doi:10.1093/scan/nst088
SCAN (2014) 9,1118 ^1126
Age-dependent changes in the neural substrates of
empathy in autism spectrum disorder
Martin Schulte-Rüther,1,2,3 Ellen Greimel,1,2,4 Martina Piefke,2,5 Inge Kamp-Becker,6 Helmut Remschmidt,6
Gereon R. Fink,2,7 Beate Herpertz-Dahlmann,3,8 and Kerstin Konrad1,2,3
1
Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, University Hospital
RWTH Aachen, Aachen, Germany, 2Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, Jülich,
Germany, 3JARA - Translational Brain Medicine, 4Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, University
Hospital Munich, Munich, Germany, 5Neurobiology and Genetics of Behavior, Department of Psychology and Psychotherapy, Witten/Herdecke
University, Germany, 6Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Philipps University Marburg,
Marburg, Germany, 7Department of Neurology, University Hospital Cologne, Cologne, Germany, and 8Department of Child and Adolescent
Psychiatry, Psychosomatics, and Psychotherapy, University Hospital RWTH Aachen, Germany
Keywords: social cognition; theory of mind; medial prefrontal cortex; facial emotion; developmental trajectories
INTRODUCTION
Empathy can be defined as the result of psychological inferences about
other persons’ mental and emotional states, allowing for socially
appropriate emotional responses. Empathy is a multidimensional
construct entailing emotional aspects (such as shared affect and emotional responses) as well as cognitive aspects (such as perspectivetaking, self-other distinction, reflection about other people’s mental
states and explicit self assessment of own evoked emotions)
(Davis, 1980; Decety and Jackson, 2004). Theory of mind (ToM) is
closely related to cognitive aspects of empathy and is defined as the
ability to represent other persons’ intentions, beliefs and desires as
different from one’s own (Premack and Woodruff, 1978). Although
a lot of research has been conducted on the typical and atypical
development of empathic abilities and ToM, less is known about the
neurodevelopmental trajectories and their disturbance in atypical
development such as autism spectrum disorder (ASD), in particular
during late childhood and young adulthood.
Aspects of empathy are evident very early in development, e.g. contagious distress can be observed in newborns in response to other
infants’ cries (Dondi et al., 1999). Basic ToM abilities typically have
Received 10 August 2012; Revised 24 April 2013; Accepted 4 June 2013
Advance Access publication 18 June 2013
The authors are grateful to all volunteers who took part in this study. They thank their colleagues in the Brain
Imaging Physics (INM-4) and Cognitive Neuroscience groups (INM-3) at the Research Center Jülich (Institute of
Neuroscience and Medicine) and the Child Neuropsychology section at the University Hospital RWTH Aachen for
their support and helpful advice. K.K. and B.H.-D. were supported by the Bundeministerium für Bildung und
Forschung grant 01GW0751. M.P. was supported by a grant from the Hans-Lungwitz-Foundation.
Correspondence should be addressed to Martin Schulte-Rüther, Child Neuropsychology Section, Department of
Child and Adolescent Psychiatry, University Hospital RWTH Aachen, Neuenhofer Weg 21, 52074 Aachen, Germany.
E-mail: mschulte@ukaachen.de
developed by 3–4 years of age (Baron-Cohen et al., 1985), paralleled by
the emergence of empathic responses such as other-oriented behavior
and instrumental acts of helping (Thompson, 1987; Zahn-Waxler and
Radke-Yarrow, 1990). Understanding of increasingly complex ToM
tasks (Wellman and Liu, 2004), e.g. social ‘faux-pas’ (Baron-Cohen
et al., 1999) continues to develop into late adolescence and is closely
linked to empathy(Ciaramelli et al., 2013). Results from behavioral and
questionnaire studies suggest improvement in empathic abilities after
childhood (Strayer, 1993; Dadds et al., 2008). Furthermore, mature
empathic understanding requires both the representation of other’s
and one’s own emotional states without confusion of both (Decety
and Jackson, 2004). Thus, self-regulatory aspects of emotional processing are important for the acquisition of fully developed empathic
abilities and these typically mature in late adolescence and young
adulthood (Diamond, 2002). Although several studies have elucidated
the neural substrates of empathic processing in children, adolescents
(Decety et al., 2008; Pfeifer et al., 2008; Light et al., 2009) and adults
(Carr et al., 2003; Schulte-Rüther et al., 2007), most do not take a
developmental perspective (but see Decety and Michalska, 2010;
Greimel et al., 2010a). Akin to the multidimensional concept of empathy, distinct brain regions have been implicated in distinct subcomponents of empathy (Schulte-Rüther and Greimel, 2011): Affective
components (in particular shared affect) have been linked to the
human mirror neuron system, in concert with limbic structures and
the insula (Carr et al., 2003; Schulte-Rüther et al., 2007; Bastiaansen
et al., 2009). In contrast, cognitive components seem to draw upon
brain regions also known to mediate ToM processing, i.e. medial prefrontal cortex (MPFC), superior temporal sulcus (STS), temporal poles
(TP) and the temporoparietal junction (TPJ) (Vogeley et al., 2001;
Frith and Frith, 2003). The MPFC, precuneus and inferior parietal
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In typical development, empathic abilities continue to refine during adolescence and early adulthood. Children and adolescents with autism spectrum
disorders (ASD) show deficits in empathy, whereas adults with ASD may have developed compensatory strategies. We aimed at comparing developmental trajectories in the neural mechanisms underlying empathy in individuals with ASD and typically developing control (TDC) subjects. Using an
explicit empathizing paradigm and functional magnetic resonance imaging, 27 participants with ASD and 27 TDC aged 12–31 years were investigated.
Participants were asked to empathize with emotional faces and to either infer the faces emotional state (other-task) or to judge their own emotional
response (self-task). Differential age-dependent changes were evident during the self-task in the right dorsolateral prefrontal cortex, right medial
prefrontal cortex, right inferior parietal cortex, right anterior insula and occipital cortex. Age-dependent decreases in neural activation in TDC were
paralleled by either increasing or unchanged age-dependent activation in ASD. These data suggest ASD-associated deviations in the developmental
trajectories of self-related processing during empathizing. In TDC, age-dependent modulations of brain areas may reflect the fine-tuning of cortical
networks by reduction of task-unspecific brain activity. Increased age-related activation in individuals with ASD may indicate the development of
compensatory mechanisms.
Neurodevelopmental trajectories of empathy in autism
1119
in both groups. We expected such differences mainly in brain regions
associated with ToM, self-related processing, the mirror neuron system
and the limbic system.
METHODS
Participants
Fifty-four male participants (aged 12–31 years) were included in the
final functional magnetic resonance imaging (fMRI) data analysis.
Twenty-seven participants were diagnosed with ASD (mean
age SD ¼ 18.52 5.10 years; n ¼ 15, age < 12–17 years; n ¼ 12,
age 18 years) and 27 were TDC participants (mean
age SD ¼ 18.22 4.41 years; n ¼ 15, age ¼ 12–17 years; n ¼ 12,
age 18 years) without a history of neurological or psychiatric disease.
Data presented here are a combined subset from the participants of
two previous studies on empathy in children and adolescents (Greimel
et al., 2010b) and adults with ASD (Schulte-Rüther et al., 2011).
Inclusion into this study depended upon a close match with respect
to age for the comparison of patients and control subjects. To exclude
psychiatric disorders in control subjects, a standardized semi-structured interview (K-SADS-PL) was conducted with children and adolescents, and the Brief Symptom Inventory (Franke, 2000) was
completed by adults. For all children and adolescent subjects, parents’
evaluations of psychopathology were obtained by the Child Behaviour
Checklist (Döpfner et al., 1994). Both groups were comparable with
respect to mean age (t test for independent samples, T52 ¼ 0.233,
P > 0.817), age distribution (Kolmogorov–Smirnov test, Z ¼ 0.544,
P > 0.930), mean G-IQ (ASD: 107.04 14.93 SD; TDC: 111.04 9.44
SD; T52 ¼ 1.177, P > 0.245) and G-IQ distribution (K-S Z ¼ 0.816,
P > 0.441). Only participants with a general IQ of at least 80 were
included (WAIS III and WISC III).
ASD subjects were diagnosed by experienced clinicians (according to
the criteria of ICD-10 and DSM-IV). For all participants, diagnosis was
confirmed with the Autism Diagnostic Observation Schedule, conducted by trained examiners (E.G. and I.K.-B). Furthermore, the
Autism Diagnostic Interview-Revised (ADI-R) was performed in children and adolescents (n ¼ 15) and in a subset of adult patients (n ¼ 6),
if a qualified informant was available. Age did not significantly correlate with total ADOS score (Pearson’s R ¼ 0.497, P > 0.497).
Demographic and clinical data are summarized in Table 1. At the
time of examination, some subjects of the ASD group were medicated
[atypical neuroleptics (nadol. ¼ 1; nadults ¼ 1), typical neuroleptics
(nadol. ¼ 1; nadults ¼ 1), atomoxetine: (nadol. ¼ 2), antidepressant
(SSNRI, NaSSA) (nadults ¼ 1)]. Medication with stimulants (n ¼ 3)
was discontinued 48 h before testing. The study was approved by the
local ethics committee (according to the Declaration of Helsinki), and
all subjects or their parents/caregivers gave written informed consent
(adults/caregivers) and assent (children and adolescents) prior to
participation.
Table 1 Demographic data
Age, mean (SD) (years)
Age range (Min–Max)
V-IQ, mean (SD)
V-IQ range (Min–Max)
P-IQ, mean (SD)
P-IQ range (Min–Max)
G-IQ, mean (SD)
G-IQ range (Min–Max)
TDC group (n ¼ 27)
ASD group (n ¼ 27)
18.22 (4.41)
12–26
112 (11.5)
89–137
108 (9.3)
91–129
111 (9.44)
95–133
18.52 (5.10)
13–31
113 (17.3)
84–144
99 (15.7)
73–128
107 (14.9)
80–134
G-IQ, General IQ; V-IQ, Verbal IQ; P-IQ, Performance IQ.
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cortex (IPC) have also been shown to play an important role for selfreferential processing and self-other distinction (Decety and
Sommerville, 2003; Vogeley and Fink, 2003). Developmental change
from childhood to adulthood suggests continuous refinements within
this network (Blakemore, 2008), e.g. due to accumulated expertise or a
shift of cognitive strategies during empathizing (Greimel et al., 2010a).
ASD are characterized by disturbances in the development of appropriate skills for social interaction and social communication. It has
been suggested that many aspects of these problems can be explained
by developmental delays in ToM abilities (Baron-Cohen et al., 1985,
2000), which are evident even in subjects with otherwise high cognitive
profiles (Happé, 1994). Furthermore, atypical self-processing has been
reported for ASD both on the neural (Lombardo et al., 2010) and
behavioral level, and might be intrinsically linked to impaired
empathic abilities (Lombardo et al., 2007). Atypical empathic behavior
in early childhood is a key symptom of ASD in children (Scambler
et al., 2007) and predicts later diagnosis (Hutman et al., 2010).
However, empathic abilities (Schwenck et al., 2011), emotional responsiveness and social behavior (Shattuck et al., 2007; Farley et al., 2009)
improve during adolescence and early adulthood in patients with ASD.
Furthermore, intact emotional empathic responses have been reported
in adult individuals with ASD (Dziobek et al., 2008; Bird et al., 2010)
despite persistent deficits in ToM (White et al., 2011) or cognitive
aspects of empathy (Dziobek et al., 2008). To date, little data are available regarding the development of empathic processing in ASD relative
to typically developing individuals. In particular, it remains unclear
whether in subjects with ASD improvements in ToM and empathy
reflect a maturation of neural circuitries as can be observed in typical
development (Greimel et al., 2010a) or rather reflect compensatory
processes due to, e.g. therapeutic interventions.
In ASD, most of the brain structures involved in empathic processing have been reported to show aberrant brain activation during
empathy-related tasks. These include frontal components of the
human mirror neuron system (Iacoboni and Dapretto, 2006;
Greimel, 2010b), MPFC, STS, TP (Happé et al., 1996; Castelli et al.,
2002; Schulte-Rüther et al., 2011), TPJ/IPC (Schulte-Rüther et al.,
2011) and anterior insula (Silani et al., 2008; Bird et al., 2010).
Virtually nothing is known about differences in developmental trajectories of these networks in patients with ASD. A better understanding
of disturbances or potential compensatory neural mechanisms is mandatory to (i) understand individual differences in the development of
empathic abilities in ASD patients and (ii) to develop age-specific
targeted interventions. The present study provides a first step toward
the investigation of developmental trajectories in ASD by using a crosssectional sample of children, adolescents and adults (aged 12–31
years), reflecting a particular interesting period of fundamental
changes in networks related to social processing (Blakemore, 2008)
and potential improvements in patients with ASD (Shattuck et al.,
2007). We employed a well-established empathizing task
(Schulte-Rüther and Greimel, 2011). This task has been shown to
engage the diverse components of the brain network associated with
empathizing (as reviewed earlier), as well as correlations of brain
activation with individual empathic abilities and emotional reactivity
(Schulte-Rüther et al., 2007, 2008, 2011; Greimel et al., 2010b). The
task requires interactive assessment of the self- and other-perspective
to allow for the construction of an interpersonal context. It taps on the
understanding and perception of an emotional state (‘other-task’), as
well as explicit emotional self-reference [such as the assessment of
one’s own emotional reaction (‘self-task’)], two closely related aspects
of empathic processing. We contrasted developmental trajectories of
neural activation related to self- and other-tasks in participants with
ASD and typically developing controls (TDC) to identify brain regions
where covariation of neural activity with age was significantly different
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MR technical parameters
MR imaging was accomplished on a 1.5-Tesla Avanto MR scanner
(Siemens, Erlangen, Germany) using a standard head coil. For functional imaging, gradient-echo, echoplanar T2*-weighted images were
acquired (TE ¼ 60 ms, TR ¼ 3000 ms, ¼ 908, FOV ¼ 200 mm, voxel
size ¼ 3.1 3.1 4 mm3, matrix size ¼ 64 64, 30 transversal slices,
slice acquisition: ascending) in one session (14 min). Anatomical
images were acquired using a T1-weighted 3D magnetization-prepared, rapid acquisition gradient echo (MP-RAGE) pulse sequence
(TE ¼ 3.93 ms, TR ¼ 2200 ms, ¼ 158, FOV ¼ 256 mm, voxel size ¼
1 1 1 mm3, matrix size ¼ 256 256, 160 sagittal slices, slice
thickness ¼ 1 mm).
A
B
Image processing and data analysis
Fifty-four subjects (27 TDC, 27 ASD) were included in the final sample
for the analysis of the fMRI data. Functional volumes were analyzed with
SPM5 (Wellcome Department of Imaging Neuroscience, London, UK;
http://www.fil.ion.ucl.ac.uk/spm) implemented in MATLAB 7 (The
Mathworks Inc., Natick, MA, USA). Two hundred eighty-five images
were realigned using rigid body transformation, normalized into the
Montreal Neurological Institute (MNI) coordinate space and resampled
at 2 2 2 mm3. Normalization parameters were determined. Prior to
statistical analysis, functional volumes were smoothed with an
8 8 8 mm3 Gaussian kernel (full width half maximum).
Boxcar functions of 19.2 s duration (corresponding to the onset of
each experimental block, starting with the first presentation of a face)
were convolved with a model of the hemodynamic response function
(HRF) and its first-order temporal derivative. Movement parameters
were included as additional regressors of no interest. A high-pass cutoff filter of 128 s was used. Parameter estimates of the resulting general
linear model were calculated for each voxel and each regressor. Using
the first regressor of the HRF model as an estimate of response height,
the effect of both self- and other-task (relative to the control-task,
respectively) was calculated and individual contrast images were created for each subject. Experimental conditions containing high and low
intensity stimuli were modeled separately, however, high and low intensity conditions were thereafter combined because the initial assessment of the data indicated that statistical sensitivity was insufficient for
separate analyses of low and high intensity conditions at a corrected
threshold.
The following analysis focused on linear and non-linear developmental trajectories of neural activation patterns related to the empathizing
tasks. A second-level analysis of covariance (ANCOVA) model was set
up, i.e. individual images of the contrasts for the other task (othertask > control-task) and the self-task (self-task > control-task) were
each entered into a ‘two-sample t test’-model with age as a covariate
(separate for both groups, centered for overall mean). Furthermore,
similar models were set up to test for the effect of age on the direct
comparison of self- and other-task (i.e. self-task > other-task and othertask > self-task). Such models allow to test for a group difference in the
linear effect of age on brain activation, irrespective of general group
C
Fig. 1 Time course of stimulus presentation during the scanning session. Subjects were instructed to empathize with the person presented on the screen and to (A) identify the emotional state (happy, neutral
and sad) observed in the face (other-task) or (B) evaluate their own emotional response (happy, neutral and sad) to that face (self-task). As a control-task (C) a perceptual decision on the width of neutral faces
(slim, normal wide) was used. Each block (19.2 s) was preceded by an instruction cue (3 s) and comprised six stimulus faces (each 2.5 s), separated by a fixation cross (jittered duration: 0.45–0.95 s). Instruction
cues were pictures of a finger pointing towards the subject (self-task), pointing away from the subject (other-task) or three dots of increasing width (control-task). Each of n ¼ 72 individual faces was presented
once displaying a happy expression, once displaying a sad expression and once displaying a neutral expression. Faces had either a low or high intensity emotional expression. Tasks varied block-wise with 6 trials
per block, resulting in 32 blocks and 192 trials overall. Prior to scanning, subjects were trained on the experimental tasks. Reprinted with permission from Schulte-Rüther, M., et al. (2011). Dysfunction in brain
networks supporting empathy: an fMRI study in adults with autism spectrum disorders. Social Neuroscience, 6(1), 1-21, Copyright 2010 Taylor & Francis Ltd. (http://www.tandfonline.com/doi/full/10.
1080/17470911003708032)
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Experimental paradigm and stimuli
We used the same experimental paradigm and stimuli described in
previous studies (Greimel et al., 2010b; Schulte-Rüther et al., 2011).
In short, subjects were asked to empathize with emotional facial
expressions presented on a computer screen by ‘feeling into’ the depicted person and either to judge the emotional state of each face
(other-task), or to report the emotions elicited in themselves by the
emotional faces (self-task). As a control task, a perceptual decision
on the width of stimulus faces was used (see Figure 1 and
Supplementary Material for more details). The software Presentation
9 (Neurobehavioral Systems, Albany, CA, USA; http://www.neurobs.
com) was used for stimulus presentation and response collection.
Additionally, eye movement data were collected during the fMRI
scan (see Supplementary Material for more details) to control for
equal attention to facial stimuli across age and diagnostic group.
After the fMRI experiment, subjects were questioned about their strategies used to perform the tasks and other performance-related aspects.
Five ASD subjects and five TD subjects were unable to describe the
difference between the self and the other task and indicated that they
had always responded ‘according to how the other person felt’ without
any reference to their own feelings. These were excluded from further
analysis. Thus, from an original sample of n ¼ 64 participants, 54 remained for fMRI data analysis. All reported results pertain to the
sample of n ¼ 54 participants.
Neurodevelopmental trajectories of empathy in autism
Analysis of behavioral data
Behavioral data were analyzed with the software package SPSS 19
(SPSS Inc., Chicago, IL). Percentage of correct (i.e. correct attribution
of the emotional state of a stimulus face in the other-task) or congruent responses (i.e. responses during the self-task mirroring the emotional state of a stimulus face) and mean reaction times (RTs) were
calculated. As Kolmogorov–Smirnov tests indicated normal distribution of all variables of interest, parametric analyses were employed to
test for statistically significant differences between groups and/or
experimental conditions and demographical variables and to test for
linear influences of age (mixed ANOVAs, t tests and ANCOVAs). For
all behavioral analyses, significance was determined using two-tailed
testing. To test for differential age effects on cognitive ability (IQ) and
behavioral measures depending on group, data were entered into GLM
analyses, modeling group as between subject factor, age as a covariate,
and the interaction of group and age. If no interaction could be
observed, data were entered into a standard ANCOVA analysis using
group as the between subject factor and age as a covariate to test for
group differences irrespective of age. To be consistent with the analysis
of neuroimaging data, behavioral data were analyzed separately for
self- and other-task but were collapsed across intensity levels.
1121
RESULTS
Behavioral data
With respect to cognitive ability, the group age interactions were not
significant [G-IQ: F(1, 50) ¼ 0.337, P ¼ 0.564; P-IQ: F(1, 50) ¼ 0.006,
P ¼ 0.937; V-IQ: F(1, 50) ¼ 0.257, P ¼ 0.615]. There were no group
differences in V-IQ [F(1, 51) ¼ 0.115, P ¼ 0.736] and G-IQ [F(1,
51) ¼ 1.322, P ¼ 0.256]; however, a significant group difference
emerged in P-IQ: F(1, 51) ¼ 6.753, P < 0.05. A significant influence
of the covariate age on IQ measures could not be observed [V-IQ:
F(1, 51) ¼ 1.082, P ¼ 0.303; H-IQ: F(1, 51) ¼ 0.051, P ¼ 0.822; G-IQ:
F(1, 51) ¼ 0.335, P ¼ 0.565)].
Percentage of congruent responses (i.e. same emotion indicated as
evident in the stimulus face) for the self-task revealed no age group
interaction [F(1, 50) ¼ 1.062, P ¼ 0.922], no influence of age [F(1,
51) ¼ 1.374, P ¼ 0.247], but a significant effect of group [F(1,
50) ¼ 12.467, P < 0.001]. Control participants responded congruently
in 68.08% (16.73 SD), ASD participants in 45.68% (28.93 SD) of
the self-trials. Note, incongruent responses in both groups were almost
exclusively neutral responses (on average less than 2.4% selection of
the opposite emotional state). Percentage of correct responses for the
other-task revealed no age group interaction [F(1, 50) ¼ 0.000,
P ¼ 0.989], no influence of age [F(1, 50) ¼ 2.095, P ¼ 0.154], and no
group effect [F(1, 50) ¼ 0.502, P ¼ 0.482]. Mean correct responses were
73.40% ( 10.69 SD) for controls and 75.37% (8.51 SD) for participants with ASD in the other-trials. A scatterplot illustrating the relationship between performance and age is given in Figure 2. The direct
comparison between correct (other-task) and congruent responses
(self-task) revealed significant differences in control participants
[T(26) ¼ 2.452, P < 0.05], as well as in participants with ASD
[T(26) ¼ 4.791, P < 0.001], suggesting that both groups were able to
distinguish properly between both tasks.
For RTs during the self-task, no group age interaction could be
observed [F(1, 50) ¼ 1.886, P ¼ 0.176], a trend for a significant effect
of age [F(1, 51) ¼ 3.434, P ¼ 0.070], and no group effect [F(1,
51) ¼ 0.015,
P ¼ 902;
meancontrols ¼ 1.193 s
(0.223
SD);
meanASD ¼ 1.189 s (0.297 SD)]. During the other-task, no group age interaction effects [F(1, 50) ¼ 0.964, P ¼ 0.331], no effect of age
[F(1, 51) ¼ 1.678, P ¼ 0.201], but a trend for a group difference [F(1,
51) ¼ 3.087, P ¼ 0.085] was evident [meancontrols: 1.123 s (0.181 SD);
meanASD ¼ 1.211 s (0.181 SD]. The analysis of eyetracking data did
not reveal any age effects or age group interactions (see
Supplementary Material for details).
Neuroimaging data
For the analyses aimed at detecting group differences for quadratic
influences of age (such as U-shaped or inverted U-shaped functions)
on brain activation, no significant results could be observed at the
selected thresholds, neither for the self-task and other task (as compared with the control task), nor for the direct comparison of self-task
versus other-task and vice versa. Concerning linear effects of age, significant group differences emerged for the comparison of the self-task
vs. control-task. In the whole-brain analysis, significant differences in
covariation of brain activation with age emerged in the occipital cortex
(including area 17/18 and extending into the cerebellum), the anterior
insula, right middle frontal gyrus and right inferior parietal lobule
(extending into superior parietal lobe). Using a ROI approach, a significant cluster also emerged in the dorsal MPFC (Table 2). No significant group differences were detected for the contrast other-control,
other-self and self-other, neither in the whole brain analysis nor in the
ROI analyses. At all coordinates of significant peak group difference,
brain activation decreased significantly with age in TDC subjects
(all R < 0.50; P < 0.01). In contrast, in ASD subjects, we observed a
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differences in activation. T-contrasts involving both covariate regressors
were used to detect differences in the slope of the regression with age.
For all models, both possible directions of slope differences were tested
(ASD > TDC, TDC > ASD). Additionally, further models were set up
that included the squared age regressor (separate for groups, centered
for overall mean). In these models, F-contrasts were used to detect any
group difference in quadratic age related effects (such as U-shape or
inverted U-shape curves). Departures from sphericity assumptions were
accommodated using the non-sphericity correction in SPM5. For these
analyses, SPMs were thresholded at P < 0.005 (voxel level, uncorrected).
We only report group differences that exceed a statistical threshold of
P < 0.05, cluster level corrected for multiple comparisons (whole-brain).
Additionally, we report significant peaks within predefined anatomical
regions of interest that exceed a threshold of P < 0.05 (family-wise error
(FWE)-corrected for ROI, voxel level). Anatomical ROIs were constructed using the software WFU pickatlas (Tzourio-Mazoyer et al.,
2002) and included MPFC, precuneus, inferior frontal gyrus, IPC,
fusiform gyrus (see Supplementary Material for more details).
At identified significant clusters, peak beta values were extracted for
each individual to determine the direction and significance of
developmental trajectories of the respective contrasts for each diagnostic
group separately (linear regression models), and model comparisons
were performed (see Supplementary Material). To investigate the
possibility of developmental delay in patients with ASD (e.g. similar
patterns in ASD adults and TDC children/adolescents), further
exploratory analyses were performed to compare developmental trajectories of TDC children/adolescents with ASD adults (see Supplementary
Material).
To assess the relationship of the identified clusters of differential
developmental trajectories to individual empathic abilities and autistic
symptoms, we correlated brain activation for the self task with individual self-rated empathic abilities (Empathy Quotient, EQ for adults
[Lawrence et al., 2004]; Bryant Index of Empathy for children/adolescents [Bryant, 1982] and ADOS score [in the ASD group]). Using
SPM, regression analyses were performed separately for groups (ASD
children/adolescents, ASD adults, TDC children/adolescents and TDC
adults). We restricted the analyses to ROIs of the previously identified
brain areas demonstrating differential developmental trajectories
(5 mm spheres around peak coordinates).
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Fig. 2 Covariation of behavioral performance and age. Scatterplot depicts behavioral performance
during the self-task as a function of age, separately for each group (blue ¼ TDC; red ¼ individuals
with ASD).
Anatomical region
H
BA
k
x
y
z
t
Occipital cortex
Anterior insula
Middle frontal gyrus
Inferior parietal lobule
MPFC*
R
R
R
R
R
18/17
48/47
6/44
39/40
8/32
643
357
669
590
10
34
38
46
8
94
24
10
54
34
12
6
36
54
44
4.65
4.26
4.07
4.04
4.13
H, hemisphere; L, left; R, right; BA, Brodmann area; k, cluster size; peak activated voxels within
significant clusters of brain activation (P < 0.05 corrected for multiple comparisons (whole brain
analysis) at the cluster level), and peak within the MPFC (*small volume correction for multiple
comparisons (FWE, P < 0.05, voxel level), in an anatomical ROI of the MPFC) x, y, z refer to MNI
coordinates of local peaks of activation for the interaction of age group.
significant increase in brain activation for DMPFC (R ¼ 0.51; P < 0.01),
rIPL (R ¼ 0.492; P < 0.01) and area 17 (R ¼ 0.51; P < 0.01), and a trend
for increased activation in right anterior insula (R ¼ 0.345; P ¼ 0.078).
No significant effect of age was observed in ASD subjects for the
middle frontal gyrus (R ¼ 0.24; P ¼ 0.231). No group differences
were detected that showed a reverse interaction pattern (i.e. decrease
in ASD and/or increase in TDC) (Figure 3). Model comparisons
revealed that linear regression with age was an appropriate model fit
for all brain regions and was not significantly improved by adding
the age-squared regressor (see Supplementary Results). Exploratory
direct comparisons of differential trajectories between the
subgroups ASD adults and TDC children/adolescents (with the hypothesis of the possibility of a developmental delay in ASD patients)
revealed a similar pattern of differential age effects (see Supplementary
Results).
Brain behavior correlations
Significant positive correlations (self-rated empathic abilities brain
activation) emerged in the right inferior parietal lobe for ASD adults.
Negative correlations could be observed in occipital cortex and right
inferior parietal lobe for ASD children/adolescents and in right anterior insula for TDC children/adolescents. A significant negative correlation with autistic symptoms was found in the right inferior parietal
lobe in adults with ASD.
DISCUSSION
To the best of our knowledge, this is the first study on neurofunctional
developmental trajectories related to empathizing in individuals with
Cognitive control, mentalizing and empathy
Our results suggest atypical functional development of cognitive control and mentalizing processes in ASD during self-related empathizing,
as reflected by differential trajectories of brain activation in lateral PFC
and DMPFC.
Developmental changes in brain activation typically reflect the maturation of brain networks (Casey et al., 2005): Focal, specialized taskrelated areas show an increase in brain activation whereas activation
within task-irrelevant brain areas declines with age (Durston et al.,
2006). The lateral PFC has been implied in a wide range of tasks related
to executive function and cognitive control (Bunge et al., 2002; Blasi
et al., 2006). The self-task poses considerable demands on executive
control, i.e. the monitoring of internal evoked emotions needs to be
coordinated with a continuous update of the observed stimuli and
their respective emotional state. In TDC, this process may require
fewer (neural) resources in adults, resulting in decreased activation.
Accordingly, a decline of dorsolateral prefrontal brain activation
with age has been shown to reflect decreased effort when solving a
task (Luna and Sweeney, 2001; Tamm et al., 2002). Our results
suggest that in subjects with ASD these refinements are impaired
and that the executive demands for explicit empathizing persist into
adulthood.
Similarly, our findings are in accordance with previous studies
demonstrating a decrease of frontal brain activation for social-cognitive tasks from adolescence to early adulthood in the MPFC (Wang
et al., 2006; Blakemore et al., 2007; Pfeifer et al., 2007; Kobayashi et al.,
2008; Gunther Moor et al., 2011; see Blakemore 2008 for a review).
These effects might sometimes be too subtle to detect (Greimel et al.,
2010a), but become evident in the direct comparison to a group with
aberrant development such as ASD. Brain activation in the MPFC has
consistently been reported for diverse ToM tasks (Castelli et al., 2000;
Vogeley et al., 2001; see Frith and Amodio, 2006; Frith and Frith, 2008
for reviews), and is particularly related to abstract mentalizing
(Gallagher and Frith, 2003). The peak of differential trajectories
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Table 2 Significant differences in covariation of brain activation with age
ASD in comparison with TDC. At the behavioral level, we observed a
group difference (ASD < TDC) for congruent responses (i.e. observed
emotional state matches the emotional state indicated by the participant) during the self-task across all age groups. Note, differences were
due to more ‘neutral’ responses of participants with ASD rather than
inappropriate emotion choices, suggesting primarily a lack of emotional contagion. Accordingly, no group difference could be observed
for correct answers (i.e., correct identification of observed emotional
state during the other-task) see also (Greimel, et al., 2010b; SchulteRüther et al., 2011). These results suggest that participants with ASD
were in principle able to identify the other person’s emotional states,
but showed a deficit in the self-assessment of an appropriate emotional
reaction. The finding is consistent with previous reports of reduced
emotional contagion (Scambler et al., 2007) and reduced self-reports
of empathy (Baron-Cohen and Wheelwright, 2004) in individuals with
ASD. There were no significant effects of age or group by age interactions. This pattern suggests that the empathizing task was suitable for
all age levels and that age-related group differences at the neural level
are due to differences in the neurofunctional development of these
brain areas, rather than to performance differences in the empathizing
task.
At the neural level, we found age by group interactions for the selftask, but no such effects for the other-task or the direct differential
comparison of both tasks, suggesting that differences in developmental
trajectories are most prominent for self-related components of
empathizing (i.e. the explicit assessment of one’s own emotional reaction in an empathic face-to-face situation).
Neurodevelopmental trajectories of empathy in autism
SCAN (2014)
1123
within DMPFC in the present study is located between anterior rostral
MPFC and a bordering, more posterior region within DMPFC which
has been implicated in goal directed behavior, including error monitoring and cognitive control (Ridderinkhof et al., 2004). The inverted
pattern of developmental trajectories within this transition zone suggests that cognitive components of empathic self-reference (i.e. mentalizing and cognitive control) are increasingly engaged in ASD,
whereas these components of empathizing may become more and
more automated and less error-prone in TDC. In accordance with
this interpretation, a shift from DMPFC to vMPFC activation for the
self-task could be observed only in TDC adults, but not in adults with
ASD (Schulte-Rüther et al., 2011).
Self- other distinction and explicit monitoring of
emotional states
The IPC has been implicated in self referential processing (Lou et al.,
2004; Uddin et al., 2005) and in particular for the distinction between
self- and other-perspective (Decety and Sommerville, 2003; SchulteRüther et al., 2007). During typical development, assessing the own
emotional response may draw stronger on these networks in children
and adolescents than in adults (Greimel et al., 2010a), in line with
behavioral studies suggesting that with increasing age empathic
responses are more and more focused on other people’s inner states
(Strayer, 1993) rather than one’s own. The increase of activation in this
brain region in participants with ASD may thus reflect that adults with
ASD develop a different strategy for assessing their own emotion in
response to a facial display. Enhanced distinction between self- and
other perspective may contribute to a diminished capability of showing
contagious emotional responses (Schulte-Rüther et al., 2011), in favor
of a more cognitively biased understanding of the observed emotion.
Interestingly, activation in the IPC was negatively correlated with ASD
symptoms in adults, but positively with self-rated empathic abilities,
suggesting that this strategy might be applied to a greater extent by
higher functioning individuals and may result in a greater amount of
self-ascribed emotional responsiveness. In adolescents with ASD, we
observed a negative correlation with self-rated empathy, supporting
the idea of age-dependent compensatory mechanisms in ASD that
develop at the transition to adulthood.
The insular cortex is involved in viscero-sensory processing of internal body states, including states of emotional arousal. It has been
suggested that the anterior insula is critically involved in interoception,
emotional awareness, and self-recognition (see Craig, 2009 for a
review), plays an important role in shared affect (Carr et al., 2003),
especially concerning disgust (Wicker et al., 2003) and pain (Singer
et al., 2004) and is related to dispositional differences in empathy
(Greimel et al., 2010b). Our results suggests that during typical development, the assessment of own emotional reactions during empathizing draws less on explicit processes (such as internal awareness of
feelings and bodily sensations) with increasing age, as reflected by a
decrease in brain activation. Typically developing children and adolescents encounter numerous socio-emotional situations involving faceto-face interactions during their lives, thus the monitoring of selfrelated emotional states may become increasingly automatic and less
relevant for explicit empathizing. In support of that notion, we
observed a negative correlation in anterior insula with self-rated empathy in TDC adolescents. In contrast, in patients with ASD, a more
explicit processing of self-related emotional states may be maintained
into adulthood.
Downloaded from http://scan.oxfordjournals.org/ by guest on April 12, 2016
Fig. 3 Covariation of brain activation and age. Statistical parametric maps of significant differences in covariation of brain activation with age (with respect to group) during the self task (see Methods section
for details). SPMs are thresholded at P < 0.005 (voxel level, uncorrected) and overlayed on a mean anatomical T1 image of all participants. Depicted clusters (red arrows) were significant at the whole brain level
(P < 0.05, cluster-level corrected for multiple comparisons), except for the peak in the MPFC (P < 0.05, voxel level corrected for multiple comparisons (ROI), white arrow). Correlation plots depict individual
contrast estimates for the self task as a function of age in the activation peak. Correlation coefficients (R) and linear best fit estimates are given separately for each group (blue ¼ TDC; red ¼ individuals
with ASD).
1124
M. Schulte-Ru«ther et al.
SCAN (2014)
Clinical implications
The finding of an overall pattern of age-related increases in brain
activation in participants with ASD in several brain regions strongly
suggest that these effects reflect compensatory processes. Increases in
brain activation in ASD may result from a greater effort or enhanced
supervisory strategies during situations that demand empathizing, in
particular when associated with emotional self-reference. Most young
adults with ASD have undergone behavioral therapeutic interventions
(e.g. social skills training). Such interventions often target at paying
explicit attention to social stimuli and situations, and teach explicit
strategies to adapt one’s own thoughts and behavior (see e.g. Bock,
2001). Such strategies may contribute to differential developmental
effects in neural networks of empathy, in particular for explicit assessment of one’s own emotional state. Our results indicate that in patients
with ASD, the functional brain networks supporting ToM and empathy continue to develop into adulthood. Note, our findings are
more in favor of qualitatively different neural compensatory mechanisms than a simple quantitative delay in development, also reflected by
the finding that developmental trajectories also differ in the comparison of ASD adults and TDC children/adolescent. Consistent with the
idea of additional compensatory processes, several studies showed that
social behavior and emotional responsiveness improve during adolescence and adulthood (Shattuck et al., 2007; Farley et al., 2009), but
though individuals with ASD can develop higher-order ToM abilities
during adolescence and adulthood they still lack intuitive ToM in
dyadic social interactions (Bowler, 1992; Happé, 1994), including
access to self-referential emotions during empathizing. Furthermore,
we found stable behavioral differences for explicit empathizing between TDC and ASD across the life-span (i.e. no behavioral age-bygroup interactions). If the changes in neural activations from adolescence to early adulthood reflect compensatory mechanisms as
hypothesized, these do not seem to be sufficient to normalize behavioral empathic performance. However, a measure of categorical affect
match, as employed in our paradigm, may not be sensitive enough to
detect subtle age-related improvements in individuals with ASD. Thus,
socio-emotional processing might have improved during the course of
development and as a result of behavioral interventions (e.g.
recognition of emotional faces, perspective taking abilities and social
skills), despite persistent reduced emotional contagion. Though speculative at present, this finding suggests that training of explicit mentalizing and ToM abilities throughout adolescence is important, but
probably not sufficient to enhance emotional contagion in individuals
with ASD in empathic situations. Ultimately, such questions can only
be answered in a longitudinal design employing a variety of empathic
performance measures. Furthermore, future studies need to include
individuals with ASD at younger ages. The age range in our sample
is particularly suited to detect compensatory changes during adolescence and young adulthood. However, ToM abilities (Baron-Cohen
et al., 1985), as well as early empathic understanding (Thompson,
1987) are rooted much earlier in development which is likely also
reflected in the neurodevelopmental trajectories of empathic abilities
(Greimel et al., 2010a). The identification of differential neural trajectories during earlier stages of typical and atypical development might
be particularly informative for early age-adapted interventional
strategies.
CONCLUSIONS
Our results provide first evidence for developmental changes in the
neural substrates of empathic processing in ASD. In a cross-sectional
approach including children, adolescents and adults, we observed that
the developmental trajectories of TDC subjects and ASD subjects differ
from each other and that atypical brain activation patterns extend into
adulthood. Furthermore, our data show that the refinement of functional brain networks related to social-cognitive abilities in ASD continues into adulthood. Interestingly, a decrease in brain activation with
age in controls was paralleled by an increase in brain activation in
individuals with ASD, mainly in brain regions relevant for cognitive
control and explicit monitoring of emotional states. These data therefore strongly suggest that during the course of development, individuals with ASD may be able to acquire a cognitive-biased strategy to
gain access to other people’s emotions.
SUPPLEMENTARY DATA
Supplementary data are available at SCAN online.
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