suggest differences in coordination across primary sensory-motor cortices versus higher-order association areas, these have not been
interregional correlation in spontaneous low-frequency hemodynamic fluctuations. Using a probabilistic atlas, we correlated
probability-weighted time series from 112 regions comprising the entire cerebrum. We then examined regional variation in correlated
was significantly higher than that between nonhomotopic (heterotopic and intrahemispheric) regions. We further demonstrated sub-
hierarchical subdivisions. Synchrony across primary cortices may reflect networks engaged in bilateral sensory integration and motor
coordination, whereas lower coordination across heteromodal association areas is consistent with functional lateralization of these
tributed processing within spatially segregated regions and long-
range integration across regions. These organizing principles be-
come especially apparent with regard to interhemispheric
coordination. Processing of sensory inputs and motor outputs
requires integration between the hemispheres, whereas higher-
order cognitive functions including language and spatial atten-
tion are predominantly lateralized (Toga and Thompson, 2003).
Such considerations suggest that the nature of interhemispheric
coordination may differ across regions, yet little is known about
regional variation in interhemispheric coordination across the
Resting-state EEG studies have long demonstrated coherent
reveal bilateral patterns of coactivation (Toro et al., 2008). The
corpus callosum appears to play a central role in mediating this
losal (Nielsen et al., 1993; Koeda et al., 1995) and callosotomized
(Johnston et al., 2008) patients. Indeed, postmortem tracing
studies in animals indicate that most callosal fibers interconnect
homotopic regions (Innocenti, 1986), defined as corresponding
anatomical areas in opposite hemispheres.
Whereas studies report a high overall degree of interhemi-
spheric coordination, with functional coupling likely subserved
masch, 1954; LaMantia and Rakic, 1990b; Aboitiz et al., 1992).
Such anatomical variability suggests functional differences in in-
terhemispheric coordination across different regions, yet studies
performed to date, limited by low spatial resolution inherent to
EEG, have not distinguished such regional differences.
Resting-state functional magnetic resonance imaging (fMRI)
13754 • TheJournalofNeuroscience,December17,2008 • 28(51):13754–13764
interhemispheric coordination. Temporally correlated patterns
of low-frequency spontaneous (nonevoked) brain activity re-
vealed by this approach recapitulate known functional and neu-
roanatomical networks. Moreover, their presence has been dem-
onstrated during task performance, sleep, sedation, anesthesia,
and at rest, suggesting that they reflect intrinsic aspects of the
al., 2007; Greicius et al., 2008). Studies have observed a high
degree of interhemispheric correlation in various cortical and
subcortical regions (Biswal et al., 1995; Lowe et al., 1998; Cordes
et al., 2000; Margulies et al., 2007). Salvador and colleagues
topic regions is a relatively ubiquitous phenomenon observable
across brain regions, although they did not directly examine re-
gional variation in interhemispheric correlation. Other resting-
10 functional networks identified by Damoiseaux and colleagues
parietal regions, were lateralized. As these higher-order associa-
tion areas are thought to subserve functionally lateralized cogni-
Here, we examine interregional correlations in spontaneous
brain activity, specifically addressing regional variation in the
degree of correlated activity between homotopic regions. We hy-
pothesized a high overall degree of correlated activity between
homotopic regions, with greater interhemispheric correlation in
primary sensory-motor cortices relative to prefrontal and tem-
poroparietal heteromodal association areas.
Subjects. Subjects included 62 right-handed volunteers (33 males, 29 fe-
males, ages 19–49 years, mean age 29.2 ? 7.9 years) with no history of
psychiatric or neurological illness as confirmed by psychiatric clinical
assessment. Informed consent was obtained before participation. Data
collection was carried out according to protocols approved by the insti-
tutional review boards of New York University (NYU) and the NYU
tigator and Doctors Milham, Stark, and colleagues as coinvestigators.
Data acquisition. A Siemens Allegra 3.0 Tesla scanner equipped for
echo planar imaging (EPI) was used for data acquisition. Each subject
underwent a resting-state scan consisting of 197 contiguous EPI whole-
brain functional volumes, resulting in a 6 min 38 s scan [repetition time
(TR) ? 2000 ms; echo time (TE) ? 25 ms; flip angle ? 90°, 39 slices,
matrix ? 64 ? 64; field of view (FOV) ? 192 mm; acquisition voxel
their eyes open. For spatial normalization and localization, a high-
resolution T1-weighted magnetization prepared gradient echo sequence
was also obtained (TR ? 2500 ms; TE ? 4.35 ms; TI ? 900 ms; flip
the present study was obtained over the course of ?1 year. Subsets of
these data have been used in previous publications (Margulies et al.,
Uddin et al., 2008a).
Preprocessing. Consistent with prior work in our lab (Margulies et al.,
2007; Castellanos et al., 2008; Di Martino et al., 2008), data processing
(http://afni.nimh.nih.gov/afni/) and fMRIb Software Library (FSL)
(http://www.fmrib.ox.ac.uk/fsl/). Image preprocessing using AFNI con-
sisted of (1) slice time correction for interleaved acquisitions using Fou-
rier interpolation, (2) three-dimensional (3D) motion correction via 3D
volume registration using least-squares alignment of three translational
outliers using a hyperbolic tangent function. Preprocessing using FSL
consisted of (4) mean-based intensity normalization of all volumes by
the same factor, (5) temporal high-pass filtering via Gaussian weighted
least-squares straight line fitting with ? ? 100.0 s, (6) temporal lowpass
(7) correction for time series autocorrelation (prewhitening). The use of
bandpass filtering to isolate the 0.005 to 0.1 Hz frequency interval was
motivated by previous studies of low-frequency fluctuations, as well as
frequency range (Fransson, 2005). The data were not spatially smoothed
as this is effectively achieved via averaging across all voxels within each
region (see below, Time series extraction) as well as to minimize artifac-
tual interhemispheric correlation because of smoothing across the me-
dial wall. Functional data were then transformed into MNI (Montreal
Neurological Institute) space using a 12 degree of freedom (df) linear
affine transformation implemented in FLIRT (fMRIb’s Linear Image
Registration Tool) (voxel size, 2 ? 2 ? 2 mm), to enable time series
extraction using standard anatomical masks.
Time series extraction. Parcellation of functional data were carried out
implemented in FSL that divides each hemisphere into regions corre-
sponding to portions of cortical gyri and subcortical gray matter nuclei
(Kennedy et al., 1998; Makris et al., 1999). Masks were generated for 112
regions (56 in each hemisphere), covering the entire cerebrum (Fig. 1).
To minimize effects because of interindividual anatomic variability,
atlas-derived values corresponding to each voxel’s probability of inclu-
sion in a given region were used to weight each voxel’s time series within
region by averaging across all voxels’ probability-weighted time series
within each region.
To minimize the effects of physiological processes such as cardiac and
respiratory fluctuations, time series were also extracted from nine nui-
sance signals [global signal, white matter (WM), CSF, and six motion
parameters]. To extract the nuisance covariate time series for WM and
CSF, we first segmented each individual’s high-resolution structural im-
segmented WM and CSF images were then thresholded to ensure 80%
tissue type probability. These thresholded masks were then applied to
each individual’s time series, and a mean time series was calculated by
averaging across all voxels within the mask. The global signal regressor
was generated by averaging across all voxels within the brain.
Each subject’s 112 regional time series were orthogonalized with re-
spect to nuisance covariates (using the Gram–Schmidt process). This
tical region for all 62 subjects.
Correlation analyses. All further analyses were carried out using R sta-
tistical analysis software (version 2.6.1, http://www.r-project.org/) and
subject, we calculated the correlation between every pairing of orthogo-
nalized time series from the set of 112 brain regions.
ship of a given pairing of brain regions can be classified according to one
of three broad categories. Homotopic regions indicate corresponding
anatomical areas in opposite hemispheres, heterotopic regions indicate
different anatomical areas in opposite hemispheres, and intrahemi-
spheric regions indicate different anatomical areas in the same hemi-
sphere. To first test whether a significant difference existed between ho-
motopic versus nonhomotopic (heterotopic and intrahemispheric)
interregional correlations, we used a linear mixed effects model, imple-
mented using the R package nlme (Pinheiro et al., 2006), to regress all
z-transformed correlations on three indicator variables: (1) homotopic,
(2) heterotopic, and (3) intrahemispheric. Age and gender were entered
as covariates and a random subject effect was included to account for
in correlated activity between homotopic regions. To characterize this
variation, we rank-ordered all 56 homotopic correlation coefficients in
each subject. We tested whether the within-subject rankings of homo-
Starketal.•VariationinInterhemisphericCoordinationJ.Neurosci.,December17,2008 • 28(51):13754–13764 • 13755
We also rank-ordered each homotopic correlation coefficient’s me-
dian ranking across subjects, resulting in the sorting of homotopic cor-
relation coefficients from 1 (the region exhibiting the highest median
ranked interhemispheric correlation) to 56 (the region exhibiting the
lowest median ranked interhemispheric correlation).
Analysis of regional variation related to hierarchical subdivisions. To
statistically test our hypothesis that regions involved in higher-order
processing exhibit lower levels of correlated activity across hemispheres,
as described by Mesulam (2000). These hierarchical subdivisions are
broadly derived from anatomical, electrophysiological, behavioral, le-
sion, and functional imaging studies in nonhuman primates and in hu-
mans. Primary sensory-motor cortices include postcentral gyrus (so-
matosensory), intracalcarine cortex and occipital pole (visual), Heschl’s
gyrus (auditory), and precentral gyrus (motor). Unimodal association
areas are those regions adjacent to primary sensory-motor cortices in-
motor modality. Heteromodal association areas, located primarily in
prefrontal and temporoparietal cortices, integrate information from
multiple sensory and motor modalities (see Fig. 4A and supplemental
Table 1 for a complete listing of each region’s functional classification,
available at www.jneurosci.org as supplemental material).
We used a linear mixed effects model, implemented in SAS, to regress
ables defining primary sensory-motor, unimodal association, and het-
eromodal association areas. Three planned contrasts were carried out,
comparing homotopic correlation strengths in primary, unimodal, and
heteromodal areas. Age and gender were again entered as covariates and
a random subject effect was included to account for within-subject
Additional mixed effects analyses were carried out in which
z-transformed homotopic correlations for primary versus unimodal as-
Consistent with previous resting-state studies (Salvador et al.,
2005), correlations between homotopic regions (mean r ? 0.62,
SD ? 0.23) were significantly higher than correlations between
heterotopic regions (mean r ? ?0.01, SD ? 0.27; t ? 159 with
385328 df, p ? 0.0001) and between intrahemispheric regions
discussion of large reported df, available at www.jneurosci.org as
homotopic regions, substantial regional variation in interhemi-
spheric correlation was observed, with mean correlation coeffi-
cients ranging from 0.33–0.88 (Fig. 3). The Friedman ?2test
strongly confirmed the existence of a systematic pattern of vari-
ation among homotopic correlations (?2? 2057 with 55 df, p ?
The presence of a notable pattern of regional variation in inter-
modeling the z-transformed interhemispheric correlations be-
probabilistic atlas that divides each hemisphere into regions corresponding to portions of cortical gyri and subcortical gray matter nuclei. Atlas-derived values corresponding to each voxel’s
Regional masks. A total of 112 regional masks (56 in each hemisphere) comprising the entire cerebrum were generated from the Harvard–Oxford Structural Atlas, a validated
13756 • J.Neurosci.,December17,2008 • 28(51):13754–13764Starketal.•VariationinInterhemisphericCoordination
division. The mean interhemispheric correlations for primary
sensory-motor, unimodal association, and heteromodal associa-
tion areas were estimated and compared based on this model.
Primary sensory-motor cortices demonstrated a significantly
dal association areas (t ? 13.10 with 3405 df, p ? 0.0001) or
heteromodal association areas (t ? 17.85 with 3405 df, p ?
0.0001). Moreover, unimodal association areas showed signifi-
cantly higher interhemispheric correlations than heteromodal
There were no significant age (t ? 0.05 with 59 df, p ? 0.96) or
gender (t ? 0.95 with 59 df, p ? 0.35) effects. These highly sig-
spectrum of interhemispheric correlation in low-frequency
spontaneous hemodynamic fluctuations, and point to the segre-
ciation areas to opposite extremes of this spectrum, with unimo-
dal association areas lying between.
Below, we describe in detail the regional variations we ob-
served in correlated spontaneous activity across hemispheres. As
anatomical terminology is often variably applied, we make an
attempt to explicitly define regions both in terms of their neuro-
ficients (averaged across subjects), as well as each region’s rank
ranked interhemispheric correlation and a rank of 56 indicates
the region with the lowest median ranked interhemispheric cor-
relation) (Figs. 3, 4B) (see also supplemental Table 1 for all val-
ues, available at www.jneurosci.org as supplemental material).
Primary sensory cortices exhibited a high degree of correlated
activity across hemispheres, with decreased interhemispheric
correlations in unimodal association areas (Fig. 5D).
In the somatosensory system, primary somatosensory cortex
(postcentral gyrus) demonstrated the highest degree of inter-
gyrus, anterior division) exhibited significantly lower interhemi-
24, and 27, respectively; t ? 10.22 with 247 df, p ? 0.0001).
tex) demonstrated a high degree of interhemispheric correlation
Compared with primary visual cortex, surrounding unimodal
occipital cortex inferior division, temporal occipital fusiform
cortex, lateral occipital cortex superior division, temporal fusi-
form cortex posterior division, inferior temporal gyrus tempo-
rooccipital part, inferior temporal gyrus anterior division, tem-
poral fusiform cortex anterior division, inferior temporal gyrus
posterior division) exhibited significantly lower degrees of inter-
hemispheric correlation (mean r range ? 0.330–0.696, rank ?
p ? 0.0001).
In the auditory system, primary auditory cortex (Heschl’s gy-
relation (mean r ? 0.621, rank ? 27). Surrounding unimodal
auditory association areas (planum temporale, planum polare,
superior temporal gyrus posterior and anterior divisions) exhib-
ited lower degrees of interhemispheric correlation (mean r
with 247 df, p ? 0.014), although the planum temporale, an
auditory association area located immediately posterior to pri-
mary auditory cortex, demonstrated slightly higher interhemi-
spheric correlation (mean r ? 0.675, rank ? 23; t ? 2.24 with 61
df, p ? 0.029).
Interhemispheric correlations for motor regions reflected the
trend observed in sensory regions (Fig. 5D). Specifically, we
found that primary motor cortex (precentral gyrus) exhibited a
high degree of correlated spontaneous activity across hemi-
spheres (mean r ? 0.736, rank ? 14), whereas unimodal motor
association areas (supplementary motor cortex, frontal opercu-
lum cortex) demonstrated significantly lower interhemispheric
correlations (mean r ? 0.720, 0.391, rank ? 18, 51, respectively;
t ? 5.73 with 123 df, p ? 0.0001).
Heteromodal association areas
Heteromodal association areas generally demonstrated relatively
low degrees of correlated spontaneous activity across hemi-
spheres compared with other regions. Prefrontal association re-
gions (inferior frontal gyrus pars opercularis and pars triangu-
laris, frontal pole, middle frontal gyrus, superior frontal gyrus)
uniformly demonstrated a low degree of correlated spontaneous
activity across hemispheres (mean r range ? 0.383–0.520,
rank ? 49, 47, 44, 40, 38, respectively). Similarly, temporopari-
dle temporal gyrus anterior and posterior divisions) exhibited
relatively low interhemispheric correlations (mean r range ?
0.368–0.578, rank ? 52, 46, 40, 39, 36, respectively). Notably,
paracingulate gyrus and precuneus cortex, both heteromodal as-
sociation areas lying within the medial wall, exhibited higher de-
grees of interhemispheric correlation than all other heteromodal
areas. Generally speaking, the majority of prefrontal and tem-
poroparietal heteromodal association areas exhibited substan-
tially lower interhemispheric correlations compared with other
intrahemispheric regions. Data points are shown for each region, averaged across subjects.
Homotopic versus nonhomotopic correlations. Correlations between homotopic
Starketal.•VariationinInterhemisphericCoordinationJ.Neurosci.,December17,2008 • 28(51):13754–13764 • 13757
homotopic regions, particularly the primary sensory and motor
areas reported above.
tial confounds to our analyses. To determine the extent to which
volumetric differences in the masks used may have influenced
our results, we performed several additional analyses. We first
regional masks. For each pair, we found no relationship between
0.122, p ? 0.369) (Fig. 6A). Furthermore, we found no relation-
ship between volumetric asymmetry index [ (left – right)/(left ?
right) ] and interhemispheric correlation (r ? 0.163, p ? 0.232).
Noting that prior studies have demonstrated higher correla-
vador et al., 2005), we sought to determine whether this effect of
distance might have confounded our results. For each pair of
left and right centroids (centers of mass). We then constructed a
regression model with Euclidean distance and hierarchical sub-
division as independent variables and interhemispheric correla-
tion as the dependent variable. As expected, shorter Euclidean
spheric correlation (coefficient ? ?0.003, t ? ?0.558, p ?
a higher-magnitude relationship between hierarchical subdivi-
?0.347, p ? 0.007). Thus, the relationship between hierarchical
subdivision and interhemispheric correlation described above
and discussed in detail below cannot be attributed to an effect of
13758 • J.Neurosci.,December17,2008 • 28(51):13754–13764Starketal.•VariationinInterhemisphericCoordination
Starketal.•VariationinInterhemisphericCoordinationJ.Neurosci.,December17,2008 • 28(51):13754–13764 • 13759
To minimize effects because of interin-
dividual anatomical variability, regional
time series were weighted according to
each voxel’s probability of inclusion in a
given region. To test the extent to which
this probability-weighting influenced our
results, we also conducted two sets of ad-
ditional analyses without probability-
weighting, using regional masks thresh-
olded to include only voxels with a ?50%
and 25% probability of inclusion, with all
included voxels given equal weighting.
Even without probability-weighting, 50%
dient of decreasing interhemispheric cor-
relation from primary, to unimodal, to
heteromodal association areas (primary
versus heteromodal, t ? 12.46 with 3281
df, p ? 0.0001; primary versus unimodal,
t ? 9.89 with 3281 df, p ? 0.0001; unimo-
df, p ? 0.0001). Analyses using less strin-
gent thresholding of 25% still yielded
highly similar results (primary versus het-
eromodal, t ? 23.36 with 3405 df, p ?
0.0001; primary versus unimodal, t ?
14.99 with 3405 df, p ? 0.0001; unimodal
versus heteromodal, t ? 13.95 with 3405
df, p ? 0.0001). Thus, probability-
weighting did not appear to systematically
affect our results.
Finally, in recognition of the fact that
higher-order regions are anatomically less
well-defined and tend to exhibit greater
interindividual variability, we repeated
our analyses, replacing the regional masks
4 mm) placed at the centroid of each re-
gional mask. This approach minimizes
interregional differences in accuracy of
anatomical labeling and eliminates volu-
metric differences. Despite this more re-
strictive method of data sampling, the
relationship between hierarchical subdivi-
sion and interhemispheric correlation was
preserved (primary versus heteromodal,
versus unimodal, t ? 5.25 with 3405 df,
t ? 2.38 with 3405 df, p ? 0.017) (supple-
Consistent with previous studies, we
found that spontaneous brain activity is
highly correlated between homotopic re-
demonstrate substantial regional variation in degree of inter-
hemispheric correlation, with a gradient of highest correlations
across primary sensory-motor cortices and lower correlations
across prefrontal and temporoparietal heteromodal association
areas. These results echo neuroanatomical findings and likely
reflect the distributed hierarchical nature of processing in the
cused on higher frequency (1–80 Hz) electrical activity. Demon-
stration of interhemispheric correlation in low-frequency (?0.1
Hz) spontaneous hemodynamic fluctuations here and in previ-
organized by hierarchical subdivision and plotted to demonstrate: A, all data points (each data point represents homotopic
cortices demonstrated a significantly higher degree of interhemispheric correlation than either unimodal association areas or
Interhemispheric correlation as a function of regional volume and interregional distance. A, No relationship was
13760 • J.Neurosci.,December17,2008 • 28(51):13754–13764Starketal.•VariationinInterhemisphericCoordination
ous resting-state fMRI studies raises the question of whether the
two types of phenomena are related, as well as their potential
functional significance. Low-frequency correlated activity may
provide an energy-efficient means of maintaining synaptic con-
nections that comprise long-range functional networks (Pinsk
and Kastner, 2007). Such activity may reflect development and
experience, as it is refined through childhood and adolescence
(Fair et al., 2007). In contrast, high-frequency correlated activity
is thought to reflect moment-to-moment processing demands
such as perceptual integration and motor coordination (Schnit-
zler et al., 2000; Mima et al., 2001).
Whereas these two frequency ranges of synchronous activity
reflect widely different temporal scales, their possible interaction
is increasingly being entertained. For example, trial-to-trial vari-
ability in behavioral and cognitive performance has been linked
to variations in spontaneous low-frequency activity (Fox et al.,
2007; Kelly et al., 2008b). Furthermore, work combining fMRI
and EEG has related hemodynamic fluctuations in resting-state
networks to power variations in ?, ?, ?, ?, and ? rhythms (Man-
tini et al., 2007). Amplitude fluctuations of interhemispherically
coherent high-frequency activity have been demonstrated at
much slower time scales ranging from seconds to minutes
variations correlate well with hemodynamic fluctuations (Logo-
thetis et al., 2001; Leopold et al., 2003; Niessing et al., 2005;
Shmuel and Leopold, 2008). Thus we propose, as have others,
that low-frequency and higher frequency phenomena are inter-
related (Buzsaki and Draguhn, 2004).
differences in interhemispheric correlation as hypothesized. We
speculate that the high degree of synchrony observed across pri-
ity in visual cortex is thought to allow temporal binding of dis-
Engel et al., 1991; Singer, 1999). Similarly, the motor system ap-
pears to maintain a default state of interhemispheric coupling
important for bilateral motor coordination (Schnitzler et al.,
In contrast, heteromodal association areas displayed a lower
degree of interhemispheric coordination, presumably reflecting
the predisposition of higher-order homotopic regions to operate
more independently. Lesion, neuropsychological, and neuroim-
aging studies demonstrate that association areas exhibit substan-
tial functional lateralization for certain cognitive domains (Toga
and Thompson, 2003). Language production and comprehen-
sion (Frost et al., 1999; Price, 2000) and spatial attention (Shep-
ard and Metzler, 1971; Ditunno and Mann, 1990) are predomi-
nantly lateralized to left and right hemispheres, respectively.
Of note, studies comparing correlated brain activity at rest
and during task performance demonstrate that interregional co-
2006). The lower degree of interhemispheric coordination ob-
served within higher-order regions may increase under condi-
tions of greater computational complexity (Belger and Banich,
regions via local inhibitory connections may allow performance
of more complex unilateral tasks (Cardoso de Oliveira et al.,
2001; Rokni et al., 2003; Wahl et al., 2007).
The high overall degree of interhemispheric synchrony we
observed is consistent with a large body of neuroanatomical and
functional evidence (Pandya et al., 1971; Innocenti, 1986; Duffy
et al., 1996; Toro et al., 2008). Moreover, the importance of an
intact corpus callosum is suggested by studies in which inter-
cording, EEG, or resting-state fMRI, is abolished or decreased
with perturbations of callosal integrity including agenesis, tran-
section, or demyelinating disease (Montplaisir et al., 1990; Engel
et al., 1991; Quigley et al., 2003; Lowe et al., 2008).
In considering possible neuroanatomical foundations of re-
gional variation in interhemispheric synchrony, we note that os-
be impacted by microstructural determinants of conduction ve-
locity, such as fiber diameter (Innocenti et al., 1995; Schuz and
Preissl, 1996; Aboitiz et al., 2003; Buzsaki and Draguhn, 2004;
Uhlhaas and Singer, 2006). Meticulous work by LaMantia and
Rakic (1990b) in monkeys, and Aboitiz and colleagues (1992) in
humans has demonstrated that primary sensory-motor and het-
eromodal association areas differ in the diameters of their inter-
are interconnected via a subset of thickly myelinated, fast-
conducting fibers, whereas heteromodal association areas are in-
terconnected via thinly myelinated, slow-conducting fibers. The
tion may depend on variation in fiber diameters and conduction
velocities will be addressed in future studies.
Still, we note that ours and previous studies demonstrate a
cortex, a region with limited callosal projections (Tootell et al.,
1998; Vincent et al., 2007). Indeed, persistence of residual inter-
hemispheric correlation in some split-brain patients (Corsi-
Cabrera et al., 1995; Uddin et al., 2008b) and conversely, de-
creased interhemispheric correlation in a patient with an
ischemic brainstem lesion (Salvador et al., 2005) suggest that
subcortical pathways may also contribute to interhemispheric
coordination of spontaneous activity. Top-down pathways may
ular exhibits reentrant feedback from higher-order visual areas
(Lamme and Roelfsema, 2000; Ban et al., 2006). Thus, whereas
direct callosal connections are likely the predominant driving
force behind homotopic interhemispheric correlations, subcor-
tical and polysynaptic feedback and feedforward mechanisms
may also contribute.
ety of disorders including schizophrenia (Spencer et al., 2003;
Liang et al., 2006), Alzheimer’s disease (Lakmache et al., 1998;
Pogarell et al., 2005), multiple sclerosis (Cover et al., 2006; Lowe
methods may be useful in future study of these diverse disease
processes. Additionally, anatomical and functional interhemi-
spheric connectivity appears to undergo lifelong changes, espe-
cially during early development and in normal aging (LaMantia
developmental changes in interhemispheric coordination may
prove informative, particularly as region-specific changes have
been noted (Bartzokis et al., 2004; Sullivan et al., 2006).
Whereas regional variation in interhemispheric correlation
closely paralleled presumptive differences in functional lateral-
ization, several deviations from this pattern merit discussion.
lower correlation than planum temporale, an abutting perisyl-
vian auditory association area. Delineation of these small and
Starketal.•VariationinInterhemisphericCoordinationJ.Neurosci.,December17,2008 • 28(51):13754–13764 • 13761
highly variable perisylvian regions has proven problematic in
previous studies, and this difficulty may have been reflected in
our results (Westbury et al., 1999; Zetzsche et al., 2001). Second,
precuneus and paracingulate gyrus, both heteromodal associa-
tion areas, demonstrated high interhemispheric correlations,
most likely because of the close proximity of these medial wall
structures to their homotopic counterparts. Post hoc analysis
demonstrated a secondary independent relationship between
founds in the Results section). Still, the presence of decreasing
interhemispheric correlations along the anterior medial wall,
mirroring known dorsal-ventral distinctions (Bush et al., 2000),
suggests that results obtained from medial wall structures are
valid. Whereas the present study discerned a broad pattern of
varying interhemispheric coordination, future work could bene-
fit from using more localized and individual-specific methods of
anatomic parcellation (Cohen et al., 2008).
Several additional limitations merit attention. We consid-
ered whether volumetric differences in regional masks, prob-
ability weighting, and method of anatomic parcellation may
have influenced our results (see Analysis of potential con-
founds in the Results section). However, we found no effect of
these factors on our pattern of results. We also considered
whether the low degree of frontal pole interhemispheric cor-
relation, whereas consistent with its implication in function-
susceptibility artifact. Nevertheless, other structures in the vi-
cinity of frontal air-filled sinuses, including frontal medial
cortex and subcallosal cortex, exhibited higher degrees of in-
terhemispheric correlation, suggesting that susceptibility arti-
fact did not impose a systematic effect on our results. Finally,
it might be argued that bilateral sensory inputs during scan-
sensory areas. However, correlated fluctuations persist across
a variety of conditions including sleep and anesthesia (Fox et
al., 2006; Fox and Raichle, 2007; Vincent et al., 2007).
In summary, we report a pattern of regional variation in
low-frequency temporally correlated brain activity across
hemispheres, suggesting that interhemispheric coordination
may differ across regions. Despite robust homotopic inter-
hemispheric correlation across all regions, lower interhemi-
spheric correlation was demonstrated in higher-order hetero-
modal association areas compared with primary sensory-
motor cortices, potentially reflective of regional functional
lateralization within the brain. Future work could benefit
from addressing this pattern in the context of developmental
changes, different clinical populations, and as it relates to re-
gional variation in white matter structure.
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