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Repetitive transcranial magnetic stimulation reveals a role for the left inferior parietal lobule in
matching observed kinematics during imitation
Arran T. Reader1, 2, * Ben P. Royce2, Jade E. Marsh2, Katy-Jayne Chivers2, Nicholas P.
Holmes3
1. Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
2. Centre for Integrative Neuroscience and Neurodynamics, School of Psychology and Clinical
Language Sciences, University of Reading, UK
3. School of Psychology, University of Nottingham, UK
*Corresponding author: Arran T. Reader. E-mail: arran.reader@ki.se
This article accepted for publication in European Journal of Neuroscience: 10.1111/ejn.13886
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Abstract
Apraxia (a disorder of complex movement) suggests that the left inferior parietal lobule
plays a role in kinematic or spatial aspects of imitation, which may be particularly important for
meaningless (i.e., unfamiliar intransitive) actions. Mirror neuron theories indicate that the inferior
parietal lobule is part of a frontoparietal system that can support imitation by linking observed and
stored actions through visuomotor matching, and have less to say about different subregions of the
left inferior parietal lobule, or how different types of action (i.e., meaningful or meaningless) are
processed for imitation. We used repetitive transcranial magnetic stimulation (rTMS) to bridge this
gap and better understand the roles of the left supramarginal gyrus and left angular gyrus in
imitation. We also examined if these areas are differentially involved in meaningful and
meaningless action imitation. We applied rTMS over the left supramarginal gyrus, left angular
gyrus, or during a no-rTMS baseline condition, then asked participants to imitate a confederate’s
actions whilst the arm and hand movements of both individuals were motion-tracked. rTMS over
both the left supramarginal gyrus and the left angular gyrus reduced the velocity of participants'
finger movements relative to the actor during imitation of finger gestures, regardless of action
meaning. Our results support recent claims in apraxia and confirm a role for the left inferior parietal
lobule in kinematic processing during gesture imitation, regardless of action meaning.
Introduction
Neuroscientific research implicates the left parietal lobe in imitation (Caspers et al., 2010;
Molenberghs et al., 2009), but neither the precise area nor its exact role are fully established. Some
neuropsychologists studying the apraxias - disorders of complex movement in which different types
of imitation can be impaired - suggest that the left parietal lobe controls kinematic (Buxbaum et al.,
2014) or spatial (Goldenberg, 2009) aspects of imitation, and that the inferior parietal lobule (IPL)
in particular is critical for imitating meaningless actions. Theories of social interaction based on the
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putative human mirror neuron system, by contrast, indicate that the IPL is part of a frontoparietal
system that links observed and stored actions through visuomotor matching (Rizzolatti et al., 2014),
in terms of movements (Iacoboni, 2009; Iacoboni & Dapretto, 2006) or goals (Hamilton, 2008,
2014). In this way, the IPL could provide a scaffolding for imitation. These theories, however, often
say less about the specific role of IPL subregions (supramarginal gyrus, SMG; angular gyrus, AG),
or how the left IPL might or might not be differentially involved in meaningful and meaningless
action imitation.
These differing views result from different approaches to studying imitation.
Neuropsychological studies of apraxia have historically distinguished lesioned regions-of-interest,
and compared different types of imitative action (Goldenberg, 2009). However, neuroimaging
studies in healthy people have often used only a single or stereotyped action type (e.g., finger
tapping) which can make it hard to draw conclusions about different imitative capacities (but see
Bien et al., 2009; Carmo et al., 2012; Decety et al., 1997; Grèzes, 1998; Higuchi et al., 2012; Koski
et al., 2003; Krüger et al., 2014; Menz et al., 2009; Mühlau et al., 2005; Rumiati et al., 2005;
Tanaka et al. 2001; Tanaka & Inui et al., 2002).
Furthermore, neuroscience-driven work on imitation in healthy individuals is often defined
by the experimental scanning environment. Experimental paradigms typically use a single
participant responding to pre-recorded stimuli in the confines of an fMRI scanner, which tends to
limit the imitative capacity afforded by viewing and acting with the entire arm and hand. We
wanted to better understand imitation as a dynamic social experience, an approach that may be
essential to understand realistic imitation behaviour (Reader & Holmes, 2016). In addition, in
research both with healthy individuals and apraxia patients there is little use of motion-tracking for
characterising imitation in an objective fashion (with some exceptions, e.g., Braadbaart et al., 2012;
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Campione & Gentilucci, 2011; Gold et al., 2008; Hayes et al., 2016, Hermsdörfer et al., 1996;
Krüger et al., 2014; Pan & Hamilton, 2015; Reader & Holmes, 2015; Sacheli et al., 2012, 2013,
2015a, 2015b; Wild et al., 2010; Williams et al., 2013), despite the fact that kinematics are an
important element of social interactions (Krishnan-Barman et al., 2017). With this in mind, we used
two-person motion-tracking in this experiment to better understand the links between actor and
imitator behaviour.
To bring work on imitation in neuropsychology and healthy participants together, we used
non-invasive brain stimulation to arbitrate between the roles of the two broad left IPL subregions
SMG and AG in imitation, and, secondary to this, to test whether these regions are differentially
involved in meaningful and meaningless action imitation. In particular, we were interested in
examining both imitation accuracy and kinematics in an exploratory fashion. We applied repetitive
transcranial magnetic stimulation (rTMS) to the left SMG, left AG, or during a no-rTMS baseline,
then asked participants to imitate a confederate’s actions in a two-person, ecologically valid and
naturalistic motion-tracking paradigm.
Materials and methods
Participants
We recruited 12 participants from the University of Reading and the surrounding area
(mean±SE age = 23.2±1.1 years, 5 males, 2 left handed). Left-handed participants were not
excluded since, in the SMG at least, praxis representation is not related to handedness (Króliczak et
al., 2016). The experiment was approved by the University of Reading ethics committee (ref:
UREC 15/49); participants gave written, informed consent; the experiments were conducted in
accordance with the Declaration of Helsinki (as of 2008).
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Stimuli and apparatus
Positions of the participant’s right arm and hand and a confederate’s left arm and hand were
recorded using a Polhemus Liberty motion tracking system (Polhemus Inc., Colchester, VT, USA)
recording 16 channels (8 per person) with 6 degrees of freedom (x, y, z, azimuth, elevation, and
roll) at 240Hz. Trackers were attached to the shoulder (acromial end of the clavicle), elbow
(olecranon), wrist (pisiform), and the thumb and finger tips, using adhesive medical tape or
Velcro™. rTMS was applied using a PowerMAG 100 (Mag & More GmbH, Munich, Germany)
with a 70mm figure-of-eight coil.
The experiment was controlled and data acquired using custom software written in
MATLAB 2014b (Mathworks, Inc.) and using the ProkLiberty interface
(https://code.google.com/p/prok-liberty/). We used LabMan and the HandLabToolbox to document
and control experiments and analyse data. The associated repositories are, or will be, freely
available at https://github.com/TheHandLaboratory, whilst raw data are available on request.
Thirty gestures were used as stimuli. This included five meaningful hand, five meaningful
finger, and twenty matched meaningless gestures. For each meaningful gesture, two matched
meaningless gestures were created. In the case of finger gestures, this was done by changing the
fingers used and/or the orientation of the hand. In the case of hand gestures, this was done by
changing the orientation and/or position of the hand. We used more meaningless than meaningful
gestures to reduce the number of times participants were exposed to these actions, reducing the
likelihood that they would associate them with a particular meaning. The finger gestures signified
"okay", "thumbs up", "shoot", "peace", and "silence". The hand gestures signified "salute", "stop",
"shock", "looking into the distance", and "I'm listening" (Figure 1A). Emblematic gestures were
used since, unlike pantomimed actions which imply an object and require continuous motion,
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emblematic gestures are point-to-point which is more appropriate for motion-tracking (i.e., it is
straightforward to extract a single velocity curve for analysis).
[FIGURE 1 ABOUT HERE]
During the imitation task, participants sat opposite a confederate at a round plastic table
(diameter=85cm), approximately 100cm apart (Figure 1B). A Blu Tack® start point was located
20cm away from each person's abdomen. To inform the confederate of the actions they needed to
perform, a computer screen (unobservable by the participant imitator) was placed parallel to the
table, approximately 50cm to the left of the actor.
TMS site localisation
Visualisation of the participant’s brain used T1 weighted MR images alongside Brainsight
2.2.13 (Rogue Research Inc., Montreal, Canada). Due to SMG and AG size, stimulation locations
were based on guidance from previous experimental activation and cytoarchitectonic maps (Caspers
et al., 2006, 2008). The stimulation site for left SMG was area PF, located by finding the dorsal
extension of the posterior end of the Sylvian fissure and the anterior end of the intraparietal sulcus,
drawing an imaginary line between them, and stimulating the centre of this line. Evidence suggests
that area PF usually falls within these limits (Caspers et al. 2006). Since AG activation in
neuroimaging studies of imitation appears to be less frequent than SMG activation, the stimulation
site was the centre of the left AG, aiming to cover both PGa (anterior) and PGp (posterior). The AG
site was located half way between the dorsal extension of the posterior superior temporal sulcus and
the intraparietal sulcus. Mean±95% CI locations are shown in Figure 1C. For both AG and SMG the
coil was oriented orthogonal to the main orientation of the gyrus limits. The location of the coil in
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the no-rTMS baseline condition was placed directly between the AG and SMG positions, but held
parallel against the head, such that no or minimal stimulation of the brain should occur.
rTMS parameters
Monophasic rTMS was applied to the left SMG, left AG, and in the no-rTMS condition (coil
held away from the head) at 1Hz and 110% of distance adjusted resting motor threshold (RMT,
Stokes et al., 2007). RMT (Rossini et al., 1994) was obtained at the start of the first session.
Mean±SE RMT was 69±4.1% of maximum stimulator output (MSO). The distance from M1, SMG,
and AG, to the scalp was measured using Brainsight. The no-rTMS site distance was measured
from the cortical tissue directly underlying the no-rTMS site to the scalp. Stimulation intensity was
limited to a maximum of 85% MSO in order to prevent the coil overheating. Mean±SE stimulation
intensity in each condition was as follows: SMG=70±4.1%, AG=72±4.4%, no-rTMS=71±3.9%
MSO.
Design and procedure
Participants took part in three sessions split at least a week apart. On a single day rTMS was
applied twice (once for meaningless, once for meaningful) for 15 minutes (900 pulses at 1Hz) to
either the left SMG, left AG, or in the no-rTMS baseline condition, in counterbalanced order across
participants. After each rTMS application, participants took part in either a meaningful or
meaningless action imitation task. Meaningless and meaningful actions were segregated into their
own separate trials (each following a single rTMS application), since there is evidence to suggest
that performing novel and known actions in a sequence could recruit a single processing route,
whilst presenting them separately recruits separate routes (Tessari & Rumiati, 2004; but see Press &
Heyes, 2008; Reader et al., under review). Task order was counterbalanced across stimulation sites.
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Imitators were not given detailed instructions regarding the task constraints, and were simply told to
imitate the confederate, with the aim that this would ensure naturalistic performance in the task.
Both confederate and imitator began with their thumb and forefinger gripping their start
points. In both meaningful and meaningless imitation tasks, action images appearing on a computer
screen (in a random order, one per trial) signalled the confederate to perform the displayed action,
which they performed and maintained briefly before returning their hand to the start point. Five
seconds after presentation of the image to the confederate, a tone indicated the participant to imitate
the action they had observed, which they performed in a mirror fashion (i.e., using their right hand
to copy the confederate’s left hand). After completing the action, they returned their hand to the
start point before the next trial. Following a single application of rTMS, each meaningful action was
presented six times, or each meaningless action was presented three times, giving a total of 60 trials
per condition and TMS site. Imitation was performed in same-sex dyads, with either a male
confederate or one of two female confederates. The same confederate was used as actor for every
condition of a given participant.
Following the third rTMS session, participants were presented with a questionnaire featuring
the meaningful and meaningless images in pseudorandom order. They were asked to state whether
they thought each gesture had a meaning and, if it did, to provide an explanation. This was done
with the aim of excluding participants if they failed to meet an arbitrary 60% agreement with our
meaningful and meaningless action categorisation, but no participants failed this criterion.
Mean±SE agreement on the meaningful gestures was 75.8±7.83%, and the mean percentage of
meaningless gestures described as meaningful was 5.83±1.83%.
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Data analysis
An automated script was used for pre-processing and extraction of variables. The analysis
routines processed the position data from each trial of each participant and rejected artefacts (e.g.,
trials with missing samples or spikes resulting from electromagnetic interference) before filtering
with a bidirectional low-pass 4th order Butterworth filter (cutoff frequency 15Hz). 5.3% of trials
were removed due to incorrect start times or artefacts.
The imitator's and actor's 3D velocity over their primary movement (movement onset to
gesture completion, mean±SE duration=1021±34.3ms) were resampled to 240 samples then
correlated in order to provide a measure of imitation accuracy (Reader & Holmes, 2015). The 3D
velocity profiles for each of the imitator's trackers were correlated with each of the actor's
corresponding trackers: shoulder (SH), elbow (EL), wrist (WR), thumb (TH), index finger (IN),
middle finger (MI), ring finger (RI), and little finger (LI). 3D velocity profiles were used since they
provide a measure of the change in the 3D position of the trackers over time, and thus the formation
of the final hand posture over time. This was considered preferable to using the x, y, and z position
values since it allowed us to reduce the number of statistical comparisons, and the likelihood of
false positives.
In order to use parametric statistics on the resulting r-values they were converted into Z-values
using Fisher's transformation (Z=0.5*ln((1+r)/(1-r))), where ln is the natural logarithm. JASP
(version 0.8.0.0, JASP Team) was used to perform two-way repeated measures ANOVAs on the
means of all relevant trials for each of these variables across each crossed condition (Table 1).
Preliminary analysis indicated that hand gestures were biasing the results (i.e., the mean Z-values
for all digits were similar since the digits generally moved together). Because of this we split the
hand and finger gestures before examining accuracy, then corrected for multiple comparisons using
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Bonferroni correction, reducing our alpha used to determine a statistically significant result to .025.
We then performed the following analysis on the hand and finger data separately.
In order to assess whether there were time-dependent significant differences in the
stimulation-site related main effects or interaction, t-statistic plots were created to examine changes
in the imitator (i.e., regardless of the actor) 3D velocity for each instance (Figure 2). We took this
time-series-driven approach in order to inform us of possible differences in peak kinematic values
in separate trackers, without the inflated type 1 error that would occur were we to examine multiple
kinematic parameters in multiple trackers. In cases where the t-value was at a significant level for
any sequence of samples in the time-series, we performed permutation testing on the relevant data.
Permutation testing was performed over 10,000 iterations to create a custom empirical null
distribution of the length of samples with significant t-statistics, which was then used to decide
whether an observed sequence was significantly long. This is similar to the use of cluster based
statistics in fMRI, where a fixed, arbitrary threshold is used for creating clusters, then a second
threshold is calculated for determining how large a cluster needs to be before it is statistically
significant. On each iteration, the condition labels for each participant's data were
pseudorandomised, and the original analyses were then repeated exactly, in order to obtain t-
statistics, and sequences of significant t-statistics for the difference between 'SMG' and 'AG'
conditions, under the null hypothesis. From this we were able to assign a p-value to our actual
results by seeing what proportion of the tail of the distribution was greater (or lesser) than or equal
to the actual result. We examined the minimum length of sequences of continuous values in which
|t|>2.201 (i.e., statistically significant at a samplewise p<.05), and also the p-values associated with
the sequences of timepoints in our recorded data where |t|>2.201.
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Where significantly long sequences were found, we examined the standard kinematic
parameters that occurred during those periods to confirm whether the differences were derived from
the SMG-AG comparison, or if there was further information to be gained from the no-rTMS
baseline. Imitator peak kinematic parameters were examined using one-tailed post-hoc paired t-
tests. To check that any differences were derived from imitator rather than actor performance, we
ran the same analysis on the actor peak values using two-tailed paired t-tests. For all post-hoc paired
t-tests Hedges’ grm was chosen to report effect sizes in repeated measures comparisons (Lakens,
2013).
Results
No significant effects of stimulation site, nor interaction between stimulation site and
meaning were observed in imitation accuracy (Table 1). In hand gestures, the shoulder and elbow
positions were significantly more correlated for meaningless than for meaningful actions. This
effect was also significant in the same direction for the shoulder in finger gestures, and, only when
uncorrected for multiple comparisons, in the elbow.
[TABLE 1 ABOUT HERE]
[FIGURE 2 ABOUT HERE]
Figure 2A shows the t-statistic plots for the main effect of SMG versus AG in hand gestures.
Figure 2B shows the t-statistic plots for the interaction between stimulation site and meaning in
hand gestures. Figure 2D shows the t-statistic plots for the interaction between stimulation site and
meaning in finger gestures. No significantly long sequences were observed in these data.
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Figure 2C shows the t-statistic plots for SMG versus AG in meaningful and meaningless
actions for finger gestures. Permutation testing for the thumb revealed a significant sequence
between 59 and 108 samples (p=.035). The index finger showed a significant sequence between 65
and 119 samples (p=.022). The ring finger showed a significant sequence between 72 and 116
samples (p=.041). The little finger showed a significant sequence between 67 and 114 samples
(p=.036). The middle finger sequence between 70 and 106 samples was not significantly long
(p=.054).
[FIGURE 3 ABOUT HERE]
The mean peak velocity (PV) for the digits in finger gestures was examined post-hoc since
the above significant sequences overlapped with this kinematic parameter. Figure 3 emphasises this
difference in the original data for the thumb. One-tailed t-tests for imitator mean digit PV (Figure 4)
indicated a Bonferroni-corrected significant (p<.025) difference, where stimulation over AG
resulted in a greater mean digit PV than over SMG (t(11)=2.23, p=.024, grm=0.207), and a similar
difference where stimulation over AG resulted in a greater mean digit PV than the sham baseline
(t(11)=2.10, p=.030, grm=0.182). There was no significant difference in mean digit PV between
stimulation over SMG and the sham baseline t(11)=-0.503, p=.303, grm=0.0465).
We then used two-tailed t-tests to perform the same analysis on the PV of the actor in their
finger gestures (Figure 4), which revealed a Bonferroni-corrected significant (p<.025) difference
between mean digit PV in the AG condition and sham baseline (t(11)=2.91, p=.014, grm=0.529).
There was no significant difference in mean digit PV following stimulation over SMG and the sham
baseline (t(11)=1.89, p=.086, grm=0.331), or between SMG and AG conditions (t(11)=-1.71,
p=.115, grm=0.222). This suggested that actor behaviour was biased, so we also decided post-hoc to
examine the imitator PV relative to the actor PV to try and control for the effects of this bias.
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[FIGURE 4 ABOUT HERE]
We examined the imitator mean digit PV relative to the actor mean digit PV in finger
gestures using two-tailed t-tests and a Bonferroni-corrected alpha criterion of .025 (Figure 5). There
were no significant differences using this corrected criterion. However, mean digit relative PV was
reduced following SMG stimulation compared to the sham baseline (t(11)=-2.37, p=.037,
grm=0.335), and also following AG stimulation compared to the sham baseline (t(11)=-2.31, p=.041,
grm=0.281). There was also no significant difference in mean digit relative PV between SMG and
AG stimulation (t(11)=-0.316, p=.758, grm=0.0424).
[FIGURE 5 ABOUT HERE]
Discussion
We tested participants’ ability to imitate meaningful and meaningless hand and finger
gestures following rTMS over the left SMG, left AG, or after a no-rTMS baseline. Whilst there
were no differences in imitation accuracy between stimulation sites, we observed that participants'
digit peak velocity was lower relative to the actor in finger gestures following left SMG or left AG
stimulation, though with a larger effect size in the SMG condition. These results provide the first
causal evidence, using brain stimulation in healthy individuals, for a role of the left IPL in
processing the kinematics of finger movements during gesture imitation.
Whilst stimulation did not influence imitation accuracy, there was some evidence for
differences between accuracy in meaningful and meaningless action performance. Interestingly,
participants matched the confederate's shoulder and elbow movements to a significantly greater
degree in meaningless actions. Meaningless actions may rely more on matching action kinematics
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(e.g., Rumiati & Tessari, 2002; Tessari & Rumiati, 2004; Wild et al., 2010) than meaningful
actions. The fact that effects were only observed in the shoulder and elbow may reflect the fact that
the differences in accuracy were easier to detect in proximal effectors with lower degrees of
freedom.
Examining t-statistics over time, we found significantly long periods during which imitator
finger velocity was significantly lower following rTMS over left SMG compared to left AG during
imitation of finger gestures, but regardless of action meaning. This effect was also reflected in mean
digit PV. To account for possible differences in actor behaviour, we examined imitator mean digit
PV relative to the actor mean digit PV, and found that participants showed significantly reduced
mean digit PV relative to the actor following SMG and AG stimulation compared to baseline. This
result seems to indicate that during the imitation of finger gestures, rTMS to the left SMG or left
AG reduces digit velocity relative to the observed actor. Further study is necessary to understand
the underlying processes altered in this experiment, but these results have the potential to bring
together both findings from apraxia and discussions of the putative mirror neuron system in healthy
individuals.
As noted in the introduction, some suggest that the putative human mirror neuron system
supports our imitative capacity. The IPL is one area of this proposed frontoparietal system
(Hamilton, 2015), that has been suggested to support imitation through visuomotor matching of
seen actions and those that are already in the motor repertoire. This theory provides one explanation
for our result in the absence of any stimulation-related effects specific to action meaning. That is,
disturbed visuomotor matching following stimulation over left IPL could reduce the velocity with
which the fingers shape complex postures relative to the actor. However, this effect does not
necessarily stem from an rTMS-induced inhibition of direct matching. Rather, by considering
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claims made in the mirror neuron literature and neuroimaging studies of healthy individuals, recent
discussions in apraxia, and recognising the possibility that different areas of the IPL may subserve
different aspects of imitation, we may be able to provide a more holistic explanation for the
observed data.
Buxbaum et al. (2014) found that damage to the left IPL was associated with deficits in
kinematic (rather than postural) aspects of movement for novel and tool-related actions. Similar
results were found in a more recent voxel-based lesion-symptom mapping study by Dressing et al.
(2016). Buxbaum et al. (2014) suggested that the left IPL computes "movement plans [as] dynamic
changes in the relative spatial positions of body parts needed to reach a goal configuration" (p. 13).
If this is the case, changes in effector movement relative to the goal (the actor movement) are a
possible consequence of left IPL stimulation. Our results indicate that this is true for both
intransitive meaningful and meaningless actions. Whilst meaningless action imitation may be more
reliant on kinematic processing (Buxbaum et al., 2014), kinematic information might still be
relevant to the way in which one must replicate a meaningful action. To take an extreme example,
the imitator is not likely to ignore explicit but irrelevant kinematics, such as a particularly slow or
rapid action which does not assist in the development of the final posture (see Forbes & Hamilton,
2017, for evidence that possibly supports this).
Our results seem, then, to confirm the importance of the left IPL in meeting the kinematic
requirements of the observed action, over and above the meaning of that action. The sensitivity of
the IPL to observed movement kinematics (Becchio et al., 2012) might substantiate this claim, and
since movement necessarily contains kinematic features, our results may help to explain why left
IPL activity is frequently reported during imitation in healthy individuals, regardless of the type of
action imitated (e.g., Jack et al., 2011; Molenberghs et al., 2010; Mühlau et al., 2005; Tanaka et al.,
2002). There is also evidence, at least for tool-related actions, that damage to the left IPL is more
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reliably associated with deficits in action performance, rather than action recognition (Tarhan et al.,
2015), potentially confirming the priority that the left IPL places on processing movement during
imitation, rather than semantic encoding (which may be more reliably served by the temporal lobe
e.g., Dressing et al., 2016; Kalénine et al., 2010).
It is worth stressing here that our assertion is made in the light of our use of intransitive
emblematic gestures, whilst the term ‘meaningful gesture’ in apraxia literature commonly refers to
action pantomimes (i.e., pretending to perform a tool-use hand action without the tool in hand). A
more detailed discussion of the distinctions between object-directed and emblematic gestures is
provided by Buxbaum et al., 2005. However, there is evidence that damage to the IPL can be
associated with deficits in both the imitation of communicative and tool-use gestures (e.g., Dressing
et al., 2016), implying that our findings may be applicable beyond the specific action types used in
this experiment. We do, of course, suggest further testing this hypothesis.
It is also worth noting that the size of the IPL indicates that it is not solely involved in the
kinematic matching process we have proposed. Indeed, this was part of our motivation for using
cytoarchitectonically defined brain regions for stimulation. It is perhaps more feasible that the left
parietal lobe is involved in multiple processes for imitation, and that these processes may be
dependent on the type of action to be imitated. For example, the parietal operculum (POp), anterior
to area PF, or the anterior intraparietal sulcus (aIPS), superior to area PF, have also been found to be
involved in imitation. Specifically, some have suggested that during imitation the left POp is
involved in comparing information about the imitator’s body with the observed actor’s body
(Krüger et al., 2014; Mengotti et al., 2013), and that activity in this area is correlated with the
subsequent accuracy of the imitative action (Krüger et al., 2014). The left aIPS, however, has been
suggested to guide object-directed hand movements (Tunik et al., 2007) and, importantly, also
appears to represent the goals of observed object-directed actions (Hamilton, 2008, 2014; Hamilton
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& Grafton, 2006, 2007; Sacheli et al., 2015). As such, the aIPS could support the imitation of
object-directed actions because it ostensibly provides a “common representational system for the
actions of self and other” (p. T84, Tunik et al., 2007). The sensitivity of the aIPS for both observed
and performed action goals (Oosterhof et al., 2010) is in keeping with claims that this area may be
part of the putative human mirror neuron system (Tunik et al., 2007). Lastly, Goldenberg &
Randerath (2015) suggested that the left IPL could have a role in the apprehension of spatial
relationships. They report that damage to this area can result in shared deficits in the imitation of
meaningless hand gestures, which require placing the hand in relation to other parts of the body
whilst the finger positions remain invariant, and in tasks such as the Token Test which require
patients to classify objects based on their spatial and physical properties.
With the above in mind we can distinguish four potential elements of imitation served by the
left parietal lobe. Left area PF and/or area PG - the parts of the SMG and AG stimulated in this
experiment - could be involved in creating movement plans based on the spatiotemporal
requirements of the to-be-performed action (i.e., the kinematics), regardless of whether the action is
imitative. The POp could then ensure that these spatiotemporal requirements are met during
imitative scenarios, by comparing the imitator’s body to that of the actor (Krüger et al., 2014;
Mengotti et al., 2013). This checking process may be essential for meaningless intransitive actions,
in the absence of objects to provide context, and could provide one explanation for the defective
imitation of meaningless gestures frequently associated with left IPL damage (Goldenberg, 2009).
The aIPS, in contrast, could be useful for informing imitation in object-directed scenarios (but see
Martin et al., 2016), where visuomotor matching between the observed and to-be-performed action
can be done at the goal rather than movement level (Hamilton, 2014). There may also be a role for
the left IPL in processing spatial relationships during imitation. This may be particularly important
for the imitation of meaningless hand gestures (Goldenberg & Karnath, 2006; Goldenberg &
Randerath, 2015), since this task could be reliant on the decomposition of visual information about
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the observed gesture into “simple spatial relationships between a limited number of defined body
parts” (p. 47, Goldenberg & Randerath, 2015). The absence of specific effects for meaningless hand
gesture imitation in our experiment is discussed later.
In light of the discussion above, it is also necessary for us to comment on the similar effects
observed for both of our active stimulation sites. Unfortunately, our study was not able to reveal
more about the possible division of labour between the left SMG and AG. The existence of similar
effects in both the left SMG and AG may not necessarily reflect similar roles for each of these
regions, but perhaps the connectivity between them. For example, the left AG stimulation may have
had an effect on information processing between the AG and SMG, with the greater effect size
observed following SMG stimulation (Hedges’ grm = 0.335 versus 0.281) partially supportive of this
claim. Since the AG is anatomically connected to the posterior SMG (Seghier, 2013), rTMS over
both areas might induce a reduction in efficient SMG functioning if information regarding the
kinematic constraints of the observed action is passed in a posterior-anterior (AG to SMG) fashion.
It is also worth noting that a previous study indicated that the AG could be involved in both
meaningful and meaningless action imitation (Vanbellingen et al., 2014). However, the location of
AG in that experiment was more ventral and anterior to ours.
An alternative explanation for the similar effects observed for both stimulation sites is that
our results reflect an influence of unspecified peripheral stimulation of another area of the IPL, for
example, cytoarchitectonic area PFm, which lies between PF (in the SMG) and PGa (AG).
Interestingly, a previous study by Weiss et al. (2013) using transcranial direct current stimulation
(tDCS), found that anodal stimulation over left area PFm improved participants’ gesture matching
ability. However, the authors suggested that their result could have been driven by a combined
effect of SMG and AG stimulation, considering the central position of PFm, and the size of tDCS
19
electrodes. Weiss et al. (2013) stated that both parts of the IPL may have to be stimulated
simultaneously in order to facilitate gesture matching. Considering our results, the same could be
true for influencing gesture imitation, and further highlights the importance of considering in detail
the role of different regions of the IPL, along with their interactions.
One response to this problem is to suggest further neurostimulation research aimed at
disentangling the relative contributions of the seven parcellated regions of the IPL (PFt, PFop [the
POp], PFcm, PF, PFm, PGa, PGp), along with the aIPS. Such an endeavour would be essential in
order to clarify how different areas of the left parietal lobe may or may not interact, and their
relative contributions to different types of action and action imitation. In addition, neuroimaging
approaches that compare brain activity with actual imitative performance (i.e., Frey & Gerry, 2006;
Krüger et al., 2014), across different types of action, are also likely to be invaluable. In general we
suggest that, where possible, researchers try to report in more detail at least the distinction between
the SMG and AG, if not their parcellated subregions, rather than stating a role for the IPL in
general.
A potential limitation of this study, and another possible explanation for the consistent
effects observed in both the SMG and AG, is that these results simply reflect a TMS-general
influence, in the absence of an active control site. TMS can cause changes in behaviour distinctly
unrelated to changes in cortical activity, whereby the clicking sound of the TMS coil alone can
facilitate or inhibit reaction times dependent on the time of stimulation within a trial (Duecker et al.,
2013; Meteyard & Holmes, 2018). However, as far as we are aware this has only been found for
online TMS protocols, and we are not aware of data suggesting that the offline approach we used is
also likely to result in ‘mere-presence’ effects of rTMS. In addition, the no-rTMS session involved
much of the same conditions as in the active stimulation sessions (e.g., 15 minutes of waiting during
TMS application, clicking sound).
20
Finally, it is interesting that our primary result was found specifically for finger gestures,
rather than hand gestures. This is partly at odds with some previous findings in apraxia, though our
stimuli were not modelled on previous distinctions between hand and finger gestures – our main
aim was to ensure there was a sufficient number of different emblematic stimuli for participants to
copy. There is some evidence to suggest that defective finger gesture imitation is more associated
with left inferior frontal gyrus damage, compared to defective hand gesture imitation, which is more
associated with left IPL damage (Goldenberg & Karnath, 2006). As stated above, some discuss the
role of the left IPL in hand gesture imitation in terms of ‘body part coding’, where hand gesture
imitation is reliant on the spatial mapping of the hand in relation to other body parts (e.g.,
Goldenberg & Karnath, 2006; Goldenberg & Randerath, 2015). However, some experimenters have
failed to find a dissociation for brain regions related to defective hand and finger imitation (Achilles
et al., 2017). It is also possible that the movement to attain the posture, rather than the final hand
position alone may be an important factor. For example, whilst the Goldenberg (1996) assessment
for meaningless gestures (as used by Goldenberg & Karnath, 2006 and Goldenberg & Randerath,
2015) considers only performance on the final gesture posture, Buxbaum et al. (2014) examined
dynamic movements, hence their suggestion that dynamic change in body part position is important.
It is possible that the difference between kinematic and postural elements of movement may take
precedence over the difference between hand and finger gestures, though further research would be
needed to clarify this. If this is the case, the fact we only observed kinematic effects in the finger
velocities for finger-specific gestures may reflect the fact that the dynamic change of each of the
fingers independently is more complex than the movement of the hand as a whole.
In conclusion, we found that the left IPL is involved in matching observed digit velocity in
action imitation, regardless of action meaning. More work is needed to expand on how imitation
21
kinematics are processed in the left IPL, and the relative contributions of the SMG and AG and their
respective cytoarchitectonic subregions. These nuances can be examined using neuronavigated
TMS. Our results highlight that brain stimulation may help close the gap in understanding imitation
in apraxia and in healthy people, particularly if it is combined with large-scale motion-tracking.
Acknowledgements
Many thanks to Dr. Anastasia Christakou and Dr. Peter Scarfe for their helpful comments on
a draft of this work. This research was supported by the Economic and Social Research Council
(grant number ES/J500148/1).
Conflict of interest statement
The authors report no conflict of interest.
Author contributions
All authors have made significant contributions to this article. In particular, experiment
design, analysis, and manuscript writing by ATR and NPH, proofing by ATR, BPR, JEM, KC, and
NPH, and participant recruitment and data collection by ATR, BPR, JEM, and KC.
Data accessibility statement:
Raw data and analysis scripts are freely available from the corresponding author (ATR).
Abbreviations
AG: angular gyrus
aIPS: anterior intraparietal sulcus
IPL: inferior parietal lobule
MF: meaningful
22
ML: meaningless
POp: parietal operculum
rTMS: repetitive transcranial magnetic stimulation
SMG: supramarginal gyrus
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Table 1: Two-way ANOVA main effects and interactions for actor-imitator correlations (Z-
values) in hand and finger gestures; significant p-values (<.025) are in bold; SH: shoulder,
EL: elbow, WR: wrist, TH: thumb, IN: index finger, MI: middle finger, RI: ring finger, LI:
little finger
Gesture
Tracker
Mean(±SE) Z-value
Main effect
Site*meaning
interaction
Site
Meaning
SMG
AG
No-
rTMS
MF
ML
Site
Meaning
F
(2,22)
p
ƞp2
F
(2,22)
p
ƞp2
F (1,11)
p
ƞp2
Hand
SH
0.504
(0.0725)
0.485
(0.0687)
0.525
(0.0534)
0.388
(0.0514)
0.622
(0.0669)
0.243
.787
.022
39.2
<.001
.781
2.84
.080
.205
EL
0.906
(0.0930)
0.860
(0.0989)
0.918
(0.0708)
0.753
(0.0862)
1.04
(0.0679)
0.253
.779
.022
28.8
<.001
.723
1.48
.250
.118
WR
1.54
(0.0833)
1.57
(0.0740)
1.50
(0.0673)
1.54
(0.0658)
1.53
(0.0822)
1.07
.359
.089
0.051
.826
.005
0.256
.777
.023
TH
1.54
(0.0760)
1.53
(0.0835)
1.52
(0.0646)
1.53
(0.0730)
1.53
(0.0708)
0.076
.927
.007
0.002
.966
<.001
0.905
.419
.076
IN
1.48
(0.0861)
1.50
(0.0772)
1.49
(0.0650)
1.48
(0.0766)
1.50
(0.0686)
0.045
.956
.004
0.263
.618
.023
0.696
.509
.060
MI
1.53
(0.0845)
1.52
(0.0744)
1.52
(0.0615)
1.51
(0.0750)
1.54
(0.0638)
0.004
.997
<.001
0.322
.582
.028
0.698
.508
.060
RI
1.53
(0.0830)
1.52
(0.0738)
1.52
(0.0616)
1.51
(0.0743)
1.53
(0.0621)
0.027
.974
.002
0.173
.685
.015
0.694
.510
.059
LI
1.52
(0.0854)
1.52
(0.0749)
1.52
(0.0624)
1.52
(0.0769)
1.52
(0.0648)
0.011
.989
.001
0.014
.909
.001
0.620
.547
.053
Finger
SH
0.122
(0.0328)
0.119
(0.0398)
0.112
(0.0280)
0.0486
(0.0309)
0.187
(0.0277)
0.040
.961
.004
37.1
<.001
.771
0.099
.906
.009
EL
0.460
(0.0845)
0.492
(0.112)
0.359
(0.0683)
0.386
(0.0791)
0.488
(0.0710)
1.14
.337
.094
6.54
.027
.373
2.03
.155
.156
WR
1.30
(0.0786)
1.34
(0.0603)
1.21
(0.0633)
1.31
(0.0594)
1.25
(0.0694)
3.19
.061
.225
1.95
.190
.151
1.33
.285
.108
TH
1.27
(0.0538)
1.31
(0.0671)
1.20
(0.0553)
1.30
(0.0614)
1.23
(0.0469)
2.18
.137
.165
2.27
.160
.171
0.045
.956
.004
IN
1.18
(0.0563)
1.24
(0.0701)
1.14
(0.0610)
1.20
(0.0654)
1.17
(0.0495)
1.53
.239
.122
.579
.463
.050
0.739
.489
.063
MI
1.22
(0.0521)
1.31
(0.0516)
1.19
(0.0513)
1.28
(0.0507)
1.20
(0.0403)
2.85
.079
.206
3.43
.091
.238
.088
.916
.008
RI
1.25
(0.0604)
1.31
(0.0511)
1.19
(0.0506)
1.27
(0.0540)
1.23
(0.0414)
2.77
.085
.201
1.17
.303
.096
0.594
.561
.051
LI
1.23
(0.0576)
1.31
(0.0595)
1.18
(0.0487)
1.28
(0.0536)
1.20
(0.0421)
2.90
.076
.208
6.45
.028
.370
0.772
.474
.066
34
Figure 1: A) Example meaningful finger and hand gestures alongside their matched
meaningless counterparts. B) Experimental set-up. Dots indicate the location of motion
trackers. The tracking box was placed under the table, and the actor's actions were cued
through images displayed on a computer screen that was not observable to the imitator. C)
95% confidence ellipsoids for the TMS target sites shown on a representative participant’s
brain. Mean±95% CI MNI coordinates for the SMG: x=-57±2.81 y=-44±4.42 z=44±3.12, and
AG: x=-37±4.37 y=-77±3.10 z=40±6.64
35
Figure 2: t-statistic plots for resampled comparisons in all trackers; A) SMG versus AG in
hand gestures B) stimulation site* meaning interaction in hand gestures C) SMG versus AG
in finger gestures D) site*meaning interaction in finger gestures; In all plots the black
horizontal lines indicate positive and negative critical t-values. Shoulder in dashed magenta,
elbow in dashed cyan, wrist in dashed grey, thumb in solid light green, index finger in solid
blue, middle finger in solid orange, ring finger in solid purple, little finger in solid dark green
(in colour online).
36
Figure 3: Original mean velocity curve for the thumb comparing SMG and AG in finger
gestures. SMG in blue, AG in red (in colour online), dashed lines indicate SE.
37
Figure 4: Mean digit peak velocity for finger gestures in imitator and actor data; Error bars
indicate standard error, whilst single points show individual participant values. * = p<.05
corrected, † = p<.05 uncorrected.
38
Figure 5: Mean digit peak velocity for finger gestures in imitator relative to actor data; Error
bars indicate standard error, whilst single points show individual participant values. † = p<.05
uncorrected.
