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Impulsivities and addictions: A multidimensional integrative framework informing assessment and interventions for substance use disorders

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Abstract

Impulse control is becoming a critical survival skill for the twenty-first century. Impulsivity is implicated in virtually all externalizing behaviours and disorders, and figures prominently in the aetiology and long-term sequelae of substance use disorders (SUDs). Despite its robust clinical and predictive validity, the study of impulsivity is complicated by its multidimensional nature, characterized by a variety of trait-like personality dimensions, as well as by more state-dependent neurocognitive dimensions, with variable convergence across measures. This review provides a hierarchical framework for linking self-report and neurocognitive measures to latent constructs of impulsivity and, in turn, to different psychopathology vulnerabilities, including substance-specific addictions and comorbidities. Impulsivity dimensions are presented as novel behavioural targets for prevention and intervention. Novel treatment approaches addressing domains of impulsivity are reviewed and recommendations for future directions in research and clinical interventions for SUDs are offered. This article is part of the theme issue ‘Risk taking and impulsive behaviour: fundamental discoveries, theoretical perspectives and clinical implications’.
royalsocietypublishing.org/journal/rstb
Review
Cite this article: Vassileva J, Conrod PJ. 2019
Impulsivities and addictions:
a multidimensional integrative framework
informing assessment and interventions for
substance use disorders. Phil. Trans. R. Soc. B
374: 20180137.
http://dx.doi.org/10.1098/rstb.2018.0137
Accepted: 15 November 2018
One contribution of 14 to a theme issue ‘Risk
taking and impulsive behaviour: fundamental
discoveries, theoretical perspectives and clinical
implications’.
Subject Areas:
behaviour, cognition, neuroscience
Keywords:
addiction, substance use disorders, personality,
impulsivity, treatment and prevention
Author for correspondence:
Patricia J. Conrod
e-mail: patricia.conrod@umontreal.ca
Impulsivities and addictions:
a multidimensional integrative framework
informing assessment and interventions
for substance use disorders
Jasmin Vassileva1,2 and Patricia J. Conrod3,4
1
Institute for Drug and Alcohol Studies, and
2
Department of Psychiatry, Virginia Commonwealth University,
Richmond, VA, USA
3
Department of Psychiatry, University of Montreal, Montreal, Canada
4
Centre de Recherche, CHU Ste Justine, Montreal, Canada
JV, 0000-0003-2397-2657; PJC, 0000-0002-5570-481X
Impulse control is becoming a critical survival skill for the twenty-first cen-
tury. Impulsivity is implicated in virtually all externalizing behaviours and
disorders, and figures prominently in the aetiology and long-term sequelae
of substance use disorders (SUDs). Despite its robust clinical and predictive
validity, the study of impulsivity is complicated by its multidimensional nature,
characterized by a variety of trait-like personality dimensions, as well as by
more state-dependent neurocognitive dimensions, with variable convergence
across measures. This review provides a hierarchical framework for linking
self-report and neurocognitive measures to latent constructs of impulsivity
and, in turn, to different psychopathology vulnerabilities, including sub-
stance-specific addictions and comorbidities. Impulsivity dimensions are
presented as novel behavioural targets for prevention and intervention. Novel
treatment approaches addressing domains of impulsivity are reviewed and
recommendations for future directions in research and clinical interventions
for SUDs are offered.
This article is part of the theme issue ‘Risk taking and impulsive behaviour:
fundamental discoveries, theoretical perspectives and clinical implications’.
1. Introduction
Impulsivity is implicated in virtually all substance-related and addictive disorders
and many other forms of psychopathology, which has caused an explosion of
research in this area over the last two decades. This research has revealed that
the construct is highly multidimensional and complex in its measurement, despite
having robust and repeatable predictive validity. However, the translation of this
knowledge to clinical practice has been strikingly slow. The novelty of this review
relative to previous reviews on this topic is the integration of multiple comp-
lementary models of addiction in which impulsivity and its different
dimensions and measurement modalities figure prominently. Furthermore, this
review will attempt to demonstrate empirical evidence in support of the link
between the multidimensional structure of impulsivity and its neurocognitive cor-
relates with contemporary views on the structure of psychopathology and risk for
different types of substance use disorders (SUDs). Finally, this multidimensional,
integrative framework will serve to highlight promising intervention strategies for
the treatment and prevention of addictive behaviours focusing on management of
dimensions of impulsivity.
2. Impulsivity in the twenty-first century
The term impulsivity has existed in human language for more than five centuries
and has been recognized as playing a key role in psychiatric conditions since the
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birth of the fields of neurology and psychiatry [1]. Impulsivity
is defined by the International Society for Research on Impul-
sivity as ‘ behaviour without adequate thought, the tendency
to act with less forethought than do most individuals of
equal ability and knowledge, or a predisposition toward
rapid, unplanned reactions to internal or external stimuli with-
out regard to the negative consequences of these reactions’.
Impulse control has become recognized as a crucial survival
skill and enhancing impulse control is viewed as a key societal
goal for the twenty-first century, as for the first time in human
history we live in a society where food is readily available,
addictive substances and technology are easily accessible,
and immediate gratification is the modus operandi of popular
culture. As eloquently argued by Terry Moffitt, impulse control
is more necessary now than ever before in human history in
order to prevent addiction, stay healthy, resist spending, and
save for retirement [2].
3. Impulsivity and drug addiction
With regards to SUDs in particular, impulsivity has been
shown to be both an antecedent risk factor considered a
vulnerability marker for SUDs [3] and a consequence of
chronic substance use associated with structural and func-
tional neurobiological changes [4]. The definition of
impulsivity maps closely to current conceptualizations of
addiction, considered to be a chronic relapsing brain dis-
ease characterized by ‘uncontrollable, compulsive drug
craving, seeking and use, even in the face of negative
health and social consequences’ [5]. Indeed, contemporary
theories of addiction [6–10] conceptualize the disorder
primarily as a syndrome of impaired impulse control, pro-
posed to be the core mechanism underlying the compulsive
pattern of drug seeking and use, which, in combination
with individual differences in other neurobiological
domains such as compulsivity or negative emotionality,
are crucial to understanding the heterogeneity of addictive
disorders [9,11].
Despite the notable advances in the study of impulsivity
in addictive disorders, the lack of clinical translation of these
advances has been striking. One source of variability in
linking impulsivity to addiction vulnerability is the attempt
by researchers to study the role of impulsivity in single
disorders, by statistically covarying other forms of psycho-
pathology, or other forms of substance use. This artificial
simulation of unitary conditions is not supported by research
on the structure of psychopathology (e.g. [12,13]), where
impulsivity also appears to be a central common vulnerability
to many externalizing conditions [12], and potentially all
psychiatric conditions [13,14]. What is lacking in the literature
is an integrative model that incorporates a multimodal
approach to the measurement of impulsivity and its different
dimensions, which attempts to explore their specificity with
respect to different types of addictions and co-occurring
mental disorders.
4. Impulsivity versus impulsivities: integrative
multimodal framework
Trait dimensions of impulsivity are measured with self-report
instruments that are based on self-evaluation of how people
view themselves relative to others. In contrast, state dimen-
sions are measured by performance on neurocognitive tasks
tapping much more narrowly defined cognitive and affective
processes, in which individuals are generally required to
choose between short-term and long-term gains and losses,
or to inhibit a response to a pre-potent urge, a valued
reward, or a delay cue [16–18]. While it is recognized
that such measures can also be used to identify trait-level
individual differences [19], they have the potential to tap
more within- person variability. In this respect, self-report
measures could reveal who is at general risk, whereas neuro-
cognitive measures, may more accurately predict who is at
immediate risk [16]. Combining the two approaches may
lead to better identification of individuals who are at parti-
cularly high risk and increase our understanding of the
relationship between multiple putative addiction phenotypes,
which may help redefine them as multi-level combination
of traits and states [20] that could be targeted by tailored
personality and neurocognitive interventions.
(a) Trait impulsivity—personality dimensions
Most models of personality propose that behaviour is gov-
erned by at least two independent systems, considered to
be heritable biological traits. One is associated with avoid-
ance behaviour, broadly reflecting the personality trait of
anxiety, and the other with approach behaviour, reflecting
impulsivity [21]. Eysenck and his biologically-based trait
theory of personality [22] inspired other similar omnibus
theories, such as those of Cloninger [23] and Zuckerman
[24], which incorporate variously named constructs related
to impulsivity (e.g. impulsiveness, venturesomeness, novelty
seeking, sensation seeking) and emphasize their importance
in relation to specific subtypes of substance abuse. Barratt
and colleagues [25,26] propose a more multifaceted conceptu-
alization of trait impulsivity, delineating attentional, motor,
and non-planning sub-components. Similarly, others
[27–29] have used the Five Factor Model of Personality [30]
to emphasize the hierarchical and context-dependent nature
of self-report impulsivity, identifying five lower-order trait
impulsivity facets: (lack of) premeditation, (lack of) persever-
ance, sensation seeking, negative urgency (tendency to act
impulsively under negative affect), and positive urgency (ten-
dency to act impulsively under positive affect). Valiant
attempts have been made to impose structure across these
various trait measures to come to some agreement on the
true nature of trait impulsivity and its dimensions, with hier-
archical factor analyses agreeing on at least two broad
dissociable trait dimensions that account for a significant por-
tion of the variance across most self-report impulsivity
measures: Impulsiveness (Unprepared/Spontaneous) and
Sensation Seeking (in [31]). Importantly, these traits were
shown to be hierarchically organized and their common var-
iance captured by a higher-order Impulsivity construct.
Within this hierarchical structure, each domain was shown
to subdivide into two additional lower order facets: Impul-
siveness into traits of preparedness and spontaneous; and
Sensation Seeking into thrill and adventure seeking and
impatient pleasure seeking. As noted by Woicik et al. [32],
many personality scales were developed when impulsivity
was considered a unitary construct and therefore fail to
fully differentiate between these facets. Sensation Seeking
[24] has items that tap impulsiveness and thrill and
royalsocietypublishing.org/journal/rstb Phil. Trans. R. Soc. B 374: 20180137
2
adventure seeking, despite its name, which causes further
confusion in the field. In figure 1, we attempt to provide
some clarification of how the most widely used impulsivity
scales map onto four empirically derived subfacets of impulsiv-
ity, as suggested in [31]. Another widely used multidimensional
scale, the UPPS/UPPS-P [27] measures three dissociable traits,
despite its numerous subscales, Negative/Positive Urgency,
(lack of) Premeditation/perseverance, and Sensation Seeking,
as revealed in meta-analysis of studies investigating the
discriminative validity of this scale [27].
Self-report measures of trait impulsivity tend to have high
predictive value with respect to substance use and misuse,
psychopathology and other health risk behaviours [2,33,34].
Numerous studies using these personality scales have
found a consistent relationship between measures of impul-
sivity, sensation seeking and drug addiction [35– 40].
Importantly, evidence from prospective studies indicates
that impulsivity measured as a childhood temperament is
linked to the later development of SUDs [41,42]. Research
indicates that trait impulsivity is a drug-vulnerability pheno-
type that exists prior to the development of SUDs, which is
also elevated in biological siblings of chronic drug users,
supporting its role as a behavioural endophenotype for
SUDs [43].
Increasingly, research has noted the need to consider
specific facets of trait impulsivity when exploring different sub-
stance misuse profiles, defined along clinical, substance-related,
and motivational dimensions [44,45]. The emotion-based
(negative and positive urgency), conscientiousness-based
(lack of premeditation and lack of perseverance), and sensation
seeking-based facets have been associated with different
patterns of substance misuse that likely reflect different
aetiological mechanisms with differential implications for treat-
ment [38,46]. Some facets, such as sensation seeking, appear to
be more adaptive because despite their associations with early
onset and heavy drinking [13,33,34,47], individuals scoring
high on this trait show greater ability to maintain some control
over drug intake and have fewer co-occurring symptoms of
psychopathology [12,38,48]. In contrast, Impulsiveness (Unpre-
pared/Spontaneous) is associated with problem drug and
alcohol use (in [45]), with some studies suggesting that the
link between this facet of impulsivity and SUD is indirect
and mediated through general tendency towards rule breaking
and misconduct (e.g. [49]). Whereas the majority of the
evidence supporting the personality model of addiction
vulnerability is based on alcohol, recent studies provide evi-
dence for the role of facets of trait impulsivity as prospective
risk factors for heavy alcohol and cannabis misuse [50,51]
and for sensation seeking as a risk factor for early initiation
of both alcohol and cannabis use [51]. Impulsivity and
sensation seeking are increasingly being recognized as separate
[52,53], genetically distinct constructs [54] implicated in risk for
substance misuse. Dissociating between them when consider-
ing the role of impulsivity in substance-related behaviours
may increase their diagnostic and prognostic utility.
Despite a widely accepted notion of personality traits
reflecting lasting and consistent individual differences, there
is unequivocal evidence that personality traits tend to
change across the lifespan [55], with some traits (e.g. con-
scientiousness, extraversion) being more stable than others
(e.g. neuroticism) [56]. For the most part, the measurement
of personality traits involves central tendency variables, is
often limited to a single assessment, and rarely involves
methods that capture within-person variability.
(F2)
z
Figure 1. Schemata for linking different impulsivity measures and facets to patterns of substance use and comorbid psychopathology through latent constructsof
impulsivity and psychopathology. Self report trait measures in dark green, neurocognitive and computational parameters in lighter green, latent neurocognitive
impulsivity constructs in orange, latent hierarchical psychopathology dimensions in light blue and observed DSM disorder in dark blue. Thick dark blue arrows
from internalising symptoms and externalising symptoms reflect indirect relationship between latent psychopathology construct and specific substance use outcomes
through externalizing and externalizing symptoms. Pfactor represents variance common to all psychiatric symptoms. Adapted from Castellanos-Ryan et al. [12] and
Conrod & Nikolaou [11].
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royalsocietypublishing.org/journal/rstb Phil. Trans. R. Soc. B 374: 20180137
(b) State impulsivity—neurocognitive dimensions
Although impulsivity is often thought of as a personality trait
or predisposition, many of its contemporary conceptualiz-
ations involve distinct performance-based neurobehavioural
manifestations, reflecting individual differences in valuation
and decision-making that could be more sensitive to fluctuat-
ing momentary lapses in impulse control, influenced by
environmental context and the current state of the individual.
Neurocognitive manifestations of impulsivity fall into two
broad domains, dissociable at both neuroanatomical and
neuropharmacological levels [21,52]. The first is ‘impulsive
action’ [57], also known as motor impulsivity, involving
response inhibition [1,58] and manifested as difficulty inhibit-
ing or stopping prepotent or ongoing responses. Common
tasks of impulsive action include stop signal tasks [59,60],
which require cancellation of an already initiated response
[61], and go/no-go type of tasks [62–64], which require inhi-
bition of prepotent responses to external or internal signals.
The second neurocognitive domain is ‘impulsive choice’ [65],
also known as cognitive impulsivity or reward-based impulsivity,
which refers to the compromised ability to make decisions in
line with long-term goals. It is related to: (i) sensitivity of
choices to delay and preference for ‘smaller-sooner over
larger-later’ rewards, measured with delay discounting para-
digms [66]; and (ii) sensitivity of choices to risk and reward,
measured with reward-based decision-making tasks such as
the Iowa Gambling Task [67] or the Cambridge Gambling
Task [68], or with risk-taking tasks such as the Balloon Ana-
logue Risk Task [69]. These neurocognitive distinctions do
show specificity with respect to relationships to facets of
self-report impulsivity when investigated in a multivariate
framework: Impulsiveness correlates with measures of
response inhibition (action impulsivity), sensation seeking cor-
relates with reward sensitivity measures on learning and
passive avoidance tasks, and self-report measures of impatient
pleasure seeking (a sub-facet of sensation seeking) most
robustly correlate with delay discounting [31].
Neuroimaging and neuropsychological studies assessing
the relationship between facets of impulsivity and risk for
SUDs suggest that self-report and neurocognitive measures
of impulsivity are implicated as both causes and consequences
of early onset substance misuse [4]. Castellanos-Ryan et al. [34]
demonstrated that neurocognitive measures of reward sensi-
tivity and action impulsivity independently mediated the
longitudinal relationships between self-report sensation seek-
ing and impulsiveness respectively, and adolescence
substance misuse. Whelan et al. [70] showed that stopping be-
haviour, a form of impulsive action involving action
cancellation, and its correlated neural network involving the
inferior frontal gyrus, was implicated specifically in frequency
of drug use, whereas other networks involved in stopping be-
haviour were related to drug use vulnerability, but not to
frequency or severity of use. More recently, Morin et al. [71]
analysed data from a longitudinal study of 4000 adolescents
who were assessed annually on a number of impulsivity and
executive cognitive functions over the course of adolescence
(from 12 to 17 years). This analysis showed that neurocogni-
tive measures of working memory and response inhibition
(impulsive action) were associated with vulnerability to sub-
stance use in adolescence, but multi-level modelling of
within-person variability on these neurocognitive measures
also showed that onset and frequency of substance use,
particularly cannabis use, were associated with, and longi-
tudinally predicted, further changes in working memory
and response inhibition (impulsive action), suggesting
action inhibition is both causal and consequential to
substance misuse.
A more recent analysis examining the role of different
measures of impulsivity in adolescent substance use beha-
viours using this same longitudinal cohort and multi-level
framework tested the independent contribution of self-
report trait impulsivity and errors of commission on a go/
no-go task (impulsive action) on adolescent drinking, binge
drinking and drug use [72]. When age-related changes in
impulsivity were modelled over a 4 year period at the
between- and within-person level, both self-report and neuro-
cognitive measures significantly contributed to the prediction
of vulnerability to drug-related behaviours. However,
between-group differences (mean level over the 4 year
period) on both on self-report trait measures and on response
inhibition/impulsive action measures were related to riskier
patterns of alcohol and drug use, whereas within- person
changes in trait impulsivity were related to within-person
variability in frequency of alcohol consumption. Two
machine-learning analyses involving large longitudinal
cohorts of European [73], Canadian and Australian [74] ado-
lescents further confirm the independent and predictive role
of trait impulsivity and sensation seeking in risk for alcohol
misuse, and suggest that neurocognitive measures of impul-
sivity do not add to model prediction of ‘who’ is at risk
when self-report traits are included. However, consistent
with [72] there is emerging evidence that more complex pre-
dictive models that account for the multi-level and variable
nature of impulsivities and substance use and that involve
multimodal measures of impulsivity might prove to be
additionally useful in determining when risk is most
likely to be expressed, rather than who is at risk. This
notion is being further investigated using more complex
computational models of task-based neurocognitive measures.
(c) State impulsivity—computational dimensions
Neurocognitive tasks of impulsivity are often deliberately
designed to be complex, in order to be ecologically valid
and capture important aspects of real-life decision-making.
Although this increases their ecological validity, it also
leads to significant heterogeneity and implies that neurocog-
nitive performance is an interaction and synthesis of several
different underlying motivational, learning, and choice pro-
cesses [75]. Therefore, failure to perform well on such tasks
may have many different causes. For example, poor perform-
ance on the Iowa Gambling Task [76] of impulsive action,
arguably the most commonly used decision-making task in
the addiction literature, may be due to increased sensitivity
to reward, to decreased sensitivity to loss, to poor learning
and retention of task contingencies, or to an erratic and
inconsistent response style. Computational models of
such complex tasks break down performance into under-
lying components and use the parameters associated
with these components to understand the mechanisms of
the neurocognitive deficits displayed by different clinical
populations [77,78].
Computational ideas permeate many areas of science, but
have had surprisingly little impact on the way psychiatric dis-
orders are phenotyped [79]. Recently, there has been an
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increased interest in the utility of computational modelling as a
novel phenotyping tool in psychiatry and a novel paradigm for
understanding psychopathology [80,81]. Computational mod-
elling has proven valuable for uncovering important
differences in neurocognitive decision processes in various
clinical populations [78]. Notably, computational parameters
have been shown to be more sensitive to substance-specific
and disorder-specific neurocognitive deficits than standard
neurobehavioural performance indices [82– 84]. For example,
computational parameters robustly discriminate between
opiate and stimulant users in protracted abstinence even
within the context of no group differences in neurobehavioural
performance [83]. Moreover, dynamic changes in specific com-
putational parameters of decision-making, such as ambiguity
tolerance, have been shown to predict imminent relapse in
abstinent opioid-dependent individuals, who were followed
up for the first seven months of treatment and retested up to
15 times with a decision-making/impulsive choice task [85].
This suggests that computational parameters may have not
only diagnostic, but also prognostic utility when used in
repeated measures designs, which can inform not only with
whom to intervene, but also when to intervene. Overall, compu-
tational parameters can serve as novel prognostic and
diagnostic state-dependent markers of addiction that can be
used to track the trajectory of various neurocognitive dimen-
sions of impulsivity and their associations with critical risk
behaviours across different stages of the addiction cycle.
There is an unexplored potential of using computational
approaches to refine neurocognitive phenotyping of addic-
tions, identify neurocognitive mediators of response to
treatment, and increase the precision of treatment inter-
ventions for addictions. While between-group differences in
self-report impulsivity and sensation seeking are robustly
predictive of risk for substance use (e.g. [74]) and hold promise
as diagnostic markers, new research designs incorporating
within-subject repeated measures, using multilevel modelling
or ecological momentary assessment along with neurocogni-
tive and computational modelling of measures of impulsive
action and impulsive choice will help to better understand
when an individual is more likely to take up use or relapse
back to problematic use.
5. Dimensions of impulsivity and risk for
psychopathology
Impulsivity is proposed to be a prime transdiagnostic marker
for a wide range of psychiatric disorders [80,86] and is one of
the most frequent diagnostic criteria in the new edition of the
Diagnostic and statistical manual of mental disorders (DSM-5;
[87]). It figures most prominently in externalizing spectrum
disorders, such as attention deficit hyperactivity disorder
(ADHD), conduct disorder, and antisocial personality dis-
order (ASPD), which are commonly comorbid with SUDs
[18]. A common characteristic of these disorders is that they
originate in childhood, have a pervasive pattern, and are
frequently lifelong. Developmentally, there is a strong associ-
ation between such disorders and subsequent drug use
[88,89].
Personality disorders in particular, such as ASPD, are
thought of as trait-like dysfunctional patterns in cognitive,
affective, interpersonal, and impulsivity domains [90] and
are highly comorbid with SUDs [91], which has led some to
question whether antisocial behaviours should be viewed as
independent of SUDs [92]. A potentially more informative
alternative to the somewhat over-inclusive diagnosis of ASPD
is psychopathy [93], an extreme variant of ASPD, consisting
of a constellation of affective, interpersonal and behavioural
characteristics. Theories of psychopathy distinguish between
primary (callous/unemotional) and secondary (antisocial/
impulsive) subtypes [94], both of which share common traits
such as hostility and aggression. However, primary psycho-
paths are characterized by grandiosity, superficiality, lack of
empathy and remorse, and low to moderate levels of anxiety,
whereas secondary types are more impulsive, anxious, with
high levels of negative affectivity and emotional disturbances
[95,96]. Trait impulsivity has been shown to be the best predic-
tor of both psychopathy and conduct disorder/ASPD [33,97].
Psychopathy, particularly secondary psychopathy, is associ-
ated with SUDs [95,98–100] but tends to be more strongly
related to illicit drug use than to alcohol use [100].
Research reveals differential relationships between exter-
nalizing disorders and neurocognitive dimensions of
impulsive choice and impulsive action. ADHD has been
associated more strongly with motor/action impulsivity
[101,102], evidenced by studies with children and adults
with ADHD, which indicate that the disorder is related
more strongly to performance-based deficits on simpler
tasks of impulsive action that involve response inhibition
[101], than on tasks of impulsive choice that involve reward
and punishment contingencies (reviewed in [102]). In contrast,
studies with individuals with psychopathic traits indicate that
psychopathy is primarily related to performance-based deficits
on more complex tasks of impulsive choice involving various
reward and punishment contingencies or delays [103–107].
A number of theorists have suggested that SUDs and
externalizing disorders represent common syndromes of disin-
hibition [108,109]. The phenotypic association between trait
impulsivity and externalizing disorders has been traced to
common aetiological factors between these phenotypes [109],
which share a common genetic basis [54], which suggests
that impulsivity could be the common underlying mechanism
linking externalizing disorders and SUDs. Longitudinal studies
that have attempted to study the intra-individual coevolution
of these two sets of problems suggest that substance misuse
tends to be secondary to the onset of conduct problems in
impulsive adolescents [33,34,49,110]. By contrast, other dimen-
sions of externalizing personality, such as sensation seeking,
have been shown to be more directly linked to early
onset alcohol misuse, with conduct problems arising after the
onset of binge drinking [110].
Recognizing that impulsivity and its facets exist along a
continuum in the general population, the IMAGEN Research
Consortium designed a longitudinal study to assess the role
of different facets of impulsivity and reward-related beha-
viours in the emergence of psychopathological symptoms
in a large sample of European adolescents (n¼2200).
Among the numerous studies from this consortium, three
studies directly test how facets of impulsivity are implicated
in different psychiatric outcomes [12,13,70]. Importantly,
these studies first represented psychiatric symptoms and
their comorbidity by deriving transdiagnostic latent dimen-
sions (represented in figure 1) using structural equation
modelling, which confirmed results from other similar studies
on the structure of psychopathology [14]. The IMAGEN
studies then showed that self-report, neurocognitive and
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functional neuroimaging measures of impulsivity and its
facets naturally dissociated along these dimensions of psycho-
pathology: variance common to all externalizing symptoms
(including substance use) was related to self-report impulsivity
and delay discounting. Variance specific to ADHD and Con-
duct Disorder symptoms was related to behavioural and
functional brain imaging measures of stopping behaviour on
go/no-go tasks of motor/action impulsivity, and variance
specific to early onset alcohol misuse was specifically related
to trait thrill seeking (SURPS-SS) and brain functional activity
during reward anticipation [12,13]. This study demonstrated
that neurocognitive and functional neuroimaging measures
provide additional specificity with respect to dissociating
latent dimensions of psychopathology that could not be fully
dissociated using self-report measures. In line with the emer-
ging evidence reviewed in figure 1, we place neurocognitive
facets as intermediary variables between self-report measures
and latent dimensions of psychopathology to demonstrate
where diagnostic specificity might be achieved.
6. Impulsivities and substance-specific addiction
vulnerabilities
Mounting evidence from the voluminous research on impul-
sivity over the past 20 years reveals that the multidimensional
conceptualization of impulsivity is relevant to prediction and
diagnosis of important addiction profiles that differ not only
on patterns of comorbid psychopathology, but also with
respect to specific class of substance of abuse.
Numerous studies show that in addition to general
liability to SUDs, substance-specific liability accounts for a
significant portion of the variance in substance-related beha-
viours and problems [12,13,111]. Merikangas and colleagues
[112] reveal substantial drug specificity in the type of drugs
abused by individuals with dependence on a range of illicit
drugs and the type of drugs abused by their relatives, with
opiates exhibiting the highest degree of specificity relative
to cocaine, cannabis, and alcohol. Similarly, there are key
differences between different classes of drugs with regards
to common versus drug-specific genetic variance, indicating
that, despite evidence for shared vulnerability factor that
underlies the misuse of marijuana, sedatives, stimulants,
heroin/opiates, and psychedelics, there are marked differ-
ences in the extent to which different classes of drugs are
influenced by shared vulnerability [113]. For instance,
heroin has larger genetic influences unique to itself than
any other drug, whereas most of the genetic influences on
marijuana, stimulants, and sedative abuse are shared across
drugs. Shared and specific environmental influences likely
cause further dissociation in patterns of drug use.
When investigating the substance-specificity of different
dimensions of impulsivity, it would be important to control
for the general liability towards substance use (and potentially
the role of impulsivity in that overall liability), and examine
whether specific dimensions of impulsivity predict more
specific behaviours and drug preferences. After accounting
for the general drug use vulnerability, personality traits can
predict which substances an individual is most likely to use
more regularly or problematically in the context of polysub-
stance use. Analyses on adult substance users, heavy
substance using college students, and high school students
all indicate that self-report measures of trait impulsivity and
sensation seeking discriminate between substance users who
are prone to stimulant versus alcohol misuse, respectively
[32,33,38,114]. Trait impulsiveness has been associated with
deficits in response inhibition/impulsive action, which predict
a pattern of substance misuse that co-occurs with other exter-
nalizing symptoms more generally, and with conduct disorder
symptoms specifically [34]. In contrast, trait sensation seeking
has been associated with reward response bias/impulsive
choice, which uniquely predicted binge drinking [34].
Interestingly, these two facets of trait impulsivity also predict
self-report motives for substance use that map onto these
different neurocognitive patterns: sensation seeking youth
reported using alcohol for enhancement reasons, while impul-
sive youth reported a motivationally-undefined pattern of use
and drinking, but were particularly at risk of misusing stimu-
lants [32]. These findings on different self-report drinking
motives have been replicated in college students and in adult
inpatient polysubstance users [38,115].
Similarly, a recent study with a community sample of
mono-dependent opiate and stimulant users who were in pro-
tracted abstinence revealed that only externalizing traits such
as impulsivity and sensation seeking discriminate between
substance-dependent individuals and non-dependent controls,
whereas internalizing traits such as hopelessness and anxiety
sensitivity do not [116]. Another study investigating the role
of personality traits in differentiating patterns of substance
misuse showed that among opioid-dependent individuals
receiving opioid substitution therapy, self-report personality
factors predicted who is likely to report illicit drug misuse
during such treatment and what type of substance they are
likely to misuse: sensation seeking predicted cannabis use,
hopelessness predicted prescription opioid misuse, anxiety
sensitivity predicted prescription sedative misuse and impul-
sivity predicted injection drug use [117]. According to the
framework presented in figure 1, some studies suggest that
opioid dependence is predicted by the callous/unemotional
factor of psychopathy (psychopathy 2) and internalizing
traits such as depression and anxiety [38,118], whereas stimu-
lant dependence is predicted by externalizing traits,
particularly impulsiveness [32] and disinhibited/impatient
sensation seeking [118], which, according to [31], likely reflects
delay discounting/impatient sensation seeking.
With regards to neurocognitive measures of impulsivity,
some neurocognitive deficits are associated with a general
tendency towards substance misuse [68]. Indeed, reliable
impairments have been documented on both impulsive
choice [66,107,119–122] and impulsive action tasks
[123–128] across a number of different drug classes. Yet,
because specific drugs of abuse also differentially affect
specific cognitive and motivational brain systems, many
have hypothesized that specific neurocognitive profiles
should be linked to misuse of specific classes of substances
of abuse with distinct pharmacological properties [11], for
which there is empirical support from animal studies (e.g.
[129,130]). Studies that have accounted for overall patterns
of substance use and then examined the relationship between
specific facets of impulsivity and specific patterns of sub-
stance use demonstrate substance-specific relationships
[32,117,131], reflected in figure 1, with all substances of
abuse loading on a general Pfactor (strongly related to
delay discounting [31]) and then personality-specific neuro-
cognitively-mediated trajectories that are either primarily
associated with specific substance use or secondarily
6
royalsocietypublishing.org/journal/rstb Phil. Trans. R. Soc. B 374: 20180137
associated through other psychopathological symptoms
(thick blue arrows).
Research designs that have attempted to more closely
examine this question have used adolescent cohort designs to
observe the role of personality in predicting drug-specific pat-
terns of use before the onset of substance dependence. Such
studies similarly show that trait impulsivity but not trait sen-
sation seeking predict stimulant prescription drug misuse,
while other more internalizing traits predict misuse of prescrip-
tion drugs with more sedative andanalgesic properties [32,49],
secondarily through psychiatric symptoms.
Using a functional neuroimaging approach to the study of
impulsivity and its multifaceted nature, Whelan and colleagues
[70] used data from the IMAGEN cohort of 2200 14-year-olds to
link facets of action impulsivity, represented by distinct brain
activation patterns during stopping, failed inhibition, and
response to feedback, to different drug use patterns. General
resilience to drug use was related to greater activation patterns
in bilateral frontal regions during stopping, while frequent
polysubstance use was associated with greater activity in the
supplementary motor area and reduced activity in a right
frontal region during stopping. Using this same dataset, Castel-
lanos-Ryan et al. [13] examined the extent to which different
substance use profiles were concurrently and longitudinally
predicted by these same functional activity patterns during
the stop task along with functional activity patterns on a
reward anticipation task of reward sensitivity and a number
of relevant neurocognitive measures. Cortical and subcortical
brain activity patterns during stopping behaviour predicted
general susceptibility to drug use and other externalizing psy-
chopathology, whereas activity patterns during reward
anticipation predicted substance-specific behaviours, particu-
larly alcohol-related behaviours. Delay discounting related to
general substance use vulnerability, whereas reduced prefron-
tal cortical activity during failed stopping behaviour and errors
of commission on a go/no-go task of impulsive action pre-
dicted conduct problems and ADHD. Other multi-site studies
involving brain and behavioural measures during neurocogni-
tive tasks of impulsivity suggest not only that impulsivity
dimensions predict future risk for substance misuse, but also
that early onset substance misuse further impacts the develop-
ment of these neurocognitive domains during adolescence and
that there might be important substance-specific effects on
certain neurocognitive functions [71,74].
A recent multi-site study investigated this question in adults
at the level of brain structure by mega-pooling structural
neuroimaging studies comparing participants with current
dependenceon at least one of five substances (alcohol, nicotine,
cocaine, methamphetamine, or cannabis) to age-, sex- and site-
matched controls [132]. This study revealed lower volume or
thickness across many brain regions in substance-dependent
individuals relative to non-dependent participants. Some of
these differences were general and affected by all substances,
whereas others were substance-specific. Structures related to
substance dependence in general, regardless of drug class,
included the insula and the medial orbitofrontal cortex,
associated with disadvantageous choices on reward- based
decision-making tasks of impulsive choice [76]. Alcohol
was associated with the most pervasive, yet specific effects,
which were particularly robust in subcortical regions, such
as the hippocampus, amygdala, and nucleus accumbens,
a region centrally implicated in reward processing.
Surprisingly, nicotine, cannabis, and methamphetamine
dependence did not have any substance-specific relation-
ships to brain volume, but that is not to say that other
imaging modalities such as task-based functional MRI might
not be more sensitive to subtle functional differences that are
not detectable at a structurallevel. Notably, opioid dependence
was not investigated, which is in line with a general trend in the
literature that has provided considerably less research attention
to opioid addiction relative to other drugs [133].
A few more recent studies have begun to compare the neu-
robehavioural performance of stimulant users with that of
opiate users on performance-based measures of impulsive
choice and impulsive action. Results generally indicate that
stimulant users show higher neurocognitive impulsivity than
opiate users [66,68,122,134–136]. However, virtually all of
these studies have been based on polysubstance users, which
significantly complicates the study of drug-specific effects
and represents one of the most formidable methodological
challenges for the study of the unique effectsof different classes
of drugs in humans. Polysubstance use may explain some key
inconsistencies between clinical and preclinical studies on
addiction, as the majority of clinical studies are based on poly-
substance using patterns, whereas the preclinical literature is
largely based on single drug administration.
An interesting research design that helps elucidate the
unique effects of specific classes of drugs while circumventing
the problem of polysubstance use in human studies involves
comparisons between unique populations of ‘pure’ substance
users who are mono-dependent on different classes of drugs
(e.g. [83,106,107,118,137– 139]). Such studies have revealed
that mono-dependent heroin and amphetamine users display
opposite relationships between trait and neurocognitive dimen-
sions of impulsivity. Specifically, in amphetamine users high
composite score on trait impulsivity (derived by factor analysis
and likely reflecting the higher-order construct) was associated
with poor response inhibition (motor/action impulsivity),
whereas in heroin users high trait impulsivity was associated
with intact response inhibition [137]. Further, a computational
modelling study of impulsive choice in ‘pure’ heroin and
amphetamine users using the Iowa Gambling Task revealed
that decision-making deficits in opiate and stimulant users are
driven by different underlying mechanisms, namely by
reduced loss aversion in opiate users versus increased reward
sensitivity in stimulant users [83]. A more recent study [118]
used machine-learning approaches to examine the predictive
utility of 54 personality, psychiatric, and neurocognitive indices
of impulsivity and related constructs to differentiate heroin and
amphetamine dependence. The study identified distinct sub-
stance-specific multivariate risk profiles that classified heroin
and amphetamine dependence in new samples with high
degree of accuracy (heroin AUC (area under the receiver-
operating characteristic (ROC) curve) 0.87, amphetamine
AUC 0.74). Amphetamine dependence was (uniquely) pre-
dicted by higher impulsive sensation seeking (disinhibition
and excitement seeking) and higher hostility, whereas heroin
dependence was uniquely predicted by lower sensation seek-
ing, lower trait motor impulsivity, higher negative urgency,
higher callous/unemotional levels of psychopathy, and
higher depression and trait anxiety. From the neurocognitive
measures of impulsivity, higher delay discounting and longer
decision-making deliberation time predicted amphetamine
dependence. In contrast, heroin dependence was predicted by
higher delay aversion, lower risk-taking, and impaired
decision-making on choice impulsivity tasks. Out of 54
7
royalsocietypublishing.org/journal/rstb Phil. Trans. R. Soc. B 374: 20180137
predictors, the antisocial/impulsive facet of psychopathy was
the only common predictor of both heroin and amphetamine
dependence. A parallel study [140] used a similar machine-
learning approach to predict current cocaine dependence and
found analogous findings, which classified out-of-sample
cocaine dependence with even higher degree of accuracy
(AUC 0.91). These findings suggest that quick, economical,
and easy to administer behavioural measures of impulsiv-
ity can serve as objective behavioural markers of distinct
addiction risk profiles, which may facilitate the develop-
ment of reliable and cost-effective risk assessment
batteries and targeted interventions.
Recognizing the highly heritable nature of addiction
vulnerability, many studies have investigated the extent to
which personality and neurocognitive dimensions of impul-
sivity might represent putative endophenotypes for
addiction. From the multiple neurocognitive dimensions of
impulsivity, delay discounting has received the strongest
support as a candidate endophenotype for SUDs [120,141],
though it is also a candidate marker for a number of exter-
nalizing disorders [80,120] and general psychopathology
[12]. The first genome-wide association study (GWAS) of
delay discounting [142] revealed that the most significant
association was with a gene implicated in the internalization
of the serotonin transporter. Recent GWAS studies of trait
impulsivity and sensation seeking [54,143] revealed that
these traits are genetically distinct, which further supports
their conceptual separation. These findings are consistent
with twin and sibling-pairs design studies reporting evi-
dence for potential substance-specificity of different trait
and state markers. For example, trait impulsiveness
(measured with the Barratt Impulsiveness Scale-11 [26])
appears to be a putative endophenotype for stimulant
dependence, as indicated by significant correlations
between sibling-pairs of stimulant-dependent individuals
and their biological non-dependent siblings [43,144]. In con-
trast, sensation seeking (measured with the Sensation
Seeking Scale—Form V [24]) was specific for sibling-pairs
discordant for heroin dependence [144]. ADHD [145] and
anxiety sensitivity [146] were specific to sibling-pairs discor-
dant for amphetamine dependence, whereas hopelessness
(measured with the Substance Use Risk Profile Scale [32])
was specific to sibling-pairs discordant for heroin depen-
dence [144], suggesting that these traits are specifically
implicated as endophenotypes, and not consequences, of
drug use. Finally, neurocognitive indices of impulsive
choice were common to both opiate and stimulant sib-
pairs (general risk), whereas an index of impulsive action,
go/no-go commission errors, was specific to amphetamine
sibling-pairs (substance-specific risk linked to ADHD) [144].
These substance-specific phenotypic associations suggest that
certain endophenotypic markers may be common across
addictions, whereas others may be specific to risk for
dependence on specific classes of drugs.
7. Impulsivities as novel diagnostic and
prognostic tools for addictions
One of the key problems in addictive disorders is their aetio-
logical and functional heterogeneity, which is not well
captured by the current psychiatric nosology, despite revolu-
tionary advances in understanding the neurobiology of
addiction. Integrating neuroscience and computationally-
based assessments with self-report trait assessments may
transform the diagnosis and classification of addictive dis-
orders and lead to better targeted and more effective
prevention and intervention approaches. To address the futi-
lity of DSM criteria to address the heterogeneity of DSM
diagnostic categories, the US National Institute of Mental
Health (NIMH) launched the Research Domain Criteria
(RDoC) [147] as an attempt to determine if a novel way of
classifying mental illness based on neurobiology, observable
behaviour, and context will be useful for diagnosing mental
disorders. The recently proposed heuristic framework for
neuroclinical assessment of addictions (Addictions Neurocli-
nical Assessment, ANA) [9] is a modified version of the
RDoC criteria adapted specifically for the assessment of
addictions. The ANA framework posits that in order to
better understand the different aetiological mechanisms
implicated in various forms of addictions, their assessment
should be multidimensional and combine clinical, personal-
ity, neurocognitive, neuroimaging and genetic approaches
that focus on three key neurofunctional domains associated
with impulsivity and compulsivity: executive function, incen-
tive salience, and negative emotionality. Another recent
conceptual framework that derived from the European con-
text focuses on variance that is unique to SUDs (chronic
and elevated patterns of consumption) in an attempt to
steer clear of psychopathological constructs and potentially
stigmatizing terms when addressing harm from substances,
but recommends a similar framework when referring to risk
for SUDs [148].
Nevertheless,thereisaconsensusacrossneuropsychia-
tric conceptualizations of addition (e.g. [149]), all of which
recommend more refined approaches to phenotyping, to
accurately and efficiently capture the hierarchical and
multidimensional nature of addiction vulnerability, par-
ticularly around brain processes linked to impulsivity,
compulsivity, and positive and negative emotionality
[9,149]. Consistent with other reviews, we recommend
incorporating self-report trait measures, as well as neuro-
cognitive state measures that include both the standard
neurobehavioural performance indices as well as compu-
tational indices that hold promise as novel phenotyping
tools. Unlike these other reviews, we also suggest that
such a multidimensional framework will further inform
patterns of comorbidity and specific patterns of substance
misuse vulnerability. Despite its complexity relative to the
very simple task of assessing severity of substance use (e.g.
the World Health Organisation’s Alcohol Use Disorder
Identification Test [150]), assessment time and burden
could be reduced significantly through adaptive design
optimization [75,151], which may facilitate the develop-
ment of ‘smart’ assessment batteries, composed of brief,
targeted, and accurate multivariate assessment modules
that may improve the precision around underlying
motivational processes driving the behaviour and
personalized treatment options.
Within this context, motor/action impulsivity and cogni-
tive/choice impulsivity, best captured by a combination of
self-report, neurocognitive, and computational measures,
seem to provide important discriminant and predictive val-
idity with respect to predicting or explaining dissociable
substance misuse and concurrent psychiatric symptom
dimensions. Measures of motor/action impulsivity appear
8
royalsocietypublishing.org/journal/rstb Phil. Trans. R. Soc. B 374: 20180137
to be useful in differentiating between individuals who might
be prone to a primary disorder of ADHD and Conduct Dis-
order (including ASPD) as well as stimulant dependence,
whereas differential responses to reward-based impulsive
choice tasks might help identify individuals who are differen-
tially susceptible to alcohol (risk sensitivity), or opiate
dependence (loss insensitivity). Other reviews focus more
attention on the progression of addiction and the impact of
substances of abuse on these brain processes (see [149]).
This distinction might further explain why reward sensitivity
is not predictive of risk for stimulant drug use but seems to
characterize stimulant drug users with a history of dependence
(e.g. [83,118,140]).
Conceptualizing individuals prone to high levels of
comorbidity across substance use and psychiatric disorders
as having transdiagnostic impairments on specific trait or
state dimensions of impulsivity might save clinicians time
in pursuing diagnostic specificity, which often delays treat-
ment and contributes to the revolving door between
addiction and mental health services for such patients. One
important outstanding question is whether treatment plan-
ning in accordance with this framework will lead to better
outcomes for individuals with SUDs. New evidence indicates
this is a promising approach.
8. Impulsivities: novel behavioural targets for
prevention and treatment of substance use
disorders
Researchers in the field have begun to address the question
about the extent to which different dimensions of impulsiv-
ity are modifiable by various psychosocial, pharmacological
and neurocognitive interventions. A number of psycho-
social interventions could now be considered helpful tools
in the management of impulsivity. For example, contin-
gency management is used to reduce delay discounting
around abstinence, cognitive-behavioural interventions are
used to help patients better manage positive and negative
urgency states in high-risk situations (e.g. relapse preven-
tion), and mindfulness-based approaches are hypothesized
to reduce negative urgency by reducing stress and rebalan-
cing fronto-striatal connectivity [152]. However, few
interventions directly arise from current research on impul-
sivity and even fewer targeted interventions have been
shown to reduce impulsivity [153].
One such personality-targeted cognitive-behavioural
approach involves modifying traditional cognitive-behaviour
therapy (CBT) strategies to target individual differences in
trait impulsivity. This programme, known as the Preventure
Programme (see [154]), uses the CBT framework to help
high-risk youth understand how individual differences in
trait impulsivity and response inhibition affect behavioural
and emotional control and decision making. Cognitive-
behavioural interventions are adapted to help impulsive
youth become better ‘stoppers’ by helping them to identify
high-risk situations and the interoceptive cues that precede
an impulsive action. They are also given cognitive strategies
to help them be more cognizant of the ‘stopping’ process
and the cognitive processes required for better stopping
(e.g. attentiveness, self-talk, goal-orientation). When deliv-
ered to adolescents with elevated scores on self-report
measures of impulsiveness, measured with the Substance
Use Risk Profile Scale (SURPS) [32], the intervention is
shown to produce long-term changes in theoretically pre-
dicted ways: substance use onset is delayed [114,155],
conduct symptoms are reduced and/or prevented [156],
and reductions in self-report levels of impulsivity specifically
mediate long-term changes in these behaviours [157]. The
Preventure Programme also includes a second intervention
targeting sensation seeking by using a different set of cogni-
tive strategies focused on managing reward sensitivity and
reward-driven behaviours. This second intervention has
been shown to produce robust effects on binge drinking
[154] and cannabis use outcomes [51], without having a sig-
nificant impact on conduct problems, aggressive behaviours
or impulsivity, suggesting some treatment specificity in per-
sonality-targeted CBT interventions. It remains to be
determined if these interventions produce changes in neuro-
cognitive measures of impulsivity, and whether these
changes mediate long-term behavioural changes [11].
Other novel therapeutic approaches target neurocognitive
dimensions of impulsivity. Some of the most promising
approaches in this area are based on behavioural economic
principles and dual-system models of decision-making that
involve executive and impulsive decision systems [158,159].
Increased delay discounting reflects greater control of the
impulsive over the executive decision system and character-
izes reinforcement pathologies, such as SUDs [160]. Delay
discounting has recently been considered a genetically-influ-
enced target for drug abuse prevention [161]. Therapeutic
approaches based on dual system models use computerized
working memory training and episodic future thinking to
modify delay discounting and expand the constricted
temporal horizon characterizing the addictive state [162].
Such training has the strongest effects in substance-dependent
individuals who display the steepest delay discounting [163].
According to this review, such interventions hold promise
for reducing risk for substance misuse and psychopathologi-
cal symptoms generally. A number of other neurocognitive
intervention strategies also show promise as adjunct treat-
ment for addictions. For example, response inhibition and
working memory training have been found to curb impulsive
drinking in problem drinkers, particularly in individuals who
score low on these functions [164– 166]. Cognitive bias modi-
fication has also shown promise in the treatment of
addictions and other forms of psychopathology [167]. Neuro-
cognitive measures of impulsivity might be more suitable
than trait measures for use in tracking treatment effects and
outcomes, owing to their state-dependent nature. For
example, excessive delay discounting predicts initiation of
drug use, improves with treatment, and returns within the
normal range with abstinence [141].
Another promising intervention approach stemming from
the neurocognitive understanding of impulsivity is the use of
repetitive Transcranial Magnetic Stimulation (TMS), which is
a non-invasive, and safe, human brain stimulation technol-
ogy based on electromagnetic induction [168] used to
depolarize brain neurons [169]. Repetitive high-frequency
(5 Hz) TMS can modulate long-term cortical excitability,
repetitive TMS (rTMS) at a low frequency (about 1 Hz) pro-
duces inhibitory effects on neurons. Systematic reviews
suggest that excitatory rTMS produces distinct effects on
different impulsivity subdomains: small-to-moderate effects
on response inhibition/impulsive action and moderate effects
9
royalsocietypublishing.org/journal/rstb Phil. Trans. R. Soc. B 374: 20180137
on delay discounting/choice impulsivity [170]. Whether
these effects translate to changes in substance use behaviour
is less well investigated, but it has been suggested that rTMS
might produce small reductions in some relevant addiction
outcomes, such as impulsivity [151] and craving [171].
There is also evidence that combining the excitatory and
inhibitory effects of rTMS in relation to different environ-
mental stimuli (e.g. drug versus natural reward cues) can
reverse the reward sensitivity resulting from neurophysiolo-
gical changes from repeated drug exposure [172]. What has
yet to be investigated is whether these effects are mediated
by changes in impulsivity or subdomains of impulsivity as
outlined in this review.
Finally, considering that some reviews suggest that meta-
cognitive executive function training for externalizing
problems is effective when combined with supervised coach-
ing with parents [173], more studies are needed in which
modular combinations of neurocognitive and cognitive-
behavioural interventions are evaluated and tested. There is
an unexplored therapeutic potential of integrating personal-
ity with neurocognitive and other intervention modules,
based on individual risk factors, which may optimize the
precision and efficacy of targeted intervention approaches
for addictions.
Data accessibility. This article has no additional data.
Competing interests. We declare we have no competing interests.
Funding. This study was supported by the Institute of Neurosciences,
Mental Health and Addiction (frn114887 and frn126053), by the
National Institute on Drug Abuse (NIDA) and the Fogarty Inter-
national Center at NIH under award number R01DA021421 to
Jasmin Vassileva, by a Canada Research Chair awarded to Patricia
Conrod, and by a Canadian Health Research Institutes Project
Grant (FRN 126053).
References
1. Bari A, Robbins TW. 2013 Inhibition and impulsivity:
behavioral and neural basis of response control.
Prog. Neurobiol. 108, 44– 79. (doi:10.1016/j.
pneurobio.2013.06.005)
2. Moffitt TE et al. 2011 A gradient of childhood self-
control predicts health, wealth, and public safety.
Proc. Natl Acad. Sci. USA 108, 2693– 2698. (doi:10.
1073/pnas.1010076108)
3. Kreek MJ, Nielsen DA, Butelman ER, LaForge KS.
2005 Genetic influences on impulsivity, risk taking,
stress responsivity and vulnerability to drug abuse
and addiction. Nat. Neurosci. 8, 1450– 1457.
(doi:10.1038/nn1583)
4. de Wit H. 2009 Impulsivity as a determinant and
consequence of drug use: a review of underlying
processes. Addict. Biol. 14, 22–31. (doi:10.1111/j.
1369-1600.2008.00129.x)
5. Leshner AI. 1997 Addiction is a brain disease, and it
matters. Science 278, 45– 47. (doi:10.1126/science.
278.5335.45)
6. de Wit H, Richards JB. 2004 Dual determinants of
drug use in humans: reward and impulsivity.
Nebraska Symp. Motiv. 50, 19–55.
7. Jentsch JD, Taylor JR. 1999 Impulsivity resulting
from frontostriatal dysfunction in drug abuse:
implications for the control of behavior by reward-
related stimuli. Psychopharmacology (Berl.) 146,
373–390. (doi:10.1007/PL00005483)
8. Koob GF, Le Moal M. 2008 Neurobiological
mechanisms for opponent motivational processes in
addiction. Phil. Trans. R. Soc. B 363, 3113– 3123.
(doi:10.1098/rstb.2008.0094)
9. Kwako LE, Momenan R, Litten RZ, Koob GF,
Goldman D. 2016 Addictions neuroclinical
assessment: a neuroscience-based framework for
addictive disorders. Biol. Psychiatry 80, 179– 189.
(doi:10.1016/j.biopsych.2015.10.024)
10. Lyvers M. 2000 ‘Loss of control’ in alcoholism and
drug addiction: a neuroscientific interpretation. Exp.
Clin. Psychopharmacol. 8, 225– 249. (doi:10.1037/
1064-1297.8.2.225)
11. Conrod PJ, Nikolaou K. 2016 Annual Research
Review: on the developmental neuropsychology of
substance use disorders. J. Child Psychol. Psychiatry
57, 371–394. (doi:10.1111/jcpp.12516)
12. Castellanos-Ryan N et al.2016Thestructureof
psychopathology in adolescence and its common
personality and cognitive correlates. J. Abnorm.
Psychol. 125, 1039– 1052. (doi:10.1037/abn0000193)
13. Castellanos-Ryan N et al. 2014 Neural and cognitive
correlates of the common and specific variance
across externalizing problems in young adolescence.
Am. J. Psychiatry 171, 1310– 1319. (doi:10.1176/
appi.ajp.2014.13111499)
14. Caspi A, Moffitt TE. 2018 All for one and one for all:
mental disorders in one dimension.
Am. J. Psychiatry 175, 831– 844. (doi:10.1176/appi.
ajp.2018.17121383)
15. MacKillop J, Weafer J, Oshri A, Palmer A, de Wit H.
2016 The latent structure of impulsivity: impulsive
choice, impulsive action, and impulsive personality
traits. Psychopharmacology (Berl.) 233, 3361– 3370.
(doi:10.1007/s00213-016-4372-0)
16. Cyders MA, Coskunpinar A. 2011 Measurement of
constructs using self-report and behavioral lab tasks:
is there overlap in nomothetic span and construct
representation for impulsivity? Clin. Psychol. Rev. 31,
965–982. (doi:10.1016/j.cpr.2011.06.001)
17. Dougherty DM, Mathias CW, Marsh-Richard DM,
Furr RM, Nouvion SO, Dawes MA. 2009 Distinctions
in behavioral impulsivity: implications for substance
abuse research. Addict. Disord. Treat. 8, 61–73.
(doi:10.1097/ADT.0b013e318172e488)
18. Moeller FG, Barratt ES, Dougherty DM, Schmitz JM,
Swann AC. 2001 Psychiatric aspects of impulsivity.
Am. J. Psychiatry 158, 1783– 1793. (doi:10.1176/
appi.ajp.158.11.1783)
19. Odum AL. 2011 Delay discounting: trait variable?
Behav. Processes 87, 1– 9. (doi:10.1016/j.beproc.
2011.02.007)
20. Bilder RM et al. 2009 Phenomics: the systematic
study of phenotypes on a genome-wide scale.
Neuroscience 164, 30– 42. (doi:10.1016/j.
neuroscience.2009.01.027)
21. Evenden JL. 1999 Varieties of impulsivity.
Psychopharmacology (Berl.) 146, 348– 361. (doi:10.
1007/PL00005481)
22. Eysenck HJ. 1985 Personality and individual
differences: a natural science approach. New York,
NY: Plenum Press.
23. Cloninger CR. 1987 Neurogenetic adaptive
mechanisms in alcoholism. Science 236, 410–416.
(doi:10.1126/science.2882604)
24. Zuckerman M. 1994 Behavioral expressions and
biosocial bases of sensation seeking. New York, NY:
Cambridge University Press.
25. Barratt ES. 1993 Impulsivity: integrating cognitive,
behavioral, biological, and environmental data. In The
impulsive client: theory, research, and treatment (eds
WG McCown, J Johnson, MB Shure), pp. 39– 56.
Washington, DC: American Psychological Association.
26. Patton JH, Stanford MS, Barratt ES. 1995 Factor
structure of the Barratt impulsiveness scale. J. Clin.
Psychol. 51, 768– 774. (doi:10.1002/1097-
4679(199511)51:6,768::AID-JCLP2270510607.3.
0.CO;2-1)
27. Cyders MA, Smith GT, Spillane NS, Fischer S, Annus
AM, Peterson C. 2007 Integration of impulsivity and
positive mood to predict risky behavior:
development and validation of a measure of
positive urgency. Psychol. Assess. 19, 107– 118.
(doi:10.1037/1040-3590.19.1.107)
28. Whiteside SPL. 2001 The five factor model
and impulsivity: using a structural model of
personality to understand impulsivity. Pers. Individ.
Dif. 30, 669–689. (doi:10.1016/S0191-8869(00)
00064-7)
29. Whiteside SP, Lynam DR, Miller JD, Reynolds SK.
2005 Validation of the UPPS impulsive behaviour
scale: a four-factor model of impulsivity. Eur. J. Pers.
19, 559–574. (doi:10.1002/per.556)
30. McCrae RR, Costa Jr PT. 1990 Personality in
adulthood. New York, NY: Guilford Press.
10
royalsocietypublishing.org/journal/rstb Phil. Trans. R. Soc. B 374: 20180137
31. Kirby KN, Finch JC. 2010 The hierarchical
structure of self-reported impulsivity.
Pers. Individ. Dif. 48, 704– 713. (doi:10.1016/j.paid.
2010.01.019)
32. Woicik PA, Stewart SH, Pihl RO, Conrod PJ. 2009 The
Substance Use Risk Profile Scale: a scale measuring
traits linked to reinforcement-specific substance use
profiles. Addict. Behav. 34, 1042– 1055. (doi:10.
1016/j.addbeh.2009.07.001)
33. Castellanos-Ryan N, O’Leary-Barrett M, Sully L,
Conrod P. 2013 Sensitivity and specificity of a brief
personality screening instrument in predicting
future substance use, emotional, and behavioral
problems: 18-month predictive validity of the
Substance Use Risk Profile Scale. Alcohol. Clin. Exp.
Res. 37(Suppl 1), E281– E290. (doi:10.1111/j.1530-
0277.2012.01931.x)
34. Castellanos-Ryan N, Rubia K, Conrod PJ. 2011
Response inhibition and reward response bias
mediate the predictive relationships between
impulsivity and sensation seeking and common and
unique variance in conduct disorder and substance
misuse. Alcohol. Clin. Exp. Res. 35, 140–155.
(doi:10.1111/j.1530-0277.2010.01331.x)
35. Allen TJ, Moeller FG, Rhoades HM, Cherek DR. 1998
Impulsivity and history of drug dependence. Drug
Alcohol Depend. 50, 137–145. (doi:10.1016/S0376-
8716(98)00023-4)
36. Cloninger CR, Sigvardsson S, Bohman M. 1988
Childhood personality predicts alcohol abuse in
young adults. Alcohol. Clin. Exp. Res. 12, 494– 505.
(doi:10.1111/j.1530-0277.1988.tb00232.x)
37. Conrod PJ, Petersen JB, Pihl RO. 1997 Disinhibited
personality and sensitivity to alcohol reinforcement:
independent correlates of drinking behavior in sons
of alcoholics. Alcohol. Clin. Exp. Res. 21,
1320–1332. (doi:10.1097/00000374-199710000-
00024)
38. Conrod PJ, Pihl RO, Stewart SH, Dongier M. 2000
Validation of a system of classifying female
substance abusers on the basis of personality and
motivational risk factors for substance abuse.
Psychol. Addict. Behav. 14, 243– 256. (doi:10.1037/
0893-164X.14.3.243)
39. Conway KP, Kane RJ, Ball SA, Poling JC, Rounsaville
BJ. 2003 Personality, substance of choice, and
polysubstance involvement among substance
dependent patients. Drug Alcohol Depend. 71,
65–75. (doi:10.1016/S0376-8716(03)00068-1)
40. Sher KJ, Bartholow BD, Wood MD. 2000 Personality
and substance use disorders: a prospective study.
J. Consult. Clin. Psychol. 68, 818– 829. (doi:10.1037/
0022-006X.68.5.818)
41. Caspi A, Moffitt TE, Newman DL, Silva PA. 1996
Behavioral observations at age 3 years predict
adult psychiatric disorders. Longitudinal evidence
from a birth cohort. Arch. Gen. Psychiatry 53,
1033–1039. (doi:10.1001/archpsyc.1996.
01830110071009)
42. Masse LC, Tremblay RE. 1997 Behavior of boys in
kindergarten and the onset of substance use during
adolescence. Arch. Gen. Psychiatry 54, 62– 68.
(doi:10.1001/archpsyc.1997.01830130068014)
43. Ersche KD, Turton AJ, Pradhan S, Bullmore ET,
Robbins TW. 2010 Drug addiction endophenotypes:
impulsive versus sensation-seeking personality
traits. Biol. Psychiatry 68, 770– 773. (doi:10.1016/j.
biopsych.2010.06.015)
44. LaBrie JW, Kenney SR, Napper LE, Miller K. 2014
Impulsivity and alcohol-related risk among college
students: examining urgency, sensation seeking and
the moderating influence of beliefs about alcohol’s
role in the college experience. Addict. Behav. 39,
159–164. (doi:10.1016/j.addbeh.2013.09.018)
45. Shin SH, Hong HG, Jeon SM. 2012 Personality and
alcohol use: the role of impulsivity. Addict. Behav.
37, 102–107. (doi:10.1016/j.addbeh.2011.09.006)
46. Dick DM, Smith G, Olausson P, Mitchell SH, Leeman
RF, O’Malley SS, Sher K. 2010 Understanding the
construct of impulsivity and its relationship to
alcohol use disorders. Addict. Biol. 15, 217–226.
(doi:10.1111/j.1369-1600.2009.00190.x)
47. Gillespie NA, Lubke GH, Gardner CO, Neale MC,
Kendler KS. 2012 Two-part random effects growth
modeling to identify risks associated with alcohol
and cannabis initiation, initial average use and
changes in drug consumption in a sample of adult,
male twins. Drug Alcohol Depend. 123, 220 –228.
(doi:10.1016/j.drugalcdep.2011.11.015)
48. Ersche KD, Jones PS, Williams GB, Smith DG,
Bullmore ET, Robbins TW. 2013 Distinctive
personality traits and neural correlates associated
with stimulant drug use versus familial risk of
stimulant dependence. Biol. Psychiatry 74,
137–144. (doi:10.1016/j.biopsych.2012.11.016)
49. Castellanos-Ryan N, Conrod PJ. 2011 Personality
correlates of the common and unique variance
across conduct disorder and substance misuse
symptoms in adolescence. J. Abnorm. Child Psychol.
39, 563–576. (doi:10.1007/s10802-010-9481-3)
50. Krank M, Stewart SH, O’Connor R, Woicik PB, Wall
AM, Conrod PJ. 2011 Structural, concurrent, and
predictive validity of the Substance Use Risk Profile
Scale in early adolescence. Addict. Behav. 36, 7 46.
(doi:10.1016/j.addbeh.2010.08.010)
51. Mahu IT, Doucet C, O’Leary-Barret M, Conrod PJ.
2015 Can cannabis use be prevented by targeting
personality risk in schools? Twenty-four-mouth
outcomes of the adventure trial on cannabis use: a
cluster randomized controlled trial. Addiction 110,
1625–1633. (doi:10.1111/add.12991)
52. Dalley JW, Everitt BJ, Robbins TW. 2011 Impulsivity,
compulsivity, and top-down cognitive control.
Neuron 69, 680–694. (doi:10.1016/j.neuron.2011.
01.020)
53. Mitchell MR, Potenza MN. 2014 Addictions and
personality traits: impulsivity and related constructs.
Curr. Behav. Neurosci. Rep. 1, 1– 12. (doi:10.1007/
s40473-013-0001-y)
54. Sanchez-Roige S et al. 2018 Genome-wide
association studies of impulsive personality traits
(BIS-11 and UPPSP) and drug experimentation in
up to 22,861 adult research participants. bioRXiv
414854. (doi:10.1101/414854)
55. Roberts BW, Walton KE, Viechtbauer W. 2006
Patterns of mean-level change in personality traits
across the life course: a meta-analysis of
longitudinal studies. Psychol. Bull. 132, 1– 25.
(doi:10.1037/0033-2909.132.1.1)
56. Hampson SE, Goldberg LR. 2006 A first large cohort
study of personality trait stability over the 40 years
between elementary school and midlife. J. Pers. Soc.
Psychol. 91, 763– 779. (doi:10.1037/0022-3514.91.
4.763)
57. Hamilton KR et al. 2015 Rapid-response impulsivity:
definitions, measurement issues, and clinical
implications. Personal. Disord. 6, 168– 181. (doi:10.
1037/per0000100)
58. Winstanley CA, Dalley JW, Theobald DE, Robbins TW.
2004 Fractionating impulsivity: contrasting effects of
central 5-HT depletion on different measures of
impulsive behavior. Neuropsychopharmacology 29,
1331–1343. (doi:10.1038/sj.npp.1300434)
59. Logan G. 1994 On the ability to inhibit thought
and action: a user’s guide to the stop signal
paradigm. In Inhibitory processes in attention,
memory and language (eds D Dagenbach, TH Carr).
San Diego, CA: Academic Press.
60. Weafer J, Mitchell SH, de Wit H. 2014 Recent
translational findings on impulsivity in relation to
drug abuse. Curr. Addict. Rep. 1, 289–300. (doi:10.
1007/s40429-014-0035-6)
61. Winstanley CA, Olausson P, Taylor JR, Jentsch JD. 2010
Insight into the relationship between impulsivity and
substance abuse from studies using animal models.
Alcohol. Clin. Exp. Res. 34, 1306–1318. (doi:10.1111/
j.1530-0277.2010.01215.x)
62. Dougherty DM, Bjork JM, Harper RA, Marsh DM,
Moeller FG, Mathias CW, Swann AC. 2003 Behavioral
impulsivity paradigms: a comparison in hospitalized
adolescents with disruptive behavior disorders.
J. Child Psychol. Psychiatry 44, 1145– 1157. (doi:10.
1111/1469-7610.00197)
63. Hester R, Fassbender C, Garavan H. 2004 Individual
differences in error processing: a review and
reanalysis of three event-related fMRI studies using
the GO/NOGO task. Cereb. Cortex 14, 986– 994.
(doi:10.1093/cercor/bhh059)
64. Voon V. 2014 Models of impulsivity with a focus on
waiting impulsivity: translational potential for
neuropsychiatric disorders. Curr. Addict. Rep. 1,
281–288. (doi:10.1007/s40429-014-0036-5)
65. Hamilton KR et al. 2015 Choice impulsivity:
definitions, measurement issues, and clinical
implications. Personal. Disord. 6, 182– 198. (doi:10.
1037/per0000099)
66. Kirby KN, Petry NM. 2004 Heroin and cocaine
abusers have higher discount rates for delayed
rewards than alcoholics or non-drug-using controls.
Addiction 99, 461–471. (doi:10.1111/j.1360-0443.
2003.00669.x)
67. Bechara A, Damasio AR, Damasio H, Anderson SW.
1994 Insensitivity to future consequences following
damage to human prefrontal cortex. Cognition 50,
7–15. (doi:10.1016/0010-0277(94)90018-3)
68. Rogers RD et al. 1999 Dissociable deficits in the
decision-making cognition of chronic amphetamine
abusers, opiate abusers, patients with focal damage
to prefrontal cortex, and tryptophan-depleted
11
royalsocietypublishing.org/journal/rstb Phil. Trans. R. Soc. B 374: 20180137
normal volunteers: evidence for monoaminergic
mechanisms. Neuropsychopharmacology 20,
322–339. (doi:10.1016/S0893-133X(98)00091-8)
69. Lejuez CW et al. 2002 Evaluation of a behavioral
measure of risk taking: the Balloon Analogue Risk
Task (BART). J. Exp. Psychol. Appl. 8, 75– 84.
(doi:10.1037/1076-898X.8.2.75)
70. Whelan R et al. 2012 Adolescent impulsivity
phenotypes characterized by distinct brain
networks. Nat. Neurosci. 15, 920– 925. (doi:10.
1038/nn.3092)
71. Morin JG, Afzali MH, Bourque J, Stewart SH, Se
´guin JR,
O’Leary-Barrett M, Conrod PJ. In press. A population-
based analysis of the relationship between substance
use and adolescent cognitive development. Am. J.
Psychiatry, appiajp201818020202. (doi:10.1176/appi.
ajp.2018.18020202)
72. Conrod PJ, Boers E, Afzali MH. In preparation.
Dimensions of psychopathology: neurocognitive
correlates and treatment outcomes. To be
presented at the Annual Meeting of the American
College of Neuropsychopharmacology (Large-Scale
Developmental Neuroimaging of Dimensional
Psychopathology Panel), 9–13 December 2018,
Hollywood, FL.
73. Whelan R et al. 2014 Neuropsychosocial profiles of
current and future adolescent alcohol misusers.
Nature 512, 185– 189. (doi:10.1038/nature13402)
74. Afzali MH, Sunderland M, Stewart S, Masse B,
Seguin J, Newton N, Teesson M, Conrod P. In press.
Machine-learning prediction of adolescent alcohol
use: a cross-study, cross-cultural validation.
Addiction. (doi:10.1111/add.14504)
75. Ahn WY, Busemeyer JR. 2016 Challenges and
promises for translating computational tools into
clinical practice. Curr. Opin. Behav. Sci. 11,17.
(doi:10.1016/j.cobeha.2016.02.001)
76. Bechara A, Tranel D, Damasio H. 2000 Characterization
of the decision-making deficit of patients with
ventromedial prefrontal cortex lesions. Brain 123,
2189– 2202. (doi:10.1093/brain/123.11.2189)
77. Busemeyer JR, Stout JC. 2002 A contribution of
cognitive decision models to clinical assessment:
decomposing performance on the Bechara gambling
task. Psychol. Assess. 14, 253– 262. (doi:10.1037/
1040-3590.14.3.253)
78. Yechiam E, Busemeyer JR, Stout JC, Bechara A. 2005
Using cognitive models to map relations between
neuropsychological disorders and human decision-
making deficits. Psychol. Sci. 16, 973– 978. (doi:10.
1111/j.1467-9280.2005.01646.x)
79. Montague PR, Dolan RJ, Friston KJ, Dayan P. 2012
Computational psychiatry. Trends Cogn. Sci. 16,
72–80. (doi:10.1016/j.tics.2011.11.018)
80. Bickel WK, Jarmolowicz DP, Mueller ET, Koffarnus
MN, Gatchalian KM. 2012 Excessive discounting of
delayed reinforcers as a trans-disease process
contributing to addiction and other disease-related
vulnerabilities: emerging evidence. Pharmacol.
Ther. 134, 287–297. (doi:10.1016/j.pharmthera.
2012.02.004)
81. Huys QJM, Maia TV, Frank MJ. 2016 Computational
psychiatry as a bridge from neuroscience to clinical
applications. Nat. Neurosci. 19, 404– 413. (doi:10.
1038/nn.4238)
82. Ahn WY, Dai J, Vassileva J, Busemeyer JR, Stout JC.
2016 Computational modeling for addiction
medicine: from cognitive models to clinical
applications. Prog. Brain Res. 224, 53– 65. (doi:10.
1016/bs.pbr.2015.07.032)
83. Ahn WY et al. 2014 Decision-making in stimulant
and opiate addicts in protracted abstinence:
evidence from computational modeling with pure
users. Front. Psychol. 5, 849. (doi:10.3389/fpsyg.
2014.00849)
84. Vassileva J et al. 2013 Computational modeling
reveals distinct effects of HIV and history of drug
use on decision-making processes in women. PLoS
ONE 8, e68962. (doi:10.1371/journal.pone.0068962)
85. Konova A, Lopez-Guzman S, Urmanche A, Ross S,
Louie K, Rotrosen J, Glimcher P (eds). 2018 Dynamic
changes in risky decision-making predict imminent
heroin use in opioid users studied longitudinally
through the first months of treatment. Abstract
73rd Annual Meeting of the Society of Biological
Psychiatry 10– 12 May 2018, New York, NY. Biol.
Psychiatry 83, S31.
86. Cyders MA. 2015 The misnomer of impulsivity:
commentary on ‘Choice Impulsivity’ and ‘Rapid-
Response Impulsivity’ articles by Hamilton and
colleagues. Personal. Disord. 6, 204– 205. (doi:10.
1037/per0000123)
87. American Psychiatric Association. 2013 Diagnostic
and statistical manual of mental disorders, 5th edn.
Arlington, VA: American Psychiatric Publishing.
88. Disney ER, Elkins IJ, McGue M, Iacono WG. 1999
Effects of ADHD, conduct disorder, and gender on
substance use and abuse in adolescence.
Am. J. Psychiatry 156, 1515– 1521. (doi:10.1176/
ajp.156.10.1515)
89. Tarter R, Vanyukov M, Giancola P, Dawes M,
Blackson T, Mezzich A, Clark D. 1999 Etiology of
early age onset substance use disorder: a
maturational perspective. Dev. Psychopathol. 11,
657–683. (doi:10.1017/S0954579499002266)
90. McCloskey MS, Phan KL, Coccaro EF. 2005
Neuroimaging and personality disorders. Curr.
Psychiatry Rep. 7, 65– 72. (doi:10.1007/s11920-005-
0027-2)
91. Regier DA et al. 1990 Comorbidity of mental
disorders with alcohol and other drug abuse.
Results from the Epidemiologic Catchment Area
(ECA) Study. JAMA 264, 2511– 2518. (doi:10.1001/
jama.1990.03450190043026)
92. Gerstley LJ, Alterman AI, McLellan AT, Woody GE.
1990 Antisocial personality disorder in patients with
substance abuse disorders: a problematic diagnosis?
Am. J. Psychiatry 147, 173– 178. (doi:10.1176/ajp.
147.2.173)
93. Hare RD. 1991 Hare psychopathy checklist—revised.
Toronto, ON: Multi-Health Systems.
94. Karpman B. 1948 The myth of the psychopathic
personality. Am. J. Psychiatry 104, 523– 534.
(doi:10.1176/ajp.104.9.523)
95. Vassileva J, Kosson DS, Abramowitz C, Conrod PJ.
2005 Psychopathy vs. psychopathies in classifying
criminal offenders. Legal Criminol. Psychol. 10,
27–43. (doi:10.1348/135532504X15376)
96. Blackburn R, Coid JW. 1998 Psychopathy and the
dimensions of personality disorder in violent
offenders. Pers. Individ. Dif. 25, 129–145. (doi:10.
1016/S0191-8869(98)00027-0)
97. Vitacco MJ, Rogers R. 2001 Predictors of adolescent
psychopathy: the role of impulsivity, hyperactivity,
and sensation seeking. J. Am. Acad. Psychiatry Law
29, 374–382.
98. Hemphill JF, Hart SD, Hare RD. 1994 Psychopathy
and substance use. J. Personal. Disord. 8, 169–180.
(doi:10.1521/pedi.1994.8.3.169)
99. Smith SS, Newman JP. 1990 Alcohol and drug
abuse-dependence disorders in psychopathic and
nonpsychopathic criminal offenders. J. Abnorm.
Psychol. 99, 430– 439. (doi:10.1037/0021-843X.99.
4.430)
100. Taylor J, Lang AR. 2006 Psychopathy and substance
use disorders. In Handbook of psychopathy (ed.
CJ Patrick), pp. 495– 511. New York, NY: Guilford
Press.
101. Barkley RA. 1997 Behavioral inhibition,
sustained attention, and executive functions:
constructing a unifying theory of ADHD.
Psychol. Bull. 121, 65– 94. (doi:10.1037/0033-2909.
121.1.65)
102. Nigg JT. 2001 Is ADHD a disinhibitory disorder?
Psychol. Bull. 127, 571– 598. (doi:10.1037/0033-
2909.127.5.571)
103. Blair RJ. 2005 Applying a cognitive neuroscience
perspective to the disorder of psychopathy. Dev.
Psychopathol. 17, 865– 891. (doi:10.1017/
S0954579405050418)
104. Hunt MK, Hopko DR, Bare R, Lejuez CW, Robinson
EV. 2005 Construct validity of the Balloon Analog
Risk Task (BART): associations with psychopathy and
impulsivity. Assessment 12, 416–428. (doi:10.1177/
1073191105278740)
105. Mitchell DG, Colledge E, Leonard A, Blair RJ.
2002 Risky decisions and response reversal: is
there evidence of orbitofrontal cortex dysfunction
in psychopathic individuals? Neuropsychologia
40, 2013–2022. (doi:10.1016/S0028-3932(02)
00056-8)
106. Vassileva J, Georgiev S, Martin E, Gonzalez R, Segala
L. 2011 Psychopathic heroin addicts are not
uniformly impaired across neurocognitive domains
of impulsivity. Drug Alcohol Depend. 114, 194–200.
(doi:10.1016/j.drugalcdep.2010.09.021)
107. Vassileva J, Petkova P, Georgiev S, Martin EM,
Tersiyski R, Raycheva M, Velinov V, Marinov P. 2007
Impaired decision-making in psychopathic heroin
addicts. Drug Alcohol Depend. 86, 287–289.
(doi:10.1016/j.drugalcdep.2006.06.015)
108. Gorenstein EE, Newman JP. 1980 Disinhibitory
psychopathology: a new perspective and a model
for research. Psychol. Rev. 87, 301– 315. (doi:10.
1037/0033-295X.87.3.301)
109. Krueger RF, Hicks BM, Patrick CJ, Carlson SR, Iacono
WG, McGue M. 2002 Etiologic connections among
substance dependence, antisocial behavior, and
personality: modeling the externalizing spectrum.
12
royalsocietypublishing.org/journal/rstb Phil. Trans. R. Soc. B 374: 20180137
J. Abnorm. Psychol. 111, 411–424. (doi:10.1037/
0021-843X.111.3.411)
110. Mackie CJ, Castellanos-Ryan N, Conrod PJ. 2011
Personality moderates the longitudinal relationship
between psychological symptoms and alcohol use
in adolescents. Alcohol. Clin. Exp. Res. 35, 703– 716.
(doi:10.1111/j.1530-0277.2010.01388.x)
111. Clark SL, Gillespie NA, Adkins DE, Kendler KS,
Neale MC. 2016 Psychometric modeling of abuse
and dependence symptoms across six illicit
substances indicates novel dimensions of misuse.
Addict. Behav. 53, 132– 140. (doi:10.1016/j.addbeh.
2015.10.015)
112. Merikangas KR et al. 1998 Familial transmission of
substance use disorders. Arch. Gen. Psychiatry 55,
973– 979. (doi:10.1001/archpsyc.55.11.973)
113. Tsuang MT et al. 1998 Co-occurrence of abuse of
different drugs in men: the role of drug-specific and
shared vulnerabilities. Arch. Gen. Psychiatry 55,
967–972. (doi:10.1001/archpsyc.55.11.967)
114. Conrod PJ, Castellanos-Ryan N, Strang J. 2010 Brief,
personality-targeted coping skills interventions and
survival as a non-drug user over a 2-year period
during adolescence. Arch. Gen. Psychiatry 67,
85–93. (doi:10.1001/archgenpsychiatry.2009.173)
115. Schlauch RC, Crane CA, Houston RJ, Molnar DS,
Schlienz NJ, Lang AR. 2015 Psychometric evaluation
of the Substance Use Risk Profile Scale (SURPS) in
an inpatient sample of substance users using cue-
reactivity methodology. J. Psychopathol. Behav.
Assess. 37, 231–246. (doi:10.1007/s10862-014-
9462-x)
116. Long EC, Milcheva S, Psederska E, Vasilev G,
Bozgunov K, Nedelchev D, Gillespie N, Vassileva J.
2018 Validation of the Substance Use Risk Profile
Scale (SURPS) with Bulgarian substance dependent
individuals. Front. Psychol. 9, 2296. (doi:10.3389/
fpsyg.2018.02296)
117. Mahu IC et al. Submitted. Specificity of personality
relationships to particular forms of concurrent
substance use among opiate agonist therapy clients.
118. Ahn WY, Vassileva J. 2016 Machine-learning
identifies substance-specific behavioral markers for
opiate and stimulant dependence. Drug Alcohol
Depend. 161, 247–257. (doi:10.1016/j.drugalcdep.
2016.02.008)
119. Bechara A, Dolan S, Denburg N, Hindes A, Anderson
SW, Nathan PE. 2001 Decision- making deficits,
linked to a dysfunctional ventromedial prefrontal
cortex, revealed in alcohol and stimulant abusers.
Neuropsychologia 39, 376– 389. (doi:10.1016/
S0028-3932(00)00136-6)
120. MacKillop J. 2013 Integrating behavioral
economics and behavioral genetics: delayed
reward discounting as an endophenotype for
addictive disorders. J. Exp. Anal. Behav. 99,
14–31. (doi:10.1002/jeab.4)
121. Vassileva J, Gonzalez R, Bechara A, Martin EM. 2007
Are all drug addicts impulsive? Effects of
antisociality and extent of multidrug use on
cognitive and motor impulsivity. Addict.
Behav. 32, 3071– 3076. (doi:10.1016/j.addbeh.
2007.04.017)
122. Verdejo-Garcia AJ, Perales JC, Perez-Garcia M. 2007
Cognitive impulsivity in cocaine and heroin
polysubstance abusers. Addict. Behav. 32, 950– 966.
(doi:10.1016/j.addbeh.2006.06.032)
123. Hester R, Garavan H. 2004 Executive dysfunction
in cocaine addiction: evidence for discordant
frontal, cingulate, and cerebellar activity.
J. Neurosci. 24, 11 017 –11 022. (doi:10.1523/
JNEUROSCI.3321-04.2004)
124. Kaufman JN, Ross TJ, Stein EA, Garavan H. 2003
Cingulate hypoactivity in cocaine users during a
GO-NOGO task as revealed by event-related
functional magnetic resonance imaging.
J. Neurosci. 23, 7839 7843. (doi:10.1523/
JNEUROSCI.23-21-07839.2003)
125. Luijten M, Machielsen MW, Veltman DJ, Hester R,
de Haan L, Franken IH. 2014 Systematic review of
ERP and fMRI studies investigating inhibitory control
and error processing in people with substance
dependence and behavioural addictions.
J. Psychiatry Neurosci. 39, 149– 169. (doi:10.1503/
jpn.130052)
126. Morein-Zamir S, Robbins TW. 2015 Fronto-striatal
circuits in response-inhibition: relevance to
addiction. Brain Res. 1628, 117– 129. (doi:10.1016/
j.brainres.2014.09.012)
127. Perry JL, Carroll ME. 2008 The role of impulsive
behavior in drug abuse. Psychopharmacology (Berl.)
200, 1–26. (doi:10.1007/s00213-008-1173-0)
128. Spronk DB, van Wel JH, Ramaekers JG, Verkes RJ.
2013 Characterizing the cognitive effects of
cocaine: a comprehensive review. Neurosci.
Biobehav. Rev. 37, 1838 1859. (doi:10.1016/j.
neubiorev.2013.07.003)
129. Badiani A, Belin D, Epstein D, Calu D, Shaham Y.
2011 Opiate versus psychostimulant addiction: the
differences do matter. Nat. Rev. Neurosci. 12,
685–700. (doi:10.1038/nrn3104)
130. Belin D, Belin-Rauscent A, Everitt BJ, Dalley JW.
2016 In search of predictive endophenotypes in
addiction: insights from preclinical research.
Genes Brain Behav. 15, 74– 88. (doi:10.1111/
gbb.12265)
131. Conrod PJ, Stewart SH, Pihl RO, Cote S, Fontaine V,
Dongier M. 2000 Efficacy of brief coping skills
interventions that match different personality
profiles of female substance abusers. Psychol.
Addict. Behav. 14, 231– 242. (doi:10.1037/0893-
164X.14.3.231)
132. Mackey S et al. In press. Mega-analysis of gray
matter volume in substance dependence: general
and substance-specific regional effects.
Am. J. Psychiatry. appiajp201817040415. (doi:10.
1176/appi.ajp.2018.17040415)
133. Vasilev G, Milcheva S, Vassileva J. 2016 Opioid use
in the twenty first century: similarities and
differences across national borders. Curr. Treat.
Options Psychiatry 3, 293– 305. (doi:10.1007/
s40501-016-0089-2)
134. Bornovalova MA, Daughters SB, Hernandez GD,
Richards JB, Lejuez CW. 2005 Differences in
impulsivity and risk-taking propensity between
primary users of crack cocaine and primary users of
heroin in a residential substance-use program. Exp.
Clin. Psychopharmacol. 13, 311– 318. (doi:10.1037/
1064-1297.13.4.311)
135. Dawe S, Gullo MJ, Loxton NJ. 2004 Reward drive
and rash impulsiveness as dimensions of
impulsivity: implications for substance misuse.
Addict. Behav. 29, 1389– 1405. (doi:10.1016/j.
addbeh.2004.06.004)
136. Verdejo-Garcia A, Perez-Garcia M. 2007 Profile of
executive deficits in cocaine and heroin
polysubstance users: common and differential
effects on separate executive components.
Psychopharmacology (Berl.) 190, 517– 530. (doi:10.
1007/s00213-006-0632-8)
137. Vassileva J, Paxton J, Moeller FG, Wilson MJ,
Bozgunov K, Martin EM, Gonzalez R, Vasilev G. 2014
Heroin and amphetamine users display opposite
relationships between trait and neurobehavioral
dimensions of impulsivity. Addict. Behav. 39,
652–659. (doi:10.1016/j.addbeh.2013.11.020)
138. Wilson MJ, Vassileva J. 2018 Decision-making under
risk, but not under ambiguity, predicts pathological
gambling in discrete types of abstinent substance
users. Front. Psychiatry 9, 239. (doi:10.3389/fpsyt.
2018.00239)
139. Wilson MJ, Vassileva J. 2016 Neurocognitive and
psychiatric dimensions of hot, but not cool,
impulsivity predict HIV sexual risk behaviors among
drug users in protracted abstinence. Am. J. Drug
Alcohol Abuse 42, 231–241. (doi:10.3109/
00952990.2015.1121269)
140. Ahn WY, Ramesh D, Moeller FG, Vassileva J. 2016
Utility of machine-learning approaches to identify
behavioral markers for substance use disorders:
impulsivity dimensions as predictors of current
cocaine dependence. Front. Psychiatry 7, 34. (doi:10.
3389/fpsyt.2016.00034)
141. Bickel WK. 2015 Discounting of delayed rewards as
an endophenotype. Biol. Psychiatry 77, 846– 847.
(doi:10.1016/j.biopsych.2015.03.003)
142. Sanchez-Roige S et al. 2018 Genome-wide
association study of delay discounting in 23,217
adult research participants of European ancestry.
Nat. Neurosci. 21, 16– 18. (doi:10.1038/s41593-
017-0032-x)
143. Gray JC et al. 2018 Genetic analysis of impulsive
personality traits: examination of a priori candidates
and genome-wide variation. Psychiatry Res. 259,
398–404. (doi:10.1016/j.psychres.2017.10.047)
144. Long EC, Kaneva R, Vasilev G, Moeller FG, Vassileva
J. 2018 Neurocognitive and personality markers for
addiction: common vs. specific (endo)phenotypes
for opiate and stimulant dependence. bioR
x
iv,
480970. (doi:10.1101/480970)
145. Ward MF, Wender PH, Reimherr FW. 1993 The
Wender Utah Rating Scale: an aid in the
retrospective diagnosis of childhood attention deficit
hyperactivity disorder. Am. J. Psychiatry 150,
885–890. (doi:10.1176/ajp.150.6.885)
146. Reiss S, Peterson RA, Gursky DM, McNally RJ. 1986
Anxiety sensitivity, anxiety frequency and the
prediction of fearfulness. Behav. Res. Ther. 24,1–8.
(doi:10.1016/0005-7967(86)90143-9)
13
royalsocietypublishing.org/journal/rstb Phil. Trans. R. Soc. B 374: 20180137
147. Insel T et al. 2010 Research domain criteria (RDoC):
toward a new classification framework for research
on mental disorders. Am. J. Psychiatry 167,
748–751. (doi:10.1176/appi.ajp.2010.09091379)
148. Anderson P et al. 2017 Reframing the science and
policy of nicotine, illegal drugs and alcohol—
conclusions of the ALICE RAP Project. F1000Res. 6,
289. (doi:10.12688/f1000research.10860.1)
149. Yucel M et al. In press. A transdiagnostic
dimensional approach towards a neuropsychological
assessment for addiction: an international Delphi
consensus study. Addiction. (doi:10.1111/add.14424)
150. Babor TFH-B, Saunders J, Monteiro MG. 2001 AUDIT.
The Alcohol Use Disorders Identification Test.
Guidelines for use in primary health care. Geneva,
Switzerland: World Health Organization.
151. Cavagnaro DR, Pitt MA, Myung JI. 2009 Adaptive
design optimization in experiments with people.
Adv. Neural Inf. Process. Syst. 22, 234– 242.
152. Witkiewitz K, Lustyk MKB, Bowen S. 2013 Retraining
the addicted brain: a review of hypothesized
neurobiological mechanisms of mindfulness-based
relapse prevention. Psychol. Addict. Behav. 27,
351– 365. (doi:10.1037/a0029258)
153. Cortese S et al. 2015 Cognitive training for
attention-deficit/hyperactivity disorder: meta-
analysis of clinical and neuropsychological outcomes
from randomized controlled trials. J. Am. Acad. Child
Adolesc. Psychiatry 54, 164– 174. (doi:10.1016/j.
jaac.2014.12.010)
154. Conrod PJ. 2016 Personality-targeted interventions
for substance use and misuse. Curr. Addict. Rep. 3,
426–436. (doi:10.1007/s40429-016-0127-6)
155. Conrod PJ et al. 2013 Effectiveness of a selective,
personality-targeted prevention program for
adolescent alcohol use and misuse: a cluster
randomized controlled trial. JAMA Psychiatry 70,
334–342. (doi:10.1001/jamapsychiatry.2013.651)
156. O’Leary-Barrett M, Topper L, Al-Khudhairy N, Pihl
RO, Castellanos-Ryan N, Mackie CJ, Conrod PJ. 2013
Two-year impact of personality-targeted, teacher-
delivered interventions on youth internalizing and
externalizing problems: a cluster-randomized trial.
J. Am. Acad. Child Adolesc. Psychiatry 52, 911– 920.
(doi:10.1016/j.jaac.2013.05.020)
157. O’Leary-Barrett M, Castellanos-Ryan N, Pihl RO,
Conrod PJ. 2016 Mechanisms of personality-
targeted intervention effects on adolescent alcohol
misuse, internalizing and externalizing symptoms.
J. Consult. Clin. Psychol. 84, 438– 452. (doi:10.1037/
ccp0000082)
158. Bickel WK, Miller ML, Yi R, Kowal BP, Lindquist DM,
Pitcock JA. 2007 Behavioral and neuroeconomics of
drug addiction: competing neural systems and
temporal discounting processes. Drug Alcohol
Depend. 90(Suppl. 1), S85–S91. (doi:10.1016/j.
drugalcdep.2006.09.016)
159. Bickel WK, Quisenberry AJ, Moody L, Wilson AG.
2015 Therapeutic opportunities for self-control
repair in addiction and related disorders: change
and the limits of change in trans-disease processes.
Clin. Psychol. Sci. 3, 140–153. (doi:10.1177/
2167702614541260)
160. Bickel WK, Johnson MW, Koffarnus MN, MacKillop J,
Murphy JG. 2014 The behavioral economics of
substance use disorders: reinforcement pathologies
and their repair. Annu. Rev. Clin. Psychol. 10, 641 –677.
(doi:10.1146/annurev-clinpsy-032813-153724)
161. Gray JC, MacKillop J. 2015 Impulsive delayed reward
discounting as a genetically-influenced target for
drug abuse prevention: a critical evaluation. Front.
Psychol. 6, 1104. (doi:10.3389/fpsyg.2015.01104)
162. Bickel WK, Moody L, Quisenberry A. 2014
Computerized working-memory training as a
candidate adjunctive treatment for addiction.
Alcohol Res. 36, 123– 126.
163. Snider SE, Deshpande HU, Lisinski JM, Koffarnus
MN, LaConte SM, Bickel WK. 2018 Working memory
training improves alcohol users’ episodic future
thinking: a rate-dependent analysis. Biol. Psychiatry
Cogn. Neurosci. Neuroimaging 3, 160– 167. (doi:10.
1016/j.bpsc.2017.11.002)
164. Houben K, Wiers RW, Jansen A. 2011 Getting a grip
on drinking behavior: training working memory to
reduce alcohol abuse. Psychol. Sci. 22, 968– 975.
(doi:10.1177/0956797611412392)
165. Houben K, Havermans RC, Nederkoorn C, Jansen A.
2012 Beer a no-go: learning to stop responding to
alcohol cues reduces alcohol intake via reduced
affective associations rather than increased response
inhibition. Addiction 107, 1280–1287. (doi:10.
1111/j.1360-0443.2012.03827.x)
166. Houben K, Nederkoorn C, Wiers RW, Jansen A. 2011
Resisting temptation: decreasing alcohol-related
affect and drinking behavior by training response
inhibition. Drug Alcohol Depend. 116, 132–136.
(doi:10.1016/j.drugalcdep.2010.12.011)
167. Sofuoglu M, DeVito EE, Waters AJ, Carroll KM. 2013
Cognitive enhancement as a treatment for drug
addictions. Neuropharmacology 64, 452– 463.
(doi:10.1016/j.neuropharm.2012.06.021)
168. Lefaucheur JP et al. 2014 Evidence-based guidelines
on the therapeutic use of repetitive transcranial
magnetic stimulation (rTMS). Clin. Neurophysiol.
125, 2150–2206. (doi:10.1016/j.clinph.2014.05.
021)
169. Terao Y, Ugawa Y. 2002 Basic mechanisms of TMS.
J. Clin. Neurophysiol. 19, 322– 343. (doi:10.1097/
00004691-200208000-00006)
170. Yang CC, Vollm B, Khalifa N. 2018 The effects of
rTMS on impulsivity in normal adults: a systematic
review and meta-analysis. Psychol. Med. 48,
737–750. (doi:10.1017/S003329171700232X)
171. Yavari F, Shahbabaie A, Leite J, Carvalho S, Ekhtiari
H, Fregni F. 2016 Noninvasive brain stimulation for
addiction medicine: from monitoring to modulation.
Prog. Brain Res. 224, 371– 399. (doi:10.1016/bs.
pbr.2015.08.007)
172. Baker TE, Lesperance P, Tucholka A, Potvin S,
Larcher K, Zhang Y, Jutras-Aswad D, Conrod P. 2017
Reversing the atypical valuation of drug and
nondrug rewards in smokers using multimodal
neuroimaging. Biol. Psychiatry 82, 819– 827.
(doi:10.1016/j.biopsych.2017.01.015)
173. Tamm L, Nakonezny PA. 2015 Metacognitive
executive function training for young children with
ADHD: a proof-of-concept study. Atten. Defic.
Hyperact. Disord. 7, 183– 190. (doi:10.1007/s12402-
014-0162-x)
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... A trait is regarded as a lasting, consistent individual feature and in humans it is usually assessed via self-report questionnaires (Odum, 2011;Roberts & Jackson, 2008;Vassileva & Conrod, 2019). In the context of impulsivity, these have proven to be particularly useful in detecting endophenotypes for substance abuse (Audrain-McGovern et al., 2009;Ersche et al., 2010;Long et al., 2018) thus strengthening the validity and clinical relevance of studying impulsivity as a trait. ...
... There has also been research on the nature of the state variables that influence impulsivity (Cyders & Coskunpinar, 2011). These alter behaviour over a short time frame and are determined by the 'environmental context and the current state of the individual' (Vassileva & Conrod, 2019). State dimensions are typically measured by performance on neurocognitive tasks (Dalley & Robbins, 2017;Dixon et al., 2006;Vassileva & Conrod, 2019), and in humans have the advantage, compared to trait measurements, of being independent of self-report biases (Cyders & Coskunpinar, 2011). ...
... These alter behaviour over a short time frame and are determined by the 'environmental context and the current state of the individual' (Vassileva & Conrod, 2019). State dimensions are typically measured by performance on neurocognitive tasks (Dalley & Robbins, 2017;Dixon et al., 2006;Vassileva & Conrod, 2019), and in humans have the advantage, compared to trait measurements, of being independent of self-report biases (Cyders & Coskunpinar, 2011). Different taxonomies have been suggested to categorize the different subtypes of impulsive behaviour (Bari & Robbins, 2013;Dalley & Robbins, 2017), with most behaviours falling under the domains of either 'response inhibition' (or motor impulsivity) or 'deferred gratification' (or choice impulsivity). ...
Thesis
Impulsivity is a multidimensional trait in humans and other mammalian species. It is widely regarded as the tendency to act rapidly, without appropriate foresight and underlies many psychiatric disorders, including attention-deficit/hyperactivity disorder (ADHD), mood disorders such as depression and mania, and substance use disorder (SUD). Research has shown that impulsivity is a non-unitary construct, characterised by distinct behavioural manifestations and, accordingly, distinct neural substrates. This thesis focuses on elucidating the psychological and neural mechanisms of waiting impulsivity in experimental rats trained on the 5-choice serial reaction time task (5CSRTT). The psychological processes involved in this behaviour are yet to be fully understood. However, aspects of sustained attention, urgency, motivation, and delay-aversion are all likely to play a role in shaping this behaviour. Although the neural substrates and neurotransmitter systems underlying waiting impulsivity have been extensively investigated, there are still many unanswered questions. Some of the questions that this work aims to address are: 1) can we refine our understanding of the psychological mechanism that influence premature responses, specifically the extent to which these are driven by motivated behaviour or, instead, negative urgency? 2) Does waiting impulsivity confer advantages in specific experimental contexts and what are the neurotransmitter mechanisms regulating this? 3) Can we refine our understanding of the precise neural circuits involved in the execution of a premature response? 4) How does impulsivity and attention on the 5CSRTT compare with performance on other tasks of sustained attention? Are there any shared neural processes? To investigate these questions, I used a range of experimental approaches spanning behavioural testing to systemic and local (intra-cerebral) pharmacology, brain lesions and chemogenetics. In chapter 2, I show that premature responses are influenced by reinforcement rate and motivation, and that negative urgency does not seem to play an important role in the genesis of these responses. In chapter 3, I found that high levels of trait impulsivity confer an advantage in contexts that require rapid focusing and action. I also showed that this advantage might be conferred putatively by elevated striatal dopamine (DA) release in the striatum. In chapter 4, I 6 show that inhibition of the mesolimbic DA system reduces premature responses but was unable to pinpoint the midbrain-striatal loop responsible for this effect. Finally, in chapter 5, two tasks were compared that assess sustained visual attention. PFC lesions effected using the excitotoxin quinolinic acid profoundly affected performance on the 5CSRTT but had negligible effects on a signal detection attentional task. Taken together, these findings suggest that impulsive (premature) responding in the 5CSRTT is linked with the motivation to perform the task but does appear to depend on negative urgency. Trait impulsivity confers an advantage in specific contexts and depends on striatal DA function. Finally, sustained attention on the 5CSRTT is distinct from other forms of sustained attention both at the psychological and neural levels. Some of these findings are consistent with studies on impulsive individuals, thus highlighting the potential for translational research between rodents and humans. Ultimately, this work expands our understanding of the psychological and neural circuit mechanisms underlying waiting impulsivity.
... The widespread utilization of the BART is motivated by its capability in recreating an ecological experience to uncover (neuro)cognitive underpinnings of risk-taking in healthy subjects (Lejuez et al., 2002(Lejuez et al., , 2003Weafer et al., 2011;De Groot, 2020;Guenther et al., 2021). However, measurements of risk-taking behavior are also interesting for clinical research since risk-taking indexed by BART scores has been associated with dysfunctional psychophysiological phenotypes, including anxiety (Maner et al., 2007;Buelow and Barnhart, 2017), clinical disorders (Hunt et al., 2005;Swogger et al., 2010;Dominguez, 2011;Cheng et al., 2012;Robbins et al., 2012;Reddy et al., 2014;Brown et al., 2015;Fischer et al., 2015;Tikàsz et al., 2019;Boka et al., 2020;Luk et al., 2021), abuse of heavy drugs (Hopko et al., 2006;Vassilva and Conrod, 2019), smoking attitudes (Lejuez et al., 2003Dean et al., 2011;Hanson et al., 2014), alcohol consumption and related symptoms (Skeel et al., 2008;Fernie et al., 2010;Ashenhurst et al., 2011;Weafer et al., 2011;DeMartini et al., 2014;King et al., 2014), gambling (Holt et al., 2003;Mishra et al., 2017), risky sexual behavior (Lejuez et al., 2004;Bornovalova et al., 2008;Lawyer, 2013; for reviews on risk-taking and related dysfunctions see: Leigh, 1999;Turner et al., 2004;Isles et al., 2019). Moreover, BART use has been suggested as a potential marker for dissecting disease-related endophenotypes (Long et al., 2020). ...
Article
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The Balloon Analog Risk Task (BART) allows to experimentally assess individuals' risk-taking profiles in an ecologically sound setting. Many psychological and neuroscientific studies implemented the BART for its simplicity and intuitive nature. However, some issues in the design of the BART are systematically unconsidered in experimental paradigms, which may bias the estimation of individual risk-taking profiles. Since there are no methodological guidelines for implementing the BART, many variables (e.g., the maximum explosion probabilities, the rationale underlying stochastic events) vary inconstantly across experiments, possibly producing contrasting results. Moreover, the standard version of the BART is affected by the interaction of an individual-dependent, unavoidable source of stochasticity with a trial-dependent, more ambiguous source of stochasticity (i.e., the probability of the balloon to explode). This paper shows the most appropriate experimental choices for having the lowest error in the approximation of risk-taking profiles. Performance tests of a series of simulated data suggest that a more controlled, eventually non-stochastic version of the BART, better approximates original risk-taking profiles. Selecting optimal BART parameters is particularly important in neuroscience experiments to optimize the number of trials in a time window appropriate for acquiring neuroimaging data. We also provide helpful suggestions to researchers in many fields to allow the implementation of optimized risk-taking experiments using the BART.
... Impulsivity, the tendency to act without sufficient consideration of potential consequences in pursuit of short-term rewards, is a vulnerability marker for SUDs (Verdejo-Garcia and Albein-Urios, 2021). The cognitive processes affected by higher impulsivity included attention, reflection, inhibition, and choices involving risk and reward (Sharma et al., 2014;Vassileva and Conrod, 2019). There is rich evidence about the role of impulsivity in SUDs and addictive disorders (D' Amour-Horvat et al., 2021;Ehlers et al., 2021). ...
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Purpose The aim of this study was to investigate and compare impulsiveness, negative emotion, cognitive function, and P300 components among gamma-hydroxybutyrate (GHB)-addicted patients, heroin-dependent patients, and methadone maintenance treatment (MMT) subjects. Methods A total of 48 men including 17 GHB addicts, 16 heroin addicts, 15 MMT subjects, and 15 male mentally healthy controls (HC) were recruited. All subjects were evaluated for symptoms of depression, anxiety, impulsiveness, and cognitive function through the Patient Health Questionnaire (PHQ-9), the Generalized Anxiety Disorder 7-item (GAD-7), the Barratt Impulsiveness Scale version II (BIS-II), the Beijing version of the Montreal Cognitive Assessment (BJ-MoCA), the behavioral test (response time), and event-related potential P300 detection. Results (1) The mean scores of BIS-II in the GHB addiction group, heroin dependence group, and MMT group were significantly higher than those of the HC group ( F = 30.339, P = 0.000). (2) The total scores of BJ-MOCA in GHB addiction group was the worst among the four groups, followed by heroin addiction, MMT group and HC group ( F = 27.880, P = 0.000). (3) The response time in the GHB addiction group was the longest among the four groups, followed by the heroin addiction, MMT, and HC groups ( F = 150.499, P = 0.000). (4) The amplitude and latency of P300 in GHB addiction subjects were significantly lower and longer than those of the MMT group and the HC group. (5) For the three types of addiction, the P300 amplitudes at Fz, Cz, Pz, T5, and T6 were negatively correlated with the scores of GAD-7, PHQ-9, and BIS-II; the P300 latencies were positively correlated with the response time and negatively correlated with the scores of the BJ-MoCA. Conclusion People with an addiction were likely to have increased impulsiveness. The cognitive function of the GHB and heroin-addicted subjects, including the heroin detoxification and the MMT groups, was severely impaired, especially for the GHB-addicted patients. The impairment manifested as abnormalities of BJ-MoCA, response time, and P300 components.
... Decision-making is often assessed using task-based measures of impulsivity. High levels of impulsivity and risk-taking are implicated in the development, maintenance, and severity of substance dependence (19,20) and mental health disorders (21) and are associated with negative treatment outcomes, including poorer treatment adherence, higher rates of rehospitalization, morbidity, and mortality (22). Where problems in decisionmaking have been implicated in mental health disorders (23)(24)(25) and substance use disorders (19), impulsivity and risk-taking are also key risks where both psychiatric disorders co-occur (26)(27)(28)(29). ...
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Background Recent studies have employed computational modeling to characterize deficits in aspects of decision-making not otherwise detected using traditional behavioral task outcomes. While prospect utility-based modeling has shown to differentiate decision-making patterns between users of different drugs, its relevance in the context of treatment has yet to be examined. This study investigated model-based decision-making as it relates to treatment outcome in inpatients with co-occurring mental health and substance use disorders. Methods 50 patients ( M age = 38.5, SD = 11.4; 16F) completed the Cambridge Gambling Task (CGT) within 2 weeks of admission (baseline) and 6 months into treatment (follow-up), and 50 controls ( M age = 31.9, SD = 10.0; 25F) completed CGT under a single outpatient session. We evaluated 4 traditional CGT outputs and 5 decisional processes derived from the Cumulative Model. Psychiatric diagnoses and discharge data were retrieved from patient health records. Results Groups were similar in age, sex, and premorbid IQ. Differences in years of education were included as covariates across all group comparisons. All patients had ≥1 mental health diagnosis, with 80% having >1 substance use disorder. On the CGT, patients showed greater Deliberation Time and Delay Aversion than controls. Estimated model parameters revealed higher Delayed Reward Discounting, and lower Probability Distortion and Loss Sensitivity in patients relative to controls. From baseline to follow-up, patients ( n = 24) showed a decrease in model-derived Loss Sensitivity and Color Choice Bias. Lastly, poorer Quality of Decision-Making and Choice Consistency, and greater Color Choice Bias independently predicted higher likelihood of treatment dropout, while none were significant in relation to treatment length of stay. Conclusion This is the first study to assess a computational model of decision-making in the context of treatment for concurrent disorders. Patients were more impulsive and slower to deliberate choice than controls. While both traditional and computational outcomes predicted treatment adherence in patients, findings suggest computational methods are able to capture treatment-sensitive aspects of decision-making not accessible via traditional methods. Further research is needed to confirm findings as well as investigate the relationship between model-based decision-making and post-treatment outcomes.
... A challenge for treating MA abuse is recurrence during abstinence . Although many factors contribute to recurrence of MADs, a possible key predictor is impulsivity (Ahn et al., 2016;Vassileva and Conrod, 2019). Impulsivity is defined as a predisposition toward rapid, unplanned reactions to internal or external stimuli with diminished regard to their negative consequences to themselves or others (Ahn et al., 2016;Psederska et al., 2021). ...
Article
Full-text available
Methamphetamine (MA) use affects the brain structure and function. However, no studies have investigated the relationship between changes in regional homogeneity (ReHo) and impulsivity in MA dependent individuals (MADs). The aim of this study was to investigate the changes of brain activity under resting state in MADs and their relationship to impulsivity using ReHo method. Functional magnetic resonance imaging (fMRI) was performed to collect data from 46 MADs and 44 healthy controls (HCs) under resting state. ReHo method was used to investigate the differences in average ReHo values between the two groups. The ReHo values abnormalities of the brain regions found in inter-group comparisons were extracted and correlated with impulsivity. Compared to the HCs, MADs showed significant increased ReHo values in the bilateral striatum, while the ReHo values of the bilateral precentral gyrus and the bilateral postcentral gyrus decreased significantly. The ReHo values of the left precentral gyrus were negatively correlated with the BIS-attention, BIS-motor, and BIS-nonplanning subscale scores, while the ReHo values of the postcentral gyrus were only negatively correlated with the BIS-motor subscale scores in MADs. The abnormal spontaneous brain activity in the resting state of MADs revealed in this study may further improve our understanding of the neuro-matrix of MADs impulse control dysfunction and may help us to explore the neuropathological mechanism of MADs related dysfunction and rehabilitation.
... 8,9 Impulsivity can function as both a determinant and outcome of problematic substance use, 6 consider impaired impulse control to be a key factor underlying patterns of compulsive drug seeking and use. 13 Amphetamine and methylphenidate are first-line pharmacotherapies for a range of neurological and neurodevelopmental disorders, including ADHD, for which therapeutic doses are effective in reducing associated symptoms such as impulsivity. 14,15 Amphetamine and other prescription psychostimulants are also increasingly used non-medically as cognitive enhancing drugs in healthy individuals, 16,17 with global estimates of the prevalence of use by students ranging from 6% to 10% in Australia and 5% to 35% in the United States. ...
Article
Evidence for acute amphetamine effects on behavioural impulsivity in healthy populations remains elusive and, at times, mixed. This review collates and reviews the clinical literature on the acute effects of amphetamines on measures of behavioural impulsivity in healthy adults. Randomised and placebo-controlled clinical trials that assessed behavioural impulsivity following the administration of an acute dose of amphetamine or a related psychostimulant (including amphetamine analogues and methylphenidate) were eligible for inclusion. The EBSCOHost, SCOPUS, PsychNet, Web of Science and ProQuest databases were searched from inception to 26 April 2021. Study selection, data extraction and the Cochrane risk of bias assessments were conducted by two independent reviewers. Reporting follows PRISMA guidelines, and the review was registered a priori on the PROSPERO database (Registration No: CRD42021249861). A total of 20 studies were included, comprising a total of 737 participants. Overall, results indicate that low-moderate doses of amphetamine and related psychostimulants may improve (i.e., reduce) impulsive responding without compromising performance, reflecting enhanced inhibitory control of behaviour. These effects are mild and appear most pronounced in individuals with high baseline impulsivity. This review highlights the need for greater consistency in behavioural task selection and future high-quality and well-designed studies to address current concerns around growing prescription psychostimulant use and misuse.
... Impulsiveness or impulsivity is a multidimensional construct that is a marker of severity in SUD (Vassileva and Conrod, 2019) and has been described as a behavioral trait and a vulnerability factor that could be part of the personality profile that antecede to starting a drug consumption and contribute to the dependence phenotype (Morein-Zamir and Robbins, 2015). We found that cognitive and motor impulsivity were correlated with different regions as confirmed by the multiple regression analysis. ...
Article
Cocaine use disorder (CUD) is characterized by a compulsive search for cocaine. Several studies have shown that cocaine users exhibit cognitive deficits, including lack of inhibition and decision-making as well as brain volume and diffusion-based white-matter alterations in a wide variety of brain regions. However, the non-specificity of standard volumetric and diffusion-tensor methods to detect structural micropathology may lead to wrong conclusions. To better understand microstructural pathology in CUD, we analyzed 60 CUD participants (3 female) and 43 non-CUD controls (HC; 2 female) retrospectively from our cross-sectional Mexican SUD neuroimaging dataset (SUDMEX-CONN), using multi-shell diffusion-weighted imaging and the neurite orientation dispersion and density imaging (NODDI) analysis, which aims to more accurately model microstructural pathology. We used Viso values of NODDI that employ a three-compartment model in white (WM) and gray-matter (GM). These values were also correlated with clinical measures, including psychiatric severity status, impulsive behavior and pattern of cocaine and tobacco use in the CUD group. We found higher whole-brain microstructural pathology in WM and GM in CUD patients than controls. ROI analysis revealed higher Viso-NODDI values in superior longitudinal fasciculus, cingulum, hippocampus cingulum, forceps minor and Uncinate fasciculus, as well as in frontal and parieto-temporal GM structures. We also found correlations between significant ROI and impulsivity, onset age of cocaine use and weekly dosage with Viso-NODDI. However, we did not find correlations with psychopathology measures. Overall, although their clinical relevance remains questionable, microstructural pathology seems to be present in CUD both in gray and white matter.
Article
Background: Impairments in neurocognitive functioning are associated with substance use behavior. Previous studies in neurocognitive predictors of substance use typically use self-report measures rather than neuropsychological performance measures and suffer from low sample sizes and use of clinical diagnostic cut offs. Methods: Crossectional data from the HUNT4 Study (Helseundersøkelsen i Trøndelag) was used to study executive neuropsychological performance and self-reported measures of neurocognitive function associated with a history of illicit substance use in a general population sample of young adults in Norway. We performed both between group comparisons and logistic regression modeling and controlled for mental health symptomatology. Results: Subjects in our cohort with a self-reported use of illicit substances had significantly higher self-reported mental health and neurocognitive symptom load. A logistic regression model with substance use as response included sex, commission errors and self-reported inattentiveness and anxiety as significant predictors. After 10-fold cross-validation this model achieved a moderate area under the receiver-operator curve of 0.63. To handle the class imbalance typically found in such population data, we also calculated balanced accuracy with a optimal model cut off of 0.234 with a sensitivity of 0.50 and specificity of 0.76 as well as precision recall-area under the curve of 0.28. Conclusions: Subtle cognitive dysfunction differentiates subjects with and without a history of illicit substance use. Neurocognitive factors outperformed the effects of depressive symptoms on substance use behavior in this cohort. We highlight the need for using adequate statistical tools for evaluating the performance of models in unbalanced datasets.
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Background Online shopping has been steadily growing in popularity, especially during the COVID-19 pandemic. The purpose of the current study was to investigate both the roles of a personality model validated in substance-related disorders and mindfulness in online compulsive buying (CB). Methods A total of 534 individuals from university (n = 334) and online communities (n = 200) completed the Substance Use Risk Profile Scale (i.e., Hopelessness, Anxiety Sensitivity, Impulsivity, and Sensation Seeking), the Five Facets Mindfulness Questionnaire and the Bergen Shopping Addiction Scale. Results Analyses indicated that higher impulsivity, higher anxiety sensitivity and lower nonreactivity and awareness mindfulness scores predicted online CB. In addition, lower nonreactivity and awareness were found to partially mediate the relationship between high impulsivity and online CB. Specifically, lower awareness accounted for 30.77% of this relationship and lower nonreactivity accounted for 7.93% of the relationship between impulsivity and CB. These findings offer support for the conceptualization of CB as a behavioural addiction and suggest that mindfulness interventions may be useful in the context of reducing online CB.
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Attention‐deficit/hyperactivity disorder (ADHD) a common neurodevelopmental disorder of childhood and often comorbid with other externalizing disorders (EDs). There is evidence that externalizing behaviors share a common genetic etiology. Recently, a genome‐wide, multigenerational sample linked variants in the Lphn3 gene to ADHD and other externalizing behaviors. Likewise, limited research in animal models has provided converging evidence that Lphn3 plays a role in EDs. This study examined the impact of Lphn3 deletion (i.e., Lphn3‐/‐) in rats on measures of behavioral control associated with externalizing behavior. Impulsivity was assessed for 30 days via a differential reinforcement of low rates (DRL) task and working memory evaluated for 25 days using a delayed spatial alternation (DSA) task. Data from both tasks were averaged into 5‐day testing blocks. We analyzed overall performance, as well as response patterns in just the first and last blocks to assess acquisition and steady‐state performance, respectively. “Positive control” measures on the same tasks were measured in an accepted animal model of ADHD – the Spontaneously Hypertensive Rat (SHR). Compared with wildtype controls, Lphn3‐/‐ rats exhibited deficits on both the DRL and DSA tasks, indicative of deficits in impulsive action and working memory, respectively. These deficits were less severe than those in the SHRs, who were profoundly impaired on both tasks compared with their control strain, Wistar‐Kyoto rats. The results provide evidence supporting a role for Lphn3 in modulating inhibitory control and working memory, and suggest additional research evaluating the role of Lphn3 in the manifestation of EDs more broadly is warranted.
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Impulsive personality traits are complex heritable traits that are governed by frontal-subcortical circuits and are associated with numerous neuropsychiatric disorders, particularly drug abuse and attention-deficit/hyperactivity disorder (ADHD). In collaboration with the genetics company 23andMe, we performed 10 genome-wide association studies on measures of impulsive personality traits [the short version of the UPPS-P Impulsive Behavior Scale, and the Barratt Impulsiveness Scale (BIS-11)] and drug experimentation (the number of drug classes an individual had tried in their lifetime) in up to 22,861 male and female adult human research participants of European ancestry. Impulsive personality traits and drug experimentation showed single nucleotide polymorphism heritabilities that ranged from 5 to 11%. Genetic variants in the CADM2 locus were significantly associated with UPPS-P Sensation Seeking (p = 8.3 × 10 ⁻⁹ , rs139528938) and showed a suggestive association with Drug Experimentation (p = 3.0 × 10 ⁻⁷ , rs2163971; r ² = 0.68 with rs139528938). Furthermore, genetic variants in the CACNA1I locus were significantly associated with UPPS-P Negative Urgency (p = 3.8 × 10 ⁻⁸ ; rs199694726). The role of these genes was supported by single variant, gene- and transcriptome-based analyses. Multiple subscales from both UPPS-P and BIS showed strong genetic correlations (>0.5) with Drug Experimentation and other substance use traits measured in independent cohorts, including smoking initiation, and lifetime cannabis use. Several UPPS-P and BIS subscales were genetically correlated with ADHD (r g = 0.30-0.51), supporting their validity as endophenotypes. Our findings demonstrate a role for common genetic contributions to individual differences in impulsivity. Furthermore, our study is the first to provide a genetic dissection of the relationship between different types of impulsive personality traits and various psychiatric disorders.
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BACKGROUND The differential utility of neurocognitive impulsivity and externalizing/internalizing traits as putative addiction endophenotypes among individuals dependent on opiates vs. stimulants is unclear. The present study aims to determine: (1) whether neurocognitive impulsivity dimensions and externalizing/internalizing traits are correlated between siblings discordant for opiate and stimulant dependence; and (2) which of these associations are common across substances and which are substance-specific. METHOD Pearson correlations between individuals with ‘pure’ heroin and ‘pure’ amphetamine dependence and their unaffected biological siblings ( n = 37 heroin sibling pairs; n = 30 amphetamine sibling pairs) were run on 10 neurocognitive measures, 6 externalizing measures, and 5 internalizing measures. Sibling pair effects were further examined using regression. RESULTS Siblings discordant for heroin dependence were significantly correlated on delay aversion on the Cambridge Gambling Task, risk-taking on the Balloon Analogue Risk Task, sensation seeking, and hopelessness. Siblings discordant for amphetamine dependence were significantly correlated on quality of decision-making on the Cambridge Gambling Task, discriminability on the Immediate Memory Task, commission errors on the Go/No-Go Task, trait impulsivity, ADHD, and anxiety sensitivity. CONCLUSIONS Dimensions of impulsivity and externalizing/internalizing traits appear to aggregate among siblings discordant for substance dependence. Risk-taking propensity, sensation seeking, and hopelessness were specific for heroin sibling pairs. Motor/action impulsivity and trait impulsivity were specific to amphetamine sibling pairs. Decisional/choice impulsivity was common across both heroin and amphetamine sibling pairs. These findings provide preliminary evidence for the utility of neurocognitive impulsivity and externalizing/internalizing traits as candidate endophenotypes for substance dependence in general and for substance-specific dependencies.
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Background: The Substance Use Risk Profile Scale (SURPS) is a 23-item self-report questionnaire that assesses four well-validated personality risk factors for substance misuse (Impulsivity, Sensation Seeking, Anxiety Sensitivity, and Hopelessness). While the SURPS has been used extensively with adolescents at risk for substance dependence, its properties with adult substance-dependent populations have been understudied. Further, the validity of the Bulgarian version of the SURPS has not been evaluated. The aims of the present study were to examine the factor structure of the Bulgarian version of the SURPS, its psychometric properties, and its ability to distinguish individuals with substance dependence from healthy controls. Methods: Participants included 238 individuals ages 18 to 50 (45% female): 36 “pure” (i.e., mono-substance dependent) heroin users, 34 “pure” amphetamine users, 32 polysubstance users, 64 controls with no history of substance dependence, 43 unaffected siblings of heroin users, and 29 unaffected siblings of amphetamine users. We explored the factor structure of the Bulgarian version of the SURPS with confirmatory factor analyses, examined its reliability and validity, and tested for group differences between substance dependent and non-dependent groups. Results: Confirmatory factor analyses (CFA) replicated the original four-factor model of the SURPS. The four subscales of the SURPS demonstrated good internal consistency (Cronbach's alphas ranged from 0.71 to 0.85) and adequate concurrent validity. Significant group differences were found on the Impulsivity and Sensation Seeking subscales, with the three substance dependent groups scoring higher than controls. Conclusions: The SURPS is a valid instrument for measuring personality risk for substance use disorders in the Bulgarian population. The Bulgarian version of the SURPS demonstrates adequate to good reliability, concurrent validity, and predictive validity. Its ability to distinguish between groups with and without a history of substance dependence was specific to externalizing traits such as Impulsivity and Sensation Seeking, on which opiate, stimulant, and polysubstance dependent individuals scored higher than non-dependent controls.
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Background The U.S. National Institutes of Mental Health Research Domain Criteria (RDoC) seek to stimulate research into biologically validated neuropsychological dimensions across mental illness symptoms and diagnoses. The RDoC framework comprises 39 functional constructs designed to be revised and refined, with the overall goal to improve diagnostic validity and treatments. This study aimed to reach a consensus among experts in the addiction field on the ‘primary' RDoC constructs most relevant to substance and behavioural addictions. Methods Forty‐four addiction experts were recruited from Australia, Asia, Europe and the Americas. The Delphi technique was used to determine a consensus as to the degree of importance of each construct in understanding the essential dimensions underpinning addictive behaviours. Expert opinions were canvassed online over three rounds (97% completion rate), with each consecutive round offering feedback for experts to review their opinions. Results Seven constructs were endorsed by ≥80% of experts as ‘primary' to the understanding of addictive behaviour: five from the Positive Valence System (Reward Valuation, Expectancy, Action Selection, Reward Learning, Habit); one from the Cognitive Control System (Response Selection/Inhibition); and one expert‐initiated construct (Compulsivity). These constructs were rated to be differentially related to stages of the addiction cycle, with some more closely linked to addiction onset, and others more to chronicity. Experts agreed that these neuropsychological dimensions apply across a range of addictions. Conclusions The study offers a novel and neuropsychologically informed theoretical framework, as well as a cogent step forward to test transdiagnostic concepts in addiction research, with direct implications for assessment, diagnosis, staging of disorder, and treatment.
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Objective A mainstay treatment for opioid addiction in North America is methadone maintenance therapy (MMT) – a form of opiate agonist therapy (OAT). While efficacious for treating opioid addiction, MMT fails to address the concurrent polysubstance use that is common among opioid dependent clients. Moreover, psychosocial approaches for addressing polysubstance use during MMT are lacking. Our study's goals were to validate the use of the four-factor personality model of substance use vulnerability in MMT clients, and to demonstrate theoretically-relevant relationships of personality to concurrent substance use while receiving MMT. Method Respondents included 138 daily-witnessed MMT clients (65.9% male, 79.7% Caucasian), mean age (SD) 40.18 (11.56), recruited across four Canadian MMT clinics. Bayesian confirmatory factor analysis was used to establish the structural validity of the four-factor personality model of substance use vulnerability (operationalized with the Substance Use Risk Profile Scale [SURPS]) in MMT clients. SURPS personality scores were then used as predictors for specific forms of recent (past 30-day) substance use. Results Using a latent hierarchal model, hopelessness was associated with recent opioid use; anxiety sensitivity with recent tranquilizer use; and sensation seeking with recent alcohol, cannabis, and stimulant use. Conclusion Personality is associated with substance use patterns and may be an appropriate target for intervention for those undergoing MMT to reduce opioid use, and potentially dangerous concurrent use of other drugs, while receiving methadone.