The Development of Executive Function and Working Memory in Children and Adolescents with Autism

Spectrum Disorder: A Longitudinal Study of Brain and Behaviour

by

Vanessa Michela Vogan

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy

Department of Applied Psychology and Human Development University of Toronto

© Copyright by Vanessa Michela Vogan (2018)

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The Development of Executive Function and Working Memory in Children and Adolescents with Autism

Spectrum Disorder: A Longitudinal Study of Brain and Behaviour

Vanessa Michela Vogan

Doctor of Philosophy

Department of Applied Psychology and Human Development University of Toronto

2018

Abstract

Children with Autism Spectrum Disorder (ASD) exhibit social and communicative problems,

repetitive and restrictive patterns of behaviour, and a range of cognitive deficits, including

executive functioning (EF) impairments. Literature in ASD has focused mostly on the

behavioural phentotype of the disorder and less research has investigated the cognitive and EF

profiles in this population. The objective of this thesis was to gain a greater understanding of EF

deficits, their longitudinal development, their underlying neural correlates using neuroimaging

techniques, and their impact on critical outcomes in children with ASD compared to typical

developing (TD) individuals.

Structural and functional brain disturbances were evident in children and adolescents with ASD,

relative to controls. In a diffusion tensor imaging (DTI) study, significant white matter

differences were found in tracts essential for higher order cognitive processing, such as EF, and

general information processing in children with ASD (ages 7-15 years). White matter

developmental trajectories did not appear to differ between groups. Functional neural systems

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associated with EF, specifically working memory, were also impaired in youth ASD (ages 7-13

years) relative to controls, particularly in parietal and temporal regions. In this functional

magnetic resonance imaging (fMRI) study, children with ASD showed inadequate modulation

of neural activity in response to increasing cognitive demands, which did not improve over two

years. Last, children with ASD (ages 7-14 years) showed marked EF impairments in everyday

settings as observed by parents, which persisted over time compared to TD children. Early EF

deficits predicted later symptoms of co-morbid psychopathology and social difficulties in ASD.

Findings suggest that children with ASD exhibit structural and functional neural deficits

associated with EF across childhood that make them vulnerable to the increasing complexity of

environmental demands as they mature. Results support EF as potential target in autism

interventions for improving executive development, and also for preventing co-morbid

psychopathology and promoting social competence in ASD.

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Acknowledgments First, I would like to express my sincerest gratitude to the families and children who participated

in this project, and for their time, effort, and commitment in support of this research.

I would also like to thank my supervisor, Dr. Margot Taylor, whom I am so thankful to have as

mentor. I am forever grateful for your continued support, faith in me, and commitment to my

ambitions, goals, and dreams. Your encouragement, guidance, and dedication to my research

and personal aspirations has helped me grow as a professional.

Thank you to my dissertation committee members, Drs. Mary Lou Smith and Rhonda

Martinussen, for your helpful feedback, contributions, and tremendous support through this

process.

I am grateful for the opportunity to have worked with an incredible group of lab members, with

whom I have developed unforgettable friendships with over the years. I want to especially

thank Ben Morgan for his support in data analyses, statistical expertise, diligence and major

contributions to my research. To Rachel and Julia, thank you for your moral support, endless

encouragement, and friendship over the past five years. I am thankful for your company during

late nights in the lab and amazing adventures abroad. I look forward to sharing many more

experiences with you post grad-school life!

Thank you to all of my clinical supervisors for their mentorship and outstanding training: Drs.

Trudi Yeger, Debbie Zweig, Elizabeth Dettmer, Sarah Glaser, Michele Peterson-Badali, Mila

Buset and Patricia Delmore-Ko. I am fortunate to have had exceptional clinical supervision and

training throughout my program. My skills working with children and families have been

shaped by each of you.

Most importantly, I want to thank my parents and my family for being pillars of support and

always believing in me throughout this journey. Your love, support, and motivation means so

much to me and continues to be a constant source of encouragement. Thank you for being so

proud of me and excited about my accomplishments, big and small. Finally, to my husband,

Fab, I cannot begin to describe my gratitude for your endless words of support, patience, and for

standing by my side throughout this journey. Thank you for being so understanding about my

absences at special events, for listening, and for helping me in whatever ways you can. I could

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not have done this without you. Your love, dedication and passion continue to inspire me, and I

am excited to share many more celebrations of our success.

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Table of Contents

Acknowledgments ....................................................................................................................... iv

Table of Contents ........................................................................................................................ vi

List of Tables ............................................................................................................................... ix

List of Figures ............................................................................................................................... x

List of Appendices ....................................................................................................................... xi

Chapter 1 Background and Rationale ....................................................................................... 1

Background and Rationale ................................................................................................. 2

1.1 Neuropathology of ASD ................................................................................................ 3

1.1.1 Brain structure ............................................................................................................ 3

1.1.2 Brain Function ........................................................................................................... 7

1.2 Executive Functioning ................................................................................................... 8

1.2.1 Overview .................................................................................................................... 8

1.2.2 Executive Functioning in ASD ................................................................................ 10

1.2.2.1 Executive Dysfunction ..................................................................................... 10

1.2.2.2 Neural Correlates of EF Deficits ...................................................................... 12

1.2.2.3 The Role of EF in Autistic Symptomatology and Outcomes .......................... 13

1.3 Present Study Objectives and Rationale ..................................................................... 14

1.3.1 Study 1: DTI investigation of white matter development in ASD ........................... 14

1.3.2 Study 2: fMRI investigation of working memory development in ASD ................. 15

1.3.3 Study 3: Development of everyday EF and its associations with functional

outcomes in ASD ................................................................................................................. 16

Chapter 2 Widespread White Matter Differences in Children and Adolescents with

Autism Spectrum Disorder ....................................................................................................... 18

Widespread White Matter Differences in Children and Adolescents with Autism

Spectrum Disorder ..................................................................................................................... 19

2.1 Abstract ........................................................................................................................ 19

2.2 Introduction .................................................................................................................. 19

2.3 Methods........................................................................................................................ 22

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2.3.1 Participants ............................................................................................................... 22

2.3.2 Image Acquisition .................................................................................................... 23

2.3.3 Image Processing ..................................................................................................... 23

2.3.4 Image Analyses ........................................................................................................ 24

2.4 Results .......................................................................................................................... 24

2.4.1 Group Comparisons Between TD and ASD ............................................................ 24

2.4.2 Correlations with ASD Symptomatology ................................................................ 30

2.5 Discussion .................................................................................................................... 30

Chapter 3 Functional Changes During Visuo-Spatial Working Memory in Autism

Spectrum Disorder: 2 Year Longitudinal fMRI Study .......................................................... 33

Functional Changes During Visuo-spatial Working Memory in Autism Spectrum

Disorder: 2 Year Longitudinal fMRI Study ............................................................................ 34

3.1 Abstract ........................................................................................................................ 34

3.2 Introduction .................................................................................................................. 35

3.3 Methods........................................................................................................................ 39

3.3.1 Participants ............................................................................................................... 39

3.3.2 fMRI visuo-spatial working memory paradigm (Colour Matching Task) ............... 40

3.3.3 Image Acquisition .................................................................................................... 43

3.3.4 CMT Behavioural Data Analyses ............................................................................ 43

3.3.5 fMRI data analyses .................................................................................................. 43

3.4 Results .......................................................................................................................... 45

3.4.1 Behavioural Data ..................................................................................................... 45

3.4.2 fMRI Analyses ......................................................................................................... 47

3.4.2.1 ROI analysis ..................................................................................................... 47

3.4.2.2 Whole brain analysis ........................................................................................ 47

3.5 Discussion .................................................................................................................... 52

3.6 Conclusions and Future Directions .............................................................................. 56

Chapter 4 Longitudinal Examination of Everyday Executive Functioning in Children with

ASD: Relations with Social, Emotional and Behavioural Functioning Over Time ............. 58

Longitudinal Examination of Everyday Executive Functioning in Children with ASD:

Relations with Social, Emotional and Behavioural Functioning Over Time ....................... 59

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4.1 Abstract ........................................................................................................................ 59

4.2 Introduction .................................................................................................................. 59

4.3 Methods........................................................................................................................ 63

4.3.1 Participants ............................................................................................................... 63

4.3.2 Procedure ................................................................................................................. 64

4.3.3 Measures .................................................................................................................. 64

4.3.3.1 Executive functioning ...................................................................................... 64

4.3.3.2 Emotional and behavioural functioning ........................................................... 65

4.3.3.3 Social functioning ............................................................................................ 65

4.3.4 Data Analysis ........................................................................................................... 65

4.3.4.1 Longitudinal trajectory of everyday executive functions ................................ 65

4.3.4.2 Relations between EF and emotional/behavioural symptomatology ............... 66

4.3.4.3 Relations between EF and social functioning .................................................. 66

4.4 Results .......................................................................................................................... 67

4.4.1 The development of everyday EF over two years ................................................... 67

4.4.2 Do prior estimates of EF predict later future emotional and behavioural

functioning? ......................................................................................................................... 68

4.4.3 Do prior estimates of EF predict future social functioning? .................................... 70

4.5 Discussion .................................................................................................................... 73

Chapter 5 General Discussion................................................................................................... 78

General Discussion ............................................................................................................. 79

5.1 Review of Objectives ................................................................................................... 79

5.2 Summary of Findings ................................................................................................... 79

5.3 Conclusions .................................................................................................................. 81

5.4 Future Directions ......................................................................................................... 82

References ................................................................................................................................... 88

Appendix A ............................................................................................................................... 112

Appendix B ............................................................................................................................... 115

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List of Tables Table 1. Participant characteristics for Study 1

Table 2. Regions showing greater FA in TD children compared to children with ASD

Table 3. Regions showing greater AD in TD children compared to children with ASD

Table 4. Participant characteristics for Study 2

Table 5. Brain regions showing a significant Group x Time interaction of the D6 versus D3

contrast during CMT

Table 6. Participant characteristics for Study 3

Table 7. Correlation matrixes of EF variables and emotional and behavioural functioning

variables for (A) typical developing children and (B) children with ASD

Table 8. Simple regression analyses: Influence of EF at T1 on emotional/behavioural and social

functioning at T2 for (A) children with ASD and (B) typical developing children

Table 9. Correlation matrixes of EF variables and social functioning for (A) typical developing

children and (B) children with ASD.

Table 10. Correlation matrix for change of EF and social functioning from T1 to T2 in ASD

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List of Figures Figure 1. FA differences between TD and ASD groups

Figure 2. AD differences between TD and ASD groups

Figure 3. FA plotted against age in select tracts in TD and ASD groups

Figure 4. Protocol description of the Colour Matching task (CMT)

Figure 5. CMT Behavioural performance

Figure 6. Group activation maps for the D6 versus D3 contrast during CMT at baseline and

follow-up

Figure 7. Between-group comparisons in the longitudinal change in functional activation of D6

versus D3

Figure 8. Graphs of signal change during D3 and D6 at baseline and follow-up in areas that

exhibited a significant Grout x Time interaction

Figure 9. Mean T scores and standard error bars of parent-reported BRIEF Behaviour

Regulation Index (BRI) and Metacognition Index (MCI) at T1 and T2

Figure 10. Mean T scores and standard error bars of parent-reported SRS Total at T1 and T2

Figure 11. Sample guide on designing and studying the effectiveness of an EF intervention for

children with ASD

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List of Appendices Appendix A. Supplementary Tables

Appendix B. Supplementary Figures

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Chapter 1 Background and Rationale

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Background and Rationale

Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental disorder

characterized, in varying degrees, by impairments in social interaction and communication, as

well as restricted and repetitive patterns of behaviours, interests and activities (American

Psychological Association [APA], 2013). The prevalence of ASD in select Canadian provinces

is estimated to be 1 in 94, with a male:female ratio of 5:1 (National Epidemiologic Database for

Study in Autism in Canada [NEDSAC], 2012), and is now the fastest growing and most

commonly diagnosed neurological disorder in Canada (“Autism Speaks Canada”, n.d.). The

causes of ASD are multifactorial, including alterations in neuronal organization (Kemper &

Bauman, 1998), cortical connectivity (Courchesne & Pierce, 2005; Travers et al., 2015) and

brain growth (Schumann, et al., 2004), as well as genetic (Yang & Gill, 2007) and epigenetic

factors (Grafodatskaya, Chung, Szatmari, & Weksberg, 2010; see Pardo & Eberhar, 2007 for

review).

Individuals with ASD also often present with a number of significant cognitive, learning and

executive function (EF) impairments (Dawson et al., 2002; Estes, Riversa, Bryan, Cali &

Dawson, 2011; Hill, 2004b; Matson & Shoemaker, 2009), but they are not seen as the core

deficits in this population. Recently, EF difficulties were included in severity measures of ASD

in the newest edition of the Diagnostic and Statistical Manual of Mental Disorders – Fifth

Edition (DSM-5; APA, 2013). Although the behavioural phenotype of ASD is well documented,

less is known about the cognitive and EF profiles of this population. One account of ASD is the

‘executive dysfunction theory’, which posits that autistic symptomatology is a consequence of

impaired higher order cognitive skills, such as EF (Hill, 2004a; Joseph, 1999). EF deficits are

associated with frontal brain regions (Silk et al., 2016; Vogan et al., 2014); the frontal lobes

have a protracted maturation (Powell & Voeller, 2004; Sowell et al., 2004) and thus functions

they support, such as EF, are susceptible to disturbances throughout development. Furthermore,

the integration of cortical and subcortical neural systems throughout the entire brain are

essential for EF (O’Hearn, Asato, Ordaz, & Beatriz, 2008). However, in ASD there is a paucity

of research investigating the underlying neural mechanisms associated with EF, their

development, and their impact on critical developmental outcomes in ASD.

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This thesis addressed these gaps in the literature by examining the development of EF and

underlying neural systems in children with ASD, compared to typically developing (TD)

children. Specifically, the aims of this research were:

1. To determine age-related changes in structural neural systems, and how they differ in

ASD, using diffusion tensor imaging (DTI) techniques.

2. To understand the longitudinal development of brain functions associated with EF,

specifically working memory, in children with ASD using functional magnetic

resonance imaging (fMRI).

3. To characterize the longitudinal trajectory of everyday executive functioning, and its link

to critical outcomes in ASD, such as social, emotional and behavioural functions.

This thesis elucidates some of the neural and cognitive abnormalities in ASD. Characterizing

neurodevelopmental profiles underlying EF will help localize brain areas that are vulnerable to

developmental disturbances—critical information for clinicians and researchers. Importantly,

information about the emergence and timing of differences in EF and underlying neurocircuitry

will inform the nature and timing of appropriate interventions. Last, a better understanding of

EF and its link to future social, emotional and behavioural outcomes will allow for more

accurate diagnosis and prognosis of co-morbid psychopathology, and will help inform treatment

planning.

1.1 Neuropathology of ASD

1.1.1 Brain structure

ASD is characterized by a number of structural and functional neurological disturbances. The

notion of an abnormal neurodevelopmental trajectory in ASD was first initiated by studies

demonstrating unusual brain growth patterns in the early life of children with ASD. Specifically,

findings from several studies have shown that toddlers with ASD (ages 2-4 years) show a period

of early accelerated brain enlargement, leading to overgrowth in total brain volume compared to

TD children, followed by a period of arrested/slowed growth or a possible decline in total brain

volume (Courchesne, Campbell, & Solso, 2011; see Redcay and Courchesne, 2005 for a meta-

analysis). However, more recent studies suggest only subtle differences in brain growth

between children with and without ASD (Raznahan et al., 2013) and that there may be a subset

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of children with ASD that drive the reported brain overgrowth (Nordahl et al., 2013). By mid-

adolescence, individuals with ASD show normal brain size compared to controls while some

structures are shown to decline further into adulthood (Lange et al., 2014). Dysregulated brain

growth in ASD seems to differ across brain regions, with the frontal and temporal lobes being

the most affected (Brun et al., 2009; Carper & Courchesne, 2005; Greimel et al., 2013; Hazlett,

Poe, Gerig, Smith, & Piven, 2006)

Any early atypical brain growth in ASD could have profound effects on microstructural

characteristics and development of neural networks, causing disruption in how areas of the brain

communicate with each other. While volumetric MRI studies, such as those described above,

can provide insights into macrostructural properties of white matter, they cannot tell us about

microstructural properties. Fortunately, white matter microstructure can be studied using

diffusion tensor imaging (DTI), a non-invasive MRI technique that measures the directionality

of water diffusion in the brain (Basser, Mettiello, & LeBihan, 1994). The extent of water

movement in a consistent direction along white matter tracts provides information about axonal

architecture and is a proxy measure of white matter integrity. Fractional anisotropy (FA), the

most commonly studied DTI metric, is a measure of the degree of directional variation of water

movement in the brain (Basser and Pierpaoli, 1996). An FA of 1 indicates diffusion in only 1

direction and FA of 0 indicates equal diffusion in all directions. FA is typically higher in thick

core white matter tracts (directional water diffusion) and lower white matter FA (distributed

water diffusion) is found in areas of microstructural disorganization, damaged myelin and

crossing fibers (Wedeen et al., 2008). Other DTI indices include axial diffusivity (AD), which

represents water diffusivity parallel to axon fibre tracts; radial diffusivity (RD), an index of

water movement perpendicular to the fiber tracts; and mean diffusivity (MD), which is the

average diffusion. Concurrent with findings of early brain overgrowth in ASD, DTI studies have

shown that infants also have higher FA than TD children in several brain regions (Ben Bashat et

al., 2007; Wolff et al., 2012).

Results of DTI studies in school age children and youth with ASD have been mixed.

Investigations examining overall white matter across the whole brain have demonstrated

consistent widespread white matter deficits as evident by reduced FA in multiple regions across

the prefrontal, temporal and parietal lobes, cingulum, corpus callosum, superior longitudinal,

occipital-frontal and uncinate fasciculi, cerebellum, and a range of projection fibres (Barnea-

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Gorlay et al., 2004; Barnea-Goraly, Lostspeich & Reiss, 2010; Cheon et al., 2011; Cheung et al.,

2009; Keller, Kana & Just, 2007; Kumar et al., 2010; Noriuchi et al., 2010; Pardini et al., 2009;

Poustka et al., 2012; Sahyoun, Belliveau, & Mody, 2010; Shukla, Keehn, & Müller, 2011;

Shukla, Keehn, Smylie, & Müller, 2011; Jou et al., 2011a,b). Corresponding increased mean

diffusivity (MD; Cheon et al., 2011; Shukla et al., 2011a,b) and/or RD (Ameis et al., 2011;

Shukla et al., 2011b), and/or reduced AD (Barnea-Goraly et al. 2010; Noriuchi et al., 2010)

compared to TD children has also been reported. Some studies have found no differences in FA

between ASD and control groups (Ameis et al., 2011; Mak-Fan et al., 2013), or even increased

FA in children with ASD (Bode et al., 2011; Ke et al., 2009; Sahyoun et al., 2010a). Other

studies using ROI approaches have also reported reduced FA in ASD, particularly in the corpus

callosum (Alexander, et al., 2007; Brito et al., 2009; Jeong, Kumar, Sundaram, Chungani, &

Chungani, 2011; Travers et al., 2015), longitudinal fasciculus (Sundaram et al., 2008),

projection fibres (Brito et al., 2009), arcuate fasciculus (Fletcher et al., 2010; Jeong et al., 2011),

and fronto-temporal tracts (Jeong et al., 2011; Sahyoun, Belliveau, Soulieres, Schwartz &

Moody, 2010; Sampson et al., 2016).

Given that ASD is a neurodevelopmental disorder, it is crucial to consider the developmental

trajectories of white matter tracts. In neurotypical individuals, development of most white

matter tracts is characterized by a relatively steep increase in FA and decrease in MD during

early childhood, which eventually plateaus in adolescence, with subtler changes occurring well

into adulthood (Lebel & Beaulieu, 2011). A longitudinal DTI study investigating white matter

tract development in high-risk infants demonstrated different developmental trajectories for

those who went on to meet diagnostic criteria for ASD at 2 years of age (Wolff et al., 2012).

Specifically, those diagnosed with ASD showed increased FA at 6 months in regions such as the

corpus callosum, fornix, inferior longitudinal fasciculus, uncinated and posterior limb of the

internal capsule compared to those who did not meet diagnostic criteria. This initial acceleration

of white matter was followed by slowed development, and by 2 years of age children with ASD

showed decreased FA values compared to children who did not have ASD. Findings from this

longitudinal investigation support volumetric MRI studies suggesting dysregulated brain growth

in early childhood ASD followed by a period of arrested growth. In school-age children, studies

have reported significant age by diagnosis interactions, with age-related increases in FA and/or

decreased MD or RD in normative populations but very minimal age-related change in ASD in

frontal, temporal and posterior lobes, and interhemispheric and long-distant white matter tracts

6

(Cheng et al., 2010; Lee et al., 2007; Mak-Fan et al., 2012). Other studies have found DTI-age

correlations for some tracts and not others. For instance, Shukla and colleagues (2011b) found

age-related correlations for FA, MD and RD in short-distance tracts in control children, but only

observed age-related change in FA in the frontal lobe of children with ASD. Furthermore,

minimal age-related changes have been reported for whole brain FA and MD in children with

ASD, whereas TD children show significant age-related development (Shukla et al., 2011a). A

closer longitudinal examination of the corpus collosum (genu and body) by Travers et al. (2015)

demonstrated atypically high FA in early childhood (until approximately 7 years old) that

decreased with age until adolescence, and sustained lower FA into adulthood. Results of our

study and others reveal no differences in age-related changes of FA and/or MD, RD, and AD

between children with and without ASD into adolescence (Bode et al., 2011) and adulthood

(Keller et al., 2007), but persistent white matter deficits across age. Inconsistencies in the ASD

DTI literature are likely to due to the different age ranges used across studies.

A handful of studies have investigated relations between white matter metrics and ASD

symptomatology or cognitive functioning in children and youth. Although findings have been

inconsistent, some studies have reported a relation between decreased FA (i.e., reduced white

matter integrity) and increased severity of autistic features including social/communication

symptoms (Cheung et al., 2009, Noriuchi et al., 2010; Cheon et al., 2011; Poustka et al., 2012;)

and repetitive behaviours (Cheung et al., 2009). However, our group and other studies did not

find relations between DTI metrics and ASD symptoms (Barnea-Goraly et al. 2010; Jou et al.,

2011b; Hong et al., 2011; Samson et al., 2016; Shukla et al., 2010; Sundaram et al., 2008).

There is also some evidence to suggest that FA in the corpus callosum is related to IQ

(Alexander et al., 2007).

The above studies are limited in sample size, with fewer than 30 participants in each group, with

the exception of Travers et al. (2015) who focused on the microstructural properties of the

corpus callosum only. Small sample sizes are problematic, particularly when studying such a

heterogeneous disorder as ASD. Furthermore, many of the studies lack comprehensive

diffusion analyses (see Travers et al., 2012 for review), and do not investigate all diffusivity

indices, which is crucial for characterizing the sources of any observed differences in white

matter development. The first study of this thesis (Chapter 2) tackled the question of white

matter development in ASD, using a large sample with over 60 participants per group.

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Overall, while structural abnormalities may be evident in infants with ASD, there is some

controversy regarding the presence and significance of gross structural abnormalities in children

over the age of 6 years. Recent studies using large-scale shared international multi-site data

sets, such the Autism Brain Imaging Data Exchange (ABIDE), have found that high-functioning

individuals with ASD (ages 6-35 years) show very weak significant anatomical differences from

controls (Haar, Berman, Behrmann, & Dinstein, 2014). As such, anatomical differences may

offer limited diagnostic value in ASD. Supplementing structural studies with fMRI studies

would contribute significantly to our understanding of the neural phenotypes in ASD.

1.1.2 Brain Function

Early disruptions of anatomical development in ASD will inevitably have significant impacts on

neural function and connectivity. Numerous fMRI studies have identified atypical neural

activation in regions associated with core ASD symptoms. Specifically, findings have

demonstrated reduced activation in areas relevant to face and emotional processing (fusiform

gyri and amygdalae) and theory of mind (frontal cortex and temporo-parietal junction), as well

as in the fronto-striatal circuitry in response to cognitive control tasks and repetitive behaviours

(Dichter, 2012) in ASD compared to TD individuals (see Hernandez, Rudie, Green,

Bookheimer, & Dapretto, 2015 for review). Language processing and communication problems

have also been linked to reduced activation in the left inferior frontal cortex and increased

activation of Wernicke’s area in ASD compared to controls (see Stigler, McDonald, Anand,

Saykin, & McDougle, 2001 for a review).

Recent fMRI work has begun to examine network-level connectivity by measuring synchronized

neural activation between nodes (i.e., functional connectivity). Studies have primarily reported

underconnectivity between distant brain regions (e.g., fronto-posterior) during task performance,

such as working memory, language processing, EF, visuo-motor coordination and facial

processing (for reviews see Hernandez et al., 2015; Ecker, Bookheimer, & Murphy, 2015).

However, some studies have also found overconnectivity in networks in ASD, specifically in

short-range tracts (see Ditcher, 2012). Additionally, fMRI connectivity studies conducted while

children are at rest (i.e., not performing any coordinated task) reveal diminished connectivity

between nodes within the default mode network (DMN; Assaf et al., 2010; Cherkassky, Kana,

Keller, & Just, 2006). The DMN is a network of brain regions including the medial prefrontal

cortex, posterior cingulate, inferior parietal lobules and lateral temporal cortices, which are

8

active during rest and suppressed during goal-directed tasks (Raichele, 2015; Whitfield-Gabriele

& Ford, 2012). The DMN shows an inverse correlation with the attentional control network

(Stevens, Pearlson, & Calhoun, 2009). A more recent fMRI resting state study utilizing the

ABIDE data set detected primarily hypo-connectivity for cortico-cortical and interhemispheric

connectivity and few findings of hyperconnectivity associated with subcortical regions in ASD

(Di Martino et al., 2014). Taken together, these studies converge to suggest that ASD is

characterized by abnormal functional connectivity, although the direction as a function of age

and brain region still needs to be firmly established. Regardless, abnormal connectivity would

have a significant impact on higher-order, complex information processing that relies on

effective integration of brain regions, such as EF. Studies of EF, its neural underpinnings and

development may provide an opportunity to better understand the neuropathological

characteristics that distinguish ASD from typical development, and their impact on cognitive

processing.

1.2 Executive Functioning

1.2.1 Overview

Executive functions are higher-order cognitive processes essential for regulating, controlling

and performing complex goal-directed behaviours (Blijd-Hoogeways, Bezemer, van Geert,

2014). EF is often considered an umbrella construct comprised of numerous interrelated but

distinct processes, including self-regulation, task initiation, cognitive flexibility, self-

monitoring, working memory, inhibition, planning and organization. These skills are

particularly essential for problem solving and responding adaptively to novel situations in which

habitual or routine behaviours are not sufficient (Gioia, Isquisth, Kenworthy, & Barton, 2002).

As such, EF abilities are critical to development and impairments may have a significant impact

in everyday life. Executive dysfunction makes it difficult for children to maintain social

interactions and meaningful relationships (Spinard et al., 2006), which would have implications

on their mood, affect, emotional responses and behaviour (Martel et al., 2007; Riggs, Blair, &

Greenberg, 2003; Gioia, Isquish, Guy, & Kenworthy, 2000).

Assessment approaches for EF include both performance-based and informant-report measures.

Neuropsychological tests are performance-based measures administered in a structured

laboratory setting under highly controlled conditions. One widely used standardized and

9

validated neuropsychological test of EF for children is the Development of Neuropsychological

Assessment (NEPSY; Korkman, Kirk, & Kemp, 1998). Other performance measures of EF

include experimental tasks designed to assess various EF domains, such as inhibition (Go/No-

Go; Drewe, 1975), cognitive flexibility (Stoop Task; Quinn and Quinn, 2005), working memory

(N-back; Owen, McMillan, Laird, & Bullmore, 2005), and planning (Tower of Hanoi; Goel and

Grafman, 1995). Although performance-based tests allow for the assessment of optimal EF

performance, such controlled conditions may not accurately reflect the complex daily lives of

children and may be less sensitive for capturing EF deficits. Consequently, these measures have

limited ecological validity and generalizability (Blijd-Hoogewys et al., 2014; Gioia et al., 2002;

Kenworthy, Yerys, Anthony, & Wallace, 2008). Thus, researchers and clinicians have also

relied on informant-report measures of EF, which capture functioning observed in unstructured,

everyday settings. One example is the Behaviour Rating Inventory of Executive Functioning

(BRIEF; Gioia et al., 2000).

In typical development, EF follows a protracted maturation, developing throughout childhood

and extending into adolescence and early adulthood. However, developmental trajectories vary

across different EF processes and depend on task complexity. A review by Best and Miller

(2010) suggests that inhibition develops earlier than other EF processes, and shows substantial

improvement during the preschool years (e.g., Garon, Bryson, & Smith, 2008) with slower

growth later on. On the other hand, working memory and cognitive flexibility show a more

gradual improvement across age, with continued refinement in adolescence (e.g., Davidson,

Amso, Anderson, & Diamond, 2006; Gathercole, Pickering, Ambridge, & Wearing, 2004),

especially for tasks of greater complexity. Furthermore, a review by Anderson (2002) shows

that planning and organizational skills undergo rapid improvements between the ages of 7 and

10 years, and gradually thereafter into adolescence. Coinciding with these behavioural

improvements of EF are neuroanatomical developmental changes in cortical and subcortical

regions (Giedd et al., 1999; Sowell, Thompson, Holmes, Jernigan, & Toga, 1999), and their

connectivity throughout the brain (see Casey, Galvan, & Hare, 2005).

Historically, EF has been associated with the frontal lobes, with emphasis on the prefrontal

cortex (Hill, 2004b). However, it is now understood that EF is not exclusively localized to

frontal structures, but is rather subserved by a widely distributed neural network and depends on

the integration of cortical and subcortical systems throughout the brain (Suchy, 2009; O’Hearn

10

et al., 2008). In other words, recent work has emphasized that, in addition to intact functioning

of the prefrontal cortex, EF depends on collaborative neural functioning with other parts of the

brain. For example, working memory is dependent on both frontal (e.g., dorsolateral prefrontal

cortex) and parietal cortices (Carlson et al., 1998; Williams, Minshew, & Goldstein, 2008).

Given the complexity of neural mechanisms underlying EF, even small disruptions within this

circuitry could result in impaired EF (Tekin & Cummings, 2002), as observed across a number

of neurodevelopmental disorders, including ASD and Attention Deficit Hyperactivity Disorder

(ADHD; Greene, Braet, Johnson, & Bellgrove, 2008; Pennington & Ozonoff, 1996).

1.2.2 Executive Functioning in ASD

1.2.2.1 Executive Dysfunction

Individuals with ASD are particularly vulnerable to EF deficits given that they are subject to

early abnormal brain development, impacting the neural systems underlying these higher-order

cognitive processes. Although EF deficits are not exclusive to the ASD population, literature

has reported a distinct pattern of executive dysfunction that differentiates ASD from other

atypical populations who share similar cognitive but different clinical profiles, such as ADHD

(Sergeant, Guerts, & Oosterlaan, 2002; Andersen et al., 2015b). Furthermore, compromised EF

performance has been reported in biological parents or siblings of children with ASD,

suggesting executive dysfunction characterizes a broader cognitive phenotype of this disorder

(Hughes, Plumet, & Leboyer, 1999), most likely explained by shared genetics.

Executive dysfunction in children with ASD is well established across a range of domains, with

most prominent, consistent deficits noted in cognitive flexibility and planning/organization

relative to typical development (Gioia et al., 2002; Granader et al., 2014; Kenworthy et al.,

2005; Lopez, Lincoln, Ozonoff, & Lai, 2005; Pellicano, Mayberry, Durking, & Maley, 2006;

Pennington & Ozonoff, 1996; Van Eylen, Boets, Steyaert, Wagemans, & Noens, 2015).

However, comprehensive reviews (Hill, 2004b; Kenworthy et al., 2008; O’Hearn et al., 2008;

Pellicano, 2012; Pennington & Ozonoff, 1996) have identified a pattern of intact and impaired

performance of EF, particularly because methodology has varied widely. For instance, a review

by Kenworthy et al. (2008) showed that deficits in cognitive flexibility and planning in ASD

were less apparent using computer-administered measures, compared to human-administered

tasks. Further, a review by Hill (2004b) suggested some spared inhibitory function in ASD.

11

However, more recent studies have demonstrated significant deficits even on simple motor

response inhibition tasks (e.g., Go/NoGo; Christ, Holt, White, & Green, 2007). In terms of

working memory, studies generally show a pattern of impaired visual-spatial working memory,

and spared verbal working memory (Boucher & Bowler, 2008; Minshew & Goldstein, 2001;

Ozonoff and Strayer, 2001; Steele, Minshew, Luna, & Sweeny, 2007; Williams, Goldstein,

Carpenter, & Minshew, 2005). EF impairment in ASD also appears to be magnified with

increasing task difficulty and complexity (see O’Hearn et al., 2008), particularly with increasing

memory load (see Barendse et al., 2013; Koolen, Vissers, Egger & Verhoeven, 2014; Minshew

& Goldstein, 2001; Russo, Flanagam, Iarocci, Berringer, Zelazo, & Burack, 2007).

Developmental studies of ASD generally support age-related improvement of EF through

childhood and adolescence, but they are delayed across development (Chen et al., 2016; Happé,

Booth, Charlton, & Hughes, 2006) or often remain impaired, never reaching adult normative

levels (Andersen, Skogli, Hovik, Egeland, & Øie, 2015; Luna, Doll, Hegedus, Minshew, &

Sweeney, 2007; see O’Hearn et al., 2008 for a review; Pellicano, 2010). Thus, these findings

suggest that while the overall degree of EF is impaired throughout development in ASD, the

maturational processes from childhood to adolescence are somewhat preserved (O’Hearn et al.,

2008), although, one study observed no improvement of working memory in children ages 9-16

years over two years (Andersen et al., 2015b).

While the above studies relied on performance-based measures, there is evidence to suggest that

EF impairments are more pronounced in natural settings of daily living than in such highly-

controlled laboratory settings in ASD (see Kenworthy et al., 2008 for review; Van Eylen et al.,

2015). This discrepancy may be explained, in part, by the fact that individuals with ASD are

more susceptible to EF deficits when facing the environmental demands of daily life, which are

controlled in lab-based measures. This has led to investigations of ecologically-valid measures

of EF, such as informant-reported questionnaires such as the BRIEF (Gioia et al., 2000), which

captures every day observations of EF that take place under socialized, real-word expectations.

Studies using the BRIEF converge to identify significant EF deficits in ASD when compared to

normative populations (Gilotty, Kenworthy, Sirian, Black, & Wagner, 2002; Gioia et al., 2002;

Winsler, Abar, Feder, Schunn, & Rubio, 2007), and no age-related improvement or even age-

related declines in children with ASD (ages 5-18; Rosenthal et al., 2013; van den Bergh,

Scheeren, Begeer, Koot, & Geurts, 2014). Interestingly, some reports found a widening

12

divergence of every day EF skills from typical development in children with ASD across age, as

they entered adolescence (Rosenthal et al., 2013). This widening gap may be due to the notion

that, as they progress through adolescence, children with ASD are more vulnerable to the

increasing complexity of environmental demands compared to typical development (van den

Bergh et al., 2014). Taken together, there is evidence for EF impairments at all ages in ASD;

however, its developmental trajectory and neural underpinnings are still not well-defined.

1.2.2.2 Neural Correlates of EF Deficits

The neural underpinnings of executive dysfunction in ASD have been increasingly studied using

fMRI techniques. Despite similar performance between individuals with and without ASD on

certain EF tasks, studies generally support abnormal neural activation in frontal and parietal

regions, as well as in their functional integration (i.e., connectivity). For instance, neuroimaging

studies of inhibition in adults with ASD have found decreased activation in the anterior

cingulate cortex, and under-connectivity between frontal-striatal and frontal-parietal circuits

(Kana, Keller, Minshew, & Just, 2007). Similarly, a study assessing planning skills recorded no

significant differences in overall brain activation between adults with and without ASD, but

decreased connectivity between frontal and parietal areas in ASD (Just, Cherkassky, Keller,

Kana, & Minshew, 2007). A report on set-shifting (a proxy for cognitive flexibility) found that

children (ages 7-14) with ASD demonstrated hypoactivation in the middle temporal gyrus and

hyperactivation in the frontal lobes (Yerys et al., 2015). These authors suggested that successful

‘switching’ in children with ASD requires greater engagement of frontal regions, resulting in

less cognitive efficiency. Studies of working memory have reported reduced activation in the

dorsolateral prefrontal cortex and posterior cingulate regions in adults with ASD compared to

controls (Luna et al., 2002). A study of adolescents by Silk and colleagues (2006) demonstrated

impaired activation in the frontal lobes in ASD, including the anterior cingulate, dorsolateral

prefrontal cortex and caudate nucleus, with normal activation in the parietal cortices, relative to

controls. More recent fMRI studies have considered the impact of increasing cognitive load on

neural systems supporting working memory, and have found that children and adolescents with

ASD show inadequate load-dependent modulation of brain activity in the insula, somatosensory,

motor and auditory cortices (Rahko et al., 2016), as well as the precuneus, dorsolateral

prefrontal cortex and medial premotor cortex (Vogan et al., 2014). Both these studies also

13

showed that DMN deactivation as a function of load in youth with ASD did not significantly

differ from controls.

Although studies of adults with ASD have begun to inform our understanding of neural

mechanisms underlying EF, few studies have investigated the neural basis of EF in children or

its development over time. Study 2 (Chapter 3) of this thesis addresses this gap by examining

the functional changes longitudinally in the brain, over 2 years, associated with the EF domain

of working memory utilizing a task of increasing cognitive load in children with and without

ASD (ages 7-14 years).

1.2.2.3 The Role of EF in Autistic Symptomatology and Outcomes

A great deal of attention has been given in the literature to the proposal that impaired executive

processes may account for some of the core ASD symptoms, giving rise to the ‘executive

dysfunction theory’ of autism (Hill, 2004a; Joseph, 1999). Other cognitive theories have been

proposed to explain the variability in the behavioural manifestations observed in ASD, such as

theory of mind hypothesis (Baron-Cohen, Tager-Flusberg, & Cohen, 2000) and central

coherence hypothesis (Happé, 2005). Although EF deficits being a leading cause of ASD is

controversial and unlikely, it is clear that executive dysfunction is related to autistic

symptomology, including restricted/repetitive behaviours (Kenworthy, Black, Harrison, Della

Rosa, & Wallace, 2009; Lopez, et al., 2005; South, Ozonoff, & McMahon, 2007) and

social/communicative problems (Gilotty, Kenworthy, Sirian, Black, & Wagner, 2002; Joseph &

Tager-Flusberg, 2004; Kenworthy et al., 2009; Leung, Vogan, Powell, Anagnostou, & Taylor,

2016; McEvoy, Rogers, & Pennington, 1993). Furthermore, EF skills are related to several other

critical areas of functioning in children with ASD, including theory of mind (Hughes &

Leekman, 2004), adaptive behaviour (Gilotty et al., 2002), quality of life (de Vries, Prins,

Schmand, & Geurts, 2015) and co-morbid psychopathology (Hollocks et al., 2014; Lawson et

al., 2015). EF deficits also are known to contribute to greater dependence and poor outcomes in

adulthood (see Hume, Loftin, & Lantz, 2009 for review).

Taken together, this information provides strong evidence to support the idea that individual

differences in EF skills may influence the development of key functional outcomes in ASD,

prompting researchers to investigate predictive links between early EF and later outcomes. For

instance, longitudinal studies have shown that early EF skills are predictive of later theory of

14

mind (Pellicano, 2010) and adaptive skills (Pugliese et al., 2015) in ASD. Less is known about

the developmental associations between early EF and later emotional and behavioural outcomes.

A single study by Andersen et al. (2015b) investigated the longitudinal link between one domain

of EF, verbal working memory, and behavioural and emotional problems in children with ASD.

This group failed to find a significant relation between increased working memory capacity and

improvement in behavioural and emotional problems over two years. Nonetheless, early

difficulties with EF in TD children (Martel et al., 2007; Riggs, Blair, & Greenberg, 2003;

Spinard et al. 2006) and youth with ADHD (Rinsky & Hinshaw, 2011) are strongly predictive of

later internalizing and externalizing problems and diminished social competence, suggesting

that EF is essential to developmental processes. The third study of this thesis (Chapter 4)

addresses this issue by investigating whether two domains of every day measures of EF

(behavioural regulation and metacognition) are associated with social, emotional and

behavioural outcomes two years later in children and adolescents with ASD.

1.3 Present Study Objectives and Rationale

1.3.1 Study 1: DTI investigation of white matter development in ASD

Early dysregulated brain growth in toddlers with ASD leave them vulnerable to developmental

disruptions of white matter microstructure and corresponding neural networks. Neuroimaging

techniques, such as DTI, can characterize white matter microstructural properties of the brain,

providing us with more information regarding axonal organization and myelination. Despite the

number of DTI studies in ASD, sample sizes have been limited to approximately 30 participants

per group, which is concerning with such a heterogeneous disorder. Small sample sizes may also

limit our ability to fully describe associations between DTI measures and ASD

symptomatology, as evident by inconsistent findings in this area (Travers et al., 2015). As a

result, our understanding of the clinical relevance of compromised white matter in ASD is

limited. Thus, recent reviews have emphasized the pressing need for larger samples in DTI

investigations of ASD (Travers et al., 2015). The aim of Study 1 (Chapter 2) of this thesis was

to characterize white matter development in youth with ASD (ages 7-15 years), relative to TD

children, and associations with ASD symptomatology.

The developmental trajectory of white matter growth provides important insight into the neural

mechanisms that are compromised in ASD, which may underlie cognitive and behavioural

15

difficulties in this population. Preliminary studies have begun to explore the use of DTI

measures to distinguish between those with and without ASD (Adluru et al., 2009; Ingalhalikar,

Parker, Bloy, Roberts, & Verma, 2011). Findings of the present study will increase our

understanding of neuropathology in ASD and its developmental trajectory, and thereby inform

efforts of using DTI methods for prognostics in autism.

1.3.2 Study 2: fMRI investigation of working memory development in ASD

EF deficits in ASD are robust. Working memory is identified as one of the primary components

of EF, and previous studies have converged to report deficits in ASD across all ages (see

Barendse et al., 2013 for review), particularly in visual-spatial working memory (Boucher &

Bowler, 2008; Luna et al., 2002; Minshew & Goldstrein, 2001; Steele et al., 2007; Williams et

al., 2005). Furthermore, EF observed by parents in every day settings has been reported to

become increasingly more impaired across time compared to TD youth (Rosenthal et al., 2013).

Despite evidence of impaired developmental trajectories of working memory in ASD, there are

no studies that have investigated the neural bases of this development. This is a significant gap

in our knowledge particularly as working memory typically has a protracted maturation (see

Best & Miller, 2010) that may be most vulnerable to disturbance caused by early abnormal brain

growth in ASD. Thus, Study 2 (Chapter 3) of this thesis describes the functional changes

longitudinally, over 2 years, in neural correlates associated with visuo-spatial working memory

in children with and without ASD (ages 7-13 years), and aims to understand the impact of

increasing cognitive load. Extant literature highlights the importance of manipulating cognitive

load when studying EF to provide a more refined profile of ability and impairments in ASD

(Koolen et al., 2014; Minshew & Goldstein, 2001; Russo et al., 2007). Thus, we utilized a

variation of the classic ‘N-back’ paradigm —The Colour Matching Task (CMT; Arsalidou,

Pascual-Leone, & Johnson, 2010)— that incorporated 6 levels of difficulty to measure the

impact of increasing load on working memory function.

Studies of development are especially important for neurodevelopmental disorders, such as

ASD. The period of pre-adolescence and adolescence is particularly interesting to study, given

that children undergo significant neuroanatomical and functional changes during this time (Paus,

2005) while facing dramatic changes in the complexity of their environmental demands.

Elucidating differences in the developmental trajectory of functional neural correlates in ASD

allows us to understand the impact of compromised neural circuitry on working memory

16

function in this population. Working memory is fundamental to cognition (Engle, Tuholksi,

Laughlin, & Conway, 1999), learning and academic achievement (Alloway, 2009), and it is

associated with many of the core symptoms of ASD (Kenworthy et al., 2009; Richmond,

Thorpe, Berryhill, Klugman, & Olson, 2013). Understanding the evolution of neural

development underlying working memory can provide valuable insights into the cognitive

profile of ASD and localize brain areas of vulnerability to developmental disturbances—both of

which could inform the nature and timing of appropriate interventions.

1.3.3 Study 3: Development of everyday EF and its associations with

functional outcomes in ASD

Children with ASD exhibit more pronounced deficits of EF in everyday settings than in highly

controlled laboratory-based assessments (see Kenworthy et al., 2008 for review), possibly due to

the fact that individuals with ASD are more sensitive to real-world expectations that take place

in socialized contexts. Furthermore, unlike EF measured by lab-based tasks, everyday EF shows

no improvement with age or age-related declines in ASD (Rosenthal et al., 2013; van den Bergh

et al., 2014), suggesting that children with ASD, compared to their typical peers, are more

vulnerable to the increasing environmental demands encountered by maturing adolescents. So

far studies of everyday EF development are cross-sectional. Longitudinal investigations are

needed to characterize developmental trajectories of real-word EF, and also to understand the

impact of EF deficits on achieving optimal outcomes later on in individuals with ASD.

While early EF deficits predict impairments in later theory of mind (Pellicano, 2010) and

adaptive skills (Pugliese et al., 2016) in children with ASD, less is known about their association

with other important functional outcomes, such as co-morbid psychopathology. This is

concerning given the prevalence of co-morbid psychopathologies in this population, particularly

anxiety and behavioural disorders (Simonoff et al., 2008). Previous studies have reported

concurrent relations between poor EF and high levels of anxiety, depression and aggression in

youth (Hollocks et al., 2014; Lawson et al., 2015) and adults with ASD (Wallace et al., 2016).

Thus, there is strong evidence to suggest the one source of variability in children’s healthy

psychological development in ASD are individual differences in EF. As such, the aims of Study

3 (Chapter 4) are two-fold: (1) to characterize the longitudinal development of real-word EF, as

measured by the BRIEF, over 2 years in children with and without ASD and (2) to understand

17

the impact of EF deficits on children’s social, emotional and behavioural functioning 2 years

later.

Overall, elucidating the developmental course of real-world EF offers insight into how EF

impairments manifest in everyday settings across age, which is informative for parents,

educators and clinicians. Furthermore, consideration of the developmental link between EF and

psychological functioning may help identify cognitive risk factors and early predictors of the

development of mental health problems, allowing for better assessment, diagnosis and prognosis

of such conditions in ASD. Identifying such a link may help to alleviate the diagnostic

challenges in identifying symptoms of mental illness in this population, and also inform

treatment planning.

18

Chapter 2 Widespread White Matter Differences in Children and

Adolescents with Autism Spectrum Disorder Vogan, V.M., Morgan, B.R., Leung, R.C., Anagnostou, E., Doyle-Thomas, K., & Taylor, M.J.

(2016). Journal of Autism and Developmental Disorders, 46(6), 2138-2147.

This chapter is a reformatted version of the manuscript published in Journal of Autism and

Developmental Disorders.

19

Widespread White Matter Differences in Children and

Adolescents with Autism Spectrum Disorder

2.1 Abstract

Diffusion tensor imaging (DTI) studies show white matter abnormalities in children with

Autism Spectrum Disorder (ASD). However, investigations are often limited by small samples,

particularly problematic given the heterogeneity of ASD. We explored white matter using DTI

in a large sample of 130 children and adolescents (7-15yrs) with and without ASD, whether age-

related changes differed between ASD and control groups, and the relation between DTI

measures and ASD symptomatology. Reduced fractional anisotropy and axial diffusivity were

observed in ASD in numerous white matter tracts, including the corpus callosum and

thalamocortical fibres—tracts crucial for interhemispheric connectivity and higher order

information processing. Widespread white matter compromise in ASD is consistent with the

view that ASD is a disorder of generalized complex information processing.

2.2 Introduction

Autism Spectrum Disorder (ASD) is characterized by complex underlying neuropathology that

is not fully understood. A large body of research has pointed to atypical functional and

structural networks in ASD, with evidence for both functional underconnectivity (Just,

Cherkassky, Keller & Minshew, 2004; Geschwind & Levitt, 2007) and overconnectivity

(Rubenstein & Merzenich, 2003; Keown et al., 2013). In the past decade, research has aimed to

better understand the underlying architecture of white matter in the brains of children with ASD

using a magnetic resonance imaging (MRI) method, called diffusion tensor imaging (DTI).

However, these studies have been limited in sample size, with fewer than 30 participants in each

group, and without comprehensive diffusion analyses (see Travers et al., 2012 for a review).

Small sample sizes are problematic, particularly in a heterogeneous disorder such as ASD.

Macrostructural brain differences between typically developing (TD) children and children with

ASD have been reported across development. Enlarged brain volume was described in early

childhood (Courchesne et al., 2001; Hazlett et al., 2005), with fewer differences by late

childhood or adolescence (see Redcay & Courchesne, 2005 for a review). More recent studies

20

suggest more subtle divergences of brain growth in children with ASD from TD control

participants (Raznahan et al., 2013; Sussman et al., 2015). However, given the exquisitely

orchestrated developmental brain processes (e.g., Becker et al., 1984; Huttenlocher &

Dabholkar, 1997; Kostović & Jovanoc-Milosevíc, 2006), even small dysregulation of brain

growth may have profound effects on the microstructural properties and development of neural

networks, impacting brain connectivity and function. Abnormal brain development is expected

to interfere with the maturation of neuronal pathways, affecting overall connectivity essential

for brain function. Thus, examining the microstructural properties of white matter tracts of the

brain over childhood could help our understanding of the underlying neural bases of this

neurodevelopmental disorder.

Diffusion imaging is a non-invasive MRI technique that measures water diffusion and is

sensitive to tissue structure (Basser, Mattiello & LeBihan, 1994). Myelinated axons have well-

defined diffusion characteristics: water diffuses freely parallel to the axon and is restricted in the

perpendicular direction. Thus, diffusion imaging can provide interesting insights into the

properties of white matter structure. Diffusion tensor imaging (DTI) is a model commonly used

to analyze diffusion data; DTI models diffusion in a voxel as a single tensor with three

orthogonal axes. Fractional anisotropy (FA), the most commonly studied DTI metric, is a

derived measure of the degree of molecular displacement, or directional variation (Basser &

Pierpaoli, 1996). It represents the ‘directionality’ of diffusion (an FA of 1 indicates diffusion in

only 1 direction, and FA of 0 indicates equal diffusion in all directions). FA is typically higher

in thick core white matter tracts and lower white matter FA is found in areas of microstructural

disorganization, damaged myelin and crossing fibres (Wedeen et al., 2008). Other DTI indices

include axial diffusivity (AD), which represents the water diffusivity parallel to axon fibre

bundles (the major axis of the DTI tensor); radial diffusivity (RD), an index of water movement

perpendicular to the fibre tracts (the average of the 2 minor axes of the DTI tensor); and mean

diffusivity (MD), which is the average diffusion across all 3 DTI tensor axes.

Findings from DTI studies in ASD have been inconsistent. Many DTI studies have suggested

microstructural disorganization and damage to neuronal tracts (i.e., reduced FA and increased

MD) in ASD. For example, several reports have found lower FA (e.g., Cheon et al., 2011;

Shukla, Keehn & Müller, 2011; Jou et al., 2011b; Nouriuchi et al., 2010; Kumar et al., 2010;

Barnea-Gorlay et al., 2010; Travers et al., 2015) and increased MD (Shukla et al., 2011; Hong et

21

al., 2011; Cheon et al., 2011; Travers et al., 2015) in ASD in the corpus callosum compared with

typical development, suggesting decreased interhemispheric connectivity in individuals with

ASD. The cingulum bundle has also been found to show reduced FA and/or increased MD

(e.g., Jou et al., 2011a,b; Kumar et al., 2010). However, there are also studies that have found

widespread decreased FA (e.g., Cheng et al., 2010, Cheung et al., 2009, Hong et al., 2011) or no

difference in FA, but only age by group interactions in AD and RD (Mak-Fan et al., 2013). As

FA is related to AD and RD, discrepant trends in these two indices could eliminate the FA

effects. Differences demonstrated in areas such as the arcuate fasciculus/superior longitudinal

fasciculus and uncinate fasciculus have also been contradictory (see Travers et al., 2012, for a

review). These inconsistencies may be due to variability in the inclusion criteria, large age

ranges with small sample sizes, as smaller samples may not offer enough power to uncover

neural atypicalities with certainty due to the heterogeneity of ASD. Also, as ASD is a

neurodevelopmental disorder, changes in brain properties are anticipated as a function of age,

and thus analyses that do not consider age would obscure age-related effects.

In one of the largest samples studied to date, Travers and colleagues (2015) examined the

developmental trajectory of the corpus callous in males with and without ASD from early

childhood to mid-adulthood. ‘Individuals with ASD demonstrated atypical developmental

patterns of white matter microstructure in the genu and body of the corpus callosum compared

to typical development. Specifically, differences were characterized by atypically high FA in

early childhood that decreased with age among the ASD group, and plateaued below the FA

values of the TD group. Although FA was persistently decreased during adolescence and

adulthood in ASD, the rate of change was similar to typical development. This study provided

evidence for atypical structural development of the corpus callosum in ASD that persists across

the life span, making affected individuals more vulnerable to abnormal cognitive and

behavioural functioning. While the corpus callosum is the largest white matter tract of the brain,

it is also crucial to examine developmental patterns of white matter across the whole brain given

the vital roles that other white matter structures have in cognition and behaviour.

Many studies have also investigated the relation between DTI findings and behavioural

symptomology in ASD. Correlations between DTI metrics and behavioural constructs have not

been found consistently, although some reports have shown at link between DTI measures and

intellectual functioning (Alexander et al., 2007; Lee et al., 2009). A few investigations have

22

found correlations between FA and socio-communicative deficits in ASD (Nouriuchi et al.,

2010; Hanaie et al., 2014), or impaired structural connectivity of socio-emotional circuits

(Ameis et al., 2011).

The present study explored white matter development in ASD relative to TD controls to clarify

controversies in this literature, using a large sample. Four DTI diffusivity indices (FA, MD, RD

and AD) were analysed to better characterize the sources of any observed differences in white

matter development, and to link this with age and ASD symptomatology (communication, social

interaction, and repetitive behaviour). Given that deficits in specific tracts in ASD are still

unclear, the present study used a whole-brain tract-based technique (Tract Based Spatial

Statistics; TBSS; Smith, 2006), which allowed for exploratory analyses without selecting a

priori regions of interest.

2.3 Methods

2.3.1 Participants

Initially 145 children were included in the study: 79 TD children and 65 children with ASD.

Out of 145 participants, 7 (2 TD) were excluded due to excessive head motion (see Image

Processing). After sex- and age-matching, the current sample consisted of 130 children, 69 TD

children (51 male) and 61 children with ASD (51 male) 7-15 years of age. The groups were

matched for age (TD M=11.1, SD=2.4; ASD M= 10.9, SD=2.0, t(128)=.54, n.s.) and sex (χ2(1)=1.8,

n.s.; see Table 1 for participant characteristics). Children with a history of chronic illness,

psychiatric (other than ASD in the ASD group) or neurological disorders, who were premature

or had standard MRI contraindications (i.e., ferromagnetic implants) and IQ<70 based on the

Wechsler Abbreviated Scale of Intelligence (WASI; Wechsler, 1999) were not included in the

study. Parents gave informed written consent and children provided informed assent for the

study approved by the Research Ethics Board at the Hospital for Sick Children.

Clinical cases of ASD were confirmed with a combination of expert clinical judgment, clinical

records and the Autism Diagnostic Observation Schedule (ADOS; Lord et al., 2000), using the

Modules 3 and 4 of ADOS-G or the updated version, ADOS-2. ADOS scores (M= 10.5, SD=

3.6) fell within the clinical criteria for ASD (≥7). Total ADOS scores were converted into

severity scores (M=6.2, SD= 2.0; Gotham, Pickles, & Lord, 2009), which facilitates

23

comparability between different versions and modules of the ADOS. Severity scores also serve

as a severity index that accounts for age and language skill, with a score of 10 being the most

severe.

Table 1. Participant characteristics

ASD (N = 61)

TD (N = 69) Difference Test

% Mean (SD) % Mean (SD) Sex (male) 83.6 73.9 n.s. Age 10.9 (2.0) 11.1 (2.4) n.s. IQ 102.2 (14.9) 112.7 (11.6) t(128) = 4.5, p<.001 ADOS Total 10.5 (3.6)

2.3.2 Image Acquisition

Sixty direction diffusion data (2mm iso; FOV= 244 x 244 x 140 mm; TR/TE/FA=8800/87/90;

b=1000 s/mm2; 9 interleaved b=0 volumes) were acquired on a 3T Siemens Trio MRI with a 12-

channel head coil at the Hospital for Sick Children, Toronto. The structural scan was a high-

resolution T1-weighted 3D MP-RAGE image Sagittal; FOV=192x240x256 mm; 1 mm iso

voxels; TR/TE/TI/FA = 2,300/2.96/900/9). Motion restriction and head stabilization were

achieved with foam padding. During these structural image acquisitions, participants watched a

movie of their choice using MR-compatible goggles and earphones.

2.3.3 Image Processing

Before processing, raw data were visually inspected for motion artefact. If more than 20 out of

60 directional volumes were corrupted, the subject was excluded from further analyses. All

volumes were registered to a reference b=0 volume (the first volume) using FSL FLIRT to

account for distortion and head motion, and b-vectors were adjusted accordingly. RESTORE,

which reduces false findings that may occur due to artefacts in DTI data, was used to robustly

estimate tensors in the presence of outliers on a voxel-level (Walker et al., 2011)

(http://cmic.cs.ucl.ac.uk/camino). FA, MD, AD and RD were extracted from the RESTORE

output. Using AFNI’s 3dvolreg (http://afni.nimh.nih.gov), a maximum displacement measure of

each volume from a single reference volume was calculated. This tool looks at the maximum

displacement across all voxels in the brain when registering each volume to the reference

volume. This was used to calculate a mean motion metric for each subject, which was used to

24

assess group differences in head motion. Although slightly more motion was found in children

with ASD (M= 0.6mm, SD=0.04) than TD children (M=0.5mm, SD=0.04), t(128)=2.2, p=.03), the

variance was the same and both groups had minimal average motion of under 0.6mm.

2.3.4 Image Analyses

Whole brain voxel-wise statistical analysis of FA, MD, AD and RD data were carried out

separately using FSL’s Tract-Based Spatial Statistics (TBSS; Smith et al., 2006). FA images

were aligned to the Montreal Neurological Institute (MNI) 152 template and a mean FA image

was calculated across subjects. This image was used to generate a mean FA skeleton. The mean

FA skeleton represented the centres of all white matter tracts common to the sample. A

threshold of 0.2 was applied to the mean FA skeleton to exclude non-white matter voxels. Each

subject’s FA, MD, AD and RD data were projected onto this skeleton. The John Hopkins

University (JHU) white matter atlas was used for tract labeling. Nonparametric permutation

tests were completed using FSL’s Randomise (Winkler et al., 2014;

http://www.fmrib.ox.ac.uk/fsl/randomise/index.html) for group comparisons of FA, MD, AD

and RD, controlling for age by using it as a covariate in the model. To understand different

developmental patterns between TD and ASD groups, we performed an interaction analysis of

group by age. Significance was determined using threshold free cluster enhancement, with a

threshold of p < 0.01 corrected for multiple comparisons. Regions were selected from areas

showing significant differences between TD and ASD groups. FA from within these significant

regions was plotted as a function of age.

We also tested the relation between ADOS Total and ADOS severity scores and DTI metrics in

the ASD group, controlling for age.

2.4 Results

2.4.1 Group Comparisons Between TD and ASD

Compared to the TD group, children with ASD demonstrated widely distributed reduced FA,

characterized by reduced AD. No significant group differences were found in MD or RD. As

shown in Figure 1, regions of significantly reduced FA included the splenium, body and genu of

the corpus callosum, brainstem tracts (cerebral peduncle) and projection fibres (anterior and

25

posterior limb of the internal capsule, anterior, posterior and superior corona radiata, and

posterior thalamic radiation; see Table 2).

Figure 1. FA differences between TD and ASD groups

Voxels (yellow) where TD children showed significantly greater FA, superimposed on FA skeleton (green). All statistics are significant at p < 0.01, corrected for multiple comparisons.

26

Table 2. Regions showing greater FA in TD children compared to children with ASD

JHU White Matter Tract Hem

FA Mean (SD)

# of Voxels TD ASD

Body of corpus callosum 0.730 (0.032) 0.708 (0.035) 1421 Splenium of corpus callosum 0.795 (0.020) 0.776 (0.028) 1313 Anterior corona radiata R 0.467 (0.034) 0.446 (0.030) 647 Genu of corpus callosum 0.777 (0.023) 0.759 (0.027) 341 Cerebral peduncle R 0.758 (0.024) 0.734 (0.034) 289 Anterior limb of internal capsule R 0.685 (0.027) 0.661 (0.033) 288 Posterior limb of internal capsule R 0.721 (0.023) 0.705 (0.028) 199 Posterior thalamic radiation L 0.572 (0.038) 0.543 (0.045) 133 Posterior thalamic radiation R 0.539 (0.042) 0.513 (0.044) 129 Posterior corona radiata R 0.503 (0.031) 0.482 (0.041) 126 Superior corona radiata R 0.497 (0.041) 0.482 (0.035) 45 Posterior corona radiata L 0.509 (0.035) 0.486 (0.041) 44 Cingulum L 0.507 (0.051) 0.486 (0.059) 35 Retrolenticular part of internal capsule R 0.665 (0.035) 0.647 (0.037) 24 Sagittal stratum L 0.593 (0.056) 0.557 (0.055) 15

Note: All regions were statistically different at p < .01, corrected for multiple comparisons.

Similarly, as shown in Figure 2, reduced AD was observed in the corpus callosum, cerebral

peduncle, sagittal stratum including the longitudinal fasciculus, cingulum and several projection

fibres (anterior and posterior limb of the internal capsule, retrolenticular internal capsule,

anterior and superior corona radiata and external capsule; see Table 3).

27

Figure 2. AD differences between TD and ASD groups

Voxels (yellow) where TD children showed significantly greater AD, superimposed on FA skeleton (green). All statistics are significant at p < 0.01, corrected for multiple comparisons.

28

Table 3. Regions showing greater AD in TD children compared to children with ASD

JHU White Matter Tract Hem

AD Mean (SD) x 10-3

# of Voxels TD ASD Body of corpus callosum 1.675 (0.066) 1.624 (0.087) 2161 Splenium of corpus callosum 1.618 (0.065) 1.560 (0.087) 1555 Genu of corpus callosum 1.615 (0.085) 1.559 (0.095) 1422 Posterior limb of internal capsule L 1.433 (0.039) 1.401 (0.056) 691 Middle cerebellar peduncle 1.147 (0.058) 1.092 (0.071) 647 Anterior corona radiata L 1.195 (0.045) 1.162 (0.046) 647 Anterior corona radiata R 1.207 (0.051) 1.180 (0.043) 615 Superior corona radiata L 1.245 (0.041) 1.219 (0.046) 555 Cerebral peduncle L 1.512 (0.054) 1.465 (0.081) 479 External capsule L 1.263 (0.038) 1.234 (0.044) 473 Cerebral peduncle R 1.444 (0.066) 1.387 (0.092) 451 Anterior limb of internal capsule L 1.285 (0.047) 1.247 (0.052) 442 Pontine crossing tract 1.145 (0.082) 1.084 (0.088) 388 Posterior limb of internal capsule R 1.349 (0.043) 1.315 (0.064) 372 Anterior limb of internal capsule R 1.234 (0.055) 1.196 (0.064) 329 Retrolenticular part of internal capsule R 1.270 (0.053) 1.244 (0.063) 279 Medial lemniscus R 1.294 (0.063) 1.246 (0.082) 183 Retrolenticular part of internal capsule L 1.399 (0.058) 1.365 (0.066) 178 Corticospinal tract R 1.157 (0.142) 1.085 (0.135) 146 Superior cerebellar peduncle R 1.564 (0.077) 1.507 (0.108) 130 Corticospinal tract L 1.194 (0.106) 1.138 (0.102) 129 Inferior cerebellar peduncle R 1.196 (0.040) 1.163 (0.061) 129 Fornix (cres) / Stria terminalis L 1.430 (0.077) 1.393 (0.082) 121 Superior cerebellar peduncle L 1.574 (0.067) 1.515 (0.097) 110 Fornix (cres) / Stria terminalis R 1.283 (0.083) 1.243 (0.092) 105 Posterior corona radiata L 1.350 (0.049) 1.324 (0.047) 104 Medial lemniscus L 1.299 (0.055) 1.263 (0.079) 101 Cingulum L 1.264 (0.062) 1.228 (0.061) 80 Superior corona radiata R 1.178 (0.063) 1.148 (0.056) 75 Sagittal stratum L 1.331 (0.066) 1.288 (0.087) 72 Fornix 2.081 (0.216) 1.968 (0.212) 65 Superior fronto-occipital fasciculus L 1.139 (0.072) 1.103 (0.066) 42 Posterior thalamic radiation L 1.590 (0.093) 1.542 (0.107) 39 Posterior corona radiata R 1.309 (0.062) 1.281 (0.057) 25 External capsule R 1.303 (0.068) 1.280 (0.068) 18 Posterior thalamic radiation R 1.643 (0.127) 1.585 (0.138) 15 Uncinate fasciculus L 1.272 (0.077) 1.237 (0.082) 15

Note: All regions were statistically different at p < .01, corrected for multiple comparisons.

There were no significant group by age interactions; age-related changes in the groups were

parallel, with the ASD group having consistently lower FA and AD indices (see Figure 3; see

Appendix A.1 for FA age model parameters in each JHU region).

29

Figure 3. FA plotted against age in TD and ASD groups in tracts that showed group differences

30

As we prioritized age- and sex-matching of groups, as well as a large sample size, there was a

group difference of IQ. However, within-group analyses did not show a correlation between IQ

and DTI metrics in any tracts for either ASD or TD groups, and IQ for children with ASD fell

within the average range (Table 1).

2.4.2 Correlations with ASD Symptomatology

There were no significant correlations found between the ADOS total or ADOS severity scores

and FA, MD, AD or RD in the ASD group.

2.5 Discussion

This study investigated microstructural properties of white matter tracts in children and

adolescents with and without ASD to date. Compared to TD children, we found widespread

reduced FA and AD in children with ASD, as discussed below, suggesting extensive white

matter compromise in this clinical population. Group differences were consistent across age;

atypical development of white matter tracts was evident in both children and adolescents.

Overall, our findings suggest that white matter differences are evident across many fibre tracts

in children with ASD compared to TD children, and can be described by reduced FA, which is

consistent with the majority of previous studies using smaller samples (see Travers et al., 2012

and Hoppenbrouwers, Vandermosten & Boets, 2014 for reviews). To better characterize these

observed group differences between ASD and TD children, our study examined multiple

diffusivity indices. These results showed that the majority of white matter tracts with reduced

FA also had corresponding decreases in AD. Findings from the few studies that have examined

AD are inconsistent, with reports of both reduced (Barnea-Goraly, Lotspeich, & Reiss, 2010;

Noriuchi et al., 2010; Shukla, Keehn, Lincoln, & Müller, 2010; Cheon et al., 2011) and

increased (Lee et al., 2007; Conturo et al., 2008; Billeci, Calderoni, Tosetti, Catani, & Muratori,

2012) AD in select white matter tracts of individuals with ASD compared to controls. Our

finding of decreased AD suggests that white matter compromise in ASD may arise primarily

from disruption of fibre coherence, and not from demyelination (Song et al., 2002). Yet

differences in AD between ASD and control groups did not completely overlap with FA

differences, suggesting that there may be other mechanisms influencing white matter

development in ASD. This finding contrasts with the limited studies that have explored the

31

components of the diffusion measures, RD and AD, in white matter, which demonstrate that

reduced FA in ASD is often accompanied by increased RD (see Travers et al., 2012 for review).

However, many possible factors impact DTI metrics, and thus caution must be used when

interpreting results (Wheeler-Kingshott & Cercigani, 2009).

We found marked white matter differences in the corpus callosum, association fibres, projection

fibres and brainstem tract. While Travers et al., 2015 found that the corpus callosum developed

atypically in ASD before the age of 10 years (i.e., different rates of change between groups), we

found no differences in the developmental trajectories throughout the brain, with persistent

group differences of FA and AD across childhood and young adolescence between children with

and without ASD. However, our study was limited to children 7-14 years; extending the analysis

to include very young children, or older adolescents and adults, may reveal different

developmental patterns. Figure 3 demonstrates sustained FA differences between the ASD and

TD in children and adolescents in tracts highlighted in the discussion. The corpus callosum is

responsible for interhemispheric information transfer and is critical for the integration of

information across the brain. White matter abnormalities have been consistently observed in the

corpus callosum of individuals with ASD (see Travers et al., 2012, 2015), and may be related to

impaired interhemispheric connectivity reported by functional imaging research (Anderson et

al., 2011; Herbert et al., 2005; Just et al., 2007). In addition, previous volumetric studies have

reported reduced size of the corpus callosum from toddlerhood to adulthood in ASD (Egaas,

Courchesne, & Saitoh, 1995; Hardan, et al., 2009), associated with cognitive dysfunction (Keary

et al., 2009). Fibre density and size are critical for the control of information transfer (i.e.,

connectivity) between hemispheres (van der Knaap & van der Ham, 2011). An underconnected

system due to atypical white matter may impair higher order processes that rely on effective

interhemispheric exchange, such as working memory, attention and inhibition (e.g., Treit et al.,

2014; Widjaja et al., 2013; Bodini et al., 2013). Future studies should examine these specific

cognitive functions in relation to the DTI metrics.

Similar to prior reports, cortico-subcortical white matter tracts were also found to differ between

control and ASD groups (Shukla, Keehn, Lincoln, & Müller, 2010; Shukla, Keehn, Müller,

2011), with children with ASD showing reduced FA and AD in the projection fibres, including

the anterior and posterior internal capsule, anterior and posterior corona radiata and posterior

thalamic radiation. Thalamocortical fibres extend into the internal capsule and link the thalamus

32

and cerebral cortex, providing essential connections for auditory, motor, visual and

somatosensory systems (Guillery & Sherman, 2002), and play a vital role in information

processing (Sherman & Guillery, 2002). The anterior limb of the internal capsule connects the

thalamus and prefrontal cortex and findings of reduced FA and AD in this tract may be related

to evidence of impaired executive processes that are mediated by frontal cortices (Russo et al.,

2007; Smith & Jonides, 1999) in ASD, such as reduced activation for visual-spatial working

memory (Luna et al., 2002; Silk et al., 2006; Vogan, Morgan, Powell, Smith, & Taylor, 2014).

The posterior limb of the internal capsule is where most of the motor fibres join and pass down

through the spinal cord, and white matter impairment in this area could compromise

corticospinal tracts critical for sensory and motor functions. A recent study found reduced FA

in the posterior limb of the internal capsule in children who had greater incidence of self-injury

(Duerden et al., 2014), which suggests that structural abnormalities in this tract may also be

related to abnormal sensory processing in ASD.

Overall, our results demonstrated widespread white matter compromise in children with ASD

that persist into adolescence. Impaired white matter structure was not limited to specific

networks, with the majority of affected regions found in the corpus callosum and

thalamocortical fibres, which are crucial for interhemispheric connectivity and information

processing, respectively. This is consistent with the view that ASD is a disorder of generalized

complex information processing (Minshew, Goldstein & Siegel, 1997). Future longitudinal

studies are required to understand whether structural brain abnormalities cause ASD

symptomology or whether atypical brain function during early childhood affects structural brain

development to improve our understanding of DTI and ASD symptom links.

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Chapter 3 Functional Changes During Visuo-Spatial Working Memory in

Autism Spectrum Disorder: 2 Year Longitudinal fMRI Study Vogan, V.M., Morgan, B.R., Smith, M.L, & Taylor, M.J.

34

Functional Changes During Visuo-spatial Working Memory in Autism Spectrum Disorder: 2 Year Longitudinal fMRI Study

3.1 Abstract

Background: Previous research has revealed persistent impairments in visuo-spatial working

memory in Autism Spectrum Disorder (ASD), relative to typically developing (TD) populations.

Extant neuroimaging studies converge to identify reduced brain activation in fronto-parietal

regions during working memory tasks in children and adults with ASD. However, our

understanding of their neurodevelopmental patterns associated with working memory is limited.

The purpose of the current study was to examine functional changes longitudinally, over 2

years, in neural correlates associated with working memory in youth with and without ASD, and

the impact of increasing cognitive load.

Methods: We used fMRI and a visuo-spatial 1-back task with four levels of difficulty. A total

of 14 children with ASD and 15 TD children (ages 7-13) were included at baseline and followed

up approximately 2 years later (from a total sample of 44 ASD and 39 TD).

Results: Despite similar task performance at baseline and follow-up between groups,

differences were evident in the developmental trajectories of neural responses. TD children

showed greater load-dependent activation which intensified over time in frontal, parietal and

occipital lobes and right fusiform gyrus, compared to those with ASD. Children with ASD

showed minimal load dependent increases with age, but greater longitudinal load-dependent

decreased activation in default-mode related areas compared to controls.

Conclusions: Our results suggest inadequate modulation of neural activity with increasing

cognitive demands in parietal-occipital cortex areas in children with ASD, which does not

mature into adolescence, unlike their TD peers. The diminished ability for children with ASD

to modulate neural activity during this period of maturation suggests that they may be more

vulnerable to the increasing complexity of social and academic demands as they progress

through adolescence than their peers.

35

3.2 Introduction

Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental disorder

characterized by deficits in social communication and interactions, as well as restricted,

repetitive patterns of behaviour and interests (APA, 2013). Individuals with ASD also often

present with a number of cognitive, learning and executive function impairments [Hill, 2004a,

Rosenthal et al., 2004). Key cognitive accounts of ASD include the theory of mind deficit

hypothesis [Baron-Cohen, Tager-Flusberg, & Cohen, 2000), central coherence hypothesis

(Happé, 2005), and the executive dysfunction hypothesis (Hill, 2004a; Joseph, 1999).

Researchers have moved away from a model analyzing a single, cognitive deficit as an

explanatory account of ASD to a framework that supports multiple cognitive differences

(Happé, Ronald, & Plomin, 2006). While executive function impairments do not have a primary

causal role in ASD, executive function problems have a significant impact on their

developmental outcomes (Pellicano, 2012). Working memory is considered to be one of the core

components of executive functions (Verté, Geurts, Roeyers, Oosterlaan, & Sergeant, 2006), and

previous studies have reported working memory deficits in ASD relative to TD populations

(Barendse et al., 2013). Furthermore, previous literature has identified associations between

working memory deficits and social function, adaptive behaviour and academic success in ASD

[Alloway, 2009; Dennis, Agostino, Roncardin, & Levin, 2009; Engle, Tuholski, Laughlin, &

Conway, 1999; Gilotty, Kenworthy, Sirian, Black, & Wagner, 2002; Killany, Moore, Rehbein,

& Moss, 2005; Richmond, Thorpe, Berryhill, & Klugman, 2013).

There is evidence for primarily visuo-spatial working memory impairment in ASD, whereas

verbal working memory appears relatively intact in individuals (Boucher & Bowler, 2008;

Minshew & Goldstein, 2001; Ozonoff & Strayer, 2001; Steele, Minshew, Luna, & Sweeney,

2007; Williams, Goldstein, Carpenter, & Minshew, 2005). Visual working memory has also

been shown to be a sensitive marker of developmental disability (Alloway, Seed, & Tewolde,

2016). Visuo-spatial working memory is important to remember anything that is seen, including

sequences of events, visual patterns and images. Thus, poor visuo-spatial working memory may

have implications for how individuals with ASD process their social and academic

environments. For instance, children with ASD may struggle to process visual cues (e.g., non-

verbal language, facial expressions) during social interactions, making it challenging for them

carry on conversations and to relate to their peers, compared to TD individuals. Moreover,

36

visuo-spatial working memory often operates as ‘mental scrap paper’ in the classroom, and thus

deficits can result in difficulties completing simple mental arithmetic, understanding concepts

and organizing thoughts. Such social and environmental demands become increasingly complex

as children mature into adolescence, and consequently, it is also important to consider the

development of associated, underlying neuropsychological systems, such as visuo-spatial

working memory processing, that are vulnerable in ASD.

Cross-sectional neuropsychological studies of working memory in ASD reveal persistent

impairments in visuo-spatial working memory throughout development (i.e., childhood,

adolescence and adulthood) (Luna, Doll, Hegedue, Minshew, & Sweeney, 2007). Using an

oculomotor delayed response task, Luna and colleagues (2007) showed that although visuo-

spatial working memory skills improved over time for both groups, a developmental delay of

working memory was observed in ASD, extending into adulthood, and remained impaired

compared to controls. In addition, Rosenthal et al. (2013) showed significantly more parent-

reported working memory impairments in older than younger children with ASD, which

contrasts with the reported general improvement of working memory in everyday settings in TD

children (Andersen et al., 2015b). All of the above studies were cross-sectional; few have used

longitudinal designs to evaluate working memory development in ASD. One recent 2-year

longitudinal study indicated that whereas verbal working memory continues to develop in

control and Attention Deficit Hyperactivity Disorder (ADHD) groups (ages 9-16), children with

ASD displayed a developmental arrest (i.e., showed no improvement 2 years later; Andersen et

al., 2015b).

Despite some evidence for differential developmental trajectories of working memory in ASD

compared to TD populations, to our knowledge there are no studies that examine the neural

basis of this development. This is a critical gap in our knowledge, given that the frontal lobes,

which support working memory (Greene, Braet, Johnson, & Bellgrove, 2008), have a protracted

maturation well into adolescence (Sowell et al., 2004), making the functions they support (e.g.,

working memory) vulnerable to developmental disturbances. Normative neurodevelopmental

studies have found age-related increases in the intensity of activation of fronto-parietal clusters

(Olesen, Macoveanu, Tegnér, & Klingberg) or only parietal regions (Spencer-Smith et al., 2013)

during visual-spatial working memory tasks. Other studies have reported developmental shifts

of neural activation during a visual categorical n-back task from reliance on the dorsal visual

37

stream in young children to reliance on the visual ventral pathway (involving prefrontal and

inferior temporal regions) in adults (Ciesielski, Lesnik, Savoy, Grant, & Ahlfors, 2005).

Although the developmental changes in working memory processing have not been studied in

ASD, a few fMRI studies have examined the neural correlates of working memory function in

either adolescents or adults with ASD separately, with only one study of pre-adolescent children

by our group (Vogan et al., 2014). Overall, studies converge to identify a fronto-parietal visual

working memory network, involving a system of prefrontal, premotor, dorsal cingulate and

posterior parietal activation, and adults with ASD show reduced activation in these areas during

working memory tasks (Koshino et al., 2005; Luna et al., 2002). Using a working memory

mental rotation task, Silk and colleagues (Silk et al., 2006) observed reduced cortical activation

in frontal regions in adolescents with ASD compared to controls, including the anterior

cingulate, dorsolateral prefrontal cortex (DLPFC) and caudate nucleus, despite no differences in

task performance. More recent fMRI investigations have considered the impact of increasing

cognitive load on neural systems underlying working memory. Rahko et al. (2016) found that

with a visuo-spatial working memory task contrasted against an attention task (e.g., 0-back),

adolescents with ASD showed reduced modulation of brain activity across increasing task

demands in the insula, somatosensory, motor and auditory cortices compared to controls. Our

group demonstrated inadequate task-load modulation in the precuneus, DLPFC and medial

premotor cortex of youth with ASD during a visuo-spatial working memory paradigm with four

levels of difficulty (Vogan et al., 2014). These findings suggest that neural functional capacity

saturates with high information load in individuals with ASD, and they are unable to modulate

brain function according to increasing demands. However, to better understand

neurodevelopmental trajectories of ASD compared to typical controls, these patterns of brain

activation should be studied longitudinally.

Findings from previous literature highlight the importance of manipulating cognitive load in

studying executive functioning to provide a more refined profile of abilities and impairments in

ASD. Specifically, a number of studies have failed to demonstrate performance differences

between individuals with and without ASD on basic working memory tasks, but report that

deficits emerge with increasing task complexity and/or working memory load in ASD (Koolen,

Vissers, Egger, & Verhoeven, 2014; Minshew & Goldsteein, 2001; Rahko et al., 2016; Russo et

al., 2014). Thus, we incorporated complexity into our variation of the ‘n-back’ protocol (The

38

Colour Matching Task [CMT]; Arsalidou, Pascual-Leone, & Johnson, 2010), using 6 levels of

difficulty. The CMT is unique in that it is a 1-back task, which systematically manipulates

memory load while keeping other executive functions constant across all difficulty levels,

allowing for a direct investigation of the influence of increasing demands on working memory.

Increasing difficulty on typical n-back tasks requires the utilization of different mental strategies

at each level (e.g., 0-back: recognition, 1-back: maintenance, 2-back: maintenance and

monitoring). This manipulation increases executive function demand in addition to memory

load, making it difficult to isolate neural responses specific to working memory processes (see

Vogan et al., 2014 for a thorough description of the CMT). Our baseline study of the CMT

demonstrated task-induced activation in working memory-related areas (e.g. frontal and parietal

areas) and deactivation in the default mode network (DMN) with increasing load in TD control

youth (Vogan et al., 2014). However, this opposing system of cognitive processes was absent in

those with ASD, who did not modulate neural activity in response to increasing task demands to

the same extent as controls.

The current study identified functional changes longitudinally (over 2 years) in neural correlates

associated with increasing working memory load in children and adolescents with and without

ASD. As children mature, environmental demands become increasingly complex, prompting

cognitive, social and affective maturation, as well as significant neuroanatomical changes (Paus,

2005; Sowell, Thompson, Tessner, & Toga, 2001), making the period of early adolescence

especially interesting to study. Not only is working memory fundamental to cognition (Engle et

al., 1999), learning and academic achievement (Alloway, 2009), it also associated with many of

the key characteristics of ASD, such as the social and communicative impairments (Gilotty et

al., 2002; Kenworthy, Black, Harrison, Della Rosa, & Wallace, 2009; Leung, Vogan, Powell,

Anagnostou, & Taylor, 2016; Richmond et al., 2013). Understanding the evolution of neural

development underlying working memory throughout childhood can provide valuable insights

into the cognitive profile of ASD and localize brain areas of vulnerability to developmental

disturbances—both of which will help inform interventions for those with ASD.

39

3.3 Methods

3.3.1 Participants

A total of 83 participants were originally recruited for this study (2011-2013), consisting of 44

children with ASD and 39 TD children between the ages of 7 and 13 years. Participants were

followed up 2 years later (9 – 15 years old). At each time point, participants underwent

neurocognitive assessments and neuroimaging. At the baseline assessment, 12 TD children and

23 children with ASD were excluded for excessive movement and inadequate fMRI task

performance or protocol completion. Out of the remaining 48 participants, 16 were excluded in

the current study due to (1) not returning at the 2-year follow-up (e.g., braces; relocated; n=9),

(2) excessive head movement (n=4), (3) poor fMRI task performance (n=3). After sex- and age-

matching, the final sample consisted of 14 children with ASD (13 boys) and 15 TD children (10

boys). Although our rates of unusable data are higher than often reported in the literature, our

task was long and complex, and thus was more taxing than those typically used with children,

particularly clinical populations. Demographic data from both time points are included in Table

4.

All participants were free from any diagnosed psychiatric comorbidities, neurological disorders,

medical illnesses, prematurity, uncorrected vision, colour blindness, as well as standard MRI

contraindicators (e.g., ferromagnetic implants). A history of developmental delay, learning

disability and ADHD was used to exclude control children only; however, these factors were

also not current primary diagnoses in any participants of the ASD group. Four children with

ASD were each on one psychotropic medication at one time point (e.g., Biphentin, Stratera and

Abilify).

Clinical diagnoses of ASD were confirmed in all cases, with a combination of expert clinical

judgment, clinical records and the Autism Diagnostic Observation Schedule (ADOS) (Lord et

al., 2003) which was administered by a trained individual who maintains inter-rater research

reliability. All participants possessed a full scale IQ estimate of 80 or above measured by the

Weschler Abbreviated Scale of Intelligence (WASI; Weschler, 1999), two subtest version. All

children completed the Backwards Digit Recall, Listening Recall, Digit Recall, Mazes Memory

and Block Recall subtest of the Working Memory Test Battery for Children (WMTB-C;

Pickering & Gathercole, 2001) at both time points. See Appendix B.1 for neuropsychological

40

test data.

Children were recruited through community support centres, parent support groups, email lists,

hospital ads and private schools. Informed consent, clinical and cognitive testing, and MRI

scanning were performed at the Hospital for Sick Children (SickKids) in Toronto, Ontario.

Experimental procedures were approved by the Research Ethics Board at SickKids. All children

provided informed assent, and the parents provided informed written consent. Table 4. Participant characteristics

CCS = Calibrated severity score; ranges from 1-10 (10=most severe) §There is no significant Group x Time interaction (p> 0.05) of IQ, and thus developmental IQ trajectories are similar across groups.

3.3.2 fMRI visuo-spatial working memory paradigm (Colour Matching

Task)

The CMT is an N-back task, in which participants are instructed to attend to coloured figures of

clowns presented in sequence, one at a time. Before scanning, participants were trained and

completed practice trials at both time points until they had an accuracy of ≥80%. Participants

were taught to ignore the face of the clown and colours that were irrelevant (blue and green),

and focus on the relevant colours (pink, yellow, red, purple, orange, grey and brown). The

number of ‘n’ relevant colours (i.e., load) in the clown figure was increased by one for every

increase in difficulty level (D). See Figure 4a for an example of a sequence presented in D3 and

D6. The CMT includes two functions that require mental attention: (1) participants must first

identify the relevant colours within the clown figure, and (2) second, determine if the colours of

Variables Time Point Controls (N=15)

ASD (N=14) Significance Test

Sex (M:F) ------ 10:5 13:1 c2 = 0.08, p = 0.08

Age (years) Baseline 11.7 (2.16) 10. 9 (1.98) t(27) = 1.05, p = 0.31

Follow-up 13.7 (2.17) 13.4 (1.82) t(27) = 0.45, p = 0.65

IQ Baseline 118.8 (11.60) 111.8 (17.21) t(27) = 1.31, p = 0.20§

Follow-up 116.5 (9.52) 107.5 (12.76) t(27) = 2.17, p = 0.04§

ADOS CCS Baseline N/A 7.1 (1.80) Follow-up N/A 7.3 (2.23) Days between visits ------ 760.8 (38.3) 791.8 (106.5) t(27) = 1.03, p = 0.32

41

the current clown match with colours of the previous clown, ignoring irrelevant colours (blue

and green). Therefore, items with n (e.g., 3) relevant colours would have a difficulty level of n +

2 (e.g., 5), rather than difficulty level 3. After each stimulus, participants indicated if the

relevant colours in the figure were the same colours as in the immediately preceding figure (i.e.,

‘1-back’), regardless of colour location or colour repetition (see Figure 4a). Participants

responded by pushing a button for ‘same’ or ‘different’ using an MR-compatible button box

with the right hand.

Four runs were presented. Each run included six 32-s blocks, one for each of the six difficulty

levels (see Figure 4b). The difficulty level was constant within each block, and all six difficulty

levels were randomized within the runs. The same four runs were presented to participants in the

same order. Each task block contained eight stimuli, yielding 168 task trials in total and

alternated with 20-s baseline blocks. For the baseline blocks, the clowns were coloured in blue

and green only, and children were instructed to look at the clowns but not to respond (Figure

4c). In the task blocks, participants had 3s to view each stimulus and respond, followed by a

fixation cross for a 1-s inter-stimulus interval. Total scan time for the fMRI protocol was

approximately 22 minutes. Participants’ data were excluded from analyses if they completed

fewer than three runs.

Accuracy and response times were recorded during the fMRI task; items were correct if the

child responded correctly within 3s of stimulus presentation. Task performance was considered

adequate if: (a) participants reached ≥60% accuracy (averaged across the 4 runs) on the two

easiest difficulty levels and (b) participants completed 2 or more runs where at least 50% of the

blocks were acceptable for performance (60% accuracy) and motion. Motion was considered

acceptable if participants moved ˂1.5 mm from the median head position in ≥60% of the

volumes per task block. The fMRI preprocessing section below describes the displacement

calculations. Motion parameters were entered into the fMRI preprocessing pipeline. An

accuracy criterion of 60% was chosen, as we could be sure that participants were performing

above chance (50%), but it was not too stringent for a clinical paediatric population.

42

Figure 4. Protocol description of the Colour Matching task (CMT)

A) Examples of a sequence presented in D3 and D6. There were six difficulty levels. The number of relevant colours (i.e., load) in the clown was increased by one colour for every increase in difficulty level. Difficulty = (# of colours) + 2. Children were taught to ignore the clown’s face, colour location, colour repetition and irrelevant colours (blue and green). After each stimulus, children indicated if the relevant colours in the current clown were the same or different from the colours in the immediately preceding clown (i.e., 1-back). B) CMT was a block design task, where each run consisted of six 32-second blocks (for each difficulty level) followed by C) 20-second baseline blocks where clowns are presented in only blue and green (irrelevant/ignore colours).

+ ++

+ ++

…

…

+

+

3s1s

Difficulty+level+3+(D3)+– 1+relevant+colour

Difficulty+level+6 (D6)+– 4+relevant+colours

SAME DIFFERENT SAME

32/s

+

A)

B)

C)

Relevant(colour: Orange Orange++++++ ++++ ++++ ++++ ++++ ++Brown+++++ BrownRESPONSE:

DIFFERENT SAME SAME

Relevant(colours: Grey Brown Brown BrownPink Grey Grey++++++ ++++ ++++ ++++ ++++ ++++ ++GreyPurple Pink Pink++++ ++++ ++++ ++++ ++++ ++++ ++++ +PinkYellow Purple Purple++++ ++++ ++++ ++++ ++++ ++++ +Purple

RESPONSE:

IGNORE:• Irrelevant/colours/

(blue/&/green)• Colour location• Colour repetition

43

3.3.3 Image Acquisition

All imaging data were acquired using a 3 T Siemens Trio MRI scanner with a 12-channel head

coil. Head stabilization and motion restriction were achieved with foam padding. A high-

resolution TI-weighted 3D MP-RAGE structural scan (Sagittal; FOV= 192 x 240 x 256 mm; 1

mm isometric voxels; TR/TE/TI/FA = 2,300/2.96/900/9), was used as an individual anatomical

reference for the fMRI images. During structural image acquisition, participants watched a

movie of their choice using MR-compatible goggles and earphones. Functional images were

acquired with single-shot echo planar imaging sequence (Axial; FOV = 192 x 192; Res = 64 x

64; 30 slices 5 mm thick; 3 x 3 x 5 mm voxels; TR/TE/FA = 2,000/30/70). Visual stimuli for the

functional task (CMT) were displayed on MR-compatible goggles. Stimuli were displayed and

performance was recorded using the software Presentation (Neurobehavioral Systems Inc.,

Berkeley, CA, USA)

3.3.4 CMT Behavioural Data Analyses

To keep consistent with our previous studies (Vogan et al., 2014; Vogan, Morgan, Powell,

Smith, & Taylor, 2016) only the first four difficulty levels (D3-D6) were analyzed, as

performance on D7 and D8 was at chance or only marginally above chance levels in the

majority of participants. Behavioural accuracy (% correct) and response times were calculated

for each difficulty level, averaging across runs for each participant, at baseline and follow-up.

Data were analyzed using a 2-way mixed ANOVA for each time point, with group (ASD and

TD) as a between subject factor and difficulty level (D3, D4, D5, and D6) as a within subject

factor. Bonferroni post-hoc pairwise comparisons were computed to explore the main effect of

difficulty level for each group separately at each time point. All reported significance levels

were adjusted using Bonferroni correction in SPSS. This adjustment involves multiplying

significance levels by the number of pairwise comparisons performed (i.e., p values were

multiplied by 6 when there are 4 difficulty levels to compare). In other words, pairwise

comparisons had to be significant at the 0.05/6 = 0.00833 level to be significant at the 0.05 level

under Bonferroni.

3.3.5 fMRI data analyses

Image preprocessing of functional data was performed using FMRIB’s Software Library (FSL)

44

(Worsley, 2001). The first three volumes of each run were discarded for scanner stabilization.

Following slice timing and motion correction, images were smoothed with a 6-mm full width at

half maximum (FWHM) Gaussian filter, temporarily filtered with a high-pass filter cut off

frequency of 0.01 Hz. To control for motion, MCFLIRT was used for volume alignment and the

standard motion parameters found from MCFLIRT were also included as a covariate of no

interest in the general linear model (GLM) of the standard FSL preprocessing. Maximum

displacement (MD) was calculated using 3dvolreg from the AFNI toolbox (Cox, 1996). This

calculates the maximum displacement (across the whole brain) of each volume to any single

reference volume. This MD metric was used to flag volumes that had unacceptable motion, as

described above. The average MD at each time point for subjects was used to explore

differences in head motion across time. Results of a paired-samples t-test showed that motion

did not change across time for control children (Mbaseline = 0.35mm, SDbaseline = 0.30mm; Mfollow-up

= 0.27mm, SDfollow-up = 0.21mm; t(14) = 0.87, p = 0.40) or children with ASD (Mbaseline = 0.65mm,

SDbaseline = 0.57mm; Mfollow-up = 0.50mm, SDfollow-up = 0.50mm; t(13) = 1.10, p = 0.29). The

average MD was calculated across time for each subject to explore overall group differences in

head motion. There was no significant differences in average head motion between children

with (M= 0.58mm, SD=0.46mm) and without ASD (M= 0.31mm, SD= 0.19mm), t(27)= 1.98, p =

0.07).

Both functional and T1 structural images were brain extracted. The functional data were

registered to its corresponding T1 image using a 6-parameter linear transformation. Each

subject’s T1 was registered to the MNI152 T1 template with a 12-parameter linear registration.

These transformations were concatenated to transform functional data to standard space for

higher level analyses.

First-level statistical analyses of BOLD activity during CMT was conducted using FSL Expert

Analysis Tool (FEAT; Woolrich et al., 2009). For each subject at each time point, data were fit

to a block-design GLM convolved with a gamma function using the task parameters (D3 to D6).

Contrasts between D6 and D3 were calculated, as well as a linear trend across D3, D4, D5 and

D6. These results were averaged across runs for each subject at each time point in a second level

analysis. Higher-level group (i.e., between-group) analyses consisted of two methods:

1). Region-of-Interest (ROI) Analysis: In our baseline study (Vogan et al., 2014), linear trend

analyses were examined to determine areas that linearly modulated as a function of CMT

45

difficulty level. Thus, to examine the longitudinal change in linear activation patterns, regions

that showed between-group differences in linear trends were obtained from our baseline study

for an ROI analysis in the current study. Using SPM8 (www.fil.ion.ucl.ac.uk/spm) small volume

correction, a mixed-design ANOVA was conducted to examine the Group (ASD, Control) x

Time (Baseline, Follow-up) in the linear activation patterns. Reported results are significant at p

< 0.05 after correction in a small volume (radius = 5mm), drawn around ROIs.

2). Whole Brain Analysis: Whole-brain statistical analyses of BOLD activity during CMT was

conducted using FMRIB’s Local Analysis of Mixed Effects type 1 (FLAME-1; Worsley, 2001).

The longitudinal change in the D6>D3 contrast was compared between participants with and

without ASD using a Group x Time interaction. This method is described in the FEAT User

Guide (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/GLM) under ANOVA: 2-groups, 2-levels per subject

(2-way Mixed Effect ANOVA). Significant interactions were reported using cluster-based

thresholding determined by Z >|2.3| and a cluster corrected significance threshold of p<0.05.

Central ROIs were selected from areas exhibiting significant interactions. Average signal

change was extracted from spherical ROIs (5mm radius) and plotted for D6>D3 and for each

group at both baseline and follow-up time points.

In summary, an ROI analysis was conducted to follow-up on a priori ROIs obtained from our

baseline study (Vogan et al., 2014). Whole-brain activation patterns were also examined for a

more comprehensive understanding of the neurodevelopmental changes in brain function during

CMT in children with and without ASD.

3.4 Results

3.4.1 Behavioural Data

There was no significant effect of group on accuracy at baseline (F(1,27) = 0.40, p=0.54) or

follow-up (F(1,27) = 1.88, p=0.18; Figure 5a). Therefore, comparisons of brain activity between

control children and children with ASD were made under comparable accuracy scores across all

levels at both time points. There was a significant main effect of difficulty level on accuracy,

with accuracy decreasing as a function of difficulty in both groups at baseline (F(2.26,27) = 44.96,

p < 0.001) and follow-up (F(2.05,27) = 37.32, p < 0.001; Greenhouse-Geisser corrected degrees of

freedom). At baseline, Bonferroni post hoc pairwise comparisons revealed that accuracy

46

significantly decreased between D4 and D5 (pcorr <0.05) for control children but not between

any other consecutive difficulty levels. Children with ASD showed significant decreases in

accuracy between all levels (pcorr <0.05), except between D5 and D6. At follow-up, control

children showed no statistically significant decreases in performance between consecutive

difficulty levels, whereas children with ASD showed a significant decline in performance

between D4 and D5 (pcorr <0.05).

At both time points, response times (RTs) increased overall as a function of difficulty in control

children, but only increased up to D5 in children with ASD (Figure 5b). There was no effect of

group on RTs at baseline (F(1,27) = 0.67, p=0.42) or follow-up (F(1,27) = 3.83, p=0.06). However,

as expected, there was a main effect of difficulty level on RTs at baseline (F(2.07,27) = 44.73, p <

0.001) and follow-up (F=(1.80,27) = 51.01, p < 0.001; Greenhouse-Geisser corrected degrees of

freedom). For control children, Bonferroni post hoc pairwise comparisons at baseline showed

significant increases in RTs in between D3 and D4, and D4 and D5, but not between D5 and D6,

whereas children with ASD only showed a significant increase in RTs between D3 and D4 (pcorr

<0.05). At follow-up, control children showed increases in RTs between all consecutive

difficulty levels, whereas children with ASD show a significant increase only between D3 and

D4, and D4 and D5 (pcorr <0.05). The decrease in RTs at the most difficult level in the ASD

group was not significant.

Figure 5. CMT behavioural performance

A) Mean proportion correct and (B) mean response times for D3 to D6 at baseline and follow-up, and standard error bars. There were no main effects of group on accuracy or response time at either time point.

a)#CMT#Accuracy b)#CMT#Response#Times

0.5

0.6

0.7

0.8

0.9

1

D3 D4 D5 D6

Accuracy'(Proportion'Correct)

Difficulty'Level'

CTL#BaselineCTL#FollowEupASD#BaselineASD#FollowEup

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47

3.4.2 fMRI Analyses

3.4.2.1 ROI analysis

There was a significant Group x Time interaction in the left Precuneus (psvc = 0.018) and left

dorsolateral prefrontal cortex (psvc = 0.008), in which controls showed greater increasing linear

trends of activation across time compared to children with ASD. No other ROIs showed

significant interactions in the linear patterns of activation over the two testing points.

3.4.2.2 Whole brain analysis

3.4.2.2.1 Group-level activation of D6 versus D3

Group-level activation during D6 versus D3 at baseline and follow-up are presented in Figure 6.

In controls, activations increased as a function of task difficulty (i.e., load; D6>D3) in the right

precuneus and left occipital cortex at baseline, and activations decreased as a function of load

(i.e., D6<D3) in typical default mode network regions including the bilateral anterior medial

frontal gyrus, posterior cingulate and angular gyri. D6 versus D3 contrasts of activation in

controls showed more widespread activity at follow-up compared to baseline. Specifically, at

follow-up, activations increased as a function of load in the bilateral inferior frontal gyri,

occipital cortex, lingual gyri, the precuneus extending into the superior parietal lobules, the

anterior cingulate extending to the dorsal medial frontal gyrus, and right dorsolateral prefrontal

cortex. Consistent with baseline testing two years earlier, activation decreased with increasing

load in the bilateral anterior medial frontal gyri, angular gyri, and posterior cingulate.

Changes in D6 versus D3 activation patterns from baseline to follow-up were observed to be

minimal in children with ASD. At baseline activation increased as a function of increasing task

difficulty in the right fusiform gyrus, bilateral middle occipital gyri and left precuneus in

children with ASD, and the right insula showed decreasing activation as a function of load. At

follow-up, children with ASD showed increasing activation with load in the bilateral fusiform

gyri, middle occipital gyri and right precuneus, and decreasing activation in the bilateral

posterior cingulate, anterior medial prefrontal gyri, angular gyri, middle temporal gyri and left

dorsolateral prefrontal cortex.

48

Figure 6. Group activation maps for the D6 versus D3 contrast during CMT at baseline and follow-up

Significant activations using cluster-based thresholding determined by Z > |2.3| and a corrected cluster significance threshold of p= 0.05. Areas in red depict regions of increasing activation from D3 to D6 (i.e., D6>D3) and areas in blue depict regions of decreasing activation from D3 to D6 (i.e., D6<D3).

3.4.2.2.2 Longitudinal change in functional activation of D6 versus D3

A significant Group x Time interaction (Z > 2.3, p < 0.05, cluster-corrected) was observed in the

D6 versus D3 contrast. Two different patterns were observed in regions exhibiting a significant

interaction (See Figure 7). In regions such as the bilateral precuneus extending into the superior

parietal lobules, right fusiform gyrus, left superior occipital gyrus/cuneal cortex and left angular

gyrus, the control group showed a greater positive change in BOLD in the D6 versus D3

contrast over time (baseline to 2-year follow-up) than children with ASD (See Table 5). In other

Baseline(((( (((( (((( (((( (((( (((( (((( ((((Follow,upCO

NTR

OL

ASD

2

4

,2

,4

Z((value

s

49

words, in these regions, controls showed increased recruitment as a function of load, and this

load-dependent change in activation increased across time, compared to children with ASD. In

regions such as the bilateral ventromedial prefrontal cortex and right parahippocampal gyrus,

the D6 versus D3 contrast became increasingly more negative over time in children with ASD

compared to control children. In other words, children with ASD showed a decrease in BOLD

signal with increasing load, and this pattern significantly increased across time compared to

control children. See Figure 8 for graphs of signal change in D3 and D6 at baseline and follow-

up for children with and without ASD.

Figure 7. Between-group comparisons in the longitudinal change in functional activation of D6 versus D3

Significant activations using cluster-based thresholding determined by Z > |2.3| and a corrected cluster significance threshold of p= 0.05. Areas in red depict regions that exhibited a significant Group x Time interaction. In the bilateral precuneus (Prec), superior parietal lobules (Sup Par Lob), right fusiform gyrus, left superior occipital gyrus (SupOcG)/cuneal cortex and left angular gyrus, the control group showed greater increase in the D6 versus D3 contrast over time (baseline to 2-year follow-up) than children with ASD. In the bilateral ventromedial prefrontal cortex (vmPFC)/orbital frontal gyrus (Orb FG) and right parahippocampal gyrus (ParahipG), the D6 versus D3 contrast became increasingly more negative over time in children with ASD compared to control children; these areas are circled in green.

Sup$Par$Lob

vmPFC

Fusiform$Gyrus

vmPFC

Prec

SupOcG/$CuneusParahipG2

4

Z$$values

R Orb$FG

50

Table 5. Brain regions showing a significant Group x Time interaction of the D6 versus D3 contrast during CMT.

Voxels MNI Coordinates

Z value P value Hem. Region x y z

1208 -20 -62 44 2.21 8.29 x 10-6 L Precuneus/Superior parietal lobule§ X -18 -68 46 3.84 L Precuneus§ X -22 -84 24 3.71 L Superior occipital cortex§ X -14 -80 38 3.55 L L inferior precuneus/Cuneal cortex§ X -16 -78 24 3.47 L Cuneal Cortex§ X -28 -62 36 3.42 L Angular gyrus§

823 -6 60 2 4.11 3.23 x 10-4 L Ventromedial prefrontal cortex* X 0 52 -6 3.87 L/R Ventromedial prefrontal cortex* X 30 54 -12 3.56 R Orbital frontal gyrus*

562 36 -44 -14 3.65 5.42x10-3 R Fusiform gyrus§ X 34 -22 -18 3.49 R Parahippocampal gyrus*

495 26 -72 48 3.21 0.01 R Superior parietal lobule§ X 36 -72 36 3.19 R Precuneus§ X 28 -74 34 2.99 R Precuneus§

MNI coordinates represent the peak Z value of the cluster; X = peak local maximas within cluster §Regions where control children showed a greater increase of activation in the D6 versus D3 contrast over time (baseline to 2-year follow-up) than children with ASD. *Regions where activation in the D6 versus D3 contrast became increasingly more negative (i.e., more deactivated) over time in children with ASD compared to control children.

51

Figure 8. Mean peak cluster signal change for control and ASD groups

Graphs of signal change during D3 and D6 at baseline and follow-up in areas that exhibited a significant Group x Time interaction of the D6 versus D3 contrast.

0

0.05

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CTL+Baseline CTL+Follow6up ASD+Baseline ASD+Follow6up

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hange'(β)

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hange'(β)

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hange'(β)

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D6

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61

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0CTL+Baseline CTL+Follow6up ASD+Baseline ASD+Follow6up

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hange'(β)

vmPFC

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0

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hange'(β)

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D6

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CTL+Baseline CTL+Follow6up ASD+Baseline ASD+Follow6up

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hange'(β)

L 'Prec/SupParLob

D3

D6

Prec =&Precuneus,+SupParLob =+Superior+parietal+lobule,+SupOcG =+Superior+occipital+gyrus,+FusG =+Fusiform+Gyrus,+vmPFC =+Ventromedial+prefrontal+cortex,+Parahip G =+Parahipocampal Gyrus

52

3.5 Discussion

This is the first longitudinal fMRI study to investigate functional changes in the brain during

working memory processing in children and adolescents with ASD. Overall, we found a

differential developmental trajectory for neural substrates underlying working memory over four

levels of increasing difficulty between the groups. We found that TD children showed

significantly greater longitudinal load-dependent increased activation in the parietal lobes

(including the bilateral precuneus extending into the superior parietal lobules), right fusiform

gyrus, left superior occipital gyrus/cuneal cortex and left angular gyrus compared to those with

ASD. However, children with ASD showed significant greater longitudinal load-dependent

decreased activation in the bilateral ventromedial prefrontal cortex and right parahippocampal

gyrus—areas associated with the DMN—compared to control children. Despite differences in

neural activation patterns, task performance and response time differences between control and

ASD groups were absent at both time points. However, there was a trend (p<0.06) for children

with ASD to have minimally longer response times on the CMT than control children at follow-

up. Thus, although task performance differences between groups are absent, children with ASD

took somewhat longer to respond to items, suggesting that they may have experienced more

challenges than controls. Results of the present study extend previous child and adolescent

fMRI studies of working memory in ASD (Silk et al., 2006; Rahko et al., 2016; Vogan et al.,

2014) by adding longitudinal information on the development of functional neural networks

underlying working memory.

Controls were able to increase their brain activation as a function of working memory load in

the parietal cortices, and this load-dependent activation intensified across development, whereas

children with ASD displayed little developmental change. Previous (non-longitudinal) studies

have found reduced task-load modulation in the parietal cortices in children with ASD

compared to typical development (Vogan et al., 2014; Rahko et al., 2016), and the current study

extends this finding to show that impairment in ASD persists as children mature. Furthermore,

behavioural studies exploring the maturation of executive functioning in ASD also demonstrate

persistent impairments in working memory throughout development (Andersen et al., 2016b;

Luna et al., 2007). Other developmental cross-sectional fMRI studies of TD youth describe

increasing parietal activity during visuo-spatial working memory with age (Ciesielski et al.,

2006; Spencer-Smith et al., 2013); this developmental change was not evident in children with

53

ASD in the present study. Additionally, whereas load-dependent activation was also observed

in the frontal cortex for controls at follow-up but not baseline (Figure 6), children with ASD did

not develop task-modulation in the frontal regions across time. Surprisingly, there was no

significant difference in frontal developmental trajectories (i.e., Group x Time interaction)

between groups, likely due to variability in the ASD group.

The parietal region is associated with increasing storage of visuo-spatial information critical for

working memory capacity (Macoveanu, Klingberg, & Tegnér, 2006), and these authors showed

that the parietal response was positively correlated with the amount of information that could be

stored in mind. Findings of the current study suggest that whereas TD controls are able to

modulate neural activity according to increasing demands as they mature, children with ASD do

not appear to have this potential. Given similar task performance in ASD, it is important to

entertain the idea that higher order processing (e.g., neural modulation) may not be mandatory

in ASD with tasks that can be efficiently processed using a perceptual processing approach due

to enhanced perceptual functioning skills (Mootron, Dawson, Soulières, Hubert, & Burack,

2006). Enhanced perceptual processing, despite a deficiency in neural modulation, may be

adequate for analyzing visual details under highly controlled conditions created by laboratory

based tasks, such as the CMT. However, it may be less adaptive for more complex information

processing, such as socialized or “real-world” tasks. In other words, observed neural deficits in

ASD may have more of an impact on functioning in everyday life. Compelling research has

demonstrated that executive functioning problems in every day settings, including working

memory, are observed in individuals with ASD, even when performance on laboratory tasks is

intact (Kenworthy, Yerys, Anthony, & Wallace, 2008). This would have implications for

working memory processing that takes place under real-world expectations, such as, for

example, during social interactions, which require one to balance multiple demands, including

decoding facial and non-verbal cues, listening and understanding verbal information, and

responding (Baddeley, Della Sala, Papgno, & Spinnler, 1997). With insufficient neural

modulation capacity, children with ASD may be more vulnerable to the increasing complexity

of real-word demands as they progress through adolescence (e.g., the need to develop

professional and romantic relationships) compared to their typical peers. It also has been

suggested that hyper-perception (i.e., hyper-focusing on fragments of details so intensely)

causes individuals with ASD to become over-saturated and overwhelmed with increasing

cognitive demands (Makram & Makram, 2010).

54

The visuo-spatial nature of the CMT and gradual increase in the number of colours that need to

be processed should require increased involvement of regions associated with categorization

(i.e., occipito-temporal) and spatial search (i.e., occipito-parietal areas) across cognitive load.

The occipito-parietal pathway, referred to as the ‘dorsal stream’, includes the precuneus and is

associated with encoding spatial properties, such as position, size and orientation. The occipital-

temporal pathway, referred to as the ‘ventral stream’, includes the fusiform gyri and is

specialized in encoding object properties such as colour, shape and texture (Pisella, 2016;

Ungerleider & Mishkin, 1982). Previous fMRI studies have observed recruitment of the visual

and dorsal streams during visuo-spatial working memory tasks in typical development

(Ciesielski et al., 2006) and ASD (Vogan et al., 2014). In addition to activation differences in

the parietal lobe, the present study also observed a developmental impairment of task-load

modulation in the right fusiform gyrus in children with ASD, compared to control children. Both

the dorsal and ventral streams are expected to activate and increase in activity as CMT presents

a gradually increasing load of colours to be located and categorized. Findings suggest that as

TD children mature, their dorsal and ventral neural pathways interact to modulate activity with

increasing visuo-spatial demands, whereas children with ASD do not demonstrate this

functional integration of networks. It is worth noting that dorsal stream deficits have been

postulated in ASD (Pellicano & Gibson, 2008; Spender et al., 2000) and other developmental

disorders (Grinter, Mayberry, & Badcock, 2010). More recent literature highlights a

developmental shift from early maturing dorsal (i.e., parietal) networks to ventral networks (i.e.,

fusiform) for working memory function in typical populations (Ciesielski et al., 2006) with the

development of semantic/categorical processing across age. We found evidence of impaired

function in both dorsal and ventral streams in ASD that persisted over the age range studied.

Another important finding of this study is the differential activity observed in areas of the DMN

in response to increasing load over time between children with and without ASD, including the

parahippocampal gyrus and ventro-medial prefrontal cortex. The present study found that

children with ASD showed significantly greater load-dependent deactivation with age in DMN

regions than TD children (i.e., deactivation in DMN as a function of difficulty intensified across

age in children with ASD, more so than controls). The DMN is a network of brain regions

which are active during rest and supressed (i.e., BOLD signal reduces) during cognitively

demanding tasks (Raichle, 2015; Whitfield-Gabrieli & Ford, 2012), and thus DMN regions are

expected to deactivate with increasing task difficulty during CMT. Previous studies have

55

observed a significant inadequate DMN suppression in response to working memory load

increase in children and adolescents with ASD compared to controls (Rahko et al., 2016).

Similarly, a number of neurodevelopmental disorders, including ASD, have been linked to

abnormal or diminished DMN function in youth during rest (Assaf et al., 2010) and tasks

(Fisher & Happ, 2005). Our study extends this literature, providing a longitudinal examination

of task-related DMN function, and suggests that this previously documented DMN impairment

in children with ASD may become less marked with age, as they showed an emerging ability to

supress DMN areas with increasing task demands.

There were some limitations of the present study to consider. To ensure that observed neural

activation occurred in response to the task, we only included individuals who were able to

perform adequately; in doing so, our sample is not representative of lower functioning children

with ASD. Future fMRI studies are required to better understand neural patterns during

working memory function in individuals with ASD with intellectual deficits who often have

very different developmental, social, cognitive and academic outcomes. Second, results are

limited by the small sample size, due to assessing a generally complex population and the

longitudinal nature of the study design, which made our sample vulnerable to attrition. One

common reason for ‘drop-out’ from baseline to follow-up in our sample was the high propensity

of adolescents to undergo orthodontic work (i.e., metal braces), which is a contraindication for

MRI scanning. Also, four participants were on medication at one time point in the study, and

for ethical reasons we did not ask them to withhold their medications. Although medication did

not have an impact on imaging findings in our baseline study of these data (Vogan et al., 2014),

it is important to take this into consideration when interpreting results of the current study.

Third, we utilized parametric statistical methods for cluster-wise inferencing, which may

increase false-positive rates (Eklund, Nicholas, & Knutsson, 2016), and thus caution should be

taken when interpreting results. However, FSL’s FLAME-1 is known to reduce family wise

error rates and returns only marginally higher false discovery rate compared to non-parametric

methods [66]. Furthermore, as outlined in Table 5, the corrected p values of our clusters are far

lower than the threshold required for significance (i.e., p < 8.29 x 10-6), with the exception of

the right superior parietal lobule. Therefore, the acceptably conservative results provided by

FLAME-1 compared to other neuroimaging analysis tools in combination with the extremely

strong statistical evidence of our data leads us to conclude that concerns raised regarding

parametric analysis of fMRI data do not compromise our findings. Finally, the present study did

56

not examine correlational links between behavioural and imaging results; given the complexity

of the experimental design and statistical analyses, we focussed on neuroimaging findings.

Future research is needed to understand brain-behaviour links underlying working memory

processing. Overall, this is the first study of its kind; the repeated measures study design over a

two-year period in the same individuals adds considerable power, and our study makes a novel

and valuable contribution to our knowledge of the neural systems underlying ASD and their

development.

3.6 Conclusions and Future Directions

The present study provides a clear neurodevelopmental profile of working memory impairment

and abilities in ASD. Overall, the current results suggest inadequate modulation of neural

activity during increased working memory load in the parietal cortex and fusiform gyrus in

children with ASD that shows no significant maturation into adolescence, in contrast to TD

peers. Additionally, as observed by increased load-dependent suppression of DMN activity

across time relative to typical children, our results may suggest that the previously reported

DMN impairment in ASD may become less marked with age. The biological mechanisms

underlying working memory development in ASD during this period of maturation reflect

persistent impairment over time in the ability to modulate brain activity with progressively more

complex cognitive demands. Children with ASD may, therefore, benefit from early intervention

and accommodations to support working memory before the demands of their social and

academic environment increase significantly in adolescence. This may include comprehensive

instruction of sophisticated ‘chunking’ (i.e., organizational) strategies to facilitate enhanced

recall of complex incoming information (Cowan, 2001) and applying various accommodations

(e.g., reducing incoming information, simplifying instruction, increasing processing time,

positive reinforcement) that support working memory capacity to various ASD interventions.

Previous studies exploring working memory interventions for children with ASD have

demonstrated mixed success (de Vries, Prins, Schmand, & Geurts, 2015; Fisher & Happé, 2005;

Solomon, Goodline-Jones, & Andres, 2004), and thus future research in this area is crucial.

Furthermore, future fMRI studies should distinguish neural function during working memory

processing beyond adolescence into adulthood, and should also include lower functioning

individuals with ASD, as neural underpinnings may vary substantially across the autism

spectrum. It will also be particularly important for future work to explore the link between

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neurodevelopmental impairments underlying working memory processing and autistic

symptomology and/or later developmental outcomes. Last, comparisons of neurodevelopment

related to visuo-spatial working memory to other atypical populations who share similar

cognitive but different clinical profiles, such as ADHD (Happé, Booth, Charlton, & Hughes,

2006), will elucidate neural patterns that are unique to ASD, and is an important avenue for

future studies.

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Chapter 4 Longitudinal Examination of Everyday Executive Functioning in

Children with ASD: Relations with Social, Emotional and Behavioural Functioning Over Time

Vogan, V.M., Leung, R.C., Martinussen, R., Smith, M.L, & Taylor, M.J.

59

Longitudinal Examination of Everyday Executive Functioning in Children with ASD: Relations with Social, Emotional and Behavioural Functioning Over Time

4.1 Abstract

Executive functioning (EF) deficits are well-documented in Autism Spectrum Disorder (ASD),

yet little is known about the longitudinal trajectory of ‘real-world’ EF and links to social,

emotional and behavioural outcomes in ASD. This study examined the profile of real-world EF

utilizing parent-reported measures over 2 years, and explored whether prior estimates of EF

were related to later co-morbid psychopathology and social functioning in 39 children with ASD

and 34 typically developing (TD) children (ages 7-14 years). According to parent reports,

children with ASD had impaired scores of EF in all domains at both time points, and showed no

significant improvement across 2 years, compared to controls. Regression analyses showed that

prior estimates of behaviour regulation difficulties uniquely predicted later emotional (i.e.,

symptoms of anxiety/depression) and behavioural (i.e., oppositionality/aggressiveness)

problems in children with ASD. Furthermore, an improvement of metacognitive skills predicted

a reduction of social difficulties over 2 years in ASD. These results imply that EF may be a

potential target of intervention for preventing and reducing co-morbid psychopathology and

promoting social competence in youth with ASD. Furthermore, the findings that EF related to

behaviour is more critical for later emotional and behavioural functioning, whereas EF related to

cognitions is more critical for social functioning, indicates that it may be beneficial to tailor

treatment. Future studies investigating the effectiveness of EF-based interventions in improving

the cognitive, psychological and social outcomes in ASD are of high priority.

4.2 Introduction

In addition to social-communicative deficits and repetitive/restricted behaviours and interests,

individuals with Autism Spectrum Disorder (ASD) often have executive functioning (EF)

impairments (Bramham, et al., 2009; Hill, 2004b; Kenworthy, Yerys, Anthony, & Wallace,

2008; Russo et al., 2007). EF is higher-order cognitive processes that regulate goal-directed

behaviour by enabling individuals to disengage from the immediate context for the coordination

and execution of future goals. EF difficulties are now explicitly described as diagnostic features

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of ASD in the Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition (DSM-5;

American Psychiatric Association, 2013). Research has demonstrated that individuals with

ASD have particular difficulty in aspects of planning and cognitive/behavioural flexibility

(Gioia, Isquish, Kenworthy, & Barton, 2002; Rosenthal et al., 2013; Hill, 2004b; Kenworthy et

al., 2005), but also in task initiation (Bramham et al., 2009), working memory (Anderson et al.,

2015b), self-monitoring (Russell, 2002), and inhibition (Lemon, Gargaro, Enticott, & Rinehart,

2011). Impairments of EF in ASD are correlated with symptom presentation (Hill & Bird,

2006; Kenworthy, Black, Harrison, Della Rossa, & Wallace, 2009; Lopez, Lincoln, Ozonoff, &

Lai, 2005; Reed, Watts, & Truzoli, 2013), adaptive behaviour problems (Giolotty, Kenworthy,

Sirian, Black, & Wagner, 2002; Pugliese et al., 2015), social competence (Leung et al., 2016;

Pellicano, 2010), academic success and psychiatric co-morbidities (Lawson et al., 2015), and are

associated with greater dependence and poor outcomes in adulthood (Hume, Loftin, & Lantz,

2009). EF depends on prefrontal cortices which have a protracted maturation (Powell and

Voeller, 2004; Sowell et al., 2004), making EF susceptible to developmental disturbances.

Thus, one crucial question is how the EF profile manifests over time in ASD and its impact on

developmental outcomes.

It is well-documented that EF develops throughout childhood and into adulthood in normative

populations (Best and Miller, 2010). Cross-sectional and longitudinal studies of individuals with

ASD generally support age-related maturation of EF through childhood and adolescence, but

they are developmentally delayed (Chen et al., 2016; Happe, Booth, Chariton, & Hughs, 2005)

and often remain impaired compared to same-age typical peers (Andersen, Skogli, Hovik,

Egeland, & Øie, 2015; Luna, Doll, Hegedus, Minshew, & Sweeney, 2007; see O’Hearn, Asato,

Ordaz & Luna, 2008 for a review; Pellicano, 2010), or show no improvement of working

memory over time (Andersen et al., 2015b). These studies rely on laboratory based assessments

of EF, which are performance measures. Although the highly controlled conditions created by

lab-based tasks allow for the assessment of optimal EF performance, they do not accurately

represent the complexity of children’s daily lives and may be less sensitive for measuring EF

deficits in ASD. Thus, some question the ecological validity and generalizability of these

measures (Blijd-Hoogeways, Bezemer & Geert, 2014; Gioia et al., 2002; Kenworthy et al.,

2008), particularly because EF problems in everyday life (e.g., informant-reported) are observed

in individuals with ASD even when lab-based measures of EF are intact (Kenworthy et al.,

2008). Unlike lab tasks, everyday observations of EF take place in a social context under real-

61

world expectations, in which children with ASD may be more susceptible than typically

developing (TD) children. Thus, gathering information from parents about EF in everyday

situations that are less structured is critical. The handful of studies that have examined the cross-

sectional development of everyday EF in ASD report no improvement of EF with age or even

age-related declines (Rosenthal et al., 2013; van den Bergh, Scheeren, Begeer, Koot, & Geurts,

2014), whereas typical populations show developmental improvements (Huizinga and Smidts,

2011). This discrepancy may be due to the fact that children with ASD are more vulnerable than

TD children to the increasing complexity of environmental demands as they progress into

adolescence, which is often controlled in standardized lab measures of EF (van den Bergh et al.,

2014). The above studies are cross-sectional, and thus, longitudinal studies are warranted to

advance our understanding of the developmental trajectories of EF in everyday settings, and

whether these skills have an impact on other developmental outcomes in ASD.

Examining social, emotional and behavioural functioning is one way to measure the capacity for

individuals with ASD to achieve optimal outcomes. Profound social deficits are central to the

disorder, involving social pragmatic impairments, poor speech prosody, limited understanding

of linguistic conventions, difficulties expressing emotions, and problems with interpersonal

interactions and theory of mind (APA, 2013). In the emotional/behavioural functioning domain,

ASD is associated with higher rates of co-morbid symptoms of both internalizing (e.g.,

depression and anxiety) and externalizing (e.g., aggressiveness and oppositionality) problems

than the population at large (Bauminger, Soloman, & Rogers, 2010; Rosenberg, Kaufmann,

Law, & Law, 2011; Strang et al., 2012). Anxiety and behavioural disorders are among the most

common psychiatric comorbidities in ASD (Simonoff et al., 2008). However, literature shows

that there are more internalizing problems (withdrawal, social problems, anxiety and depression)

than externalizing problems in ASD (Sturum, Fernell, & Gillberg, 2008). Given that social and

emotional/behavioural problems are highly prevalent in children with ASD and there may be

diagnostic challenges in identifying these conditions based solely on mental health assessment,

it is crucial to identity risk factors and early predictors associated with the development of such

symptoms.

Impairment in EF is one factor of importance for the development of social and emotional

problems in ASD. Previous studies of TD children and young adults have found strong

associations between poor EF and externalizing and internalizing behaviours (Castaneda,

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Tuulio-Henriksson, Marttunen, Suvisaari, & Lönnqvist, 2008; Hughes & Ensor, 2008; Snyder,

2013). Furthermore, longitudinal studies of TD children show that early EF difficulties predict

later internalizing and externalizing problem behaviour (Martel et al., 2007; Riggs, Blair, &

Greenberg, 2003), and social competence (Spinrad et al., 2006), suggesting that EF is critical for

developmental processes. In a review article, Hofmann and colleagues (2012) also outline that

working memory, inhibition and cognitive flexibility may generally subserve successful self-

regulation. Research examining the relation between EF and social, emotional and behavioural

functioning in individuals with ASD is limited. Some of these studies report concurrent relations

between poor EF and high levels of anxiety, depression and aggression in youth with ASD,

controlling for IQ (Hollocks et al., 2014), age and gender (Lawson et al., 2015). A study of

adults with ASD found that EF components were differentially associated with certain

emotional disorders, with cognitive flexibility associated with anxiety and planning/organization

associated with depression (Wallace et al., 2016), above and beyond attentional problems. In

contrast, other studies have failed to find both concurrent and longitudinal relations between

laboratory tests of EF and severe mood dysregulation problems (Simonoff et al., 2012),

behavioural difficulties, and emotional symptoms (Andersen et al., 2014a,b) in children with

ASD. With regards to social deficits in ASD, studies have reported links between social

difficulties and weaknesses of various EF processes, including task initiation, working memory

and cognitive flexibility (Gilotty et al., 2002; Leung et al., 2016). Furthermore, Pellicano (2010)

reported developmental links between early lab-based measures of EF and autistic children’s

emerging theory of mind skills.

In summary, these findings provide compelling evidence that one source of variability in social,

emotional and behavioural outcomes in ASD are individual differences in the development of

EF. However, surprisingly little is known about the predictive linkages between early every day

EF skills and later social and psychological outcomes in ASD. The present study extends prior

work by characterizing the longitudinal changes of EF as observed in everyday settings, and

investigates whether prior estimates of EF difficulties predict later social functioning and

psychopathology (i.e., anxious/depressive symptoms and oppositionality/aggressiveness) in

youth with ASD across 2 years. As abilities on laboratory tasks may differ from real world

observations, the current study focused on parent-reported EF measures using the Behaviour

Rating Inventory of Executive Functioning (BRIEF; Gioia, Isquith, Guy, & Kenworthy, 2000),

which captures daily scenarios of EF in individuals with ASD. We examined two EF domains,

63

behavioural regulation and metacognition, to elucidate the emergence of these related but

distinguishable skills, and because distinct EF processes may be differentially associated with

outcomes. Overall, knowledge of developmental trajectories of everyday EF in ASD offers

insight into the cognitive profile of the disorder, which is extremely informative for parents,

educators and clinicians. Furthermore, an enhanced understanding of EF development in ASD

and its link to future social and emotional functioning may allow for better assessment and will

inform treatment planning for targeting cognitive, psychological and social outcomes.

4.3 Methods

4.3.1 Participants

We utilized data from a longitudinal study of brain and behaviour neuroimaging in children with

and without ASD (2011-2013). The complete sample for this study consisted of 73 participants:

39 children with ASD (34 males) and 34 age-matched TD (20 males). At baseline, children were

between 7 and 14 years old (TD M=11.2 years, SD=2.1; ASD M=10.6 years, SD=1.8), and were

followed up with approximately 2 years later, when they were between the ages of 9 and16

years old (TD M=13.3 years, SD=2.1; ASD M=12.9 years, SD=1.8). All participants were free

from any diagnosed psychiatric comorbidities, overt neurological damage, and prematurity.

Diagnosed developmental delay, learning disability and attention deficit hyperactivity disorder

(ADHD) was used to exclude control children only; however, these disorders were also not

primary diagnoses in any children with ASD. Clinical diagnosis of ASD was confirmed in all

cases, with a combination of expert clinical judgment, clinical records and the Autism

Diagnostic Observation Schedule (ADOS; Lord et al. 2000) or the Autism Diagnostic

Observation Schedule, Second Edition (ADOS-2; Lord et al. 2012), which was administered by

a trained assessor who maintains inter-rater research reliability. All participants possessed a Full

Scale IQ estimate at 80 or above measured by the Wechsler Abbreviated Scale of Intelligence

(WASI; Wechsler, 1999) – two subtest version. Full Scale IQ estimates were taken from the

participants’ first evaluation (i.e., at baseline). Table 6 provides information on the

characteristics of the sample at both time points.

64

Table 6. Participant characteristics

TD (N=34)

ASD (N=39) Difference Test

% Mean (SD)

% Mean (SD) Sex (male) 59 87 c2 = 7.6, p<0.01

Age at T1 (years) 11.2 (2.1) 10.6 (1.9) n.s. Age at T2 (years) 13.3 (2.1) 12.9 (1.8) n.s.

IQ 115.4 (11.7) 103.3 (14.7) t (71) = 3.9, p<0.01

4.3.2 Procedure

Children were recruited through community support centres, parent support groups, email lists,

hospital ads and private schools. Inclusion criteria were assessed through pre-screening

interviews. All children provided informed assent, and the parents provided informed written

consent. Clinical and cognitive testing and parent questionnaires were completed at baseline

(T1) and approximately 2 years later (T2) at the Hospital for Sick Children in Toronto, Ontario.

Intelligence and clinical testing batteries were consistent across time points. The parent

questionnaires provided were the same at both time points, with an additional questionnaire

measuring emotional and behavioural functioning at T2. This study was approved by the

Hospital for Sick Children Research Ethics Board.

4.3.3 Measures

4.3.3.1 Executive functioning

The Behaviour Rating Inventory of Executive Functioning, Parent Form (BRIEF; Gioia et al.,

2000) was completed by parents at both time points. The BRIEF is an 86-item informant report

questionnaire that assesses EF in everyday settings during the past 6 months (i.e., real world EF)

for children and adolescents between 5 and 18 years-old. The BRIEF has six subscales that are

collapsed into two main indices: the Behaviour Regulation Index (BRI), which includes three

scales (inhibit, emotional control and shift), and the Metacognition Index (MCI), which includes

five scales (initiate, organize/plan, organization of materials, working memory, and monitor).

The present study utilized T scores. Higher scores are indicative of more EF problems, with T

scores ≥ 65 representing clinical symptomatology. The BRIEF has acceptable reliability and

65

well-established internal consistency, and convergent, discriminant, content validity (Gioia et

al., 2000).

4.3.3.2 Emotional and behavioural functioning

The Child Behavior Checklist (CBCL; Achenbach and Rescorla, 2001) was completed by

parents at T2. The CBCL is an informant report questionnaire that evaluates behaviour and

emotional symptoms during the past 6 months for children between 5 and 18 years-old. The

questionnaire is made up of 113 items that yield eight syndrome scales, six DSM-IV oriented

scales, and three broader band scales. The Anxious/Depressed and Aggressive Behaviour

syndrome scales were of interest in the current study. The Anxious/Depressed scale assesses

symptoms of both anxiety and depression, while the Aggressive Behavior scale consists of

symptoms consistent with oppositionality, conduct and disruptive behaviours. The present study

utilized T-scores. Higher scores are indicative of more behavioural and emotional problems,

with T-scores ≥ 65 representing clinical impairment. The CBCL has demonstrated good

psychometric properties overall (Ivanova et al., 2007), as well as in identifying psychopathology

in ASD (Gjevik, Sanstad, Andreassen, Myhre, & Sponhein, 2014).

4.3.3.3 Social functioning

The Social Responsiveness Scale (SRS; Constantino & Gruber, 2005) was completed by parents

at both time points. The SRS is a 65-item informant report that measures the range of severity of

social impairment in ASD across the entire range of the spectrum, from non-existent to severe.

Items assess social awareness, social cognition, social communication, social motivation,

restricted interests and repetitive behaviour. SRS Total T-scores were used for the purpose of the

current study. Higher SRS Total scores are indicative of greater social impairment, and T-scores

≥ 65 represent clinical symptomatology.

4.3.4 Data Analysis

All statistical analyses were performed with SPSS for Macintosh, Version 24.

4.3.4.1 Longitudinal trajectory of everyday executive functions

Mixed between-within ANOVAs were conducted to assess the interaction between group (ASD

66

and TD) and time on real world EF. Significant Group x Time interactions were followed up

with repeated measures ANOVAs for each group. Separate analyses controlling for the effect of

age at baseline were conducted.

4.3.4.2 Relations between EF and emotional/behavioural

symptomatology

Pearson correlations were completed to examine the bivariate relations among predictor

variables (BRIEF BRI and MCI at T1) and outcome variables (CBCL Anxious/Depression and

Aggressive Behaviour at T2) within each group. EF variables and potential covariates that

correlated significantly (p<0.05) with CBCL scales were included as predictors in subsequent

regression analyses. To examine whether prior estimates of EF at T1 predicted

emotional/behavioural functioning at T2, a series of simple regressions for each group were

conducted using the T2 CBCL scale scores (Anxious/Depressed and Aggressiveness) as

dependent variables. Due to strong correlations between the two predictor variables of interest

(TD: r=0.68, p<0.001; ASD: r=0.59, p<0.001), measures of EF were analyzed separately using

simple regressions and a series of partial correlations were completed to understand the unique

contribution of MCI and BRI to CBCL variables at T2. Due to the number of comparisons in

these models, we used Bonferroni correction. IQ and sex were not correlated with study

variables and thus were not entered into regression models.

4.3.4.3 Relations between EF and social functioning

Pearson correlations were performed to examine the bivariate relations among predictor

variables (BRIEF BRI and MCI at T1), covariates (sex and IQ) and outcome variables (SRS

Total score at T2) within each group. EF variables and potential covariates that correlated

significantly (p<0.05) with SRS Total were included as predictors in subsequent regression

analyses. To determine whether prior estimates of EF at T1 predicted social functioning at T2, a

series of simple regressions for each group was conducted using the T2 SRS Total score as a

dependent variable. Partial correlations were completed to better understand unique

contributions (see rationale above). Due to the number of comparisons in these models, we used

Bonferroni correction. IQ and sex were not correlated with study variables, and were excluded

from regression analysis.

67

As measures of EF and social functioning were collected at both time points, we had the benefit

of exploring the relationship between change of EF and change of social functioning over time

(T2 – T1). Change scores were computed by subtracting T-scores at T1 from T2; negative change

scores indicate improvement of abilities because higher T-scores reflect more problems. In order

to describe the developmental trajectory of social functioning over time, mixed ANOVAs were

conducted with Group as the between factor and Time as the within factor. A separate analysis

controlling for the effect of age at baseline was conducted. Bivariate Pearson correlation

analyses between potential co-variates (IQ and sex), predictor variables (BRIT2-T1 and MCI T2-

T1), and outcome variables (SRS Total T2-T1) were completed. To determine whether the change

of EF abilities predict change of social functioning over time, similar regression analyses were

run as described above.

4.4 Results

4.4.1 The development of everyday EF over two years

The longitudinal course of EF abilities for BRI and MCI is presented in Figure 9. For children’s

BRIEF scores, mixed ANOVAs revealed no significant Group x Time interaction on BRI or

MCI. While there was no significant effect of time, a significant effect of group was found

(p<0.001), with participants in the ASD group showing more impaired scores on measures of

BRI and MCI than control children. In terms of the BRIEF individual subtest scores that make

up the BRI, there was no significant Group x Time interaction on inhibition, shift and emotional

control. However, there was a significant effect of Group (p<0.001), with impaired scores on

inhibition, shifting and emotional control for participants with ASD. Furthermore, there was a

significant effect of time on inhibition (p=0.04). Post hoc testing using a repeated measures

ANOVA for each group revealed a significant improvement of scores of inhibition over time for

children with ASD only (p=0.02). There was no significant Group x Time interaction on any

other subtest scores that make up the MCI. There was a significant group effect (p<0.001), with

the ASD group showing more impairments on all measures, but there was no effect of time. The

longitudinal course of BRIEF subscale scores is presented in Appendix B.2. To examine

whether initial age at baseline (T1) affected the change in EF over 2 years, we controlled for age

and found no significant Time x Age interaction on any scales or indices of the BRIEF,

indicating that effects of time were not impacted by initial age of the participants.

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Figure 9. Mean T scores and standard error bars of parent-reported BRIEF Behaviour Regulation Index (BRI) and Metacognition Index (MCI) at T1 and T2

4.4.2 Do prior estimates of EF predict later emotional and behavioural

functioning?

Independent sample t-tests were performed to determine whether children with and without

ASD differed on emotional and behavioural symptoms at T2. Results indicate that children with

ASD had significantly more parent-reported symptoms of anxiety/depression (ASD M=67.26,

SD=12.04; TD M=52.62, SD=4.95; t(52)= -6.95, p<0.001) and aggressive/oppositional behaviour

(ASD M=59.77, SD=9.08; TD M=51.73, SD=3.93; t(53)= -5.02, p<0.001) than TD (degrees of

freedom adjusted for Levene’s tested for equality of variances).

Bivariate correlations showed that behaviour regulation difficulties at T1 were associated with

greater symptoms of anxiety/depression (r=0.45, p<0.01) and aggressiveness (r=0.61, p<0.001)

2 years later in children with ASD; more metacognitive difficulties at T1 were also significantly

correlated with greater symptoms of aggressiveness 2 years later (r=0.41, p=0.01) (see Table 7).

40

45

50

55

60

65

70

75

Time1 Time2

Tscore

TimePoint

BRIEFScores

ASDBRI

ASDMCI

TDCBRI

TDCMCI

69

In contrast, prior estimates of EF were not correlated with any later emotional/behavioural

symptoms in TD, and thus, regressions were not computed for this group.

Table 7. Correlation matrixes of EF variables and emotional and behavioural functioning variables for (a) TD children and (b) children with ASD

A) TD Group 1 2 3 4 5 6 1. CBCL Anxious/Depressed T2 -- 2. CBCL Aggressiveness T2 0.41* -- 3. BRIEF BRI T1 0.23 0.28 -- 4. BRIEF MCI T1 -0.04 -0.05 0.68*** -- 5. IQ -0.05 0.22 0.29 0.13 -- 6. Sex 0.20 0.04 -0.06 -0.20 0.22 --

*p<0.05, **p<0.01, ***p<0.001

B) ASD Group

1 2 3 4 5 6 1. CBCL Anxious/Depressed T2 -- 2. CBCL Aggressiveness T2 0.52*** -- 3. BRIEF BRI T1 0.45** 0.61*** -- 4. BRIEF MCI T1 0.30 0.41** 0.59*** -- 5. IQ -0.20 -0.25 -0.11 0.03 -- 6. Sex -0.04 0.06 -0.06 -0.04 -0.02 --

*p<0.05, **p<0.01, ***p<0.001

In children with ASD, more BRI problems at T1 predicted later symptoms of anxiety/depression

(p<0.01), accounting for 18% of adjusted variance. Furthermore, BRI (p<0.001) and MCI

(p=0.01) difficulties at T1 predicted later aggressive behaviour, accounting for 36% and 14% of

adjusted variance, respectively (see Table 8). The unstandardized regression coefficients of BRI

and MCI in models predicting Aggressiveness were not significantly different (t(64) = 0.26,

p=0.79). However, the partial correlation between MCIT1 and CBCL AggressivenessT2 was not

significant when BRI was partialled out (r=0.071, p=0.67), indicating that the variance in

aggressive behaviour explained uniquely by MCI is very minimal in ASD. To note, BRIT1 was

significantly correlated with later aggressiveness even when controlling for MCI (r=0.50,

p=0.001). All reported regression models survived Bonferroni correction for multiple

comparisons

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Table 8. Simple regression analyses: Influence of EF at T1 on emotional/behavioural and social functioning at T2 for (a) children with ASD and (b) TD children

A) ASD Group

Variable Adjusted R2 F B SE B t Partial

Correlation Predicting CBCL Anxious/Depressed at T2 N/A

BRI 0.18 9.18 0.43 0.14 3.03** Predicting CBCL Aggressiveness at T2

BRI 0.36 22.07 0.44 0.09 4.70*** r=0.50*** MCI 0.14 7.23 0.40 0.15 2.71** r=0.07

Predicting SRS Total social functioning at T2 BRI 0.34 19.67 0.57 0.13 4.43*** r=0.42** MCI 0.24 12.85 0.65 0.18 3.58*** r=0.25

Predicting Change in SRS Total social functioning from T1 to T2 N/A

MCI Change 0.18 8.72 0.43 0.14 2.95** *p<0.05, **p<0.01, ***p<0.001 Bolded values = significant after correction for multiple comparisons using Bonferroni

B) TD Group

Variable Adjusted R2 F B SE B t Partial

Correlation Predicting SRS Total social functioning at T2

BRI 0.22 10.37** 0.35 0.11 3.22** r=0.38* MCI 0.09 4.24 0.23 0.11 2.05* r=-0.01

*p<0.05, **p<0.01, ***p<0.001 Bolded values = significant after correction for multiple comparisons using Bonferroni

4.4.3 Do prior estimates of EF predict future social functioning?1

Independent sample t-tests revealed that children with ASD demonstrated significantly more

parent-reported impairment in social functioning (SRS Total score) than TD at T2 (ASD;

M=71.97, SD=12.01; TD: M=44.85, SD=5.98), t(56) = -12.32, p<0.001 (degrees of freedom

adjusted for Levene’s test for equality of variances)).

Results of bivariate correlations show that behaviour regulation (TD: r=0.5, p<0.01; ASD:

1 The SRS was not submitted at T2 by one parent in the ASD group, and thus the sample for this analysis varies

slightly from that described above (TD n=32; ASD n=38). Demographic data varied minimally, with no meaningful differences from the sample described in Table 6.

71

r=0.59, p<0.001) and metacognitive abilities (TD: r=0.34, p<0.05; ASD: r=0.51, p<0.001) at T1

were significantly correlated with later social functioning at T2 for both groups (see Table 9).

Table 9. Correlation matrixes of EF variables and social functioning for (a) TD and (b) children with ASD.

A) TD

1 2 3 4 5 1. SRS Total T2 -- 2. BRIEF BRI T1 0.50** -- 3. BRIEF MCI T1 0.34* 0.70*** -- 4. IQ -0.08 0.28 0.13 -- 5. Sex -0.21 -0.07 -0.19 0.20 --

*p<0.05, **p<0.01, ***p<0.001 B) ASD Group

1 2 3 4 5 1. SRS Total T2 -- 2. BRIEF BRI T1 0.59*** -- 3. BRIEF MCI T1 0.51*** 0.59*** -- 4. IQ -0.17 -0.17 0.01 -- 5. Sex 0.02 -0.05 -0.04 0 --

*p<0.05, **p<0.01, ***p<0.001

Results of regressions showed that in children with ASD, both behaviour regulation and

metacognitive difficulties at T1 predicted social impairments 2 years later (p<0.001), accounting

for 34% and 24% of adjusted variance, respectively (Table 8). The unstandardized regression

coefficients of BRI and MCI in these models were not significantly different (t(72) = 0.36,

p=0.72). However, the partial correlation between MCIT1 and SRS TotalT2 was weak when BRI

was partialled out (r=0.250, p=0.135), whereas the partial correlation between BRI and SRS

Total remained significant even when controlling for MCI (r=0.42, p<0.01). These findings

suggest that the unique contribution of MCI in explaining the variance of later social functioning

is minimal, much less than BRI. In TD, both BRI (p<0.01) and MCI (p=0.05) predicted later

social functioning, accounting for 22% and 9% of adjusted variance. However, after Bonferroni

correction, MCI was no longer a significant predictor of SRS Total in this model.

An exploratory analysis2 was performed to understand the relation between changes in real-

2 Sample for this analysis consisted of 32 TD and 37 ASD, after excluding participants who failed to complete the SRS at both time points, Demographic data varied minimally, with no meaningful differences from sampled described in Table 6.

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world EF and changes in social functioning from T1 to T2 in ASD. A mixed ANOVA revealed a

significant Group x Time interaction (p<0.001) on children’s SRS Total scores (Figure 10).

Furthermore, results showed a significant effect of time and group (p<0.001). Post hoc analysis

revealed a significant improvement of social functioning over time in children with ASD only

(p<0.001), but who were still impaired at both time points. The TD group showed very minimal

change in social functioning over 2 years (i.e., less than one standard score). When controlling

for age, we found no significant Time x Age interaction on the SRS Total, indicating that effects

of time on social function were not impacted by initial age of the participants. Independent

sample t-tests revealed that children with ASD demonstrated significantly more parent-reported

impairment in social functioning (SRS Total score) than TD children at T2 (ASD; M=71.97,

SD=12.01; TD: M=44.85, SD=5.98), t(56) = -12.32, p<0.001 (degrees of freedom adjusted for

Levene’s test for equality of variances)).

Figure 10. Mean T scores and standard error bars of parent-reported SRS Total at T1 and T2

40

50

60

70

80

90

Time1 Time2

Tscore

TimePoint

SRSTotal

ASD

TDC

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We examined the predictive link between change in scores of EF and social functioning over 2

years in children with ASD. Preliminary Pearson’s correlations between change of BRIEF BRI

and MCI and change of SRS Total revealed a significant positive correlation between change in

MCI and change in SRS Total (r=0.45, p<0.01). We found no significant correlation between

change in BRI and change in SRS Total (r=0.27, p=0.11; See Table 10). Table 10. Correlation matrix of change of EF and social functioning from T1 to T2 in ASD

1 2 3 4 5 1. Change in SRS Total -- 2. Change in BRIEF BRI 0.27 -- 3. Change in BRIEF MCI 0.45** 0.56*** -- 4. IQ -0.11 -0.20 -0.15 -- 5. Sex -0.06 -0.08 -0.10 -0.01 --

*p<0.05, **p<0.01, ***p<0.001

The simple regression indicated that improvements in metacognitive abilities (BRIEF MCI)

significantly predicted improvement in social abilities (p<0.01), accounting for 18% of adjusted

variance (Table 8).

4.5 Discussion

This is the first study to (1) track the development of everyday EF in children with and without

ASD over 2 years utilizing parent ratings and (2) investigate how two domains of EF

(behavioural regulation and metacognition) are associated with later social, emotional and

behavioural outcomes. Results indicate that children with ASD showed persistent impairments

over time in all aspects of everyday EF compared to TD. Furthermore, these early difficulties of

EF were predictive of later social, emotional and behavioural problems in youth with ASD.

In line with previous cross sectional studies investigating the development of EF as observed in

everyday settings (i.e., informant-reported EF; Rosenthal et al., 2013; van den Bergh et al.,

2014), we found no improvements of behavioural regulation and metacognitive abilities over

two years in both groups, with the exception of inhibition, which showed significant

improvement over time in children with ASD. Age related improvements of inhibition in ASD

have also been reported by van den Bergh and colleagues (2014), and may suggest a delayed or

protracted development of inhibition. Findings of no EF maturation in TD may be due to

74

possible ceiling effects for this group. Closer examination of EF subdomains revealed greatest

difficulties in flexibility (highest T- scores on the BRIEF Shift subscale) at both time points for

children with ASD relative to TD, which is consistent with previous research (Granader et al.

2014; Russo et al., 2007; Wallace et al., 2016). While many studies employing laboratory tasks

demonstrate that EF problems become less marked with age (Best and Miller, 2010; Happé et

al., 2006; Pellicano, 2010), the present study documented significant EF difficulties in everyday

settings (i.e., parent-report) that persisted over time in youth with ASD compared to TD.

Everyday EF deficits have been shown in adults with ASD as well (Wallace et al., 2016). These

findings indicate that while environmental demands increase dramatically as children enter

adolescence, refinements of EF problems are impaired in ASD, making it challenging for them

to adapt to and keep pace with growing real world demands.

The present study also found that prior estimates of everyday EF were predictive of key

outcomes in ASD: emotional (i.e., anxiety/depression symptoms), behavioural (i.e.

aggressive/oppositional behaviour) and overall social functioning. Specifically, behavioural

regulation difficulties at baseline predicted internalizing symptoms (anxiety and depression) two

years later in children with ASD. Furthermore, both behavioural regulation and metacognitive

problems at baseline predicted externalizing symptoms two years later, specifically

oppositionality, conduct and aggressive/disruptive behaviours. However, it should be noted that

when controlling for behaviour regulation skills, metacognitive abilities were no longer

significantly associated with externalizing symptoms. Thus, these results indicate that deficits in

EF related mainly to behaviour (i.e., inhibition, shifting, emotional control) are relevant for the

development of future emotional and behavioural problems in ASD. Similarly, Lawson et al.

(2015) reported that specific deficits in parent-reported flexibility predicted greater

anxiety/depression and aggressive symptoms in childhood ASD. Moreover, parent-reported

inflexibility in adults with ASD is associated with anxiety-related symptoms, while

metacognition problems are related to depression symptoms, above and beyond the influence of

attentional problems (Wallace et al., 2016). Our findings also parallel the results of studies

employing lab-based tasks of EF, which have demonstrated links between poor shifting and

inhibition (i.e., behavioural EF domains), but not working memory (i.e., metacognitive EF

domain), and greater symptoms of anxiety in youth with ASD (Hollocks et al., 2014). In

contrast, a longitudinal study by Andersen et al. (2015b) reported that behaviour and emotional

improvement over time was not associated with increased verbal working memory capacity in

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ASD. One explanation for this discrepancy may be that EF related to behaviour, which captures

the ability to appropriately control behavioural and emotional responses, is more critical for

emotional and behavioural functioning in this group than metacognitive processes of EF, which

on the other hand reflect the ability to cognitively self-manage tasks. Overall, the present study

extends previous literature by demonstrating a two-year predictive developmental association

between everyday manifestations of EF and co-morbid emotional and behavioural

psychopathology.

We also found that behavioural regulation EF processes predicted social functioning two years

later in all children, but there was a distinct developmental link between metacognition (i.e.,

initiating, planning, organization, working memory, self-monitoring) and later social

functioning in children with ASD only, consistent with previous findings from our group

(citation removed for blinded peer review). However, exploratory analyses examining the

association between change in EF and change in social functioning over time in ASD revealed

that an improvement in metacognitive processes only predicted an improvement in social

functioning across two years. Similarly, previous literature (non-longitudinal) has also linked

real-world metacognitive deficits, but not behaviour regulation, to weak adaptive social skills

(Gilotty et al., 2002). That a reduction in social deficits is associated with improvement in

metacognitive skills, but not behavioural regulation, suggests that the ability to take initiative,

plan and carry out possible actions, maintain task-relevant information in mind, and

monitor/control ongoing mental operations is particularly crucial for social development in

ASD. Research has consistently reported an association between metacognitive skills and

theory of mind abilities, which can be considered a proxy for social functioning (see Hughes &

Leekman, 2004 for review). Additionally, interventions with a focus on metacognitive skills

have demonstrated effectiveness in improving social functioning in children and adolescents

with ASD (Kenworthy et al., 2014) and intellectual disabilities (Rosenthal-Malek and Yoshida,

1994).

The current study had a number of limitations. First, our sample was relatively small, and a

larger sample may have yielded additional information regarding the relations between EF,

emotional/behavioural functioning and social functioning, and would have allowed us to

conduct more complex analysis. Furthermore, given our sample size, we were limited in the

number of statistical analyses that could be conducted and, in turn, we focused on BRIEF index

76

scores (i.e., BRI and MCI). Future research examining the longitudinal relations between EF

sub-functions (i.e., BRIEF subscales), co-morbid psychopathology and social functioning is

needed to capture more specific EF impairments that may be related to developmental outcomes

in ASD. In the current study, we failed to find a relation between IQ and study variables;

relations may have been discovered using separate measures of verbal and non-verbal IQ. We

administered a 2-subtest version of the WASI and, consequently, were unable to examine verbal

and non-verbal IQ scores in the present study. Additionally, we did not have measures of

behaviour and emotional functioning at time 1 and were unable to explore whether change over

time was related to change of EF. Lastly, findings are restricted to development over two years

and further longitudinal investigations into adulthood are crucial to better understand the

trajectory of EF, and its relations to social, emotional and behavioural functioning across the

lifespan.

These findings represent the first steps toward characterizing the developmental trajectory of EF

in everyday settings and its developmental links with important areas of functioning in ASD,

and have a number of important implications. Findings suggest that the inclusion of EF

assessment in standard mental health evaluations for children with ASD may allow for a better

diagnosis and prognosis of social and emotional problems and will help inform treatment

planning. Further, findings suggest that everyday EF deficits are potential risk factors for the

development of later social, emotional and behavioural problems in children with ASD, and thus

early interventions targeting EF abilities, alongside traditional psychosocial or autism therapies,

could mitigate or possibly prevent poor social functioning or co-morbidity later in life. More

specifically, given that EF related to behaviour is more critical for later emotional and

behavioural functioning, and EF related to cognitions is more critical for social functioning, it

may be beneficial to tailor treatment. For instance, specific interventions for inhibition,

cognitive flexibility and emotional control (i.e., behavioural regulation) could yield potential

improvements in anxious/depressive symptoms and a reduction in aggressive/oppositional

behaviour, whereas training in planning, initiation, working memory, self-monitoring and

organization (i.e., metacognition) may result in improved social functioning. EF interventions

have yielded improvement in not only EF (Diamond, Barnett, Thomas, & Munro, 2007) but also

externalizing and internalizing behaviour (Riggs, Greenberg, Kusché, & Pentz, 2006) in TD

children, although the long-term impacts of such interventions are not well defined. Recent

investigations of an EF school-based curriculum (Unstuck and On Target!; Cannon, Kenworthy,

77

Alexander, Werner, & Anthony, 2011) targeting flexibility and metacognitive skills adapted

specifically for children with ASD demonstrated effectiveness in improving EF, classroom

behaviour and social skills (Kenworthy et al., 2014). Fisher and Happé (2005) also showed that

EF training in children with ASD contributed to improvements in theory of mind, but not EF.

Although this initial work is promising, it is limited and the impact of EF-based treatments on

co-morbid psychopathology remains unknown; as such, more research is needed in this area. If

early EF interventions are shown to improve social, emotional and behavioural outcomes in

ASD, this would not only strengthen the evidence for links between these factors, but also

provide important clinical insights. At this time, EF skills are not often the focus of

interventions for children with ASD, and the implications of our findings support the need to

adapt traditional treatment approaches to include EF training throughout childhood and

adolescence. Doing so may enhance prevention of co-morbid psychopathology and promote

social competence in youth with ASD, particularly during a time of cognitive development.

In conclusion, our study found that prior estimates of everyday EF predict later social

functioning and co-morbid psychopathology in youth with ASD. Specifically, findings show

that EF related to behaviour (i.e., behavioural regulation) are more critical for later emotional

and behavioural functioning, whereas EF related to cognitions (i.e., metacognition) are more

critical for social skill development over time in youth with ASD. Findings support the

importance of EF problems in influencing psychological and social outcomes in ASD, and as

potential intervention targets, alongside traditional autism and mental health therapies. Future

studies are needed to examine the effectiveness of EF-based interventions in diminishing co-

morbid psychopathology and social difficulties in children and adolescents with ASD.

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Chapter 5 General Discussion

79

General Discussion

5.1 Review of Objectives

The over-arching objective of this dissertation was to gain a greater understanding of EF

deficits, their development, and underlying neural correlates in children with ASD. Individuals

with ASD show abnormal brain growth patterns that have profound effects on white matter

maturation and on the way the brain communicates. Studies exploring white matter development

are limited by sample size, which is a concern in such a heterogeneous population as ASD.

Dysregulated brain growth in ASD leaves affected individuals particularly vulnerable to

disturbance of functions, such as EF, that rely on effective integration of brain regions. While

impairments in all domains of EF are documented in ASD, its neural underpinnings and their

development are not well-defined. EF deficits have been linked to core ASD symptomatology,

theory of mind and adaptive skills. However, less is known about its relation to other important

areas of functioning, such as emotional and behavioural functioning, and long-term impacts on

developmental outcomes. As such, this dissertation addressed gaps in the literature by (1)

determining the differences in age-related change of white matter between children with and

without ASD in a large sample; (2) investigating the functional development of neural correlates

associated with one domain of EF, working memory, and the impact of cognitive load in

children with ASD; and (3) exploring the links between everyday EF (observed in every day

settings) and later social, emotional and behavioural functioning.

5.2 Summary of Findings

The first study (Chapter 2) addressed critical limitations in the literature by exploring structural

brain differences in white matter development in children with and without ASD (ages 7-15

years) using DTI in a relatively large sample. Children with ASD showed reduced FA

compared to TD children in white matter tracts, including the corpus callosum, cingulum and

various projection fibers (e.g., thalamocortical fibres, corona radiate, cerebral peduncle),

consistent with widespread impaired white matter development. The majority of white matter

tracts with reduced FA also had corresponding decreased AD, suggesting that white matter

compromise in ASD arises primarily from disruptions of fiber coherence, rather than from

demyelination (Song et al., 2002). However, AD and FA differences between ASD and control

80

groups did not completely overlap, suggesting the involvement of other mechanisms influencing

white matter development in ASD. Another important finding of this study was that group

differences were consistent across age (i.e., white matter developmental trajectories did not

appear to differ between children and adolescents with and without ASD); thus, there was not

evidence of the ASD individuals catching up to controls over childhood. Furthermore,

symptomatology as measured by the ADOS did not correlate with DTI metrics in the ASD

group. In sum, results demonstrated widespread white matter compromise in children with ASD

that persisted over time. Affected white matter was not limited to specific networks or tracts

(i.e., long versus short distant tracts, frontal lobe tracts), with the majority of deficits in tracts

responsible for interhemispheric connectivity and information processing—essential for higher-

order, complex cognitive constructs, such as EF.

The next study (Chapter 3) investigated functional brain activity associated with EF, specifically

in visuo-spatial working memory, in ASD compared to controls (ages 7-13 years), and

developmental changes over two years. Despite similar task performance between groups,

marked differences were evident in the developmental trajectories of neural responses during the

visuo-working memory task that had four levels of difficulty. TD children showed increased

recruitment in the parietal cortex and fusiform gyrus as a function of load, and this load-

dependent activation increased with age, in contrast to the ASD group. This suggests that

children with ASD show inadequate modulation of neural activity during increasing working

memory load that does not mature over time, unlike their typical peers. Another important

finding of this study was that children with ASD showed greater load-dependent deactivation of

regions associated with the DMN (i.e., parahippocampal gyrus and ventro-medial prefrontal

cortex), which intensified across age, more so than control children. This suggests that

previously reported DMN deficits in ASD (e.g., Assaf et al., 2010) may become less marked

with age. In sum, neural mechanism underlying cognitive development in ASD during this

period of maturation reflect persistent impairment over time in the ability to modulate brain

activity with progressively more complex demands. As such, children with ASD may benefit

from early intervention and/or accommodations to support working memory deficits before the

demands of their social and academic worlds increase in adolescence.

The final study (Chapter 4) characterized the longitudinal developmental trajectory of EF in

every day, real-life settings, and the impact of EF deficits on the development of co-morbid

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psychopathology and social difficulties two years later. According to parent-reports, children

and adolescents (ages 7-14 years) with ASD were impaired on all domains of EF of the BRIEF

at both time points, compared to control children. Improvements were not observed across time

in any EF domain for children with ASD, with the exception of inhibition, which showed a

significant improvement over time. Prior estimates of behaviour regulation difficulties (i.e.,

shifting, inhibition, emotional control) predicted emotional (i.e., anxiety/depression) and

behavioural (i.e., oppositionality/aggressiveness) problems, as measured by the CBCL, two

years later in children with ASD. Furthermore, an improvement in metacognitive abilities (i.e.,

initiation, working memory, planning/organization, organizing materials) predicted an

improvement in social skills, as measured by the SRS over time in ASD. In sum, results

demonstrate links between EF and social, emotional and behavioural functioning, suggesting

that targeting EF may be effective in preventing and/or reducing co-morbid psychopathology

and social problems in youth with ASD. More specifically, findings that EF related to behaviour

(i.e., behaviour regulation skills) is critical for later emotional and behavioural outcomes,

whereas EF related to cognition (i.e., metacognitive skills) is crucial for social competence,

suggest that it may be beneficial to tailor treatment for these outcomes. Overall, results support

the need to include EF in traditional models of autism interventions as a way to improve

executive development, and also to enhance the prevention of co-morbid psychopathology and

promotion of social competence in youth.

5.3 Conclusions

Taken together, this thesis demonstrated that children and adolescents with ASD display both

structural and functional brain disturbances compared to typical developing children. Youth

with ASD show widespread white matter differences, with primary affected regions found in

tracts essential for higher order cognitive processes (i.e., EF) and general information

processing. Furthermore, functional neural systems associated with executive processes, such as

working memory, are impaired in youth with ASD, specifically in their ability modulate neural

activity in response to increasing cognitive demands, and show no maturation over time relative

to controls. While neural activation in regions associated with working memory does not

improve over time, DMN function appears to mature with age in ASD, as observed by an

increasing ability to suppress these regions with age with increasing cognitive demands Despite

these functional neural deficiencies in children with ASD, they demonstrate similar behavioural

82

task performance to typical developing children. However, children with ASD show marked EF

impairments in everyday settings as observed by parents. Everyday EF in children with ASD

does not improve over time, making it challenging for them to compensate for the increasing

complexity of real-world demands as they mature. These findings may suggest that while higher

order processing (i.e., neural modulation) may not be mandatory for EF performance in highly

controlled laboratory tasks, it may be less adaptive for functioning in real life socialized

environments, to which individuals with ASD are more vulnerable than controls. In other words,

neural deficits in ASD may have more consequences to functioning in day-to-day life than in

experimental situations. EF deficits in ASD also have lasting negative impacts on children’s

social, emotional and behavioural functioning. I showed that parent-reported EF difficulties in

children with ASD predict symptoms of co-morbid psychopathology and social difficulties two

years later. Thus, children and adolescents with ASD demonstrate marked EF deficits across

age and in underlying neural systems, impacting cognitive, social, and psychological

development. These findings support EF as a potential target in autism interventions.

5.4 Future Directions

Many areas of future investigations of EF in children with ASD are warranted. First, this

dissertation highlights importance of combining research methods in future studies. While a

number of studies, including my own, have reported aberrant structural (see Travers et al., 2012)

and functional connectivity (see Hernandez et al., 2015), few have linked fMRI and DTI

measures, and thus the relations between white matter development and changes in brain

activity in ASD are unknown. Strong relations between strength of default mode connectivity

and white matter metrics (FA) have been reported, and relations to EF performance, in typical

developing children (Gordon et al., 2011). Moreover, one study reported correlations between

FA values in frontal-parietal white matter and neural activation in nearby grey matter regions

during performance on a working memory task in normative populations (Olesen, Nagy,

Westerberg, & Klingberg, 2003). As such, white matter maturation could affect cortical activity

and, in turn, cognitive capacity, particularly in neurodevelopmental disorders such as ASD, yet

limited work has been done in this area. Future research combining fMRI and DTI techniques is

needed to explore how differences in brain function may be impacted by variations in

microstructural brain development in ASD. Longitudinal studies beginning in early childhood

(toddlerhood) will help disentangle causal links and mechanisms between alterations of brain

83

microstructure and function, and cognitive/behavioural deficits, which may inform biological

interventions for autism. Studies of brain, behaviour and cognition in the same sample will

facilitate a better understanding of the impact of abnormal neural development on functioning in

day-to-day life.

Additionally, given discrepancies between EF performance in laboratory settings and in every-

day life, future studies should incorporate multiple assessment tools to obtain more insight into

the cognitive and EF profiles of individuals with ASD. Researchers have begun to explore more

ecologically valid methods of assessing EF in ASD. For instance, Mackinlay and colleagues

(2006) aimed to capture everyday EF problems experimentally in children with ASD by

combining a laboratory measure of multitasking, which has high ecological validity, with

comprehensive EF informant questionnaires. This study found group differences in

multitasking; compared to TD children, participants with ASD were less efficient at planning,

attempted fewer tasks, struggled to switch between tasks, and broke performance rules more

frequently. Such multi-tool assessment models will improve our understanding of such a

complex cognitive construct, like EF, in ASD.

Another important consideration for future neuroimaging studies of ASD is sample composition

and size. First and foremost, studies of individuals with low functioning ASD (i.e., ASD plus

intellectual deficits) are generally missing from neuroimaging literature. Results from studies

included in this dissertation, and the majority of previous studies, include ASD samples with

IQs within the average range, limiting results to relatively high functioning ASD (i.e., no

intellectual deficits). One DTI study of young adult males with ASD and intellectual disability

found reduced FA in the frontal cortex, including the orbitofrontal and anterior cingulate

regions, and relations between severity of intellectual impairment and extent of white matter

abnormalities (Pardini et al., 2009).

In terms of fMRI studies investigating EF, no studies have been reported in low-functioning

individuals. This is a significant gap in the literature, as low functioning children could

potentially show significant brain abnormalities and stronger links with behavioural

impairments. Given the heterogeneity of ASD, future research is encouraged to concentrate on

low-functioning groups, and how their neurodevelopmental profiles differ from higher

functioning individuals. As such, larger sample sizes will also be needed to identify

distinguishable ASD subgroups. Given challenges recruiting large pediatric clinical samples, it

84

will be crucial to make use of large-scaled shared international multi-site data sets, such as

ABIDE.

Additionally, there are limited studies exploring brain function associated with EF in pre-

adolescent children. As there are vital periods during development when altered brain function

influences the onset of behavioural symptoms, future studies that focus on younger populations

are warranted. Last, by using TD children as a comparison group in the current dissertation,

findings can only provide information about differences from the norm. Comparisons to other

atypical populations who share similar cognitive but different clinical profiles, such as ADHD,

are needed to understand patterns that are unique to ASD, potentially explaining the distinctive

behaviours in this disorder. Although, studies have identified distinguishable EF profiles

between ASD and ADHD (Andersen et al., 2015b; Happé et al., 2006), imaging studies are

needed to further our understanding about the neural patterns that are distinct to autism.

Additional longitudinal research examining both structural and functional changes in the brain

across the lifespan (into adulthood) is needed, given that ASD is a life-long condition. This is

particularly important when examining EF, as it has a protracted maturation with refinements

observed into young adulthood. A clearer conceptualization of how EF and associated structural

and functional neural correlates develop across the lifespan would not only provide detailed

developmental theoretical accounts of ASD, but also lead to important insights regarding

optimal timing and nature of critical interventions. Longitudinal investigations exploring

interventions targeting EF in children with ASD are also desperately needed. Despite increasing

literature focused on EF interventions in typical development (e.g., Karbach & Unger, 2014;

Melby-Lervåg & Hulme, 2013; Traverso, Viterbori & Usai, 2015) and ADHD (e.g., Dovis, Van

der Oord, Wiers, & Prins, 2015; Kray, Karbach, Haening, & Freitag, 2012; Rapport, Orban,

Kofler, & Friedman, 2013; Re, Capodieci, & Carnoldi, 2015; Shuai et al., 2017; Tamm,

Nakonezny & Hughes, 2014) in the past decade, few have explored this issue in ASD.

Moreover, prior studies examining EF interventions have demonstrated mixed success. One

study found no improvement of EF in children with ASD following a short-term clinic-based EF

training in a small sample (Fisher & Happé, 2005). A randomized controlled trial investigating

working memory and flexibility training in children with ASD showed EF improvement

following treatment but no significant differential intervention effects from the placebo

condition (de Vries et al., 2015). Cannon and colleagues (2011) developed a cognitive-

85

behavioural school-based intervention adapted for individuals with ASD, called ‘Unstuck and

on Target!’ (UOT), that targets cognitive flexibility, metacognitive skills (i.e., big picture

thinking and planning) and problem-solving. A recent randomized control trial comparing UOT

with a social skills intervention in school-age children with ASD found that UOT demonstrated

greater effectiveness in improving not only EF, but also classroom behaviour (e.g., following

rules, making transitions; Kenworthy et al., 2014). Children in both interventions made

equivalent improvements in social skills. Although this initial work is promising, the

development and study of EF interventions in ASD is greatly needed.

Given previous literature and personal clinical experience, EF intervention should occur within

the naturalistic settings, such as home or school environments in order to maximize the potential

for generalization of learned skills. Parent and/or teacher training is important for facilitating

such contextually-based interventions, and would result in a high dosage of intervention while

minimizing costs to public systems. Interventions should be developmentally sensitive (e.g., use

of play-based approaches for young children) and adapted for children with ASD (e.g., use of

role play, visual schedules, social stories, and reinforcements). Finally, a combination of

environmental accommodations and remedial strategies (i.e., student-focus teaching) may be

most effective in supporting executive function skill development in children with ASD. See

Figure 11 for an sample guide in designing an EF intervention study for children with ASD.

86

Figure 11. Sample guide on designing and studying the effectiveness of an EF intervention for children with ASD

This dissertation demonstrated a developmental link between EF deficits and symptoms of

anxiety/depression, aggressive behaviour and social difficulties in ASD. These results provide

preliminary support for future investigations exploring the effects of EF intervention on not only

children’s executive development, but also on other important outcomes, such as social,

emotional and behavioural functioning. Research exploring school-based interventions, such as

the Promoting Alternative Thinking program (PATH; Riggs et al., 2006) and Rochester

Resilience Project (Wyman et al., 2010), teaching self-regulation and problem solving skills for

neurotypical children, have generally demonstrated effectiveness in reducing externalizing and

internalizing behaviour and in promoting social competence. However, little is known about the

generalization effects of EF interventions on psychological and social development in ASD.

87

Currently, EF is not traditionally the focus of autism interventions. However, future work in

this area will not only further our understanding of the mechanistic links between EF and

various outcomes, but also answer questions about whether EF should be considered in models

of ASD interventions moving forward. Lastly, considerably more research is needed to explore

the impact of EF-based intervention on neural functioning in ASD. One possible avenue for

future research is the use of fMRI methods to monitor the effectiveness of EF interventions in

enhancing brain function in critical regions, such as the parietal and temporal areas (as reported

in the current thesis). Enhancement of brain function may potentially be another indicator of

invention effectiveness.

To conclude, this dissertation adds to the limited research investigating cognitive and EF

profiles, and their underlying neural mechanisms in children with ASD. Youth with ASD

showed marked white matter differences from typical development in tracts essential for

complex information processing, such as EF. Functional neural deficiencies associated with

executive processes were also observed in this group. Specifically, children with ASD

demonstrated inadequate modulation of neural activation in frontal and temporal areas in

response to increasing working memory load, and impairment persisted over time compared to

controls. Furthermore, parent-report EF deficits predicted later symptoms of co-morbid

psychopathology and social impairments in children with ASD. These findings localize brain

areas vulnerable to developmental disturbance and provide preliminary support for future

research investigating EF as a target in autism interventions for reducing cognitive and

behavioural impairments, and also enhancing psychological and social development.

88

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1

Appendix A

Supplementary Tables

1

Table A.1. FA age model parameters in JHU tracts (Study 1)

JHU Index Region

FA as a Function of Agea

Slope Y Intercept CONTROL ASD CONTROL ASD

1 Middle cerebellar peduncle 9.50E-04 1.55E-03 0.45 0.43 2 Pontine crossing tract (a part of MCP) -2.04E-03 1.60E-03 0.45 0.41

3* Genu of corpus callosum 9.00E-04 1.99E-03 0.53 0.51 4* Body of corpus callosum 3.99E-03 3.40E-03 0.54 0.53 5* Splenium of corpus callosum 2.74E-03 4.14E-03 0.66 0.64 6 Fornix (column and body of fornix) 1.04E-04 -2.32E-03 0.39 0.41 7 Corticospinal tract R 6.50E-03 5.59E-03 0.41 0.42 8 Corticospinal tract L 4.67E-03 6.40E-03 0.44 0.41 9 Medial lemniscus R 5.28E-03 5.34E-03 0.47 0.47

10 Medial lemniscus L 6.13E-03 4.93E-03 0.46 0.47 11 Inferior cerebellar peduncle R 3.91E-03 7.56E-03 0.40 0.35 12 Inferior cerebellar peduncle L 5.80E-03 7.16E-03 0.36 0.34 13 Superior cerebellar peduncle R 2.74E-03 3.76E-03 0.48 0.46 14 Superior cerebellar peduncle L 2.59E-03 4.29E-03 0.45 0.43

15* Cerebral peduncle R 3.82E-03 4.22E-03 0.58 0.56 16 Cerebral peduncle L 5.14E-03 4.09E-03 0.55 0.55

17* Anterior limb of internal capsule R 2.91E-03 3.91E-03 0.49 0.47 18 Anterior limb of internal capsule L 3.58E-03 4.37E-03 0.45 0.43

19* Posterior limb of internal capsule R 2.82E-03 5.03E-03 0.57 0.54 20 Posterior limb of internal capsule L 1.79E-03 3.62E-03 0.56 0.53

21* Retrolenticular part of internal capsule R 3.81E-03 4.03E-03 0.50 0.50 22 Retrolenticular part of internal capsule L 2.34E-03 3.31E-03 0.50 0.48

23* Anterior corona radiata R 4.56E-04 1.10E-03 0.42 0.40 24 Anterior corona radiata L 9.87E-04 1.67E-03 0.41 0.40

25* Superior corona radiata R 4.35E-03 4.08E-03 0.42 0.42 26 Superior corona radiata L 3.06E-03 2.92E-03 0.40 0.40

27* Posterior corona radiata R 4.33E-03 4.97E-03 0.40 0.38 28* Posterior corona radiata L 2.94E-03 4.30E-03 0.39 0.37 29* Posterior thalamic radiation R 4.21E-03 2.65E-03 0.50 0.52 30* Posterior thalamic radiation L 2.70E-03 2.85E-03 0.51 0.49 31 Sagittal stratum R 4.68E-03 2.25E-03 0.45 0.47

32* Sagittal stratum L 2.80E-03 2.25E-03 0.41 0.41 33 External capsule R 2.91E-03 2.72E-03 0.35 0.34 34 External capsule L 2.02E-03 2.42E-03 0.34 0.33 35 Cingulum (cingulate gyrus) R 6.99E-03 9.53E-03 0.34 0.31

36* Cingulum (cingulate gyrus) L 5.70E-03 8.84E-03 0.39 0.35 37 Cingulum (hippocampus) R 3.09E-03 3.00E-03 0.32 0.32 38 Cingulum (hippocampus) L 3.01E-03 3.39E-03 0.29 0.29 39 Fornix (cres) / Stria terminalis R 9.98E-04 2.50E-03 0.45 0.43 40 Fornix (cres) / Stria terminalis L 6.62E-04 1.19E-03 0.44 0.43

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41 Superior longitudinal fasciculus R 5.09E-03 4.21E-03 0.39 0.40 42 Superior longitudinal fasciculus L 4.07E-03 4.00E-03 0.36 0.36 43 Superior fronto-occipital fasciculus R 3.27E-03 1.01E-03 0.43 0.45 44 Superior fronto-occipital fasciculus L 1.87E-03 1.04E-03 0.40 0.40 45 Uncinate fasciculus R 1.98E-03 4.51E-03 0.42 0.38 46 Uncinate fasciculus L 2.34E-03 2.73E-03 0.38 0.37 47 Tapetum R 2.14E-03 4.86E-03 0.35 0.31 48 Tapetum L 5.33E-04 4.21E-03 0.29 0.24

a Slope and Y-intercept were calculated using the average FA of all voxels in each JHU tract, including voxels where FA did not differ between TD and ASD groups. * = JHU tracts showing greater FA in TD children compared to children with ASD (p < .01, corrected for multiple comparisons)

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Appendix B

Supplementary Figures

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Figure B.1. Mean standard scores and standard error bars for subtests of the Working Memory Test Battery for Children (WMTB-C) at baseline and follow-up (Study 2)

Standard scores on the administered subscales of the WMTB-C were compared across time and group using five 2-way mixed ANOVAs (for each subtest) to understand the longitudinal changes on neuropsychological measures of working memory, and if they differ between children with and without ASD. The mixed ANOVA for Digit Recall and Listening Recall showed no significant Group x Time interaction effects; there was an effect of group (p = 0.02, p=0.047, respectively), with poorer scores for children with ASD. The mixed ANOVAs for Block Recall and Mazes Memory revealed no significant interaction, effect of time, or effect of group. For the Backwards Digit Recall subtest, no significant group x time interaction or effect of group was seen. However, all children showed significant improvement in scores across time (p < 0.01).

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Figure B.2. Longitudinal trajecotry of BRIEF subscale scores over 2 years for children with and without ASD (Study 3)

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