ArticlePDF AvailableLiterature Review

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

Philosophical Transactions B

December 2018
374(1766)

DOI:10.1098/rstb.2018.0137

License
CC BY 4.0

Authors:

Jasmin Vassileva

Virginia Commonwealth University

Patricia Conrod

Université de Montréal

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’.

Access to this full-text is provided by The Royal Society.

Learn more

Content available from Philosophical Transactions B

This content is subject to copyright.

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

Institute for Drug and Alcohol Studies, and

Department of Psychiatry, Virginia Commonwealth University,

Richmond, VA, USA

Department of Psychiatry, University of Montreal, Montreal, Canada

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 substance-specific

addictions and comorbidities. Impulsivity dimensions are presented as novel be-

havioural 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

&2019 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution

License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original

author and source are credited.

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

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.

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].

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.

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

royalsocietypublishing.org/journal/rstb Phil. Trans. R. Soc. B 374: 20180137

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

royalsocietypublishing.org/journal/rstb Phil. Trans. R. Soc. B 374: 20180137

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

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

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

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

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.

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

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. 11,1–7.

(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.

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