The structural MRI analysis of social
cognition networks in ASD.
Sofie Valk, University of Amsterdam Supervisor:
Boris Bernhardt, Max Planck Institute of Cognitive and Brain Sciences
Social impairments are part of the core symptoms of autism spectrum disorder (ASD). In this study, the difference in brain structures involved in social cognition in individuals with high functioning ASD and matched healthy controls is investigated. To this end, we assessed a MR-‐based measurement of cortical thickness. We found no difference in regional cortical thickness between ASD and healthy controls. However, morphometric correlation analysis revealed patterns of hypoconnectivity in the theory of mind network and patterns of disrupted connectivity in the empathy network in ASD. Our data provides anatomical evidence for connectivity disruptions in the networks related to social cognition in ASD.
Introduction
Autism spectrum disorder (ASD) is a group of neuro-‐developmental disorders of early onset that persists into adulthood and is characterized by abnormalities in language, social interaction, together with manifestations of stereotyped and repetitive behavior1. The social impairments that are part of the ASD phenotype
have been linked with impairments in mentalizing and the inference of mental states from other persons2, 3. These processes are believed to rest on a network
formed by temporo-‐parietal and medial prefrontal cortices4 , 5. Moreover,
alexithymia, a sub-‐clinical trait related to impairments and difficulties in describing feelings and in distinguishing feelings from bodily sensations is reported to be found in severe degrees in the ASD population6, 7. There is less
consistent evidence about the relation between alexithymia and the social cognitive and emotional processing in the brain8, 9 10, 11, 12. Research indicates that
such emotional deficits may arise from dysfunctions in cortical networks involved in social cognition, for example empathy and emotional reactivity; the insula and the anterior cingulate cortex13, 14, 15 and/or in regions associated with
theory of mind; the prefrontal cortex and the temporal parietal junction11.
Whether ASD relates to abnormal brain structure is poorly understood. Some divergence of previous magnetic resonance imaging (MRI) studies in adult individuals with ASD has been attributed to studying heterogeneous patient groups, to abnormal developmental trajectories, or to incomplete matching between patients and controls. However, even restricting the analysis to high-‐ functioning and extensively matched patients with ASD, studies in the neocortex have yielded inconclusive results, with some analyses showing gray matter
increases16, 17 while others have shown decreases in gray matter18, 19 or mixed
results of increases and decreases between ASD and controls20, 21, 22, 23. Indeed,
previous MRI-‐based cortical thickness analyses have reported increases in frontal, temporal-‐limbic, and parietal networks in high-‐functioning ASD relative to control subjects 16, 24, but also decreases in similar regions18.
A possible explanation for the lack of consistent findings in ASD is that previous analysis have mostly performed univariate regional analyses, and thus failed to sufficiently characterize a structural substrate of ASD that may more precisely be defined as a disruption of interconnected and distributed networks. The currently dominant theory of brain connectivity in ASD holds that there are alterations in long-‐range as well as local connectivity25, 26. Most of the analysis of
signal correlations derived from functional resting-‐state MRI demonstrated reduced long-‐range functional connectivity, especially between frontal and parietal regions located in the frontal cortex, but also in other regions27,28.
However some analysis report mixed results of reduced and increased functional connectivity in ASD29, 30. EEG studies show the same patterns of results, with
mixed results of hypo-‐ and hyperconnectivity31, 32. In the structural domain,
diffusion tensor imaging analysis report reduced fractional anisotropy (FA) in ASD33 , 34 but also mixed results of increased and decreased FA in frontal,
temporal and occipital regions35, 36. Morphometric correlation analysis has been
proposed as an efficient means to detect subtle alterations in large-‐scale structural brain connectivity37 , 38 , 39 , 40 , 41. In ASD, the single previous
covariance between the amygdala and the fusiform cortex, and that this relationship relates to impairments in emotional face processing42.
Our purpose is to identify abnormalities in ASD in the organization of emotional and cognitive perspective taking networks. We aim to test possible structural abnormalities in individuals with autism spectrum disorder directly while taking into account the increased level of alexithymia in this group. We therefore investigated the structural brain basis of social cognition in a group of individuals with autism spectrum conditions with a wide distribution of alexithymia scores and a matched control group.
Using MRI-‐based cortical thickness measurements43 we will map the topography
of structural alterations in patients with ASD relative to controls. We will employ the framework of vertex wise morphometric correlation analysis, seeding from the insular and cingulate cortex as regions consistently activated in studies of empathy13 and from temporo-‐parietal and medial prefrontal cortical regions as
regions consistently activated in studies of cognitive perspective taking and mentalizing 4, 5.
Methods Subjects:
Both high functioning autism spectrum disorder (ASD) participants and healthy controls matched for Alexithymia, age, sex and partly matched for IQ participated in this study. Subjects were previously recruited at the Institute of Cognitive Neuroscience of the University of London. Structural MR data was
acquired between 2004 and 2007 as part of different functional MR studies on Alexithymia and/or autism.
We selected our subjects based on scanner parameters and degree of Alexithymia, (TAS-‐20)44, 45. Our final sample consists of 16(4 female) participants
of the autism spectrum and 16(7 female) controls. Groups were statistically gender-‐matched (Chi-‐Squared=0.57, p=0.55). Groups were not significantly different in terms of age (ASD: mean±sd 34.8±13.3 years, range: 21-‐60 years; Controls: 36.2±13.0 years, range: 23-‐63 years, t(30)=0.8, p = 0.76) and IQ (ASD: mean±sd 119.8±15.2, range: 91-‐140; Controls: 113.4±12.3, range: 98-‐149, t(30)=1.3, p = 0.21, whereby IQ was assessed with the Wechsler Adult Intelligence Scale (WAIS-‐III UK46). Although there was no significant difference
between groups for Alexithymia, there was a trend for higher Alexithymia scores in ASD (ASD: mean±sd 57.3±12.7, range: 37-‐78; Controls: 49.1±12.4, range 27-‐ 65, t(30) p = 0.08.
All participants with ASD are high functioning and previously diagnosed with autism or Asperger Syndrome by an independent clinician according to the standard Diagnostic and Statistical Manual of Psychiatric Disorders-‐IV47 criteria
for their participation in the studies of the University of London. Control participants were pre-‐screened for any neurological or psychiatric disorders and did not exhibit autistic features.
MRI acquisition:
MRI data was acquired on a 1.5T Siemens sonata scanner (Siemens Medical Systems, Erlangen). We used a 3D IR/GR T1-‐weighted sequence (TR=20.66 ms; TE=8.46 ms; flip angle=25°; 256 coronal slices; matrix size = 176 x 224;
FOV=224 mm; slice thickness= 1mm), yielding a final voxel size of 1.0 x 1.0 x 1.0 mm.
Fig 1. Three stages from the FreeSurfer cortical analysis pipeline. A. skull stripped image. B. white matter segmentation. C. surface between white and gray (yellow line) and between gray and pia (red line) overlaid on the original volume.
Cortical thickness measurements:
We used FreeSurfer to semi-‐automatically reconstruct representations of the gray/white matter boundary and the cortical surface from the T1-‐weighted images (Version 5.1.0; http://surfer.nmr.mgh.harvard.edu). Previous work has validated FreeSurfer against histological analysis48 and manual measurements49.
FreeSurfer has shown good test-‐retest reliability across scanner manufacturers and across field strengths. The processing steps have been described in detail elsewhere43, 50, 51, 52 (see figure 1). Following surface extraction, sulcal and gyral
features across individual subjects were aligned by morphing each subjects brain to an average spherical representation that allows for accurate matching of cortical thickness measurement locations among participants, while minimizing metric distortion. The entire cortex in each subject was visually inspected, and segmentation inaccuracies manually corrected. For whole-‐brain analysis, thickness data was smoothed on the tessellated surfaces using a 20mm FWHM Gaussian kernel prior to statistical analysis. Selecting a surface-‐based kernel
reduces measurement noise but preserves the capacity for anatomical localization, as it respects cortical topological features53.
Statistical analyses:
Statistical analyses were performed using the SurfStat 54 (http://
www.math.mcgill.ca/keith/surfstat) toolbox for Matlab (R2010a, The Mathworks, Natick, MA)
a) Analysis of cortical thickness differences. We used vertex wise t-‐tests to
map differences in cortical thickness at each vertex between ASD and controls.
Figure 2. Morphometric correlation analysis. The left scatterplot is an example of a positive correlation between the mean thickness of the seed region of each participant and the thickness of a point located in the left frontal cortex. The right scatterplot is an example of no correlation between the mean thickness of the seed region and a point located in the right central cortex.
b) Morphometric correlation analysis (Fig 1). We studied structural networks
of the seeds involved in alexithymia and emotional reactivity ipsilaterally (peaks left dorsal anterior insula13: x =-‐40, y=22, z=0; right dorsal anterior
insula13: x=39, y=23,z=-‐4; left anterior midcingulate cortex13: x=-‐2, y=23,
overlapped in a meta-‐analysis of studies on empathy13. To assess
networks of cognitive perspective taking (left temporal parietal junction4:
x=-‐54, y=-‐60, z=21; right temporal parietal junction4: x=51, y=-‐54, z=27,
left medial prefrontal cortex, constructed by mirroring the right coordinate on the x-‐place: x=-‐1, y=59, z=16, right medial prefrontal cortex5: x=1, y=59, z=16), clusters are created by smoothing the region
around the peak voxel coordinate with a 10mm FWHM Gaussian kernel. Coordinates are presented in MNI space. Correlation analysis between the mean thicknesses of each seed with cortical thickness at each vertex was used to map structural networks in controls and ASD separately. A significant correlation was interpreted as a connection. We also used linear interaction models to assess differences in connectivity between both groups.
c) Correction for multiple comparisons. We employ corrected significances
based on the structural correlation analysis using random field theory for non-‐isotrophic images55. This controls the chance of ever reporting a false
positive finding to below 0.05. In addition, we also display uncorrected trends at p<0.025.
Results
Group differences in vertex wise cortical thickness.
There was no FWE corrected difference in thickness of the cortex between ASD and control, (fig 3.). However there was a trend for thickening in ASD in the medial central frontal region. There was no significant difference of thickness between groups in the region of interests (see appendix table 1).
Fig. 3. The vertex wise cortical thickness differences between ASDs and HCs.
Group differences in affective processing ROIs.
The left dAi in controls is correlated to the inferior prefrontal cortex, the superior temporal lobe and the posterior insular region. There are trends for correlation to the occipital lobe, the posterior temporal lobe and the posterior cingulate cortex.
The left dAI in ASDs is correlated to the lateral orbitofrontal cortex and the medial frontal cortex. There are trends for correlation to the posterior/mid cingulate cortex and the inferior medial occipital lobe.
The interaction between groups for correlation showed only trends; positive interaction in the in the medial prefrontal region and the prefrontal cortex, negative interaction in the occipital lobe.
The right dAi in controls is correlated to the inferior prefrontal cortex, the posterior insular region, the temporal lobe, the occipital cortex and the fusiform gyrus. The correlations are extensive. There are trends for correlation in the medial central cortex as well as in t he medial prefrontal region.
Figure 5. The dorsal anterior insula correlations. On the left the left dorsal anterior insular seed and the correlations in the left hemisphere. On the right the right dorsal anterior insula seed and the correlations in the right hemisphere. The significant clusters are delineated.
The right dAI in ASD is correlated to the orbitofrontal cortex, the medial prefrontal cortex and the superior temporal lobe. There are trends for correlation to the posterior insular region, the praecuneus, the fusiform gyrus and the occipital lobe.
The interaction between groups shows no significant correlations. There are trends for positive interaction in the praecuneus. There are trends for negative interaction in the medial central regions as well as in the medial occipital lobe.
The left MCC/ACC in controls did not show any significant correlations. However there are trends of correlation to the medial central lobe, the frontal regions and the temporal parietal junction.
The left MCC/ACC in ASDs did not show any significant correlations. There were only trends localized in the direct region of the seed.
Figure 5. The ACC/MCC correlations. On the left the left ACC/MCC seed and the correlations in the left hemisphere. On the right the right ACC/MCC seed and the correlations in the right hemisphere. The significant clusters are delineated.
The interaction between groups did not show any significant interactions. However there are trends for negative interaction in the anterior temporal lobe and the frontal cortex.
The right MCC/ACC in controls did not show any significant correlations. There were some trends for correlation in the central sulcus and the parietal occipital fissure.
The right MCC/ACC in ASDs did not show any significant correlations. There were trends in the region of the MCC/ACC itself and the prefrontal cortex.
The interaction between groups did not show significant correlations. There were positive trends in the mid cingulate cortex, the temporal junction and the anterior insular region. There were trends for negative interaction in the central sulcus, the posterior central lobe and in the parietal occipital fissure.
Group differences in theory of mind ROIs.
The left TPJ in controls shows extensive correlation to the occipital regions, the posterior insula, the central lobe, the medial central lobe, the praecuneus and the medial occipital lobe. There are trends for correlation to the inferior and anterior temporal lobe, the prefrontal cortex and the mid cingulate cortex.
The left TPJ in ASDs is correlated to the region of the temporal junction itself. There are trends for correlation to the prefrontal cortex, the superior occipital cortex and the medial prefrontal cortex.
Figure 6. The temporal parietal junction correlations. On the left the left temporal parietal junction seed and the correlations in the left hemisphere. On the right the right temporal parietal junction seed and the correlation in the right hemisphere. The significant clusters are delineated.
The interaction between groups shows only trends for positive interaction in the medial prefrontal region. However there are significant negative interactions in
the central cortex, the medial central cortex and the mid cingulate cortex. There are trends for negative interaction in the occipital lobe, the inferior temporal lobe and the praecuneus.
The right TPJ in controls shows no significant correlations. There are trends for correlation to the parietal occipital fissure, the praecuneus and the central sulcus and to the region of the TPJ itself.
The right TPJ in ASDs shows no significant correlations. There are trends for correlation to the inferior lateral prefrontal cortex, the superior temporal lobe and in the region of the temporal parietal junction itself.
The interaction between groups shows only trends. There are positive trends for interaction in the inferior medial prefrontal region. There are negative trends for interaction in the central sulcus and the perfumes.
The left mPFC in controls has extended correlations in the central and prefrontal cortex stretching to the central sulcus. There are few trends, one in the calcarine fissure and the superior occipital lobe.
The left mPFC in ASDs has extended correlations in the orbitofrontal cortex and the medial frontal cortex and the anterior insular region. There are trends for correlation in the fusiform gyrus and the posterior cingulate cortex.
The interaction between groups has negative interaction in the central superior lobe stretching to the central sulcus. There are trends for positive interaction in the posterior-‐ and anterior cingulate cortex and the fusiform gyrus.
The right mPFC in controls is extensively correlated to the prefrontal cortex, the occipital cortex, the medial prefrontal cortex and the cuneus. There are trends
for correlation in the medial and posterior temporal lobe and the region of the temporal parietal junction.
Figure 7. The medial prefrontal correlations. On the left the left medial prefrontal seed and the correlations in the left hemisphere. On the right the right medial prefrontal seed and the correlations in the right hemisphere. The significant clusters are delineated.
The right mPFC in controls is correlated to the prefrontal cortex, the insular region and the medial prefrontal cortex. There are trends for correlation in the central lobe, the region of the temporal parietal junction, the occipital lobe, the mid cingulate cortex, the cuneus and the parietal occipital fissure.
The interaction between groups is not significant, however there are positive trends in the ventral medial prefrontal cortex. There are small clusters of negative trends across the cortical surface.
Discussion
The paradigm of the present study is unique in that it enabled us to assess morphometric correlation analysis on a group of high functional ASD subjects with a wide distribution of alexithymia scores and matching controls. To our
knowledge, this is the first structural MR study that examines subjects with ASD that are matched with a control group on alexithymia. Alexithymia is a subclinical trait that is associated with hypo activation of the theory of mind regions11 and with the empathic social cognitive symptoms in ASD14, 15. Also only
one other study employed morphometric correlation analysis to investigate possible abnormal brain networks in ASD42. Previous studies on the structure of
the brain in ASD have reported contradictory results16, 17, 18, 19, 20, 22 of both cortical
thickening and thinning in ASD. Moreover, studies assessing the connectivity in ASD have reported divergent results as well27, 29, 30,31, 36.
The present study aimed at identifying abnormalities in ASD in the organization of emotional and cognitive perspective taking networks. In contrast to previous studies16, 17, 18, 19, we found no structural alterations in patients with ASD relative
to controls. Employing the framework of vertex wise morphometric correlation analysis, we found less connectivity in ASD seeding from regions consistently activated in studies on empathy13, the insular and cingulate cortex. We found
hypoconnectivity in ASD seeding from regions consistently activated in studies of cognitive perspective taking and mentalizing 4, 5. Our data provides anatomical
evidence for connectivity disruptions in the networks related to social cognition in ASD.
Regional thickness differences
Contrary to previous reports16, 14, 18, 19 we found no regional grey matter
thickness differences between ASDs and healthy controls. This indicates there is no regional difference between the diagnostic groups. Our ASD group consisted of high functioning ASD and the groups were matched for alexithymia. This could
explain why we found no regional difference. Our group consisted only of adults, with a mean age of 34.8 and 36.2 years in respectively ASDs and controls. Subjects in many other studies were children and adolescents56, 26. ASD is a
neurodevelopmental disorder and there is a relation between age and cortical thinning in ASD 19. This could explain why our results differ from previous
findings. Another possible explanation is a lack of statistical power, since our group size was relatively small.
Connectivity of ROIs associated with theory of mind
To assess the connectivity of the seeds with the rest of the brain we employed a correlation analysis between the thickness of the seed region and the thickness of the vertices of the ipsilateral hemisphere. We found hypoconnectivity in the theory of mind networks in ASDs. This is in line with the currently dominant theory of brain connectivity in ASD25,26. There was more extended connectivity
between the left TPJ seed and the cortex in controls and negative interaction in the superior lateral and medial central regions. We found only trends for hypoconnectivity in the right TPJ. The TPJ is bilaterally associated with theory of mind4. The TPJ is reported to be less selectively activated by ASD subjects in a
specific mentalizing task compared to controls57 and is typically associated with
self-‐other distinction58, 59, 60. However the right TPJ function is not restricted to
theory of mind alone61, but is also involved in attention mechanisms62, 63. The
involvement of the right TPJ in multiple processes could be a explanation for the reduced correlations we found in this hemisphere. Also, we found more extended network connectivity in the left and right medial PFC. Although the connectivity stretched to more posterior medial regions in controls, the
connectivity in ASD stretched only to lateral frontal regions. Previously it has been suggested the medial prefrontal cortex can be functionally split in a part that is related to more emotional cognition involved in theory of mind and a part that is related to a more cognitive and action monitoring components of theory of mind64. Moreover, it has been reported controls employ both emotional and
cognitive mechanisms in theory of mind, while ASDs only employ cognitive strategies65. The altered connectivity patterns in ASD may indicate ASD subjects
use different cognitive resources to employ theory of mind related strategies on the structural level as well.
Connectivity of ROIs associated with empathy and affective processing
We found less connectivity between the empathy regions in the rest of the brain in ASDs compared to the connectivity in controls. The results show mixed patterns of correlations in both groups, and no significant interactions. This could be the result of the alexithymia matching in our groups, and our results are in line with the functional findings of the study of Bird et al14. They found no
difference in activation in the left anterior insular region between ASD and controls that were corrected for alexithymia. We found a more extended difference in connectivity between ASDs and controls in the right dorsal anterior insular seed. The right dorsal anterior insular region specifically has been reported to be hypo-‐activated in ASD in a meta-‐analysis of a mix of social cognition studies66. This may contribute to the more extensively impaired
networks relating to the right dAI in ASD. ACC/MCC seed has no significant correlations with other brain areas in both ASDs and controls. This could be because a lack of statistical power in our group. However there are trends for
more connectivity of left ACC/MCC in controls and also for negative interaction. The ACC/MCC that was reported as a significant cluster in the coordinate based meta-‐analysis13 was located in the left hemisphere. It has been proposed the
ACC/MCC contributes to the appropriate motor responses to painful, negative, events in general67 and its empathy related activation when pain in occurs in
others illustrates the shared representation account of understanding others68.
Because the divergence of functions of the MCCACC, it is possible that, just as in the right TPJ, the cortical thickness correlations of this region are reduced because this region is involved in many interacting processes.
Limitations
There are some limitations in the current study. The size of our group was relatively small. It is clear from trends in correlations of the seed region that there is the need for more statistical power, since now sometimes a seed has no significant correlation to any region and there are very big differences in the number of correlated clusters between hemispheres.
Our groups were matched for alexithymia; both groups had a wide distribution of alexithymia scores. In regular controls and ASD individuals this distribution is different, since high levels of alexithymia is prevalent in 1 in 10 people in a healthy population. In this research 1 in 3 healthy controls had high levels of alexithymia. This has both been a limitation as well as an asset. Our groups were atypical, but we could control for the possible modulating effect of alexithymia. Also our seed regions were derived from different studies, and not all were meta-‐analysis. Only the seeds of the dAI and the ACC/MCC resulted from meta-‐ analysis of empathy for pain studies. The seeds we used were the regions of
activation that were found to be overlap between the different studies. The left dAI seed however was located partly in the inferior frontal gyrus and consisted of two seeds. The right dAI also seems to be located in the inferior frontal gyrus. The right MCC/ACC seed was very small. The other seeds were from the Mitchell et al. (2002)5 study, the mPFC seeds and the TPJ seeds were from Saxe et al.
(2003)4 study. Here the peak voxels of the activation are smoothed with a
Gaussian kernel of 10mm in order to account for the region of the peak voxel as well. We chose a relatively dorsal mPFC peak. We mirrored the seed found in Mitchell, because they only reported of a peak located in the right hemisphere, in order to construct a bilateral seed of the medial prefrontal cortex.
Our results are in line with previous accounts of hypoconnectivity in ASD18,19, 22,23. Since regions relating to social cognition are highly specialized57, it is
likely there are mixed patterns of connectivity in ASD compared to healthy controls, with each region of interest modulating different functional roles.
Final remarks
The present data provide novel insights into the structure of brain networks involved in social cognition in healthy controls and ASD subjects. We provide a fine-‐graded distinction between the structural correlates of two different capacities underlying social cognition: mentalizing ability and empathic ability, in order to dissociate the possible impairments of both networks. We accounted for a wide distribution alexithymia scores in both groups, in order to control for possible structural differences related to alexithymia. Importantly, our data support the theory of structural hypoconnectivity in ASD and argue these
structural alterations are connected to ASD symptoms in the social cognitive domain.
Appendix Table 1. :
Thickness t-‐test of the seeds between groups.
ROI: t-‐value
Whole brain thickness t(30)=0.92, p<0.36
Left dAI t(30)=1.55, p<0.13 Right dAI t(30)=0.62, p<0.54 Left MCCACC t(30)=0.45, p<0.65 Right MCCACC t(30)=0.81, p<0.42 Left TPJ t(30)=1.36, p<0.18 Right TPJ t(30)=1.32, p<0.20 Left mPFC t(30)=0.17, p<0.87 Right mPFC t(30)=1.18, p<0.25
Table 2.: Correlation between seeds and Tas-‐score
ROI Autism Healthy Control
Left dAi r=-‐0.12, p<0.66 r=0.16, p<0.56 Right dAI r=-‐0.13, p<0.63 r=0.40, p<0.12 Left MCCACC r=-‐0.07, p<0.79 r=0.10, p<0.72 Right MCCACC r=-‐0.11, p<0.69 r=-‐0.11,p<0.69 Left mPFC r=-‐0.07, p<0.79 r=0.24, p<0.36 Right mPFC r=-‐0.47, p<0.07 r=0.49, p<0.06 Left TPJ r=0.22, p<0.41 r=0.52, p<0.04 Right TPJ r=-‐0.16, p<0.54 r=0.45, p<0.08
Table 3: Correlation between seeds in the left hemisphere (A= ASD, C= HC). MCCACC TPJ mPFC dAi A: r=-‐0.09, p<0.73 H: r=0.04, p<0.87 A: r=0.39, p<0.14 H: r=0.18, p<0.50 A: r=0.75, p<0.00 H: r=-‐0.08, p<0.77 MCCACC A: r=-‐0.02 p<0.94 H: r=0.51, p<0.05 A: r=-‐0.22, p<0.40 H: r=0.29, p<0.28 TPJ A: r=0.47, p<0.07 H: r=-‐0.07, p<0.79
Correlation between seeds in the right hemisphere (A= ASD, C= HC).
MCCACC TPJ mPFC dAi A: r=0.04, p<0.88 H: r=-‐0.06, p<0.84 A: r=0.26, p<0.33 H: r=0.38, p<0.15 A: r=0.36, p<0.17 H: r=0.50, p<0.05 MCCACC A: r=-‐0.21, p<0.44 H: r=0.06, p<0.83 A: r=0.17, p<0.54 H: r=-‐0.02, p<0.93 TPJ A: r=0.15, p<0.57 H: r=0.00, p<0.99
Literature
1 APA, A. P. A. (2000). Diagnostic and statistical manual of mental disorders (DSM-‐IV-‐TR) (4th
edition ed.). Washington D.C.
2 Frith, U., Happe, F., (2006) Autism spectrum disorder. Current Biology 15(19), R786-‐790
3 Singer, T., Seymour, B., O'Doherty, J., Kaube, H., Dolan, R. J., & Frith, C. D. (2004). Empathy for
pain involves the affective but not sensory components of pain. [Research Support, Non-‐U.S. Gov't]. Science, 303(5661), 1157-‐1162. doi: 10.1126/science.1093535
4 Saxe, R., Kanwisher, N., (2003) People thinking about thinking people. The role of the temporal-‐
parietal junction in “Theory of mind”. NeuroImage 19, 1835-‐1842
5 Mitchell, J.P., Heatherton, Macrae, C., (2002) Distinct neural systems subserve person and object
knowledge. P.N.A.S. 99(23), 15238-‐15243
6 Berthoz, S., & Hill, E. L. (2005). The validity of using self-‐reports to assess emotion regulation
abilities in adults with autism spectrum disorder. [Research Support, Non-‐U.S. Gov't Validation Studies]. Eur Psychiatry, 20(3), 291-‐298. doi: 10.1016/j.eurpsy.2004.06.013
7 Hill, E., Berthoz, S., & Frith, U. (2004). Brief report: cognitive processing of own emotions in
individuals with autistic spectrum disorder and in their relatives. [Research Support, Non-‐U.S. Gov't]. J Autism Dev Disord, 34(2), 229-‐235.
8 Heinzel, A., Minnerop, M., Schafer, R., Muller, H. W., Franz, M., & Hautzel, H. (2011). Alexithymia
in healthy young men: A voxel-‐based morphometric study. J Affect Disord. doi: 10.1016/j.jad.2011.06.012
9 Borsci, G., Boccardi, M., Rossi R., Rossi, G., Perez, J., Bonetti, M., Frisoni, G.B., (2009) Alexithymia
in healthy women: a brain morphology study. J Affect Disord. 114(1-‐3), 208-‐215
10 Gundel, H., Lopez-‐Sala, A., Ceballos-‐Baumann, A. O., Deus, J., Cardoner, N., Marten-‐Mittag, B., . . .
Pujol, J. (2004). Alexithymia correlates with the size of the right anterior cingulate. Psychosom
Med, 66(1), 132-‐140.
11 Moriguchi, Y., Ohnishi, T., Lane, R.D., Maeda, M., Mori, T., Nemoto, K., Matsuda, H., Komaki, G.,
(2006) Impaired self-‐awareness and Theory of mind: an fMRI study of mentalizing in alexithymia. NeuroImage 32(3), 1472-‐1482
12 Swart, M., Kortekaas, R., Aleman, A., (2009) Dealing with feelings: characterization of the trait
alexithymia on emotion regulation stratigies and cognitive-‐emotional processing. PLoS One 4(6), e5751
13 Lamm, C., Decety, J., & Singer, T. (2011). Meta-‐analytic evidence for common and distinct
neural networks associated with directly experienced pain and empathy for pain. [Meta-‐Analysis Research Support, Non-‐U.S. Gov't Review]. NeuroImage, 54(3), 2492-‐2502. doi:
10.1016/j.neuroimage.2010.10.014
14 Bird, G., Silani, G., Brindley, R., White, S., Frith, U., & Singer, T. (2010). Empathic brain responses
in insula are modulated by levels of alexithymia but not autism. [Research Support, Non-‐U.S. Gov't]. Brain, 133(Pt 5), 1515-‐1525. doi: 10.1093/brain/awq060
15 Silani, G., Bird, G., Brindley, R., Singer, T., Frith, C., & Frith, U. (2008). Levels of emotional
awareness and autism: an fMRI study. [Comparative Study Research Support, Non-‐U.S. Gov't]. Soc
16 Hardan, A.,Y., Muddasani, S., Vermulapalli, M., Keshavan, M.S., Mishew, N.J., (2006) An MRI
study of increased cortical thickness in autism. Am J Psychiatry 163(7), 1290-‐1292
17 Rojas, D.C., Peterson, E., Winterrowd, E., Reite, M.L., Rogers, S.J., Tregellas, J.R. (2006) Regional
gray matter volumetric changes in autism associated with social and repetivtive behavior symptoms. BMC Psychiatry 13, 6-‐56
18 Hadjikhani, N., Joseph, R. M., Snyder, J., & Tager-‐Flusberg, H. (2006). Anatomical differences in
the mirror neuron system and social cognition network in autism. [Comparative Study Controlled Clinical Tria Research Support, N.I.H., Extramural Research Support, Non-‐U.S. Gov't]. Cereb
Cortex, 16(9), 1276-‐1282. doi: 10.1093/cercor/bhj069
19 Wallace, G.L., Dankner, N., Kenworthy, L., Giedd, J.N., Martin, A. (2010) Age-‐related temporal
and parietal cortical thinning in autism spectrum disorder. Brain 133(12), 3745-‐3754
20 Raznahan, A., Toro, R., Daly, E., Robertson, D., Murphy, C., Deeley, Q., Bolton, P.F., Paus, T.,
Murphy, D.G., (2010) Cortical anatomy in autism spectrum disorder: an in vivo MRI study on the effect of age. Cereb Cortex 20(6), 1332-‐1340
21 Bigler, E.D., Tate, D.F., Neeley, E.S., Wolfson, L.J., Miller, M.J., et al. (2003) Temporal lobe,
autism, and macrocephaly. American Journal of Neuroradiology 24, 2066-‐2076
22 Jiao, Y., Chen, R., Ke, X., Chu, K., Lu, Z., Herskovits, E.H., (2010) Predictive models of autism
spectrum disorder based on brain regional cortical thickness. NeuroImage 50(2), 589-‐599
23 Hyde, K.L., Samson, F., Evans, A.C., Mottron, L., (2010) Neuroanatomical differences in brain
areas implicated in perceptual and other core features of autism revealed by cortical thickness analysis and voxel-‐based morphometry. Human Brain Mapping 31(4), 556-‐566
24 Mak-‐Fan, K. M., Taylor, M. J., Roberts, W., & Lerch, J. P. (2011). Measures of Cortical Grey Matter
Structure and Development in Children with Autism Spectrum Disorder. J Autism Dev Disord. doi: 10.1007/s10803-‐011-‐1261-‐6
25 Courchesne, E., Pierce, K., (2005) Why the frontal cortex in autism might be only talking to
itself: local over-‐connectivity but long-‐distance disconnection. Curr. Opin. Neurobiol. 15, 225-‐230
26 Vissers, M.E., Cohen, M.X., Geurts, H.M., (2012)Brain connectivity and high functioning autism:
A promising path of research that needs refined models, methodological convergence, and stronger behavioral links.
27 Kana, R.K., Keller, T.A., Cherkassky, V.L., Minshew, N.J., Just, M.A. (2009) Atypical frontal-‐
posterior synchronization of Theory of mind regions in autism during mental state attribution.
Soc. Neuroscience 4(2), 135-‐152
28 Ebisch, S.J., Gallese, V., Willems, R.M., Mantini, D., Groen, W.B., Romani, G.L., Buitelaar, J.K.,
Bekkering, H., (2011) Altered intrinsic functional connectivity of anterior and posterior insula regions in high-‐functioning participants with autism spectrum disorder. Human Brain Mapping 32(7), 1013-‐1028
29 Assaf, M., Agannathan, K., Calhoun, V.D., Miller, L., Stevens, M.C., Sahl, R., O’Boyle, J.G., Schultz,
R.T., Pearlson, G.D. (2010) Abnormal functional connectivity of default mode sub-‐networks in autism spectrum disorder patients. Neuroimage, 53(1), pp. 247-‐256
30 Shih, P., Shen, M., Ottl, B., Keehn, B., Gaffrey, M.S., Muller, R.A. (2010) Atypical network
connectivity for imitation in autism spectrum disorder. Neuropsychologia. 48(10), pp. 2931-‐2939
31 Barttfeld, P., Wicker, B, Cukier, S., Navarta S., Lew, S., Sigman, M. (2011) A big-‐world networks
in ASD: dynamical connectivity analysis reflects a deficit in long-‐range connections and an excess of short-‐range connections.
32 Leveille, C., Barbeau, E.B., Bolduc, C., Limoges, E., Berthiame, C., Chevrier, E., Mottron, L.,
Godbout, R., (2010) Enhanced connectivity between visual cortex and other regions of the brain in autism: a REM sleep EEG coherence study. Autism Research 3(5), 280-‐285
33 Catani, M., Jones, D.K., Daly, E., Embiricos, N., Deeley, Q., Pugliese, L., Curran, S., Robertson, D.,
Murphy, D.G.M., (2008) Altered cerebellar feedback projections in Asperger syndrome.
NeuroImage 41, 1184-‐1191
34 Thakkar, K.N., Polli, F.E., Joseph, R.M., Tuch, D.S., Hadjikhani, N., Barton, J.J., Manoach, D.S.,
(2008) Response monitoring, repetitive behavior and anterior cingulate abnormalities in autism spectrum disorders (ASD) Brain 131, 2464-‐2478
35 Cheng, Y., Chou, K.H., Chen, I.Y., Fan, Y.T., Decety, J., Lin, C.P., (2010) Atypical development of
white matter microstructure in adolescents with autism spectrum disorders. NeuroImage 50, 873-‐882
36 Cheung, C., Chua, S.E., Cheung, V., Khong, P.L., Tai, K.S., Wong, T.K., Ho, T.P., McAlonan, G.M.,
(2009) White matter fractional anistropy differences and correlates of diagnostic symptoms in autism. J. Child Psychol. Psychiatry 50, 1102-‐1112
37 Rykhlevskaia, E., Gratton, G., & Fabiani, M. (2008). Combining structural and functional
neuroimaging data for studying brain connectivity: a review. [Research Support, N.I.H., Extramural Research Support, Non-‐U.S. Gov' Review]. Psychophysiology, 45(2), 173-‐187. doi: 10.1111/j.1469-‐8986.2007.00621.x
38 Lerch, J., Worsley, K, Shaw, W., Greenstein, D., Lenroot, R., Giedd, J. & Evans, A. (2006) Mapping
anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI.
NeuroImage, 31(3), 993-‐1003
39 Bullmore E., Frangou, S. & Murray, R. (1997) The dysplastic net hypothesis: an integration of
developmental and dysconnectivity theories of schizophrenia. Schizophr. Res. 28(2-‐3) 143-‐156
40 Mitelman S., Buchsbaum M., Brickman A. & Shihabuddin L. (2005) Cortical intercorrelations of
frontal area volumes in schizophrenia. NeuroImage 27(4). 753-‐770
41 Bernhardt, B., Worsley, K., Besson, P., Concha, L., Lerch, J., Evans, A. & Bernasconi, N. (2008)
Mapping limbic network organization in temporal lobe epilepsy using morphometric
correlations: insights on the relation between mesiotemporal connectivity and cortical atrophy.
NeuroImage 42(2), 515-‐524
42 Dziobek, I., Bahnemann, M., Convit, A., & Heekeren, H. R. (2010). The role of the fusiform-‐
amygdala system in the pathophysiology of autism. [Research Support, Non-‐U.S. Gov't]. Arch Gen
Psychiatry, 67(4), 397-‐405. doi: 10.1001/archgenpsychiatry.2010.31
43 Fischl, B. & Dale, A. (2000). Measuring the thickness of the human cerebral cortex from
magnetic resonance images. PNAS 97, 11050-‐11055.
44 Bagby R., Parker J. & Taylor, G. (1994). The twenty-‐item Toronto Alexithymia Scale—I. Item
selection and cross-‐validation of the factor structure. J Psychosom Res. 38(1). 23-‐32
45 Bagby, R., Taylor, G. & Parker, J. (1994). The twenty-‐item Toronto Alexithymia Scale—II.
Convergent, discriminant, and concurrent validity. J Psychosom Res. 38(1). 33-‐40
46 Wechsler, D. (1999) Wechsler Adult Intelligence Scale -‐3rd UK Edition (WAIS ® -‐ III UK).
London: The Psychlogical Corporation
47 APA, A. P. A. (2000). Diagnostic and statistical manual of mental disorders (DSM-‐IV-‐TR) (4th
edition ed.). Washington D.C.
48 Rosas, H., Liu, A., Hersch, S., Glessner, M., Ferrante, R., Salat, D., van der Kouwe, A., Jenkins, B.,
Dale, A., & Fischl., B. (2002). Regional and progressive thinning of the cortical ribbon in Huntington's disease. Neurology 58, 695-‐701
49 Kuperberg, G., Broome, M., McGuire, P., David, A., Eddy, M., Ozawa, F., Goff, D., West, W.,
Williams, S., van der Kouwe, A., Salat, D., Dale, A. & Fischl, B. (2003). Regionally localized thinning of the cerebral cortex in schizophrenia. Arch. Gen. Psychiatry 60, 878-‐888.
50 Han, X., Jovicich, J., Salat, D., van der Kouwe, A., Quinn, B., Cyanner, S., Busa E., Pacheco, J.,
Albert, M, Killiany, R., Maguire, P., Rosas, D., Makris, N., Dale, A., Dickerson, B. & Fischl, B. (2006). Reliability of MRI-‐derived measurments of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer. NeuroImage 32, 180-‐194
51 Dale, A., Fischl, B. & Sereno, M. (1999) Cortical surface-‐based analysis. I. Segmentation and
surface reconstruction. NeuroImage 9, 179-‐194
52 Fischl, B., Sereno, M. & Dale, A. (1999). Cortical surface-‐based analysis. II: Inflation, flattening,
and surface-‐based coordinate system. NeuroImage 9, 195-‐207
53 Lerch, J. & Evans, A. (2005). Cortical thickness analysis examined through power analysis and a
population simulation. NeuroImage 24, 163-‐173
54 Worsley, K., Taylor, J., Carbonell, F., Chung, M., Duerden, E., Bernhardt, B., Lyttelton, O., Boucher,
M. & Evans, A. (2009) SurfStat: A Matlab toolbox for the statistical analysis of univariate and multivariate surface and volumetric data using linear mixed effects models and random field theory. NeuroImage 47, S102
55 Worsley, K., Andermann, M., Koulis, T., MacDonald, D. & Evans, A. (1999) Detecting changes in
nonisotropic images. Hum. Brain Mapp. 8, 98-‐101
56 Chen, R., Jiao, Y., Herskovits, E. H., (2011) Structural MRI in Autism Spectrum Disorder. Pediatric Research 69(5), 63-‐69
57 Lombardo, M.V., Chakrabarti, B., Bullmore, E., Baron-‐Cohen, S., (2011) Specialization of right
temporo-‐parietal junction for mentalizing and its relation to social impairments in autism.
NeuroImage 56(3), 1832-‐1838
58 Decety, J., Grezes, J. (2006) The power of simulation: Imaging one’s own and other’s behavior. Brain Research 1079, 4-‐14
59 Schulte-‐Ruther, M., Markowitsch, H. J., Fink, G.R., Piefke, M., (2007) Mirror neuron and the
theory of mind mechanisms involved in face-‐to-‐face interactions: A functional magnetic resonance imaging approach to empathy. Journal of Cognitive Neuroscience 19, 1354-‐1372
60 Vogeley, K., Fink, G.R., (2003) Neural correlates of the first-‐person-‐perspective. Trends in Cognitive Sciences 7, 38-‐42
61 Mitchell, J.P. (2008) Activity in Right Temporo-‐Parietal Junction is not selective for Theory of
mind. Cerebral Cortex 18(2), 262-‐271
