personality, executive functions, and behavioral disinhibition in adolescence

University of Colorado, BoulderCU ScholarPsychology and Neuroscience Graduate Theses &Dissertations Psychology and Neuroscience

Spring 1-1-2014

Personality, Executive Functions, and BehavioralDisinhibition in AdolescenceJoanna M. VandeverUniversity of Colorado Boulder, [email protected]

Follow this and additional works at: https://scholar.colorado.edu/psyc_gradetds

Part of the Biological Psychology Commons, and the Genetics Commons

This Dissertation is brought to you for free and open access by Psychology and Neuroscience at CU Scholar. It has been accepted for inclusion inPsychology and Neuroscience Graduate Theses & Dissertations by an authorized administrator of CU Scholar. For more information, please [email protected].

Recommended CitationVandever, Joanna M., "Personality, Executive Functions, and Behavioral Disinhibition in Adolescence" (2014). Psychology andNeuroscience Graduate Theses & Dissertations. 69.https://scholar.colorado.edu/psyc_gradetds/69

https://scholar.colorado.edu?utm_source=scholar.colorado.edu%2Fpsyc_gradetds%2F69&utm_medium=PDF&utm_campaign=PDFCoverPages

https://scholar.colorado.edu/psyc_gradetds?utm_source=scholar.colorado.edu%2Fpsyc_gradetds%2F69&utm_medium=PDF&utm_campaign=PDFCoverPages


https://scholar.colorado.edu/psyc?utm_source=scholar.colorado.edu%2Fpsyc_gradetds%2F69&utm_medium=PDF&utm_campaign=PDFCoverPages


http://network.bepress.com/hgg/discipline/405?utm_source=scholar.colorado.edu%2Fpsyc_gradetds%2F69&utm_medium=PDF&utm_campaign=PDFCoverPages

http://network.bepress.com/hgg/discipline/29?utm_source=scholar.colorado.edu%2Fpsyc_gradetds%2F69&utm_medium=PDF&utm_campaign=PDFCoverPages

https://scholar.colorado.edu/psyc_gradetds/69?utm_source=scholar.colorado.edu%2Fpsyc_gradetds%2F69&utm_medium=PDF&utm_campaign=PDFCoverPages

mailto:[email protected]

Personality, Executive Functions, and Behavioral Disinhibition in Adolescence

by

Joanna M. Vandever

B.S., Black Hills State University, 2008

A thesis submitted to the

Faculty of the Graduate School of the

University of Colorado in partial fulfillment

of the requirement for the degree of

Doctor of Philosophy

Department of Psychology and Neuroscience

2014

This thesis entitled:


written by Joanna M. Vandever

has been approved for the Department of Psychology and Neuroscience

Michael C. Stallings

Naomi P. Friedman

John K. Hewitt

Matthew B. McQueen

Yuko Munakata

Soo Hyun Rhee

Date 04/11/14

The final copy of this thesis has been examined by the signatories, and we

Find that both the content and the form meet acceptable presentation standards

Of scholarly work in the above mentioned discipline.

IRB protocol # ___0600.01, 0109.47, 0109.48, 0324.97, 10-0325_____________

iii

Vandever, Joanna M. (Ph.D., Psychology)


Thesis directed by Associate Professor Michael C. Stallings

Prior studies suggest there are common genetic vulnerabilities underlying antisocial

behavior and substance use disorders, which are often comorbid. It has been proposed that

cognitive and personality factors related to behavioral disinhibition may explain some of the

association between these behaviors. This dissertation uses adolescent twins from the Colorado

Center for Antisocial Drug Dependence (CADD) to investigate (a) whether behavioral

disinhibition and factors common and specific to executive functions share genetic influences,

and (b) how genetic relations change with specific stages of substance use. Then, a subset of

items from the Tridimensional Personality Questionnaire (TPQ) is examined for its usefulness in

predicting antisocial behavior and substance use problems.

In the first two studies, latent constructs reflected variance shared among either executive

function tasks or behavioral disinhibition measures. A set of updating tasks and a set of shifting

tasks were each represented by latent factors. All three types of tasks (updating, shifting, and

inhibiting) loaded on a third executive function factor. The behavioral disinhibition factor

included conduct disorder, substance use or dependence vulnerability, and the TPQ novelty

seeking dimension. The first study showed that genetic influences on the common executive

function factor were more highly correlated with genetic influences on behavioral disinhibition

when substance use, rather than dependence vulnerability, was included in the model. Results

iv

from the second study indicated a higher proportion of shared genetic influences between the

common executive function factor and age-of-onset than between executive functioning and later

problem-use stages. The final study identified a set of TPQ items that reflected disinhibitory

personality. Although the new measure predicted antisocial behavior and substance use

disorders, it did not show significant improvement over the original novelty seeking dimension

commonly used in studies of behavioral disinhibition. Implications for these findings are

discussed.

Dedication

To my parents Chuck and Nancy Vandever

For their endless love and encouragement

I especially want to thank my mother for the myriad ways

in which she actively supported me throughout my education,

and for instilling me with a passion for learning.

vi

Acknowledgements

I cannot express how grateful I am to my advisor Michael Stallings. He has taught me

much about research in behavioral genetics, professional development, and the importance of a

balanced life. I wish to thank Robin Corley, Susan Young, and Naomi Friedman for their

assistance with my research. I am also grateful for the guidance and support of my fellow

graduate students—Melissa Munn-Chernoff, Raven Astrom, Josh Bricker, Rohan Palmer, and

Brooke Huibregtse. I want to give special thanks to Hannah Snyder for her friendship. Through

ups and downs she has lent a kind ear and has proven to be the perfect hiking companion. Finally

I would like to thank my fiancé John Crabtree for being there for me during the writing process

and for being my best cheerleader.

This work would not have been possible without funding from the National Institutes of

Health. The longitudinal sample and data were maintained by a grant from the National Institute

of Child Health and Human Development (HD010333). Data collection was supported by grants

from the National Institute of Mental Health (MH63207) and the National Institute of Drug

Abuse (DA011015). I am also grateful for the support from the National Institute of Child Health

and Human Development, which included an institutional training grant awarded to the Institute

for Behavioral Genetics (T32 HD007289) and a Research Project Grant awarded to Rand Donald

Conger (R01HD064687-04).

vii

Contents

Chapter

1 Introduction

1.1 Genetic influences common to substance use disorders and antisocial

behavior 2

1.2 Genetic effects on personality contribute to antisocial behaviors and

substance use disorders 3

1.3 Executive functions and behavioral disinhibition 4

1.4 Summary 5

2 Behavioral Disinhibition and Executive Functions: Genetic Correlations are Stronger for

Substance Use than Dependence Vulnerability

2.1 Introduction 7

2.2 Methods 8

2.2.1 Participants 8

2.2.2 Behavioral disinhibition measures 10

2.2.2.1 Conduct disorder 10

2.2.2.2 Substance measures 12

2.2.2.3 Novelty seeking 16

2.2.2.4 Data transformation 19

2.2.3 Executive function tasks 22

2.2.3.1 General procedure 22

2.2.3.2 Inhibiting tasks 23

2.2.3.2.1 Antisaccade 23

2.2.3.2.2 Stop signal 23

2.2.3.2.3 Stroop 23

viii

2.2.3.3 Updating tasks 24

2.2.3.3.1 Keep track 24

2.2.3.3.2 Letter memory 24

2.2.3.3.3 Spatial 2-back 25

2.2.3.4 Shifting tasks 25

2.2.3.4.1 Number-letter 25

2.2.3.4.2 Color-shape 26

2.2.3.4.3 Category switch 26


2.2.4 The twin design 31

2.2.5 Statistical analyses 33


2.2.5.2 Modeling 34

2.3 Results 38

2.3.1 Preliminary analyses 38

2.3.1.1 Executive functioning 38

2.3.1.2 Behavioral disinhibition 39

2.3.2 Substance use vs. dependence vulnerability in behavioral

disinhibition 44

2.3.3 Behavioral disinhibition and the common executive function

factor 49

2.3.4 Behavioral disinhibition and the updating- and shifting-specific

factors 51

2.4 Discussion 51

ix

3 The Role of Executive Functioning in the Progression from Substance Use to

Dependence

3.1 Introduction 56

3.2 Methods 57


3.2.2 Measures 57

3.2.2.1 Age-of-onset 57

3.2.2.2 Problem use 61

3.2.2.3 Dependence 61

3.2.2.4 Executive function tasks 62

3.2.3 Statistical analyses 62


3.2.3.2 Modeling 64

3.3 Results 67

3.3.1 Substance stages 67

3.3.2 Substance stages and common executive functioning 72

3.4 Discussion 81

4 Using Items from the Tridimensional Personality Questionnaire to Assess Behavioral

Disinhibition

4.1 Introduction 84

4.2 General Methods 87

4.2.1 Samples 87

4.2.1.1 Community twin sample 87

4.2.1.2 Selected family sample 88

x


4.2.3 Measures 89

4.2.3.1 Personality assessment 89

4.2.3.2 Behavioral disinhibition measures 90

4.3 Study 1: Multivariate twin analysis of the Tridimensional Personality

Questionnaire 90

4.3.1 Methods 90

4.3.1.1 Tridimensional Personality Questionnaire 90


4.3.1.3 Modeling 92

4.3.2 Results 93

4.4 Study 2: Exploratory factor analyses of disinhibitory personality 97

4.4.1 Methods 97

4.4.1.1 Disinhibitory personality 97

4.4.1.2 Exploratory factor analyses 98

4.4.1.2.1 Data preparation 99

4.4.1.2.2 EFA specification 100

4.4.2 Results 102

4.5 Study 3: Disinhibitory personality in a second community sample 105

4.5.1 Confirmatory factor analyses 105

4.5.2 Disinhibitory personality, novelty seeking and behavioral

disinhibition 106

4.5.2.1 Methods 106

4.5.2.2 Results 107

xi

4.6 Study 4: Disinhibitory personality in a case-control sample selected for

antisocial substance dependence 110

4.7 Discussion 111

5 Summary and Conclusions

5.1 Introduction 114

5.2 Summary of Results 114

5.2.1 Chapter 2: Behavioral Disinhibition and Executive Functions: Genetic

Correlations are Stronger for Substance Use than Dependence

Vulnerability 114

5.2.2 Chapter 3: The Role of Executive Functioning in the Progression from

Substances Use to Substance Dependence 116

5.2.3 Chapter 4: Using Items from the Tridimensional Personality Questionnaire

to Assess Behavioral Disinhibition 117

5.3 Conclusions 118

References 120

xii

List of Tables

Table

2.1 Ethnicity (N = 773) 9

2.2 Participants with available data 10

2.3 Conduct disorder symptoms in males and females 12

2.4 Dependence symptoms by substance type 14

2.5 Number of substances used repeatedly 15

2.6 Number of participants using repeatedly by substance type 16

2.7 Novelty seeking item endorsement by subscale 18

2.8 Sex differences and age correlations for behavioral disinhibition measures 20

2.9 Sex differences and age correlations for executive function tasks 27

2.10 Descriptive information for executive function tasks 39

2.11 Twin correlations and univariate results for behavioral disinhibition measures 40

2.12 Phenotypic correlations among behavioral disinhibition measures 41

2.13 Cross-twin cross-trait correlations for behavioral disinhibition measures 42

2.14 Model comparison for behavioral disinhibition 43

2.15 Standardized path coefficients for behavioral disinhibition independent pathway

models 45

2.16 Model fitting results for behavioral disinhibition with executive functioning 50

2.17 Genetic correlations for behavioral disinhibition and executive functioning 50

3.1 Age at which repeated users first tried a substance 59

3.2 Age-of-onset by reporting age 60

3.3 Age-of-onset for males and females 60

3.4 Problem use for male and female users 61

xiii

3.5 Dependence for male and female users 62

3.6 Number of complete twin pairs for problem use and dependence stages 64

3.7 Twin concordance rates for substance stages 68

3.8 Polychoric twin correlations and univariate results for substance stages 69

3.9 Polychoric correlations among substance stages 70

3.10 Cross-trait cross-twin correlations for substance stages 70

3.11 Bivariate results for substance stages 71

3.12 Model fitting results for common executive functioning and substance stages 73

3.13 Genetic correlations between multi-substance stages and common executive

functioning 79

3.14 Genetic correlations between common executive functioning and substance-specific

stages 80

4.1 Sample information by study 89

4.2 TPQ dimensions and subscales 89

4.3 Reliability coefficients for TPQ dimensions 91

4.4 Descriptive information for TPQ dimensions 91

4.5 Sex difference and age correlations for TPQ dimensions 92

4.6 Twin correlations and univariate results for TPQ dimensions 94

4.7 Phenotypic correlations for TPQ dimensions 95

4.8 Fit indices for independent pathway models 96

4.9 Hypothesized subscales for disinhibitory personality 98

4.10 Eigenvalues for the sample correlation matrix 101

4.11 Model fit for one, four, and eight factors 103

4.12 Factor loadings for the eight-factor solution 104

xiv

4.13 Factor intercorrelations from the eight-factor solutions 105

4.14 Correlation estimates from the seven-factor confirmatory factor analysis 106

4.15 Twin correlations and univariate results for novelty seeking and disinhibitory

personality 108

4.16 Correlations among personality dimensions and behavioral disinhibition measures 108

4.17 Results for behavioral disinhibition measures regressed on personality dimensions 110

xv

List of Figures

Figure

2.1 Distributions of substance use and dependence vulnerability before and after log

transformation 21

2.2 Distributions of conduct disorder and novelty seeking before and after log

transformation 22

2.3 Distributions of inhibition tasks before and after log transformation 28

2.4 Distributions of updating tasks before and after log transformation 29

2.5 Distributions of shifting tasks before and after log transformation 30

2.6 Univariate twin model 32

2.7 Independent pathway model 36

2.8 Common pathway model 37

2.9 Full behavioral disinhibition and executive function model with substance use 47

2.10 Full behavioral disinhibition and executive function model with dependence

vulnerability 48

3.1 Trivariate Cholesky model 65

3.2 Example model with standardized path coefficients for executive functions 66

3.3 Trivariate model for multi-substance use 72

3.4 Standardized path coefficients for multi-substance stages with Common EF 74

3.5 Standardized path coefficients for alcohol stages with Common EF 75

3.6 Standardized path coefficients for tobacco stages with Common EF 76

3.7 Standardized path coefficients for cannabis age-of-onset and Common EF 77

4.1 Standardized path coefficients for the multivariate TPQ model 97

4.2 Scree plot for EFA 102

1

CHAPTER 1

Introduction

Antisocial behavior and substance use disorders are often comorbid in adults (Kessler,

Chiu, Demler, & Walters, 2005) and adolescents (Armstrong & Costello, 2002; Bukstein, Brent,

& Kaminer, 1989; Disney, Elkins, McGue, & Iacono, 1999). Furthermore, evidence suggests a

common underlying liability to substance use disorders, antisocial behavior, and other behaviors

such as risky sex. This liability is often referred to as behavioral disinhibition. Behavioral

disinhibition has been described as a lack of control of response tendencies, such that immediate

rewards are obtained at the expense of long-term gains (Gorenstein & Newman, 1980).

Therefore, behavioral disinhibition likely includes personality traits and executive functions that

are part of a bottom up mechanism of increased reward seeking, and a top down mechanism

related to a lack of control (Iacono et al., 2008). While several studies have examined genetic

and environmental influences on behavioral disinhibition, few have explored influences in

common with the cognitive and personality traits thought to reflect behavioral disinhibition.

The purpose of this dissertation is to better understand the nature of behavioral

disinhibition. Twins reared together were used to examine genetic and environmental influences

on individual differences in behavioral disinhibition, as well as related personality traits and

executive functions. In addition, genetic and environmental influences on the dimensions of the

Tridimensional Personality Questionnaire (TPQ) were explored, followed by the development of

a new personality dimension characteristic of behavioral disinhibition. A better understanding of

the genetic link between disinhibited behavior, personality, and cognition can advise research on

the biological underpinnings of risky behaviors.

2

This chapter provides an overview of the quantitative-genetic literature on antisocial

behavior and substance use disorders. This is followed by an examination of genetic influences

on personality and executive functions that contribute to antisocial behavior and substance use

disorders. Chapter 2 examines genetic correlations between behavioral disinhibition and a

common executive functioning factor composed of inhibiting, updating and shifting tasks.

Genetic correlations with updating- and shifting-specific factors are also explored. In addition,

differences in genetic correlations were examined when dependence vulnerability versus

substance use was included in the behavioral disinhibition construct. Chapter 3 follows up on the

findings from Chapter 2 by focusing on the genetic covariance between executive functioning

and substance use behavior. It explores whether executive functioning is differentially related to

stages along the substance use trajectory. In Chapter 4 a multivariate analysis is used to

determine if, in addition to specific influences, there are genetic and environmental influences

common to the four dimensions of the TPQ (harm avoidance, novelty seeking, reward

dependence, and persistence). Then items from the TPQ are used to create a personality

dimension more reflective of behavioral disinhibition than the novelty seeking dimension, which

has traditionally been used in studies of behavioral disinhibition. Lastly, Chapter 5 summarizes

the results from all four studies and suggests how they can inform future research.

1.1 Genetic influences common to substance use disorders and antisocial behavior

Evidence suggests there are common genetic vulnerabilities underlying substance use

disorders and antisocial behavior. In a study of parent-offspring similarity, a factor characterized

by antisocial personality disorder and substance use disorders was transmitted in families

(Kendler, Davis, & Kessler, 1997). In a sample selected for antisocial drug dependence,

transmittable family factors for antisocial personality symptom-counts and alcohol problems

3

were highly correlated (Stallings et al., 1997). A study using the same sample also found that

relatives of probands were more likely to have conduct disorder, antisocial personality disorder

and substance abuse than relatives of controls (Miles et al., 1998). These studies were consistent

with common genetic influences for antisocial behavior and substance use disorders, but the

possibility that the transmission was due to environmental factors could not be ruled out.

In a genetically informative sample of adult twins reared apart, common genetic factors

were shown to influence substance use problems and antisocial behavior (Grove et al., 1990).

Similar results have been found in samples of adult twins reared together (Kendler, Prescott,

Myers, & Neale, 2003; Pickens, Svikis, McGue, & LaBuda, 1995; Slutske et al., 1998). Research

using our adolescent community twin sample has shown that the covariance between conduct

disorder and a non-specific measure of dependence vulnerability can be explained by common

genetic influences (35%), shared environmental influences (46%), and non-shared environmental

influences (19%; Button et al., 2006). A follow-up study (Button et al., 2007) indicated that the

genetic contribution to the comorbidity between alcohol dependence and illicit drug dependence

was partially explained by the genetic influences they shared with conduct disorder. In a family

study of adolescent twins and their parents, a highly heritable latent liability accounted for most

of the familial resemblance in antisocial behavior and substance dependence (Hicks, Krueger,

Iacono, McGue, & Patrick, 2004). Overall, the literature suggests common genetic factors

contribute to the comorbidity between antisocial behavior and substance use problems in adults

and adolescents.

1.2 Genetic effects on personality contribute to antisocial behaviors and substance use

disorders

Personality dimensions related to sensation seeking, impulsivity, behavioral under-

control, and social non-compliance have been shown to predict substance problems in adults

4

(Jang, Vernon, & Livesley, 2000) and adolescents (Chassin, Flora, & King, 2004; Elkins, McGue,

Malone, & Iacono, 2004; Grekin, Sher, & Woods, 2006; Krueger, 1999; Sher & Trull, 1994).

Measures of disinhibited personality traits have also been included in studies on the genetic

covariance between substance use disorders and antisocial behavior. Slutske and colleagues

(2002) found that behavioral under-control (from the TPQ and Eysenck Personality

Questionnaire) accounted for the majority of common genetic risk for alcohol dependence and

conduct disorder. Genetic influences on the novelty seeking dimension of the TPQ accounted for

some of the variance shared among two antisocial disorders (oppositional defiant disorder and

conduct disorder) and attention deficit hyperactivity disorder (Hink et al., 2013). Finally, two

studies reported heritable behavioral disinhibition factors which included novelty seeking

(Young et al., 2000) and the constraint dimension of the Multidimensional Personality

Questionnaire (Krueger et al., 2002). Interestingly, genetic influences on constraint (or lack

thereof) were shown to contribute less to the variation in substance dependence symptom count

with age (Vrieze, Hicks, Iacono, & McGue, 2012). This finding raises the question of whether

genetic effects on disinhibited personality have less influence on the covariation between

antisocial and substance measures with age.

1.3 Executive functions and behavioral disinhibition

Cognitive under-control has been put forth as a component of behavioral disinhibition

that increases liability for risky behaviors. Many studies have examined substance dependence in

relation to cognitive tasks. Reviews of these studies have concluded that addicts often exhibit

deficits in executive functions and that this is true for different types of substances (Hester,

Lubman, & Yücel, 2010; Loeber et al., 2012; Montgomery, Fisk, Murphy, Ryland, & Hilton,

2012; Murphy et al., 2012). In most cases it was unclear whether these deficits occurred after

5

prolonged use or were present in individuals prior to their exposure to substances. Many studies

have also examined the role of executive functions in conduct disorder and attention deficit

hyperactivity disorder (e.g. Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005).

There are relatively few biometrical studies of executive functions as most twin analyses

of cognition have focused on IQ. However a highly heritable common factor was shown to

account for the covariance among three executive functions in adolescent twins (Friedman et al.,

2008). Genetic influences specific to two executive functions, updating and shifting, indicated

that these executive functions were also separable to some extent. Furthermore the executive

function factors were shown to reflect variation independent of IQ and perceptual speed. One

study examined the genetic relations between behavioral disinhibition and inhibition in

adolescents (Young et al., 2009). Inhibition is a commonly studied executive function that

represents the intentional control of pre-potent responses. In this study it was modeled as a latent

factor, which consisted of three laboratory inhibition tasks. Behavioral disinhibition was also a

latent factor representing variance shared among substance use, conduct disorder, attention

deficit hyperactivity disorder and novelty seeking. Findings indicated that genetic influences on

behavioral disinhibition were negatively correlated with inhibition in 12 year-olds (rg = -.60) and

17 year-olds (rg = -.61).

1.4 Summary

Genetic influences contribute to the covariance between antisocial behavior and

substance use disorders. Part of this covariance is accounted for by genetic influences on

personality traits related to novelty seeking, impulsivity, and a lack of control. Antisocial

behavior, substance use/dependence and disinhibited personality have been included in latent

behavioral disinhibition factors, which have also been shown to be heritable. Furthermore, in an

6

adolescent sample genetic influences on behavioral disinhibition were correlated with genetic

effects on inhibition (Young et al., 2009).

This dissertation sought to address four questions. First, are genetic influences on

behavioral disinhibition related to genetic influences on a common executive functioning factor,

updating-specific factor, and shifting-specific factor? If so, do the relationships change if

behavioral disinhibition consists of different substance measures: substance use versus

dependence vulnerability? Third, are genetic influences on the common executive function factor

associated with particular stages of substance use? Finally, are there items in the TPQ that better

predict antisocial behavior and substance disorders than the novelty seeking dimension alone?

By using genetically informative samples we can obtain a better understanding of the complex

etiology of behavioral disinhibition, which can then inform studies on the biological

underpinnings of risky behavior.

.

7

CHAPTER 2

Behavioral Disinhibition and Executive Functions: Genetic Correlations are Stronger for

Substance Use than Dependence Vulnerability

2.1 Introduction

Novelty seeking, impulsivity, a lack of persistence, sensitivity to reward, and insensitivity

to punishment are thought to be elements of behavioral disinhibition that play a role in the

development of antisocial behavior and substance use disorders. Another possible component

includes cognitive under-control or poor executive functioning. Executive functions are

cognitive processes important in controlled, organized, and goal-directed thoughts and behavior.

As the term behavioral disinhibition implies, deficits in inhibition (the ability to stop pre-

potent responses) likely underlie both antisocial behavior and substance use disorders. This was

supported by a twin study in which genetic influences on laboratory measures of inhibition were

negatively correlated with genetic influences on behavioral disinhibition (age 12 r = -.60, age 17

r = -.61; Young et al., 2009). Inhibition, however, may simply be an effect of active maintenance

of abstract information (such as goals) in the prefrontal cortex (PFC; Munakata et al., 2011). In a

factor analysis of three commonly studied executive functions (inhibition, updating, and

shifting), inhibition was entirely subsumed under a highly heritable factor common across all

nine executive function tasks (Friedman et al., 2008). This common executive function factor

(Common EF) may represent active maintenance in the PFC, as all of the executive function

tasks required the ability to ‘hold onto’ the goal and various contexts of the task. The primary

aim of this study was to examine whether genetic influences on Common EF were related to

genetic influences on behavioral disinhibition.

8

In addition to Common EF, the executive function model included updating- and

shifting-specific factors (Friedman et al., 2008). Updating takes place when information in

working memory is replaced with newer, more relevant information. Shifting occurs when an

individual must disengage from one task to take part in another task. The second aim, therefore,

was to explore the genetic and environmental relations between these factors and behavioral

disinhibition.

Lastly, this study examined whether the executive function factors were differentially

correlated with a behavioral disinhibition construct that included substance use (Young, Stallings,

& Corley, 2000; Young et al., 2009) and one that included substance dependence (Krueger et al.,

2002). Behavioral disinhibition may be tapping different liabilities depending on whether

substance use or dependence is included, and these liabilities may relate differently to executive

functioning.

2.2 Methods

2.2.1 Participants

Participants were part of the Colorado Longitudinal Twin Study (LTS; Rhea, Gross,

Haberstick, & Corley, 2006). Same-sex twins were identified through birth records and included

in the study if they were born between 1984 and 1990, had a normal gestational period and birth

weight, and lived within a three-hour drive from the Institute for Behavioral Genetics (Rhea et al.,

2006). The current study included 773 adolescents: 205 monozygotic (MZ) twin pairs (110

female, 95 male) and 178 dizygotic (DZ) twin pairs (90 female, 88 male). Seven singletons

(female: 1 MZ, 3 DZ; male: 2 MZ, 1 DZ) were included in descriptive analyses but did not

contribute to the genetic analyses. Twins were mostly White (81.1%, see Table 2.1), which is

9

consistent with demographics of the state of Colorado. Hispanics represented the second most

common ethnicity (10.0%).

Table 2.1

Ethnicity (N = 773)

n %

White 627 81.1

Hispanic 78 10.0

American Indian/Alaska Native 10 1.3

Native Hawaiian/Pacific Islander 2 0.3

More than One Race 46 6.0

Unknown 10 1.3

Beginning at age 12, LTS twins were included in the Colorado Center for Antisocial

Drug Dependence (CADD; PI: John K. Hewitt) funded by the National Institute on Drug Abuse

(DA011015). At-home assessments included self-report questionnaires, a clinical interview, and

zygosity judgment. Additionally, twins came into the laboratory to complete a battery of nine

executive function tasks. Participants who completed the executive function tasks and a second

assessment (the questionnaire or interview) between 16 and 18 years of age were included in the

current study (N = 773). Table 2.2 shows the number of participants with data for each measure.

Eighty-four percent had usable data for all fourteen measures. The average age at assessment

was 17 for the interview/questionnaire (M = 17.19, SD = 0.52) and executive function tasks (M =

17.28, SD = 0.48). All participants gave informed consent (if 18 years) or assent (if 17 years or

younger) prior to participation. Parents also provided informed consent for participants under age

18. The Institutional Review Board of the University of Colorado approved the study.

10

Table 2.2

Participants with available data

n

Personality (TPQ) 772

Substance use (CIDI–SAM) 768

Conduct disorder

DISC 747

DIS 24

Executive functions

Antisaccade 754

Stop Signal 716

Stroop 734

Keep Track 749

Letter Memory 760

Spatial2back 752

Number-Letter 752

Color-Shape 743

Category Switch 743

Note. TPQ = Tridimensional Personality Questionnaire; CIDI–SAM = Composite International

Diagnostic Interview–Substance Abuse Module; DISC = Diagnostic Interview Schedule for

Children (DSM–IV); DIS = Diagnostic Interview Schedule (DSM–IV).

Zygosity was initially determined from a nine-item questionnaire of physical

characteristics (Nichols & Bilbro, 1966) in which 85% agreement from at least four raters was

required. Ratings were later confirmed using 11 highly informative short tandem repeat (STR)

genetic polymorphisms. Concordance across all polymorphisms between co-twins indicated MZ

status, while discordant markers for members of a twin pair indicated their DZ origin. Senior

staff resolved any discrepancies between the rater judgment and DNA calls, and resampled and

genotyped the DNA if necessary.

2.2.2 Behavioral disinhibition measures

2.2.2.1 Conduct disorder. Conduct disorder was assessed using the DSM–IV versions of

the Diagnostic Interview Schedule (DIS; Robins et al., 2000) and the Diagnostic Interview

11

Schedule for Children (DISC; Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000). The DIS

was administered to 18-year-olds who no longer lived with their parents (n = 24). All other

participants completed the DISC and two-thirds were given a supplemental conduct disorder

interview developed through the CADD. Fifteen symptoms, reflecting a range of externalizing

behaviors, were scored from interview responses. Six symptoms required a minimum number of

instances of the behavior in question, which the DISC and DIS assessed for the past year. In

order to measure lifetime conduct disorder, items from the supplement were used to assess

whether, depending on the symptom, a participant had ever met the minimum frequency or met

the minimum in any one-year period. For example, a participant would meet criteria for bullying

if he or she had bullied others five or more times in a one-year period.

The most common symptom for participants assessed with the DISC was stealing without

confronting the victim (see Table 2.3). For those assessed with the DIS, lying was most

common. Participants were diagnosed with conduct disorder if they met DSM criteria for 3 of the

15 symptoms at some time in their life. Conduct disorder diagnoses were low for females (3.2%).

The rate for males (10.4%) was consistent with U.S. population estimates (Nock, Kazdin, Hiripi,

& Kessler, 2006). For this study conduct disorder was measured as lifetime symptom count.

Although scores could potentially range from 0 to 15, scores in the twin sample ranged from 0 to

7 symptoms (M = 0.76, SD = 1.18) with 38% percent of females and 50% of males endorsing at

least one symptom. Interview type (DISC vs. DIS) was not a significant predictor of conduct

disorder scores, t(769) = 0.8, p = .937.

12

Table 2.3

Conduct disorder symptoms in males and females

Females (n = 404) Males (n = 367)

n % n %

Bullies, threatens 9 2.2 34 9.3

Initiates fights 4 1.0 18 4.9

Fights or threatens with a weapon 7 1.7 17 4.6

Physically cruel towards people 1 0.2 3 0.8

Physically cruel towards animals 3 0.7 10 2.7

Steals with confrontation 2 0.5 4 1.1

Forces sex 0 0.0 1 0.3

Sets fires 0 0.0 0 0.0

Destroys property 18 4.5 56 15.3

Breaking and entering 4 1.0 21 5.7

Lies 21 5.2 27 7.4

Steals without confrontation 136 33.7 155 42.2

Stays out late 1 0.2 6 1.6

Runs away 13 3.2 6 1.6

Truant 3 0.7 7 1.9

2.2.2.2 Substance measures. The Composite International Diagnostic Interview–

Substance Abuse Module (CIDI–SAM; Robins, Cottler, & Babor, 1993), which employs DSM–

IV criteria, assesses alcohol, tobacco, marijuana, and eight categories of illicit drugs. If

participants were not currently using, past substance use behavior was scored. Participants were

diagnosed with substance dependence if they met DSM criteria for three or more symptoms for a

particular substance. Thirteen percent of participants (14% males, 12% females) met criteria for

lifetime dependence, which is consistent with U.S. population estimates (Compton, Thomas,

Stinson, & Grant, 2007; Hasin, Stinson, Ogburn, & Grant, 2007; Young et al., 2002). As shown

in Table 2.4, DSM–IV symptoms are related to tolerance, withdrawal, increased time spent

obtaining a substance, and continued use of a substance despite interference with important life

activities (American Psychiatric Association, 1994). In the current sample the most common

13

symptoms were tolerance, taking larger amounts or over a longer period than was intended, and a

persistent desire (or unsuccessful efforts) to cut down or control substance use. Among users,

dependence symptoms across substances ranged from 0 to 23 (M = 2.23, SD = 3.88). Nearly one-

half of users (48%) endorsed at least one dependence symptom.

14

Table 2.4

Dependence symptoms by substance type

Tolerance Withdrawal Larger amounts

longer

Unsuccessful

quit attempts

Increased time

obtaining

substance

Important

activities

forgone

Continued use

despite

problems

Alcohol 63 14% 12 3% 93 21% 78 18% 37 8% 11 2% 24 5%

Tobacco 59 13% 57 13% 59 13% 109 25% 67 15% 17 4% 22 5%

Marijuana 35 8% 19 4% 21 5% 12 3% 21 5% 14 3% 53 12%

Other

Drugs

16 4% 12 3% 17 4% 6 1% 17 4% 8 2% 30 7%

Note. Numbers and percentages are based on the number of users (n = 444).

15

Two substance variables were used in the analyses: substance use and dependence

vulnerability. Substance use was measured as the number of substances used repeatedly—as

defined by the CIDI–SAM. The current version of the CIDI–SAM defines repeated use as

smoking at least 20 cigarettes, using alcohol at least once, and using tobacco (pipe, cigar, or

chewing tobacco) or illicit drugs six or more times. As shown in Table 2.5, the majority of

participants reported repeated use for at least one substance (M = 1.08, SD = 1.32), and a

substantial proportion reported poly-substance use, or use of two or more substances. Over one-

half of participants had used alcohol and one in five reported repeated use of tobacco and/or at

least one illicit substance (see Table 2.6).

Table 2.5

Number of substances used repeatedly

n %

0 324 42.2

1 240 31.3

2 95 12.4

3 70 9.1

4 20 2.6

5 10 1.3

6 6 0.8

7 1 0.1

8 1 0.1

9 1 0.1

Note. Percentages are based on the number of participants with substance use data (n = 768).

Repeated use for illicit drugs is defined as using 6 or more times. Use for alcohol is defined as

having ever used. For tobacco, repeated use consists of smoking a pipe or cigar 6 or more times,

chewing tobacco 6 or more times, or smoking at least 20 cigarettes.

16

Table 2.6

Number of participants using repeatedly by substance type

n %

Alcohol 437 56.9

Tobacco 159 20.7

Cannabis 157 20.4

Stimulants 14 1.8

Sedatives 4 0.5

Club Drugs 13 1.7

Cocaine 19 2.5

Opioids 8 1.0

PCP 0 0.0

Hallucinogens 18 2.3

Inhalants 1 0.1

Note. Repeated use for illicit drugs is defined as using 6 or more times. Use for alcohol is defined

as having ever used. For tobacco, repeated use consists of smoking a pipe or cigar 6 or more

times, chewing tobacco 6 or more times, or smoking at least 20 cigarettes.

Dependence vulnerability was calculated by taking the total lifetime dependence

symptom count across substances, divided by the number of substances used repeatedly. For

example, a participant who used two substances repeatedly and had four dependence symptoms

received a score of 2. Participants reporting no substance use were scored 0. Dependence

vulnerability scores ranged from 0 to 5.33 (M = 0.47, SD = 0.96). Dependence vulnerability has

been shown to be highly heritable and successful in discriminating community controls from

cases with substance use disorders (Stallings et al., 2003).

2.2.2.3 Novelty seeking. Participants completed the 18-item novelty seeking dimension

from the short form (Cloninger, Przybeck, & Svrakic, 1991) of the Tridimensional Personality

Questionnaire (TPQ; Cloninger, 1987). The true-false items assess exploratory and impulsive

behavior. Endorsement (response = true) rates were between 20% and 80% for all but the

following two items (see Table 2.7): “I am slower than most people to get excited about new

17

ideas and activities” (17.5%) and “I like to stay at home better than to travel or explore new

places” (13.7%). Internal consistency (Cronbach’s α = .72; female α = .75; male α = 069) fell

within the range reported in psychometric studies (Kuo, Chih, Soong, Yang, & Chen, 2004; Otter,

2003; Sher, Wood, Crews, & Vandiver, 1995). Ten of the items were reverse-scored so higher

scores indicated higher novelty seeking. As is common with self-report questionnaires,

participants sometimes skipped items or circled both true and false. Therefore, scores were

calculated as the mean number of items endorsed instead of the sum of endorsements. Because

the items were coded as 0 and 1, mean novelty seeking scores were effectively the proportion of

items endorsed for each participant. Participants were required to have answered 16 (~90%) of

the items to be included in the analysis (n = 768). Novelty seeking scores ranged from .06 to 1.0

(M = 0.52, SD = 0.19).

18

Table 2.7

Novelty seeking item endorsement by subscale

All Females Males

n % n % n %

Exploratory Excitability

1 - I often try new things just for fun or thrills, even if most people

think it is a waste of time.

616 79.8 309 76.7 307 83.2

2 - When nothing new is happening, I usually start looking for

something that is thrilling or exciting.

531 68.9 257 63.9 274 74.3

3 - I am slower than most people to get excited about new ideas

and activities. (R)

135 17.5 57 14.1

78 21.2

4- I like to stay at home better than to travel or explore new places.

(R)

105 13.7 41 10.2 64 17.4

Impulsivity

5 - I like to think about things for a long time before I make a

decision. (R)

412 53.8 224 56.1 188 51.2

6 - I often follow my instincts, hunches, or intuition without

thinking through all the details.

467 60.5 247 61.3 220 59.6

7 - I usually think about all the facts in detail before I make a

decision. (R)

464 60.5 236 58.9 228 62.3

8 - I nearly always think about all the facts in detail before I make

a decision, even when other people demand a quick decision. (R)

366 47.5 174 43.3 192 52.0

9 - I hate to make decisions based on my first impressions. (R) 495 64.2 262 65.0 233 63.3

Extravagance

10 - I am much more reserved and controlled than most people.

(R)

508 66.3 252 63.2 256 69.8

11 - I am better at saving money than most people. (R) 443 57.5 218 54.4 215 58.3

12 - I often spend money until I run out of cash or get into debt

from using too much credit.

163 21.2 82 20.4 81 22.0

13 - Because I often spend too much money on impulse, it is hard

for me to save money – even for special plans like a holiday.

172 22.4 79 19.7 93 25.3

14 - I enjoy saving money more than spending it on entertainment

or thrills. (R)

311 40.3 160 39.7 151 40.9

Disorderliness

15 - I often do things based on how I feel at the moment without

thinking about how they were done in the past.

408 53.0 204 50.6 204 55.6

16 - I often break rules and regulations when I think I can get away

with it.

209 27.2 91 22.8 118 32.1

17 - I can usually do a good job at stretching the truth to tell a

funnier story or to play a joke on someone.

477 61.9 222 55.1 255 69.3

18 - I have trouble telling a lie, even when it is meant to spare

someone else’s feelings. (R)

328 42.6 182 45.3 146 39.7

Note. Percentages are based on the available n for each item. R = reverse-scored.

19

2.2.2.4 Data transformation. Basic t tests were used to examine sex differences for all

behavioral disinhibition measures (see Table 2.8). The dependence in the data was accounted for

by weighting the scores of complete twin pairs by 0.5 and the scores of singletons by 1.0. On

average males had more conduct disorder symptoms than females, which is consistent with the

literature on adolescent antisocial behavior (Moffitt, 2001). Males also used more substances

repeatedly than females. In a study of adolescents, some of whom overlapped with this sample,

there were few differences between male and female rates of substance use and dependence

(Young et al., 2002). However, substance use was defined as having ever used, which may

explain the discrepancy in findings. Correlations between scores and age at test are also shown in

Table 2.8. If age, or age and sex differences were observed, basic regression (within sex) was

used to correct for age. When only sex differences were observed, scores were regressed on sex.

Conduct disorder scores were corrected within instrument (DIS or DISC) and then combined to

create one variable. Non-normal distributions were log transformed and re-standardized to obtain

z scores with a mean of zero and a variance of one. Figure 2.1 and Figure 2.2 show distributions

of the original and transformed variables.

20

Table 2.8

Sex differences and age correlations for behavioral disinhibition measures

n M SD t-test (p) Age correlation (p)

Conduct disorder–DISC

M 362 0.99 1.39 -5.43a (< .001) .143 (.010)

F 385 0.51 0.86 .036 (.458)

Conduct disorder –DIS

M 5 1.40 1.52 -.809

F 19 1.26 1.41 .241

Substance use

M 364 1.21 1.33 -2.48a (.014) .280 (< .001)

F 404 0.97 1.30 .237 (< .001)

Dependence vulnerability

M 364 0.52 0.98 -1.37 (.170) .244 (< .001)

F 404 0.43 0.95 .224 (< .001)

Novelty seeking

M 367 0.53 0.18 -0.86 (.393) .055 (.297)

F 401 0.51 0.20 .038 (.445)

Note. P values are based on tests of means and correlations where the dependence in the data was

accounted for, not the actual means and correlations shown. There were not enough participants

with DIS conduct disorder scores for meaningful test statistics.

M = males; F = females; DISC = Diagnostic Interview Schedule for Children; DIS = Diagnostic

Interview Schedule; Age = age at test. a Equal variances could not be assumed under Levene’s Test for Equality of Variances; separate

variance t-tests were utilized.

21

Figure 2.1. Distributions of substance use and dependence vulnerability before and after log

transformation

Figure 2.1. Age and sex corrected substance use (a.) and dependence vulnerability (b.).

a.

b.

22

Figure 2.2. Distributions of conduct disorder and novelty seeking before and after log

transformation

Figure 2.2. Age and sex corrected conduct disorder (a.) and novelty seeking (b.).

2.2.3 Executive function tasks

2.2.3.1 General procedure. Participants came into the laboratory and completed nine

tasks in PsyScope 1.2.5 (Cohen, MacWhinney, Flatt, & Provost, 1993). Inhibition, updating, and

shifting were assessed with three tasks each. Stimuli were counterbalanced and randomized, and

the order of stimuli within tasks was the same for all participants. Practice trials were included to

b.

a.

23

ensure participants understood instructions. Reaction times (RT) were measured using a button

box with millisecond accuracy. A headset was attached to the button box to record RTs for

verbal responses. Descriptions of each task are provided below (for more details, see Friedman et

al., 2008).

2.2.3.2 Inhibiting tasks.

2.2.3.2.1 Antisaccade. Participants were required to override their natural tendency to

look at a black square and instead direct their attention to a target stimulus (arrow) and report the

direction of the arrow (up, left, or right) (adapted from Roberts, Hager, & Heron, 1994). After

175 ms the target was masked with gray cross-hatching until the participant responded. There

were 22 practice trials and 90 target trials. Scores were measured as the proportion of correct

responses with higher scores indicating higher inhibition.

2.2.3.2.2 Stop signal. Participants categorized words as either animals or non-animals as

quickly as possible (Logan, 1994). During trials with an auditory signal, participants were

instructed to withhold their response. The first 48 trials were used to build up a pre-potent

categorization response and calculate each participant’s average RT. In the remaining four

blocks (96 trials each) a signal (tone approximately 100 ms in duration) was emitted on 25% of

trials. For each participant the signal occurred equally often at three time points: 225 ms before

his or her average RT (long stop-signal delay), 50 ms before his or her average RT (medium

stop-signal delay), or 50 ms after the onset of the trial (short stop-signal delay). The dependent

measure was the estimated time at which the stopping process finished (averaged across trials),

or the stop-signal RT. Slower RTs indicate lower executive functioning.

2.2.3.2.3 Stroop. Participants named the color of the stimulus (string of asterisks, color

word, or neutral word) as quickly as possible (Stroop, 1935). Asterisks were in six different

24

colors (red, green, blue, orange, yellow, or purple) and were of variable length (corresponding to

the lengths of the color words). Color words were sometimes in a different color (e.g. GREEN

presented in blue). After 18 practice trials, there were 60 trials each of asterisks, color words, and

neutral words. Neutral word trials were not used in the present analysis. The dependent measure

was the reaction time difference between trials with asterisks and trials where the word and color

were incongruent. This provides an index of inhibition, as participants are required to inhibit

their natural tendency to read the word and instead report the color of the word. The RTs for

asterisk trials were subtracted to adjust for individual differences in reaction time.

2.2.3.3 Updating tasks.

2.2.3.3.1 Keep track. During each trial of the keep track task (adapted from Yntema,

1963) participants were presented with 15 words, one at time, while a list of categories remained

at the bottom of the screen. At the end of each trial, participants were asked to report the last

word that was presented for each of the categories. Participants were required to recall words for

two, three, or four categories, thus creating three levels of difficulty. Before the task, participants

were shown the words belonging to each of six categories (animals, colors, countries, distances,

metals, and relatives). After three practice trials, participants completed four trials of each

difficulty level (12 trials for a total of 36 words). Scores were measured as the proportion of

correctly recalled words, with higher scores indicating higher executive functioning.

2.2.3.3.2 Letter memory. In each trial of the letter memory task (adapted from Morris &

Jones, 1990) five, seven, or nine letters were presented one at a time and participants were

instructed to say the last three letters out loud. Thus, before saying the new list of three letters

participants were required to mentally drop the fourth letter back and add the most recent letter.

Participants were not required to say the letters in order, just recall the last three letters. There

25

were three practice trials and 12 trials (four of each length). Due to a programing error only the

first 10 trials were scored. The proportion of all letters correctly recalled was the dependent

variable.

2.2.3.3.3 Spatial 2-back. Ten squares were scattered across the screen (Friedman et al.,

2008). One at a time boxes appeared to flash (24 flashes per block). Participants reported if the

square that flashed was the same square that had flashed two trials earlier. Four blocks followed

a practice block. The proportion of correct responses (yes or no) was the dependent measure.

2.2.3.4 Shifting tasks.

For all three shifting tasks a cue indicating which subtask to perform was presented prior

to the stimulus onset. The dependent measure for all switching tasks was the switch cost, or

difference between the average RT for trials that required a switch and the average RT for no-

switch trials. Smaller RT differences (switch costs) were indicative of higher executive

functioning. The RTs immediately following trials with errors were excluded. Each task had four

blocks consisting of 24 switch and 24 no-switch trials. In addition there were two practice blocks

and six warm-up trials at the beginning of each actual block. The cue was presented 150 ms

(block 1 and 3) or 1500 ms (block 2 and 4) prior to the stimulus onset. Only the first and third

blocks were scored.

2.2.3.4.1 Number-letter. The cue was the appearance of a box either above or below a

line dividing the computer screen in half. A number-letter pair then appeared in the box. When

the box was above the line, participants were required to specify whether the number was odd or

even. When the box was below the line, participants specified whether the letter was a consonant

or a vowel (adapted from Rogers & Monsell, 1995).

26

2.2.3.4.2 Color-shape. In each trial a colored rectangle with either a circle or a triangle

inside was presented along with a cue (letter C or S above the rectangle; Miyake, Emerson,

Padilla, & Ahn, 2004). Participants indicated whether the shape in the rectangle was a circle or a

triangle when the cue was S and whether the color of the rectangle was green or red when the

cue was C.

2.2.3.4.3 Category switch. When the symbol above the word on the screen was a heart,

participants specified whether the word could be described as living or nonliving (adapted from

Mayr & Kliegl, 2000). When the symbol was an arrow cross, participants specified whether the

word represented something that is smaller or larger than a soccer ball. There were 16 words:

table, bicycle, coat, cloud, pebble, knob, marble, snowflake, shark, lion, oak, alligator,

mushroom, sparrow, goldfish, and lizard.

2.2.3.5 Data transformation. To improve normality of executive function task data, all

accuracy data was arcsine transformed and observations three or more standard deviations from

the mean were replaced with the value at three standard deviations. Reaction time errors and all

RTs below 200 ms were eliminated. Then a within-subject trimming procedure (Wilcox &

Keselman, 2003) was applied and observations that were 3.32 times above or below the median

value were excluded. Finally, RT measures were reversed so higher scores reflected better

performance. Like the behavioral disinhibition measures, executive function scores were

corrected for sex differences and age at test using basic regression. On average, females scored

significantly higher than males on the stop signal, stroop, number-letter and color-shape tasks

(see Table 2.9). Males scored higher on the antisaccade and keep track tasks. Age correlations

varied among tasks and between males and females. Non-normal distributions were log-

transformed and re-standardized. Figures 2.3 through 2.5 show distributions of the original and

27

log-transformed variables for inhibition, updating, and shifting tasks, respectively.

Table 2.9

Sex differences and age correlations for executive function tasks

n M SD t test (p) Age correlation (p)

Antisaccade

M 357 1.09 0.18 -5.89 (< .001) -.037 (.001)

F 397 0.99 0.20 -.002 (.479)

Stop signala

M 331 286 64.67 -2.60b (.010) .118 (.000)

F 385 278 59.87 .028 (.401)

Stroopa

M 339 223 94.01 -3.52b (< .001) .021 (.021)

F 395 206 85.10 -.026 (.905)

Keep track

M 349 0.94 0.19 -2.17b (.030) -.123 (.008)

F 400 0.93 0.17 -.030 (.621)

Letter memory

M 359 1.07 0.24 -0.07 (.947) -.023 (.001)

F 401 1.10 0.25 -.039 (.880)

Spatial 2-back

M 354 1.16 0.18 -0.68b (.495) -.100 (.052)

F 398 1.18 0.17 .068 (.029)

Number-lettera

M 353 344 196.66 -2.57b (.010) -.052 (.603)

F 399 320 169.02 .033 (.254)

Color-shapea

M 346 350 198.50 -3.04b (.002) -.054 (.808)

F 397 316 181.35 .029 (.383)

Category switcha

M 345 317 180.84 1.18 (.237) -.031 (.302)

F 398 351 181.51 .035 (.310)

Note. P values are based on tests of means and correlations where the dependence in the data was

accounted for, not the actual means shown.

M = males; F = females. aLower means indicate better performance. bEqual variances could not be assumed under Levene’s Test for Equality of Variances; separate

variance t-tests were utilized.

28

Figure 2.3. Distributions of inhibition tasks before and after log transformation

Figure 2.3. Age- and sex-corrected antisaccade (a.), stop signal (b.) and stroop (c.). Because stop

signal and stroop were measured using RTs, they were reverse scored so higher scores indicated

better executive functioning.

a.

b.

c.

29

Figure 2.4. Distributions of updating tasks before and after log transformation

Figure 2.4. Age- and sex-corrected keep track (a.), letter-memory (b.) and spatial 2-back (c.).

a.

b.

c.

30

Figure 2.5. Distributions of shifting tasks before and after log transformation

Figure 2.5. Age- and sex-corrected number-letter (a.), color-shape (b.) and category switch (c.).

Because all tasks were measured using RTs, they were reverse-scored so higher scores indicated

better executive functioning.

a.

b.

c.

31

2.2.4 The twin design

Genetic analyses were conducted using the classic twin design, which uses structural

equation modeling to compare the trait resemblance (or covariance) of MZ and DZ twins reared

together (for a good tutorial on the twin design see Neale & Cardon, 1992). With the classic twin

design the phenotypic variance of a trait can be divided into genetic and environmental variance.

When the MZ trait correlation is greater than the DZ correlation, additive genetic effects are

implicated. For complex traits additive genetic effects (A) include the effects of many genes,

whose alleles act in an additive manner. In the model, the additive genetic correlation is set to 1.0

for MZ twins because they share all of their genes (see Figure 2.6). The genetic correlation for

DZ twins is set to .5 because they share half of the additive effects of their segregating genes on

average. When the DZ correlation is less than half the MZ correlation there is evidence that non-

additive genetic effects are operating in addition to additive genetic influences. Non-additive

genetic effects include interactions between alleles at a given locus. Based on biometrical

principles, the non-additive genetic correlations are set to 1.0 for MZ twins and .25 for DZ twins.

32

Figure 2.6. Univariate twin model

Figure 2.6. A = additive genetic influences; C = shared environmental influences; D = non-

additive genetic influences; E = non-shared environmental influences;

rMZ = monozygotic twin correlation; rDZ = dizygotic twin correlation.

Variance not accounted for by genetics is due to the environment and measurement error.

In the classic twin design the environment is not directly measured. Rather, the influence of the

environment is statistically estimated. When the DZ trait correlation is greater than half the MZ

correlation, shared environmental effects (C) are implicated. By definition, the shared

environment is the environment experienced by both twins that makes them more similar. It can

include influences such as family, nutrition, and peer groups. For the shared environment, twin

correlations are set to 1.0 for both MZ and DZ twins (see Figure 2.6). Importantly, D and C may

33

both influence a trait. However, with just the MZ and DZ covariances only one can be estimated

at a time. For complex traits, it is biologically implausible to have D without A (e.g. DCE;

Eaves, 1988), so the relationship between the MZ and DZ correlation (described above) is used

to decide whether to estimate D or C.

The non-shared environmental variance (E) can be calculated by subtracting the MZ

covariance from the phenotypic variance. In other words, when the MZ correlation is less than

one, non-shared environment is implicated. The non-shared environment includes environments

unique to each twin and measurement error, both of which contribute to the dissimilarity of

twins. Thus, twin correlations for the non-shared environment are set to 0 in the model (see

Figure 2.6).

Multivariate models are an extension of univariate models in which the variance and

covariation among variables are examined. The multivariate twin model is a direct extension of

the basic twin model (see Neale & Cardon, 1992). The extent to which variables share genetic

and environmental effects is estimated, as well as influences that are unique to each variable.

Multivariate models are useful for understanding the complex etiology of comorbid behaviors.

2.2.5 Statistical analyses

2.2.5.1 General procedure. Descriptive statistics were obtained with SPSS version 21

(IBM Corp, 2012). Structural equation modeling using the statistical programs Mx (Neale,

Boker, Xie, & Maes, 2003) and Mplus (Muthén & Muthén, 1998-2012) was used to decompose

the variance of behavioral disinhibition, executive functioning, and their covariance into genetic

and environmental sources. Parameters were calculated using maximum likelihood estimation

(ML). Both Mx and Mplus can use raw data files and accommodate missing data (instead of

requiring covariance matrices with complete data). This allowed for the maximum use of data for

34

each variable. For example, if a twin pair was missing executive functioning data their

information was still used to decompose the variance of behavioral disinhibition into genetic and

environmental sources. The number of complete twin pairs ranged from 334 to 371 for the

executive function tasks and 378 to 381 for the behavioral disinhibition measures. Consistent

with most twin studies MZ pairs and female pairs were over-represented. On average there were

24 more MZ than DZ pairs (SD = 6.61) and 24 more female than male pairs (SD = 9.05).

Each variable was examined for outlying twin pairs using scatterplots for twin 1 and twin

2 scores. No extreme outliers were observed. Twin 1 and twin 2 variances were also examined

(by zygosity and sex) for each variable. When twin 1 variances differ greatly from twin 2

variances there may be a systematic bias in twin-number designation. When such biases occur

twin-number can be a confounding variable. Variances were around 1.0 because all variables

were standardized. Differences between twin 1 and twin 2 variances ranged from 0.01 to 0.26 for

behavioral disinhibition measures (M = 0.11, SD = 0.07) and 0.003 to 0.386 for executive

function tasks (M = 0.190, SD = 0.102). Therefore, bias due to twin number designation was not

of concern.

2.2.5.2 Modeling. An advantage of structural equation modeling is its use of latent

variables, which make it possible to examine the underlying construct of interest. Latent

variables represent only the variance that is shared among variables. For example, the keep track,

letter memory and spatial 2-back tasks are thought to require a common cognitive process

(updating information in working memory). Variances specific to each measure are modeled as

well. The specific variance for an executive function task, for example, may contain

measurement error and cognitive processes unique to the task (e.g. visual or auditory

processing).

35

Miyake et al. (2000) used this latent variable approach in an individual differences study

of executive functioning. Their results suggested that three of the commonly studied executive

functions (inhibition, updating, and shifting) were separate but correlated constructs. My

colleagues followed up on these findings by using genetic analyses to examine why there was

variation common and specific to the three executive functions (Friedman et al., 2008). A highly

heritable common factor (Common EF) explained the covariance among the three types of

executive functions. Genetic influences specific to the updating and shifting factors indicated

that there was also variation common to the tasks over and above Common EF. Interestingly,

variance in the inhibition tasks was entirely accounted for by Common EF. These findings were

represented in a nested factors model, which was used for the executive function component of

this study (see Figure 2.9, p. 47).

The main goal of this study was to use genetic correlations to examine whether the

behavioral disinhibition and the executive function factors (common and specific) shared genetic

influences. Then the difference in the proportion of genetic influences shared with executive

functioning was examined when behavioral disinhibition included dependence vulnerability

versus substance use.

Prior to the above analyses the genetic and environmental structure of behavioral

disinhibition was examined. An independent pathway model was implemented, followed by a

common factor model. In an independent pathway model part of the covariation among variables

is explained by a specified number of genetic and environmental factors (see Figure 2.7). Each

factor has a unique (independent) path to each variable. Therefore, the effects of A (or C, D, or

E) on two variables may differ in magnitude and direction (positive vs. negative). Factors

specific to each variable are also estimated. The common pathway model is a more constrained

36

version of the independent pathway model in which a phenotypic latent factor (e.g. behavioral

disinhibition) explains the covariation among variables (see Figure 2.8). Estimates of genetic

and environmental influences on the latent factor and influences specific to each variable are

obtained.

Figure 2.7. Independent pathway model

Figure 2.7. Only twin 1 is depicted. CD = conduct disorder; SU = substance use;

DV = dependence vulnerability; NS = novelty seeking; A = additive genetic influences;

C = shared environmental influences; E = non-shared environmental influences.

37

Figure 2.8. Common pathway model

Figure 2.8. Only twin 1 is depicted. CD = conduct disorder; SU = substance use;

DV = dependence vulnerability; NS = novelty seeking; A = additive genetic influences;

C = shared environmental influences; E = non-shared environmental influences.

In all models the variance of the genetic and environmental latent variables was set to one

so that the variance components could be obtained by squaring the standardized path

coefficients. Information from twin correlations was used to specify the initial models. In follow-

up models non-significant As and Cs were set to zero and the model fit was calculated to

determine if their exclusion resulted in a significant decrement in fit. To examine the fit of the

models, three goodness-of-fit indices were used: Akaike’s information criteria (AIC; Akaike,

1987), the root-mean-square error of approximation (RMSEA; Steiger & Lind, 1980), and the

38

Tucker-Lewis index (TLI; Tucker & Lewis, 1973). For AIC, larger negative values indicate

better fit. The RMSEA and TLI are good indicators of fit with complex multivariate models

because they take into account the degrees of freedom of the model (Hu & Bentler, 1998). An

RMSEA < .06 and TL1 > .95 indicate an adequate fit of the model. When comparing the relative

fit of nested models, χ2 difference tests were used. P-values less than .05 indicated significantly

worse fit of a reduced/constrained model compared to the original model.

2.3 Results

2.3.1 Preliminary analyses

2.3.1.1 Executive functioning. Because over 75% of participants with executive

functioning data were used in our prior study of the genetics of executive functions (Friedman et

al., 2008), it was important that results for the tasks be similar. Task means and distributions

(prior to sex- and age-correction) were consistent with those reported earlier (see Table 2.10).

Genetic influences accounted for nearly all the variation in the common executive function factor

(96%) and the updating factor (100%; see Figure 2.9, p. 47). Genetic influences explained 71%

of the variance in the shifting factor, with non-shared environmental influences accounting for

the rest. Specific variance components were also consistent with Friedman et al. (2008) and the

standardized individual factor loadings were within at least 0.08.

39

Table 2.10

Descriptive information for executive function tasks

n M SD Min Max Skew Kurtosis

Antisaccadea 754 1.04 0.20 0.50 1.57 -0.10 -0.26

Stop signal 716 282 ms 62 151 489 1.12 1.48

Stroop 734 214 ms 90 0 488 0.59 0.23

Keep tracka 749 0.93 0.18 0.38 1.49 0.28 0.54

Letter memorya 760 1.09 0.25 0.38 1.57 0.29 -0.19

Spatial 2-backa 752 1.17 0.17 0.65 1.57 -0.96 1.67

Number-letter 752 332 ms 183 -14 923 1.06 1.18

Color-shape 743 331 ms 190 -196 916 0.77 0.77

Category switch 743 335 ms 182 -34 899 0.98 0.91

Note. The descriptive information followed trimming procedures (see 2.2.3.5 Data

Transformation) but was prior to age- and sex-correction. aAccuracy scores were arcsine transformed.

2.3.1.2 Behavioral disinhibition. Monozygotic twin correlations were consistently

higher than DZ correlations, indicating genetic influences (see Table 2.11). Dizygotic

correlations greater than half the MZ correlations suggested shared-environmental influences on

conduct disorder, substance use, and dependence vulnerability. There was evidence for non-

additive genetic influences on novelty seeking. Non-additive effects have been reported for many

measures of personality (e.g. Eaves, Heath, Neale, Hewitt, & Martin, 1998; Loehlin, 1992),

including novelty seeking (Heiman, Stallings, Young, & Hewitt, 2004; Keller, Coventry, Heath,

& Martin, 2005). Variance components and the model fit statistics for the best-fitting univariate

models are also shown in Table 2.11. An AE model adequately described conduct disorder,

while the inclusion of shared environmental influences was necessary for the substance

measures. This supports many studies in which the shared environment explained a substantial

portion of the variance in adolescents (Stallings, Gizer, & Young-Wolff, in press). Correlations

suggested a DE model for novelty seeking. Because a DE model is not plausible, an AE model

40

was used with the understanding that variance explained by non-additive genetic influences

would go into the additive variance component.

Table 2.11

Twin correlations and univariate results for behavioral disinhibition measures

Females Males

Variance

Components Model Fit

MZ DZ MZ DZ A C E -2LL AIC RMSEA TLI

CD .60 .31 .58 .32 .59 .41 2083.2 547.2 .026 .99

SU .77 .65 .75 .58 .27 .48 .24 1921.6 393.6 .075 .99

DV .77 .55 .62 .51 .37 .34 .29 1983.4 455.4 .073 .99

NS .34 -.02 .34 .05 .32 .68 2155.9 625.9 .081 .89

Note. CD = conduct disorder; SU = substance use; DV = dependence vulnerability; NS = novelty

seeking; MZ = monozygotic; DZ = dizygotic; A = additive genetic; C = shared environment;

E = non-shared environment; -2LL = -2 log likelihood; AIC = Akaike’s information criteria;

RMSEA = Root mean square error approximation; TLI = Tucker Lewis index.

It is important to note that the DZ twin correlation for novelty seeking was essentially

zero. This suggested that factors other than dominance or additive-additive epistasis influenced

this measure. It is possible that complex epistatic interactions occurred, which this study was

unable to examine due to the nature of the classic twin design. Also, special MZ twin

environments could account for the large difference between the MZ and DZ correlation. Again,

this hypothesis could not be tested with the classic twin design.

All behavioral disinhibition measures were significantly correlated (see Table 2.12)

which supports the implementation of a multivariate model. Novelty seeking had the weakest

relationship with the other measures and, as expected, substance use and dependence

vulnerability were highly correlated (r = .636). Similar correlation patterns were observed for

both males and females. Table 2.13 shows cross-trait cross-twin correlations, which were used to

41

determine if the observed covariation was partly due to genetic factors. Higher MZ than DZ

correlations indicated genetic covariance between conduct disorder and dependence

vulnerability. Correlations among the remaining measures were inconsistent as one twin1/twin2

pairing was higher in MZs while the correlation for the other twin1/twin2 pairing was not.

Table 2.12

Phenotypic correlations among behavioral disinhibition measures

CD SU DV NS

All CD —

SU .484 —

DV .435 .636 —

NS .258 .248 .204 —

By Sex* CD — .508 .493 .331

SU .458 — .652 .250

DV .376 .618 — .173

NS .195 .251 .233 —

Note. All correlations are significant at the .01 level (two-tailed) after accounting for dependence

in the data (weighted .5 for complete pairs, 1.0 for singletons).

CD = conduct disorder; SU = substance use; DV = dependence vulnerability; NS = novelty

seeking.

*Males and females are above and below the diagonal, respectively.

42

Table 2.13

Cross-twin cross-trait correlations for behavioral disinhibition measures

MZ Twins DZ Twins

CD1 SU1 DV1 NS1 CD1 SU1 DV1 NS1

CD2 .587 .432 .400 .053 .316 .337 .243 .075

SU2 .424 .756 .616 .039 .467 .615 .429 .138

DV2 .466 .514 .703 .057 .354 .522 .526 .136

NS2 .241 .215 .272 .345 -.039 .105 -.030 .010

Note. Twin correlations for single traits are on the diagonal in boldface type. Cross-trait cross-

twin correlations are on the off diagonals.

MZ = monozygotic; DZ = dizygotic; CD = conduct disorder; SU = substance use; DV =

dependence vulnerability; NS = novelty seeking.

Model fitting results for behavioral disinhibition are shown in Table 2.14. Because twin

correlations for novelty seeking suggested a DE model, a D factor specific to novelty seeking

was included. It was not possible to estimate D in the univariate model. Here, however, additive

genetic influences (shared with the other measures) were also operating. When the D specific

factor was excluded there was no decrement in fit. Therefore it was eliminated in all subsequent

models. All models fit significantly worse when the C factor on behavioral disinhibition was

dropped, which supported an ACE model. The ACE independent pathway models fit slightly

better then the ACE common pathway models. This may suggest that influences shared among

substance use behavior, antisocial behavior, and related traits may operate differentially instead

of through a common mechanism like behavioral disinhibition. The differences in fit were minor,

however. So for completeness, executive functioning was also modeled with behavioral

disinhibition characterized by a common pathway model.

43

Table 2.14

Model comparison for behavioral disinhibition

Model -2LL df AIC ∆χ2 ∆df ∆p TLI RMSEA

BD–SU IP ACDE* 5921.2 2286 1349.2 .97 .057

ACE 5924.4 2287 1350.4 3.2 1 .076 .97 .059

AE 5942.1 2290 1362.1 17.7 3 < .001 .95 .070

CP ACE 5946.7 2291 1364.7 22.3 4 < .001 .95 .070

BD–DV IP ACDE* 6022.3 2286 1450.3 .96 .058

ACE 6024.4 2287 1450.4 2.4 1 .149 .96 .059

AE 6032.3 2290 1452.3 7.9 3 .047 .96 .060

CP ACE 6041.3 2291 1459.3 16.9 4 < .001 .95 .069

Note. The best-fitting models are indicated in boldface type. The ACE common pathway models were nested under the best-fitting

independent pathway models.

BD–SU = behavioral disinhibition with substance use; BD–DV = behavioral disinhibition with dependence vulnerability; IP =

independent pathway; CP = common pathway; A = additive genetic component; C = shared environmental component; E = non-

shared environmental component; -2LL = -2 log likelihood; AIC = Akaike’s information criteria; ∆χ2 = chi-square difference test;

TLI = Tucker Lewis index; RMSEA = Root mean square error approximation.

*Non-additive (D) specific effect on novelty seeking.

44

2.3.2 Substance use vs. dependence vulnerability in behavioral disinhibition

For the independent pathway portion of the full models (with executive functioning) the

additive genetic factor loaded highest on substance use and dependence vulnerability, followed

by conduct disorder and novelty seeking (see Table 2.15). All loadings were positive suggesting

that genetic influences were not acting on the variables in an opposite matter. The additive

genetic factor loaded higher on conduct disorder when dependence vulnerability was modeled

than when substance use was incorporated. However this difference was questionable as

confidence intervals for these estimates overlapped. The full models incorporating the

independent pathway structure were modeled in the statistical program Mplus. It is important to

note that in this program the residual variance was obtained instead of ACEs specific to each

variable.

45

Table 2.15

Standardized path coefficients for behavioral disinhibition independent pathway models

Model Variable a c e Residual variance

BD–SU CD .28

[.08, .47] .44

[.30, .58] .25

[.06, .44] .67

[.56, .77]

SU .53

[.38, .68] .69

[.58, .79] .14

[.03, .25] .23

[.18, .27

NS .32

[.16, .48] .55

[.21, .90] .59

[.21, .97]

BD–DV CD .40

[.22, .58] .36

[.18, .53]

.18

[-.49, .86] .69

[.45, .93]

DV .55

[.38, .73] .65

[.50, 80]

.02

[-.08, .12] .30

[.24, .35]

NS .39

[.20, .58]

.59

[-1.5, 2.7]

.56

[-2.0, 2.9]

Note. Estimates are from the behavioral disinhibition portion of the full models with executive functioning. Boldface type indicates

significance at the .05 level (two-tailed). Values in brackets are the 95% confidence intervals.

BD–SU = behavioral disinhibition with substance use; BD–DV = behavioral disinhibition with dependence vulnerability;

CD = conduct disorder; SU = substance use; DV = dependence vulnerability; NS = novelty seeking; a = additive genetic; c = shared

environment; e = non-shared environment.

46

Figure 2.9 shows executive functioning and behavioral disinhibition with substance use.

The results for behavioral disinhibition with dependence vulnerability are presented in Figure

2.10. The behavioral disinhibition factor was more heritable when dependence vulnerability (h2 =

.79) was used than when substance use (h2 = .28) was included. An opposite pattern was

observed for the proportion of variance explained by the shared environment. Substance use

loaded higher (.83) on behavioral disinhibition than dependence variability (.64). The loading for

conduct disorder was higher when dependence vulnerability was included in the model (.69) than

when substance use was used (.59), which supported findings from the independent pathway

model.

47

Figure 2.9. Full behavioral disinhibition and executive function model with substance use

Figure 2.9. Numbers on arrows and underneath the lower ACEs are standardized path coefficients. Proportions of variance of the latent

variables are represented as percentages. Double-headed arrows are correlation coefficients. Solid lines indicate p < .05. Dashed

lines indicate non-significance (p > .05).

BD = behavioral disinhibition; CD = conduct disorder; SU = substance use; NS = novelty seeking; Common EF = common executive

function factor; Anti = antisaccade; Stop = stop signal; Keep = keep track; Letter = letter memory; S2ba = spatial-2back;

Num = number-letter; Col = color-shape; Cat = category switch.

BD

SU

NS

CD

A

C

E

28% 59% 13%

.59 .82 .32

.50 .00 .63 .40 .09 .39 .50 .80

Common

EF

Stop

Stroop

Anti

A

C

E

96% 0% 4%

.47 .52 .40

.53 .00 .71 .43 .00 .74 .55 .00 .73

Updating

Specific

Letter

S2ba

Keep

A

C

E

100% 0% 0%

.20 .24 .69 .23 .00 .71 .00 .29 .86

Shifting

Specific

Col

Cat

Num

A

C

E

71% 0% 29%

.51

.01 .29 .69 .24 -.07 .78 .13 .31 .62

.37 .38 .37 .42 .49 .39

.42 .52

.54 .56 .20

-.54

.13

.11

.27

C

A

E

A

E

C

A

E

C

A

E

C

A

E

C

A

E

C

A

E

C

A

E

C

A

E

C

A

E

C

A

E

C

A

E

48

Figure 2.10. Full behavioral disinhibition and executive function model with dependence vulnerability

Figure 2.10. Numbers on arrows and underneath the lower ACEs are standardized path coefficients. Proportions of variance of the

latent variables are represented as percentages. Double-headed arrows are correlation coefficients. Solid lines indicate p < .05. Dashed

lines indicate non-significance (p > .05).

BD = behavioral disinhibition; CD = conduct disorder; DV = dependence vulnerability; NS = novelty seeking; Common EF =

common executive function factor; Anti = antisaccade; Stop = stop signal; Keep = keep track; Letter = letter memory; S2ba = spatial

2back; Num = number-letter; Col = color-shape; Cat = category switch.

BD

DV

C

A

E

NS

A

E

CD

C

A

E

A

E

79% 2%

.69 .64 .33

.35 .00 .64 .34 .44 .53 .48 .81

Common

EF

Stop

C

A

E

Stroop

C

A

E

Anti

C

A

E

A

C

E

96% 0% 4%

.48 .50 .42

.52 .00 .71 .44 .00 .75 .55 .00 .73

Updating

Specific

Letter

C

A

E

S2ba

C

A

E

Keep

C

A

E

A

C

E

100% 0% 0%

.11 .23 .69 .29 .00 .71 .00 .30 .86

Shifting

Specific

Col

C

A

E

Cat

C

A

E

Num

C

A

E

A

C

E

70% 0% 30%

.53

.00 .29 .69 .23 .09 .78 .13 .31 .62

.38 .38 .36 .43 .48 .39

.42 .51

.56 .53 .21

-.23

.03

.09

.08

C

19%

49

2.3.3 Behavioral disinhibition and the common executive function factor

For the full models with behavioral disinhibition and executive functioning, RMSEA

indicated adequate fit, while TLI suggested a somewhat poorer fit (see Table 2.16). When

substance use was included there was a significant genetic correlation between the common

executive function factor and behavioral disinhibition (rg = -.54, see Figure 2.9). Again, the

results for behavioral disinhibition with dependence vulnerability are presented in Figure 2.10.

Although genetic effects accounted for more of the variance in behavioral disinhibition (79%)

the genetic correlation with common executive functioning was smaller (rg = -.23). The

difference between the two correlations was not significant, ∆χ2 = 3.78, ∆df = 1, ∆p = .052. This

difference in genetic correlations was also observed when behavioral disinhibition was structured

as an independent pathway model (see Table 2.17). The negative direction of the correlations

suggested that genetic effects contributing to disinhibited behavior were related to genetic effects

that conferred poor executive functioning. And this was especially the case when the behavioral

disinhibition measure included substance use instead of dependence vulnerability.

50

Table 2.16

Model fitting results for behavioral disinhibition with executive functioning

Model –2LL χ2 df p AIC TLI RMSEA

CP: BD–SU 23639.31 8944 5751 .93 .037

CP: BD–DV 23746.17 8944 5858 .91 .039

IP: BD–SU 705.62 567 < .01 23770 .93 .035

IP: BD–DV 726.62 564* < .01 23887 .92 .038

SU 19610.77 7417 4777 .95 .034

DV 19688.44 7417 4854 .93 .039

Note. CP = common pathway; IP = independent pathway; BD–SU = behavioral disinhibition

with substance use; BD–DV = behavioral disinhibition with dependence vulnerability;

SU = substance use; DV = dependence vulnerability; -2LL = -2 log likelihood; χ2 = chi-square

test of model fit; AIC = Akaike’s information criteria; TLI = Tucker Lewis index;

RMSEA = Root mean square error approximation.

*The residuals for novelty seeking could not be equated so four parameters were estimated (MZ-

NS1, MZ-NS2, DZ-NS1, DZ-NS2) instead of one (NS).

Table 2.17

Genetic correlations for behavioral disinhibition and executive functioning

Common EF Updating Shifting

CP: BD-SU -.54 .13 .11

CP: BD-DV -.23 .03 .09

IP: BD-SU -.23 -.04 .18

IP: BD-DV -.14 .07 .20

SU -.43 .08 .01

DV -.17 -.04 -.11

Note. Boldface type indicates significance at the .05 level.

CP = common pathway model; IP = independent pathway model; BD–SU = behavioral

disinhibition with substance use; BD–DV = behavioral disinhibition with dependence

vulnerability; SU = substance use; DV = dependence vulnerability; EF = executive functioning.

51

To examine whether substance use and dependence vulnerability were indeed driving the

difference, genetic correlations were obtained between common executive functioning and each

of the substance variables alone (see Table 2.17). The genetic correlation was significantly

larger for substance use (rg = -.43) than dependence vulnerability (rg = -.17), ∆χ2 = 4.52, ∆df = 1,

∆p = .034. Therefore, results were consistent with the idea that the type of substance measure

included in behavioral disinhibition was largely responsible for different genetic correlations

with Common EF.

2.3.4 Behavioral disinhibition and the updating- and shifting-specific factors

For the full models with behavioral disinhibition structured as both a common pathway

and an independent pathway, genetic correlations with the updating-specific factor ranged from

-.04 to .13 (see Table 2.17). Genetic correlations were similar when just substance use or

dependence vulnerability was included in the model. All estimates were non-significant. For

most models genetic correlations were slightly higher with shifting, but still non-significant.

Although the shifting factor had measurable non-shared environmental influences (E ≈ .30), they

were not significantly correlated with non-shared environmental influences on behavioral

disinhibition or on the individual substance measures.

2.4 Discussion

This chapter used biometrical analyses of adolescent twins from a nonclinical sample to

examine (a) the genetic relationships between behavioral disinhibition and factors common and

specific to executive functions, and (b) how these relationships changed when measures of

substance use versus dependence vulnerability were used in the behavioral disinhibition

construct.

52

First, multivariate twin analyses were used to examine the structure of behavioral

disinhibition with either substance use or dependence vulnerability. Fit indices indicated better

model fit for the independent pathway models. The magnitude of additive genetic effects on the

substance measures was similar. Conduct disorder loaded highest on (a) the genetic factor when

dependence vulnerability was included, and (b) the shared environmental factor when behavioral

disinhibition contained substance use. Variance in behavioral disinhibition modeled with

substance use may reflect aspects of antisocial behavior that are influenced by the shared

environment. For example, peer pressure. Whereas behavioral disinhibition modeled with

dependence vulnerability may represent antisocial behavior more highly influenced by genetics.

When behavioral disinhibition was modeled as a latent construct (common pathway

model), heritability was higher when dependence vulnerability was used and shared-

environmental effects were higher when substance use was included. These findings suggest that

the etiology of behavioral disinhibition is complex and it may reflect somewhat different

processes depending on the type of substance measure used. Research on the decision to try

substances and on the role of the shared environment in behavioral disinhibition may benefit

from using a measure of substance initiation or substance use. On the other hand, substance

problems or dependence may be more appropriate if a maximally heritable behavioral

disinhibition construct is desired.

For both representations of behavioral disinhibition, there were no significant genetic or

environmental correlations with the updating- and shifting-specific factors. This indicated that

the variance shared among updating tasks and among shifting tasks (controlling for variance in

common with all executive function tasks) was not related to individual differences in behavioral

disinhibition. For executive functioning common across inhibition, updating, and shifting

53

(Common EF), negative genetic correlations were observed. Despite lower heritability, genetic

influences on behavioral disinhibition with substance use were more highly correlated with

Common EF. The same pattern was observed when substance use and dependence vulnerability

were each modeled separately with executive functioning.

These results suggest that individual differences or deficits in Common EF may be more

important for behaviors relating to the initiation and regular use of substances than for the

development of problem use and/or dependence. Genetic influences shared with executive

functions likely play some role in the transition from regular use to dependence. However, given

our findings of a smaller genetic correlation with the more highly heritable dependence

vulnerability, their contribution to substance dependence variation may be less than that for other

genetic factors. For example, evidence suggests homeostasis in the reward system is altered with

heavy substance use (Koob & Le Moal, 2001), and individual differences in susceptibility to

such changes may contribute more to substance dependence variance than genetic influences in

common with executive functioning.

Long term substance use has also been shown to alter brain circuitry associated with

executive functioning (George & Koob, 2010; Goldstein & Volkow, 2011). And reviews focused

on various substances report evidence for executive function deficits in addicts (Hester, Lubman,

& Yücel, 2010; Loeber et al., 2012; Montgomery, Fisk, Murphy, Ryland, & Hilton, 2012;

Murphy et al., 2012). Therefore one important limitation of this study was the timing of

executive function tasks and the diagnostic interview. Some participants had used substances for

a considerable amount of time before they completed the executive function tasks. Therefore, it

is impossible to know whether any observed deficits in Common EF were present prior to

54

substance use or a result of prolonged substance exposure. Future research would benefit from

obtaining executive function data prior to the age of risk for substances use.

Another limitation of this study was the reliance on estimated genetic correlations

between variables. Genetic correlations may have represented identical polymorphisms that

influenced both behavioral disinhibition and executive functioning. However, genetic

correlations may also reflect spatial and statistical associations between different polymorphisms

in different variables. More sophisticated genetic analyses are needed to understand the

biological underpinnings of the observed negative genetic correlation between behavioral

disinhibition and Common EF.

Also of concern was the dependence vulnerability measure. Participants in our

community sample had less variation on this measure (s2 = 0.931) than on substance use (s2 =

1.79). This suggests a more cautious interpretation of how meaningful the genetic correlation is

between dependence vulnerability and Common EF. Another issue to consider was the inclusion

of non-users (score = 0) in the dependence vulnerability measure. It has been argued that non-

users should be included in measures of dependence because part of the protective factor against

dependence includes the decision not to use substances in the first place. Another perspective is

that non-users have not been exposed to substances and therefore their risk of becoming

dependent on one of them is unknown. (For a good summary on this issue see Palmer et al.,

2012.) Participants in this study were adolescents who hadn’t passed through the period in life

when most adult users begin to use substances. Therefore, it may have been beneficial to only

score dependence vulnerability among users.

Finally, there are potential limitations that stem from assumptions inherent in the twin

design. Estimates may have been biased if assortative mating, a special twin environment, or

55

gene-environment correlations existed for the any of the variables studied. Also, results may not

generalize to non-twin individuals or individuals outside of the adolescent age range.

A strength of this study was the use of latent variables. Genetic and environmental

influences were estimated for the variance shared among variables, which was of theoretical

interest and less likely to contain error. Other strengths were the large sample size and use of a

variety of measures including laboratory tasks, diagnostic interviews, and a self-report

questionnaire.

In summary, results suggest additive genetic influences on behavioral disinhibition are

related to additive genetic influences on Common EF. This finding is consistent with a study of

behavioral disinhibition by my colleagues (Young et al., 2009) in which genetic effects were

correlated with inhibition. The inhibition factor was identical to that used in this study, which

was subsumed under Common EF. Therefore, executive functions that influence individual

differences in behavioral disinhibition may include more than an inhibition component.

A second important finding was that negative genetic correlations with Common EF were

higher for behavioral disinhibition with substance use than for behavioral disinhibition with

dependence vulnerability. It may be that individual differences in Common EF are more

predictive of initiation and the number of substances used than problem substance use. There

were also differences in the genetic and environmental structure of behavioral disinhibition with

substance use versus dependence vulnerability. Therefore the type of substance measure used

should be considered in future studies of behavioral disinhibition.

56

CHAPTER 3

The Role of Executive Functioning in the Progression from Substance Use to Dependence

3.1 Introduction

Findings from Chapter 2 indicate that for processes common to executive functions

(Common EF factor containing inhibiting, updating, and shifting tasks; Friedman et al., 2008)

the proportion of genetic influences shared with substance use was larger that that for

dependence vulnerability. The same pattern was observed for genetic correlations with

behavioral disinhibition containing either dependence vulnerability or substance use. These

results suggest that genetic factors influencing executive functioning may be more important for

particular substance behaviors. Executive functions likely play a role from substance initiation

through the development of substance dependence. However, individual differences in executive

functions may have more consequences for (and be better able to predict) behaviors associated

with earlier stages of the substance-use trajectory.

The goal of this chapter was to follow up on the findings from Chapter 2 by exploring

the genetic relationship between Common EF and specific substance use stages. The

implementation of a stage model addressed the concern from Chapter 2 that non-users were

included in the dependence vulnerability measure. In a stage-transition model only individuals

who meet the set threshold of a stage are included in the following stage. For example,

participants who didn’t use substances repeatedly would not be scored on dependence

vulnerability. Of course this model has limitations as well.

In Chapter 2 the definitions of ‘repeated substance use,’ for alcohol (ever tried) and

tobacco (20+ cigarettes) were different from the other drugs. The high endorsement rates may

57

have skewed the results for substance use and dependence vulnerability. In this study, therefore,

it was of special interest to examine these substances separately. Model results were compared

for alcohol, tobacco, cannabis, and multi-substance use.

3.2 Methods

3.2.1 Participants

See Chapter 2 for a description of the sample and information on zygosity determination.

3.2.2 Measures

The Composite International Diagnostic Interview—Substance Abuse Module (CIDI–

SAM; Robins, Cottler, & Babor, 1993), which uses DSM–IV criteria, was used to assess

substance behaviors. Lifetime information was obtained on alcohol, tobacco, cannabis, and eight

categories of illicit drugs. Only participants with repeated use on at least one substance were

questioned about their substance-related behavior. See Chapter 2 for more details on repeated

substance use. A majority of participants (tested between ages 16 and 18) reported repeated use

of at least one substance (57.8%). The most commonly used substances were alcohol (56.9%),

tobacco (20.7%), and cannabis (20.4%). To capture different stages of the substance use

trajectory, three stage-variables were created: early versus late age-of-onset, progression to

problem use, and progression to dependence. Because the prevalence of most illicit drugs was

low, these stages were examined only for alcohol, tobacco, cannabis and multi-substance use.

Multi-substance use was defined as repeated use of two or more substances (including alcohol,

tobacco, cannabis and the eight illicit drugs).

3.2.2.1 Age-of-onset. Table 3.1 shows the average age when repeated users first tried a

particular substance. For the multi-substance use category the youngest age at first use was

58

selected. Age-of-onset was used instead of a dichotomous initiation variable (no use/ever used)

because genetic and environmental parameters for two dichotomous variables are likely to be

biased in the context of substance initiation and problem substance use. (Heath, Martin, Lynskey,

Todorov, & Madden, 2002). An individual can’t simultaneously be a non-user and a problem

user, so the correlation between non-shared environmental influences can’t be estimated.

Because substances tend to be more available to high school-age adolescents, participants

who started using at age 15 or older were designated as late users and given a score of 1.

Participants using before age 15 were considered early users and given a score of 2. Evidence

suggests that early users are at greater risk for developing substance use problems (Agrawal et al.,

2006; Lando et al., 1999; Lewinsohn, Rohde, & Seeley, 1996). Thus, scoring followed the likely

level of risk from non-users (0) to early users (2). The age cut-off for alcohol was 14 instead of

15 because alcohol use prevalence was high and the average age of onset was on the younger end

of the spectrum (M = 14.64, SD = 1.66).

59

Table 3.1

Age at which repeated users first tried a substance

n Min Max M SD

Alcohol 437 6 18 14.64 1.66

Tobacco 159 5 17 13.43 2.44

Cannabis 136 11 18 15.41 1.10

Cocaine 15 14 18 15.87 1.19

Stimulants 11 10 17 14.91 2.07

Hallucinogens 10 15 17 16.00 0.82

Club drugs 9 11 18 15.33 2.12

Opioids 5 13 17 15.40 1.52

Sedatives 3 15 17 16.00 1.00

Inhalants 1 15 15 15.00 —

PCP 0 — — — —

Note. Repeated use for illicit drugs was defined as using 6 or more times. Alcohol use

was defined as having ever used. For tobacco, repeated use consisted of smoking a pipe

or cigar 6 or more times, chewing tobacco 6 or more times, or smoking at least 20

cigarettes.

Participants were interviewed at either age 16, 17, or 18. With each successive age group

the proportion of late users was larger and non-users was smaller (see Table 3.2). This was

expected given that participants in the older cohorts had more time to have started using. The

proportion of early users (< 15 years) also increased with reporting age, which suggested

possible cohort differences in age-of-onset. More males than females had early-onset substance

use (see Table 3.3).

60

Table 3.2

Age-of-onset by reporting age

Sixteen (n = 267) Seventeen (n = 432) Eighteen (n = 69)

Multi-substance

< 15 19.5% 27.8% 37.7%

≥ 15 28.8% 30.3% 55.1%

No use 51.7% 41.9% 7.2%

Alcohol

< 14 9.4% 9.7% 13.0%

≥ 14 38.2% 47.5% 78.3%

No use 52.4% 42.8% 8.7%

Tobacco

< 15 6.4% 12.7% 29.0%

≥ 15 4.5% 9.7% 18.8%

No use 89.1% 77.6% 52.2%

Cannabis

< 15 2.6% 3.2% 10.1%

≥ 15 8.6% 14.6% 31.9%

No use 88.8% 82.2% 58.0%

Note. Reporting age = age at the time of the CIDI–SAM interview.

Table 3.3

Age-of-onset for males and females

Males (n = 364) Females (n = 404)

n % n %

Multi-substance

Age-of-onset < 15 109 30 89 22

≥ 15 118 32 128 32

Alcohol

Age-of-onset < 14 44 12 32 8

≥ 14 179 49 182 45

Tobacco

Age-of-onset < 15 52 14 40 10

≥ 15 39 17 28 7

Cannabis

Age-of-onset < 15 15 4 13 3

≥ 15 61 17 47 12

61

3.2.2.2 Problem use. Participants meeting criteria for at least one dependence symptom

were considered problem users. As discussed in Chapter 2, nearly one-half of users (48%) had

at least one dependence symptom. Problem use scores from 0 to 4 equaled zero to four

symptoms and a score of 5 represented five or more symptoms. Problem use was contingent on

the age-of-onset variable as only users were scored (non-users = missing). Table 3.4 shows the

distribution of problem use for males and females. A majority of users had no dependence

symptoms and each category had a similar number of males and females. Importantly, the

problem use variable was only used in models of multi-substance use. Originally problem use

was scored for individual substances (zero symptoms = 0, one/two symptoms = 1). However, for

each substance there were cells in the cross-twin cross-trait correlations with missing data. This

prevented the use of multivariate genetic models.

Table 3.4

Problem use for males and female users


n % n %

0 113 49.8 116 53.4

1 25 11.0 29 13.4

2 22 9.7 16 7.4

3 20 8.8 14 6.4

4 7 3.1 8 3.7

5+ 40 17.6 34 15.7

Note. Problem use was only scored for multi-substance use.

3.2.2.3 Dependence. Participants with one or two dependence symptoms were assigned

a 0 and those with three or more a score of 1. Multi-substance use participants were scored a 1

only if they had at least three symptoms for the same substance. For example, one alcohol

62

symptom and two tobacco symptoms would result in a score of 0. Dependence was contingent on

problem use (multi-substance) or age-of-onset (individual substances). Substance dependence

rates were similar for males and females (see Table 3.5).

Table 3.5

Dependence for male and female users


n % n %

Multi-substance 51 23 48 22

Alcohol 22 10 24 11

Tobacco 37 16 37 17

Cannabis 16 7 9 4

Note. Dependence = 3 or more dependence symptoms.

3.2.2.4 Executive function tasks. Inhibition, updating, and shifting were assessed with

three laboratory tasks each. See Chapter 2 for procedural information and a description of the

individual tasks. Executive function scores were corrected for sex and age differences and

transformed to improve normality. This study focused on the Common EF factor on which all the

tasks loaded (for more details see Friedman et al., 2008).

3.2.3 Statistical analyses

3.2.3.1 General procedure. Descriptive statistics were obtained with SPSS version 21

(IBM Corp, 2012). The structural-equation modeling program Mplus (Muthén & Muthén, 1998-

2012) was used to estimate the proportion of the variances and covariances due to genetic and

environmental influences. This required assumptions based on the biometrical principles of the

twin design (see Chapter 2). Mplus was chosen for its ease in incorporating continuous

(executive function) and categorical (substance stage) variables in the same model. Each

63

ordinal/categorical variable was associated with a continuous underlying latent response variable.

Thresholds designated the points along the continuous latent distribution where one measured

category ended and another began. Parameters were calculated with mean- and variance-adjusted

weighted least squares (WLSMV) estimation. Mean- and variance-adjusted estimation was used

because it is more robust to small sample sizes than regular WLS (Muthén, du Toit, & Spisic,

1997).

The number of complete twin pairs ranged from 334 to 371 for the executive function

tasks. Complete twin pairs for the substance stages are shown in Table 3.6. No outliers were

observed for twin 1 and twin 2 scores on the executive function tasks and differences between

twin 1 and twin 2 variances ranged from 0.003 to 0.386 (M = 0.190, SD = 0.102). For substance

stages (for each substance type), the counts of twin 1 and twin 2 participants in each category

were compared. Differences for age-of-onset variables ranged from 0 to 9 (M = 4.17, SD = 2.48).

For problem use categories the number of twin 1s differed from twin 2s by 4.17 on average (SD

= 3.86, range 0 to 12). Differences ranged from 1 to 13 (M = 4.56, SD = 3.74) for dependence

variables. This descriptive information indicated that low twin-pair coverage and bias due to twin

number designation were not of concern.

64

Table 3.6

Number of complete twin pairs for substance stages

MZ DZ

Multi-substance Age-of-onset 204 177

Problem use 88 91

Dependence 33 37

Alcohol Age-of-onset 204 177

Dependence 85 89

Tobacco Age-of-onset 204 177

Dependence 26 25

Cannabis Age-of-onset 204 177

Dependence 20 23

Note. MZ = monozygotic; DZ = dizygotic.

3.2.3.2 Modeling. First, the variance and covariance structures of the substance stages

were specified with univariate and Cholesky bi- and tri-variate models. In a Cholesky model the

number of genetic factors (A) equals the number of variables (n). All variables load on the first

factor, n-1 variables load on the second factor, etc. (see Figure 3.1). Environmental variation (C,

E) is modeled the same way. Second, Cholesky models were used to examine whether genetic

influences on particular substance stages were also operating on Common EF. Dummy latent

variables were created for the substance stages so genetic and environmental factors were at the

same level as those for executive functioning (see Figure 3.2, p. 66). It is important to note that

in a Cholesky model the order of the variables should be theoretically driven because the

questions addressed by the paths will change with a different order. Common EF followed the

substance stages, which were ordered to reflect the progression from onset to dependence.

65

Figure 3.1. Trivariate Cholesky model

Figure 3.1. Only twin 1 is depicted. Non-shared environmental factors are not shown but the

loading pattern is identical to that for additive genetic effects (A) and shared environmental

effects (C). Onset = early vs. late age-of-onset; Prb = problem use; Dep = dependence.

66

Figure 3.2. Example model with standardized path coefficients for executive functions

Figure 3.2. The proportion of variance accounted for by residual variance is shown below each executive function task.

Environmental effects followed the same pattern but are not shown. Only twin 1 is depicted.

A = additive genetic effects; Common EF = common executive functioning; Anti = antisaccade; Stop = stop signal; Keep = keep

track; Letter = letter memory; S2ba = spatial-2back; Num = number-letter; Col = color-shape; Cat = category switch.

Onset

A1

Age-of-onset

Stop

Stroop

Anti

δ

.78 .77

Letter

S2ba

Keep

.61 .54 .83

Col

Cat

Num

.58 .70 .48

Common EF Updating

Specific Shifting

Specific

Dependence

Problem Use

a11 a

21 a

31

a22

a41

a32 a

42

a33 a

43 a

44

.79

1 1 1 1

1.0 .47

1 1

A2 A

3 A

4

1.0 1.0

Prb Dep

.48 .46 .37 .38 .36 .42 .37

.48

.50 .56

.20 .49

.41

.53

δ δ δ δ δ δ δ δ

67

To calculate the total genetic covariance between Common EF and a particular substance

stage, covariances through all relevant factors were added. For example, the genetic covariance

between Common EF and problem use was equal to their covariance through A1 plus their

covariance through A2 (see Figure 3.2). Previous results indicated that for both updating- and

shifting-specific factors there were no significant correlations with substance measures (see

Chapter 2). Therefore, only the covariation between Common EF and substance stages was

examined. However, for an accurate representation of Common EF it was necessary to include

the updating- and shifting-specific factors in the model.

3.3 Results

3.3.1 Substance stages

For all substance categories MZ concordance rates were higher than DZ rates, indicating

genetic influences (see Table 3.7). This was supported by polychoric twin correlations (see

Table 3.8). Polychoric correlations are estimated for ordinal variables with two or more levels

and assume a continuous-normal underlying distribution. For most substances the relative

magnitude of the MZ and DZ correlations suggested shared environmental influences. Model fit

indices indicated adequate fit for tobacco and cannabis age-of-onset, multi-substance problem

use, and multi-substance and cannabis dependence. For the remaining models there were mixed

conclusions regarding fit (see Table 3.8). Genetic influences accounted for 23% to 47% of the

variance in age-of-onset variables and 23% to 91% percent in dependence variables. For age-of-

onset and problem use variables the variance accounted for by the shared environment was equal

to or greater than that for genetic influences. In contrast the influence of the shared environment

on the dependence variables was negligible. This is consistent with Chapter 2 and other studies

68

in which the shared environment had a smaller effect on dependence than it did on less severe

substance-related behaviors (Stallings, Gizer, & Young-Wolff, in press).

Table 3.7

Twin concordance rates for substance stages

Multi-substance Alcohol Tobacco Cannabis

Age-of-onset MZ .66 .69 .89 .89

DZ .60 .64 .73 .80

Problem use MZ .48

DZ .44

Dependence MZ .88 .93 .96 .75

DZ .65 .80 .60 .70

Note. MZ = monozygotic; DZ = dizygotic.

69

Table 3.8

Polychoric twin correlations and univariate results for substance stages

Correlations

Variance

Components Model Fit

MZ DZ A C E χ2 df p RMSEA TLI

Onset Multi .77 .65 .23 .54 .23 17.27 7 .012 .087 .99

Alcohol .71 .54 .35 .36 .29 13.52 7 .060 .069 .99

Tobacco .91 .68 .47 .45 .08 9.16 7 .241 .040 .99

Cannabis .84 .70 .27 .57 .16 8.35 7 .303 .032 .99

Prb Multi .83 .71 .25 .58 .17 18.16 19 .512 .000 1.00

Dep Multi .87 .50 .76 .11 .13 3.26 3 .354 .034 .99

Alcohol .86 .51 .87 .13 6.43 4 .169 .068 .98

Tobacco .91 .32 .91 .09 1.67 4 .796 .000 1.00

Cannabis .26 .07 .23 .00 .77 1.32 3 .724 .000 1.00

Note. Boldface type indicates significance at the .05 level (two-tailed).

Onset = early versus late age-of-onset; Prb = problem use; Dep = dependence; Multi = multi-

substance use; MZ = monozygotic; DZ = dizygotic; A = additive effects; C = shared

environmental effects; E = non-shared environmental effects; χ2 = chi-square test of model fit;

RMSEA = Root mean square error approximation; TLI = Tucker Lewis index.

Polychoric correlations indicated covariation between some substance stages (see Table

3.9). For multi-substance use, age-of-onset was more highly correlated with the subsequent stage,

problem use, than with dependence. Cannabis age-of-onset and dependence were not correlated

so they were examined separately in the full executive function models. Cross-trait cross-twin

correlations were necessary to determine if the observed covariation was partly due to genetic

factors. For multi-substance age-of-onset and problem use, higher MZ than DZ cross-trait cross-

twin correlations indicated genetic covariance (see Table 3.10). The rest of the correlations were

inconclusive as the correlation for one twin1/twin2 pairing was higher in MZs while the

correlation for the other twin1/twin2 pairing was not.

70

Table 3.9

Polychoric correlations among substance stages

Onset Problem Use Dependence

Multi-substance Onset —

Problem Use .424 —

Dependence .190 .574 —

Alcohol Dependence .328

Tobacco Dependence .076

Cannabis Dependence .020

Note. Onset = early versus late age-of-onset.

Table 3.10

Cross-trait cross-twin correlations for substance stages

MZ Twins DZ Twins

Onset1 Prb1 Dep1 Onset1 Prb1 Dep1

Multi-substance Onset2 .770 .525 .314 .654 .404 .363

Prb2 .463 .832 .686 .412 .706 .533

Dep2 .128 .342 .874 .106 .531 .495

Alcohol Onset2 .712 .327 .535 .382

Dep2 .400 .859 .333 .508

Tobacco Onset2 .913 .358 .680 .167

Dep2 .105 .914 .287 .322

Note. Univariate twin correlations are in boldface type.

Onset = early vs. late age-of-onset; Prb = problem use; Dep = dependence; MZ = monozygotic;

DZ = dizygotic.

Results from the bivariate models are shown in Table 3.11. The shared environment

appeared to contribute to the covariance between the age-of-onset and dependence stages,

71

however the cross paths (c21) were not significant. For multi-substance use, the shared

environment contributed to the covariance between (a) age-of-onset and problem use, and (b)

problem use and dependence. Genetic cross paths (a21) were substantial but not significant.

Similar results were observed for the multi-substance trivariate model (see Figure 3.3).

Adequate or near-adequate fit was observed for the bivariate (see Table 3.11) and trivariate

models, χ2 = 51.3 (44), p = .209; RMSEA = .029; TLI = .99.

Table 3.11

Bivariate results for substance stages

Standardized Path Coefficients Model Fit

a11 a21 a22 c11 c21 c22 e11 e21 e22 χ2 (df) p RMSEA TLI

Onset

w/ Dep

Multi .48 .06 .86 .73 .36 .00 .48 -.10 .34 26.1(15) .037 .062 .99

Alc .59 .14 .77 .61 .49 .00 .54 -.06 .38 28.1 (15) .021 .067 .97

Tob .66 -.08 .87 .68 .34 .00 .31 -.36 .00 18.4 (17) .363 .021 .99

Onset

w/ Prb

Multi .48 .39 .31 .73 .43 .63 .48 -.17 .37 36.9 (31) .216 .031 .99

Prb w/

Dep

Multi .52 .55 .63 .76 .49 .00 .40 -.26 .00 30.7 (29) .382 .021 .99

Note. Boldface type indicates significance at the .05 level.

Multi = multi-substance; Alc = alcohol; Tob = tobacco; Onset = early versus late age-of-onset;

Prb = problem use; Dep = dependence; a = additive genetic effects; c = shared environmental

effects; e = non-shared environmental effects; χ2 = chi-square test of model fit; RMSEA = Root

mean square error approximation; TLI = Tucker Lewis index.

72

Figure 3.3. Trivariate model for multi-substance use

Figure 3.3. Solid lines indicate significance at the .05 level. Dashed lines indicate non-

significance (p > .05).

Onset = age-of-onset; Prb = problem use; Dep = dependence; A = additive genetic factor; E =

non-shared environmental factor; C = non-shared environmental factor.

3.3.2 Substance stages and common executive functioning

The substance stages with Common EF had adequate fit to the data (see Table 3.12). The

minimum covariance coverage was not met for cannabis dependence and Common EF, so only

Onset

A1

Prb

A2

C2 C

1

Dep

A3

C3

.48 .39

-.05 .29 .40 .60

Onset Prb

E2 E

1

Dep

E3

.73 .43 .35

.64 .48 .00

.48 -.17

-.10 .38 .06

.35

1 1 1

1 1 1

1 1 1

73

cannabis age-of-onset with Common EF is shown. Figures 3.4 through 3.7 show standardized

path estimates for each model. For all substances except cannabis a significant path from the age-

of-onset genetic factor (A1) to Common EF was observed. Interestingly, genetic factors specific

to Common EF were only observed for alcohol and tobacco. For multi-substance use, shared and

non-shared environmental influences contributed to the covariation between age-of-onset and

problem use.

Table 3.12

Model fitting results for common executive functioning and substance stages

χ2 df p RMSEA TLI

Multi-substance 655.84 568 .006 .028 .95

Alcohol 521.20 470 .051 .024 .96

Tobacco 561.76 469 .002 .032 .94

Cannabisa 437.21 390 .050 .025 .96

Note. χ2 = chi-square test of model fit; RMSEA = Root mean square error approximation;

TLI = Tucker Lewis Index. aCannabis age-of-onset with Common EF.

74

Figure 3.4. Standardized path coefficients for multi-substance stages with Common EF

Figure 3.4. Only latent variables for twin 1 are pictured. See Figure 3.2 for the full factor model

with observed and latent variables. Solid lines indicate significance at the .05 level. Dashed lines

indicate non-significance (p > .05).

A = additive genetic factor; E = non-shared environmental factor; C = non-shared environmental

factor.

Age-of-onset

Problem Use

Dependence

E1

1

E2

1

E3

1

E4

1

.50

.45 .07

.28

-.44

.66 -.15 .35 .85 .03

A1 1

A2

1

A3

1

A4

1

.47

-.24 -.18

.31

.03

-.33 .27 .00 .00 .00

Common EF

C1

1

C2

1

C3

1

.73

.41

.63 .31

.44

.00

75

Figure 3.5. Standardized path coefficients for alcohol stages with Common EF





factor.

Alcohol Age-of-onset

Alcohol Dependence

Common EF

E1

1

E2

1

E3

1

.60

.41 -.33

.78

-.02 .92

A1 1

A2

1

A3

1

.53

-.29 .01

.00

.00

C1

1

C2

1

.60

.38

.00

76

Figure 3.6. Standardized path coefficients for tobacco stages with Common EF





factor.

Tobacco

Age-of-onset

Tobacco Dependence

Common EF

E1

1

E2

1

E3

1

.67

-.12 -.47

.88

.22 .83

A1 1

A2

1

A3

1

.31

-.30 .21

.00

.00 .00

C1

1

C2

1

.68

.35

.00

77

Figure 3.7. Standardized path coefficients for cannabis age-of-onset with Common EF





factor.

Cannabis

Age-of-onset Common EF

E1

1

E2

1

.52

-.74

.64

A1 1

A2

1

.41

.18

.00

C1

1

.75

78

Genetic correlations were calculated for multi-substance use (Table 3.13) and individual

substances (Table 3.14). See Figure 3.2 on p.66 for a visual representation of the additive

genetic path labels. For all substance types, genetic influences on age of onset were negatively

correlated with genetic influences on Common EF. And for multi-substance use, a substantial

portion of genetic effects on age-of-onset was shared with genetic effects on problem use (see

Figure 3.4). Additive genetic effects accounted for a large proportion (.83 to .99) of the total

covariance between age-of-onset and Common EF. This was not surprising given that Common

EF had very little non-shared environmental influences. Overall these findings suggest that

genetic factors contributing to better executive functioning in adolescence may also contribute to

no use/later age-of-onset and fewer substance use problems.

79

Table 3.13

Genetic correlations between multi-substance stages and common executive functioning

Variables Expected

Variance

Expected rg with

Common EF Formula

ONSET .25 a112

PRB .28 a212 + a22

2

DEP .56 a312 + a32

2 + a332

CEF .94 a412 + a42

2 + a432 + a44

2

ONSET -.45 (a11 x a41) / √(a2ONSET x a

2CEF)

PRB

common w/onset

-.39 (a21 x a41) / √(a2PRB x a2

CEF)

PRB -.08 (a22 x a42) / √(a2PRB x a2

CEF)

DEP

common w/onset

.04 (a31 x a41) / √(a2DEP x a2

CEF)

DEP

common w/prb

-.14 (a32 x a42) / √( a2DEP x a2

CEF)

DEP .41 (a33 x a43) / √(a2DEP x a2

CEF)

Note. rg = genetic correlation; ONSET = age-of-onset; PRB = problem use; DEP = dependence;

CEF = common executive functioning; a = additive genetic effects.

80

Table 3.14

Genetic correlations between common executive functioning and substance-specific stages

Model

Variable Expected Genetic

Variance

Expected rg with

Common EF Formula

Alcohol ONSET .36 a112

DEP .78 a212 + a22

2

CEF .85 a312 + a32

2 + a332

ONSET -.36 (a11 x a31) /

√(a2ONSET+ a

2CEF)

DEP

common

w/onset

-.17 (a21 x a31) /

√( a2DEP x a2

CEF)

DEP .02 (a22 x a32) /

√( a2DEP x a2

CEF)

Tobacco ONSET .45 a112

DEP .79 a212 + a22

2

CEF .96 a312 + a32

2 + a332

ONSET -.47 (a11 x a31) /

√(a2ONSET+ a

2CEF)

DEP

common w/

onset

.07 (a21 x a31) /

√( a2DEP x a2

CEF)

DEP .22 (a22 x a32) /

√( a2DEP x a2

CEF)

Cannabis ONSET .27 a112

CEF .96 a212 + a22

2

ONSET -.54 (a11 x a21) /

√( a2ONSET x a2

CEF)

Note. rg = genetic correlation; ONSET = age-of-onset; DEP = dependence; CEF = common

executive functioning; a = additive genetic effects.

There were substantial genetic influences specific to alcohol dependence (a = .78, Figure

3.5). However these effects were not correlated with genetic effects on Common EF. Genetic

81

influences specific to tobacco dependence and multi-substance dependence on the other hand,

were positively correlated with genetic influences on Common EF (tobacco rg = .22; Multi rg

= .41). These correlations suggest that some genetic effects contribute to better executive

functioning and also to the development of substance dependence. However the correlations

were likely non-significant and should be interpreted with caution. For all substance types there

were substantial shared environmental influences on age-of-onset. And shared-environmental

influences on multi-substance age-of-onset were correlated with problem use.

3.4 Discussion

The aim of this chapter was to examine genetic and environmental covariation between

Common EF and specific substance use stages. The first stage was age-of-onset, which

represented no use, late use, and early use. Then early and late users were included in the

problem use stage, which represented the number of dependence symptoms across substance

type. Participants with at least one dependence symptom were included in the third and final

stage—dependence. Individuals received a score of 1.0 on dependence if they met criteria for at

least three dependence symptoms on the same substance.

Another goal of the study was to expand on Chapter 2 by examining these stages for

specific substances. Alcohol, cannabis, and tobacco were modeled in addition to multi-substance

use. For both alcohol and tobacco, additive genetic influences were higher on dependence

(AAlcohol = .78, ATobacco = .79) than on age-of-onset (AAlcohol = .36, ATobacco = .45) There were no

shared environmental influences specific to dependence, but shared environmental effects on

age-of-onset contributed to the covariance between the stages. Genetic influences also accounted

for some of the covariance in alcohol.

82

Negative genetic correlations between age-of-onset and Common EF were observed for

alcohol (rg = -.36), tobacco (rg = -.47), and cannabis (rg = -.54). These correlations were

consistent with multi-substance use (rg = -.45). Genetic influences on age-of-onset accounted for

83% of the covariance between Common EF and problem use, but only 7% of the covariance

between Common EF and dependence. Even though repeated use was defined differently for

alcohol/tobacco than for other drugs, the pattern of results for these substances was consistent

with multi-substance use. However the multi-substance use results may have been partly driven

by alcohol and tobacco.

Overall these results suggest that genetic influences shared with Common EF may play

more of a role in initiation/age-of-onset than in substance dependence. This is consistent with

Chapter 2, however the extent to which age-of-onset captured the same variance as substance

use (number of substances used repeatedly) is not clear. As in Chapter 2, the direction of

causality between substance use and executive function deficits could not be determined.

Another limitation of this study was the lack of power to detect significant effects in the

later substance stages. Decreasing sample size is an inherent part of stage models as fewer and

fewer participants are included in each subsequent stage. This effect is magnified in genetic

studies because participants are further categorized by zygosity. In this study the number of

participants decreased by at least half with each stage (in multi-substance use for example: nage-of-

onset = 762, nproblem use = 358, ndependence = 140), which may partly explain the many non-significant

path coefficients.

A third concern was that participants were only asked about age of onset if they met

criteria for repeated use. Therefore, age-of-onset was the age of onset for individuals who

eventually used the substance repeatedly. Future research would benefit from a model with a

83

persistence stage (experimentation vs. repeated use) between the age-of-onset and problem use

stages. Age-of-onset would then reflect the age at which an individual first tried a substance,

independent from continued use.

In summary, additive genetic influences that contributed to an earlier age of onset for

substance use also contributed to poor Common EF. Furthermore, these genetic influences

accounted for the majority of the covariance observed between Common EF and later substance

stages. Individual differences in executive functions may be more important in earlier stages of

the substance use trajectory than in the progression to dependence.

84

CHAPTER 4

Using Items from the Tridimensional Personality Questionnaire to Assess

Behavioral Disinhibition

4.1 Introduction

Novelty seeking was included with antisocial behavior and substance use as an indicator

of behavioral disinhibition both in Chapter 2, and in studies by my colleagues (Palmer et al.,

2010; Young, Stallings, & Corley, 2000; Young et al., 2009). The dimension, which is part of the

Tridimensional Personality Questionnaire (TPQ; Cloninger, 1987), was developed so that

individuals high in novelty seeking express excitement and exploratory activity in response to

novel and appetitive stimuli. Novelty seeking consists of four subscales: exploratory excitability,

impulsivity, extravagance, and disorderliness (individual items are listed in Chapter 2, Table

2.7).

The subscales reflect an overall lack of control, which is a significant aspect of

behavioral disinhibition. However, it has also been suggested that the liability to behavioral

disinhibition includes a lack of persistence, inattention, insensitivity to social constraints and an

inability to learn from consequences (Gorenstein & Newman, 1980; Iacono, Malone, & McGue,

2008). The goal of this chapter was to identify a set of TPQ items that reflected these traits and,

when used with novelty seeking items, provided a personality measure more representative of

behavioral disinhibition. An example of a broader personality measure of behavioral

disinhibition is the constraint dimension of the Multidimensional Personality Questionnaire

(MPQ; Tellegen & Waller, 2001), which has been used in studies by the Minnesota Twin and

Family Study (Iacono, McGue, & Krueger, 2006). Constraint (or lack thereof) contains three

subscales: control, harm avoidance, and traditionalism. (Traditionalism addresses the extent to

85

which individuals have a sense of morality and respect and obey social norms.) A study using

factor analyses indicated that TPQ novelty seeking was related to MPQ constraint, mostly

through the high loading on the control scale (Waller, Lilienfeld, Tellegen, & Lykken, 1991).

Participants high in novelty seeking tended to have low scores on control. Also, the fearlessness

subscale of TPQ harm avoidance was related to the harm avoidance subscale in MPQ constraint.

These findings supported the idea that items from TPQ dimensions other than novelty seeking

could be used to measure disinhibitory personality.

TPQ dimensions (other than novelty seeking) from which to draw items were harm

avoidance, reward dependence and persistence. Harm avoidance was created to reflect the

behavioral inhibition system, such that individuals high on the dimension respond strongly to

aversive stimuli and are quick to inhibit behavior associated with negative outcomes. Reward

dependence reflects variation in the behavioral maintenance system. Individuals high in reward

dependence respond strongly both to positive and negative reinforcement and are resistant to

extinction of rewarded behavior. Persistence addresses how long/hard individuals work to finish

tasks and their desire for achievement.

When the TPQ was created, emphasis was placed on the relations between the factors.

For example, individuals high in novelty seeking and low in harm avoidance were described as

impulsive, danger seeking, and aggressive. Still, the dimensions were thought to be largely

uncorrelated (i.e. specific profiles did not occur more often than others). Correlations among the

TPQ dimensions would provide initial support for using items from different dimensions in the

same measure. Indeed, results from exploratory and confirmatory factor analytic studies have

been mixed in their support of the TPQ structure (Howard, Kivlahan, & Walker, 1997).

86

Evidence of shared genetic influences among TPQ dimensions would further support the

creation of a disinhibitory personality factor. In his original theory Cloninger (1986) proposed

that each dimension was genetically independent. This was supported by findings from a

multivariate twin study of the four dimensions, at least in men (Stallings, Hewitt, Cloninger,

Heath, & Eaves, 1996). There were significant genetic correlations between harm avoidance and

novelty seeking, and novelty seeking and reward dependence in women. The average age for this

study was 67 years old. Negative genetic correlations between harm avoidance and novelty

seeking/reward dependence were observed (for males and females) in a large Australian adult

twin sample (Heath, Cloninger, & Martin, 1994). They also found positive genetic correlations

between novelty seeking and reward dependence. To our knowledge, no multivariate analysis of

the TPQ has been reported for adolescents.

This chapter consists of four studies. First, a multivariate twin-analysis was used to

examine the extent to which genetic and environmental influences contributed to the covariation

among TPQ dimensions. In the second study TPQ items thought to reflect behavioral

disinhibition were selected and analyzed using an exploratory factor analysis. The resulting

factor structure was used in a confirmatory factor analysis in Study 3, which consisted of a

different sample. Next, characteristics of the newly created disinhibitory personality dimension

were compared to those of novelty seeking. This was followed by an examination of the extent to

which both dimensions predicted conduct disorder and substance use. In the final study

regression analyses were used to determine if disinhibitory personality was predictive of case-

control status (over and above novelty seeking) in a sample selected for antisocial substance

dependence.

87

4.2 General Methods

4.2.1 Samples

4.2.1.1 Community twin sample. Twins were drawn from two samples in the Colorado

Twin Registry (Rhea, Gross, Haberstick, & Corley, 2006). Twins born in 1968 forward who

attended primary school or were born in the state of Colorado were invited to participate in the

Community Twin Sample (CTS). For the Longitudinal Twin Sample (LTS) same-sex twins were

identified through Colorado birth records. Twins born between 1984 and 1990 were included in

the study if they had a normal gestational period and birth weight, lived within a three-hour drive

from the Institute for Behavioral Genetics, and were available for initial testing at 14 months. In-

person assessments with different interviewers for each twin were conducted in the participants’

homes. They also completed a set of self-report questionnaires, which included the TPQ. There

were multiple waves of data collection; therefore, a small number of twins had completed the

TPQ twice between the ages of 16 and 18. For these individuals the most recent data was

analyzed. All participants gave informed consent (if 18 years) or assent (if 17 years or younger)

prior to participation. Parents also provided informed consent for participants under age 18. The

Institutional Review Board of the University of Colorado approved the study.

Zygosity was determined from a nine-item questionnaire of physical characteristics based

on Nichols and Bilbro (1966). For twins recruited through the school system, initial zygosity was

based on two questions: “how frequently are the twins mistaken for each other by people who

know them?” and “are they ‘as alike as two peas in a pod?’” An 85% agreement from at least

four raters was required for assigning monozygotic (MZ) or dizygotic (DZ) status. Zygosity was

confirmed using DNA collected from cheek swabs. Eleven highly informative short-tandem

repeat (STR) genetic polymorphisms were genotyped using standard polymerase chain reaction

88

technology. Concordance across all polymorphisms between twins indicated MZ status.

Discordance indicated DZ status. A panel of researchers resolved discrepancies between the

initial zygosity judgment and genotyping. If necessary, DNA was resampled.

4.2.1.2 Selected family sample. Participants were also drawn from the Colorado

Adolescent Substance Abuse (ASA) family study (Miles et al., 1998; Stallings et al., 2003). The

ASA is a selected sample of adolescent probands, matched adolescent controls, and all

consenting first-degree biological relatives. Probands were recruited beginning in 1993 from one

of three treatment facilities for substance abuse. The treatment facilities were located in the

Denver metropolitan area and operated by the Division of Substance Dependence of the

University of Colorado School of Medicine. The majority of probands were referred to the

treatment centers by juvenile justice and social service agencies. To be included in the sample,

they were required to have an IQ score greater than 80, exhibit no current psychotic symptoms,

and pose no imminent danger to themselves or others. Controls were matched within 1 year of

age, for sex, for ethnicity, and for geographic location (zip code). Participants underwent

diagnostic interviews and completed a set of self-report questionnaires, including the TPQ. The

Institutional Review Board of the University of Colorado approved the study.

4.2.2 Participants

For all four studies, participants were included if they had completed the TPQ between

16 and 18 years of age. Table 4.1 shows descriptive information for the participants in each

study. Participants in the community samples were largely White (CTS = 72.6%, LTS = 81.8%).

Hispanics represented the second most common ethnicity (CTS = 11.03%, LTS = 9.7%). This

pattern was reversed in the ASA family sample (Hispanic = 34.8%, White = 19.7%).

89

Table 4.1

Sample information by study

Study Sample N % Female Mean Age (SD)

1-TPQ multivariate twin analysis CTS, LTS 1944 53.0 17.49 (0.80)

2-DP: exploratory factor analyses CTS 1414 52.9 17.65 (0.89)

3-DP: relations with NS and BD LTS 826 52.5 17.21 (0.56)

4-DP: cases compared to controls ASA 604 4.5 17.18 (0.73)

Note. TPQ = tridimensional personality questionnaire; DP = disinhibitory personality;

NS = novelty seeking; BD = behavioral disinhibition; CTS = community twin sample;

LTS = longitudinal twin sample; ASA = adolescent substance abuse family study.

4.2.3 Measures

4.2.3.1 Personality assessment. Participants completed the 54-item version (Cloninger,

Przybeck, & Svrakic, 1991) of the TPQ (Cloninger, 1987). Dimensions originally consisted of 18

true-false items. Persistence was a subscale of reward dependence before it was considered a

separate factor. Therefore, persistence and reward dependence consisted of 5 and 13 items,

respectively. Table 4.2 shows the four dimensions along with their subscales. Ten of the items

were reversed so higher scores indicated higher harm avoidance, novelty seeking, etc.

Table 4.2

TPQ dimensions and subscales

Harm avoidance Novelty seeking Reward dependence Persistence

Anticipatory worry Exploratory excitability Sentimentality

Fear of uncertainty Impulsivity Attachment

Shyness with strangers Extravagance Dependence

Fatigability Disorderliness

Note. Persistence was originally the fourth subscale under reward dependence.

90

4.2.3.2 Behavioral disinhibition measures. The DSM–IV version of the Diagnostic

Interview Schedule (DIS; Robins et al., 2000) was used to assess conduct disorder in 18-year-

olds who no longer lived with their parents. All other participants completed the DSM–IIIR and

DSM–IV versions of the Diagnostic Interview Schedule for Children (DISC; Shaffer et al., 1996;

Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000). Substance use was measured with the

DSM–III-R and DSM–IV version of the Composite International Diagnostic Interview–

Substance Abuse Module (CIDI–SAM; Robins, Cottler, & Babor, 1993). The CIDI–SAM

assesses alcohol, tobacco, cannabis, and eight categories of illicit drugs. Past substance use

behavior was scored if participants were not currently using.

4.3 Study 1: Multivariate twin analysis of the Tridimensional Personality Questionnaire

4.3.1 Methods

4.3.1.1 Tridimensional Personality Questionnaire. Internal consistency (see Table 4.3)

was comparable to that reported in psychometric studies of the TPQ (Kuo, Chih, Soong, Yang, &

Chen, 2004; Otter, 2003; Sher, Wood, Crews, & Vandiver, 1995). Item endorsement rates were

between 20% and 80% for 47 of the 54 true-false items, which suggests good variance. The most

commonly endorsed (response = true) items were: “People find it easy to come to me for help,

sympathy, and warm understanding” (84.1%) and “Usually I am more worried than most people

that something might go wrong in the future” (79.1%).

91

Table 4.3

Reliability coefficients for TPQ dimensions

Cronbach α

All Female Male

Harm avoidance .824 .837 .800

Novelty seeking .699 .718 .674

Reward dependence .677 .656 .660

Persistence .542 .534 .557

Participants sometimes skipped items or circled both true and false. Therefore, the mean

number of items endorsed was calculated instead of the sum. Items were coded so false = 0 and

true = 1, which resulted in scores that were essentially the proportion of items endorsed. Only

participants who answered all five persistence items were scored on that dimension (excluded n

= 33). To be scored on the other three personality dimensions participants were required to

answer ~90% of the items (maximum excluded n = 14). See Table 4.4 for a summary of each

dimension.

Table 4.4

Descriptive statistics for TPQ dimensions

N Min Max M SD

Harm avoidance 1938 .00 .94 0.32 0.23

Novelty seeking 1930 .00 1.0 0.51 0.19

Reward dependence 1932 .00 1.0 0.63 0.21

Persistence 1911 .00 1.0 0.63 0.28

4.3.1.2 Data transformation. As shown in Table 4.5 females scored higher on harm

avoidance and reward dependence on average, which is consistent with many TPQ studies

(Miettunen, Veijola, Lauronen, Kantojärvi, & Joukamaa, 2007). Males scored higher on novelty

92

seeking, which supports the normative data collected for the TPQ (Cloninger et al., 1991).

However, there have been inconsistent results for sex differences in novelty seeking. Basic

regression was used to correct for sex. Then non-normal distributions were log transformed and

re-standardized.

Table 4.5

Sex differences and age correlations for TPQ dimensions

n M SD t-test (p) Age correlation (p)

Harm Avoidance

M 909 0.28 0.21 6.81a (< .001) .056 (.049)

F 1029 0.35 0.23 .014 (.652)

Novelty Seeking

M 908 0.53 0.18 -3.01 (.003) -.026 (.796)

F 1022 0.50 0.19 -.087 (.030)

Reward Dependence

M 906 0.57 0.21 9.66a (< .001) -.045 (.595)

F 1026 0.68 0.20 .034 (.184)

Persistence

M 891 0.62 0.29 1.82 (.070) -.051 (.508)

F 1020 0.64 0.27 -.003 (.588)

Note. P values are based on means and correlations where the dependence in the data was

accounted for (weighted .5 for complete pairs, 1.0 for singletons), not the actual means and

correlations shown.

M = male; F = female. aEqual variances could not be assumed under Levene’s Test for Equality of Variances.

4.3.1.3 Modeling. Genetic analyses were conducted using the classic twin design (see

Chapter 2). Univariate models were examined first to determine if the dimensions were

heritable. Then phenotypic correlations were used to inform the structure of the multivariate

models. An independent pathway model was used to determine if there were genetic influences

93

shared across the four TPQ dimensions. Paths unique to each variable made it possible to

examine whether shared genetic factors differentially influenced the personality dimensions.

Analyses were performed with the structural-equation modeling program Mx (Neale,

Boker, Xie, & Maes, 2003), which uses maximum likelihood estimation. Akaike’s information

criteria (AIC; Akaike, 1987), the root-mean-square error of approximation (RMSEA; Steiger &

Lind, 1980), and the Tucker-Lewis index (TLI; Tucker & Lewis, 1973) were used to examine the

fit of the models. Non-significant paths were set to zero in follow-up models and χ2 difference

tests were used to determine if their exclusion resulted in a decrement in fit.

There were 923 same-sex twin pairs (472 MZ, 336 DZ) and 145 opposite-sex dizygotic

pairs (OS). Thirty-eight singletons were used in descriptive analyses but did not contribute to the

genetic analyses. No extreme outliers were observed when scatterplots for twin 1 and twin 2

scores were examined, and differences between twin 1 and twin 2 variances were minor.

4.3.2 Results

Univariate results indicated that all four dimensions were heritable and genetic influences

explained about 25% to 40% of the variation (see Table 4.6). Twin correlations suggested non-

additive genetic influences (D) for novelty seeking. This is consistent with findings in Chapter 2

and other studies of the TPQ (Heiman, Stallings, Young, & Hewitt, 2004; Keller, Coventry,

Heath, & Martin, 2005). When D was included in the model, however, the estimate for additive

genetic effects was zero. Only additive influences (A) were estimated in subsequent models

because (a) genetic factors with entirely non-additive effects are improbable and (b) the twin

design is not able to tease apart the sources of non-additive variation.

94

Table 4.6

Twin correlations and univariate results for TPQ dimensions

Zygosity Variance Components Model Fit

MZ DZ OS A E -2LL AIC RMSEA TLI

HA .39 .20 .01 .37 .63 5415 1545 .010 .99

NS .34 -.00 .13 .30 .70 5428 1574 .048 .96

RD .33 .12 .07 .31 .69 5425 1567 .046 .96

PS .25 .11 -07 .23 .77 5397 1581 .036 .96

Note. HA = harm avoidance; NS = novelty seeking; RD = reward dependence; PS = persistence;

MZ = monozygotic; DZ = dizygotic; OS = opposite sex dizygotic; LL = log likelihood;

AIC = Akaike’s information criteria; RMSEA = Root mean square error approximation;

TLI = Tucker Lewis index.

Phenotypic correlations, although small, were consistent with the possibility of shared

genetic influences (see Table 4.7). Harm avoidance and novelty seeking were negatively

correlated, which suggested that individuals high on harm avoidance tended to have lower scores

on novelty seeking. Harm avoidance and persistence were both correlated around .1 or higher

with reward dependence and novelty seeking, but not with each other. Therefore, two additive

genetic factors were included in the independent pathway model. Harm avoidance, novelty

seeking, and reward dependence loaded on the first factor. Persistence, novelty seeking, and

reward dependence loaded on the second. Finally, the magnitude of cross-trait cross-twin

correlations was compared for MZ and DZ twins. Eight of the 12 comparisons suggested genetic

influences contributed to the covariation between dimensions.

95

Table 4.7

Phenotypic correlations for TPQ dimensions

HA NS RD PS

HA —

NS -.257 —

RD .155 -.021 —

PS .075 .139 .096 —

Note. Correlations in boldface type are significant at the .01 level (two-tailed) after accounting

for dependence in the data (weighted .5 for complete pairs, 1.0 for singletons).

HA = harm avoidance; NS = novelty seeking; RD = reward dependence; PS = persistence.

The hypothesized independent pathway model with two additive-genetic factors fit

significantly better than a model where all dimensions loaded on a single genetic factor (see

Table 4.8). Standardized parameter estimates from the best fitting model (AAE Model in Table

4.8) are presented in Figure 4.1. Path coefficients that could not be dropped from the model,

without causing a significant decrement in fit, are represented by solid lines. Dashed lines

indicate non-significant paths. The first genetic factor was shared by harm avoidance and novelty

seeking and it appeared to contribute to a high harm avoidance/low novelty seeking or a low

harm avoidance/high novelty seeking profile. The later of which is consistent with high

behavioral disinhibition. Additive genetic influences accounted for 32% of the phenotypic

correlation between harm avoidance and novelty seeking. For the second factor, significant path

coefficients were observed for all the dimensions. The proportions of the phenotypic correlations

due to the second set of genetic effects were between 70% and 89%. Although the correlations

among the four personality dimensions were modest, these results suggested that shared genetic

influences played a substantial role in their covariation.

96

Table 4.8

Fit indices for independent pathway models

Model -2LL df AIC ∆χ2 ∆df ∆p TLI RMSEA

AAE 21421 7689 6043.2 .94 .032

AE 21476 7692 6091.6 54.42 3 < .001 .86 .049

Note. The best-fitting model is indicated in boldface type. AAE represents the model with two

additive genetic factors.

A = additive genetic component; C = shared environmental component; E = non-shared

environmental component; -2LL = -2 log likelihood; AIC = Akaike’s information criteria;

∆χ2 = chi-square difference test; TLI = Tucker Lewis index; RMSEA = Root mean square error

approximation.

97

Figure 4.1. Standardized path coefficients for the multivariate TPQ model

Figure 4.1. Solid lines indicate p < .05. Dashed lines indicate non-significance (p > .05). Double-

headed arrows signify that the variance of the genetic and environmental factors was set to one.

4.4 Study 2: Exploratory factor analyses of disinhibitory personality

4.4.1 Methods

4.4.1.1 Disinhibitory personality. In Study 1 there were phenotypic and genetic

correlations among the four dimensions of the TPQ. These results supported the overall goal of

this chapter, which was to obtain items from the TPQ that together would reflect an underlying

disinhibitory personality factor. The purpose of this study was to (a) identify items, and (b)

examine the validity of using items from different TPQ dimensions. Items were chosen if they

‘fit’ into one of three recognized components of behavioral disinhibition: lack of control, the

pursuit of potentially harmful experiences/environments, and disregard for social convention. All

Harm Avoidance

A1 A2

E1

Novelty Seeking

Reward Dependence

Persistence

1

1

1

.24 -.36

.19 .35

.20 .39

.80 -.23 .13

.07

98

four original dimensions were used. Most of the novelty seeking and all of the persistence items

were included. Seven subscales were hypothesized from the 30 items selected (see Table 4.9).

Items were coded as 0/1 and scored so that 1s were consistent with higher behavioral

disinhibition.

Table 4.9

Hypothesized subscales for disinhibitory personality

Factor Number of

Indicators Example Item

Fearlessness 2* I nearly always stay relaxed and carefree, even when

nearly everyone else is fearful.

Sensation seeking 4 When nothing new is happening, I usually start looking

for something that is thrilling or exciting.

Impulsivity 7 I often follow my instincts, hunches, or intuition without

thinking through all the details.

Reckless spending 4 I often spend money until I run out of cash or get into debt

from using too much credit.

Lack of

persistence

5 I am usually so determined that I continue to work long

after other people have given up. (reverse-scored)

Social

indifference

4 I don’t care very much whether other people like me or

the way I do things.

Social

noncompliance

4 I often break rules and regulations when I think I can get

away with it.

Note. *The fearlessness factor by itself was not identified, rather it was identified through

correlations with other factors.

4.4.1.2 Exploratory factor analyses. Exploratory factor analyses (EFA), conducted in

the statistical package Mplus (Muthén & Muthén, 1998-2012), were used to determine the factor

99

structure of the selected items. In addition to determining which indicators loaded on each factor,

EFA also provided information on how well the factor solution accounted for the variance in

each item. Importantly, it was not possible to conduct a true EFA because the disinhibitory

personality variables are dichotomous and violate the required assumption of continuous,

normally distributed data. Therefore, a model similar to confirmatory factor analysis was used,

only each indicator (item) was allowed to load on all factors and all factors were allowed to

correlate (Muthén, 1984; Muthén & Asparouhov, 2002). Each dichotomous indicator was

associated with an underlying latent response variable, which was thought to be continuous and

normally distributed. This assumption is not always appropriate. However, personality traits are

usually conceived of as normally distributed (Bouchard & Loehlin, 2001) and it is not

unreasonable to assume the items themselves reflect continuous-normal distributions. Finally,

robust weighted least squares (WLS) estimation (mean and variance adjusted) was used to

accommodate the dichotomous indicators (Muthén, 1997).

4.4.1.2.1 Data preparation. First, the dataset was examined for missing values. The

number of missing values per indicator ranged from zero to nine. Seven participants were

missing more than three indicators ( > 10%) and were excluded from further analyses. For the

EFA with WLS estimation a listwise-present analysis was required (n = 1341). Second, item

endorsement rates were examined to ensure sufficient variability within the sample. All but two

indicators had endorsement rates between 20% and 80%. The least endorsed (response = true)

items were part of the sensation-seeking subscale: “I like to stay at home better than to travel or

explore new places” (12.6%) and “I am slower than most people to get excited about new ideas

and activities” (19.4%). The largest male-female differences were for the two fearlessness

100

indicators. Males were about 1.8 times more likely than females to report a lack of fear in

situations where “most others would be fearful.”

Next, factorability was assessed using tetrachoric correlations. An indicator was included

in the analyses if it was correlated ≥ .2 with at least one other indicator (a) inside the

hypothesized subscale and (b) outside the hypothesized subscale. Twenty-seven of the 30

indicators met this requirement and were included in the EFA. Finally, the data was examined

for outliers. Since items were dichotomous the usual tests for univariate and multivariate outliers

were not applicable. Instead, the 27 indicators were summed within participants and the resulting

scores examined for outliers. No extreme scores were observed.

4.4.1.2.2 EFA specification. Both the slopes of the scree plot and the eigenvalues for the

sample correlation matrix were used to assess dimensionality. Eight eigenvalues greater than 1.0

(Kaiser rule) were observed (Table 4.10). The scree plot was consistent with the eigenvalues in

that it suggested roughly one, four, or eight factors (see Figure 4.2). Therefore, factor solutions

with one through eight factors were extracted. Oblique rotation was used so that the factors were

allowed to correlate. Finally, it was necessary that the model control for the dependence in the

sample, which consisted of many sets of relatives (mostly twins). A cluster variable

corresponding to family ID was created so Mplus could account for the lack of independence in

the data.

101

Table 4.10

Eigenvalues for the sample correlation matrix

Factors Eigenvalue

1 4.90

2 2.95

3 2.43

4 2.18

5 1.65

6 1.59

7 1.14

8 1.09

… …

27 0.12

102

Figure 4.2. Scree plot for EFA

Figure 4.2. Eigenvalues are presented for one through 27 factors (which reflect the 27 items

included in the EFA)

4.4.2 Results

Table 4.11 shows the model fit for the one, four, and eight-factor solutions. For the first

two models, χ2 values with p < .001 suggested the sample models did not reflect the population

and that estimated correlations were significantly different from the observed correlation

matrices. This may have been partly due to sample size (n = 1341) as χ2 is sensitive to large

samples. However, the lack of fit in the one-factor model was supported by the residual

103

correlation matrix, in which 44% of the residual correlations were ≥ .1. The Comparative fit

index (CFI) and TLI approximate fit indices suggested inadequate fit for both models, while the

RMSEA and Standardized root mean square residual (SRMR) were decent for the four-factor

model. All fit indices indicated the eight-factor model best fit the data. There were no residual

correlations were greater than .1 and the factor solution explained at least 40% of the variance in

20 of the 27 indicators.

Table 4.11

Model fit for one, four, and eight factors

Factors χ2 (mean and variance

adjusted)

p CFI TLI RMSEA SRMR

1 3545.61 < .001 0.50 0.46 .086 .136

4 1077.81 < .001 0.87 0.82 .050 .065

8 179.58 .177 0.99 0.99 .010 .022

Note. CFI = Comparative fit index; TLI = Tucker Lewis index; RMSEA = Root mean square

error approximation; SRMR = Standardized root mean square residual.

The seven predicted subscales emerged as well as an additional factor (Factor 2 in Table

4.12). Three indicators loaded highest on their hypothesized factor, but also loaded ≥ .3 on

Factor 2. The four indicators that loaded highest on Factor 2 (TPQ2, TPQ4, TPQ12, and TPQ14)

also had substantial partial correlations with their predicted factors. Therefore, seven factors

(instead of eight) were used in subsequent analyses. Each factor was significantly correlated with

at least three other factors (see Table 4.13) and four of the factors were correlated with 5 or more.

Therefore, at least part of the variation shared within a factor was related to variation in other

104

factors. Although the correlations were modest, they provided some support for using the items

together to represent disinhibitory personality.

Table 4.12

Factor loadings for the eight-factor solution

Hypothesized

Subscales TPQ

Item

Extracted Factor

1 2 3 4 5 6 7 8

Fearlessness 6 1.00 .002 -.005 -.030 .054 .052 -.015 -.007

16 .413 .246 .029 .086 -.050 -.089 .029 .117

Sensation

Seeking

2 .035 .581 .026 .564 -.031 -.009 .044 -.028

4 .018 .546 .029 .441 -.022 -.037 -.024 -.033

25 .016 -.085 .041 .415 -.028 -.085 -.162 .036

53 -.130 .028 -.042 .714 .049 .010 -.011 .004

Impulsivity

29 .077 .018 .688 .135 -.022 -.100 .048 .079

30 -.011 .478 .481 -.013 -.035 .009 -.003 -.031

33 -.011 .041 .960 -.117 .022 .044 -.019 -.047

34 -.021 -.103 .762 .050 .026 .002 -.017 .012

44 .092 -.033 .387 .025 .021 .024 .037 .069

12 -.015 .460 .236 -.053 .037 .047 -.033 .031

Social

Noncompliance

14 -.031 .538 .000 .100 .086 .191 .055 .347

36 .057 .368 .006 -.020 .011 -.027 -.131 .445

38 .007 -.008 .015 -.031 -.044 .028 .026 .829

Reckless

Spending

39 .021 -.028 .019 .246 .961 -.006 .006 -.021

40 .070 .205 .061 -.039 .754 -.053 .009 .020

41 -.058 .217 .004 -.053 .801 -.005 .065 -.059

48 -.011 -.042 -.043 .366 .702 .088 -.104 .080

Social

Indifference

52 -.028 -.213 .033 .020 .014 .009 .857 .118

9 .070 .250 -.034 -.025 .098 -.120 .335 -.022

15 .024 .254 -003 .047 -.009 -.041 .376 .042

35 .023 .016 -.018 -.110 -.030 .065 .762 -.055

Lack of

Persistence

24 -.038 .047 .093 .026 .034 .767 .017 .013

26 .135 -.274 .014 .162 -.064 .588 .048 -.087

28 .039 .448 -.079 -.079 -.009 .793 -.021 .026

54 -.121 -.086 .144 -.045 .046 .557 -.004 .084

Note. Values in boldface type represent the highest factor loading for the item. Other factor

loadings greater than .3 are italicized.

105

Table 4.13

Factor intercorrelations from the eight-factor solution

1 2 3 4 5 6 7 8

1

2 .298

3 .137 .134

4 .140 -.159 .134

5 -.058 .136 .240 -.137

6 -.046 -.173 .139 -.197 .161

7 .150 .125 .113 -.059 .060 .188

8 .106 .070 .256 .123 .150 .219 .084

Note. Correlations in boldface type were significant at the .05 level.

4.5 Study 3: Disinhibitory personality in a second community sample

4.5.1 Confirmatory factor analyses

A better fitting one-factor model from the EFA would have provided stronger evidence

that the TPQ items were reflective of a single process, such as disinhibitory personality.

However, because the factors in the eight-factor EFA were at least correlated, the validity of

using the 27 items was explored further. The main goal of Study 3 was to use confirmatory factor

analyses (CFA) to examine whether the correlated seven-factor model still fit reasonably well

when applied to a different sample. In CFA models, which indicators load on which factor is

specified ahead of time and the continuous latent variables may or may not be allowed to

correlate.

A total of 773 participants from the longitudinal twin sample had complete TPQ data and

were used in the CFA. The model consisted of the seven factors that were originally

hypothesized in Study 2, and all of the factors were allowed to correlate. Again, a cluster

variable based on family ID was used to account for the dependence inherent in twin samples.

106

Model fit for the CFA was somewhat poor, χ2 = 758.88, p < .001; RMSEA = .044; CFI =

0.86; TLI = 0.84. All items loaded significantly on their predicted factors except TPQ25, which

had one of the lowest endorsement rates. Each factor correlated with at least four other factors

(Table 4.14) and the magnitudes of the correlations were higher than those observed in the EFA.

Results from the CFA suggested that the factor structure from the EFA was reasonable in a

second sample. However the fit was less than adequate and it was likely that sample-specific

influences were contributing.

Table 4.14

Correlation estimates from the seven-factor confirmatory factor analysis

1 2 3 4 5 6 7

1 —

2 .558 —

3 .181 .348 —

4 .370 .400 .466 —

5 -.004 .103 .387 .396 —

6 .404 .089 .239 .343 .151 —

7 -.170 -.281 .362 .368 .348 .133

Note. Correlations in boldface type were significant at the .05 level (two-tailed).

4.5.2 Disinhibitory personality, novelty seeking, and behavioral disinhibition

4.5.2.1 Methods. The second goal of Study 3 was to compare the disinhibitory

personality dimension (DP) with novelty seeking (NS), which is the TPQ measure used in

several studies of behavioral disinhibition. First, descriptive information, distributions, and

heritabilities were compared. Then the extent to which the personality dimensions predicted

behaviors associated with behavioral disinhibition was examined.

107

Scores for both NS (18 items) and DP (27 items) were calculated as the mean number of

items endorsed. Participants were required to answer at least 90% of the items to be scored (N =

826). Because DP included 17 novelty-seeking items, differences between the two dimensions

could be attributed to ten unique items.

Behavioral disinhibition variables included a measure of conduct disorder and two

substance use measures (see Chapter 2 for a more detailed description). Conduct disorder was

defined as the number of symptoms endorsed (maximum = 15). Similarly, substance use was

measured as the number of substances used repeatedly. Dependence vulnerability assesses

dependence risk by examining how many dependence symptoms an individual has developed in

relation to the number of substances he or she has tried (Stallings et al. 2003). It was calculated

by taking the number of dependence symptoms divided by the number of substances used

repeatedly. All three measures were corrected for age and sex differences as well as for non-

normal distributions.

4.5.2.2 Results. Both personality dimensions were normally distributed and had good

internal consistency (αNS = .716, αDP = .709). Males (M = 0.514, SD = 0.156) scored

significantly higher on disinhibitory personality than females (M = 0.474, SD = 0.162), t(767) = -

3.09, p = .002. No sex differences were observed for novelty seeking. Age was not significantly

correlated with either dimension (rNS = .018, rDP = .003). Disinhibitory personality was

regressed on sex and the resulting standardized residuals were used in subsequent analyses.

Novelty seeking was standardized to be consistent with the other variables.

Table 4.15 presents twin correlations and univariate results for the personality

dimensions. Twin correlations were similar and suggested non-additive effects. Both dimensions

had modest heritability estimates around .30 with overlapping 95% confidence intervals. These

108

results suggested no difference in the proportion of the variance in each dimension that was

accounted for by genetic effects. The RMSEA and TLI indicated slightly better fit for

disinhibitory personality. Phenotypic correlations were also slightly higher for disinhibitory

personality (see Table 4.16). As expected the two personality dimensions were highly correlated

(r = .891). The correlation between novelty seeking and the 10 items unique to disinhibitory

personality was much smaller (r = .284).

Table 4.15

Twin correlations and univariate results for novelty seeking and disinhibitory personality

Zygosity Variance

Components

Model Fit

MZ DZ A E -2LL AIC RMSEA TLI

NS .35 .01 .32 .68 2161 631 .081 0.89

DP .32 .02 .29 .71 2164 632 .065 0.92

Note. NS = novelty seeking; DP = disinhibitory personality; MZ = monozygotic; DZ = dizygotic;

LL = log likelihood; AIC = Akaike’s information criteria; RMSEA = Root mean square error

approximation; TLI = Tucker Lewis index.

Table 4.16

Correlations among personality and behavioral disinhibition measures

NS DP DV SU CD

NS

DP .891

DV .204 .208

SU .248 .294 .637

CD .258 .300 .436 .483

Note. NS = novelty seeking; DP = disinhibitory personality (27 items including NS);

DV = dependence vulnerability, SU = substance use; CD = conduct disorder.

109

Regression analyses were used to examine how well the personality dimensions predicted

the behavioral disinhibition variables. One twin from each pair was randomly selected for

inclusion in the analyses (n = 383). For each behavioral disinhibition measure two models were

compared. Predictors in model 1 included the intercept, disinhibitory personality, and novelty

seeking. Disinhibitory personality was dropped from the 2nd model. The 1 df test of model fit

asked whether the model with disinhibitory personality was better able to predict behavioral

disinhibition than the model with novelty seeking alone. However, because novelty seeking and

disinhibitory personality shared many items, they would be redundant predictors if included in

the same model. Therefore, disinhibitory personality was calculated as the mean endorsement of

the 10 items not shared with novelty seeking. A tolerance value of .916 confirmed that the 10-

item disinhibitory personality was not redundant with novelty seeking.

Controlling for novelty seeking, disinhibitory personality was a significant predictor of

conduct disorder and substance use (Table 4.17). Beta coefficients were difficult to interpret

because of the data transformation procedures used. However, the positive coefficients indicated

that higher scores on the personality dimensions were associated with more conduct disorder

symptoms and greater substance use. Compared to a simple mean model, there was a significant

reduction in the proportion of error (R2) for both models. The model with both personality

indicators had an R2 greater than that for the second model with novelty seeking alone. Although

results indicated that disinhibitory personality increased the proportion of variance explained, the

size of its effect was very small. This suggested that little was gained by using it in addition to

novelty seeking.

110

Table 4.17

Results for behavioral disinhibition measures regressed on personality dimensions

Dependent Variable Model β1NS (p) β2DP (p) R2 ∆R2

Dependence DV=β0+ β1NS + β2DP .195 (< .001) .091 (.079) .058

Vulnerability DV=β0 + β1NS .221 (< .001) .050 – .008

Substance Use SU = β0 + β1NS + β2DP .191 (< .001) .176 (.001) .083

SU = β0 + β1NS .241 (< .001) .056 – .027

Conduct Disorder CD = β0 + β1NS +

β2DP

.268 (< .001) .118 (.024) .103

CD = β0 + β1NS .302 (< .001) .090 – .013

Note. The error terms are not shown. R2 = the proportional reduction in error compared to the

simple mean model (not shown).

DV = dependence vulnerability; SU = substance use; CD = conduct disorder; NS = novelty

seeking; DP = disinhibitory personality.

4.6 Study 4: Disinhibitory personality in a case-control sample selected for antisocial

substance dependence

Study 4 further examined the novelty seeking and disinhibitory personality dimensions

by comparing them in a selected case-control sample (Miles et al., 1998; Stallings et al., 2003).

Adolescent probands (cases) were recruited from substance abuse treatment centers and matched

with adolescent controls. In probands the prevalence of drug/alcohol dependence and conduct

disorder was significantly higher compared to controls. For this study 351 probands and 253

controls were 16 to 18 years-of-age and had complete TPQ data. Probands (MNS = .632, SDNS

= .177; MDP = .615, SDDP = .142) had higher scores, on average, than controls (MNS = .509, SDNS

= .177; MDP = .510, SDDP = .157). For disinhibitory personality, probands scored 0.67 SD higher

on average than controls. A similar effect size was observed for novelty seeking (d = 0.69).

Regression analyses were conducted using models identical to those in Study 3. However,

it was necessary to use logistic regression because the dependent variable (case-control status)

111

was not continuous. The coefficients were converted from log-odd units into odds ratios to

increase interpretability. The ten-item disinhibitory personality measure significantly predicted

case-control status over and above novelty seeking (p = .01). For every one-unit increase, the

odds of being assigned proband status were 3.66 higher. A one-unit increase corresponded to a

10% increase in the proportion of items endorsed. In this sample there was no difference in the

percentage of participants correctly classified by the two models (65%). This, coupled with the

fact that the beta coefficient for novelty seeking was about 3 times higher, suggested

disinhibitory personality did not considerably improve the prediction of case-control status.

4.7 Discussion

This chapter aimed to develop a disinhibitory personality dimension using items from the

TPQ. The effectiveness of the new dimension was assessed using the following criteria. First, it

was necessary that the dimension include non-novelty seeking items so that other aspects of

behavioral disinhibition, such as lack of persistence, disregard for social norms, and fearlessness

were being tapped. Second, the dimension needed to possess good psychometric qualities

including a normal distribution and acceptable internal consistency. And an absence of age and

sex differences was preferable. A third requirement was that the dimension be heritable with an

estimate at least as high as those observed for the original dimensions. Finally, it was important

that disinhibitory personality predicted behavioral disinhibition variables better than novelty

seeking alone, both in community samples and a sample selected for antisocial substance

dependence.

Study 1 showed that the four original dimensions were correlated and had some genetic

influences in common, which supported the use of items from the different dimensions. There

112

was a negative genetic correlation between novelty seeking and harm avoidance, and a positive

genetic correlation between novelty seeking and reward dependence. The same pattern was

observed in a study of adults (Heath et al., 1994) and in adult females (Stallings et al., 1996). The

lack of genetic correlations for men in Stallings et al. (1996) may be partly due to age differences.

The mean age of their sample was 67, compared to 42 years in Heath et al. (1994) and 17 years

in this study. (Some evidence indicates personality and its etiology may change across the

lifespan; Bouchard & Loehlin, 2001).

In the second study EFA items loaded on their predicted subscales. However, the one-

factor model had poor fit (χ2 = 3545.61, p < .001; TLI = 0.46; RMSEA = .086), which did not

indicate a strong, single disinhibitory personality factor. In the CFA from Study 3, indicators

loaded highly on their factors and factors were correlated, but the fit was lacking. Overall the

correlated factors in the EFA and CFA provided modest support for a disinhibitory personality

dimension.

Study 3 showed that the new dimension was normally distributed and had acceptable

reliability (αDP = .709). But males (M = 0.514, SD = 0.156) scored higher than females (M =

0.474, SD = 0.162), t(767) = -3.09, p = .002. The heritability estimate (h2 = .29) and correlations

with behavioral disinhibition variables were both satisfactory. Finally, regression analyses in the

LTS (community sample) and case-control sample indicated that disinhibitory personality was a

significant predictor over and above novelty seeking. At first glance this appeared to support the

use of disinhibitory personality. However, the effect sizes were not consistent with a meaningful

gain in prediction.

In summary, findings suggest that the four TPQ dimensions are correlated and share

some genetic influences. This is consistent with several studies reporting correlations for the

113

dimensions in Cloninger’s Temperament and Character Inventory (Miettunen, Lauronen,

Kantojärvi, Veijola, & Joukamaa, 2008) and with biometrical results reported for women

(Stallings et al., 1996) and Australian adults (Heath et al., 1994). The disinhibitory personality

dimension, which was created using TPQ items from both novelty seeking and other dimensions,

had good psychometric properties and was partially supported by studies using EFA and CFA. It

predicted conduct disorder and substance use, as well as case-control status in a sample selected

for antisocial substance dependence. However there was no substantial gain in predictive power

over that of novelty seeking. Despite our best efforts to increase prediction with items

theoretically related to disinhibited behavior, results suggest that when using the TPQ in studies

of behavioral disinhibition novelty seeking is an appropriate choice.

114

Chapter 5

Summary and Conclusions

5.1 Introduction

Behavioral disinhibition has been characterized as an underlying liability to substance use

disorders, antisocial behavior, and other behaviors such as risky sex. As the term implies, deficits

in inhibition may play a role in these comorbid behaviors. Inhibition is part of a set of executive

functions important in controlled and goal-oriented thoughts and behavior. Deficits in other

executive functions such as working memory and shifting may also reflect the liability to

substance use disorders and antisocial behavior. Finally, personality traits such as impulsivity,

novelty seeking and negative emotionality have been shown to predict substance use behavior

(Elkins, McGue, Malone, & Iacono, 2004; Grekin, Sher, & Woods, 2006; Jang, Vernon, &

Livesley, 2000) and antisocial behavior (Tackett, Waldman, Van Hulle, & Lahey, 2011;

Waldman et al., 2011). This dissertation used genetically informative samples to better

understand the nature of behavioral disinhibition. Specifically, cognitive and personality factors

thought to reflect behavioral disinhibition were examined.

5.2 Summary of Results

5.2.1 Chapter 2: Behavioral Disinhibition and Executive Functions: Genetic Correlations

are Stronger for Substance Use than Dependence Vulnerability

In Chapter 2, univariate twin analyses indicated that individual differences in conduct

disorder (CD) and novelty seeking (NS) could be explained by additive genetic influences (ACD

= .59, ANS = .32) with the remaining variance due to non-shared environmental effects. Shared-

environmental influences were detected for substance use (SU; C = .48) and dependence

115

vulnerability (DV; C = .34) in addition to additive genetic effects (ASU = .27, ADV = .37).

Multivariate analyses suggested that independent pathway models with additive genetic (A),

shared-environmental (C), and non-shared environmental (E) effects were the best fitting for

both behavioral disinhibition with SU and behavioral disinhibition with DV. Common pathway

models also showed adequate fit to the data and were used to be consistent with other studies of

behavioral disinhibition (Young et al., 2000; Young et al., 2009; Krueger et al., 2002).

Behavioral disinhibition was more heritable when DV was used (A = .79) than when SU was

included (A = .28). An opposite pattern was observed for the proportion of variance explained by

the shared environment (CDV = .19, CSU = .59).

Although genetic influences on behavioral disinhibition with SU were smaller, they were

more highly correlated with genetic influences on the common executive function factor

(Common EF; r = -.54) than genetic influences on behavioral disinhibition with DV (rg = -.23).

This finding was supported by models in which Common EF was correlated with SU (rg = -.43)

and DV alone (rg = -.17), ∆χ2 = 4.52, ∆df = 1, ∆p = .034. Environmental influences on Common

EF (E = .04) were not related to environmental influences on behavioral disinhibition. Neither

genetic nor environmental effects on behavioral disinhibition were correlated with the updating-

and shifting-specific factors. These findings suggests that individual differences or deficits in

executive functions may be more important for behaviors relating to the initiation and regular use

of substances than for the development of problem use and/or dependence. Differences in the

genetic and environmental structure of behavioral disinhibition suggest that the type of substance

measure to be included in the construct should be carefully considered.

116

5.2.2 Chapter 3: The Role of Executive Functioning in the Progression from Substances

Use to Substance Dependence

Genetic covariances between Common EF and each stage in a substance use model were

estimated in Chapter 3. In addition to multi-substance use—alcohol, tobacco, and cannabis were

examined. Univariate results indicated additive genetic influences (A = .23 to .47) and

substantial shared environmental influences (C = .36 to .57) on age-of-onset regardless of

substance type. Shared environmental influences were also observed for the problem use stage of

multi-substance use (C = .58). For all substances except cannabis, additive genetic effects (A

= .76 to .91) were responsible for most of the variance in the dependence stage. Non-shared

environmental influences explained the remainder of the variance. This pattern of findings is

consistent with studies in which the shared environment had a larger effect on earlier substance

use stages than on substance dependence (Stallings, Gizer, & Young-Wolff, in press).

Substance stages were correlated for all substances except cannabis. Bivariate results

indicated that covariance between age-of-onset and dependence was mostly due to shared-

environmental influences. Despite their large magnitude however, these cross paths were non-

significant. In the multi-substance trivariate model, genetic influences on age-of-onset

contributed to problem use (a12 = .48) but not dependence (a13 = -.05). The remaining A, C, and

E factors (independent of influences on age-of-onset) accounted for most of the covariance

between problem use and dependence (71%).

Negative genetic correlations between age-of-onset and Common EF were observed for

alcohol (rg = -.36), tobacco (rg = -.47), cannabis (rg = -.54), and multi-substance use (rg = -.45).

Furthermore, genetic influences on multi-substance age-of-onset accounted for 83% of the

covariance between Common EF and problem use, but only 7% of the covariance between

Common EF and dependence. These findings suggest that genetic influences shared with

117

Common EF may be more important in earlier stages of the substance use trajectory. However,

results should be interpreted with caution because many path coefficients were non-significant.

This was likely due the small number of participants represented in the later substance use stages.

5.2.3 Chapter 4: Using Items from the Tridimensional Personality Questionnaire to Assess

Behavioral Disinhibition

The goal of Chapter 4 was to identify items from the TPQ short-version that were

theoretically related to behavioral disinhibition and could predict antisocial behavior and

substance use disorders better than the original novelty seeking dimension. Four separate studies

were used to address these aims. In Study 1, univariate results for the four dimensions (harm

avoidance, reward dependence, novelty seeking, and persistence) indicated that AE models

provided adequate fit to the data. The proportion of variance in the dimensions explained by

additive genetic effects ranged from .23 to .37. Significant correlations were observed among the

dimensions, which supported the use of a multivariate twin analysis. An independent pathway

model with two additive genetic factors best fit the data, -2LL = 21421, df = 7689; AIC = 6043;

TLI = .94; RMSEA = .032. Additive genetic covariance between harm avoidance and novelty

seeking (rg = -.72) suggested a negative relationship in which genetic influences contributed to

high novelty seeking/low harm avoidance and low novelty seeking/high harm avoidance profiles.

Novelty seeking, reward dependence, and persistence all loaded positively on the second additive

genetic factor.

An exploratory factor analysis (EFA) conducted in Study 2 suggested an eight-factor

model with correlated factors could be used to characterize the thirty selected items. The

hypothesized subscales were reflected in seven of the eight factors. Because the factors were

correlated there was some support for using items from the TPQ dimension to represent

disinhibitory personality. Using a separate sample, a confirmatory factor analyses was conducted

118

in Study 3. The factor structure from the EFA was replicated, however fit to the data was less

than adequate. Disinhibitory personality (DP) and novelty seeking (NS) were both normally

distributed and had similar internal consistency (αNS = .716, αDP = .709) and heritability

estimates (h2NS = .32, h2

DP = .29). They were both correlated with the three behavioral

disinhibition measures: substance use (SU), dependence vulnerability (DV) and conduct disorder

(CD). Regression analyses on CD and SU indicated disinhibitory personality was a significant

predictor over and above novelty seeking (pCD = .024, pSU = .001). However its inclusion

resulted in a very small increase in the proportional reduction of error (∆R2CD = .008, ∆R2

SU

= .027. Similar results were observed in logistic regression analyses in Study 4. DP significantly

predicted case-control status in a sample selected for antisocial substance dependence (p = .01),

but there was no substantial increase in prediction over NS. Results from the four studies suggest

that additional disinhibitory personality items do not provide better assessment of behavioral

disinhibition than NS items.

5.3 Conclusions

Together these studies have attempted to better understand behavioral disinhibition by

exploring its genetic and environmental relations to executive functions and personality. First,

findings showed that behavioral disinhibition was more heritable when a measure of dependence

vulnerability was used in the construct than when substance use was included. Second, genetic

influences on Common EF were negatively correlated with genetic influences on behavioral

disinhibition. Third, the correlation was stronger when substance use was included in the

measure of behavioral disinhibition than when dependence vulnerability was used. Fourth,

genetic influences on Common EF were negatively correlated with genetic influences on age-of-

119

onset, independent of substance type. Finally, items from the TPQ though to reflect behavioral

disinhibition did not improve upon novelty seeking’s ability to predict antisocial behavior and

substance use disorders. Findings suggest genetic influences on executive functions and

personality are related to behavioral disinhibition. More work is needed to determine if

personality and deficits in executive function are risk factors that could be used to predict the

onset of antisocial behavior and problem substance use.

120

References

Agrawal, A., Grant, J. D., Waldron, M., Duncan, A. E., Scherrer, J. F., Lynskey, M. T., et al.

(2006). Risk for initiation of substance use as a function of age of onset of cigarette, alcohol

and cannabis use: Findings in a Midwestern female twin cohort. Preventive Medicine, 43(2),

125–128. doi:10.1016/j.ypmed.2006.03.022

Akaike, H. (1987). Factor Analysis and the AIC. Psychometrika, 52, 317–332.

American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders

(4th ed.). Washington, DC: Author.

Armstrong, T. D., & Costello, E. J. (2002). Community studies on adolescent substance use,

abuse, or dependence and psychiatric comorbidity. Journal of Consulting and Clinical

Psychology, 70(6), 1224–1239. doi:10.1037//0022-006X.70.6.1224

Bouchard, T. J., & Loehlin, J. C. (2001). Genes, evolution, and personality. Behavior Genetics,

31(3), 243–273.

Bukstein, O. G., Brent, D. A., & Kaminer, Y. (1989). Comorbidity of substance abuse and other

psychiatric disorders in adolescents. Am J Psychiatry, 146(9), 1131–1141.

Button, T. M. M., Hewitt, J. K., Rhee, S. H., Young, S. E., Corley, R. P., & Stallings, M. C.

(2006). Examination of the causes of covariation between conduct disorder symptoms and

vulnerability to drug dependence. Twin Research and Human Genetics, 9(1), 38–45.

Button, T. M. M., Rhee, S. H., Hewitt, J. K., Young, S. E., Corley, R. P., & Stallings, M. C.

(2007). The role of conduct disorder in explaining the comorbidity between alcohol and

illicit drug dependence in adolescence. Drug and Alcohol Dependence, 87(1), 46–53.

doi:10.1016/j.drugalcdep.2006.07.012

Chassin, L., Flora, D. B., & King, K. M. (2004). Trajectories of Alcohol and Drug Use and

Dependence From Adolescence to Adulthood: The Effects of Familial Alcoholism and

Personality. Journal of Abnormal Psychology, 113(4), 483–498. doi:10.1037/0021-

843X.113.4.483

Cloninger, C. R. (1986). A unified biosocial theory of personality and its role in the development

of anxiety states. Psychiatric Developments, 3, 167-226.

Cloninger, C. R. (1987). The tridimensional personality questionnaire: Version 4. St Louis,

MO: Department of Psychiatry, Washington University School of Medicine.

Cloninger, C.R., Przybeck, T. R., Svrakic, D. M. (1991). The Tridimensional Personality

Questionnaire: U.S. normative data. Psychological Reports, 69(3), 1047–1057.

121

Cohen, J. D., MacWhinney, B., Flatt, M., & Provost, J. (1993). PsyScope: A new graphic

interactive environment for designing psychology exper- iments. Behavioral Research

Methods, Instruments, & Computers, 25, 257–271.

Compton, W. M., Thomas, Y. F., Stinson, F. S., & Grant, B. F. (2007). Prevalence, correlates,

disability, and comorbidity of DSM-IV drug abuse and dependence in the United States:

results from the national epidemiologic survey on alcohol and related conditions. Archives of

General Psychiatry, 64(5), 566.

Disney, E. R., Elkins, I. J., McGue, M., & Iacono, W. G. (1999). Effects of ADHD, conduct

disorder, and gender on substance use and abuse in adolescence. American Journal of

Psychiatry, 156(10), 1515–1521.

Eaves, L. J., Heath, A. C., Neale, M. C., Hewitt, J. K., & Martin, N. G. (1998). Sex differences

and non-additivity in the effects of genes on personality. Twin Res. 1:131–137

Elkins, I. J., McGue, M., Malone, S. M., & Iacono, W. G. (2004). The effect of parental alcohol

and drug disorders on adolescent personality. American Journal of Psychiatry, 161(4), 670–

676.

Friedman, N. P., Miyake, A., Young, S. E., DeFries, J. C., Corley, R. P., & Hewitt, J. K. (2008).

Individual differences in executive functions are almost entirely genetic in origin. Journal of

Experimental Psychology: General, 137(2), 201–225. doi:10.1037/0096-3445.137.2.201

George, O., & Koob, G. F. (2010). Neuroscience and Biobehavioral Reviews. Neuroscience &

Biobehavioral Reviews, 35(2), 232–247. doi:10.1016/j.neubiorev.2010.05.002

Goldstein, R. Z., & Volkow, N. D. (2011). Dysfunction of the prefrontal cortex in addiction:

neuroimaging findings and clinical implications, 1–18. doi:10.1038/nrn3119

Gorenstein, E. E., & Newman, J. P. (1980). Disinhibitory psychopathology: a new perspective

and a model for research. Psychological Review, 87(3), 301.

Grove, W. M., Eckert, E. D., Heston, L., Bouchard, T. J., Segal, N., & Lykken, D. T. (1990).

Heritability of substance abuse and antisocial behavior: a study of monozygotic twins reared

apart. Biological Psychiatry, 27(12), 1293–1304.

Hasin, D. S., Stinson, F. S., Ogburn, E., & Grant, B. F. (2007). Prevalence, correlates, disability,

and comorbidity of DSM-IV alcohol abuse and dependence in the United States: results from

the National Epidemiologic Survey on Alcohol and Related Conditions. Archives of General

Psychiatry, 64(7), 830.

Heath, A. C., Cloninger, C. R., & Martin, N. G. (1994). Testing a model for the genetic structure

of personality: A comparison of the personality systems of Cloninger and Eysenck. Journal

of Personality and Social Psychology, 66(4), 762–775.

122

Heath, A. C., Martin, N. G., Lynskey, M. T., Todorov, A., & Madden, P. A. F. (2002).

Estimating two-stage models for genetic influences on alcohol, tobacco or drug use initiation\

and dependence vulnerability in twin and family data. Twin Research, 5(2), 113–124.

Heiman, N., Stallings, M. C., Young, S. E., & Hewitt, J. K. (2004). Investigating the genetic and

environmental structure of Cloninger's personality dimensions in adolescence. Twin

Research, 7(5), 462–470.

Hester, R., Lubman, D. I., & Yücel, M. (2010). The role of executive control in human drug

addiction. In Behavioral Neuroscience of Drug Addiction (pp. 301-318). Springer Berlin

Heidelberg.

Hicks, B. M., Krueger, R. F., Iacono, W. G., McGue, M., & Patrick, C. J. (2004). Family

transmission and heritability of externalizing disorders: a twin-family study. Archives of

General Psychiatry, 61(9), 922.

Howard, M. O., Kivlahan, D., & Walker, R. D. (1997). Cloninger’s tridimensional theory of

personality and psychopathology: Applications to substance use disorders. Journal of Studies

on Alcohol, 58(1), 48–66.

Hu, L., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to

underparameterized model misspecification. Psychological Methods, 3(4), 424–453.

IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY:

IBM Corp.

Iacono, W. G., Malone, S. M., & McGue, M. (2008). Behavioral Disinhibition and the

Development of Early-Onset Addiction: Common and Specific Influences. Annual Review of

Clinical Psychology, 4(1), 325–348. doi:10.1146/annurev.clinpsy.4.022007.141157

Iacono, W.G., McGue, M., Krueger, R.F., 2006. Minnesota Center for twin and family research.

Twin Res. Hum. Genet. 9, 978–984.

Jang, K. L., Vernon, P. A., & Livesley, W. J. (2000). Personality disorder traits, family

environment, and alcohol misuse: A multivariate behavioural genetic analysis. Addiction,

95(6), 873–888.

Keller, M. C., Coventry, W. L., Heath, A. C., & Martin, N. G. (2005). Widespread Evidence for

Non-Additive Genetic Variation in Cloninger’s and Eysenck’s Personality Dimensions using

a Twin Plus Sibling Design. Behavior Genetics, 35(6), 707–721. doi:10.1007/s10519-005-

6041-7

Kendler, K. S., Davis, C. G., & Kessler, R. C. (1997). The familial aggregation of common

psychiatric and substance use disorders in the National Comorbidity Survey: a family history

study. The British Journal of Psychiatry, 170(6), 541–548. doi:10.1192/bjp.170.6.541

123

Kendler, K. S., Prescott, C. A., Myers, J., & Neale, M. C. (2003). The structure of genetic and

environmental risk factors for common psychiatric and substance use disorders in men and

women. Archives of General Psychiatry, 60(9), 929.

Kessler, R. C., Chiu, W. T., Demler, O., & Walters, E. E. (2005). Prevalence, Severity, and

Comorbidity of 12-Month DSM-IV Disorders in the National Comorbidity Survey

Replication. Archives of General Psychiatry, 62(6), 617. doi:10.1001/archpsyc.62.6.617

Koob, G. F., & Le Moal, M. (2001). Drug addiction, dysregulation of reward, and allostasis.

Neuropsychopharmacology, 24(2), 97–129.

Krueger, R. F. (1999). Personality Traits in Late Adolescence Predict Mental Disorders in Early

Adulthood: A Perspective�Epidemiological Study. Journal of Personality, 67(1), 39–65.

Krueger, R. F., Hicks, B. M., Patrick, C. J., Carlson, S. R., Iacono, W. G., & McGue, M. (2002).

Etiologic connections among substance dependence, antisocial behavior and personality:

Modeling the externalizing spectrum. Journal of Abnormal Psychology, 111(3), 411–424.

doi:10.1037//0021-843X.111.3.411

Kuo, P.-H., Chih, Y. C., Soong, W. T., Yang, H. J., & Chen, W. J. (2004). Assessing personality

features and their relations with behavioral problems in adolescents: Tridimensional

Personality Questionnaire and Junior Eysenck Personality Questionnaire. Comprehensive

Psychiatry, 45(1), 20–28.

Lando, H. A., Thai, D. T., Murray, D. M., Robinson, L. A., Jeffery, R. W., Sherwood, N. E. &

Hennrikus, D. J. (1999). Age of initiation, smoking patterns, and risk in a population of

working adults. Preventive Medicine, 29, 590–598.

Lewinsohn, P.M., Rohde, P., Seeley, J.R., 1996. Alcohol consumption in high school

adolescents: frequency of use and dimensional structure of associated problems. Addiction,

91, 375–390.

Loeber, S., Nakovics, H., Kniest, A., Kiefer, F., Mann, K., & Croissant, B. (2012). Drug and

Alcohol Dependence. Drug and Alcohol Dependence, 120(1-3), 81–87.

doi:10.1016/j.drugalcdep.2011.07.001

Loehlin, J. C. (1992). Genes and Environment in Personality Development, Sage, Newbury

Park, CA.

Logan, G. D. (1994). On the ability to inhibit thought and action: A user’s guide to the stop

signal paradigm. In D. Dagenbach & T. H. Carr (Eds.), Inhibitory processes in attention,

memory, and language (pp. 189–239).

Mayr, U., & Kliegl, R. (2000). Task-set switching and long-term memory retrieval. Journal of

Experimental Psychology: Learning, Memory, and Cognition, 26(5), 1124–1140.

doi: 10.1037/0278-7393.26.5.1124

124

Miettunen, J., Lauronen, E., Kantojärvi, L., Veijola, J., & Joukamaa, M. (2008). Inter-

correlations between Cloninger's temperament dimensions — A meta-analysis. Psychiatry

Research, 160(1), 106–114. doi:10.1016/j.psychres.2007.05.003

Miettunen, J., Veijola, J., Lauronen, E., Kantojärvi, L., & Joukamaa, M. (2007). Sex differences

in Cloninger's temperament dimensions—a meta-analysis. Comprehensive Psychiatry, 48(2),

161–169. doi:10.1016/j.comppsych.2006.10.007

Miles, D. R., Stallings, M. C., Young, S. E., Hewitt, J. K., Crowley, T. J., & Fulker, D. W.

(1998). A family history and direct interview study of the familial aggregation of substance

abuse: the adolescent substance abuse study. Drug and Alcohol Dependence, 49(2), 105–

114.

Miyake, A., Emerson, M. J., Padilla, F., & Ahn, J.-C. (2004). Inner speech as a retrieval aid for

task goals: The effects of cue type and articulatory suppression in the random task cuing

paradigm. Acta Psychologica, 115(2-3), 123–142. doi: 10.1016/j.actpsy.2003.12.004

Moffitt, T. E. (Ed.). (2001). Sex differences in antisocial behaviour: Conduct

disorder, delinquency, and violence in the Dunedin Longitudinal Study.

Cambridge University Press.

Montgomery, C., Fisk, J. E., Murphy, P. N., Ryland, I., & Hilton, J. (2012). The effects of heavy

social drinking on executive function: a systematic review and meta-analytic study of

existing literature and new empirical findings. Human Psychopharmacology: Clinical and

Experimental, 27(2), 187–199. doi:10.1002/hup.1268

Morris, N., & Jones, D. M. (1990). Memory updating in working memory: The role of the

central executive. British Journal of Psychology, 81(2), 111–121.

Munakata, Y., Herd, S. A., Chatham, C. H., Depue, B. E., Banich, M. T., & O’Reilly, R. C.

(2011). A unified framework for inhibitory control. Trends in Cognitive Sciences, 15(10),

453–459. doi:10.1016/j.tics.2011.07.011

Murphy, P. N., Bruno, R., Ryland, I., Wareing, M., Fisk, J. E., Montgomery, C., & Hilton, J.

(2012). The effects of “ecstasy” (MDMA) on visuospatial memory performance: findings

from a systematic review with meta-analyses. Human Psychopharmacology: Clinical and

Experimental, 27(2), 113–138. doi:10.1002/hup.1270

Muthén, B. O. (1984). A general structural equation model with dichotomous, ordered

categorical, and continuous latent variable indicators. Psychometrika, 49(1), 115–132.

Muthén, B., & Asparouhov, T. (2002). Latent variable analysis with categorical outcomes:

Multiple-group and growth modeling in Mplus. Mplus web notes, 4(5), 1-22.

Muthén, B., Du Toit, S. H., & Spisic, D. (1997). Robust inference using weighted least squares

and quadratic estimating equations in latent variable modeling with categorical and

125

continuous outcomes. Psychometrika, 75, 1-45.

Muthén, L. K., & Muthén, B. O. (1998-2012). Mplus User's Guide. Seventh Edition. Los

Angeles, CA: Muthén & Muthén.

Neale, M. C., Boker, S. M., Xie, G., & Maes, H. H. (2003). Mx: Statistical modeling (6th ed.).

Richmond: Virginia Commonwealth University Department of Psychiatry.

Neale, M. C. & Cardon, L. R. (1992). Methodology for genetic studies of twins and families.

Norwell, MA: Kluwer Academic Publishers.

Nichols, R. C., & Bilbro, W. C., Jr. (1966). The diagnosis of twin zygosity. Acta genetica et

statistica medica, 16(3), 265–275. doi: 10.1159/000151973

Nock, M. K., Kazdin, A. E., Hiripi, E., & Kessler, R. C. (2006). Prevalence, subtypes, and

correlates of DSM-IV conduct disorder in the National Comorbidity Survey Replication.

Psychological Medicine, 36(05), 699–710.

Otter, C. (2003). Cloninger's Tridimensional Personality Questionnaire: Reliability In An

English Sample, 1–10.

Palmer, R. H. C., Knopik, V. S., Rhee, S. H., Hopfer, C. J., Corley, R. C., Young, S. E., et al.

(2010). Prospective effects of adolescent indicators of behavioral disinhibition on DSM-IV

alcohol, tobacco, and illicit drug dependence in young adulthood. Addictive Behaviors,

38(9), 2415–2421. doi:10.1016/j.addbeh.2013.03.021

Pickens, R. W., Svikis, D. S., McGue, M., & LaBuda, M. C. (1995). Common genetic

mechanisms in alcohol, drug, and mental disorder comorbidity. Drug and Alcohol

Dependence, 39(2), 129–138.

Rhea, S. A., Gross, A. A., Haberstick, B. C., & Corley, R. P. (2006). Colorado twin registry.

Twin Research and Human Genetics, 9(6), 941–949.

Robins, L. N., Cottler, L. B., Babor, T. F. (1993). WHO-NIH Composite International

Diagnostic Interview–Substance Abuse Module. St Louis, MO: Washington University

School of Medicine, Department of Psychiatry.

Robins, L. N., Cottler, L. B., Bucholz, K. K., Compton, W. M., North, C. S., & Rourke, K. M.

(2000). Diagnostic Interview Schedule for the DSM-IV (DIS-IV).

Roberts, R. J., Hager, L. D., & Heron, C. (1994). Prefrontal cognitive processes: Working

memory and inhibition in the antisaccade task. Journal of Experimental Psychology:

General, 123(4), 374–393. doi: 10.1037/0096-3445.123.4.374

Rogers, R. D., & Monsell, S. (1995). Costs of a predictable switch between simple cognitive

tasks. Journal of Experimental Psychology: General,124(2), 207–231.

doi: 10.1037/0096-3445.124.2.207

126

Shaffer, D., Fisher, P., Dulcan, M. K., Davies, M., Piacentini, J., Schwab-Stone, M. E., ... &

Regier, D. A. (1996). The NIMH Diagnostic Interview Schedule for Children Version 2.3

(DISC-2.3): Description, acceptability, prevalence rates, and performance in the MECA

study. Journal of the American Academy of Child & Adolescent Psychiatry, 35(7), 865-877.

Shaffer D, Fisher P, Lucas CP, Dulcan MK, Schwab-Stone ME (2000). NIMH Diagnostic

Interview Schedule for Children Version IV (NIMH DISC-IV): description, differences

from previous versions, and reliability of some common diagnoses. J Am Acad Child

Adolesc Psychiatry, 39(1):28-38. pubmed 10638065

Sher, K. J., & Trull, T. J. (1994). Personality and disinhibitory psychopathology: alcoholism and

antisocial personality disorder. Journal of Abnormal Psychology, 103(1), 92.

Sher, K. J., Wood, M. D., Crews, T. M., & Vandiver, P. A. (1995). The Tridimensional

Personality Questionnaire: Reliability and validity studies and derivation of a short form,

7(2), 195–208.

Slutske, W. S., Heath, A. C., Dinwiddie, S., Madden, P. A. F., Bucholz, K. K., Dunne, M. P., et

al. (1998). Common genetic risk factors for conduct disorder and alcohol dependence.

Journal of Abnormal Psychology, 107(3), 363.

Slutske, W. S., Heath, A. C., Madden, P. A. F., Bucholz, K. K., Statham, D. J., & Martin, N. G.

(2002). Personality and the genetic risk for alcohol dependence. Journal of Abnormal

Psychology, 111(1), 124–133. doi:10.1037//0021-843X.111.1.124

Stallings, M. C., Cherny, S., Young, S. E., Miles, D. R., Hewitt, J. K., & Fulker, D. W. (1997).

The familial aggregation of depressive symptoms, antisocial behavior, and alcohol abuse.

American Journal of Medical Genetics, 74(2), 183–191.

Stallings, M. C., Corley, R. P., Hewitt, J. K., Krauter, K. S., Lessem, J. M., Mikulich, S. K., et al.

(2003). A genome-wide search for quantitative trait loci influencing substance dependence

vulnerability in adolescence. Drug and Alcohol Dependence, 70(3), 295–307.

doi:10.1016/S0376-8716(03)00031-0

Stallings, M. C., Gizer, I. R. and Young-Wolff, K. C. (in press). Genetic epidemiology and

molecular genetics, in Sher, K (Ed.) Oxford Handbook of Substance Use Disorders, Oxford

University Press.

Stallings, M. C., Hewitt, J. K., Cloninger, C. R., Heath, A. C., & Eaves, L. J. (1996). Genetic and

environmental structure of the Tridimensional Personality Questionnaire: Three or four

temperament dimensions? Journal of Personality and Social Psychology, 70(1), 127.

Steiger, J. H. and J. C. Lind. 1980. ‘‘Statistically based tests for the number of common factors.’’

Paper presented at the annual meeting of the Psychometric Society, May, Iowa, City, IA.

Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental

127

Psychology, 18(6), 643–662. doi: 10.1037/h0054651

Tackett, J. L., Waldman, I. D., Van Hulle, C. A., & Lahey, B. B. (2011). Shared genetic

influences on negative emotionality and major depression/conduct disorder comorbidity.

Journal of the American Academy of Child & Adolescent Psychiatry, 50(8), 818-827.

Tellegen A., Waller N. G. (2001). Exploring personality through test construction: Development

of the Multidimensional Personality Questionnaire. Minneapolis, University

of Minnesota Press.

Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor

analysis. Psychometrika, 38, 1-10.

Vrieze, S. I., Hicks, B. M., Iacono, W. G., & McGue, M. (2012). Decline in Genetic Influence on

the Co-Occurrence of Alcohol, Marijuana, and Nicotine Dependence Symptoms From Age

14 to 29. American Journal of Psychiatry.

Waldman, I. D., Tackett, J. L., Van Hulle, C. A., Applegate, B., Pardini, D., Frick, P. J., &

Lahey, B. B. (2011). Child and adolescent conduct disorder substantially shares genetic

influences with three socioemotional dispositions. Journal of abnormal psychology, 120(1),

57.

Waller, N. G., Lilienfeld, S. O., Tellegen, A., & Lykken, D. T. (1991). The tridimensional

personality questionnaire: Structural validity and comparison with the multidimensional

personality questionnaire. Multivariate Behavioral Research, 26(1), 1–23.

Wilcox, R. R., & Keselman, H. J. (2003). Modern robust data analysis methods: Measures of

central tendency. Psychological Methods, 8(3), 254 – 274.

doi: 10.1037/1082-989X.8.3.254

Willcutt, E. G., Doyle, A. E., Nigg, J. T., Faraone, S. V., & Pennington, B. F. (2005). Validity of

the Executive Function Theory of Attention-Deficit/Hyperactivity Disorder: A Meta-

Analytic Review. Biological Psychiatry, 57(11), 1336–1346.

doi:10.1016/j.biopsych.2005.02.006

Yntema, D. B. (1963). Keeping track of several things at once. Human Factors, 5, 7–17.

Young, S. E., Corley, R. P., Stallings, M. C., Rhee, S. H., Crowley, T. J., & Hewitt, J. K. (2002).

Substance use, abuse and dependence in adolescence: prevalence, symptom profiles and

correlates. Drug and Alcohol Dependence, 68(3), 309–322.

Young, S. E., Friedman, N. P., Miyake, A., Willcutt, E. G., Corley, R. P., Haberstick, B. C., &

Hewitt, J. K. (2009). Behavioral disinhibition: Liability for externalizing spectrum disorders

and its genetic and environmental relation to response inhibition across adolescence. Journal

of Abnormal Psychology, 118(1), 117–130. doi:10.1037/a0014657

128

Young, S. E., Stallings, M. C., & Corley, R. P. (2000). Genetic and environmental influences on

behavioral disinhibition. American Journal of Medical Genetics (Neuropsychiatric Genetics),

96, 684–695.

personality, executive functions, and behavioral disinhibition in adolescence

Documents