Cerebral activation effects of acupuncture using Zusanli (ST36) and Yanglingquan (GB34) points based on Regional Homogeneity indices: A resting-state fMRI study

Abstract

OBJECTIVE:

This study aimed to observe the cerebral activation effects of acupuncturing the Zusanli (ST36) plusYanglingquan (GB34) points in young healthy volunteers based on Regional Homogeneity (ReHo) indices.

METHODS:

Ten healthy volunteers were enrolled, including 4 males and 6 females between the ages 20 and 34 years with a median age of 23 years. Magnetic resonance imaging (GE Signa HDxt 3.0T) was performed in four groups: Before acupuncture (Control Group), after acupuncture at Zusanli (ST36 Group), after acupuncture at Yanglingquan (GB34 Group) and after acupuncture at both Zusanli and Yanglingquan (Compatibility Group). Differences in the brain ReHo indices of the 4 groups were analyzed by statistical parametric mapping (SPM8) and ReHo data processing methods. The significantly different brain regions were obtained using a false discovery rate correction (FDR-Corrected).

RESULTS:

The ReHo indices revealed that the main significant effect was in the Compatibility Group. Compared with the resting state of the Control Group, the ReHo values of the Compatibility Group increased in the right middle frontal gyrus (BA8, 9), left superior temporal areas (BA22), ventral anterior cingulate area (BA24) and right inferior parietal lobe (BA40); in contrast, the ReHo values decreased in the left thalamus, right insular cortex (BA13), left inferior frontal lobe (BA9) and right dorsal anterior cingulate area (BA32). Our analysis showed that the Compatibility Group had higher ReHo values than the left inferior parietal lobule (BA40) and right frontal cortex (BA6) of the ST36 Group and the posterior lobe of the right cerebellum, dorsal anterior cingulate (BA32), left and right middle frontal gyrus (BA46, BA9), left precuneus (BA7), right inferior parietal love (BA40) of the GB34 Group.

CONCLUSION:

The results of our neuroimaging study suggest that the combination of acupoints could more widely activate areas of the brain compared to a single acupoint. Additionally, the combination of acupoints can activate some new brain areas and generate new curative effects.

Keywords

Acupuncture Zusanli (ST36)Yanglingquan (GB34)Regional Homogeneity (ReHo) indices functional magnetic resonance imaging

1 Introduction

Acupuncture, a significant and mystical therapy of Traditional Chinese Medicine (TCM), has been commonly used to treat various diseases for thousands of years. Since ancient times, a number of methods have been used to understand the mechanisms of acupuncture. However, a full comprehension has not yet been achieved. In recent decades, the application of imaging techniques [1] such as positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have provided practical methods to explore the mechanisms of acupuncture treatment and visually identify connections within the brain [2 –6], which is crucial to the mechanism of acupuncture. Given the previous successful application of imaging technology in clinical studies of acupuncture, the specificity of meridians and acupoints has become an important topic [7], and its specificity has been partly confirmed by numerous studies [7 –10]. However, the specificity of acupuncture stimulation requires further elucidation [11].

In fMRI studies, two major methods are widely used for characterizing the regional properties, namely, “Amplitude of Low-Frequency Fluctuation” (ALFF) and “Regional Homogeneity” (ReHo). Either method can reveal changes in brain activity among certain brain regions. Due to the superiority of ReHo, this analysis has been applied to study Alzheimer’s disease, Parkinson’s disease, [12] migraines, attention-deficit/hyperactivity disorder, Mild Cognitive Impairment (MCI) [13], and other conditions. The utilization of ReHo has become a global trend in acupuncture research. Therefore, we applied ReHo as an analytical tool in our study using healthy volunteers as subjects, and we were able to identify the active or inactive brain areas.

Acupoints are reactive positions of diseases on the surface of the body and the crucial components of acupuncture based on the theories of TCM and acupuncture, the objective of which is obtaining close relationships among meridians, specific visceral organs [7] and the spirit, which is stored in the brain. Theoretically, acupoints are intended to act as a point of penetration, and therefore, many studies have chosen acupoints such as LR3, SJ5, ST36, and GB34 to investigate the effects of acupuncture on brain function using fMRI [12 –16]. Based on previous research, it is clear that the selection of single acupoints puts greater emphasis on the specific acupoints than the combination of other points; however, in TCM theory, a combination of acupoints, particularly a unique combination, is expected to produce special effects [17, 18]. In a majority of studies, acupoints are selected in pairs or in groups that tend to connect with the corresponding diseases, although either the functions or the empirical clinical applications [19] do not completely match with TCM theories. According to the “Miraculous Pivot”, a famous ancient Chinese medical work, “the Qi of meridian gathers together into He-sea points” is an important description of these cases.

This indicates that the combination of He-sea points would more clearly illustrate the effects of acupuncture treatment. To confirm the theory that acupuncture at pairs of acupoints would be more effective than at a single acupoint [20 –23], we selected the Zusanli (ST36) and Yanglingquan (GB34) acupoints, which are the He-sea points of the Yangming Stomach Meridian the Shaoyang Gallbladder Meridian, respectively. In addition, some studies have indicated that the combination of the Zusanli and Yanglingquan acupoints possesses a therapeutic effect in certain diseases [24, 25]. Although ST36 and GB34 differ in their meridian categories, they belong to the L5 dermatome skin areas [26], which means that they share the same nerve innervations and the same histological type [27]. Whether these acupoints possess some special connection requires further study regarding brain activity and the multiple effects of ST36 and GB34 in clinical practice, which could provide evidence for their combined use in clinical practice.

We utilized fMRI to test the hypothesis that acupuncture stimulation at pairs of acupoints would produce brain activation or deactivation in several regions, and that, compared with single acupoints, it would activate or deactivate additional functional regions that would not be affected by single acupoints. We designed our study to apply the ReHo method in healthy volunteers to observe acupuncture stimulation at both ST36 and GB34 using resting-state fMRI compared with acupuncture stimulation at ST36 and GB34 separately.

2 Materials and methods

2.1 Clinical data

This study included 10 healthy volunteers consisting of 4 males and 6 females between the ages of 20 to 34 years old, with a median age of 23 years. All of the volunteers were previously healthy, had not taken drugs or undergone acupuncture within the last month, had no MRI contraindications and had signed informed consent forms.

2.2 Equipment and techniques

The following apparatus and equipment were used: 0.32 mm×40.00 mm disposable sterile acupuncture needles (Tianxie Acupuncture Instruments Corporation in Suzhou); a Signa HDxt 3.0T MRI scanner manufactured by GE organization; and an MRI chamber located at the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine.

2.3 Experimental design and data collection

2.3.1 Acupuncture methods

The puncture was performed by one highly qualified acupuncture doctor. The acupoints were the left GB34 and the left ST36. Manipulation consisted of puncture for 15 min after getting a needle reaction and a twist of the needle once every 2 min (10 s each twist). The brains were scanned immediately after finishing the acupuncture.

2.3.2 Imaging design for resting-state cerebral function

Resting-state fMRI data as well as two-dimensional (two-dimensional, 2D) and three-dimensional (three-dimensional, 3D) anatomical image data were collected for the four groups. The scanning interval for each group was 24 hours.

2.3.3 MRI Scan

Quadrature coil neck coil. The volunteers were in a supine position on the examination bed, were awake with calm breathing, and had no proactive thinking movement but were informed of the scanning environment in advance. A foam pad was used to fix the head, and rubber earplugs were used to reduce the noise. Scanning steps: Structural positioning imaging, fast spin echo sequence (FSE) and axial T1-weighted fluid-attenuated inversion recovery (T1-FLAIR) were used to perform the scan, with the baseline parallel to the anterior and posterior combine-line (AC-PC) of the corpus callosum. A total of 28 slices covering the entire brain were acquired. Resting-state blood oxygenation level dependent (BOLD) imaging, echo planar imaging (EPI) sequence and T1-FLAIR image positioning were used with the same number of scanning layers to structurally position the image, and 8400 images were obtained. A high-resolution image of the entire brain scan, including a three-dimensional structure used for brain volume imaging (BRAVO) sequences, was constructed and used to obtain 136 images. The parameters of each sequence scan are listed in Table 1.

2.4 Data processing and fMRI statistics

2.4.1 Data preprocessing

The original image was input to an offline workstation. The package for statistical parametric mapping software (SPM8, http://www.fil.ion.ucl.ac.uk/spm/), which is run using MATLAB2009b, and a software package based on SPM8 and DPARSF of REST 1.2 were used. The main steps were as follows: The first 10 images were removed to exclude effects due to the time required for the magnetic fields to reach the steady state and for the volunteers to adapt to the environment. Time correction: This step aimed to reduce the differences of the moment to obtain each picture. Head movement correction: This step aimed to reduce the effect of signals due to the noise generated by the movement of the head. Data corresponding to the three-dimensional translation of the head moving > 1 mm and a three-dimensional rotation > 1° were removed. Spatial normalization: The functional sequence of the images of all of the volunteers is spatially normalized to the Montreal Neurological Institute (MNI) template to locate the active region of the brain. Spatial smoothing: All normalized data were processed using Gaussian smoothing, i.e., processed data were convoluted with an isotropic Gaussian kernel using a full width half maximum (FWHM) of 8 mm to improve the image quality. Low-pass filtering: The obtained signal was subjected to low-pass filtering of 0.01∼0.08 Hz to remove the interference generated by high- and low-frequency signals. Finally, the obtained frequency wave signals were analyzed using ReHo.

2.4.2 ReHo analysis

Resting state fMRI data analysis toolkit (REST), which is a software developed by the National Key Laboratory of Cognitive Neuroscience and Study of Beijing Normal University (more information at http://sourceforge.net/projects/resting-fmri), was adopted for ReHo analysis. Consistency in the time series of every voxel with its adjacent voxel in the brain was calculated to obtain the Kendall’s coefficient of concordance (KCC). The KCC was the ReHo of the voxel, and the ReHo of every voxel constituted the subjects’ ReHo brain. The following Equation (1) for the KCC for a fixed point was used: $W = \frac{\sum (R_{i})^{2} - n (\bar{R})^{2}}{\frac{1}{12} K^{2} (n^{3} - n)}$ (1) , where W is the KCC of a fixed point with the range 0∼ 1 ; n is the time point (n = 250 in this study); K is the total number of given voxels with their adjacent voxel point (K = 27 in the study), i.e., the given point itself and its 26 adjacent points comprise a voxel cluster; R_i is the total level of the voxel values of 27 points at the ith time point; and $\bar{R}$ is the mean of R_i¹⁰.

2.4.3 Image viewing

Brain images that were statistically significant after the above processing were superimposed on standard brain images to obtain images that displayed anatomical and statistical information, including image information for the sagittal, coronal, horizontal position or 3D mode images. Figure plugins such as Slice Viewer and Xjview were used to read the coordinates of the activated brain areas, the anatomical location and the T values in the MNI template.

2.5 Statistical methods

Standardized ReHo brain diagrams of the Compatibility Group and every other group were obtained using a two-sample paired t-test with SPM8 software, with the results from two one-sample t-tests set used as a mask. In this context, the results were corrected for the false discovery rate (FDR). Significance was defined as P < 0.05, T = 2.262 and K > 405. The regions of no significance, for which the voxel values were less than 10, were removed to obtain the difference in ReHo values for the cerebral areas pre- and post-puncture at GB34.

3 Results

3.1 Cerebral ReHo differences between the Compatibility Group and the Control Group

In Fig. 1, the ReHo results demonstrated that the right middle frontal gyrus (BA8, 9), left superior temporal areas (BA22), ventral anterior cingulate area (BA24) and right inferior parietal lobe (BA40) were increased in the Compatibility Group compared with the pre-acupuncture condition (Table 2). In contrast, the results in the left thalamus, right insular cortex (BA13), left inferior frontal lobe (BA9) and right dorsal anterior cingulate area (BA32) were decreased (Table 3).

3.2 Differences in cerebral ReHo values between the Compatibility Group and the ST36 Group

In Fig. 2, the ReHo results demonstrated that the left inferior parietal lobule (BA40) and the right frontal cortex (BA6) were increased in the Compatibility Group compared with the ST36 Group (Table 4), whereas the values in the left occipital lobe cortex (BA19) and the left inferior frontal gyrus (BA9) were decreased (Table 5).

3.3 Differences in cerebral ReHo values between the Compatibility Group and the GB34 Group

In Fig. 3, the results demonstrated that the ReHo values of the posterior lobe of the right cerebellum, the dorsal anterior cingulate (BA32), the left middle frontal gyrus (BA46), the left precuneus (BA7), the right middle frontal gyrus (BA9) and the right inferior parietal lobe (BA40) were increased in the Compatibility Group compared with GB34 Group (Table 6), whereas the values of the left insular lobe (BA13) and the left paraventricle white matter were decreased (Table 7).

4 Discussion

The selection of an analytical method is essential for an fMRI study. When comparing the two major methods used in fMRI studies, the ALFF method is based on the intensity of the blood oxygenation level dependent (BOLD) signal in each voxel, and it thus directly reflects the spontaneous activity of neurons [28]. The ReHo [29] method involves the synchronism of a time series in regional brain areas that is calculated using Kendall’s coefficient of concordance (KCC) and also reflects the spontaneous activity of local neurons in brain areas synchronously and indirectly [30]. Owing to its test-retest reliability [31]. ReHo dispenses with prior knowledge. Therefore, the vital advantage of using the ReHo method over other methods is that it can rapidly map the level of regional activity through every voxel across the entire brain [32].

The main finding of this fMRI study was that an acupuncture point combination of ST36 and GB34 activated a distinct set of brain regions. Compatibility of ST36 and GB34 activated more brain regions than acupuncture of ST36 or GB34 alone. Based on the hypothesis that brain function activities are highly correlated with acupoint compatibility or the rule of acupoint compatibility, the brain is able to receive signals from different compatible acupoints and then produce a comprehensive analysis while processing these signals. The processed signals project onto a specific target to demonstrate that different compatibilities of acupoints have different clinical efficacies [33]. The study by Hui also suggested that acupuncture mobilizes the anti-correlated functional networks of the brain to mediate its actions and that the effect is dependent on the psychophysical response [34]. This current study provides neurobiological evidence for the existence of acupoint specificity. The clinical efficacy of ST36 and GB34 is correlated with triggering of the related brain areas [35].

In the present study, we found that acupuncture induces dynamic responses in broad brain areas, similar to the results of studies by Li and Yeo [12, 36]. Acupuncture stimulation at ST36 evoked pronounced changes, especially in the homeostatic afferent processing network of functional dyspepsia patients compared to healthy subjects. GB34 appears to be a suitable acupoint for treating the symptoms of Parkinson’s disease by activating the prefrontal cortex and the precentral gyrus. Our study shows that after acupuncturing ST36 and GB34 at the same time, the brain areas with ReHo alterations included the BA8, BA9 (the right middle frontal gyrus), BA22, BA24, BA40, BA9 (the left inferior frontal lobe), BA13, BA32 and the left thalamus, an area that can perceive and integrate visual information. For example, the anterior cingulate cortex contributes to behavior by modifying responses, particularly those to challenging cognitive and physical states that require additional effortful cognitive control [37]. The left superior temporal areas (BA22) appear to be related to auditory hallucinations in schizophrenia [38]. The inferior parietal cortex convexity is an important association area that integrates auditory, visual, and somatosensory information [39]. The right middle fontal gyrus (MFG) has been proposed as a site of convergence of the dorsal and ventral attention networks by serving as a circuit-breaker to interrupt ongoing endogenous attentional processes in the dorsal network and reorient attention to an exogenous stimulus [40]. The insular cortex is associated with neuropsychiatric symptoms in Alzheimer’s disease patients [41]. These brain areas are intimately linked with executive, cognitive, feeling and speech functions, meaning that simultaneous acupuncturing of ST36 and GB34 exerts a therapeutic effect by activating specific related brain areas. Our study also indicates that compared with the ST36 Group or the GB34 Group, more brain areas were activated in the Compatibility Group, which enhanced the individual effect of ST36, such as activating the right frontal cortex (BA6), which is an “essential cortical node” in the mentalizing network [42]. The cerebellum is involved in regulating balance and muscle tension. It also participates in the regulation of motor learning and memory, cardiovascular activity, movement coordination, executive function, language features and intelligence [43]. The Compatibility Group specifically activated the posterior lobe of the right cerebellum, which suggests that acupuncturing ST36 and GB34 at the same time could treat some symptoms of a variety of disorders, such as disorders of somatesthesia, locomotion and muscle tone. Additionally, the Compatibility Group also modulates BA32, BA46, BA7, BA9, BA40, BA13 and the left paraventricle white matter. This shows that the combination of two acupoints stimulates or suppresses new brain areas related to emotion, cognitive function, calculation function, intelligence and other functions.

5 Conclusions

The results of our neuroimaging study suggest that the combination of acupoints can activate a broader range of brain areas than a single acupoint. Furthermore, a combination of acupoints could activate some uninvestigated brain areas, suggesting that matching acupoints may generate new curative effects. Given the complexity of various combinations of acupoints, the study of ST36 and GB34 is not sufficient to illustrate that stimulating multiple acupoints would be much more beneficial than separate acupoints, and thus, additional comparisons among different types of acupoint combinations are required in larger, future studies to determine which combinations would be effective.

Author disclosure statement

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

Authors’ contributions: Liansheng Liu, Changhong Liang designed the experiment; Liansheng Liu, Hengguo Li organized the experiment; Daohui Zeng, Jiayi Zheng performed the experiments; Changzheng Shi, Minjie Yang analyzed the data; Liansheng Liu, Yuefeng Wu wrote the manuscript; All authors discussed the data and the analysis methods and contributed to the manuscript. All authors read and approved the final manuscript.

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