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c o r t e x 4 9 ( 2 0 1 3 ) 3e1 7

Available online at

Journal homepage: www.elsevier.com/locate/cortex

Review

Pathways to alcohol-induced brain impairment in youngpeople: A review

Daniel F. Hermens a,*, Jim Lagopoulos a, Juliette Tobias-Webb a, Tamara De Regt a,Glenys Dore b, Lisa Juckes b, Noeline Latt b and Ian B. Hickie a

aClinical Research Unit, Brain & Mind Research Institute, University of Sydney, Sydney, NSW, AustraliabNorthern Sydney Drug & Alcohol Service, Herbert Street Clinic, Royal North Shore Hospital, St Leonards, NSW, Australia

a r t i c l e i n f o

Article history:

Received 15 November 2011

Reviewed 13 January 2012

Revised 2 April 2012

Accepted 23 May 2012

Action editor Henry Buchtel

Published online 17 June 2012

Keywords:

Young people

Alcohol

Binge drinking

Neuropsychology

Neuroimaging

* Corresponding author. Clinical Research UnNSW 2050, Australia.

E-mail address: daniel.hermens@sydney.0010-9452/$ e see front matter ª 2012 Elsevhttp://dx.doi.org/10.1016/j.cortex.2012.05.021

a b s t r a c t

Classically, disorders associated with ‘alcohol-related brain damage’ (ARBD) occur as

a result of chronic excessive alcohol misuse and confer significant physical and psycho-

logical disability to the individual as well as to the community. These phenotypes are often

difficult to detect at early stages and therefore early intervention and treatment is limited.

It remains unresolved as to whether there are neurobiological markers of the early stages

of such brain damage in young ‘at-risk’ drinkers, who probably experience ‘alcohol-

induced brain impairment’ prior to the onset of ARBD, per se. This review focuses on

neurobiological (in particular, neuropsychological and neuroimaging) markers that are

associated with alcohol misuse in young people (13e24 years of age). The findings from this

review suggest that a clearer understanding of alcohol misuse (particularly with regards to

binge drinking) is needed. Despite this, neurocognitive profile along with supporting neu-

roimaging evidence appears to be particularly important in the early detection of brain

changes that result from excessive alcohol use. In young alcohol misusers, these

preventable and potentially reversible deficits may be progressive but if left unresolved

such deficits eventually become major contributors to poor outcome (long term) and

hamper adherence to treatment. We address five key themes in this review: (i) there are

specific drinking patterns in young people; (ii) youth represents a critical period in brain

development that is particularly vulnerable to alcohol misuse; (iii) the extent to which

there are pre-existing versus alcohol-induced neurobiological changes remains unclear;

(iv) vulnerability markers may be mediated by mental health and substance use comor-

bidities; and (v) cognitive remediation would be a likely candidate for early prevention and

treatment as it could help to develop efficient meta-cognitive skills to prevent relapse in

young drinkers.

ª 2012 Elsevier Ltd. All rights reserved.

it, Brain & Mind Research Institute, University of Sydney, 100 Mallett Street, Camperdown,

edu.au (D.F. Hermens).ier Ltd. All rights reserved.

c o r t e x 4 9 ( 2 0 1 3 ) 3e1 74

1. Introduction adolescent (13e18 years) and young adult (19e24 years) age

It is estimated that excessive alcohol use causes a net harm of

3.7% of all deaths and 4.4% of the global burden of disease

(Anderson, 2006; WHO, 2007). Of particular concern are the

damaging effects of alcohol abuse on the developing brain in

young people, before the age of 25 years, with retardation of

intellectual function, rational decision making and emotional

maturation (Spear, 2011). At the other extreme of the spec-

trum, the long-term effects of excessive chronic alcohol

consumption on the brain are well described within the

literature (Crews and Nixon, 2009; Harper, 2009). Up until

recently, the vastmajority of this research has tended to focus

on brain damage that ensues following excessive, long-term

drinking. The term ‘alcohol-related brain damage’ (ARBD) is

commonly used to describe chronic neurological conditions

such as acute Wernicke’s encephalopathy, Korsakoff’s

syndrome, WernickeeKorsakoff syndrome, frontal lobe

syndrome, cerebrocortical degeneration, cerebellar degener-

ation, cortical atrophy and alcoholic dementia. However,

ARBD also applies to other stages of the lifespan, albeit in

different ways (Marshall et al., 2009). The earliest forms of

ARBD are in the fetal alcohol spectrum disorders, which are

due to excessive alcohol consumption during pregnancy and

they are associated with life-long behavioral and cognitive

effects.

Despite the recognition that adolescent and young adult

brains are particularly vulnerable to the effects of excessive

alcohol consumption, the mechanisms by which ARBD occurs

(or commences) is poorly understood within this cohort. In

a recent review, Clark et al. (2008) suggest that the asynchro-

nous development of the prefrontal cortex with respect to the

limbic system in adolescence or young adulthood drives such

a heightened vulnerability to the effects of alcohol. Hence,

they offer a conceptual framework whereby neuroimaging

studies, particularly those that follow patients longitudinally,

can help to clarify this. Furthermore, binge drinking (in

contrast to chronic long-term drinking) is now acknowledged

as being the dominant type of alcoholmisuse in this age group

(Archie et al., 2012; Deas, 2006; SAMHSA, 2009). However

despite its prevalence, there is a paucity of studies investi-

gating the effects of short-term excessive drinking in young

people. Thus, rather than focusing on brain damage per se,

research effort may be better directed at examining the early

stages of brain impairment induced by alcohol misuse in this

vulnerable age group. The identification of early neuro-

cognitive changes associated with excessive drinking in

adolescents and young adults has the potential to result in

a better understanding of the biomarkers associated with

long-termARBD risk and help to prevent alcohol related harm.

Themain goal of this review is to help answer an important

and difficult question: ‘Are young alcohol misusers on the

same pathway as those who eventually develop ARBD’? (i.e.,

brain damage resulting from long-term repetitive alcohol use).

Accordingly, this review focuses on studies that have specifi-

cally examined the neuropsychological and/or neurobiolog-

ical (via neuroimaging techniques) effects of alcoholmisuse in

young people. Here, ‘young people’ are defined as those

between 13 and 24 years of age, a combination of the

ranges (NLM, 2012). After describing the drinking patterns that

are most commonly associated with young people this review

summarizes the neuropsychological and neuroimaging find-

ings relevant to this group as ameans to better understand the

underlying neurobiology of early alcohol misuse. As a means

to integrate the neuropsychological and neuroimaging find-

ings we offer a summary of potential ‘vulnerability markers’

for this area of research with the hope that such biomarkers

may help to distinguish the specific effects of alcohol misuse

from pre-existing neurobiological differences of those who

misuse alcohol at very early stages. Finally, this review

concludes by discussing: (i) why the younger brain is at

a heightened risk when exposed to binge-style drinking;

(ii) how comorbid mental health problems, common in youth,

contribute to and exacerbate the neurobiological effects of

alcohol misuse; (iii) what may be the implications for early

intervention or treatment given the potential identification of

early warning signs; and (iv) the potential impacts of other

substance misuse (in particular, marijuana misuse) in young

drinkers.

2. Drinking patterns in young people

Alcohol is themost widely used substance among adolescents

(Johnston et al., 2009) and there is a considerable prevalence of

escalated drinking levels (particularly per occasion e see

below) occurring within this critical stage in brain develop-

ment (Brown et al., 2008). When describing drinking patterns

in young people it is imperative to look beyond the traditional

‘alcohol use disorders’ [AUDs: alcohol abuse and alcohol

dependence (APA, 2000)] which may account for only

a proportion of those who misuse alcohol. For example,

‘harmful use’ is defined as a repetitive pattern of drinking at

levels that result in actual physical or mental harm but do not

fulfill criteria for the dependence syndrome (Babor and

Higgins-Biddle, 2001). Other terms like ‘risky drinking’,

‘hazardous drinking’ or ‘binge drinking’ add to the complexity

but it is important to note the differences (and overlap) when

interpreting data from epidemiological and clinical studies.

Binge (or ‘heavy episodic’) drinking is becoming increas-

ingly recognized as being a drinking pattern that is particu-

larly relevant to young people. Unfortunately, there is no

universal definition of what is considered to be binge drinking,

however, more recently it has been defined as consuming five

drinks or more on the same occasion (SAMHSA, 2011). In the

past, there have been variations in the definition according to

factors such as gender, bodymass index or blood alcohol level.

Furthermore, binge drinking is often followed by a period of

abstinence with no severe withdrawal symptoms being

experienced by the individual (Crego et al., 2009; Jarvenpaa

et al., 2005), unless the individual is alcohol dependent.

Binge drinking is more common among young adults, espe-

cially university students (Crego et al., 2009). This is of

increasing concern given the evidence from animal studies

showing that the adolescent brain is more vulnerable to the

effects of binge drinking compared to the adult (Crews et al.,

2000).

c o r t e x 4 9 ( 2 0 1 3 ) 3e1 7 5

The use and abuse of alcohol often begins in adolescence

with alcohol intake and binge drinking increasing sharply

(that is, from 1.0% to 45.5%) between the ages of 12e25

(SAMHSA, 2011). Critically, it has been suggested (Bates et al.,

2002) that neurocognitive impairments in younger alcohol

misusersmay bemore amenable to recovery, with abstinence.

However, more research is needed to clarify this as the only

supporting evidence comes from studies where the ‘younger’

alcoholic patients were less than 50 years of age (Brandt et al.,

1983; Rourke and Grant, 1999). In the general community,

although young people drink less often than older adults, they

tend to drink more per occasion, with highest rates of binge

drinking being for those between 21 and 24 years of age

(SAMHSA, 2009). Similarly, drinking to intoxication is reported

to be common in teenagers (White and Hayman, 2006) and

more young people die from the acute effects of alcohol rather

than the long-term ones (NHMRC, 2009). A large survey of US

students found that by the time they reach the 8th grade,

almost 40% of students have consumed alcohol and nearly

one-fifth (18%) report being drunk at least once. Moreover, by

the time students have reached grade 12, well over two-thirds

(72%) have consumed alcohol and more than half (55%) have

been drunk (Johnston et al., 2009).

Prevalence studies often examine AUDs collectively,

sometimeswithout clearly defining either diagnosis. In theUS,

a high prevalence of alcohol dependence was reported in

young people (between the ages of 18 and 20 years) who began

drinking at an early age (Grant et al., 2004a). Alcohol abuse is,

however, more common in young people. Drawing from the

2007National Survey ofMental Health andWellbeing, Teesson

et al. (2010) reported that the prevalence of lifetime alcohol

abuse and dependence was 18.3% and 3.9%, respectively, from

a large (N¼ 8841) sampleofAustralianadults (16e85 years). For

participants between the ages of 16e25 years, 19.2% reported

a lifetimeAUDand11.1%reporteda12-monthAUD. In contrast

26.5% of participants between the ages of 25e44 reported

a lifetime AUD but only 4.7% reported a 12-month AUD.

The earlier the age of drinking onset, the more likely an

individualwill experience long-termproblemsassociatedwith

alcohol. In 2010, data fromthe (US)National SurveyofDrugUse

and Health found that individuals who first used alcohol at 14

years of age or younger were five times more likely to develop

an AUD by the time they were adults, compared to those who

commenced drinking after 21 years of age (SAMHSA, 2011).

Similarly, Hingson and Zha (2009) found that, by the time they

reachedadulthood, thosewhostarteddrinkingat youngerages

were more likely to: (i) experience an AUD; (ii) binge drink at

least weekly; (iii) drive under the influence of alcohol; and (iv)

engage in risk-takingbehaviorafterdrinking. Ina large studyof

Australian youth (18e24 years), Bonomoet al. (2004) found that

90% of respondents drank at levels that placed them at high

risk of acute harm including periods of unconsciousness,

unsafe sexual practices, injury and accidental death, in addi-

tion to homicide and suicide.

The Australian 2007 National Drug Strategy Household

Survey (AIHW, 2008) reported that 16% versus 19% of young

(18e24 years of age) women and men, respectively, had

engaged in ‘risky’ or ‘high-risk’ drinking, at least once per

week, in the preceding 12 months. This was double the

comparable rate of risky/high-risk drinking females and

males over the age of 25 years (5% and 8%, respectively).

Despite differences between the sexes in patterns of drinking,

there is evidence to suggest that females may be more

susceptible to alcohol related brain impairment compared to

males (Caldwell et al., 2005). In an effort to determinewhether

definitions of risky drinking have any utility in predicting

long-term outcomes for young people, Moore et al. (2009)

conducted a 10-year follow-up study of 1520 adolescents.

Compared with females, more males were in the higher risk

categories, particularly with respect to short-term risk. By the

end of the study, 27% of males and 13% of femalesmet criteria

for an AUD. Perhaps the most striking result of the study was

that adolescents identified as low-risk drinkers were just as

likely as their risky/high-risk drinking peers to develop an

adverse outcome (including an AUD) in adulthood.

While studies show that risky or binge drinking patterns in

young people are highly prevalent and problematic (with far

reaching harmful consequences) there have been no major

advances in the early detection of the impairments that ensue.

To this end there is a real need to identify markers and thresh-

olds of impairments at an early stage in those who have risky

drinking patterns while they are in vulnerable stages of brain

development. A further complicating factor is the high preva-

lence (particularly in young people) of comorbid mental health

problems that often exacerbate alcohol-induced impairment

and likely contribute to the early stages of brain changes and

damage (Conway et al., 2006; Grant, 1995; Grant et al., 2004b).

3. Early neuropsychological effects of alcoholmisuse in young people

The relationship between chronic exposure to alcohol and

cognitive impairment is well established in older adults

(Oscar-Berman and Marinkovi�c, 2007) and specific links have

been established between neuropsychological impairment

and alcohol related neurodegeneration in animals (Crews and

Nixon, 2009). Far from affecting all cognitive functions

equally, chronic alcoholmisuse is thought to primarily disrupt

memory and ‘fluid’ cognitive abilities, such as problem solving

and concept formation (Bates et al., 2002). Themost consistent

findings are visuospatial deficits, as well as deficits in execu-

tive functions (including problem solving, mental flexibility,

and response inhibition) and the controlled processing of

attention and working memory (Scheurich, 2005; Yucel et al.,

2007). On the other hand, automatic and over-learned func-

tions seem to be preferentially spared (Bates et al., 2002).

While there is an expanding literature describing the

effects of chronic alcohol misuse, there is a paucity of studies

that describe the effects of excessive alcohol use in adoles-

cents and young adults with respect to their neuro-

psychological functioning (Squeglia et al., 2009b). Although

the observed impairments are generally thought to be similar

to those observed in older adults young people with AUDs

probably present with more subtle cognitive changes which

then rapidly deteriorate as the brain reaches maturation. To

this end adolescents and young adults may be more suscep-

tible to alcohol-induced memory impairments which include

problems with retrieval as well as poor attention, impair-

ments in cognitive processing and (compared to older adults)

c o r t e x 4 9 ( 2 0 1 3 ) 3e1 76

poorer language skills (Tapert and Schweinsburg, 2005).

Table 1 summarizes key studies that have examined the

effects of alcohol on the neuropsychological performance of

young people.

Squeglia et al. (2009b) conducted a 3-year longitudinal

study of females and males whose baseline assessment was

conducted prior to the onset of any drinking (i.e., when they

were 12e14 years of age). It was found that the females who

went on to undertake moderate or heavy drinking patterns

showed specific impairments in their visuospatial perfor-

mance. On the other hand, for males there was an association

between increased hangover symptoms and poorer perfor-

mance in sustained attention. These cognitive deficits were

found to be over and above baseline performance, suggesting

that there may be specific neuropsychological markers asso-

ciated with early alcohol misuse. An earlier study by Moss

et al. (1994) reported that AUD adolescents (aged 15.5 � 1.5

years) had poorer language skills in the absence of any

‘significant brain damage’ (inferred from their overall neuro-

psychological performance) compared with controls

(14.7 � 1.8 years); there were no differences between AUD

females andmales. Based on (separate) evidence showing that

the sons of alcoholic fathers have poorer language abilities,

Moss et al. (1994) took their findings to suggest that poor verbal

skills may be a risk factor for alcoholism, however their study

may have been limited by a significant level of poly-substance

abuse within the cohort.

A study of detoxified adolescents (15e16 years) reports that

these patients have poorer retention of verbal and non-verbal

information, but have intact learning and recognition abilities

compared to healthy controls (Brown et al., 2000). The impaired

recall of verbal information in these alcohol dependent

adolescents is thought tobeduetoapoorutilizationofsemantic

clustering, anexecutiveskill that assistswithefficientencoding

of information (Brown et al., 2000). Tapert et al. (2004) reported

that AUD adolescents (15e17 years) showed no performance

impairments in spatial working memory (SWM) and simple

motor tasks (however, there were impairments revealed by

neuroimaging; reported in the following section). In a previous

(and similar) study by the same group (Tapert et al., 2001), 10

young adult (18e25 years) females were impaired in SWM,

attentional switching and digit span tasks; impairments were

greatest in those individualswhohadexperiencedmorealcohol

withdrawal symptoms in the past 2 years.

A more recent study of Spanish binge drinking youth

(Sanhueza et al., 2011) has reported that both moderate

(19.0 � 1.4 years) and heavy (19.0 � 1.2 years) drinkers per-

formedsimilarly (acrossa rangeofneuropsychological tests) to

agroupofelderly (69.3�4.8years)non-drinkersandperformed

worse than their age-matched non-drinking peers (18.8 � 1.7

years). The young drinkers performed worse than the young

and elderly control groups in executive function tasks, partic-

ularly with respect to perseverative errors. There was also

evidence of an effect of ‘intensity of alcohol consumption’

given that the heavy drinkers tended to be worse than their

moderatedrinkingpeersacrossall tests. Ina longitudinal study

of college students (20� .4 years), Goudriaan et al. (2007) found

that while decision making was increasingly impaired by the

level of bingedrinking, itwasnot related to impulsivitynorwas

it associated with age of onset.

Compared with their teetotaler peers, binge drinking

British university students (18e23 years) demonstrated poorer

sustained attention, episodic memory and planning ability,

they also had increased levels of self-rated anxiety and

depression (Hartley et al., 2004). In a similar sample of

‘healthy’ volunteers (18e29 years), Scaife and Duka (2009)

found that binge drinkers were worse than their non-binge

peers in a visual memory task with female binge drinkers

exhibiting additional deficits in tasks of cognitive flexibility

and SWM. The authors concluded that female binge drinkers

had dorsolateral prefrontal cortex impairments in addition to

the temporal lobe impairments that appear to occur in binge

drinkers (of both genders). In another study (Townshend and

Duka, 2005) of British social drinkers (18e30 years), female

binge drinkers showed impaired SWM and vigilance. Notably,

binge drinkers (both male and female) were faster than their

non-binge peers in choice reaction (and movement) times

however, their current mood states were less positive (and

this was not related to alcohol withdrawal).

Despite the general consensus that neuropsychological

impairments are associatedwith chronic alcohol use, there still

remains contention regarding the actual relationship between

the duration and/or the amount of alcohol use and the associ-

ated cognitive deficits. A younger age of onset of AUDs may be

associated with greater cognitive deficits and the degree of

neurological damage (Chanraudet al., 2006; Pishkin et al., 1985).

Previous accounts in adult populations have proposed that

neuropsychological impairments are apparent only after

considerable exposure to alcohol; for example, Eckardt et al.

(1998) suggested that impairments arise after 10 years of

alcohol abuse. The effects of the age of onset and theamount of

alcohol use are less clear in adolescents, and indeed represent

an area inneedofmore research (Yucel et al., 2007). Overall, the

studies summarized above (and in Table 1) indicate that young

alcohol misusers tend to show the same patterns of neuro-

psychological impairments seen in much older, chronic alco-

holism; in particular, they show memory and executive

functioningdeficits.Whilethesestudiessupport thehypothesis

that alcohol causes premature aging of the brain (at least in

termsof cognition), they also suggest that thehypothesis needs

revision, with respect to: (i) the stage at which such premature

agingmay occur; and (ii) that heavy episodic (or binge) drinking

appears to play a considerable role in the early stages of ARBD.

While the actual relationship between early binge drinking and

cognitive deficits has been difficult to tease out there is strong

evidence (particularly from neuroimaging studies) to suggest

that binge drinking has direct effects on the brain; for example,

Squeglia et al. (2009a) summarize findings showing that even

subtle binge drinking in adolescents impacts brain develop-

ment [in particular, alteredwhitematter (WM) integrity]. Thus,

beyond neuropsychological impairment, a range of neuro-

imaging techniques have further revealed neurobiological

changes associated with young alcohol misusers.

4. Early neurobiological effects of alcoholmisuse in young people

As with neuropsychological findings, the relationship

between chronic exposure to alcohol and neurobiological

Table 1 e Neuropsychological studies of young (13e24 yrs) alcohol users sorted according to average age (youngest to oldest).

Authors Age (mean � SD) Sample (N) Aims Neuropsychological measures Key neuropsychological findings

Squeglia

et al. (2009b)

ALC (F): 13.9 � .8 yrs

CTRL (F): 13.4 � .7 yrs

ALC (M): 13.8 � .8 yrs

CTRL (M): 13.5 � .8 yrs

ALC (23M; 13F)

CTRL (24M; 16F)

Prospectively examine influence

of alcohol on neuropsychological

functioning in alcohol naıve

boys and girls

Executive functioning (D-KEFS);

Verbal learning & memory (CVLT);

Visuospatial (ROCFT, WAIS-BD);

Achievement & general intellectual

(WISC-III-C, WRAT-3);

Attention/working memory (DVT, WISC-III-DS)

ALC (F): Y Visuospatial

ALC (M): Y Attention

Moss et al. (1994) AUD: 15.5 � 1.5 yrs

CTRL: 14.7 � 1.8 yrs

AUD (20M; 18F)

[DEP ¼ 27; AB ¼ 11]

CTRL (29M: 40F)

Investigate neuropsychological

characteristics of combined

clinical & community sample

of abstinent AUD adolescents

Verbal learning & memory (CVLT, CVM);

Achievement & general intellectual (PIAT; WAIS-R);

Attention (TMT);

Executive functioning (WCST)

AUD (M & F): Y language skills

Brown et al. (2000) DEP: 16.2 � .6 yrs

CTRL: 15.9 � .6 yrs

DEP (19M; 14F)

CTRL (14M; 10F)

Examine neuropsychological

functioning in adolescent

AUD following 3 weeks

of detoxification

Executive functioning (BCT, COWAT);

Language (BNT);

Verbal learning & memory (CVLT-C);

Visuospatial (EFT);

Attention (TMT);

Achievement & general intellectual (WISC-R);

Visual learning & memory (WMS-VR)

DEP (M & F): Y Verbal &

non-verbal memory

Tapert et al. (2004) AUD: 16.8 � .7 yrs

CTRL: 16.5 � .8 yrs

AUD (10M; 5F)

[DEP ¼ 8; AB ¼ 7]

CTRL (11M; 8F)

Determine whether there

are neurocognitive changes

in younger people with an AUD

Attention/Working memory (SWM);

Verbal learning & memory (CVLT-C);

Visuospatial (ROCFT);

Attention (TMT);

Executive functioning (SCWT);

Achievement & general intellectual (WAIS-III,

WISC-II, WRAT-3-R; -A)

Nil

Sanhueza et al. (2011) BD: 19.0 � 1.2 yrs

non-BD: 19.0 � 1.4 yrs

CTRL: 18.8 � 1.7 yrs

Old: 69.3 � 4.8 yrs

BD (8M; 13F)

non-BD (9M; 13F)

CTRL (8M; 12F)

Old (11M; 15F)

Compare neurocognition in

young binge drinkers to

young & elderly non-drinkers

Visual learning & memory (BVRT);

Verbal learning & memory (CVLT);

Executive functioning (ToH, SCWT);

Attention/Working memory (WMS-DS)

BD (M & F): Y

Executive functioning

Goudriaan et al. (2007) Hi BD: 20.0 � .4 yrs

Incr. BD: 20.0 � .3 yrs

Mod BD: 19.9 � .3 yrs

Low BD: 19.9 � .4 yrs

Each group (25M; 25F) Investigate (longitudinally)

relationship between heavy

drinking and decision

making in young adults

Executive functioning (IGT) Hi BD (M & F): Y

Executive functioning

Scaife and Duka (2009) BD: 20.7 � 3.0 yrs

CTRL: 22.3 � 5.2 yrs

BD (18M 12F)

CTRL (13M; 17F)

Compare binge and non-binge

young “social drinkers” in tasks

of prefrontal cortex functioning

Executive functioning (CANTAB-IDED);

Visual learning & memory (CANTAB-PAL);

Attention/Working memory

(CANTAB-RTI; -SWM)

BD (M & F): Y Visual

learning & memory

BD (F): Y Working

memory & executive

functioning

Townshend

and Duka (2005)

BD: 20.9 � 2.6 yrs

CTRL: 20.9 � 2.5 yrs

BD (23M; 15F)

CTRL (13M; 21F)

Compare cognition and mood

state in binge and non-binge

young “social drinkers”

Attention/Working memory

(CANTAB-SWM; -VST, GDS-V)

BD (F): Y Attention/Working

memory

Hartley et al. (2004) BD (F): 21.0 � .8 yrs

CTRL (F): 21.4 � .4 yrs

BD (M): 21.8 � .3 yrs

CTRL (M): 20.3 � .6 yrs

BD (9M; 5F)

CTRL (6M; 7F)

Compare cognition and

mood state in ‘teetotaller’

and binge drinking

university students

Attention/Working memory (PASAT,

CANTAB-SWM)

Memory (unspecified recall tests);

Visual learning & memory (CANTAB-PRT; -SRT)

Executive functioning (CANTAB-IDED; -SoC)

BD (M & F): Y Attention/

Working memory &

executive functioning

(continued on next page)

cortex

49

(2013)3e17

7

Table

1e

(con

tinued

)

Auth

ors

Age(m

ean�

SD)

Sam

ple

(N)

Aim

sNeuro

psy

chologicalm

easu

res

Key

neuro

psy

chologicalfindings

Tapert

etal.(2001)

DEP(F):21.5

�2.5

yrs

CTRL(F):19.6

�1.2

yrs

DEP(10F)

CTRL(10F)

Characterize

neuro

cognitive

functionsandassociatedbrain

regionsin

youngAUD

females

Atten

tion

/Workingmem

ory

(SW

M,TMT,W

AIS-III-D

S)

Execu

tivefunctioning(SCW

T)

DEP(F):YAtten

tion

/

Workingmem

ory

Notes:

AB

¼Alcoholabusing;ALC

¼Alcoholdrinkers

(i.e.,nodefinition

ofbinging/abusingordependence

);BD

¼Bingedrinkers

(non-B

Dco

nsu

med

<6(F)or8(M

)unitsofalcoholperse

ssion);

CTRL¼

Controlgro

up;DEP¼

Alcoholdependent;F¼

Female;M

¼Male.

Neu

ropsycholog

icalm

easu

res:BVRT¼

Bentonvisualretentiontest;B

CT¼

Bookletca

tegory

test;B

NT¼Bostonnamingtest;C

OW

AT¼

Controlledoralw

ork

associationtest;C

VLT(-C)¼Californ

iaverb

al

learn

ingtest

(-Children’s

Version);CANTAB¼

Cambridgeneuro

psy

chologicaltest

automatedbattery

[subtestsinclude:ID

ED

¼Intra/extra-d

imensionalse

tsh

ift;PAL¼

Pairedassociateslearn

ing;

PRT

¼Pattern

reco

gnition;RTI¼

Reaction

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c o r t e x 4 9 ( 2 0 1 3 ) 3e1 78

changes is well established in older long-term drinkers. Neu-

roimaging studies have demonstrated that excessive alcohol

use often leads to both structural and functional changes

(Bates et al., 2002; Harper, 2009). Damage is particularly severe

for individuals who have additional nutritional deficiencies

(e.g., thiamine) however, even ‘uncomplicated’ cases (with no

specific neurological or hepatic problems) demonstrate brain

damage (Harper, 2009). Chronic alcohol abuse can lead to

alterations in blood flow and deficits in metabolic processes,

in addition to atrophy of numerous brain regions. The most

prominent neurological damage, in older people (average age

more than 45 years) with chronic alcohol exposure, is typically

observed in the prefrontal and temporal cortices, as well as in

the thalamic, insular, and cerebellar regions (Beresford et al.,

2006; Ende et al., 2005; Gazdzinski et al., 2005; Gilman et al.,

1990; Mann et al., 2001; Pfefferbaum et al., 2006). Such exten-

sive neurological damage, in long-term drinkers, tends to

manifest in three major ways: (i) neuropsychological impair-

ment, (ii) personality changes, and (iii) affective dysregulation

(Bates et al., 2002). In young people, these three features are

often inter-related (socially and neurobiologically) and there-

fore particularly vulnerable to the effects of alcohol (Brown

et al., 2008).

The following studies are summarized in Table 2 (which is

ordered in terms of increasing age as well as differing imaging

modality). In a young (15e17 years) sample of individuals with

an AUD, Medina et al. (2008) found that, despite similar levels

of drinking, adolescent AUD females and males showed

opposite patterns in prefrontal cortex morphometry, whereby

girls showed smaller regional volumes and males showed

larger volumes compared to their gender-matched controls.

Other studies utilizing structural magnetic resonance imaging

(MRI) have found that adolescent AUD subjects (15e18 years)

show significant reductions in left hippocampal volumes

(Medina et al., 2007c; Nagel et al., 2005). Similarly, De Bellis

et al. (2000) found that 12 adolescent/young adults (13e21

years) with AUD had smaller bilateral hippocampal volumes

compared to healthy controls and furthermore, total hippo-

campal volume correlated positively with the age at onset and

negatively with the duration of the AUD. Interestingly the

AUD group did not differ from controls in gray orWMvolumes

in any other brain structures. In a slightly larger sample

(N ¼ 14) of adolescents with AUD and comorbid mental

disorder, De Bellis et al. (2005) reported that young drinkers

(13e21 years) had reduced prefrontal lobe volumes (including

WM reductions) as well as hippocampal volume loss in

comparison with controls.

When specifically investigating WM changes, De Bellis

et al. (2008) utilized diffusion tensor imaging (DTI), to

examine 32 adolescents (13e19 years) with AUD and comorbid

mental disorder. From this study they reported that young

AUDs showed increased functional anisotropy (FA) and

decreased mean diffusivity (MD) in the corpus callosum. This

was interpreted as evidence for accelerated myelin matura-

tion which may enhance the risk for developing an AUD. On

the other hand, McQueeny et al. (2009) found adolescent

(16e19 years) binge drinkers had lower FA, and therefore,

compromised WM fiber coherence in 18 different frontal,

cerebellar, temporal and parietal regions including the corpus

callosum, superior longitudinal fasciculus, corona radiata,

ble 2 e Neuroimaging studies of young (13e24 yrs) alcohol users sorted according to imaging modality (sMRI; DTI; fMRI) d average age (youngest to oldest).

thors Age (mean � SD) Sample (N) Aims Ne oimagingodality

Key neuroimaging findings

dina et al. (2008) AUD (M): 16.6 � .7 yrs

CTRL (M): 16.6 � .7 yrs

AUD (F): 17.1 � .6 yrs

CTRL (F): 16.5 � 1.0 yrs

AUD (9M; 5F)

CTRL (10M; 7F)

Compare PFC volumes

in AUD M & F

sMRI AUD (M): [ PFC

AUD (F): Y PFC

gel et al. (2005) AUD: 16.7 � .7 yrs

CTRL: 16.5 � .9 yrs

AUD (9M; 5F)

[DEP ¼ 9; AB ¼ 5]

CTRL (10M; 7F)

Examine HC volumes in

adolescent AUD

sMRI AUD (M & F): Y left HC

dina et al. (2007c) ALC: 16.9 � .7 yrs

CTRL: 17.5 � 1.1 yrs

ALC þ MJ: 17.6 � .9 yrs

ALC (11M; 5F)

CTRL (14M; 7F)

ALC þ MJ (19M; 7F)

Examine HC volume and asymmetry

in adolescent alcohol/MJ users

sMRI AUD (M & F): Y left HC

Bellis et al. (2005) AUD: 17.0 � 2.1 yrs

CTRL: 16.9 � 2.3 yrs

AUDa (8M; 6F) [DEP ¼ 9; AB ¼ 5]

CTRL (16M; 12F)

Compare PFC; thalamus;

CRBL of AUD

sMRI AUD (M & F): Y HC & PFC

Bellis et al. (2000) AUD: 17.2 � 2.2 yrs

CTRL: 17.0 � 2.4 yrs

AUDb (5M; 7F) [DEP ¼ 7; AB ¼ 5]

CTRL (10M; 14F)

Compare HC volumes in

young people with AUD

sMRI AUD (M & F): Y HC

Bellis et al. (2008) AUD: 16.9 � 1.2 yrs

CTRL: 15.9 � 1.1 yrs

AUDb (25M; 7F) [DEP ¼ 16; AB ¼ 16]

CTRL (17M; 11F)

Examine microstructural

differences in CC of adolescent AUD

DTI AUDb (M & F): [ FA, Y MD in CC

Queeny et al. (2009) BD: 18.1 � .7 yrs

CTRL: 17.9 � .9 yrs

BD (12M; 2F)

CTRL (12M; 2F)

Assess the microstructural

WM integrity of adolescent BD

DTI BD (M & F): Y FA (‘globally’)

obus et al. (2009) BD: 17.3 � .8 yrs

CTRL: 17.3 � .8 yrs

BD þ MJ: 18.2 � .7 yrs

BD (12M; 2F)

CTRL (12M; 2F)

BD þ MJ (11M; 3F)

Examine WM integrity in

adolescent BD (& MJ use)

DTI BD (M & F): Y FA (‘globally’)

ldwell et al. (2005) AUD (M): 16.6 � .7 yrs

CTRL (M): 16.6 � .9 yrs

AUD (F): 16.9 � .8 yrs

CTRL (F): 16.2 � 1.1 yrs

AUD (11M; 7F) [DEP-M ¼ 5;

AB-M ¼ 6; DEP-F ¼ 3; AB-F ¼ 4]

CTRL (12M; 9F)

Investigate BOLD response

to SWM/vigilance in adolescent AUD

fMRI AUD (M): [ BOLD in PFC

AUD (F): Y BOLD in PFC

pert et al. (2004) c AUD: 16.8 � .7 yrs

CTRL: 16.5 � .8 yrs

AUD (10M; 5F) [DEP ¼ 8; AB¼7]

CTRL (11M; 8F)

Investigate BOLD response to

SWM in adolescent AUD

fMRI AUD (M & F): [ BOLD in parietal

AUD (M & F): Y BOLD in PCC, CRBL

hweinsburg et al. (2010) BD: 18.2 � .8 yrs

CTRL: 17.8 � .9 yrs

BD (10M; 2F)

CTRL (8M; 4F)

Examine BOLD response to verbal

encoding in adolescent/young adult BD

fMRI BD (M & F):[ BOLD in right PFC,

bilateral PC

BD (M & F):Y BOLD in Occ

pert et al. (2001) c DEP (F): 21.5 � 2.5 yrs

CTRL (F): 19.6 � 1.2 yrs

AUD (10F)

CTRL (10F)

Investigate BOLD response to

SWM in adolescent/young adult AUD

fMRI DEP (F): Y BOLD (‘globally’)

tes: AB ¼ Alcohol abusing; ALC ¼ Alcohol drinkers (no definition of binging/abusing or dependence); BD ¼ Binge drinkers; CTRL ¼ Control grou ; DEP ¼ Alcohol dependent; F ¼ Female; M ¼ Male;

¼ Marijuana users.

uroimaging: CC ¼ Corpus callosum; CRBL ¼ Cerebellum; FA ¼ Fractional anisotropy; ‘globally’ ¼ at least three different brain regions; HC ¼ Hip ocampus; Occ ¼ Occipital cortex; PFC ¼ Prefrontal

rtex; PC ¼ Parietal cortex; PCC ¼ Pre-central cortex.

All with comorbid mental or substance use disorder.

Included comorbid mental or substance use disorder.

lso appears in Table 1.

cortex

49

(2013)3e17

9

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c o r t e x 4 9 ( 2 0 1 3 ) 3e1 710

internal and external capsules, as well as the commissural

limbic, brainstem and cortical projection fibers. Furthermore,

WM coherence was also related to lifetime hangover symp-

toms and estimated peak blood alcohol concentrations in the

body and genu of the corpus callosum, internal and external

capsules, anterior and posterior corona radiata and the infe-

rior peduncle. These findings indicate that even intermittent

heavy drinking may result in significant changes to the

underlying WM microstructure. Interestingly, in contrast to

De Bellis et al. (2005), this study revealed no areas of increased

FA in binge drinkers. In a similar study, the same group

(Jacobus et al., 2009) found pathological characteristics of

adolescent (16e19 years) binge drinkers including reduced

WM integrity (indexed by decreased FA) seen in the superior

corona radiata, inferior longitudinal fasciculus, inferior-

fronto-occipital fasciculus and superior longitudinal

fasciculus.

A recent functional MRI (fMRI) study by Caldwell et al.

(2005) reported that adolescent (14e17 years) AUD males had

increased activity [as reflected by an increased blood

oxygenation level dependent; blood oxygen level-dependent

(BOLD) response] within the prefrontal cortex in response to

a SWM task. In contrast, the adolescent AUD females showed

a ‘greater departure from the normal activation’, suggesting

that females may be more vulnerable to the effects of alcohol.

Similarly, Tapert et al. (2004) reported increased BOLD activity

(during a SWM task) in the parietal cortices for adolescent

(15e17 years; males and females with) AUD; however cases in

this study also showed reduced responses within precentral

and cerebellar regions compared to controls. These differ-

ences in the BOLD response occurred in the absence of any

overt differences in the SWM task but interestingly they were

associated with the level of alcohol consumption. The Tapert

group later showed that during a novel verbal encoding task,

adolescent (16e18 years) binge drinkers had an increased

BOLD response in the right superior frontal and bilateral

posterior parietal cortices, and less response in the occipital

cortex (Schweinsburg et al., 2010). This fronto-parietal

response in adolescent binge drinkers was interpreted as

a greater reliance on an alternate memory system (e.g., more

effort to suppress irrelevant information), whereas the

reduced occipital response [consistent with their previous

study (Tapert et al., 2004)] was interpreted as reflecting a lack

of visual and linguistic processing when learning verbal

material. The net result being poorer performance in verbal

learning compared to their non-drinking peers. In another

fMRI study that assessed only females, Tapert et al. (2001)

found that young (18e25 years) alcohol dependent females

showed a decreased BOLD response (to a SWM task) in the

right superior and inferior parietal, right middle frontal, right

postcentral, and left superior frontal cortices, after controlling

for the baseline vigilance response.

Taken together, the aforementioned neuroimaging studies

showed somewhat disparate findings; however, it is likely that

differences in methodology are a major source of such varia-

tion. Despite this, young AUD or binge drinkers tended to

show impairments in temporal and frontal structures,

consistent with the findings in neuropsychological studies;

there was however more evidence from neuroimaging

research showing differences between the genders.

In recent years there has been an increasing focus of

research centered on the use of magnetic resonance spec-

troscopy (MRS) which has seen this modality being applied

successfully to neuropsychiatric disorders as well as alcohol

studies (Bendszus et al., 2001; Nery et al., 2010; Thoma et al.,

2011). MRS provides a means to non-invasively determine

the relative quantitation of metabolites in specific brain

regions essentially allowing the ‘chemical sampling’ of the

brain’s metabolites. All alcohol studies to date have employed

proton MRS (1H-MRS) which is ideally suited to investigating

the viability of neurons through the N-acetylaspartate (NAA)

metabolite as well as the brain’s main excitatory neuro-

transmitter glutamate. 1H-MRS allows for the in vivo charac-

terization of neurochemical changes occurring as a result of

alcohol. To our knowledge no studies have assessed MRS in

subjects with alcoholmisuse under the (mean) age of 25 years.

Two studies of older (i.e., in terms of mean age) individuals

are worth noting in the context of this review. Lee et al. (2007)

studied 1H-MRS in 13 recently abstinent young alcoholic men

(33.8� 5.8 years) and found that they had increased glutamate

levels within the anterior cingulate cortex compared to

controls. Furthermore, glutamate levels were found to corre-

late positively with alcohol consumption and verbal memory

performance. These findings were interpreted as evidence for

a recovery of glutamate levels (and associated functioning)

with abstinence. Similarly, Bendszus et al. (2001) scanned 17

alcohol dependent participants (40.5� 8.4; 21e60 years) at day

1e3 and again at day 36e39 post-abstinence. Compared to

healthy controls, at the beginning of detoxification the alcohol

dependant participants demonstrated decreased levels of

NAA in both the frontal lobe and the cerebellum. After 5e6

weeks of abstinence, the alcohol dependent participants

showed normalization of NAA, as there were no longer

differences in frontal or cerebellar NAA levels compared with

controls. The normalization of frontal NAA was significantly

correlated with improved performance in verbal learning and

memory, while the recovery of cerebellar neuronal loss was

significantly correlated with improvements in attention

span. Moreover, a volumetric analysis revealed significantly

enlarged cerebrospinal fluid spaces for alcohol dependent

patients immediately after detoxification, which normalized

with prolonged abstinence (Bendszus et al., 2001). While there

are a handful of other MRS studies in older alcohol dependent

samples, more research in younger cohorts is clearly needed.

This is particularly relevant given the non-invasive nature of

MRS as compared to positron emission tomography and

single-photon emission computed tomography which both

rely on the injections of radioactive labels and are therefore

much more difficult lines of research in young subjects.

5. Vulnerability markers

It is not entirely clear whether short- or long-term brain

damage is the direct result of alcohol misuse or a reflection of

pre-morbid abnormalities that predispose individuals to

misuse alcohol.While animalmodels of alcohol abuse suggest

that exposure to alcohol indeed causes neuronal damage,

researchers (of human alcohol misusers) have struggled to

differentiate between pre-morbid brain defects that may

c o r t e x 4 9 ( 2 0 1 3 ) 3e1 7 11

produce (or trigger) alcohol misuse itself and the brain

abnormalities that are caused by alcoholmisuse (Crews, 2003).

Clearly, determining how alcohol misuse leads to and/or

causes neurobiological damage in younger drinkers is crucial

to understanding the relationship between overt ‘sign and

symptoms’ (i.e., neuropsychological impairment, mood and

personality changes, etc) and early brain changes. Equally,

there is a great need to establish a better understanding of any

predisposed neurobiological factors that contribute to alcohol

misuse and the associated brain damage.

As a means to identify potential brain changes that may

precede (and therefore, possibly contribute to an increased

vulnerability for) alcohol misuse, Hanson et al. (2010) exam-

ined hippocampal volumes in non-using adolescents (12e14

years) with a family history (FH) of alcoholism. They found

that compared to their FH negative peers, FH positive

adolescents showed no overall differences in hippocampal

volume symmetry. These findings were interpreted as

evidence to suggest that hippocampal volume differences

found in AUD adolescents (see above) may not predate

significant alcohol use nor be explained by FH. There was,

however, an interaction effect to suggest that FH may influ-

ence hippocampal development as a function of gender (in

particular, males). Taking a similar approach, Norman et al.

(2011) utilized fMRI to determine whether levels of brain

activation during a response inhibition task would predict

alcohol use outcomes in a longitudinal study of adolescents

(12e14 years; when their substance use was minimal). After 4

years of follow-up, subjects were categorized as being either:

‘transitioned to heavy alcohol use’ (TU) or ‘healthy controls’.

As predicted, the TU group was found to have significantly

less activation (across 12 brain regions) during the ‘go/no-go’

task recorded at baseline as compared with the controls. This

finding supports the notion of a particular vulnerability

marker, whereby reduced neural activity during a task that

requires optimal inhibitory processing is linked to a predis-

position for future alcohol misuse. More longitudinal studies

of young people, like these [i.e., Norman et al. (2011) and

Squeglia et al. (2009b); see above] are needed to better estab-

lish our understanding of the predictive and casual neurobi-

ological factors that contribute to ongoing problems with

alcohol. Other potential candidates for vulnerability markers

include impulsivity and cue-reactivity (Squeglia et al., 2009a),

with some evidence (albeit cross-sectional) indicating that

such markers can distinguish light versus heavy young adult

drinkers (Ihssen et al., 2011; Papachristou et al., 2012; Tapert

et al., 2003).

6. Discussion

Results of the above studies provide evidence that young

people who drink excessively are at risk of functional and

structural brain damage, whichmay have long lasting adverse

consequences. Of the 20 studies examined in this review (see

Tables 1 and 2) half have included young people with an AUD,

whereas eight studies specifically assessed young people

identified as binge drinkers. The two remaining studies

(Medina et al., 2007c; Squeglia et al., 2009b) did not determine

whether theymet criteria for an AUD or binge drinking. While

it has been suggested that binge drinking patterns could

increase the risk for alcohol dependence (Townshend and

Duka, 2002) there is also evidence showing that binge

drinking may be a precursor to alcohol abuse, particularly in

youth (Archie et al., 2012; Deas, 2006). Clearly, there are links

between bring drinking and AUDs, however it is important to

distinguish between them.

6.1. Heightened risk: a young brain and binge drinking

Hunt (1993) provides two lines of evidence to support the

notion that binge drinkers aremore at risk of developing brain

damage and cognitive dysfunction. One line of evidence stems

from the interaction between alcohol and the glutamate-

N-methyl-d-aspartate (NMDA) receptor system. Chronic

exposure to alcohol leads to an up-regulation of NMDA

receptors in the hippocampus which then mediates seizures

following alcohol withdrawal (Grant et al., 1990). The other

line of evidence is also associated with the withdrawal period

whereby excessive glucocorticoid release causes stressful

events, including seizures. Both scenarios are exacerbated by

the fluctuations between periods of increased alcohol intake

and periods of abstinence. Critically, animal models of binge

drinking show that the adolescent rat brain ismore vulnerable

to the effects of alcohol compared with the adult rat brain, in

particular the frontal association cortex and the hippocampus

(Crews et al., 2000; Silvers et al., 2003). Similarly, adolescent

rats exposed to intermittent, binge-style alcohol administra-

tion show similar working memory deficits as young adults;

characterized by accelerated forgetting of novel information

(Schulteis et al., 2008). As pointed out by the authors, these

findings parallel the “subtle but reliable” memory decrements

observed in adolescent (human) alcohol misusers (Brown

et al., 2000; Tapert et al., 2001).

According to Bava and Tapert (2010) the adolescent brain is

more susceptible to alcohol misuse because of the “windows

of vulnerability” created by the asynchronous maturation of

prefrontal and limbic systems. In adults, some recovery of

neuropsychological functions can occur with abstinence over

periods of up to several years, with the most notable

improvements usually seen in working memory, visuospatial

skills, and attention (Merkl et al., 2011). However, deficits in

executive functioning appear to remain, even after abstinence

(Lyvers, 2000). Older drinkers are the least likely to recover

their normal levels of function even with abstinence as they

tend to show the most pronounced alcohol related cognitive

changes (Bates et al., 2002). Notably, there is evidence showing

that young drinkers have persistent memory deficits after 3

weeks of abstinence, however after extended periods of

abstinence (several years) neurocognitive function has been

shown to normalize (Brown and Tapert, 2004). Investigating

the ‘pathways to alcohol-induced brain impairment’ in

younger samples offers a unique opportunity to better explore

the neurocognitive effectswhen reversibility of harmmay still

be possible.

6.2. The complication of comorbidity

Psychiatric comorbidity in those with AUDs is the rule rather

than the exception. Patients with comorbid psychiatric

c o r t e x 4 9 ( 2 0 1 3 ) 3e1 712

disorders constitute the majority of the alcohol abusing pop-

ulation (Drake et al., 1993; Schade et al., 2004). Individuals with

excessive alcohol use have increased depressive symptoms,

and depressed patients are more likely to abuse alcohol (for

review see De Bellis et al., 2005). In their recent meta-analysis

of epidemiological data, Boden and Fergusson (2011) demon-

strated a casual association between AUD and major depres-

sion, with data modeling showing that the former increases

the risk of the latter. In a US national comorbidity study, Grant

et al. (2004b) found that 13% of patients with an AUD had

major depression, 7% had a mania/hypomania diagnosis and

17% had an anxiety disorder. Conversely, in patients with

a primary psychiatric diagnosis, many also have a comorbid

AUD [e.g., 16% in depression; 23% in mania; 24% in hypo-

mania; and 13% in anxiety disorders (Grant et al., 2004b;

Kessler et al., 1997)]. Often, patients with such comorbidities

are excluded a priori from neuropsychological and neuro-

imaging research, which may compromise the generaliz-

ability of the findings (Squeglia et al., 2009a).

A recent Canadian study (Archie et al., 2012) of more than

17,000 young (15e24 years) community members found that

7% of respondents had depression while over 30% were binge

drinkers. Notably, 2.7% of females and 2.1% of males were

identified as binge drinkers with depression. In males, this

comorbidity was just as prevalent as depression alone.

Beyond determining the prevalence of comorbid depression

and binge drinking, very little is known about the neurobio-

logical underpinnings associatedwith this observation and, as

such, little is known about the combined impact that the two

conditions assume on the younger developing brain.

Given the neuropsychological impairments frequently

observed in patients with an AUD or Axis I diagnosis in

isolation, it is plausible that the combination of the two

disordersmay have compounding adverse effects on cognitive

functioning, although this is an area of ongoing debate (De

Bellis et al., 2005; Uekermann et al., 2003; Umhau et al.,

2010). Durazzo et al. (2008) found that alcohol dependent

adult patients (28e66 years) with a unipolar mood disorder

(i.e., major depressive disorder or substance induced mood

disorder with depressive symptoms) had 14.5 times greater

risk of relapse than patients without a mood disorder.

However, these findings are not uniformly supported as

research in the adult population (average age range 32e60

years) has indicated no additive effect on cognition, with no

difference found between patients with comorbidities versus

either condition alone (Uekermann et al., 2003). Together

these findings indicate that regardless of the direct neuro-

logical effect of comorbid mood and AUDs, clinicians should

treat the symptoms of both conditions simultaneously to

maximize outcomes and prevent alcohol relapse, and there-

fore, further neurological insult. This is especially critical in

young adults who, by virtue of their age, have a greater

window of opportunity for intervention and therefore an

increased potential for the prevention of irreversible struc-

tural damage caused by excessive alcohol use.

6.3. Implications for treatment

Neurocognitive impairments can have significant, adverse

effects that interfere with the AUD treatment process, for

example, developing efficient meta-cognitive skills to

prevent relapse (Bates et al., 2002). It is anecdotal whether or

not the associated cognitive deficits resolve spontaneously

from the strategies that help enhance abstinence or reduce

drinking. Arguably, the most optimal outpatient program

could be centered on cognitive remediation which empha-

sizes behavioral interventions that help to improve various

aspects of cognition in those patients who have suffered

a decline in cognitive functioning (Fals-Stewart et al., 1994;

Medalia and Richardson, 2005). Individual or group-based

sessions usually focus on remediation that improve specific

cognitive skills (i.e., memory, attention, problem solving and

visual spatial skills), promote neuroplasticity, and accelerate

cerebral recovery from brain damage (Allen et al., 1997;

Naismith et al., 2010). This treatment alternative is particu-

larly relevant in alcohol settings as the aforementioned

cognitive deficits associated with AUDs have been found to

be at least moderately predictive of treatment outcome.

While spontaneous cognitive recovery can occur within the

short-term [i.e., the first few weeks or months post-

detoxification (Allen et al., 1997; Bates et al., 2002)],

complete recovery of cognitive abilities can take years ormay

not occur at all. Also, patients with the highest degree of

cognitive impairment are more likely to drop out of treat-

ment and have the poorest treatment outcomes (Grohman

and Fals-Stewart, 2003; Parsons et al., 1990; Vocci, 2008).

Moreover, as many alcohol dependent patients show varying

degrees of impairments in a variety of cognitive domains

[i.e., executive functioning, attention, and working memory;

(Bates et al., 2002)], cognitive remediation is advantageous in

that it can be tailored to meet the requirements of each

individual or group of individuals (Allen et al., 1997; Fals-

Stewart et al., 1994).

To date, there has been very little research examining the

role of cognitive remediation in adolescents or young adults

with an AUD. Fals-Stewart and Lucente (1994) focused their

cognitive remediation on relatively young (29 � 6 years)

substance abusing patients (alcohol was the primary drug of

choice in 25% of the sample). Patients were divided into four

groups: cognitive rehabilitation, progressive muscle relaxa-

tion, computer typing, or no treatment. Tested monthly,

patients in the cognitive rehabilitation group showed cogni-

tive recovery over the first 2 months of treatment, more effi-

cient cognitive functioning over the first 4 months, and

(consistent with the findings that spontaneous recovery of

cognitive functions often occurs within the first 6 months of

detoxification) all groups demonstrated a comparable amount

of recovery at 6 months. The researchers also found that over

the 6-month duration of the intervention, the cognitive

rehabilitation group had significantly more positive relation-

ships and attitudes, and better program/work participation

ratings (as evaluated by the intervention therapists) when

compared to the other groups. These results suggest that even

moderately young adults who presumably had relatively few

years of alcohol abuse benefit from cognitive remediation.

Overall, the research suggests that cognitive remediation in

addition to other techniques established in the substance use

field (e.g., motivational interviewing) could provide the best

outcome for individuals with AUD without psychotropic

interventions.

c o r t e x 4 9 ( 2 0 1 3 ) 3e1 7 13

6.4. Impact of other substance misuse

As noted by Clark et al. (2008) to get a better understating of

the specific effects that alcohol misuse has on the brain, the

potential effects of other substances need to be taken into

account. The most commonly used illicit substance in young

people, marijuana, is frequently used in conjunction with

alcohol (SAMHSA, 2011). Accordingly, there has been focused

research interest in examining the neurobiological effects of

early, frequent/heavymarijuana use (Block et al., 2000;Wilson

et al., 2000) as well as the combinatorial effects of marijuana

and alcohol use (Jacobus et al., 2009; Squeglia et al., 2009a).

While there is evidence of broad neuropsychological

impairments in abstinent (previously heavy marijuana using)

adolescents (Medina et al., 2007a), few neuroimaging studies

have detected significant changes in gray matter (GM) or WM

volumes. This kind of mismatch has prompted some to

assume that heavymarijuana use may produce other kinds of

brain abnormalities, not detectable via structural MRI tech-

niques (Block et al., 2000). Notably, there is some evidence that

gross morphometric changes due to marijuana use may

depend on the age an individual commenced their use;Wilson

et al. (2000) found that those who started using prior to 17

years of age had smaller whole brain and percent cortical GM

(as well as larger percent WM) volumes compared to their

peers who started their use later.

Squeglia et al. (2009a) summarizes neuroimaging studies

from their group (Medina et al., 2007c; Nagel et al., 2005)

which found that while heavy alcohol use was associated

with reductions in hippocampal volume, individuals who

used marijuana in conjunction with alcohol showed no

hippocampal changes. An explanation offered to account for

such findings was that alcohol and marijuana may trigger

opposing processes, such as myelination suppression (by the

former) and neuroinflammation (by the latter), which effec-

tively ‘cancel each other out’ in terms of overall macro-

morphometric change. Furthermore, evidence that

marijuana using adolescents show increased prefrontal GM

volume (Medina et al., 2008) suggests that itsmisuse assumes

a role in disrupting normal synaptic pruning processes

(Squeglia et al., 2009a). Notably, increased marijuana use in

adolescents interacts with smaller WM volume to exacerbate

depressive symptoms (Medina et al., 2007b). Functional

imaging reveals even more information about early, heavy

marijuana use; Schweinsburg et al. (2008) found that while

abstinent adolescent marijuana users showed no perfor-

mance differences in a SWM task, they showed an abnormal

pattern of activation suggestive of a compensatory neural

response to encoding new information. Critically, the afore-

mentioned summary highlights that there may be substan-

tially different neurobiological effects in young people

depending on the substance (and combination, thereof) they

misuse (Squeglia et al., 2009a).

7. Conclusions

The following key themes have emerged as being central to

this review of the literature:

1. Specific drinking patterns in young people: alcohol use is

usually initiated during adolescence. Adolescents and

young adults drink less frequently than adults but they

drink much more per occasion, commonly to intoxication,

making them more at risk of a range of acute harms.

2. A critical period in brain development: brain maturation typi-

cally is not complete until up to 25 years; the limbic system

is well-developed prior to the maturation of the prefrontal

cortex, which results in an increased propensity for risky,

impulsive behaviors and a ‘window of vulnerability’ (Bava

and Tapert, 2010). Such vulnerability may be exploited by

alcohol (and other concomitant substance) misuse.

Furthermore, females and males may be differentially

affected or have different levels of risk.

3. Pre-existing versus alcohol-induced neurobiological changes:

heavy alcohol use in young people may affect brain matu-

ration: studies have found structural and functional (neu-

rocognitive) changes in heavy drinking adolescents

compared with controls. It is, however, unclear if some of

these changes may be pre-existing and therefore predis-

posing to alcohol misuse. Notably, the impairments seen in

frontal and temporal structures are consistent with those

seen in older chronic alcohol misusers. Moreover, it should

be borne in mind that these structures are undergoing

significant change (e.g., synaptic pruning) in normal

adolescent development.

4. Vulnerability markers: early brain changes (whether pre-

existing or alcohol-induced) may potentially make young

people vulnerable for later or ongoing alcohol dependence

and ARBD; animal studies highlight that this vulnerability

is exposed by binge-style drinking. Vulnerability markers

will be better understood via familial and longitudinal

studies and they may be mediated by mental health and

substance use comorbidities.

5. Early prevention and treatment: more strategies are needed to:

(a) delay the onset of alcohol use and reduce binge drinking;

(b) identify early onset ‘alcohol-induced brain impairment’

in adolescents/young adults; and (c) provide effective early

intervention treatments for young people identified as at-

risk as well as more comprehensive treatment for young

problem drinkers. Such treatment would need to account

for current neuropsychological impairment as well as

comorbid mental health problems and concurrent other

substance misuse. Cognitive remediation is a likely candi-

date to help develop efficient meta-cognitive skills to

prevent relapse in young drinkers.

Research into alcohol misuse in young people is still in its

infancy and there are many questions that remain unre-

solved. For example, it remains unclear why problem drinking

influences some adolescents more than others, how alcohol

affects other life outcomes (such as mental health disorders

and AUDs), what brain structures or biochemical imbalances

may precede or indeed cause alcohol abuse, and precisely

what neurobiological pathways are impacted by alcohol

misuse during a critical stage in normal brain development

(Witt, 2010). To this end there is an urgent need for large

multicenter, longitudinal studies where young adults are

clearly diagnosed with either alcohol dependence or (non-

dependent) alcohol abuse and that if terms such as ‘binge

c o r t e x 4 9 ( 2 0 1 3 ) 3e1 714

drinking’, or ‘risky drinking’ are to be used, they be universally

accepted and appropriately categorized. Multimodal studies

that span neuropsychological, neuroimaging and intervention

domains should be conducted, so as to tease apart the

neurobiological changes associated with either alcohol

dependence or (non-dependent) alcohol abuse as well as

those associated with cognitive remediation resulting in the

best treatment outcomes.

Conflicts of interest

We have no conflicts of interest to declare.

Source of funding

This work was supported by a grant from the NSWMinistry of

Health, Mental Health and Drug & Alcohol Office as well as an

NH&MRC Australia Fellowship (No. 511921) awarded to IBH.

Acknowledgments

The authors would like to thank Dr. Nick Glozier for

comments on the manuscript.

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