social perception and anatomical connectivity in autism: a...

Social perception and anatomical connectivity in autism:

a cross sectional study of the correlation between eye-tracking and

MR-DTI

Internship supervised by Pr Monica Zilbovicius

Laboratoire INSERM U1000 “Imagerie et Psychiatrie”

Florence Brun

Cogmaster M2 - Supervisor Sharon Peperkamp

2017/2018

Defense: June

Language: English

Internship Cogmaster M2 Florence Brun 2018

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« Je ne constitue pas autrui, je le rencontre. »

Jean-Paul Sartre


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Declaration of originality:

In recent years, a great number of studies have investigated brain anatomical connectivity in

autism spectrum disorders (ASD) through white matter microstructure properties. Using

diffusion tensor MRI (MR-DTI), previous studies have revealed impaired white matter

microstructural properties in children with ASD compared to children with typical development

(TD). However there is not yet a consensus in how the white matter is affected in ASD1. Some

studies have associated ASD with broadly reduced degree of white matter (WM) tracts

organisation and or myelination2, while other studies suggest more localized abnormalities,

mainly regarding fibre tracts within the so called “social brain” that are involved in social

perception and social cognition3,1,4.

In that context, the main goal of our study was to investigate whether white matter

microstructural abnormalities in ASD are broadly spread across the brain or localised in tracts

connecting the “social brain” areas such as the cerebro-cerebellar circuits, the hippocampo-

amygdala tracts and the fronto-temporal and parieto-temporal fibres especially. The tract-

based-spatial-statistics (TBSS) method should allow us to tackle this question, since it performs

whole brain analyses without a priori hypotheses, in contrast with methods based on regions-

of-interest5. TBSS is quite recent, in 2012 only 21% of the 48 DTI studies in autism reviewed

by Travers et al. was using this method6. Furthermore, the authors of this review highlighted

that very few studies have focused on the age-related maturation of white matter in ASD,

although it seems that this developmental trajectory is abnormal in autism. Given that, we

further aimed to investigate the white matter microstructural properties across the age in ASD

and in TD.

The core feature of autism spectrum disorders is deficits in social interaction, mainly

characterized by deficits in eye contact. For the last two decades, several studies have

investigated gaze behaviour in ASD using eye-tracking methods. It has been reported that

subjects with ASD showed a decreased gaze preference to social stimuli compared to subjects

with TD7. However, very few studies so far have characterized the developmental trajectory of

this atypical gaze pattern in ASD. Moreover, as far as we know, very little work has drawn the

link between anatomical and behavioural data1, although white matter and gaze preference

abnormalities have been both associated with the severity of social deficits in ASD8,9.

Therefore, we wanted to investigate whether white matter microstructural properties and gaze

behaviour would be correlated, in ASD and in TD. Finally, we wanted to investigate the link


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between white matter microstructural properties and social deficits assessed by the diagnostic

interview-revised for ASD in children (ADI-R) and the autism spectrum quotient in adults

(ASQ).

Declaration of contribution:

This study results from a collective work with different contributions as summarised above:

- Scientific question: Monica Zilbovicius, Ana Saitovitch, Florence Brun

- Scientific literature review: Monica Zilbovicius, Ana Saitovitch, Florence Brun

- Design setting, experimental coding and improvement : Monica Zilbovicius, Ana

Saitovitch, Elza Rechtman

- Participants recruitment: Ana Saitovitch, Elza Rechtman

- Realisation of the experiments: Monica Zilbovicius, Ana Saitovitch, Elza Rechtman,

Alice Vinçon-Leite, Florence Brun, Louis Dufour, Jennifer Boisgontier

- Extraction of the data: Ana Saitovitch, Elza Rechtman, Alice Vinçon-Leite, Florence

Brun

- Analyse of the data: Hervé Lemaître, Alice Vinçon-Leite, Florence Brun

- Interpretation, conclusions: Monica Zilbovicius, Ana Saitovitch, Florence Brun, Hervé

Lemaître

- Report redaction, figures: Florence Brun

- Report review: Monica Zilbovicius, Ana Saitovitch, Hervé Lemaître


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Acknowledgments

I would like to thank all the people involved, who made this work possible.

I thank first all the children and adults with autism and their families. I thank them for their

kindness, their opening and their courage. Meeting each of them was a special enrichment.

Then, I thank all the children and adults with typical development and their families, who accept

to spend their time, to help the research and whose involvement is invaluable.

This work was also made possible thanks to the team INSERM U1000, who has begun this

study on autism since many years already, and I thank J-L. Martinot the director of this

INSERM unit for welcoming me in the team.

I warmly thank Dr.M. Zilbovicius for welcoming me to her lab, for her advice and the transfer

of her experience, for her enriching discussion and her precious help on my formation. I warmly

thank A. Saitovitch for her administrative help, her advice about my formation, for what she

taught me about the clinical care of autism, and for her listening skills. I warmly thank the

engineer H. Lemaître, whose help on statistics and images processing was especially precious

and who has been always patient with me. I thank the post-doctoral researcher E. Rechtman

and the PhD student A. Vinçon-Leite, whose previous work I carried on and who gladly

answered my questions, even though having already left the lab. I thank the engineer L. Fillon

for helping me with the Linux vocabulary, for his sense of humour and his kindness. I thank the

post-doctoral researcher J. Boisgontier for initiating me to TBSS, for her energy and her good

mood. I thank the paediatrician L. Dufour for transferring me his medical knowledge, for his

interesting discussion and his good mood. I also thank Pr. N. Boddaert for her collaboration to

the project and her opening.

Finally, I thank my great family and friends, who have been always supporting me.


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Summary:

Pre-registration ……………………………………………………………………........page 8

Report

Introduction ……………………………………………………………………page 18

Hypotheses ……………………………………………………………………..page 25

Method

Participants’ recruitement …………………………………………….page 26

Participants’ information …………………………………………..…page 27

Eye-tracking

Apparatus …………………………………………………..……page 28

Stimuli …………………………………………………………..page 28

Procedure ………………………………………………………..page 29

Measures ……………………………………………………..… page 29

Statistical analysis ……………………………………………….page 30

MR-DTI

Apparatus …………………………………………………..……page 31

Procedure ………………………………………………………..page 31

Images processing ………………………………………………page 31

Statistical analysis ………………………………………………page 32

Results

Eye-tracking

Group comparison in children and adolescents ..………………………...page 33

Group comparison in adults ……………………………………………..page 34

Correlation with the age, cross sectional study ……………………….....page 35


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Correlation with the severity of social deficits …………………………page 36

MR-DTI

Group comparison in children and adolescents…..………………………page 38

Group comparison in adults …………………………………………….page 39

Correlation with the age, cross sectional study …………………………page 39

Correlation with the severity of social deficits …………………………page 42

Correlation with the gaze behaviour, cross sectional study …………….page 42

Discussion………………………………………………………………………page 44

Conclusion………………..…………………………………………………………….page 51

References………………..…………………………………………………………….page 52


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Pre-registration

I Introduction

Autism spectrum disorder (ASD) affects 1 in 160 children worldwide10. Although ASD covers

a wide heterogeneity of symptoms, social interaction deficits are ubiquitous across patients with

the disorder11, impairing both social integration and functional dependence. Social interaction

deficits reflect the inability to infer others’ intentions and mental states from the gaze

direction12,13 and emotional content14. Thus, social interaction relies on more basic social

perception processes, with the detection of non-verbal social cues, such as body gestures and

eyes contact.

Social perception can be investigated by the eye-tracking method and studies have highlighted

an atypical gaze pattern in patients with ASD. Indeed, previous eye-tracking studies have shown

that patients demonstrate a lack of interest for the faces and especially for the eyes while

watching a social scene7. Moreover, the brain regions involved in social perception have been

associated with neuro-anatomical impairments in ASD patients compared to control subjects.

Thus, grey matter loss15,16,17,18, hypo-activation during social tasks19,20,21 and an altered

functional connectivity22,23,24 have been found in the superior temporal sulcus (STS), the

fusiform face area (FFA), the fusiform gyrus (FFG) and the amygdala.

Studies have also associated ASD with an altered anatomical connectivity measured with the

diffusion tensor MRI method (MR-DTI) that infers the white-matter (WM) tracts organisation

degree; the measure reflects the WM’s state of myelination, axonal coherence or axonal

calibre25. However the results remain heterogeneous1. Some studies have associated ASD with

broadly reduced degree of WM tracts organisation2, while other studies suggest local reductions

in the “social brain” regions that are involved in social perception and social cognition3,1,4.

Given these findings, we want to assess whether white matter microstructural abnormalities in

ASD are broadly spread across the brain or localised in tracts connecting the “social brain”

areas such as the cerebro-cerebellar circuits, the hippocampo-amygdala tracts and the fronto-

temporal and parieto-temporal fibres especially.

The recent tract – based – spatial – statistics (TBSS) method should allow us to tackle this

question, since it performs whole brain analyses without a priori hypotheses. Using this

method, a study previously conducted in our lab in children with ASD and children with typical

development (TD) has highlighted local white matter abnormalities in the corpus callosum and


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the arcuate fasciculus (Vinçon-Leite et al., submitted). This work has shown that altered

anatomical connectivity in ASD varies across the age, especially through brain maturation in

adolescence26,27, but also through age-invariant changes in the trajectories28. However, this

evolution from childhood to adulthood still remains underinvestigated29. We have also noticed

that very few studies have drawn so far the link between anatomical and behavioural data, even

if WM and gaze pattern abnormalities have been both associated with social symptoms of the

disorder8,9.

Behavioural and anatomical data were acquired in our lab, in adults and children with autism

and in typically developing controls. In the project presented here, we aim at using the eye-

tracking and MR-DTI raw data to test whether WM abnormalities would correlate with gaze

pattern abnormalities and whether WM abnormalities would correlate with social deficits that

were assessed by the autism diagnostic interview-revised for ASD (ADI-R). Then, we will be

able to compare and contrast our results in adults with the results in children previously found

in our lab, to characterize the white matter developmental trajectories in patients with autism.

II Method

Data in children and adults were previously acquired in our lab, as described below.

Participants

We included in our study 28 children (22 men, 6 women, mean age 8.4 ± 3.9 range: 2.4 to 16)

and 17 adults (men, mean age 22 ± 3.2 range: 18 to 29) with ASD, 26 children (16 men, 10

women, mean age 10 ± 2.9 range: 6.0 to 17) and 43 adults with typical development (TD) (36

men and 8 women, mean age 22 years old ± 2.6 range: 18 to 31).

Children and adults with ASD were recruited in university hospitals, designed as reference

centres for autism diagnosis by the French Health Ministry. ASD diagnosis was established by

a multidisciplinary team, including psychiatrists, psychologist and speech therapists. The

clinician judgement was informed by the Diagnostic and Statistical Manual of mental disorder

criteria revised (DSM-IV-R, APA, 2000 and 2013) as well as by the Autism Diagnostic

Interview-Revised (ADI-R, Lord 2004). Exclusion criteria were medical conditions accounting

for the autistic symptoms (epilepsy for example). Intellectual quotient (IQ) was assessed by the

clinicians. Children and adults with TD were volunteers recruited in an advertisement.

Exclusion criteria were psychiatric, neurological and general health problems. Moreover, TD


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children had a normal scholarship, without any learning disabilities. Intellectual quotient was

determined according to the standardised procedures of the fourth version of the Wechsler

intelligence scale for children and the third revision of the Wechsler adult intelligence scale.

All participants had normal or corrected-to-normal vision abilities and none of them had

contraindications for MRI scans.

Written informed consent to participate to the experiment was obtained from each participant

or from its parent(s) or legal guardian according to ethical and legal guidelines and the study

was approved by the Necker Ethics Committee.

Eye-tracking

Apparatus

The experiment was conducted using a Tobii T120 Eye Tracker Equipment (17-inch TFT

monitor with a resolution of 1280 * 1024 pixels). The stimuli were presented on a screen,

recording the gaze simultaneously with a rate accuracy of 0.5 degrees and a sampling rate of 60

Hz for both eyes. We choose this sampling rate to compensate for the children movements30.

The eye-tracking procedure was non-invasive and without any head or body movement

constraint.

Stimuli

The stimuli were similar to a previous study conducted in our lab31. 7 movie fragments (10

seconds each) were showed successively, with 5 fragments displaying social scenes and 2

fragments displaying non-social scenes. The social scenes correspond to peer to peer social

interaction between two characters and were extracted from the movie Le Petit Nicolas®. The

non-social scenes correspond to the movement of a red balloon flying in a blue sky and were

extracted from the movie Le ballon rouge®. This non-social fragments were used to control for

non-biological movement perception. Factors such as scene background, characters’ position,

balloon size, or speed were not controlled for.

Procedure

The participants were seated at an approximately 60 cm viewing distance of the screen. Before

each set of stimuli, five registration points were used to calibrate the eye-tracker. The


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registration points were adapted for the children with the presentation of a toy. The calibration

had to achieve the accurate recording quality criteria, as indicated by Tobii StudioTM software.

The participants were instructed that they would see a sequence of movie fragments, and they

just had to watch them. Little information was provided about the tracking of eye-movement,

to optimize the ecological aspect of the experiment.

Measures

Gaze patterns were characterized with respect to dynamic areas of interests (AOI) manually

defined on the visual scene, allowing "frame by frame" measurements throughout the film.

Faces AOI were defined with oval shapes, whereas eyes and mouth were defined with

rectangular shapes. The rest of the visual scene was considered as non-social background AOIs.

AOIs size and shape were stable across the different frames.

We recorded the number of fixations inside an AOI using Tobii StudioTM software that defines

fixation as the gaze remaining in a 0.5 degree visual field for at least 100 millisecond minimum.

The number of fixation inside an AOI is our dependant variable that reveals the eyes movement

toward the region, interpreted as the participant’s level of interest.

We will consider the age, the sex and the IQ score as covariates.

MR-DTI

Apparatus

All brain imaging were acquired with a GE-Signa 1.5 Tesla MR scanner located at Necker

hospital in Paris using a 12-channel head coil.

Anatomical scans were acquired using a three-dimensional T1-weighted FSPGR sequence

(repetition time (TR): 16.4 milliseconds, echo time (TE): 7.2 milliseconds, flip angle 13°,

matrix size: 512 x 512, field of view: 22 x 22 cm, 228 axial slices at a thickness of 0.6 mm).

This sequence was used to exclude from the study participants with anatomical damage.

The diffusion weighted image sequence was acquired using an echo-planar imaging sequence

with diffusion gradients applied on either side of the radiofrequency (repetition time (TR):

15000 or 13000 milliseconds according to the cranial size of the subject, echo time (TE): 70

milliseconds, voxels size 2 x 1.8 x 1.8 mm3, total acquisition time of 5 minutes). Diffusion

weighting was encoded along 40 independent orientations, and the b value was set at 1000

s.mm-2.


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Procedure

Participants underwent an MRI separately from the eye-tracking session. For the children with

ASD, the standard premedication protocol (7.5 mg/kg of pentobarbital per rectum) was applied

only when required (12 out of 28 children).

Predictions

Eye-tracking in the adults

Deficits in social perception have been described in children and adults with ASD, mainly

characterized by a lack of preference for social stimuli. The previous study conducted in our

lab highlighted that children with ASD presented significantly fewer fixations to the eyes and

faces and significantly more fixations to non-social background in social scenes compared to

TD children, while no differences were observed concerning the mouth (Vinçon-Leite et al.,

submitted). Thus, we expect that when watching a social scene, adults with ASD will present

fewer gaze fixations to the face and the eyes and more gaze fixations to the mouth and the non-

social background compared to TD adults (hypothesis 1). We also expect that in adults with

ASD, the number of gaze fixations to the social areas would negatively correlate with the

severity of the disease (hypothesis 2a), whereas the number of gaze fixations to non-social areas

would positively correlate with the severity of the disease (hypothesis 2b). The severity of the

disease will be evaluated by the autism diagnostic interview-revised (ADI-R) and we will focus

on its sub score B assessing social interaction symptoms.

Eye-tracking: comparison between the adults and the children

Studies using the eye-tracking method have shown significant differences in visual social

perception between typically developing children and children with ASD as early as from 24

months of age32. These impairments in visual social perception have been found to persist in

adults with ASD33 even though they usually benefit from therapies and treatments. However, a

recent cross sectional study suggests that socio-visual interactions would differ between

children and adults with ASD ranging from 7 to 30 years old34. We will push this investigation

further, including autistic patients from 2 to 30 years old and refining the results found. Thus,

we hypothesize that the visual social perception between children and adults with ASD would


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be different, with a group by age interaction on the gaze behaviour (hypothesis 3a).

Alternatively, we hypothesize that there would be no such differences (hypothesis 3b).

MR-DTI in the adults

Neuroimaging studies have shown that there are white matter (WM) microstructure

abnormalities in the brain of adults with ASD, but it remains unclear whether these

abnormalities are broadly disseminated or locally distributed in specific brain areas. We

hypothesize that WM abnormalities would target especially the tracts connecting the brain areas

involved in social perception and social cognition. Thus, we expect fractional anisotropy values

within the WM tracts connecting the “social brain” areas to be lower in the ASD group

compared to the control group (hypothesis 4).

MR-DTI: comparison between the adults and the children

Very few studies have been conducted to assess the effect of the age on white matter differences

between ASD and controls. Results on this question are currently very heterogeneous in the

literature, describing a blunting, an aggravation or a similar pattern of white matter

abnormalities across the age in subjects with ASD compared to subjects with TD. For example,

childhood differences in the thalamus WM microstructure between ASD and controls have been

shown to be less robust in adolescence and adulthood35. On the contrary, the white matter

fractional anisotropy in the bilateral superior frontal gyrus has been shown to decrease with the

age in adolescents with ASD, whereas it increases with the age in adolescents with TD36.

Finally, other studies suggest that the abnormal white matter microstructural properties white

matter found in ASD in childhood persist into adulthood37.

It is worth noticing that these studies mentioned above were mostly restricted to a narrow age-

range and / or investigated specific brain areas with a priori hypotheses. As far as we know, no

study has been conducted so far on a large age-range cohort with a whole brain approach to

characterize the evolution of white matter abnormalities in autism. Given that, we will

investigate age-related changes of white matter microstructure in participants with ASD and

TD. We hypothesize that white matter tracts involved in the “social brain” will evolve with the

age in a different manner in participants with ASD subjects compared to participants with TD,

leading to either a blunting, or an aggravation of white matter abnormalities between childhood

and adulthood (hypothesis 5a). Alternatively, we hypothesize that the white matter

abnormalities found in adults with ASD would be consistent with the abnormalities found in


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children with ASD, revealing a similar age-evolution in participants with ASD and participants

with TD (hypothesis 5b).

Eye tracking and MR-DTI correlation in adults and children

We hypothesize that adults and children who look less at social stimuli would present lower

white matter fractional anisotropy, especially in the regions of the “social brain”. Thus, we will

assess the correlation between the FA values and the number of gaze fixations toward social

AOIs within the control and the ASD groups. We suppose that in both groups, the white matter

fractional anisotropy in the “social brain” would positively correlate with the gaze preference

toward social scenes (hypothesis 6a). If it is not the case, we will perform the same analysis

separating the groups. Indeed, we suppose that the positive correlation detailed above would be

found in both groups, with different correlation rates (hypothesis 6b). Moreover, we

hypothesize that WM microstructural alterations found in adults and children with ASD would

correlate with clinical outcome. Thus, we will assess the correlation between the FA values and

the symptoms severity in ASD patients measured by the sub score B of the ADI-R in children

and by the ASQ score in adults, assuming that we will find a negative correlation between the

two (hypothesis 7).

Data analysis

Eye tracking in the adults

The gaze fixations to the eyes, the mouth, the faces and the non-social background were

recorded throughout time using Tobii StudioTM software.

We will use number of fixations to the referred regions of interest. For each participant, these

data will be extracted and analysed using R software to compute multiple linear models with

the diagnosis as a main factor and the sex and the age as covariates.

Eye-tracking: comparison between the adults and the children

Taken the data acquired in children into account, we will assess the effect of age, in both groups

or separately, with the sex as covariate.


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MR-DTI in the adults

The preprocessing, processing, and statistical analyses of all MRI data will be performed using

FMRIB software library (FSL) and its module TBSS that focuses on diffusion data.

The diffusion images will be converted from DICOM to NIFTI formats and the images affected

by large artefacts or with a very low signal will be removed from the study. The eddy current

artefacts and subjects’ motions will be corrected using the EDDY tool in FSL. Subjects with

head rotation superior to 5° will be excluded to avoid bias in the computation of the diffusion

tensors38. The cerebral tissues will be extracted using the BET tool in FSL. Then, a tensor model

will be fitted to each voxel and derivative measure such as fractional anisotropy (FA) will be

computed using the DTIFIT tool in FDT FSL. A quality control will be performed to detect

eventual outliers or issues from the precedent steps.

FA will be aligned into a common space from the Montreal Neurology Institute 152 (MNI 152),

using the FSL nonlinear registration tool (FNIRT). Next, the mean FA image was created and

thinned to construct a mean FA skeleton. This skeleton represents the centres of all tracts

common to the group. Each subject's aligned FA data was then projected onto this skeleton and

threshold at 0.3.

Whole brain voxel-wise statistics will be performed on these FA skeletonised maps to assess

group comparisons. These statistics will be performed within the general linear model

framework and run with 5000 permutations tests using Randomise option in FSL. Multiple

comparisons across voxels will be corrected applying the family-wise error correction (FWE)

and the threshold-free cluster enhancement option (TFCE)39. The significant clusters obtained

will be localised using the J. Hopkins University WM atlases tool in FSL.

MR-DTI: comparison between the adults and the children

Whole brain voxel-wise statistics within the general linear model framework will be performed

on the FA skeletonised maps to assess the correlation with the age.

Eye tracking and MR-DTI correlation in adults and children

Whole brain voxel-wise statistics within the general linear model framework will be performed

on the FA skeletonised maps to assess the correlation with the number of fixations to the eyes.

We will compare the results we will get in adults with the results we will get in children.


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Interpretation

If the general linear model reveals that adults in the ASD group present a significant lower

number of gaze fixations in the face areas and / or in the eyes areas and a significant higher

number of gaze fixations in non-social areas compared to the control group, we will conclude

that our hypothesis 1 is verified.

If the general linear model reveals a significant positive correlation in subjects with ASD

between the number of gaze fixations to non-social ROI and the severity of social deficits

assessed by the sub score B of the ADI-R, we will conclude that our hypothesis 2a is verified.

If there is a significant negative correlation between the number of gaze fixations to social ROI

and the severity of social deficits, we will conclude that our hypothesis 2b is verified.

If the general linear model reveals a group by age interaction on the gaze preference to social

areas, we will conclude that our hypothesis 3a is verified. Alternatively, we will conclude that

our hypothesis 3b is verified.

If the general linear model reveals that adults with ASD present significant lower FA values in

WM tracts in the “social brain” compared to adults with TD, we will conclude that our

hypothesis 4 is verified.

If the general linear model reveals a group interaction on the age-related evolution of FA in

WM tracts in the “social brain”, our hypothesis 5a is verified. If the group differences of the

FA values are consistent between children and adults, our hypothesis 5b is verified.

If the general linear model reveals a significant negative correlation in adults between the FA

values in WM tracts in the “social brain” and the number of gaze fixations to social stimuli, we

will conclude that our hypothesis 6a is verified. If it is not the case, we will separate the groups

to assess whether there is a correlation between these two indexes in participants with ASD on

the one hand and in participants with TD on the other hand. If it is the case, our hypothesis 6b

is verified and we will be able to determine how this correlation differs between the two groups.

If the general linear model reveals a significant negative correlation in adults with ASD between

the FA values and the symptoms severity, we will conclude that our hypothesis 7 is verified.


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III Expected contribution

Participants have been previously recruited and scanned in our lab according to the procedures

detailed above. I will personally lead the analyses presented in this work, to assess the

hypotheses I have built on the basis of what has be done so far.

All the steps required for the realisation of the study are listed below, with the expected

contribution of each member of the project.

- Experimental question: M. Zilbovicius, A. Saitovitch, F. Brun

- Bibliography research: A. Vinçon-Leite, A. Saitovitch, E. Rechtman, F. Brun

- Participants recruitment: A. Saitovitch, E. Rechtman

- Eye-tracking experiment: A. Saitovitch, E. Rechtman, A. Vinçon-Leite

- MR-DTI experiment: M. Zilbovicius, A. Saitovitch, E. Rechtman, A. Vinçon-Leite

- Eye-tracking analyses: A. Saitovitch, A. Vinçon-Leite, F. Brun

- MR-DTI analyses: H. Lemaître, J. Boisgontier, L. Fillon, F. Brun

- Correlation analyses: H. Lemaître, L. Fillon, F. Brun


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Report

Introduction

Autism spectrum disorder (ASD) is a neuro-developmental disorder that affects around 1 in 160

children worldwide40. According to the last version of the diagnostic and statistical manual of

mental disorder (DSM-V), autism spectrum disorder is characterised by difficulties in social

interaction and communication and by unusually restricted, repetitive behaviour and interests.

ASD is referred to a spectrum, because it covers a wide heterogeneity of profiles through a

strong variation in the severity of the two symptoms mentioned. Moreover, ASD is associated

in more than 70% of the cases with comorbidities41 that largely differ between patients. The

main comorbidities are other developmental disorder (language42 or general cognitive

impairments, attention-deficit hyperactivity disorder43, motor impairments44), psychiatric

disorders (including anxiety and depression45, bipolar disorder46, Tourette syndrome,

addictions, obsessive compulsive disorder) and other pathologies (including epilepsy47, hyper

or hypo sensitivity48, gastro-intestinal problems, sleep disturbances49 and immunological

disruptions). Despite this wide heterogeneity in terms of severity and comorbidity, the two core

symptoms are ubiquitous across the disorder11.

The prevalence of autism has risen dramatically in the last years. The Centres for Disease

Control and Prevention in the United States reported in 1996 a prevalence of 3.4 in 10 000

children and in 2018 a prevalence of 1 in 59 children50. This increase could be explained by a

more frequent diagnostic and an enlargement of the diagnostic criteria, but also by a higher

exposure to environmental risk factors. Indeed, foetal exposure to psychotropic drugs51 and

treatments for epilepsy52, to toxins and insecticides41 have been associated with a higher risk

for ASD and the increased exposure to some of these agents have been found to positively

correlate with the increased prevalence of autism53. Atmospheric pollution is suspected to play

a role in the increased prevalence of ASD54, although findings in this field are controversial due

to the presence of several confounding factors55.

Social interaction and communication deficits are especially determining factors for the severity

of the disorder, impairing both social integration and functional dependence of the subjects with

ASD. Social interaction is based on the ability to infer others’ intentions and mental states. This

ability is referred as the theory of mind and is conveyed especially through the processing of

others’ gaze direction12,13 and emotional content14. Indeed, humans are able to depict far more

information from the eyes direction and emotion, than other primates can do. This special


19

sensitivity seems to be supported by the very contrasted polarity of the human eyes with its very

bright cornea and dark-coloured iris56. It has been shown that the contrasted luminance within

the eye is crucial in the perception of gaze direction57. Moreover, this special sensitivity seems

to be supported by an early gaze preference to the eyes. Infants with 3 days of life prefer already

faces with direct eyes contact compared to faces looking away58 and infants with 4 months of

life are able to follow gaze direction59. Then, this gaze preference to the eyes evolves to other

social skills. By 6 months infants are able to discriminate emotional expressions60. At 1 year

old they use joint attention to communicate their interest toward external objects and they

develop imitative behaviours61. Later, the theory of mind emerges, with the ability to understand

others’ interests and intentions as different from one's own62. This ability potentiates the socio-

emotional maturation that lies beyond.

In autism, gaze abnormalities had been described since the first publication of Kanner

describing autism in 1943 and it is now systematically taken into account by the current

diagnostic evaluations. Within the first year of life, infants who will later develop a diagnosis

of ASD show less oriented preference to social stimuli than infants with typical development

(TD). Moreover, a reduced social smiling and a reduced eye contact has been observed63. At 4

years old, children with ASD react less to faces64 and show decreased joint attention and

imitative behaviours compared to children with TD65. Later, deficits in the theory of mind have

been observed66. These symptoms are associated with deficits in the development of social

interaction and communication.

Reflecting the clinical observations mentioned above, objective quantitative measures of the

atypical gaze pattern in ASD have been developed with eye-tracking techniques. Eye-tracking

techniques are currently non-invasive30 and allow to study the position, the direction and the

behaviour of the gaze. These techniques are based on the reflection of infrared light by the pupil

and the exterior cornea, recorded by a camera throughout time. The luminous point of the pupil

reflection corresponds to gaze motions, whereas the luminous point of the corneal reflection do

not move with gaze motions. Therefore, the corneal reflection is used as a reference point that

is a crucial step. Indeed, a subject can move the head to the left and maintain his gaze on the

same visual stimulus. Without reference point that follows head motion to the left, the eye

tracking would register a pupil movement to the left, while the gaze remains static. The corneal

reflection point defines the position of the eye that allows to discriminate pupil motions from

head motions67. To infer what a subject is looking at on the screen, a calibration step is needed.

During the calibration, the subject is asked to look at different targets on the screen. The


20

coordinates of the targets are adjusted to the spatial coordinates of the pupil reflection point,

whereas the corneal reflection point is set as the reference. When the position of the reference

moves, reflecting head motions, the displacements are taken into account in the calculation of

the pupil position. Once the pupil position is constraint in a 1° visual angle area for at least 100

milliseconds, the eye tracking records it as a point of fixation. Indeed, it can be assumed that

the subject is able to process the visual information displayed on this area68.

Current eye-tracking techniques provide indexes such as the number of fixations or the duration

of fixations to a given visual object, allowing researchers to measure with high precision what

a participant is looking at and for how long. These indexes reflect gaze preference to a given

visual object that is drawn by spontaneous attentional bias and consciously directed attention

to the stimulus69,70.

Spontaneous gaze behaviour can be characterized with the passive watching of dynamic social

scenes. Such characterisations have been shown to be closely related to social phenotype71.

Moreover, since the technique is non-invasive and does not imply the realisation of a task, it

can be used with children and adults regardless of their level of non-verbal and verbal

functioning. Hence, such experiments provide the opportunity to study social perception across

ages and phenotypes in a similar way.

In the first study using eye tracking70, Klin et al. recorded the gaze behaviour of subjects with

ASD watching extracts of the movie from E. Albee “Who’s afraid of Virginia Woolf?”. The

authors reported significant differences of gaze preference between subjects with ASD and

subjects with TD. While subjects with TD were focused on the social relevant stimuli namely

the actors’ faces and body movements, subjects with TD were looking at non-social stimuli

from the background. Such reductions of fixations to others’ faces would decrease the

opportunity to perceive and take advantage of social cues such as the gaze direction and

emotional content. Hence, this atypical spontaneous gaze behaviour would be directly linked to

the social interaction deficits reported in ASD. The authors hypothesized that these social

deficits would be primarily driven by difficulties in orienting the gaze to social stimuli

preferentially.

Since this first study, several works have been conducted on gaze behaviour, using a wide range

of stimuli and reporting heterogeneous findings. Indeed, in some studies, no group difference

was observed72. This result has been hypothesized to be due to the stimuli used. Thus, a higher

sensitivity to gaze differences between groups has been associated to more ecological stimuli.


21

For example, dynamic stimuli displaying only one actor addressing to the subject, rather than a

group of people engaged in social interactions have led to the absence of group difference73.

Similarly, the passive watching of cartoons or pictures have been shown to be less sensitive to

atypical gaze behaviour in ASD than the passive watching of dynamic social scenes74.

Across the literature, previous works using the passive watching of dynamic social scenes

agreed on the fact that autism is characterised by an atypical spontaneous gaze pattern. It has

been described that subjects with ASD spend significantly less time and look significantly less

at the eyes and at the faces than subjects with typically developing controls. This atypical gaze

pattern was reported in children with 2 and 5 years old, but also in 6 months-infants later

diagnosed for ASD75,76,77,78 and in adolescents and adults with ASD79,80,70.

The gaze behaviour abnormalities mentioned above have been associated with the severity of

the disease70, leading hopes to use such objective characterization in early diagnosis for autism.

Nevertheless, few studies have focused on the trajectory of this atypical gaze pattern, although

it is known that gaze preference to the faces and the eyes evolves with the age. Indeed, social

expertise emerges across development through implicit learning, leading to an age-related

increase of gaze preference to social stimuli81. This phenomenon seems to be different in ASD.

Studies have reported aggravation of social deficits between 6 and 8 years old82 and after

adolescence34. On the contrary, other studies have highlighted improvements during

adolescence, in the sense of behavioural learning and plasticity to overcome social deficits in

ASD83. Despite the encouraging results of these last studies, social difficulties associated with

ASD persist in adulthood84,70. We aim at clarifying these observations and characterising the

developmental trajectory of social behaviour in ASD compared to TD, from childhood to

adulthood.

The first insights on brain impairments in ASD were provided in the 1990s by post mortem

characterizations and cerebral imaging using positrons emission tomography. Since these

pioneer works, a large number of studies have been conducted to understand the cerebral basis

of ASD. Grey matter loss15,16,17,18, hypo-perfusion at rest17 and hypo-activation during social

tasks such as gaze perception, processing of facial emotion, or tasks involving the theory of

mind19,20,21,85 have been described in brain areas within the social brain, particularly in the

superior temporal sulcus (STS). The STS is strongly implicated in the perception of biological

motion, human voice, gaze direction and in the theory of mind. Similarly, as cerebral bases for

faces recognition, the fusiform face area and the fusiform gyrus have been associated to

impairments in ASD. Finally, involved in emotional processing, the amygdala has been linked


22

with functional and anatomical disruptions in ASD. Moreover, since these brain areas are highly

connected in functional networks, an altered functional connectivity has been described in

ASD22,23,24. Functional connectivity is based on structural connectivity, which reinforces the

importance of investigations on white matter integrity.

White matter properties can be inferred using Magnetic Resonance Diffusion Tensor Imaging

(MR-DTI) that provides indexes on water diffusion in the brain. Brain water diffuses

spontaneously in the tissues and this random spatial movement can be characterised by its

degree of anisotropy. The degree of anisotropy reveals how strongly directional the water

diffusion is, that means how strongly the diffusion is constraint by the environment. In cerebral

tissues, the dominant physical constraint of the environment corresponds to the axons. The

fractional anisotropy (FA) is an index of the degree of anisotropy. This index is comprised

between 0 and 1, with high values reflecting very anisotropic water diffusions. Thus, high

values of fractional anisotropy correspond to a high degree of white matter fibres coherence,

organization, axonal density, diameter and / or myelination of the fibres86. On the contrary, low

fractional anisotropy values correspond to weaker white matter microstructure. However, from

MR-DTI data only, abnormally low FA values are not specific and can be due to different

combinations of all the factors mentioned25.

Currently, MR-DTI data analyses follow two different methodological processing. The first

method consists in volumes- and fibres-of-interest approach, restricting the investigations to

specific volumes or fibres. The volumes-of-interest are defined either manually or automatically

that is subjected to the experimenters’ subjectivity or to inadequate spatial normalisation. The

fibres-of-interest analysis uses white matter tracts reconstruction to define fibres-of-interest.

This approach is less susceptible to the lack of spatial accuracy than the volumes-of-interest

approach but highly depends on the method used to reconstruct the white matter fibres. That

leads to inconsistent results across the literature. Moreover, volumes- and fibres-of-interests

analyses are restricted to specific areas- and fibres-of-interest, missing a global overview of

white matter microstructure over the brain.

The second method consists in a non-biased whole brain analysis that treats the data without a

priori hypotheses. In this analysis, comparisons are performed voxel-to-voxel across the whole

brain. As a result, the number of multiple comparisons requires statistical corrections. This

whole analysis can be performed with different methods such as voxel-based morphometry

(VBM) and tract-based-spatial statistics (TBSS). One of the first steps in VBM is to align the

images of the subjects for inter-subjects comparisons. This alignment is highly sensitive to the


23

anatomical heterogeneity between subjects and misalignments can create artefacts, leading to

misinterpretations87. To reduce this effect, smoothing filters can be applied to the images.

However, these smoothing filters need to be threshold and the thresholds used vary across the

studies, leading to heterogeneous results88. Similarly, TBSS requires an alignment step for

comparisons. However, TBSS focuses on voxels in the white matter fibres especially, rather

than in the whole brain. The highest FA values across all the subjects are attributed to the centre

of the white matter tracts and allow to create a skeleton of the fibres. Then, only the voxels

located at this skeleton are taken into account for the analyses. This processing reduces the

impact of potential structural mismatch between subjects and remove the step of smoothing89.

However, the analysis is restricted to the major white matter bundles and does not take into

account small fibres.

In ASD, using volumes- and fibres-of-interest approaches, previous studies have revealed

alterations of the white matter microstructure, through lower fractional anisotropy values in

many brain areas and tracts1. Using a whole brain approach, studies reported that these white

matter abnormalities were widespread over the brain2,90,91. From that emerged the hypothesis

of autism as a global hypo connectivity disorder, with the white matter affected in different

brain networks.

On the contrary, other whole brain studies suggested lower fractional anisotropy values in local

fibres and brain areas, in the so called “social brain”3,4. Thus, lower FA values have been found

in children and adolescent with autism in the cingulum (CG) and corpus callosum (CC), in the

uncinated fasciculus (Uc), in the inferior and superior longitudinal fasciculi (ILF and SLF) and

in the inferior fronto-occipital fasciculus (IFOF)1,92. Despite a crucial lack of studies conducted

in adulthood, comparable results have been found in adults with ASD93. The microstructural

abnormalities reported in these brain areas are coherent with the function they were assigned to

and with respect to the symptomatology of autism. Thus, the CC is crucial in the integration of

visual and verbal information94 and abnormalities in this region correlated with repetitive and

stereotypical behaviour especially95. The Uc is involved in the integration of information

between emotional and cognitive processes1 and the white matter fractional anisotropy in the

left uncinated fasciculus has been shown to correlate with social and emotion regulation

deficits96. Moreover, a disrupted functional connectivity between the Uc and its connected areas

and a lower activation in these areas have been found in ASD both at rest97 and during a socio-

emotional task98. The right SLF connects especially the frontal lobe to the superior temporal

sulcus and the fusiform face area that are recruited in social perception and interaction17. White


24

matter abnormalities in this fibre have been associated to the severity of autistic traits99. The

left SLF connects cortical language regions such as Wernicke’s, Broca’s and Geschwind’s areas

and white matter abnormalities in this fibre have been related to poor language skills and

communication deficits assessed by the sub score D of the ADI-R99,8. The ILF connects the

temporal and the occipital lobes and the degree of white matter fractional anisotropy in the left

ILF has been associated to autistic traits in subjects with TD100. Finally, the IFOF has been

shown to connect areas involved in social cognition in the temporal, frontal and parietal lobes

such as the fusiform gyrus, the amygdala, the superior temporal sulcus and the prefrontal cortex

and abnormalities in this fibre have been shown to induce difficulties in facial emotional

recognition101.

Given the opposition between a global hypo connectivity and a local disrupted connectivity in

autism, we aimed to investigate whether lower fractional anisotropy values in ASD would be

widespread over the brain or localised in specific fibres in the “social brain”, using TBSS.

Studies on white matter microstructural properties in ASD have been conducted on

heterogeneous populations and it emerges that abnormalities would vary across age. An

aggravation of white matter impairments in ASD has been shown in the frontal fibres especially

during the adolescence36 and a faster white matter microstructural decline in ASD has been

reported in aging, located at the right ILF and SLF especially102. Thus, it seems that the

developmental trajectory of white matter microstructure would be abnormal in ASD6. However,

that has not yet been characterized using a whole brain approach and within a large age-range

cohort103.

Given these findings, we aimed to characterize the developmental trajectory of white matter

microstructure in ASD. We investigated the age-related evolution of fractional anisotropy in

ASD and TD subjects in a large age-range cohort, with a whole brain approach.

Finally, very few studies have drawn so far the link between anatomical and behavioural data,

although atypical gaze behaviours and white matter abnormalities have been both associated

with social deficits in ASD8,9. Thus, we aimed to test the correlation between gaze preference

to social stimuli and white matter fractional anisotropy. We also tested whether white matter

fractional anisotropy would correlate with the severity of social interaction deficits assessed by

the sub score B of the autism diagnostic interview-revised for ASD (ADI-R) and the autism

spectrum quotient (ASQ). That would allow us to bring insights on the cerebral bases for social

deficits associated to ASD.


25

Hypotheses

Hypothesis 1: Participants with ASD will present a significant lower number of gaze fixations

to the face and / or the eyes and a significant higher number of gaze fixations to non-social

background compared to participants with TD.

Hypothesis 2a: The number of fixations to social AOIs will positively correlate with age.

Hypothesis 2b: This positive correlation will be significantly different in participants with ASD

and participants with TD.

Hypothesis 3: In children and adolescents with ASD, the social deficits assessed by the sub

score B of the ADI-R will positively correlate with the number of gaze fixations to non-social

AOIs and negatively correlate with the number of gaze fixations to social AOIs. In adults with

ASD and adults with TD, the same correlations will be observed with the score of the ASQ.

Hypothesis 4: Adults and children with ASD will present significant lower fractional anisotropy

values in white matter tracts in the “social brain” compared to adults and children with TD.

Hypothesis 5a: The fractional anisotropy values will positively correlate with the age.

Hypothesis 5b: This correlation will be significantly different between participants with ASD


Hypothesis 6: In children and adolescents with ASD, the social deficits assessed by the sub

score B of the ADI-R will negatively correlate with the fractional anisotropy values in the

“social brain”. In adults with ASD and adults with TD, the same correlation will be observed

with the ASQ score.

Hypothesis 7a: The fractional anisotropy values in the “social brain” will negatively correlate

with the number of gaze fixations to social AOIs.

Hypothesis 7b: This correlation will be significantly different between participants with ASD



26

Method

Participants’ recruitment

Children, adolescents and adults with ASD were recruited in university hospitals, designed as

reference centres for autism diagnosis by the French Health Ministry. ASD diagnosis was

established by a multidisciplinary team, including psychiatrists, psychologists and speech

therapists, in accordance to the diagnostic and statistical manual of mental disorder criteria

Revised (DSM-IV-R, APA, 2000 and 2013) and to the autism diagnostic interview-revised

(ADI-R, Lord 2004). Exclusion criteria were medical conditions accounting for the autistic

symptoms (epilepsy for example). In our study, children with ASD were evaluated again with

the standardised procedure of the ADI-R, whereas adults with ASD were evaluated with the

autism spectrum quotient (ASQ, Baron Cohen 2001). The ADI-R is a semi-structured interview

conducted by the clinician with a parent or caretaker, who is familiar to the developmental

history and current behaviour of the subject being evaluated. The interview allows to score the

severity of deficits in language and communication (sub scores C: verbal and non-verbal), in

reciprocal social interactions (sub score B) and in restricted, repetitive and stereotyped

behaviours and interests (sub score D). The ASQ proposes statements for which the subject has

to indicate his degree of agreement. The questionnaire allows to score social and

communication skills, but also imagination ability, attention to details and tolerance of change.

We used the ASQ rather than the ADI-R to measure the severity of social deficits in adulthood,

because the ADI-R is mostly based on symptoms from the childhood. This can create distortions

between the final score and the severity of the symptoms in the adult evaluated104. The

intellectual quotient of each patient (IQ) was assessed by the clinicians.

Children, adolescents and adults with typical development (TD) were volunteers recruited in

an advertisement. Exclusion criteria were psychiatric, neurological and general health

problems. Moreover, TD children and adolescents had a normal scholarship, without any

learning disabilities. In our study, the intellectual quotient was assessed according to the

standardised procedures of the fourth version of the Wechsler intelligence scale for children

and adolescents and the third revision of the Wechsler adult intelligence scale. In adults, autistic

traits were also assessed according to the standardised procedures of the ASQ.

All participants had normal or corrected-to-normal vision abilities and none of them had

contraindications for MRI scans.


27

Written informed consent to participate to the experiment was obtained from each participant

or from its parent(s) or legal guardian according to ethical and legal guidelines and the study

was approved by the Necker Ethics Committee.

Participants’ information

Our cohort included 105 participants aged from 2.3 to 29.4 years old and distributed between

children, adolescents and adults as follows. A) 28 children and adolescents with ASD (20 men,

8 women, mean age 8.4 ± 4.0 range: 2.3 to 16.0) and 26 children and adolescents with TD (16

men, 10 women, mean age 10.8 ± 3.2 range: 6.0 to 18.0) were included in our study. The ADI-

R scores are reported Table 1, we were not able to perform the ADI-R for 3 children with ASD.

B) 14 adults with ASD (men, mean age 21.5 ± 3.2 range: 18.3 to 29.4) and 37 adults with TD

(29 men and 8 women, mean age 22.3 years old ± 2.9 range: 18.1 to 30.8) were included in our

study, the ADI-R and ASQ scores are reported Table 2.


28

Eye-tracking

Apparatus

The experiment was conducted using a Tobii T120 Eye Tracker Equipment (17-inch TFT

monitor with a resolution of 1280 * 1024 pixels). The stimuli were presented on a screen,

recording the gaze simultaneously with a rate accuracy of 0.5 degrees and a sampling rate of 60

Hz for both eyes. We choose this sampling rate to compensate for the children movements30.

The eye-tracking procedure was non-invasive and without any head or body movement

constraint.

Stimuli

The stimuli were similar to a previous study conducted in our lab31. Seven movie fragments

were showed successively, with 5 fragments displaying social scenes and 2 fragments

displaying non-social scenes. All movie fragments lasted 10 seconds that allows us to capture

spontaneous gaze preference without directed attentional biases. The social scenes correspond

to peer to peer social interaction between two characters and were extracted from the movie Le

Petit Nicolas®. The non-social scenes correspond to the movement of a red balloon flying in a


29

blue sky and were extracted from the movie Le ballon rouge®. This non-social fragments were

used to control for non-biological movement perception. Factors such as scene background,

characters’ position, balloon size, or speed were not controlled for, Figure 1.

Procedure

The participants were seated at an approximately 60 cm viewing distance of the screen. Before

each set of stimuli, five registration points were used to calibrate the eye-tracker. The

registration points were adapted for the children, a toy was presented. The calibration had to

achieve the accurate recording quality criteria, as indicated by Tobii StudioTM software.

The participants were instructed that they would see a sequence of movie fragments, and they

just had to watch them. Little information was provided about the tracking of eye-movement,

to optimize the ecological aspect of the experiment.

Measures

Gaze patterns were characterized with respect to dynamic areas of interests (AOIs) manually

defined on the visual scene, allowing "frame by frame" measurements throughout the film.

Faces AOIs were defined by oval shapes, whereas eyes and mouth AOIs were defined by


30

rectangular shapes. The rest of the visual scene was considered as non-social background AOIs.

AOIs size and shape were stable across the different frames, Figure 2.

We recorded the number of fixations inside an AOI using Tobii StudioTM software that defines

a fixation as the gaze remaining in a 0.5 degree visual field for at least 100 milliseconds.

Statistical analyses

The number of fixations inside an AOI is our dependant variable that reveals the eyes movement

to the region, interpreted as the participant’s gaze preference. We summed the number of

fixations to our different areas of interest (eyes, faces, mouth or non-social background) on the

5 fragments displaying social scenes (total number of fixations).

We performed groups’ comparisons in the multiple linear models framework, taking age and

sex as covariates. We also studied the effect of the age on gaze behaviour, taking sex and group

as covariates. We performed similar analyses to define the link between gaze behaviour and

social deficits assessed by the sub score B of the ADI-R for the children and the adolescents,

and by the ASQ for the adults.


31

MR-DTI

Apparatus

All brain images were acquired with a GE-Signa 1.5 Tesla MR scanner located at Necker

hospital in Paris using a 12-channel head coil.

Anatomical scans were acquired using a three-dimensional T1-weighted FSPGR sequence

(repetition time (TR): 16.4 milliseconds, echo time (TE): 7.2 milliseconds, flip angle 13°,

matrix size: 512 x 512, field of view: 22 x 22 cm, 228 axial slices at a thickness of 0.6 mm,

total acquisition time of 5 minutes). This sequence was used to assess the absence of clinical

MRI abnormalities.

The diffusion weighted image sequence was acquired using an echo-planar imaging sequence

with diffusion gradients applied on either side of the radiofrequency (repetition time (TR):

15000 or 13000 milliseconds according to the cranial size of the subject, echo time (TE): 70

milliseconds, voxels size 2 x 1.8 x 1.8 mm3, 47 axial slices, total acquisition time of 5 minutes).

Diffusion weighting was encoded along 40 independent orientations, and the b value was set at

1000 s.mm-2.

Procedure

Separately from the eye-tracking session, participants underwent an MRI. For the children with

ASD, the standard premedication protocol (7.5 mg/kg of pentobarbital per rectum) was applied

only when required (12 out of 28 children).

Images processing

The preprocessing, processing, and statistical analyses of the MRI data were performed using

FMRIB software library (FSL) and its module TBSS that focuses on diffusion data.

The diffusion images were converted from DICOM to NIFTI formats and the images affected

by large artefacts or with a very low signal were removed from the study. The eddy current

artefacts and subjects’ motions were corrected using the eddy correct tool in FSL. Subjects with

head rotation superior to 5° were excluded to avoid bias in the computation of the diffusion

tensors38. Thus, we excluded from our study 2 children and 3 adults with ASD and one child

with TD. The cerebral tissues were extracted using the bet tool in FSL. Then, a tensor model

was fitted to each voxel and derivative measure such as fractional anisotropy (FA) were

computed using the dtifit tool in FSL. A quality control was performed to detect eventual

outliers or issues from the precedent steps.


32

FA maps were aligned into a common space from the Montreal Neurology Institute 152 (MNI

152), using the FSL nonlinear registration tool (fnirt). A mean FA image was created on all the

subjects and thinned to construct a mean FA skeleton, threshold to 0.3. This skeleton represents

the centre of the major white matter tracts, Figure 3.

Then, each subject's aligned FA map was projected onto this skeleton (FA skeletonised map).

Statistical analyses

Whole brain voxel-wise statistics were performed on the FA skeletonised maps to assess group

comparisons. These statistics were performed within the general linear model framework and

run with 5000 permutations tests using Randomise option in FSL. Multiple comparisons across

voxels were corrected applying the family-wise error correction (FWE) and the threshold-free

cluster enhancement option (TFCE)39.

We performed group comparisons taking age and sex as covariates. We investigated the

correlation between age and FA values taking the group as covariate. We performed similar

analyses to assess the correlation between FA values and the number of fixations to the eyes or

the severity of social deficits assessed by the sub score B of the ADI-R for children and

adolescents with ASD and by the ASQ for adults. Affected voxels were labelled using the JHU

White-Matter Tractography atlas available in FSL.


33

Results

Eye-tracking

Group comparison in children and adolescents

Children and adolescents with ASD looked significantly less at the faces (t (50) = -4.1, p < .01,

Cohen’s 1.4) and at the eyes (t (50) = -3.2, p < .01, Cohen’s 0.99) and more at the non-social

background (t (50) = 3.4, p < .01, Cohen’s 0.92) than children and adolescents with TD. We

did not find any group difference for the mouth (p > .05).

We did not find any group difference in the weighted gaze samples (p > .05). However, we

found that children and adolescents with ASD looked significantly less at the balloon than

children and adolescents with TD (t (47) = 6.1, p < .001, Cohen’s 1.6).

These results confirm our hypothesis 1. Our results are summarised Figure 4.


34

Group comparison in adults

Adults with ASD looked significantly less at the faces (t (47) = 2.8, p < .01, Cohen’s d 1.0) and

at the eyes (t (47) = 2.0, p ≤ .05, Cohen’s 0.4) and more at the non-social background than

adults with TD (t (47) = 3.0, p < .01, Cohen’s 0.45). We did not find any group difference for

the mouth (p > .05).

We found no group difference neither in the weighted gaze samples during the whole

experiment, nor in the number of fixations to the non-biological movement of the red balloon

(p > .05).

These results confirm our hypothesis 1. Our results are summarised Figure 5.


35

Correlation with the age, cross-sectional study

We found a significant positive correlation between age and the number of fixations to the faces

(t (101) = 3.8, p < .001, estimate: 1.2 unit.year-1) and to the eyes (t (101) = 4.0, p < .001,

estimate: 1.4 unit.year-1) in participants with ASD and participants with TD. Very interestingly,

we found a group by age interaction on the gaze preference to the faces (t (100) = 1.9, p ≤ .05),

with an increase’s rate of 1.7 unit.year-1 in the ASD group (t (39) = 3.7, p < .001) and of 0.74

unit.year-1 in the TD group (t (60) = 1.7, p < .1). The groups interaction did not reached

significance for the eyes (p > .05), Figure 6.


36

We did not find any effect of the age on the number of fixations to the mouth and the non-social

AOIs (p > .05).

These results confirm our hypothesis 2a and for the faces, our hypothesis 2b.

Correlation with the severity of social deficits

We found a negative correlation in children and adolescents with ASD between the social

deficits assessed by the sub score B of the ADI-R and the number of fixations to the faces (t

(20) = -3.0, p < .01, estimate: -2.2) and to the eyes (t (20) = -1.5, p < .05, estimate: -1.5),

meaning that children with ASD who looked less at social stimuli were those who had higher

severity of social deficits. Moreover, we found a positive correlation between the sub score B

of the ADI-R and the number of fixations to the non-social background (t (20) = 4.4, p < .001,

estimate: 2.2), Figure 7.


37

In adults with ASD and TD, we found a negative correlation between the severity of autistic

traits assessed by the ASQ and the number of fixations to the face (t (48) = -3.0, p < .01,

estimate: -0.95) and to the eyes (t (48) = -2.6, p ≤ .01, estimate: -1.0), meaning that adults who

looked less at social stimuli were those who had higher severity of autistic traits. Moreover, we

found a positive correlation between the ASQ and the number of fixation to the non-social

background (t (48) = 2.3, p < .05, estimate: 0.31), Figure 8.

These results confirm our hypothesis 3.


38

MR-DTI

Group comparison in children and adolescents

We found a trend for a lower mean FA over the whole skeleton in participants with ASD

compared to participants with TD (p < .1, t (101) = -1.7, Cohen’s d = 0.67).

Voxel-wise, we found significant lower FA values in children and adolescents with ASD

compared to children and adolescents with TD localised in specific fibres namely the corpus

callosum (p < .01, FWE TFCE corrected) and the inferior longitudinal fasciculus bilaterally (p

< .01, FWE TFCE corrected), Figure 9. The mean FA value was extracted in these fibres using

the JHU WM Tractography atlas for each subject, to estimate the size effect. We get for the

corpus callosum a Cohen’s d of 0.67 and for the inferior longitudinal fasciculus a Cohen’s d of

0.67 bilaterally.

These results confirm our hypothesis 4 in children and adolescents.


39

Group comparison in adults

We did not find any group difference of fractional anisotropy values in adults, performing the

same analyses than in children and adolescents (p > .05).

Correlation with the age, cross-sectional study

Vowel-wise, we found a positive correlation between age and FA values widespread over the

brain in both groups (p < .05, FWE TFCE corrected). Very interestingly, we found a significant

group by age interaction widespread over the brain (t (100) = 4.1, p < .001), in the sense of a

higher age-related increase of fractional anisotropy in ASD than in TD participants (p < .05,

FWE TFCE corrected). Thus, the mean FA values on the whole skeleton increases with age at

a rate of 3.6.10-3 unit.year-1 in the ASD group (t (39) = 11, p < .001) and at a rate of 1.5.10-3

unit.year-1 in the TD group (t (60) = 4.4, p < .001), Figure 10A.

This result confirms our hypotheses 5a and 5b.

To further investigate the age-related increase of fractional anisotropy in both groups, we

focused on the cluster in which FA values differed between children and adolescents with ASD

and children and adolescents with TD. We extracted for each subject the mean FA value in this

cluster and we found a significant group by age interaction (t (100) = 5.4, p < .001), with a rate


40

of 3.3.10-3 unit.year-1 in the ASD group (t (39) = 7.8, p < .001) and a non-significant age-related

regression in the TD group (p > .05), Figure 10B.

To refine our results, we investigated the age-related FA increase in the fibres from the cluster

in which FA values differed between children and adolescents with ASD and children and

adolescents with TD, but taken individually. The JHU WM Tractography atlas allowed us to

study the CC and the ILF separately. We found a significant group by age interaction in the

corpus callosum (t (100) = 3.4, p < .001), with an age-related increase of 2.5.10-3 unit.year-1 in

the ASD group (t (39) = 5.4, p < .001) and a non-significant age-related regression in the TD

group (p > .05), Figure 10C. Similarly, we found a significant group by age interaction in the

ILF bilaterally (t (100) = 3.3, p ≤ .001), with an age-related increase of 3.7.10-3 unit.year-1 in

the ASD group (t (39) = 9.8, p < .001) and of 1.8.10-3 unit.year-1 in the TD group (t (60) = 5.42,

p < .001), Figure 10D.


41


42

Correlation with the severity of social deficits, cross-sectional study

We did not find any correlation between FA values and the sub score B of the ADI-R in children

and adolescents with ASD (p > .05). We did not find any significant correlation between the

ASQ score and the FA values neither in ASD nor in TD adults (p > .05).

This leads us to reject our hypothesis 6.

Correlation with the gaze behaviour, cross-sectional study

Voxel-wise, we found a significant positive correlation between FA values and the number of

fixations to the eyes in children and adolescents with ASD, along the corpus callosum and the

inferior and superior longitudinal fasciculi bilaterally (p < .05, FWE TFCE corrected). In

children and adolescents with TD, we found a trend for a positive correlation between FA values

and the number of fixations to the eyes in the same fibres (p < .05 uncorrected). However, in

TD, the areas concerned were much more localised, namely in the anterior part of the SLF and

in the posterior part of the ILF. Consistent with these different patterns between groups, we

found a trend for a higher correlation between FA values and the number of fixations to the

eyes in the ASD group than in the TD group, localised in the right anterior part of the inferior

and superior longitudinal fasciculi (p < .1, FWE TFCE corrected), Figure 11.

These results confirm our hypotheses 7a and 7b in children and adolescents.

In adults, we did not find any correlation between FA values and the number of fixations to the

eyes neither in the ASD group nor in the TD group (p > .05).


43


44

Discussion

Summary

First, we wanted to confirm previous results on the atypical spontaneous gaze behaviour

reported in autism. Moreover, we aimed to characterize the typical development of this

spontaneous gaze preference to social stimuli from childhood to adulthood and to define

whether this evolution was different in autism. Second, we wanted to investigate the white

matter microstructure to define whether the impairments reported in ASD were widespread over

the brain or localised in specific fibres. Third, our goal was to investigate the typical evolution

of white matter microstructure from childhood to adulthood and to define whether this

trajectory differed in autism. The last objective of our study was to assess the correlation

between social gaze behaviour and white matter microstructure in ASD and TD.

Our study confirmed previous results reporting a lower gaze preference to social stimuli such

as the faces and the eyes in children, adolescents and adults with ASD. We found a positive

correlation between atypical gaze behaviour and the severity of social difficulties assessed by

the ADI-R in children and adolescents with ASD and by the ASQ in adults. Moreover, our work

revealed that in typical development, the gaze preference to social stimuli evolves from

childhood to adulthood with a continuous age-related increase up to young adulthood. We found

this continuous age-related evolution in participants with ASD, but very interestingly, we found

for the gaze preference different patterns between groups.

Our whole brain study revealed significant lower fractional anisotropy values in children and

adolescents with ASD in the corpus callosum and the inferior longitudinal fasciculus. These

abnormalities were not observed in adults with ASD. Furthermore, our study showed an age-

related increase of fractional anisotropy in both ASD and TD groups widespread over the brain.

Very interestingly, we found a higher age-related increase of FA values in subject with ASD

than in subjects with TD in the whole brain, and in the corpus callosum and the inferior

longitudinal fasciculus especially.

Finally, our study showed a positive correlation in children and adolescents with ASD and TD

between white matter microstructure and gaze preference to the eyes in the corpus callosum

and in the inferior and superior longitudinal fasciculi. Very interestingly, in subjects with ASD,

the correlation was localised along the whole inferior and superior longitudinal fasciculi,

whereas in subjects with TD it was localised only in the posterior part of the ILF and in the

anterior part of the SLF. Thus, we found a trend for a higher correlation between FA values and


45

gaze preference to the eyes in subjects with ASD compared to subjects with TD, in the right

anterior part of the inferior and superior longitudinal fasciculi. We did not find this type of

correlations in adults.

A cohort reflecting the heterogeneity of ASD

In our population, the repartition of children under 6 years old was biased toward the ASD

group. However, when we performed each analysis without this age range, we get similar

results (data not shown). Similarly, the repartition of men was biased toward the ASD group.

We took the sex as covariate in our analyses, since we were not interested in investigating

gender difference. This bias toward men in ASD is representative of the prevalence found in

the disorder105. The score of the ADI-R in our cohort reflect a wide heterogeneity in the severity

of the symptoms, ranging from 20 to 63 and involving verbal as well as non-verbal participants

with ASD (n = 8 non-verbal children with ASD out of 28 and n = 1 non-verbal adult with ASD

out of 14). Since some IQ data could not be collected in due course, we do not include this

index in the present study, keeping in mind that it can be a confound factor.

A decreased gaze preference to social stimuli in ASD

In children, adolescents and adults with ASD, we found a lower number of fixations to the faces

and the eyes and a higher number of fixations to the non-social background compared to

participants with TD (Figure 4, Figure 5). To ensure that these results are not due to a lower

general interest of participants with ASD to the stimuli displayed, we controlled for the total

weighted gaze samples and for the gaze preference to a non-biological movement. We did not

find any group difference in adults, however, we found a decreased gaze preference to the non-

biological movement represented by a flying balloon in children and adolescents with ASD.

Examining the data in more details, we observed that this effect was probably driven by the

oriented preference of children with ASD at the strings of the balloon rather at the balloon itself.

We should refine our areas-of-interests taking that into account. The atypical spontaneous gaze

behaviour that we observed in ASD is consistent with previous studies conducted in children,

adolescents and adults32,79,70.

In our study, we did not find any group difference in the gaze preference to the mouth, whereas

other works reported a higher time spent looking at the mouth in subjects with ASD watching


46

dynamic social scene70,106. Klin et al. proposed especially that visual orientation to the mouth

would be an adaptive behaviour to take advantage of audio visual cues in the processing of

language information. However, it appears that an increased gaze preference to the mouth in

ASD has not find any robust support across the literature73.

We found a negative correlation between gaze preference to social stimuli and the severity of

social deficits evaluated by the sub score B of the ADI-R in children and adolescents with ASD

(Figure 7). We found similar associations with the ASQ in adults (Figure 8). These findings

are consistent with previous studies70 and reinforce the relevance of objective measures for gaze

preference, which seem to reflect accurately the severity of ASD.

A continuous acquisition of social expertise up to adulthood

Our study revealed a typical age-related development of social expertise, through a continuous

linear increase of gaze preference to social stimuli up to young adulthood (Figure 6). As far as

we know, no such cross-age behavioural characterization had been done so far, since most

studies focus specifically on the early evolution of social perception78. Thus, our results

emphasize for the first time the importance of behavioural development and implicit learning

in social perception, up to late stages in life.

A different age-related acquisition of faces perception in ASD

In participants with ASD, we also observed an age-related increase of gaze preference to social

stimuli. Thus, children with ASD also acquire a gradual development of social perception

across the age, although not reaching the typical values.

We found in TD an early maturation of the “optimal” gaze preference for the faces, reflected

by a low age-related increase. On the contrary, in ASD, gaze preference to the faces increases

with the age (Figure 6). Strikingly, this significantly different age-related evolution of gaze

preference to the faces leads subjects with ASD to reduce their initial deficits in faces

perception. Indeed, we noticed that group differences on gaze preference to faces were stronger

in children and adolescents than in adults. Hence, it seems that subjects with ASD are able to

partly compensate for their initial lack of gaze preference to faces from childhood to young

adulthood, through a developmental acquisition of faces perception more progressive over time

than subjects with TD.

Regarding gaze preference to the eyes, it continuously increases over time in typical

development, highlighting the relevance and the complexity of this stimulus in social cognition.


47

In ASD, it increases with the age in a similar way, leading to the persistence of a similar degree

of difficulties in eyes perception between adults and children with ASD. As far as we know,

these findings have never been reported in the literature. Indeed, previous studies were unable

to detect such age-related behavioural evolutions, mainly because they focused on narrower

age-ranges34. However, since our study is cross-sectional, we must keep in mind that our results

could be explained by a sampling effect and they should be confirmed in a longitudinal study.

Taken together, our results give hopes on behavioural plasticity and learning, considering that

social perception continues to develop up to young adulthood and that participants with ASD

are able to improve their faces perception. In addition, it points out the crucial importance of

orienting the gaze to the eyes especially, since participants with ASD do not achieve to

compensate for eyes perception across their development, which could partially account for the

persistence of social difficulties up to adulthood. Further studies are needed, to investigate on

large longitudinal cohorts the age-related evolution of social perception in TD and ASD and to

assess the impact of behavioural therapies.

An abnormal white matter microstructure localised in the “social brain” in ASD

Concerning the MR-DTI data, our whole brain analysis showed lower values of fractional

anisotropy in specific fibres namely the corpus callosum and the posterior part of the inferior

longitudinal fasciculus (Figure 9). Hence, our results lead us to assume that white matter

abnormalities are localised in specific fibres in the “social brain” and to stand against the theory

of a general anatomical hypo connectivity in autism. We argue that this theory has emerged

from methodological issues, since some studies did not control for head motions. Head motions

during acquisition induce shifts between the first and the last scans acquired and despite re

alignment of all the scans, it can create artefacts and biases the tensor computation. These

artefacts can have different distributions between the groups, leading to false positive group

differences followed by misinterpretations107. Indeed, Koldewyn et al. have recently

demonstrated that, without controlling for head motion, group differences were widespread

over all their fibres-of-interest in the tractography analysis, whereas after matching participants

with ASD and TD for head motion the group differences were localised in the right inferior

longitudinal fasciculus exclusively108.

The lower fractional anisotropy values that we found in the corpus callosum and the inferior

longitudinal fasciculus confirm previous ASD reports in childhood and young adulthood1,109,92.

However, these studies associated autism with abnormalities in the corpus callosum and the


48

inferior longitudinal fasciculus among other white matter tracts. Thus, our results extend those

findings to the conclusion that these two structures would constitute the core white matter

disruptions in ASD.

Furthermore, the corpus callosum and the inferior longitudinal fasciculus have been associated

to the “social brain” networks. The ILF connects the temporal and the occipital lobes and its

white matter integrity has been shown to correlate with autistic traits, assessed by the ASQ100,

and more precisely with facial emotion perception on its right part110 and with language

functions on its left part111. The CC links the two hemispheres and white matter abnormalities

in this structure have been shown to correlate with repetitive and stereotypical behaviour

especially95. Nevertheless, other studies suggest that abnormalities of white matter

microstructure in the corpus callosum in ASD could reflect non-verbal cognitive delays112, not

specific from the social deficits of the disorder.

A typical white matter microstructure in adults with ASD

We did not find the white matter abnormalities in adults with ASD. That does not seem to be

due to the smaller sample size in our adults’ cohort. Indeed, assuming that the size effect

observed in children and adolescents would be similar in adults and setting type I error at 5%

and type II error at 20%, a sample size of n = 7 should be enough to detect such a group

difference (http://rpsychologist.com/d3/cohend/).

Moreover, other studies including more participants did not detect any group difference in

adults, focusing on the corpus callosum113, or investigating the whole brain without a priori

hypothesis114. This last study argues that as a confound factor, the IQ score could explain the

heterogeneity of results found in the literature.

A different age-related evolution of white matter microstructure in ASD and TD

Investigating the white matter microstructure over time, we found in TD an age-related increase

of fractional anisotropy widespread over the brain, consistent with previous findings115,116.

These fractional anisotropy changes would reflect increases of myelination, axonal packing,

and / or coherence with age, probably associated with changes in cognitive development.

Similarly, the global fractional anisotropy increases with the age in participants with ASD.

However, we found a different rate of age-related evolution of white matter microstructure

between the groups. Indeed, we observed a general faster increase of fractional anisotropy

values over time in ASD than in TD, widespread over the brain. This was especially the case in

http://rpsychologist.com/d3/cohend/


49

the corpus callosum and the inferior longitudinal fasciculus. Indeed, the mean value of

fractional anisotropy in the CC did not increase with the age in TD consistent with previous

findings116, but showed a strong age-related increase in ASD. In the ILF, the mean value of

fractional anisotropy increased with the age in the TD group, but significantly slower than in

the ASD group. This group by age interaction of FA values in the CC and in the ILF would

explain why we observed white matter abnormalities in children, and not in adults (Figure 10).

Similarly, it would explain why McLaughlin et al. reported group differences in the white

matter microstructural properties in thalamo-cortical fibres in children, and not in adolescents35.

It is worth noticing that this study is longitudinal, bringing strong support to our work.

The faster age-related changes of white matter properties in ASD could reflect a delayed white

matter maturation in earlier stages of development and / or an increased plasticity to compensate

for initial disruptions. That could be the neural bases supporting the social cognitive

improvements we detailed above.

A different white matter network required for social perception in ASD

We found a positive correlation between fractional anisotropy and gaze preference to the eyes

in children and adolescents in both groups. This correlation was located at the corpus callosum

and at the inferior and superior longitudinal fasciculi that are known to be recruited in social

perception, interaction and communication100,17. It is worth noticing that we did not find any

correlation between fractional anisotropy and the severity of social deficits assessed by the

ADI-R. That leads us to the conclusion that an objective characterization of gaze parameters,

using the eye-tracking, is a more sensitive index of the symptoms severity in autism compared

to the ADI-R and the ASQ.

In participants with ASD, the correlation between fractional anisotropy values and gaze

preference to the eyes was widespread along the ILF and SLF bilaterally. In participants with

TD, the correlation was in the same fibres, but in a much more localised way, namely in the

posterior part of the ILF and in the anterior part of the SLF (Figure 11). Very interestingly, the

posterior part of the ILF corresponds to the brain areas where the white matter microstructure

was found as abnormal in children with ASD. Thus, it seems that subjects with ASD would

compensate for local reductions of white matter fractional anisotropy in the posterior part of

the ILF, expanding the network of white matter fibres required in the processing of eyes

perception into a more anterior part of the ILF. That was especially the case in the right

hemisphere, consistent with the fact that the right ILF is involved in faces perception. Similarly,


50

subjects with ASD would expand the network of white matter fibres required for eyes

perception in the SLF to a more anterior part of the fibre, especially on the right hemisphere,

since the right SLF especially is involved in faces perception.

The recruitment of wider white matter networks for social perception echoes our finding of a

significant faster age-related increase of fractional anisotropy in the ILF and the SLF in subjects

with ASD (data not shown). In ASD, the white matter microstructure would mature with the

age through an expansion of the typical networks required for social perception to wider areas.

That could be an adaptive development in line with improvements in social abilities.

Considering that the white matter microstructural properties increases up to young adulthood,

with an important plasticity in the myelination degree especially117, hopes are provided on

behavioural therapies to compensate for initial deficits. The efficiency of such therapies on

white matter microstructural properties has been shown in poor readers118.


51

Conclusion

In this Master work, we aimed to investigate social perception and anatomical connectivity over

time using eye-tracking and diffusion tensor imaging, in a wide age-range cohort of children

and adults with autism spectrum disorder (ASD) and with typical development (TD).

We confirmed social perception deficits in children and adults with ASD, characterized by a

lower gaze preference for the eyes and the faces of characters during the passive visualisation

of social scene. Moreover, we found a different acquisition of social expertise over time in

autism compared to typical development. Indeed, in participants with ASD, gaze preference to

the faces increases with the age, while it remains stable in typical development, reflecting a

maturation early in life.

We found white matter microstructure abnormalities that were specifically localized in the

corpus callosum and the inferior frontal fasciculus in children with ASD compared to typically

developing controls. Our results stand against the assumption of ASD as a generalized hypo

connectivity developmental disorder. Moreover, these abnormalities were not present in adults

with ASD, and we observed a different pattern of white matter microstructure maturation over

time in autism. These results could reflect a delayed white matter maturation in earlier stages

of development and / or an increased plasticity to compensate for initial disruptions.

Interestingly, it could be associated with the social difficulties, since deficits in social

perception seem to be less severe over time in our study, even though this hypothesis require

further studies to be conclusive.

Finally, we showed that gaze preference to the eyes was associated with the white matter

microstructure in wider parts of the inferior and superior longitudinal fasciculi in participants

with ASD compared to participants with TD. Since these fibres are recruited in social

perception, our finding could reflect an adaptive development in ASD. Taken together, our

findings bring new evidences on the neurophysiological mechanisms implicated in ASD.


52

References

1. Ameis, S. H. & Catani, M. Altered white matter connectivity as a neural substrate for social

impairment in Autism Spectrum Disorder. Cortex J. Devoted Study Nerv. Syst. Behav. 62,

158–181 (2015).

2. Aoki, Y., Abe, O., Nippashi, Y. & Yamasue, H. Comparison of white matter integrity

between autism spectrum disorder subjects and typically developing individuals: a meta-

analysis of diffusion tensor imaging tractography studies. Mol. Autism 4, 25 (2013).

3. Sharda, M. et al. Language Ability Predicts Cortical Structure and Covariance in Boys with

Autism Spectrum Disorder. Cereb. Cortex N. Y. N 1991 27, 1849–1862 (2017).

4. Barnea-Goraly, N. et al. White matter structure in autism: preliminary evidence from

diffusion tensor imaging. Biol. Psychiatry 55, 323–326 (2004).

5. Di, X., Azeez, A., Li, X., Haque, E. & Biswal, B. B. Disrupted focal white matter integrity

in autism spectrum disorder: A voxel-based meta-analysis of diffusion tensor imaging

studies. Prog. Neuropsychopharmacol. Biol. Psychiatry (2017).

doi:10.1016/j.pnpbp.2017.11.007

6. Travers, B. G. et al. Diffusion tensor imaging in autism spectrum disorder: a review. Autism

Res. Off. J. Int. Soc. Autism Res. 5, 289–313 (2012).

7. Saitovitch, A. et al. Social cognition and the superior temporal sulcus: implications in autism.

Rev. Neurol. (Paris) 168, 762–770 (2012).

8. Lo, Y.-C., Chen, Y.-J., Hsu, Y.-C., Tseng, W.-Y. I. & Gau, S. S.-F. Reduced tract integrity

of the model for social communication is a neural substrate of social communication deficits

in autism spectrum disorder. J. Child Psychol. Psychiatry 58, 576–585 (2017).

9. Franchini, M. et al. Social orienting and joint attention in preschoolers with autism spectrum

disorders. PloS One 12, e0178859 (2017).


53

10. WHO | Autism spectrum disorders. WHO Available at:

http://www.who.int/mediacentre/factsheets/autism-spectrum-disorders/en/. (Accessed: 7th

November 2017)

11. DSM-IV Diagnostic Classifications. Autism Society Available at: http://www.autism-

society.org/dsm-iv-diagnostic-classifications/. (Accessed: 7th November 2017)

12. Engell, A. D. & Haxby, J. V. Facial expression and gaze-direction in human superior

temporal sulcus. Neuropsychologia 45, 3234–3241 (2007).

13. Caruana, N., Spirou, D. & Brock, J. Human agency beliefs influence behaviour during

virtual social interactions. PeerJ 5, e3819 (2017).

14. Jones, C. R. G. et al. The association between theory of mind, executive function, and

the symptoms of autism spectrum disorder. Autism Res. Off. J. Int. Soc. Autism Res. (2017).

doi:10.1002/aur.1873

15. Boddaert, N. et al. Superior temporal sulcus anatomical abnormalities in childhood

autism: a voxel-based morphometry MRI study. NeuroImage 23, 364–369 (2004).

16. Hadjikhani, N., Joseph, R. M., Snyder, J. & Tager-Flusberg, H. Anatomical differences

in the mirror neuron system and social cognition network in autism. Cereb. Cortex N. Y. N

1991 16, 1276–1282 (2006).

17. Zilbovicius, M. et al. Autism, the superior temporal sulcus and social perception. Trends

Neurosci. 29, 359–366 (2006).

18. Ruggieri, V. L. [The amygdala and its relation to autism, behavioural disorders and other

neurodevelopmental disorders]. Rev. Neurol. 58 Suppl 1, S137-148 (2014).

19. Martino, A. D. et al. Functional Brain Correlates of Social and Nonsocial Processes in

Autism Spectrum Disorders: An Activation Likelihood Estimation Meta-Analysis. Biol.

Psychiatry 65, 63–74 (2009).


54

20. Moseley, R. L. & Pulvermüller, F. What can autism teach us about the role of

sensorimotor systems in higher cognition? New clues from studies on language, action

semantics, and abstract emotional concept processing. Cortex J. Devoted Study Nerv. Syst.

Behav. (2017). doi:10.1016/j.cortex.2017.11.019

21. Schultz, R. T. et al. Abnormal ventral temporal cortical activity during face

discrimination among individuals with autism and Asperger syndrome. Arch. Gen.

Psychiatry 57, 331–340 (2000).

22. Takahashi, T. et al. Band-specific atypical functional connectivity pattern in childhood

autism spectrum disorder. Clin. Neurophysiol. Off. J. Int. Fed. Clin. Neurophysiol. 128,

1457–1465 (2017).

23. D’Mello, A. M. & Stoodley, C. J. Cerebro-cerebellar circuits in autism spectrum

disorder. Front. Neurosci. 9, 408 (2015).

24. Just, M. A., Cherkassky, V. L., Keller, T. A., Kana, R. K. & Minshew, N. J. Functional

and anatomical cortical underconnectivity in autism: evidence from an FMRI study of an

executive function task and corpus callosum morphometry. Cereb. Cortex N. Y. N 1991 17,

951–961 (2007).

25. Beaulieu, C. The basis of anisotropic water diffusion in the nervous system - a technical

review. NMR Biomed. 15, 435–455 (2002).

26. Solomon, M., Hogeveen, J., Libero, L. & Nordahl, C. An altered scaffold for

information processing: Cognitive control development in adolescents with autism. Biol.

Psychiatry Cogn. Neurosci. Neuroimaging 2, 464–475 (2017).

27. Travers, B. G. et al. Atypical development of white matter microstructure of the corpus

callosum in males with autism: a longitudinal investigation. Mol. Autism 6, 15 (2015).

28. Lainhart, J. E. Brain imaging research in autism spectrum disorders: in search of

neuropathology and health across the lifespan. Curr. Opin. Psychiatry 28, 76–82 (2015).


55

29. Murphy, D. G. M., Beecham, J., Craig, M. & Ecker, C. Autism in adults. New

biologicial findings and their translational implications to the cost of clinical services. Brain

Res. 1380, 22–33 (2011).

30. Sasson, N. J. & Elison, J. T. Eye tracking young children with autism. J. Vis. Exp. JoVE

(2012). doi:10.3791/3675

31. Saitovitch, A. et al. Tuning Eye-Gaze Perception by Transitory STS Inhibition. Cereb.

Cortex N. Y. N 1991 26, 2823–2831 (2016).

32. Murias, M. et al. Validation of eye-tracking measures of social attention as a potential

biomarker for autism clinical trials. Autism Res. Off. J. Int. Soc. Autism Res. 11, 166–174

(2018).

33. Caruana, N. et al. Joint attention difficulties in autistic adults: An interactive eye-

tracking study. Autism Int. J. Res. Pract. 1362361316676204 (2017).

doi:10.1177/1362361316676204

34. Fedor, J. et al. Patterns of fixation during face recognition: Differences in autism across

age. Autism Int. J. Res. Pract. 1362361317714989 (2017). doi:10.1177/1362361317714989

35. McLaughlin, K. et al. Longitudinal development of thalamic and internal capsule

microstructure in autism spectrum disorder. Autism Res. Off. J. Int. Soc. Autism Res. 11, 450–

462 (2018).

36. Cheng, Y. et al. Atypical development of white matter microstructure in adolescents

with autism spectrum disorders. NeuroImage 50, 873–882 (2010).

37. Keller, T. A., Kana, R. K. & Just, M. A. A developmental study of the structural integrity

of white matter in autism. Neuroreport 18, 23–27 (2007).

38. Koldewyn, K. et al. Differences in the right inferior longitudinal fasciculus but no

general disruption of white matter tracts in children with autism spectrum disorder. Proc.

Natl. Acad. Sci. U. S. A. 111, 1981–1986 (2014).


56

39. Smith, S. M. & Nichols, T. E. Threshold-free cluster enhancement: addressing problems

of smoothing, threshold dependence and localisation in cluster inference. NeuroImage 44,

83–98 (2009).

40. Autism spectrum disorders. Available at: http://www.who.int/news-room/fact-

sheets/detail/autism-spectrum-disorders. (Accessed: 9th June 2018)

41. Sharma, S. R., Gonda, X. & Tarazi, F. I. Autism Spectrum Disorder classification,

diagnosis and therapy. Pharmacol. Ther. (2018). doi:10.1016/j.pharmthera.2018.05.007

42. Kjelgaard, M. M. & Tager-Flusberg, H. An Investigation of Language Impairment in

Autism: Implications for Genetic Subgroups. Lang. Cogn. Process. 16, 287–308 (2001).

43. Johnson, M. H., Gliga, T., Jones, E. & Charman, T. Annual research review: Infant

development, autism, and ADHD--early pathways to emerging disorders. J. Child Psychol.

Psychiatry 56, 228–247 (2015).

44. Ming, X., Brimacombe, M. & Wagner, G. C. Prevalence of motor impairment in autism

spectrum disorders. Brain Dev. 29, 565–570 (2007).

45. Skokauskas, N. & Gallagher, L. Psychosis, affective disorders and anxiety in autistic

spectrum disorder: prevalence and nosological considerations. Psychopathology 43, 8–16

(2010).

46. Munesue, T. et al. High prevalence of bipolar disorder comorbidity in adolescents and

young adults with high-functioning autism spectrum disorder: a preliminary study of 44

outpatients. J. Affect. Disord. 111, 170–175 (2008).

47. Jokiranta, E. et al. Epilepsy among children and adolescents with autism spectrum

disorders: a population-based study. J. Autism Dev. Disord. 44, 2547–2557 (2014).

48. Ward, J. et al. Atypical sensory sensitivity as a shared feature between synaesthesia and

autism. Sci. Rep. 7, 41155 (2017).


57

49. Johnson, K. P., Giannotti, F. & Cortesi, F. Sleep patterns in autism spectrum disorders.

Child Adolesc. Psychiatr. Clin. N. Am. 18, 917–928 (2009).

50. Autism Prevalence | Autism Speaks. (2012). Available at:

https://www.autismspeaks.org/what-autism/prevalence. (Accessed: 29th May 2018)

51. Rai, D. et al. Antidepressants during pregnancy and autism in offspring: population

based cohort study. BMJ 358, j2811 (2017).

52. Christensen, J. et al. Prenatal valproate exposure and risk of autism spectrum disorders

and childhood autism. JAMA 309, 1696–1703 (2013).

53. Nevison, C. D. A comparison of temporal trends in United States autism prevalence to

trends in suspected environmental factors. Environ. Health Glob. Access Sci. Source 13, 73

(2014).

54. Flores-Pajot, M.-C., Ofner, M., Do, M. T., Lavigne, E. & Villeneuve, P. J. Childhood

autism spectrum disorders and exposure to nitrogen dioxide, and particulate matter air

pollution: A review and meta-analysis. Environ. Res. 151, 763–776 (2016).

55. Weisskopf, M. G., Kioumourtzoglou, M.-A. & Roberts, A. L. Air Pollution and Autism

Spectrum Disorders: Causal or Confounded? Curr. Environ. Health Rep. 2, 430–439 (2015).

56. Kobayashi, H. & Kohshima, S. Unique morphology of the human eye and its adaptive

meaning: comparative studies on external morphology of the primate eye. J. Hum. Evol. 40,

419–435 (2001).

57. Ando, S. Luminance-induced shift in the apparent direction of gaze. Perception 31,

657–674 (2002).

58. Farroni, T., Menon, E. & Johnson, M. H. Factors influencing newborns’ preference for

faces with eye contact. J. Exp. Child Psychol. 95, 298–308 (2006).


58

59. Grossmann, T., Johnson, M. H., Farroni, T. & Csibra, G. Social perception in the infant

brain: gamma oscillatory activity in response to eye gaze. Soc. Cogn. Affect. Neurosci. 2,

284–291 (2007).

60. Safar, K. & Moulson, M. C. Recognizing facial expressions of emotion in infancy: A

replication and extension. Dev. Psychobiol. 59, 507–514 (2017).

61. Zambrana, I. M., Ystrom, E., Schjølberg, S. & Pons, F. Action imitation at 1½ years is

better than pointing gesture in predicting late development of language production at 3 years

of age. Child Dev. 84, 560–573 (2013).

62. Brooks, R. & Meltzoff, A. N. Connecting the dots from infancy to childhood: a

longitudinal study connecting gaze following, language, and explicit theory of mind. J. Exp.

Child Psychol. 130, 67–78 (2015).

63. Zwaigenbaum, L. et al. Behavioral manifestations of autism in the first year of life. Int.

J. Dev. Neurosci. Off. J. Int. Soc. Dev. Neurosci. 23, 143–152 (2005).

64. Johnson, M. H. et al. The emergence of the social brain network: evidence from typical

and atypical development. Dev. Psychopathol. 17, 599–619 (2005).

65. Sigman, M., Mundy, P., Sherman, T. & Ungerer, J. Social interactions of autistic,

mentally retarded and normal children and their caregivers. J. Child Psychol. Psychiatry 27,

647–655 (1986).

66. Baron-Cohen, S., Leslie, A. M. & Frith, U. Does the autistic child have a ‘theory of

mind’? Cognition 21, 37–46 (1985).

67. Morimoto, C. H. & Mimica, M. R. M. Eye gaze tracking techniques for interactive

applications. Comput. Vis. Image Underst. 98, 4–24 (2005).

68. Rucci, M. & Poletti, M. Control and Functions of Fixational Eye Movements. Annu.

Rev. Vis. Sci. 1, 499–518 (2015).


59

69. Birmingham, E., Bischof, W. F. & Kingstone, A. Saliency does not account for fixations

to eyes within social scenes. Vision Res. 49, 2992–3000 (2009).

70. Klin, A., Jones, W., Schultz, R., Volkmar, F. & Cohen, D. Visual Fixation Patterns

During Viewing of Naturalistic Social Situations as Predictors of Social Competence in

Individuals With Autism. Arch. Gen. Psychiatry 59, 809–816 (2002).

71. Klin, A., Jones, W., Schultz, R., Volkmar, F. & Cohen, D. Visual fixation patterns

during viewing of naturalistic social situations as predictors of social competence in

individuals with autism. Arch. Gen. Psychiatry 59, 809–816 (2002).

72. Guillon, Q., Hadjikhani, N., Baduel, S. & Rogé, B. Visual social attention in autism

spectrum disorder: Insights from eye tracking studies. Neurosci. Biobehav. Rev. 42, 279–297

(2014).

73. Guillon, Q., Hadjikhani, N., Baduel, S. & Rogé, B. Visual social attention in autism

spectrum disorder: Insights from eye tracking studies. Neurosci. Biobehav. Rev. 42, 279–297

(2014).

74. Saitovitch, A. et al. Studying gaze abnormalities in autism: Which type of stimulus to

use? Open J. Psychiatry 03, 32 (2013).

75. Shic, F., Bradshaw, J., Klin, A., Scassellati, B. & Chawarska, K. Limited activity

monitoring in toddlers with autism spectrum disorder. Brain Res. 1380, 246–254 (2011).

76. Hosozawa, M., Tanaka, K., Shimizu, T., Nakano, T. & Kitazawa, S. How Children With

Specific Language Impairment View Social Situations: An Eye Tracking Study. Pediatrics

129, e1453–e1460 (2012).

77. Chawarska, K., Macari, S. & Shic, F. Decreased Spontaneous Attention to Social Scenes

in 6-Month-Old Infants Later Diagnosed with Autism Spectrum Disorders. Biol. Psychiatry

74, 195–203 (2013).


60

78. Jones, W. & Klin, A. Attention to eyes is present but in decline in 2-6-month-old infants

later diagnosed with autism. Nature 504, 427–431 (2013).

79. Riby, D. & Hancock, P. J. B. Looking at movies and cartoons: eye-tracking evidence

from Williams syndrome and autism. J. Intellect. Disabil. Res. 53, 169–181

80. Bird, G., Press, C. & Richardson, D. C. The Role of Alexithymia in Reduced Eye-

Fixation in Autism Spectrum Conditions. J. Autism Dev. Disord. 41, 1556–1564 (2011).

81. Gredebäck, G., Fikke, L. & Melinder, A. The development of joint visual attention: a

longitudinal study of gaze following during interactions with mothers and strangers. Dev.

Sci. 13, 839–848 (2010).

82. Starr, E., Szatmari, P., Bryson, S. & Zwaigenbaum, L. Stability and change among high-

functioning children with pervasive developmental disorders: a 2-year outcome study. J.

Autism Dev. Disord. 33, 15–22 (2003).

83. Howlin, P., Moss, P., Savage, S. & Rutter, M. Social outcomes in mid- to later adulthood

among individuals diagnosed with autism and average nonverbal IQ as children. J. Am. Acad.

Child Adolesc. Psychiatry 52, 572-581.e1 (2013).

84. Howlin, P., Goode, S., Hutton, J. & Rutter, M. Adult outcome for children with autism.

J. Child Psychol. Psychiatry 45, 212–229 (2004).

85. Spencer, M. D. et al. A novel functional brain imaging endophenotype of autism: the

neural response to facial expression of emotion. Transl. Psychiatry 1, e19 (2011).

86. Alba-Ferrara, L. M. & de Erausquin, G. A. What does anisotropy measure? Insights

from increased and decreased anisotropy in selective fiber tracts in schizophrenia. Front.

Integr. Neurosci. 7, (2013).

87. Simon, T. J. et al. Volumetric, connective, and morphologic changes in the brains of

children with chromosome 22q11.2 deletion syndrome: an integrative study. NeuroImage

25, 169–180 (2005).


61

88. Jones, D. K., Symms, M. R., Cercignani, M. & Howard, R. J. The effect of filter size on

VBM analyses of DT-MRI data. NeuroImage 26, 546–554 (2005).

89. Smith, S. M. et al. Tract-based spatial statistics: voxelwise analysis of multi-subject

diffusion data. NeuroImage 31, 1487–1505 (2006).

90. Müller, R.-A. The study of autism as a distributed disorder. Ment. Retard. Dev. Disabil.

Res. Rev. 13, 85–95 (2007).

91. Deoni, S. C. L. et al. White-matter relaxation time and myelin water fraction differences

in young adults with autism. Psychol. Med. 45, 795–805 (2015).

92. Shukla, D. K., Keehn, B. & Müller, R.-A. Tract-specific analyses of diffusion tensor

imaging show widespread white matter compromise in autism spectrum disorder. J. Child

Psychol. Psychiatry 52, 286–295 (2011).

93. Mueller, S. et al. Convergent Findings of Altered Functional and Structural Brain

Connectivity in Individuals with High Functioning Autism: A Multimodal MRI Study. PloS

One 8, e67329 (2013).

94. Symington, S. H., Paul, L. K., Symington, M. F., Ono, M. & Brown, W. S. Social

cognition in individuals with agenesis of the corpus callosum. Soc. Neurosci. 5, 296–308

(2010).

95. Thomas, C., Humphreys, K., Jung, K.-J., Minshew, N. & Behrmann, M. The anatomy

of the callosal and visual-association pathways in high-functioning autism: a DTI

tractography study. Cortex J. Devoted Study Nerv. Syst. Behav. 47, 863–873 (2011).

96. Samson, A. C. et al. White matter structure in the uncinate fasciculus: Implications for

socio-affective deficits in Autism Spectrum Disorder. Psychiatry Res. Neuroimaging 255,

66–74 (2016).

97. Jann, K. et al. Altered resting perfusion and functional connectivity of default mode

network in youth with autism spectrum disorder. Brain Behav. 5, e00358 (2015).


62

98. Wicker, B. et al. Abnormal cerebral effective connectivity during explicit emotional

processing in adults with autism spectrum disorder. Soc. Cogn. Affect. Neurosci. 3, 135–143

(2008).

99. Bakhtiari, R. et al. Differences in white matter reflect atypical developmental trajectory

in autism: A Tract-based Spatial Statistics study. NeuroImage Clin. 1, 48–56 (2012).

100. Bradstreet, L. E., Hecht, E. E., King, T. Z., Turner, J. L. & Robins, D. L. Associations

between autistic traits and fractional anisotropy values in white matter tracts in a nonclinical

sample of young adults. Exp. Brain Res. 235, 259–267 (2017).

101. Philippi, C. L., Mehta, S., Grabowski, T., Adolphs, R. & Rudrauf, D. Damage to

Association Fiber Tracts Impairs Recognition of the Facial Expression of Emotion. J.

Neurosci. 29, 15089–15099 (2009).

102. Koolschijn, P. C. M. P., Caan, M. W. A., Teeuw, J., Olabarriaga, S. D. & Geurts, H. M.

Age-related differences in autism: The case of white matter microstructure. Hum. Brain

Mapp. 38, 82–96 (2017).

103. Libero, L. E., Burge, W. K., Deshpande, H. D., Pestilli, F. & Kana, R. K. White Matter

Diffusion of Major Fiber Tracts Implicated in Autism Spectrum Disorder. Brain Connect. 6,

691–699 (2016).

104. Fusar-Poli, L. et al. Diagnosing ASD in Adults Without ID: Accuracy of the ADOS-2

and the ADI-R. J. Autism Dev. Disord. 47, 3370–3379 (2017).

105. Moseley, R. L., Hitchiner, R. & Kirkby, J. A. Self-reported sex differences in high-

functioning adults with autism: a meta-analysis. Mol. Autism 9, (2018).

106. Jones, W., Carr, K. & Klin, A. Absence of preferential looking to the eyes of

approaching adults predicts level of social disability in 2-year-old toddlers with autism

spectrum disorder. Arch. Gen. Psychiatry 65, 946–954 (2008).


63

107. Walker, L. et al. Diffusion Tensor Imaging in Young Children with Autism: Biological

Effects and Potential Confounds. Biol. Psychiatry 72, 1043–1051 (2012).

108. Koldewyn, K. et al. Differences in the right inferior longitudinal fasciculus but no

general disruption of white matter tracts in children with autism spectrum disorder. Proc.

Natl. Acad. Sci. U. S. A. 111, 1981–1986 (2014).

109. Ameis, S. H. et al. Impaired Structural Connectivity of Socio-Emotional Circuits in

Autism Spectrum Disorders: A Diffusion Tensor Imaging Study. PLoS ONE 6, (2011).

110. Fischer, D. B. et al. Right inferior longitudinal fasciculus lesions disrupt visual-

emotional integration. Soc. Cogn. Affect. Neurosci. 11, 945–951 (2016).

111. Ivanova, M. V. et al. Diffusion-tensor imaging of major white matter tracts and their

role in language processing in aphasia. Cortex J. Devoted Study Nerv. Syst. Behav. 85, 165–

181 (2016).

112. Alexander, A. L. et al. Diffusion tensor imaging of the corpus callosum in Autism.

NeuroImage 34, 61–73 (2007).

113. Nickel, K. et al. Altered white matter integrity in adults with autism spectrum disorder

and an IQ >100: a diffusion tensor imaging study. Acta Psychiatr. Scand. 135, 573–583

(2017).

114. Kirkovski, M., Enticott, P. G., Maller, J. J., Rossell, S. L. & Fitzgerald, P. B. Diffusion

tensor imaging reveals no white matter impairments among adults with autism spectrum

disorder. Psychiatry Res. 233, 64–72 (2015).

115. Mohammad, S. A. & Nashaat, N. H. Age-related changes of white matter association

tracts in normal children throughout adulthood: a diffusion tensor tractography study.

Neuroradiology 59, 715–724 (2017).


64

116. Lebel, C., Treit, S. & Beaulieu, C. A review of diffusion MRI of typical white matter

development from early childhood to young adulthood. NMR Biomed. (2017).

doi:10.1002/nbm.3778

117. Fields, R. D. A new mechanism of nervous system plasticity: activity-dependent

myelination. Nat. Rev. Neurosci. 16, 756–767 (2015).

118. Keller, T. A. & Just, M. A. Altering Cortical Connectivity: Remediation-Induced

Changes in the White Matter of Poor Readers. Neuron 64, 624–631 (2009).

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