Timing and Location of Brain Areas Involved in Cognitive ...€¦ · viii List of Figures Figure 1: The stimuli for the false belief task.14 Figure 2: Boxplots illustrating the scores

Timing and Location of Brain Areas Involved in Cognitive Theory of Mind in Children with Autism Spectrum Disorder

by

Veronica Wai-Jong Yuk

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

Department of Psychology University of Toronto

© Copyright by Veronica Wai-Jong Yuk, 2015

ii

Timing and Location of Brain Areas Involved in Cognitive Theory

of Mind in Children with Autism Spectrum Disorder

Veronica Wai-Jong Yuk

Master of Arts in Psychology

Department of Psychology

University of Toronto

2015

Abstract

Cognitive theory of mind (ToM), or the ability to recognize the different mental states of others,

is a social cognitive skill that is often impaired in children with autism spectrum disorder (ASD).

This study enriches our understanding of ToM in ASD by using magnetoencephalography

(MEG) to determine the temporal properties of brain regions active during a false belief task, a

domain that has not been explored in this field, and yet is a significant component of brain

activity. We found that whereas typically-developing children activate familiar ToM regions,

such as the precuneus and the left temporoparietal junction, children with ASD appear to rely

more on working memory and inhibition regions, such as the right dorsal temporoparietal

junction and the right inferior frontal gyrus. This atypical activation suggests that children with

ASD make use of alternative strategies to compensate for their deficits in ToM.

iii

Acknowledgments

The path to scientific discovery is a long and arduous journey, in which no one person can travel

alone. Therefore, I would like to thank a number of people for their leadership and

companionship throughout this harrowing adventure.

First and foremost, I would like to thank my supervisor, Dr. Margot Taylor for all her

knowledge, guidance, and encouragement throughout this past year. Without her, this project and

thesis would not have been possible, and I would have never had the chance to work in such an

innovative field with the most wonderful people.

I would also like to express my gratitude to my committee members, Dr. Daphna Buchsbaum

and Dr. Evdokia Anagnostou, for their valuable comments and fresh perspectives.

I especially would like to thank Sarah Mossad, whose extensive knowledge and support were of

immense help to this project. Our countless hours spent analyzing data together in the lab were

much less agonizing because of her presence and good humour.

A very special thanks goes to Dr. Charline Urbain, whose boundless wisdom and expertise were

indispensable, and without whom I and countless others would still be lost.

I would further like to thank Anne Keller for her constant positivity and clever ideas, and for her

perseverance, despite all the setbacks we endured during analysis. Because of her, this project

and many others were able to move forward swiftly and efficiently.

I am also grateful to Dr. Elizabeth Pang, whose advice and encouragement were incredibly

helpful during challenging times.

Many thanks also go to Rachel Leung, MyLoi Huynh, Amanda Robertson, Marc Lalancette,

Tammy Rayner, and Ruth Weiss for all their assistance with testing participants and data

analysis. Rachel’s knowledge and experience with autism was particularly informative for this

thesis, and MyLoi and Amanda were a tremendous help in gathering data for this project.

I would also like to thank the rest of my colleagues, Julia Young, Vanessa Vogan, Wayne Lee,

Ben Morgan, and Ben Dunkley for all their constructive input and suggestions for this project.

iv

Finally, I would like to convey my deep appreciation for my friends and family. They saw me

through the worst and most stressful of times this past year, and I would have never been able to

accomplish this work without their constant encouragement and joy that they bring to my life.

v

Table of Contents

Acknowledgments .......................................................................................................................... iii

Table of Contents ............................................................................................................................ v

List of Tables ................................................................................................................................ vii

List of Figures .............................................................................................................................. viii

List of Appendices ......................................................................................................................... ix

Chapter 1 Introduction .................................................................................................................... 1

1.1 Behavioural Studies ............................................................................................................ 2

1.2 Neuroimaging Studies ......................................................................................................... 3

1.2.1 The Posterior Superior Temporal Sulcus ................................................................ 4

1.2.2 The Temporoparietal Junction ................................................................................ 4

1.2.3 The Medial Prefrontal Cortex ................................................................................. 7

1.2.4 Temporal Aspects of the Mentalizing Network ...................................................... 9

Chapter 2 Objectives and Hypotheses .......................................................................................... 11

Chapter 3 Methods ........................................................................................................................ 12

3.1 Participants ........................................................................................................................ 12

3.2 Neurocognitive Assessments and Questionnaires ............................................................ 12

3.3 Task ................................................................................................................................... 13

3.4 MEG Data Acquisition ..................................................................................................... 15

3.5 MRI Data Acquisition ....................................................................................................... 15

3.6 Analysis ............................................................................................................................. 16

3.6.1 Behavioural Data Analysis ................................................................................... 16

3.6.2 MEG Data Analysis .............................................................................................. 17

Chapter 4 Results .......................................................................................................................... 19

4.1 Assessments ...................................................................................................................... 19

vi

4.2 Task Performance ............................................................................................................. 21

4.3 Neuroimaging ................................................................................................................... 23

4.3.1 Within-group comparisons .................................................................................... 23

4.3.1.1 TD, FB > TB ......................................................................................................... 23

4.3.1.2 ASD, FB > TB ...................................................................................................... 24

4.3.2 Between-group comparisons ................................................................................. 25

4.3.2.1 TD > ASD, FB ...................................................................................................... 25

4.3.2.2 ASD > TD, FB ...................................................................................................... 25

Chapter 5 Discussion .................................................................................................................... 36

5.1 Performance on Neurocognitive and Behavioural Measures ............................................ 36

5.2 Timing and Location of Theory of Mind Regions in the Brain ........................................ 37

5.2.1 Timeline of Activation in TD Children ................................................................ 38

5.2.2 Timeline of Activation in Children with ASD ...................................................... 40

5.2.3 Typically-Developing Children Rely Mainly on ToM Regions for False-Belief

Processing, Whereas Children with ASD Additionally Use Working Memory

and Inhibition ........................................................................................................ 42

5.2.4 The Intersection between Theory of Mind and Working Memory ....................... 43

5.3 Summary ........................................................................................................................... 44

5.4 Limitations and Future Directions .................................................................................... 44

5.5 Conclusions ....................................................................................................................... 45

References ..................................................................................................................................... 46

Appendices .................................................................................................................................... 66

vii

List of Tables

Table 1: Scores on Neurocognitive Assessments and Questionnaires 21

Table 2: Coordinates of Significant Peaks of Brain Activation of Interest in TD Children and

Children with ASD in the False-Belief Task 27

viii

List of Figures

Figure 1: The stimuli for the false belief task. 14

Figure 2: Boxplots illustrating the scores of the TD and ASD group on the neurocognitive

assessments. 20

Figure 3: Boxplots demonstrating how participants were rated on the parent questionnaires. 21

Figure 4: Boxplots comparing accuracy and reaction time on the FB and TB trials of the false-

belief task. 22

Figure 5: Timeline of brain activation for TD and ASD groups in response to the false-belief

task 33

ix

List of Appendices

Supplementary Figure 1: Glass brain images that depict areas that are and are not preserved in

the TD to ASD group comparison of false belief when

controlling for working memory load. 66

1

Chapter 1 Introduction

Theory of mind (ToM) was first defined by Premack and Woodruff (1978) as the ability to

attribute mental states to others, or the understanding that others may have thoughts, feelings,

and perspectives independent from our own. Though Premack and Woodruff’s (1978) work

concerned chimpanzees, their concept was extended to humans by Baron-Cohen, Leslie, and

Frith (1985), who proposed that a lack or impairment of ToM may be the cause of some of the

social deficits characteristic of autism spectrum disorder (ASD). Baron-Cohen (1988) also later

suggested that ToM may consist of two components: cognitive and affective. Cognitive ToM

refers to the recognition that everyone possesses separate mental states, whereas affective ToM is

more specifically concerned with the knowledge that others may feel differently than oneself in a

given situation (Baron-Cohen, 1988).

Although advances in neuroimaging in the past few decades have allowed researchers to probe

the brain areas underlying cognitive and affective ToM (Shamay-Tsoory, Tomer, Berger,

Goldsher, & Aharon-Peretz, 2005; Kalbe et al., 2010; Nandrino et al., 2014), relatively few

neuroimaging studies have looked at ToM in the ASD population, who consistently show deficits

in ToM (Baron-Cohen et al., 1985; Perner et al., 1989; Buitelaar, van der Wees, Swaab-

Barneveld, & van der Gaag, 1999; Beaumont & Newcombe, 2006; Matthews et al., 2012), and

even fewer have looked specifically at cognitive ToM in those with ASD, which may underlie

affective ToM abilities. While research in this field has focused mainly on locations of brain

activation (Happé et al., 1996; Vaidya et al., 2011; Lombardo, Chakrabarti, Bullmore, MRC

AIMS Consortium, & Baron-Cohen, 2011; von dem Hagen, Stoyanova, Rowe, Baron-Cohen, &

Calder, 2014), none has explored their timing in ASD; it has only been described in typically-

developing (TD) children (Sabbagh & Taylor, 2000; Vistoli, Brunet-Gouet, Baup-Bobin, Hardy-

Bayle, & Passerieux, 2011). While the specific areas involved in cognitive ToM in ASD are

extremely important to establish, timing information is equally as meaningful, since the

behavioural impairments seen in those with ASD may arise from both temporal and spatial

abnormalities in neural activation. Moreover, it is important to describe such differences in brain

activity during development as children and adolescents with ASD have also been shown to

recruit different brain regions in a variety of situations, compared to their TD peers (Leung et al.,

2

2014; Solomon et al., 2014; Doyle-Thomas et al., 2015), and any form of atypical brain activity

during childhood may affect the groundwork for behaviours exhibited in adulthood.

Given the importance of the temporal aspect of brain activation, this study investigated

differences in both timing and location of brain areas activated in children with ASD during a

false-belief task, a test often used to assess cognitive ToM abilities (Wellman, Cross, & Watson,

2001; Gweon & Saxe, 2013), using magnetoencephalography (MEG), a neuroimaging technique

that provides sensitive temporal and spatial measures of brain activity (Hari & Salmelin, 2012).

With the knowledge gained from this study, we can extend the current behavioural and

neuroimaging literature on cognitive ToM in children with ASD into the temporal domain,

allowing us to better characterize ToM deficits in the ASD population, which in turn will be

informative for designing, enhancing, and monitoring interventions to improve social outcomes

in children with ASD.

As the remainder of this work will mainly focus on cognitive ToM, the term ToM will

henceforth refer simply to the cognitive domain, unless otherwise specified.

1.1 Behavioural Studies

Behavioural studies investigating ToM in ASD began with Baron-Cohen et al.’s (1985) seminal

study, where they compared the performance of children with ASD to that of children with

Down’s syndrome and TD children on a classic false-belief ToM task, the Sally-Anne task. In

this task, Sally puts a marble in her basket. Sally leaves, and Anne moves the marble from the

basket to a box. The participant must then answer where Sally will look for her marble. While

the majority of Down’s syndrome and TD children passed the ToM task, a significant majority of

children with ASD failed the task (Baron-Cohen et al., 1985), signifying some fundamental

difference in ToM processing in ASD. In fact, this discrepancy in performance in these ToM

tasks may be not only due to a deficit, but also due to a delay in ToM ability acquisition (Baron-

Cohen, 1991; Sparrevohn & Howie, 1995; Serra, Loth, van Geert, Hurkens, & Minderaa, 2002).

It has been suggested that verbal ability in those with ASD is related to ToM aptitude, such that

an individual with ASD who has a high verbal age generally will do better on tests of ToM than

one who has a low verbal age (Happé, 1995; Sparrevohn & Howie, 1995; Steele, Joseph, &

Tager-Flusberg, 2003). However, because these studies linking verbal and ToM abilities are all

3

correlational, it is also equally likely that in addition to verbal abilities potentiating ToM

abilities, it may be that being able to apply ToM can also lead to improved verbal abilities in

children with ASD, suggesting a bidirectional relationship between the two (de Villiers, 2007).

For instance, Capps et al. (2000) found that while children with ASD and children with

developmental delays, who were matched for language ability, both had difficulties

understanding a narration, only in children with ASD were narrative abilities correlated with

ToM abilities. Furthermore, a number of studies have shown that individuals with ASD have

difficulties understanding pragmatic language, even though they have normal language abilities

(Martin & McDonald, 2004; Hale & Tager-Flusberg, 2005; Colle, Baron-Cohen, Wheelwright,

& van der Lely, 2008; Li, Law, Lam, & To, 2013).

An alternative explanation for the ToM deficits seen in ASD is that poor social motivation may

lead to a poor understanding of others’ mental states. There has been substantial research into the

lack of social motivation observed in those with ASD (Demurie, Roeyers, Baeyens, & Sonuga-

Barke, 2011; Chevallier, Grezes, Molesworth, Berthoz, & Happe, 2012; Stavropoulos & Carver,

2014; Wang et al., 2014) and how it may affect their cognitive development (Klintwall, Macari,

Eikeseth, & Chawarska, 2014; Vivanti, Trembath, & Dissanayake, 2014). Chevallier, Kohls,

Troiani, Brodkin, and Schultz (2012) make a persuasive argument that low social motivation

may lead specifically to social cognitive (i.e. ToM) deficits in individuals with ASD, in that

diminished social motivation manifests as a reduced desire to seek out social situations, resulting

in less practice processing social stimuli, which in turn leaves social cognitive abilities under-

developed. However, few studies have explored this hypothesis by directly comparing level of

social motivation to ToM abilities (Assaf et al., 2013), an avenue that may be worth pursuing to

explain ToM deficits in those with ASD.

1.2 Neuroimaging Studies

In order to comprehend the basis of these impairments in ToM in ASD, researchers investigating

ToM in TD individuals have identified a constellation of brain areas consistently activated

during a variety of ToM tasks, known collectively as the mentalizing network. This network

includes the precuneus/paracingulate cortex (Walter et al., 2004; Hervé, Razafimandimby,

Jobard, & Tzourio-Mazoyer, 2013), the temporal poles (Olson, Plotzker, & Ezzyat, 2007;

Reniers, Völlm, Elliott, & Corcoran, 2014), and, most importantly, the posterior superior

4

temporal sulcus (pSTS), the temporoparietal junction (TPJ), and the medial prefrontal cortex

(mPFC) (Gallagher & Frith, 2003; Carrington & Bailey, 2009; Dodell-Feder, Koster-Hale,

Badny, & Saxe, 2011).

1.2.1 The Posterior Superior Temporal Sulcus

The pSTS is involved in processing biological motion (Allison, Puce, & McCarthy, 2000;

Pelphrey et al., 2003; Herrington, Nymberg, & Schultz, 2011; Dayan et al., 2014). This function

may explain its role in perceiving or rationalizing the actions of others (Grezes, Frith, &

Passingham, 2004; Fukui et al., 2006; Wyk, Hudac, Carter, Sobel, & Pelphrey, 2009), as

people’s intentions are not always clear. Interestingly, Materna, Dicke, and Their (2008) found

that the pSTS was similarly activated during two social communicative gestures, a directed eye

gaze and a finger point toward a stimulus. Considering that individuals with ASD typically have

difficulties with following another’s eye gaze (Richler & Coss, 1976; Baron-Cohen, Baldwin, &

Crowson, 1997; Leekam, Hunnisett, & Moore, 1998), it is possible that they have a deficiency in

the pSTS, which might also lead to issues with other social communicative actions.

This idea is supported by both a behavioural study by Camaioni, Perucchini, Muratori, Parrini,

and Cesari (2003), which showed that young children with ASD failed to understand experience-

sharing pointing (as opposed to pointing to simply request something), and by an fMRI study by

Vaidya et al. (2011), which demonstrated that the pSTS was active for arrows (non-social

stimuli) that were used to direct attention, but not for gazes (social stimuli), whereas in TD

children, the opposite pattern of pSTS activation was observed. These findings are in line with a

recent EEG study by Stavropoulos and Carver (2014), who found that although children with

ASD processed rewards accompanied by non-social stimuli normally, they had reduced

responses to those accompanied by social stimuli.

1.2.2 The Temporoparietal Junction

Although the TPJ is adjacent to the pSTS, they have fairly unique functions. Gobbini, Koralek,

Bryan, Montgomery, and Haxby (2007) proposed that the pSTS is involved in interpreting

actions and biological movements, whereas the TPJ deals more with mentalizing in a false belief

scenario. The significance of the TPJ in ToM was brought to prominence in a paper by Saxe and

Wexler (2005), who showed that the right TPJ was specifically involved in attributing

5

behaviours to people, in that if a behaviour or mental state of a person was incongruent with their

previously described character, right TPJ activity would increase.

However, there is some uncertainty in the literature as to whether the left, right, or bilateral TPJ

is crucial for ToM reasoning. To date, few studies have shown that only the left TPJ is necessary

for ToM reasoning (Samson, Apperly, Chiavarino, & Humphreys, 2004). In a study by

Bahnemann, Dziobek, Prehn, Wolf, and Heekeren (2010), the left TPJ, but not the right TPJ, was

activated during a ToM task, where participants were asked to make a judgment on a character’s

mental state. They also found activity in the right TPJ, but it was correlated more with a moral

judgment task, where participants were asked to determine the congruency of a character’s

actions to their mental state, which may explain why the previous study by Saxe and Wexler

(2005) only found activity in the right TPJ and not the left. A meta-analysis by Schurz,

Aichhorn, Martin, and Perner (2013) found that the left TPJ, in addition to the left middle

occipital gyrus and precuneus, were all implicated in two important ToM processes, false belief

and visual perspective taking, but not the right TPJ.

In contrast, Rabin et al. (2010) found that while autobiographical memory and ToM processing

networks have some overlap, the right TPJ was exclusively activated in the ToM task. Similarly,

Mitchell (2008) observed that both an attentional and ToM task activated similar regions of the

right TPJ, prompting Scholz, Triantafyllou, Whitfield-Gabrieli, Brown, and Saxe (2009) to

respond with an experiment showing that they actually activate distinct areas in the right TPJ.

Despite these findings, one cannot ignore the number of studies that show bilateral activation of

the TPJ during ToM tasks, compared to unilateral activation (Rothmayr et al., 2011; Young,

Scholz, & Saxe, 2011; van Veluw & Chance, 2014). One study by Saxe and Powell (2006)

showed that both the left and right TPJ responded to stories about a character’s thoughts, but not

to stories about one’s internal state. In another experiment by Kobayashi, Glover, and Temple

(2007), the bilateral TPJ responded to both verbal and non-verbal false belief tasks in both TD

children and adults.

To clarify these seemingly disparate findings, Aichhorn et al., 2009 sought to elucidate the

specific roles of the left and right TPJ in ToM by comparing brain activation in TD individuals

on a variety of mentalizing tasks. They found that the left TPJ responded to false belief and false

sign tasks, whereas the right TPJ was activated by both true and false belief stories, which points

6

toward the left TPJ playing a role in incongruence detection, and the right TPJ in mentalizing

about others’ beliefs. These results appear to be contradictory to the studies mentioned above,

but it may be due to the fact that, for example, Bahnemann et al. (2010) used emotionally-

valenced stimuli (e.g. one character punching another), and so the emotional processes involved

interacted with and altered activity in the left and right TPJ.

In the ASD population, the TPJ often shows less or atypical activation in tasks involving ToM,

especially in the right TPJ. In an experiment by Williams et al. (2006), they found that

adolescents with ASD had greater activation in the right TPJ during action observation as

opposed to imitation, whereas TD adolescents showed the opposite. This inverse in function,

combined with the evidence that the pSTS is associated with understanding actions, illustrates

the likelihood of some dysfunction in the mentalizing network in those with ASD, where perhaps

the TPJ takes over the role of an impaired pSTS.

Children with ASD appear to have structural abnormalities that contribute to their deficits, in

addition to functional ones. In a study comparing grey matter differences between ASD,

attention deficit/hyperactivity disorder (ADHD), and control children and adolescents, the

participants with ASD had grey-matter abnormalities near the right TPJ, compared to the other

two groups (Brieber et al., 2007). These findings do not necessarily indicate that ToM deficits in

the ASD population are caused by an abnormal TPJ, since individuals with ADHD have also

been shown to have ToM difficulties (Buitelaar et al., 1999; Caillies, Bertot, Motte, Raynaud, &

Abely, 2014). This does not absolutely rule out the possibility of a dysfunctional TPJ, either, as it

is possible that those with ADHD have deficits elsewhere that lead to their ToM impairments.

Another study by Lombardo et al. (2011) noticed that in control adults, the right TPJ was

selectively active for mentalizing versus observing physical events, but in adults with ASD, the

right TPJ was active for both conditions, even when mentalizing about the self and others, and it

was less active overall than in controls. Moreover, this activation was negatively correlated with

social impairment symptom severity seen in the ASD group. A recent study by Kana, Libero,

Hu, Deshpande, and Colburn (2014) found similar results, where the ASD group recruited the

bilateral TPJ during a task where they had to determine the intentions of a character, although the

activation was weaker than in controls. This lower activation was paralleled by significantly

worse behavioural performance on the intentionality task. Taken together, these studies

7

demonstrate that impairments in the activation and recruitment of the TPJ are likely associated

with ToM deficits in ASD.

1.2.3 The Medial Prefrontal Cortex

An important component of the mentalizing network is the mPFC, and its contribution to ToM

abilities has been extensively studied. In one experiment, participants were asked to imagine

meeting a stranger and invent a narrative about their mental state, which instigated mPFC

activation (Calarge, Andreasen, & O’Leary, 2003). Saxe and Powell (2006) found that the mPFC

was activated similarly in three different tasks where they read about a character’s thoughts,

personal thoughts, or social information about a person, indicating that the mPFC may be a hub

for various social cognitive functions. These effects in mPFC were not limited to verbal

processes, as comparable results were found in non-verbal cartoon tasks looking at ToM

(Gallagher et al., 2000; Rothmayr et al., 2011). There is also some indication that the mPFC may

have distinct regions that subserve unique functions, as a recent study by Hartwright, Apperly,

and Hansen (2014) found that during a ToM task, the dorsal mPFC was related to the cognitive

load or demand, while the rostral mPFC was correlated with reasoning about the intentions of a

person. In addition to these two areas, the posterior mPFC has been shown to be causally related

to discriminating between the differing perspectives of others and oneself using repetitive

transcranial magnetic stimulation (rTMS), which is able to temporarily inhibit activity in a

specific area of the brain (Schuwerk et al., 2014). Considering that, as described previously,

Lombardo et al. (2011) found right TPJ activation for both self and other mentalizing in adults

with ASD, these findings suggest that the right TPJ and posterior mPFC may be functionally

connected, such that both areas depend on each other to separate one’s own thoughts from the

potentially dissimilar thoughts of others.

Lesion studies have also provided evidence for the mPFC’s significance in the mentalizing

network. Damage to the mPFC was associated with poor performance on more challenging ToM

tasks, such as deception (Stuss, Gallup Jr., & Alexander, 2001) and social faux pas (Stone,

Baron-Cohen, & Knight, 1998). A case study, however, described a patient with a large bilateral

mPFC lesion, covering areas implicated in previous studies of ToM, who had no impairments on

a wide range of ToM tasks, including the social faux pas task mentioned above (Bird, Castelli,

Malik, Frith, & Husain, 2004). These authors suggested that if, in fact, mPFC is involved in ToM

8

reasoning, then it may be the case that in older adults (as the patient was 62 at the time of her

infarction), the functions of the mPFC can be performed in another region of the mentalizing

network. This hypothesis is somewhat supported by a study by Moriguchi, Ohnishi, Mori,

Matsuda, and Komaki (2007), where the location of the peak of activation in the mPFC was

correlated with the age of the participant, such that in younger children, a ToM task activated

more ventral parts of the mPFC, and in the older children, activation was observed in more

dorsal parts of the mPFC, indicating that development plays a role in the site of mPFC activity

associated with ToM. However, since most studies investigating ToM development focus on

childhood rather than increasing age groups, it is difficult to say whether these changes in

location occur throughout the lifespan.

In ASD, issues with ToM have also been related to dysfunction in the mPFC. Early work by

Happe et al. (1996) indicated that while controls activated the left mPFC during a ToM task

where they were asked to think about why a character acted in a certain way, individuals with

Asperger’s syndrome exhibited no such activation for the same task. Marsh and Hamilton (2011)

showed that during a hand movement comprehension task, adults with ASD had no difference in

activation in the mPFC between a rational and an irrational hand movement, whereas the mPFC

had a greater response to an irrational hand movement in controls. In the language processing

domain, Colich et al. (2012) found that even though children and adolescents with and without

ASD were able to interpret and discriminate between ironic and sincere remarks, those without

ASD only activated left-lateralized language areas, while those with ASD recruited bilateral

language and mentalizing brain areas, which the authors attributed as compensatory mechanisms.

Thus, these findings in the ASD population complement the TD literature on the role of the

mPFC, and it appears that this brain region may be involved in interpreting the meaning behind

one’s actions by integrating information about another’s mental state.

Although most of the studies mentioned above found differences in only one of the regions of the

mentalizing network, there are a great number of studies showing activation of a combination of

these areas during ToM tasks. For instance, Dodell-Feder et al. (2011) and Dufour et al. (2014)

found that the pSTS, TPJ, and mPFC all responded consistently to stories involving false belief.

von dem Hagen et al. (2014) found similar results, where a direct gaze, which may prompt

automatic mental state processes, led to activation of the pSTS, TPJ, and mPFC in controls,

whereas these same areas were activated only when individuals with ASD looked at a face with

9

an averted gaze. These three areas were also found to be less functionally correlated in the ASD

group than in controls.

A number of studies have outlined the different functions of these areas by comparing their

activity in a variety of ToM tasks. Ciaramidaro et al. (2007) differentiated between the functions

of the left TPJ, the right TPJ, and the mPFC by showing that the right TPJ was involved mainly

in processing intentions, while the left TPJ and the mPFC were responsible for understanding

intentions. Vistoli et al. (2011) found that the left TPJ and pSTS perceive social cues, while the

right TPJ and pSTS recognize intentions. Also, Murdaugh, Nadendla, and Kana (2014) observed

that although both the TPJ and the mPFC were activated when thinking about the intentions of

others in a sequence of actions, the TPJ was more strongly correlated with the rest of the

mentalizing network. This suggests that the TPJ may play a more central role to ToM processing,

which is supported by another finding in their study that showed individuals with ASD had lower

functional connectivity in the mentalizing network, especially in the TPJ.

1.2.4 Temporal Aspects of the Mentalizing Network

The majority of temporal information regarding ToM-related brain activity has been determined

through electroencephalography (EEG) studies in adults. Sabbagh and Taylor (2000) were the

first to report the timing of brain activity during a false belief task, where left frontal areas

showed greater positive activity between 300-400 ms, and left parietal areas had more negative

activity between 600-840 ms. In a later study, Sabbagh, Apperly, Chiavarino, and Humphreys

(2004) asked participants to ascertain a person’s mental state based solely on the expression in

their eyes, and they found a negative EEG component from 270-400 ms in the orbitofrontal and

medial temporal regions, which may correspond to the activity seen in the mPFC in fMRI studies

(Gallagher & Frith, 2003). Similarly, using a mental reasoning task, where participants had to

predict which action a character would take based on what they were told of the character’s

mental state, Cao, Li, Li, and Li (2012) reported that the second negative EEG component in the

prefrontal cortex elicited by this task was both earlier and smaller between 240-440 ms than in

tasks that did not require reasoning about mental states. A study by McCleery, Surtees, Graham,

Richards, and Apperly (2011) found that a positive EEG component localized to the TPJ,

normally peaking around 450 ms, exhibited longer, sustained activity when participants were

10

asked to take the perspective of another person, especially when the other person’s perspective

was inconsistent with their own.

These results are supported by studies using MEG, which has a much better spatial resolution

than EEG (Anninos, Anogianakis, Lehnertz, Pantev, & Hoke, 1987). For instance, Pylkkanen

and McElree (2007) contrasted neural responses to sentences with implied meanings to those

with explicit meanings using MEG, and they observed that only the sentences with implied

connotations activated the ventral mPFC between 350-450 ms. In addition, Vistoli et al. (2011)

found that the right TPJ, right STS, and right inferior parietal cortex were recruited from 200-600

ms during an attribution of intentions task.

As mentioned above, there have been no studies as of yet that have analyzed the temporal

relationship between areas activated by tasks involving ToM in the ASD population. Only one

study by Hasegawa et al. (2013) has attempted to do so by connecting ToM-related brain activity

to Autism Quotient (AQ) scores in TD adults. They found that direct gaze elicited greater

activity in the right pSTS between 150-250 ms, and this activity positively correlated with AQ

scores. However, these results cannot be generalized to the ASD population, as children with

ASD undergo atypical neural development (Fishman, Keown, Lincoln, Pineda, & Müller, 2014;

Leung, Ye, Wong, Taylor, & Doesburg, 2014; Orekhova et al., 2014; Vogan et al., 2014), and so

replication with a clinically-diagnosed ASD sample is necessary to validate these results.

Although the roles of the pSTS, TPJ, and mPFC are not clearly defined, the existing literature

suggests that the pSTS is involved in processing and understanding biological motion, that the

TPJ may be responsible for processing actions and their intentions, and that the mPFC may be

implicated in understanding the intentions of social actions. It is impossible to sketch a complete

profile of the functions of these areas, though, without crucial timing information. While EEG

and MEG studies have shown that these regions are involved in fairly early neural processing

steps, generally between 200-400 ms post-stimulus, these results have yet to be consistently

reproduced and supported by separate studies.

11

Chapter 2 Objectives and Hypotheses

This study will add to our present knowledge of the timing of brain areas in the mentalizing

network by providing fundamental temporal and spatial information of brain areas activated

during a ToM task in children with ASD, which is a group that reliably shows ToM deficits

(Baron-Cohen et al., 1985; Happe, 1995; Buitelaar et al., 1999; Matthews et al., 2012), but for

which no timing information regarding brain activity during ToM processing has been reported.

We tested ToM abilities in two groups of children, those with ASD and those without (TD),

using a false-belief task similar to the classic Sally-Anne task (Baron-Cohen et al., 1985). To

capture the information on timing and location simultaneously, we recorded the children’s brain

activity as they did the task using MEG, which detects weak magnetic fields emitted from

neuronal activity with a temporal resolution of 1 ms and spatial resolution of 3 mm (Hari &

Salmelin, 2012). In response to the false-belief situations, we hypothesized that the TD children

would recruit the pSTS, TPJ, and mPFC, while the children with ASD would recruit only the

pSTS and the TPJ, though to a lesser degree, and they would draw on additional brain regions to

adjust for their impairments in ToM processing. With regards to timing, we predicted that, based

on the hierarchy of complexity of these areas, the pSTS would be involved in early (~200-300

ms) ToM processing, while the TPJ and mPFC would be associated with later ToM processes

(~300-400 ms and 400-500 ms post-stimulus, respectively) in TD children, but the children with

ASD would show a disordered and delayed temporal relation in the brain areas they recruit.

12

Chapter 3 Methods

3.1 Participants

For this study, 44 typically-developing (TD) participants and 34 participants with high-

functioning autism spectrum disorder (ASD) between the ages of 8 and 12 were recruited. Of

these participants, 11 TD and 15 children with ASD were not included in the analyses due to

incompatibility with the MEG scanner (i.e. noise due to metal artefacts), excessive movement,

and poor (≤ 50%) performance on the task. Participants were then matched for age and sex,

resulting in a sample of 22 TD children (19 males, 10.34 ± 1.32 years old) and 19 children with

ASD (16 males, 10.52 ± 1.45 years old). All participants were screened for low IQ (< 70) and

premature birth, and TD participants were not included if they reported any history of

psychological, neurological, or developmental disorders. Participants with ASD were not

excluded if they presented with comorbid disorders, although a primary diagnosis of ASD was

confirmed with a combination of expert clinical judgement and the Autism Diagnostic

Observation Schedule, Generic (ADOS-G; Lord et al., 2000) or Second Edition (ADOS-2; Lord

et al., 2012). Three children with ASD were each taking psychotropic medication at the time of

scanning, specifically Prozac and Concerta. Informed assent was given by all children, and

informed consent was obtained from their parents. All aspects of testing, including informed

consent, cognitive testing, and MEG and MRI scanning, took place at the Hospital for Sick

Children in Toronto, Canada. This study was approved by the Research Ethics Board at the

Hospital for Sick Children.

3.2 Neurocognitive Assessments and Questionnaires

A battery of neurocognitive tests was administered to all children to assess their general

executive functioning and cognitive skills. The two-subtest version of the Weschler Abbreviated

Scale of Intelligence, Second Edition (WASI-II; Weschler, 2002), which includes the

Vocabulary and Matrix Reasoning subtests, was used as an estimate of IQ. The Forward and

Backward Digit Recall subtests of the Working Memory Test Battery for Children (WMTB-C;

Pickering & Gathercole, 2001) were administered to detect working memory differences

between the two groups. Two subtests of the Developmental Neuropsychological Assessment,

13

Second Edition (NEPSY-II; Korkman, Kirk, & Kemp, 2007), namely Inhibition and Theory of

Mind, were also used to assess their respective titular cognitive domains.

Parents completed two questionnaires regarding their child’s executive functioning and social

behaviour: the Behavior Rating Inventory of Executive Function (BRIEF; Gioia, Isquith, Guy, &

Kentworthy, 2000) and the Social Responsiveness Scale, First (SRS; Constantino & Gruber,

2005) and Second Edition (SRS-2; Constantino & Gruber, 2012).

3.3 Task

Participants completed a nonverbal (picture based) false-belief task (Figure 1A) modelled after

the task used by Dennis et al. (2012) and adapted for the MEG scanner. In each trial, children

saw two consecutively presented visual stimuli, in which there are two characters, Jack and Jill.

In the first picture, Jack is holding a ball over one of two hats, and Jill is present and watching

him. The first picture was then replaced by the second one, where one of four scenarios was

presented: (1) Witnessed-Unswitched, where Jill watches (Witnessed) as Jack puts the ball in the

same hat he was holding it over in the first picture (Unswitched); (2) Witnessed-Switched, where

Jill watches (Witnessed) as Jack changes his mind and puts the ball in the other hat (Switched);

(3) Unwitnessed-Unswitched, where Jill goes away (Unwitnessed), and then Jack puts the ball in

the same hat (Unswitched); and (4) Unwitnessed-Switched, where Jill goes away (Unwitnessed),

and then Jack changes his mind and puts the ball in the other hat (Switched). The first three

conditions – Witnessed-Unswitched, Witnessed-Switched, and Unwitnessed-Unswitched – each

represented a true belief, as Jill would know the correct location of the ball in all these

conditions, whereas the other condition – Unwitnessed-Switched – represented a false belief,

since Jill would be mistaken as to the ball’s location, as Jack switched the ball without Jill’s

knowledge. The participants were asked to indicate, using a button box, in which one of the two

hats Jill thinks the ball is. They then received feedback on their response, either a green

checkmark for a correct answer or a red cross for an incorrect answer.

All children were given a short practice session outside of the MEG scanner to familiarize them

with the task. During the practice session, the experimenter explained the four different

conditions of the task and gave detailed feedback if the child did not understand parts of the task.

The practice session ended when the child was able to correctly answer the trials and verbally

indicated that they understood the requirements of the task.

14

True Belief False Belief

Witnessed-Unswitched Witnessed-Switched Unwitnessed-Unswitched Unwitnessed-Switched

A

B

Figure 1 - The stimuli for the false belief task, which were adapted from Dennis et al. (2012). (A) An example of

the four different types of trials in this task: Witnessed-Unswitched, Witnessed-Switched, Unwitnessed-

Unswitched, and Unwitnessed-Switched. The first three conditions tested true belief abilities, while the

Unwitnessed-Switched condition assessed false belief. Subsequent analyses focused on the Witnessed-Switched

and Unwitnessed-Switched conditions, outlined in green and red above. (B) The timing of the stimuli in the task.

The first picture of each trial appeared for 500 ms, followed by the second stimulus, which was shown for up to

3000 ms or until the child responded, whichever came first. After the response, feedback in the form of a green

checkmark or red cross appeared for 1000 ± 100ms, and the next trial began immediately after.

15

The participants then went into the magnetically-shielded room, where they performed the task

while lying supine in the MEG scanner. The stimuli for the task were presented using

Presentation 0.70 (Neurobehavioral Systems, 2014) and back-projected onto a screen at a

distance of 79 cm from their eyes. A photodiode attached to the screen was used to precisely

detect stimulus onset time. Each trial began with the presentation of the first stimulus for 500 ms,

then the second picture for a maximum of 3000 ms, or until the child responded, followed by

feedback for the trial, which was displayed for 1000 ms with a jitter of ± 100 ms (Figure 1B).

The task was designed such that the ball was put into each of the hats an equal number of times,

although in two-thirds of the trials, the ball’s position was switched from its initial position, and

in the remaining one-third, it was not switched. Additionally, in half the trials, Jill was present,

and in the other half, she was not. The trials were arranged in this way to capture more of the

Unwitnessed-Switched and Witnessed-Switched trials, as they were our two main conditions of

interest, without alerting the participants to this fact. The task ended when either the child

correctly completed a total of 300 trials over the four conditions (100 Unwitnessed-Switched,

100 Witnessed-Switched, 50 Unwitnessed-Unswitched, and 50 Witnessed-Unswitched), or after

15 minutes had passed, whichever came first. The participants’ responses and their latencies

were recorded with their MEG data.

3.4 MEG Data Acquisition

A CTF MEG system consisting of 151 axial gradiometers (MISL, Coquitlam, British Columbia,

Canada) was used to acquire the MEG data. Fiducial coils were placed on three reference points

– the nasion, and the left and right pre-auricular points – on each participant to track head

position. Children lay supine with only their head in the MEG dewar, during which time they

performed the task described above. Data were sampled at 600 Hz with continuous head

localization. To optimize the signal-to-noise ratio, a third-order spatial gradient was used, and the

signal was bandpassed from 0-150 Hz.

3.5 MRI Data Acquisition

A 3.0 T MRI scanner (MAGNETOM Tim Trio, Siemens AG, Erlangen, Germany) with a 12-

channel head coil in the Hospital for Sick Children was used to acquire anatomical MRIs to

allow accurate determination of sources of MEG activity. Radio-opaque markers were placed in

16

the same locations as the fiducial coils used in the MEG scan to ensure accurate MEG-MRI co-

registration. Participants completed a T1-weighted MRI scan using the 3D SAG MPRAGE

sequence with the following parameters: GRAPPA = 2, TR/TE/FA = 2300ms/2.96ms/90°, FOV

= 28.8 × 19.2cm, 256 × 256 matrix, 192 slices, slice thickness = 1.0mm isotropic voxels.

3.6 Analysis

3.6.1 Behavioural Data Analysis

Scores on the neurocognitive assessments and questionnaires, as well as the participants’

response times and accuracy on the task, were analyzed using R 3.1.2 (R Core Team, 2014). A

chi-square test and a t-test were performed to confirm homogeneity between the TD and ASD

groups with regards to sex and age, respectively. These tests revealed no difference in sex (Χ2 =

0, p = 1) or age (t(39) = 0.42, p = 0.67). T-tests were also used to compare the scores of the two

groups on each of the neurocognitive assessments and questionnaires. For the WASI-II, IQ was

used as an indicator of their performance on this test, as it is a classical measure of intelligence.

Composite scores that were created from the sum of the scaled scores of the Forward and

Backward Digit Recall tests of the WMTB-C were used for analysis, as we were interested in

differences in their general working memory abilities and not on their success on each of these

subtests. On the NEPSY-II Inhibition subtest, differences on the Inhibition vs. Naming scaled

score were examined, rather than the simple Inhibition scaled score, as we believed the Inhibition

vs. Naming score is a better measure of inhibition skills, since it is not confounded by their

ability to name the objects in the test. The total raw scores on the NEPSY-II Theory of Mind

subtest were contrasted as the NEPSY-II does not provide age-adjusted standard scores for this

subtest. However, the TD and ASD groups were age-matched, so their raw scores could be

directly compared. For the BRIEF and the SRS and SRS-2, their total scaled scores (the Global

Executive Composite (GEC) score for the BRIEF, and the Total Score for the SRS and SRS-2)

were analyzed to evaluate how the children were scored by their parents on these questionnaires.

Linear mixed-effects models were fit to determine differences in response time and accuracy on

the task, with group (TD or ASD), switching (Switched or Unswitched), and witnessing

(Witnessed or Unwitnessed) as predictors, and IQ as a covariate. Main effects of group,

switching, witnessing, and IQ were included in the analysis, as well as the interactions between

group and switching, group and witnessing, switching and witnessing, and group and IQ. To

17

account for the repeated measurement of each participant over the different conditions,

participants were included as random effects. Thus, the models constructed were as follows:

response time ~ group * switching * witnessing + group * IQ + (1 | participant)

accuracy ~ group * switching * witnessing + group * IQ + (1 | participant)

As mentioned above, our analyses focused mainly on differences between the Unwitnessed-

Switched and Witnessed-Switched conditions. We chose these two conditions for all our

analyses as they reflected the differences between false and true beliefs most clearly, with Jill’s

absence or presence indicating the generation of a false or true belief, respectively, while

controlling for participants’ understanding of switching the ball’s position. Therefore, although

our task paradigm contains three different true belief scenarios, only the Witnessed-Switched

condition will hereafter be referred to as the true belief (TB) condition, and the Unwitnessed-

Switched condition as the false belief (FB) condition.

3.6.2 MEG Data Analysis

MEG data were analyzed in MATLAB 2014b (The MathWorks, 2014) with customized scripts

that made use of SPM12 software (FIL Methods Group, 2014). Data were filtered from 1-50 Hz

with a fifth-order Butterworth bandpass filter. Trials were epoched from -200 to 600 ms relative

to the presentation of the second picture stimulus and subsequently baseline corrected. Head

motion artefacts were controlled by discarding trials in which the participant moved 5 mm within

the trial or 10 mm between trials. Independent component analysis (ICA) as implemented by

FieldTrip (Oostenveld, Fries, Maris, & Schoffelen, 2011) was used to detect and remove

heartbeat and eyeblink artefacts in the data. Artefacts were further removed from the data by

rejecting trials in which the signal exceeded 2500 fT at any of the channels, and by excluding

MEG channels in which more than 20% of trials surpassed this threshold. Data were then

averaged across trials for each condition and each participant.

Coregistration of MEG data and corresponding anatomical MRIs was done for each individual

based on the three reference points (fiducials) mentioned above. The forward model was

calculated based on the single-shell model for computing the lead field matrix (Nolte, 2003), and

the inverse model was generated using the minimum norm estimation method in SPM (Litvak et

al., 2011). Results from the inversion were averaged over a 50 ms sliding time window, with an

18

overlap of 25 ms, between 50-600 ms post-stimulus, leading to the creation of 21 summary time

windows (e.g. 50-100 ms, 75-125 ms, etc.) for each participant and each condition, which were

then exported as 3D NIFTI contrast images in MNI space. These images were spatially smoothed

by a 12 mm FWHM Gaussian kernel before being input into a 2 x 2 x 2 (group x switching x

witnessing) factorial ANOVA to model the effects of group (TD or ASD) and each condition

(switching – Switched or Unswitched – and witnessing – Witnessed or Unwitnessed) on brain

activity. Because two of the factors (switching and witnessing) are repeated across participants,

the statistics model estimation was adjusted for violations of sphericity using the Restricted

Maximum Likelihood (ReML) method (Friston, Stephan, Lund, Morcom, & Kiebel, 2005).

Planned comparison t-tests were performed to evaluate specific within- and between-group

effects of interest, namely (1) brain regions that were more strongly activated in the FB than the

TB condition in TD (TD, FB > TB) and ASD children (ASD, FB > TB); and (2) brain regions

found in (1) that were more active in TD (TD > ASD, FB) and in ASD children (ASD > TD, FB),

which was done by analyzing these latter two contrasts in the context of the interaction between

group (TD or ASD) and condition (FB or TB). Significant peaks of activity are reported at

puncorrected < 0.009 and were labelled according to the CA_ML_18_MNIA atlas (Eickhoff et al.,

2005) available in the Analysis of Function NeuroImages (AFNI) software (Cox, 1996).

Visualization of these areas were created using MRIcron (Rorden, Karnath, & Bonilha, 2007).

19

Chapter 4 Results

4.1 Assessments

For the neurocognitive assessments, t-tests revealed that the TD group had an overall higher IQ

(mean = 119.55 ± 9.49; t(39) = 2.92, p = 0.006; Figure 2A) than the ASD group (mean = 109.68

± 12.10), as measured by the WASI-II, although it is important to note that both groups had

average to above average IQs. The TD group (mean = 25.27 ± 1.80) and the ASD group (mean =

22.82 ± 3.34) also differed on the Theory of Mind subtest of the NEPSY-II (t(37) = 2.94, p =

0.006; Figure 2D), with the TD children scoring higher than the children with ASD on the test,

which is consistent with the classical ToM deficits described the literature on ASD (Baron-

Cohen et al., 1985; Perner et al., 1989). In contrast, there were no significant differences between

the TD group (mean = 216.64 ± 32.71) and the ASD group (mean = 204.00 ± 37.01) on the

WMTB-C (t(39) = 1.16, p = 0.25; Figure 2B), nor on the Inhibition subtest of the NEPSY-II

(t(37) = 1.06, p = 0.30; TD mean = 10.95 ± 3.05, ASD mean = 9.82 ± 3.64; Figure 2C).

On the parent questionnaires, for the BRIEF, the ASD group (mean = 67.41 ± 10.02) scored

considerably higher (t(37) = 7.13, p = 1.92 x 10-8

; Figure 3A) than the TD group (mean = 44.86

± 9.62), indicating that the children with ASD were seen to have more executive functioning

deficits than their TD peers. Likewise, on the SRS and SRS-2, the ASD group (mean = 73.13 ±

10.66) was rated much more severely (t(35) = 7.93, p = 2.49 x 10-9

; Figure 3B) than the TD

group (mean = 44.67 ± 10.92) in terms of their social difficulties. In fact, all of the children with

ASD scored in the mild/moderate to severe clinical range of social impairments, whereas only

two of the twenty-two children in the TD group scored in the mild/moderate range, with the rest

of the TD participants having normal scores.

For ASD symptomology, all but one child with ASD scored above the ADOS comparison score

cutoff for ASD (mean = 6.84 ± 2.12). The one child who did not meet cutoff had his ASD

diagnosis confirmed by his paediatrician using the DSM-IV criteria.

A summary of these results can be found in Table 1.

20

A B

C D

Figure 2 - Boxplots illustrating the scores of the TD and ASD group on the four neurocognitive assessments: (A)

WASI-II, which measured IQ; (B) WMTB-C, which measured working memory capacity; (C) NEPSY-II Inhibition

subtest, which measured inhibitory control; and (D) NEPSY-II ToM subtest, which measured ToM abilities. In (A)

and (D), t-tests showed group differences (p < 0.05), with TD children scoring higher than ASD children in both

cases, whereas in (B) and (C), there were no differences (p > 0.05).

Note: * p < 0.05; ** p < 0.01; *** p <0.001

21

Table 1

Scores on Neurocognitive Assessments and Questionnaires.

4.2 Task Performance

A linear mixed effect model showed main effects of switching and witnessing, as well as an

interaction between the two, for both accuracy (switching: F(1,117) = 100.29, p < 0.0001;

witnessing: F(1,117) = 83.00, p < 0.0001; interaction: F(1,117) = 17.35, p = 0.0001) and reaction

time (switching: F(1,117) = 64.77, p < 0.0001; witnessing: F(1,117) = 113.92, p < 0.0001;

interaction: F(1,117) = 12.26, p = 0.0007). There was no main effect of group (accuracy: F(1,37)

WASI-II WMTB-C NEPSY-II

Inhibition

NEPSY-II Theory of

Mind BRIEF

SRS / SRS-2

ADOS-G / ADOS-2

TD (N)

119.55 ±

9.49

(22)

216.64 ±

32.71

(22)

10.95 ±

3.05

(22)

25.27 ±

1.80

(22)

44.86 ±

9.62

(22)

44.67 ±

10.92

(21)

---

ASD (N)

109.68 ±

12.10

(19)

204.00 ±

37.01

(19)

9.82 ± 3.64

(17)

22.82 ±

3.34

(17)

67.41 ±

10.02

(17)

73.13 ±

10.66

(16)

6.84 ±

2.12

(19)

Note. Mean scores and standard deviations are given here. Below each of the scores is the number of participants

for which data was available.

A B

Figure 3 - The two boxplots above demonstrate how participants were rated on the (A) BRIEF and the (B) SRS and

SRS-2. On both these questionnaires, parents of children with ASD rated their children as having more difficulties

with executive function (p < 0.05; A) and social functioning (p < 0.05; B) than parents of TD children.

Note: * p < 0.05; ** p < 0.01; *** p <0.001

22

= 1.55, p = 0.22; reaction time: F(1,37) = 1.87, p = 0.18), interaction between group and

switching (accuracy: F(1,117) = 0.006, p = 0.94, reaction time: F(1,117) = 0.01, p = 0.90),

interaction between group and witnessing (accuracy: F(1,117) = 1.006, p = 0.32, reaction time:

F(1,117) = 2.92, p = 0.09), or interaction between group, switching, and witnessing (accuracy:

F(1,117) = 0.11, p = 0.74, reaction time: F(1,117) = 0.86, p = 0.36) for either accuracy or

reaction time. Although IQ was significantly higher in the TD group than the ASD group,

including it as a covariate in the model did not account for this difference in accuracy (F(1,37) =

3.114, p = 0.09) or reaction time (F(1,37) = 0.23, p = 0.64), nor did the interaction between

group and IQ (accuracy: F(1,37) = 2.95, p = 0.09; reaction time: F(1,37) = 0.13, p = 0.72).

Post-hoc t-tests revealed highly significant differences between the FB and TB conditions in

terms of accuracy (t(40) = 8.28, p = 1.68 x 10-10

; Figure 4A) and reaction time (t(40) = 6.33, p =

8.21 x 10-8

; Figure 4B). Specifically, participants were less accurate on the FB trials (mean =

79.71 ± 10.93 % correct) than on the TB trials (mean = 92.46 ± 5.85 % correct), and they also

took longer to respond to the FB trials (mean = 1.06 ± 0.22 seconds) compared to the TB trials

(mean = 0.96 ± 0.21 seconds). Taken together, these results suggest that while TD and ASD

children performed similarly on our ToM task, both groups found the FB condition more

challenging than the TB condition, as evident by their poorer accuracy on and longer reaction

time to the FB trials.

A B

Figure 4 – These boxplots contrast the children’s accuracy (A) and reaction time (B) on the FB and TB trials of the

false-belief task. There were no group-level differences in terms of accuracy or RT, i.e. TD children and children

with ASD performed similarly on the task (p > 0.05). However, both groups of children achieved a lower accuracy

(p < 0.05; A) and had a higher reaction time (p < 0.05; B) on the FB trials compared to the TB trials.

Note: * p < 0.05; ** p < 0.01; *** p <0.001

23

4.3 Neuroimaging

A wide variety of brain regions were shown to be active in the within- and between- group

comparisons performed. Table 2 lays out the coordinates, labels, and timing of these areas, along

with their p-values, while Figure 5 gives a visual representation of their activations. Here we

describe areas of activation that are significant at p < 0.009 and that are of particular interest.

4.3.1 Within-group comparisons

4.3.1.1 TD, FB > TB

In the first two time windows of 50-100 ms and 75-125 ms, TD children activated the right

inferior temporal gyrus (rITG) and an area encompassing the left middle temporal gyrus and left

angular gyrus (lMTG/lAG) during the FB condition compared to the TB condition. The latter

region was activated again in the 300-350 ms, 325-375 ms, and 350-400 ms time windows,

spreading to the left middle occipital gyrus (lMOG), and again at 425-475 ms and 450-500 ms.

Between 100-150 ms, the right angular gyrus (rAG) was more active for the FB trials, though its

peak was more superior than its contralateral counterpart in the previous time window ([-50, -70,

22] for lAG vs [34, -70, 50] for rAG).

There were no activations of interest in the 125-175 ms time window, but in the subsequent two

time periods of 150-200 ms and 175-225 ms, the right precuneus (rPreCun) was recruited more

strongly for the FB trials.

Again, nothing of interest appeared between 200-250 ms, although at 225-275 ms, the TD group

showed greater activation for FB in the right middle frontal gyrus (rMFG) and the right middle

orbital gyrus (rMOrbG), which continued until the 275-325 ms time period. The rMFG was also

recruited from the 425-475 ms to 500-550 ms time window. Additionally, the left inferior

temporal gyrus (lITG) was active during the 250-300 ms and 275-325 ms time windows.

From 325-375 ms to 350-400 ms, the rPreCun was active, but this segment of the rPreCun was

more anterior and superior (12, -78, 44) than that previously activated (10, -48, 72).

Subsequently, the left superior parietal lobule (lSPL) was more activated for the FB condition

than the TB condition between 350-400 ms and 375-425 ms. At the same time, the right superior

24

parietal lobule (rSPL), which was adjacent to the rAG mentioned above, was also active, and its

activation extended to the 400-450 ms time window.

In the next few time windows of 400-450 ms to 450-500 ms, the left inferior parietal lobule

(lIPL) and the right inferior temporal gyrus (rITG) were both activated, though the rITG was

much more posterior and superior (52, -46, -18) to the rITG found in the earliest two time

windows (50, -6, -42).

The final region of note, the left inferior temporal gyrus (lITG), was activated starting at the 425-

475 ms time window until 550-600 ms.

4.3.1.2 ASD, FB > TB

In the FB compared to the TB trials, children with ASD began with activating the left middle

temporal gyrus (lMTG) at 50-100ms and 75-125 ms, as well as the left calcarine and left lingual

gyri (lCalG/lLG) from the 75-125 ms to 125-175 ms time window.

At 125-175 ms, the right superior lobule (rSPL) was activated. The site of its peak activity is

very close to that activated in TD children ([36, -62, 58] in the TD group vs [26, -60, 60] in the

ASD group), although it was activated earlier in the children with ASD and for a shorter

duration. The left middle frontal gyrus (lMFG) was also active at the same time, but for a more

sustained period of time, up to the 275-325 ms time window. Interestingly, the location of the

lMFG here almost exactly corresponds to that of the contralateral rMFG seen in the TD children,

and their timing of activation overlapped from the 225-275 ms to the 275-325 ms time window,

with both areas tapering off almost simultaneously. In addition, between 150-200 ms and 200-

250 ms, the left middle occipital gyrus (lMOG) was more active for the FB condition.

The right fusiform gyrus (rFG) was shown to be active beginning at 175-225 ms up to the 225-

275 ms time window. Following this, during the 250-300 ms and 275-325 ms time windows, the

right middle orbital gyrus (rMOrbG) was active. Its region of activity overlapped that of the

rMOrbG in the TD children ([30, 54, -8] in TD children and [40, 52, -10] in children with ASD),

and even their timing almost completely coincided.

In the 275-325 ms time period, the right superior parietal lobule (rSPL) was activated, as was the

left inferior frontal gyrus (lIFG). This rSPL activation abutted a region of simultaneously

25

significant activity in the right angular gyrus (rAG) and right inferior parietal lobule (rIPL),

which lasted until 325-375 ms. This rSPL/rAG/rIPL area was active again from 450-500 ms up

to 550-600 ms and extended to the right supramarginal gyrus (rSMG) at 550-600 ms. The lIFG

remained active up to the 325-375 ms time window. The lIFG appeared again at 450-500 ms, this

time covering a much wider area of the brain and continuing to be active until 550-600 ms.

The most striking activation in this comparison of the FB and TB trials in children with ASD

was the right inferior frontal gyrus (rIFG), which was recruited starting at 300-350 ms and

persisting until 550-600 ms. The timing of the rIFG directly followed the first appearance of an

equivalent rMFG region in the TD group, and it also overlapped both spatially and temporally

during the latter’s second appearance, from the 425-475 ms to the 500-550 ms time window.

Lastly, from 450-500 ms, children with ASD invoked a more anterior portion of the left middle

temporal gyrus (lMTG) than seen at 50-100 ms ([-68, -38, 0] earlier vs [-66, -20, -8] here), until

the 550-600 ms time window.

4.3.2 Between-group comparisons

4.3.2.1 TD > ASD, FB

Only a few areas were significantly more active in the TD than in the ASD group on the FB

compared to the TB trials. The lMTG/lAG was more active at 325-375 ms and, also at 425-475

ms. A contralateral region, the rSPL of the rSPL/rAG/rIPL cluster, was found to be recruited

more strongly from 375-425 ms. Finally, during the next time periods of 425-475 ms and 450-

500 ms, the lITG was more activated in TD children than their ASD counterparts.

4.3.2.2 ASD > TD, FB

In comparison, children with ASD displayed stronger and more persistent activation in several

distinct regions compared to TD children in the FB condition. Firstly, the lMTG was more active

only between 50-100 ms, but the lCalG/lLG showed greater activity for a longer period of time,

from the 75-125 ms to 125-175 ms time window, and from 175-225 ms to 200-250 ms, the rFG

was recruited more by the children with ASD.

During the 275-325 ms time window, the rSPL/rAG/rIPL area was activated significantly more

in the ASD group, and also from 500-550 ms up to 550-600 ms. From the 300-350 ms time

26

window to 325-375 ms, both the lIFG and rIFG were more robustly activated, with the lIFG also

being more active at 475-525 ms up to 550-600 ms, and the rIFG continuing to be more active

until 375-425 ms, and again at 500-550 ms and 525-575 ms.

In the last few time windows from 500-550 ms to 550-600 ms, activation in the lMTG was

shown to be greater in children with ASD relative to TD children.

27

Table 2

Coordinates of Significant Peaks of Brain Activation of Interest in TD Children and Children with ASD in the False-

Belief Task

Region MNI coordinates

Z score p (uncorrected)

x y z

50-100 ms

TD, FB > TB

R Inferior Temporal Gyrus1 50 -6 -42 2.71 1.31 x 10

-5

L Middle Temporal Gyrus2 -46 -64 14 2.57 0.005

ASD, FB > TB

L Middle Temporal Gyrus3 -68 -38 0 2.99 0.001 †

75-125 ms

TD, FB > TB

R Inferior Temporal Gyrus1 50 -4 -34 2.79 0.003


ASD, FB > TB

L Calcarine Gyrus4 -4 -88 -18 4.54 2.84 x 10

-6 †

L Middle Temporal Gyrus3 -66 -38 -2 2.46 0.007

100-150 ms

TD, FB > TB

R Angular Gyrus5 34 -70 50 3.45 0.0002

ASD, FB > TB

L Lingual Gyrus4 -6 -88 -16 5.47 2.30 x 10

-8 †

125-175 ms

TD, FB > TB

No significant regions of interest

ASD, FB > TB

L Calcarine Gyrus4 -6 -88 -14 4.93 4.02 x 10

-7 †

R Superior Parietal Lobule6 26 -60 60 2.67 0.004

L Middle Frontal Gyrus7 -38 52 10 2.37 0.008

150-200 ms

TD, FB > TB

R Precuneus8 12 -78 44 4.40 5.52 x 10

-6

ASD, FB > TB


L Middle Occipital Gyrus9 -36 -72 24 2.73 0.003

Note. L = left, R = right. * denotes that the peak was also significant in the TD > ASD, FB contrast. † denotes that

the peak was also significant in the ASD > TD, FB contrast. Numbers in superscript (e.g.1) denote peaks that were

considered to be part of the same region. Bolded peaks are significant at a pFWE < 0.05.

28

Table 2 (Continued)


Belief Task


Z score p (uncorrected) x y z

175-225 ms

TD, FB > TB

R Precuneus8 8 -74 40 2.59 0.005

ASD, FB > TB



R Fusiform Gyrus10

38 -62 -20 2.61 0.005 †

200-250 ms

TD, FB > TB

No significant regions of interest

ASD, FB > TB


R Fusiform Gyrus10

36 -62 -22 2.95 0.002 †


225-275 ms

TD, FB > TB

R Middle Orbital Gyrus11

30 54 -8 2.75 0.003

R Middle Frontal Gyrus12

40 48 16 2.71 0.003

ASD, FB > TB


R Fusiform Gyrus10

38 -62 -22 2.56 0.005

250-300 ms

TD, FB > TB


40 48 10 3.22 0.0006


36 54 -2 3.20 0.0006

L Inferior Temporal Gyrus13

-46 4 -34 2.43 0.008

ASD, FB > TB


40 52 -10 2.92 0.002





29

Table 2 (Continued)


Belief Task



275-325 ms

TD, FB > TB


42 46 2 3.11 0.0009


-48 2 -38 2.58 0.005

ASD, FB > TB

L Inferior Frontal Gyrus15

-48 32 0 3.25 0.0006


42 50 -10 2.74 0.003


R Angular Gyrus6 50 -62 42 2.56 0.005 †


300-350 ms

TD, FB > TB

L Angular Gyrus2 -48 -72 24 2.83 0.002

ASD, FB > TB


-48 32 -6 3.37 0.0003 †

R Inferior Parietal Lobule6 48 -58 44 2.77 0.003

R Inferior Frontal Gyrus16

48 28 24 2.65 0.004 †


42 46 18 2.42 0.008

325-375 ms

TD, FB > TB

R Precuneus17

10 -48 72 3.92 4.46 x 10-5

L Middle Temporal Gyrus2 -52 -70 16 3.25 0.0006 *

ASD, FB > TB


48 30 24 3.64 0.0001 †


-48 34 -6 3.42 0.0003 †





30

Table 2 (Continued)


Belief Task



350-400 ms

TD, FB > TB

R Precuneus17

10 -48 72 4.06 2.46 x 10-5

L Superior Parietal Lobule18

-14 -78 50 3.61 0.0002



ASD, FB > TB


48 30 18 4.48 3.70 x 10-6

†

375-425 ms

TD, FB > TB

R Superior Parietal Lobule5 38 -58 56 3.80 7.20 x 10

-5 *

L Superior Parietal Lobule18

-20 -76 52 2.61 0.004

ASD, FB > TB


48 30 18 4.07 2.40 x 10-5

†

400-450 ms

TD, FB > TB

R Inferior Temporal Gyrus19

52 -46 -18 3.76 8.64 x 10-5


L Inferior Parietal Lobule20

-42 -52 52 2.47 0.007

ASD, FB > TB


50 30 14 3.16 0.0008

425-475 ms

TD, FB > TB


52 -50 -24 3.37 0.0004 *


-38 -62 48 3.14 0.0009

L Middle Occipital Gyrus2 -48 -84 12 3.08 0.001 *


40 48 24 2.76 0.003

ASD, FB > TB


48 30 16 2.89 0.002




31

Table 2 (Continued)


Belief Task



450-500 ms

TD, FB > TB


40 50 22 3.62 0.0001



-42 -56 54 2.74 0.003


52 -44 -28 2.53 0.006

ASD, FB > TB


50 30 18 3.65 0.0001

R Angular Gyrus6 36 -64 42 3.00 0.001



-46 20 -8 2.69 0.004

L Temporal Pole15

-36 20 -24 2.64 0.004

L Middle Temporal Gyrus21

-66 -20 -8 2.40 0.008


-56 -4 -30 2.39 0.008

475-525 ms

TD, FB > TB


40 52 18 3.80 7.33 x 10-5


-52 -60 -20 2.88 0.002

ASD, FB > TB


-48 22 -10 4.04 2.68 x 10-5

†


52 30 22 3.60 0.0001



-66 -24 -2 2.55 0.005

500-550 ms

TD, FB > TB


-52 -60 -18 2.87 0.002


38 50 20 2.81 0.002

ASD, FB > TB


-48 22 -14 4.10 2.08 x 10-5

†


50 32 22 3.50 0.0002 †

R Inferior Parietal Lobule6 54 -56 40 3.20 0.0007 †


-66 -26 0 2.66 0.004 †




32

Table 2 (Continued)


Belief Task



525-575 ms

TD, FB > TB


-54 -62 -16 2.73 0.003

ASD, FB > TB


-48 24 -14 4.02 2.91 x 10-5

†


50 32 22 3.48 0.0002 †



-66 -26 0 2.74 0.003 †

550-600 ms

TD, FB > TB


-54 -62 -16 2.69 0.004

ASD, FB > TB


-48 26 -16 3.96 3.69 x 10-5

†


50 32 22 3.48 0.0003


R Supramarginal Gyrus6 62 -48 26 2.48 0.006


-66 -26 0 2.74 0.003 †




33

TD Group Time (ms)

Region 50-100 75-125 100-150 125-175 150-200 175-225 200-250

rITG1

lMTG/lAG2

rAG/rSPL5

rPreCun8

ASD Group Region 50-100 75-125 100-150 125-175 150-200 175-225 200-250

lMTG3

lCalG/lLG4

rSPL/rAG/rIPL6

lMFG7

lMOG9

rFG10

Figure 5 - Bar graphs and brain images showing the timeline of brain activation for the TD children (top, blue) and the children with ASD (bottom, orange/red). The

numbers in superscript correspond to the ones found in Table 2.

l

M

T

G

rITG lMTG/lAG

lMTG lCalG/lLG

rAG/rSPL

rSPL/rAG/rIPL

lMFG

lMOG

rPreCun

rPreCun

rFG

■ TD, FB > TB

■ ASD, FB > TB

34

TD Group Time (ms)

Region 225-275 250-300 275-325 300-350 325-375 350-400 375-425

lMTG/lAG2

rAG/rSPL5

rMOrbG11

rMFG12

lITG13

rPreCun17

lSPL18

Figure 5 (Continued) - Bar graphs and brain images showing the timeline of brain activation for the TD children (top, blue) and the children with ASD (bottom,

orange/red). The numbers in superscript correspond to the ones found in Table 2.

ASD Group Region 225-275 250-300 275-325 300-350 325-375 350-400 375-425

rSPL/rAG/rIPL6

lMFG7

rFG10

rMOrbG14

lIFG15

rIFG16

rMOrbG

rMFG

lMFG

rFG

lITG

rMOrbG

rSPL/rAG/rIPL

lIFG

lMTG/lAG

rIFG

rIFG

rPreCun rAG/rSPL

lSPL

rAG/rSPL

■ TD, FB > TB

■ ASD, FB > TB

35

TD Group Time (ms)

Region 400-450 425-475 450-500 475-525 500-550 525-575 550-600

lMTG/lAG2

rAG/rSPL5

rMFG12

rITG19

lIPL20

lITG22

ASD Group Region 400-450 425-475 450-500 475-525 500-550 525-575 550-600

rSPL/rAG/rIPL6

lIFG15

rIFG16

lMTG21

Figure 5 (Continued) - Bar graphs and brain images showing the timeline of brain activation for the TD children (top, blue) and the children with ASD (bottom,

orange/red). The numbers in superscript correspond to the ones found in Table 2.

rAG/rSPL

rITG

lIPL

rIFG

lMTG/lAG

rMFG

rSPL/rAG/rIPL

lIFG

lMTG lITG

■ TD, FB > TB

■ ASD, FB > TB

36

Chapter 5 Discussion

ToM is a valuable social skill that is often lacking in children with ASD (Baron-Cohen et al.,

1985; Perner et al., 1989). Aligning with recent literature, this study demonstrated that the brain

regions underlying ToM abilities differed between children with and without ASD, but we

additionally observed that the timing of similarly activated areas also distinguished these two

groups. Based on our results, we propose that whereas TD children utilize commonly identified

ToM brain areas to correctly recognize a false belief, children with ASD draw on supplementary

working memory resources to make up for their inherent deficits in ToM to perform at the same

level as their TD counterparts.

5.1 Performance on Neurocognitive and Behavioural Measures

The neurocognitive assessments used in this study revealed that our two groups of children

differed in IQ and ToM abilities, but not on tests of working memory or inhibition. The

discrepancy in IQ is not surprising, as intellectual problems are common in children with ASD

(Lai, Lombardo, & Baron-Cohen, 2014), and it can be difficult to find IQ-matched children with

ASD (Rao, Raman, & Mysore, 2015). However, all children in our sample fell within two

standard deviations of the median IQ, and the average IQ in our ASD sample was close to, but

above the median. Furthermore, IQ was not a predictor of accuracy or reaction time in our false-

belief task, even when group status was considered, implying that the overall lower IQ in the

children with ASD did not affect their understanding of the task.

The difference in performance on the ToM subtest of the NEPSY-II was also consistent with the

literature on ToM deficits in ASD (Baron-Cohen et al., 1985; Perner et al., 1989), with

participants with ASD performing more poorly on the test than TD participants. Interestingly, the

results from this test and from our false-belief task were incongruent, as the former showed

group differences and the latter did not. This disparity is likely due to two factors. First, the

NEPSY-II ToM subtest and our task measure different aspects of ToM, in that the NEPSY-II

does not gauge solely ToM abilities, but also affective ToM abilities, and the stimuli presented in

this assessment contain richer social contexts compared to the stimuli in the task. Second, these

two measures are administered differently; whereas the NEPSY-II requires interaction with the

37

examiner, the false-belief task only involves responding to a computer screen. A recent study by

Chevallier et al. (2014) found that while children with ASD performed similarly to controls

when given a ToM task on a computer, the control children did comparatively better on the task

when it was administered by a human examiner, as they benefited from the social interaction.

Thus, because these two tests were given in dissimilar social environments, it is likely that it

could have affected participants’ responses.

The significant differences on the parent ratings of executive functioning and social impairment

are consistent with previous work that has found that children with ASD have reduced executive

functioning, as measured by the BRIEF, and that these scores are related to their SRS scores

(Leung, Vogan, Powell, Anagnostou, & Taylor, 2015). Our finding that children with ASD had

higher SRS scores is also in line with the fact that ratings on the SRS have been correlated with

autistic symptomology, as measured by the 3di, ADI-R, and ADOS (Constantino et al., 2003;

Duvekot, van der Ende, Verhulst, & Greaves-Lord, 2015).

In contrast, the two groups of children performed similarly on our false-belief task. We expected

that the children with ASD would be less accurate and take longer to respond to the FB trials, but

instead they did not differ from the TD children. Ceiling effects were not an issue, since no

children were able to achieve 100% accuracy on the trials, and the mean accuracy of both groups

were below 90%. This does not affect the interpretation of our neuroimaging data, as many have

found neural differences despite no behavioural differences (e.g., Colich et al., 2012; Vogan et

al., 2014). However, we found that participants, both TD and ASD, completed fewer correct

trials and responded more slowly to the FB than TB trials, likely indicating the participants found

the FB trials more difficult.

5.2 Timing and Location of Theory of Mind Regions in the Brain

The TD and ASD groups clearly differed in their recruitment of brain regions in response to the

false-belief task, even though they were quite similar behaviourally. While they shared some

areas in common, the onset and latency of these regions were fairly distinct. Below we discuss

the contributions these areas may have to false-belief reasoning and their diverse roles in

children with and without ASD.

38

5.2.1 Timeline of Activation in TD Children

Within the first 125 ms of recognizing a false-belief scenario, TD children activated the right

inferior temporal gyrus (rITG), the left middle temporal gyrus (lMTG; part of the left middle

temporal gyrus/left angular gyrus (lMTG/lAG) cluster), and the right angular gyrus (rAG; part of

the right angular gyrus/right superior parietal lobule (rAG/rSPL) cluster). Given the early

engagement of these areas, they are likely involved in basic visual processing. The rITG is

involved in object recognition in the ventral visual pathway (Mishkin, Ungerleider, & Macko,

1983; Milner & Goodale, 2008), and the lMTG is used for processing the motion of objects

(Beauchamp, Lee, Haxby, & Martin, 2002; Weisberg, van Turennout, & Martin, 2007) and

human bodies, in conjunction with the pSTS (Grosbras, Beaton, & Eickhoff, 2012; Ferri,

Kolster, Jastorff, & Orban, 2013). In addition, the rAG is implicated in visual search (Taylor,

Muggleton, Kalla, Walsh, & Eimer, 2011; Bocca, Töllner, Müller, & Taylor, 2015; Petitet,

Noonan, Bridge, O'Reilly, & O'Shea, 2015). Together, these regions may be detecting the

apparent movement of the ball and Jill in the FB trials by tracking their locations.

Between 150-225 ms, the right precuneus (rPreCun) responded to the FB trials. At this time, its

activation may be reflecting the TD participants realizing that their belief about the ball’s

location is separate from Jill’s, as the rPreCun has been shown to identify disparities between

one’s own perspective and another’s (Vogely et al., 2004; Schurz et al., 2015), and in other false-

belief tasks (Kobayashi, Glover, & Temple, 2006; Sommer et al., 2007; Schneider, Slaughter,

Becker, & Dux, 2014).

The right middle frontal gyrus (rMFG) and right middle orbital gyrus (rMOrbG) were

subsequently activated from 225-325 ms. Their roles may be in re-orienting of attention (Japee,

Holiday, Satyshur, Mukai, & Ungerleider, 2015), which is plausible in this context, since the

regions noted above are activated once again, and in practically the same order, with the

contralateral left inferior temporal gyrus (lITG) being active from 250-325 ms, the lMTG/lAG

from 300-400 ms, the right superior parietal lobule (rSPL; part of the rAG/rSPL cluster) from

350-450 ms, and the rPreCun from 325-400 ms. Whereas their activation before 200 ms was

perhaps automatic in response to the FB situation, the rMFG and rMOrbG may be recruiting

them again to parse the scene more carefully, which may be reflected in the fact that they are

active for longer periods of time. Notably, the peak of the lMTG/lAG area is more superior in

39

this later time window, centred more strongly on the lAG than the lMTG, and then shifts down

toward the lMTG as time passes. As the lAG is part of the lTPJ, and since the lTPJ has shown to

be involved in false belief and perspective taking tasks (Schurz et al., 2013), it may be that

during this period, the lMTG/lAG area is integrating the child’s interpretation of Jill’s thoughts

with the visual scene. Furthermore, the peak of the rAG/rSPL cluster is closer to the rSPL during

this time, and given the rSPL’s involvement in top-down attention and working memory

(Humphreys & Lambon Ralph, 2014), and its connection with the rMFG (Ma et al., 2012;

Schmidt et al., 2014), the rAG/rSPL cluster is perhaps drawing on working memory resources to

remember what happened before Jill went away to understand the FB scenario. Also, the region

of the rPreCun activated is more superior than at 150-225 ms. As the dorsal parietal cortex may

be responsible for top-down attention to memory, while the ventral parietal cortex may be

concerned instead with bottom-up attention to memory (Burianová, Ciaramelli, Grady, &

Moscovitch, 2012), perhaps the rPreCun at this time is actively, rather than automatically,

recognizing that Jill’s perspective is different.

The left superior parietal lobule (lSPL) is subsequently activated between 350-425 ms. Previous

work has implicated it in the mirror neuron system (Molenberghs, Brander, Mattingley, &

Cunnington, 2010), a network of brain regions that has been often linked to ToM processing

(Gallese & Goldman, 1998; Iacoboni et al., 2006; Uddin, Iacobini, Lange, & Keenan, 2007;

Libero et al., 2014). It has also been associated with increasing working memory load, along

with the rITG (Mazoyer, Wicker, & Fonlupt, 2002), which is also active at around the same time,

between 400-500 ms. In addition, the concurrent recruitment of the left inferior parietal lobule

(lIPL) between 400-500 ms may reflect an understanding that Jill likely has a false belief, since

the lIPL has been shown to be selectively active for the resolution of incongruities (Chan et al.,

2013), deception (Lisofsky, Kazzer, Heekeren, & Prehn, 2014), and counterfactual reasoning

(Van Hoeck et al., 2014). The reactivation of the rMFG between 425-550 ms, may suggest that it

is interacting with the above three regions. Considering the latency of this activation, and that the

rMFG is involved in logical reasoning (Porcaro et al., 2014), it is possible that it is working with

the lSPL, rITG, and lIPL to help resolve the disparity between Jill’s belief of where the ball is

and its actual location using the working memory and mirror neuron systems.

Since the lITG has been implicated in visual memory (Hamamé et al., 2012; Vilberg & Rugg,

2012), then perhaps from 475-600 ms, TD children are using their lITG to ensure that their

40

assumption that Jill has a false belief is correct by trying to remember where Jill saw the ball last.

The lITG also may play a role in response inhibition and selection (Chiang et al., 2013), so it

might additionally be involved in choosing the correct answer to the FB trials, which is the

opposite of their own knowledge of the situation.

5.2.2 Timeline of Activation in Children with ASD

Children with ASD, similar to TD children, activated the lMTG from 50-125 ms, perhaps also to

observe the movement of the objects and characters in the trial, but they relied on the left

calcarine gyrus/left lingual gyrus (lCalG/lLG) between 75-175 ms instead of the rITG to process

the basic features of the visual stimulus, as they are both classical visual processing and encoding

regions (Arroyo, Lesser, Poon, Webber, & Gordon, 1997; Klein, Paradis, Poline, Kosslyn, & Le

Bihan, 2000; Machielsen, Rombouts, Barkhof, Scheltens, & Witter, 2000; Hayakawa, Miyauchi,

Fujimaki, Kato, & Yagi, 2003; Moradi et al., 2003). The children with ASD also activated the

rSPL (part of the right superior parietal lobule/right angular gyrus/right inferior parietal lobule

(rSPL/rAG/rIPL) cluster) from 125-175 ms possibly to encode the visual scene and keep it in

working memory, as suggested above. Also, the location of the rSPL overlapped with the

contralateral lMTG/lAG region activated early on in TD children, and so it may be triggering

visual search to look for relevant cues.

At around 125-325 ms, the left middle frontal gyrus (lMFG) was active. Previous work has

shown its involvement with ToM (Péron et al., 2010), and in particular, Zhang, Sha, Zheng,

Ouyang, and Li (2009) demonstrated its role in inhibiting one’s personal knowledge during a

false-belief task. Thus, the lMFG may be involved in monitoring responses, as children with

ASD are known to have difficulty with response inhibition (Chmielewski & Beste, 2015).

During the same time as the lMFG, the left middle occipital gyrus (lMOG), right fusiform gyrus

(rFG), and rMOrbG were also active, with the lMOG being recruited from 150-250 ms, the rFG

from 175-275 ms, and the rMOrbG from 250-325 ms. The lMOG has been shown to be active

during false-belief and visual perspective-taking tasks, and it has been suggested that it plays a

part in transforming a scene to understand a different perspective (Schurz et al., 2013). In

contrast, the rFG is responsible for processing objects and faces (Vandenbulcke, Peeters, Fannes,

& Vandenberghe, 2006; Morris, Pelphrey, & McCarthy, 2007; Bruffaerts et al., 2013) and

biological motion in children (Lichtensteiger, Loenneker, Bucher, Martin, & Klaver, 2008). In

41

addition, the rMOrbG was likely performing a similar task as in the TD children, where it was

responsible for re-orienting attention. Therefore, during this time period, these three regions were

likely working together to comprehend Jill’s view of the scene, with the lMOG taking Jill’s

perspective, the rFG processing the complex visual scene, and the rMOrbG utilizing working

memory resources, as the lMFG tried to suppress the children’s own viewpoint.

The activation of the rAG of the rSPL/rAG/rIPL cluster between 275-375 ms may have served

the same function as in the TD group, where the rAG was promoting a visual search. However,

unlike the TD children, the children with ASD activated the contralateral IPL, the right inferior

parietal lobule (rIPL), during this time. The rIPL is proposed to be involved in understanding

actions (Gobbini et al., 2007; Marsh, Mullett, Ropar, & Hamilton, 2014), which has been

replicated in MEG (Vistoli, Brunet-Gouet, Lemoalle, Hardy-Baylé, & Passerieux, 2011), and

generally in both verbal and non-verbal ToM tasks (Kobayashi, Glover, & Temple, 2007). The

left inferior frontal gyrus (lIFG) was simultaneously active from 275-375 ms, and it also has

been shown to be active in individuals with ASD while processing the intentions of actions

(Libero et al., 2014). Furthermore, this region has traditionally been defined as Broca’s area,

which deals with language production (Broca, 1861), and so several studies have linked its

activation with working memory and active maintenance of information (Braver et al., 1997;

Cohen et al., 1997), specifically with verbal rehearsal of this information (Zarahn, Rakitin,

Abela, Flynn, & Stern, 2005; Powell, Kemp, & Garcia-Finaña, 2012). The rIPL may also play a

role in keeping information in memory through verbal repetition (Zarahn et al., 2005; Urbain et

al., 2013); thus, these three regions may have been working in concert to understand the

consequences of Jack’s movement of the ball on Jill’s beliefs by resorting to verbal strategies.

Beginning at 300 ms, children with ASD employed the right inferior frontal gyrus (rIFG), up

until 600 ms. The rIFG has been shown consistently to be involved in inhibition and executive

control (Aron, Fletcher, Bullmore, Sahakian, & Robbins, 2003; Kenner et al., 2010; Zhang,

Hughes, & Rowe, 2012; Hughes, Johnston, Fulham, Budd, & Michie, 2013). In the context of

this task, and bearing in mind the duration of this activation, children with ASD were likely

relying strongly on the rIFG to inhibit their own beliefs about where the ball is located.

The rSPL/rAG/rIPL and the lIFG were reactivated between 450-600 ms, and the lMTG was

additionally recruited for the same time period. The former two areas likely served the same

42

purposes as before, namely attempting to integrate the true location of the ball with what Jill

previously thought (before leaving the scene) through verbal rehearsal. It is important to note,

though, that the rSPL/rAG/rIPL at this time point extended to the right supramarginal gyrus/right

superior temporal sulcus (rSMG/rSTS) area, which is part of the rTPJ. While some studies have

shown that the rTPJ is central to ToM processing (Saxe & Wexler, 2005; Rabin et al., 2010),

there is converging evidence that the dorsal rTPJ is concerned with working memory and re-

orienting of attention, while the ventral TPJ is exclusive for ToM (Decety & Lamm, 2007;

Scholz et al., 2009). Based on the location of this rSPL/rAG/rIPL region, it appeared to be

situated in the more dorsal portion of the TPJ, and so it may instead reflect attentional processes.

The lMTG here is much more anterior than the portion of the lMTG that analyzes object motion,

so instead of being involved in relatively basic visual processing, it probably is participating in

resolving the difference between the children’s own thoughts and those of Jill, as the lMTG has

been implicated in detecting incongruities (Chan et al., 2013) and counterfactual thinking (Van

Hoeck et al., 2013). Hence, these three regions may be coordinating the union of reality and Jill’s

perspective to form her false belief.

5.2.3 Typically-Developing Children Rely Mainly on ToM Regions for False-Belief Processing, Whereas Children with ASD Additionally Use Working Memory and Inhibition

Based on the interpretations given above, TD children and children with ASD appear to recruit

fairly distinct networks of brain areas for false-belief processing, with the TD children utilizing

classical ToM regions, such as the precuneus and the lTPJ, and the children with ASD drawing

on a number of areas responsible for working memory and inhibition, such as the rIPL, the lIFG,

and the rIFG. A direct comparison of the two groups reveals a similar conclusion.

The lAG/lMTG, rSPL, and lITG all had significantly greater activity in TD children than in

children with ASD. The lAG/lMTG was more active at 325-375 ms and 425-475 ms, the rSPL at

375-425 ms, and the lITG at 425-500 ms. What is immediately striking is that all these

differences occur relatively late, past 300 ms, suggesting that while automatic processes may be

similar between the two groups, TD children are applying a different top-down cognitive

approach than children with ASD. As proposed above, TD children are perhaps appropriately

making use of ToM regions, such as the lAG/lMTG, and selectively using working memory and

inhibition regions, such as the rSPL and lITG, though not reliant on the latter two.

43

On the other hand, a variety of regions were more active in children with ASD compared to TD

children, such as the lMTG, lCalG/lLG, rFG, rSPL/rAG/rIPL, lIFG, rIFG, and a more anterior

lMTG region. The significant activation in the posterior lMTG, lCalG/lLG, and rFG from 50-100

ms, 75-200 ms, and 175-250 ms, respectively, likely reflect atypical visual processing that has

been established in ASD (Behrmann, Thomas, & Humphreys, 2006; Simmons et al., 2009).

Between 275-325 ms, and again between 475-600 ms, the recruitment of the rSPL/rAG/rIPL,

lIFG, rIFG, and anterior lMTG shows the greater dependence that the children with ASD have

on working memory and inhibitory processes to interact with ToM-related regions. These results

demonstrate how children with ASD may use alternative neural strategies to perform a ToM task

similarly to TD children.

5.2.4 The Intersection between Theory of Mind and Working Memory

Given that the children with ASD appeared to rely on working memory during our false-belief

ToM task, it is important to briefly discuss the apparent overlap that the ToM and working

memory networks have in the literature. As mentioned above, different sections of one of the key

ToM network hubs, the TPJ, have been shown to serve distinct functions, with the dorsal region

being responsible for attention, and the ventral region for ToM (Decety & Lamm, 2007; Scholz

et al., 2009), and it may also underlie several other functions (Igelström, Webb, & Graziano,

2015). In addition, other crucial ToM network regions, such as the IPL, AG, and precuneus, have

been implicated in attention and working memory studies, as well (Kizilirmak, Rösler, Bien, &

Khader, 2015; Urbain, Pang, & Taylor, 2015). Taking into consideration this overlap between

ToM and working memory, it raises the question of how specific these proposed ToM regions

are to ToM itself, and it highlights the need for adequate working memory controls in ToM tasks,

an issue which has also been brought up by Spreng and Mar (2012).

Clearly, the false-belief task used in this present study was not without working memory

confounds, as the TD group activated working memory regions as well. However, we performed

exploratory analyses of our data to examine the extent to which certain regions were recruited for

working memory, by comparing the FB trials to another set of TB trials, the Unwitnessed-

Unswitched condition, which requires a similar cognitive load (as Jill also disappears in these

trials), but which is also an appropriate ToM contrast. This analysis showed that, for example, in

the 375-425 ms time window, even when working memory was accounted for, TD children still

44

showed higher activation in the rSPL compared to children with ASD, but on the other hand,

children with ASD no longer showed greater activation in the lIFG or the rIFG, emphasizing

their roles as working memory regions (Supplementary Figure 2). In other words, when the

children with ASD were presented with an equally memory-intensive TB scenario, they recruited

similar areas for the TB and FB trials, so it can be concluded that these regions are related to

working memory processes. The rationale for not using this condition to represent true belief,

though, was due to the fact that the study’s design included fewer trials in this condition, namely

50 trials, and so it cannot be compared as robustly to the FB condition, which had 100 trials.

Regardless, this comparison compellingly suggests that children with ASD have a strong

working memory component when engaging in ToM.

5.3 Summary

Our neuroimaging results suggest that unlike TD children, who primarily use ToM regions

during false-belief reasoning, children with ASD, while capable of correctly inferring others’

thoughts, are utilizing additional brain areas that are responsible for working memory and

inhibition, perhaps because their ToM regions are not functioning efficiently. Their reliance on

these two domains makes sense in the context of their neurocognitive results, which showed no

difference in working memory or inhibition measures compared to controls.

5.4 Limitations and Future Directions

While this study demonstrated distinct differences between the TD and ASD groups, it is not

without its caveats. Although we found IQ to be different between the two groups of children, it

was not controlled for in our neuroimaging analyses. This decision was due to the fact that IQ

did not adequately predict accuracy or reaction time on the false-belief task, and it has been

suggested that controlling for IQ may lead to misinterpretation of results, as IQ differences are a

central and defining symptom of certain disorders (Dennis et al., 2013).

It was surprising that neither the rTPJ nor the mPFC appeared prominently in our results,

considering their ubiquitous presence in the ToM literature. However, it is possible that these

areas did not play a role in our study as their functions were either present in all trials or were not

necessary for this task. The rTPJ has been shown to be activated for both TB and FB scenarios

(Aichhorn et al., 2009), and as we compared these two trials in our analyses, the rTPJ’s activity

45

was likely cancelled out. Thus, it may be interesting to compare the FB trials instead to a

different control condition, such as physical causality, to observe the rTPJ’s role. Also, the

mPFC appears to be more involved in attribution of intentions and specifically processing social

intentions (Brunet, Sarfati, Hardy-Baylé, & Decety, 2000; Ciaramidaro et al., 2007), but since

our false-belief task requires neither of these processes, it is logical, in that case, that the mPFC

need not be recruited. Alternatively, since activation of the rTPJ and mPFC has been largely

reported in adult studies, it may be that these areas are still developing in children, and so their

roles and activation are not as apparent in our study.

As our study involved the use of working memory, future work should have an appropriate

working memory comparison task or condition. In addition, neuroimaging results could be

improved by including time-course information for regions of interest to better delineate these

regions’ exact timing, since the time windows used above averaged activity over the 50-ms time

period. Finally, it would be interesting to investigate the connections between frontal and parietal

regions in the ToM network during this task using connectivity and diffusion tensor imaging

measures, as our results showed many simultaneous frontal and parietal activations, suggesting

some coherence between them.

5.5 Conclusions

This study investigated differences in the timing and location of the neural correlates of ToM

processing between TD children and children with ASD. Using MEG, not only were we able to

detect the distinct areas that were activated, but we were also able to observe differences in

timing of similarly recruited regions between the two groups due to MEG’s ability to tease apart

temporal differences, which no other neuroimaging technique can tell us with comparable

resolution. In addition, measuring this temporal domain allowed us to establish the sequence of

activation and thus the roles each area might play in ToM processing. We propose that while TD

children activate appropriate ToM regions during false-belief reasoning, children with ASD

instead activate additional working memory and inhibition-related areas to perform similarly to

the TD children. These results are significant to the literature on ASD as they not only inform us

on how ToM processes can be preserved in some children with high-functioning ASD, but they

also demonstrate strategies that could be used in interventions to improve ToM in ASD, and thus

lead to better social outcomes for children with ASD.

46

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Appendices

A TD > ASD, FB

B ASD > TD, FB

Supplementary Figure 1 – Glass brain images from SPM depicting the TD to ASD group comparison of false

belief (A) and vice versa (B) when controlling for working memory load. The Unwitnessed-Unswitched trials

(another true belief condition) were used as the working memory control. In (A), the right superior parietal lobule,

denoted by the red arrow, is more active in TD children compared to children with ASD, as was the case when

using the Witnessed-Switched (TB) trials as the comparison. However, the inverse of this contrast (B) showed no

significant regions of activation, even though our results with the TB trials as control trials showed greater

activation in the left and right inferior frontal gyrus.

Documents

Timing and Location of Brain Areas Involved in Cognitive ...€¦ · viii List of Figures Figure 1: The stimuli for the false belief task.14 Figure 2: Boxplots illustrating the scores