Intrinsic Functional Relations Between Human Cerebral Cortex and Thalamus
Dongyang Zhang,1Abraham Z. Snyder,1,2Michael D. Fox,1Mark W. Sansbury,1Joshua S. Shimony,1
and Marcus E. Raichle1,2,3,4,5
1Department of Radiology,2Department of Neurology,3Department of Neurobiology,4Department of Psychology, and5Department
of Biomedical Engineering, Washington University, St. Louis, Missouri
Submitted 14 April 2008; accepted in final form 10 August 2008
Zhang D, Snyder AZ, Fox MD, Sansbury MW, Shimony JS,
Raichle ME. Intrinsic functional relations between human cerebral
cortex and thalamus. J Neurophysiol 100: 1740–1748, 2008. First
published August 13, 2008; doi:10.1152/jn.90463.2008. The brain is
active even in the absence of explicit stimuli or overt responses. This
activity is highly correlated within specific networks of the cerebral
cortex when assessed with resting-state functional magnetic resonance
imaging (fMRI) blood oxygen level–dependent (BOLD) imaging.
The role of the thalamus in this intrinsic activity is unknown despite
its critical role in the function of the cerebral cortex. Here we mapped
correlations in resting-state activity between the human thalamus and
the cerebral cortex in adult humans using fMRI BOLD imaging.
Based on this functional measure of intrinsic brain activity we parti-
tioned the thalamus into nuclear groups that correspond well with
postmortem human histology and connectional anatomy inferred from
nonhuman primates. This structure/function correspondence in rest-
ing-state activity was strongest between each cerebral hemisphere and
its ipsilateral thalamus. However, each hemisphere was also strongly
correlated with the contralateral thalamus, a pattern that is not attrib-
utable to known thalamocortical monosynaptic connections. These
results extend our understanding of the intrinsic network organization
of the human brain to the thalamus and highlight the potential of
resting-state fMRI BOLD imaging to elucidate thalamocortical
I N T R O D U C T I O N
Spontaneous fluctuations of the blood oxygen level–depen-
dent (BOLD) functional magnetic resonance imaging (fMRI)
signal are temporally coherent within anatomically and func-
tionally related areas of the brain (e.g., see Damoiseaux et al.
2006; Fox et al. 2005; Greicius et al. 2003; Hampson et al.
2002; Vincent et al. 2007). Functionally organized systems
defined on this basis were first described by Biswal and
colleagues (1995) within the somatomotor network and since
then have been demonstrated in multiple systems within the
cerebral cortex (for a recent comprehensive review of this
literature see Fox and Raichle 2007). Less is known regarding
participation of the thalamus in this intrinsic activity despite its
central role in the function of the cerebral cortex (Jones 2007;
Sherman and Guillery 2006).
Given the unique cytoarchitecture and firing patterns in the
thalamus (Jones 2007; Sherman and Guillery 2006), it is
unclear whether and how spontaneous BOLD fluctuations in
the thalamus and cortex will be related. Previous resting-state
functional connectivity studies using fMRI BOLD imaging
have occasionally noted significant thalamocortical correla-
tions (Anand et al. 2005; Beckmann et al. 2005; De Luca et al.
2006; Dosenbach et al. 2007; Fox and Raichle 2007; Greicius
et al. 2007; Seeley et al. 2007; Stein et al. 2000). However, in
many cases, the observed correlations extended over large
areas of the thalamus without respecting classical nuclear
boundaries and there has been no attempt to systematically
characterize thalamic nuclei on the basis of spontaneous activ-
ity. The lack of specificity with regard to the thalamus in prior
resting-state BOLD correlation mapping likely reflects not only
shared neuronal signals propagated over multiple brain areas
but also spatially uniform fluctuations in the BOLD signal
generated by cardiac and respiratory activity (Birn et al. 2006;
Triantafyllou et al. 2005).
The present work examines patterns of coherence within the
thalamocortical system derived from partial correlation map-
ping of spontaneous fluctuations in the fMRI BOLD signal.
Highly specific patterns of resting-state connectivity were seen
between specific regions of the cerebral cortex and the thala-
mus. Much of the observed functional connectivity reflects
known anatomical connections, consistent with evidence that
resting-state networks are synchronized by the underlying
axonal connections (Greicius et al. 2008; Honey et al. 2007;
Johnston et al. 2008; Quigley et al. 2003; Vincent et al. 2007).
However, some aspects of the connectivity (e.g., the bilaterally
symmetric correlations of the cerebral cortex in the thalamus)
are uniquely revealed by this imaging approach, adding a new
dimension to our understanding of the organization of the
brain’s intrinsic activity.
M E T H O D S
Resting-state BOLD sensitized fMRI data (4 ? 4 ? 4 mm voxels,
TE 25 ms, TR 2.16 s) were acquired in 17 normal young adults using
a 3T Siemens Allegra MR scanner in the course of a previous study
(Fox et al. 2007). Each data set included four 7-min runs of 194
frames each (28 min total) during which subjects visually fixated on
a crosshair. No task was imposed except to remain still and not fall
Structural images acquired for definitive atlas transformation in-
cluded a 1 ? 1 ? 1.25-mm sagittal T1-weighted magnetization-
prepared rapid gradient echo (MP-RAGE) (TR ? 2.1 s, TE ? 3.93
ms, flip angle ? 7°) and a T2-weighted (T2W) fast spin echo scan.
Preprocessing of imaging data
Preprocessing included compensation for systematic, slice depen-
dent time shifts using sinc interpolation, elimination of systematic
Address for reprint requests and other correspondence: D. Zhang, Wash-
ington University, Department of Radiology, Campus Box 8225, 510 South
Kingshighway Blvd., St. Louis, MO 63110 (E-mail: zhangd@npg.
The costs of publication of this article were defrayed in part by the payment
of page charges. The article must therefore be hereby marked “advertisement”
in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
J Neurophysiol 100: 1740–1748, 2008.
First published August 13, 2008; doi:10.1152/jn.90463.2008.
17400022-3077/08 $8.00 Copyright © 2008 The American Physiological Society www.jn.org
odd–even slice intensity differences due to interleaved acquisition,
rigid body correction for head motion within and across runs, and
normalization of the signal intensity across each run (not counting the
first four frames) to obtain a whole brain mode value of 1,000
(Ojemann et al. 1997). Atlas transformation was achieved by compo-
sition of affine transforms connecting the first functional volume
(averaged over all fMRI runs after cross-run realignment) with the
T2-weighted and T1-weighted structural images. Common mode
image registration (e.g., to correct for head motion within and across
fMRI runs and affine warping of T1-weighted structural images to the
atlas-representative target image) was performed using in-house ver-
sions of standard algorithms (Woods et al. 1998). Cross-modal reg-
istration (e.g., T2- to T1-weighted images) was performed using the
vector gradient matching algorithm of Rowland et al. (2005). Our
atlas-representative template includes MP-RAGE data from 12 nor-
mal individuals and was made to conform to the 1988 Talairach atlas
(Talairach and Tournoux 1988) according to the method of Lancaster
et al. (1995). To prepare our BOLD data for the present analyses, each
fMRI run was resampled to 2-mm3voxels in atlas space. For display
purposes, voxel boundaries were smoothed using fourfold interpola-
tion and then displayed using in-house software written on the
MATLAB platform (The MathWorks, Natick, MA).
Linear trends across runs were removed voxelwise and the data
were low-pass filtered using a second-order Butterworth filter to retain
frequencies ?0.1 Hz. No high-pass filter was used. Several sources of
spurious variance were removed by regression of the following
nuisance variables along with their first derivatives: 1) the six param-
eters resulting from rigid body correction for head motion; 2) a signal
from a ventricular region of interest (ROI); 3) and a signal from a
white-matter ROI (Fox et al. 2005). Regression of the whole brain
signal, which normally is included in our seed ROI-based functional
connectivity procedure (Fox et al. 2005), was omitted because of our
observation that the thalamus highly correlates with this signal (D
Zhang and MD Fox, unpublished observations). Spatial blurring was
also omitted to maximize resolution in the analyses of small brain
structures, in particular the thalamus.
Cortical ROI definition
To investigate the specific functional relationships between the
cortex and the thalamus, the cortex of each hemisphere was parti-
tioned into five disjoint regions. In detail, the MP-RAGE image
obtained in a normal young adult volunteer (not included in this study)
was segmented and the brain surface extracted and deformed to the
population-average, landmark- and surface-based (PALS)-B12 (Van
Essen 2005) atlas using SureFit and CARET (Van Essen and Drury
1997; Van Essen et al. 2001). The partition boundaries were manually
drawn on the basis of major sulcal landmarks, largely following
previous work (Behrens et al. 2003) taking into account the known
anatomical connectivity of the thalamus and the cortex. In this
manner, five disjoint cortical ROIs were defined: 1) frontopolar and
frontal cortex including the orbital surface and anterior cingulate;
2) motor and premotor cortex (Brodmann areas 6 and 4) excluding
adjacent portions of cingulate cortex; 3) somatosensory cortex (Brod-
mann areas 3, 1, 2, 5, and parts of 40); 4) parietal and occipital cortex
including posterior cingulate and lingual gyrus; 5) temporal cortex
including the lateral surface, temporal pole, and parahippocampal
areas (Fig. 1A). All of the insula and adjacent opercular regions as
well as midcingulate cortex were excluded from the present analyses.
To generate five volume space ROIs, the surface regions obtained by
the partitioning scheme were assigned a thickness of 3 mm (1.5 mm
above and below the fiducial surface corresponding to “layer IV”).
Partial correlation mapping between the cerebral cortex
and the thalamus
The average BOLD time course was extracted from each cortical
ROI. Using these time courses, partial and total correlations were
computed for each voxel in the thalamus. The equations for total and
partial correlations appear in supplemental material.1We computed
partial correlations between each thalamic voxel and each of five
cortical ROIs. The partial correlation between the local thalamic
signal and cortical ROI 1 is the correlation after eliminating the
influence of all other cortical ROIs. It should be noted that partial
correlation can be related to regression and total correlation by the
following example. For given signals A, B, and C, the partial corre-
lation of A and B eliminating the influence of C is the same as
regressing the influence of C from A and B, and then computing the
total correlation of the residuals of A and B.
For purposes of calculating statistical significance, partial correlation
coefficients were converted to a normal distribution using Fisher’s r-to-z
transform. In computing the statistical significance of correlations
when successive observations are independent, the degree of freedom
is equal to the number of samples less a small correction. However,
since consecutive BOLD frames are not independent, it is necessary to
correct the degrees of freedom according to Bartlett’s theory (Fox et
al. 2005; Jenkins and Watts 1968). In the present data, the propor-
tionality between frames and degrees of freedom was computed to be
3.183. Accordingly, for purposes of significance testing under a
fixed-effects model, the z-transformed partial correlation values were
converted to Z-scores by multiplying by?(n ? k ? 3, where n is the
degrees of freedom (number of frames divided by 3.183) and k is the
order of the partial correlation (places to the right of the point in
Yule’s notation in Eq. 3; see supplemental material) (Weatherburn
1949). It should be noted that statistical parametric mapping is an
alternative, equally valid method to partial correlation mapping (Fris-
ton 2007). Z-score maps were combined across subjects using a
fixed-effects analysis. Although partial correlations are defined over
all voxels in the brain, we confine the reporting of our results to the
thalamus, superior and inferior colliculi, striatum, and globus pallidus.
Boundaries enclosing these structures were created by manual tracing
of the atlas template. Resulting images were corrected for multiple
comparisons using a bootstrap method (see supplemental materials).
R E S U L T S
Results obtained by partial correlation mapping of the thal-
amus are shown in Fig. 1 and Supplemental Fig. S2. Each
cortical ROI representing bilaterally homologous areas of the
cerebral cortex gave rise to significantly positive partial corre-
lations in spatially restricted zones of the thalamus. Moreover,
inspection of all five partial correlation maps revealed that
most voxels within the thalamus showed strongly positive,
nonoverlapping correlations with one and only one cortical
ROI. Thus for example, the map of the somatosensory ROI
(Fig. 1B, panel 4) showed strong positive correlations in a zone
corresponding to a void in the motor ? premotor map (Fig. 1B,
panel 3). Systematic evaluation of the partial correlation of
each thalamic voxel with each of the five cortical ROIs (Fig. 2)
confirmed that the great majority of thalamic voxels were
strongly positively correlated with one and only one cortical
ROI. This nonoverlapping property suggested creating winner-
take-all (WTA) representations by labeling each thalamic
voxel according to the cortical ROI with the highest partial
correlation (Fig. 1C and Supplemental Fig. S3). The WTA
maps were highly ordered: similarly labeled voxels were clus-
tered together in aggregate regions, suggesting thalamic nu-
1The online version of this article contains supplemental data.
1741THALAMOCORTICAL FUNCTIONAL CONNECTIVITY
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