ORIGINAL RESEARCH ARTICLE
published: 18 May 2010
Frontiers in Systems Neuroscience www.frontiersin.org May 2010 | Volume 4 | Article 10 | 1
tigations have focused on cortical–cortical interactions, subcorti-
cal structures and their cortical interactions also develop during
childhood and adolescence (Jones, 2007). Indeed, recent rs-fcMRI
studies have suggested distinct developmental changes that occur
between cortical and subcortical structures (Fair et al., 2007a, 2009;
Kelly et al., 2009; Supekar et al., 2009). Thus, important insights
regarding brain development are likely to emerge from additional
examination of cortical–subcortical functional relationships.
Knowledge concerning the developmental trajectory of rs-fcMRI
is especially lacking with regards to the thalamus, a key structure for
nearly all brain operations. Innovative work by Zhang et al. (2008,
2009) in adults has recently used rs-fcMRI to map thalamo-corti-
cal interactions. This methodology utilizes known cortical con-
nectional anatomy (Alexander and Crutcher, 1990; Jones, 2007)
to identify functional boundaries in the thalamus and other sub-
cortical structures. In this procedure (Zhang et al., 2008), regions
of interest (ROIs) are identifi ed that encompass major subdivisions
of the cortex (see Figure 1). The average spontaneous signal gener-
ated in each cortical ROI is then correlated with all of the voxels
in the thalamus. Using a ‘winner take all’ strategy, where the corti-
cal subdivision that correlates strongest with a given voxel ‘wins,’
Zhang et al. partitioned the thalamus into distinct subdivisions
Recent years have witnessed a surge of investigations examining
brain function and organization using the relatively new technique
of resting-state functional connectivity MRI (rs-fcMRI) (Biswal
et al., 1995). rs-fcMRI measures correlate, low frequency (usually
<0.1 Hz) blood oxygenation level dependent (BOLD) fl uctuations
between brain regions while subjects are at rest, not performing goal-
directed tasks (Biswal et al., 1995; Fox et al., 2005; Fair et al., 2007a,b,
2008; Fox and Raichle, 2007). By cross correlating the BOLD signal
time series between different regions or voxels, one can determine
which regions are ‘functionally connected’ (see Friston et al., 1993;
Lee et al., 2003 for specifi c defi nition). To date, this method has been
used in several domains to examine systems-level brain organization
in typical and atypical populations (Biswal et al., 1995; Fox et al.,
2006; Hampson et al., 2006; Andrews-Hanna et al., 2007; Dosenbach
et al., 2007; Fair et al., 2007a, 2008, 2009; Greicius et al., 2007; Seeley
et al., 2007; Uddin et al., 2008; Church et al., 2009).
Recent work has shown that rs-fcMRI is also quite useful for
studying the maturation of functional brain networks. This work
has led to key insights regarding typical and atypical brain develop-
ment (Fair et al., 2007a, 2008, 2009; Fransson et al., 2007; Kelly et al.,
2009; Supekar et al., 2009). Whereas the majority of these inves-
Maturing thalamocortical functional connectivity across
Damien A. Fair1*, Deepti Bathula1, Kathryn L. Mills1, Taciana G. Costa Dias1, Michael S. Blythe1, Dongyang
Zhang2, Abraham Z. Snyder2, Marcus E. Raichle2, Alexander A. Stevens1,3, Joel T. Nigg1,3 and Bonnie J. Nagel1,3*
1 Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
2 Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
3 Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR, USA
Recent years have witnessed a surge of investigations examining functional brain organization
using resting-state functional connectivity MRI (rs-fcMRI). To date, this method has been used
to examine systems organization in typical and atypical developing populations. While the
majority of these investigations have focused on cortical–cortical interactions, cortical–subcortical
interactions also mature into adulthood. Innovative work by Zhang et al. (2008) in adults have
identifi ed methods that utilize rs-fcMRI and known thalamo-cortical topographic segregation
to identify functional boundaries in the thalamus that are remarkably similar to known thalamic
nuclear grouping. However, despite thalamic nuclei being well formed early in development, the
developmental trajectory of functional thalamo-cortical relations remains unexplored. Thalamic
maps generated by rs-fcMRI are based on functional relationships, and should modify with the
dynamic thalamo-cortical changes that occur throughout maturation. To examine this possibility,
we employed a strategy as previously described by Zhang et al. to a sample of healthy children,
adolescents, and adults. We found strengthening functional connectivity of the cortex with
dorsal/anterior subdivisions of the thalamus, with greater connectivity observed in adults versus
children. Temporal lobe connectivity with ventral/midline/posterior subdivisions of the thalamus
weakened with age. Changes in sensory and motor thalamo-cortical interactions were also
identifi ed but were limited. These fi ndings are consistent with known anatomical and physiological
cortical–subcortical changes over development. The methods and developmental context
provided here will be important for understanding how cortical–subcortical interactions relate
to models of typically developing behavior and developmental neuropsychiatric disorders.
Keywords: development, thalamus, functional connectivity, subcortical, MRI, fcMRI, nuclei
Lucina Q. Uddin, Stanford University,
University of Pittsburgh, USA
Max Planck Institute, Germany
Damien A. Fair, Department of
Psychiatry, Oregon Health and Science
University, 3181 SW Sam Jackson Park
Road UHN80R1, Portland, OR 97239,
Bonnie J. Nagel, Department of
Psychiatry, Oregon Health and Science
University, 3181 SW Sam Jackson Park
Road DC7P , Portland, Oregon 97239,
Frontiers in Systems Neuroscience www.frontiersin.org May 2010 | Volume 4 | Article 10 | 2
Fair et al. Maturing thalamo-cortical interactions
(Figure 1). The spatial organization of these thalamic subdivisions
is in substantial agreement with known thalamic nuclear group-
ing based on postmortem human studies (Morel, 2007; Mai et al.,
2008) and anatomical track-tracing data from other mammalian
species (Nieuwenhuys, 1988; Webster et al., 1995; Jones, 2007).
The rs-fcMRI results also are remarkably similar to tract-tracing
results based on diffusion tensor imaging (DTI) (Behrens et al.,
2003; Johansen-Berg et al., 2005; Zhang et al., 2009).
While anatomically distinct nuclear groups are well formed
within the thalamus early in development (Jones, 2007), it is
unknown whether thalamo-cortical fcMRI is the same in children
and adults. Because the thalamic maps generated by fcMRI are based
on functional relationships, we hypothesized they should not mimic
organization found in adulthood, but should track the dynamic tha-
lamocortical changes that are believed to occur throughout matura-
tion (e.g., the pruning of temporal-thalamic contacts, and increased
frontal-subcortical coherence over age) (Giedd et al., 1999; Jones,
2007; Galinanes et al., 2009). This developmental characterization
between thalamus and cortex has the potential to lay the ground-
work for a better understanding of how cortical–subcortical interac-
tions contribute to the shift from refl exive, stimulus-bound behavior
in childhood, to the goal-directed and more fl exible functioning
found in adulthood. It will also provide the neural context necessary
to examine how cortical-thalamic interactions relate to prominent
models of several developmental neuropsychiatric disorders. Hence,
we employed a strategy previously detailed by Zhang et al. to study
correlated spontaneous brain activity between the cortex and the
thalamus in healthy children, adolescents, and adults.
MATERIALS AND METHODS
Participants were recruited through a combination of public
advertisements, county mailings, and via the Oregon Health &
Sciences University local outreach systems. Participants were
screened with a series of interviews and questionnaires for inclu-
sion. Informed consent was obtained from all subjects in accord-
ance with the guidelines and approval of the Oregon Health &
Science University Human Investigation Review Board. A total
of 52 subjects (17 aged 7–9; 21 aged 11–16; 14 aged 19–32) were
included in the study (see Table 1; Table S1 in Supplementary
Material). All participants were free of major sensory, medical,
neurological, or psychiatric (including substance abuse) illness
and had normal-range intelligence.
DATA ACQUISITION AND PROCESSING
Participants were scanned using a 3.0 Tesla Siemens Magnetom
Tim Trio scanner with a twelve-channel head-coil at the OHSU
Advanced Imaging Research Center (AIRC). One high resolution T1-
weighted MPRAGE sequence (orientation = sagittal, TE = 3.58 ms,
TR = 2300 ms, 256 × 256 matrix, resolution = (1 mm)3, 1 average,
total scan time = 9 min 14 s) was collected. Blood-oxygen level
dependent (BOLD)-weighted functional imaging was collected in
an oblique plane (parallel to the ACPC line) using T2*-weighted
echo-planar imaging. Resting data from adult participants were
originally collected as part of a separate study. As such, acquisi-
tion parameters were slightly different for adults and children/
adolescents (adults: TR = 2000 ms, TE = 30 ms, fl ip angle = 90°,
FOV = 240 mm, 36 slices, slice thickness = 3.5 mm in-plane res-
olution = 3.75 mm2; children: TR = 2000 ms, TE = 30 ms, fl ip
angle = 90°, FOV = 240 mm, 36 slices covering the whole brain,
slice thickness = 3.8 mm, in-plane resolution = 3.8 mm2). Steady
state magnetization was assumed after fi ve frames (∼10 s). The
parameters for this acquisition have been optimized (e.g., oblique
acquisition) to reduce susceptibility artifact, which causes signal
drop out in orbitofrontal cortex. Three rest runs of 3.5-min dura-
tion obtained for all children. Two rest runs of 5-min duration
were obtained for all adolescents and adults. During rest periods,
subjects were verbally instructed to continue to stay still, view a
FIGURE 1 | The ‘winner take all’ strategy for identifying subdivisions of the
thalamus (Zhang et al., 2008), utilizes known thalamocortical topographic
segregation (Alexander and Crutcher, 1990; Jones, 2007) to identify functional
boundaries in the thalamus. (A) Regions of interest (ROIs) used for the current
analysis. These regions are composed of fi ve disjoint cortical subdivisions, which
include the prefrontal cortex (blue), motor/premotor (green), somatosensory
(yellow), parietal/occipical cortex (purple), and temporal corex (red). (B) The average
spontaneous signal generated from each cortical ROI is then correlated with all of
the voxels in the thalamus. This creates fi ve voxelwise statistical maps of the
thalamus. (C) The strength of connectivity is then compared for each cortical ROI
within each voxel. The cortical subdivision that correlates strongest with any given
voxel ‘wins. ’ The given voxel is then assigned the color of the winning cortical ROI.
The resulting thalamic subdivisions are in substantial agreement with known
thalamic nuclear grouping based on postmortem human studies (Morel, 2007; Mai
et al., 2008) and anatomical track-tracing data from other mammalian species
(Nieuwenhuys, 1988; Webster et al., 1995; Jones, 2007)
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Fair et al. Maturing thalamo-cortical interactions
was similar except that the whole brain signal was not included as
a nuisance regressor. The correlation procedures associated with
these two separate pre-processing strategies are described below (in
Correlations between cortical subdivisions and thalamus).
CORTICAL ROI DEFINITION
Cortical ROI defi nition was as in Zhang et al. (2008). In short, the
cortex in each hemisphere was partitioned into fi ve subregions
(Figure 1). The MP-RAGE image from a normal young adult volun-
teer (not included in this study) was segmented along the gray/white
boundary and deformed to the population-average, landmark and
surface-based (PALS)-B12 atlas (Van Essen, 2005) using SureFit and
CARET software (Van Essen and Drury, 1997; Van Essen et al., 2001).
Partition boundaries were manually drawn based on major sulcal
landmarks, following work by Behrens et al. (2003). Five cortical
ROIs were thus defi ned: (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 (Brodmann areas 3,
1, 2, 5, and parts of 40); (4) parietal and occipital cortex including
posterior cingulate and lingual gyrus; (5) temporal cortex includ-
ing the lateral surface, temporal pole, and parahippocampal areas
(Figure 1). For details see (Zhang et al., 2008, 2009).
CORRELATIONS BETWEEN CORTICAL SUBDIVISIONS AND THALAMUS
Resting state (fi xation) data from 52 subjects (17 aged 7–9; 21 aged
11–16; 14 aged 19–32) were included in the analyses. The adolescent
age range was chosen for two reasons. First, while there are sev-
eral ways of defi ning adolescence, we chose an age-range that best
encompasses the peripubertal years – a time of dynamic behavioral
and neural maturation (Paus, 2005). By age 11, many children have
initiated early pubertal processes (especially among females), and
by age 16, most youth have attained pubertal maturation (Kreipe,
1992). The second consideration regarded our prior connectivity
results, which have shown signifi cant transitions in connectivity
between similar age groups (e.g., see Fair et al., 2007a). All par-
ticipants contributed between 420–630 s of resting-state data. The
data were fi rst analyzed with a total correlation procedure, which
included whole brain signal regression in the initial pre-processing
steps (see Functional Connectivity Pre-processing). In this case, for
the fi ve cortical subdivisions, an average resting state timeseries
was extracted and correlated (r) with all voxels of the thalamus
separately and for each individual. Shared variance among the fi ve
cortical subdivisions is accounted for in this instance with the initial
whole brain signal regression. This procedure is similar to the total
correlation procedure used in Zhang et al. (2008). In the second
analysis, whole brain signal regression was not used in the initial
pre-processing. Rather, shared variance was accounted for by partial
correlation, wherein the correlation between a cortical ROI and the
thalamus was computed after covarying out the other four cortical
regions. Total correlation yielded slightly less specifi city but more
uniformity across subjects in comparison to partial correlation. In
both analyses, to calculate statistical signifi cance within each age
group, we fi rst applied Fischer’s z transformation to the correlation
coeffi cients (r) to improve normality. From here, these values were
converted to Z scores by dividing by the square root of the variance
within each group, as in Fox et al. (2005). Z-score maps were then
Table 1 | Subject characteristics.
Adults Adolescents Children
Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.
Mvmt. (rms) 0.36
cross in the middle of the screen, and be sure to stay awake. The
stimulus display consisted of the standard fi xation-cross in the
center of the stimulus fi eld.
Functional images were processed to reduce artifacts (Miezin
et al., 2000). These steps included: (i) removal of a central spike
caused by MR signal offset, (ii) correction of odd vs. even slice
intensity differences attributable to interleaved acquisition with-
out gaps, (iii) correction for head movement within and across
runs, and (iv) within run intensity normalization to a whole brain
mode value of 1000. Atlas transformation of the functional data
was computed for each individual via the MP-RAGE scan. The
fMRI data then were resampled (3 mm cubic voxels) in Talairach
atlas space (Talairach and Tournoux, 1988) as defi ned by the spa-
tial normalization procedure (Lancaster et al., 1995). This resam-
pling combined movement correction and atlas transformation
in one interpolation. All subsequent operations were performed
on the atlas-transformed volumetric time series. For presentation
purposes, voxel boundaries were interpolated to 1 mm3 voxels and
displayed using CARET software (Van Essen et al., 2001).
Participant head motion was measured and corrected using rigid
body translation and rotation. Summary statistics were calculated
as root mean square (RMS) values for translation and rotation
about the x, y, and z-axes. Total RMS values were calculated on a
run-by-run basis for each participant. BOLD runs with excessive
movement (>2 mm RMS) were excluded from further analysis.
Movement was relatively low in all groups (see Table 1).
FUNCTIONAL CONNECTIVITY PRE-PROCESSING
The functional data were additionally pre-processed in two ways for
two separate analysis strategies, as outlined below. In the fi rst analysis
(total (marginal) correlation – see below) pre-processing was carried
out as previously described (Fox et al., 2005; Fair et al., 2007a,b, 2008,
2009) to reduce spurious variance unlikely to refl ect neuronal activity
(Fox and Raichle, 2007). These steps included: (i) a temporal band-
pass fi lter (0.009 Hz < f < 0.08 Hz), (ii) regression of six parameters
obtained by rigid body head motion correction, (iii) regression of
the whole brain signal averaged over the whole brain, (iv) regres-
sion of ventricular signal averaged from ventricular region of inter-
est (ROI), and (v) regression of white matter signal averaged from
a white matter ROI. Regression of fi rst derivative terms for whole
brain, ventricular, and white matter signals were also included in the
correlation pre-processing. These pre-processing steps are, in part,
intended to remove developmental changes in connectivity driven
by changes in respiration and heart rate over age. Pre-processing in
preparation for the second analysis (partial correlation – see below)
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Fair et al. Maturing thalamo-cortical interactions
combined across subjects using a fi xed effects analysis. Results pre-
sented here are restricted to the thalamus, whose boundaries were
created by manual tracing of the atlas template. For the ‘winner take
all’ strategy (see Figure 3), the cortical subdivision that correlated
most strongly for any given voxel was assigned the designated color
for the ‘winning’ cortical subdivision.
To test signifi cant change over development, direct comparisons
between the two end point groups, children and adults, were per-
formed. This between-group end point approach, as opposed to using
the entire sample, has been shown to be more robust to potential
non-linear changes (Fair et al., 2006). For such direct comparisons,
we performed two-sample, two-tailed t-tests (random effects analysis
assuming unequal variance; p ≤ 0.05) for each cortical subdivision
applied to Fischer z-transformed r values. For the voxelwise, fi xed
effects maps, thresholding based on Monte Carlo simulation was
implemented (Forman et al., 1995). To obtain multiple comparisons
corrected, p < 0.05 voxel clusters, a threshold of 35 contiguous voxels
with a Z-value > 2.5 was used. Maps showing statistically signifi cant
changes with age were uncorrected (as few voxels passed our stringent
correction), and displayed with a threshold of Z > 2.
FUNCTIONAL CONNECTIVITY OF CORTICAL SUBDIVISIONS WITHIN THE
THALAMUS IN ADULTS SHOW A SPATIAL ORGANIZATION IN
AGREEMENT WITH KNOWN THALAMIC NUCLEAR GROUPING
Replicating prior reports, (Zhang et al., 2008, 2009), in our adult
sample, correlations between the thalamus and each cortical subdi-
vision were distinct, with substantial correspondence with known
axonal connectivity with thalamic nuclei in primates (Jones, 2007;
Morel, 2007). Specifi cally, the parietal-occipital cortical subdivi-
sion showed strong correlations with the lateral and posterior por-
tions of the thalamus. This portion of the thalamus and dorsal
brain stem are comprised of lateral pulvinar, lateral geniculate, and
superior colliculus, which contain combinations of afferent input,
and projections, to parietal occipital association areas and primary
visual cortex (Lock et al., 2003; Jones, 2007) (see Figure 2 – row 1,
column 2; Figure S1 in Supplementary Material). The temporal
cortical ROI correlated strongly with medial, inferior, and posterior
portions of the thalamus. This segment of the thalamus and dorsal
brainstem presumptively corresponds to medial pulvinar, inferior/
superior colliculi, medial geniculate, and medial dorsal nucleus
(Webster et al., 1995; Jones, 2007), which have combinations of
inputs from, and projections to, temporal cortex (see Figure 2 – row
1, column 5; Figure S2 in Supplementary Material). The prefrontal
cortical subdivision showed strong interactions with dorsal, medial,
and anterior portions of the thalamus. This thalamic area contains
medial dorsal and the anterior nuclear groups, with inputs and
outputs to cingulate and prefrontal portions of the cortex (Jones,
2007) (see Figure 2 – row 1, column 1; Figure S3 in Supplementary
Material). Somatosensory cortical areas strongly correlated with
ventral, lateral, and posterior thalamic regions, which presump-
tively correspond to ventral posteriolateral and posteriomedial
nuclei (Jones, 2007); see Figure 2, row 1, column 4, and Figure
S4 in Supplementary Material. Finally, the motor cortex subdivi-
sion correlated strongly with lateral and ventral thalamic areas that
presumptively correspond to ventral lateral and ventral anterior
nuclei (see Figure 2 – row 1, column 3; Figure S5 in Supplementary
Material). As can be seen in Figure 3, these fi ndings are perhaps
most clearly evident in ‘winner take all’ displays (Zhang et al., 2008,
2009); also see Figure S6 in Supplementary Material).
FUNCTIONAL CONNECTIVITY OF CORTICAL SUBDIVISIONS WITH THE
THALAMUS CHANGES SUBSTANTIALLY OVER DEVELOPMENT
Although thalamic nuclear groups are defi ned early in development
(Jones, 2007), we saw substantial differences in connectivity pat-
terns between our younger participants and adults. Figure 2 (rows
1–3) suggests a transitional change from childhood, through ado-
lescence, to adulthood for thalamo-frontal and thalamo-temporal
interactions. Specifi cally, frontal lobe correlations are weak in child-
hood and appear to strengthen by adulthood. Temporal lobe cor-
relations with the thalamus, however, are much stronger in children,
and weaken progressively in adolescence and adulthood. This fi nd-
ing was obtained both by total correlation (Figure 2) and partial
correlation analyses (Figure S7 in Supplementary Material). The
fi nding also held true when looking at raw correlation values (r),
rather than z statistics (Figure S8 in Supplementary Material).
This particular fi nding (i.e., increased thalamo-frontal interac-
tions and decreased thalamo-temporal interactions over age) is
clearly seen in the ‘winner take all’ displays. In adolescents (Figure 3),
the picture was slightly modifi ed from what was seen in the adult
group. Along the midline bilaterally, the thalamo-temporal correla-
tions encompassed a greater portion of anterior and midline thala-
mus, while frontal lobe correlations encompassed much less of the
anterior portions of the thalamus. This trend, in which frontal (and
to a lesser extent, motor) correlations give way to temporal correla-
tions, was observed to an even greater extent in the youngest age
group. In children, thalamo-temporal correlations were stronger
and more widespread – not only encroaching on areas occupied by
thalamo-frontal correlations in adults, but also parts of the thala-
mus functionally connected with motor/premotor, somatosensory,
and occipital/parietal areas. In contrast, the spatial extent of the
thalamo-frontal interaction was minimal in children and somewhat
stronger in the adolescent group although, still limited relative to
the adults. This dynamic can also be observed in Movie S1–S4
in Supplementary Material. Again, Figure S9 in Supplementary
Material shows these fi ndings were largely unchanged when using
partial correlations. (Our child and adolescent groups had a slight
excess of females and males, respectively. Accordingly, we repeated
our analysis using equal numbers of males and females in each
group. These fi ndings, shown in Figure S10 in Supplementary
Material, suggest that gender had minimal effect on the overall
patterns observed here.)
Direct statistical comparisons between children and adults con-
fi rmed the qualitative observations for the frontal–thalamic and
temporal–thalamic interactions. Thalamo-cortical interactions
observed with somatosensory cortex were qualitatively similar, but
showed statistical differences between groups of both increased
(lateral/inferior) and decreased (medial/dorsal) connectivity
with specifi c parts of the thalamus (Figure 4). Similar fi ndings
were observed for motor-premotor cortex. Correlations with the
occipital/parietal ROI appeared qualitatively unchanged across age
groups. This observation was also confi rmed with the direct sta-
tistical comparisons (Figure 4). The direct comparisons between
children and adults using partial correlations (without whole brain
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Fair et al. Maturing thalamo-cortical interactions
signal regression – Figure S11 in Supplementary Material) were
slightly different in that statistical differences observed between
somatosensory and motor-premotor ROIs appeared weaker.
In this report we replicate prior fi ndings that demonstrate spatially
distinct BOLD correlations between the thalamus and specifi c corti-
cal subdivisions that are in substantial correspondence with known
thalamic connectivity in primates (Jones, 2007; Morel, 2007). We
also saw signifi cant development in thalamo-cortical correlations
over maturation via rs-fcMRI. Specifi cally, we showed a progres-
sive strengthening of functional connectivity of the frontal cortex
with dorsal/anterior subdivisions of the thalamus. We also saw a
systematic weakening of temporal lobe connectivity with ventral/
midline/posterior subdivisions of the thalamus. Premotor-motor
and somatosensory cortical subdivisions also showed increased
connectivity in lateral/inferior portions of the thalamus and
decreased connectivity in medial/dorsal portions of the thalamus.
Occipital–parietal correlations with the thalamus were relatively
stable over our samples.
Of note, considering the nature of the BOLD response, it is
conceivable that developmental differences in the hemodynamic
response could affect our results (D’Esposito et al., 2003). We
feel this is unlikely considering reports suggesting that changes
observed over development with fMRI are not the product of
changes in hemodynamic response mechanisms over age (Kang
et al., 2003; Wenger et al., 2004). Similarly, we also note that our
observations could be affected by physiologic noise such as res-
pirations and heart rate, but believe this is also unlikely for two
reasons. First, most of these nuisance signals are likely removed
with our band-pass fi lter and/or the removal of shared variance
via partial correlations or whole brain signal regression. Second,
the observations in this report (and elsewhere, Fair et al., 2007a,
2009; Kelly et al., 2009; Supekar et al., 2009) show age-related
changes over development that occur in both directions (i.e.,
strengthen and weaken). It would be diffi cult to explain how a dif-
ference in heart rate or respiration could account for these oppos-
ing dynamics. With that said, it would be benefi cial for future
reports to include these additional physiologic noise parameters
as regressors into the processing strategy of rs-fcMRI; however,
it will be equally interesting in future reports to determine how
brain oscillations might actually drive cardiac and respiratory
fl uctuations. Assuming that the age-related alterations described
here represent true change in functional connections, the ques-
tion then becomes: What are the neurobiological underpinnings
of that change?
FIGURE 2 | Fixed effects functional mapping of the thalamus for each
cortical ROI, in each age group (Children, Adolescents, Adults). This
qualitative comparison appears to show substantial change over development
regarding the thalamo-cortical functional interactions. The functional
neuroanatomy of the adult group is quite similar to previous publications
(Zhang et al., 2008, 2009). The most prominent differences between age
groups shows frontal lobe interactions that are weaker in children and appear
to increase over age. In contrast, temporal lobe interactions are much stronger
in children, but weaken progressively in adolescents and adults: Transverse
Z = +8
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Fair et al. Maturing thalamo-cortical interactions
CHANGES IN THE FUNCTIONAL RELATIONSHIPS OF CORTICAL
STRUCTURES WITH THE THALAMUS MAY, IN PART, REFLECT CHANGES
IN THE UNDERLYING NEURAL SUBSTRATE
There are multiple sources of developmental change that may
account for the changes in functional connectivity observed from
the child to adult thalamus reported here. Based on comparisons
of this method with similar methods using DTI in adults (Zhang
et al., 2009), it is clear that some of the functional relationships
seen here are related to large fi ber-tracts connecting the cortex
with specifi c subdivisions of the thalamus. This is not to say that
nuclear groupings are not fully developed early in development
(at birth thalamic nuclei are known to be composed of connec-
tional and functionally distinct cell types; Jones, 2007). Nor is this
to say that large fi ber bundles are growing or being eliminated
over the age span studied here; these are in place by ∼9 months
of age (Conel, 1939–1963). However, there does continue to be
signifi cant refi nement in connectional anatomy that occurs well
into young adulthood.
For example, axonal retraction and the elimination of
axon collaterals continues throughout development and aging
(O’Leary, 1989; Luo and O’Leary, 2005). Following a proliferation
of synapses early in development, there is a protracted period
of synaptic pruning that reaches adult levels in the late second
decade of life (Huttenlocher, 1979; Huttenlocher et al., 1982;
Elman et al., 1996; Huttenlocher and Dabholkar, 1997; Casey
et al., 2005; Jones, 2007). Importantly, these regressive processes
are not random. They are selective, reduce connectivity between
specifi c regions, and occur in both cortical and subcortical struc-
tures (Ebbesson, 1980; Greenough et al., 1987; Luo and O’Leary,
2005; Jones, 2007).
Such phenomena may account for some of the fi ndings shown
here. For example, work conducted by Webster et al. (1995) in
developing macaques provides a compelling parallel with regard
to weakening thalamo-temporal connectivity over age. In this
study, the authors compared the subcortical connections of inferior
temporal cortex (Areas TE and TEO) in infant vs. adult monkeys.
While the connectional anatomy was similar in infant and adult
monkeys, there was signifi cant refi nement of the subcortical con-
nections. Specifi cally, while projections from TE (and to a more
limited extent TEO) to the nucleus medial dorsalis were present
in infants and adults, they were more widespread in infants and
signifi cantly reduced in the adult animals. The same was true for
connections from area TE projecting to the superior colliculus.
Similar changes potentially occur in medial and polar aspects of
the temporal lobe (Russchen et al., 1987). Importantly, both frontal
and temporal cortical subdivisions share anatomical connections
to the nucleus medial dorsalis (Webster et al., 1995; Jones, 2007),
which is consistent with the thalamo-temporal and thalamo-frontal
connectivity seen here (Figure 3). Hence, the refi nement of inferior
temporal lobe projections directly to nucleus medial dorsalis, may,
in part, account for the reduced thalamo-temporal connectivity
observed during development.
The reduced connectivity associated with somatosensory and
motor-premotor cortical subdivisions is also likely related to
similar phenomena as the temporal lobe. For example, in mice,
relay neurons of the ventral posterior medial nucleus undergo
signifi cant dendritic refi nement over age, with more than 300%
reduction in the extent of their dendritic fi elds from age P6 to
adulthood (Brown et al., 1995; Zantua et al., 1996; Jones, 2007).
Work by Dennis O’Leary and colleagues (Luo and O’Leary, 2005)
has shown in rodents, that cortical layer V neurons, after early
extensive interstitial branching, acquire functionally appropriate
connections through selective elimination, dictated by the cortical
area in which the neuron is located. For example, in newborns,
motor and visual neurons project to common targets in the brain-
stem and spinal cord. During maturation, functionally appropri-
ate connections are acquired through selective axon elimination,
determined by the cortical area (i.e., motor or visual) in which the
neuron is located.
In addition to a reduction in thalamo-temporal correla-
tions, we also demonstrated increased thalamo-frontal correla-
tions across development. One commonly cited contributor to
increased connectivity between regions is myelination. Indeed,
myelination has been shown to be closely related to rs-fcMRI
measures (Hagmann et al., 2008; Greicius et al., 2009) (although
this has not yet been examined in children). Myelination increases
at least through young adulthood. It proceeds from primary
sensory and motor regions to association areas (Flechsig, 1920;
FIGURE 3 | ‘Winner take all’ displays across age. As previously observed,
the color-coded thalamus based on the winner take all strategy in adults
shows a functional organization that is remarkably similar to known nuclear
groupings in the primate thalamus. However, the picture is different in
adolescents and children. In agreement with Figure 2, in both adolescents
and to a greater extent children, thalamo-temporal interactions encompass a
greater portion of anterior and midline thalamus, while the frontal lobe
interactions encompass much less of the anterior portions of the thalamus. In
children, thalamo-temporal interactions not only encroach on areas that, in
adults, are dominated by thalamo-frontal interactions, but also impinge on
thalamic zones that later become functionally more connected with motor/
premotor, somatosensory, and occipical/parietal cortex (also see Movies in
Supplementary Material); Transverse Z = +8, Sagittal X = −12, Coronal Y = −27 .
Frontiers in Systems Neuroscience www.frontiersin.org May 2010 | Volume 4 | Article 10 | 7
Fair et al. Maturing thalamo-cortical interactions
These types of functional changes identifi ed by Galinanes
and colleagues highlight an important aspect regarding tha-
lamic organization and the developmental changes observed
in this report. As put by Sherman and Guillery (2006), ‘It is
important to distinguish the functional input that carries the
messages for transmission to the cortex, the driver, from the
many other inputs, the modulators, which can modify the way
in which the message is transmitted…’ The circuitry of the tha-
lamus is complex. Its function is determined not only by the
driver connectional anatomy to the cortex, but also modulators
(interneurons, other subcortical inputs, various neurotransmit-
ter systems) (Sherman and Guillery, 2006; Jones, 2007). Indeed,
throughout the thalamus, driver synapses to relay cells encompass
only a fraction of the total number synapses (∼2–10%) (Sherman
and Guillery, 2006). To the contrary, modulatory synapses to
relay cells account for over 90% of synaptic contacts with relay
neurons. Importantly, many properties related to modulatory
action continue to develop postnatally (Jones, 2007). Hence, it
is likely that increases and decreases in thalamo-cortical con-
nectivity seen here over age are infl uenced by maturation of
modulatory systems (Jones, 2007).
In this report, we showed dynamic maturing functional interac-
tions between the thalamus and cortex. The gross partitioning of
cortical regions, as used in the present study, is adequate to produce
connectivity maps with thalamic nuclei that are consistent with
known structural connectivity, and is promising. In addition, there
appears to be little difference in results obtained by partial vs. total
correlation analysis. However, it is important to note that while
the currently applied methods appear well suited for identifying
distinct subcortical structures in adults, they do not provide the
same specifi city in children. This difference likely refl ects func-
tional and anatomic maturational mechanisms. Alternative rs-
fcMRI techniques may be better suited for identifying nuclear
Brody et al., 1987; Paus et al., 2001; Sowell et al., 2002), roughly
following the hierarchical organization introduced by Felleman
and Van Essen (1991). As such, increased signal propagation,
through the maturation of the myelin sheath, is a likely contribu-
tor to the increased interaction between the frontal cortex and
subregions of the thalamus, particularly those in the anterior
and medial dorsal portion (Luna and Sweeney, 2004; Fair et al.,
2007a, 2008; Kelly et al., 2009).
CHANGES IN THE FUNCTIONAL RELATIONSHIPS OF CORTICAL
STRUCTURES WITH THE THALAMUS LIKELY REFLECT CHANGES BEYOND
THE MATURING NEURAL SUBSTRATE
Changes in cortical-subcortical dynamics, particularly with the
frontal cortex, are likely not solely due to changes in the underlying
neural substrate (Honey et al., 2007; Fair et al., 2009; Lewis et al.,
2009). It is now apparent that the connectivity signal measured
via rs-fcMRI is not a pure representation of monosynaptic ana-
tomical connectivity (Vincent et al., 2007; Hagmann et al., 2008;
Zhang et al., 2008), and thus, other explanations must be consid-
ered. For example, modeling work has shown that complex spatial
and temporal patterns of synchronous activity can develop over
time in the absence of external input and without changes in the
neuroanatomy (Honey et al., 2007). Other work by Galinanes et al.
(2009) has shown, in mice, that subcortical neurons in the striatum
are more temporally tuned to frontal cortical rhythms in adults
than they are in infancy (i.e., they are more strongly functionally
connected – albeit in different frequency ranges than examined
here). Importantly, Galinanes found that these changes in func-
tional properties are unlikely to be secondary to direct develop-
ment of anatomical changes per se, but rather the modulation of
functional properties through the maturation of the dopaminergic
system. While this work targeted the striatum, similar phenomena
could be occurring indirectly (or directly) in the thalamus with-
out direct changes in the gross neuroanatomy, and independently
FIGURE 4 | Direct comparison between the end groups (i.e., children and
adults). The direct comparison between children and adults confi rmed many of
the qualitative fi ndings shown in Figure 3. Frontal connectivity with the
thalamus increases with age, and Temporal connectivity with the thalamus
weakens with age. Differences in connectivity between children and adults with
premotor/motor and somatosensory cortex were also revealed with the direct
comparisons, while the occipical/parietal ROI showed very little difference
between the groups.
Frontiers in Systems Neuroscience www.frontiersin.org May 2010 | Volume 4 | Article 10 | 8
Fair et al. Maturing thalamo-cortical interactions
groupings specifi c to children and adolescents (e.g., Cohen et al.,
2008; Margulies et al., 2009). Other complementary connectivity
methods may additionally assist in differentiating changes across
development related to extent versus strength or magnitude in
connectivity. For example, as noted in the Introduction section,
anatomically distinct nuclear groups and large fi ber tracts are well
formed within the thalamus early in development (Jones, 2007). As
such, anatomically based methods that utilize large fi ber bundles
(see Behrens et al., 2003; Asato et al., 2010, as opposed to function-
ally based methods, may prove successful in further differentiating
nuclear groupings and extent of connectivity in children.
In future work it will also be necessary to observe how changes
in thalamo-cortical functional connectivity relate to develop-
mental changes in behavior. It is likely that the cortical-subcor-
tical interactions observed here will correspond to a shift from
refl exive, stimulus-bound behavior in childhood, to the goal-
directed, self-organized, and more fl exible functioning in young
adulthood (Stuss, 1992) – a distinct hypothesis that can be tested.
For example, recent theories suggest that, during childhood and
early adolescence, goal-directed behavior is governed by principles
of approach and avoidance, with regulation and balance of this
system developing across adolescence and into adulthood (Ernst
et al., 2009). While early approach and avoidance is thought to
be subserved by subcortical and limbic brain regions (consistent
with greater thalamo-temporal interactions in children), emerging
control of these affective and appetitive behaviors (among others
– see Bunge et al., 2002) occurs in concert with the maturation
of subcortical to prefrontal cortex interactions (consistent with
emerging increased thalamo-frontal interactions shown across our
sample) (Chambers et al., 2003; Casey et al., 2008). Along the same
lines, identifying how these cortical–subcortical interactions relate
to models of neuropsychiatric disorders will also be of interest in
Research was supported by the Oregon Clinical and Translational
Research Institute (Fair), Medical Research Foundation (Fair),
UNCF Merck postdoctoral fellowship (Fair), Ford Foundation
(Fair), R01 MH59105 (Nigg), NS06833 (Raichle), K08 NS52147
(Nagel), Portland Alcohol Research Center (P60 AA010760 –
Nagel), and the OHSU Neuropsychiatric Institute (Nigg).
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Conflict of Interest Statement: The
authors declare that the research was con-
ducted in the absence of any commercial or
fi nancial relationships that could be con-
strued as a potential confl ict of interest.
Received: 05 February 2010; paper pen-
ding published: 01 March 2010; accepted:
06 April 2010; published online: 18 May
Citation: Fair DA, Bathula D, Mills KL,
Dias TGC, Blythe MS, Zhang D, Snyder
AZ, Raichle ME, Stevens AA, Nigg JT and
Nagel BJ (2010) Maturing thalamocor-
tical functional connectivity across deve-
lopment. Front. Syst. Neurosci. 4:10. doi:
Copyright © 2010 Fair, Bathula, Mills,
Dias, Blythe, Zhang, Snyder, Raichle,
Stevens, Nigg and Nagel. This is an open-
access article subject to an exclusive license
agreement between the authors and the
Frontiers Research Foundation, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the
original authors and source are credited.