Group Title: Molecular Pain 2008, 4:55
Title: FMRI connectivity analysis of acupuncture effects on an amygdala-associated brain network
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Title: FMRI connectivity analysis of acupuncture effects on an amygdala-associated brain network
Series Title: Molecular Pain 2008, 4:55
Physical Description: Archival
Creator: Qin W
Tian J
Bai L
Pan X
Yang L
Chen P
Dai J
Ai L
Zhao B
Gong Q
Wang W
von Deneen KM
Liu Y
Publication Date: 39765
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Volume ID: VID00001
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Holding Location: University of Florida
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Molecular Pain ioMed



Research

FMRI connectivity analysis of acupuncture effects on an
amygdala-associated brain network
Wei Qin1,2, Jie Tian*1,2, Lijun Bai2, Xiaohong Pan6, Lin Yang4, Peng Chen5,
Jianping Dai3, Lin Ai3, Baixiao Zhao5, Qiyong Gong7, Wei Wang8,
Karen M von Deneen9 and Yijun Liu*9


Address: 'Medical Image Processing Group, Institute of Automation, Chinese Academy of Sciences, Beijing, 100080, PR China, 2Life Science
Research Center, School of Electronic Engineering, Xidian University, Xi'an, Shaanxi 710071, PR China, 3Department of Radiology, BeijingTiantan
Hospital, Capital University of Medical Sciences, Beijing, 100050, PR China, 4Department of Anatomy and Embryology, Capital Medical
University, Beijing, 100069, PR China, 5Beijing University of Chinese Medicine, Beijing, 100029, PR China 6Department of Psychology, School
of Education Science, East China Normal University, Shanghai, 200241, PR China, 7Huaxi MR Research Center, Department of Radiology, Medical
Imaging Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610054, PR China, 8The Fourth Military Medical University, Xi'an,
Shaanxi 710038, PR China and 9Departments of Psychiatry and Neuroscience, McKnight Brain Institute, University of Florida, Gainesville, FL
32610, USA
Email: Wei Qin wei.qin@ia.ac.cn; Jie Tian* jie.tian@ia.ac.cn; Lijun Bai bailijun@life.xidian.edu.cn;
Xiaohong Pan xhpan@dedu.ecnu.edu.cn; Lin Yang yanglini 1873@hotmail.com; Peng Chen chenpenger@yahoo.com.cn;
Jianping Dai djp@public.bta.net.cn; Lin Ai ailinl36@yahoo.com.cn; Baixiao Zhao baixiaol00@yahoo.com.cn;
Qiyong Gong qygong05@126.com; Wei Wang m_dwang@hotmail.com; Karen M von Deneen vondenk@ufl.edu;
Yijun Liu* yijunliu@ufl.edu
* Corresponding authors


Published: 13 November 2008
Molecular Pain 2008, 4:55 doi: 10.1 186/1744-8069-4-55


Received: 12 October 2008
Accepted: 13 November 2008


This article is available from: http://www.molecularpain.com/content/4/1/55
2008 Qin et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.



Abstract
Background: Recently, increasing evidence has indicated that the primary acupuncture effects are
mediated by the central nervous system. However, specific brain networks underpinning these
effects remain unclear.
Results: In the present study using fMRI, we employed a within-condition interregional covariance
analysis method to investigate functional connectivity of brain networks involved in acupuncture.
The fMRI experiment was performed before, during and after acupuncture manipulations on
healthy volunteers at an acupuncture point, which was previously implicated in a neural pathway
for pain modulation. We first identified significant fMRI signal changes during acupuncture
stimulation in the left amygdala, which was subsequently selected as a functional reference for
connectivity analyses. Our results have demonstrated that there is a brain network associated with
the amygdala during a resting condition. This network encompasses the brain structures that are
implicated in both pain sensation and pain modulation. We also found that such a pain-related
network could be modulated by both verum acupuncture and sham acupuncture. Furthermore,
compared with a sham acupuncture, the verum acupuncture induced a higher level of correlations
among the amygdala-associated network.
Conclusion: Our findings indicate that acupuncture may change this amygdala-specific brain
network into a functional state that underlies pain perception and pain modulation.


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central


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Background
Acupuncture is one of the most important therapeutic
modalities in traditional Chinese medicine (TCM). It uti-
lizes fine needles that may pierce through specific ana-
tomical points (named 'acupoints') so that certain healing
effects are produced. In clinical practice, sensation
induced by needling at acupuncture points is asserted as
'deqi', and the resulting effects of acupuncture stimulation
have been ascribed to treatment of various diseases [1].
While acupuncture has gained much popularity in the
Western medical community, the underlying mechanisms
remain mostly unknown.

Previous human neuroimaging studies have shown that
acupuncture stimulation activates extensive brain regions,
including the primary somatosensory cortex (SI), second-
ary somatosensory cortex (SII), anterior cingulate cortex
(ACC), insular cortex, prefrontal cortex (PFC), amygdala,
hippocampus, periaquaductal gray (PAG) and hypothala-
mus [2-7]. These distributed brain regions are associated
closely with a wider pain matrix for modulating sensa-
tions and affective pain perception. Some of these brain
regions are also implicated in endogenous anti-nocicep-
tive signaling. Using functional magnetic resonance imag-
ing (fMRI), Wager et al [8] demonstrated that expectancy
might modulate the pain matrix, along with a considera-
ble overlap among the brain areas in response to placebo
and expectation. A recent PET study by Pariente et al [9]
has identified different areas of activations induced by
both the expectation of acupuncture and actual acupunc-
ture. These findings suggest that actual acupuncture may
not only activate a brain network associated with expecta-
tion and placebo response but also the brain regions
implicated in the actual effect of acupuncture analgesia.

Acupuncture, however, is a complex intervention that is
intimately intertwined with placebo, patients, and practi-
tioners. We thereby hypothesized that acupuncture may
affect this pain matrix in both specific and non-specific
manners which contribute to its specific therapeutic
effects, as well as the effects of expectation for pain relief.
We further questioned whether there are interactions
among these brain regions activated during acupuncture
intervention. We speculate that these brain regions
involved in the pain matrix may constitute various net-
works to mediate both specific and non-specific effects of
acupuncture, which can be assessed using fMRI connectiv-
ity analysis methods.

Recently, new but promising fMRI connectivity analysis
methods have provided insight into the brain networks
mediating acupuncture effects. The term fMRI connectiv-
ity describes brain regions that are functionally related
and interdependently connected [10,11] by detecting the
coherence in fMRI signals among these regions during


either a behavioral task or a resting state engaging no task.
The between-region correlation during a resting state may
represent synchronous fluctuations with a high temporal
coherence and reflect intrinsic neuronal connections that
coordinate activities in the brain, even for those regions in
remote locations [12-14]. Most resting state connectivity
studies have employed a 'seeding' approach, in which a
seed voxel or several voxels are selected as a functional ref-
erence, and then the averaged time course of the fMRI sig-
nal from the seeding area is cross-correlated with the time
course of each voxel over the entire brain to generate con-
nectivity maps (for details see Methods). Such a func-
tional reference is often determined by a region of interest
(ROI) in a brain activation study using specific behavioral
tasks or external stimuli [14]. The choice of such ROI is
therefore vital and should be carefully defined in func-
tional connectivity analysis.

Acupuncture may recruit distributed cortical and subcorti-
cal brain networks that are also implicated in both inhib-
itory and facilitating effects in the pain-modulation
system for both sensation and affective pain perception.
Accumulating evidence suggests that the amygdala is an
important neural substrate of such reciprocal interaction,
and one that also appears to play a key role in the modu-
lation of pain behavior and nociceptive processing at dif-
ferent levels of the pain matrix [15-17]. Furthermore,
increasing attention has been paid to the acupuncture-
induced deactivation of the amygdala in humans using
ST36 or LI4 when contrasting acupuncture needle stimu-
lation with non-stimulation baseline [2,3], to further
demonstrate this in relation to the analgesic effect of acu-
puncture. Results of animal studies have demonstrated
that the amygdala formation has abundant opiates recep-
tors and participates in both opioid analgesia and acu-
puncture analgesia [18]. These findings are noteworthy
because the amygdala modulation may demonstrate an
acupuncture specificity [19]. In the aforementioned anal-
ysis, therefore, we selected the amygdala as the seeding
ROI to conduct our functional connectivity analysis. Tar-
geting the brain circuits involving the amygdala using
fMRI may improve our understanding of neural mecha-
nisms underlying both acupuncture specific and non-spe-
cific effects.

Previous fMRI activation studies have been mostly based
on a block paradigm design that detects acupuncture
effects according to a presumable temporal pattern of
brain activation induced by acupuncture administration
[2,3,20]. A block design, or model-dependent approach in
general, may not be optimal to the study of acupuncture
effects. For example, in a model-dependent block design
for specific visual or motor tasks, the corresponding visual
or motor cortical areas are assumed to be activated almost
simultaneously. This approach, however, is not valid in


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cases when limited or no prior temporal information is
available, such as testing the acute effects of a new drug or
food intake on the brain [21]. According to the theory of
TCM, acupuncture may induce long lasting post-adminis-
tration effects [22]. Thus, the actual temporal information
for acupuncture-induced changes in brain activity remains
lacking. In addition, because of a sustained effect, the 'off-
state' in the block design may still retain some acupunc-
ture effect, which has not ideally returned to a baseline.
Therefore, using several stimulation blocks in a short
period of time, investigators may not be able to dissociate
the long lasting effects from other confounding changes,
such as the effect of needle manipulation during the
experiment. In the current study, a new experimental par-
adigm, namely the non-repeated event-related fMRI
(NRER-fMRI) design, was employed for investigating sus-
tained effects after acupuncture administration by using
functional connectivity analysis.


Methods
Subjects
The experiment was performed on 18 right-handed
healthy Chinese college students (9 males and 9 females
age of 24.2 2.9 years old). Subjects with a medical his-
tory of any neurological or psychiatric diseases were
excluded from study. All the participants have given
informed consent approved by a local review board for
human studies. None of them had previous acupuncture
experience or had been exposed to a high magnetic field.

Experimental protocol
We first used a conventional block design adapted from
Hui et al to study brain activation during acupuncture
administration [2,3]. In the BLOCK run (Fig. 1A), the par-
ticipant underwent a conventional block of acupuncture
stimulation at ST 36 for 8 minutes with a needle inserted
perpendicularly to the skin at a depth of 2-3 cm. One


BLOCK


SNeedle in
I


+


Needle out


Sm,
rI


IA *


0 mS2 3

Omin 8


min


ACUP or SHAM


4


Time points for connectivity analysis I


REST


Time points for connecti' ii, analysis


Figure I
Experimental design. A: The design for a conventional block run (BLOCK) with three acupuncture stimulation periods (S I,
S2, and S3) lasting for 8 minutes; B: The design for an verum acupuncture (ACUP) run or sham (SHAM) run totally lasting for
15.0 minutes; C: The design for a resting state (REST) run lasting for 12.5 minutes. The images acquired during the time points
labeled by blue-color were used for connectivity analyses.


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I Needle in


1B


0 min I min


2.5 min


Needle out


I-


15 min


0 min


12.5 min


---


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minute later, the needle was manipulated by rotating it
clockwise and counterclockwise 60 times per min for 1
minute. Three stimulation blocks S1, S2 and S3 were sep-
arated by 2 minutes for S1-S2 or 1 minute for S2-S3 with
the needle kept in the point. The scanning continued for
1 minute after S3.

We then employed the new NRER-fMRI design, in which
two functional runs: verum acupuncture (ACUP) and
sham acupuncture (SHAM) were conducted and only one
single stimulation period was given during each of these
two runs (Fig. 1B). All participants' eyes were covered so
that they could not observe any of the procedures, verum
or sham, while they were occurring. The acupoint of ST 36
is located 3 mm lateral and distal to the anterior tubercle
of the tibia, which has been examined in various studies
and has been proved to have various effects, including
efficacy in the treatment of gastric and intestinal diseases
in humans and animals. In the verum acupuncture run, an
acupuncture needle was inserted at ST 36 from the begin-
ning, and after resting for 1 min, the needle was manipu-
lated for 1.5 minutes; then, the needle remained inserted
at the acupoint for another 12.5 minutes. In the sham acu-
puncture run, the procedure was the same as verum run
except that the stimulation was administered at a non-
acupoint (2-3 cm apart from ST 36). For both ACUP and
SHAM runs, the method for needle manipulation and the
depth were all identical to those in the BLOCK run. For a
baseline control, a resting state (REST) scan was con-
ducted for 12.5 minutes without any stimulation (Fig.
1C). During the REST run, the participant was asked to
remain relaxed without engaging in any mental tasks. All
of the subjects affirmed to keep awake during the whole
process according to their report after the scanning.

Despite considerable neuroimaging studies on specific
effects of acupuncture, the results are controversial and do
not demonstrate clear effects of acupuncture over the pla-
cebo or controls used in many of previous studies. Some
of the drawbacks may be due to poor paradigm design
and very limited understanding of the concepts involved
in both verum acupuncture and sham. Therefore, the true
extent of acupuncture's specific effect cannot be defined
without the knowledge of the nature and mechanism of
placebo. The choice of placebo or control is vital and
needs careful consideration. Our sham control involves
the insertion of needles in non-acupoint using the same
needling manipulation as in real acupuncture, which is
thought to be a classic placebo and well accepted in TCM.
In fact, this control has a physiological effect possibly
through the mechanisms such as diffuse noxious inhibi-
tory control [23], as well as many of the central neural
substrates that are involved in pain [24]. Actual acupunc-
ture given in the guise of this control which has more
resembled needling sensation as real acupuncture and


stronger blinding to subjects would provide strong sup-
port for the existence of acupuncture specific effects.

Because there is a potential long-lasting effect following
acupuncture administration [22], a 24-hour interval was
taken between the four of above fMRI runs. The presenta-
tion sequence of the above four conditions (BLOCK,
ACUP, SHAM and REST run) was randomized and bal-
anced throughout the subjects, and every subject performs
only one run in each day. And all the subjects completed
the four runs. The subjects were informed of acupuncture
being administered on the right lower extremity (on ST 36
or sham acupoint), and would feel various sensations, but
were blinded to the sequence of the stimulation condi-
tions. The subjects lied down supine inside of the magnet
and kept their eyes closed for the entire fMRI run. The
stimulation was administered by a balanced 'tonifying
and reducing' technique using a sterile disposable 38
gauge stainless steel acupuncture needle, 0.2 mm in diam-
eter and 40 mm in length. The entire acupuncture proce-
dure was conducted by the same experienced and licensed
acupuncturist (CP).

Functional Imaging
Subjects were scanned in a 3.0 Tesla Signa (GE) MR whole
body Scanner. A foam pillow and a band (across the fore-
head) were used to restrict head movement. Functional
images were collected in a sagittal orientation parallel to
the AC-PC plane with 5 mm slice thickness (no gaps)
using a single-shot gradient-recalled echo planar imaging
(EPI) sequence. The EPI pulse sequence had the following
parameters: TE = 30 ms, TR = 1500 ms, flip angle = 90
degree; matrix size = 64 x 64, FOV 240 x 240 mm2, giving
an in-plane resolution = 3.75 x 3.75 mm. The scan cov-
ered the entire brain including the cerebellum and brain-
stem. High-resolution structural scans were acquired
using 3D MRI sequences with a voxel size of 1 mm3 for
anatomical localization.

At the end of each verum or sham run, the participant was
questioned about aching, pressure, soreness, heaviness,
fullness, warmth, coolness, numbness, tingling, dull or
sharp pain and any other sensations felt during the stim-
ulation. The intensity of each sensation was measured on
a scale from 0 to 10 (0 = no sensation, 1-3 = mild, 4-6 =
moderate, 7-8 = strong, 9 = severe and 10 = unbearable
sensation). The participants signaled their feelings by rais-
ing one finger if the needling sensation became very
strong (the subjective rating score > 8), and raising 2 fin-
gers for experiencing sharp pain. The acupuncturist would
then adjust the intensity of stimulation by reducing the
rotation angle of the needle. In general, the discomfort
would disappear almost immediately [2]. Subjects were
excluded from functional connectivity analysis if they
experienced sharp pain (greater than the mean by more


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than two standard deviations). The psychophysical data
obtained after ACUP or SHAM runs were summarized in
Figure 2. Among eighteen participants, only one was
excluded from further analysis due to remarkable sharp
pain.

Image pre-processing
For BLOCK and REST runs, the data were preprocessed by
removing the first 5 time points to eliminate nonequilib-
rium effects of magnetization. For both ACUP and SHAM
runs, only the datasets after manipulation were selected
(total of 500 time points, the same time points as in REST
run), and the first 5 time points were discarded in order to
obtain a stable resting state. The remaining time points
(labeled by blue-color in Fig. 1) were used for functional
connectivity analyses.

All functional images were motion-corrected by using a
new arithmetic model proposed by Freire et al. [25] in
order to reduce global correlation. This method can effec-
tively attenuate the contribution of the global movement
to the correlation coefficient. In this work, we used a med-
ical pulse oximeter to monitor the cardiac component,
and thereby obtained its spectrum in the low-frequency
band. Through a down-sampling process, physiological
noise sources and significant artifacts could be removed.
The translation and rotation were checked, and the images
with head movement greater than 1 mm in any direction
or head rotation greater than one degree were excluded.
The data were further processed with spatial normaliza-
tion in the MNI space and re-sampled at 2 x 2 x 2 mm3
using SPM2. Global means and linear trends were
removed to eliminate both global correlation and gross
signal drifts using Gramm-Schmitt orthogonalization
[26]. The datasets were spatially smoothed with a 6 mm
FWHM Gaussian kernel.

The low-frequency components of fMRI time series have
been shown to have interregional correlations between
functionally related brain areas [27]. A finite-impulse
response band-pass filter was applied to the dataset used
for functional connectivity analyses in order to remove
the frequency out of the 0.01-0.1 Hz signals.

Regions of interest
Data from the BLOCK run were used to localize ROIs for
further functional connectivity analysis. For this purpose,
a four-step process was undertaken. (i) Statistical analysis
was performed on individual data by cross-correlating the
temporally smoothed boxcar reference function with the
time courses of each voxel [28], hence the individual r-
map was obtained. (ii) In order to eliminate variance for
each condition of interest across subjects, a random-effect
analysis was performed with a one-sample t-test (df = 16)
at each voxel across subjects based on their individual r-


maps [29]. Voxels with Itl > 3.5 (P < 0.001, uncorrected)
and clusters with a threshold size > 6 voxels were then
superimposed on a high-resolution anatomical image
(Fig. 3). (iii) The peak voxel in the brain region (in our
case, it is the voxel with the largest negative t value in the
left amygdala was obtained). The peak voxel and its 6
nearest neighbors were defined as the ROI. (iv) Due to
anatomical variance across the subjects, the subject-spe-
cific peak voxel and subject-specific ROI were defined on
individual r-maps as follows: the ROI from the above
group analysis was taken as a mask and then, based on
individual r-maps, the voxel with the largest r-value
within this mask was taken as the subject-specific peak
voxel. Regarding the subject variance and improving the
ratio of signal to noise, this voxel together with its 4 near-
est neighbors were used as a subject-specific ROI. The
averaged time courses of voxels within the ROI were used
as a single low-frequency reference function named as the
'seeding' time course.

Functional connectivity analysis
For each subject, the 'seeding' time courses of both con-
tralateral amygdala were respectively cross-correlated with
all low-pass filtered voxels to generate functional connec-
tivity maps within each of the three conditions. Since the
brain responses during the block run (Table 1) were found
similar to the previous study [2], only the left amygdala
was used in our further connectivity analyses of the related
brain network. This approach was termed as within-con-
dition interregional covariance analysis (WICA) [14]. The
resulting correlation coefficient r-maps were normalized
and corrected to roughly standard normal distributions
using the methods previously described [30,31]. The nor-
mality of the distribution was then tested using Kurtosis
tests (P < 0.05). The three z-maps of each individual were
entered into one-sample t-tests respectively [29] to deter-
mine whether the group data was significantly different
from zero.

In order to quantitatively compare the functional connec-
tivity among these three conditions (i.e. REST, ACUP,
SHAM), the strength of a connection between two brain
regions was calculated by weighing the t-values and vol-
umes on the basis of the averaged cross-correlations with
the 'seed' and then normalized [14]. Furthermore, the
paired t-tests were applied on a voxel-by-voxel basis over
all the subjects to contrast the functional connectivity
maps (i.e. ACUP REST or SHAM REST). The difference
map was used to reveal how acupuncture or sham stimu-
lation may modulate the resting-state functional connec-
tivity. Similarly, paired t-tests were used to contrast
between ACUP and SHAM conditions for showing an acu-
puncture specific modulation (i.e. ACUP SHAM). All the
resulting t-maps were then cluster-filtered to remove cor-
relations involving less than 3 contiguous voxels and then


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0




0.8




0.6

o
(II
C-
LL



0.2




0.0


Soreness Numbness Fullness

A


Sharp pain Dull pain


Soreness Numbness Fullness Sharp pain Dull pain

B


Figure 2
Psychophysical ratings. A: The averaged index of subjective pain experienced by all participants (n = 18); only five sensations
were listed with mean scale above zero (meaning that at least one subject experienced this sensation). Significant difference of
verum acupuncture stimulation compared to the sham stimulation condition was observed in soreness, fullness and sharp pain
level. Error bars were based on 95% Confidence Interval. B: The frequency of the sensations occurred among all the subjects.
Error bars were based on 95% Confidence Interval. Although the sensations such as soreness, numbness and fullness occurred
at higher rates among the subjects under acupuncture intervention as compared to sham stimulation, no significant difference
in the overall frequency of experience was found between acupuncture and sham conditions using Fisher's test (P > 0.05).




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A Acu vs B Sham


















,, Pnini


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Table I: The main results of group analysis of block data.


Ta


1-2E
Figure 3
Functional activation map determined by group anal-
ysis in a conventional fMRI study using BLOCK
design. Only negative activation (indicated by t values) in the
BLOCK run was shown for localizing an ROI in the left amy-
gdala (the blue circle). This ROI was used as a seeding area
for ensuing connectivity analysis.


superimposed on high-resolution anatomical images
using a P < 0.01 cutoff threshold (FDR corrected). The
above image processing programs were coded in
MATLAB7 (The MathWorks, Inc.).

Results
In the BLOCK experiment, acupuncture stimulation
induced fMRI-BOLD signal changes over extensive brain
areas such as the hippocampus, hypothalamus, ACC, pos-
terior cingulate cortex (PCC), anterior insula, thalamus,
and SII. These results are consistent with previous fMRI
studies, especially well-defined deactivation in the left
amygdala (Fig. 3) [2]. Based on the activation study, three
brain networks were defined in the ensuing connectivity
analyses using the activated left amygdala as a reference
(Table 2 and Fig. 4). We found an amygdala-associated
brain network, consisting of extensive areas in the frontal
gyrus, temporal gyrus, ACC, PCC, thalamus and basal
ganglia (Table 2). Besides showing the overlapped regions
with the above network, the post-acupuncture condition
engaged other brain regions including the medial prefron-
tal cortex (MPC), postcentral gyrus (PCG), insula, and
PAG. In addition, the post-sham network covers more
areas (reflected in the number ofvoxels) in the cingulate
and basal ganglia, and has stronger between-region corre-
lations (reflected in the t values). While the post-acupunc-
ture condition overlapped extensive regions with the post-
sham network, there were changes in the connectivity pat-
tern with respect to both the size and strength of the local-


Anatomical regions


Amygdala


Hippocampus


Hypothalamus


Block


Talairach
Hem BA x y z t

R 21 -4 -13 -6.33
L -24 -6 -12 -6.18

R 30 -19 -10 -5.01
L -22 -18 -21 -5.32


ACS


ACP


PMC


Posterior cingulate


Anterior insula

Thalamus


R 24
L 24


R 24 2 -18 31 -4.08
L 24 -9 -15 35 -4.38

R 23 5 -38 31 5.01
L 23 -3 -31 30 5.12

R 40 10 9 4.92


18 -26 6 6.98
-13 -33 4 6.32


R 62 -7 21 5.81
L -60 -3 28 5.62


(df= 16, P< 0.001, uncorrected)
ACS, Anterior cingulate-subgenual; ACP, Anterior cingulate-
pregenual; PMC, Posterior middle cingulated; Abbreviations: BA-
Brodmann Area; Hem -Hemisphere.

ized correlations in the inferior temporal gyrus (ITC),
PCG, cingulate, insula and PAG (Table 2 and Fig. 4).

For a quantitative presentation of the connectivity
changes over different conditions, eight ROIs including
the amygdala, were defined in Figure 4 to construct a basic
connectivity module [14]. We selected these eight regions
by considering that (1) they were activated in our BLOCK
experiment during acupuncture stimulation and (2) they
have been implicated in both sensation and affective pain
perception, as well in the pain-modulation system
[2,32,33]. While the regions in this brain module were
active during or even following acupuncture stimulation,
the connections among these regions may or may not be
expressed (as shown by the statistical significance level of
the correlations in Fig. 4). Furthermore, the between-
region correlations changed over different conditions. For
instance, while the connections between the amygdala


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MFC PCG

/ -


PAG INS

REST


PAG INS

ACUP


SHAM


Figure 4
Amygdala-associated functional brain networks. A: the resting condition (REST); B: the post-sham condition (SHAM);
C: the post-acupuncture condition (ACUP). The functional connectivity maps on the left show the statistical significance level
of the correlation (t > 5.8, df = 16, P < 0.01 FDR corrected) in a coronal brain section through the amygdala. The amygdala-
associated networks on the right show the strength of a functional connection (the weighted and normalized correlations)
between the left amygdala and other selected brain regions. Dotted lines indicate statistically insignificant correlations. For
abbreviations refer to Table I.


and cingulate remained almost the same, all the other
selected connections were significantly increased during
SHAM relative to REST, suggesting modulation of the rest-
ing network by sham stimulation. Most importantly, the
increases in connectivity with the amygdala during ACUP
(relative to SHAM) were found specifically in the PAG and
INS. While functional connections to the ACC and thala-
mus remained the same between SHAM and ACUP, the
connections to the MFC, PCG (the SII) and PCC were
decreased. Note that the connectivity maps covered the
contralateral amygdala and it also showed a different
degree of correlation with the reference under each condi-
tion. For simplicity, the results in the cerebellum were not
shown in the above analysis.

Figure 5 and Table 3 showed the contrast of connectivity
maps between the two conditions by voxel-wise t-tests,
further indicating the brain regions in which the REST
connectivity was modulated by the ACUP or SHAM con-
dition. Both verum and sham induced significant changes
in the resting-state functional connectivity. Moreover,
while the locations of these connectivity changes had
extensive overlap between verum and sham there were
apparent differences between post-acupuncture modula-
tion and post-sham modulation (Fig. 5 and Table 3).

To further demonstrate acupuncture-specific effects on the
modulation of resting-state connectivity, we directly com-
pared the SHAM and ACUP connectivity maps using
voxel-by-voxel paired t-tests (Fig. 6 and Table 4). Our
results showed that the SI, SII, ITC and cerebellum were
more associated with the amygdala during sham relative


to verum acupuncture; these regions constructed a post-
sham network. In contrast, the ACUP-induced increases in
the amygdala connectivity were primarily found in the
PAG and insula, which constructed a post-acupuncture
network.

Based on the selected ROIs that were implicated in both
verum and sham networks, we performed a regression
analysis to characterize how the dynamic changes in these
specific brain regions interacted with the amygdala activi-
ties (Fig. 7). The slopes in the PAG and insula were appar-
ently steeper during verum than those during sham
acupuncture (the upper two panels in Fig. 7), which are
consistent with the above results that the PAG and insula
are more strongly involved in specific acupuncture effects.
On the other hand, the slopes in the SII and cerebellum
are steeper during SHAM than those during verum acu-
puncture (the lower two panels in Fig. 7), indicating that
these two structures are more strongly involved more in
the sham response.

Discussion
In this paper, we employed functional connectivity analy-
sis methods with a new NRER-fMRI design to investigate
the sustained effects of acupuncture. In most previous
fMRI studies, the model-dependent analysis methods
using a block design require prior knowledge of event tim-
ing from which an anticipated hemodynamic response
can be modeled. However, this type of analysis methods
cannot be used without a predictable hemodynamic
response reflecting the actual BOLD signal changes
induced by acupuncture. In the current study, we used a


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Table 2: Functional localization of the brain regions showing significant correlations with the ROI in the left amygdala (df= 16, P < 0.01,
FDR corrected).


ACUP


SHAM


Anatomical regions


Medial Prefrontal
Cortex (MFC)

Postcentral Gyrus

Temporal Gyrus


Anterior Cingulate
Cortex (ACC)

Posterior Cingulate
Cortex (PCC)

Thalamus (THA)


Talairach Talairach
Hem BA x y z t Vol BA x y z


Talairach
Vol BA x y z


L 10 -6 51 6 5.56 270 II -II 49 -16 9.51 189
R 6 6 0 51 5.05 81

L 3/40 -30 -36 57 5.59 378 1/3 -42 -21 60 5.72 810


L 20/37 -51 -39 -21 6.28 1242 20 -54 -48 -18 7.20 1377 22 -51 -60 12 4.50
R 20/37 51 -39 -21 6.24 1620 20 39 -12 -33 5.71 189 20 51 -54 -18 4.89


32 9 36 -9 6.01 162 32/33 6 21 24 5.42 135

29 -9 -45 3 5.12 54 30/31 -12 -63 9 5.66 216
30/31 15 -54 3 5.38 54


-3 -18 0 6.58 1647
6 -18 0 6.30 2133


-3 -12 2 5.60 1674
6 -9 9 6.76 3483


25 -6 -23 -10 4.01
32 10 41 -3 4.21

30 -12 -63 9 4.64
30 15 -54 3 4.68


-9 -21 0 4.51


Insula (INS)

Putamen


Caudate


Periaqueductal Gray
(PAG)


R 13 39 3 9 5.23 135 13 39 9 -6 4.9 54


-27 3 0 6.87 3348
21 3 -3 6.03 1620

-9 15 3 5.92 1836
6 12 -6 5.81 162

-4 -29 -12 6.18 2268
2 -22 -9 5.46 567


-27 -6 -6 6.69 2918
27 -3 -6 6.05 2170

-15 6 18 6.01 1890


-21 0 -6 5.48
-24 0 -6 5.32

-12 18 -6 4.75


+4 -25 -8 4.11 54


Abbreviations: BA-Brodmann Area; Hem -Hemisphere; Vol-Volume (mm2)



model-independent approach, i.e. the new NRER-fMRI
design, to explore systematic changes of the BOLD signal
without a decrease in the statistical power and the bias on
the results.

Our work presents the first fMRI connectivity study of acu-
puncture using a modular approach in connectivity anal-
ysis [14], followed by a more recent connectivity study on
the defaut mode network [33]. We have defined a resting-
state brain network that is associated with an amygdalar
region activated during acupuncture stimulation (Fig. 4).
Our results further showed that the amygdalar-specific
network consisting of brain regions overlapped with the
pain-matrix to some extent. These regions include exten-
sive areas in the SI and SII, insula, ACC and PFC as well as
the hypothalamus and PAG (Table 1). While these brain
regions were activated by sham or acupuncture stimula-
tion, the corresponding network associated with sham
could be dissociated from acupuncture modulation


effects by differentiating functional connectivity patterns
among these regions (Fig. 4).

As the amygdala plays a dual role of facilitating as well
inhibiting in the modulation of pain behavior and nocic-
eptive processing at different levels of the pain matrix, the
amygdala-associated network during the resting state may
be crucial in both pain and analgesia systems implicated
in the effects of acupuncture stimulation [2,20,34]. Many
previous studies have found a basic network derived from
the important concept of "default mode" in the resting
brain [10,35]. Within this default mode, the temporal cor-
relation of the fMRI signal during a resting-state provides
complementary information about the intrinsic interac-
tion between different brain regions. With such a baseline,
the verum acupuncture- or sham-induced changes in the
temporal correlation may represent the modulation of
region-to-region interactions. It should be noted that the
low-frequency temporal correlations in the resting state



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Figure 5
Changes in the resting-state functional network following verum acupuncture or sham stimulation. Difference
maps were obtained by comparisons between the connectivity maps (ACUP-REST or SHAM-REST) using paired t-tests with a
threshold of t > 4.0 (df = 16, P < 0.01, FDR corrected). The color-coded maps indicate brain regions that have significant
increases in functional connectivity relative to the resting-state.


are also related to uncontrolled brain activities [36]. Con-
tinuous stimulation during ACUP or SHAM may result in
a higher degree of low frequency correlation than the
"resting" uncontrolled stimulus state in these regions,
which may provide an explanation for the increase in con-
nectivity in both sham and acupuncture conditions (Figs.
4,5).

The change in the region-to-region connectivity is an indi-
cation of functional changes of the network, and may pro-
vide complementary information for exploring
modulating effects of acupuncture or sham on the net-
work of interest (Figs. 4, 5). Based on the pain-related net-
work in the resting brain, we can expect that either verum
or sham acupuncture (using pain-related stimulation)
may modulate the functional connectivity in some spe-
cific brain regions implicated in this network. The connec-
tivity network during the post-acupuncture condition was
similar to the sham except for more extensive and stronger
connectivity in the limbic system. Furthermore, since the
procedure during SHAM intervention, as a whole, is the
similar to that of ACUP and all of subjects are naive to
acupuncture, we expect that SHAM is believed to be the
same procedure as verum acupuncture to our subjects in


terms of potential placebo effects of pain relief. Therefore,
the observed differences between these two conditions
may constitute a specific physiological effect. Comparing
ACUP and SHAM, we found connectivity increases specif-
ically in the PAG and INS (Fig. 4). The PAG has abundant
opiate receptors and participates in both opioid analgesia
and acupuncture analgesia [5,8]. In addition, a recent PET
study involving patients in pain, has clearly identified a
hyperactivation of the ipsilateral insula, suggesting a spe-
cific neural structure underpinning the effect of acupunc-
ture for the treatment of chronic musculoskeletal pain.
The insula is a key modulator of the visceromotor system.
The increases in the connectivity of the amygdala with
both the insula as well PAG indicate that the uncrossed
visceroceptive autonomic pathways may be engaged,
which seems to be crucial for acupuncture analgesia
effects reflecting its specific action on the central nervous
system.

It is now believed that sham does have a physiological
effect, as well as many of the central neural substrates may
be involved in the sham-related pain sensation. Com-
pared to the ACUP, an increased connectivity was shown
among the MFC, PCG (and the SII) and PCC during the


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Table 3: Localization of connectivity maps by comparing between the ACUP vs. Rest, and SHAM vs. Rest using paired t-tests (df= 16,
P < 0.01, FDR corrected).


Acu-Rest


SHAM-Rest


Talaiarch
Hem BA x y


Talaiarch
z t Vol BA x y


II -4 24 -12 4.98 108


24/32 -9 41 6 4.88 98
24/32 8 40 2 4.93 126


-36 2 8 5.72 243
44 2 12 5.49 261


L 20 -50 -10 -24 5.01 232
R 20 43 -10 -30 5.12 118

L 21 -62 -20 12 5.21 124
R 21 59 -50 -12 6.38 612

L -12 16 12 5.83 1182
R 16 14 II 5.71 136

L -6 -II 4 5.97 646
R 8 -16 2 5.88 312

L 4 -7 -22 52 5.62 204
R

L 3/4 -37 -22 54 6.31 513
R 3/4 39 -22 50 6.19 465


II -10 43 -II 5.67
II 9 48 -12 5.32

24 -3 3 29 4.08


26/23 -4 -38 21 4.64
26/23 10 -47 25 4.30


-39 -II 12 4.98
32 16 5 4.61


20 -40 -10 -26 6.39
20 45 -10 -36 6.21

21 62 -18 -12 6.92
21 -61 20 II 5.12

-7 16 -8 6.32
8 17 II 6.12

-4 -10 10 6.02
6 -10 5 5.92

4/6 -35 -21 63 5.56
4/6 29 -22 65 5.41

3/4 -47 -24 51 6.73
3/4 50 -22 40 6.24


5 -28 -II 4.91


Positive t values indicate ACUP or SHAM > Rest.
Abbreviations: ITC-lnferior Temporal Cortex; MTC-Middle Temporal Cortex.


SHAM. The MFC has been shown to be involved in the induced by sham stimulation. As a result, we speculate
modulation of pain by regulating attention and affective that the action of sham may involve non-specific effects
emotion [8,9]. The MFC and PCC may also transform the supporting both sensory and affective pain perception.
memory or sensory information to assign meaning to Although no significant statistical differences between
pain, and subserve planning and execution of coping acupuncture and sham scores on subjects' perceptions of
strategies. Previous imaging studies have shown that the sensations (P < 0.05), the sharp pain levels showed an ele-
SII and PCC have connections to memory-related tempo- vated tendency during the sham stimulation (paired t-test,
ral-lobe structures and the motor system, suggesting that P < 0.07), primarily due to the low scores (typically less
these cortices may contribute to learning and memory of than 1) measured during the acupuncture stimulation.
pain, as well as to pain-motor integration [37-40]. In
addition, the PCC near the 'unpleasantness region' is asso- The involvement of the ACC in both sham and acupunc-
ciated with response selection, conflict monitoring and ture is an interesting finding in our study. The functional
attention, which is considered to be more reliably acti- connectivity results are consistent with the previous neu-
vated by pain [37-39]. Therefore, the increased connec- roanatomical and electrophysiological findings that the
tions with the amygdala shown in the SII and PCC during efferents of the amygdala have bidirectional relations with
SHAM may be due to more intense sensations commonly the ACC [41-43]. Although a considerable volume of lit-



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Anatomy regions


MFC


ACC


PCC


Insula


ITC


MTC


Caudate


Thalamus


Precentral


Postcentral


PAG


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Figure 6
Paired t test between post-acupuncture connectivity map and post-sham connectivity map. The significance
threshold was t > 4.0, df = 16, p < 0.01 FDR correct. The red regions with positive t values indicate ACUP > SHAM, and the
blue regions with negative t values indicate SHAM > ACUP. y (mm): Talairach coordinates indicate the locations of the coronal
brain sections (ACV: anterior commissure verticalization).








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Table 4: Localization of acupuncture specific effects by comparing between the ACUP and SHAM connectivity maps using paired t-
tests (t = 4.0, df= 16, P < 0.01, FDR corrected).


ACUP-SHAM


Anatomy regions


Medial Frontal Gyrus (MFG)


Precentral Gyrus


Inferior Frontal Gyrus (IFG)

Postcentral Gyrus (SII)

Superior Parietal Gyrus

Inferior Temporal Gyrus (ITG)


Insula (INS)


Talairach
BA x y


9 39 -9

L 6 -33 3 27 -6.28 270
R 4 36 -21 54 5.93 54

R 44 54 9 15 -5.75 216

R 2 54 -30 57 -5.68 54

R 7 27 -57 48 -5.43 81

L 20 -42 -21 -33 -6.50 216
R 21 39 -6 -36 -5.84 27


13 -33 -3 18 4.88
13 45 -27 18 5.33


Anterior Cingulate Cortex (ACC)


L 24/32
R 24/32


-6 30 12 5.11 81
9 36 -9 5.46 270


R 20 36 -12 -36 -5.77


12 15 12 -6.27 108


24 12 9 -5.11


Periaqueductal Gray (PAG)


Cerebellar Culmen


Cerebellar Tonsil

Cerebellar Declive


Cerebellar Pyramis

Cerebellar Tuber


0 -22 -8 5.28 182

-36 -54 -27 -6.73 216
39 -54 -27 -5.75 189

-27 -57 -48 -6.83 216

-39 -60 -21 -6.02 513
36 -57 -27 -6.63 108

-18 -72 -36 -5.55 108

-54 -60 -33 -5.7 54


Positive t values indicate ACUP > SHAM, while negative t values indicate SHAM > ACUP.
Abbreviations: BA-Brodmann Area; Hem Hemisphere; Vol-Volume (mm3).


erature documented the role of the ACC in autonomic reg-
ulation and emotion [44], some studies pointed out that
a pain response as shown in the ACC might be associated
with the "suffering" component of pain [45] or with the
opioid pathway [46,47]. Placebo effects following condi-
tioning with surreptitious variation of heat pain can
induce decreased activity within the ACC. We hypothe-
sized that placebo analgesia may arise from changes in the


expectation of pain within higher cognitive centers such as
the ACC. The ACC contains a high concentration of opi-
oid receptors [48], and has been regarded as a functional
region in opioid analgesia and in other forms of pain
modulation [49,50], which may suggest a similar involve-
ment of higher order control of opioid-dependent pla-
cebo analgesia. We therefore suggest that the ACC
activation found in both ACUP and SHAM is linked to the


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Uncus

Caudate

Putamen


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0.8

0.7

0.6

O(
a-_
< 0.5

0.4

0.3

0.2


0.8

0.7

0.6


0.5

0.4

0.3


0.2
0


0.5
Amygdala


*KIsI State (r 0. 10) i*** '"
o Pos t SHAM (r = 0. 12) .',"; t
'Post AC(P (r 0. i; .. ..



t., .. .. ,* . ;
'+ 1 .L n. ,
+ 'r i *.
S. ... ; .' -
.. :* ..


-. -''




f_ <+. + ++= +


-


Amygdala


Figure 7
Regression results of amygdale and other representive brain regions. Dynamic changes in the interaction between
the amygdala and other representative brain regions implicated in pain sensation and pain modulation over different states:
REST, Post-SHAM, and Post-ACUP. Regression analyses were based on normalized BOLD signal intensities calculated individu-
ally from the time courses of the ROls: the amygdala, PAG, Insula (INS), SII and cerebellum (CBM). The resulting regression
slopes (r) were averaged over 17 subjects (for clarification, the scattered data points shown are from only one subject.


expectation of therapeutic benefit and exerts a top-down
effect on the midbrain which is at the same site of the PAG
activation reported by Petrovic et al [51]. Previous studies
also showed that acupuncture might stimulate both pain
modulation and analgesia systems by releasing endog-


enous opioids [52]. However, there was no significant dif-
ference in the amygdala-ACC connectivity between SHAM
and ACUP (Fig. 4.), suggesting that the ACC may not
mediate the effects specific to the sustained effects of acu-


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-


* REST State (0. 18) .
o Post SHIAM (0.. -'
* Prit ACMIP (0.2 :'



.
--'1





. ; .. .. ..,
S; ., ..*' ,* -" .. .
- ..r.. -. ." ," ,, .


0.5
Amygdala


* REST State (n. 12 .. :
o P[iY lLM1 n(. i( "- -'
* I', '" ," "" "

"



4 . . .


0.8

0.7


0.6


z 0.5

0.4


0.3

0.2


0.8

0.7


0.6

Co0.5

0.4

0.3


0.2
1 0


Molecular Pain 2008, 4:55


0.5
Amygdata


I


* REST State (0. 08).-.*'
o Post SHAM (0. 9 .


.
-" A l 111' 41 ,, a .J .


r .
, ...
+ +


}


I


-


I








http://www.molecularpain.com/content/4/1/55


puncture, but participate in non-specific components
such as expectation and pain-related affective processes.

The activation of the insula has been reported in previous
acupuncture studies [9,53,54]. The increased connectivity
of the insula in our study is consistent with electrophysio-
logical studies and clinical investigations [55], which
showed the insula's involvement in emotional processing
(fear, uneasiness, etc) and ascending visceral symptoms
[56]. The involvement of the insula in the post-acupunc-
ture functional network is also consistent with TCM's
viewpoint that the known healing effects of Zusanli
(ST36) acupuncture on gastroenteric disorders such as
gastroenteritis and gastroenteric spasm may be mediated
through the insular visceral feedback pathway. Further-
more, the unaltered connectivity of the ACC may there-
fore suggest that the expectation during the treatment may
have a physiological effect on the brain, which mediates a
potentially powerful non-specific response to acupunc-
ture. On the other hand, our results showed that the SII
and cerebellum were more associated with SHAM, sug-
gesting that the post-sham effects may mostly be repre-
sented in modulating responses in sensory processes [2].
More compelling evidence supporting central effects spe-
cific to acupuncture was from direct comparisons between
the ACUP and SHAM connectivity maps (Fig. 6 and Table
4). Our ROI-based temporal analyses in Figure 7 indicated
a dynamic relationship between the amygdala and the
other four regions implicated in the pain-related network.
When the region-to-region connectivity patterns were fur-
ther explored, our results showed differential modulatory
effects of acupuncture and sham stimulations on the cor-
responding networks, in which the modulatory effects
were mediated in a time-dependent way. Therefore, these
findings suggest that the difference in functional connec-
tivity is region-specific, which provides indirect evidence
in support of the discrepancy between verum and sham
acupuncture.

It has been recently suggested that acupuncture may be
effective in pain relief regardless of acupoint locations,
although there are differences in their efficacies [57].
However, as shown in our results using a new fMRI
approach, the verum acupuncture and sham stimulation
(at a non-acupoint) induced the activations of differential
brain networks. The specific pattern of correlation during
the post-acupuncture condition provides a reasonable
explanation for the actual analgesia effect of acupuncture
as well as direct evidence supporting that an acupuncture
point may have its own functional specificity.

Conclusion
Using connectivity analysis with the new NRER-fMRI
design, we demonstrated that there is an amygdala-associ-
ated resting brain network, which can be further modu-


lated by sham and acupuncture stimulations, and that the
specific effects of acupuncture may result from the coop-
eration of brain regions engaged in the resting functional
network. In addition, this network encompasses the brain
structures that are implicated in both pain sensation and
pain modulation.

Competing interests
The authors declare that they have no competing interests.

Authors' contributions
WQ carried out the experiment and wrote the manuscript.
IT participated in the design and coordination of this
study. LB participated in the data processing and provided
assistance in writing the manuscript. XP participated in
the design of this study. PC performed the entire acupunc-
ture procedure. JD participated in the design and coordi-
nation of this study. KMvD provided assistance in writing
and revising the manuscript. YL provided fMRI methodol-
ogy in the study and assisted in writing the manuscript. All
authors read and approved the final manuscript.

Acknowledgements
This paper is supported by the Project for the National Key Basic Research
and Development Program (973) under Grant No.2006CB705700,
Changjiang Scholars and Innovative Research Team in University (PCSIRT)
under Grant No.IRT0645, CAS Hundred Talents Program, CAS scientific
research equipment develop program (YZ0642,YZ200766), 863 program
under Grant No. 2006AA04Z216, 2008AA0 I Z41 I, the Joint Research
Fund for Overseas Chinese Young Scholars under Grant No.30528027, the
National Natural Science Foundation of China under Grant No. 30672690,
30600151, 30500131, 30873462, 30870685, 60532050, 60621001,
90209008, Beijing Natural Science Fund under Grant No. 4071003 .

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