Social perception and anatomical connectivity in autism: a...
Transcript of 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
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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
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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
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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.
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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
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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
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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
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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.
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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
and participants with TD.
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
and participants with TD.
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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.
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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.
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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
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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
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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
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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.
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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).
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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
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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
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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.
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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
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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
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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,
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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.
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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.
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