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Original Paper
Neuropsychological consequences
of alcohol and drug abuse on different
components of executive functions Journal of Psychopharmacology
24(9) 1317–1332
!The Author(s) 2010
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DOI: 10.1177/0269881109349841
jop.sagepub.com
Marı
´a Jose
´Ferna
´ndez-Serrano
1
, Miguel Pe
´rez-Garcı
´a
1,2
,
Jacqueline Schmidt Rı
´o-Valle
3
and Antonio Verdejo-Garcı
´a
1,2
Abstract
Several studies have shown alterations in different components of executive functioning in users of different drugs, including cannabis, cocaine and heroin.
However, it is difficult to establish a specific association between the use of each of these drugs and executive alterations, since most drug abusers are polysub-
stance abusers, and alcohol is a ubiquitous confounding factor. Moreover, in order to study the association between consumption of different drugs and executive
functioning, the patterns of quantity and duration of drugs used must be considered, given the association between these parameters and the executive
functioning alteration degree. Based on the multicomponent approach to executive functions, the aims of the present study were: (i) to analyse the
differential contribution of alcohol versus cocaine, heroinand cannabis use on executive functions performance; and (ii) to analyse the contribution made
by the severity of the different drugs used (quantity and duration patterns) on these functions in a sample of polysubstance abusers that requested
treatment for cannabis-, cocaine- or heroin-related problems. We administered measures of fluency, working memory, analogical reasoning, interference,
cognitive flexibility, decision-making and self-regulation to two groups: 60 substance-dependent individuals (SDIs) and 30 healthy control individuals
(HCIs). SDIs had significantly poorer performance than HCIs across all of the executive domains assessed. Results from hierarchical regression models
showed the existence of common correlates of the use of alcohol, cannabis and cocaine on verbal fluency and decision-making; common correlates of quantity of
cannabis and cocaine use on verbal working memory and analogical reasoning; common correlates of duration of cocaine and heroin use on shifting; and specific
effects of duration of cocaine use on inhibition measures. These findings indicate that alcohol abuse is negatively associated with fluency and decision-making
deficits, whereas the different drugs motivating treatment have both generalized and specific deleterious effects on different executive components.
Keywords
alcohol, cannabis, cocaine, executive functions, heroin, severity of use
Introduction
Drug use has increased notably among the world population,
according to the United Nations World Drug Report 2008 (from
the United Nations Office of Drugs and Crime, UNODC,
2008). It has been estimated that 4.9% of the world’s popula-
tion aged 15–64 have used drugs at least once over 2007–2008,
and 0.6% of the world’s population have drug-related problems,
poly-consumption of diverse substances, such as heroin, cocaine,
cannabis, amphetamines and ecstasy (MDMA), being the pre-
dominant abuse pattern (UNODC, 2008) especially in those
individuals that demand treatment (European Monitoring
Centre for Drugs and Drug Addiction, EMCDDA, 2008). In
parallel with the increase of drug-related problems, there is
increasing consensus on the notion of addiction as a brain
disorder characterized by longstanding changes in cognitive
functioning, especially in so-called executive functions (i.e.
higher-order skills responsible for selection, monitoring and
fine-tuning of goal-directed behaviour) (Goldstein and
Volkow, 2002; Lubman et al., 2004). Recent evidence from
animal and human studies indicate that specific components
of executive functions, including dysfunctional impulsivity and
decision-making, may predate initiation of drug use and medi-
ate the transition between drug use and drug dependence
(Belin et al., 2008; Dalley et al., 2007; Tarter et al., 2003).
Accordingly, human studies have shown mild executive deficits
in recreational users of cannabis and psychostimulants (De
Win et al., 2007; Leland and Paulus, 2005). On the other hand,
there is evidence that intensive exposure leading to dependence
to different drugs, including cannabis, psychostimulants and
opioids, dose-dependently impair several domains of executive
functions (i.e. selective attention, inhibition, flexibility) and
prefrontal cortex structure and function in animals (Jentsch
et al., 2002; Stalnaker et al., 2009; Verrico et al., 2004; Yang
et al., 2007) and humans (Bolla et al., 2003, 2004, 2005;
1
Departamento de Personalidad, Evaluacio
´n y Tratamiento Psicolo
´gico,
Universidad de Granada, Campus de Cartuja, s/n, 18071 Granada, Spain.
2
Institute of Neurosciences F Olo
´riz, Universidad de Granada, Spain.
3
Departamento de Enfermerı
´a, Escuela de Ciencias de la Salud,
Universidad de Granada, Spain.
Corresponding author:
Marı
´a Jose
´Ferna
´ndez-Serrano and Antonio Verdejo-Garcı
´a, Departamento
de Personalidad, Evaluacio
´n y Tratamiento Psicolo
´gico, Facultad de
Psicologı
´a de la Universidad de Granada, Campus de Cartuja, s/n, 18071
Granada, Spain. Email: mjfser@ugr.es and averdejo@ugr.es
at Biblioteca Universitaria de Granada on March 28, 2016jop.sagepub.comDownloaded from
Verdejo-Garcia et al., 2004; Whitlow et al., 2004). As compared
with recreational users, executive deficits in individuals with
substance dependence are more generalized (i.e. affecting
mechanisms of access, working memory, inhibition, planning,
flexibility and decision-making) and greater in magnitude (i.e.
effect sizes ranging 0.5–2.2) (Verdejo-Garcı
´a and Pe
´rez-
Garcı
´a, 2007). Executive dysfunction is especially relevant in
the context of substance dependence treatment, since perfor-
mance on indices of executive functioning has been strongly
associated with treatment retention and drug relapse
(Aharonovich et al., 2006, 2008; Passetti et al., 2008; Streeter
et al., 2008). Currently, cannabis, heroin and cocaine are the
illegal drugs that generate more treatment demands in the
European Union (EMCDDA 2008), and therefore there is a
need to better understand the selective effects of these drugs on
executive functions among substance dependents.
Despite being traditionally considered as a general cogni-
tive domain (Denckla and Reiss, 1997; Zelazo et al., 1997), the
literature agrees on the existence of a number of executive
components or sub-functions (such as access, working
memory, inhibition, flexibility or decision-making) (Fisk and
Sharp, 2004; Miyake et al., 2000; Verdejo-Garcı
´a and Pe
´rez-
Garcı
´a, 2007). Evidence from lesion research and functional
neuroimaging studies has supported this view by showing that
discrete executive mechanisms are endorsed by differentiated
neural systems. Hence, there is evidence of the prominent roles
of the dorsolateral prefrontal cortex in working memory
(D’Esposito et al., 1999), the inferior frontal gyrus and supple-
mentary motor area in response inhibition (Aron et al., 2003;
Picton et al., 2007), the lateral orbitofrontal cortex in cognitive
flexibility (Cools et al., 2002), the frontal pole (Area 10) in multi-
tasking (Dreher et al., 2008; Gilbert et al., 2006) and the medial
orbitofrontal cortex in decision-making (Bechara et al., 1994).
Although distinct, these processes are flexibly assembled in
response to complex task demands (Collette et al., 2005).
Therefore, the abuse of different drugs may both selectively
and commonly impair these separate but interrelated executive
components. In the last few years, several studies have shown
decrements in differentiated components of executive function-
ing in cannabis, cocaine and heroin abusers/dependents, the
type of addicted individuals forming our sample. Studies have
found impairments in working memory, decision-making,
attention and planning in cannabis abusers/dependents
(Bolla et al., 2005; Medina et al., 2007; Verdejo-Garcı
´a et al.,
2007a; Wadsworth et al., 2006), impairments in decision-
making, working memory and inhibition in cocaine abusers/
dependents (Bolla et al., 2003; Fillmore et al., 2002; Ku
¨bler
et al., 2005; Verdejo-Garcı
´a et al., 2007a) and impairments in
decision-making, inhibition and flexibility in heroin abusers/
dependents (Brand et al., 2008; Fishbein et al., 2007; Lee and
Pau, 2002; Pau et al., 2002; Verdejo-Garcı
´a et al., 2005a).
However, it is difficult to establish a selective association bet-
ween decrements in separate executive tasks and the abuse/
dependence of any given drug, since virtually all of these studies
have been conducted in polysubstance using groups. Along with
the potential detrimental effects of aging and lower education
on executive decline (Van der Elst et al., 2006; Verhaeghen and
Cerella, 2002), one of the main confusing variables in most of
these studies is co-abuse of alcohol, which is ubiquitous among
polysubstance abusers. Alcohol abuse and dependence are
related to long-lasting executive impairments affecting fluency,
working memory, inhibition, flexibility and decision-making,
and decreases in prefrontal cortex structure (Chanraud et al.,
2007; Loeber et al., 2009; Pitel et al., 2009). Furthermore, there
is evidence of dose-dependent effects of severity of alcohol use
on executive performance decrements (Glass et al., 2009).
More importantly, there is some evidence that alcohol abuse
may be more strongly associated with certain aspects of exec-
utive dysfunction (i.e. sustained attention, planning or flexibil-
ity) than the co-abuse of other drugs, such as cocaine (Bolla
et al., 2000; Goldstein et al., 2004) or heroin (Fishbein et al,
2007). Therefore, alcohol co-abuse is a relevant confounding
variable that complicates the interpretation of previously
observed associations between cannabis, cocaine or heroin
abuse and impairment of separate executive processes (Abi-
Saab et al., 2005; Di Sclafani et al., 2002; Fishbein et al.,
2007; Robinson et al., 1999). Nicotine is also a relevant con-
founding variable, but its neurocognitive effects appear to be
more related to processing speed and memory functioning,
with less deleterious effects on executive functions (Swan and
Lessov-Schlaggar, 2007). On the other hand, in order to exam-
ine the association between abuse/dependence of different
drugs and executive functioning, the patterns of quantity and
duration of use of these drugs must be considered. As we
explained earlier, there is a strong association between the
intensity of drug use (in terms of both quantity and duration
of use) and the degree of executive functions impairment and
frontal cortex dysfunction (see Beveridge et al., 2008). In this
regard, several studies have shown consistent associations
between the severity of cannabis use and alterations in inhibi-
tion, flexibility and decision-making (Bolla et al., 2002, 2005;
Verdejo-Garcı
´a et al., 2005b), between the severity of cocaine
use and inhibition impairments (Bolla et al., 2000; Fillmore
and Rush, 2002; Roselli and Ardila, 1996; Verdejo-Garcı
´a
et al., 2005b) and between the severity of opioid consumption
and cognitive flexibility decrements (Lyvers and Yakimoff, 2003).
Therefore, based on the multicomponent approach to
executive functions, this study is aimed at: (i) analysing the
independent impact of the three main drugs motivating treat-
ment demand (cocaine, heroin and cannabis) versus the
impact of alcohol co-abuse on polysubstance dependents’
executive functions performance, and (ii) analysing the con-
tribution made by the quantity and duration of consumption
of the different drugs analysed on executive functions perfor-
mance. We expect that alcohol and other drugs of abuse have
a differential contribution in the separate components of the
executive functions analysed. To reach both aims, we chose to
make a three-stage multiple regression approach aimed to
differentiate between detrimental effects due to the effects of
demographic variables (age and education), those related to
the effects of alcohol, and those related to the effects of the
main drugs of choice motivating treatment (especially after
discounting demographic and alcohol effects).
Method and materials
Participants
Sixty substance-dependent individuals (SDIs) (eight female),
aged 21–49 years, and 30 healthy control individuals (HCIs)
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(six female), aged 18–49 years, participated in this study. All
participants were Spaniards (European background) and
spoke Spanish as their native language. SDIs and HCIs par-
ticipants were matched for gender, but not for age or educa-
tion level, which were used as independent variables in
regression analyses. In Table 1, we present the main socio-
demographic characteristics of both groups. SDIs were
selected during their treatment at the ‘Proyecto Hombre’
rehabilitation centre, an intreatment therapeutic community
in Granada, Spain. This centre provides a controlled environ-
ment for dishabituation and treatment of drug abuse. SDIs
were in a situation of controlled abstinence and urine toxicol-
ogy screening (One Step Syva rapid tests for alcohol, canna-
bis-THC, amphetamines, benzodiazepines, cocaine and
opiates) was conducted on these individuals weekly, allowing
us to rule out drug use throughout the entire period of absti-
nence. Selection criteria for participants in the SDIs group
were: (i) meeting the DSM-IV criteria for substance depen-
dence; (ii) absence of documented comorbid mood or person-
ality disorders as assessed by clinical reports; (iii) absence of
documented head injury or neurological disorders; (iv) not
being enrolled in opioid substitution treatment; and (v) min-
imum abstinence duration of 15 days before testing, although
the median duration of abstinence for any drug in the group
was of 32 weeks, so that it was possible to rule out the pres-
ence of alterations associated with the acute or short-term
effects of the drugs. The SDIs sample was principally com-
posed of polysubstance abusers who requested treatment for
cocaine, heroin or cannabis use. Although five SDIs showed
high level of MDMA use (a lifetime consumption of more
than 50 pills), none of them requested treatment for this
MDMA consumption. In Table 2, we present the consump-
tion characteristics of the SDIs group.
Control participants were selected by means of local
advertisements and snowball communication among adult
people from the community. Selection criteria for these con-
trol participants were: (i) absence of current or past substance
abuse, excluding past or current social drinking (less than six
units of alcohol per week); (ii) absence of documented major
psychiatric disorders; (iii) absence of documented head injury
or neurological disorder; and (iv) not being on any medica-
tion affecting the central nervous system (CNS), including
antidepressants, mood stabilizers, anxiolytics, antiepileptics
or antipsychotics. The mean amount of alcohol use in male
HCIs was 5.43 units/month (SD ¼5.24) and the mean of
alcohol duration consumption was 6.12 years (SD ¼6.06).
In female HCIs the mean amount of alcohol use was 5.33
units/month (SD ¼8.35) and the mean of alcohol duration
consumption was 9.00 years (SD ¼11.45).
Instruments and assessment procedures
Background information: In order to examine the lifetime
use of different substances, we used the Interview for Research
on Addictive Behaviour (IRAB) (Lo
´pez-Torrecillas et al.,
2001). This instrument evaluates the dosing, frequency (con-
sumption episodes per month) and duration of use of a
number of substances. For every substance the subject had
actually consumed, including cannabis, alcohol, cocaine,
heroin, amphetamines, benzodiazepines and MDMA, the fol-
lowing information was requested:
(1) The average amount of each target drug taken in each
episode of use (number of joints for cannabis; number
of grams for cocaine and heroin; and number of units
for alcohol, considering that a glass of Scotch whisky
equals one unit, while a glass of wine or beer equals 0.5
units), and the frequency of these consumption episodes
per month (daily, between one and three times per week,
once a week, between one and three times per month or
once a month).
(2) The number of years elapsed since the onset of use.
Table 1. Descriptive scores for the sociodemographic characteristics of substance-dependent individuals (SDIs) and
healthy control individuals (HCIs)
SDIs HCIs
Socio-demographic variables Mean SD Mean SD t/
2
p-value
Age 30.58 7.08 26.40 8.03 2.52* 0.013
Years of education 9.88 2.48 11.63 2.04 3.33* 0.001
Gender (%) 0.67** 0.538
Men 86.7 80
Women 13.3 20
*Value of Student’s t.
**Value of chi-squared
2
.
Table 2. Descriptive scores for patterns of quantity and duration of drug
use in the group of substance-dependent individuals (SDIs)
SDIs
Substances Variables Mean SD
Cocaine Grams/month 49.53 42.22
Duration (years) 8.07 5.57
Cannabis Joints/month 148.65 179.87
Duration (years) 8.27 7.63
Heroin Grams/month 10.90 24.05
Duration (years) 1.90 4.14
Alcohol Units/month 506.98 445.58
Duration (years) 10.40 7.17
Abstinence (weeks) 33.28 Median¼32.00
Ferna
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From these data, two independent measures of quantity
(average amount taken in each episode of use monthly fre-
quency) and duration (years) of consumption were calculated
for each drug abused by the participants.
Neuropsychological tests: We used a selective battery of
neuropsychological tests designed to assess several compo-
nents of executive functions, including fluency, working
memory, analogical reasoning, interference and cognitive flex-
ibility (which have been associated with the functioning of
different sections of the lateral prefrontal cortex) (Koechlin
and Summerfield, 2007), and decision-making and self-
regulation during multitasking (which are proposed to
relate to more medial and rostral sections of the prefrontal
cortex) (Bechara et al., 2000; Levine et al., 2000). Below we
describe the tasks used grouped by executive components.
Fluency tests:
.FAS (verbal fluency) (Lezak, 2004): participants were
asked to produce in 1 min the greatest possible number
of words that start first with the letter ‘F’, next with the
letter ‘A’ and finally with the letter ‘‘S’’. The main depen-
dent variable was the sum of the words produced with
these three letters.
.Ruff figural fluency test (RFFT) (Ruff, 1996): consists of
five parts that present a similar structure, made up of 35
boxes with five dots in each. Participants were required to
draw as many different figures as possible joining with
straight lines at least two of the five dots each box con-
tains. The main dependent variable used in this test was
the total number of original figures produced.
Working memory tests:
.Letter number sequencing (LNS) (Wechsler adult intelli-
gence scale, WAIS-III) (Wechsler, 1997a): the participant is
read a sequence in which letters and numbers are combined,
and they are asked to reproduce the sequence heard, first
placing the numbers in ascending order and then the letters
in alphabetical order. The test consists of seven elements,
and each element consists of three tries. In each element, the
sequence is read at one letter or number per second. The
administration is stopped when the participant misses three
tries in the same element. The main dependent variable used
on this test was the number of correct answers.
.Spatial span (Wechsler memory scale, WMS-III) (Wechsler,
1997b): this task consists of a platform on which a series of
10 three-dimensional cubes are placed and organized
according to a pre-determined pattern. The test consists
of two parts: forward and backward span. In both cases
the evaluator touches a series of cubes (whose number
increases in successive trials) with his finger, and the partic-
ipant must touch the same cubes as the evaluator (1) in the
same order (forward span) or (2) in inverse order (backward
span). The main dependent variable used in these tests was
the total number of correct responses.
Analogical reasoning tests:
.Similarities (WAIS-III) (Wechsler, 1997a): pairs of words
that represent common objects or concepts are read, and
participants have to indicate how these objects/concepts
are similar. This task consists of 19 pairs of words. The
administration is stopped when the participant misses four
consecutive elements. The main dependent variable ana-
lysed in this test was the number of correct answers.
.Category test (DeFilippis, 2002): a computerized version
of this test was administered. The task consists of 208
stimuli that have different types of designs (squares, trian-
gles, circles, letters, etc.) grouped in seven subtests with
different rules. For all of the stimuli included in the same
subtest, there is an underlying rule that determines the
appropriateness of the responses throughout this subtest.
However, this rule changes in the next subtest, so that the
participant’s performance on the test depends on the abil-
ity to infer these rules, and modify them when they are no
longer valid. Test instructions are intentionally ambigu-
ous: we explained to the participant that different types
of designs will appear consecutively on the screen, and
that each design is associated with one of the first four
numbers: 1, 2, 3 or 4. For each stimulus the participant
must press the key with the number they think is asso-
ciated with that design, and the computer provides audi-
tory feedback related to the correctness or incorrectness of
the response provided. The main index of performance on
the test was the total number of errors on the seven
subtests.
Tests of interference and shifting:
.Stroop: this test consists of three forms, each of which
contains 100 elements distributed in five columns of 20
elements each. The first form (WORDS condition) is
made up of the words ‘RED’, ‘GREEN’ and ‘BLUE’
ordered randomly and printed in black ink. In this condi-
tion the participant is asked to read aloud, as quickly as
possible, the words written on this page in a time set at
45 s. The second form consists of strings of ‘XXXX’
(COLORS condition) printed in red, blue or green ink.
In this condition, the participant is asked to read aloud
as quickly as possible the colour of these elements with a
time limit of 45 s. The third form (COLOR-WORD con-
dition) introduces the condition of interference, and it con-
sists of the words from the first form printed in the colours
of the second. In this condition, the subject is asked to
name the colour of the ink the word is written in, ignoring
the word, also in 45 s. The main dependent variable used in
this test was the interference score, obtained by subtracting
subjects’ response latency to WORDS and COLOR (using
the formula: WORDS*COLORS/WORDS+COLORS)
from their response latency to the COLOR-WORD con-
dition (Golden, 1978).
.Five digit test (5DT) (Sedo
´, 2005): this consists of four
parts of independent application, in which a series of 50
boxes are presented, each of which contains one to five
digits (parts 1, 3 and 4) or stars (part 2), organized in
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patterns similar to those on domino pieces or playing
cards. In part 1 (reading), the participant is asked to
read as quickly as possible the digit each box contains.
In part 2 (counting), they are asked to count how many
stars each box contains. In part 3 (interference), they are
asked to count the number of digits each box contains,
producing an effect of interference as the boxes present
groups of digits that do not correspond to their arithmetic
value (e.g. in a box with five twos, the correct response
would be five and not two). Finally, in part 4 (shifting),
the participant is asked to count, just as in part 3, or read,
as in part 1, depending on whether the outline of the box is
normal (count, 80% of the stimuli) or of double thickness
(read, 20% of the stimuli). Parts 1 and 2 constitute basic
measures of attention and processing speed. In contrast,
parts 3 and 4 are sensitive to executive processes of inhibi-
tion. Therefore, the main dependent variables used in this
test were the difference between the performance time on
part 3 and the mean of parts 1 and 2 (differential ‘inter-
ference’ score), and the difference between the performance
time on part 4 and the means of parts 1 and 2 (differential
‘shifting’ score).
.Oral Trail Making (OTM) (Sedo
´et al., 1995): this test
includes two independent parts. The first part (OT 1)
assesses visuo-spatial and naming abilities. It contains 20
items consisting of numbers (1–20) paired with four famil-
iar fruit images (apple, banana, grapes and orange).
The items (containing the number and the paired fruit
represented together in 20 little boxes) are spread all
over the test form. Participants are asked to visually
search for the items by number, and to name the fruit
paired with each item (one apple, two orange and so
forth). The second part (OT 2) assesses visuo-spatial and
cognitive flexibility skills. This portion of the test uses a
presentation identical to that in the first part, except that
the fruits paired with the numbers are printed in different
non-natural colours, in such a way that the shape and the
colour of the fruit are always incongruent (e.g. red
banana). Participants are asked to visually search for
each item by number and to name the fruit paired accord-
ing to the colour and not the shape (thus, a red banana
should be named as ‘apple’). The dependent measure was
the interference score obtained by subtracting time in part
1 from time in part 2 (OT 21).
Decision making:
.Iowa gambling task (IGT) (Bechara et al., 1994): this is a
computerized task that factors several aspects of decision-
making including uncertainty, risk, and evaluation of
reward and punishing events. The IGT involves four
decks or cards, decks A0,B
0,C
0and D0. Participants were
instructed to win as much money as possible by picking one
card at a time from each of the four decks in any order until
the computer instructed them to stop (after the selection of
the 100th card). Each time a participant selects a card, a
specified amount of play money is awarded. However, inter-
spersed among these rewards, there are probabilistic
punishments (monetary losses with different amounts).
Two of the decks of cards, decks A0and B0, produce high
immediate gains; however, in the long run, these two decks
will take more money than they give, and are therefore con-
sidered to be the disadvantageous decks. The other two
decks, decks C0and D0, are considered advantageous, as
they result in small, immediate gains, but will yield more
money than they take in the long run. The main dependent
variable used on this task was the difference between
the number of advantageous and disadvantageous
choices [(C+D)(A+B)] on each of the five blocks of 20
trials of the task.
Self-regulation:
.Revised Strategy Application Test (R-SAT) (Levine et al.,
2000; Spanish adaptation by Verdejo-Garcı
´a et al., 2007b):
this is an unstructured paper-and-pencil multitasking test
sensitive to disturbed self-regulation. It consists of three
simple activities: figure tracing, sentence copying and
object numbering. Each activity has to be performed in
two different stacks (A and B), each containing 10 pages
with approximately 12 items each. The items differed in
two dimensions: size (they can be large or small) and
time required to complete them (they can be brief, taking
a couple of seconds, medium or long, taking more than
one minute). The different types of stimuli are intermixed
within each page, but the number of brief items decreases
progressively within each stack. The main goal of the task
was to win as many points as possible, considering that
large items scored 0 points and small items scored 100
points each. Nonetheless, points were used in the instruc-
tions only to see whether participants would respond
accordingly, but the dependent variable in this task is the
number of items and not points. In order to complete more
items, given the limited time, the most efficient strategy
(which the participant must discover as they perform the
task) is to complete brief items to the exclusion of lengthy
items. This requires the inhibition of a tendency to com-
plete all of the items in sequence, which is established on
the early pages of each stack, where all of the items are
brief. Therefore, the main dependent variable from the
R-SAT was the proportion of brief items completed (not
including the first page of each stack) with regards to the
total number of items attempted.
Procedure
Participants were assessed individually between April 2003
and November 2007 in a single session that lasted approxi-
mately 3 h and 45 min (including breaks) or on two consecu-
tive days, depending on the rehabilitation centre availability.
Participants did not consume food, caffeine or nicotine during
tests administration, although smoking (a maximum of one
cigarette) was allowed during the breaks to avoid nicotine
withdrawal effects. SDIs and HCIs were not tested at
a fixed time of the day, but to the best of the authors’
knowledge there is no consistent evidence of biasing
effects of this variable on neuropsychological performance
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in this population. The tests included in the study were part of
a more comprehensive battery aimed at examining neuropsy-
chological functions in SDIs. Test administration was
blocked for all participants and arranged to alternate between
verbal and non-verbal tasks and between more- and less-
demanding tasks. The order of administration was: FAS,
RFFT, LNS, Stroop, Similarities, Category Test, R-SAT,
OTM, Spatial Span, 5DT and IGT.
All of the participants in the study were informed
about the objectives, benefits and possible inconveniences
associated with the research protocol. Likewise, all of the
participants signed an informed consent form certifying
their voluntary participation. The SDIs and HCIs partici-
pants who requested it received a neuropsychological report
about their performance on the tests. In addition, the control
participants were paid E18 for their participation to ensure
motivation.
Data analysis
First, in order to characterize neuropsychological performance
differences between SDIs and HCIs, we conducted indepen-
dent-samples t-tests (for those dependent variables unrelated
to age and education: 5DT shifting score, 5DT interference
score and R-SAT) or univariate analyses of covariance
(ANCOVAs; for those dependent variables significantly asso-
ciated with age, education or both: FAS, RFFT, LNS, Spatial
span, Similarities, Category test total errors, OTM shifting
score, Stroop interference score and IGT) using group (SDIs
versus HCIs) as a between-subjects factor and age and educa-
tion as covariates. Next, we explored dependent variables to
examine the possible presence of outliers (defined as atypical
values by the Explore command of SPSS v.15). Two outliers
were detected in the R-SAT proportion of brief items distribu-
tion, two outliers were detected in the 5DT–interference score
distribution, and three outliers were detected in the 5DT–shift-
ing score distribution. These subjects were removed from fur-
ther analyses with the corresponding dependent variables;
therefore SDIs sample size for R-SAT analyses, N¼58, for
5DT interference, N¼58 and for 5DT shifting, N¼57. Next,
we performed a series of multiple regression models to examine
the impact of demographic variables, alcohol use and illegal
drugs use on executive performance. Since the three illegal
drugs motivating treatment demand in this sample were the
main focus of the study, we first conducted multiple regression
models including only cannabis, cocaine and heroin (both
quantity and duration) as predictor variables; these analyses
were included to assess the variance attributable to drug use
before inclusion of demographics and alcohol. Next, to test the
main aim of the study (i.e. to disentangle specific effects of
alcohol versus illegal drugs on different executive components
and determine the impact of cannabis, cocaine and heroin after
discounting the effect of demographics and alcohol abuse), we
conducted hierarchical multiple regression analyses. These
models were set on three stages: (i) demographic variables
associated with executive performance (age and years of edu-
cation); (ii) total consumption of alcohol, which is the main
substance of co-abuse; and (iii) quantity and duration of con-
sumption of cannabis, cocaine and heroin. We developed inde-
pendent regression model series for the variables quantity and
duration of consumption of the different substances, to deter-
mine the specific effects of both parameters and avoid colli-
nearity effects. These separate models also allowed us to adjust
the number of predictors as a function of sample size: we used a
maximum of five predictor variables for a sample size of 90;
ratio of 15 cases by predictor variable (a ratio of at least 10
cases by predictor is considered appropriate) (Hair et al.,
2000). Therefore, for each analysis we introduced three differ-
entiated blocks of predictor variables in a sequential manner:
the first block included the variables of age and years of educa-
tion, the second block included the total alcohol consumption
(i.e. a combined quantity duration measurement was calcu-
lated to avoid that alcohol consumption of healthy control
individuals, similar to that of SDIs in duration, yet of quite
lower intensity, could slant the contribution of this factor), and
finally, the third block included the quantity or duration of
consumption of cannabis, cocaine and heroin. The different
performance indices of the executive function neuropsycholo-
gical tests were included as dependent variables. For each new
block of variables entered in the regression model, we esti-
mated the R
2
of the prediction change associated with that
block and its statistical significance, with the aim of determin-
ing the differential contribution of each of the blocks to the
regression model. To facilitate reading, in text we only present
results from hierarchical models showing significant effects of
alcohol or drug use after discounting the effects of demo-
graphic variables. Data from regression models including
only drug use variables are presented in Tables (two first col-
umns), along with hierarchical models.
Results
Group differences
SDIs performed significantly poorer than controls on all of
the executive indices assessed, with effect sizes ranging from
0.6 (e.g. shifting) to 2.3 (analogical reasoning); see Table 3.
All executive indices (with the exception of OTM shifting)
yielded effect sizes circa or superior to 0.8 for differences
between SDIs and HCIs, which are considered large effects
according to Cohen (Zakzanis, 2001).
Regression models
The coefficients obtained with the hierarchical regression
analyses are represented in Table 4, for the models including
quantity of consumption, and in Table 5, for the models
including duration of consumption. The impact of the entry
of each block in each of the steps of the regression model is
represented by means of the determination coefficient values
(R
2
). The results of multiple regressions including only drug
measures (quantity or duration) are also included in the first
two columns of Tables 4 and 5, respectively.
Quantity of consumption
Fluency: In the FAS test, after controlling for the effects
of demographics, the block of total alcohol consumption was
a significant predictor of performance. However, the entry of
the block of quantity of consumption of other drugs
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significantly increased the predictive value of demographics
and alcohol. The global model revealed that the quantity
of cannabis was the most predictive variable of perfor-
mance on this test. In the RFFT, neither the block of
total alcohol consumption nor the block of illegal drugs con-
sumption were predictors of performance on this task.
Working memory: In the LNS test, the block of total alco-
hol consumption showed a trend to significant effects
(p¼0.057), but the entry of the block of quantity of consump-
tion of drugs significantly improved the prediction of the
former blocks. In the global model we observed that the
quantity of cocaine and cannabis use had the highest predic-
tive value of performance. In the Spatial Span task, we
observed that the block of total alcohol consumption failed
to predict performance on this task. Nevertheless, inclusion of
the quantity of consumption of other drugs significantly
improved the prediction of demographics and alcohol. The
analysis of the global model coefficients showed that the
quantity of cocaine use had the highest predictive value of
performance on this task.
Reasoning: In the Similarities test, we observed that, after
controlling for demographics and alcohol, the block of quan-
tity of consumption of drugs was a significant predictor of
performance. The analysis of the global model coefficients
showed that the quantity of consumption of cannabis and
cocaine were the variables with the highest predictive value
of performance on this task. Similarly, in the Category test,
the block of quantity of consumption of drugs showed a trend
to significantly increase prediction of performance
(p¼0.062), and the analysis of global model coefficients
revealed that the quantity of cocaine was the most predictive
variable of performance on this task.
Interference and shifting: In the 5DT (both interference
and shifting scores) and the Stroop tests, neither the block of
total alcohol consumption nor the block of other drugs had
predictive capability of performance on these measures. In the
OTM test, the entry of the block of quantity of consumption
of drugs improved the prediction of previous blocks signifi-
cantly, and quantity of heroin consumption was the most
predictive variable.
Decision-making: In the IGT we observed that both the
block of total alcohol consumption and the block of other
drugs significantly predicted performance on this task. When
analysing the global model, we observed that total alcohol
consumption, quantity of cannabis and quantity of cocaine
were the most predictive variables of performance on this task.
Self-regulation: In the R-SAT we observed that only the
block of total alcohol consumption showed a trend to signifi-
cantly predict performance on this task (p¼0.077). The inclu-
sion of the block of drug measures failed to increase the
prediction of the global model significantly.
Duration of consumption: In this section we only refer to
the predictive value of the variables of cannabis, cocaine and
heroin consumption, since the predictive value of the blocks
of total alcohol consumption variable is the same as that of
the previously described models.
Fluency: In the FAS test we observed that the block of
consumption of drugs (cannabis, cocaine and heroin)
improved the predictive value of demographics and alcohol
significantly, with duration of cocaine consumption being the
Table 3. Descriptive scores, independent group t-tests/univariate analyses of covariance (ANCOVAs), and effect sizes on the neuropsychological
measures for substance-dependent individuals (SDIs) and healthy control individuals (HCIs)
SDIs HCIs
Domain Task Mean SD Mean SD F/t p-value Cohen’s delta
Fluency FAS 33.81 10.55 51.43 9.80 46.52
a
0.000 1.71
RFFT 82.86 23.72 110.33 19.94 20.11
a
0.000 1.21
Working memory LNS 9.33 2.40 15.13 2.33 93.50
a
0.000 1.57
Spatial Span 14.89 3.36 19.90 4.35 24.55
a
0.000 1.35
Reasoning Similarities 18.11 4.51 27.76 3.01 90.55
a
0.000 2.37
CT_tot_errors 66.89 24.01 31.80 25.32 30.86
a
0.000 1.41
Shifting 5DT_shift 25.47 9.38 20.10 5.45 2.89
b
0.005 0.64
OTM_shift 21.89 15.32 14.13 7.57 2.18
a
0.143 0.58
Interference 5DT_interf 15.74 6.46 11.10 3.77 4.28
b
0.000 0.81
Strp_interf 1.67 6.07 4.15 7.24 9.63
a
0.003 0.89
Decision-making IGT 2.21 22.60 37.20 26.15 47.84
a
0.000 1.65
Self-regulation R-SAT 82.95 16.46 93.98 5.70 3.55
b
0.001 0.79
a
F-value.
b
Student’s tvalue.
FAS, Verbal fluency; RFFT, Ruff figural fluency test; LNS, Letter number sequencing; CT_tot errors, total number of errors on Category test; 5DT_shift, Five digit test shifting
score; OTM_shift, Oral trail making shifting score; 5DT_interf, Five digit test interference score; Strp_interf, Stroop interference score; IGT, Iowa gambling task; R-SAT,
Revised strategy application test.
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Table 4. Multiple hierarchical regression models of the association between demographic variables, alcohol total consumption and quantity of cannabis, cocaine and heroin use and neuropsychological
performance
Model including only cannabis, cocaine and heroin Hierarchical three-stage model including demographics, alcohol and drugs use
Domain Test
R
2
adjusted
(p-value) Significant contributors
Demographics
R
2
change
(p-value)
Alcohol R
2
change
(p-value)
Cannabis/cocaine/
heroin
Quantity R
2
change (p-value)
Full model
R
2
adjusted
(p-value) Significant contributors
Fluency FAS 0.297 (0.000) Cann Quant (0.000);
Coc Quant (0.027);
0.096 (0.013) 0.086 (0.003) 0.177 (0.000) 0.313 (0.000) Cann Quant (0.001)
Heroin Quant (0.039)
RFFT 0.061 (0.038) Cann Quant (0.066) 0.133 (0.002) 0.017 (0.199) 0.034 (0.399) 0.124 (0.009) Educ (0.005)
WM LNS 0.286 (0.000) Cann Quant (0.035);
Coc Quant (0.000)
0.171 (0.000) 0.034 (0.057) 0.180 (0.000) 0.341 (0.000) Educ (0.014); Cann Quant
(0.032); Coc Quant (0.001)
Spatial Span 0.096 (0.009) Coc Quant (0.015) 0.109 (0.007) 0.006 (0.443) 0.081 (0.049) 0.137 (0.006) Coc Quant(0.032)
Reasoning Similarities 0.207 (0.000) Cann Quant (0.022);
Coc Quant (0.004)
0.173 (0.000) 0.030 (0.075) 0.118 (0.004) 0.272 (0.000) Educ (0.005); Cann Quant
(0.026); Coc Quant (0.032)
CT_tot_errors 0.085 (0.016) Coc Quant (0.027) 0.074 (0.039) 0.012 (0.308) 0.079 (0.062) 0.103 (0.022) Coc Quant (0.047)
Shifting 5DT_shift 0.058 (0.047) 0.014 (0.556) 0.004 (0.558) 0.079 (0.080) 0.029 (0.213)
OTM_shift 0.130 (0.002) Coc Quant (0.037);
Heroin Quant (0.018)
0.105 (0.008) 0.008 (0.375) 0.092 (0.028) 0.148 (0.004) Heroin Quant (0.047)
Interference 5DT_interf 0.058 (0.046) 0.005 (0.814) 0.011 (0.330) 0.078 (0.081) 0.027 (0.225)
Strp_interf 0.021 (0.191) 0.087 (0.020) 0.002 (0.667) 0.036 (0.347) 0.061 (0.083) Age (0.073)
DM IGT 0.260 (0.000) Cann Quant (0.003);
Coc Quant (0.011)
0.049 (0.111) 0.127 (0.000) 0.160 (0.000) 0.288 (0.000) Alcoh tot cons (0.037); Cann
Quant (0.004); Coc Quant (0.067)
Self-regulation R-SAT 0.030 (0.136) Cann Quant (0.084) 0.011 (0.619) 0.036 (0.077) 0.057 (0.168) 0.038 (0.164)
Note: WM, Working Memory; DM, Decision-making; FAS, Verbal fluency; RFFT, Ruff figural fluency test; LNS, Letter number sequencing; CT_tot errors, total number of errors on Category test; 5DT_shift, Five digit test shifting score;
OTM_shift, Oral trail making shifting score; 5DT_interf, Five digit test interference score; Strp_interf, Stroop interference score; IGT, Iowa gambling task; R-SAT, Revised strategy application test; Cann, Cannabis; Quant, Quantity; Educ,
Years of education; Coc, Cocaine; Alcoh tot cons, Alcohol total consumption.
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Table 5. Multiple hierarchical regression models of the association between demographic variables, alcohol total consumption and duration of cannabis, cocaine and heroin use and neuropsychological
performance
Model including only cannabis,
cocaine and heroin Hierarchical three-stage model including demographics, alcohol and drugs use
Domain Test
R
2
adjusted
(p-value)
Significant
contributors
Demographics
R
2
change
(p-value)
Alcohol
R
2
change
(p-value)
Cannabis/cocaine/
heroin Duration
R
2
change (p-value)
Full model
R
2
adjusted
(p-value) Significant contributors
Fluency FAS 0.160 (0.000) Coc Durat (0.012) 0.096 (0.013) 0.086 (0.003) 0.130 (0.002) 0.262 (0.000) Age (0.002); Educ (0.026);
Coc Durat (0.012)
RFFT 0.093 (0.010) Coc Durat (0.007) 0.133 (0.002) 0.017 (0.199) 0.179 (0.033) 0.179 (0.001) Educ (0.003); Coc Durat (0.015)
WM LNS 0.179 (0.000) Coc Durat (0.020) 0.171 (0.000) 0.034 (0.057) 0.113 (0.005) 0.269 (0.000) Educ (0.001); Coc Durat (0.069)
Spatial Span 0.098 (0.008) Cann Durat (0.025) 0.109 (0.007) 0.006 (0.443) 0.080 (0.050) 0.137 (0.006) Educ (0.012); Cann Durat (0.014)
Reasoning Similarities 0.193 (0.000) Coc Durat (0.004) 0.173 (0.000) 0.030 (0.075) 0.144 (0.001) 0.300 (0.000) Educ (0.000); Coc Durat (0.009)
CT_tot_errors 0.106 (0.006) Cann Durat (0.090) 0.074 (0.039) 0.012 (0.308) 0.073 (0.084) 0.096 (0.028)
Shifting 5DT_shift 0.003 (0.432) 0.014 (0.556) 0.004 (0.558) 0.052 (0.224) 0.000 (0.430) Coc Durat (0.074)
OTM_shift 0.223 (0.000) Coc Durat (0.002);
Heroin Durat (0.002)
0.105 (0.008) 0.008 (0.375) 0.167 (0.001) 0.228 (0.000) Educ (0.062); Coc Durat (0.016);
Heroin Durat (0.003)
Interference 5DT_interf 0.064 (0.035) Coc Durat (0.066) 0.005(0.814) 0.011(0.330) 0.098(0.036) 0.048(0.124) Coc Durat (0.036)
Strp_interf 0.102 (0.007) Coc Durat (0.089) 0.087(0.020) 0.002(0.667) 0.076(0.065) 0.104(0.019) Educ (0.065)
DM IGT 0.125 (0.002) Coc Durat (0.076) 0.049 (0.111) 0.127 (0.000) 0.060 (0.099) 0.181 (0.001) Alcoh tot cons (0.013)
Self-regulation R-SAT 0.019 (0.710) 0.011 (0.619) 0.036 (0.077) 0.006 (0.916) 0.017 (0.602)
Note: WM, Working Memory; DM, Decision Making; FAS, Verbal fluency; RFFT, Ruff figural fluency test; LNS, Letter number sequencing; CT_tot errors, total number of errors on Category test; 5DT_shift, Five digit test shifting score;
OTM_shift, Oral trail making shifting score; 5DT_interf, Five digit test interference score; Strp_interf, Stroop interference score; IGT, Iowa gambling task; R-SAT, Revised strategy application test; Cann, Cannabis; Durat, Duration; Educ,
Years of education; Coc, Cocaine; Alcoh tot cons, Alcohol total consumption.
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variable with the highest predictive value. In the RFFT the
block of duration of drug consumption improved prediction
of previous blocks significantly, and duration of cocaine con-
sumption was the variable with the highest predictive value.
Working memory: In the LNS test, the block of duration
of drug consumption improved the predictive value of demo-
graphics and alcohol significantly. Duration of cocaine con-
sumption was the best predictor variable of performance on
this task. In the Spatial span task, the block of duration of
drug consumption improved prediction of demographics and
alcohol significantly, but in this case the variable with the
highest predictive value was duration of cannabis
consumption.
Reasoning: In the Similarities task, the block of duration
of drug consumption improved the predictive value of demo-
graphics and alcohol significantly, and duration of cocaine
consumption was the best predictor of performance on this
task. In the Category test, we observed that the block of
durations of drugs consumption failed to significantly predict
performance on this measure.
Interference and shifting: In the Stroop test, the block of
duration of drug consumption was only marginally significant
for prediction of performance on this task, and none of the
individual drug variables (cannabis, cocaine or heroin)
showed significant -coefficients. In the 5DT, we observed
that, for the interference score, the block of duration of
drugs consumption produced a significant improvement in
prediction, being the duration of cocaine consumption the
best predictor of performance. As for the shifting score, the
block of duration of drugs use failed to significantly predict
performance, and only duration of cocaine use had a margin-
ally significant effect (p¼0.074). In the OTM test, we
observed that the block of drugs produced a significant
improvement in prediction, with duration of heroin and
cocaine being the variables with the highest prediction of
performance.
Decision-making: In the IGT, we observed that the block
of duration of drug consumption did not improve prediction
of performance on this task, as compared with the block of
alcohol consumption.
Self-regulation: In the R-SAT, we observed that the block
of duration of drug consumption did not improve prediction
of performance on this task, as compared with the block of
alcohol consumption.
Summary
Group comparisons showed that SDIs performed signifi-
cantly poorer than controls on all of the executive indices
assessed, showing large effect sizes for differences on tests of
fluency, working memory, reasoning, inhibition and decision-
making. The hierarchical regression models showed a signif-
icant contribution of total alcohol consumption on verbal
fluency and decision-making. As for quantity of consumption
of the drugs that motivated treatment, we observed that: (i)
the quantity of cannabis consumption predicts performance
on verbal working memory, verbal reasoning, verbal fluency
and decision-making; (ii) the quantity of cocaine consump-
tion predicts performance on verbal and visual–spatial work-
ing memory, verbal and visual reasoning, and decision-
making; and (iii) the quantity of heroin consumption predicts
performance on visual–spatial shifting. As for duration, we
observed that: (i) the duration of cannabis consumption pre-
dicts performance on visual working memory only; (ii) the
duration of cocaine consumption predicts performance on
verbal working memory and reasoning, both verbal and
non-verbal fluency and shifting, and interference-based inhi-
bition; and (iii) the duration of heroin consumption predicts
performance on visual-spatial shifting (Table 6).
Discussion
Results showed that SDIs have a broad range of executive
impairments, including fluency, working memory, reasoning,
inhibition, shifting and decision-making deficits, of moderate
to large magnitude according to effect sizes (Cohen’s drange:
0.6–2.4). Importantly, these decrements are observed in SDIs
with a median abstinence duration of 8 months, and therefore
they should be regarded as long-term effects with relevant
implications for the notion of addiction as a chronic brain
disorder associated with frontal systems dysfunction
(Goldstein and Volkow, 2002). Previous studies had obtained
similar results (see the review by Verdejo-Garcı
´a et al., 2004),
but the fact that virtually all SDIs are polysubstance abusers
complicates the attribution of specific or generalized executive
deficits to the effects of alcohol or any given drug. In this
respect, the results from regression models revealed that
severity of alcohol use is robustly associated with verbal flu-
ency and decision-making decrements. As for the main drugs
motivating treatment (cannabis, cocaine and heroin), results
showed that quantity of cannabis and cocaine use have
common detrimental effects on verbal working memory, ana-
logical reasoning and decision-making measures, and that
duration of cocaine and heroin use have common detrimental
effects of visual–spatial shifting measures. On the other hand,
we found specific effects of duration of cannabis use on
visual–spatial working memory, and of duration of cocaine
use on response inhibition.
Our first aim was to separate the effects of alcohol
versus drugs use on different components of executive func-
tions. Severity of alcohol use showed significant detrimental
effects on verbal fluency and decision-making (on the IGT),
and a trend to significant effects on working memory, but not
on other executive components. Previous studies had pro-
posed that severity of alcohol abuse was significantly asso-
ciated with decrements on executive components of planning
and flexibility in psychostimulants and heroin abusers co-
abusing alcohol (Bolla et al., 2000; Fishbein et al., 2007;
Goldstein et al., 2004). However, these studies were con-
ducted in short-term abstinent SDIs (range of 2–4 weeks),
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whereas one of the few studies available in long-term absti-
nent alcoholics found that decision-making performance
(measured with the IGT) was impaired pervasively in these
individuals even after six years of sobriety; being the magni-
tude of disadvantageous decision-making associated with the
duration of peak alcohol use (Fein et al., 2004). Moreover,
the alcoholic individuals who had impaired IGT performance
had significant grey matter reductions in the amygdala, a key
region for the operation of decision-making processes
(Bechara et al., 2003). A recent structural magnetic resonance
study have also provided evidence of significant structural
reductions of grey matter (up to 20% lower) in the bilateral
dorsolateral prefrontal cortex of alcoholics (Chanraud et al.,
2007). This region has been proposed to be involved in verbal
fluency and other executive operations associated with the
updating of information in working memory (D’Esposito
and Postle, 2002; Gauthier et al., 2009). Functional imaging
studies have also demonstrated dysfunctional frontotemporal
activation during verbal fluency performance using functional
spectroscopy (Schecklmann et al., 2007), and significant cor-
relations between PET-indexed left dorsolateral prefrontal
hypometabolism and reduced verbal fluency performance in
abstinent alcoholics (Dao-Castellana et al., 1998). There is
also evidence that acute ethanol administration decreases
left dorsolateral prefrontal cortex activation and impairs
verbal fluency performance in healthy individuals (Wendt
and Risberg, 2001). Overall, these studies support our results
showing a prominent association between severity of alcohol
use and poorer fluency and decision-making skills. Although
fluency and decision-making are independent executive com-
ponents (Verdejo-Garcı
´a and Pe
´rez-Garcı
´a, 2007) they have
in common being complex multifaceted operations encom-
passing access to long-term memory, clustering, monitoring
and switching of information (in the case of fluency) (Fisk
and Sharp, 2004; Troyer et al., 1998), and episodic/working
memory, motivation and feedback processing and reversal
learning (in the case of decision-making) (Bechara et al.,
2005; Busemeyer and Stout, 2002; Gupta et al., 2009).
Therefore, we may speculate that alcohol severity specifically
affects some of the component operations of fluency and/or
decision-making (e.g. working memory updating), or
alternatively affects in a broad sense to multi-component
executive processes.
Our second aim was to determine the contribution of
quantity and duration of consumption of the main drugs
that motivated treatment to decrements on executive compo-
nents functioning. In this regard, we found common detri-
mental effects of quantity of cannabis use and cocaine use
on measures of verbal updating of working memory, analo-
gical reasoning and decision-making. A principal component
analysis performed on a comprehensive battery of executive
functions tests concluded that measures of working memory
and analogical reasoning (along with fluency measures) load
together on a factor that we and others have labelled
‘updating’ (Verdejo-Garcı
´a and Pe
´rez-Garcı
´a, 2007); which
consists of continuous refreshing/updating of working
memory contents in order to set task demands and optimize
performance (Miyake et al., 2000; Stuss and Alexander, 2007;
Verdejo-Garcı
´a and Pe
´rez Garcı
´a, 2007). These results are
consistent with several sources of evidence, including animal
studies showing cocaine and cannabinoid dose-related mod-
ulation of working memory performance (Deadwyler et al.,
2007; Egerton et al., 2006; George et al., 2008), human studies
showing dose-related negative effects of severity of cannabis
and cocaine use on updating measures in polydrug abusers
(Medina et al., 2007; Verdejo-Garcı
´a et al., 2007a), and the
conclusions of a recent meta-analysis of neuropsychological
studies in cocaine abusers showing moderate effect sizes for
updating indices, which are durable across abstinence
(Jovanovski et al., 2005). Functional imaging studies have
linked these updating deficits to prefrontal cortex, cingulate
cortex and superior parietal cortex dysfunctions (Jager et al.,
2006; Ku
¨bler et al., 2005). Nonetheless, there is also intriguing
evidence showing that cannabis users have abnormally
increased hippocampal activation in response to executive
tasks demands (Eldreth et al., 2004; Nestor et al., 2008).
Moreover, a recent structural magnetic resonance imaging
study has revealed significant volumetric reductions (circa
12%) in the hippocampus of long-term cannabis users
(Yu
¨cel et al., 2008). Therefore, hippocampal dysfunction
may also play a prominent role on cannabis-induced updating
deficits. In fact, duration of cannabis was also linked to
Table 6. Summary of significant associations between the different substances analysed and the different components of cold and hot executive
functions
CONSUMPTION VARIABLES COLD EXECUTIVE FUNCTIONS HOT EXECUTIVE FUNCTIONS
Working memory Reasoning Fluency Shifting Interference Decision-making Self-regulation
verbal visual verbal visual verbal visual verbal visual verbal visual
ALCOHOL Total consumption
CANNABIS Quantity
Duration
COCAI
´NE Quantity
Duration
HEROI
´N Quantity
Duration
Consumption parameter that significantly predicted this process.
Consumption parameter showing a trend to significant prediction on this process.
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poorer spatial working memory, a process that has been asso-
ciated with the hippocampal endocannabinoid system activa-
tion in animal models (Deadwyler et al., 2007). Similarly, for
decision-making, very recent studies have shown that both
cannabis and cocaine abuse have dose-related detrimental
effects on IGT performance (Bolla et al., 2003, 2005;
Verdejo-Garcı
´a et al., 2007a). However, results from func-
tional imaging and cognitive models studies suggest that
both groups may fail to make advantageous decisions for
different reasons: cannabis abusers display PET-indexed
prominent activation in non-specialized areas (e.g. cerebellum
and occipital cortex) during IGT performance (Bolla et al.,
2005), whereas cocaine abusers exposed to the same paradigm
show dysfunctional activation of regions typically involved in
reward processing and decision-making (e.g. striatum and
orbitofrontal cortex) (Bolla et al., 2003). Moreover, cognitive
decision models of the IGT have shown that cannabis abusers
fail the task because they place more attention on recent than
distal outcomes, whereas cocaine abusers fail because they
place more attention on gains than on losses (Busemeyer
and Stout, 2002).
Regression models have also shown common effects of
cocaine and heroin duration of use on cognitive shifting.
Animal models have shown that repeated administration of
cocaine produces impairments in cognitive flexibility, specifi-
cally in perseveration and reversal learning linked to orbito-
frontal cortex functioning (Jentsch et al., 2002; Schoenbaum
et al., 2004; Stalnaker et al., 2006, 2009). These findings have
been nicely translated to humans by several studies showing
relatively specific effects of cocaine abuse on cognitive shifting
(Ersche et al., 2008; Verdejo-Garcı
´a and Pe
´rez-Garcı
´a, 2007)
and electrophysiological indices of decreased error-related
processing and impaired behavioural correction of errors in
cocaine abusers (Franken et al., 2007). Although there is no
equivalent body of animal research on the opioid modulation
of cognitive shifting, a number of human neuropsychological
studies have shown that heroin abusers have significant
impairments in intradimensional set-shifting, perseveration,
risk-taking and decision-making tasks (Fishbein et al., 2007;
Lyvers and Yakimoff, 2003; Ornstein et al., 2000; Verdejo-
Garcia et al., 2005a; see also the review by Gruber et al.,
2007), which have been attributed to grey matter decrements
in the medial and inferior prefrontal cortex, insula and tem-
poral cortex (Lyoo et al., 2006) and dysfunctional activation
of the rostral anterior cingulate cortex in response to error
feedback (Forman et al., 2004). Therefore, abnormal error
processing and subsequent failure of ‘quality control’ execu-
tive mechanisms may underlie flexibility deficits in both
cocaine and heroin abusers.
Finally, we found specific effects of duration of cocaine
abuse on one inhibition measure, the 5DT interference
index. Previous results from our lab and others have sup-
ported relatively specific deleterious effects of psychostimu-
lants on a number of neuropsychological indices of response
inhibition, including the Stroop test, the Go–No Go, the
Continuous Performance test or the Stop-Signal task (Bolla
et al., 2004; Colzato et al., 2007; Li et al., 2008; Verdejo-
Garcı
´a et al., 2007c). Furthermore, these deficits have been
linked to patterns of severity of drug use (Bolla et al., 2004;
Verdejo-Garcı
´a et al., 2005b) and to brain measures of
reduced activation of the anterior cingulate and lateral pre-
frontal cortices during inhibition trials (using PET or fMRI)
(Bolla et al., 2004; Li et al., 2008), and white matter decre-
ments in the genu of the corpus callosum (using diffusion
tensor imaging) (Moeller et al., 2005). These effects may be
explained by a more intense neuromodulatory effect of psy-
chostimulants on the cingulate cortex-striatal system (Bolla
et al., 2003; Paulus et al., 2002, 2003, 2005; see also the review
by Li and Sinha, 2008). However, this result may be inter-
preted with caution for several reasons. First, there is growing
evidence that disinhibition deficits may predate initiation of
drug use and constitute a liability marker for substance use
disorders (see Dalley et al., 2007 and Belin et al., 2008 for
animal evidence; see Verdejo-Garcı
´a et al., 2008 for a review
of human evidence); therefore, we cannot draw conclusions
on the causality of inhibition deficits. Second, there is no
consistency between our findings on the 5DT and the results
of other inhibition tests, such as the Stroop. We think this
may be due to the fact that Stroop performance is more
influenced by age and educational factors (Kaplan et al.,
2009), making it harder to establish a drug-related effect.
However, more research is warranted to investigate the spe-
cific effects of cocaine and other psychostimulants on inhibi-
tory control processes.
Overall, these results obtained in mid-term abstinent sub-
stance abusers may have important implications for their
quality of life and their ability to take advantage of cognitive
behavioural therapy-based treatment programs. Deficits in
working memory, reasoning, fluency and cognitive flexibility
may be associated with difficulties in retaining complex
instructions, selecting relevant information from clinical ses-
sions or group interactions, and generalizing specific learning
to other familiar and social interactive activities. On the other
hand, treatment headways require that addicted individuals
reverse strong habits and over-rehearsed decision patterns.
Cognitive deficits have been associated with poorer clinical
progression levels (Leber et al., 1985), a lower level of partic-
ipation and implication in the treatment (Fals-Stewart and
Lucente, 1994) and higher rates of treatment dropout and
drug relapse (Aharonovich et al., 2003, 2006, 2008; Passetti
et al., 2008; Streeter et al., 2008; Teichner et al., 2002). In this
respect, our results stress the need to promote rehabilitation
programs targeted to restore or compensate executive dys-
function in SDIs.
Finally, several limitations of this study should be men-
tioned. First, there is evidence of age-related cognitive decline
from the thirties onwards (Herndon et al., 1997; Salthouse,
2009), and therefore some of the executive declines in our
sample may be related to normal aging. However, our regres-
sion models adequately controlled for the effects of age and
education, and all of the drug effects reported were obtained
after removing the effect of these variables. Second, due to a
lower prevalence of female inpatients during recruitment, our
sample was predominantly composed of males. Future studies
should investigate how these findings may or may not gener-
alize to a female population of SDI. Third, some executive
indices that were impaired in SDI failed to show any associ-
ation with alcohol or drug use (e.g. the R-SAT). It is possible
that in these cases the relatively medium sample size (further
limited after outliers exclusion) may have contributed to type
1328 Journal of Psychopharmacology 24(9)
at Biblioteca Universitaria de Granada on March 28, 2016jop.sagepub.comDownloaded from
II error or, alternatively, that these deficits relate to different
aspects of the addiction phenomenon (e.g. age of first use,
personality patterns). Furthermore, there is an inherent limi-
tation linked to the reliability of self-reports of drug use;
nonetheless, when considering the limitations of other meth-
ods, such as toxicological analyses or structured interviews
categorical approaches, to catch the time line, peak effects
and dimensional aspects of drug history, self-reports end up
as the approach with highest face validity (see Verdejo-Garcı
´a
et al., 2004 for a discussion of this methodological challenge
of drug abuse cognitive studies). Finally, as mentioned above,
the current cross-sectional data do not allow us to determine
whether these alterations preceded drug use and contributed
to higher severity patterns, or if they occur as a consequence
of persistent drug use. Longitudinal studies are warranted to
address this relevant question.
Acknowledgements
This work is supported by the Spanish Ministry of Science and
Innovation (MICINN), under the FPU national plan (grant reference
AP 2005-1411) and Research Paper SEJ 2006-08278, and the Junta de
Andalucı
´a through the Research Project P07.HUM 03089. This study
complies with the current laws of Spain and all international ethical
guidelines for human studies.
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