Investigation of acupoint specificity by multivariate granger causality analysis from functional MRI

12 Pages·2011·0.59 MB·English

CME
JOURNALOFMAGNETICRESONANCEIMAGING34:31–42(2011)
Original Research
Investigation of Acupoint Specificity by
Multivariate Granger Causality Analysis From
Functional MRI Data
Yuanyuan Feng, PhD,1 Lijun Bai, PhD,1 Wensheng Zhang, PhD,1* Ting Xue, PhD,2
Yanshuang Ren, PhD,3,4 Chongguang Zhong, PhD,1 Hu Wang, PhD,1 Youbo You, PhD,1
Zhenyu Liu, PhD,1 Jianping Dai, MD,5 Yijun Liu, PhD,6 and Jie Tian, PhD1,2
Purpose:Toinvestigatetheacupointspecificitybyexplor- parison with the fusiform gyrus following acupuncture at
ing the effective connectivity patterns of the poststimulus GB37. Red nucleus was also a target following acupunc-
resting brain networks modulated by acupuncture at the ture at PC7, but with fewer inputs than those of PC6. In
PC6, with the same meridian acupoint PC7 and different addition,themostimportanttargetfollowingacupuncture
meridianacupointGB37. atPC7waslocatedattheparahippocampus.
Materials and Methods: ThefunctionalMRI (fMRI)study Conclusion: Our findings demonstrated that acupunc-
was performed in 36 healthy right-handed subjects tureatdifferentacupointsmayexertheterogeneousmod-
receiving acupuncture at three acupoints, respectively. ulatory effects on the causal interactions of brain areas
Due to the sustained effects of acupuncture, a novel ex- during the poststimulus resting state. These preliminary
perimental paradigm using the nonrepeated event-related findings provided a clue to elucidate the relatively func-
(NRER) design was adopted. Psychophysical responses tion-orientedspecificityofacupunctureeffects.
(deqisensations)werealsoassessed.Finally,anewlymul-
Key Words: acupuncture specificity; mGCA; fMRI;
tivariate Granger causality analysis (mGCA) was used to
delayedeffect
analyze effective connectivity patterns of the resting fMRI
J.Magn.Reson.Imaging2011;34:31–42.
datatakenfollowingacupunctureatthreeacupoints.
VC 2011Wiley-Liss,Inc.
Results: Following acupuncture at PC6, the red nucleus
and substantia nigra emerged as central hubs, in com-
ORIGINATED IN ANCIENT China, acupuncture has
1KeyLaboratoryofComplexSystemsandIntelligenceScience,Institute currently been recognized in the Western medicine as
ofAutomation,ChineseAcademyofSciences,Beijing,China. an important complementary therapy (1). However,
2LifeScienceResearchCenter,SchoolofLifeSciencesandTechnology, the mechanism underlying this traditional interven-
XidianUniversity,Xi’an,Shaanxi,China.
tion has not been clear and needs further investiga-
3Department of Radiology, Xuanwu Hospital, Capital Medical
tions. In the past decades, functional MRI (fMRI) has
University,Beijing,China.
4DepartmentofRadiology,Guang’anmenHospital,ChineseAcademy been used to study the anatomy and physiological
ofTraditionalmedicine,Beijing,China. functionunderlyingtheacupuncture.
5DepartmentofRadiology,BeijingTiantanHospital,CapitalUniversity Previousstudiesmainlyfocusedontheneuralactiv-
ofMedicalSciences,Beijing,China.
ities involving the acute effects of acupuncture (2–4).
6Departments of Psychiatry and Neuroscience, McKnight Brain
Institute,UniversityofFlorida,Gainesville,Florida,USA. Hui and colleagues reported that acupuncture can
Contract grant sponsor: the Project for the National Key Basic exertmodulatoryeffectsonthelimbicsystemandsub-
Research and Development Program (973) under Grant No. cortical gray structures in the human brain (3). The
2011CB707700, the Chinese Academy of Sciences; Contract grant
number:KGCX2-YW-129;Contractgrantsponsor:theNationalHigh modulation of cerebellar activities underlying the acu-
Technology Research and Development Program of China (863 puncturestimulationwasfurtherprovedlater(4).One
Program); Contract grant number: 2008AA01Z121; Contract grant
recent report has provided additional evidence to sup-
number: 2007AA01Z338; Contract grant sponsor: the National
Natural Science Foundation of China; Contract grant number: port that acupuncture can modulate the limbic-para-
30873462; Contract grant number: 30970774; Contract grant limbic-neocortical network (2). Overall, these studies
number: 60901064; Contract grant number: 90924026; Contract
generally adopted the block-designed fMRI paradigm,
grantnumber:81071137;Contractgrantnumber:81071217.
Drs.FengandBaicontributedequallytothiswork. andthereforethetemporalchangesinthebloodoxygen
*Address reprint requests to: W.Z., Institute of Automation, Chinese level-dependent(BOLD)signalaspredictedbythegen-
AcademyofScience,P.O.Box2728,Beijing,100190,China.E-mail: eralized linear model (GLM) conform to the ‘‘on-off’’
[email protected]
specifications.Accordingtothetheoryofthetraditional
ReceivedMay20,2010;AcceptedMarch3,2011.
Chinese medicine (TCM), acupuncture can induce
DOI10.1002/jmri.22585
Viewthisarticleonlineatwileyonlinelibrary.com. long-lasting effects even after the needling
VC 2011Wiley-Liss,Inc. 31
32 Fengetal.
manipulation being terminated (5). Considering such
delayed effect, the ‘‘off-state’’ in the block design may
remainsomeeffectsofacupuncture,andcannotideally
return to the baseline level (6). In addition, one recent
study has already pointed that neural responses
induced by acupuncture have the saliently time-vary-
ing characteristics (6–9). This temporal aspect of the
Figure 1. Experimentalparadigm.Theentirerunforeachacu-
BOLD response to acupuncture may violate the
pointlastedfor4min.Forstatisticalanalyses,thesignalinten-
assumptions of the block-designed GLM estimates,
sityduringthe1minrestphaseservedasacontrolbaselinefor
whichmaybesusceptibletoerrorsofstatisticalsignifi-
detecting the changes in signal intensity during acupuncture
cance (6). Therefore, a newly experimental paradigm
stimulation,therebyfunctionallydefiningtheregionsofinterest.
namely the NRER design was adopted to explore the In addition, the data from the 1 min rest phase were used for
neuralresponsesinducedbyacupuncture.Thisdesign mGCA.[Colorfigurecanbeviewedintheonlineissue,whichis
incorporatesthesustainedphasewhichfocusesonthe availableatwileyonlinelibrary.com.]
dynamic of acupuncture-related brain activities. Con-
sidering the sustained effects of acupuncture, the
tively specific modulatory effect underlying acupunc-
NRERdesignmaybemoresuitabletoelucidateitsspe-
ture at different acupoints.Bydetecting the specificity
cificmechanismunderlyingacupuncture.
of brain effective connectivity underlying the same
Several studies have paid attention to the sustained
meridian and different meridian acupoints, we can
effect of the acupuncture and its influence on the
provide further evidence to explore the functional
poststimulus resting state networks (8–12). Dhond et
specificity ofacupuncture.
al revealed that acupuncture can influence intrinsic
connectivity in the resting brainnetworks, i.e., default
MATERIALSANDMETHODS
mode network (11). Our group has investigated the
functional connectivity of brain networks involved in Subjects
acupuncture and demonstrated that acupuncture can
Thirty-six healthy right-handed college students (18
exert modulatory effect on the insula-anchored brain males, aged 21.4 6 1.3) were recruited from a homo-
network during the poststimulus resting period (8).
geneous group to reduce intersubject variabilities. All
One recent study further proved the existence of a
subjects were acupuncture na¨ıve according to the
large anti-correlated functional connectivity networks
Edinburgh Handedness Inventory (16). After given a
of resting brain modulated by acupuncture (12).
completedescriptionofthestudy,eachofthesubjects
These studies demonstrated the existence of function-
signed the informed consent form. All subjects were
guided brain networks underlying the specific effect of
free of any major medical illnesses, any head trauma,
acupuncture. Most of them adopted the standard
any neuropsychiatric disorders, any intake of pre-
functional connectivity analysis, which was a kind of
scription medications within the last month, as well
undirected graph analysis of temporal correlations
as any contraindications for exposure to a high mag-
between time series in different brain regions. How-
netic field. Subjects were allocated to receive once
ever, little was known about the direction and
acupuncture at one of the three acupoints in a ran-
strength of the information flow between these brain
dom sequence by using an envelope method, with the
regions modulated by acupuncture. Further investiga- gender ratio balanced (analysis of variance, P > 0.6)
tion of the interregional causal interactions may be
among different groups. All protocols were approved
helpful to explain the neurophysiological action
byalocalsubcommitteeonHumanStudies.
underlying acupuncture. Recently, a newly multivari-
ateGrangercausalityanalysis(mGCA)hasbeenintro-
ExperimentalParadigm
duced as an effective connectivity method to analyze
causal relations among multiple brain areas from All subjects underwent a resting state scan. The
fMRI data (13–15). By exploring this approach to ana- NRER scanning for every acupoint incorporated 2-min
lyze the causal influences of the activated regions needling manipulations, preceded by a 1-min rest,
modulated by acupuncture, we can account for the and followed by another 1-min rest scanning (shown
acupuncture modulatory effects on multiple relevant in Fig. 1). During the experiment, the subjects were
regions simultaneously. Unraveling the organization instructed to keep their eyes closed and remain
ofthelarge-scalecorticalnetworksfollowingacupunc- relaxed without engaging in any mental tasks. Each
ture would pave the way to better understand the subject only received once acupuncture at one of the
neurophysiologicalfunctionunderlyingacupuncture. three acupoints in a random order: PC6 (Neiguan),
In the present study, we combined mGCA (14) with located between the tendon of palmaris longus and
the NRER design to evaluate the effective connectivity flexor carpi radialis at a depth approximately 3 cm
patterns among multiple brain regions during the above the midpoint of the transverse crease of the
poststimulus resting state following acupuncture at wrist (17); PC7 (Daling), located between the tendons
the PC6, with the same meridian acupoint PC7 and of the long palmar muscle and radial flexor muscle at
different meridian acupoint GB37. We examined the the midpoint of the crease of the wrist (18); GB37
directionality and strength of causal influence (Guangming), located in the lateral aspect of the lower
between multiple brain regions following acupuncture leg with needle being inserted on the anterior border
at three acupoints to find whether there was the rela- of the fibula (19). A sterile disposable 38 gauge
AcupointSpecificitybyGrangerCausalityAnalysis 33
stainless steel acupuncture needle (0.2 mm in diame- response between stimulus and baseline conditions
ter and40mminlength)wasusedtodeliveracupunc- was estimated at every voxel across the whole brain
ture stimulation. During acupuncture, the needle was volume by using the general linear model (GLM) in
rotatedmanuallyclockwiseandcounterclockwisefor1 SPM5. The obtained t-maps at individual levels were
minatarateof60timesperminbyabalanced‘‘tonify- then entered into the ‘‘random effect’’ group analysis
ing and reducing’’ technique (3). The precise locations framework by the one-sample t-test (d.f. ¼ 11) sum-
of needling, the presumed acupuncture effects, and mary statistic (P < 0.001, uncorrected). The statistical
the stimulation paradigm were not divulged. The pro- maps indicated the brain activation in response to
cedurewasperformedbythesameexperiencedandli- acute effects of acupuncture stimuli, thereby func-
censedacupuncturistonallsubjects. tionally defining the regions of interest (ROIs). Each
The subjects were questioned about aching, pres- peak voxel with its nearest 10 neighbors was defined
sure, soreness, heaviness, fullness, warmth, coolness, asagroupROI.
numbness, tingling, dull or sharp pain and any other Considering the anatomical variance across sub-
sensations they felt during the scan after each fMRI jects, subject-specific peak voxels and subject-specific
scanning (20,21). The questionnaire used a 10-point ROIs were defined on individual t-maps as follows.
visual analogue scale (VAS), which was scaled at 0 ¼ The given group ROI was used as a mask and then,
no sensation, 1–3 ¼ mild, 4–6 ¼ moderate, 7–8 ¼ based on individual t-maps, the voxel with the largest
strong, 9 ¼ severe and 10 ¼ unbearable sensation. t-value withinthis mask served asthesubject-specific
The subjects were excluded from further analysis if peak voxel. ROIs were selected based onthe acupunc-
they experienced sharp pain (greater than the mean ture-stimulation results. First, the time series corre-
by more than 2 standard deviations) (20). None expe- sponding to the poststimulus period of BOLD signal
riencedthesharppainamongthe36participants. intensities from these selected ROIs were selected.
Then, the time series were averaged across voxels
withineach ROIand normalizedacross subjectssepa-
Data Acquisition andAnalysis
rately for each group to form a single vector per ROI.
MRI data were acquired using a 3.0 Tesla (T) Signa In this manner, we obtained a total of 25 ROI time se-
(GE) MR scanner. Head movements were prevented by ries related to acupuncture stimulation for each acu-
a custom-built head holder. The images were parallel point for furtheranalysis.
to the anterior commissure–posterior commissure line
and covered the whole brain. Thirty-two axial slices
mGCA
wereobtainedusingaT2*-weightedsingle-shot,gradi-
ent-recalled echo planar imaging (EPI) sequence (field In this study, we used mGCA to describe the effective
of view [FOV] ¼ 240 mm (cid:1) 240 mm, matrix ¼ 64 (cid:1) connectivity in the postacupuncture resting brain
64, thickness ¼ 5 mm, repetition time [TR] ¼ 2000 (14). This approach detected causal interactions
ms, echotime [TE]¼30ms, flipangle¼90(cid:2)). After the between brain regions by computing directed transfer
functional run, high-resolution structural information function (DTF) from a multivariate autoregressive
on each subject was also acquired using three-dimen- model of the time-course of selected ROIs. Based on
sional (3D) MRI sequences with a voxel size of 1 mm3 the principle of Granger causality, the DTF was ren-
for anatomical localization (TR ¼ 2.7 s, TE ¼ 3.39 ms, dered in a multivariate formulation (23). Therefore the
matrix ¼ 256 (cid:1) 256, FOV ¼ 256 mm (cid:1) 256 mm, flip DTF can effectively model the inherently multivariate
angle ¼7(cid:2),slicethickness¼1mm). nature of neuronal networks. Preprocessed dataset
After that, all images were preprocessed using sta- were implemented using this method. The algorithm
tistical parametric mapping (SPM5, http://www.fil.io- was coded in MATLAB7 (The MathWorks Inc.) and
n.ucl.ac.uk/spm/). First, the image data underwent detailswereasfollows.
slice-timing correction and realignment for head First, we need to construct the multivariate autore-
motions using least-squares minimization. None of gressive (MVAR) model of the times series. Let X(t) ¼
the subjects had head movements exceeding 1 mm on [x1(t), x2(t),..., xM(t)] be a matrix representing data
any axis and head rotation greater than one degree. from M ROIs, in which x(t) is the time series corre-
i
Thus all subjects (n ¼ 36) were left. A mean image sponding to ith ROI. The MVAR model of order p is
created from the realigned volumes was coregistered given bythefollowing:
with the subject’s individual structural T1-weighted Xp
volume image. Then, the standard MNI template pro- XðtÞ¼ AðnÞXðt(cid:3)nÞþEðtÞ ½1(cid:4)
vided by SPM5 was used in spatial normalization with x(cid:3)1
resampling at 2 mm (cid:1) 2 mm (cid:1) 2 mm (22). In the end,
Traditionally,themodelorderwasdeterminedusing
the functional images were spatially smoothed with a
3DGaussiankernel(FWHM¼6mm). the Akaike information criterion (24). Here, a model
orderofonewaschosen(14).A(n)isthematrixofpre-
diction coefficients composed of elements aij(n). E(t) is
Definition ofRegionsof Interest the vector corresponding to the residual errors. Then
applying the Fourier transform to Eq. [1] is given as
Taking into account the sustained effects of acupunc-
follows:
ture, the mean signal intensity of the resting period
preceded by the active stimulation was taken as the
XðfÞ¼A(cid:3)1ðfÞEðfÞ¼HðfÞEðfÞ ½2(cid:4)
baseline. For each subject, the difference in the BOLD
34 Fengetal.
Where
Xp
aijðfÞ¼dij(cid:3) aijðnÞe(cid:3)i2pfnandHðfÞ¼A(cid:3)1ðfÞ ½3(cid:4)
n¼1
Here, dij is the Dirac-delta function, which is 1
when i ¼ j and 0 otherwise. Also, i ¼ 1. . .M, j ¼ 1. .
.M. H(f) is the frequency domain transfer matrix and
hij(f) represents its ith row and jth column element.
hij(f) is defined as the nonnormalized DTF (25) corre-
sponding to the influence of ROI j onto ROI i. The
direct DTF (dDTF) was obtained by multiplying hij(f)
with the partial coherence between ROIs i and j (25).
This operation ensures that direct connections are
emphasized and mediated influences are de-empha-
sized. To calculate the partial coherence, the cross-
spectrawerecomputedasfollows:
SðfÞ¼HðfÞVH(cid:5)ðfÞ ½4(cid:4)
Here, V is the variance of the matrix E(f), and the
asterisk denoted transposition and complex conjuga-
tion. Then we obtained the partial coherence between
ROIsiand j:
M2ðfÞ
vijðfÞ¼M ðfijÞM ðfÞ ½5(cid:4)
ii ij
where Mij(f) is the minor obtained by removing the ith
row and jth column from the matrix S (26). The par-
tial coherence lies in the range of (0, 1). Here, a value
of 0 indicates no direct association between the ROIs
Figure 2. Averagedpsychophysicalresponse(N ¼ 36).A: The
with the influence of all other ROIs removed. While if
percentage of subjects who reported having experienced the
the value is 1 indicates there exists complete direct
givensensation(atleastonesubjectexperiencedthesevensen-
association. Finally the dDTF is defined as the sum of
sationslisted).B:Theintensityofreportedsensationsmeasured
all frequency components of the product of the non-
byanaveragescore(withstandarderrorbars)onascalefrom0
normalized DTF and partial coherence as given in the denotingnosensationto10denotinganunbearablesensation.
equationbelow: *Denoting significant differencesunder Fisher’s exact test (P<
X 0.005).[Colorfigurecanbeviewedintheonlineissue,whichis
dDTFij ¼ hijðfÞvijðfÞ ½6(cid:4) availableatwileyonlinelibrary.com.]
f
flows). In-Outdegreecanidentifyhubregionsthathave
In the end, we obtained the value of dDTF, which
importantcausaleffectsonnetworkdynamics(28).
only reflects the magnitude of causal influence
betweentheROIs.
The statistical significance of the path weights was RESULTS
ascertained using surrogate data (25,27). Surrogate
Psychophysical Results
data were generated by randomizing the phase of the
original time series spectrum while retaining its mag- In this study, the prevalence of deqi sensation was
nitude. A null distribution was obtained by generating expressed as the percentage of individuals in the
2500 sets of surrogate data and calculating the dDTF group that reported the given sensations (Fig. 2A). No
(for every connection) from these 2500 datasets. The difference was found in regard to the prevalence of
dDTF value obtained from the original time series was the listed sensations elicited by acupuncture stimula-
verified using with the null distribution for a one- tion (P > 0.05) when grouped across all acupoints.
tailed test with the significant a P-value of 0.05. The However, there were some differences with respect to
effective connectivity network of the 25 ROIs was con- the type of sensations. In the case of PC6, soreness
structed by visualizing the significant dDTF (P < 0.05) was 53%, seemed more frequent than the case of PC7
obtained after running the statistical significant test. and GB37 which were 28% and 33%. Numbness was
Additionally, we adopted In-Out degree to compare in the same case with soreness (65% versus 43% and
the effective connectivity network for three acupoints 35%). Following acupuncture at PC7, fullness was
(28). The In-Out degree of a node is defined as the dif- 67%, occurred more commonly than PC6 and GB37
ference between its In-degree (the number of causal which were 48% and 42%. Compared with PC6 and
in-flows) and Out-degree (the number of causal out- GB37, dull pain was also in the same case with
AcupointSpecificitybyGrangerCausalityAnalysis 35
s pos-sorymid-
VVoxel 81435712513315203252493913533059165336675417116 entralatosen¼TG
vmM
¼ sox;
tValue 3.384.494.03-4.025.496.393.223.773.335.006.315.085.373.38-2.843.835.613.775.047.914.734.18-3.058.055.54 men;VPLNsecondarygulatecorte
GB37 Talairach emxyz R6-48-19L-44-44-20R32-77-26R2-56-27L-18-8-11L-22-33-2L-28-14-11R32-2711L-6-22-4R16-20-4L-24-1113L-18-191L-16-235L-32-332L-8-6-3L-16-979L-44-40-18L-18-8717R51-1554L-51-435R592219R32-5054R41122L-6-3422R40-5525 ¼substanianigra;PTMputa¼middleoccipitalgyrus;SII¼ecortex;PCCposteriorcin
cted) H ¼SN¼OGngulat
<upunctureatPC6,PC7andGB37(P0.001,Uncorre PC7 TalairachtVHemxyzvaluevoxels R6-56-22-2.733R10-34-15-5.20443L-6-71-27-3.5823R2-56-27-4.0282L-18-8-115.4951L-12-37-2-4.8886L-28-35-3-2.742R42-3818-3.3834R4-22-4-4.9631R8-12-8-3.3714L-24-1113-2.741L-22-218-2.864L-16-218-2.853R24-3613-3.1513L-8-6-3-2.843R16-8836-4.7540R-42-87-26-5.3741L38-176-3.288R67-1918-3.335R20-2553-3.853R57183-2.682R65-4222-3.7414R41122-3.054R6-2825-2.954L-65-29-4-4.9523 ¼¼ampalgyrus;HIPPhippocampus;RNrednucleus;¼¼date;HYPOhypothalamus;FGfusiformgyrus;M¼¼cortex;IPLinferiorparietallobule;ACCanteriorci
PeakVoxelWithin-GroupROIsFollowingAc PC6 TalairachtVyzvaluevoxels -61-19-3.7621-53-12-4.82413-77-264.0357-63-24-2.9922-4-12-4.2887-53-6-2.824-414-2.841-62-6.68146-22-4-2.954-22-7-3.216-16-6-3.5919-173-4.3145-2355.3713-3416-5.2517-6-3-2.843-55-7-3.4319-47-14-3.7755-87173.7716-2036-4.54144-1741-5.3020183-2.682-2938-3.4225-1638-3.003-42-15-2.815-29-4-4.9523 ¼¼stigium;AMYamygdala;PHparahippoc¼ventralposteriormedialnucleus;CDcau¼motorarea;DLPFCdorsolateralprefrontal
the x 812321022-26-284081228-18-1622-8-24-36-18656157-40-20-46-65 ¼fa¼Nary
andtScoreof Hem RRRRRLLRRRRLLRLLLLRRRLLLL sphere;FGMnucleus;VPM¼supplementgyrus.
Table1Coordinates Regions FGMCulmenUvulaNoduleAMYPHHIPPInsulaRNSNPTMVPLNVPMNCDHYPOCuneusFGMOGSIISMADLPFCIPLACCPCCMTG ¼Hemhemiteriorlateralcortex;SMAdletemporal
36 Fengetal.
Figure 3. MultivariateGrangercausalityrelationshipswithsignificantconnections(P<0.05)followingacupunctureatPC6,PC7,
andGB37.Relativestrengthofpathweights(inarbitraryunits)wasindicatedbythewidthofthearrows.Abbreviations:Hem,hemi-
sphere;ACC,anteriorcingulatecortex;AMY,amygdala;CD,caudate;DLPFC,dorsolateralprefrontalcortex;FGM,fastigium;FG,
fusiformgyrus;HIPP,hippocampus;HYPO,hypothalamus;IPL,inferiorparietallobule;MOG,middleoccipitalgyrus;MTG,middle
temporalgyrus;PCC,posteriorcingulatecortex;PH,parahippocampalgyrus;PTM,putamen;RN,rednucleus;SII,secondarysoma-
tosensorycortex;SMA,supplementarymotorarea;SN,substanianigra;VPLN,ventralposteriorlateralnucleus;VPMN,ventralposte-
riormedialnucleus.[Colorfigurecanbeviewedintheonlineissue,whichisavailableatwileyonlinelibrary.com.]
fullness (67% versus 25% and 33%) following acu- (SII) and uvula. In addition, the red nucleus received
puncture at PC7. In contrast, tingling was saliently causal inflows from more than half regions in the
more frequent for GB37 than PC6 and PC7 (58% ver- brain network, primarily from the caudate, fastigium,
sus 25% and 33%). The average stimulus intensities substantia nigra, and SII. Of interest, we found that
(mean 6 SE) were approximately similar during acu- the red nucleus and substantia nigra were not only
puncture on PC6 (1.85 6 1.80), PC7 (1.93 6 2.03) central hubs but also have significant causal influ-
and GB37 (1.83 6 1.79) (Fig. 2B). However, soreness ence with each other. The path weights of mGCA
was more intense for PC6 than PC7 and GB37 (P < result for PC6 were tabulated in Table 2 with signifi-
0.005) whiledullpainwas stronger forPC7and GB37 cantconnectionsshown inredcolor.
compared with PC6 (P < 0.005). In addition, numb- The brain regions with extensive causal interactions
ness was more intense for PC6 and PC7 than GB37 (P following acupuncture at PC6 were mainly located at
< 0.005) while tingling exhibited more intense for the limbic/paralimbic-cerebellum and subcortical
GB37 comparedwithPC6andPC7(P<0.005). areasasmentionedabove.Ofinterest,severalofthese
brain regions (the fastigium, hippocampus, PH and
uvula) also have remarkably causal interactions fol-
mGCA Resultof Resting BrainNetworks Modu- lowing acupuncture at PC7. Furthermore, the red nu-
lated by Acupunctureat ThreeAcupoints cleus received two significant causal inflows from the
culmen and uvula. In addition, the PH was a signifi-
For bilaterally activated regions, we only selected the
cant target, receiving causal inflows from the hippo-
hemisphere anatomical area with a more significant t
campus, uvula, culmen and fastigium. In contrast,
value as the representative ROI (shown in Table 1).
the brain regions with extensive causal interactions
The mGCA was performed on the averaged time series
following acupuncture at GB37 were primarily located
of voxels within each ROI, separately for each acu-
at the vision-related cortex (the fusiform gyrus), with
point during the poststimulus phase. Effective con-
limited extent of the limbic system. The fusiform
nectivity graphs were constructed using the thickness
gyrus received causal inflows primarily from the SII,
of connecting lines and arrows to indicate the
thalamus (ventral posterior lateral nucleus [VPLN],
strength and direction of the causal influences (indi-
ventral posterior medial nucleus [VPMN]). Moreover,
cated in Fig. 3). Only links that showed significant
we identified that several brain areas (the insula and
effective connectivity were presented in the network (P
nodule) only have causal interactions following acu-
< 0.05). Graphs were visualized using Pajek software
puncture at PC6 compared with PC7 and GB37. The
(www.vlado.fmf.uni-lj.si/pub/networks/pajek). The
path weights of mGCA result for PC7 and GB37 were,
In-Out degrees of three effective connectivity networks
respectively, tabulated in Tables 3 and 4 with signifi-
were sorted in a descending order as shown in Figure
cantconnectionsshown inredcolor.
4. Only the brain regions with a nonzero value of In-
Outdegreewerelisted.
Following acupuncture at PC6, the mGCA result
DISCUSSION
showed that the substantia nigra and red nucleus
emerged as central hubs. The substantia nigra In this study, a newly mGCA combined with a novel
received causal inflows from almost all other nodes in NRER design were used to investigate the specific
the brain network, including the amygdala, caudate, effective connectivity during the poststimulus resting
fastigium, hippocampus, parahippocampal gyrus period following acupuncture at PC6, PC7, and GB37.
(PH), red nucleus, secondary somatosensory cortex Our results demonstrated that acupuncture at
AcupointSpecificitybyGrangerCausalityAnalysis 37
Figure 4. Ranking of the In-
Out degrees of the effective
connectivity network following
acupuncture at each acupoint
in a descending order. The In-
Out degree of a node is
defined as the difference
between its In-degree (the
number of causal in-flows)
and Out-degree (the number
of causal out-flows). Only the
brain regions with a nonzero
value of In-Out degree are
listed here. Abbreviations: L,
lefthemisphere;R,righthemi-
sphere; MOG, middle occipital
gyrus; DLPFC, dorsolateral
prefrontal cortex; SII, second-
ary somatosensory cortex;
VPMN, ventral posterior
medial nucleus; PCC, poste-
riorcingulatecortex;ACC,an-
terior cingulate cortex; VPLN,
ventral posterior lateral nu-
cleus; IPL, inferior parietal
lobule; PH, parahippocampal
gyrus; SMA, supplementary
motorarea; MTG, middletem-
poralgyrus.
different acupoints may exert heterogeneous modula- settings. Our findings may be helpful to understand
tory effects on the causal interactions of brain areas the basic neurophysiological mechanisms underlying
during the poststimulus resting state. As a peripheral thefunctionalspecificityofacupuncture.
input to transducing signals into the brain, acupunc- Several studies have begun to gaze on the modula-
ture may induce the reorganization of the effective tory effect of acupuncture on the resting brain net-
connectivity across different neural subsystems. works, primarily using the functional connectivity
These different effective connectivity patterns may be analysis(8–12).Popular functionalconnectivityanaly-
related to the special effects of acupuncture in clinical sis primarily focused on the correlation patterns
s;a-al
Uvula 0.040.160.280.000.240.090.270.100.200.010.410.220.370.000.260.160.430.310.430.220.020.410.090.120.83 ocampu¼Mputormedi
VPLNVPMN 0.010.190.020.141.190.150.180.380.990.120.890.311.210.210.010.460.000.070.120.360.960.150.000.180.690.160.000.030.050.120.000.070.600.090.820.261.030.271.330.290.010.101.220.241.220.590.391.840.060.25 ¼s;HIPPhippmpalgyrus;PT¼ventralposteri
SMASN 1.340.071.920.014.610.030.050.194.070.022.720.024.750.022.480.011.870.000.000.174.940.000.091.303.500.040.020.060.001.820.070.814.120.003.410.014.880.004.370.030.260.090.005.660.002.193.640.012.640.01 usiformgyruparahippocaeus;VPMN
PTMRNSII 0.010.500.080.900.040.282.000.870.090.010.330.241.660.610.031.560.560.062.200.900.100.020.740.290.010.610.120.000.010.042.280.840.110.030.600.221.890.710.100.000.010.021.120.070.310.020.400.081.780.480.051.660.650.190.860.102.872.260.111.080.010.040.122.560.870.111.330.540.081.620.790.081.450.070.28 ¼¼fastigium;FGf¼gulatecortex;PHposteriorlateralnucl
PH 0.060.150.190.010.260.130.150.200.240.000.260.030.200.010.110.040.350.100.220.150.000.270.050.110.18 FGMorcinentral
CC 000024011606170213051047 26070795100813072514000318 ex;steri¼v
P 0.0.0.0.0.0.0.0.0.0.0.0. 0.0.0.0.0.0.0.0.0.0.0.0.0. ortpoLN
MOGMTGNodule 0.000.020.020.270.210.030.560.000.070.070.010.000.410.000.030.200.030.060.430.000.110.160.030.050.220.000.070.000.010.040.540.020.190.190.030.030.810.000.120.000.260.000.250.000.460.220.020.030.480.010.120.440.000.160.540.000.160.530.000.110.140.020.000.520.000.130.220.000.030.310.000.030.360.000.12 orsolateralprefrontalc¼mporalgyrus;PCC¼substanianigra;VP
deN
Insula 0.000.000.040.010.020.000.040.000.010.000.020.090.020.010.010.040.010.010.020.020.030.020.000.010.02 ¼PFCmiddletarea;S
IPL 0.010.030.030.000.030.020.030.030.020.000.050.010.030.000.030.010.040.030.040.040.000.040.010.030.02 e;DL¼TGmotor
PC6 HYPO 0.000.000.000.000.010.010.000.000.000.090.000.000.000.000.010.010.000.000.000.000.000.000.020.020.00 ¼caudatgyrus;Mmentary
of PP 03131209240308215100180414000807340411050017000212 Dalple
Analyses FGHI 0.210.0.320.0.270.0.100.0.310.0.160.0.230.0.650.0.260.0.020.0.350.0.020.0.130.0.070.0.090.0.020.0.370.0.060.0.170.0.170.0.010.0.290.0.010.0.160.0.080. ygdala;Cdleoccipit¼MAsup
sality FGM 0.190.240.89 0.120.64 0.341.030.390.170.030.78 0.410.540.000.290.190.470.590.800.87 0.080.91 0.370.69 0.34 ¼am¼midex;S
Units)FromMultivariateGrangerCau CDCulmenCuneusDLPFC 0.370.060.040.500.800.590.070.050.672.790.070.230.260.780.010.092.060.020.330.630.800.030.082.372.400.090.190.781.200.590.120.160.680.130.150.140.040.000.030.192.181.130.000.201.160.050.070.071.940.070.160.590.020.040.000.320.520.000.020.380.700.010.050.141.580.240.880.021.410.810.050.051.981.290.090.182.271.220.170.180.380.150.030.222.380.241.180.030.710.230.061.231.841.160.160.170.940.000.090.24 05)areshowninbold.¼Canteriorcingulatecortex;AMY¼IPLinferiorparietallobule;MOG¼SIIsecondarysomatosensorycort
Table2PathWeights(Arbitrary ACCAMY ACC0.300.03AMY0.040.24CD0.040.05Culmen0.020.02Cuneus0.040.04DLPFC0.060.08FGM0.050.05FG0.100.12HIPP0.020.06HYPO0.000.00IPL0.070.13Insula0.010.00MOG0.000.06MTG0.020.03Nodule0.010.15PCC0.000.00PH0.050.10PTM0.010.05RN0.050.07SII0.020.06SMA0.090.01SN0.070.08VPLN0.010.01VPMN0.030.02Uvula0.010.04 <Significantpaths(P0.¼Hemhemisphere;AC¼HYPOhypothalamus;¼men;RNrednucleus;nucleus.
s;a-al
Uvula 0.040.010.000.410.040.240.240.030.420.000.060.010.020.230.180.120.480.030.360.080.190.040.010.030.78 ocampu¼Mputormedi
VPLNVPMN 0.020.030.260.170.280.320.020.000.320.210.150.120.060.040.090.130.010.010.380.360.010.000.200.110.070.190.020.010.090.090.140.070.080.100.790.67 0.030.000.290.330.120.040.030.060.790.970.870.860.010.03 ¼s;HIPPhippmpalgyrus;PT¼ventralposteri
SMASN 0.000.010.140.090.040.120.180.010.070.060.010.120.210.060.000.350.110.050.000.040.370.020.120.190.030.350.020.040.110.100.090.080.060.050.040.020.100.000.000.030.450.090.060.680.060.020.020.050.110.04 usiformgyruparahippocaeus;VPMN
PTMRNSII 0.010.100.010.020.260.000.080.010.041.490.000.000.080.240.010.030.130.000.010.480.000.030.140.030.010.800.010.070.070.020.000.000.010.030.000.000.020.160.020.000.970.000.030.050.010.030.170.000.030.920.010.230.000.050.002.260.010.110.160.110.020.510.000.010.000.000.200.060.030.190.000.041.050.010.01 ¼¼fastigium;FGf¼gulatecortex;PHposteriorlateralnucl
PH 0.030.030.061.88 0.391.011.51 0.362.94 0.230.440.250.010.240.700.72 3.380.461.35 0.370.410.230.280.412.08 FGMorcinentral
CC 00011711350027051500020504001862130805001207090509 ex;steri¼v
P 0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0. ortpoLN
MOGMTGNodule 0.000.180.020.000.070.000.250.050.100.000.200.020.060.000.050.000.190.010.060.060.080.230.180.030.010.050.070.030.040.010.160.040.030.070.050.000.640.010.010.010.850.000.040.000.190.040.000.050.000.060.040.060.010.020.040.360.000.110.000.020.030.030.040.300.040.020.040.020.020.140.010.020.010.250.04 orsolateralprefrontalc¼mporalgyrus;PCC¼substanianigra;VP
deN
Insula 0.040.350.000.000.010.290.000.180.020.000.431.000.110.050.000.080.070.130.000.000.270.260.210.140.01 ¼PFCmiddletarea;S
IPL 0.000.070.010.000.020.110.030.110.040.000.250.110.070.010.050.010.030.000.000.010.010.120.000.000.02 e;DL¼TGmotor
PC7 HYPO 0.000.010.010.000.020.010.000.010.000.160.000.000.010.010.000.000.010.050.000.020.000.010.060.070.00 ¼caudatgyrus;Mmentary
of PP 0000042006062305350106010102130930 0112020803000019 Dalple
Analyses FGHI 0.000.0.000.0.070.0.060.0.050.0.130.0.120.0.460.0.070.0.020.0.200.0.080.0.170.0.100.0.060.0.040.0.0.050.060.0.030.0.110.0.000.0.220.0.040.0.070.0.020. ygdala;Cdleoccipit¼MAsup
sality FGM 0.010.050.240.360.170.030.610.160.390.000.070.000.060.040.270.270.270.030.130.020.270.050.040.030.19 ¼am¼midex;S
Units)FromMultivariateGrangerCau CDCulmenCuneusDLPFC 0.040.000.000.000.020.010.000.140.880.000.020.060.050.080.010.090.320.010.020.010.040.010.001.180.360.040.010.050.140.010.010.330.090.050.010.220.080.000.010.110.050.000.000.500.000.000.000.340.350.000.010.000.050.020.000.270.470.010.010.070.250.010.020.000.020.040.010.350.280.000.020.130.000.040.010.070.270.000.010.010.080.030.010.030.150.000.000.180.250.000.030.200.320.000.020.160.000.040.000.36 05)areshowninbold.¼Canteriorcingulatecortex;AMY¼IPLinferiorparietallobule;MOG¼SIIsecondarysomatosensorycort
Table3PathWeights(Arbitrary ACCAMY ACC0.120.03AMY0.000.85CD0.010.02Culmen0.000.16Cuneus0.000.00DLPFC0.000.10FGM0.000.06FG0.000.01HIPP0.000.00HYPO0.000.06IPL0.000.24Insula0.010.30MOG0.000.01MTG0.030.07Nodule0.010.00PCC0.000.01PH0.000.01PTM0.000.08RN0.010.09SII0.010.00SMA0.000.25SN0.000.10VPLN0.000.22VPMN0.000.17Uvula0.010.01 <Significantpaths(P0.¼Hemhemisphere;AC¼HYPOhypothalamus;¼men;RNrednucleus;nucleus.
s;a-al
Uvula 0.050.060.010.000.070.050.040.180.080.080.020.000.090.030.000.000.210.140.000.150.050.190.070.010.50 ocampu¼Mputormedi
VPLNVPMN 0.000.020.040.000.380.000.060.000.280.000.330.010.160.040.540.000.040.020.130.000.190.000.140.010.230.010.360.000.160.010.030.010.140.000.060.000.090.010.320.000.460.000.430.000.850.010.070.080.130.00 ¼s;HIPPhippmpalgyrus;PT¼ventralposteri
SMASN 0.010.090.090.060.520.150.070.000.460.330.500.490.020.070.860.620.010.190.140.300.390.080.180.060.160.100.800.420.310.030.060.000.310.450.050.010.130.100.700.531.110.470.560.960.590.450.050.020.100.34 usiformgyruparahippocaeus;VPMN
PTMRNSII 0.000.130.000.010.030.110.040.340.020.030.100.010.000.000.060.000.300.110.000.030.000.000.120.150.020.010.030.000.020.130.040.150.000.000.090.030.000.030.050.000.000.130.010.000.020.000.180.000.020.000.160.070.220.010.020.990.000.000.000.250.000.110.160.000.100.150.010.100.090.000.080.000.020.000.07 ¼¼fastigium;FGf¼gulatecortex;PHposteriorlateralnucl
PH 0.010.370.000.030.120.330.000.280.290.360.030.010.110.190.000.010.840.240.000.520.240.410.130.010.35 FGMorcinentral
CC 14010100220506000600103007131844010208000200010500 ex;steri¼v
P 0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0. ortpoLN
MOGMTGNodule 0.020.020.180.040.050.020.010.130.150.010.000.000.050.210.240.000.060.000.040.040.120.030.180.050.010.000.120.000.050.000.000.130.210.040.170.370.200.110.100.060.360.290.040.180.590.030.100.230.030.080.000.010.010.080.010.000.000.040.180.040.030.250.160.020.170.020.050.150.110.020.000.050.030.020.00 orsolateralprefrontalc¼mporalgyrus;PCC¼substanianigra;VP
deN
Insula 0.020.010.000.000.020.000.010.000.000.000.010.030.010.020.030.030.000.000.000.010.010.000.010.000.00 ¼PFCmiddletarea;S
IPL 0.000.050.290.120.160.020.030.110.080.030.520.110.000.190.190.120.020.270.080.000.180.040.120.010.02 e;DL¼TGmotor
GB37 HYPO 0.000.050.000.010.010.040.000.020.050.190.010.020.000.020.000.000.080.010.000.100.020.060.030.000.03 ¼caudatgyrus;Mmentary
of PP 10030401000606014611060402000906150901050009021207 Dalple
Analyses FGHI 0.060.0.070.0.290.0.050.0.320.0.500.0.030.0.880.0.010.0.110.0.200.0.070.0.150.0.440.0.070.0.010.0.300.0.000.0.100.0.520.0.680.0.580.0.540.0.020.0.310. ygdala;Cdleoccipit¼MAsup
sality FGM 0.020.000.010.010.020.000.120.000.010.000.010.010.020.010.020.010.000.000.000.000.000.010.020.050.01 ¼am¼midex;S
Units)FromMultivariateGrangerCau CDCulmenCuneusDLPFC 0.000.040.050.120.000.080.150.100.650.040.260.230.130.210.010.040.120.001.340.070.180.010.100.830.040.010.270.010.480.210.010.480.060.000.000.120.000.010.090.200.360.050.410.040.040.000.790.000.030.010.320.010.230.000.810.140.170.000.570.000.020.000.650.100.000.010.190.340.260.060.030.000.220.020.000.260.040.010.340.390.300.010.560.380.440.110.000.470.290.010.440.330.000.000.030.070.010.000.190.09 05)areshowninbold.¼Canteriorcingulatecortex;AMY¼IPLinferiorparietallobule;MOG¼SIIsecondarysomatosensorycort
Table4PathWeights(Arbitrary ACCAMY ACC0.050.15AMY0.010.73CD0.000.00Culmen0.010.26Cuneus0.000.08DLPFC0.010.08FGM0.010.03FG0.000.06HIPP0.010.04HYPO0.000.21IPL0.000.07Insula0.020.09MOG0.000.14MTG0.000.10Nodule0.020.02PCC0.010.01PH0.000.33PTM0.000.13RN0.010.02SII0.000.33SMA0.000.06SN0.010.05VPLN0.000.03VPMN0.010.02Uvula0.000.08 <Significantpaths(P0.¼Hemhemisphere;AC¼HYPOhypothalamus;¼men;RNrednucleus;nucleus.