AN EXAMINATION OF EXECUTIVE FUNCTIONING IN A PEDIATRIC SAMPLE WITH ATTENTION-DEFICIT/HYPERACTIVITY DISORDER
By
DANIELLE LAINE COOKE
A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF
FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE
OF MASTER OF SCIENCE
UNIVERSITY OF FLORIDA
2017
© 2017 Danielle Laine Cooke
To my patients, who inspire me, and to my family, who believe in me
4
ACKNOWLEDGEMENTS
This thesis would not have been possible without the encouragement, support
and guidance of Dr. Gary R. Geffken, Dr. Joseph P.H. McNamara, Mr. Andrew Giles
Guzick, Ms. Andrea Guastello, Ms. Lacie Lazaroe, and Dr. Brian Olsen. I would
especially like to thank my incredible research assistants, particularly Mr. Luc Loyola,
Ms. Haley Ehrlich, Mr. Jesse Blogg, Ms. Kassidy Schaeffer, Ms. Darby Monagle, and
Ms. Pooja Surkanti. I would also like to thank Dr. W. Keith Berg, Dr. Carol Mathews, Dr.
Michael Marsiske and Dr. Shelley Heaton who all took time out of their busy schedules
to guide this project.
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TABLE OF CONTENTS
page
ACKNOWLEDGEMENTS ............................................................................................... 4
LIST OF TABLES ............................................................................................................ 7
LIST OF FIGURES .......................................................................................................... 8
LIST OF ABREVIATIONS ............................................................................................... 9
ABSTRACT ................................................................................................................... 10
CHAPTER
1 INTRODUCTION ........................................................................................................ 12
ADHD ......................................................................................................................... 14
ADHD And Anxiety ..................................................................................................... 15
Executive Measures Of ADHD ................................................................................... 17
Study Aims ................................................................................................................. 18
2 METHODS ................................................................................................................. 20
Participants ................................................................................................................ 20
Procedure .................................................................................................................. 20
Measures ................................................................................................................... 21
Demographics ....................................................................................................... 21
Delis-Kaplan Executive Function System .............................................................. 21
The Wechsler Intelligence Scale For Children, Fourth Edition ................................ 22
Behavior Rating Inventory Of Executive Functioning ............................................. 22
Statistical Analyses .................................................................................................... 24
Aim 1. Examination Of Common Factors Between Tests ....................................... 24
Hypothesis 1: Subsections of the BRIEF, D-KEFS and WISC-IV will load onto
processing speed, working memory, and inhibitory control. ............................... 24
Aim 2: Predicting Comorbid Anxiety Diagnosis In Children With ADHD ................. 25
Hypothesis 2a: On parental measures, children with an anxiety diagnosis will
demonstrate poorer behavioral regulation. ......................................................... 25
Hypothesis 2b: On performance measures, children with an additional anxiety
diagnosis will have better working memory than those without an anxiety
diagnosis. ........................................................................................................... 25
6
3 RESULTS ................................................................................................................... 27
Preliminary Correlational Analyses ............................................................................ 27
Aim 1. Examination of Common Factors Between Tests ........................................... 27
Aim 2: Predicting Comorbid Anxiety Diagnosis In Children With ADHD..................... 29
Hypothesis 2a: On Parental Measures, Children With An Anxiety Diagnosis Will
Demonstrate Poorer Behavioral Regulation. ...................................................... 29
Hypothesis 2b: On Performance Measures, Children With An Additional Anxiety
Diagnosis Will Have Better Working Memory Than Those Without An Anxiety
Diagnosis. ........................................................................................................... 30
4 DISCUSSION ............................................................................................................. 41
REFERENCES .............................................................................................................. 46
BIOGRAPHICAL SKETCH ............................................................................................ 51
7
LIST OF TABLES
Table page
2-1 Sample demographics ........................................................................................ 26
3-1 Correlations between performance-measures of executive functioning and subsections of the BRIEF ................................................................................... 32
3-2 Communalities ................................................................................................... 34
3-3 Total variance explained .................................................................................... 35
3-4 Pattern matrix ..................................................................................................... 37
3-5 Structure matrix .................................................................................................. 38
3-6 Factor correlation matrix .................................................................................... 38
3-7 Hierarchical binary logistic regression: step 1 .................................................... 39
3-8 Hierarchical binary logistic regression: step 2 .................................................... 39
3-9 Hierarchical binary logistic regression: step 3 .................................................... 40
3-10 Classification table .............................................................................................. 40
8
LIST OF FIGURES
Figure page
3-1 Scree plot ........................................................................................................... 36
9
LIST OF ABBREVIATIONS
ADHD Attention-Deficit/Hyperactivity Disorder
ADHD + AD Attention-deficit/hyperactivity disorder with comorbid anxiety
disorders
BRIEF Behavior Rating Inventory of Executive Function
D-KEFS Delis-Kaplan Executive Function System
DSM-IV Diagnostic and Statistical Manual of Mental Disorders, fifth edition
DSM-V Diagnostic and Statistical Manual of Mental Disorders, fourth edition
EF Executive functioning
IC Inhibitory control
TD Typically developing
WM Working memory
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Abstract of Thesis Presented to the Graduate School of the University of Florida in
Partial Fulfillment of the Requirements for the Degree of Master of Science
AN EXAMINATION OF EXECUTIVE FUNCTIONING IN A PEDIATRIC SAMPLE WITH ATTENTION-DEFICIT/HYPERACTIVITY DISORDER
By
Danielle Laine Cooke
May 2017
Chair: David M. Janicke Major: Psychology
Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder
that is associated with impairments in important areas of executive functioning (EF),
including processing speed, shifting, inhibition, and working memory. Children with
anxiety are hypothesized to show similar deficits. This study attempted to evaluate the
utility of using EF measures to distinguish children with ADHD from children with ADHD
and comorbid anxiety (ADHD+AD). Archival data was analyzed from a clinical sample of
209 youth (aged 7 to 18) with an ADHD diagnosis referred for a psychoeducational
assessment; of this sample, 90 youth had an additional anxiety diagnosis. Parent-report
of EF was assessed using the BRIEF, while subsections of the WISC-IV and D-KEFS
were used to assess performance-based executive functioning. A factor analysis
revealed that the BRIEF subtests cross load along the Metacognition and Behavioral
Regulation Indices, while the D-KEFS and WISC-IV subscores loaded onto a distinct
factor. A binary logistic regression revealed that performance-based EF tasks, but not
parent-report of EF, predicted comorbid anxiety diagnosis in children with ADHD
11
significantly better than chance. Better performance on a working memory task and
worse performance on a processing speed and switching task predicts a comorbid
anxiety diagnosis in children with ADHD. These preliminary findings suggest that
performance-based measures of EF may be useful in helping to differentiate between
children with ADHD and children with ADHD+AD. Limitations of this study include
examination of a limited testing battery and high rates of comorbidity in the sample.
12
CHAPTER 1 INTRODUCTION
Executive functioning is a broad term for higher order cognitive functions
(Friedman & Miyake, 2016). It impacts how human behavior is expressed (Jurado &
Roselli, 2007; Banich, 2009) and is important for almost all areas of daily living
(Diamond, 2013). Many studies have examined and conceptualized executive
functioning, leading to a variety of different, and at times conflicting, definitions of what
comprises and constitutes “executive functioning” (Banich, 2009; Chung, Weyandt &
Swentosky, p. 4-6; Juardo & Rosselli, 2007).
Recent research has demonstrated that executive functioning tasks require the
use of a complex frontoparietal control network (Banich, 2009; Chung et al., 2014;
Niendam et al., 2012), with the prefrontal, dorsal anterior cingulate, and parietal cortices
being consistently implicated across domains of executive functioning (Niendam et al.,
2012). Given the complexity of this system, it comes as little surprise that researchers
have debated whether executive functioning can be viewed as a single construct or as a
compilation of several distinct functions. Currently there is cautious consensus that
executive functioning is composed of at least a few distinct, general areas. However, a
number of studies have had difficulty in separating these areas, and attempts to specify
their contributions to specific executive tasks had only moderate success (Diamond,
2013, Jurado & Rosselli, 2007), resulting in a variety of heterogeneous tasks that utilize
a variety of executive domains (Miyake, 2000). Evidence has shown that broad areas of
executive functioning areas are clearly distinguishable, and yet, at the same time, are
not completely independent of one another (Miyake et al., 2000). This has resulted in
the general agreement that executive functioning is comprised of broad areas that work
13
independently and in conjunction: working memory, inhibitory control, shifting (Diamond,
2013; Friedman & Miyake, 2016), and processing speed (Anderson, 2002).
Working memory (WM) can be defined as the capacity to temporarily retain and
manipulate relevant information (Chung et al., 2014; Jurado & Rosselli, 2007; McCabe,
Roediger, McDaniel, Balota, & Hambrick, 2010; Niendam et al., 2012; Olivers, Peters,
Houtkamp & Roelfsema, 2011). WM tasks require maintenance, manipulation, and
updating of information (Friedman & Miyake, 2017). While the “updating” portion of WM
has previously been described as a distinct process that is “closely related” to WM
(Miyake, Friedman, Rettinger, Shah, & Hegarty, 2001), many other authors have used
WM (Jurado & Rosselli, 2007; McCabe et al., 2010; Niendam et al., 2012; Olivers et al.,
2011) to describe Miyake’s “updating” process, and both terms have been used
interchangeably (Salthouse et al., 2003). Therefore, this study defines the processes of
WM as the capacity not only to store, but to actively manipulate task-relevant
information, and thus, for the purpose of this study, the terms WM and updating will be
considered interchangeable.
Other areas of executive functioning include inhibitory control (IC), or the deliberate,
intentional decision to override dominant responses (Friedman & Miyake, 2017), and
shifting (also referred to as switching, set shifting, or cognitive flexibility) which refers to
the ability to switch flexibly back and forth between tasks or mental sets (Diamond,
2013; Friedman & Miyake, 2017). Though not traditionally defined as an aspect of
executive functioning, processing speed will also be considered throughout this study.
Processing speed, often referred to as perceptual speed, can be defined as a measure
of how quickly cognitive functions take place.
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ADHD
Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder
that is characterized by a persistent pattern of inattention and/or hyperactivity and
impulsivity that occurs across multiple settings and interferes with functioning or
development (American Psychiatric Association, 2013). It is associated with several
impairments in important areas of executive functioning, including shifting, IC, and WM
(Snyder, Miyake, & Hankin, 2015b). Recent meta-analyses estimate prevalence rates of
ADHD to range from 7.2 to about 9.5% (Polanczyk, Willcutt, Salum, Kieling & Rohde,
2014; Thomas, Sanders, Doust, Beller & Glasziou, 2015).
ADHD has been shown to cause impairments in several areas of life, including
work performance (Brook, Brook, Zhang, Seltzer, & Finch, 2013), employment (Erskine
et al., 2016), school performance (Taanila et al., 2014), self-esteem, social functioning
(Harpin, Mazzone, Raynaud, Kahle, & Hodgkins, 2016; Tarver, Daley, & Sayal, 2014)
and both parent and child rated quality of life (Lee et al., 2016). Additionally, ADHD
carries a high societal burden; with the annual incremental costs of ADHD ranging from
$143 to $266 billion in the United States alone (Doshi et al., 2012). Of this estimate,
children and adolescents are estimated to only account for $38 to $72 billion while
adults account for $105 to $194 billion. The majority of the adult cost can be found in
productivity and income losses (Doshi et al., 2012), suggesting that early diagnosis of
ADHD is vital.
Executive functioning is crucial to the effective treatment of ADHD. Unfortunately,
many studies examining executive dysfunction within ADHD have been inconclusive,
inconsistent, or reported only small to medium effect sizes (Eycke & Dewey, 2016,
15
Snyder et al., 2015b; Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005). The
strongest and most consistent effects can be found on measures of IC and WM; even
after the effects of comorbid disorders, intelligences and academic achievement are
controlled (Willcutt et al., 2005). Children with ADHD have been shown to perform more
poorly than controls on almost all neuropsychological functions examined, including
inhibition, working memory and shifting, when compared to their typically developing
(TD) peers (Sjöwall, Roth, Lindqvist, & Thorell, 2013). At least part of this deficit
appears to be biological in nature with children with ADHD demonstrating reduced WM
task specific brain activation in comparison to TD peers (Fassbender et al., 2011).
Furthermore, a recent meta-analysis suggested that adults and adolescents with ADHD
demonstrated reduced IC task specific brain activation when compared to controls
(Hart, Radua, Nakao, Mataix-Cols, & Rubia, 2013). Processing speed impairments have
been associated with ADHD, and it has been suggested that observed impairments in
WM may be better accounted for by lower processing speed (Weigard & Huang-Pollock,
2016).
ADHD And Anxiety
ADHD and anxiety disorders are highly comorbid disorders, with up to 25% of
children with ADHD meeting criteria for a comorbid anxiety disorder (Adler, Spencer &
Willins, 2015; Efron, Bryson, Lycett, & Sciberras, 2016; Schatz & Rostain, 2006). Given
this, the impact of a comorbid anxiety diagnosis on executive functioning is of significant
clinical interest. Limited research has examined executive impairment in anxiety
disorders, and what has been done has yielded contradictory results. Similar to
research with ADHD, anxiety disorders (panic disorder, social anxiety disorders and
16
generalized anxiety disorders) have been associated with impairments in shifting, IC,
and WM though these results have not been reliably replicated (Snyder et al. 2015b).
Although obsessive-compulsive disorder is not currently classified as an anxiety
disorder in the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-
V; American Psychological Association, 2013) research has consistently demonstrated
executive deficits in this population. Namely, individuals with this diagnosis have been
shown to have impaired performance for shifting, inhibition and WM when compared to
controls (Snyder, Kaiser, Warren & Heller, 2015a). Given that obsessive-compulsive
disorder displays impairments similar to those posited in anxiety spectrum disorders,
and that our subjects were diagnosed using Diagnostic and Statistical Manual of Mental
Disorders, fourth edition (DSM-IV) criteria when obsessive-compulsive disorder was
classified as an anxiety disorder, obsessive-compulsive disorder will be considered a
“comorbid anxiety disorder” for the purpose of this study.
With nearly one fourth of those with ADHD meeting criteria for at least one
anxiety disorder (Adler et al., 2015; Efron et al., 2016; Schatz & Rostain, 2006), the
impact of a comorbid anxiety diagnosis on executive impairment in ADHD is important
to examine. As with anxiety disorders alone, research examining ADHD with a comorbid
anxiety disorder (ADHD + AD) has been somewhat limited and contradictory. Some
studies have suggested that children with ADHD + AD have better performance on
inhibitory tasks and greater impairments in WM (Jarrett, Wolff, Davis, Cowart &
Ollendick, 2016). On the BRIEF, children with ADHD + AD were rated significantly
higher on the inhibition scale than children with “pure” ADHD or anxiety (Sørensen,
Plessen, Nicholas, & Lundervold, 2011).
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Executive Measures Of ADHD
Both the Delis-Kaplan Executive Function System (D-KEFS) and Behavior Rating
Inventory of Executive Function (BRIEF) are commonly administered clinical measures
posited to examine executive functioning. The D-KEFS is a traditional neurocognitive
exam that could be posited to measure optimal executive functioning while the BRIEF, a
parental report-based measure, could be considered to capture “everyday” executive
functioning. The BRIEF has been shown to distinguish between children with ADHD and
TD peers better than performance-based measures of executive functioning (Davidson,
Cherry & Corkum, 2016). Individuals with ADHD have variable performance on
neurocognitive batteries. The current recommendation is to provide both rating scales
and performance tasks (Davidson et al., 2016); rating scales are hypothesized to
capture behavioral aspects of executive functioning, while performance tasks are
hypothesized to capture cognitive aspects of executive functioning (Davidson et al.,
2016; McAuley et al., 2010). The reason for this discrepancy between performance
measures and rating scales is unclear; while it is possible that this variable performance
could be due to our current diagnostic criteria not adequately capturing differences in
cognitive impairment ranging across ADHD subtypes (Roberts, Martel, & Nigg, 2017), it
is also possible that this discrepancy is a result of variations in operational definitions of
executive constructs (Eycke & Dewey, 2016), and the issue of task impurity.
Given that executive functioning is composed of broad subcomponents that work
together and independently (Friedman & Miyake, 2017), various tasks differ in their
dependence on certain subcomponents of executive functioning thereby contributing to
unexpected variations in performance (Miyake et al., 2000). Additionally, as executive
18
tasks increase in complexity, they tend to utilize other cognitive functions (Miyake et al.,
2000). This issue of “task impurity,” or the idea that executive tasks will tap into more
than one area of executive functioning, is a common impediment to executive
functioning research. It has, in part, contributed to the vast collection of contradictory
executive functioning research, including low intercorrelations between different
executive tasks and low internal and test-retest reliability in complex executive tasks
(Miyake et al., 2000). Thus, this limitation is important to recognize, and it was
necessary to attempt to select for tasks that tapped predominantly into each of the
primary components of executive functioning examined in this study.
Given the question of task impurity, the Processing Speed section of the fourth
edition of Wechsler Adult Intelligence Scale (WISC-IV) was utilized in order to examine
processing speed; the WM section of the WISC-IV was utilized in order to examine WM;
the D-KEFS Trail-Making, Condition 4, Letter-Number Switching Scaled Score and D-
KEFS Color-Word, Condition 3, Inhibition-Switching Scaled Score were utilized in in
order to examine IC; and the D-KEFS Trail-Making, Condition 4, Letter-Number
Switching Scaled Score and D-KEFS Color-Word, Condition 3, Inhibition-Switching
Scaled Score were utilized.
Study Aims
Aim 1. To investigate the common factors between test subsections on the
BRIEF, D-KEFS and WISC-IV.
Hypothesis 1: Subsections of the BRIEF, D-KEFS and WISC-IV will load onto
processing speed, working memory, and inhibitory control.
Aim 2. To investigate if parental and performance measures (test subsections
from Aim 1) predict a comorbid anxiety diagnosis in children with ADHD
19
Hypothesis 2a: On parental measures, children with an additional anxiety
diagnosis will demonstrate poorer behavioral regulation.
Hypothesis 2b: On performance measures, children with an additional anxiety
diagnosis will have better working memory than those without.
20
CHAPTER 2 METHODS
Participants
Participants in the study consisted of 209 children and adolescents referred for
psychoeducational testing and evaluation selected from a clinical database. Participants
ranged in age from 7 to 19, and had a diagnosis of ADHD as determined by a licensed
psychologist or psychiatrist. All participants in this sample completed the D-K EFS and
had a parent or guardian complete the BRIEF. The population was predominately male
(n=162, 71.7%) and ranged in age from 94 to 225 months (mean = 146.50 months; SD
= 31.28 months). Of this sample, nearly 40% also met criteria for a DSM-IV anxiety
diagnosis (n=90, 39.8%). Anxiety disorders included in the analysis were separation
anxiety disorder, social phobia, generalized anxiety disorder, panic disorder, obsessive-
compulsive disorder, anxiety not otherwise specified, and unspecified anxiety disorder.
Sample demographics are presented in Table 2-1.
Procedure
A retrospective chart review was performed in order extract relevant data.
Existing medical records and psychoeducational reports from UF Health Shands
Springhill and Vista were examined. Only individuals with a diagnosis of ADHD, who
were aged 7 to 19, who had completed both the D-KEFS and the BRIEF were
considered for inclusion. The study was approved by the University of Florida
institutional review board.
21
Measures
Demographics
Demographic data, including participant age, sex and diagnoses were collected
from a clinical database and a retrospective chart review.
Delis-Kaplan Executive Function System (D-KEFS)
The D-KEFS is a standardized parent-rated neurocognitive exam for individuals
aged 8 to 89 years of age that has been validated in a number of clinical populations as
well as a representative national sample (Delis, Kramer, Kaplan, & Holdnack, 2004;
Homack, Lee, & Riccio, 2005). From the D-KEFS, three tests were examined: the Trail
Making Test (composed of Condition 1: Visual Scanning, Condition 2: Number
Sequencing, Condition 3: Letter Sequencing, Condition 4: Letter-Number Sequencing
and Condition 5: Motor Speed), the Verbal Fluency Test (composed of Condition 1:
Letter Fluency, Condition 2: Category Fluency, and Condition 3: Category Switching),
and the Color-Word Interference Test (composed of Condition 1: Color Naming,
Condition 2: Word Reading, Condition 3: Inhibition, and Condition 4:
Inhibition/Switching).
The Trail Making Test is intended isolate set-shifting from letter sequencing and
visual scanning by including four baseline conditions (Condition 1: Visual Scanning,
Condition 2: Number Sequencing, Condition 3: Letter Sequencing, and Condition 5:
Motor Speed). The primary executive task in this test is Letter-Number Sequencing,
which measures the speed in which a participant can physically sequence a series of
numbers and letters.
22
The Verbal Fluency Test is composed of three tasks in which participants are
given 60 seconds to generate as many items as possible per trial. In the letter fluency
condition the examinee is asked to generate as many words as they can think of that
begin with the letters “f,” “a,” and “s.” In the category fluency condition, the examinee is
asked to generate as many different words as possible from specific categories;
“animals” and “boy’s names.” In the category switching condition, the final and primary
executive task, the examinee is asked to alternate between naming a type of fruit and a
piece of furniture.
The Color-Word Interference test required recalling and stating words of visual
stimuli presented in multiple rows. The first task, Color Naming, measures the speed of
verbalization when asked to identify blocks of colors and the second, Word Reading,
measures the speed in which a child can read the names of different colors printed in
black ink. The primary executive tasks are the third and fourth conditions. The third task,
Inhibition, is a Stroop-style task that measures the speed of verbalization in which a
child states the ink colors of the words “red,” “green” and “blue” that are printed in
incongruous ink (ignoring what the words presented said or meant and attending only to
the ink colors of these words). The fourth and final task, Inhibition/Switching, is identical
to the previous task, however half the words are outlined by a box. The participant is
required to read the word if it appears in a box, and state the ink color of the word if it
does not. The third task is styled after a well-known measure of IC, while the fourth task
requires the child to apply new instructions to the task and is intended to be a measure
of a child’s ability to switch attention between visual stimuli.
23
The Wechsler Intelligence Scale for Children, Fourth Edition (WISC-IV)
The WISC-IV is a standardized intelligence exam that is composed of four first
order factor index scores. This study utilized two of these scores: the WM Index,
composed of the Letter-Number Sequencing and Digit Span subsections, and the
Processing Speed Index, composed of the Coding, and Symbol Search subsections.
Behavior Rating Inventory Of Executive Function (BRIEF)
The BRIEF is a psychometrically sound, parent-rated questionnaire for children
aged 5 to 18 years of age (Gioia et al., 2000). It is one of the first attempts to measure
executive functioning via self and informant reports of every day functioning (Gioia et
al., 2000). It provides two indices: the Behavioral Regulation Index (composed of three
scaled scores: inhibit, shift and emotional control) and the Metacognition Index
(composed of five scaled scores: initiate, working memory, plan/organize, organization
of materials and monitor). Additionally, it provides an overall score, the General
Executive Composite (GEC) that incorporates all of the BRIEF clinical scales.
Within the Behavioral Regulation Index, the inhibit scale is intended to assess IC,
the shift scale is intended to assess a child’s ability to move freely between tasks and
problems as needed, and the emotional control scale is intended to assess the impact
of an executive deficit on a child’s ability to control his or her emotional response. Within
the Metacognition Index, the initiate scale is intended to reflect a child’s ability to begin
a task or activity independently , the working memory scale is intended to assess a
child’s WM, the plan/organize scale is intended to reflect a child’s ability to manage
current and future-oriented task demands, the organization of materials is intended to
24
reflect a child’s ability to organize spaces of work and play, and the monitor scale is
intended to reflect a child’s ability to assess his or her task performance.
Statistical Analyses
To address the primary goal of determining the relationship between the
submeasures within the BRIEF and subsections of the D-KEFS and WISC-IV, a
correlation matrix was generated.
Aim 1. Examination Of Common Factors Between Tests Hypothesis 1: Subsections of the BRIEF, D-KEFS and WISC-IV will load onto processing speed, working memory, and inhibitory control
In order to assess whether the parental-ratings (BRIEF) of executive dysfunction
in and ADHD population reflected the performance-based deficits observed in this
population, an exploratory factor analysis was performed. Factor analyses allow for the
examination of the internal structure of a set of variables or indicators by generating a
theoretical set of factors (Henson & Roberts, 2006). To assess whether the individual
subsections of the BRIEF, D-KEFS and WISC-IV loaded onto distinct factors
representing processing speed, WM, and IC, an exploratory factor analysis was
performed in order to investigate the common factors between test subsections. Given
the independent and dependent nature of executive functioning, a promax rotation was
used to allow for general factor variation. Eight factors were examined from the BRIEF:
Inhibit, Shift, Emotional Control, Initiate, Working Memory, Plan/Organize, Organization
of Materials and Monitor. Only the primary executive tasks from the D-KEFS were
included: from the Verbal Fluency test, Category Switching was included, from the Trail
Making test, Letter-Number Sequencing was included and from the Color-Word
Interference test, Inhibition and Inhibition-Switching were included. Finally, from the
25
WISC-IV, the Processing Speed and Working Memory composite scores were included.
A total of fourteen variables were entered into the final model.
Aim 2. Predicting Comorbid Anxiety Diagnosis In Children With ADHD Hypothesis 2a: On parental measures, children with an anxiety diagnosis will demonstrate poorer behavioral regulation
In order to assess whether the parental-ratings (BRIEF) of executive dysfunction
was able to distinguish between ADHD and ADHD + AD, a hierarchical binary logistic
regression was performed using the BRIEF Behavioral Regulation Index and the BRIEF
Metacognition Index to predict diagnosis. In the first step, sex was entered in order to
control for the skewed sample size. In the second step, the BRIEF Behavioral
Regulation Index and the BRIEF Metacognition Index scores were entered.
Hypothesis 2b: On performance measures, children with an additional anxiety diagnosis will have better working memory than those without an anxiety diagnosis
In order to assess whether performance measures of executive functioning were
able to distinguish between ADHD and ADHD + AD, another hierarchical binary logistic
regression was performed. As in the previous analysis, sex was entered in the first step.
In the second step, a number of measures representing a variety of executive functions
were entered. From the assessment battery, the WISC-IV Processing Speed Composite
was used to assess processing speed, the WISC-IV Working Memory Composite was
used to assess working memory, the D-KEFS Letter-Number Switching score was used
to assess motor switching, the D-KEFS Category Switching score was used to assess
verbal switching, and the D-KEFS Inhibition and Inhibition Switching scores were used
to assess IC.
26
Table 2-1. Sample demographics (n=226)
Age in years M (SD) 12.2 (2.6) Sex N (%)
Male Female
162 (71.7) 64 (28.3)
Anxiety N (%) Separation Anxiety Disorder Social Phobia Generalized Anxiety Disorder Panic Disorder Obsessive-Compulsive Disorder Anxiety NOS Unspecified Anxiety Disorder
90 (39.8)
27
CHAPTER 3 RESULTS
Preliminary Correlational Analyses
An initial evaluation of the correlation matrix suggested that some measures of
the BRIEF were correlated with some performance measures (Table 3-1). Specifically,
the WISC-IV processing speed index was related to the emotional control, initiate,
working memory and monitor scales on the BRIEF. On the D-KEFS, the color-word
inhibition scale was related to the BRIEF working memory and monitor scales and the
color-word inhibition-switching scale was related to the BRIEF initiate, working memory,
and monitor scales. All performance measures showed a correlation with one another.
Aim 1. Examination Of Common Factors Between Tests
All scales examined have variances from ranging from 0.136-0.726 (Table 3-2).
An exploratory factor analysis with a promax rotation to allow for general factor variation
was performed. An examination of the factor’s eigenvalue indicates that there are three
factors that are above the criteria of <1 (Table 3-3), with a fourth factor approaching
significance. An examination of the cumulative variance suggests that the three factors
explain about 50% of the total variance. These result should be interpreted with caution,
however, as promax rotation and oblique rotations will miss shared variance.
The screeplot (Figure 3-1) is fairly clear, suggesting that there are three distinct
factors. As seen in the eigenvalue, there is a fourth factor approaching significance, but
there is still a clear inflection point. In sum, the scree plot does not support the four
factors, and although it is slightly unclear, there does appear to be a distinct inflection
point after three factors.
28
An examination of the Pattern Matrix (Table 3-4), for factor 1, shows that there
are high loadings for four parent-report scales (BRIEF Plan/Organize, BRIEF Working
Memory, BRIEF Initiate, and BRIEF Organization of Materials). All scales load strongly
onto the first factor and do not appear to cross load. The next six scales are the
performance-measures (Category Switching, Letter-Number Sequencing, Inhibition,
Inhibition-Switching, Processing Speed and Working Memory) and load primarily on the
second factor. As with the first factor, the scales appear to load strongly onto this factor
and do not appear to cross load between either of the other factors. The final factor
contains the remaining parent-report scales (BRIEF Emotional Control, BRIEF Shift,
BRIEF Inhibit, and BRIEF Monitor). The final scale, BRIEF monitor, is almost perfectly
cross loads between factor 1 and factor 3 with loadings of .458 and .465, respectively.
However, all other scales load strongly onto factor three without cross loading.
The Structure Matrix does not give any further evidence that is not provided by
the pattern matrix, with the exception of BRIEF Monitor loading onto factor 1 instead of
factor 3. As before, however, it appears to be almost perfectly cross load between
factors (Table 3-5).
An examination of the scales and their loadings suggests that the first factor is
best referred to as “Metacognition Index,” as all scales that comprise the Metacognition
Index load onto the first factor, excluding BRIEF Monitor. Likewise, the third factor could
be referred to as “Behavioral Regulation Index” as all scales that comprise the
Behavioral Regulation Index load onto this factor, excluding BRIEF Monitor. The third
factor, as it is comprised exclusively of the executive functioning performance
measures, could be referred to as “Executive Performance.”
29
The Behavioral Regulation Index is positively related to the Metacognition Index,
with the factor correlation matrix (Table 3-6) reflecting moderate magnitudes (0.554).
Executive Performance does not appear to be related to either the Metacognition Index
or the Behavioral Regulation Index, with a negative relationship of small magnitude
reflected in the correlation matrix.
Aim 2. Predicting Comorbid Anxiety Diagnosis In Children With ADHD
In the first step, sex was entered in order to control for its impact on diagnosis.
The omnibus test for the model was significant, χ2(1, N=209) = 4.775, p <0.029,
representing a significant improvement over the null model (Table 3-7). Sex was
significant (p=0.029), suggesting that the female sex was associated with 1.9% higher
odds of an anxiety diagnosis. Although the omnibus model tests revealed significance,
Cox and Snell pseudo r-squared was 0.023 and Nagelkerke pseudo r-squared was
0.031; thus this suggests that the model accounted for very little of the individual
differences in diagnosis.
Hypothesis 2a: On Parental Measures, Children With An Anxiety Diagnosis Will Demonstrate Poorer Behavioral Regulation
In the second step, the BRIEF Metacognition Index and the BRIEF Behavioral
Regulation Index were entered. The omnibus test for the model was significant, χ 2(3,
N=209) = 8.048, p <0.045, representing a significant improvement over the null model.
However, the omnibus test for the block was nonsignificant, χ 2(2, N=209) = 3.273, p
=.195 which suggests that there was no significant improvement over the previous
model. Therefore, the significance of this model can likely be attributed to the effects of
sex (Table 3-8), which remained significant (p<0.015). Neither the BRIEF Metacognition
Index nor the BRIEF Behavioral Regulation Index was significant. As would be
30
expected, Cox and Snell pseudo r-squared and Nagelkerke pseudo r-squared increased
only slightly (0.038 and 0.051, respectively) thus this suggests that this model accounts
for very little of the individual differences in diagnosis.
Hypothesis 2b: On Performance Measures, Children With An Additional Anxiety Diagnosis Will Have Better Working Memory Than Those Without An Anxiety Diagnosis
In the third step, performance measures were entered. Specifically, the WISC-IV
Processing Speed Composite score, the WISC-IV Working Memory Composite score,
the D-KEFS Letter-Number Switching scaled score, the D-KEFS Category Switching
scaled score, the D-KEFS Inhibition scaled score and the D-KEFS Inhibition Switching
scaled score were included in the model. The omnibus test for the model was
significant, χ 2(9, N=209) = 30.832, p <.001, representing a significant improvement over
the null model (Table 3-9). The omnibus test for the block was significant, χ 2(6, N=209)
= 22.784, p =.001 representing a significant improvement over the previous model.
Although the omnibus model tests revealed significance, Cox and Snell pseudo r-
squared was 0.137 and Nagelkerke pseudo r-squared was 0.186; thus this suggests
that the model accounted for relatively little of the individual differences in diagnosis.
In this model, sex remained significant (p<0.001), and of the entered
performance measures, the WISC-IV Working Memory Composite (p=0.007), WISC-IV
Processing Speed Composite (p=0.002), and the D-KEFS Verbal Fluency, Category
Switching (p=.034) are all significant. Each unit increase in WISC-IV Working Memory
Composite score was associated with 3.6% higher odds of having a comorbid anxiety
diagnosis, each unit decrease in WISC-IV Processing Speed Composite score was
associated with 4.7% higher odds of having a comorbid anxiety diagnosis, and each unit
31
decrease in D-KEFS Verbal Fluency, Category Switching scaled score was associated
with 10% higher odds of having a comorbid anxiety diagnosis.
Overall, 61.2% of patients were correctly classified (Table 3-10). 77.8% of those
who do not have an anxiety diagnosis were correctly predicted, while 36.1% of those
who did have a comorbid anxiety diagnosis were correctly identified, giving this model a
sensitivity of 36.1% and a specificity of 77.8%. Chance classification would be 0.53 and
25% better than chance would be 0.657. Actual correct classification was 67.9%,
suggesting that this model predicts diagnosis better than chance.
An examination of the model suggests that the C-statistic is 0.718 and
significantly greater than 0.5 (p < .001), and thus meets the 0.7 minimum criteria
suggested by Hosmer and Lemeshow (1982). Agresti’s point-biserial is 0.136 and
suggests a weak association between model-estimated individual probabilities of
correctly predicting a comorbid anxiety disorder.
32
Table 3-1. Correlation Matrix
D-KEFS: Number-
Letter Switching
D-KEFS: Category Switching
D-KEFS: Inhibition
D-KEFS: Inhibition-Switching
WISC-IV: WM
WISC-IV: Processing
Speed
BRIEF: Inhibit
BRIEF: Shift
BRIEF: Emotional
Control
BRIEF: Initiate
BRIEF: Memory
BRIEF: Plan/
Organize
BRIEF: Organize Materials
D-KEFS: Category Switching
.233** 1
D-KEFS: Inhibition
.392** .277** 1
D-KEFS: Inhibition-Switching
.315** .251** .699** 1
WISC-IV: Working Memory
Composite
.423** .141* .286** .140* 1
WISC-IV: Processing
Speed Composite
.413** .275** .474** .367** .368** 1
BRIEF: Inhibit
0.023 0.049 -0.049 0.014 -0.047 -0.043 1
BRIEF: Shift
0.005 -0.032 -0.049 -0.016 -0.012 -0.061 .511** 1
33
Table 3-1. Continued
D-KEFS: Number-
Letter Switching
D-KEFS: Category Switching
D-KEFS: Inhibition
D-KEFS: Inhibition-Switching
WISC-IV: WM
WISC-IV: Processing
Speed
BRIEF: Inhibit
BRIEF: Shift
BRIEF: Emotional
Control
BRIEF: Initiate
BRIEF: Memory
BRIEF: Plan/
Organize
BRIEF: Organize Materials
BRIEF: Emotional
Control -.048 .019 -.024 .012 -.015 -.156* .582** .663** 1
BRIEF: Initiate
-.037 -.045 -.106 -.147* -.012 -.198** .346** .469** .438** 1
BRIEF: Memory
-.057 -.022 -.131* -.161* .012 -.173** .422** .347** .259** .617** 1
BRIEF: Plan/
Organize -.002 .012 -.061 -.120 .034 -.105 .343** .384** .256** .638** .681** 1
BRIEF: Organize Materials
.089 .043 -.023 -.023 .033 -.096 .388** .222** .277** .434** .465** .491** 1
BRIEF: Monitor
-.076 .020 -.148* -.128* -.056 -.179** .613** .583** .503** .588** .573** .617** .438**
**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).
34
Table 3-2. Communalities
Initial Extraction
BRIEF - Inhibit 0.514 0.494
BRIEF - Shift 0.583 0.623
BRIEF - Emotional Control 0.592 0.726
BRIEF - Initiate 0.570 0.590
BRIEF - Memory 0.588 0.672
BRIEF - Plan/Organize 0.614 0.718
BRIEF - Organization of Materials 0.340 0.348
BRIEF - Monitor 0.636 0.685
D-KEFS - Trail Making: Number-Letter Switching 0.339 0.382
D-KEFS - Verbal Fluency: Category Switching 0.586 0.622
D-KEFS - Color-Word: Inhibition 0.537 0.471
D-KEFS - Color-Word: Inhibition-Switching 0.129 0.136
WISC-IV - Working Memory Composite 0.282 0.219
WISC-IV - Processing Speed Composite 0.377 0.431
Extraction method: principal axis factoring.
35
Table 3-3. Total Variance Explained
Factor
Initial Eigenvalues Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadingsa
Total % of
Variance Cumulative
% Total % of
Variance Cumulative
% Total
1 4.414 31.526 31.526 4.033 28.810 28.810 3.520
2 2.697 19.262 50.788 2.135 15.250 44.060 2.269
3 1.341 9.581 60.369 .950 6.786 50.846 3.113
4 .990 7.072 67.441
5 .850 6.074 73.515
6 .698 4.986 78.501
7 .568 4.056 82.557
8 .549 3.919 86.476
9 .464 3.317 89.793
10 .359 2.562 92.355
11 .325 2.320 94.676
12 .270 1.929 96.605
13 .257 1.836 98.441
14 .218 1.559 100.000
Extraction method: principal axis factoring. a. When factors are correlated, sums of squared loadings cannot be added to obtain a total variance.
36
Figure 3-1. Scree plot
37
Table 3-4. Pattern Matrix
Factor
1 2 3
BRIEF - Plan/Organize .895 .062 -.074
BRIEF - Working Memory .845 .005 -.046
BRIEF - Initiate .667 -.037 .151
BRIEF - Organization of Materials .579 .124 .034
D-KEFS - Color-Word: Inhibition -.059 .781 .065
WISC-IV - Processing Speed Composite .003 .643 -.099
D-KEFS - Color-Word: Inhibition-Switching -.204 .638 .215
D-KEFS - Trail Making: Number/Letter Switching
.154 .626 -.084
WISC-IV - Working Memory Composite .166 .469 -.106
D-KEFS - Verbal Fluency: Category Switching .083 .374 .007
BRIEF - Emotional Control -.124 -.040 .911
BRIEF - Shift .088 .010 .738
BRIEF - Inhibit .124 .042 .629
BRIEF - Monitor .458 -.078 .465
Extraction method: principal axis factoring, 3 factors extracted, 14 iterations required.
38
Table 3-5. Structure Matrix
Factor
1 2 3
BRIEF - Plan/Organize 0.843 -0.087 0.417
BRIEF – Working Memory 0.819 -0.138 0.421
BRIEF - Initiate 0.757 -0.163 0.523
BRIEF - Monitor 0.729 -0.190 0.724
BRIEF - Organization of Materials 0.577 0.021 0.346
D-KEFS - Color-Word: Inhibition -0.158 0.787 -0.023
D-KEFS - Color-Word: Inhibition-Switching -0.195 0.658 0.057
WISC-IV Processing Speed Composite -0.163 0.650 -0.142
D-KEFS - Trail Making: Number-Letter Switching -0.001 0.605 -0.043
WISC-IV Working Memory Composite 0.026 0.448 -0.048
D-KEFS - Verbal Fluency: Category Switching 0.023 0.359 0.027
BRIEF - Emotional Control 0.388 -0.083 0.846
BRIEF - Shift 0.495 -0.057 0.786
BRIEF - Inhibit 0.465 -0.024 0.695
Extraction method: principal axis factoring, 3 factors extracted, 14 iterations required.
Table 3-6. Factor correlation matrix
Factor 1 2 3
1 1.000 -.173 .554
2 -.173 1.000 -.070
3 .554 -.070 1.000
Extraction method: principal axis factoring. Rotation method: promax with Kaiser
normalization.
39
Table 3-7. Hierarchical binary logistic regression, step 1
Block 1 B S.E. Exp(B) 95% CI for Exp(B)
Predictor Lower Upper
Sex .684* .313 1.981 1. 072 3.661
Note. * p <.05, ** p < .01
Table 3-8. Hierarchical binary logistic regression, step 2
Block 2 B S.E. Exp(B) 95% CI for Exp(B)
Predictor Lower Upper
Sex .799** .327 2.222 1. 170 4.220
BRIEF Behavioral Regulation Index
-.009 .013 .991 .967 1.016
BRIEF Metacognition Index -.019 .020 .981 .944 1.019
Note. * p <.05, ** p < .01
40
Table 3-9. Hierarchical binary logistic regression, step 3
Block 3 B S.E. Exp(B) 95% CI for
Exp(B)
Predictor Lower Upper
Sex 1.345** .381 3.836 1.817 8.103
BRIEF Behavioral Regulation Index -.009 .014 .991 .964 1.018
BRIEF Metacognition Index -.034 .022 .967 .926 1.009
WISC-IV Working Memory Composite .035** .013 1.036 1.010 1.063
WISC-IV Processing Speed Composite -.048** .016 .953 .925 .983
D-KEFS Color-Word – Inhibition .070 .069 1.073 .936 1.229
D-KEFS Trail-Making – Letter-Number Switching
.060 1.381 1.061 .961 1.172
D-KEFS Verbal Fluency – Category Switching
-.106* 4.516 .900 .816 .992
D-KEFS Color-Word – Inhibition-Switching
.062 .736 1.064 .923 1.228
Note. * p <.05, ** p < .01
Table 3-10. Classification table
Observed
Predicted
Anxiety Diagnosis Status
Percentage
Correct ADHD alone
ADHD and
anxiety
Anxiety
Diagnosis
Status
ADHD alone 104 22 82.5
ADHD and anxiety 45 38 45.8
Overall Percentage: 67.9
41
CHAPTER 4 DISCUSSION
Although preliminary results from the correlational analysis suggested that
parental-ratings from the BRIEF and performance measures may have some
relationship, results from this study reveal that performance measures and parental-
report based measures do not capture similar constructs. Indeed, contrary to the
hypothesis, performance measure and parental-report measures appear to capture
entirely different constructs. This contributes to the growing body of research, which
suggests that parent-report and performance based measures of executive functioning
measure different constructs (Davidson et al., 2016; Eycke & Dewey, 2016). Indeed, in
this study there appeared to be almost no correlation between the BRIEF subscales and
the performance subscales selected from the D-KEFS and WISC-IV.
The factors generated by the exploratory factor analysis appear to reflect three
distinct measures. The first, referred to as the “Behavioral Regulation Index,” is almost
perfectly composed of scales composing the Behavioral Regulation Index on the
BRIEF. The second factor, referred to as the “Metacognition Index,” likewise reflects the
BRIEF’s Metacognition Index. The scale “monitor” was almost perfectly cross loads on
these two factors, suggesting that it captures a construct within this specific population
that is similar to both the Metacognition Index and the Behavioral Regulation Index. The
final factor, referred to as “Executive Performance,” contained all performance
measures. This is likely reflective of the timed nature of these tasks, and is thus
reflective of the common nature of these measurements, rather than being suggestive
of their ability to measure a common construct. Taken together, this factor analysis
suggests that the BRIEF’s Metacognition Index and Behavioral Regulation Index each
42
measure unique constructs within a pediatric ADHD population. Furthermore, the BRIEF
does not reflect a common construct with any performance measures examined.
The results of the hierarchical binary logistic regression suggest that the BRIEF
cannot distinguish between children with ADHD and children with ADHD + AD.
However, the performance measures examined were able to predict a comorbid anxiety
diagnosis in the ADHD population 25% better than chance, such that better
performance on the WISC-IV Working Memory Composite was associated with higher
odds of having a comorbid anxiety diagnosis and worse performance on WISC-IV
Processing Speed Composite and D-KEFS Verbal Fluency, Category Switching was
associated higher odds of having a comorbid anxiety diagnosis. While limited previous
research has been done on the executive functioning differences between these
populations, previous research has implicated greater impairment in processing speed
in an ADHD + AD population, as compared to an ADHD population (Bloemsma et al.,
2013). However, the improved performance in WM runs contrary to previous research,
which has suggested that an additional anxiety diagnosis is associated with poorer
performance on WM tasks (Jarrett et al., 2016). There are a variety of possible
explanations for this; one explanation is that it is reflective of methodological
differences, specifically differences in task selection for WM, or the high rates of
additional comorbidity within our sample. Another explanation that should be considered
is the potential for misdiagnosis. Anxiety disorders are associated with poor executive
attention (Mogg et al., 2015), and many complaints associated with anxiety bare
remarkable resemblance to the diagnostic criteria for ADHD (such as poor attention and
43
concentration). It is, therefore, possible that some children within this sample have
symptoms better accounted for by only an anxiety diagnosis, rather than ADHD + AD.
Few conclusions can be drawn from this. While the BRIEF failed to predict
diagnosis classification in this sample, it would be premature to state that parental-
measures do not distinguish between ADHD + AD and ADHD. While performance
measures did predict comorbidity, the weak sensitivity of the model examined suggests
that it is a less than desirable method of differentiating between these two populations.
However, these results are suggestive of some objectively measurable, cognitive
difference between these populations. In short, while parental measures may not predict
ADHD + AD, performance measures may. This is of particular interest given that this
sample contained children with a number of comorbid diagnoses that were not taken
into account in this analysis, offering some advantage to clinical application. While it
may be argued that diagnostically “pure” samples will best illuminate cognitive
differences with some degree of certainty, this holds little practical application given the
rampant rate of comorbidity in ADHD (Cuffe et al., 2015) and across the DSM (Cramer,
Waldorp, van der Maas, & Borsboom, 2010).
Though findings from this analysis support recent literature reaching similar
conclusions regarding the relationship between parental-ratings and performance
measures (Davidson et al., 2016; Eycke & Dewey, 2016), these results are preliminary
in nature and are limited by relatively few children diagnosed with ADHD + AD
(n=83/209). These findings were reflective of population rates (Barkely, 2007), with our
sample containing more males than females at a ratio approaching 3:1. However, given
the heterogeneous nature of ADHD (Cuffee et al., 2015), and the differences in
44
executive functioning on both parental-ratings and performance measures among males
and females with ADHD (Skogli, Teicher, Andersen, Hovik, & Øie, 2013), sex
differences may of be great interest. Furthermore, as previously mentioned, this sample
contained children with a number of comorbid diagnoses that were not considered in
these analyses. While this may offer some advantage in terms of realistic application of
results to a clinical sample, it does little to elucidate the true cognitive differences
between children with ADHD and ADHD + AD. These findings are also limited by a
narrow test battery. The only parental rating considered was the BRIEF, and the only
performance-measures examined were the D-KEFS and the composite scores of the
WISC-IV Processing Speed Index and Working Memory Index. As such, these results
should be interpreted with caution.
Future studies should examine this with a larger sample size, and a more
comprehensive test battery. While the results of these analyses suggest that the BRIEF
does not distinguish between children with ADHD and children with ADHD + AD,
another parental measure might. Furthermore, a larger test battery may be able to
distinguish between children with ADHD and children with ADHD + AD with greater
sensitivity and specificity than the current model. Future research should also consider
examining a “pure” sample, in order to illuminate cognitive differences between children
with ADHD, ADHD + AD, and children with only anxiety disorders. Such findings may be
better able to guide the development of a neurocognitive battery to identify and
distinguish between these populations.
Finally, rating measures should continue to be examined in order to determine
their relationship to performance-based measures. Previous research has suggested
45
that teacher rating scales may more accurately reflect cognitive impairment, due to
greater familiarity with behavioral responses to increased cognitive demand (Eycke &
Dewey, 2016). While rating scales may currently capture the behavioral expression of
executive dysfunction, the development or identification of a rating measure that
accurately reflects cognitive impairment holds incredible clinical and diagnostic
relevance. Psychological assessment is often expensive, and should reflect
improvements in treatment or prevention efforts in order to justify the monetary cost
(Yates & Taub, 2003). Rating scales that accurately reflect cognitive impairment may
decrease the monetary cost while simultaneously providing improvements in treatment
and prevention efforts, especially to families with limited access to treatment.
ADHD is associated with neurocognitive impairments in addition to behavioral
dysfunction. Anxiety disorders are highly comorbid with ADHD, and many anxiety
disorders are associated with difficulty with attention and concentration (American
Psychological Association, 2013). Some evidence suggests children with ADHD + AD
experience poorer outcomes in treatment when compared to counterparts without
ADHD (Halldorsdottir & Ollendick, 2013). Results from this study suggest that children
with ADHD + AD and children with ADHD evidence some differences in cognitive
impairment. Therefore, accurately distinguishing between ADHD + AD and ADHD alone
could help children and families access the treatment best suited to their needs.
46
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BIOGRAPHICAL SKETCH
Danielle Laine Cooke is a second-year graduate student in the Department of
Clinical and Health Psychology at the University of Florida. She obtained her bachelor’s
degrees in psychology and English from the University of Florida in 2015 and has been
working with the UF OCD Clinic since 2013. Since that time she has had the opportunity
to work with a wide variety of adults and children with anxiety-spectrum disorders and
eating disorders. Danielle was offered the opportunity to begin graduate studies in
clinical psychology at the University of Florida in the fall of 2015 and has continued
working in the Division of Medical Psychology as a graduate assistant. She identifies
strongly with the scientist-practitioner model and is primarily interested in treatment
outcome research, and the impact of transdiagnostic factors, including executive
functioning and sleep.
