicating FHP individuals may be less fearful and less likely to develop risk aversion.⁸³ Furthermore, FHP adolescents demonstrated an attenuated emotion-modulated startle response in comparison to their FHN counterparts.⁸⁴ Similarly, while no differences were observed on risk-taking behaviors in decision-making tasks,^85,79 FHP participants demonstrated less activation in the right cerebellum when making risky decisions compared to their FHN counterparts,⁸⁵ again suggesting that FHP participants are less risk responsive.

Working memory and spatial performance

FHP individuals also perform differently on working memory tasks.^86–88 For example, FHP youth had slower reaction times on a verbal working memory task and reduced activity in the PFC during said task.⁸⁶ Similarly, FHP was related to decreased activation in the right cerebellum during a spatial working memory task; however, there were no FHN versus FHP group performance differences.⁸⁷ FHP youth have demonstrated reduced connectivity between contralateral cerebellar regions and anterior PFC, independent of task-related activation.⁸⁹ This altered activation and connectivity may explain FHP individual’s altered responses to visuospatial tasks in some studies.^13,16,59 Specifically, FHP males took more trials to learn how to perform a spatial task than FHN males.¹⁶ In another study, 7- to 15-year-old FHP children had lower scores with block design and digit span; however, three years later, in the same cohort (now 11–17 years old), there were no observable group differences,⁹⁰ thereby fitting the aforementioned neurodevelopmental lag hypothesis in FHP youth and adolescents.

Other

A number of other neurocognitive differences have been found. Namely, FHP individuals may have difficulty with shifting attention⁷⁵ and emotional regulation.⁸⁴ They may also experience more negative affect and poorer mood regulation than FHN individuals.⁹¹ Being FHP was related to increased early life adversity, which also predicted poorer mood regulation and negative affect.⁹¹

Taken together, a family history of AUD has been linked to altered performance on various neurocognitive tasks. Specifically, FHP participants are more impulsive, variably reward-responsive, less risk and loss-averse, and have altered activity and performance on working and spatial memory tasks that, in many cases, predate the development of alcohol problems.

Conclusion

Family history of AUD has been multimodally studied for several decades, and it is safe to say is related to much more than simply increased risk of alcoholism. In imaging studies, FHP individuals demonstrate differences in brain volumes, activity, connectivity, and neurochemistry, as well as increased impulsivity and reward sensitivity. A re-occurring theme throughout the literature is that FHP youth, but not adults, are much more likely to demonstrate volumetric differences across several brain regions. Although the neurotoxic effects of alcohol may be at play in young drinkers, the extant literature suggests that FHP may be associated with a neurodevelopmental lag in brain maturation. Currently, there is no direct outcome data on this hypothesized neurodevelopmental lag in FHP participants. One study has linked childhood neural activity in FHP youth to resiliency in adulthood,⁸² which indicates that neurodevelopmental differences in FHP children may affect later outcomes. Interestingly, there is significant overlap between altered brain structures associated with FHP and brain structures associated with Attention Deficit-Hyperactivity Disorder (ADHD), including the cerebellum, basal ganglia, and PFC,⁹² and a similar phenomenon of neurodevelopmental delay has been reported in ADHD youth. Brain networks associated with ADHD symptoms demonstrated a developmental lag in ADHD children, with the most severe presentations being associated with increasing delay.⁹³ While there is no research on how a neurodevelopmental lag directly affects ADHD children, a childhood ADHD diagnosis is associated with long-term adverse outcomes, i.e., unemployment.⁹⁴

In addition to neuroanatomical, neurophysiological, and psychological differences at baseline, FHP subjects also respond differently to numerous substances including alcohol and ketamine. FHP adults are more sensitive to alcohol’s stimulant (ascending limb) effects while simultaneously being less sensitive to its sedative (descending limb) effects. FHP individuals also demonstrate altered brain activity to alcohol-related cues and experience more detrimental outcomes, such as hangovers, from drinking even when consumption levels are similar. After alcohol, ketamine has been the most studied drug in FHP subjects, demonstrating attenuated dissociative, psychotomimetic, and acute dysphoric reactions during administration and, in FHP TRD, greater magnitude and maintenance of antidepressant efficacy to subanesthetic dose infusion.

Lastly, future research is warranted across numerous domains. Although neuroimaging findings are consistent with the developmental lag hypothesis, large sample, longitudinal studies are needed to more properly examine differential brain developmental trajectories. Additionally, more research is needed to investigate various risk and protective factors in FHP individuals, in order to remediate risks and promote resiliency. More research is also needed to understand the neurobiology of altered antidepressant response to subanesthetic dose ketamine based on family history of AUD. The knowledge gained by this research may assist prescribers to better predict which patients may have an augmented and even exceptional antidepressant response to ketamine and potentially other glutamate-based interventions in a “precision psychiatry” treatment framework.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: The authors acknowledge funding from National Institute on Alcohol Abuse and Alcoholism.

References

American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: DSM-5, Arlington, VA: American Psychiatric Association, 2013.

Grant

Chou

Saha

, et al. Prevalence of 12-month alcohol use, high-risk drinking, and DSM-IV alcohol use disorder in the United States, 2001-2002 to 2012-2013: results from the National Epidemiologic Survey on Alcohol and Related Conditions. JAMA Psychiatry 2017; 74: 911–923.

Stephan

Alhassoon

Allen

, et al. Meta-analyses of clinical neuropsychological tests of executive dysfunction and impulsivity in alcohol use disorder. Am J Drug Alcohol Abuse 2017; 43: 24–43.

Schulte

Oberlin

Kareken

, et al. How acute and chronic alcohol consumption affects brain networks: insights from multimodal neuroimaging. Alcohol Clin Exp Res 2012; 36: 2017–2027.

Cloninger

Bohman

Sigvardsson

. Inheritance of alcohol abuse. Cross-fostering analysis of adopted men. Arch Gen Psychiatry 1981; 38: 861–868.

Knopik

Heath

Madden

, et al. Genetic effects on alcohol dependence risk: re-evaluating the importance of psychiatric and other heritable risk factors. Psychol Med 2004; 34: 1519–1530.

Merikangas

Stolar

Stevens

, et al. Familial transmission of substance use disorders. Arch Gen Psychiatry 1998; 55: 973–979.

Verhulst

Neale

Kendler

. The heritability of alcohol use disorders: a meta-analysis of twin and adoption studies. Psychol Med 2014; 45: 1061–1072.

Dager

McKay

Kent

Jr , et al. Shared genetic factors influence amygdala volumes and risk for alcoholism. Neuropsychopharmacology 2014; 40: 412.

10.

Alvanzo

AAH

Wand

Kuwabara

, et al.

Family history of alcoholism is related to increased D2/D3 receptor binding potential: a marker of resilience or risk?

Addict Bio 2017; 22: 218–228.

11.

Zai

Manchia

Zai

, et al. Association study of BDNF and DRD3 genes with alcohol use disorder in schizophrenia. Neurosci Lett 2018; 671: 1–6.

12.

Begleiter

Porjesz

Bihari

, et al. Event-related brain potentials in boys at risk for alcoholism. Science 1984; 225: 1493–1496.

13.

Drejer

Theikjaard

Teasdale

, et al. A prospective study of young men at high risk for alcoholism: neuropsychological assessment. Alcohol Clin Exp Res 1985; 9: 498–502.

14.

Acheson

Franklin

Cohoon

, et al. Anomalous temporoparietal activity in individuals with a family history of alcoholism: studies from the Oklahoma Family Health Patterns Project. Alcohol Clin Exp Res 2014; 38: 1639–1645.

15.

Hill

De Bellis

Keshavan

, et al. Right amygdala volume in adolescent and young adult offspring from families at high risk for developing alcoholism. Biol Psychiatry 2001; 49: 894–905.

16.

Garland

Parsons

Nixon

. Visual-spatial learning in nonalcoholic young adults with and those without a family history of alcoholism. J Stud Alcohol 1993; 54: 219–224.

17.

Squeglia

Jacobus

Brumback

, et al. White matter integrity in alcohol-naive youth with a family history of alcohol use disorders. Psychol Med 2014; 44: 2775–2786.

18.

Ramirez MA. A Phenomenological Study of Childhood Experiences of Adult Children Who Had Parents With an Alcohol Use Disorder [ProQuest Dissertations & Theses Global 2015], Ann Arbor, MI: ProQuest Dissertation Publishing, HQ located.

19.

Park

Schepp

. A systematic review of research on children of alcoholics: their inherent resilience and vulnerability. J Child Fam Stud 2015; 24: 1222–1231.

20.

Söderpalm Gordh

AHV

Brkic

Söderpalm

. Stress and consumption of alcohol in humans with a type 1 family history of alcoholism in an experimental laboratory setting. Pharmacol Biochem Behav 2011; 99: 696–703.

21.

Morzorati

Ramchandani

Flury

, et al. Self-reported subjective perception of intoxication reflects family history of alcoholism when breath alcohol levels are constant. Alcohol Clin Exp Res 2002; 26: 1299–1306.

22.

Earleywine

. Confirming the factor structure of the anticipated Biphasic Alcohol Effects Scale. Alcohol Clin Exp Res 1994; 18: 861–866.

23.

Erblich

Earleywine

Erblich

, et al.

Biphasic stimulant and sedative effects of ethanol: are children of alcoholics really different?

Addict Behav 2003; 28: 1129–1139.

24.

Söderpalm Gordh

AHV

Söderpalm

. Healthy subjects with a family history of alcoholism show increased stimulative subjective effects of alcohol. Alcohol Clin Exp Res 2011; 35: 1426–1434.

25.

King

Houle

de Wit

, et al. Biphasic alcohol response differs in heavy versus light drinkers. Alcohol Clin Exp Res 2002; 26: 827–835.

26.

Schuckit

. Low level of response to alcohol as a predictor of future alcoholism. Am J Psychiatry 1994; 151: 184–189.

27.

Schuckit

Smith

. The relationships of a family history of alcohol dependence, a low level of response to alcohol and six domains of life functioning to the development of alcohol use disorders. J Stud Alcohol 2000; 61: 827–835.

28.

Schuckit

Smith

Pierson

, et al. Relationships among the level of response to alcohol and the number of alcoholic relatives in predicting alcohol-related outcomes. Alcohol Clin Exp Res 2006; 30: 1308–1314.

29.

O’Malley

Maisto

. Effects of family drinking history and expectancies on responses to alcohol in men. J Stud Alcohol 1985; 46: 289–297.

30.

Erblich

Earleywine

Erblich

. Positive and negative associations with alcohol and familial risk for alcoholism. Psychol Addict Behav 2001; 15: 204–209.

31.

Lundahl

Lukas

. The impact of familial alcoholism on alcohol reactivity in female social drinkers. Exp Clin Psychopharmacol 2001; 9: 101–109.

32.

Tapert

Cheung

Brown

, et al. Neural response to alcohol stimuli in adolescents with alcohol use disorder. Arch Gen Psychiatry 2003; 60: 727–735.

33.

Kareken

Bragulat

Dzemidzic

, et al. Family history of alcoholism mediates the frontal response to alcoholic drink odors and alcohol in at-risk drinkers. NeuroImage 2010; 50: 267–276.

34.

Dager

Anderson

Stevens

, et al. Influence of alcohol use and family history of alcoholism on neural response to alcohol cues in college drinkers. Alcohol Clin Exp Res 2013; 37: E161–E171.

35.

Elliott

Carey

Bonafide

. Does family history of alcohol problems influence college and university drinking or substance use? A meta-analytical review. Addiction 2012; 107: 1774–1785.

36.

Ranganathan

Sewell

Carbuto

, et al. Effects of Δ9-tetrahydrocannabinol in individuals with a familial vulnerability to alcoholism. Psychopharmacology 2014; 231: 2385–2393.

37.

Krishnan-Sarin

Krystal

Shi

, et al. Family history of alcoholism influences naltrexone-induced reduction in alcohol drinking. Biol Psychiatry 2007; 62: 694–697.

38.

Kampov-Polevoy

Garbutt

Khalitov

. Family history of alcoholism and response to sweets. Alcohol Clin Exp Res 2003; 27: 1743–1749.

39.

Eiler

WJA

Dzemidzic

Soeurt

, et al. Family history of alcoholism and the human brain response to oral sucrose. NeuroImage: Clinical 2018; 17: 1036–1046.

40.

U.S. Food and Drug Administration. KETALAR (ketamine hydrochloride) injection. https://www.accessdata.fda.gov/scripts/cder/daf/index.cfm?event=overview.process&ApplNo=016812. Accessed July 9, 2019.

41.

Hamilton J. FDA approves esketamine nasal spray for hard-to-treat depression. Morning Edition. npr.org 2019. Accessed July 9, 2019.

42.

Peltoniemi

Hagelberg

Olkkola

, et al. Ketamine: a review of clinical pharmacokinetics and pharmacodynamics anesthesia and pain therapy. Clin Pharmacokinet 2016; 55: 1059–1077.

43.

Zanos

Moaddel

Morris

, et al. Ketamine and ketamine metabolite pharmacology: insights into therapeutic mechanisms. Pharmacol Rev 2018; 70: 621–660.

44.

Petrakis

Limoncelli

Gueorguieva

, et al. Altered NMDA glutamate receptor antagonist response in individuals with a family vulnerability to alcoholism. Am J Psychiatry 2004; 161: 1776–1782.

45.

Narayanan

Stevens

Jiantonio

, et al. Effects of memantine on event-related potential, oscillations, and complexity in individuals with and without family histories of alcoholism. J Stud Alcohol Drugs 2013; 74: 245–257.

46.

Luckenbaugh

Ibrahim

Brutsche

, et al. Family history of alcohol dependence and antidepressant response to an N-methyl-D-aspartate antagonist in bipolar depression. Bipolar Disord 2012; 14: 880–887.

47.

Phelps

Brutsche

Moral

, et al. Family history of alcohol dependence and initial antidepressant response to an N-methyl-D-aspartate antagonist. Biol Psychiatry 2009; 65: 181–184.

48.

Niciu

Luckenbaugh

Ionescu

, et al. Clinical predictors of ketamine response in treatment-resistant major depression. J Clin Psychiat 2014; 75: e417–e423.

49.

Niciu

Luckenbaugh

Ionescu

, et al. Ketamine’s antidepressant efficacy is extended for at least four weeks in subjects with a family history of an alcohol use disorder. Int J Neuropsychopharmacol 2014; 18: pyu039.

50.

Pennybaker

Niciu

Luckenbaugh

, et al. Symptomatology and predictors of antidepressant efficacy in extended responders to a single ketamine infusion. J Affect Disord 2017; 208: 560–566.

51.

Permoda-Osip

Skibinska

Bartkowska-Sniatkowska

, et al. Factors connected with efficacy of single ketamine infusion in bipolar depression. Psychiatria Polska 2014; 48: 35–47.

52.

Tessner

Hill

. Neural circuitry associated with risk for alcohol use disorders. Neuropsychol Rev 2010; 20: 1–20.

53.

Hill

Wang

Carter

, et al. Amygdala volume in offspring from multiplex for alcohol dependence families: the moderating influence of childhood environment and 5-HTTLPR variation. J Alcohol Drug Depend 2013; Suppl 1: 001.

54.

Benegal

Antony

Venkatasubramanian

, et al. Gray matter volume abnormalities and externalizing symptoms in subjects at high risk for alcohol dependence. Addict Bio 2007; 12: 122–132.

55.

McHugh

Hofmann

Asnaani

, et al. The serotonin transporter gene and risk for alcohol dependence: a meta-analytic review. Drug Alcohol Depend 2010; 108: 1–6.

56.

Sjoerds

Van Tol

M-J

van den Brink

, et al. Family history of alcohol dependence and gray matter abnormalities in non-alcoholic adults. World J Biol Psychiatry 2013; 14: 565–573.

57.

Matthies

Rüsch

Weber

, et al. Small amygdala – high aggression? The role of the amygdala in modulating aggression in healthy subjects. The World J Bio Psychiatry 2012; 13: 75–81.

58.

Cservenka

Gillespie

Michael

, et al. Family history density of alcoholism relates to left nucleus accumbens volume in adolescent girls. J Stud Alcohol Drugs 2015; 76: 47–56.

59.

Henderson

Vaidya

Kramer

, et al. Cortical thickness in adolescents with a family history of alcohol use disorder. Alcohol Clin Exp Res 2018; 42: 89–99.

60.

Gujar

Maheshwari

Björkman-Burtscher

, et al. Magnetic resonance spectroscopy. J Neuro-Ophthalmol 2005; 25: 217–226.

61.

Cohen-Gilbert

Sneider

Crowley

, et al. Impact of family history of alcoholism on glutamine/glutamate ratio in anterior cingulate cortex in substance-naïve adolescents. Dev Cog Neurosci 2015; 16: 147–154.

62.

Alba-Ferrara

De Erausquin

. What does anisotropy measure? Insights from increased and decreased anisotropy in selective fiber tracts in schizophrenia. Front Integ Neurosci 2013, pp. 7: 9.

63.

Clark

Nuechterlein

Asarnow

, et al. Mean diffusivity and fractional anisotropy as indicators of disease and genetic liability to schizophrenia. J Psychiatr Res 2011; 45: 980–988.

64.

Acheson

Wijtenburg

Rowland

, et al. Combining diffusion tensor imaging and magnetic resonance spectroscopy to study reduced frontal white matter integrity in youths with family histories of substance use disorders. Hum Brain Mapp 2014; 35: 5877–5887.

65.

Jones

Nagel

. Altered frontostriatal white matter microstructure is associated with familial alcoholism and future binge-drinking in adolescence. Neuropsychopharmacology 2019. 44: 1076–1083.

66.

Hill

Muddasani

Prasad

, et al. Cerebellar volume in offspring from multiplex alcohol dependence families. Biol Psychiatry 2007; 61: 41–47.

67.

Hill

Wang

Carter

, et al. Cerebellum volume in high-risk offspring from multiplex alcohol dependence families: association with allelic variation in GABRA2 and BDNF. Psychiatry Res Neuroimaging 2011; 194: 304–313.

68.

Hill

Lichenstein

Wang

, et al. Volumetric differences in cerebellar lobes in individuals from multiplex alcohol dependence families and controls: their relationship to externalizing and internalizing disorders and working memory. Cerebellum 2016; 15: 744–754.

69.

Mon

Durazzo

Abe

, et al. Structural brain differences in alcohol-dependent individuals with and without comorbid substance dependence. Drug Alcohol Depend 2014; 144: 170–177.

70.

Cardenas

Studholme

Meyerhoff

, et al. Chronic active heavy drinking and family history of problem drinking modulate regional brain tissue volumes. Psychiatry Res Neuroimaging 2005; 138: 115–130.

71.

Hanson

Medina

Nagel

, et al. Hippocampal volumes in adolescents with and without a family history of alcoholism. Am J Drug Alcohol Abuse 2010; 36: 161–167.

72.

Wilson

Bair

Thomas

, et al. Problematic alcohol use and reduced hippocampal volume: a meta-analytic review. Psychol Med 2017; 47: 2288–2301.

73.

Mazursky-Horowitz H, Woods KE, Chronis-Tuscano, A. Executive Functioning. In Bornstein MH (ed), The SAGE Encylopedia of Lifespan Human Development (pp. 802–807). Thousand Oaks, CA: SAGE Publications, Inc., 2018.

74.

Yarosh

Hyatt

Meda

, et al. Relationships between reward sensitivity, risk-taking and family history of alcoholism during an interactive competitive fMRI task. PLoS One 2014; 9: e88188.

75.

Gierski

Hubsch

Stefaniak

, et al.

Executive functions in adult offspring of alcohol-dependent probands: toward a cognitive endophenotype?

Alcohol Clin Exp Res 2013; 37: E356–E363.

76.

Acheson

Richard

Mathias

, et al. Adults with a family history of alcohol related problems are more impulsive on measures of response initiation and response inhibition. Drug Alcohol Depend 2011; 117: 198–203.

77.

Andrews

Meda

Thomas

, et al. Individuals family history positive for alcoholism show functional magnetic resonance imaging differences in reward sensitivity that are related to impulsivity factors. Biol Psychiatry 2011; 69: 675–683.

78.

Bjork

Knutson

Hommer

. Incentive-elicited striatal activation in adolescent children of alcoholics. Addiction 2008; 103: 1308–1319.

79.

Acheson

Robinson

Glahn

, et al. 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 Alcohol Depend 2009; 100: 17–23.

80.

Acheson

Tagamets

Rowland

, et al. Increased forebrain activations in youths with family histories of alcohol and other substance use disorders performing a Go/NoGo Task. Alcohol Clin Exp Res 2014; 38: 2944–2951.

81.

DeVito

Meda

Jiantonio

, et al. Neural correlates of impulsivity in healthy males and females with family histories of alcoholism. Neuropsychopharmacology 2013; 38: 1854.

82.

Martz

Zucker

Schulenberg

, et al. Psychosocial and neural indicators of resilience among youth with a family history of substance use disorder. Drug Alcohol Depend 2018; 185: 198–206.

83.

Glahn

Lovallo

Fox

. Reduced amygdala activation in young adults at high risk of alcoholism: studies from the Oklahoma Family Health Patterns Project. Biol Psychiatry 2007; 61: 1306–1309.

84.

Miranda

Jr Meyerson

Buchanan

, et al. Altered emotion-modulated startle in young adults with a family history of alcoholism. Alcohol Clin Exp Res 2002; 26: 441–448.

85.

Cservenka

Nagel

. Risky decision-making: an fMRI study of youth at high risk for alcoholism. Alcohol Clin Exp Res 2012; 36: 604–615.

86.

Cservenka

Herting

Nagel

. Atypical frontal lobe activity during verbal working memory in youth with a family history of alcoholism. Drug Alcohol Depend 2012; 123: 98–104.

87.

Mackiewicz Seghete

Cservenka

Herting

, et al. Atypical spatial working memory and task-general brain activity in adolescents with a family history of alcoholism. Alcohol Clin Exp Res 2013; 37: 390–398.

88.

Lovallo

Yechiam

Sorocco

, et al. Working memory and decision-making biases in young adults with a family history of alcoholism: studies from the Oklahoma Family Health Patterns project. Alcohol Clin Exp Res 2006; 30: 763–773.

89.

Herting

Fair

Nagel

. Altered fronto-cerebellar connectivity in alcohol-naïve youth with a family history of alcoholism. NeuroImage 2011; 54: 2582–2589.

90.

Corral

Holguín

Cadaveira

. Neuropsychological characteristics of young children from high-density alcoholism families: a three-year follow-up. J Stud Alcohol 2003; 64: 195–199.

91.

Sorocco

Carnes

Cohoon

, et al. Risk factors for alcoholism in the Oklahoma Family Health Patterns project: impact of early life adversity and family history on affect regulation and personality. Drug Alcohol Depend 2015; 150: 38–45.

92.

Nigg

Casey

. An integrative theory of attention-deficit/ hyperactivity disorder based on the cognitive and affective neurosciences. Dev Psychopathol 2005; 17: 785–806.

93.

Sripada

Kessler

Angstadt

. Lag in maturation of the brain’s intrinsic functional architecture in attention-deficit/hyperactivity disorder. Proc Natl Acad Sci U S A 2014; 111: 14259–14264.

94.

CHADD. ADHD and long-term outcomes. https://chadd.org/about-adhd/long-term-outcomes/. Accessed July 9, 2019.

95.

Yoon G, Pittman B, Limoncelli D, et al. Familial alcoholism risk and ratio of stimulant to sedative effects of ketamine. Biol Psychiatry 2016; 79: e69–e70.

96.

Rong C, Park C, Rosenblat JD, et al. Predictors of response to ketamine in treatment resistant major depressive and bipolar disorder. Int J Environ Res Public Health 2018; 15: e771–780.