Investigating Mirror System (MS) Activity in Adults with ASD When Inferring Others’ Intentions Using Both TMS and EEG
Journal of Autism and Developmental Disorders
Investigating Mirror System (MS) Activity in Adults with ASD When Inferring Others' Intentions Using Both TMS and EEG
Eleanor J. Cole 0
Nick E. Barraclough 0
Peter G. Enticott 0
0 Cognitive Neuroscience Unit, Faculty of Health, Deakin University Burwood Campus , 221 Burwood Highway, Melbourne, VIC 3125 , Australia
1 Eleanor J. Cole
ASD is associated with mentalizing deficits that may correspond with atypical mirror system (MS) activation. We investigated MS activity in adults with and without ASD when inferring others' intentions using TMS-induced motor evoked potentials (MEPs) and mu suppression measured by EEG. Autistic traits were measured for all participants. Our EEG data show, high levels of autistic traits predicted reduced right mu (8-10 Hz) suppression when mentalizing. Higher left mu (8-10 Hz) suppression was associated with superior mentalizing performances. Eye-tracking and TMS data showed no differences associated with autistic traits. Our data suggest ASD is associated with reduced right MS activity when mentalizing, TMS-induced MEPs and mu suppression measure different aspects of MS functioning and the MS is directly involved in inferring intentions.
Autism spectrum disorder (ASD); Mirror system (MS); Mentalizing; Transcranial magnetic stimulation (TMS); Electroencephalography (EEG); Intentions
Experimental evidence and anecdotal reports suggest that
individuals with autism spectrum disorder (ASD) diagnoses
display difficulties inferring the thoughts, feeling and beliefs
of others, collectively known as ‘mentalizing’
et al. 1997; Castelli et al. 2002; Jolliffe and Baron-Cohen
1999; Kana et al. 2014; Senju et al. 2009)
. ASD is a term
used by the most recent edition of the Diagnostic and
Statistical Manual of Mental Disorders (DSM-5) to describe
a number of neurodevelopmental disorders characterised
by difficulties in social communication as well as restricted
and repetitive behaviours
. Due to the spectral nature of ASD, individuals
with and without ASD diagnoses display varying degrees
The Department of Psychology, The University of York,
Heslington, York, North Yorkshire YO10 5DD, UK
of autistic traits. Individuals without an ASD diagnosis but
who display relatively high levels of autistic traits have also
been shown to exhibit mentalizing deficits
(Gökçen et al.
The ‘broken mirror’ theory of ASD suggests that
dysfunction in brain areas known collectively as the mirror
system (MS) underlie some of the social communication
difficulties experienced by individuals with ASD
and Dapretto 2006; Oberman and Ramachandran 2007)
main components of the human MS are considered to be the
inferior frontal gyrus (IFG) and the inferior parietal lobe
(IPL; Rizzolatti and Craighero 2004; Rizzolatti and
. Areas of the MS are active during the
performance of an action as well as the observation of similar
(di Pellegrino et al. 1992; Rizzolatti et al. 1996)
. It is
thought that by displaying similar activation patterns during
the observation of actions as when performing actions, the
MS simulates observed actions in the observer’s own motor
system to facilitate action understanding (Rizzolatti and
Craighero 2004). This is known as the motor resonance
(Agnew et al. 2007; Landmann et al. 2011; Leslie et al.
2004; Rizzolatti et al. 2002)
. According to the broken
mirror theory, atypical MS activation in individuals with ASD
results in reduced understanding of the actions of others,
which in turn underlies some of the social communication
difficulties these individuals experience
Dapretto 2006; Oberman and Ramachandran 2007)
Although the broken mirror hypothesis is an attractive
theory, the literature supporting the possibility of
atypical MS activation in individuals with ASD is limited,
particularly in adults. A number of behavioural studies, fMRI
studies and studies using electromyographic recordings have
shown children with ASD display both behavioural
impairments and atypical MS activity during tasks typically
associated with MS functioning such as imitation
(Dapretto et al.
2006; Hobson and Hobson 2008; Rogers et al. 2003;
Williams et al. 2004)
, action planning
(Cattaneo et al. 2007;
Dowd et al. 2012; Fabbri-Destro et al. 2009)
(Dewey et al. 2007)
. In contrast, adults with
ASD generally display typical behavioural performances
on tasks traditionally associated with MS functioning
et al. 2007; Avikainen et al. 2003)
and the majority of
neuroimaging studies (fMRI, EEG and TMS) have shown
that adults with ASD display typical levels of MS activity
(Avikainen et al. 1999; Dinstein et al. 2010; Enticott et al.
2013b; Marsh and Hamilton 2011)
. Only a limited number
of studies have provided evidence to suggest MS activation
is atypical during these tasks in adults with ASD
et al. 2007; Enticott et al. 2012; Honaga et al. 2010;
Martineau et al. 2010)
and adults with high levels of autistic
(Cooper et al. 2013; Lepage et al. 2010; Puzzo et al.
. Therefore, evidence to support general dysfunction of
the MS in ASD, particularly in adults, is limited (Hamilton
Despite the limited evidence suggesting atypical MS
activity in adults with ASD during tasks traditionally
associated with MS functioning (e.g. imitation and action
planning), fMRI studies have found reduced MS activation
(IFG and IPL) in adults with ASD during mentalizing tasks
compared to control participants
(Baron-Cohen et al. 1999;
Holt et al. 2014; Kana et al. 2014)
. A wide body of
neuroimaging literature has provided evidence for MS
involvement in mentalizing in typically developing adults: higher
MS activity has been shown during mentalizing tasks than
(fMRI; Adams et al. 2010; Centelles
et al. 2011; de Lange et al. 2008; Schurz et al. 2014; PET;
Brunet et al. 2000)
. Additionally, both fMRI and TMS
studies have found higher MS activation during the
observation of actions with social context compared to non-social
actions even in the absence of mentalizing tasks
et al. 2013; Ciaramidaro et al. 2014; Enticott et al. 2013b;
Iacoboni et al. 2004)
. Lesions to IFG, both in brain damaged
(Besharati et al. 2016; Dal Monte et al. 2014)
when temporary functional lesions are induced via direct
current stimulation in patients undergoing surgery to treat
(Herbet et al. 2014)
, have been shown to impair
mentalizing performances. Collectively, these data show that
MS has a role in mentalizing and that MS functioning is
atypical in adults with ASD when the mentalizing system is
engaged. Therefore, it is possible that reduced MS activity
during mentalizing tasks may contribute to the mentalizing
difficulties these adults experience.
Despite numerous studies providing evidence for a role
of the MS in mentalizing, some fMRI studies have not
found higher levels of MS activity during mentalizing tasks
compared to non-mentalizing tasks in typically
(Castelli et al. 2000, 2002; Gallagher et al.
2000; Spunt et al. 2011; White et al. 2014)
. Differences in
the stimuli used are likely to have contributed to
inconsistencies in the existing literature. fMRI studies have shown
that different brain areas are active during mentalizing tasks
depending on the stimuli used
(Gobbini et al. 2007; Schurz
et al. 2014)
, higher MS activation is elicited when dynamic
stimuli are used rather than static stimuli and when stimuli
depict bodies rather than faces (Schlochtermeier et al. 2015).
The majority of mentalizing tasks that have not elicited MS
activation have used simplistic cartoons, still images or
passages of text as stimuli
(Castelli et al. 2000, 2002; Gallagher
et al. 2000; White et al. 2014)
. If MS functioning is
atypical in adults with ASD during mentalizing tasks then these
individuals may display more prominent differences in brain
activation and greater behavioural impairments on
mentalizing tasks that typically elicit greater levels of MS activity.
Transcranial magnetic stimulation (TMS) and
electroencephalography (EEG) are two techniques that have often
been used to non-invasively obtain indirect indices of MS
activity, but it is unknown precisely how these two indices
of MS activity relate to each other. TMS involves
administering brief magnetic pulses through a magnetic coil placed
on the scalp in order to induce transient changes in activity
in the underlying region of the cortex
single TMS pulses are applied to the primary motor cortex
(M1), the resulting increases in corticospinal activity can
be measured by recording increased activity in
contralateral hand muscles via electromyography
et al. 2002; Fadiga et al. 2005)
. These increases in muscle
activity induced by TMS (known as motor evoked
potentials; MEPs) are larger when individuals view hand actions
compared to when TMS is applied at rest and therefore
these increases in MEP sizes during action observation are
regarded as an index of MS activity
(Fadiga et al. 2005;
Patuzzo et al. 2003; Strafella and Paus 2000)
. In contrast, mu
rhythm; large amplitude oscillations in the alpha frequency
band (8–12 Hz) over sensorimotor cortex detected by EEG,
is suppressed during action observation as well as the
performance of actions and thereby provides another index of
(Fox et al. 2016; Frenkel-Toledo et al. 2014;
Oberman et al. 2007)
. Two previous studies that have
combined EEG and single-pulse TMS to measure MS activity in
typically developing populations have shown that although
measurements from both these techniques are sensitive to
motor resonance mechanisms, they are not correlated with
(Andrews et al. 2015; Lepage et al. 2008)
Therefore, it is possible that these measurements reflect different
aspects of MS functioning. It is important to note that these
indices of MS activity also differ in their spatial and
temporal properties; EEG measures the sum of post-synaptic
neuronal activity over a large area, and an index of mu
suppression is typically taken over a relatively long time period (i.e.,
> 1 s). By contrast, TMS measures brief induced increases
in corticospinal activity from peripheral muscles
et al. 2015; Pineda 2005; Rossini et al. 1994)
. Using both
of these non-invasive measures of MS activity
simultaneously allows a more complete picture of MS functioning to
This study aimed to investigate whether adults with
diagnoses of ASD display atypical MS activity when mentalizing
and whether levels of MS activation correspond to
mentalizing performance. In this study, participants watched hand
action videos, and after each video they had to either make
decisions about the intention of the actor (mentalizing task)
or the success of the action (non-mentalizing task). The
video stimuli used showed different actors performing
naturalistic hand actions to ensure the stimuli were sufficiently
complex and optimally activated the MS. TMS-induced
MEPs and mu suppression were both used as indices of MS
activity. A preliminary TMS study carried out on typically
developing individuals, using the same stimuli, identified
higher MS activation during a mentalizing task compared
to a non-mentalizing task once the actors’ intentions had
(Cole and Barraclough 2017)
. Therefore, in
our study, single-pulse TMS was applied to the primary
motor cortex (M1) at the end of each hand action when the
outcome of the action or the intention of the actor had been
revealed. Simultaneous EEG recordings were made
throughout the experiment. Participants without a diagnosis were
grouped based on low or high levels of autistic traits. It was
predicted that larger TMS-induced MEP sizes and greater
levels of mu suppression would be found during the
mentalizing task compared to the non-mentalizing task,
indicating higher levels of MS activity. It was also predicted
that high levels of autistic traits would be associated with
reduced task-related changes in MS activity and that lower
levels of MS activity would be related to poorer mentalizing
Forty-three adults were recruited for this study, of which 13
had a formal diagnosis of either Asperger’s disorder (11)
or ASD. All of the participants with a diagnosis met the
DSM-5 criteria for ASD and none of the participants had
any existing learning difficulties or experienced delayed
language development. Participants without an ASD
diagnosis were recruited based on the level of autistic traits they
displayed as measured by the Autistic Spectrum Quotient
(AQ; Baron-Cohen et al. 2001)
. The average AQ score in the
general population is 16.94
(Ruzich et al. 2015)
were excluded from the study if they had AQ scores between
16 and 19. Participants with scores between 0 and 15 were
assigned to the ‘low AQ’ group and participants with AQ
scores of 20 or higher were assigned to the ‘high AQ’ group.
This resulted in three participant groups: low AQ (n = 15),
high AQ (n = 15) and ASD (n = 13). Participants without an
ASD diagnosis were grouped into low and high AQ groups
due to findings of subtler versions of the behavioural and
neural characteristics associated with ASD in individuals
without a diagnosis but relatively high levels of autistic traits
(Best et al. 2015; Di Martino et al. 2009; Lindell et al. 2009;
Ridley et al. 2011; van Boxtel and Lu 2013)
. AQ scores were
used to group these individuals initially and further
psychological assessments were later used to quantify the level of
autistic traits displayed in more detail (see Psychological
Assessments). The participant groups did not significantly
differ in age, verbal IQ, gender or years of formal education
and all participants had verbal IQ scores within the normal
range (> 70; see Table 1).
All participants were screened for symptoms of
psychiatric disorders using the Mini-International
Neuropsychiatric Interview (MINI)
(Sheehan et al. 1998)
Years of formal
p values were obtained from one-way MANOVA unless otherwise stated
aThe verbal IQ scores were measured using the test of pre-morbid functioning
were not eligible to take part if they were diagnosed with
any psychiatric disorders or were identified by the MINI
as displaying symptoms of any psychiatric disorders. An
exception was made for mood disorders, anxiety and ADHD
in the participants with ASD due to the high prevalence of
(Matson et al. 2013; Matson and
. In the ASD group, six participants were taking
psychotropic medication to treat ADHD, depression or
anxiety (see Table 2). None of the participants without an ASD
diagnosis were taking psychotropic medication.
Participants were also screened for contraindications for
TMS; history of seizures, serious head injuries, brain related
conditions, severe headaches, implanted metal or medical
devices, family history of epilepsy and current pregnancy
(Rossi et al. 2009)
This research project was approved by the Human
Research Ethics Committee at Deakin University and was
performed in accordance with the ethical standards laid
down in the 1990 Declaration of Helsinki. All participants
provided signed informed consent.
All participants completed the Autism Spectrum Quotient
(AQ), Autism Diagnostic Observation Schedule
(ADOS2), The Awareness of Social Inference Test (TASIT), Social
Responsiveness Scale (SRS-2) and Empathy Quotient (EQ).
The AQ and ADOS-2 are designed to measure the level of
autistic traits displayed, the SRS-2 and TASIT measure
social functioning and the EQ provides a measure of
empathy. The three groups significantly differed from each other
on all these measures (see Table 3). These psychological
tests have been shown to display good psychometric
(Allison et al. 2011; Hurst et al. 2007; McDonald et al.
2006; Oosterling et al. 2010)
. The AQ was administered in
the form of an online questionnaire before the participants
took part in the experiment. The other assessments were
administered at the Cognitive Neuroscience Unit at Deakin
University as a 2-h session prior to the TMS testing session.
Psychological assessment sessions always took place on a
Participants sat 600 mm away from an Eyelink 1000 plus
eye-tracker (SR Research, Ontario) placed in front of a
24″ LED computer monitor. Single EEG electrodes were
placed at locations FCz, F3 and F4 according to the
international 10–20 system of electrode placement. Typically,
EEG recordings are taken from central electrodes (C3, C4,
Cz) when investigating MS activity. However, due to the
placement of the TMS coil over the primary motor cortex
(M1) EEG recordings were taken from frontal electrodes
(F3, F4, FCz) to reduce TMS-induced artefacts, and to allow
sufficient contact between the TMS coil and the scalp. It has
previously been shown that mu suppression can be measured
across the entire scalp when observing and imitating hand
actions and ASD participants have been shown to display
differences in mu power over frontal regions (Dumas et al.
2014). Reference electrodes were placed on the left and right
mastoid bones and the ground electrode was placed on the
forehead. Electrooculogram (EOG) electrodes were placed
above and below the left eye to in order to identify EEG
artefacts caused by blinking. EEG signals were recorded using
Curry Neuroimaging Suite 7 (Compumetics Ltd, Australia).
EEG signals were amplified using NeuroScan SynAmps RT
(NeuroScan SynAmps, Compumedics Ltd.) and digitised at
1 kHz. All electrode impedances were below 5 KΩ. EEG
analyses and bandpass filtering were conducted offline.
TMS was administered using a Magstim BiStim2
stimulator (Magstim Company Ltd., Carmarthenshire, Wales, UK).
Firstly, the location of the primary motor cortex (M1) was
identified in each participant by measuring the position on
the scalp five centimetres lateral and 1 cm anterior to Cz
(according to the international 10–20 system of electrode
placement). TMS pulses were then applied to this position
on the scalp using a standard figure of eight 70 mm coil
held tangentially over the scalp at a 45° angle to the
midline. An initial intensity of 40% stimulator output was used
and then the intensity of TMS stimulation was increased in
5% increments until MEPs were produced. Stimulation was
also applied around the estimated location of M1 in order to
confirm that this was the optimal scalp location to produce
MEPs in muscles of the right hand. MEPs were measured
from the first dorsal interosseous (FDI) and abductor digiti
minimi (ADM) muscles using Ag/AgCl surface electrodes.
EMG signals were amplified using PowerLab 4/35 (with
dual BioAmp; AD Instruments, Colorado Springs, CO).
Once the optimal location for stimulation had been
identified, the intensity of the TMS stimulation was adjusted
in order to find the participants’ resting motor threshold
(RMT). Participants’ RMT was defined as the minimum
stimulation intensity needed to induce MEPs with an average
peak-to-peak magnitude of 1 mV over 5 consecutive trials.
Twenty MEPs induced by stimulation at RMT were used as
a measure of baseline corticospinal excitability (CSE).
The experiment comprised of two blocks; a mentalizing
task and a non-mentalizing task. In both tasks, participants
watched short videos (4 s) of actors passing or attempting
to pass a poker chip through slots in a wooden board to
another person on the other side of the board (who was out
of view; see Fig. 1). After each video, participants were
asked to make a decision about the action; either about the
intention of the actor (mentalizing task) or the success of the
action (non-mentalizing task). Participants indicated their
response by pressing buttons on the computer keyboard with
their left hand. In the mentalizing task, participants watched
videos that either showed an actor accidentally dropping a
poker chip and therefore failing to pass the poker chip to
the other player (‘clumsy’ action) or an actor deliberately
not passing the poker chip (‘spiteful’ action). After each
video, participants indicated whether they thought the action
was ‘clumsy’ or ‘spiteful’. In the non-mentalizing block,
participants watched videos in which actors either
successfully passed the poker chip to the other player (successful
action) or accidentally dropped the poker chip
(unsuccessful action). After each video, participants had to indicate
whether the action was ‘successful’ or ‘unsuccessful’. The
unsuccessful actions shown in the non-mentalizing block
were the same as the ‘clumsy’ actions shown in the
mentalizing block. Before each block, participants were told which
decision they would be required to make after each video in
the upcoming block (the instructions given to participants
are available in the supplementary material). Participants
also completed 16 practice trials (8 mentalizing trials) before
the main experiment so that they knew what the tasks would
entail. The actors shown in the practice trials were not shown
in the main experiment to avoid any preconceptions about
the actors influencing decisions made in the main
experiment. The videos shown in this experiment are a subset
of the videos used in a previous study
(Cole et al. 2017)
Grasping and pushing actions were shown; grasping actions
involved actors grasping a poker chip and placing it through
a slot at head height, pushing actions involved pushing the
poker chip with the index finger through a slot that was level
with the table in front of them (see Fig. 1). Twenty actors (10
male) were shown performing each action (clumsy grasp,
clumsy push, spiteful grasp, spiteful push, successful grasp,
(successful grasp). d Accidentally not passing a poker chip through
the bottom slot (clumsy push). e Deliberately not passing a poker
chip through the bottom slot (spiteful push). f Passing a poker chip
through the bottom slot (successful push)
successful push) resulting in 80 action videos in each of the
two blocks (clumsy actions were seen in both blocks). Two
types of actions (grasping and pushing) were used in order
to make the stimuli more varied and these particular actions
were chosen as they both utilise the FDI muscle.
A single TMS pulse at 1 mV RMT was delivered at the
end of each video. A light sensor was used in order to time
lock the TMS pulses to the timing of the videos. A black
square was added to the top left corner of the videos and
this black square was replaced with a white square for the
last three frames in each video. The light sensor detected this
change and sent a 5 V TTL pulse to the TMS stimulator via
a BNC cable which triggered a single TMS pulse to be fired.
The TMS machine subsequently sent a trigger to a PowerLab
4/35 (ADInstruments Pty Ltd) to trigger EMG recording.
EEG was continuously recorded throughout both the
mentalizing and non-mentalizing tasks but triggers were sent
to the EEG machine at the start of each trial to record the
type of action being shown. The order in which mentalizing
and non-mentalizing blocks were completed was
counterbalanced across all participants and within each participant
group. Once participants had completed both the
mentalizing and the non-mentalizing task, twenty single TMS pulses
were administered at RMT in order to compare baseline
corticospinal excitability before and after the experiment.
First, the ADOS-2, AQ, EQ, SRS-2 and TASIT scores were
calculated and a one-way MANOVA was used to identify
group differences in these scores. Then, the numbers of
correct responses on the mentalizing and non-mentalizing tasks
were calculated for each participant. Data screening
identified that the behavioural data were not normally distributed
and therefore a log transformation was applied. The log
transformed data still violated the assumption of normality
so non-parametric analyses were conducted. Potential group
differences in behavioural performances were explored using
a Kruskal–Wallis test and a possible task-related difference
in performances across all participants was examined using
a Wilcoxon signed rank test.
TMS was not performed on two participants in the high
AQ group and four participants in the ASD group; two
participants in the ASD group and one participant in the high
AQ group found TMS too uncomfortable and the
remaining three participants had motor thresholds deemed too
high to continue (> 75% stimulator output). Trials in which
muscle activity (± 0.1 mv) was identified within a 200 ms
time window before the TMS pulse or trials in which FDI
peak to peak MEP amplitudes were smaller than 0.2 mV
were removed from the analysis (4.02% of all MEPs were
excluded). Two participants in the high AQ group were
removed from the analyses for having only 50% or fewer
valid FDI MEPs for either task. This resulted in 35
participants (15 low AQ, 11 high AQ and 9 ASD) being included
in the EMG analysis.
Preliminary analyses were carried out on the EMG data
in order to clarify that RMTs were not significantly different
between groups, that the experiment did not alter
participants’ resting corticospinal activity and that the number of
excluded MEPs did not significantly differ across tasks or
participant groups. Group differences in RMTs were
investigated using a one-way ANOVA. Changes in corticospinal
activity as a result of the experiment were investigated by
first calculating median MEP sizes (peak-to-peak amplitude
[mV]) for both the 20 single TMS pulses given before the
experiment and after the experiment for both muscles. Then,
separate mixed-model ANOVAs were performed for each
muscle investigating the influences of group (low AQ, high
AQ, ASD) and time point (before or after the experiment)
on MEP sizes. The data regarding the number of excluded
MEPs violated the assumption of normality even after a log
transformation was applied so non-parametric tests were
used. An independent-samples Kruskal–Wallis test was used
to investigate group differences and a related-samples
Wilcoxon signed rank test was used to investigate differences in
the number of MEPs excluded between tasks.
For the main TMS data analyses, median MEP values
were calculated for both the FDI and the ADM muscles for
each participant and each task
(mentalizing/non-mentalizing). Median baseline MEP values were also calculated for
both muscles for each participant by combining MEPs from
both pre- and post-experiment baseline measures. The raw
median MEP values for each task were then converted into
motor resonance values by computing the relative MEP sizes
in comparison to MEP sizes at baseline:
MR = (median MEP during task − median MEP at baseline)∕
median MEP at baseline] ∗ 100
Data screening found that the motor resonance data for
both FDI and ADM muscles violated the assumption of
normality so a log transformation was used. This transformation
cannot be performed on negative values so a constant of 100
was added to each motor resonance value prior to
transformation to ensure that all values were positive. After the
log transformation, the distribution of the FDI data did not
significantly differ from a normal distribution but the ADM
data still violated the assumption of normality. Therefore,
parametric analyses were used for the log transformed FDI
data, but non-parametric analyses were conducted on the log
transformed ADM muscle data.
The FDI motor resonance data were analysed using
a mixed-model ANOVA to investigate the influences of
group (low AQ, high AQ, ASD) and task
(mentalizing/nonmentalizing) on MEP sizes. Potential group differences in
the ADM motor resonance data were investigated using a
Kruskal–Wallis test and potential differences in motor
resonances across experimental tasks were explored using a
Wilcoxon signed rank test.
Offline analyses of the EEG data were performed using
Curry 7 Neuroimaging Suite software (Compumetics Ltd,
Australia). Epochs of EEG data were created for videos
shown in each task (mentalizing and non-mentalizing).
Although, the action videos were 4000 ms long, the last
350 ms of each epoch was removed in order to eliminate the
artefact created by the TMS pulse. Therefore, each video
epoch was 3650 ms long and 80 epochs of each type were
created for every participant. EEG data collected when
participants were viewing a fixation cross were used as a
baseline measure. There were 160 fixation cross epochs (80
for each task), each 1500 ms long. The first 500 ms of each
fixation cross epoch were removed from the analysis because
the fixation cross was shown directly after participants were
required to make a response and therefore removing the
first 500 ms reduced the possibility of increased mu power
during fixation as a result of participants moving their left
hand back to a resting position after they had made their
responses. This resulted in 160 fixation epochs for each
participant that were each 1000 ms long.
EEG data were baseline corrected and band-pass filtered
(1–30 Hz). Blink artefacts were detected by scanning the
data from the EOG electrodes for voltages greater than 100
or − 100 µV. Once the blink artefacts were detected, these
were corrected using covariance analysis (Curry
Neuroimaging Suite 7, Compumetics Ltd, Australia). Any epochs that
still contained non-cerebral artefacts (> 75 µV) were
identified and removed from the analysis. Two participants were
removed from the analysis (one participant from the high
AQ group and one participant from the ASD group) because
62.5% or less of the epochs were valid for one or more of
the individual conditions (mentalizing videos,
non-mentalizing videos, fixation crosses in the mentalizing block,
fixation crosses in the non-mentalizing block). Excluding
these participants, only 5.6% of epochs were invalid across
all participants. Preliminary analyses were carried out on
the EEG data in order to clarify that the number of epochs
that were excluded did not significantly differ between
participant groups or experimental conditions. The numbers of
excluded epochs were not normally distributed even after a
log transformation was applied so non-parametric analyses
were conducted. A Kruskal–Wallis test was used to
investigate group differences in the number of epochs excluded and
a Friedman’s ANOVA was used to identify differences in the
number of excluded epochs across experimental conditions.
A Fast Fourier Transform (FFT) was used to calculate mu
power in both the low alpha frequency range (8–10 Hz) and
high alpha frequency range (10–12 Hz) during all epochs.
The majority of previous studies investigating MS activity
using EEG have used activity in the entire alpha frequency
band (8–12/13 Hz) as a measure of mu power
(Andrews et al.
2015; Oberman et al. 2005, 2008; Perry et al. 2011; Ulloa
and Pineda 2007)
. However, there is accumulating evidence
to suggest that lower (8–10 Hz) and higher (10–12 Hz) alpha
bands reflect different processes and should therefore be
(Dumas et al. 2014; Frenkel-Toledo et al.
2014; Neuper et al. 2009; Pfurtscheller et al. 2000)
Additionally, a previous study found reduced mu suppression in
the 10–12 Hz range over frontal regions in adults with ASD
but not the 8–10 Hz range when observing hand movements
(Dumas et al. 2014)
. Consequently, lower and higher mu
frequency bands were analysed separately in this study.
Average mu power in both frequency bands was then
calculated for each epoch type (four epoch types: mentalizing
videos, non-mentalizing videos, mentalizing fixation and
non-mentalizing fixation) for every participant. The degree
of mu suppression during each experimental condition was
calculated by comparing average mu power during the video
epochs compared to the fixation epochs in the same
condition: [(fixation-video)/fixation]*100. Larger values indicated
greater degrees of mu suppression. The mu suppression data
for both frequency bands violated the assumption of
normality so the data were log transformed. Log
transformations cannot be carried out on negative values so a
constant of 1300 was added to each data point to ensure that all
values were positive before the log transformation. After
the log transformation was applied, the data still violated
the assumption of normality and therefore non-parametric
analyses were conducted. Kruskal–Wallis tests were used to
investigate group differences in mu suppression and
relatedsamples Wilcoxon signed rank tests were carried out to
investigate task-related changes in mu suppression. Analyses
were carried out separately for both frequency bands.
The eye-tracking data were analysed using EyeLink
DataViewer software (SR Research Ltd., Ontario, Canada). Three
dynamic rectangular regions of interest (ROIs) were created
for each action video individually. These ROIs corresponded
to the head of the actor, the actor’s hand and the poker chip
(see Fig. 2). These three interest areas were chosen based
on eye-tracking data from a previous behavioural study
using the same stimuli
(Cole et al. 2017)
. The total number
Fig. 2 The dynamic regions of interest (ROIs) used in the
eye-tracking data analysis for one of the action videos are shown overlaid onto
screenshots from a the start and b the end of that particular video.
Three dynamic ROIs corresponding to (1) The poker chip, (2) The
actor’s head and (3) The actor’s hand, were created for each of the
120 videos individually
and total duration of fixations in each ROI during each task
(mentalizing/non-mentalizing) were calculated for each
participant. ROIs were analysed separately as these data are
not independent (participants cannot fixate in more than
one ROI at once). The eye-tracking data were not normally
distributed even after a log transformation was applied and
therefore non-parametric analyses were conducted.
Independent-samples Kruskal–Wallis tests were used to
investigate group differences and related-samples Wilcoxon signed
rank tests examined differences between tasks for each ROI.
For all analyses (behavioural, EMG, EEG and eye-tracking),
any significant task-related differences that were identified
were investigated further by analysing the data collected
during the presentation of clumsy actions across the two
tasks. Identical clumsy actions were shown during both the
mentalizing and non-mentalizing tasks. Analysing the data
in this way eliminates the possibility that apparent effects of
the task are due to differences in observed action kinematics.
Due to the spectral nature of ASD, any significant group
differences that were found were also examined across the
continuum of autistic traits. A principal components analysis
(PCA) was performed on all the psychological test scores
in order to obtain a single score for each participant that
reflected the level of autistic traits that they displayed. Linear
regression analyses were then used to examine whether the
levels of autistic traits significantly predicted the outcome
variables e.g. levels of mu suppression. These additional
analyses were conducted to further support the relationships
between the outcome variables and ASD.
Bayes factors were also calculated for all non-significant
results to provide evidence for, or against, our hypotheses,
irrespective of sample size
. Bayes factors of
1 indicate equal amounts of evidence to support both the
null hypothesis and the alternative hypothesis, higher Bayes
factors indicate more evidence for the alternative
hypothesis and lower values suggest more evidence for the null
hypothesis. Bayes factors lower than 1/3 are considered to
reflect substantial evidence for the null hypothesis and Bayes
factors higher than 3 indicate substantial evidence for the
A one-way MANOVA identified that scores on all
psychological tests (ADOS-2, AQ, EQ, TASIT and SRS-2) were
significantly different between groups (see Table 3).
Posthoc pairwise comparisons identified that all groups were
significantly different from each other on the ADOS-2, AQ
and SRS-2 measures (Bonferroni correction applied). The
Bonferroni corrected multiple comparisons identified that
the high AQ and ASD groups did not significantly differ in
TASIT scores (p = 0.92, B = 0.51). EQ scores did not
significantly differ between low and high AQ groups (p= 0.08,
B = 4.62), however, the Bayes factor indicated there was
evidence for a difference in EQ score between low and high
AQ groups. All other group comparisons were significant
(p < 0.001 except difference in AQ scores between high AQ
and ASD groups; p < 0.01, and TASIT scores between low
and high AQ groups; p = 0.02). In all cases, where significant
group differences were found, the ASD group had scores that
reflected the highest level of autistic traits and the low AQ
group had scores reflected the lowest level of autistic traits.
Principal component analysis (PCA) was conducted using
the psychological test scores in order to obtain a single value
for each participant that represented the level of autistic traits
that they displayed. The psychological test scores correlated
with each other (all rs > 0.35) meaning that they were
suitable for PCA. The Kasier–Meyer–Olkin measure of sampling
accuracy was 0.84 (above 0.6), Barlett’s test of sphericity
was significant χ2(10) = 146.07, p < 0.001 and the
communalities were all above 0.7 which collectively supported the
inclusion of all the psychological tests in the PCA. PCA with
varimax rotation was used. The initial eigenvalues from the
PCA analysis showed that one factor (with an eigenvalue of
3.57) explained 71.36% of the variance in psychological test
scores. No other factors had eigenvalues higher than Kaiser’s
criterion of 1 and therefore only one factor was extracted.
This factor was labelled ‘autistic traits’.
Group Differences There were significant differences
in the levels of mu suppression in the 8–10 frequency
band between groups during the mentalizing task at F4
(H(2) = 6.21, p < 0.05). Additionally, linear regression
analysis demonstrated that the level of autistic traits that
participants displayed significantly predicted the amount of mu
suppression in 8–10 Hz band at F4 during the mentalizing
task [F(1,38) = 0.47, p = 0.04, R2 = 0.11; see Fig. 3].
Pairwise-comparisons with adjusted p values (using the
Bonferroni correction) are reported and demonstrate a borderline
significant difference between the high AQ group and the
ASD group (p = 0.05, r = 0.47; with lower levels of mu
suppression in the ASD group). [After applying a Bonferroni
correction, the new significance threshold was p= 0.017
(0.05/3)]. There were no significant differences between
ASD and low AQ groups (p = 0.18, r = 0.37, B = 1.44) or the
between the low and high AQ groups (p = 1.00, r = − 0.10,
B = 0.46).
Mu suppression in the 8–10 Hz frequency band was
significantly different between groups during the
nonmentalizing task at F3 (H(2) = 10.10, p = 0.006) and FCZ
(H(2) = 7.32, p = 0.03). Pairwise comparisons showed that
the high AQ group displayed significantly lower levels of mu
suppression than the low AQ group at F3 (p = 0.01, r = 0.54).
No other group differences were significant once threshold
significance values had been adjusted using the Bonferroni
correction (p = 0.02; see supplementary material). Linear
regression analysis demonstrated that the level of autistic
traits that participants displayed was not a significant
predictor of the amount of mu suppression in 8–10 Hz band
during the non-mentalizing task at F3 [F(1,38) = 0.02, p = 0.90,
R2 < 0.001, B = 0.44] or FCZ [F(1,38) = 0.03, p = 0.86,
R2 < 0.01, B = 0.35].There were no other significant group
differences in mu suppression in the 8–10 Hz frequency band
(see Supplementary Material).
Task‑Related Differences Initial analyses identified that mu
suppression in the 8–10 Hz band was significantly lower
during the mentalizing task than the non-mentalizing task
at F3 across all participants (T = 581, p = 0.02, r = 0.38).
However, when this apparent significant task-related
difference in mu suppression was investigated using data from
the clumsy actions alone (in order to control for differences
in action kinematics), there was no significant difference in
mu suppression in the 8–10 Hz range at F3 between clumsy
actions shown in the mentalizing task compared to the
nonmentalizing task (T = 518, p = 0.15, r = 0.23, B = 0.23).
There were also no task-related differences in mu
suppression in the 8–10 Hz band at FCZ (T = 501, p = 0.21, r = 0.20,
B = 0.22) or F4 (T = 495, p = 0.25, r = 0.18, B = 0.22).
There were no significant differences in mu suppression in
the 10–12 Hz frequency band between groups or across tasks
at any of the cortical sites (see Supplementary Material).
Fig. 3 The relationship between the level of autistic traits that
participants displayed and the level of mu suppression in the 8–10 Hz
frequency range at F4. Levels of autistic traits significantly predicted the
degree of mu suppression at F4; participants that exhibited higher
levels of autistic traits showed lower levels of mu suppression (8–10 Hz)
at F4 [F(1,38) = 0.47, p = 0.04, R2 = 0.11]. The curved lines represent
95% confidence intervals
Across all participants, there was no significant difference
in motor resonance values in the FDI muscle between the
mentalizing and non-mentalizing tasks (F(1,32) = 0.30,
p = 0.59, ηp2 < 0.01), there was no significant interaction
between participant group and the task (F(2,32) = 0.73,
p = 0.49, ηp2 = 0.04) and there were no significant group
differences in motor resonance values (F(2, 32) = 0.73,
p = 0.49, ηp2 = 0.04). Bayesian t-tests indicated there was
significant evidence against higher motor resonance
values during the mentalizing task compared to the
non-mentalizing task (B = 0.29). Bayes factors indicated that there
was neither evidence for, nor against, group differences in
motor resonance values; between low and high AQ groups
(B = 0.83), between high AQ and ASD groups (B = 1.25) or
between low AQ and ASD groups (B = 1.29).
There were no significant task or group differences for the
ADM data (see Supplementary Material).
Across all participants, significantly more and longer
fixations were made in the hand and head ROIs during the
mentalizing task compared to the non-mentalizing task
[hand ROI: number: (T = 169, p < 0.001, r = − 0.52),
duration: (T = 288, p = 0.03, r = − 0.33); head ROI: number:
(T = 271, p = 0.02, r = − 0.34), duration: (T = 344, p = 0.02,
r = − 0.35)]. There was borderline significantly more
fixations in the poker chip ROI during the mentalizing than the
non-mentalizing task (T = 297, p = 0.05, r = 0.29). However,
there was no significant task-related difference in the total
duration of fixations within the poker chip ROI (T = 431,
p = 0.61, r = − 0.08, B = 0.66).
When the eye-tracking data from the clumsy actions were
analysed alone, all previously significant results (including
the borderline significant difference) were still significant
except for the duration of fixations within the head ROI
(T = 344, p = 0.12, r = − 0.24, B = 0.97; see supplementary
material for all results).There were no significant group
differences in the eye-tracking data (see Supplementary
Relationships Between Data from Different
EEG and Behavioural Performance
When investigating the relationship between EEG and
behavioural performances, linear regression analysis found
that mu suppression in the 8–10 Hz frequency band at F3
during the mentalizing task significantly predicted
performance on this task across all participants [F(1,38) = 5.64,
p = 0.02, R2 = 0.13; see Fig. 4]. There were no other
significant relationships between the EEG data and behavioural
performance (see Supplementary Material).
EyeT‑racking and Behavioural Performance
The total duration of fixations within the poker chip ROI
during the non-mentalizing task significantly predicted
performance on the non-mentalizing task [F(1,41) = 5.14,
p = 0.03, R2 = 0.11]. There were no other significant
relationships between eye-tracking data and behavioural
performance (see Supplementary Material).
EyeT‑racking and EEG
Linear regression analyses found that the degree of mu
suppression did not significantly predict fixation patterns for any
of the cortical sites (see Supplementary Material).
TMS and Other Measures
Linear regression analyses found that motor resonance
values did not significantly predict behavioural performances or
levels of mu suppression in either task (see Supplementary
This study aimed to investigate the possible association
between ASD and atypical MS activity when mentalizing, as
well as the relationship between MS activity and mentalizing
performance. Both TMS-induced MEPs and mu suppression
(measured by EEG) were used as indices of MS activity. The
EEG data show that higher levels of autistic traits (across
clinical and non-clinical populations) were associated with
lower levels of MS activation in the right hemisphere when
mentalizing. These lower levels of MS activity in the right
hemisphere were not associated with poorer mentalizing
performances. In contrast, lower levels of MS activity in
the left hemisphere were associated with poorer mentalizing
performance but not associated with the levels of autistic
traits that participants displayed. The TMS data did not show
differences in MS activity between groups or a relationship
between MS activity and mentalizing performances.
Consequently, although our sample size is small, the EEG data
provide evidence for MS involvement in mentalizing and
reduced MS activity in adults with high levels of autistic
traits. The different lateralisation of MS activity associated
with task performance and MS activity associated with high
levels of autistic traits means our data do not provide
evidence that atypical MS functioning underlies mentalizing
difficulties associated with ASD.
Across all participants, the level of autistic traits
displayed significantly predicted levels of mu suppression in
the 8–10 Hz frequency band at F4 during the mentalizing
task (see Fig. 3). These data imply that high levels of autistic
traits are associated with reduced MS activity in the right
hemisphere when mentalizing. Our results support previous
fMRI studies which have found reduced MS activation in
adults with ASD during mentalizing tasks
et al. 1999; Hadjikhani et al. 2009; Holt et al. 2014; Kana
et al. 2014; Wicker et al. 2008)
The lower levels of right mu suppression in individuals
with high levels of autistic traits during the mentalizing task
were not observed during the non-mentalizing task. These
data suggest mentalizing induces atypical suppression of the
right MS in these adults. Mentalizing tasks reliably induce
activation in a cortical system known as the ‘mentalizing
(Ciaramidaro et al. 2014; de Lange et al. 2008;
Lombardo et al. 2010; Spunt et al. 2011; Van Overwalle and
. Atypical connectivity between the
mentalizing system and the MS has previously been reported in
(Damarla et al. 2010; Fishman et al. 2014; Just et al.
2007, 2004; Kennedy and Courchesne 2008; Noonan et al.
2009; Shih et al. 2010)
. It is possible that the reduced right
MS activity we observed in adults with high levels of
autistic traits was the result of atypical connectivity between the
mentalizing system and the MS when inferring the
intentions of others from their actions.
Although our EEG data suggest that MS activation in
the right hemisphere is reduced in adults with high levels
of autistic traits when mentalizing, no significant
relationship was found between right-lateralised MS activity
and mentalizing performance. Consequently, our data do
not provide evidence that the atypical right MS
activation identified underlies mentalizing difficulties associated
with ASD. However, the lack of a significant relationship
between MS activity and mentalizing performance may
be due to compensatory strategies that individuals with
high levels of autistic traits have adopted in order to
successfully infer the intentions of others from action
kinematics, despite atypical disengagement of the right MS.
All participants in this study were high-functioning adults
with IQ scores within the typical range (> 70). It is
possible that if younger or lower functioning individuals were
recruited they may not have developed sufficient
compensatory mechanisms and a relationship between right mu
suppression and mentalizing performance may have been
found. Therefore, although our EEG data provide evidence
against the broken mirror theory, the use of compensatory
strategies as well as our small sample size may have
contributed to the lack of a relationship between MS
activation and mentalizing performance.
In contrast to the right-lateralised EEG data,
left-lateralised mu suppression was not related to levels of autistic
traits but was positively associated with mentalizing
performance. Participants who exhibited superior performances
on the mentalizing task also displayed higher levels of mu
suppression in the 8–10 Hz frequency band at F3 during this
task. These data support the motor resonance (or motor
(Decety and Grèzes 2006; Landmann et al.
2011; Uithol et al. 2011)
. This theory states that observed
actions are internally simulated in the observer’s own MS
in order to infer the internal states of the individuals
performing the actions. In our study, right-handed actions were
viewed and therefore internal simulation of these actions
would be predicted to result in particularly increased
activation in left hemisphere motor areas
(Aziz-Zadeh et al. 2002)
The relationship found between MS activity and mentalizing
performance in our EEG data supports the notion that
internal simulation of observed hand actions by the contralateral
MS is an important process in order to successfully infer
others intentions. This compliments previous fMRI and
TMS studies that have shown higher left MS activation
when viewing social right-handed actions compared to those
without social context
(Becchio et al. 2012; Bucchioni et al.
2013; Enticott et al. 2013b)
and the poorer mentalizing
performances observed in patients with MS lesions
et al. 2016; Dal Monte et al. 2014)
Initial analyses of task-related differences in mu
suppression suggested that left-lateralised MS activity was lower
during the mentalizing task than the non-mentalizing task.
However, this task-related difference in MS activity was
eliminated when only identical (‘clumsy’) actions were
analysed across tasks. This implies that the apparent task-related
difference in MS activity in the left hemisphere was likely to
be the product of differences in action kinematics between
the videos shown across the mentalizing and
non-mentalizing tasks. Determining the success of the successful actions
shown in the non-mentalizing task required processing the
actors’ hands returning to their side of the board without the
poker chip. It is therefore likely that the successful actions
were internally simulated for slightly longer periods of time
than the spiteful actions shown in the mentalizing task,
resulting in overall greater levels of MS activation during
the non-mentalizing task.
Our EEG data show differences in mu suppression in
the 8–10 Hz frequency band rather than the 10–12 Hz
frequency band were associated with autistic traits and
mentalizing performance. No significant relationships were
found between mu suppression in the 10–12 Hz frequency
band and any other measures. These data support previous
EEG studies which have found mu suppression in the lower
alpha frequency band (8–10 Hz) but not the higher alpha
frequency band during action observation
Simon and Mukamel 2016)
. These EEG data also support
the functional segregation of mu rhythm into two discrete
sub-bands, complimenting previous work that found
distinct mu responses in low and high alpha bands
et al. 2014; Frenkel-Toledo et al. 2014; Neuper et al. 2009;
Pfurtscheller et al. 2000)
. The majority of previous studies
investigating mu rhythm in individuals with ASD have not
split mu rhythm into sub-bands
(Raphael Bernier et al. 2013;
Dumas et al. 2014; Fan et al. 2010; Oberman et al. 2005,
2008; Raymaekers et al. 2009)
. A previous EEG study that
did investigate mu suppression in two discrete sub-bands
in adults with ASD found reduced mu suppression in the
11–13 Hz frequency band when passively observing hand
actions and no atypicalities in the 8–10 Hz frequency band
(Dumas et al. 2014). Similar to this previous study, our data
show no atypicalities in mu suppression in the 8–10 Hz
range in adults with ASD when performing a
non-mentalizing task. The reduced mu suppression in the upper sub-band
during passive action observation in the previous study may
be the result of the slightly higher frequency band used. This
frequency band encroaches into the beta frequency range
(Haenschel et al. 2000; Kilavik et al.
2013; Miller 2007)
. Similar to mu suppression, oscillatory
activity in the beta frequency range is suppressed when
observing biological motion
(Babiloni et al. 2002; Milston
et al. 2013; Perry et al. 2010)
and atypical oscillatory
activity in the beta frequency range has previously been reported
in adults with ASD
(Cooper et al. 2013; Honaga et al. 2010)
In summary, mu suppression in the 8–10 Hz frequency
subband (and not 10–12 Hz) appears to reflect MS activity in the
left hemisphere that is related to mentalizing performance,
and MS activity in the right hemisphere is reduced in adults
with high levels of autistic traits when mentalizing.
The TMS data show no relationship between motor
resonance values and either mentalizing performance or autistic
traits and no differences in motor resonance values across
tasks. We had expected larger motor resonance values during
the mentalizing task
(Enticott et al. 2013b)
, and reduced
task-related differences in motor resonance values in adults
with high levels of autistic traits
(Enticott et al. 2012; Puzzo
et al. 2009; Théoret et al. 2005)
. TMS stimulation was
applied to the left hemisphere meaning that motor resonance
values reflected left MS activity. The lack of a relationship
between autistic traits and motor resonance values as well
as no task-related difference in motor resonance values
complement our left-lateralised EEG data, when differences in
action kinematics were controlled for. However, our TMS
data did not replicate the relationship found between left MS
activation and mentalizing performance in the EEG data.
A possible reason for the inconsistency between the TMS
data and the EEG data is that these methods measure
different aspects of MS functioning. Across all participants, motor
resonance values did not significantly predict the degree of
mu suppression (see Supplementary Material) supporting
(Andrews et al. 2015; Lepage et al. 2008)
Results from both MEG studies
(Cheyne et al. 2003; Jones
et al. 2009)
and a combined MRI-EEG study
(Arnstein et al.
suggest that mu rhythms correspond to activation in
S1. Although S1 is not considered to be a ‘core region’ of
the MS, S1 has been reliably shown to display mirror
(Confalonieri et al. 2012; Gazzola and Keysers 2009;
Molenberghs et al. 2012; Porro et al. 1996)
. TMS on the
other hand, is very unlikely to cause MEPs in muscles of
the hand through means other than the stimulation of M1
(Lepage et al. 2008). TMS-induced MEPs measured
during action observation are thought to measure increased
excitability in M1 as the result of excitatory cortico-cortical
connections from prefrontal MS areas
et al. 2005; Loporto et al. 2011)
. Therefore, if mu
suppression measured by EEG reflects MS activity in S1 and
TMSinduced MEPs provide an index of prefrontal MS activity
then this could explain the differences between the results
from these two measures.
An alternative reason for the inconsistency between the
EEG and TMS data could be due to the differences in the
spatial and temporal properties of these two measurements
of MS activity. The EEG measurements in this study reflect
the sum of post-synaptic neuronal activity over a large
cortical area and a relatively long time period (throughout video
or fixation cross display) whereas TMS measures brief
induced increases in corticospinal activity from peripheral
muscles, induced by stimulating a relatively small
population of neurons at a discrete time point
(Andrews et al. 2015;
Pineda 2005; Rossini et al. 1994)
. Therefore, the EEG and
TMS datamay differ due to differences in the spatial and
temporal properties of the measurements.
The total duration of fixations in the poker chip ROI
predicted non-mentalizing task performance but there were no
significant relationships between any of the eye-tracking
measures and mentalizing performance. This implies that
the visual information within the poker chip ROI was vital
for performance on the non-mentalizing task; this is to be
expected as performances relied upon identifying whether
the poker chip was successfully passed to another player
or was dropped before being passed to another player. In
contrast, during the mentalizing task, the final location of
the poker chip was always the same (all actions were
unsuccessful) but participants were required to infer the intentions
of the actors from their action kinematics. The lack of any
significant relationships between the eye-tracking data and
mentalizing performance suggests that participants did not
have a rigid method in which they did this. This is supported
by the greater number of fixations made during the
mentalizing task compared to the non-mentalizing task, suggesting
a greater degree of re-diverting attention, perhaps reflecting
an increased level of uncertainty regarding where to direct
their visual attention.
There were no significant differences in the eye-tracking
data associated with high levels of autistic traits during the
mentalizing task. This means that the lower levels of MS
activity during the mentalizing task exhibited by adults with
high levels of autistic traits were not due to reduced fixation
on the observed action kinematics in these individuals.
There are a number of limitations associated with this
study including the small sample size, particularly for the
TMS data, which may have resulted in limited power to
detect differences in MS functioning associated with ASD.
The particularly small sample size for the TMS data was
due to a number of participants (n = 6) not being able to
complete the TMS element of this study either due to not
tolerating stimulation or having particularly high motor
thresholds. This particularly small sample size may have
contributed to the lack of differences in motor resonance
values found both across tasks and between groups. The lack
of significant mentalizing deficits in adults with high levels
of autistic traits in our study may have also limited our
ability to detect a relationship between motor resonance values
and autistic traits; participants with higher levels of autistic
traits and significant mentalizing deficits were recruited then
a significant relationship may have been observed. However,
previous studies have also reported typical motor resonance
values in adults with ASD
(Enticott et al. 2013b; Kirkovski
et al. 2016)
and the lower levels of mu suppression found
in our study suggest MS atypicalities were detectable in
our participant sample regardless of typical behavioural
It is possible that mu suppression variability was higher
both between participants and within individuals with high
levels of autistic traits compared to those with low levels
of autistic traits. Therefore, the reduced levels of right mu
suppression observed in individuals with high levels of
autistic traits when mentalizing may reflect intermittent
displays of typical mu suppression rather than consistently
reduced mu suppression. The investigation of individual
and within group variability in mu suppression is beyond
the scope of this study but could be an interesting avenue
for future research. Although a significant relationship was
found between autistic traits and right-lateralised mu
suppression (8–10 Hz), significant group differences were not
observed. Examining neural differences in terms of the
continuous measure of autistic traits may have been a more
sensitive measure than examining group differences due to
within group variability in autistic traits, and therefore
neural measures, may have reduced chances of observing group
differences. The inclusion of medicated participants in this
study may have also influenced the data; six participants
in the ASD group were taking psychotropic medications
which have been shown to increase corticospinal
(Gilbert et al. 2006; Minelli et al. 2010)
. Due to the high
comorbidity of ADHD, depression and anxiety in ASD, the
inclusion of adults taking psychotropic medication is
common in TMS studies with ASD participants
(Enticott et al.
2010, 2013a; Oberman et al. 2014)
. Despite the possible
influence of psychotropic medication on corticospinal
excitability, our TMS data show no group differences in resting
motor thresholds and there was no significant difference
in resting motor threshold values between medicated and
non-medicated participants (see Supplementary Material).
Despite these limitations, our EEG data add to the existing
literature by identifying lower levels of right MS activity
in adults with high levels of autistic traits when inferring
the intentions of others from their actions and higher
levels of left MS activity associated with superior mentalizing
performances. These EEG data suggest that the MS has a
role in inferring the intentions of others from their actions,
providing support for the motor resonance theory of social
(Agnew et al. 2007; Landmann et al. 2011;
Leslie et al. 2004; Rizzolatti et al. 2002)
. Additionally, adults
with high levels of autistic traits appear to display atypical
top-down suppression from the mentalizing system to the
MS in the right hemisphere when inferring the intentions of
others. Therefore, this study provides evidence for reduced
MS activity in adults with high levels of autistic traits when
mentalizing and a potential role of the MS in inferring the
intentions of others.
Acknowledgments Eleanor Cole was funded by an Economic Social
Research Council (ESRC) 1+3 Ph.D. studentship. We thank Michael
Barham for his help with out of hours data collection as well as Sou
Bekkali, Charlotte Davies, Claire McNeel, Daniel Corp and Melissa
Kirkovski for their support during data collection and all the
participants for their contributions.
Author contributions EJC designed the experiment, collected and
analysed the data, wrote the first draft of the manuscript, edited the
manuscript, and approved the final version. NEB helped design the
experiment, edited the manuscript and approved the final version. PGE
assisted with the experimental design, constructing the experimental
set-up, guided the analysing of the data, edited the manuscript and
approved the final version of the manuscript.
Compliance with Ethical Standards
Conflict of interest The authors declare that they have no conflict of
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://creativeco
mmons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give appropriate
credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
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