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Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
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Functional connectivity-based parcellation of the thalamus:
An unsupervised clustering method and its validity investigation
Yang Fan1, 2, Lisa D. Nickerson3, 4, Huanjie Li1, 2, Yajun Ma1, 2, Bingjiang Lyu1, 2,
Xinyuan Miao5, Yan Zhuo5, Jianqiao Ge1, Qihong Zou1, *, Jia-Hong Gao1, 2, 6, *
1
2
Center for MRI Research, Peking University, Beijing, China
Beijing City Key Lab for Medical Physics and Engineering, School of Physics,
Peking University, Beijing, China
3
4
5
McLean Imaging Center, McLean Hospital, Belmont, MA, USA
Harvard Medical School, Harvard University, Boston, MA, USA
State Key Laboratory of Brain and Cognitive Science, Beijing MRI Center for Brain
Research, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
6
McGovern Institute for Brain Research, Peking University, Beijing, China
*Please send correspondences to:
Jia-Hong Gao, Ph.D. and Qihong Zou, Ph.D.
Center for MRI Research, Peking University, Beijing, China, 100871
Emails: jgao@pku.edu.cn and zouqihong@pku.edu.cn
Phone: +86-10-62752918
Running head: Functional connectivity-based parcellation of the thalamus
Key Words: Thalamus, Parcellation, Resting-state fMRI, Functional connectivity,
K-means, Visuomotor task.
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Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
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Abstract
Node definition, or delineating how the brain is parcellated into individual
functionally-related regions, is the first step to accurately map the human connectome.
As a result, parcellation of the human brain has drawn considerable attention in the
field of neuroscience. The thalamus is known as a relay in the human brain, with its
nuclei sending fibers to cortical and subcortical regions. Functional MRI techniques
offer a way to parcellate the thalamus in vivo based on its connectivity properties.
However, the parcellations from previous studies show that both the number and the
distribution of thalamic subdivisions vary with different cortical segmentation
methods. Here, we used an unsupervised clustering method that does not rely on a
priori information of the cortical segmentation to parcellate the thalamus. Instead, this
approach is based on the intrinsic resting-state functional connectivity profiles of the
thalamus with the whole brain. A series of cluster solutions were obtained, and an
optimal solution was determined. Furthermore, the validity of our parcellation was
investigated through: 1) identifying specific resting-state connectivity patterns of
thalamic parcels with different brain networks; and 2) investigating the task activation
and psychophysiological interactions of specific thalamic clusters during 8-Hz
flashing checkerboard stimulation with simultaneous finger tapping. Together, the
current study provides a reliable parcellation of the thalamus and enhances our
understating of thalamic. Furthermore, the current study provides a framework for
parcellation that could be potentially extended to other subcortical and cortical
regions.
2
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Brain Connectivity
Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
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3
Acronyms
AAL: Automated Anatomical Labeling
DC: Dice’s coefficient
fMRI: functional MRI
FWHM: full-width at half-maximum
FWE: family-wise error
GLM: general linear model
ICA: independent component analysis
LFP: local field potential
MNI: Montreal Neurological Institute
MPRAGE: magnetization-prepared rapid gradient echo
MRI: magnetic resonance imaging
MD: mediodorsal nucleus
PPI: psychophysiological interactions
ROI: region of interest
VI: variation of information
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Brain Connectivity
Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
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4
1. Introduction
The human brain is a complex system with densely interconnected regions and
mapping of this “human connectome” has become a major focus of neuroscience
research (Fornito et al., 2013; Sporns, 2013; Wig et al., 2014). The first crucial step to
delineate the topological properties of the human brain is node definition, or
delineating how the brain is parcellated into individual subunits that can be assessed
for functional or structural connections with each other (Fornito et al., 2013). As a
result, more and more researchers have lent their attention to parcellation of the
human brain, in both cortical and subcortical areas (Bzdok et al., 2013; Craddock et
al., 2012; Kim et al., 2013; Shen et al., 2013; Wig et al., 2013).
The thalamus acts as a “gateway” between subcortical areas and cerebral cortex
(Liang et al., 2013; Sherman and Guillery, 2002), with nearly all areas of neocortex
receiving afferents from the thalamus (Guillery and Sherman, 2002). Moreover, the
thalamus plays an important role in regulating states of sleep and wakefulness
(Steriade and Llinas, 1988) and thalamo-cortical connectivity is reported to be
fundamental for the establishment of oscillatory brain waves (Jones, 2001).
Dysfunction of the thalamus is implicated in the pathophysiology of neurological and
neuropsychiatric disorders (Cauda et al., 2009; Welsh et al., 2010; Woodward et al.,
2012). Based on its cytoarchitecture, the thalamus can be divided into several nuclei
(Morel et al., 1997) that show distinct anatomical connectivity with different cortical
and subcortical regions (Jones, 2007; Sherman and Guillery, 2002). In vivo
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Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
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5
parcellation of the thalamus into different subdivisions would provide locations of
specific thalamic nuclei and thus be of great importance for understanding thalamic
functioning and revealing thalamic involvement in the pathophysiology of brain
disorders.
Magnetic resonance imaging (MRI) techniques are powerful methods for parcellating
brain structures in vivo based on either anatomical or functional connectivity to
provide insight beyond cytoarchitecture information. For example, there are a few
reports that have used a diffusion tensor MRI-based fiber tractography technique
(Basser et al., 1994) to parcellate the thalamus into subdivisions according to its
anatomical connectivity with seven cortical areas (Behrens et al., 2003;
Johansen-Berg et al., 2005; Mastropasqua et al., 2014). Those studies (Behrens et al.,
2003; Johansen-Berg et al., 2005)(Mastropasqua et al., 2014) were limited to the
investigation of connections between thalamus and large cortical regions. However, as
noted by Behrens et al. (2003), the non-human primate literature provides support for
a finer-grained topographic mapping between subregions of, e.g., the mediodorsal
nucleus of the thalamus and smaller regions in the prefrontal cortex (Goldman‐
Rakic and Porrino, 1985; Kievit and Kuypers, 1977).
Furthermore, while anatomical structure is thought to be the foundation of brain
function, studies have found a discrepancy between anatomical and functional
connectivity in human brain (Honey et al., 2009; Honey et al., 2010). Thus, for
5
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Brain Connectivity
Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
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6
functional MRI (fMRI) studies it is equally essential to parcellate the brain based on
its functional connectivity. Over the last decade, resting-state fMRI has emerged as a
powerful tool for mapping the spontaneous or intrinsic functional connectivity of the
human brain with high sensitivity in a completely non-invasive manner that is easy to
implement (Biswal et al., 1995; Kim et al., 2013; Zhang et al., 2008; Zhang et al.,
2010). Kim and his colleagues (Kim et al., 2013) used an ICA based approach to
parcellate thalamus and basal ganglia into several sub-divisions and Zhang et al. used
seed-based resting-state fMRI to parcellate the thalamus into five sub-divisions with a
Winner-Take-All method to label each thalamic voxel (Zhang et al., 2008; Zhang et
al., 2010). The number of thalamic sub-divisions in ICA-based approach is directly
determined by the number of independent components. Different definition of
independent components will result in varied thalamic parcellation results. By
definition, the approach utilized by Zhang et al. is based on a priori knowledge of
how the cerebral cortex is segmented. As a result, parcellation of the thalamus with
different templates of cortical areas, for example, using the Automated Anatomical
Labeling (AAL) template (Tzourio-Mazoyer et al., 2002) or the Brodmann Area
template (Brodmann, 1909), will result in a different parcellation of thalamic nuclei,
both in the number of parcels and in the spatial distribution of each parcel. Previous
study have shown that using functionally inaccurate ROIs, for example, anatomically
defined ROIs, for network analysis is extremely damaging to network estimation
(Smith et al., 2011).
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Brain Connectivity
Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
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7
To obviate those mentioned issues, in the present study, a recently proposed approach
for parcellating brain regions based on the similarity of their functional profiles with
other regions of the brain (Craddock et al., 2012; Kahnt et al., 2012; Kelly et al., 2012;
van den Heuvel et al., 2008) was adopted to parcellate the thalamus into different
spatially continuous subdivisions without any spatial restrictions. An optimal solution
of the number of thalamic subdivisions was obtained by evaluating the stability of
diverse parcellation results. The validity of this approach was investigated by
assessing: 1) specific functional connectivity fingerprints of the thalamic parcels with
known brain networks; 2) brain activation and psychophysiological interactions (PPI)
of specific thalamic nuclei during a visuomotor task (8-Hz flashing checkerboard
stimulation with simultaneous finger tapping). The visuomotor task allows us to
examine the validity of our parcellation because it activates specific thalamic
subdivisions (i.e. lateral dorsal and ventral posterior nuclei (Purushothaman et al.,
2012; Stepniewska et al., 2007; Strick, 1976)) and it can be used to assess PPIs, which
characterize direct connections between thalamic nuclei and other regions during task
performance. For example, Purushothaman and his colleagues found that the posterior
part of thalamus, which sends fibers to primary visual areas, was involved in visual
signal processing.
2. Materials and Methods
2.1. Data Acquisition
Both resting-state fMRI and visuomotor task fMRI datasets were included in this
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Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
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8
study.
Resting-state fMRI dataset: Resting-state fMRI data that were acquired at Beijing
Normal University and have been made freely available on the website of the 1000
functional Connectomes Project (www.nitrc.org/projects/fcon_1000) were used for
this study. The data from 60 subjects (30 females/30 males, 20.8 2.6 years) with
complete coverage of cortical regions were selected from the available scans. All
subjects provided written informed consent before the experiment, and the study was
approved by the institutional review board of Beijing Normal University.
MR Images from this dataset were acquired using a Siemens 3T Trio MRI scanner.
Resting-state functional images were obtained using a T2*-weighted echo-planar
imaging sequence with the following parameters: 33 axial slices, thickness/gap =
3.0/0.6 mm, FOV = 200 200 mm2 with in-plane matrix = 64 64, TR/TE/ =
2000 ms/30 ms/90o, and scanning duration = 7 minutes and 30 seconds with 225
functional volumes. A high-resolution structural MRI 3D image was acquired using a
T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) imaging
sequence (spatial resolution = 1 mm 1 mm 1.33 mm, TR/TE/ = 2530
ms/3.39 ms/7o, inversion time (TI) = 1100 ms). During the resting-state scanning
session, subjects were asked to keep their eyes closed but not fall asleep.
Visuomotor task fMRI dataset: Visuomotor task data were acquired in eighteen
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Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
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9
healthy subjects (9 females/9 males; 22.8 1.8 years) at Chinese Academy of
Science. This study was approved by the institutional review board of Chinese
Academy of Science.
MR images of this dataset were also collected on a Siemens 3T Trio scanner. Task
fMRI image parameters of this dataset are as follows: 22 axial slices without gap,
slice thickness = 5 mm, FOV = 220 220 mm2 with in-plane matrix = 64 64,
and TR/TE/ = 2500 ms/30 ms/90o. A high-resolution structural MRI 3D image was
acquired using a T1-weighted MPRAGE sequence (spatial resolution = 1 mm3
isotropic, TR/TE/ = 2530 ms/ 3.39 ms/7o, TI = 1100 ms). In the visuomotor task
scans, a block-design was used. It began with a 30-s “off” block and consisted of
twelve cycles of alternated 20-s “on” and 20-s “off” blocks. During the “on” block,
subjects were asked to view a screen with 8-Hz flashing checkerboard stimulation and
tap their finger simultaneously.
2.2. Resting-state data processing
The
resting-state
fMRI
images
were
processed
using
SPM8
(www.fil.ion.ucl.ac.uk/spm/) and REST (Song et al., 2011; Yan and Zang, 2010) after
discarding the first ten functional volumes of each subject to avoid transient signal
changes before the longitudinal magnetization reached a steady state. Pre-processing
steps are shown in a flow chart (Figure 1). Steps in SPM8 included slice timing
correction, head motion correction and spatial normalization to Montreal Neurological
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Brain Connectivity
Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
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10
Institute (MNI) space (Collins et al., 1994). The voxel size of the normalized
functional images was 3 mm isotropic. No subject had head motion larger than 2 mm
in translation and/or 2 degree in rotation. As a result, no subject was excluded for
large head motion. Linear trend removal and temporal band-pass filtering (0.01-0.08
Hz) (Biswal et al., 1995) were then performed using REST. Spatial smoothing with a
Gaussian kernel of 4 mm full-width at half-maximum (FWHM) was applied to our
imaging data. To reduce to potential contamination of non-neuronal sources of noise,
the anatomical CompCor (aCompCor) method was adopted for noise estimation and
removal of noise signals (Behzadi et al., 2007).
To further reduce head motion effects, a “temporal scrubbing” method was applied to
the pre-processed fMRI data with the threshold for frame-wise displacement (FD) set
to 0.5 mm (Power et al., 2012). After the “scrubbing” procedure, most subjects (44/60)
had no volumes scrubbed and the most volumes removed were 35 (in one subject).
Scrubbing has been shown to improve functional connectivity maps, and in this study,
this procedure will have minimal impact from slight differences in degrees of freedom
from subject to subject (since most subjects did not have any volumes scrubbed and
this is a single-group study). If the "scrubbed" data is less than 5 minutes, then
exclude that data. Here, no subjects are excluded in this procedure.
2.3. Resting-state functional connectivity-based parcellation
The first goal of this study was to segment the entire thalamus (both left and right
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Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
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11
parts together) into distinct subdivisions based on their resting-state functional
connectivity patterns with the whole brain. The template of the thalamus was
extracted from the AAL template (Tzourio-Mazoyer et al., 2002). The parcellation
approach used here was modified from previous studies (Craddock et al., 2012; Kelly
et al., 2012; van den Heuvel et al., 2008). A flow chart of parcellation procedure is
shown in Figure 1. For each subject, functional connectivity between each thalamic
voxel and every voxel of the whole brain was calculated (Figure 1). Then, an
individual similarity matrix was computed using Pearson correlation coefficient to
evaluate similarity between those spatial functional connectivity maps of each pair of
thalamic voxels. A K-means clustering algorithm was applied to this similarity matrix
to obtain individual thalamic parcellations, generating an individual adjacency matrix
(Craddock et al., 2012). The group adjacency matrix was obtained by averaging all
individual adjacency matrixes. Then, the group adjacency matrix was input to the
K-means clustering algorithm to obtain the group level clustering result. For each
clustering procedure (both single-subject and group levels), the number of clusters
was chosen from two to fifteen.
2.4. Determination of the optimal solution
Since the cluster number, K, is a freely chosen parameter, we further assessed whether
an optimal solution, i.e., stable solution, existed. The variation of information (VI)
index (Kahnt et al., 2012; Meilă, 2007) and Dice’s coefficient (DC) (Craddock et al.,
2012; Zhang et al., 2014b) were calculated here to assess the stability of parcellation
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Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
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12
procedure and determine an optimal solution. The computation of VI and DC is
shown in Supplementary Materials. To obtain those indices, a split-half comparison
approach was used, which divided subjects randomly into two subgroups (30 subjects
in each group) for 100 instances. For each instance, the above-mentioned parcellation
approach was applied to each subgroup and VI and DC values were computed
according to the parcellation results of the two subgroups. The K value whose values
of VI was statistically indistinguishable from that of the K - 1 solution (Kahnt et al.,
2012; Kelly et al., 2010) or with local maximum DC value are defined as a stable
solution. Combining the values of VI and DC, we can obtain the optimal solution.
2.5. Functional connectivity fingerprints of thalamic subdivisions
To define the functional connectivity fingerprints of the estimated thalamic
subdivisions from the optimal solution, we calculated the Pearson correlation
coefficient between mean time courses of each thalamic subdivision and those of
several common resting-state brain networks (Smith et al., 2009). Nine of ten
functional networks were selected, including three visual networks, default mode
network, motor network, auditory network, executive control network, left and right
frontal-parietal networks (Figure S1). The tenth network reported by Smith et al.
(2009), which covered cerebellum, was not assessed in the current study because the
fMRI data from resting-state fMRI dataset did not fully cover the cerebellum. The
Pearson correlation coefficients were then transformed to Fisher Z-scores, and then a
series of one-sample t-tests were used to assess the statistical significance of each
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Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
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13
correlation. The statistical threshold was FWE corrected at p < 0.05.
2.6. Specific functional response of the thalamic subdivisions to visuomotor task
For the task fMRI dataset, preprocessing steps were as follows: 1) slice timing; 2)
head motion correction; 3) spatial normalization; 4) linear trend removal and 5)
spatial smoothing with 4 mm FWHM. For the first-level analyses, a general linear
model (GLM) was constructed to detect task-based activation areas in each subject.
Specifically, the block design convolved with a hemodynamic response function and
six head motion parameters were used as predictors in the GLM. The spatial maps of
regression coefficients for the block-design predictor were used as contrast maps.
Group activation maps were generated from a higher-level GLM analysis
implementing a one-sample t-test of the contrast maps from the first-level GLM.
Whole-brain results were corrected for multiple tests to control for family-wise error
(FWE) using Monte Carlo simulations (FWE corrected at p < 0.01, with uncorrected p
< 0.001 and minimum cluster size = 270 mm3).
To investigate whether specific thalamic clusters responded during the visuomotor
task, we averaged the raw regression coefficients in each cluster from the optimal
solution. Then a one-sample t-test of average activation amplitude (computed from
raw regression coefficients) for each thalamic cluster was conducted across subjects
with a significance threshold of p < 0.05. To investigate the functional connectivity
profiles of specific clusters during task performance, each thalamic cluster was used
13
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Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
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14
as seed region to perform whole brain voxel-wise PPI analysis (Friston et al., 1997).
As running in SPM, the centered block design convolved with hemodynamics
response function, the first eigenvalue of BOLD signals of a given thalamic
subdivisions and their interactions were used as regressors in PPI analysis. Only the
element for beta value of interactions tern was set to one in contrast matrix. A series
of two-tailed one-sample t-tests were then conducted to test for significance of
previous results with a whole-brain multiple comparison correction based on Monte
Carlo simulations (corrected p < 0.01 with uncorrected p < 0.001 and minimum
cluster size was 270 mm3). In the case of a simple first-level GLM the averaged
activation amplitudes can be used to test hypotheses related to the simple question of
“what regions in the brain have activity that is being modulated by the task”. In the
case of PPI, the hypotheses being tested are asking the question “what regions of the
brain show increased connectivity with the thalamic nucleus during the visuomotor
stimulation itself (e.g., relative to the control condition).” These are very different
hypotheses that inform different aspects of thalamic nuclei behavior during
visuomotor stimulation. Thus, a PPI analysis provides strong evidence for testing our
parcellation scheme as direct connections of thalamic nuclei with visual and motor
regions should be separately disentangled (whereas the GLM activation results will
just show nuclei involved in task response, but not their direct connections during the
task).
3. Results
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Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
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15
3.1. Thalamus parcellation results
Parcellation results with different cluster solutions obtained from the resting state data
are shown in Figure 2. The K = 2 cluster solution revealed two spatially symmetric
subdivisions of the thalamus. The larger cluster (Cluster 1) stretched from the ventral
anterior part to the medial and almost covered the complete dorsal part of the
thalamus. The other cluster (Cluster 2) covered only the ventral posterior and ventral
lateral part of the thalamus. Interestingly, the two subdivisions appeared in both the
left and right hemispheres and showed excellent symmetry. As we parcellated the
entire thalamus together, the symmetric results indicated that the parcellation was
dependent on the functional connectivity profiles, and not constrained by the distance
relationship (Mars et al., 2012; Tomassini et al., 2007). Compared with the K = 2
cluster solution, Cluster 3 from the K = 3 solution mostly stemmed from Cluster 2
(the ventral posterior cluster) of the K = 2 solution. Cluster 4 from the K = 4 solution
was spilt from the medial cluster from K = 3, and it covered the posterior part of the
medial thalamus. Cluster solutions with K = 5 to K = 8 resulted in further splittings of
these clusters into subdivisions to show a clear hierarchical structure of the
parcellations. As it is shown in Figure 2, Cluster 9 from the K = 9 solution was
lateralized to the left part of the thalamus. Similarly, when K > 9, one or more clusters
were lateralized to a single hemisphere, thus those results are not shown here.
3.2. Determination of the optimal cluster solution
To determine the optimal solution, a split-half procedure with 100 randomly generated
15
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Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
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16
split-half groups was used and VI and DC values were computed (Figure 3) to assess
the stability of the parcellation and the similarity between the adjacency matrices
calculated for each group in the split-half procedure. VI increased as a function of K,
indicating that the stability decreased as K increased (Figure 3A). After performing a
series of paired t-test statistics, VI was found to be significantly different with K (p <
0.001), except for K = 7. VI indices for the K = 6 and K = 7 parcellation were
statistically undistinguishable (p = 0.988), Moreover, the parcellation with K = 7
corresponded to a local maximum of the DC values (Figure 3B), thus K = 7 was
selected as the optimal solution.
3.3. Functional connectivity fingerprints between the thalamic parcellation and
resting state brain networks
The functional connectivity fingerprints of the thalamic subdivisions from the optimal
thalamic parcellation (K = 7) are shown in Figure 4 and the connectivity strength
computed as values of the group averaged correlation coefficients between mean time
courses of each thalamic subdivision and each resting-state brain network are listed in
Table 1. It can be seen that different subdivisions of the thalamus showed distinct
functional connectivity fingerprints. Table 1 shows that all statistically significant
connections are positive. Cluster 1 was significantly correlated with default mode
network, motor network, auditory network, executive control network and bilateral
frontal parietal networks. (Figure 4). Cluster 2 demonstrated significant functional
connectivity with all networks, except for bilateral frontal parietal networks. Among
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Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
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17
these connections, the one with motor network has the greatest connectivity strength
(r = 0.341). Cluster 3 showed significant but weak correlations with motor, auditory
and executive control networks. Cluster 4 and Cluster 6, which are almost the same
cluster in the K = 5 solution, showed a similar connectivity fingerprint, with very
significant connections with default mode network and executive control network.
Cluster 5 and Cluster 7, though with distinct connectivity patterns, both showed
significant connectivity with almost all networks except for occipital pole visual and
lateral visual networks.
3.4. Activations and PPI of the thalamic subdivisions during the visuomotor task
Visuomotor task activation of thalamic regions is shown in Figure 5A. The activated
regions are located mainly in the bilateral ventral posterior part and left ventral lateral
part of the thalamus, which overlap primarily with Clusters 2, Cluster 3 and Cluster 5
for the K = 7 solution. The percentage overlap between visuomotor activated regions
and the thalamic subdivisions are listed in Table 2. The activation amplitude of each
thalamic cluster is shown in Figure 5B. Clusters 2, 3 and 5 showed statistically
significant responses to visuomotor stimulation.
Psychophysiological interactions during the visuomotor task condition relative to
baseline were investigated between every subdivisions of the optimal solution (K = 7)
and the whole brain. Only clusters (Cluster 2, Cluster 3 and Cluster 5) activated by the
visuomotor task showed significant negative PPIs with visual cortex or/and motor
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Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
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18
cortex (Figure 5C). Clusters 2 and 5 showed negative PPIs with visual and motor
cortices, while Cluster 3 only showed a negative PPI with visual cortex. There were
no significant positive PPIs of thalamic subdivisions with other regions of the brain.
4. Discussion
In the present study, we parcellated the thalamus into different subdivisions using an
unsupervised approach based on their resting-state functional connectivity profiles
with the whole brain. The validity of our thalamic parcellation approach was tested by
assessing: 1) the functional connectivity fingerprints of our thalamic parcels with
known brain networks; and 2) the thalamic responses and PPIs with visual and motor
regions during visuomotor stimulation.
4.1. Connectivity-based parcellation of the thalamus
Behrens and colleagues proposed a novel method to parcellate the thalamus based on
its anatomical connectivity with a priori segmented cortical areas (Behrens et al.,
2003; Johansen-Berg et al., 2005; Mastropasqua et al., 2014). Depending on the
number of cortical regions used for the parcellation, they obtained different
parcellation results, i.e., parcellation of the thalamus into five or seven sub-divisions.
In the human brain, the anatomical structure is assumed to be the foundation of its
function. With equal importance, the underlying anatomical structure is continually
modulated by function in relation to experience (Damoiseaux and Greicius, 2009;
Zhang et al., 2010). It is commonly assumed that functional connectivity between
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Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
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brain regions reflects the anatomical connectivity between them (Damoiseaux and
Greicius, 2009; Honey et al., 2009; Honey et al., 2010). However, the relationship
between functional and structural connectivity appears not be so straightforward.
Studies have shown that the strength of functional and structural connectivity is
positively correlated when structural connectivity is present, functional connectivity is
also observed between brain regions without structural connectivity (Honey et al.,
2009). Thus, a thalamic parcellation based on functional connectivity properties is
needed for functional MRI studies.
To that end, a few studies have defined thalamic subdivisions based on functional
connectivity with a template of brain cortical regions (Zhang et al., 2008; Zhang et al.,
2010). Kim and colleagues proposed a multi-level independent component analysis
(ICA) technique to parcellate the thalamus and basal ganglia (Kim et al., 2013).
However, ICA has some intrinsic methodological limitations (Hyvarinen, 2011),
specifically, ICA-based schemes will depend on the choice of dimensionality of the
ICA. In the current study, we adopted a technique based on the intrinsic functional
connectivity profiles of thalamus. Compared to previous seed-based connectivity
parcellation methods, it does not rely on a priori definition of cortical areas.
Compared to ICA-based techniques, our approach is more hypothesis-driven in the
sense of the determination of number of nuclei. Moreover, the ICA will separate out
the connectivity of each parcel into different components according to networks they
belong to. This makes interpretation a little trickier as one have to worry about
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Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
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“mixing” in many different components. However, seed-based connectivity approach,
as what we do, can assess the whole connectivity profile of each parcel. Thus, the
results of the proposed methods is perhaps easier to interpret.
4.2. Resting-state functional connecitivity with specific brain networks
Distinct and anatomically relevant functional connectivity fingerprints of each
subdivision with brain networks provided evidence for the validity of our parcellation
approach. For instance, the Cluster 6 mainly overlapped with the mediodorsal part of
the thalamus. Significant functional connectivity was observed between this
sub-division and the executive control and default mode networks (Figure 4).
Previous studies have shown that the mediodorsal nucleus anatomically projects to
prefrontal cortex and posterior cingulate cortex (Jones, 2007), which are key regions
in the executive control network and the default mode network, respectively. Thus,
our findings suggest a close correspondence between the functional and anatomical
connectivity of Cluster 6. Notably, Clusters 2 and 3 are primarily located in the
ventral lateral and ventral posterior parts of the thalamus, which sends fibers to motor
and sensory cortices. Figure 4 shows that these subdivisions correlated with motor
network. Moreover, Cluster 4 mainly overlapped with the medial part of the pulvinar
nucleus. This part of thalamus is anatomically connected with prefrontal cortex,
cingulate cortex, and visual and auditory cortices (Jones, 2007). Thus, the significant
functional connectivity observed between Cluster 4 and these cortical areas (Figure 4,
Table 1) is consistent with the known anatomical connections of the pulvinar.
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4.3. Response of specific thalamic clusters to visuomotor stimulation
4.3.1. Visuomotor task activation
Activity of the thalamic clusters from the K = 7 parcellation showed responses in
certain clusters to visuomotor stimulation that are consistent with previous reports of
visuomotor task activation (Calhoun et al., 2001; Miao et al., 2014; Witt et al., 2008),
providing validity of our thalamic parcellation results. Specifically, Clusters 2, Cluster
3 and Cluster 5 were activated during the visuomotor task. Cluster 2 was primarily
comprised of the posterior lateral part of the thalamus, a region that has been shown to
project to both primary and higher order visual areas and to contribute to visual
perception and attention (Fischer and Whitney, 2012; Purushothaman et al., 2012).
The ventral lateral and ventral posterior parts of the thalamus, which were in Cluster 3
and Cluster 5, relay somatosensory and motor information to and among the brain
(Morel et al., 1997; Stepniewska et al., 2007; Strick, 1976) and these regions were
activated during the finger-tapping task when the flashing checkerboard stimulation
was on.
4.3.2. PPI during the visuomotor task
Interestingly, only those thalamic clusters which were activated by the visuomotor
task, showed a significant PPI with visual and motor cortices. However, the PPI
analysis revealed that in fact Clusters 2, 3 and 5 showed strong negative connectivity
with visual and/or motor cortices. Although this may seem counterintuitive,
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theoretically, it can be explained. The statistical model used to detect brain activation
(which is a GLM with a predictor that corresponds to the task plus any nuisance
predictors) detects regions with increased or decreased BOLD signal responses to a
given task (e.g., a main effect of the task), while a PPI analysis, which is also a GLM,
but includes a region of interest (ROI) time course and an interaction regressor,
investigates the interaction between the seed ROI and other regions of the brain
during task performance. In other words, the PPI helps distinguish whether two
regions activated by a task become more strongly functionally connected during task
performance (e.g., have a stronger positive or negative correlation), or whether the
regions act independently with both becoming more active during the task. Several
previous studies have reported negative PPIs of thalamus with other brain regions
during task performance. For example, Chang et al. (2011) reported a negative PPI
between the ventral lateral thalamus and primary auditory cortex during a speech
production task and Zhang et al. (2014a) reported a negative PPI between the
thalamus and medial prefrontal cortex during a stop signal task. Second, a recent
animal electrophysiology study found that the increases in thalamic action potential
firing rate during sensory stimulation correlated with decreases in the amplitude of
low frequency local field potential (LFP) fluctuations of neurons from barrel cortex
(Poulet et al., 2012). BOLD fMRI signals are closely related to LFP signals
(Logothetis et al., 2001), so an increase in thalamic action potential firing leading to a
decrease in the amplitude of the LFP could be one possible mechanism for the strong
negative PPIs we observed between these two regions during visuomotor stimulation,
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although clearly this is speculative. Thus, the presence of a strong PPI effect between
those activated clusters and visual and motor cortices provides further evidence to the
validity of our parcellation scheme. E.g., it is consistent that thalamic nuclei would
interact with visual and motor cortices during a visuomotor task. Our findings reveal
that this occurs via negative PPIs with the thalamic nuclei, which sheds insight into
mechanisms underlying thalamocortical interactions. Future work to understand the
exact processes giving rise to these negative PPIs is warranted.
4.5. Limitations
There are potential limitations of our parcellation procedure that should be considered.
First, the parcellation results were performed on group-averaged data. More work is
needed to obtain stable individual parcellation results. Second, considering the
convergence and divergence between functional connectivity and structural
connectivity (Buckner et al., 2008; Honey et al., 2010), a multi-modal diffusion-based
anatomical scan (e.g., Diffusion Spectrum Imaging) to assess structural connectivity
combined with functional connectivity measurements would shed great light on the
correspondence between structural and functional connectivity and perhaps result in a
more robust thalamic parcellation. Third, improvements in signal noise ratio and
spatial resolution would result in a parcellation of the thalamus with higher precision.
Fourth, considering that thalamus is surrounded by the ventricular system, spatial
smoothing might be a potential source of contamination of thalamic signals. In this
study, to minimize the effect of smoothing, a 4-mm FWHM smooth kernel was used,
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Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
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with 3 mm voxel size of normalized functional images. In addition, the seed-based
functional connectivity analysis method has intrinsic shortcomings. For example, it is
less sensitive in detecting inter-individual variability than ICA-based analyses (Smith
et al., 2014) and one must contend with the global signal regression problem. Here,
we used the aCompCor approach for removing physiological noise signals that
contribute to global signals and thus obviated the issue of GSR.
5. Conclusion
In the current study, we used an unsupervised clustering approach to parcellate the
thalamus based on resting-state functional connectivity profiles. Our optimal thalamic
parcellation scheme resulting in five symmetric thalamic clusters was validated by
assessing the distinct fingerprints of functional connectivity profiles of the resulting
thalamic clusters with brain networks and by assessing the thalamic responses and
PPIs during a visuomotor task.
Acknowledgements
This work was supported by China’s National Strategic Basic Research Program (973)
(2012CB720700 and 2015CB856400) and the Natural Science Foundation of China
(81227003, 81430037, 31421003, 31200761 and 81201142).
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Figure Legends
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Figure 1. Flow chart. Procedures of resting-state fMRI data analysis and thalamic
parcellation.
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Figure 2. The resting-state functional connectivity-based thalamic parcellation.
Resting-state functional connectivity-based parcellation of the thalamus with different
cluster solutions (K = 2, 3 … 9). Seven slices (Z values from -1 mm to 17 mm, slice
thickness: 3 mm) covering the entire thalamus are shown. Different colors represent
different thalamic subdivisions from the parcellation. L and R denote the left and right
hemispheres, respectively.
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Figure 3. VI and DC values for each cluster solution (K). (A) Values of VI as a
function of K. Values of VI for each K cluster solution are significantly different from
the K+1 solution (p < 0.001) except for the difference between K = 6 and K = 7 (p =
0.988), (B) Values of DC as a function of K. K = 7 is a local maximum and thus
considered to be a relatively stable solution.
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Figure 4. Functional fingerprints of thalamic subdivisions from the optimal
solution (K = 7). Fingerprints of Clusters 1-7 from K = 7 solution. Each value of
connectivity strength in fingerprint is the group averaged correlation coefficient
between mean time course of each thalamic subdivision and each functional network.
The MNI axial slice for the overlay corresponds to Z = 11 mm. Abbreviations: VN1 –
Medial Visual Network, VN2 – Occipital Pole Visual Network, VN3 – Lateral Visual
Network, DMN – Default Mode Network, MN – Motor Network, AN – Auditory
Network, ECN – Executive Control Network, rFPN – right Frontal-Parietal Network,
lFPN – left Frontal Parietal Network.
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Figure 5. Response of specific thalamic clusters from the optimal solution (K = 7)
to visuomotor stimulation. (A) Task activation results are shown in the upper panel
(corrected p < 0.01) with the corresponding thalamic parcellation at the same Z
locations. (B) Bar and error bar plots of activation amplitude for each thalamic
subdivision, averaged across subjects. Clusters 2, 3, and 5 show significant activation
during the task. (C) Regions showing significant PPI effects with thalamic clusters
during the visuomotor task period relative to the control period.
36
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Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
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37
Table 1 - Mean functional connectivity strength between thalamic subdivisions
and brain networks
Cluster Number
Brain
Networks
1
2
3
4
5
6
7
VN1
0.034
0.196
0.011
0.117
0.134
0.209
0.150
VN2
-0.006
0.139
0.000
0.125
0.087
0.206
0.101
VN3
-0.036
0.175
0.030
-0.026
0.093
0.018
0.030
DMN
0.172
0.176
0.013
0.335
0.163
0.392
0.310
MN
0.127
0.341
0.157
0.117
0.294
0.063
0.151
AN
0.164
0.225
0.134
0.009
0.313
0.153
0.249
ECN
0.355
0.136
0.141
0.299
0.289
0.543
0.459
rFPN
0.278
0.073
0.067
0.171
0.160
0.228
0.283
lFPN
0.261
0.069
0.048
0.147
0.120
0.192
0.247
Thalamic subdivisions are those from the optimal solution (K = 7) using the resting
state FMRI data. Functional connectivity values in bold indicate statistically
significant connectivity across subjects (corrected p < 0.05). VN1: Medial Visual
Network, VN2: Occipital Pole Visual Network, VN3: Lateral Visual Network, DMN:
Default Mode Network, MN: Motor Network, AN: Auditory Network, ECN:
Executive Control Network, rFPN: right Frontal-Parietal Network, lFPN: left Frontal
Parietal Network.
37
Brain Connectivity
Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
This article has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof.
Page 38 of 41
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Table 2 - Percentage overlap between thalamic subdivisions and visuomotor task
activations
Cluster Number
Task
1
2
3
4
5
0%
50.8%
15.9%
3.4%
25.9%
data (with global signal regression).
38
6
7
Activations
0%
4.0%
Thalamic subdivisions are those from K = 7 parcellation using the resting state FMRI
Page 39 of 41
Brain Connectivity
Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
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39
Supplementary Materials
Computation of VI and DC
The VI index was used to evaluate the stability of various parcellation results (Kahnt
et al., 2012; Kelly et al., 2012; Meilă, 2007). VI values for each K were computed as
follows:
VIk C1, C2 Hk C1 Hk C2 2Ik C1, C2
(1)
where C1 and C2 represent clustering results from group 1 and group 2, respectively.
Hk (C)
is the entropy of clustering results of a subgroup and
Ik (C1, C2 )
mutual information between clustering results of the two subgroups.
is the
H k and I k can
be computed as:
H k C P(k ) log P k
K
k 1
I K C1 , C2 P k1 , k2 log
K
K
k1 1k2 1
(2)
P k1 , k2
P k1 P k2
(3)
where P(k) is the probability that a voxel belongs to cluster k; P(k1,k2) is the
probability that a voxel belongs to cluster k1 in clustering result C1 and cluster k2 in C2.
Low VI indicates that clustering results estimated from the two subgroups share more
information, i.e., they are more similar. In contrast, high VI values indicate low
similarity. We defined a stable solution as the K value whose VI was statistically
indistinguishable from that of the K - 1 solution (Kahnt et al., 2012; Kelly et al.,
2010).
Dice’s Coefficient (DC; (Craddock et al., 2012; Zhang et al., 2014) was used to
evaluate the similarity between the results from the two subgroups during the
39
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Brain Connectivity
Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
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40
split-half procedure. DC is computed as follows:
DC
2 C1 C2
C1 C2
(4)
This, DC values are between zero and one, where a value of one indicates perfect
correspondence, and zero indicates no similarity. The higher the DC values, the more
similar the parcellation results from the two subgroups, and the more stable the cluster
solution.
40
Page 41 of 41
Brain Connectivity
Functional connectivity-based parcellation of the thalamus: An unsupervised clustering method and its validity investigation (doi: 10.1089/brain.2015.0338)
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41
Figure S1. Resting-state brain networks.
These brain networks are generated using ICA (Smith et al., 2009). Nine networks out
of ten were selected in the present study. We did not investigate the cerebellar network
because the cerebellum was not fully covered in our data. Abbreviations: VN1 –
Medial Visual Network, VN2 – Occipital Pole Visual Network, VN3 – Lateral Visual
Network, DMN – Default Mode Network, MN – Motor Network, AN – Auditory
Network, ECN – Executive Control Network, rFPN – right Frontal-Parietal Network,
lFPN – left Frontal Parietal Network.
41