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Binge Drinking in the Adolescent and Young Brain

Mini Review ARTICLE

Front. Psychol., 19 January 2018 | https://doi.org/10.3389/fpsyg.2018.00012

Binge Drinking and the Young Brain: A Mini Review of the Neurobiological Underpinnings of Alcohol-Induced Blackout

Daniel F. Hermens1,2* and Jim Lagopoulos2
  • 1Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
  • 2Sunshine Coast Mind and Neuroscience Thompson Institute, University of the Sunshine Coast, Sunshine Coast, QLD, Australia

Binge drinking has significant effects on memory, particularly with regards to the transfer of information to long-term storage. Partial or complete blocking of memory formation is known as blackout. Youth represents a critical period in brain development that is particularly vulnerable to alcohol misuse. Animal models show that the adolescent brain is more vulnerable to the acute and chronic effects of alcohol compared with the adult brain. This mini-review addresses the neurobiological underpinnings of binge drinking and associated memory loss (blackout) in the adolescent and young adult period. Although the extent to which there are pre-existing versus alcohol-induced neurobiological changes remains unclear, it is likely that repetitive binge drinking in youth has detrimental effects on cognitive and social functioning. Given its role in learning and memory, the hippocampus is a critical region with neuroimaging research showing notable changes in this structure associated with alcohol misuse in young people. There is a great need for earlier identification of biological markers associated with alcohol-related brain damage. As a means to assess in vivo neurochemistry, magnetic resonance spectroscopy (MRS) has emerged as a particularly promising technique since changes in neurometabolites often precede gross structural changes. Thus, the current paper addresses how MRS biomarkers of neurotransmission (glutamate, GABA) and oxidative stress (indexed by depleted glutathione) in the hippocampal region of young binge drinkers may underlie propensity for blackouts and other memory impairments. MRS biomarkers may have particular utility in determining the acute versus longer-term effects of binge drinking in young people.

Introduction

Binge drinking (BD) is the dominant type of alcohol misuse in young people (SAMHSA, 2009; Archie et al., 2012; Hermens et al., 2013). Alcohol use typically begins in adolescence with the prevalence of BD increasing sharply between 12 and 25 years old (to ∼40–50%), which is a pattern observed across Western countries (SAMHSA, 2011; Archie et al., 2012; AIHW, 2014; Schuckit et al., 2015). Although young people drink less frequently than older adults, they tend to drink more on each occasion (SAMHSA, 2009) and drinking to intoxication is especially common in teenagers (White and Hayman, 2006). Hence, single incident-excessive alcohol consumption or BD is often accompanied with adverse effects. These include increased risk of injury or accidental death, drink driving, unsafe sexual practices, periods of unconsciousness, as well as an increased likelihood of being a perpetrator or victim of assault (Bonomo et al., 2004; Mundt et al., 2012). A universal definition of BD remains lacking, however, it is generally accepted that it refers to “a single drinking session leading to intoxication” (Berridge et al., 2009). The USA’s National Institute on Alcohol Abuse and Alcoholism (NIAAA, 2017) has a more specific definition of: “a pattern of drinking that brings blood alcohol concentration (BAC) levels to 0.08 g/dL.” Furthermore, this would be within a period of about 2 h, which “typically occurs after four drinks for women and five drinks for men.” Despite this, numerous studies and surveys have opted for a simpler definition of BD as five or more drinks per single drinking occasion, for both sexes (SAMHSA, 2011; Degenhardt et al., 2013).

Prevalence and Patterns of Binge Drinking in Young People

National surveys in the United States and Australia show that around 40% of young adults (aged ∼20–25 years1) report at least monthly BD. Similarly, in both countries around 5–6% of adolescents (aged 12–17 years) report this pattern of drinking (with a sharp increase to ∼15% by 16–17 years) (AIHW, 2017; SAMHSA, 2017). Across 35 European countries, around one third of 16 year olds report monthly BD (EMCDDA/ESPAD, 2016). The Australian survey (AIHW, 2017) also asked about any ‘loss of memory after drinking.’ Of those reporting monthly BD, 16–17 year olds had the highest rates of such memory loss (32%) with the next highest being the 20–24 year olds (24%). In terms of those with yearly but not monthly BD, 100% of 12–15 year olds reported alcohol-related memory loss, compared to the next highest group the 18–19 year olds (49%)2.

Longitudinal studies have provided important insights into the longer-term effects that adolescent BD may have on memory loss. Degenhardt et al. (2013) conducted a 15-year prospective study of N = 1943 Australians (from 14 to 15 years old) and found that 52% of males and 34% of females reported past-week adolescent BD. Furthermore, the vast majority continued to be BD into their adulthood and this was more likely in males, those who had antisocial behaviors and adverse consequences of drinking in adolescence. Notably, the adverse consequences included ‘intense drinking’ (i.e., when the subject could not remember the night before) as well as social problems, and alcohol-related sexual risk taking and injury/violence. Similarly, a longitudinal study of N = 1402 English adolescents who reported drinking alcohol prior to 15 years showed that 29% experienced alcohol-induced blackout (AIB)3. At follow-up, 57 and 74% had AIBs by 16 and 19 years, respectively (Schuckit et al., 2015). Although this study did not evaluate BD per se, the authors found that there was a general association between increased alcohol quantities and AIBs. One of the trajectories identified (30% of the sample) was thought to be prone to AIBs at age 16 due to links between their extroversion, peer substance use and BD (high BAC). However, the authors would not rule out other potential factors including family history of alcohol problems. Taken together, these findings suggest that young people who undertake BD are particularly prone to experiencing AIBs (Schuckit et al., 2015; Wetherill and Fromme, 2016). As a further complication, it remains a challenge to distinguish between the acute versus longer-term effects of BD in young people. These differential impacts of BD are addressed in the following sections.

Early Binge Drinking: A Window of Vulnerability

The prevalence of BD in young people is particularly concerning given the damaging effects of alcohol on the developing adolescent-to-young adult brain (Hermens et al., 2013; Cservenka and Brumback, 2017). Despite this, there remains a relative paucity of neurobiological studies investigating the acute and longer-term effects of BD in young people (Hermens et al., 2013), particularly with respect to AIBs. Clark et al. (2008) suggest that the asynchronous development of the prefrontal cortex with respect to the limbic system in adolescence/young adulthood drives the heightened vulnerability to the effects of alcohol. Brain maturation continues well into the third decade of life, particularly in regards to prefrontal executive functions (EFs) (De Luca et al., 2003), which can result in an increased propensity for risky, impulsive behaviors and experimentation. In this period there are substantial changes in brain structure, with gray matter (GM) decreasing non-linearly in the cerebral cortex and linearly in the cerebellum and subcortical structures (caudate, putamen, pallidum), whereas in other subcortical structures (amygdala, hippocampus) slight, non-linear increases in GM volume are observed (Ostby et al., 2009). Additionally, white matter (WM) increases non-linearly in the cerebrum and cerebellum (Ostby et al., 2009). Hence, the period of adolescence-to-young adulthood is often viewed as a ‘window of vulnerability,’ particularly in the context of substance misuse (Bava and Tapert, 2010; Hermens et al., 2013). Young alcohol misusers first show impairments in memory and EF, which correspond with structural changes in hippocampal and prefrontal brain regions (Bava and Tapert, 2010; Hermens et al., 2013; Squeglia et al., 2015; Gropper et al., 2016; Wilson et al., 2017). Given its progressive development throughout adolescence the hippocampus is thought to be particularly susceptible to alcohol, including acute dysfunction causing blackout (Zeigler et al., 2005). Such dysfunction may be due to the increased sensitivity of the adolescent brain to the acute effects of alcohol and/or the maturational changes and associated heightened vulnerability driving longer-term effects of exposure. Due to ethics and legal issues, research on the acute effects of alcohol on younger people is not possible, and as such animal studies (see below) have been crucial in our understandings of how the adolescent brain is particularly vulnerable to BD (Zeigler et al., 2005). Despite this, several human studies have provided important insights into the cognitive effects of acute alcohol ingestion. Acheson et al. (1998) conducted a randomized, repeated-measures placebo-controlled trial of alcohol (0.6 g/kg) in N = 12 healthy adults. They found that compared to placebo alcohol significantly impaired the acquisition of both semantic and non-verbal memory. Importantly, younger subjects (21–24 years) performed worse in the alcohol condition compared to their older peers (25–29 years) in immediate and delayed recall (visuo-spatial) and delayed recognition (verbal memory). Similarly, Vinader-Caerols et al. (2017) examined the acute effects of alcohol (i.e., doses of 0, 0.3–0.5, or 0.54–1.1 g/L) in past 12-month refrainers or BD aged 18–19 years. Compared to their BD and non-drinking peers those who consumed the highest acute dose showed the most impaired immediate visual and working memory, while the lower dose BD group showed impaired immediate visual memory only.

Other studies have examined the potential longer-term, dose-dependent effects of BD on cognitive performance. Nguyen-Louie et al. (2016) examined verbal learning and memory in adolescents (12–16 years) who were determined (6 years after baseline) to be moderate, binge or extreme-binge drinkers (≤4, 5+, or 10+ drinks/occasion). At follow-up, the extreme-BD group performed significantly worse than the moderate drinkers in verbal learning, as well as cued and free short delayed recall (BD performed at an intermediate level). Furthermore, for every additional drink consumed in adolescence, there was a linearly increasing deleterious effect on a range of learning, recall and recognition measures. In contrast, a more recent longitudinal study (Boelema et al., 2015) of N = 2230 Dutch adolescents found no differences among non-, light-, and heavy-drinkers in terms of the maturation of four measures of EF (i.e., inhibition, working memory, and shift- and sustained attention).

Animal Models

Earlier studies by Swartzwelder and colleagues utilized rat hippocampal slices to demonstrate the effects of acute alcohol exposure on the pre-pubertal/adolescent brain. Swartzwelder et al. (1995b) showed that alcohol has greater suppression of N-methyl-D-aspartate (NMDA) receptor-mediated synaptic potentials in pre-pubertal as compared with adult rats. Thus, the authors suggested that young drinkers may be at greatest risk of compromised cognitive function (i.e., anterograde memory formation) related to hippocampal NMDA activity. In other similar studies, this group provided further evidence of perturbed hippocampal function in adolescent but not adult rats; with attenuated long-term potentiation (LTP; important in the acquisition of spatial memory as well as learning and memory formation or ‘synaptic plasticity’) being observed across three different doses, including those more representative of human intoxication (Swartzwelder et al., 1995a; Pyapali et al., 1999). More recently, Risher et al. (2015) utilized ‘adolescent intermittent ethanol’ exposure via intragastric gavage for 16 days (until adulthood) before examining the acute effects of alcohol on hippocampal slices, and found enduring structural and functional abnormalities, reflecting synaptic immaturity.

Two subsequent studies probed and evaluated the longer-term effects of alcohol in adolescent and adult rats performing memory tasks. Markwiese et al. (1998) injected rats with alcohol (1.0 or 2.0 g/kg) or saline 30 min before trials on a spatial memory task, over a 5-day period. Notably, alcohol significantly impaired adolescent but not adult rats in spatial memory acquisition. As a follow-up to this, White et al. (2000) exposed rats to binge-style alcohol (i.e., 5.0 g/kg, 48-h intervals) or saline over a 20 day period. Animals were then tested (20 days post final dose) on an elevated plus maze and trained to perform spatial working memory task. Interestingly, prior exposure to alcohol and group status did not affect plus maze behavior nor spatial working memory performance, however, the animals exposed to binge-style alcohol as adolescents showed significant impairments in working memory when undertaken during an alcohol challenge (1.5 g/kg) compared to the other three groups (including binge-exposed adults). Importantly, the overall findings of studies utilizing intraperitoneal injections have been observed in similar studies utilizing self-administration protocols. Vargas et al. (2014) showed that voluntary binge drinking during adolescence produced enduring WM deficits in prefrontal circuitry and poorer performance in working memory, which was over and above the effects of vapor exposure (modeling dependence; over a longer period) during adulthood, suggesting that the adolescent brain has a heightened sensitivity to alcohol.

Acute Alcohol Use, Memory Loss: Blackout

‘Blackout’ or the loss of memory during an episode of drinking was first documented as an important indicator of alcoholism (Jellinek, 1946). However, it is now understood as phenomenon that can be experienced by any drinker, as it is typically induced by BD with a rapid increase in BAC; although there are a range of factors that are thought to increase the likelihood of blackout (Rose and Grant, 2010). Most definitions of blackout refer to there being a breakdown in the transfer of information from short-term to long-term storage (Acheson et al., 1998; White, 2003; Siqueira and Smith, 2015). Importantly, this occurs while immediate (very brief short-term) and remote (long-term; formed prior to intoxication) memory abilities remains intact (White, 2003). More specifically, an AIB leads to a failure in forming new explicit memories (i.e., facts and events) (Lister et al., 1991). Such anterograde amnesia occurs despite the subject continuing to participate in events (e.g., holding a conversation) that they will not remember later (White, 2003; Lee et al., 2009).

There is no objective test to determine that one is experiencing a blackout (Goodwin, 1995; Pressman and Caudill, 2013; Wetherill and Fromme, 2016). Thus, observers rely on the subject’s self-report which is itself constrained by the concept of being asked to ‘remember not remembering’ (Wetherill and Fromme, 2016). Detailed research has led to the identification of two qualitatively different types of blackouts: ‘en bloc’ (complete) and fragmentary (partial), first described almost 50 years (Goodwin et al., 1969a,b) these terms remain valid today (White, 2003; Rose and Grant, 2010). AIBs should not be confused with losing consciousness (i.e., “passing out”), rather an AIB is the memory lost from the conscious state whereby en bloc blackouts represent the complete interruption of memory transfer (an absence of encoding) and fragmentary blackouts (FBs) reflect partial obstruction of memory formation (a deficiency of encoding), which may be ameliorated via cueing (Lee et al., 2009; Rose and Grant, 2010).

For Acheson et al. (1998), AIBs stems from two processes: first, alcohol reduces one’s ability to process new information (Maylor and Rabbitt, 1993), then it facilitates faster forgetting (Maylor and Rabbitt, 1987). Importantly, rapid forgetting is a hallmark of hippocampal dysfunction (Squire et al., 2004), however, not all BD experience blackout, implying that genetic factors also play a role (Lee et al., 2009). Genetic epidemiological research supports this assumption. An Australian study of 2324 twin pairs reported a 52.5% heritability rate of lifetime AIBs (Nelson et al., 2004). Interestingly, it was speculated that genes whose products mediate alcohol’s effects on hippocampal neurotransmission probably underlie such risk. On the other hand, early alcohol exposure may have specific impacts on longer-term hippocampal functioning as suggested by a longitudinal study of N = 1145 young adults (Marino and Fromme, 2016). Whereby, earlier drinking age was associated with more frequent blackouts (over 3-year period) which persisted despite a reduction in BD episodes.

A paucity of neuroimaging studies has directly examined AIB. However, functional magnetic resonance imaging (fMRI) studies undertaken to date provide evidence for neurobiological vulnerabilities that may exist prior to alcohol use onset and become more evident after BD patterns emerge (Wetherill and Fromme, 2016). Wetherill et al. (2012) utilized two fMRI sessions (nil vs. alcohol ingestion) to compare N = 12 university students (21–23 years) with a past 12-month history of FB to N = 12 peers without FB in a contextual memory task. The groups did not differ in performance or neural activity during the nil alcohol session. However, in the alcohol session (0.08% breath alcohol concentration) the FB group showed decreased blood-oxygen-level dependency (BOLD) response during encoding and recollection of contextual details in dorsolateral prefrontal and parietal regions.

Subsequently, this same group conducted an fMRI study in substance-naïve 13 year olds (Wetherill et al., 2013). At 5-year follow-up, the investigators compared inhibitory processing in those who remained substance naïve (n = 20) versus those who had transitioned into heavy drinkers with (n = 20) or without (n = 20) a history of AIB. Interestingly, at baseline the AIB group showed greater activation (increased BOLD) in frontal and cerebellar brain regions during inhibitory processing compared to both other groups. The authors suggested this provided evidence of inherent vulnerabilities to inhibitory processing difficulties that likely contribute to alcohol-induced memory impairments (Wetherill and Fromme, 2016).

Magnetic Resonance Spectroscopy: Probing the Neurochemistry of Blackout

Magnetic resonance spectroscopy (MRS) has provided evidence of in vivo neurochemical perturbations associated with alcohol misuse in human (Lee et al., 2007; Hermann et al., 2012; Ende et al., 2013; Yeo et al., 2013) and animal (Hermann et al., 2012) studies. However, only two MRS studies have specifically examined AIBs. Silveri et al. (2014) examined neurochemical profiles in the frontal and parietal-occipital lobes of BD aged 18–24 years. Compared to their light-drinking (LD) peers (N = 31), BD (N = 21) showed reduced gamma-aminobutyric acid (GABA) and N-acetylaspartate (NAA; a marker of neuronal integrity) in the anterior cingulate cortex (ACC). Furthermore, BD with a history of AIBs also showed significantly reduced glutamate compared LD. Follow-up analyses suggested that the reductions in GABA and NAA were more pronounced in BD with AIBs. There was also a trend for a reduction in glutamate in this subgroup. Importantly, all subjects had experience as college students, had high-average to superior IQ and none had an alcohol use disorder (AUD). Thus, the authors suggested that these findings might serve as early markers of risk in young individuals who continue hazardous drinking. Notably, only GABA was found to be significantly associated with cognitive performance, with lower levels of ACC-GABA being associated with worse performance in attentional switching and response inhibition.

To our knowledge, only one other study has specifically investigated AIB utilizing MRS. Our group (Chitty et al., 2014) examined the relationship between in vivo glutathione (GSH; the brain’s primary anti-oxidant) levels in young people with bipolar disorder (aged 18–30 years), given the high levels of alcohol use common to this psychiatric group and alcohol’s propensity to trigger oxidative stress (via the production of reactive oxygen species) in the brain (Nordmann et al., 1990). Despite no significant difference in overall risky drinking levels compared to healthy controls, the bipolar disorder group showed an association between increased alcohol use and decreased frontal (ACC) and hippocampal GSH. We supposed that this association might be evidence of memory impairment related to alcohol-induced oxidation, since increases in oxidative stress have also been linked to impairments in synaptic plasticity and memory, and decreased capacity to exhibit LTP (Pellmar et al., 1991; Auerbach and Segal, 1997).

Hippocampus: The Target of Further Investigation

Although mechanisms around AIBs are becoming increasingly understood, a detailed understanding of the neurobiological vulnerability (and why some individuals experience blackouts) remains unknown (Wetherill and Fromme, 2016). We would argue that more research targeting the neurochemistry and functioning of the hippocampus is needed to address this. More broadly, the hippocampus has been implicated in the pathogenesis of AUD (White and Swartzwelder, 2004). Furthermore, a substantive amount of work has led to the hippocampus being a focal point in studies of both the acute and chronic effects of alcohol use (Abrahao et al., 2017), particularly given its inhibition of glutamate binding [suppression of NMDA receptors (NMDAr)] (Strelnikov, 2007). It is also well-established that with chronic alcohol use, NMDAr binding sites increase in number and level of functioning (up-regulation), as demonstrated in rodents who show increased glutamate transmission in the hippocampus after repeated ethanol administration (Chefer et al., 2011). Furthermore, upon alcohol withdrawal, excessive glutamate activity resulting from increased numbers of NMDAr leads to a state of excitoxicity that can contribute to neurodegeneration (Hunt, 1993). Thus, periods of BD followed by abstinence may trigger cycles of neural responses that facilitate such neurotoxicity and associated cognitive impairments (Zeigler et al., 2005). Future studies should explore this by specifically examining factors associated with (and without) AIB, in particular, the underlying neurochemistry. This is crucial given the two key mechanisms underlying AIBs (Rose and Grant, 2010); that is: (i) a breakdown or blocking of short-term memory transfer, followed by; (ii) compromised subsequent retrieval caused by disruptions in hippocampal pyramidal cell activity. Crucially, the neurochemical processes underpinning these steps are: (i) potentiation of GABA-mediated inhibition; and (ii) interference of hippocampal NMDAr activation, leading to decreased LTP (Rose and Grant, 2010). The role of GSH may be important too given its status as a marker of oxidative stress. Furthermore, glutamate is a precursor of both GABA and GSH therefore the relationship between these metabolites (all measured via MRS) may be crucial to understanding individual differences in AIBs.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Funding

DH was supported by grants from the National Health and Medical Research Council (NHMRC) including a Centre of Research Excellence (No. 1061043).

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Footnotes

  1. ^The age range in the Australian Institute of Health Welfare (AIHW) survey was 20–24 years; whereas in the Substance Abuse and Mental Health Services Administration (SAMHSA) survey it was 20–25 years.
  2. ^For the 16–17 year old group with yearly but not monthly BD, the rate of ‘loss of memory after drinking’ could not be confirmed because of high sampling error.
  3. ^Schuckit et al. (2015) used the term ‘alcohol-related blackout’ however, alcohol-induced blackout is more commonly used and therefore “AIB” is term used throughout this paper.

References

Abrahao, K. P., Salinas, A. G., and Lovinger, D. M. (2017). Alcohol and the brain: neuronal molecular targets, synapses, and circuits. Neuron 96, 1223–1238. doi: 10.1016/j.neuron.2017.10.032

PubMed Abstract | CrossRef Full Text | Google Scholar

Acheson, S. K., Stein, R. M., and Swartzwelder, H. S. (1998). Impairment of semantic and figural memory by acute ethanol: age-dependent effects. Alcohol. Clin. Exp. Res. 22, 1437–1442. doi: 10.1111/j.1530-0277.1998.tb03932.x

PubMed Abstract | CrossRef Full Text | Google Scholar

AIHW (2014). National Drug Strategy Household Survey Detailed Report 2013. Drug Statistics Series No. 28. Cat No. PHE 183. Canberra, ACT: Australian Institute of Health and Welfare.

Google Scholar

AIHW (2017). National Drug Strategy Household Survey (NDSHS) 2016—Key Findings. Drug Statistics Series No. 31. Cat No. PHE 214. Canberra, ACT: Australian Institute of Health and Welfare).

Archie, S., Zangeneh Kazemi, A., and Akhtar-Danesh, N. (2012). Concurrent binge drinking and depression among Canadian youth: prevalence, patterns, and suicidality. Alcohol 46, 165–172. doi: 10.1016/j.alcohol.2011.07.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Auerbach, J. M., and Segal, M. (1997). Peroxide modulation of slow onset potentiation in rat hippocampus. J. Neurosci. 17, 8695–8701.

PubMed Abstract | Google Scholar

Bava, S., and Tapert, S. F. (2010). Adolescent brain development and the risk for alcohol and other drug problems. Neuropsychol. Rev. 20, 398–413. doi: 10.1007/s11065-010-9146-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Berridge, V., Herring, R., and Thom, B. (2009). Binge drinking: a confused concept and its contemporary history. Soc. Hist. Med. 22, 597–607. doi: 10.1136/jech.2006.056721

PubMed Abstract | CrossRef Full Text | Google Scholar

Boelema, S. R., Harakeh, Z., Van Zandvoort, M. J., Reijneveld, S. A., Verhulst, F. C., Ormel, J., et al. (2015). Adolescent heavy drinking does not affect maturation of basic executive functioning: longitudinal findings from the TRAILS study. PLOS ONE 10:e0139186. doi: 10.1371/journal.pone.0139186

PubMed Abstract | CrossRef Full Text | Google Scholar

Bonomo, Y. A., Bowes, G., Coffey, C., Carlin, J. B., and Patton, G. C. (2004). Teenage drinking and the onset of alcohol dependence: a cohort study over seven years. Addiction 99, 1520–1528. doi: 10.1111/j.1360-0443.2004.00846.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Chefer, V., Meis, J., Wang, G., Kuzmin, A., Bakalkin, G., and Shippenberg, T. (2011). Repeated exposure to moderate doses of ethanol augments hippocampal glutamate neurotransmission by increasing release. Addict. Biol. 16, 229–237. doi: 10.1111/j.1369-1600.2010.00272.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Chitty, K. M., Lagopoulos, J., Hickie, I. B., and Hermens, D. F. (2014). The impact of alcohol and tobacco use on in vivo glutathione in youth with bipolar disorder: an exploratory study. J. Psychiatr. Res. 55, 59–67. doi: 10.1016/j.jpsychires.2014.03.024

PubMed Abstract | CrossRef Full Text | Google Scholar

Clark, D. B., Thatcher, D. L., and Tapert, S. F. (2008). Alcohol, psychological dysregulation, and adolescent brain development. Alcohol. Clin. Exp. Res. 32, 375–385. doi: 10.1111/j.1530-0277.2007.00601.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Cservenka, A., and Brumback, T. (2017). The burden of binge and heavy drinking on the brain: effects on adolescent and young adult neural structure and function. Front. Psychol. 8:1111. doi: 10.3389/fpsyg.2017.01111

PubMed Abstract | CrossRef Full Text | Google Scholar

De Luca, C. R., Wood, S. J., Anderson, V., Buchanan, J.-A., Proffitt, T. M., Mahony, K., et al. (2003). Normative data from the CANTAB. I: development of executive function over the lifespan. J. Clin. Exp. Neuropsychol. 25, 242–254. doi: 10.1076/jcen.25.2.242.13639

PubMed Abstract | CrossRef Full Text | Google Scholar

Degenhardt, L., O’loughlin, C., Swift, W., Romaniuk, H., Carlin, J., Coffey, C., et al. (2013). The persistence of adolescent binge drinking into adulthood: findings from a 15-year prospective cohort study. BMJ Open 3:e003015. doi: 10.1136/bmjopen-2013-003015

PubMed Abstract | CrossRef Full Text | Google Scholar

EMCDDA/ESPAD (2016). ESPAD Report 2015: Results from the European School Survey Project on Alcohol and Other Drugs. Lisbon: Publications Office of the European Union.

Google Scholar

Ende, G., Hermann, D., Demirakca, T., Hoerst, M., Tunc-Skarka, N., Weber-Fahr, W., et al. (2013). Loss of control of alcohol use and severity of alcohol dependence in non-treatment-seeking heavy drinkers are related to lower glutamate in frontal white matter. Alcohol. Clin. Exp. Res. 37, 1643–1649. doi: 10.1111/acer.12149

PubMed Abstract | CrossRef Full Text | Google Scholar

Goodwin, D. W. (1995). Alcohol amnesia. Addiction 90, 315–317. doi: 10.1111/j.1360-0443.1995.tb03779.x

CrossRef Full Text | Google Scholar

Goodwin, D. W., Crane, J. B., and Guze, S. B. (1969a). Alcoholic "blackouts": a review and clinical study of 100 alcoholics. Am. J. Psychiatry 126, 191–198. doi: 10.1176/ajp.126.2.191

PubMed Abstract | CrossRef Full Text | Google Scholar

Goodwin, D. W., Crane, J. B., and Guze, S. B. (1969b). Phenomenological aspects of the alcoholic "blackout". Br. J. Psychiatry 115, 1033–1038. doi: 10.1192/bjp.115.526.1033

CrossRef Full Text | Google Scholar

Gropper, S., Spengler, S., Stuke, H., Gawron, C. K., Parnack, J., Gutwinski, S., et al. (2016). Behavioral impulsivity mediates the relationship between decreased frontal gray matter volume and harmful alcohol drinking: a voxel-based morphometry study. J. Psychiatr. Res. 83, 16–23. doi: 10.1016/j.jpsychires.2016.08.006

PubMed Abstract | CrossRef Full Text | Google Scholar

Hermann, D., Weber-Fahr, W., Sartorius, A., Hoerst, M., Frischknecht, U., Tunc-Skarka, N., et al. (2012). Translational magnetic resonance spectroscopy reveals excessive central glutamate levels during alcohol withdrawal in humans and rats. Biol. Psychiatry 71, 1015–1021. doi: 10.1016/j.biopsych.2011.07.034

PubMed Abstract | CrossRef Full Text | Google Scholar

Hermens, D. F., Lagopoulos, J., Tobias-Webb, J., De Regt, T., Dore, G., Juckes, L., et al. (2013). Pathways to alcohol-induced brain impairment in young people: a review. Cortex 49, 3–17. doi: 10.1016/j.cortex.2012.05.021

PubMed Abstract | CrossRef Full Text | Google Scholar

Hunt, W. A. (1993). Are binge drinkers more at risk of developing brain damage? Alcohol 10, 559–561.

PubMed Abstract | Google Scholar

Jellinek, E. M. (1946). Phases in the drinking history of alcoholics. Analysis of a survey conducted by the official organ of Alcoholics Anonymous (Memoirs of the Section of Studies on Alcohol). Q. J. Stud. Alcohol 7, 1–88. doi: 10.15288/QJSA.1946.7.1

CrossRef Full Text | Google Scholar

Lee, E., Jang, D.-P., Kim, J.-J., An, S. K., Park, S., Kim, I.-Y., et al. (2007). Alteration of brain metabolites in young alcoholics without structural changes. Neuroreport 18, 1511–1514. doi: 10.1097/WNR.0b013e3282ef7625

PubMed Abstract | CrossRef Full Text | Google Scholar

Lee, H., Roh, S., and Kim, D. J. (2009). Alcohol-induced blackout. Int. J. Environ. Res. Public Health 6, 2783–2792. doi: 10.3390/ijerph6112783

PubMed Abstract | CrossRef Full Text | Google Scholar

Lister, R. G., Gorenstein, C., Fisher-Flowers, D., Weingartner, H. J., and Eckardt, M. J. (1991). Dissociation of the acute effects of alcohol on implicit and explicit memory processes. Neuropsychologia 29, 1205–1212. doi: 10.1016/0028-3932(91)90034-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Marino, E. N., and Fromme, K. (2016). Early onset drinking predicts greater level but not growth of alcohol-induced blackouts beyond the effect of binge drinking during emerging adulthood. Alcohol. Clin. Exp. Res. 40, 599–605. doi: 10.1111/acer.12981

PubMed Abstract | CrossRef Full Text | Google Scholar

Markwiese, B. J., Acheson, S. K., Levin, E. D., Wilson, W. A., and Swartzwelder, H. S. (1998). Differential effects of ethanol on memory in adolescent and adult rats. Alcohol. Clin. Exp. Res. 22, 416–421. doi: 10.1111/j.1530-0277.1998.tb03668.x

CrossRef Full Text | Google Scholar

Maylor, E. A., and Rabbitt, P. M. (1987). Effect of alcohol on rate of forgetting. Psychopharmacology 91, 230–235. doi: 10.1007/BF00217069

CrossRef Full Text | Google Scholar

Maylor, E. A., and Rabbitt, P. M. (1993). Alcohol, reaction time and memory: a meta-analysis. Br. J. Psychol. 84(Pt 3), 301–317. doi: 10.1111/j.2044-8295.1993.tb02485.x

CrossRef Full Text | Google Scholar

Mundt, M. P., Zakletskaia, L. I., Brown, D. D., and Fleming, M. F. (2012). Alcohol-induced memory blackouts as an indicator of injury risk among college drinkers. Inj. Prev. 18, 44–49. doi: 10.1136/ip.2011.031724

PubMed Abstract | CrossRef Full Text | Google Scholar

Nelson, E. C., Heath, A. C., Bucholz, K. K., Madden, P. A., Fu, Q., Knopik, V., et al. (2004). Genetic epidemiology of alcohol-induced blackouts. Arch. Gen. Psychiatry 61, 257–263. doi: 10.1001/archpsyc.61.3.257

PubMed Abstract | CrossRef Full Text | Google Scholar

Nguyen-Louie, T. T., Tracas, A., Squeglia, L. M., Matt, G. E., Eberson-Shumate, S., and Tapert, S. F. (2016). Learning and memory in adolescent moderate, binge, and extreme-binge drinkers. Alcohol Clin. Exp. Res. 40, 1895–1904. doi: 10.1111/acer.13160

PubMed Abstract | CrossRef Full Text | Google Scholar

NIAAA (2017). Drinking Levels Defined. Available at: https://www.niaaa.nih.gov/alcohol-health/overview-alcohol-consumption/moderate-binge-drinking [accessed November 2, 2017].

Nordmann, R., Ribiere, C., and Rouach, H. (1990). Ethanol-induced lipid peroxidation and oxidative stress in extrahepatic tissues. Alcohol Alcohol. 25, 231–237. doi: 10.1093/oxfordjournals.alcalc.a044996

CrossRef Full Text | Google Scholar

Ostby, Y., Tamnes, C. K., Fjell, A. M., Westlye, L. T., Due-Tonnessen, P., and Walhovd, K. B. (2009). Heterogeneity in subcortical brain development: a structural magnetic resonance imaging study of brain maturation from 8 to 30 years. J. Neurosci. 29, 11772–11782. doi: 10.1523/JNEUROSCI.1242-09.2009

PubMed Abstract | CrossRef Full Text | Google Scholar

Pellmar, T. C., Hollinden, G. E., and Sarvey, J. M. (1991). Free radicals accelerate the decay of long-term potentiation in field CA1 of guinea-pig hippocampus. Neuroscience 44, 353–359. doi: 10.1016/0306-4522(91)90060-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Pressman, M. R., and Caudill, D. S. (2013). Alcohol-induced blackout as a criminal defense or mitigating factor: an evidence-based review and admissibility as scientific evidence. J. Forensic Sci. 58, 932–940. doi: 10.1111/1556-4029.12134

PubMed Abstract | CrossRef Full Text | Google Scholar

Pyapali, G. K., Turner, D. A., Wilson, W. A., and Swartzwelder, H. S. (1999). Age and dose-dependent effects of ethanol on the induction of hippocampal long-term potentiation. Alcohol 19, 107–111. doi: 10.1016/S0741-8329(99)00021-X

PubMed Abstract | CrossRef Full Text | Google Scholar

Risher, M. L., Fleming, R. L., Risher, W. C., Miller, K. M., Klein, R. C., Wills, T., et al. (2015). Adolescent intermittent alcohol exposure: persistence of structural and functional hippocampal abnormalities into adulthood. Alcohol. Clin. Exp. Res. 39, 989–997. doi: 10.1111/acer.12725

PubMed Abstract | CrossRef Full Text | Google Scholar

Rose, M. E., and Grant, J. E. (2010). Alcohol-induced blackout. Phenomenology, biological basis, and gender differences. J. Addict. Med. 4, 61–73. doi: 10.1097/ADM.0b013e3181e1299d

PubMed Abstract | CrossRef Full Text | Google Scholar

SAMHSA (2009). Results from the 2008 National Survey on Drug Use and Health: Summary of National Findings. NSDUD Series H-38, HHS Publication No. (SMA) 09-4434. Rockville, MD: Office of Applied Studies).

Google Scholar

SAMHSA (2011). Results from the 2010 National Survey on Drug Use and Health: Summary of National Findings. NSDUD Series H-41, HHS Publication No. (SMA) 11-4658. Rockville, MD: Office of Applied Studies.

Google Scholar

SAMHSA (2017). Key Substance Use and Mental Health Indicators in the United States: Results from the 2016 National Survey on Drug Use and Health. HHS Publication No. SMA 17-5044, NSDUH Series H-52. Rockville, MD: Office of Applied Studies.

Google Scholar

Schuckit, M. A., Smith, T. L., Heron, J., Hickman, M., Macleod, J., Munafo, M. R., et al. (2015). Latent trajectory classes for alcohol-related blackouts from age 15 to 19 in ALSPAC. Alcohol. Clin. Exp. Res. 39, 108–116. doi: 10.1111/acer.12601

PubMed Abstract | CrossRef Full Text | Google Scholar

Silveri, M. M., Cohen-Gilbert, J., Crowley, D. J., Rosso, I. M., Jensen, J. E., and Sneider, J. T. (2014). Altered anterior cingulate neurochemistry in emerging adult binge drinkers with a history of alcohol-induced blackouts. Alcohol. Clin. Exp. Res. 38, 969–979. doi: 10.1111/acer.12346

PubMed Abstract | CrossRef Full Text | Google Scholar

Siqueira, L., and Smith, V. C. (2015). Binge drinking. Pediatrics 136, e718–e726. doi: 10.1542/peds.2015-2337

PubMed Abstract | CrossRef Full Text | Google Scholar

Squeglia, L. M., Tapert, S. F., Sullivan, E. V., Jacobus, J., Meloy, M. J., Rohlfing, T., et al. (2015). Brain development in heavy-drinking adolescents. Am. J. Psychiatry 172, 531–542. doi: 10.1176/appi.ajp.2015.14101249

PubMed Abstract | CrossRef Full Text | Google Scholar

Squire, L. R., Stark, C. E., and Clark, R. E. (2004). The medial temporal lobe. Annu. Rev. Neurosci. 27, 279–306. doi: 10.1146/annurev.neuro.27.070203.144130

CrossRef Full Text | Google Scholar

Strelnikov, K. (2007). Can mismatch negativity be linked to synaptic processes? A glutamatergic approach to deviance detection. Brain Cogn. 65, 244–251. doi: 10.1016/j.bandc.2007.04.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Swartzwelder, H. S., Wilson, W. A., and Tayyeb, M. I. (1995a). Age-dependent inhibition of long-term potentiation by ethanol in immature versus mature hippocampus. Alcohol. Clin. Exp. Res. 19, 1480–1485.

PubMed Abstract | Google Scholar

Swartzwelder, H. S., Wilson, W. A., and Tayyeb, M. I. (1995b). Differential sensitivity of NMDA receptor-mediated synaptic potentials to ethanol in immature versus mature hippocampus. Alcohol. Clin. Exp. Res. 19, 320–323.

PubMed Abstract | Google Scholar

Vargas, W. M., Bengston, L., Gilpin, N. W., Whitcomb, B. W., and Richardson, H. N. (2014). Alcohol binge drinking during adolescence or dependence during adulthood reduces prefrontal myelin in male rats. J. Neurosci. 34, 14777–14782. doi: 10.1523/JNEUROSCI.3189-13.2014

CrossRef Full Text | Google Scholar

Vinader-Caerols, C., Duque, A., Montanes, A., and Monleon, S. (2017). Blood alcohol concentration-related lower performance in immediate visual memory and working memory in adolescent binge drinkers. Front. Psychol. 8:1720. doi: 10.3389/fpsyg.2017.01720

PubMed Abstract | CrossRef Full Text | Google Scholar

Wetherill, R. R., Castro, N., Squeglia, L. M., and Tapert, S. F. (2013). Atypical neural activity during inhibitory processing in substance-naive youth who later experience alcohol-induced blackouts. Drug Alcohol Depend. 128, 243–249. doi: 10.1016/j.drugalcdep.2012.09.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Wetherill, R. R., and Fromme, K. (2016). Alcohol-induced blackouts: a review of recent clinical research with practical implications and recommendations for future studies. Alcohol. Clin. Exp. Res. 40, 922–935. doi: 10.1111/acer.13051

PubMed Abstract | CrossRef Full Text | Google Scholar

Wetherill, R. R., Schnyer, D. M., and Fromme, K. (2012). Acute alcohol effects on contextual memory BOLD response: differences based on fragmentary blackout history. Alcohol. Clin. Exp. Res. 36, 1108–1115. doi: 10.1111/j.1530-0277.2011.01702.x

PubMed Abstract | CrossRef Full Text | Google Scholar

White, A. M. (2003). What happened? Alcohol, memory blackouts, and the brain. Alcohol Res. Health 27, 186–196.

PubMed Abstract | Google Scholar

White, A. M., Ghia, A. J., Levin, E. D., and Swartzwelder, H. S. (2000). Binge pattern ethanol exposure in adolescent and adult rats: differential impact on subsequent responsiveness to ethanol. Alcohol. Clin. Exp. Res. 24, 1251–1256. doi: 10.1111/j.1530-0277.2000.tb02091.x

PubMed Abstract | CrossRef Full Text | Google Scholar

White, A. M., and Swartzwelder, H. S. (2004). Hippocampal function during adolescence: a unique target of ethanol effects. Ann. N. Y. Acad. Sci. 1021, 206–220. doi: 10.1196/annals.1308.026

PubMed Abstract | CrossRef Full Text | Google Scholar

White, V., and Hayman, J. (2006). Australian Secondary School Students’ Use of Over-the-counter and Illicit Substances in 2005. Victoria, BC: The Cancer Council.

Google Scholar

Wilson, S., Bair, J. L., Thomas, K. M., and Iacono, W. G. (2017). Problematic alcohol use and reduced hippocampal volume: a meta-analytic review. Psychol. Med. 47, 2288–2301. doi: 10.1017/S0033291717000721

PubMed Abstract | CrossRef Full Text | Google Scholar

Yeo, R. A., Thoma, R. J., Gasparovic, C., Monnig, M., Harlaar, N., Calhoun, V. D., et al. (2013). Neurometabolite concentration and clinical features of chronic alcohol use: a proton magnetic resonance spectroscopy study. Psychiatry Res. 211, 141–147. doi: 10.1016/j.pscychresns.2012.05.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Zeigler, D. W., Wang, C. C., Yoast, R. A., Dickinson, B. D., Mccaffree, M. A., Robinowitz, C. B., et al. (2005). The neurocognitive effects of alcohol on adolescents and college students. Prev. Med. 40, 23–32. doi: 10.1016/j.ypmed.2004.04.044

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: binge drinking, alcohol-induced blackout, adolescent, young adult, hippocampus, memory, magnetic resonance spectroscopy

Citation: Hermens DF and Lagopoulos J (2018) Binge Drinking and the Young Brain: A Mini Review of the Neurobiological Underpinnings of Alcohol-Induced Blackout. Front. Psychol. 9:12. doi: 10.3389/fpsyg.2018.00012

Received: 01 August 2017; Accepted: 04 January 2018;
Published: 19 January 2018.

Edited by:

Salvatore Campanella, Université Libre de Bruxelles, Belgium

Reviewed by:

Caroline Quoilin, Université catholique de Louvain, Belgium
Sonia S. Sousa, University of Minho, Portugal
Anita Cservenka, Oregon State University, United States

Copyright © 2018 Hermens and Lagopoulos. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Daniel F. Hermens, dhermens@usc.edu.au; daniel.hermens@sydney.edu.au