Cerebral Cortex May 2011;21:1134--1146
Advance Access publication October 12, 2010
Decreased Interhemispheric Functional Connectivity in Autism
Jeffrey S. Anderson1,2,3, T. Jason Druzgal1, Alyson Froehlich4, Molly B. DuBray2,4, Nicholas Lange5,6,7, Andrew L. Alexander8,9,
Tracy Abildskov10,11, Jared A. Nielsen2,4, Annahir N. Cariello2,4, Jason R. Cooperrider4, Erin D. Bigler3,10,11and Janet E. Lainhart2,3,4
1Department of Neuroradiology,2Program in Neuroscience,3The Brain Institute,4Department of Psychiatry, University of Utah, Salt
Lake City, UT 84132, USA,5Department of Psychiatry, Harvard Medical School, 401 Park Drive, Boston, MA 02215, USA,6Department
of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA,7Neurostatistics Laboratory,
McLean Hospital, 115 Mill Street, Belmont, MA 02478, USA,8Department of Medical Physics, University of Wisonsin-Madison, 1111
Highland Ave, Madison, WI 53705, USA,9Department of Psychiatry, University of Wisconsin-Madison, 6001 Research Park Blvd,
Madison, WI 53719, USA,10Neuroscience Center, Brigham Young University, 1055 SWKT, Provo, UT 84062, USA and11Department
of Psychology, Brigham Young University, 1001 SWKT, Provo, UT 84062, USA
Address correspondence to Jeffrey S. Anderson, Department of Neuroradiology, University of Utah, 1A71 School of Medicine, Salt Lake City, UT
84132, USA. Email: email@example.com.
The cortical underconnectivity theory asserts that reduced long-
range functional connectivity might contribute to a neural
mechanism for autism. We examined resting-state blood oxygen
level--dependent interhemispheric correlation in 53 males with high-
functioning autism and 39 typically developing males from late
childhood through early adulthood. By constructing spatial maps of
correlation between homologous voxels in each hemisphere, we
found significantly reduced interhemispheric correlation specific to
regions with functional relevance to autism: sensorimotor cortex,
anterior insula, fusiform gyrus, superior temporal gyrus, and
superior parietal lobule. Observed interhemispheric connectivity
differences were better explained by diagnosis of autism than by
potentially confounding neuropsychological metrics of language, IQ,
or handedness. Although both corpus callosal volume and gray
matter interhemispheric connectivity were significantly reduced in
autism, no direct relationship was observed between them,
suggesting that structural and functional metrics measure different
aspects of interhemispheric connectivity. In the control but not the
autism sample, there was decreasing interhemispheric correlation
with subject age. Greater differences in interhemispheric correla-
tion were seen for more lateral regions in the brain. These findings
suggest that long-range connectivity abnormalities in autism are
spatially heterogeneous and that transcallosal connectivity is
decreased most in regions with functions associated with
behavioral abnormalities in autism. Autism subjects continue to
show developmental differences in interhemispheric connectivity
into early adulthood.
Keywords: autism spectrum disorders, brain development, fcMRI, fMRI,
resting state fMRI
Abnormalities in long-range, functional connectivity in autism
have been demonstrated in multiple prior imaging studies
(Castelli et al. 2002; Just et al. 2004, 2007; Koshino et al. 2005,
2008; Cherkassky et al. 2006; Kana et al. 2006; Kennedy et al.
2006; Kennedy and Courchesne 2008; Kleinhans et al. 2008). In
paradigms involving working memory (Koshino et al. 2005,
2008; Mostofsky et al. 2009), executive function (Just et al.
2007), face processing (Kleinhans et al. 2008), motor function
(Mostofsky et al. 2009), and language (Just et al. 2004; Kana
et al. 2006), ASD individuals exhibited significantly decreased
correlation between task-related regions of interest (ROIs).
Subsequently, resting state paradigms have shown atypical
network properties of the default mode network (Kennedy
et al. 2006; Kennedy and Courchesne 2008; Monk et al. 2009;
Weng et al. 2009) and decreased functional connectivity of
anterior/posterior projectional pathways (Cherkassky et al.
2006; Koshino et al. 2008). These observations and their
relationship to observed differences in cytoarchitecture of the
cortex are summarized in a theory of connectivity in
autism characterized by local over connectivity but long-
distance underconnectivity (Just et al. 2004; Courchesne and
Pierce 2005; Geschwind and Levitt 2007; Casanova and Trippe
Structural imaging has also demonstrated abnormalities in
long-range white matter pathways. Corpus callosal volume has
been considered one index of interhemispheric connectivity
(Manes et al. 1999; Hardan et al. 2000; Vidal et al. 2006; Keary
et al. 2009). Decreased size of the corpus callosum is one of the
most replicated structural findings in autism (Lainhart et al.
2005; Alexander et al. 2007; Stanfield et al. 2008; Keary et al.
2009). Diffusion tensor analysis of the corpus callosum
demonstrated significant microstructural differences between
autism and control groups in fractional anisotropy, mean
diffusivity, and radial diffusivity (Alexander et al. 2007).
Moreover, functional connectivity abnormalities have shown
correlation with size of the genu, anterior portion, and total
size of the corpus callosum in a theory of mind task (Mason
et al. 2008) and with the genu in an executive function task in
individuals with autism (Just et al. 2007).
(Alexander et al. 2007; Mason et al. 2008) projection pathways
would be ideally measured by direct correlation between
regions in the brain in corresponding positions in the opposite
hemisphere, where transcallosal connectivity is greatest. Such
a measurement might provide an excellent paradigm for
evaluating long-range connectivity in the brain because in-
terhemispheric connections have been shown to be among the
strongest relationships observed with functional connectivity
(Stark et al. 2008). Previous investigations of functional
connectivity in autism have applied either ROI-based analyses
(Just et al. 2004, 2007; Koshino et al. 2005, 2008; Kana et al.
2006; Kleinhans et al. 2008), task-based deactivation paradigms
(Kennedy et al. 2006), or focused on a defined network, such as
the default mode network (Di Martino et al. 2009; Monk et al.
2009; Weng et al. 2009).
? The Author 2010. Published by Oxford University Press. All rights reserved.
For permissions, please e-mail: firstname.lastname@example.org
To evaluate functional connectivity abnormalities in the
corpus callosum, we examined high-functioning autism and
typically developing control participants using resting-state
functional magnetic resonance imaging (fMRI) (Biswal et al.
1995; Fox and Raichle 2007). We compared interhemispheric
correlations between the autism and control samples to
evaluate the spatial heterogeneity of interhemispheric connec-
tivity abnormalities in autism. Based on structural, microstruc-
tural, and functional connectivity study findings to date,
we hypothesized decreased interhemispheric correlations
between homotopic cortical areas in the right and left
hemispheres in autism.
Materials and Methods
Fifty-three high-functioning adolescent and young adult males with
autism spectrum disorders (all had autism except 1 Asperger syndrome
and 3 PDD-NOS) were compared with 39 male typically developing
control volunteers, group matched by age. Verbal IQ (vIQ), perfor-
mance IQ (pIQ), handedness, autism diagnostic surveys, social re-
sponsiveness, and language function testing were obtained for most
participants (Table 1). Two control subjects were ambidextrous, one
control subject was left-handed, and 4 autism subjects were left-
handed, and all of the remaining subjects were right-handed.
Handedness did not differ significantly between the groups. The
participants had no history of hearing problems; all had English as their
As expected, language and motor function were impaired in the
autism participants as a group. vIQ and pIQ scores showed small, but
significant, decreases in the autism group. Language was significantly
impaired in the autism group relative to the control group.
All experiments were undertaken with the understanding and
written consent of each subject, with the approval of the University
of Utah Institutional Review Board and in compliance with national
legislation and the Code of Ethical Principles for Medical Research
Involving Human Subjects of the World Medical Association.
Diagnosis and Exclusion Criteria
Diagnosis of autism was established in the majority of subjects by the
Autism Diagnostic Interview-Revised (ADI-R) (Lord et al. 1994), Autism
Diagnostic Observation Schedule-Generic (ADOS-G) (Lord et al. 2000),
Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition
(DSM-IV) (American Psychiatric Association 1994), and International
Statistical Classification of Diseases and Related Health Problems, 10th
Revision (ICD-10) criteria. The diagnosis was established by an autism
expert (J.E.L.) using the ADI-R and DSM-IV/ICD-10 criteria in 2 subjects
and DSM-IV/ICD-10 criteria in 3 subjects. Individuals with medical
causes of autism, identified by history, Fragile-X gene testing, karyotype,
and examination, were excluded.
Control participants underwent tests of IQ, language, and neuro-
psychological function and were assessed with a standardized psychi-
atric interview (Leyfer et al. 2006). Most controls also were assessed
with the ADOS-G (Lord et al. 2000) to confirm typical development.
Controls with any history of developmental, learning, cognitive,
neurological, or neuropsychiatric conditions were excluded.
The Edinburgh Handedness Inventory (Oldfield 1971), a standardized
assessment of hand preference, was obtained for each subject. This
inventory consists of a numerical score between –100 and 100, where
–100 represents strong left-handedness and 100 represents strong
vIQ and pIQ were measured with the Wechsler Adult Intelligence
Scale, WAIS III (Wechsler 1997) or Wechsler Abbreviated Scale of
Intelligence, WASI (Wechsler 1999). The Differential Abilities Scale was
used to test several children (Elliott 1990).
Clinical Evaluation of Language Fundamentals—Third Edition (CELF-3)
(Semel et al. 1995) was used to assess language skills. It is
a comprehensive and nationally normalized clinical assessment tool
that provides a quantitative measure of language level. The CELF-3
includes subtests that measure grammar, syntax, semantics, and
working memory for language and provides an overall assessment of
higher order receptive and expressive language and total language
The Social Responsiveness Scale (SRS) is a normed, quantitative, 65-
item rating scale that measures social impairments characteristic of
autism spectrum disorders.
Images were acquired on Siemens 3 T Trio scanner. The scanning
protocol consisted of initial 1-mm isotropic magnetization prepared
rapid gradient echo (MP-RAGE) acquisition for an anatomic template.
Blood oxygen level--dependent (BOLD) echoplanar images (time
repetition = 2.0 s, time echo = 28 ms, Generalized Autocalibrating
Partially Parallel Acquisition (GRAPPA) with acceleration factor = 2, 40
slices at 3-mm slice thickness, 64 3 64 matrix) were obtained during
the resting state, where subjects were instructed to ‘‘Keep your eyes
open and relax. Remain awake and try to let thoughts pass through
your mind without focusing on any particular mental activity.’’
Prospective motion correction was performed during BOLD imaging
with PACE sequence (Siemens). An 8-min scan (240 volumes) was
obtained for each subject.
Characterization of control (n 5 39) and autism (n 5 53) populations
Age EHI vIQpIQ ADOS-SADOS-CSRS CELF-3
P value (2-tailed t-test)
?93 to 100
?60 to 100
9.4 3 10228
2.3 3 10223
1.5 3 10219
4.3 3 1027
Note: Summary statistics are provided for age, Edinburgh Handedness Inventory (EHI), vIQ (from either WASI or WAIS III), pIQ (from either WASI or WAIS III), Autism Diagnostic Observation Schedule-
Social (ADOS-S) and Communication (ADOS-C) subtests, SRS, and CELF-3 (total score).
Cerebral Cortex May 2011, V 21 N 5 1135
fMRI Postprocessing and Statistical Analysis
Offline postprocessing was performed in Matlab (Mathworks) using
SPM8 (Wellcome Trust) software. Initial slice timing correction was
performed to adjust for interleaved slice acquisition. Field map
sequence was acquired for each subject for distortion correction, and
all images were motion corrected using realign and unwarp procedure.
BOLD images were coregistered to MP-RAGE anatomic image sequence
for each subject. All images were normalized to Montreal Neurological
Institute (MNI) template brain (T1.nii in SPM8).
Because BOLD images contain information not only from neural
activity but also from physiological and artifactual sources such as
respirations, heart rate, and scanner drift, more accurate connectivity
measurements can be obtained by using a technique for removing
global signal sources not associated with neural activity (Fox et al.
2009). Because physiological waveforms of heart rate and respiration
were not available for all subjects during the scans, we used a regression
algorithm using time series from voxels in the soft tissues, cerebrospi-
nal fluid (CSF) and white matter to correct for artifactual correlations in
the BOLD data (Fox et al. 2009). No global signal regression was
performed to avoid introducing artifactual anticorrelations in the data
(Murphy et al. 2009; Anderson, Druzgal, et al. 2010).
Scalp and facial soft tissues, CSF, and white matter regression were
performed after automated gray matter, white matter, and CSF
segmentation of each subject’s MPRAGE image using SPM8. These
segmented images were manually inspected to confirm appropriate
identification of tissue components. The CSF time series for each
subject was measured from the lateral ventricles. This was obtained
from selecting voxels from the CSF segmented image for each subject
within the bounding box defined by MNI coordinates: –35 <x <35, –60
< y < 30, 0 < z < 30. White matter time series for each subject were
obtained from the mean time series of voxels within 2 ROIs in
the bilateral centrum semiovale (MNI coordinates: left: x = –27, y = –7,
z = 30; right: x = 27, y = –7, z = 30, each ROI had 10-mm radius). Before
extracting the white matter time series, an exclusive mask was
performed with the gray matter segmented image from each subject
to eliminate voxels containing gray matter. A soft tissue mask of the
facial and scalp soft tissues was used as previously described (Anderson,
Druzgal, et al. 2010). The mean soft tissue, CSF, and white matter time
series were then used as regressors in a general linear model (glmfit.m
in Matlab Statistics Toolbox) for the BOLD time series at each voxel in
the brain, and the best fit was subtracted from the voxel’s time
series data, producing the signal-corrected time series images. Each
voxel’s time series was band-pass filtered with a frequency window of
0.001--0.1 Hz (Cordes et al. 2001) and linearly detrended to correct for
scanner drift. These images were used for subsequent analysis.
For each subject, the time series at each voxel in the image was
computed and a corresponding voxel in the opposite hemisphere was
obtained by inverting the MNI space x coordinate. A cross-correlation
curve was computed between each pair of time series, and the zero lag
correlation (equivalent to Pearson correlation coefficient) was used to
construct an image of interhemispheric correlation (Fig. 1). This image
was converted using Fisher Z-transformation by computing the
hyperbolic arctangent of the value of each voxel to obtain an image
of Z-scores for correlation (Kennedy and Courchesne 2008; Fox et al.
2009; Murphy et al. 2009). Because there are small variations in gray
matter anatomy between the left and right hemispheres, these
interhemispheric Z-score images were spatially smoothed to mitigate
noise in the images using SPM8 (full-width at half-maximum of 8 3 8 3
Histograms of interhemispheric correlation distribution were com-
puted for gray matter voxels by using a binary mask obtained from
SPM8 gray.nii image. The same gray matter mask was used for all
subjects in this step so the same number of voxels was used for each
subject’s normalized data. Histograms and 95% confidence intervals
were computed on z-transformed values.
Interhemispheric correlation images after Fisher’s z-transform (Lowe
et al. 1998) were used for a second-level analysis comparing autism and
control groups using SPM8, in which a 2-tailed t-test design was used to
calculate significance of the interhemispheric correlation differences
between autism and control samples. The second-level analysis was
repeated using neuropsychiatric and demographic covariates of age,
vIQ, pIQ, language (CELF-3 total score), SRS, ADOS-G algorithm score
(sum of social and communication scores), and handedness as
covariates. Analysis was performed with each metric individually in
the general linear model, as well as with all metrics combined into
a single general linear model.
Calculation of Corpus Callosal Volume
Cortical reconstruction and volumetric segmentation were performed
with the FreeSurfer image analysis version 4.0.4 (Athinoula A. Martinos
Center for Biomedical Imaging, 2005). Details of the procedures are
described in prior publications (Dale and Sereno 1993; Dale et al. 1999;
Fischl, Sereno, and Dale 1999; Fischl and Dale 2000; Fischl et al. 2001,
2002; Han et al. 2006; Jovicich et al. 2006). Briefly, nonbrain tissue was
removed using a hybrid watershed/surface deformation procedure
(Segonne et al. 2004), automated Talairach transformation, segmenta-
tion of the subcortical white matter (WM) and deep gray matter (GM)
volumetric structures (Fischl et al. 2002, 2004), intensity normalization
(Sled et al. 1998) tessellation of the GM-WM boundary, automated
topology correction (Fischl et al. 2001; Segonne et al. 2007), and
surface deformation following intensity gradients to optimally place the
GM/WM and GM/CSF borders at the location where the greatest shift
in intensity defines the transition to the other tissue class (Dale and
Sereno 1993; Dale et al. 1999; Fischl and Dale 2000). The resulting
cortical models were registered to a spherical atlas, utilizing individual
cortical folding patterns to match cortical geometry across subjects
(Fischl, Sereno, Tootell, et al. 1999). The cerebral cortex was
parcellated into regions based on gyral and sulcal structure (Desikan
et al. 2006). Results for each subject were visually inspected to ensure
accuracy of registration, skull stripping, segmentation, and cortical
surface reconstruction. Corpus callosal volumes of posterior, mid
posterior, mid, mid anterior, and anterior corpus callosum were added
to produce a total corpus callosal volume for each subject used in
The procedure used in constructing maps of interhemispheric
correlation is illustrated in Figure 1, where the value at each
Figure 1. Calculating interhemispheric correlation. For each voxel in the image,
a corresponding voxel in the opposite hemisphere was obtained by inverting the MNI
x coordinate. Time series for each pair of voxels was obtained, and the value of the
cross-correlogram at zero lag (Pearson correlation coefficient) was used to construct
an image of interhemispheric correlation. This image was Fisher transformed by
evaluating hyperbolic arctangent and then spatially smoothed.
Interhemispheric Connectivity in Autism
Anderson et al.
voxel represents the correlation coefficient between the time
series at that voxel and the corresponding voxel in the
opposite hemisphere. Such interhemispheric correlation maps
showed reproducible patterns of interhemispheric correlation
at the single subject level. These patterns are shown in Figure 2,
in which Z-transformed maps of interhemispheric correlation
were averaged from 39 control subjects, overlaid on a canonical
MNI-normalized MP-RAGE image.
Interhemispheric Correlation in Typical Development
We first characterized the spatial distribution of voxelwise
interhemispheric correlation in the control population. The
group map of interhemispheric correlation in control subjects
shows spatial heterogeneity, consistent with the ROI technique
for interhemispheric correlation observed in a prior study
(Stark et al. 2008) and similar to results obtained using a related
voxelwise technique to study age-related changes in inter-
hemispheric correlation (Zuo et al. 2010). First, interhemi-
spheric correlations appear higher among gray matter voxels
than white matter voxels, as would be expected if interhemi-
spheric correlation is a measure of synchronized underlying
neural activity in areas of relatively higher anatomic connec-
tivity. It is also possible that this reflects our postprocessing
strategy of CSF and white matter regression because mean
brain signal or gray matter signal was not regressed, but this is
considered unlikely because a similar pattern was seen in
interhemispheric correlation results from data before they
were subjected to the regression postprocessing technique.
Figure 2. Interhemispheric correlation averaged over 39 control subjects. Scale bar shows Fisher-transformed correlation (Z-score).
Cerebral Cortex May 2011, V 21 N 5 1137
Interhemispheric correlation appears higher for voxels
closer to the midline. Relatively higher values of correlation
are seen in the frontal pole, occipital cortex and medial parietal
lobe, deep gray nuclei, and cerebellum, all of which are
relatively close to the midline. The trend toward higher
connectivity near the midline does not apply uniformly,
however. Areas of lateral sensorimotor cortex, visual cortex,
primary auditory cortex, and the anterior insula show, for
example, greater correlation than surrounding brain structures
of similar distance to the midline. Higher connectivity in
sensorimotor areas might be expected given known strong
thalamocortical contributions in these areas, with shared
inputs from sensory and motor signals that exhibit left/right
symmetry. Common inputs from the thalamus might be
expected to produce greater synchronization in activity. Given
these patterns, interhemispheric correlation appears consistent
with known underlying anatomical connectivity. Similar spatial
heterogeneity was observed in the group map of interhemi-
spheric correlations in the autism group.
Differences in Interhemispheric Correlation in Autism
Interhemispheric correlation shows a trend toward lower
values in autism throughout the brain, but some areas are
affected more than others. Figure 3 shows regions where
control subjects showed significantly higher interhemispheric
correlation than autistic subjects, with all clusters significant at
an acceptable false discovery rate q < 0.001. Peak coordinates
for significant clusters are listed in Table 2. No voxels showed
significantly higher correlation for autistic subjects than
control subjects. Regions showing higher interhemispheric
correlation for control subjects include sensorimotor cortex,
frontal insula, and superior parietal lobule extending from the
parietooccipital junction to the intraparietal sulcus.
Differences in interhemispheric correlation in autism do not
merely occur in areas of highest interhemispheric correlation
in the control population. Rather, many areas with high
correlation, such as visual cortex, medial frontal lobes, and
striatum, do not show significant differences between autism
and control samples. Moreover, the effect is also not well
described by changes only to voxels of intermediate connec-
tivity, as might be seen if the effect were due to a greater
dynamic range among subjects in voxels with intermediate
correlation. Many areas in this range do not show group
differences, such as dorsolateral prefrontal cortex and caudate
nuclei, among others. Differences in interhemispheric connec-
tivity also show poor match to spatial distribution of
physiological confounds, such as cerebral blood volume and
respiratory variation (Birn et al. 2006, 2008).
Covariates and Interhemispheric Correlation
To account for factors other than diagnosis that might underlie
these regional differences in interhemispheric correlation, we
included in a general linear model covariates of age, vIQ, pIQ,
language total score (CELF-3), SRS, ADOS-G algorithm score,
and handedness. Regions showing control greater than autism
Figure 3. Control [ autism interhemispheric correlation. Regions of greater
interhemispheric correlation for 39 controls than in 53 autism subjects. All clusters
were significant at q \ 0.001, false discovery rate. No voxels showed significantly
greater interhemispheric correlation for autism than control subjects.
Peak MNI coordinates of control [ autism interhemispheric correlation
Lateral sensorimotor cortex
Lateral sensorimotor cortex
Lateral parietooccipital junction
Posterior superior parietal
Note: Significant results fell in an anterior (insula, lateral sensorimotor) and a posterior (superior
parietal lobule) cluster.
Figure 4. Increased interhemispheric correlation associated with younger age. All
clusters were significant at q \ 0.001, false discovery rate. No voxels showed
significantly higher interhemispheric correlation with older age or higher or lower vIQ,
pIQ, Edinburgh Handedness Inventory, ADOS, SRS, or CELF-3 scores.
Interhemispheric Connectivity in Autism
Anderson et al.
interhemispheric correlation remained significant when these
factors were included as regressors in the model. Each
covariate was analyzed separately with diagnosis as well as in
a combined general linear model with all of the covariates. In
each case, the only covariate that showed significant associa-
tions with interhemispheric correlation was age, shown in
Figure 4. Younger subjects showed higher interhemispheric
correlation near the midline, particularly in the supplementary
motor area, precuneus, and occipital lobe.
Mean Interhemispheric Correlation and Age
We obtained an estimate of mean interhemispheric correlation
by averaging overall gray matter voxels within the interhemi-
spheric correlation images for each subject. Mean gray matter
interhemispheric correlation was reduced in the autism sample
relative to the control sample (control 0.253 ± 0.071 standard
deviation [SD], autism 0.232 ± 0.071 SD) To evaluate the
significance of this difference, we included age as a covariate
because a significant relationship was seen in the voxelwise
analysis of Figure 4. Using a general linear model with diagnosis
and age as regressors and including an interaction term, we
confirmed the hypothesis that control subjects showed
significantly higher interhemispheric correlation than autism
subjects (P = 0.023, one-tailed t-statistics), with a significant
interaction between age and diagnosis (P = 0.043). This is
illustrated in Figure 5, showing greater decreases in mean
interhemispheric correlation with age among control subjects
than among autism subjects.
The changes seen in interhemispheric correlation appear
most significant among the younger subjects. We divided our
autism and control samples into subjects younger than or equal
to age 20 and subjects older than 20. In the younger control
group, the correlation with age was significant (r = –0.48, P =
0.019, one-tailed t-test). In the older group, the correlation was
not significant (r = –0.09, P = 0.36). In the autism sample,
neither the younger group (r = 0.03, P = 0.43) nor the older
group (r = –0.22, P = 0.15) showed a significant correlation
between mean interhemispheric correlation and age. In the
combined autism and control sample, no voxels showed
a significant relationship with one-tailed t-tests between mean
interhemispheric correlation and neuropsychological metrics
of vIQ (r = –0.09, P = 0.19), pIQ (r = 0.05, P = 0.31), handedness
(r = –0.11, P = 0.16), Autism Diagnostic Observation Schedule-
Social (r = –0.03, P = 0.40), Autism Diagnostic Observation
Schedule-Communication (r = –0.09, P = 0.23), or language
function testing (r = –0.01, P = 0.45) in our data. SRS scores
showed a strong trend toward decreased social impairment
with higher mean interhemispheric correlation (r = –0.18,
P = 0.058).
Corpus Callosum Volume and Interhemispheric
Corpus callosum mean volume was significantly reduced in the
autism sample relative to the control sample (control 3523 ±
450 SD, autism 3271 ± 600, one-tailed t-test, P = 0.015). In order
to evaluate the relationship of functional interhemispheric
correlation with callosal volume, we compared the mean gray
matter interhemispheric correlation values for each subject
with total corpus callosal volume and found no significant
correlation (r = –0.009, P = 0.93). No significant correlation was
seen in either the autism or control sample between gray
Figure 5. Relationship of mean gray matter interhemispheric correlation with subject
age. (A) Each point represents mean gray matter interhemispheric correlation for 1 of
39 control subjects (above), with best straight line fit through the data. (B) The same
is shown below for 53 autism subjects. (C) Distributions of interhemispheric
correlation across gray matter voxels. Distributions were computed for each subject
and shaded averages above show pointwise 95% confidence intervals for
distributions from autism and control populations, computed on Fisher-transformed
Cerebral Cortex May 2011, V 21 N 5 1139
matter functional interhemispheric correlation and corpus
callosal volume when samples were analyzed separately. These
data are shown in Figure 6.
Interhemispheric Correlation and Distance from Midline
Voxelwise data showed significant differences in interhemi-
spheric correlation in autism were lateral to the midline. To
evaluate whether interhemispheric correlation in autism is
related to a voxel’s position on the medial--lateral axis, we
computed the difference in mean interhemispheric correlation
for each voxel between the control and autism samples and
averaged this value for all gray matter voxels in each sagittal
slice, shown in Figure 7. There is increasing interhemispheric
correlation for control subjects relative to autism subjects with
distance from the midline with a peak at 60 mm. Greater than
one SD of gray matter voxels at this distance show greater
interhemispheric correlation for control subjects, while essen-
tially no difference in interhemispheric correlation is seen at
Alternate Method: Regional Parcellation
Another method for calculating interhemispheric correlation
was used in addition to the voxelwise technique described
above. The supratentorial brain was parcellated into 90 regions
using the AAL brain atlas (Tzourio-Mazoyer et al. 2002; Maldjian
et al. 2003). These consist of 45 left/right homologous regions.
For each region, the mean BOLD time course was extracted for
each subject, and correlation was compared with the homol-
ogous region in the opposite hemisphere, similar to a method
previously used to measure interhemispheric correlation (Stark
et al. 2008). While this method has lower spatial resolution, it
might also be less sensitive to noise present in the voxelwise
Figure 6. Interhemispheric correlation does not vary with corpus callosal volume.
Mean interhemispheric correlation of gray matter voxels is compared with corpus
callosal volume from the MP-RAGE scan for each subject.
Figure 7. Differences in interhemispheric correlation increase with distance from the
midline. The difference of mean interhemispheric correlation from gray matter voxels
between control and autism subjects is shown for each sagittal slice. Error bars
represent SD across voxels in the slice for difference in mean correlation between
control and autism samples.
Figure 8. Effect of distance between regions on interhemispheric correlation. (A) The
supratentorial brain was parcellated into 45 pairs of left/right homologous regions.
Each point shows the mean interhemispheric correlation between left and right
homologues for one region. Error bars show standard error of the mean across autism
subjects. Dark bars were statistically significant after Bonferroni correction. (B) The
same regions as above were used to plot the difference between control and autism
mean interhemispheric correlation for each region against the Euclidean distance
between the centroids of the left and right homologues for the region.
Interhemispheric Connectivity in Autism
Anderson et al.
Mean control and autism population values for each region
are shown in Figure 8. Forty of the 45 regions showed greater
interhemispheric correlation for controls. This was statistically
significant across subjects after multiple comparison correction
in 7 regions: rolandic operculum, insula, superior and mid
occipital, fusiform, postcentral, and superior temporal. These
are largely the same regions seen in the voxelwise analysis of
Figure 3. Strong trends in the fusiform gyrus and superior
temporal gyrus were also seen in the voxelwise data. When the
distance from midline was measured from the centroid of each
region, there was again a significant trend toward greater
interhemispheric correlation in controls with distance from
the midline (r = 0.35, P = 0.009).
We report regionally specific decreases in interhemispheric
correlation in autism subjects compared with typically de-
veloping controls. The resting-state fMRI interhemispheric
correlational differences in autism were localized to sensori-
motor cortex, superior parietal lobule, frontal insula, superior
temporal gyrus, fusiform gyrus, and perisylvian posterior
inferolateral premotor cortex. Interhemispheric correlation
differences were more associated with the diagnosis of autism
than with quantitative measures of IQ, language function, and
handedness. Significant age-related decrease in mean inter-
hemispheric correlation observed in controls was absent in the
group with autism.
Resting-State fMRI Interhemispheric Correlations
Correlation of the fMRI BOLD time series of gray matter is
believed to be an indirect index of synchrony in spontaneous
neural activity and likely related to the intrinsic functional
architecture of the brain (Biswal et al. 1995, 2010; Fox and
Raichle 2007; Buckner et al. 2009). In response to stimuli,
animals have coherent fluctuations in electrical activity in
cortical neurons that are connected interhemispherically, and
fluctuations in electrical activity and BOLD hemodynamic
responses are statistically associated (Innocenti 2009). Similar
to interhemispheric electroencephalography coherence, the
degree of correlation in the BOLD time series between 2
regions at rest is used as an estimate of the strength of their
baseline, nontask--related functional connectivity. It is impor-
tant to note, however, that a variety of factors other than
spontaneous neural activity can affect the observed correla-
tions (Jones et al. 2010). The importance of understanding
interregional correlations in spontaneous neural activity has
been recently emphasized. Observed correlations in fMRI time
series between brain regions during performance of a task and
autism-control differences in the correlations are likely driven
by fluctuations in neuronal activity that are unrelated to the
task (Jones et al. 2010).
Regionally Specific Decreased Interhemispheric
Correlations in Autism
We found an overall decrease in the correlation of the time
series in homologous voxels in the 2 hemispheres in the autism
sample, with greatest effect seen in areas of the brain relevant
to clinical abnormalities observed in autism. The largest
difference in correlation observed was in the anterior (frontal)
insula. The frontal insulae are core components of social
processing networks and constitute a hub mediating integra-
tion of external and internal stimuli with consistent hypo-
activity inautism studies
Frontoinsula is also a core region of the recently described
salience network involved in identification of novel or relevant
stimuli across sensory modalities (Seeley et al. 2007). The left
frontoinsular region was specifically hypoactive in autism
subjects in a study of novelty detection using an auditory
oddball paradigm (Gomot et al. 2006).
Superior temporal gyrus has been implicated as a site of
abnormal auditory processing in autism, as well as a locus
associated with social intelligence. An fMRI study of social
intelligence reported activation of superior temporal gyrus
during processing of social judgments (Baron-Cohen et al.
1999). Volumetric studies have shown atypical structure--
function relationships suggesting abnormal lateralization of
superior temporal gyrus in autism (Bigler et al. 2007).
Abnormalities of white matter microstructure in the superior
temporal gyrus, detected by diffusion tensor imaging of autism,
include significantly decreased fractional anisotropy and in-
creased mean and radial diffusivity (Lee et al. 2007). Auditory
processing of rapid (Oram Cardy et al. 2005) and steady-state
(Wilson et al. 2007) stimuli, known to involve the superior
temporal gyrus, was found to be abnormal in autism subjects
using magnetoencephalography analysis. A recent fMRI study of
language processing showed hypoactivity in autism at the
junction of the superior temporal gyrus and left posterior
insula (Anderson, Lange, et al. 2010), a locus included in the
abnormal superior temporal gyrus interhemispheric correlation
Other areas of reduced interhemispheric connectivity in
autism included primary sensorimotor and lateral inferior
premotor cortex. This finding might be related to well-known
gross and fine motor skill impairments associated with autism
(Vilensky et al. 1981; Frith 1991; Jansiewicz et al. 2006;
Mostofsky et al. 2009). Alteration in the volume of white matter
deep to primary motor cortex correlates with greater
impairment in basic motor skills in children with autism
(Mostofsky et al. 2007). Impairments in basic motor control are
among the earliest deficits observed in some infants who
develop autism (Brian et al. 2008; Zwaigenbaum et al. 2009).
Lower correlations involving the superior parietal lobule in
autism were observed in a study of the functional connectivity
of residuals after the effects of task response were removed
(Jones et al. 2010).
Abnormalities in function in the fusiform gyrus are also
reported in the literature in the context of social function
and face processing in autism (Pierce and Redcay 2008;
Corbett et al. 2009). Functional connectivity of the fusiform
gyrus to frontal regions has been reported to be lower in
autism during facial processing tasks (Kleinhans et al. 2008;
Koshino et al. 2008). A postmortem study in 7 autism patients
and 10 controls showed decreased numbers of neurons
specifically in the fusiform gyrus in autism (van Kooten et al.
If autism is manifest by an overabundance of short-range
connections and impaired long-range connections (Casanova
and Trippe 2009), this can also be seen as a failure of the
segregation and integration process. Segregation is thought to
involve pruning of local connections, whereas integration
involves strengthening of long-range connections within
distributed functional networks (Fair et al. 2007, 2009). The
regions where our data show greater interhemispheric
(Uddinand Menon 2009).
Cerebral Cortex May 2011, V 21 N 5 1141
connectivity are precisely those that are longer-range, more
lateral (lateral sensorimotor and premotor but not medial), and
in areas of association cortex, such as superior parietal lobule,
frontal insula, and posterior lateral frontal lobe, which are more
likely to be strengthened during integration. This hypothesis
would also explain functional connectivity results obtained
during a Go/No Go task where connectivity from inferior
frontal gyrus to supplementary motor area showed greater
differences with age between autism and control samples, as
would be expected from delayed or impaired integration (Lee
et al. 2009).
Age-Related Changes in Interhemispheric Correlations
We report atypical age-related changes in interhemispheric
correlations in autism. Across the age range we studied, 8--42
years, control subjects showed decreasing interhemispheric
correlations, particularly in medial cortical regions. The autism
subjects showed much less change with age, and the relation-
ship was not statistically significant either in the younger (less
than 20 years) or older subgroups. The relative lack of
age-related changes in the autism group appeared to be driven
by abnormally decreased interhemispheric correlations in
a substantial subgroup of younger individuals and to a lesser
extent somewhat increased interhemispheric correlations
relative to controls in a small subgroup of older individuals. A
diffusion tensor study of the corpus callosum, the main
interhemispheric tract, found a similar pattern between 7 and
33 years of age: significant age-related changes in total corpus
callosum volume, fractional anisotropy, and radial diffusivity
present in typically developing controls were absent in the
autism group (Alexander et al. 2007). Although caution is
requiredwhen making inferences
processes from cross-sectional data (Kraemer et al. 2000), the
combined findings suggest a hypothesis that needs to be tested
with longitudinal data: that the developmental trajectory of
interhemispheric connectivity deviates from typical develop-
ment in a nonlinear manner from childhood into adulthood in
autism and is related to aberrant tensor scalar measures of
corpus callosum microstructure. Abnormalities of interhemi-
spheric connectivity in childhood and different abnormalities
in adulthood provide additional support to a growing body of
literature that suggests brain development during late as well as
early childhood and brain maturation during young adulthood
are abnormal in autism.
Our finding is particularly important in the developmental
context of maturing connectivity during late adolescence and
early adulthood. The brain has been shown to undergo
segregation and integration across development (Varela et al.
2001; Fair et al. 2007; Supekar et al. 2009), where segregation
corresponds to specificity of neural function in local spatial
regions and integration represents development of relation-
ships between spatially disparate regions in the brain with
related function. For example, default mode regions are only
sparsely connected in childhood and strengthen with age
(Fair et al. 2008). In other networks, a similar pattern is seen
where correlated ensembles develop locally first, with distrib-
uted networks occurring in adolescence or early adulthood
(Fair et al. 2009). Using independent component analysis to
define networks, it has also been shown that distributed
networks show increasing efficiency and less mutual interde-
pendence with age (Stevens et al. 2009). These data suggest
a neurodevelopmental mechanism of overconnectivity fol-
lowed by pruning or weakening of short-range functional
connections. Our data in typically developing controls are
consistent with these results in that connections near the
A prior study evaluating corpus callosal microstructure
found increased fractional anisotropy with age in controls but
not in autism subjects. This is curious in that in the present
study mean gray matter interhemispheric correlation decreases
with age. To reconcile these findings, we note that the
decrease in correlation was driven by more medial voxels
such as the ones showing greatest association with age in
Figure 4. It is possible that in these voxels decreasing
correlation might represent the effects of integration, where
midline structures are increasingly connected to other distant
brain regions with age (such as default mode and attention
control networks) and that these strengthened connections
represent a greater proportion of the variance of the BOLD
signal, lowering the interhemispheric synchrony for midline
regions. Also possible is that interhemispheric correlation
measures features of connectivity not directly related to
colossal projections (particularly given the absence of direct
correlation between corpus callosal volume and interhemi-
spheric correlation that we found), such as shared input from
the thalamus or subcortical structures.
Corpus Callosum Volume and Interhemispheric
Compared with typically developing controls, our autism
sample had decreased mean total corpus callosum volume
and a decrease in the overall mean correlation of time series in
homologous interhemispheric gray matter voxels. These
findings are consistent with replicated findings of decreased
callosal size (Manes et al. 1999; Chung et al. 2004; Waiter et al.
2005; Vidal et al. 2006; Freitag et al. 2009; Keary et al. 2009) and
fractional anisotropy of white matter (Alexander et al. 2007;
Keller et al. 2007; Brito et al. 2009) in the corpus callosum. Size
decreases in the corpus callosum have been shown to be
correlated with neuropsychological abnormalities in a motor
task in autism (Keary et al. 2009), frontoparietal functional
underconnectivity (Just et al. 2007), and reduced gyral
window, consistent with minicolumnar findings in the cortex
in autism and a bias toward local relative to long-range
connectivity (Casanova et al. 2009).
Corpus callosum volume and interhemispheric resting-state
time series correlation of homologous voxels were not related
within individuals in our sample. The tendency toward smaller
corpus callosum volume and decreased interhemispheric
correlation might be biologically independent phenomena, at
least at the level of the total corpus callosum and overall mean
connectivity. This finding is in contrast to studies of functional
connectivity between intrahemispheric and interhemispheric
cortical areas during the performance of neuropsychological
tasks. These studies have found significant positive correlations
between size of the total corpus callosum or its subregions
and functional connectivity in autism samples but usually not
in controls. Based on the findings, it has been proposed
that corpus callosum volume or mid-sagittal area constrains
task-related functional connectivity.
Interhemispheric Connectivity in Autism
Anderson et al.
Our findings suggest that corpus callosum volume, although
decreased in autism, is associated but not correlated with
decreased interhemispheric functional connectivity between
homologous gray matter voxels in the resting state. The
functional information we describe might characterize differ-
ent aspects of connectivity not present in volumetric data
alone. Volumetric and diffusion tensor measurements of white
matter only relay information on white matter axonal
architecture in the corpus callosum and not differences related
to synaptic efficiency or number that can affect functional
correlation. In addition, the interhemispheric correlations we
observed might not reflect transcallosal connectivity but rather
shared inputs from other sources. The mechanism for
correlation might be more complicated, possibly involving
corticostriatal, corticothalamic, or corticocerebellar pathways.
It is also possible that other commissural fibers such as the
fornix or anterior commissure contribute to interhemispheric
correlation. Correlation with diffusion imaging and other
metrics of white matter microstructure might help determine
the extent to which transcallosal connections drive the high
functional connectivity we observed in controls and the
differences we found in autism.
Alternative Explanations for Observed Decreased in
Interhemispheric Correlation in Autism
It is possible that the abnormalities we observed in interhemi-
spheric connectivity in autism arose from case-control differ-
ences other than a primary deficit in correlated spontaneous
neural activity in homologous gray matter voxels. The de-
creased correlations could be the secondary result of atypical
experience rather than a primary component of the neural
mechanism of autism. Case-control differences in cognitive and
behavioral characteristics might have confounded the results.
Yet, across multiple neuropsychological indices, correlation
abnormalities were more associated with diagnosis of autism
than other metrics. It is not yet known if the increased rates of
macrocephaly and megalencephaly in autism affect structural
and functional connectivity. In evolution, larger brains are
associated with increased cortical volume and folding, de-
creased corpus callosum size relative to total brain volume,
decreased interhemispheric connectivity, and increased intra-
hemispheric functional specialization (Casanova et al. 2009).
But enlarged brain volume appears to be mainly a phenomenon
of early childhood in autism, with mean brain volume not
differing significantly from typical in older individuals with the
disorder (Lainhart et al. 2005). The differences in interhemi-
spheric correlation of homologous voxels could result from
functionally different neurons in homologous voxels in cases
and controls. Intersubject and interhemispheric variability exist
in how cytoarchitectonic functional areas map onto MRI-
defined voxels (Scheperjans et al. 2008). If variability is
systematically increased in autism (Muller et al. 2003; Bailey
et al. 2005), and resting-state interhemispheric connectivity is
stronger between functionally related areas than between
structurally homologous areas, decreased correlation between
fMRI time series in homologous voxels could result from
differences in variability of structure--function maps between
groups. Even if this is the case in our samples, the findings would
still suggest that increased interhemispheric structure--function
variability is not uniform in autism but occurs in specific areas
relevant to core and associated features of the disorder.
Finally, resting-state examinations are difficult to control,
given the relatively unconstrained mental activity that occurs.
Interindividual variations in mental state during rest, particu-
larly systematic differences related to autism in how the
‘‘resting’’ task is performed, might contribute to our results
rather than underlying structural connectivity differences.
Because performing an explicit task might reduce interindi-
vidual variation in mental state, methods such as using the
correlation of residual fluctuations when the effects of task-
related activation are removed might provide complementary,
albeit still indirect, information about correlated spontaneous
neural activity (Jones et al. 2010).
Limiting the autism sample to high-functioning males reduces
complex heterogeneity prevalent in autism and increases
statistical power but does not allow us to determine if the
findings extend to females, younger children, and lower
functioning individuals with autism. The cross-sectional design
limits conclusion about developmental changes but generates
hypotheses for longitudinal studies. Measuring interhemi-
spheric correlations at the homologous voxel level results
in a huge number of comparisons but allows an anatomically
finer grain analysis of case-control differences than permitted
by studies that use parcellated cortical areas as ROIs. It is
reassuring that both techniques identified similar areas of
significant differences between the autism and control
samples. The samples we used are limited by the relatively
broad age range included. It appears that most of the changes
seen with age occur during childhood and adolescence, with
relatively few changes after age 20. A more focused study with
respect to age might help clarify the timing of changes in
interhemispheric correlation. Corpus callosal volumes were
calculated with FreeSurfer, which has limited validation on
children and adolescent populations included in the present
study. Finally, we note that differences seen between autism
and control samples are greater for more lateral brain regions
but acknowledge that distance from midline is a crude
measurement of path length. Future studies might clarify
whether this relationship represents medial/lateral axis differ-
ences or path length by examining specific connections using
probabilistic tractography or other multimodal methods to
estimate path length between regions.
The neuroimaging manifestations of autism are widespread
with many brain regions implicated. We found decreased
interhemispheric correlations between homologous voxels in
some but not all regions of the brain in our autism sample
relative to controls. Our finding adds to growing evidence
that abnormalities of interhemispheric connectivity in autism
are widespread but regionally specific and related to cognitive
and neurological impairments commonly found in the disorder.
Abnormalities in interhemispheric connectivity might be
due to a common neural mechanism underlying seemingly
disparate aspects of the autism phenotype. Our findings
might also be part of a more widespread albeit selective
disturbance of spontaneous neuronal activity in autism (Jones
et al. 2010).
Interhemispheric connectivity might represent a useful
screening method for evaluating neurological disorders where
Cerebral Cortex May 2011, V 21 N 5 1143
neural connectivity is implicated in the pathophysiology.
Robust interhemispheric connectivity comprises one of the
dominant modes of functional connectivity (Stark et al. 2008)
and might allow for a simple screening technique for regional
differences in connectivity in pathophysiological states. If
a different distribution of interhemispheric connectivity
abnormalities is seen in other disorders, this would increase
confidence that results in this study are not an iceberg effect of
generalized connectivity reduction but pathway-specific ab-
normalities related to neurodevelopmental integration of
function in distributed networks.
Fellowship (1677); University of Utah Multidisciplinary Re-
searchSeed Grant; National
Disorders and Stroke (R01NS34783); NRSA Predoctoral Fellow-
ship (NIH/NIDCD 1F31 DC10143-01); National Institutes of
Health (NIDCD T32DC008553); Ben B. and Iris M. Margolis
The authors appreciate the assistance of Melody Johnson and Henry
Buswell of the University of Utah Center for Advanced Imaging
Research for technical assistance in data acquisition. They acknowledge
Drs William McMahon, Judith Miller, Mikle South, and Nicanor Garcia,
and past members of the Utah Autism Research Program. They also
thank Barbara Young and Celeste Knoles of the Utah Autism
Neuroscience Program and express their sincere gratitude to the
young people and their families who participated in the study. The
content is solely the responsibility of the authors and does not
necessarily represent the official views of the National Institute of
Mental Health, National Institute of Neurological Disorders and Stroke,
or the National Institutes of Health. Conflict of Interest: None declared.
Alexander AL, Lee JE, Lazar M, Boudos R, DuBray MB, Oakes TR,
Miller JN, Lu J, Jeong EK, McMahon WM, et al. 2007. Diffusion tensor
imaging of the corpus callosum in Autism. Neuroimage. 34:61--73.
American Psychiatric Association 1994. Diagnostic and Statistical
Manual of Mental Disorders: DSM-IV. 4th ed. Washington (DC):
American Psychiatric Association.
Anderson JS, Druzgal TJ, Lopez-Larson M, Jeong EK, Desai K, Yurgelun-
Todd D. 2010. Network anticorrelations, global regression, and
phase-shifted soft tissue correction. Hum Brain Mapp. Published
Online 9 Jun 2010.
Anderson JS, Lange N, Froehlich A, DuBray M, Druzgal T, Froimowitz M,
Alexander A, Bigler E, Lainhart J. 2010. Decreased left posterior
insular activity during auditory language in autism. AJNR Am
J Neuroradiol. 31:131--139.
Bailey AJ, Braeutigam S, Jousmaki V, Swithenby SJ. 2005. Abnormal
activation of face processing systems at early and intermediate
latency in individuals with autism spectrum disorder: a magneto-
encephalographic study. Eur J Neurosci. 21:2575--2585.
Baron-Cohen S, Ring HA, Wheelwright S, Bullmore ET, Brammer MJ,
Simmons A, Williams SC. 1999. Social intelligence in the normal and
autistic brain: an fMRI study. Eur J Neurosci. 11:1891--1898.
Bigler ED, Mortensen S, Neeley ES, Ozonoff S, Krasny L, Johnson M,
Lu J, Provencal SL, McMahon W, Lainhart JE. 2007. Superior
temporal gyrus, language function, and autism. Dev Neuropsychol.
Birn RM, Diamond JB, Smith MA, Bandettini PA. 2006. Separating
respiratory-variation-related fluctuations from neuronal-activity-related
fluctuations in fMRI. Neuroimage. 31:1536--1548.
Birn RM, Smith MA, Jones TB, Bandettini PA. 2008. The respiration
response function: the temporal dynamics of fMRI signal fluctua-
tions related to changes in respiration. Neuroimage. 40:644--654.
Biswal B, Yetkin FZ, Haughton VM, Hyde JS. 1995. Functional
connectivity in the motor cortex of resting human brain using
echo-planar MRI. Magn Reson Med. 34:537--541.
Biswal BB, Mennes M, Zuo XN, Gohel S, Kelly C, Smith SM,
Beckmann CF, Adelstein JS, Buckner RL, Colcombe S, et al. 2010.
Toward discovery science of human brain function. Proc Natl Acad
Sci U S A. 107:4734--4739.
Brian J, Bryson SE, Garon N, Roberts W, Smith IM, Szatmari P,
Zwaigenbaum L. 2008. Clinical assessment of autism in high-risk
18-month-olds. Autism. 12:433--456.
Brito AR, Vasconcelos MM, Domingues RC, Hygino da Cruz LC, Jr.,
Rodrigues LD, Gasparetto EL, Calcada CA. 2009. Diffusion tensor
imaging findings in school-aged autistic children. J Neuroimaging.
Buckner RL, Sepulcre J, Talukdar T, Krienen FM, Liu H, Hedden T,
Andrews-Hanna JR, Sperling RA, Johnson KA. 2009. Cortical hubs
revealed by intrinsic functional connectivity: mapping, assessment
of stability, and relation to Alzheimer’s disease. J Neurosci.
Casanova M, Trippe J. 2009. Radial cytoarchitecture and patterns of
cortical connectivity in autism. Philos Trans R Soc Lond B Biol Sci.
Casanova MF, El-Baz A, Mott M, Mannheim G, Hassan H, Fahmi R,
Giedd J, Rumsey JM, Switala AE, Farag A. 2009. Reduced gyral
window and corpus callosum size in autism: possible macroscopic
correlates of a minicolumnopathy. J Autism Dev Disord. 39:751--764.
Castelli F, Frith C, Happe F, Frith U. 2002. Autism, Asperger syndrome
and brain mechanisms for the attribution of mental states to
animated shapes. Brain. 125:1839--1849.
Cherkassky VL, Kana RK, Keller TA, Just MA. 2006. Functional
connectivity in a baseline resting-state network in autism. Neuro-
Chung MK, Dalton KM, Alexander AL, Davidson RJ. 2004. Less white
matter concentration in autism: 2D voxel-based morphometry.
Corbett BA, Carmean V, Ravizza S, Wendelken C, Henry ML, Carter C,
Rivera SM. 2009. A functional and structural study of emotion and
face processingin children
Cordes D, Haughton VM, Arfanakis K, Carew JD, Turski PA, Moritz CH,
Quigley MA, Meyerand ME. 2001. Frequencies contributing to
functional connectivity in the cerebral cortex in ‘‘resting-state’’ data.
AJNR Am J Neuroradiol. 22:1326--1333.
Courchesne E, Pierce K. 2005. Why the frontal cortex in autism might
be talking only to itself: local over-connectivity but long-distance
disconnection. Curr Opin Neurobiol. 15:225--230.
Dale AM, Fischl B, Sereno MI. 1999. Cortical surface-based analysis. I.
Segmentation and surface reconstruction. Neuroimage. 9:179--194.
Dale AM, Sereno MI. 1993. Improved localization of cortical activity by
combining EEG and MEG with MRI cortical surface reconstruction:
a linear approach. J Cogn Neurosci. 5:162--176.
Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D,
Buckner RL, Dale AM, Maguire RP, Hyman BT, et al. 2006. An
automated labeling system for subdividing the human cerebral
cortex on MRI scans into gyral based regions of interest. Neuro-
Di Martino A, Shehzad Z, Kelly C, Roy AK, Gee DG, Uddin LQ,
Gotimer K, Klein DF, Castellanos FX, Milham MP. 2009. Relationship
between cingulo-insular functional connectivity and autistic traits in
neurotypical adults. Am J Psychiatry. 166:891--899.
Elliott CD. 1990. Differential Ability Scales: Introductory and technical
handbook. New York: The Psychological Corporation.
Fair DA, Cohen AL, Dosenbach NU, Church JA, Miezin FM, Barch DM,
Raichle ME, Petersen SE, Schlaggar BL. 2008. The maturing
architecture of the brain’s default network. Proc Natl Acad Sci
U S A. 105:4028--4032.
Fair DA, Cohen AL, Power JD, Dosenbach NU, Church JA, Miezin FM,
Schlaggar BL, Petersen SE. 2009. Functional brain networks develop
with autism. PsychiatryRes.
Interhemispheric Connectivity in Autism
Anderson et al.
from a ‘‘local to distributed’’ organization. PLoS Comput Biol.
Fair DA, Dosenbach NU, Church JA, Cohen AL, Brahmbhatt S,
Miezin FM, Barch DM, Raichle ME, Petersen SE, Schlaggar BL.
2007. Developmentof distinct
segregation and integration. Proc Natl Acad Sci U S A. 104:
Fischl B, Dale AM. 2000. Measuring the thickness of the human cerebral
cortex from magnetic resonance images. Proc Natl Acad Sci U S A.
Fischl B, Liu A, Dale AM. 2001. Automated manifold surgery:
constructing geometrically accurate and topologically correct
models of the human cerebral cortex. IEEE Trans Med Imaging.
Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der
Kouwe A, Killiany R, Kennedy D, Klaveness S, et al. 2002. Whole
brain segmentation: automated labeling of neuroanatomical struc-
tures in the human brain. Neuron. 33:341--355.
Fischl B, Salat DH, van der Kouwe AJ, Makris N, Segonne F, Quinn BT,
Dale AM. 2004. Sequence-independent segmentation of magnetic
resonance images. Neuroimage. 23(Suppl 1):S69--S84.
Fischl B, Sereno MI, Dale AM. 1999. Cortical surface-based analysis. II:
inflation, flattening, and a surface-based coordinate system. Neuro-
Fischl B, Sereno MI, Tootell RB, Dale AM. 1999. High-resolution
intersubject averaging and a coordinate system for the cortical
surface. Hum Brain Mapp. 8:272--284.
Fox MD, Raichle ME. 2007. Spontaneous fluctuations in brain activity
observed with functional magnetic resonance imaging. Nat Rev
Fox MD, Zhang D, Snyder AZ, Raichle ME. 2009. The global signal and
observed anticorrelated resting state brain networks. J Neuro-
Freitag CM, Luders E, Hulst HE, Narr KL, Thompson PM, Toga AW,
Krick C, Konrad C. 2009. Total brain volume and corpus callosum
size in medication-naive adolescents and young adults with autism
spectrum disorder. Biol Psychiatry. 66:316--319.
Frith U. 1991. Autism and Asperger syndrome. Cambridge: Cambridge
Geschwind DH, Levitt P. 2007. Autism spectrum disorders: develop-
mental disconnection syndromes. Curr Opin Neurobiol. 17:103--111.
Gomot M, Bernard FA, Davis MH, Belmonte MK, Ashwin C, Bullmore ET,
Baron-Cohen S. 2006. Change detection in children with autism: an
auditory event-related fMRI study. Neuroimage. 29:475--484.
Han X, Jovicich J, Salat D, van der Kouwe A, Quinn B, Czanner S, Busa E,
Pacheco J, Albert M, Killiany R, et al. 2006. Reliability of MRI-derived
measurements of human cerebral cortical thickness: the effects of
field strength, scanner upgrade and manufacturer. Neuroimage.
Hardan AY, Minshew NJ, Keshavan MS. 2000. Corpus callosum size in
autism. Neurology. 55:1033--1036.
Jansiewicz EM, Goldberg MC, Newschaffer CJ, Denckla MB, Landa R,
Mostofsky SH. 2006. Motor signs distinguish children with high
functioning autism and Asperger’s syndrome from controls. J Autism
Dev Disord. 36:613--621.
Jones TB, Bandettini PA, Kenworthy L, Case LK, Milleville SC, Martin A,
Birn RM. 2010. Sources of group differences in functional
connectivity: an investigation applied to autism spectrum disorder.
Jovicich J, Czanner S, Greve D, Haley E, van der Kouwe A, Gollub R,
Kennedy D, Schmitt F, Brown G, Macfall J, et al. 2006. Reliability in
multi-site structural MRI studies: effects of gradient non-linearity
correction on phantom and human data. Neuroimage. 30:436--443.
Just MA, Cherkassky VL, Keller TA, Kana RK, Minshew NJ. 2007.
Functional and anatomical cortical underconnectivity in autism:
evidence from an FMRI study of an executive function task and
corpus callosum morphometry. Cereb Cortex. 17:951--961.
Just MA, Cherkassky VL, Keller TA, Minshew NJ. 2004. Cortical
activation and synchronization during sentence comprehension in
high-functioning autism: evidence of underconnectivity. Brain.
Kana RK, Keller TA, Cherkassky VL, Minshew NJ, Just MA. 2006.
Sentence comprehension in autism: thinking in pictures with
decreased functional connectivity. Brain. 129:2484--2493.
Keary CJ, Minshew NJ, Bansal R, Goradia D, Fedorov S, Keshavan MS,
Hardan AY. 2009. Corpus callosum volume and neurocognition in
autism. J Autism Dev Disord. 39:834--841.
Keller TA, Kana RK, Just MA. 2007. A developmental study of the
structural integrity of white matter in autism. Neuroreport.
Kennedy DP, Courchesne E. 2008. The intrinsic functional organization
of the brain is altered in autism. Neuroimage. 39:1877--1885.
Kennedy DP, Redcay E, Courchesne E. 2006. Failing to deactivate:
resting functional abnormalities in autism. Proc Natl Acad Sci U S A.
Kleinhans NM, Richards T, Sterling L, Stegbauer KC, Mahurin R,
Johnson LC, Greenson J, Dawson G, Aylward E. 2008. Abnormal
functional connectivity in autism spectrum disorders during face
processing. Brain. 131:1000--1012.
Koshino H, Carpenter PA, Minshew NJ, Cherkassky VL, Keller TA,
Just MA. 2005. Functional connectivity in an fMRI working memory
task in high-functioning autism. Neuroimage. 24:810--821.
Koshino H, Kana RK, Keller TA, Cherkassky VL, Minshew NJ, Just MA.
2008. fMRI investigation of working memory for faces in autism:
visual coding and underconnectivity with frontal areas. Cereb
Kraemer HC, Yesavage JA, Taylor JL, Kupfer D. 2000. How can we learn
about developmental processes from cross-sectional studies, or can
we? Am J Psychiatry. 157:163--171.
Lainhart J, Lazar M, Bigler E, Alexander A. 2005. Advances in
neuroimaging: looking into the brain in autism. In: Casanova M,
editor. Recent developments in autism research. New York: Nova
Science Publishers, Inc. p. 57--108.
Lee JE, Bigler ED, Alexander AL, Lazar M, DuBray MB, Chung MK,
Johnson M, Morgan J, Miller JN, McMahon WM, et al. 2007.
Diffusion tensor imaging of white matter in the superior temporal
gyrus and temporal stem in autism. Neurosci Lett. 424:127--132.
Lee PS, Yerys BE, Della Rosa A, Foss-Feig J, Barnes KA, James JD,
VanMeter J, Vaidya CJ, Gaillard WD, Kenworthy LE. 2009. Functional
connectivity of the inferior frontal cortex changes with age in
children with autism spectrum disorders: a fcMRI study of response
inhibition. Cereb Cortex. 19:1787--1794.
Leyfer OT, Folstein SE, Bacalman S, Davis NO, Dinh E, Morgan J,
Tager-Flusberg H, Lainhart JE. 2006. Comorbid psychiatric
disorders in children with autism: interview development and
rates of disorders. J Autism Dev Disord. 36:849--861.
Lord C, Risi S, Lambrecht L, Cook EH Jr, Leventhal BL, DiLavore PC,
Pickles A, Rutter M. 2000. The autism diagnostic observation
schedule-generic: a standard measure of social and communication
deficits associated with the spectrum of autism. J Autism Dev
Lord C, Rutter M, Le Couteur A. 1994. Autism Diagnostic Interview-
Revised: a revised version of a diagnostic interview for caregivers of
individualswith possible pervasive
J Autism Dev Disord. 24:659--685.
Lowe MJ, Mock BJ, Sorenson JA. 1998. Functional connectivity in single
and multislice echoplanar imaging using resting-state fluctuations.
Maldjian JA, Laurienti PJ, Kraft RA, Burdette JH. 2003. An automated
method for neuroanatomic and cytoarchitectonic atlas-based in-
terrogation of fMRI data sets. Neuroimage. 19:1233--1239.
Manes F, Piven J, Vrancic D, Nanclares V, Plebst C, Starkstein SE. 1999.
An MRI study of the corpus callosum and cerebellum in mentally
retarded autistic individuals. J Neuropsychiatry Clin Neurosci.
Mason RA, Williams DL, Kana RK, Minshew N, Just MA. 2008. Theory
of mind disruption and recruitment of the right hemisphere
during narrative comprehension in autism. Neuropsychologia. 46:
Monk CS, Peltier SJ, Wiggins JL, Weng SJ, Carrasco M, Risi S, Lord C.
2009. Abnormalities of intrinsic functional connectivity in autism
spectrum disorders. Neuroimage. 47:764--772.
Cerebral Cortex May 2011, V 21 N 5 1145
Mostofsky SH, Burgess MP, Gidley Larson JC. 2007. Increased motor Download full-text
cortex white matter volume predicts motor impairment in autism.
Mostofsky SH, Powell SK, Simmonds DJ, Goldberg MC, Caffo B, Pekar JJ.
2009. Decreased connectivity and cerebellar activity in autism
during motor task performance. Brain. 132:2413--2425.
Muller RA, Kleinhans N, Kemmotsu N, Pierce K, Courchesne E. 2003.
Abnormal variability and distribution of functional maps in autism:
an FMRI studyof visuomotor
Murphy K, Birn RM, Handwerker DA, Jones TB, Bandettini PA. 2009.
The impact of global signal regression on resting state corre-
lations: are anti-correlated networks introduced? Neuroimage.
Oldfield RC. 1971. The assessment and analysis of handedness: the
Edinburgh inventory. Neuropsychologia. 9:97--113.
Oram Cardy JE, Flagg EJ, Roberts W, Brian J, Roberts TP. 2005.
Magnetoencephalography identifies rapid temporal processing
deficitin autismand language
Pierce K, Redcay E. 2008. Fusiform function in children with an
autism spectrum disorder is a matter of ‘‘who’’. Biol Psychiatry.
Scheperjans F, Eickhoff SB, Homke L, Mohlberg H, Hermann K,
Amunts K, Zilles K. 2008. Probabilistic maps, morphometry, and
variability of cytoarchitectonic areas in the human superior parietal
cortex. Cereb Cortex. 18:2141--2157.
Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H,
Reiss AL, Greicius MD. 2007. Dissociable intrinsic connectivity
networks for salience processing and executive control. J Neurosci.
Segonne F, Dale AM, Busa E, Glessner M, Salat D, Hahn HK, Fischl B.
2004. A hybrid approach to the skull stripping problem in MRI.
Segonne F, Pacheco J, Fischl B. 2007. Geometrically accurate topology-
correction of cortical surfaces using nonseparating loops. IEEE
Trans Med Imaging. 26:518--529.
Semel E, Wiig EH, Secord WA. 1995. Clinical evaluation of language
fundamentals—3rd edition (CELF-3). San Antonio (TX): Psycholog-
Sled JG, Zijdenbos AP, Evans AC. 1998. A nonparametric method for
automatic correction of intensity nonuniformity in MRI data. IEEE
Trans Med Imaging. 17:87--97.
Stanfield AC, McIntosh AM, Spencer MD, Philip R, Gaur S, Lawrie SM.
2008. Towards a neuroanatomy of autism: a systematic review and
meta-analysis of structural magnetic resonance imaging studies. Eur
Stark DE, Margulies DS, Shehzad ZE, Reiss P, Kelly AM, Uddin LQ,
Gee DG, Roy AK, Banich MT, Castellanos FX, et al. 2008. Regional
variation in interhemispheric coordination of intrinsic hemody-
namic fluctuations. J Neurosci. 28:13754--13764.
learning. AmJ Psychiatry.
Stevens MC, Pearlson GD, Calhoun VD. 2009. Changes in the
interaction of resting-state neural networks from adolescence to
adulthood. Hum Brain Mapp. 30:2356--2366.
Supekar K, Musen M, Menon V. 2009. Development of large-scale
functional brain networks in children. PLoS Biol. 7:e1000157.
Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O,
Delcroix N, Mazoyer B, Joliot M. 2002. Automated anatomical
labeling of activations in SPM using a macroscopic anatomical
parcellation of the MNI MRI single-subject brain. Neuroimage.
Uddin LQ, Menon V. 2009. The anterior insula in autism: under-connected
and under-examined. Neurosci Biobehav Rev. 33(8):1198--1203.
van Kooten IA, Palmen SJ, von Cappeln P, Steinbusch HW, Korr H,
Heinsen H, Hof PR, van Engeland H, Schmitz C. 2008. Neurons in
the fusiform gyrus are fewer and smaller in autism. Brain.
Varela F, Lachaux JP, Rodriguez E, Martinerie J. 2001. The brainweb:
phase synchronization and large-scale integration. Nat Rev Neuro-
Vidal CN, Nicolson R, DeVito TJ, Hayashi KM, Geaga JA, Drost DJ,
Williamson PC, Rajakumar N, Sui Y, Dutton RA, et al. 2006. Mapping
corpus callosum deficits in autism: an index of aberrant cortical
connectivity. Biol Psychiatry. 60:218--225.
Vilensky JA, Damasio AR, Maurer RG. 1981. Gait disturbances in patients
with autistic behavior:a preliminary
Waiter GD, Williams JH, Murray AD, Gilchrist A, Perrett DI, Whiten A.
2005. Structural white matter deficits in high-functioning individ-
uals with autistic spectrum disorder: a voxel-based investigation.
Wechsler D. 1997. Wechsler Adult Intelligence Scale—Third Edition
(WAIS-III). San Antonio (TX): The Psychological Corporation.
Wechsler D. 1999. Wechsler Abbreviated Scale of Intelligence (WASI).
San Antonio (TX): The Psychological Corporation.
Weng SJ, Wiggins JL, Peltier SJ, Carrasco M, Risi S, Lord C, Monk CS.
2009. Alterations of resting state functional connectivity in the
default network in adolescents with autism spectrum disorders.
Brain Res. 1313:202--214.
Wilson TW, Rojas DC, Reite ML, Teale PD, Rogers SJ. 2007. Children and
adolescents with autism exhibit reduced MEG steady-state gamma
responses. Biol Psychiatry. 62:192--197.
Zuo XN, Kelly C, Di Martino A, Mennes M, Margulies DS, Bangaru S,
Grzadzinski R, Evans AC, Zang YF, Castellanos FX, et al. 2010.
Growing together and growing apart: Regional differences in the
J Neurosci. (forthcoming).
Zwaigenbaum L, Bryson S, Lord C, Rogers S, Carter A, Carver L,
Chawarska K, Constantino J, Dawson G, Dobkins K, et al. 2009.
Clinical assessment and management of toddlers with suspected
autism spectrum disorder: insights from studies of high-risk infants.
Interhemispheric Connectivity in Autism
Anderson et al.