THE SPECIFICITY AND NEURAL BASIS OF IMPAIRED INHIBITORY CONTROL

by

Jonathan Shilo Lipszyc

A thesis submitted in conformity with the requirements for the degree of Master of Science

Institute of Medical Science University of Toronto

© Copyright by Jonathan Lipszyc 2009

ii

The Specificity and Neural Basis of Impaired Inhibitory Control

Jonathan Shilo Lipszyc

Master of Science

Institute of Medical Science

University of Toronto

2009

Abstract

Impaired inhibition is a deficit of several psychopathological disorders, particularly attention-

deficit hyperactivity disorder (ADHD). In the first study, a meta-analysis was conducted to

determine whether impaired inhibition as measured by the Stop Signal Task (SST) is specific to

ADHD, or whether it could be found in other psychopathological disorders. The meta-analysis

found an inhibitory deficit in ADHD, but also in obsessive compulsive disorder (OCD) and

schizophrenia (SCZ), suggesting that deficient inhibition is not specific to ADHD. A common

neural mechanism may underlie deficient inhibition in ADHD, OCD, and SCZ. Study 2 aimed to

determine the neural basis of inhibition using a lesion-deficit approach in children with traumatic

brain injury (TBI). Only TBI children with white matter lesions in the superior frontal gyrus

(SFG) region showed impaired inhibition compared with orthopedic injury (OI) controls. This

suggests that deficient inhibition may stem from frontal lobe white matter damage, particularly in

the SFG.

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Acknowledgments

I would like to thank Dr. Russell Schachar for his guidance and support over these past two

years. This thesis could not have been completed without Dr. Schachar’s knowledge, expertise,

and encouragement. I would also like to thank my program advisory committee members, Dr.

Maureen Dennis and Dr. Brian Levine, for their input and advice on the thesis. Moreover, I owe

a debt of gratitude to my fellow graduate students, Mehereen Wadiwalla and Troy Climans, for

their support. I would also like to thank Dr. Charles Raybaud, Dr. Jennifer Crosbie and Andre

Chevrier for their useful comments. This thesis was supported in part by a RESTRACOMP

Graduate Studentship from The Hospital for Sick Children, an Institute of Medical Science Entry

Award, and a University of Toronto Open Fellowship.

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Table of Contents

Abstract ........................................................................................................................................ ii

Acknowledgements ..................................................................................................................... iii

Table of Contents .........................................................................................................................iv

List of Tables.................................................................................................................................v

List of Figures ..............................................................................................................................vi

General Introduction .....................................................................................................................1

Chapter 1.0 ....................................................................................................................................4

1.1 Abstract ...................................................................................................................................5

1.2 Introduction .............................................................................................................................6

1.3 Methods.................................................................................................................................14

1.4 Results ...................................................................................................................................19

1.5 Discussion/Conclusion ..........................................................................................................25

Chapter 2.0 ..................................................................................................................................30

2.1 Abstract .................................................................................................................................31

2.2 Introduction ...........................................................................................................................32

2.3 Methods.................................................................................................................................42

2.4 Results ...................................................................................................................................48

2.5 Discussion/Conclusion ..........................................................................................................54

General Discussion......................................................................................................................61

References ...................................................................................................................................65

Tables ..........................................................................................................................................99

Figures .......................................................................................................................................150

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List of Tables

Table 1.1 Characteristics of studies...................................................................................... 99

Table 1.2 Means and standard deviations for the Stop Task outcome variables and sample

sizes .................................................................................................................... 108

Table 1.3 Summary of effect sizes by group ...................................................................... 117

Table 1.4 Weighted mean effect sizes and homogeneity analysis by group and Stop Task

outcome variable ................................................................................................ 126

Table 1.5 Fixed and mixed effects meta-regression analyses for mean reaction time across

the ADHD studies............................................................................................... 129

Table 2.1 Demographic characteristics of TBI children, OI controls, and population

controls ............................................................................................................... 130

Table 2.2 Mean T-scores for the clinical scales, indices, and global executive composite of

the Behavior Rating Inventory of Executive Function....................................... 132

Table 2.3 V-scale scores for the Vineland Adaptive Behavior Scale maladaptive behavior

domain ................................................................................................................ 134

Table 2.4 Distribution of lesions in TBI patients ............................................................... 135

Table 2.5 Comparison of the frequency of patients between the good and poor SSRT

subgroups according to lesion location and tissue type...................................... 137

Table 2.6 Comparison of the frequency of patients between the good and poor MRT

subgroups according to lesion location and tissue type...................................... 141

Table 2.7 Comparison of the frequency of patients between the good and poor SDRT

subgroups according to lesion location and tissue type...................................... 143

Table 2.8 Stop Task performance of TBI patients, OI controls, and population controls.. 145

Table 2.9 Stop Task performance of TBI patients with frontal white matter lesions, OI

controls, and population controls ....................................................................... 146

Table 2.10 Stop Task performance of TBI patients with SFG white matter lesions, OI

controls, and population controls ....................................................................... 147

Table 2.11 Effect sizes (Cohen’s d) for group comparisons ................................................ 148

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List of Figures

Figure 1.1 Funnel plot of SSRT effect sizes for the difference between ADHD patients and

controls ............................................................................................................... 151

Figure 1.2 Fixed effects meta-regression of SSRT on MRT effect sizes across the ADHD

studies ................................................................................................................. 152

Figure 1.3 Fixed effects meta-regression of SSRT on SDRT effect sizes across the ADHD

studies ................................................................................................................. 153

1

General Introduction

Executive functions are higher order cognitive processes that underlie goal-directed

behavior. Examples of executive functions include inhibition, working memory, planning, set-

shifting, and fluency (e.g., Pennington & Ozonoff, 1996). Executive dysfunction has been

associated with psychopathology (e.g., Pennington et al., 1996) and neuropathology (e.g.,

Ewing-Cobbs, Prasad, Landry, Kramer, & DeLeon, 2004). Barkley (1997) purported that

inhibition is the primary executive function, and that it regulates four other executive functions,

including working memory, self-regulation of affect-motivation-arousal, internalization of

speech, and reconstitution. These four executive functions are necessary for self-regulation.

Deficient inhibition leads to secondary impairments in these four executive functions, and in

turn, deficient self-regulation. Inhibition is a broad construct that has received considerable

attention, particularly in the literature on ADHD. Nigg (2000) distinguished between three forms

of inhibition: executive inhibition, motivational inhibition, and automatic inhibition of attention.

Executive inhibition is the ability to deliberately suppress a response for the purpose of attaining

a goal. Executive inhibitory control has been considered the primary deficit in ADHD (Barkley,

1997). It refers to the ability to inhibit a prepotent (restraint) or ongoing (cancellation) response.

Commonly used measures of inhibition are the SST (Logan, 1994) and the Go/No-Go

(GNG) Task. The SST is primarily a measure of cancellation, while the GNG task is a measure

of restraint (Schachar et al., 2007a). Cancellation refers to inhibition of a response that has

already been triggered. Restraint, on the other hand, refers to inhibition of a response that has yet

to be triggered. Studies have suggested involvement of the right inferior frontal gyrus (IFG) and

basal ganglia in cancellation, while more dorsolateral regions of the prefrontal cortex (PFC) have

been implicated in restraint (Chevrier, Noseworthy, & Schachar, 2007). In addition, various PFC

2

regions have been found to be common to both restraint and cancellation (Rubia et al., 2001c;

Zheng, Oka, Bokura, & Yamaguchi, 2008).

The SST has been used the most frequently as a measure of motor response inhibition. It

has been regarded as the best measure of pure disinhibition (Quay, 1997). The most consistently

found deficit in ADHD children has been in cancellation (hereafter referred to as “inhibition”) as

measured by the SST (Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005b). The SST is not

confounded by reading or language ability. It has also been found to be sensitive to stimulant

medication (Tannock, Schachar, Carr, Chajczyk, & Logan, 1989). For these reasons, the focus of

the present thesis is on the SST.

Several psychopathological disorders have been associated with deficient inhibition as

measured by the SST, particularly ADHD (e.g., Schachar, & Logan, 1990). Previous meta-

analyses of the SST (e.g., Oosterlaan, Logan, & Sergeant, 1998c; Lijffijt, Kenemans, Verbaten,

& van Engeland, 2005; Alderson, Rapport, & Kofler, 2007) have revealed a moderate inhibition

deficit in ADHD. Studies have also suggested that inhibition is deficient in other

psychopathologies, such as OCD (Chamberlain et al., 2007; Penades et al., 2007), and SCZ (e.g.,

Enticott, Ogloff, & Bradshaw, 2008; Huddy et al., 2008). Yet, the magnitude of this deficit has

not been systematically reviewed in psychopathological disorders other than ADHD,

oppositional defiant disorder/conduct disorder (ODD/CD), and to a lesser extent, anxiety (ANX).

Chapter 1 presents a meta-analysis of 80 SST studies to determine whether deficient inhibition is

specific to ADHD, or whether it could be found in other psychopathological disorders, including

ANX, autism, bipolar disorder, major depressive disorder (MDD), ODD/CD, OCD, reading

disability (RD), SCZ, and Tourette syndrome. After showing the importance of deficient

3

inhibition in several psychopathological disorders, Chapter 2 investigates the neural basis of

inhibition.

Lesion-deficit studies in adults have revealed that the frontal lobes underlie inhibition

(Rieger, Gauggel, & Burmeister, 2003), particularly the right IFG (e.g. Aron, Fletcher, Bullmore,

Sahakian, & Robbins, 2003a) and the right superior medial frontal region (Floden & Stuss,

2006). The basal ganglia have also been implicated (Rieger et al., 2003). It is presently unknown

whether lesions in these regions would also impair inhibition in children. Moreover, previous

lesion studies have not examined the effect of lesion tissue type (e.g., gray matter, white matter,

gray-white junction) on inhibition. Chapter 2 uses a lesion-deficit approach to investigate the

effect of various lesions on inhibition in children with TBI, taking tissue type into consideration.

4

Chapter 1 Inhibitory Control and Psychopathology: A Meta-Analysis of

Studies using the SST

5

Abstract

Impaired inhibition is a deficit of several psychopathological disorders, particularly ADHD. A

meta-analysis was conducted to determine whether deficient inhibition as measured by the SST

is specific to patients with ADHD, or whether it is found in other psychopathological disorders.

Stop Signal Reaction Time (SSRT) is the main outcome measure of the SST, which reflects the

speed of the inhibitory process. Five criteria were used to assess the methodological quality of

the ADHD studies. Results showed moderate SSRT effect sizes (ESs) for ADHD (ES = 0.62, p <

0.001), OCD (ES = 0.79; p < 0.001) and SCZ (ES = 0.73, p < 0.01), but not for ANX (ES = 0.09;

ns), autism (ES = 0.40, ns), bipolar disorder (ES = 0.25; ns), MDD (ES = 0.25; ns), ODD/CD

(ES = 0.15; ns), RD (ES = 0.39; p .< 0.001), or Tourette syndrome (ES = 0.3, ns). In addition, a

large SSRT ES was found for comorbid ADHD and RD (ADHD + RD) (ES = 0.82, p < 0.001), a

near moderate SSRT ES for comorbid ADHD and ANX (ADHD + ANX) (ES = 0.49, p < 0.01),

and a small-to-medium SSRT ES for comorbid ADHD and ODD/CD (ADHD + ODD/CD) (ES

= 0.29, p < 0.05). Study quality did not significantly affect the SSRT ESs across the ADHD

studies. This confirms the presence of an inhibition deficit in ADHD, and also suggests that

ADHD + RD and ADHD + ODD/CD may represent distinct forms of ADHD. Further studies are

needed to firmly establish a deficit in OCD and SCZ.

6

Introduction

Inhibition is an important construct in current models of psychopathology (e.g., Barkley,

1997), neuropathology (Aron et al., 2003a) and development (Harnishfeger & Pope, 1996). In

particular, deficient inhibition is believed to underlie several psychopathological disorders, most

notably ADHD (Barkley, 1997). ADHD is a disorder most strongly associated with disinhibited

behaviour. It is characterized by symptoms of severe inattention, impulsiveness and

hyperactivity. Of the many models developed to explain the underlying cognitive deficits in

ADHD, the inhibition model put forth by Barkley (1997) has formed the basis of much recent

research. This model suggests that inhibition is the primary deficit in ADHD. Several forms and

measures of inhibition have been studied (Schachar et al., 2007a), but motor response inhibition

has been studied the most often, commonly using the SST. Deficient inhibition as measured by

the SST has been implicated in several psychopathologies, particularly in ADHD (e.g. Schachar

et al., 1990). A meta-analysis of studies examining executive functioning in ADHD children

found inhibition to be the most consistently reported deficit (Willcutt et al., 2005b). Yet, the

magnitude of this deficit in several other psychopathologies has not been subjected to systematic

investigation. Moreover, the effect of comorbid ADHD on inhibition in various

psychopathological groups has not been systematically reviewed. Consequently, the specificity

of the link between deficient inhibition and any particular psychopathological condition has yet

to be established completely.

Meta-analysis is a quantitative statistical technique that is used to combine the results of

multiple studies. As noted by Ioannidis and Lau (1999), performing a meta-analytic review has

several advantages. Notably, meta-analysis may increase the power of studies with smaller

sample sizes. It can be useful for identifying sources of variability across studies. The statistical

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technique may also improve the generalizability of research findings. As well, meta-analytic

methods have been developed to aid in the detection of publication bias, a phenomenon in which

studies with significant results are more likely to be published than studies with non-significant

results.

A number of meta-analyses have been published on the SST (Oosterlaan et al., 1998c;

Lijffijt et al., 2005; Alderson et al., 2007). In general, these meta-analyses have: (a) focused

almost exclusively on ADHD; (b) failed to take methodological quality of studies into account,

instead applying strict inclusion criteria which could have resulted in the exclusion of

informative studies; (c) failed to thoroughly assess publication bias; (d) tended not to consider

the potential impact of comorbidity on their findings; and (e) focused predominantly on children,

despite the considerable SST literature on adults. Since the most recently published SST meta-

analysis (i.e., Alderson et al., 2007), 25 studies have been published on ADHD alone and there is

now a substantial literature on nine other psychopathological disorders, including ANX, autism,

bipolar disorder, MDD, ODD/CD, OCD, RD, SCZ, and Tourette syndrome. The literature has

also reported on three comorbid ADHD groups only: ADHD + ANX, ADHD + ODD/CD, and

ADHD + RD. Consequently, available meta-analyses of inhibition as measured by the SST have

been limited in focus, and do not fully address questions of sensitivity and specificity that are

crucial for cognitive models of psychopathology.

The SST measures cancellation of an ongoing speeded motor response. This contrasts

with the GNG Task, which measures response restraint (Schachar et al., 2007a). These two

subcomponents of inhibition were studied in children with ADHD and normal controls (Schachar

et al., 2007a). ADHD children showed deficits in both restraint and cancellation compared with

controls. In controls, restraint and cancellation were significantly correlated. Conversely, there

8

was no significant correlation between restraint and cancellation in ADHD children. This

suggests that there may be differences in the processes underlying the two subcomonents of

inhibition.

The SST is widely considered to provide the most sensitive assessment of motor response

inhibition. Nichols & Waschbusch (2004) conducted a review of cognitive tasks that are used in

the assessment of ADHD symptoms. Considerable support was found for the validity of the SST.

They noted that the SST can distinguish between ADHD children and normal controls.

Performance on the SST was reported to be associated with parent and teacher ratings of ADHD

symptoms. It was further noted that methylphenidate improves performance on the SST.

Moreover, the SST has acceptable test-retest reliability, with an intraclass-correlation coefficient

of 0.72 (Soreni, Crosbie, Ickowicz, & Schachar, 2009).

During the SST, participants are required to respond as quickly and accurately as

possible to a primary task stimulus, also referred to as the go stimulus. On a subset of trials,

typically 25 percent, the go stimulus is followed by a stop signal (usually an auditory tone), at

which point, participants are required to withhold their response to the go stimulus. The SST is

founded on a theory of inhibition known as the race-model, which purports that whether or not a

particular response will be inhibited depends on the outcome of a race between two independent

processes: the go process, which is triggered by the go stimulus, and the stop process, triggered

by the stop signal. If the stop process finishes first, the response will be inhibited, while if the go

process finishes first, the response will be executed. Logan (1994) showed that the outcome of

the race between going and stopping could be affected as well by response variability. The

current meta-analysis considers three SST outcome variables: SSRT, mean reaction time (MRT),

and the within-subject standard deviation of reaction time (SDRT). SSRT provides an estimate of

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the latency of the inhibitory process. MRT and SDRT reflect the latency and variability in the

latency of the go process, respectively. Lijffijt et al. (2005) argued that longer and more variable

MRTs accounted for differences in SSRT between ADHD patients and normal controls. This

study subjected this argument to closer scrutiny by examining the influence of MRT and SDRT

on SSRT across ADHD studies.

The first meta-analysis of the SST reviewed 8 studies published between 1990 and 1997

(Oosterlaan et al., 1998c). Participants ranged in age from 6 to 12 years. Four clinical groups

were examined: an ADHD group, a CD group, a comorbid ADHD + CD group, and an ANX

group. There were seven studies in the ADHD group, four studies in each of the CD and ADHD

+ CD groups, and three studies in the ANX group. SST outcome variables of interest that were

examined in the meta-analysis included SSRT and MRT. SDRT was not taken into

consideration. The ADHD and pure CD groups showed significantly longer SSRTs compared

with controls. However, there was no significant difference in SSRT between children with ANX

and controls. The ADHD group also had a significantly longer MRT than controls. A search for

moderator effects was not performed.

The second SST meta-analysis reviewed 29 studies published between 1998 and 2004

(Lijffijt et al., 2005). Participants were adults and children with ADHD who ranged in age from

6 to 59 years. The following SST outcome variables were examined in the meta-analysis: SSRT,

SDRT, MRT, and MRT-SSRT (difference in the lengthening of SSRT relative to MRT). Both

adults and children with ADHD showed significantly longer SSRTs and more variable RTs

relative to controls. Only the ADHD children had significantly longer MRTs than controls. There

was no significant difference in the lengthening of SSRT compared with MRT in ADHD

children versus controls. The authors indicated that, the latter result, combined with the finding

10

of increased RT variability, suggests that general attention is impaired rather than inhibition in

children. In contrast, SSRT was significantly prolonged relative to MRT in adults with ADHD

versus controls. This was interpreted as suggesting that inhibition is impaired in adults with

ADHD. Age explained a significant portion of the variability across the ESs for all SST outcome

variables. It was noted that stop-signal method (i.e. fixed, variable, or tracking), comorbidity

with ODD/CD, IQ, and ADHD subtype were not significant moderators. An issue with the meta-

analysis is that it included studies that used the SST to examine the effect of feedback on

performance.

The most recent meta-analysis of the SST reviewed 24 studies published between 1990

and 2004 (Alderson et al., 2007). Participants were ADHD children ranging in age from 7-12

years. SST outcome variables examined in the meta-analysis included SSRT, SDRT, MRT, and

stop signal delay (SSD), which refers to the time interval between the presentation of the go

stimulus and stop signal. ADHD children showed significantly longer SSRTs compared with

normal controls. They also had longer and more variable RTs than controls. However, there was

no significant difference in SSD between ADHD children and controls. The authors concluded

that the findings reflect a general deficit in attention or cognition. It was noted that younger

children, rating scales as opposed to more comprehensive diagnostic procedures, dynamic rather

than fixed SSDs, a greater total number of experimental trials, and visual-spatial instead of

phonological primary task stimuli produced large effect sizes for MRT. Stop-signal target

density (percentage of stop-trials in an experimental block) was not a significant moderator of

MRT.

Willcutt et al. (2005b) performed a meta-analysis of studies that investigated executive

functioning in children with ADHD. Studies that used the SST to investigate inhibition were

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included. However, the SST was not the primary focus of the meta-analysis. Results revealed a

significant difference in SSRT between ADHD children and normal controls in 22 of 27 SST

studies reviewed. Overall, ADHD children showed moderately prolonged SSRTs compared with

normal controls. The authors did not look at other SST outcome measures, such as MRT and

SDRT.

Although deficient inhibition is generally held to be a hallmark of ADHD (Barkley,

1997), available research suggests that the same deficit may be shared by several

psychopathological groups. Notably, recent studies have found that inhibition is impaired in

patients with OCD and SCZ (Chamberlain et al., 2007; Enticott et al., 2008). If this is confirmed

by systematic investigation, it would have implications for the diagnostic utility of deficient

inhibition. It would also suggest that the neural pathways implicated in disorders sharing a

common inhibition deficit may, to some extent, overlap.

Comorbidity refers to the occurrence of two or more psychopathological disorders in the

same individual at the same time. There is a high rate of comorbidity between ADHD and other

psychopathological disorders (Biederman, Newcorn, & Sprich, 1991). Most commonly, a

comorbid condition is thought to be a hybrid of two distinct disorders, possibly because one

disorder (e.g., ADHD) increases the risk of developing a second disorder (e.g., ODD/CD).

Another common explanation for comorbidity is the phenocopy hypothesis, which suggests that

patients with one disorder (e.g., ODD/CD) will manifest the symptoms of a second disorder (e.g.,

ADHD), but without the underlying cognitive deficits that are typical of the second disorder.

There is also the etiologic subtype hypothesis, which predicts that the magnitude of the inhibition

deficit in ADHD + RD children will be proportional to the combined inhibition deficits in pure

ADHD children and pure RD children (Purvis & Tannock, 2000). Failure to take into account the

12

impact of comorbid ADHD on inhibitory control could lead to erroneous conclusions about the

specificity of deficient inhibition.

ADHD and ODD/CD co-occur in approximately 35 percent of children (Biederman et al.,

1991). In the meta-analysis by Oosterlaan et al. (1998c), no significant difference in SSRT was

found between children with ADHD + CD and those with ADHD only. Schachar & Tannock

(1995b) considered ADHD + ODD/CD as being a hybrid of pure ADHD and ODD/CD. Yet,

more recent studies have supported the phenocopy hypothesis (e.g., Schachar et al., 1990;

Schachar, Mota, Logan, Tannock, & Klim, 2000). For example, Schachar et al. (2000) noted that

children with ADHD, but not those with ADHD + CD, showed impaired inhibition compared

with controls. If the phenocopy hypothesis explains the comorbidity between ADHD and

ODD/CD, then the ADHD + ODD/CD group will be comparable to that of the ODD/CD group

in inhibition.

The rate of comorbidity between ADHD and RD ranges from 15 to 40 percent (Semrud-

Clikeman et al., 1992). It has been suggested by van der Schoot, Licht, Horsley, & Sergeant

(2002) that the impulsive reading style (abnormally fast and inaccurate reading) seen in some

patients with RD may represent a mild form of ADHD. Most SST studies have supported the

etiologic subtype hypothesis (e.g., Purvis et al., 2000; Willcutt et al., 2001). Willcutt et al. (2001)

reported that ADHD + RD children showed impaired inhibition compared with ADHD children,

RD children, and controls. If the etiologic subtype hypothesis explains the comorbidity between

ADHD and RD, then the ADHD + RD group will show greater impairment in inhibition than

both the ADHD and RD groups.

ADHD co-occurs with ANX in approximately 25 percent of children (Biederman et al.,

1991). In the Oosterlaan et al. (1998c) meta-analysis, the effect of comorbid ANX on SST

13

performance was not examined. Oosterlaan & Sergeant (1998a) reviewed evidence suggesting

that the presence of ANX may mitigate impaired inhibition. One study has reported that ADHD

+ ANX children showed significantly better inhibition than pure ADHD children (e.g., Manassis,

Tannock, & Barbosa, 200l). More studies, however, have reported that comorbid ANX does not

significantly affect inhibition in ADHD children (e.g., Pliszka, Borcherding, Spratley, Leon, &

Irick, 1997; Korenblum, Chen, Manassis, & Schachar, 2007). Pliszka et al. (1997) noted that

both children with ADHD alone and ADHD + ANX showed significantly longer SSRTs than

normal controls. If the latter is the case, then the ADHD + ANX group will be comparable to the

ADHD group in inhibition.

In summary, this meta-analysis updates the literature on inhibition as measured by the

SST, expands the systematic review to all disorders for which sufficient evidence is available

(including disorders comorbid with ADHD), examines the impact of response speed and

variability on group differences in inhibition, estimates the impact of publication bias, and

models study quality to determine whether published results are affected by design and execution

of the study.

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Method

A literature search was conducted using PubMed and PsycINFO electronic databases

from 1966 to April 2009. The following search terms were used: response inhibition, stop signal

task, and stop task. Articles identified by this search strategy were subsequently reviewed for

further relevant references. The following SST outcome variables were assessed: SSRT, MRT,

and SDRT. Included studies were those that: (a) used the SST to measure inhibition; (b) included

a healthy control group; and (c) reported the means and standards deviations for the SST

outcome variables. Studies that contained data sets presented in other studies were excluded (the

study with the larger sample size was used). Also excluded were studies that provided feedback

on performance because of the suspected effect of reward on inhibition. Studies using atypical

SST paradigms, such as the Selective SST, were also excluded. The present meta-analysis did,

however, include studies that used the Change Task, which is a modified version of the SST

where participants shift to a secondary response once they have inhibited an ongoing response.

In general, the change task yields longer SSRTs than does simple stopping (Logan & Burkell,

1986).

All effect size (ES) calculations were computed with Comprehensive Meta Analysis

software (Borenstein, Hedges, Higgins, & Rothstein, 2005). ES reflects the magnitude of

difference between two groups. A positive ES indicates that the experimental group performed

worse than the control group, whereas a negative ES indicates that the experimental group

performed better. In accordance with Lipsey and Wilson (2001), Hedge’s g ESs were used to

correct for sample size. Cohen (1988) indicates that ESs can be classified as small (0.2), medium

(0.5) or large (0.8). Individual study ESs greater than 2.5 standard deviations from the weighted

15

mean ES of any SST outcome variable were considered outliers and removed from the analysis

(Lipsey and Wilson, 2001).

A test of homogeneity using the Q-statistic was performed on the three SST outcome

variables (Lipsey and Wilson, 2001). A rejection of homogeneity (p < 0.1) indicates that the

variability among the ESs is not due to chance, suggesting that moderator variables may be

contributing to ES variability. If heterogeneity was not significant, a fixed effects model was

used. Where significant heterogeneity existed (p < 0.1), a mixed effects model was applied.

Significant heterogeneity was further investigated using a fixed effects multiple meta-regression

model where 15 or more ESs were available. A mixed effects multiple meta-regression was also

performed to check the robustness of the fixed effects model. The multiple meta-regression was

conducted with macros designed for SPSS (Lipsey and Wilson, 2001). This study conducted two

additional meta-regressions, each with one covariate, to investigate the influence of MRT and

SDRT on SSRT across the ADHD studies using Comprehensive Meta Analysis software

(Borenstein et al., 2005). For all meta-regression analyses, a significant Q for the regression (QR)

indicates that the regression model accounts for significant variability across the ESs, and a

significant Q for the residual (QE) indicates the presence of significant remaining variability that

cannot be explained by sampling error.

Analog to the analysis of variance (ANOVA) was used to compare the SST

performance of the ADHD group with that of the other nine psychopathological groups and the

three comorbid ADHD groups. The analog to the ANOVA was also used to determine whether

SST performance was significantly influenced by ADHD study quality (high, medium, and low).

A significant Q-between (QB) indicates that the mean ESs across groups differ by more than

chance, and a significant Q-within (QW) indicates that QB does not sufficiently explain the extra

16

variability (Lipsey and Wilson, 2001). If QW was not significant (p < 0.1), a fixed effects model

was used, whereas if QW was significant, a mixed effects model was applied.

Inclusion Exceptions and Exclusions

Thirty-three studies were excluded for the following reasons: samples had been

presented in other studies (Geurts et al., 2008; Lee et al., 2008; Menzies et al., 2007; Verte,

Geurts, Roeyers, Oosterlaan, & Sergeant, 2006a; Verte, Geurts, Roeyers, Oosterlaan, &

Sergeant, 2006b; Bekker et al., 2005b; Geurts, Verte, Oosterlaan, Roeyers, & Sergeant, 2005;

Liotti, Pliszka, Perez, Kothmann, & Woldorff, 2005; Langley et al., 2004; Nigg, Blaskey,

Huang-Pollock, & Rappley, 2002; Chhabildas, Pennington, & Willcutt, 2001; Crosbie and

Schachar, 2001; Scheres, Oosterlaan, & Sergeant, 2001b; Rubia et al., 1999); participants

received feedback on their performance (Huang-Pollock, Mikami, Pfiffner, & McBurnett, 2007;

Slusarek, Velling, Bunk, & Eggers, 2001; Konrad, Gauggel, Manz, & Scholl, 2000b; Oosterlaan

& Sergeant, 1998b); insufficient data to calculate ESs (DeVito et al., 2009; Matthews et al.,

2009; Nigg et al., 2008; Huang & Chan, 2007; Vink, Ramsey, Raemaekers, & Kahn, 2006;

Michel, Kerns, & Mateer, 2005; Stevens, Quittner, Zuckerman, & Moore, 2002; Aman, Roberts,

& Pennington, 1998; Brandeis et al., 1998; Jennings, van der Molen, Pelham, Debski & Hoza,

1997; Daugherty, Quay, & Ramos, 1993); use of atypical SST paradigms (Geurts, van der Oord

and Crone, 2006; MacLaren, Taukulis, & Best, 2007; Armstrong and Munoz 2003; Bedard et al.,

2003). Further details can be obtained from the authors.

Moderators

Methodological quality. The quality scale covered five items. Items 1 and 2 considered

the source of diagnostic information (i.e., parent/teacher if a child, or patient/informant if an

17

adult) and the diagnostic procedure (interview/questionnaire) used to identify cases of ADHD.

More specifically, item one asked whether information about the child’s ADHD symptoms were

obtained from parents or caregivers (or the patient, if the participant was an adult), and item two

asked whether information about ADHD symptoms were gathered from the child’s classroom

teacher (or an informant, such as a spouse, if the participant was an adult). Studies employing

rating scales or questionnaires received 0.5 points, while those that used an interview either with

or without the use of rating scales or questionnaires were allocated one point.

Item 3 evaluated the diagnostic criteria used in making the diagnosis. One point was

given to studies that used Diagnostic and Statistical Manual of Mental Disorders (DSM)-III-R or

-IV criteria.

Item 4 addressed the medication status of participants. Research has found that

methylphenidate improves SSRT in children with ADHD (Tannock et al., 1989). Studies

explicitly stating that participants were free of stimulant medication at the time of testing

received one point.

Item 5 covered the validity of task performance. One point was applied to studies that

reported a mean go accuracy of at least 66 percent for both the ADHD and control groups, which

indicates that participants understood the task requirements.

The overall quality score could range from 0 (low) to 5 (high). Studies were arbitrarily

divided into 3 groups based on their quality score: high (greater than or equal to 4.5), medium

(greater than or equal to 3.5, but less than 4.5), and low (less than or equal to 3). The quality

assessment instrument was specifically designed for and applied to the ADHD studies only.

18

Study characteristics. The following variables were coded for each study: age (less than

18 years and greater than or equal to 18 years), IQ (average IQ of ADHD participants), and

gender (percentage of male ADHD participants). In addition, ADHD subtype (percentage of

patients with the inattentive and combined subtypes) was coded for the ADHD studies. All

information was extracted blindly (i.e., without knowledge of the effect sizes yielded by the

studies).

Publication Bias

Funnel plots of ES against standard error were generated to visually check for

publication bias (Light, Singer, & Willett, 1994). Asymmetry in the funnel plot is an indication

of publication bias. Egger's regression is a formal test (Egger, Davey-Smith, Schneider, &

Minder, 1997) designed to quantitatively measure funnel plot symmetry, where p < 0.05

indicates the presence of significant publication bias (Sterne, Gavaghan, & Egger, 2000). If

publication bias was detected, the trim-and-fill method (Duval & Tweedie, 2000) was applied to

adjust for funnel plot asymmetry.

19

Results

The studies identified using the inclusion and exclusion criteria included a total of 4689

patients with one of ten psychopathological conditions (including three comorbid ADHD

groups). Table 1.1 presents the study characteristics of the 10 psychopathological groups and 3

comorbid ADHD groups. The means for the three SST outcome variables are provided in Table

1.2, in addition to the sample size of each study. Table 1.3 presents individual study ESs by SST

outcome variable.

ES Analysis

SSRT. Table 1.4 presents the weighted mean ESs and homogeneity analysis by SST

outcome variable. Prior to analysis, one outlier was omitted from each of the ADHD and

ODD/CD groups (Johnstone, Barry, & Clarke, 2007; Oosterlaan & Sergeant, 1996; respectively).

Moderate ESs were found for ADHD (g = 0.62, p < 0.001), OCD (g = 0.79, p < 0.001), and SCZ

(g = 0.73, p < 0.001). ESs were small-to-medium or small for the other groups. Significant

heterogeneity was observed for autism (p = 0.01) and Tourette syndrome (p = 0.08). There was

no significant difference between ADHD ESs and those of autism (QB = 0.04, p = 0.84; QW =

68.75, p = 0.10), OCD (QB = 0.96, p = 0.33; QW = 65.93, p = 0.2), or SCZ (QB = 0.42, p = 0.52;

QW = 63.93, p = 0.22). ADHD ESs were significantly larger than those of ANX (QB = 17.1, p =

0.000; QW = 66.73, p = 0.26), bipolar disorder (QB = 7.27, p = 0.007; QW = 68.11, p = 0.15),

MDD (QB = 3.81, p = 0.05; QW = 62.95, p = 0.22), ODD/CD (QB = 11.23, p = 0.001; QW = 69.34,

p = 0.19), RD (QB = 7.21, p = 0.007; QW = 67.44, p = 0.21), and Tourette syndrome (QB = 4.23, p

= 0.04; QW = 67.46, p = 0.14).

20

A large SSRT ES was found for ADHD + RD (g = 0.82, p < 0.001). ADHD + ANX

produced a borderline moderate ES (g = 0.49, p = 0.003), and a small-to-medium ES was noted

for ADHD + ODD/CD (g = 0.29, p = 0.02). ESs for ADHD did not differ significantly from

those of ADHD + ANX (QB = 0.68, p = 0.41; QW = 65.58, p = 0.18), but were significantly larger

than those of ADHD + ODD/CD (QB = 6.72, p = 0.01; QW = 67.19, p = 0.22) and significantly

smaller than those of ADHD + RD (QB = 3.45, p = 0.06; QW = 62.69, p = 0.28).

MRT. Six outliers were omitted from the ADHD group prior to analysis (Bitsakou,

Psychogiou, Thompson, & Sonuga-Barke, 2008; Liotti et al., 2007; Pliszka et al., 2006; Murphy,

2002; Purvis et al., 2000; and Oosterlaan et al., 1998a). ESs were moderate for autism (g = 0.64,

p = 0.11), ODD/CD (g = 0.63, p < 0.001), and RD (g = 0.59, p = 0.001). The other groups

produced small-to-medium or small ESs. Heterogeneity was significant for ADHD (p = 0.006),

autism (p = 0.07), bipolar disorder (p = 0.06), MDD (p = 0.006), RD (p = 0.07), and SCZ (p =

0.05). There was no significant difference between ADHD ESs and those of ANX (QB = 0.73, p

= 0.39 QW = 72.61, p = 0.004), autism (QB = 0.41, p = 0.52; QW = 66.75, p = 0.005), bipolar

disorder (QB = 1.03, p = 0.31; QW = 71.12, p = 0.003), MDD (QB = 1.71, p = 0.19; QW = 73.78, p

= 0.001), ODD/CD (QB = 1.64, p = 0.20; QW = 73.79, p = 0.006), OCD (QB = 1.02, p = 0.31; QW

= 64.77, p = 0.02), RD (QB = 1.35, p = 0.25; QW = 72.35, p = 0.003), or SCZ (QB = 0.000, p = 1;

QW = 69.43, p = 0.004). ADHD ESs were significantly larger than those of Tourette syndrome

(QB = 12.33, p = 0.000; QW = 64.44, p = 0.02).

MRT ESs were moderate for ADHD + ODD/CD (g = 0.55, p < 0.001) and ADHD + RD

(g = 0.69, p < 0.001). A small-to-medium ES was noted for ADHD + ANX (g = 0.34, p = 0.07).

ADHD ESs did not differ significantly from those of ADHD + ANX (QB = 0.05, p = 0.83; QW =

21

63.91, p = 0.01) or ADHD + ODD/CD (QB = 1.31, p = 0.25; QW = 67.14, p = 0.01), but were

significantly smaller than those of ADHD + RD (QB = 5.05, p = 0.03; QW = 64.56, p = 0.01).

SDRT. Prior to analysis, two outliers were removed from the ADHD group (Oosterlaan

et al., 1998a; Rubia et al., 2001a). ESs were moderate or large for ADHD (g = 0.77, p < 0.001),

ANX (g = 0.56, p = 0.05), ODD/CD (g = 0.86, p < 0.001), RD (g = 0.86, p < 0.001), and SCZ (g

= 0.62, p = 0.35). Data were insufficient to calculate mean ESs for autism, bipolar disorder,

MDD, OCD, and Tourette syndrome. There was significant heterogeneity for ANX (p = 0.05),

RD (p = 0.04) and SCZ (p = 0.02). No significant differences were found between ADHD ESs

and those of ANX (QB = 0.59, p = 0.44; QW = 45.59, p = 0.09), ODD/CD (QB = 0.25, p = 0.62;

QW = 45.86, p = 0.15), RD (QB = 0.14, p = 0.71; QW = 47.89, p = 0.07), or SCZ (QB = 0.06, p =

0.80; QW = 42.95, p = 0.09).

ADHD + RD and ADHD + ODD/CD yielded large (g = 1.13, p < 0.001) and small-to-

medium (g = 0.41, p = 0.07) SDRT ESs, respectively. Insufficient data were available to

calculate a mean ES for ADHD + ANX. There was significant heterogeneity for ADHD +

ODD/CD (p = 0.09). ADHD ESs were significantly larger than those of ADHD + ODD/CD (QB

= 5.32, p = 0.02; QW = 44.35, p = 0.11), but significantly smaller than those of ADHD + RD (QB

= 6.84, p = 0.009; QW = 40.01, p = 0.19).

Meta-Regression Analyses

This study did not investigate potential moderators of SSRT or SDRT across the ADHD

studies given the absence of significant heterogeneity. Significant heterogeneity across the

ADHD studies for MRT justified the investigation of moderator variables. A fixed effects

multiple meta-regression was performed. Results indicated that the model was significant (QR =

22

10.30, df = 5, p = 0.07). Two significant moderators emerged (see Table 1.5): percentage of

patients with ADHD combined subtype (z = -2.26, p = 0.02) and percentage of patients with

ADHD inattentive subtype (z = -2.33, p = 0.02), suggesting that studies with a higher proportion

of combined or predominantly inattentive patients were associated with significantly smaller

MRT effect sizes. Age was a borderline significant moderator (z = -1.64, p = 0.10), which

suggests that studies with a higher proportion of older patients were associated with borderline

significantly smaller MRT effect sizes. Yet, significant variability remained that could not be

explained by sampling error (QE = 36.65, df = 24, p = 0.05). This suggests that other moderator

variables may have been influencing MRT. The multiple meta-regression was repeated with

mixed effects to assess the robustness of the fixed effects model. In this analysis, no significant

moderators were found (see Table 1.5). Although, percentage of patients with ADHD combined

subtype was a borderline significant moderator. Due to the small number of studies in the other

psychopathological groups (including the comorbid ADHD groups), meta-regression was not

performed.

Two additional fixed effects meta-regression analyses were performed, each with one

covariate, to examine the influence of MRT and SDRT on SSRT across the ADHD studies.

Results showed no significant relationship between ESs for SSRT and those of either MRT (QR =

0.88, df = 1, p = 0.35, QE = 49.07, df = 37, p = 0.09; Figure 2) or SDRT (QR = 0.03, df = 1, p =

0.87, QE = 38.75, df = 29, p = 0.11; Figure 3).

Study Quality

For the SST outcome measures, high, medium, and low quality studies yielded the

following respective weighted mean ESs: SSRT, 0.70 (k = 13), 0.61 (k = 22), and 0.58 (k = 20);

MRT, 0.45 (k = 9), 0.29 (k = 16), and 0.41 (k = 15); and SDRT, 0.67 (k = 8), 0.76 (k = 10), and

23

0.82 (k = 14). There were no significant differences in ESs between the quality groups for SSRT

(QB = 1.97, p = 0.37; QW = 60.36, p = 0.2), MRT (QB = 1.36, p = 0.51; QW = 61.54, p = 0.007), or

SDRT (QB = 2.30, p = 0.32; QW = 35.49, p = 0.19).

Publication Bias

SSRT. Egger’s test and visual inspection of the funnel plots showed evidence of

significant publication bias for ADHD (p = 0.047; see Figure 1) and OCD (p < 0.01). Applying

the trim and fill method reduced the weighted mean ES from 0.62 to 0.58 (CI = 0.52-0.63) for

ADHD, and from 0.79 to 0.65 (CI = 0.37-0.94) for OCD. The funnel plots also showed evidence

of possible publication bias for ANX. The trim and fill method reduced the weighted mean ES

from 0.09 to 0.02 (CI = -0.21-0.25). Data were insufficient to assess publication bias for autism

or MDD. In addition, the funnel plots showed possible publication bias for ADHD + ODD/CD.

The trim and fill method reduced the weighted mean ES from 0.29 to 0.16 (CI = -0.06 – 0.38).

MRT. The funnel plots showed possible publication bias for Tourette syndrome. The

trim and fill method reduced the weighted mean ES from -0.11 to -0.13 (CI = -0.37-0.11). Due to

insufficient data, publication bias was not assessed for autism. The funnel plots also showed

possible publication bias for ADHD + RD. The trim and fill method reduced the weighted mean

ES from 0.69 to 0.6 (CI = 0.39-0.81). Data were insufficient to assess publication bias for ADHD

+ ANX.

SDRT. The funnel plots showed possible publication bias for ADHD. The trim and fill

method reduced the weighted mean ES from 0.78 to 0.74 (CI = 0.67-0.82). Due to insufficient

data, publication bias was not assessed for autism, bipolar disorder, MDD, OCD, SCZ, or

Tourette syndrome. The funnel plots also showed possible publication bias for ADHD + RD.

24

The trim and fill method reduced the weighted mean ES from 1.13 to 1.00 (CI = 0.78-1.22). Data

were insufficient to assess publication bias for ADHD + ANX.

25

Discussion

The present meta-analysis aimed to determine whether deficient inhibition as measured

by the SST was specific to ADHD, and whether the presence of comorbid ADHD influenced the

specificity of the deficit in various psychopathological groups. This study comprehensively

examined ten psychopathological conditions, took comorbidity into consideration, examined the

role of response speed and variability in observed inhibition deficits, used an instrument to assess

the methodological quality of ADHD studies, and considered publication bias.

Consistent with previous SST meta-analyses (i.e., Oosterlaan et al., 1998c; Lijfijjit et

al., 2005; Alderson et al., 2007), it was confirmed that ADHD patients showed a moderate

inhibition deficit compared with controls, supporting prior research indicating that inhibition is

impaired in ADHD (e.g. Barkley, 1997; Quay, 1997). Results also indicated that OCD, SCZ, and

autism patients did not differ significantly in inhibition from ADHD patients. OCD and SCZ

patients showed a moderate difference in inhibition compared with controls, while autistic

patients showed a small-to-medium difference. However, patients with deficient inhibition in the

autism group tended to show ADHD symptoms (Verte, Geurts, Roeyers, Oosterlaan, & Sergeant,

2005). It is not known whether the ADHD symptoms in the autism patients were comparable to

those of pure ADHD patients, although the finding suggests that common mechanisms may

underlie both disorders. These results suggest that there may be common cognitive and neural

mechanisms underlying the inhibition deficit in ADHD, OCD, and SCZ.

In the bipolar disorder, MDD, RD, and Tourette syndrome groups, patients showed

small-to-medium differences in inhibition compared with controls. The bipolar disorder finding

may be attributed to the inclusion of patients on lithium at the time of testing in most of the

bipolar disorder studies reviewed (i.e., McClure et al., 2005; Leibenluft et al., 2007; Strakowski

26

et al., 2009). Strakowski et al. (2009) reported that bipolar disorder patients on lithium showed

significantly longer SSRTs than both lithium-free patients and normal controls. For RD, the

finding could be related to the fact that, in some of the studies reviewed, patients showed

subclinical manifestations of ADHD (e.g., Willcutt et al., 2001). The Tourette syndrome finding

is partially related to the presence of comorbid ADHD (Verte et al., 2005). These four groups

showed significantly better inhibition than the ADHD group.

Patients in the ANX and ODD/CD groups showed small differences in inhibition

compared with normal controls. The ODD/CD finding differs from that reported in the meta-

analysis by Oosterlaan et al. (1998c), which noted a moderate difference in inhibition between

ODD/CD children and normal controls. The ANX finding is essentially in agreement with that of

Oosterlaan et al. (1998c). In the present meta-analysis, both the ANX and ODD/CD groups

showed significantly better inhibition than the ADHD group.

Comorbidity of ADHD with other psychopathological disorders introduces a significant

confound into studies of inhibition. A large difference in inhibition was found between ADHD +

RD patients and controls. The ADHD + RD group had significantly longer SSRTs than the

ADHD group, providing support for the etiologic subtype hypothesis. It may also be possible

that the ADHD + RD group is cognitively distinct from that of the ADHD and RD groups. Other

explanations cannot yet be ruled out, such as the possibility that ADHD + RD participants have

more severe ADHD than those without comorbid RD, or the possibility of a bias in sample

selection. A small-to-medium difference in inhibition was found between ADHD + ANX

patients and controls. The ADHD + ANX group did not differ significantly in SSRT from the

ADHD group, suggesting that comorbid ANX has no significant effect on inhibition. Results

also showed a small difference in inhibition between ADHD + ODD/CD patients and controls.

27

The ADHD + ODD/CD group had significantly better inhibition than the ADHD group, which is

in line with the phenocopy hypothesis. Alternatively, there is evidence to suggest that many

children with pure ODD/CD are misdiagnosed as having ADHD, when in fact they do not have

the disorder (Schachar, Sandberg, & Rutter, 1986). There is also the possibility that ADHD +

ODD/CD represents a cognitively distinct form of ADHD.

In general, the ten psychopathological groups showed slower and more variable MRTs

than controls. The ADHD group had a small-to-medium difference in MRT compared with

controls. This finding is consistent with the meta-analyses of Oosterlaan et al. (1998c), Lijffijt et

al. (2005), and Alderson et al. (2007), all of which reported small-to-medium differences in MRT

between ADHD patients and controls. None of the non-ADHD groups, except the Tourette

syndrome group, differed significantly in MRT from the ADHD group. The meta-regression

showed a borderline significant relationship between ADHD studies with a greater percentage of

combined-type patients and smaller MRT ESs, which may be associated with the presence of

hyperactive-impulsive symptoms. For SDRT, there was a moderate difference between ADHD

patients and controls. This finding agrees with the meta-analyses of Lijffijt et al. (2005) and

Alderson et al. (2007), both of which found moderate differences in SDRT between ADHD

patients and controls. Of the non-ADHD groups for which weighted mean SDRT ESs could be

calculated, none differed significantly from the ADHD group. Previous studies have interpreted

increased response variability as an indication of impaired attention (Ghajar & Ivry, 2009).

These findings indicate that slow and variable responding is not specific to ADHD. This study

did not find a significant relationship between ADHD studies with prolonged SSRTs and those

with longer or more variable MRTs, suggesting that MRT and SDRT do not influence SSRT to

the extent previously thought (e.g.., Lijffijt et al., 2005; Alderson et al., 2007).

28

Study quality did not significantly affect the ESs across the ADHD studies. Results

showed, however, that the high and medium quality studies in the ADHD group were associated,

albeit not significantly, with longer SSRTs and less variable MRTs compared with low quality

studies. For MRT, the high and low quality studies were associated with larger ESs than the

medium quality studies. It would be useful to expand on the quality instrument to more

thoroughly assess SST performance.

Some limitations of this meta-analysis should be noted. In the non-ADHD groups, a

number of studies included patients with comorbid ADHD (e.g., Verte et al., 2005), which may

have resulted in an overestimation of the difference in inhibition between patients and controls.

A number of studies in the non-ADHD groups also included patients on medication at the time of

testing (e.g., Strakowski et al., 2009), possibly confounding the difference in SSRT between

patients and controls. Another limitation is the relatively small number of studies in the non-

ADHD groups compared with the ADHD group, which limits the generalizability of the

findings. It is also of note that most studies in the OCD group and all studies in the SCZ group

consisted of adult participants. Future studies on patients with psychopathology other than

ADHD should control for the use of medication and the presence of comorbid ADHD. Research

should also aim to examine inhibition more extensively in patients with psychopathology other

than ADHD, especially in children.

This meta-analysis confirms the presence of an inhibition deficit in ADHD, and extends

beyond previous SST meta-analyses by showing that this deficit is also evident in OCD and

SCZ. These findings, which persisted even after adjusting for publication bias, suggest that

impaired inhibition is not specific to ADHD. On the other hand, the inhibitory deficit does not

appear to be a non-specific marker of psychopathology, considering that not all groups showed a

29

significant impairment in inhibition. In contrast, nearly all groups showed a moderate or large

difference in inhibition compared with controls, suggesting that increased response variability is

a non-specific marker of psychopathology. For MRT, no specific pattern of performance was

observed. The meta-analysis also shows that ADHD + RD and ADHD + ODD/CD may represent

distinct forms of ADHD. This meta-analysis points to the possibility that deficient inhibition may

play a greater role in OCD and SCZ than previously thought. However, more studies are needed

to confirm the presence of an inhibition deficit in both disorders. It will be critical to examine the

effect of comorbid ADHD on inhibition in OCD and SCZ. Neuroimaging research using

functional and structural methods is also needed to elucidate the neural mechanism(s) underlying

the common inhibition deficit.

30

Chapter 2 Effect of Specific Lesions on Inhibitory Control in Children with

TBI

31

Abstract

Previous lesion-deficit studies in adults have largely implicated discrete anatomical regions of

the PFC in deficient inhibition as measured by the SST. Lesion research in children has been

limited to the comparison between frontal and non-frontal regions, which has failed to yield a

significant lesion-deficit relationship. The primary aim of this study was to determine whether

lesions associated with deficient inhibition in adults also impair inhibitory control in children.

Participants included 30 children aged 7-16 years with moderate-to-severe TBI, 23 OI controls,

and 30 population controls (PCs). Patients in the TBI and OI groups underwent magnetic

resonance imaging (MRI) and performed the SST at 3-months post-injury. In the TBI group,

lesions were located in the following regions: SFG, middle frontal gyrus (MFG), IFG, orbital

frontal gyrus (OFG), other frontal, and non-frontal. Four lesion tissue types were examined: gray

matter, white matter, both gray and white matter, and gray-white matter junction. Results

revealed that TBI children as a group had impaired inhibition compared with PCs, but did not

differ from OI controls. However, a subset of TBI children with white matter lesions of the SFG

showed significantly longer SSRTs than both control groups. This suggests that deficient

inhibition in children following TBI may be due to frontal white matter damage, particularly in

the SFG region. Future research should use diffusion tensor imaging (DTI) to examine the

integrity of white matter tracts.

32

Introduction

Research suggests that specific cognitive processes can be localized to discrete PFC

regions (Picton et al., 2007). Functional MRI (fMRI) is used to identify the brain regions that are

associated with a particular process. FMRI studies in children and adults have suggested

involvement of various regions in inhibition as measured by the SST, particularly the

ventrolateral PFC (VLPFC) and the dorsolateral PFC (DLPFC) (e.g., Rubia, Smith, Brammer,

Toone, & Taylor, 2005). Complementary to fMRI is the lesion-deficit method, which plays a

critical role in identifying the brain regions that are necessary for a particular process. Lesion

studies in adults have implicated the right IFG and SFG in deficient inhibition (Aron et al.,

2003a; Floden et al., 2006, respectively). In contrast, lesion research in children has not yielded a

significant lesion-deficit relationship, likely because a comparison has been made only between

frontal and non-frontal regions (e.g. Leblanc et al., 2005). It is unknown whether the lesions

found to impair inhibition in adults would have the same effect in children. In addition, there is

emerging evidence suggesting that frontal white matter damage is associated with impaired

inhibition (e.g., Levin et al., 2008a). Yet, no previous lesion studies have examined the effect of

lesion tissue type on inhibition. This study addresses these gaps in the literature by investigating

the impact of lesions arising from TBI on inhibition in children. TBI children were selected for

three reasons: (a) they are particularly susceptible to frontal damage (Levin et al., 1997); (b) the

most frequently affected site following TBI in children is the frontal lobe white matter (Levin et

al., 1997); and (c) TBI has been found to impair inhibition in children (e.g. Ornstein et al., 2009;

Konrad, Gauggel, Manz, & Scholl, 2000a);

Motor inhibition consists of various components, such as the ability to restrain or to

cancel an ongoing response (Schachar et al., 2007a). The SST has been used most frequently to

33

study cancellation (Logan, 1994). The GNG task is the most commonly used measure of

restraint, in which participants are required to perform a response on go trials, and to inhibit their

responding on no-go trials. The number of errors participants make on no-go trials provides a

measure of inhibitory control. In the present study, the SST was used because it provides a more

sensitive index (i.e., SSRT) of motor inhibition. Although the focus of the literature review is on

the SST, GNG studies are also reviewed, given that brain regions common to both cancellation

and restraint have been identified (Rubia et al., 2001c).

FMRI studies have investigated the neural correlates of inhibition in healthy children.

Rubia et al. (2005) noted that, during successful relative to unsuccessful inhibition, healthy

adolescents activated the left DLPFC, the right inferior and mesial PFC, the anterior cingulate

gyrus, the left parietal cortex, the cerebellar vermis and right cerebellar hemisphere. Pliszka et al.

(2006) found that, during stop relative to go trials, healthy children activated the right DLPFC.

Activation in the right IFG and the right superior temporal gyrus was associated with successful

inhibition. Rubia et al. (1999) found that, during stop trials, healthy adolescents activated the

right medial/inferior PFC, the right mesial frontal cortex, the right supplementary motor area

(SMA), and the bilateral caudate nuclei. These studies suggest that inhibition involves activation

of various brain regions, particularly the VLPFC and DLPFC.

Meta-analyses have been performed on fMRI studies of inhibition. Aron and Poldrack

(2005) looked at 11 fMRI studies using the Stop and GNG tasks in healthy participants, and

found extensive activation throughout the frontal cortex which was predominantly right-

lateralized, particularly in the VLPFC. Swick, Ashley, and Turken (2008) performed a meta-

analysis of 39 studies reporting activations during Stop and GNG tasks. The regions most

commonly activated by successful inhibition included the right MFG, insular cortex, SFG, and

34

right inferior parietal lobule/precuneus. Buchsbaum, Greer, Chang, and Berman (2005) looked at

18 studies using the GNG task, and observed the most pronounced activation in the right

prefrontal cortex, which included the IFG and MFG. Garavan, Hester, Murphy, Fassbender, and

Kelly (2006) conducted a meta-analysis of 5 studies using the GNG task and noted that

successful inhibition activated a predominantly right hemispheric network involving prefrontal,

parietal, subcortical, and midline regions. These studies further support the suggestion that the

VLPFC and DLPFC are involved in inhibition.

One study to date has examined brain activation in TBI patients during the SST. Easdon,

Levine, O’Connor, Tisserand, and Hevenor (2004) noted that, during stop trials, adults with TBI

showed significantly less activation in bilateral DLPFC than controls. During successful

inhibition, TBI patients showed significantly less activation in the left DLPFC and visual cortex

than controls. This suggests that, in TBI patients, the DLPFC is involved in inhibition. Several

neuroimaging studies have examined brain activation in ADHD patients during the SST. The

symptomatology of TBI is often similar to that of ADHD (Levin et al., 2007). Methylphenidate,

which is most commonly used in the management of ADHD (Tannock et al., 1989), has also

been found effective in treating the cognitive and behavioral problems associated with TBI in

children (Hornyak, Nelson, & Hurvitz, 1997). Thus, fMRI studies examining inhibition in

ADHD children may provide further insight into the neural mechanism underlying this cognitive

process. Rubia et al. (2008) reported that ADHD children showed significantly reduced

activation in the left DLPFC compared with controls during successful inhibition. During failed

stop versus go trials, ADHD children showed significantly reduced activation in the posterior

cingulate gyrus relative to controls. Rubia et al. (2005) found that, compared with controls,

adolescents with ADHD showed significantly less activation in the right inferior prefrontal

cortex during successful response inhibition. During inhibition failure, adolescents with ADHD

35

showed significantly less activation in the posterior cingulate gyrus and precuneus relative to

controls. These studies suggest involvement of the DLPFC in inhibition. However, studies using

a lesion-deficit approach are required to determine which brain regions are necessary for

inhibition.

Five studies have used the SST to examine inhibition in TBI children. Leblanc et al.

(2005) investigated the recovery of inhibition over a period of two years in 136 TBI children and

117 children with no history of TBI. Younger patients had a greater initial impairment in

inhibition, but showed greater recovery than older patients. Lesion characteristics did not

significantly affect inhibition. Konrad et al. (2000a) examined 27 children with moderate-to-

severe TBI tested at least 6 months postinjury, 31 children with developmental ADHD, and 26

normal controls aged 8-12 years. The TBI and ADHD groups had impaired inhibition compared

with controls. Schachar, Levin, Max, Purvis, and Chen (2004) looked at 137 children with

closed-head injury tested between 2.1 and 15 years postinjury, and 63 children with no history of

closed head injury aged 5-17 years. Only children with severe closed head injury and a high level

of secondary ADHD symptoms showed deficient inhibition compared with normal controls.

Levin et al. (2008b) found that 80 children aged 5-15 years with mild TBI showed an

improvement in inhibition over a one year period. As well, Stewart and Tannock (1999) used the

selective SST to investigate inhibition in 42 patients with mild head injury and 42 case-control

participants aged 8-72 years. Patients with mild head injury had impaired inhibition compared

with case-control participants. These studies suggest that TBI impairs inhibition in children, at

least in the early stages postinjury, although it has yet to be determined whether discrete PFC

lesions are responsible for the deficit.

36

Three studies have used the SST to investigate inhibition in adults with TBI. Rieger and

Gauggel (2002) found no significant difference in inhibition performance between 27 patients

with TBI due to traffic accidents or falls tested within 8 weeks of injury, and 27 OI controls aged

17-68 years. In addition, TBI patients with frontal lesions did not differ significantly from TBI

patients with non-frontal lesions. Similarly, Dimitrov et al. (2003) found that 22 patients with

non-progressive frontal lobe lesions due to penetrating missile or shrapnel wounds did not differ

significantly in inhibition performance from 22 normal controls aged 44-70 years. They also

found no significant difference between patients with frontal and non-frontal lesions. In contrast,

DeHaan et al. (2007) found that 17 patients with mild TBI tested within 2 days of injury had

significantly better inhibition than 17 normal controls. In general, therefore, it appears that TBI

does not impair inhibition in adults, which contrasts with studies in children. The possibility

remains that discrete PFC lesions arising from TBI may impair inhibition in adults, but this has

yet to be examined.

Lesion studies have used the SST to examine inhibition in adults with brain damage

resulting from various etiologies. Aron et al. (2003a) examined inhibition in 18 patients with

lesions of the right frontal lobe due to aneurysm or hemorrhage, or excisions of meningioma, and

16 controls. They looked at five frontal regions of interest: IFG, SFG, medial frontal gyrus, MFG

and OFG. Patients with right frontal lesions had impaired inhibition compared with normal

controls. They found a significant relationship between SSRT and volume of IFG damage in

patients with right, but not left, frontal lesions. Clark et al. (2007) confirmed the findings of Aron

et al. (2003a) with a larger sample of 40 patients. Floden et al. (2006) examined inhibition in 23

patients with single focal frontal lesions due to cerebral vascular accident, tumour/epilepsy

resections, or traumatic focal contusions, and 19 normal controls. Patients with damage to right

superior medial frontal regions (involving the SMA and pre-SMA) showed deficient inhibition

37

compared with normal controls. Also, Rieger et al. (2003) examined three groups of patients

with lesions due to cerebrovascular disorders or brain tumour resections aged 18-70 years: 17

patients with frontal lesions, 20 patients with non-frontal lesions, and 8 patients with basal

ganglia lesions. Compared with 20 OI controls, patients in the frontal and basal ganglia groups

had impaired inhibition. Of the patients with frontal lesions, those with right and bilateral lesions

showed deficient inhibition relative to patients with left lesions. These studies suggest that

discrete frontal regions (i.e., the right IFG and right superior medial frontal region) are necessary

for inhibition in adults.

Studies have also used the GNG task to examine inhibition in adults with brain damage.

It should be re-emphasized that the GNG task provides a measure of restraint. The SST, which is

the focus of the present study, is predominantly a measure of cancellation. However, regions

have been identified that are involved in both restraint and cancellation (e.g., Rubia et al.,

2001c). Consequently, lesion studies using the GNG task may shed some light on the brain

regions necessary for inhibition. Drewe (1975) examined four groups of 12 patients with cortical

brain damage: right frontal, left frontal, right non-frontal, and left non-frontal. Patients with

frontal lesions had impaired inhibition compared to patients with non-frontal lesions. Lesion side

had no significant effect on inhibition performance. Leimkuhler and Mesulam (1985) found that

a patient with a meningioma in the falx affecting medial frontal regions bilaterally made many

commission errors on the GNG task. Following tumour excision, the patient performed normally

on the task. Decary and Richer (1995) found that 8 patients with frontal excisions showed

deficient inhibition relative to 8 patients with temporal excisions and 8 controls. Similarly,

Godefroy, Lhullier, and Rousseaux (1996) reported that 11 patients with frontal damage had

impaired inhibition compared to 11 patients with posterior damage and 20 controls. Picton et al.

(2007) investigated 43 patients with focal frontal lesions localized to one of four neuroanatomic

38

locations: left lateral, right lateral, inferior medial, or superior medial. Patients with superior

medial frontal lesions showed deficient inhibition compared with 38 controls. Patients with left

superior medial lesions had impaired inhibition relative to the other patient groups and controls.

As well, Swick et al. (2008) looked at 12 patients with damage to the left IFG, 5 patients with

orbitofrontal cortex damage, and 16 controls. Patients with left IFG damage showed deficient

inhibition compared with controls, while those with orbitofrontal damage performed comparably

to controls. These GNG studies suggest that frontal regions, particularly in the left DLPFC and

VLPFC, are necessary for restraint inhibition. This contrasts with studies using the SST, which

suggest that frontal regions in the right hemisphere are necessary for cancellation inhibition.

Inhibition as measured by the SST has also been studied with transcranial magnetic

stimulation (TMS), a lesion-deficit approach in which magnetic fields are used to temporarily

disrupt neural activity in a particular brain region. Chambers et al. (2006) found that deactivation

of the right IFG impaired inhibition in healthy adults, whereas deactivation of either the MFG or

angular gyrus had no significant effect on inhibition. Similarly, Chambers et al. (2007) reported

that deactivation of the right IFG during a combined stop-signal/flanker task impaired inhibition

on incongruent trials, whereas deactivation of the right dorsal premotor cortex (located within the

precentral gyrus) did not significantly affect inhibition. These TMS studies lend support to

previous research suggesting that the right IFG is necessary for inhibition. A limitation of TMS

is that it penetrates only 1-2 cm into the brain (Schutter, 2009). As a result, the effects of TMS

are generally restricted to the superficial layers of the cortex.

Studies using the SST have reported age-related differences in performance and neural

activation that need to be considered. Williams, Ponesse, Schachar, Logan, and Tannock (1999)

noted that SSRT becomes faster with increasing age throughout childhood and remains constant

39

across adulthood. These age-related changes in SSRT could not be attributed to RT speed.

Cognitive development has been linked to PFC maturation (Dempster, 1992). Research suggests

that the PFC matures later than other regions of the frontal cortex (Gogtay et al., 2004). Rubia et

al. (2000) used fMRI to look at differences in brain activation between adolescents and adults

performing the SST. The two age groups did not differ significantly in SST performance.

Compared with adults, however, adolescents showed increased power of response in the right

caudate nucleus and in the right IFG, and decreased power of response in the left MFG and in the

left IFG. Bunge, Dudukovic, Thomason, Vaidya, Gabrieli (2002) used fMRI to examine

differences in brain activation between children aged 8-12 years and adults performing a dual

GNG flanker task. Children performed more poorly than adults in terms of inhibition and

interference suppression. FMRI results revealed that both cognitive functions activated a large

region of the right ventrolateral PFC in adults, but not children. This research raises the

possibility that findings in adults may not be completely generalizable to children.

Overall, it appears that cognitive processes are localizable to discrete PFC regions (Picton

et al., 2007). FMRI studies have suggested that specific PFC regions are involved in inhibition

(e.g., Rubia et al., 2005). Previous lesion studies in adults have largely implicated regions of the

right DLPFC and VLPFC in inhibition, particularly the right SFG and IFG (Aron et al., 2003a;

Floden et al., 2006). On the other hand, lesion research in children has been limited to the

comparison between frontal and non-frontal regions, which has not yielded a significant lesion-

deficit relationship. Consequently, questions remain about which PFC regions are necessary for

inhibition in children. In order to address this issue, it is important to take into account the

distinctiveness of discrete PFC regions.

40

Furthermore, to date, no studies have explored the effect of lesion tissue type on

inhibition. Diffuse axonal injury (DAI), which is a common consequence of TBI, refers to the

disruption of white matter connections (see Povlishock & Katz, 2005 for a more detailed

description). DAI has been associated with executive dysfunction (Fork et al., 2005; Wallesch,

Curio, Galazky, Jost, and Synowitz, 2001). Marquez de la Plata et al. (2007) reported an

association between greater DAI volume and poorer functional outcome. In children, DAI has

been linked to poor prognosis (Ciurea, Coman, Rosu¸ Ciurea, & Baiasu¸ 2005). Indeed, frontal

white matter lesions have been found to impair executive functioning (Arnett et al., 1994). Yet,

lesion studies of inhibition have not distinguished between gray and white matter. As a result, it

is unknown whether deficient inhibition is related to gray matter or white matter damage.

Consequently, the primary aim of this study was to examine the impact of lesions arising

from TBI on inhibition in children 3 months after injury. It was hypothesized that TBI children

will show impaired inhibition compared with PCs. In contrast, previous research suggests that

adults with TBI are not impaired in inhibition compared with OI controls (Rieger et al., 2002).

This may have been due to the fact that OI patients are included to control for the effects of

hospitalization as well as risk factors that predispose patients to injury, such as behavioral

problems (Stancin et al., 1998). Thus, it was hypothesized that TBI children will not differ

significantly in inhibition performance from OI controls. This study looked at five frontal

regions: IFG, SFG, MFG, OFG, and other frontal. Four lesion tissue types were examined: gray

matter, white matter, both gray and white matter, and gray-white matter junction. It was

hypothesized that TBI children with IFG or SFG lesions in the gray or white matter will show

deficient inhibition compared with OI controls. This study also evaluated the effect of the

following variables on inhibition: injury severity, number of lesions, and volume of lesions.

41

Based on previous research using the SST (Schachar et al., 2004; Leblanc et al., 2005), it was

hypothesized that none of these variables would relate to inhibition in TBI children.

42

Method

This study involved 30 TBI patients, 22 OI controls, and 30 PCs. Patients in the TBI and

OI groups were recruited from consecutive admissions to hospitals in Houston, Dallas, and

Miami. Participating hospitals in Houston included Ben Taub General Hospital, Texas

Children’s Hospital, and The Institute for Rehabilitation and Research. In Dallas, patients were

recruited from Children’s Medical Center and Our Children’s House at Baylor, and in Miami,

from Jackson Memorial Hospital. PCs were recruited from visitors to the Ontario Science Centre

in Toronto, and matched for age and sex with the TBI children.

Inclusion/Exclusion Criteria

All patients in the TBI group had a closed head injury. The OI group was comprised of

patients who sustained a traumatic bone fracture (upper-extremity, lower-extremity, or pelvic

fractures). Patients in the TBI and OI groups were screened for preexisting psychiatric disorders

shortly after injury by means of a clinical interview with parents. Exclusion criteria were as

follows: preinjury ADHD, pervasive developmental disorder, SCZ, preexisting neurological

disorders associated with cerebral dysfunction and/or impaired cognition (e.g., cerebral palsy,

epilepsy, mental retardation), and history of child abuse. Patients with hypoxia or hypotension

lasting for 30 minutes or more after resuscitation were also excluded. For the PC group, vision,

hearing, and motor impairments were exclusion criteria.

MRI

MRI Acquisition. Children in the TBI and OI groups were imaged without sedation on

Philips Intera 1.5 T MRI scanners (Philips, Best, The Netherlands) at 3 months postinjury. The

3-month time interval was selected based on previous TBI research showing that

43

neuropsychological outcome measures correlate with MRI at follow-up, but not at baseline

(Wilson et al., 1988). Imaging parameters were as follows: slice thickness = 5 mm; gap = 0.01

mm; field of view = 220 mm; voxel size = 0.86 x 0.86 x 5.00 mm.

Lesion Analysis. A neuroradiologist marked focal areas of signal abnormality on

contiguous coronal T2-weighted fluid-attenuated inversion recovery images using Picture

Archiving and Communication System software. Volumes were obtained by multiplying the

areas by 0.5 cm. Focal areas were originally recorded in cm2. Lesions were localized using the

method described by Damasio and Damasio (1989) and Damasio (1995). This involved tracing

lesions onto standard brain templates. There are some issues surrounding the use of the template

method to localize lesions. A notable shortcoming is that the brains under study will not

correspond completely to the templates (Makale et al., 2002). This may affect the accuracy of

lesion localization. In addition, the method involves some subjectivity. The latter issue may be

addressed, however, by having a qualified expert trace the lesions (Stamatakis & Tyler, 2005).

Despite its limitations, the template method is an accepted standard for localizing lesions

(Makale et al., 2002).

This study primarily looked at four frontal regions: SFG (Brodmann Area [BA] 6, 8, 9,

10, 46), MFG (BA 6, 8, 9, 46), IFG (BA 44, 45, 47), and OFG (BA 11, 12, 47). An “other

frontal” group was also examined, which consisted of patients with lesions in the gyrus rectus

(BA 11, 12), precentral gyrus (BA 4, 6), and frontal lobe.

Inhibitory Control

The SST measures the ability to cancel a speeded motor response (Logan, Schachar, &

Tannock, 1997). During the task, children were required to respond as quickly and accurately as

44

possible to a primary task (choice reaction time task), also called a “go” task. The stop signal

occurred on 25 percent of trials, which involved the presentation of a tone instructing

participants to withhold their response to the go stimulus on that particular trial. The SST is

based on a theory of inhibition known as the race model which purports that inhibition depends

on the outcome of a race between go and stop processes. If the go process finishes first, the

response will be executed, whereas if the stop process finishes first, the response will be

inhibited. Go and stop processes are independent. The primary outcome measure of the SST is

the SSRT which provides an estimate of the latency of the stopping process.

The probability of inhibiting a response is determined by the latency of the go process,

the latency of the stop process, and the SSD. Shorter delays make it easier for participants to

inhibit, whereas longer delays make inhibition more difficult. The SSD was adjusted

dynamically according to the participant’s stopping performance. Initially, the SSD was set at

250 ms. If the child inhibited successfully, the delay was increased by 50 ms, whereas if the child

failed to inhibit, the delay was decreased by 50 ms. This tracking algorithm maintained an

inhibition success rate of approximately 50 percent. SSRT was estimated by subtracting mean

SSD from mean go reaction time. Other relevant outcome measures include MRT, which reflects

the latency of the go process, and SDRT, which measures variability in the latency of the go

process.

Go stimuli were the uppercase letters X and O, presented one at a time in the center of

the screen for 1,000 msec. An equal number of X and O stimuli were presented. Children were

instructed to press one button with their left index in response to an “X,” and another button with

their right index finger in response to an “O.” Trials began with a 500 ms fixation point which

also appeared in the center of the screen. The screen then went blank for 2000 ms. The stop

45

signal was a 1000 Hz tone. The Stop Task was presented in 4 blocks of 24 trials, of which, 8

were stop trials (for a more detailed description of the SST, see Schachar et al., 2000).

Go accuracy is expressed as the percentage of correct responses to the go stimulus.

Participants who did not respond correctly to at least 66 percent of go trials were excluded. This

is an indication that the participant did not properly attend to the task. Scores greater than 3

standard deviations from the mean were not included in the analysis.

TBI Severity

TBI severity was determined using the Glasgow Coma Scale (GCS) (Teasdale & Jennett,

1974). The GCS score ranges from 3 (worst) to 15 (best) and is based on three separate patient

responses: eye opening, verbal and motor responses. This study included TBI patients with

moderate and severe TBI. Severe TBI was defined by a lowest post-resuscitation GCS score of

3-8, whereas moderate TBI was defined by a lowest post-resuscitation GCS score of 9-12 or 13-

15 plus a lesion.

Socioeconomic Status (SES)

SES was determined using the Socioeconomic Composite Index (SCI) (Yeates et al.,

1997). This involved calculating z scores based on the TBI and OI groups for three variables:

annual family income coded on an 8-point scale taken from the Life Stressors and Social

Resources Inventory-Adult Form (Moos & Moos, 1994); maternal education coded on a 7-point

scale; and maternal occupational prestige according to the Total Socioeconomic Index (Hauser &

Warren, 1997). The SCI score was the average z-score for the three variables.

Although SES was not directly measured for the PCs, it was expected that they would be

46

more likely to have a higher SES and IQ than patients in the TBI and OI groups. However, this is

unlikely to affect the inhibition results, since previous studies have found no significant

correlation between either SES or IQ and SSRT (e.g., Leblanc et al., 2005; Schachar, Tannock,

Marriott, & Logan, 1995a).

Behavioral Measures

Behavior Rating Inventory of Executive Function (BRIEF). The BRIEF is designed for

use with children aged 5 to 18 years (Gioia, Isquith, Guy, & Kenworthy, 2000). It consists of 86

items that assess executive functioning as reflected in everyday life. The parent form of the brief

was administered to a caregiver of each participant in the TBI and OI groups. The BRIEF

consists of eight clinical scales: Initiate, Working Memory, Plan/Organize, Organization of

Materials, Monitor, Inhibit, Shift, and Emotional Control. The first five clinical scales comprise

the Metacognitive Index, whereas the last three clinical scales comprise the Behavioral

Regulation Index. When combined, these two indexes form a Global Executive Composite. The

BRIEF has been shown to be valid and reliable for use in both healthy children (Gioia et al.,

2000) and children with TBI (Mangeot, Armstrong, Colvin, Yeates, & Taylor, 2002). T-scores

were used in the analysis, which have a mean of 50 and a standard deviation of 10. Scores of 65

or greater were considered clinically significant.

Vineland Adaptive Behavior Scale (VABS). The VABS (Sparrow, Balla, & Cicchetti,

1984) was administered to a caregiver of each patient in the TBI and OI groups. This semi-

structured interview was used to assess Maladaptive Behavior. The VABS has well-established

reliability and validity (Sparrow et al., 1984), and is sensitive to TBI-related adaptive functioning

deficits in children (Fletcher, Ewing-Cobbs, Miner, Levin, & Eisenberg, 1990). V-scale scores

were used in the analysis, which have a mean of 15 and a standard deviation of 3.

47

Medication Status

There is evidence that methylphenidate stimulant medication improves inhibition in

ADHD children (Tannock et al., 1989). Consequently, participants were instructed not to take

stimulant medication on the night before and day of testing.

Analysis

Group differences in participant characteristics were compared using a one way ANOVA

for continuous variables and chi-square tests for categorical variables. TBI patients were initially

divided by median split into “good” and “poor” groups according to their SST performance.

Frequencies were compared between groups with Fisher’s exact test. Where significant,

comparisons with controls were performed using a one-way ANOVA. Planned contrasts were

conducted where appropriate. ESs were calculated using Cohen’s d (Cohen, 1988). ESs can be

classified as small (0.2), medium (0.5) or large (0.8).

48

Results

Patient Characteristics

Table 2.1 provides the demographic information of the TBI, OI, and PC groups. The most

common mechanism of injury in the TBI group was motor vehicle accidents (23.3 percent),

whereas in the OI group, the most frequent mechanism of injury was sports or play related (17.4

percent). The mean GCS score of the TBI patients was 8.69 (SD = 3.44), 16 of whom had severe

TBI and 11 had moderate TBI. GCS was not available for 3 TBI patients. There was no

significant difference in age between the three groups (F = 2.04, p = 0.14). Furthermore, the TBI

and OI groups did not differ significantly in their age at injury (F = 2.77, p = 0.10). There was no

significant difference in gender between the three groups (χ2

= 0.06, p = 0.97). As well, the TBI

and OI groups did not differ significantly in SES.

Behavioral Measures

Table 2.2 presents the mean T-scores of the TBI and OI groups on the BRIEF. The mean

T-scores for the TBI group fell below the clinically significant range on all scales and indices.

Still, TBI children were rated significantly higher than OI controls on all clinical scales (p <

0.05) except the Organization of Materials subscale (p = 0.13). Compared with the OI group, the

TBI group had significantly higher scores on the Behavioral Regulation Index (p = 0.002), the

Metacognitive Index (p = 0.009), and the Global Executive Composite (p = 0.003).

Table 2.3 provides the mean V-scale scores of the TBI and OI groups on the Maladaptive

Behavior Domain of the VABS. Compared with the OI group, the TBI group received

significantly higher scores on the Maladaptive Behavior Index (p = 0.001), reflecting greater

49

behavioral problems. TBI children had significantly higher scores than OI controls on the

externalizing subdomain (p = 0.001), but not on the internalizing subdomain (p = 0.26).

Lesion Characteristics

All patients in the TBI group had at least one lesion. The mean total lesion volume in the

TBI group was 12.84 cm3, and the mean number of lesions was 11.23. Table 2.4 presents the

distribution of lesions in the TBI group. Ninety-seven percent of TBI patients had non-frontal

lesions, which affected the left and right hemispheres almost equally. Eighty-seven percent of

TBI patients had frontal lesions, which occurred most often in the SFG. They were also

commonly found in the MFG, IFG, and OFG. No patients had lesions in the medial frontal gyrus,

cingulate gyrus, or operculum. Lesions of the SFG and OFG occurred equally in the left and

right hemispheres, whereas MFG and IFG lesions were identified more frequently in the right

hemisphere. Both frontal and non-frontal lesions were found most often in the gray matter.

It is of note that no OFG lesions were observed in the white matter, and only one patient

had a white matter lesion of the IFG. The effect of basal ganglia damage on inhibition was not

examined because only three patients had lesions in this region. Moreover, this study did not

assess the effect of lesion laterality on inhibition because few patients had lesions only in the left

hemisphere.

Lesion Analysis

SSRT. Table 2.5 provides the number of patients with lesions in the good and poor SSRT

subgroups by lesion location. SSRTs in the poor subgroup ranged from 229.8 ms to 503.3 ms,

whereas SSRTs in the good subgroup ranged from 151.4 ms to 221.7 ms. There was no

significant difference between the two SSRT subgroups in the frequency of patients with non-

50

frontal lesions across the four brain tissue types. In contrast, there was a significantly higher

frequency of patients with frontal white matter lesions in the poor SSRT subgroup than in the

good SSRT subgroup (80 percent versus 20 percent, respectively, p = 0.003). When examined

more closely, the poor SSRT subgroup had a significantly higher frequency of patients with SFG

white matter lesions compared with the good SSRT subgroup (47 percent versus 7 percent,

respectively, p = 0.04).

MRT. Table 2.6 presents the number of patients with lesions in the poor and good MRT

subgroups according to lesion location and brain tissue type. MRTs in the poor subgroup ranged

from 555.2 ms to 1413 ms, and in the good subgroup, from 346 ms to 546.6 ms. There was no

significant difference between the MRT subgroups in the frequency of patients with frontal or

non-frontal lesions across any of the four brain tissue types, although, the poor MRT subgroup

had a borderline significantly higher frequency of patients with frontal lesions at the gray-white

matter junction compared with the good MRT subgroup.

SDRT. Table 2.7 presents the number of patients with lesions in the poor and good SDRT

subgroups according to lesion location and brain tissue type. SDRTs ranged from 138.5 ms to

326.5 ms in the poor subgroup, and from 49.3 ms to 132.8 ms in the good subgroup. The two

SDRT subgroups did not differ significantly in the frequency of patients with frontal or non-

frontal lesions across any of the four brain tissue types.

SST Performance

The moderate and severe TBI groups did not differ significantly in SSRT (F = 2.83, p =

0.11), MRT (F = 1.23, p = 0.28), SDRT (F = 0.78, p = 0.39), probability of inhibition (F = 1.15,

p = 0.29), or in the percentage of correct responses to go stimuli (F = 0.73, p = 0.40).

51

Consequently, SST data from the moderate and severe groups were combined for subsequent

analyses.

Regression analyses showed no significant relationship between SSRT and the total

volume of lesions (β = 0.37, p = 0.08) or the total number of lesions (β = -0.12, p = 0.57);

between MRT and the total volume of lesions (β = -0.006, p = 0.98) or the total number of

lesions (β = 0.37, p = 0.08); or between SDRT and the total volume of lesions (β = 0.03, p =

0.87) or the total number of lesions (β = 0.3, p = 0.16).

Table 2.8 presents the SST performance of the TBI patients, OI controls, and PCs.

Results showed a significant difference in SSRT between the three groups (F = 3.192, p =

0.046). Planned contrasts revealed that the TBI and OI groups had significantly longer SSRTs

than the PCs (t = 2.501, p = 0.014). There was no significant difference in SSRT between the

TBI and OI groups (t = 0.125, ns). Significant group differences in MRT were also found (F =

13.84, p = 0.000). The TBI and OI groups had significantly longer MRTs than the PCs (t =

5.193, p = 0.000). TBI patients did not differ significantly from the OI controls (t = 0.113, ns).

There was also a significant difference between the three groups in the probability of inhibition

(F = 6.030, p = 0.005). The TBI and OI groups had a significantly higher probability of

inhibition than the PCs (t = 3.379, p = 0.001). The TBI group did not differ significantly from the

OI group (t = 1.251, ns). The three groups did not differ significantly in SDRT (F = 1.805, p =

0.171) or in the percentage of correct responses to go stimuli (F = 0.391, p = 0.678).

Given the significantly higher frequency of patients with frontal white matter lesions in

the poor relative to the good SSRT subgroup, a separate analysis was conducted to investigate

the SST performance of patients with frontal white matter lesions. Table 2.9 presents the SST

performance of patients with frontal white matter lesions, OI controls, and PCs. There was a

52

significant difference in SSRT between the three groups (F = 7.77, p = 0.001). Patients with

frontal white matter lesions had significantly longer SSRTs than OI controls (t = 1.96, p = 0.05).

The frontal white matter subgroup and the OI group together had significantly longer SSRTs

than PCs (t = 3.64, p = 0.001). The three groups also differed significantly in MRT (F = 11.29, p

= 0.000). The frontal white matter subgroup and OI group had significantly longer MRTs

compared with the PCs (t = 4.78, p = 0.000). Yet, the frontal white matter subgroup did not differ

significantly from the OI subgroup (t = 0.55, ns). Significant group differences in SDRT were

also found (F = 3.37, p = 0.04). The frontal white matter subgroup had significantly more

variable RTs compared with the OI group (t = 2.19, p = 0.03). However, the frontal lobe white

matter subgroup and the OI group, when combined, did not differ significantly from the PCs (t =

1.67, ns). Finally, there was a significant difference in the probability of inhibition between the

three groups (F = 4.39, p = 0.02). The frontal white matter subgroup and the OI group had a

significantly higher probability of inhibition than the PCs (t = 2.99, p = 0.007). The frontal white

matter group did not differ significantly from the OI group (t = 1.54, ns). There was no

significant difference between the groups in the percentage of correct response to go stimuli (F =

0.38, ns).

The SST performance of patients with SFG white matter lesions was also examined for

the same reason as described for the frontal white matter subgroup. Table 2.10 presents the SST

performance of patients with SFG white matter lesions, OI controls, and PCs. There were

significant group differences in SSRT (F = 7.76, p = 0.001). Planned contrasts indicated that

patients with SFG white matter lesions had significantly longer SSRTs than OI controls (t = 2.26,

p = 0.03). The OI group and SFG white matter subgroup also had significantly longer SSRTs

than the PCs (t = 3.81, p = 0.000). There was a significant difference in MRT between groups (F

= 7.83, p = 0.005). Patients with SFG white matter lesions and OI controls had significantly

53

longer MRTs than PCs (t = 3.55, p = 0.003). The SFG white matter subgroup did not differ

significantly from the OI group (t = -0.19, ns). No significant group differences were found in

SDRT (F = 0.80, ns), the percentage of correct responses to go stimuli (F = 0.48, ns), or in the

probability of inhibition (F = 2.35, ns).

In Table 2.11, effect sizes are presented for the group comparisons using Cohen’s d.

Behavioral Outcomes for the Frontal White Matter and SFG White Matter Subgroups

BRIEF. The mean T-scores for the frontal white matter and SFG white matter subgroups

fell below the clinically significant range on all scales and indices. Even so, the frontal white

matter subgroup was rated significantly higher than the OI group on the Behavioral Regulation

Index (F = 7.73, p = 0.01) and the Metacognitive Index (F = 5.48, p = 0.03), as well as on the

Global Executive Composite (F = 7.12, p = 0.02). In addition, the SFG white matter subgroup

had significantly higher scores than the OI group on the Metacognitive Index (F = 5.1, p = 0.03),

but did not differ significantly on the Behavioral Regulation Index (F = 3.90, p = 0.08) or on the

General Executive Composite (F = 4.3, p = 0.07).

VABS. The frontal white matter subgroup received significantly higher scores than the OI

group on the Maladaptive Behavior domain (F = 7.07, p = 0.01). Similarly, the SFG white matter

subgroup had significantly higher scores compared with the OI group on the Maladaptive

Behavior domain (F = 7.33, p = 0.01).

54

Discussion

The results are consistent with previous studies showing that TBI children with moderate

to severe TBI are slower to stop than PCs (e.g., Konrad et al., 2000a; Schachar et al., 2004;

Leblanc et al., 2005). TBI children did not, however, differ significantly in SSRT from OI

controls. This is consistent with the study of Rieger et al. (2002), which found that adults with

TBI did not differ significantly in SSRT from OI patients. The finding suggests that TBI

children, in general, are no more impaired in inhibition than are OI controls. This may be related

to the significant behavioral problems seen in OI patients (Loder, Warschausky, Schwartz,

Hensinger, & Greenfield, 1995). However, this study did find that subsets of TBI children with

specific lesions had impaired inhibition compared with OI controls.

This was the first study to examine whether lesions previously found to impair inhibition

in adults would have the same effect in children. Patients with frontal white matter lesions

showed borderline significantly longer SSRTs than the OI controls. This comparison yielded a

moderate effect size. In addition, the subgroup of TBI patients with SFG white matter lesions

had significantly longer SSRTs than the OI group. The effect size for this comparison was large.

This finding is consistent with the study of Floden et al. (2006), which found significantly longer

SSRTs in patients with damage to right superior medial frontal regions compared with normal

controls. However, the finding conflicts with that of Aron et al. (2003a), who implicated the right

IFG in deficient inhibition. Yet, only one TBI child had an IFG lesion of white matter, which

may explain the lack of a deficit in the IFG group.

Recent studies have suggested involvement of the SFG in inhibition. Chen, Muggleton,

Tzeng, Hung, and Juan (2009) found that deactivation of the left superior medial frontal cortex

using TMS impaired SSRT, but not MRT, in healthy adults. Li, Huang, Constable, and Sinha

55

(2006) used fMRI and reported that healthy adults with more efficient inhibition (i.e., shorter

SSRTs) showed significantly greater activation in the left SFG and left precentral gyrus

compared to patients with less efficient inhibition (i.e., longer SSRTs). Duann, Ide, Luo, & Li

(2009) suggested that a functional connection exists between the pre-SMA and both the

subthalamic nucleus (STN) and caudate head, as well as between the inferior frontal cortex and

pre-SMA. Yet, the inferior frontal cortex was not found to be functionally connected with either

the STN or caudate head. The authors interpreted this as suggesting that the role of the pre-SMA

is in mediating motor inhibition, while the role of the IFG is in mediating attention to the stop

signal.

These findings do not necessarily suggest that the SFG alone is responsible for inhibition.

To the contrary, various frontal and subcortical regions have been implicated in inhibition (see

Robbins, 2007 for a review). Indeed, Aron et al. (2007a) proposed that a fronto-basal-ganglia

network underlies inhibition, including the pre-SMA, IFG, and STN. The SFG contains parts of

the pre-SMA (John et al., 2006). Lesions in various parts of the network could impair inhibition.

Findings from the present study suggest that frontal white matter lesions especially impair the

ability to inhibit an ongoing motor response. This may explain why TBI children with SFG white

matter lesions showed impaired inhibition compared with OI controls.

The role of the SFG is not limited to that of inhibition. Du Boisgueheneuc et al. (2006)

reported that patients with left SFG lesions showed impaired working memory compared with

three control groups: patients with prefrontal lesions not involving the SFG, patients with right

parietal lesions, and healthy controls. Indeed, Clark et al. (2007) reported a significant

correlation between SSRT and total between-search errors on a spatial working memory task in

patients with right, but not left, frontal damage. Max et al. (2006) noted an association between

56

SFG lesions and personality change following TBI in children. It appears, therefore, that there

are several functions of the SFG.

White matter lesions have been associated with deficient inhibition in patients with

disorders other than TBI, such as MS and Alzheimer’s disease (AD). MS largely affects the

white matter of the brain and spinal cord. Arnett et al. (1994) compared three groups of patients

with MS on the Wisconsin Card Sorting Task: a frontal white matter lesion group, a minimal

frontal lesion group, and a control group of MS patients with few lesions. The frontal white

matter lesion group performed significantly worse on the Wisconsin Card Sorting Task

compared with the other two groups. A recent study also found that cognitively impaired MS

patients made significantly more commission errors on a GNG task than controls (Smith et al.,

2009). White matter lesions have been reported as a risk factor for AD (Vermeer et al., 2003).

Tullberg et al. (2004) reported that the most common site of white matter lesions in patients with

AD is the PFC. Research suggests that patients with AD are slightly impaired in SST

performance (Amieva et al., 2002). Crawford et al. (2005) reported that AD patients made

significantly more errors on the GNG task than normal elderly participants.

DTI is a novel MRI technique designed to assess the integrity of white matter tracts. It

has been found to be more sensitive than conventional MRI for detecting white matter damage in

children with moderate to severe TBI at 3 months postinjury (Levin et al., 2008a). A recent study

found that higher fractional anisotropy (FA) values (indicates greater white matter integrity) in

the white matter of the left dorsolateral frontal region was significantly associated with fewer

errors in the no-go condition of a Flanker Task (Levin et al., 2008a). Lipton et al. (2009) reported

that lower FA values (indicating lower white matter integrity) in the white matter of the DLPFC

was significantly correlated with greater executive dysfunction as measured by the Continuous

57

Performance Task and the Executive Maze Task in adults with mild TBI. From these studies, it is

evident that executive functioning is related to the integrity of white matter tracts in patients with

TBI.

Frontal white matter lesions may lead to greater cognitive impairment in children than in

adults. Research has found that healthy children have significantly lower FA values in the frontal

white matter than healthy adults (Klingberg, Vaidya, Gabrieli, Moseley, & Hedehus, 1999).

White matter maturation during childhood has been related to cognitive development (Nagy,

Westerberg, & Klingberg, 2004). In accordance with previous research (Levin et al., 1997), this

study found that lesions occurred most commonly in the frontal lobe white matter. Accordingly,

frontal white matter damage may further reduce the cognitive function of children compared

with adults.

TBI children did not differ significantly in MRT from the OI controls. However, the TBI

and OI groups showed significantly longer MRTs than the PCs. The effect size for the difference

in MRT between the TBI and PC groups was large. Konrad et al. (2000a) also found that TBI

children with moderate to severe TBI had significantly longer MRTs than normal controls. This

does not suggest that the inhibitory deficit is due to a generalized slowing of response. According

to the race model, the stop and go processes are independent of one another. The SFG white

matter subgroup had non-significantly shorter MRTs than OI controls. This is of note, as the

SFG white matter subgroup also had significantly longer SSRTs than the OI group.

The poor MRT subgroup had a borderline significantly higher frequency of patients with

frontal lesions at the gray-white junction than the good MRT subgroup. DAI is most frequently

observed at the gray-white junction. Wallesch et al. (2001) found that TBI patients with DAI had

58

significantly longer reaction times on the GNG task than TBI patients without DAI. This

suggests that DAI may impair MRT in children with TBI.

TBI children did not differ significantly in SDRT from PCs. The TBI and OI groups also

showed no significant difference in SDRT from the PCs. In contrast, patients with frontal white

matter lesions showed significantly more variable RTs than the OI controls. This comparison

yielded a medium effect size. There was no significant difference in SDRT between patients with

SFG white matter lesions and the OI controls, but this was likely due to the small sample size in

the SFG white matter subgroup. Ghajar and Ivry (2008) suggested that increased response

variability in TBI patients may be a consequence of damage to white matter tracts connecting the

PFC, parietal lobe, and cerebellum.

Results showed no significant difference between patients with moderate and severe TBI

on any of the SST outcome variables. This is in line with previous evidence suggesting that GCS

score alone does not relate to SSRT. In children, the GCS does not seem to adequately predict

cognitive outcome (Keenan & Bratton, 2006). Alternatively, the finding may suggest that

inhibitory control is impaired by TBI irrespective of severity. Also in accordance with previous

studies (e.g., Leblanc et al., 2005), there was no significant relationship between the mean total

number of lesions and SSRT. Similarly, the mean total lesion volume was not significantly

associated with SSRT. These variables, however, may not sufficiently capture the diffuse nature

of TBI.

The TBI group as a whole and the subgroup of TBI patients with frontal white matter

lesions showed greater executive dysfunction than the OI group as measured by the BRIEF. Both

groups were rated significantly higher on the Behavioral Regulation and Metacognitive Indices,

as well as on the Global Executive Composite. However, the mean scores for both groups were

59

not in the clinically significant range. Similarly, the mean scores for the subgroup of TBI patients

with SFG white matter lesions fell below the clinically significant range. Patients with SFG

white matter lesions had significantly higher scores than the OI controls on the Metacognitive

Index. However, they did not differ significantly from OI controls on the Behavioral Regulation

Index or on the General Executive Composite, which was likely due to the small number of

patients with SFG white matter lesions. These findings are in accordance with previous studies

using the BRIEF in TBI patients (Sesma, Slomine, Ding, and McCarthy, 2008), suggesting that a

dissociation exists between executive functioning in everyday life as assessed by the parent form

of the BRIEF and executive inhibitory control as measured by a laboratory task of inhibition.

The TBI group also received significantly higher scores on the Maladaptive Behavior

Domain than the OI controls, indicating greater behavioral problems. TBI children received

significantly higher scores than the OI controls on the externalizing subdomain of the

Maladaptive Behavior Domain, but not the internalizing one. This may be due to the fact that

impulsivity is a common consequence of TBI in children (Levin et al., 2007). Similarly, the

frontal white matter and SFG white matter subgroups received significantly higher scores than

the OI group on the Maladaptive Behavior domain. These findings suggest that behavioral

problems are present in TBI children, irrespective of lesion location.

Some limitations of this study should be noted. First, due to the diffuse nature of TBI, the

lesion groups were not mutually exclusive. For example, patients with SFG lesions may have

also had MFG lesions. Patients were divided into groups based on the presence of a particular

lesion. There is also the issue that structural MRI is not as sensitive as DTI for detecting white

matter damage (Ghajar et al., 2008).

60

In summary, this study revealed that TBI children are significantly impaired in inhibition

compared with PCs. Conversely, TBI children did not differ significantly in inhibition

performance from OI controls, which may be related to the behavioral problems typically seen in

patients with OI (Loder et al., 1995). However, a subgroup of TBI children with frontal white

matter lesions had a borderline-significant deficit in inhibition compared with OI controls, while

a subgroup of TBI children with SFG white matter lesions had a significant deficit. These

findings suggest that frontal white matter damage, particularly in the SFG region, is responsible

for impaired inhibition in TBI children. Future research should use DTI to assess the integrity of

white matter tracts. It may also be useful to longitudinally assess the impact of SFG lesions on

inhibition in TBI children. In the future, research should also examine whether methylphenidate

improves inhibition in patients with SFG white matter lesions.

61

General Discussion

This thesis undertook the most comprehensive systematic investigation of inhibition as

measured by the SST across 10 psychopathological disorders. This thesis was also the first to

examine the impact of specific lesions on inhibition in children with TBI. Overall, a great deal

has been learned about the specificity of impaired inhibition and the nature of its underlying

neural mechanism.

Chapter 1 showed that impaired inhibition is not specific to ADHD. A moderate

inhibitory deficit was found in ADHD, which agrees with previous meta-analyses of the SST

(Oosterlaan et al., 1998c; Lijffijt et al., 2005; Alderson et al., 2007). Results also revealed

moderate inhibition deficits in OCD and SCZ. Existing research has suggested that deficient

inhibition may underlie the repetitive behaviors of OCD (Rosenberg, Dick, O'Heam, Sweeney,

1996). In SCZ, thought disorder symptoms (i.e. disorganized speech) have previously been

associated with impaired inhibition (Allen, Liddle, & Frith, 1993). The inhibition deficit does not

appear to be a non-specific marker of psychopathology, given that not all psychopathological

groups showed impaired inhibition. It is possible that a common neural mechanism may underlie

the inhibition deficit in ADHD, OCD, and SCZ. For example, fMRI studies in ADHD children

have found that various brain regions are involved in inhibition, particularly the VLPFC and

DLPFC (Rubia et al., 2005). Hoptman et al. (2002) used DTI and found that lower FA values in

the white matter of the right IFG was significantly associated with higher motor impulsiveness in

men with SCZ. Roth et al. (2007) used fMRI and found that adults with OCD showed

significantly less activation in the right IFG and MFG compared with normal controls during a

GNG task. However, further research is needed to clarify the neural correlates of inhibition.

62

Another notable finding of the meta-analysis was the small inhibition deficit in ODD/CD.

This contrasts with the meta-analysis of Oosterlaan et al. (1998c), which noted a moderate

inhibition deficit in ODD/CD. This finding is consistent with the minimal evidence for executive

dysfunction in ODD/CD (Pennington et al., 1996).

In terms of the comorbid ADHD groups, ADHD + RD showed a large deficit in

inhibition, which was greater than that of both the ADHD and RD groups. Therefore, the ADHD

+ RD group exhibited a pattern of inhibition consistent with the “etiological subtype” hypothesis.

This may also suggest that children with ADHD + RD may have more severe ADHD than those

without comorbid RD. The ADHD + ODD/CD group showed a small-to-medium deficit in

inhibition, lending support to the phenocopy hypothesis.

Chapter 2 revealed that TBI children and OI controls are impaired in inhibition compared

with PCs. Conversely, TBI children did not differ significantly in inhibition performance from

OI controls. This suggests that TBI, in general, does not impair inhibition. It further points to the

possibility that OI controls may not be typically developing children. The study did find that

subsets of TBI children with specific lesions showed deficient inhibition compared with OI

controls. TBI patients with frontal white matter lesions had a borderline-significant deficit in

inhibition compared with OI controls. Moreover, TBI children with SFG white matter lesions

had a significant inhibiton deficit compared with OI controls. In both cases, TBI children did not

differ significantly in MRT from OI controls.

These findings suggest that frontal white matter damage, particularly in the SFG region,

underlies impaired inhibition in TBI children. This finding is consistent with previous research

showing impaired inhibition in adults with damage to right superior medial frontal regions (e.g.

Floden et al., 2006). Interestingly, the findings did not implicate the right IFG. Floden et al.

63

(2006) also found little evidence of deficient inhibition in patients with damage to the right IFG.

The results of the present study, which suggest that white matter lesions especially impair

inhibition, do not necessarily conflict with those of Aron et al. (2003a), given that only one child

in the TBI sample had an IFG lesion of white matter. It is also possible that more than one region

may be necessary for inhibition. According to Aron et al. (2007a), a fronto-basal-ganglia

network is responsible for inhibition, including the pre-SMA, IFG, and STN. This accords with

the present study, since parts of the pre-SMA are in the SFG region.

There are several clinical implications associated with these findings. ADHD is

considered the architypal disorder of deficient inhibition. Yet, the meta-analysis revealed that

deficient inhibition is not specific to ADHD, but can be found in OCD and SCZ. In fact,

inhibition appeared to be slightly more impaired in patients with OCD and SCZ. This

information may help clinicians make more informed diagnostic decisions. The meta-analysis

also found a small difference in inhibition between ADHD + ODD/CD children and controls,

which accords with research suggesting that many children with pure ODD/CD are misdiagnosed

as having ADHD (Schachar et al., 1986). This suggests that clinicians should exercise more

discretion in diagnosing ADHD. It was further shown that ADHD + RD children had more

impaired inhibition than ADHD children, which points to the possibility that children with

ADHD + RD may have more severe ADHD than those without comorbid RD. If this is the case,

then ADHD + RD children may require a modified treatment regimen.

The lesion-deficit study revealed that specific subsets of TBI children showed impaired

inhibition compared with OI controls. More specifically, TBI children with SFG white matter

lesions had an inhibition deficit, while TBI children with frontal white matter lesions had a

borderline deficit. It is possible that patients with frontal white matter lesions may require

64

additional care. The lesion-deficit study found no significant difference in inhibition between

TBI children and OI controls. This suggests that OI children are not typically developing. They

may benefit from a more thorough clinical evaluation at hospital admission.

In summary, this thesis has established that impaired inhibition is not specific to ADHD.

There may be a common cognitive deficit underlying impaired inhibition in these disorders. This

raises the possibility of a common neural mechanism underlying the deficit. Neuroimaging

studies have suggested involvement of the VLPFC and DLPFC in inhibition. Previous lesion

studies in adults have specifically implicated the IFG and SFG. This thesis used a lesion-deficit

approach to examine the effect of specific lesions on inhibition in TBI children. The results

showed that damage to the SFG white matter impairs inhibition in TBI children. It appears,

therefore, that impaired inhibition may result from damage to white matter tracts. Further studies

are needed to confirm the inhibitory deficit in OCD and SCZ, and to establish the presence of a

common cognitive deficit. Future research should also use more advanced neuroimaging

techniques, such as DTI, to assess the integrity of white matter tracts.

65

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Tab

le 1

.1

Characteristics of Studies

Stu

dy

Age

(yea

rs)

IQ

% M

ale

% A

DH

D-I

%

AD

HD

-C

Qual

ity I

nd

ex

AD

HD

Sch

achar

et

al. (1

990)

9.3

107

- -

- 3.5

Sch

achar

et

al. (1

995

a)

8.7

105.1

100

- -

4

Sch

achar

et

al. (1

995b)

9.2

103.1

100

- -

4

Oost

erla

an e

t al

. (1

996)

9.3

93

86.7

-

- 3

Pli

szka

et a

l. (

1997)

7.2

-

- -

- 4.5

Oost

erla

an e

t al

. (1

998a)

10.4

90.2

100

- -

3

Rubia

et

al. (1

998)

9.0

96.5

100

- -

2

Nig

g e

t al

. (1

999)

9.6

104.6

68

0

100

3

10

0

Konra

d e

t al

. (2

000a)

10.5

95

90.3

29

58.1

3.5

Man

assi

s et

al.

(2000)

10.1

95.2

73.3

-

- 2

Pli

szka

et a

l. (

2000)

11.0

-

100

0

100

3.5

Purv

is e

t al

. (2

000)

9.1

110.7

94.1

-

- 5

Sch

achar

et

al. (2

000)

9.0

96.0

-

- -

5

Epst

ein e

t al

. (2

001)

33.6

-

40

56

40

4

Kunts

i et

al.

(2001)

8.8

93.5

52.9

-

- 2

Rubia

et

al. (2

001a)

15.7

-

81.3

-

- 3.5

Sch

eres

et

al. (2

001a)

10.1

92.2

75

37.5

29.2

4

Sola

nto

et

al. (2

001)

8.5

98.6

86

- 100

3.5

Wil

lcutt

et

al. (2

001)

10.8

101.1

-

- -

2.5

Murp

hy (

2002)

27-5

8

110

100

0

100

2

Over

toom

et

al. (2

002)

10.4

95.4

100

0

100

3.5

Ruck

lidge

et a

l. (

2002)

15.2

102.2

57.1

85.7

8.6

4.5

Aro

n e

t al

. (2

003b

) 26.2

109

76.9

23.1

61.5

3.5

Dim

osk

a et

al.

(2003)

9.8

98.2

100

15.4

84.6

2.5

McI

ner

ney e

t al

. (2

003

) 10.1

-

90

0

100

4

Oss

man

n e

t al

. (2

003)

19.2

116.7

58.3

-

- 3.5

10

1

Wodush

ek e

t al

. (2

003)

35.0

-

31.1

-

- ??

?

Geu

rts

et a

l. (

2004

) 9.3

99.5

100

29.6

66.7

4.5

Sch

achar

et

al. (2

004)

8.7

102.1

76.2

26

55

5

Sch

eres

et

al. (2

004)

8.7

97.6

100

34.8

65.2

3.5

Wal

cott

et

al. (2

004)

9.3

-

100

0

100

3.5

Alb

rech

t et

al.

(2005)

10.8

94.4

100

0

100

2.5

Bek

ker

et

al. (2

005

a)

34.3

-

50

0

100

5

Rubia

et

al. (2

005)

13.0

100

100

0

100

2.5

Sch

achar

et

al. (2

005)

9.5

103.9

70

25

55

5

Wil

lcutt

et

al. (2

005a)

11.2

104.3

65.5

65

35

3

Kle

in e

t al

. (2

006)

10.5

96.6

85.9

19.3

77.2

??

?

Pli

szka

et a

l. (

2006)

13.2

106.8

62.5

0

100

4.5

Bid

wel

l et

al.

(2007

) 11.2

101.8

71.4

72.2

27.8

3

Cla

rk e

t al

. (2

007)

28.0

108.3

65

20

50

3.5

Johnst

one

et a

l. (

2007)

11.9

109.1

84

48

52

3.5

Kore

nblu

m e

t al

. (2

007)

8.9

102.6

78.2

-

- 5

Lam

pe

et a

l. (

2007

) 30.0

111

63.6

63.6

31.8

2.5

Lio

tti

et a

l. (

2007)

12.3

107.4

69.4

0

100

4.5

10

2

Nig

g e

t al

. (2

007)

9.5

104.9

69.4

28.4

71.6

3.5

Rubia

et

al. (2

007)

11.0

99

93.8

0

100

2.5

Sch

achar

et

al. (2

007

a)

9.5

104.5

79

32

55

5

Sch

achar

et

al. (2

007b)

8.8

101.8

77.9

29.5

43.9

4

Ald

erso

n e

t al

. (2

008

) 8.7

5

100.9

2

100

0

100

3.5

Bit

sakou e

t al

. (2

008

) 11.8

94.2

81.6

0

100

3.5

Bla

skey e

t al

. (2

008)

9.8

1

102.0

5

73.5

2

30.9

9

69.0

1

4.5

More

in-Z

amir

et

al. (2

00

8)

9.8

0

- 73.3

26.7

66.7

4

Rom

mel

se e

t al

. (2

008)

12.0

98.8

75.5

8

86.3

3.5

Rubia

et

al. (2

008)

13.2

96

100

0

100

3

Shan

ahan

et

al. (2

008

) 10.7

102

72

64

36

3

McA

lonan

et

al. (2

009

) 8.8

7

114.0

9

100

- -

3

AN

X

Sch

achar

et

al. (1

990)

9.9

2

107

-

Oost

erla

an e

t al

. (1

996)

10.1

99.7

65.0

Oost

erla

an e

t al

. (1

998a)

10.5

91.3

55

Man

assi

s et

al.

(2000)

10.1

95.2

58

10

3

Epst

ein e

t al

. (2

001)

37.7

-

40

Kore

nblu

m e

t al

. (2

007)

8.8

104.1

81.0

Lau

et

al. (2

007)

36.1

115.8

28

Auti

sm

Ozo

noff

et

al. (1

997)

13.9

101.0

100

Ver

te e

t al

. (2

005)

9.1

99.2

93

Bip

ola

r D

isord

er

Leb

ow

itz

et a

l. (

2004)

29.2

105.6

46

McC

lure

et

al. (2

005)

12.9

107.4

55

Dic

kst

ein e

t al

. (2

007)

13.1

110.2

58

Lei

ben

luft

et

al. (2

007

) 13.6

106.7

46

Str

akow

ski

et a

l. (

2009)

30

107

41

MD

D

Lau

et

al. (2

007)

39.2

113.9

42

10

4

Hal

ari

et a

l. (

2009)

16.2

96.4

47.6

1

Yan

g e

t al

. (2

009

) 16

- 46.1

5

OC

D

Johan

nes

et

al. (2

001)

35.0

-

30

Kri

kori

an e

t al

. (2

004

) 25

112

29

Cham

ber

lain

et

al. (2

006

) 35.3

115.7

20

Cham

ber

lain

et

al. (2

007

) 32.1

114.2

20

Pen

ades

et

al. (2

007

) 33.7

-

67

Wooll

ey e

t al

. (2

008)

14.3

102

100

OD

D/C

D

Sch

achar

et

al. (1

990)

9.7

5

110

-

Sch

achar

et

al. (1

995b)

10.1

107

100

Oost

erla

an e

t al

. (1

996)

9.3

92.3

77.8

Oost

erla

an e

t al

. (1

998a)

9.5

99.4

82

Sch

achar

et

al. (2

000)

9.5

109.6

-

Sch

eres

et

al. (2

001a)

10.7

86.9

90.4

10

5

Alb

rech

t et

al.

(2005)

10.9

6

96.9

100

Rubia

et

al. (2

008)

13

100

100

RD

Purv

is e

t al

. (2

000)

9.5

101.7

47

van

der

Sch

oot

et a

l. (

20

00)

10.6

68

-

Wil

lcutt

et

al. (2

001)

10.4

100.1

-

Ruck

lidge

et a

l. (

2002)

15.1

99.9

50

van

der

Sch

oot

et a

l. (

20

02)

10.6

-

68

Wil

lcutt

et

al. (2

005a)

11.0

96.8

51

SC

Z

Rubia

et

al. (2

001b)

40

101

-

Bad

cock

et

al.

(2002)

32.7

101.3

79

Enti

cott

et

al. (2

008)

36.1

104.6

72

Hudd

y e

t al

. (2

008)

23.6

83.6

64

10

6

Toure

tte

Synd

rom

e

Johan

nes

et

al. (2

001)

34.4

-

90

Ver

te e

t al

. (2

005)

10.0

104.8

83

Goudri

aan e

t al

. (2

006)

36.8

122.5

70

Ray

Li

et a

l. (

2006)

12

106

80

AD

HD

+ A

NX

Pli

szka

et a

l. (

1997)

6-1

2

- 85

- -

Man

assi

s et

al.

(2000)

10.1

95.2

58

- -

Kore

nblu

m e

t al

. (2

007)

8.9

103.6

71.1

-

-

AD

HD

+ O

DD

/CD

Sch

achar

et

al. (1

990)

9.3

3

104

- -

-

Sch

achar

et

al. (1

995b)

8.8

103.4

100

- -

Pli

szka

et a

l. (

1997)

6-1

2

- 85

- -

Sch

achar

et

al. (2

000)

9.2

103.1

-

- -

Sch

eres

et

al. (2

001a)

10.9

82.7

92.6

37.0

44.4

Alb

rech

t et

al.

(2005)

10.3

93.0

100

0

100

10

7

AD

HD

+ R

D

Purv

is e

t al

. (2

000)

9.2

97.6

82

- -

Wil

lcutt

et

al. (2

001)

10.6

99.2

-

- -

Ruck

lidge

et a

l. (

2002)

14.9

101.1

63

66.7

25

Wil

lcutt

et

al. (2

005a)

11.1

92.4

63

65

35

Note. D

ashes

indic

ate

that

dat

a w

ere

not

report

ed.

The

qual

ity i

ndex

could

ran

ge

from

0 (

low

) to

5 (

hig

h).

IQ

=

Inte

llig

ence

quoti

ent;

% A

DH

D-I

= p

erce

nta

ge

of

par

tici

pan

ts w

ith t

he

pre

dom

inan

tly i

nat

tenti

ve

subty

pe

of

AD

HD

; %

AD

HD

-C =

per

centa

ge

of

par

tici

pan

ts w

ith t

he

com

bin

ed s

ubty

pe

of

AD

HD

.

10

8

Tab

le 1

.2

Means for the Stop Task Outcome Variables (ms) and Sample Sizes

Ex

per

imen

tal

Gro

up

Contr

ol

Gro

up

Stu

dy

n

SS

RT

M

RT

S

DR

T

n

SS

RT

M

RT

S

DR

T

AD

HD

Sch

achar

et

al. (1

990)

13

437

901

255

10

269

901

194

Sch

achar

et

al. (1

995

a)

14

472

841

281

22

355

719

199

Sch

achar

et

al. (1

995b)

22

493

822

- 16

354

732

-

Oost

erla

an e

t al

. (1

996)

15

256

428

116

17

224

352

81

Pli

szka

et a

l. (

1997)

13

329

839

- 14

221

731

-

Oost

erla

an e

t al

. (1

998a)

10

334

529

156

21

283

393

83

Rubia

et

al. (1

998)

11

330

603

133

11

260

602

95

10

9

Nig

g e

t al

. (1

999)

25

405

714

209

25

295

652

170

Konra

d e

t al

. (2

000a)

31

431

612

- 26

357

572

-

Man

assi

s et

al.

(2000)

15

288

672

- 16

237

567

-

Pli

szka

et a

l. (

2000)

10

428

625

197

10

337

679

152

Purv

is e

t al

. (2

000)

17

308

658

235

17

265

534

155

Sch

achar

et

al. (2

000)

72

332

664

247

33

264

579

215

Epst

ein e

t al

. (2

001)

25

252

692

168

30

210

572

117

Kunts

i et

al.

(2001)

51

239

527

142

118

222

476

114

Rubia

et

al. (2

001a)

16

271

590

205

23

229

611

90

Sch

eres

et

al. (2

001a)

24

164

417

91

41

154

362

59

Sola

nto

et

al. (2

001)

56

436

764

- 29

290

769

-

Wil

lcutt

et

al. (2

001)

40

306

689

- 102

260

666

-

Murp

hy (

2002)

18

179

472

- 18

135

537

-

Over

toom

et

al. (2

002)

16

454

598

- 16

262

508

-

Ruck

lidge

et a

l. (

2002)

35

216

440

177

37

152

404

123

Aro

n e

t al

. (2

003b

) 13

195

426

- 13

153

450

-

Dim

osk

a et

al.

(2003)

13

360

724

248

13

260

645

173

McI

ner

ney e

t al

. (2

003

) 30

364

- -

30

289

- -

11

0

Oss

man

n e

t al

. (2

003)

24

258

576

152

24

207

613

134

Wodush

ek e

t al

. (2

003)

23

289

- -

22

234

- -

Geu

rts

et a

l. (

2004

) 54

321

498

139

41

237

487

112

Sch

achar

et

al. (2

004)

151

314

635

- 41

234

578

-

Sch

eres

et

al. (2

004)

21

226

- -

18

169

- -

Wal

cott

et

al. (2

004)

26

434

- -

23

311

- -

Alb

rech

t et

al.

(2005)

10

272

583

158

11

245

598

161

Bek

ker

et

al. (2

005

a)

24

237

468

112

24

185

463

105

Rubia

et

al. (2

005)

16

210

809

254

21

255

758

193

Sch

achar

et

al. (2

005)

60

320

628

223

24

205

569

189

Wil

lcutt

et

al. (2

005a)

113

340

672

218

151

281

660

178

Kle

in e

t al

. (2

006)

57

313

563

204

53

277

512

142

Pli

szka

et a

l. (

2006)

17

726

1055

- 15

644

1175

-

Bid

wel

l et

al.

(2007

) 266

380

- 214

332

286

- 166

Cla

rk e

t al

. (2

007)

20

172

424

- 16

173

420

-

Johnst

one

et a

l. (

2007)

25

501

717

275

13

541

667

238

Kore

nblu

m e

t al

. (2

007)

78

320

- -

21

238

- -

Lam

pe

et a

l. (

2007

) 16

237

595

178

17

138

476

119

11

1

Lio

tti

et a

l. (

2007)

16

283

854

198

30

210

966

170

Nig

g e

t al

. (2

007)

134

418

- -

72

322

- -

Rubia

et

al. (2

007)

32

279

- -

34

214

- -

Sch

achar

et

al. (2

007

a)

58

579

729

283

52

457

611

171

Sch

achar

et

al. (2

007b)

804

332

- -

67

250

- -

Ald

erso

n e

t al

. (2

008

) 12

666

836

322

11

323

670

170

Bit

sakou e

t al

. (2

008

) 77

290

594

174

50

232

618

142

Bla

skey e

t al

. (2

008)

71

404

720

- 45

297

643

--

More

in-Z

amir

et

al. (2

00

8)

15

338

591

149

15

200

561

129

Rom

mel

se e

t al

. (2

008)

350

285

- -

259

251

- -

Rubia

et

al. (2

008)

20

251

757

237

20

238

756

195

Shan

ahan

et

al. (2

008

) 25

223

626

167

30

190

551

124

McA

lonan

et

al. (2

009

) 22

451

589

195

29

356

543

139

AN

X

Sch

achar

et

al. (1

990)

13

297

845

188

10

269

901

194

Oost

erla

an e

t al

. (1

996)

20

235

385

95

17

224

352

81

Oost

erla

an e

t al

. (1

998a)

11

254

448

109

21

283

393

83

11

2

Man

assi

s et

al.

(2000)

15

237

568

- 16

237

567

-

Epst

ein e

t al

. (2

001)

15

228

633

134

30

210

572

117

Kore

nblu

m e

t al

. (2

007)

10

257

- -

40

238

- -

Lau

et

al. (2

007)

26

245

1285

- 31

246

1306

-

Auti

sm

Ozo

noff

et

al. (1

997)

13

272

605

- 13

286

571

-

Ver

te e

t al

. (2

005)

61

321.9

547

150

47

223.4

515

119

Bip

ola

r D

isord

er

Leb

ow

itz

et a

l. (

2004)

26

173

521

- 24

172

449

-

McC

lure

et

al. (2

005)

38

251

- -

22

217

- -

Dic

kst

ein e

t al

. (2

007)

32

- 760

- 22

- 754

-

Lei

ben

luft

et

al. (2

007

) 26

216

734

- 17

230

739

-

Str

akow

ski

et a

l. (

2009)

49

186

608

- 30

150

638

-

MD

D

11

3

Lau

et

al. (2

007)

38

291

1461

- 31

246

1306

-

Hal

ari

et a

l. (

2009)

21

164

525

- 20

159

669

-

Yan

g e

t al

. (2

009

) 13

- 665

- 13

- 696

-

OC

D

Johan

nes

et

al. (2

001)

10

- 596

- 10

- 582

-

Kri

kori

an e

t al

. (2

004

) 7

- 481

- 10

- 430

-

Cham

ber

lain

et

al. (2

006

) 20

212

429

- 20

168

421

-

Cham

ber

lain

et

al. (2

007

) 20

224

459

- 20

172

407

-

Pen

ades

et

al. (2

007

) 27

256

604

- 25

155

588

-

Wooll

ey e

t al

. (2

008)

10

238

843

- 9

219

819

OD

D/C

D

Sch

achar

et

al. (1

990)

9

322

920

231

10

269

901

194

Sch

achar

et

al. (1

995b)

5

301

822

- 16

354

732

-

Oost

erla

an e

t al

. (1

996)

18

273

398

29

17

224

352

81

Oost

erla

an e

t al

. (1

998a)

11

306

475

128

21

283

393

83

Sch

achar

et

al. (2

000)

13

294

643

77

33

264

579

215

11

4

Sch

eres

et

al. (2

001a)

21

161

416

32

41

154

362

59

Alb

rech

t et

al.

(2005)

8

274

649

50

11

245

598

161

Rubia

et

al. (2

008)

13

188

733

29

20

238

756

195

RD

Purv

is e

t al

. (2

000)

17

302

565

180

17

265

534

155

van

der

Sch

oot

et a

l. (

20

00)

40

263

777

289

20

257

677

191

Wil

lcutt

et

al. (2

001)

75

283

- -

102

260

- -

Ruck

lidge

et a

l. (

2002)

12

181

439

135

37

152

404

123

van

der

Sch

oot

et a

l. (

20

02)

40

267

808

344

20

216

692

225

Wil

lcutt

et

al. (2

005a)

109

357

707

233

151

281

660

178

SC

Z

Rubia

et

al. (2

001)

6

- 534

143

7

- 605

147

Bad

cock

et

al.

(2002)

24

258

666

208

34

227

525

118

Enti

cott

et

al. (2

008)

18

256

- -

17

224

- -

Hudd

y e

t al

. (2

008)

33

234

531

- 24

158

471

-

11

5

Toure

tte

Synd

rom

e

Johan

nes

et

al. (2

001)

10

- 590

- 10

- 582

-

Ver

te e

t al

. (2

005)

24

237

510

125

47

223

515

119

Goudri

aan e

t al

. (2

006)

46

146

393

- 50

114

397

-

Ray

Li

et a

l. (

2006)

30

208

590

- 28

210

628

-

AD

HD

+ A

NX

Pli

szka

et a

l. (

1997)

17

236

- -

31

176

- -

Man

assi

s et

al.

(2000)

18

239

585

- 16

237

567

-

Kore

nblu

m e

t al

. (2

007)

38

301

631

- 40

238

574

-

AD

HD

+ O

DD

/CD

Sch

achar

et

al. (1

990)

14

328

952

235

10

269

901

194

Sch

achar

et

al. (1

995b)

18

446

873

- 16

354

732

-

Pli

szka

et a

l. (

1997)

8

275

- -

31

176

- -

Sch

achar

et

al. (2

000)

47

270

648

229

33

264

579

215

11

6

Sch

eres

et

al. (2

001a)

27

162

428

88

41

154

362

59

Alb

rech

t et

al.

(2005)

11

256

594

156

11

245

598

161

AD

HD

+ R

D

Purv

is e

t al

. (2

000)

17

370

638

270

17

265

534

155

Wil

lcutt

et

al. (2

001)

41

354

- -

102

260

- -

Ruck

lidge

et a

l. (

2002)

24

220

506

234

37

152

404

123

Wil

lcutt

et

al. (2

005a)

64

383

732

239

151

281

660

178

Note. D

ashes

indic

ate

that

dat

a w

ere

not

report

ed.

11

7

Tab

le 1

.3

Summary of ESs by Group

Stu

dy

SS

RT

ES

M

RT

ES

S

DR

T E

S

AD

HD

Sch

achar

et

al. (1

990)

0.9

7

0

0.8

2

Sch

achar

et

al. (1

995

a)

0.6

5

0.7

2

1.0

3

Sch

achar

et

al. (1

995b)

0.7

4

0.5

7

-

Oost

erla

an e

t al

. (1

996)

0.6

3

1.1

6

1.2

4

Pli

szka

et a

l. (

1997)

1.3

3

1.1

9

-

Oost

erla

an e

t al

. (1

998a)

0.8

5

1.7

7

2.0

8

Rubia

et

al. (1

998)

1.1

2

0.0

2

1.3

7

Nig

g e

t al

. (1

999)

0.8

7

0.4

8

0.7

7

Konra

d e

t al

. (2

000a)

0.9

5

0.6

2

-

11

8

Man

assi

s et

al.

(2000)

0.3

2

0.8

3

-

Pli

szka

et a

l. (

2000)

0.7

2

-0.4

1

1.3

2

Purv

is e

t al

. (2

000)

0.3

8

1.2

4

1.1

1

Sch

achar

et

al. (2

000)

0.5

1

0.6

3

0.4

Epst

ein e

t al

. (2

001)

0.5

7

0.6

7

0.8

6

Kunts

i et

al.

(2001)

0.2

3

0.5

2

0.7

8

Rubia

et

al. (2

001a)

0.5

3

-0.2

1

2.4

Sch

eres

et

al. (2

001a)

0.1

6

0.8

5

1.2

6

Sola

nto

et

al. (2

001)

0.6

1

-0.0

3

-

Wil

lcutt

et

al. (2

001)

0.4

7

0.1

3

-

Murp

hy (

2002)

1.2

3

-0.5

6

-

Over

toom

et

al. (2

002)

0.9

0

0.6

5

-

Ruck

lidge

et a

l. (

2002)

0.6

8

0.3

4

0.7

6

Aro

n e

t al

. (2

003b

) 0.9

4

-0.2

5

-

Dim

osk

a et

al.

(2003)

1.2

7

0.8

2

1.3

7

McI

ner

ney e

t al

. (2

003

) 0.8

5

- -

Oss

man

n e

t al

. (2

003)

0.6

5

-0.2

2

0.3

1

Wodush

ek e

t al

. (2

003)

0.8

3

- -

11

9

Geu

rts

et a

l. (

2004

) 0.9

6

0.0

9

0.5

3

Sch

achar

et

al. (2

004)

0.5

1

0.4

2

-

Sch

eres

et

al. (2

004)

0.6

1

- -

Wal

cott

et

al. (2

004)

1.2

3

- -

Alb

rech

t et

al.

(2005)

0.6

3

-0.2

1

-0.0

9

Bek

ker

et

al. (2

005

a)

0.7

6

0.0

6

0.2

8

Rubia

et

al. (2

005)

-0.1

5

0.3

4

0.7

9

Sch

achar

et

al. (2

005)

0.8

8

0.3

9

0.3

9

Wil

lcutt

et

al. (2

005a)

0.4

9

0.1

0.6

8

Kle

in e

t al

. (2

006)

0.4

1

0.3

9

0.9

7

Pli

szka

et a

l. (

2006)

0.5

3

-0.6

2

-

Bid

wel

l et

al.

(2007

) 0.7

0

- 0.7

8

Cla

rk e

t al

. (2

007)

-0.0

3

0.0

6

-

Johnst

one

et a

l. (

2007)

-0.3

4

0.3

5

0.4

7

Kore

nblu

m e

t al

. (2

007)

0.5

0

- -

Lam

pe

et a

l. (

2007

) 1.0

3

0.7

8

1.0

6

Lio

tti

et a

l. (

2007)

0.5

6

-0.6

9

0.5

8

Nig

g e

t al

. (2

007)

0.6

8

- -

12

0

Rubia

et

al. (2

007)

0.7

1

- -

Sch

achar

et

al. (2

007

a)

0.8

6

0.7

6

0.9

4

Sch

achar

et

al. (2

007b)

0.5

4

- -

Ald

erso

n e

t al

. (2

008

) 1.5

9

0.6

9

1.5

5

Bit

sakou e

t al

. (2

008

) 0.5

4

-0.1

8

0.5

5

Bla

skey e

t al

. (2

008)

0.8

7

0.6

2

-

More

in-Z

amir

et

al. (2

00

8)

0.9

6

0.2

7

0.5

3

Rom

mel

se e

t al

. (2

008)

0.5

3

- -

Rubia

et

al. (2

008)

0.0

8

0.0

07

0.7

8

Shan

ahan

et

al. (2

008

) 0.5

3

0.9

5

1.1

4

McA

lonan

et

al. (2

009

) 0.6

2

0.4

2

1.2

4

AN

X

Sch

achar

et

al. (1

990)

0.2

3

-0.3

9

-0.1

Oost

erla

an e

t al

. (1

996)

0.2

2

0.5

0.6

5

Oost

erla

an e

t al

. (1

998a)

-0

.5

1.0

1

1.4

1

Man

assi

s et

al.

(2000)

0.0

02

0.0

02

-

Epst

ein e

t al

. (2

001)

0.4

5

0.3

2

0.3

2

12

1

Kore

nblu

m e

t al

. (2

007)

0.1

9

- -

Lau

et

al. (2

007)

-0.0

1

-0.1

-

Auti

sm

Ozo

noff

et

al. (1

997)

-0.2

1.1

3

-

Ver

te e

t al

. (2

005)

0.9

0.3

1

0.7

0

Bip

ola

r D

isord

er

Leb

ow

itz

et a

l. (

2004)

0.0

1

0.7

-

McC

lure

et

al. (2

005)

0.4

9

- -

Dic

kst

ein e

t al

. (2

007)

- 0.0

6

-

Lei

ben

luft

et

al. (2

007

) -0

.26

-0.0

4

-

Str

akow

ski

et a

l. (

2009)

0.5

3

-0.2

7

-

MD

D

Lau

et

al. (2

007)

0.3

6

0.4

8

-

Hal

ari

et a

l. (

2009)

0.0

6

-0.7

7

-

12

2

Yan

g e

t al

. (2

009

) -

-0.2

4

-

OC

D

Johan

nes

et

al. (2

001)

- 0.1

7

-

Kri

kori

an e

t al

. (2

004

) -

0.4

7

-

Cham

ber

lain

et

al. (2

006

) 0.8

0.1

-

Cham

ber

lain

et

al. (2

007

) 0.8

2

0.4

7

-

Pen

ades

et

al. (2

007

) 1.0

7

0.1

2

-

Wooll

ey e

t al

. (2

008)

0.0

7

0.1

3

-

OD

D/C

D

Sch

achar

et

al. (1

990)

0.4

4

0.1

3

0.6

9

Sch

achar

et

al. (1

995b)

-0.6

1

0.6

1

-

Oost

erla

an e

t al

. (1

996)

1.2

5

0.8

3

1.1

7

Oost

erla

an e

t al

. (1

998a)

0.3

3

1.2

4

1.8

2

Sch

achar

et

al. (2

000)

0.3

7

0.5

7

0.5

4

Sch

eres

et

al. (2

001a)

0.1

5

0.9

4

0.7

6

Alb

rech

t et

al.

(2005)

0.6

7

0.5

2

0.5

7

12

3

Rubia

et

al. (2

008)

-0.3

8

-0.1

4

0.4

6

RD

Purv

is e

t al

. (2

000)

0.4

1

0.2

8

0.5

van

der

Sch

oot

et a

l. (

20

00)

0.0

6

0.9

4

1.2

3

Wil

lcutt

et

al. (2

001)

0.2

2

- -

Ruck

lidge

et a

l. (

2002)

0.5

2

0.3

0.2

2

van

der

Sch

oot

et a

l. (

20

02)

0.2

8

1.1

2

1.4

2

Wil

lcutt

et

al. (2

005a)

0.5

8

0.3

8

0.8

4

SC

Z

Rubia

et

al. (2

001b)

- -0

.64

-0.1

2

Bad

cock

et

al.

(2002)

0.5

1

0.8

2

1.2

2

Enti

cott

et

al. (2

008)

0.7

1

- -

Hudd

y e

t al

. (2

008)

1

0.5

4

-

Toure

tte

Synd

rom

e

12

4

Johan

nes

et

al. (2

001)

- 0.1

2

-

Ver

te e

t al

. (2

005)

0.1

8

-0.0

5

0.1

7

Goudri

aan e

t al

. (2

006)

0.6

8

-0.0

9

-

Ray

Li

et a

l. (

2006)

-0.0

4

-0.3

0

-

AD

HD

+ A

NX

Pli

szka

et a

l. (

1997)

0.8

4

- -

Man

assi

s et

al.

(2000)

0.0

2

0.1

7

-

Kore

nblu

m e

t al

. (2

007)

0.5

1

0.4

2

-

AD

HD

+ O

DD

/CD

Sch

achar

et

al. (1

990)

0.4

0.3

3

0.5

7

Sch

achar

et

al. (1

995b)

0.4

6

0.8

1

-

Pli

szka

et a

l. (

1997)

1.0

3

- -

Sch

achar

et

al. (2

000)

0.0

7

0.5

4

0.1

9

Sch

eres

et

al. (2

001a)

0.1

6

0.7

4

0.9

0

Alb

rech

t et

al.

(2005)

0.2

4

-0.0

6

-0.1

5

12

5

AD

HD

+ R

D

Purv

is e

t al

. (2

000)

0.7

8

0.8

0

1.3

4

Wil

lcutt

et

al. (2

001)

0.9

1

- -

Ruck

lidge

et a

l. (

2002)

0.8

2

0.8

9

1.4

5

Wil

lcutt

et

al. (2

005a)

0.7

7

0.6

1.0

0

Note. D

ashes

indic

ate

that

dat

a w

ere

not

report

ed.

12

6

Tab

le 1

.4

Weighted Mean ESs and Homogeneity Analysis by Group and Stop Task Outcome Variable

ES

and 9

5%

confi

den

ce i

nte

rval

Tes

t of

null

(2-t

ail)

Het

ero

gen

eity

Dis

ord

er

Var

iable

k

g

SE

95%

CI

Z-v

alue

P-v

alue

Q

df

(Q)

P-v

alue

SS

RT

55

0.6

2

0.0

3

0.5

6-0

.68

20.7

4

<0.0

01

62.3

3

54

0.2

0

MR

T

40

0.3

8

0.0

6

0.2

7-0

.49

6.7

8

<0.0

01

63.5

3

39

0.0

08

AD

HD

SD

RT

32

0.7

8

0.0

4

0.7

-0.8

6

18.6

1

<0.0

01

37.7

9

31

0.1

9

SS

RT

7

0.0

9

0.1

3

-0.1

6-0

.33

0.7

1

0.4

8

4.4

0

6

0.6

2

MR

T

6

0.2

0

0.1

4

-0.0

6-0

.47

1.4

9

0.1

4

9.0

8

5

0.1

1

AN

X

SD

RT

4

0.5

6

0.2

9

-0.0

09-1

.13

1.9

3

0.0

5

7.8

1

3

0.0

5

Auti

sm

SS

RT

2

0.4

0

0.5

4

-0.6

7-1

.47

0.7

3

0.4

6

6.4

2

1

0.0

1

12

7

MR

T

2

0.6

4

0.4

0

-0.1

5-1

.42

1.6

0.1

1

3.2

2

1

0.0

7

SD

RT

-

- -

- -

- -

- -

SS

RT

4

0.2

5

0.1

3

-0.0

1-0

.51

1.8

7

0.0

6

5.7

8

3

0.1

2

MR

T

4

0.1

6

0.2

1

-0.2

6-0

.58

0.7

4

0.4

6

7.5

9

3

0.0

6

Bip

ola

r D

isord

er

SD

RT

-

- -

- -

- -

- -

SS

RT

2

0.2

5

0.1

9

-0.1

3-0

.62

1.3

0.2

0.6

2

1

0.4

3

MR

T

3

-0.1

6

0.4

1

-0.9

5-0

.64

-0.3

8

0.7

0

10.2

5

2

0.0

06

MD

D

SD

RT

-

- -

--

- -

- -

-

SS

RT

4

0.7

9

0.1

7

0.4

6-1

.11

4.7

2

<0.0

01

3.6

1

3

0.3

1

MR

T

6

0.2

3

0.1

4

-0.0

6-0

.51

1.5

8

0.1

2

1.2

4

5

0.9

4

OC

D

SD

RT

-

- -

- -

- -

- -

SS

RT

7

0.1

5

0.1

4

-0.1

2-0

.42

1.0

9

0.2

7

7.0

2

6

0.3

2

MR

T

8

0.6

3

0.1

4

0.3

6-0

.9

4.5

8

<0.0

01

10.2

6

7

0.1

7

OD

D/C

D

SD

RT

7

0.8

6

0.1

5

0.5

7-1

.14

5.9

0

<0.0

01

8.0

7

6

0.2

3

SS

RT

6

0.3

9

0.0

8

0.2

3-0

.55

4.7

3

<0.0

01

5.1

1

5

0.4

0

MR

T

5

0.5

9

0.1

7

0.2

6-0

.93

3.4

5

0.0

01

8.8

2

4

0.0

7

RD

SD

RT

5

0.8

6

0.1

9

0.4

9-1

.23

4.5

9

<0.0

01

10.1

4

0.0

4

SC

Z

SS

RT

3

0.7

3

0.1

7

0.4

0-1

.06

4.3

4

<0.0

01

1.6

1

2

0.4

5

12

8

MR

T

3

0.3

8

0.3

3

-0.2

7-1

.04

1.1

5

0.2

5

5.9

0

2

0.0

5

SD

RT

2

0.6

2

0.6

7

-0.6

9-1

.93

0.9

3

0.3

5

5.1

6

1

0.0

2

SS

RT

3

0.3

0.2

2

-0.1

4-0

.73

1.3

5

0.1

8

5.1

4

2

0.0

8

MR

T

4

-0.1

1

0.1

3

-0.3

6-0

.14

-0.8

6

0.3

9

0.9

1

3

0.8

2

Toure

tte

Syndro

me

SD

RT

-

- -

- -

- -

- -

SS

RT

3

0.4

9

0.1

6

0.1

7-0

.80

3.0

2

0.0

03

3.2

5

2

0.2

MR

T

2

0.3

4

0.1

9

-0.0

3-0

.71

1.8

0

0.0

7

0.3

8

1

0.5

4

AD

HD

+ A

NX

SD

RT

-

- -

- -

- -

- -

SS

RT

6

0.2

9

0.1

3

0.0

4-0

.53

2.2

6

0.0

2

4.8

6

5

0.4

3

MR

T

5

0.5

5

0.1

4

0.2

8-0

.81

4.0

6

<0.0

01

3.6

1

4

0.4

6

AD

HD

+

OD

D/C

D

SD

RT

4

0.4

1

0.2

3

-0.0

4-0

.86

1.7

9

0.0

7

6.5

6

3

0.0

9

SS

RT

4

0.8

2

0.1

0

0.6

2-1

.03

7.8

8

<0.0

01

0.3

6

3

0.9

5

MR

T

3

0.6

9

0.1

2

0.4

4-0

.93

5.5

5

<0.0

01

1.0

3

2

0.6

AD

HD

+ R

D

SD

RT

3

1.1

3

0.1

3

0.8

8-1

.39

8.7

8

<0.0

01

2.2

2

2

0.3

3

Note. D

ashes

indic

ate

that

dat

a w

ere

not

report

ed.

P <

0.1

was

consi

der

ed s

tati

stic

ally

sig

nif

ican

t. k

= n

um

ber

of

studie

s;

g =

Hed

ge’

s E

S;

SE

= s

tandar

d e

rror.

12

9

Tab

le 1

.5

Fixed and Mixed Effects Meta-Regression Analyses for MRT across the ADHD Studies

F

ixed

Eff

ects

M

ixed

Eff

ects

Var

iable

s B

S

E

Z-v

alue

P-v

alue

β

B

SE

Z

-val

ue

P-v

alue

β

Age

-0.0

1

0.0

08

-1.6

4

0.1

0

-0.2

8

-0.0

1

0.0

1

-1.4

2

0.1

6

-0.3

1

IQ

-0.0

1

0.0

07

-1.3

4

0.1

8

-0.2

1

-0.0

08

0.0

09

-0.9

1

0.3

6

-0.1

7

Per

cent

Mal

e -0

.005

0.0

05

-1.0

1

0.3

1

-0.2

3

-0.0

05

0.0

06

-0.8

0

0.4

2

-0.2

2

Per

cent

AD

HD

-I

-0.0

1

0.0

04

-2.3

3

0.0

2

-0.7

5

-0.0

08

0.0

05

-1.4

7

0.1

4

-0.5

9

Per

cent

AD

HD

-C

-0.0

09

0.0

04

-2.2

6

0.0

2

-0.7

3

-0.0

08

0.0

05

-1.6

4

0.1

0

-0.6

7

Note

. P

< 0

.1 w

as c

onsi

der

ed s

tati

stic

ally

sig

nif

ican

t. B

= u

nst

andar

diz

ed r

egre

ssio

n c

oef

fici

ent;

SE

= s

tandar

d e

rro

r of

unst

andar

diz

ed r

egre

ssio

n c

oef

fici

ent;

β =

sta

ndar

diz

ed r

egre

ssio

n c

oef

fici

ent;

% A

DH

D-I

= p

erce

nta

ge

of

par

tici

pan

ts

wit

h t

he

pre

dom

inan

tly i

nat

tenti

ve

subty

pe

of

AD

HD

; %

AD

HD

-C =

per

centa

ge

of

par

tici

pan

ts w

ith t

he

com

bin

ed

subty

pe

of

AD

HD

.

13

0

Tab

le 2

.1

Demographic Characteristics of TBI Patients, OI Controls, and PCs

TB

I (n

= 3

0)

OI

(n =

23

) P

C (

n =

30)

Char

acte

rist

ic

Mea

n

SD

M

ean

SD

M

ean

SD

f

p

Age

at t

esti

ng

13.7

4

2.7

4

12.4

5

2.3

3

13.7

8

2.7

7

2.0

4

0.1

4

Sex

(%

mal

e)

67

70

67

0.9

7

Age

at i

nju

ry

13.3

7

2.8

12.1

6

2.3

3

2.7

7

0.1

0

SE

S

-0.0

2

0.8

1

0.0

3

0.8

6

0.0

5

0.8

3

46.7

34.8

43.3

34.8

6.7

30.4

Eth

nic

ity (

%)

C

auca

sian

H

ispan

ic

A

fric

an A

mer

ican

A

mer

ican

India

n

3.3

0

Mec

han

ism

of

inju

ry (

%)

13

1

23.3

8.7

6.7

8.7

10

4.3

10

4.3

6.7

13.0

3.3

17.4

13.3

8.7

A

uto

, tr

uck

, bus

(chil

d p

asse

nger

or

dri

ver

)

M

oto

rcycl

e, m

oped

(ch

ild p

asse

nger

or

dri

ver

)

R

V, A

TV

, off

-road

veh

icle

(ch

ild p

asse

nger

or

dri

ver

)

B

icycl

e (C

hil

d d

river

or

pas

sen

ger

)

F

all

S

port

s or

pla

y

H

it b

y m

oto

r veh

icle

O

ther

3.3

4.3

Gla

sgo

w C

om

a S

cale

8.6

9

3.4

4

13

2

Tab

le 2

.2

Mean T-Scores for the Clinical Scales, Indices, and Global Executive Composite

of the Behavior Rating Inventory of Executive Function

T

BI

OI

T-S

core

s M

ean

SD

M

ean

SD

p

Cli

nic

al s

cale

s

I

nit

iate

53.3

5

12.7

9

43.1

5

5.8

9

0.0

02

W

ork

ing M

emory

56.3

9

12.9

7

47.3

0

8.6

9

0.0

1

P

lan/O

rgan

ize

54.8

3

13.6

7

46.5

8

6.8

6

0.0

2

O

rgan

izat

ion o

f M

ater

ials

50.6

1

10.9

45.6

5

9.7

7

0.1

3

M

onit

or

54.9

1

14.8

8

45.8

9

8.5

6

0.0

2

I

nhib

it

55.3

0

13.1

6

46.0

0

6.2

5

0.0

05

S

hif

t 51.5

7

10.3

2

44.2

0

5.0

0

0.0

05

E

moti

onal

Contr

ol

53.0

4

13.1

42.2

6

6.4

5

0.0

01

Indic

es

13

3

M

etac

ognit

ive

Index

55.1

3

14.0

4

45.7

4

7.7

3

0.0

09

B

ehav

iora

l R

egula

tio

n I

nd

ex

53.8

7

13.0

7

43.3

7

6.1

8

0.0

02

Glo

bal

Ex

ecuti

ve

Com

posi

te

54.7

8

13.5

6

44.3

7

7.3

1

0.0

03

13

4

Tab

le 2

.3

V-Scale Scores for VABS Maladaptive Behavior Domain

T

BI

OI

Sta

ndar

d S

core

s M

ean

SD

M

ean

SD

p

Mal

adap

tive

Beh

avio

r In

dex

15.7

7

2.0

7

13.7

0

1.5

9

0.0

01

I

nte

rnal

izin

g s

ubdom

ain

14.9

6

2.3

2

14.3

0

1.5

9

0.2

6

E

xte

rnal

izin

g s

ubdom

ain

15.8

5

2.7

4

13.6

0

1.1

9

0.0

01

13

5

Tab

le 2

.4

Distribution of Lesions in TBI Patients O

ver

all

WM

G

M

G +

W

G/W

Junct

ion

Les

ion L

oca

tion

T

R

L

T

R

L

T

R

L

T

R

L

T

R

L

Fro

nta

l 26

24

22

15

13

8

21

19

16

6

6

2

12

8

10

S

uper

ior

fronta

l g

yru

s 16

14

13

8

6

6

10

10

8

6

6

9

6

7

M

iddle

fro

nta

l g

yru

s 14

12

6

6

6

1

7

6

3

3

2

1

3

1

3

I

nfe

rior

fronta

l g

yru

s 14

13

7

1

1

12

11

6

2

1

2

3

2

2

P

rece

ntr

al g

yru

s 1

1

1

1

C

ingula

te g

yru

s

A

nte

rior

cin

gula

te g

yru

s

P

ost

erio

r ci

ngula

te g

yru

s

M

edia

l fr

onta

l g

yru

s

O

rbit

al g

yru

s 13

10

11

12

9

10

1

1

2

2

2

G

yru

s re

ctus

7

6

6

5

4

4

2

2

2

13

6

O

per

culu

m

O

ther

fro

nta

l 4

4

2

4

4

2

Non-f

ronta

l 29

23

25

16

10

9

24

19

22

3

3

12

8

7

Note

: V

alues

ref

lect

num

ber

of

pat

ients

. G

roups

are

not

mutu

ally

ex

clusi

ve.

Over

all

= a

cross

the

four

tiss

ue

types

;

WM

= w

hit

e m

atte

r; G

M =

gra

y m

atte

r; G

+ W

= a

ffec

ting b

oth

gra

y a

nd w

hit

e m

atte

r; G

-W j

unct

ion =

gra

y-w

hit

e

junct

ion;

T =

tota

l num

ber

of

pat

ients

; R

= n

um

ber

of

pat

ients

wit

h a

les

ion i

n t

he

right

hem

ispher

e; L

= n

um

ber

of

pat

ients

wit

h a

les

ion i

n t

he

left

hem

ispher

e.

13

7

Tab

le 2

.5

Comparison of the Frequency of Patients between

the Good and Poor SSRT Subgroups according to

Lesion Location and Tissue Type

S

SR

T

Tis

sue

type

G

ood

Poor

P

F

ronta

l

Gra

y

11

10

1

Whit

e 3

12

0.0

03

Both

2

4

0.6

5

Gra

y-W

hit

e Ju

nct

ion

4

8

0.2

6

N

on-F

ronta

l

Gra

y

13

11

0.6

5

13

8

Whit

e 7

9

0.7

2

Both

1

2

1

Gra

y-W

hit

e Ju

nct

ion

7

5

0.7

1

S

FG

Gra

y

5

5

1

Whit

e 1

7

0.0

4

Both

2

4

0.6

5

Gra

y-W

hit

e Ju

nct

ion

3

6

0.4

3

M

FG

Gra

y

2

5

0.3

9

Whit

e 1

5

0.1

7

Both

0

3

0.2

2

Gra

y-W

hit

e Ju

nct

ion

2

1

1

13

9

IF

G

Gra

y

7

5

0.7

1

Whit

e 0

1

1

Both

1

1

1

Gra

y-W

hit

e Ju

nct

ion

0

3

0.2

2

O

FG

Gra

y

8

4

0.2

6

Whit

e 0

0

1

Both

1

0

1

Gra

y-W

hit

e Ju

nct

ion

1

1

1

O

ther

Fro

nta

l

14

0

Gra

y

3

2

1

Whit

e 1

4

0.3

3

Both

0

0

1

Gra

y-W

hit

e Ju

nct

ion

0

2

0.4

8

14

1

Tab

le 2

.6

Comparison of the Frequency of Patients

between the Good and Poor MRT Subgroups

according to Lesion Location and Tissue Type

M

RT

Tis

sue

type

Good

Poor

P

F

ronta

l

Gra

y

10

11

1

Whit

e 7

8

1

Both

4

2

0.6

5

Gra

y-W

hit

e Ju

nct

ion

3

9

0.0

6

N

on-F

ronta

l

Gra

y

12

12

1

14

2

Whit

e 7

9

0.7

2

Both

2

1

1

Gra

y-W

hit

e Ju

nct

ion

6

6

1

14

3

Tab

le 2

.7

Comparison of the Frequency of Patients between

the Good and Poor SDRT Subgroups according to

Lesion Location and Tissue Type

S

DR

T

Tis

sue

type

Good

Poor

P

F

ronta

l

Gra

y

10

11

1

Whit

e 5

10

0.1

4

Both

4

2

0.6

5

Gra

y-W

hit

e Ju

nct

ion

4

8

0.2

6

N

on-F

ronta

l

Gra

y

13

11

0.6

5

14

4

Whit

e 7

9

0.7

2

Both

1

2

1

Gra

y-W

hit

e Ju

nct

ion

4

8

0.2

6

14

5

Tab

le 2

.8

Stop Task Performance of TBI Patients, OI Controls, and PCs

TB

I (n

= 3

0)

OI

(n =

23

) P

C (

n =

30)

TB

I vs.

OI

TB

I +

OI

vs.

PC

M

easu

re

M

SD

M

S

D

M

SD

F

P

T

P

T

P

SS

RT

266.0

0

94.8

0

263.0

5

75.6

2

216.2

4

77.6

3

3.1

9

0.0

46

0.1

25

0.9

01

2.5

01

0.0

14

MR

T

589.0

0

153.5

8

583.8

5

168.3

6

436.3

1

96.4

6

13.8

4

0.0

00

0.1

13

0.9

11

5.1

93

0.0

00

SD

RT

143.6

0

63.8

3

122.7

0

41.3

4

120.3

2

43.2

2

1.8

1

0.1

71

1.4

51

0.1

51

1.0

86

0.2

81

PI

55.8

5

10.7

5

52.5

5

8.4

9

49.4

7

2.5

2

6.0

3

0.0

05

1.2

51

0.2

17

3.3

79

0.0

01

% c

orr

ect

96.5

2

3.3

9

95.7

3

5.6

2

96.8

8

3.1

8

0.3

9

0.6

78

0.5

96

0.5

55

-0.8

53

0.3

97

14

6

Tab

le 2

.9

Stop Task Performance of Patients with Frontal White Matter (FWM) Lesions, OI Controls, and PCs

FW

M (

n =

15)

OI

(n =

23

) P

C (

n =

30)

FW

M v

s. O

I F

WM

+ O

I vs.

PC

M

easu

re

M

SD

M

S

D

M

SD

F

P

T

P

T

P

SS

RT

316.6

6

96.9

6

263.0

5

75.6

2

216.2

4

77.6

3

7.7

7

0.0

01

1.9

6

0.0

5

3.6

4

0.0

01

MR

T

616.2

5

173.7

5

583.8

5

168.3

6

436.3

1

96.4

6

11.2

9

0.0

00

0.5

5

0.5

9

4.7

8

0.0

00

SD

RT

159.3

7

70.9

9

122.7

0

41.3

4

120.3

2

43.2

2

3.3

7

0.0

4

2.1

9

0.0

3

1.6

7

0.1

0

PI

58.7

5

14.0

5

52.5

5

8.4

9

49.4

7

2.5

2

4.3

9

0.0

2

1.5

4

0.1

4

2.9

9

0.0

07

% c

orr

ect

96.7

7

3.1

1

95.7

3

5.6

2

96.8

8

3.1

8

0.3

8

0.6

8

0.7

3

0.4

7

-0.6

9

0.5

14

7

Tab

le 2

.10

Stop Task Performance of Patients with SFG White Matter (SFG-WM) Lesions, OI Controls, and PCs

SF

G-W

M (

n =

8)

OI

(n =

23

) P

C (

n =

30)

SF

G-W

M v

s. O

I S

FG

-WM

+ O

I vs.

PC

M

easu

re

M

SD

M

S

D

M

SD

F

P

T

P

T

P

SS

RT

337.6

2

100.1

0

263.0

5

75.6

2

216.2

4

77.6

3

7.7

6

0.0

01

2.2

6

0.0

3

3.8

1

0.0

00

MR

T

570.1

6

162.4

9

583.8

5

168.3

6

436.3

1

96.4

6

7.8

3

0.0

05

-0.1

9

0.8

5

3.5

5

0.0

03

SD

RT

163.8

2

92.5

1

122.7

0

41.3

4

120.3

2

43.2

2

0.8

0

0.4

64

1.2

1

0.2

59

1.2

3

0.2

43

PI

58.0

9

16.4

6

52.5

5

8.4

9

49.4

7

2.5

2

2.3

5

0.1

3

0.9

1

0.3

88

1.9

0

0.0

91

% c

orr

ect

96.7

5

3.6

5

95.7

3

5.6

2

96.8

8

3.1

8

0.4

8

0.6

19

0.5

8

0.5

7

-0.5

4

0.5

94

14

8

Tab

le 2

.11

Effect Sizes (Cohen’s d) for Group Comparisons

Mea

sure

S

SR

T

MR

T

SD

RT

TB

I co

mp

aris

ons

T

BI

vs.

OI

0.0

3

0.0

3

0.3

9

T

BI

vs.

PC

0.5

7

1.1

9

0.4

3

FW

M c

om

par

isons

F

WM

vs.

OI

0.6

2

0.1

9

0.6

3

F

WM

vs.

PC

1.1

4

1.2

8

0.6

6

F

WM

vs.

TB

I 0.5

3

0.1

7

0.2

3

SF

G-W

M c

om

par

isons

S

FG

-WM

vs.

PC

1.3

6

1.0

0

0.6

0

S

FG

-WM

vs.

OI

0.8

4

-0.0

8

0.5

7

S

FG

-WM

vs.

TB

I 0.7

3

-0.1

2

0.2

5

OI

vs.

PC

0.6

1

1.0

8

0.0

6

14

9

150

Figure Caption

Figure 1.1. Funnel plot of SSRT ESs for the difference between ADHD patients and controls.

Open circles correspond to original studies, while filled circles denote imputed studies.

Diamonds represent the ES estimates before (open) and after (filled) adjustment for publication

bias.

Figure 1.2. Fixed effects meta-regression of SSRT on MRT ESs across the ADHD studies.

Figure 1.3. Fixed effects meta-regression of SSRT on SDRT ESs across the ADHD studies.

15

1

-2.0

-1

.5

-1.0

-0

.5

0.0

0.5

1.0

1.5

2.0

0.0

0.1

0.2

0.3

0.4

0.5

Standard Error

SS

RT

ES

s

15

2

MR

T E

Ss

SSRT ESs

-0.5

6

-0.3

7

-0.1

8

0.0

1

0.2

0

0.3

9

0.5

8

0.7

7

0.9

7

1.1

6

1.3

5

2.0

0

1.7

8

1.5

6

1.3

4

1.1

2

0.9

0

0.6

8

0.4

6

0.2

4

0.0

2

-0.2

0

15

3

SD

RT

ES

s

SSRT ESs

-0.2

5

-0.0

6

0.1

4

0.3

4

0.5

3

0.7

3

0.9

3

1.1

2

1.3

2

1.5

2

1.7

1

2.0

0

1.7

8

1.5

6

1.3

4

1.1

2

0.9

0

0.6

8

0.4

6

0.2

4

0.0

2

-0.2

0

1