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. 2023 Jul 31;13(9):e3174. doi: 10.1002/brb3.3174

Tuina therapy promotes behavioral improvement and brain plasticity in rats with peripheral nerve injury and repair

Shu‐Jie Ma 1,2, Jun‐Peng Zhang 3,2, Xu‐Yun Hua 2,4, Jia‐Jia Wu 2,5,✉, Mou‐Xiong Zheng 2,4,✉, Jian‐Guang Xu 2,3,✉
PMCID: PMC10498059  PMID: 37522806

Abstract

Introduction

Tuina is currently one of the popular complementary and alternative methods of rehabilitation therapy. Tuina can improve patients' pain and mobility function. However, the underlying physiological mechanism remains largely unknown, which might limit its further popularization in clinical practice. The aim of this study is to explore the short‐term and long‐term changes in brain functional activity following Tuina intervention for peripheral nerve injury repair.

Methods

A total of 16 rats were equally divided into the intervention group and the control group. Rats in the intervention group received Tuina therapy applying on the gastrocnemius muscle of the right side for 4 months following sciatic nerve transection and immediate repair, while the control group received nerve transection and repair only. The block‐design functional magnetic resonance imaging scan was applied in both groups at 1 and 4 months after the surgery. During the scan, both the injured and intact hindpaw was electrically stimulated according to a “boxcar” paradigm.

Results

When stimulating the intact hindpaw, the intervention group exhibited significantly lower activation in the somatosensory area, limbic/paralimbic areas, pain‐regulation areas, and basal ganglia compared to the control group, with only the prefrontal area showing higher activation. After 4 months of sciatic nerve injury, the control group exhibited decreased motor cortex activity compared to the activity observed at 1 month, and the intervention group demonstrated stronger bilateral motor cortex activity compared to the control group.

Conclusion

Tuina therapy on the gastrocnemius muscle of rats with sciatic nerve injury can effectively alleviate pain and maintain the motor function of the affected limb. In addition, Tuina therapy reduced the activation level of pain‐related brain regions and inhibited the decreased activity of the motor cortex caused by nerve injury, reflecting the impact of peripheral stimulation on brain plasticity.

Keywords: cortical plasticity, functional magnetic resonance imaging, manipulation, massage, peripheral nerve injury, tuina

Comparison of cortical activation between the experimental and control groups during the right (injured) hindpaw stimulation task. Each column in the figure displays the difference in the limbic/paralimbic system, pain‐related brain regions, and somatosensory cortex between experimental and control groups. Each row in the figure displays the difference in activation between experimental and control groups at two observation points. The warm tone represents higher activation in the intervention group than that in the control group, while the cold tone represents lower activation.

graphic file with name BRB3-13-e3174-g002.jpg

1. INTRODUCTION

Tuina, as the traditional Chinese massage, serves as a complementary and alternative method in the rehabilitation therapy. It consists of many classic manipulation methods which are grouped into four distinct categories: pushing‐rolling, squeezing/pressing, moving joints, and vibrating (Fang et al., 2018). Accumulating literature have reported its application in multiple diseases including back pain, cervical vertigo, insomnia, headaches, and hypertension (F. Huang et al., 2020; T. Li et al., 2021; Nie et al., 2019; X. Yang et al., 2014; Y. Zhang et al., 2020). According to existing clinical trials, application of Tuina in several diseases has been proven to be inspiringly effective, especially in pain relieving (Happe et al., 2016; Sousa, Coimbra, et al., 2015; Tang et al., 2016; Wang, 2012). However, the underlying physiological mechanism still remains largely unaddressed.

Early clinical studies have shown that Tuina therapy can effectively alleviate chronic neck pain and exhibit long‐term effects after 12 weeks (Pach et al., 2018). In addition, the combination of Tuina therapy and traditional Chinese exercise is also beneficial in reducing pain and improving disability (Zhou et al., 2022). Studies have shown that Tuina therapy intervention in rats with sciatic nerve pathological pain reduced the spinal dorsal horn C‐fiber response, suggesting that the analgesic effect of Tuina therapy is related to the increase in pain threshold of C‐fiber‐induced field potential of ipsilateral and contralateral nerves (Jiang et al., 2016). The analgesic mechanism of Tuina therapy in peripheral pathological pain is mainly manifested by regulating the TLR4 pathway and miRNA to inhibit peripheral inflammation, regulating ion channels, inhibiting the activation of neuroglia cells, and regulating brain function changes. Tuina therapy has analgesic effects by acting on different levels of targets and is an effective treatment for peripheral neuropathic pain (Z. F. Liu et al., 2022).

A systematic review of the effects of Tuina therapy on poststroke sequelae improvement showed that, in addition to traditional therapies, therapeutic massage, especially Tuina therapy, has a significant effect on improving the motor function and reducing spasticity of stroke survivors (Cabanas‐Valdes et al., 2021). The use of Tuina has shown positive effects in promoting the recovery of patients' motor function after injury (Kang et al., 2022).

As Tuina is a therapy performed locally, researchers used to focus on the physiological mechanism of its peripheral effects. For example, a previous study revealed that Tuina might decrease the activation level of peripheral nociceptive C‐fiber (Jiang et al., 2016). It reasoned that Tuina may induce long nerve fiber signaling and thereby inhibit transmission of pain signal to the central nervous system by activating inhibitory neurons. Some other researchers suggested that neurotransmitters might also play an important role in the therapeutic effect of Tuina (Guo et al., 2016; S. R. Huang et al, 2003; Sousa, Moreira, et al., 2015). In a biomolecular study,  the gene expression at the point of nerve injury and the myelin integrity was modulated by Tuina, which was related to the functional recovery following peripheral nerve injury repair (T. Lv, Mo, Yu, Zhang, et al., 2020). However, the effect of Tuina on the central nervous system following peripheral nerve injury is rarely referred to.

Peripheral nerve injury is one of the major neuropathies that lead to lifelong disabilities (R. Li et al., 2014). Current therapeutic theory suggests that nerve regeneration and target muscle atrophy are two most important factors that influence functional outcomes (Q. Bao et al., 2021; He et al., 2022; Ruven et al., 2017; Sun et al., 2022; Zainul et al., 2022). Previous studies demonstrated that brain plasticity was also an important factor that involves in the recovery other than peripheral regeneration (Navarro et al., 2007). As more studies regarding to Tuina therapy in the treatment of peripheral nerve injury have been reported, investigations on brain plasticity have become essential for a better understanding of its effects and appropriate application in clinical practice (Z. Liu et al., 2021; T. T. Lv, Mo, Yu, Shao, et al., 2020).

Functional magnetic resonance imaging (fMRI) is a safe and noninvasive method widely used in neuroscience research (Fox, 2018; Rocca & Filippi, 2006). It is often used to explore the central nervous system mechanisms of both central nervous system diseases (Shan et al., 2023) and other peripheral diseases (Bhat et al., 2017; Xing et al., 2020, 2021).

Previous fMRI studies have found that the anterior cingulate cortex (ACC), insular cortex, and primary and secondary somatosensory cortices (S1 and S2) are involved in the complex experience of pain (H. Li et al., 2022). In our previous study on electroacupuncture intervention for knee osteoarthritis, we observed that electroacupuncture reduced the centrality and nodal efficiency of the right ACC, which is a central component of the reward circuitry (J. P. Zhang et al., 2023). This inhibition of activity in the reward circuitry contributed to pain suppression and reduced the transmission of pain signals from the source to the central nervous system. Currently, research on the brain mechanisms of pain has expanded from the study of a single brain region to the level of functional connectivity and functional networks (B. B. Bao et al., 2022). Researchers hope to use changes in the cortical function and plasticity as prognostic factors related to pain (Jenkins et al., 2023).

In the present study, the fMRI study was used to describe the alteration of brain activity induced by Tuina therapy as a rehabilitation treatment following peripheral nerve injury repair. The Tuina therapy was applied on the gastrocnemius muscle of the right side for 4 months following sciatic nerve transection and immediate repair, while the fMRI scan and the behavior test were applied at the first and fourth months after the surgery. The aim of this study is to explore the short‐ and long‐term changes in brain functional activity induced by Tuina therapy in peripheral nerve stimulation tasks, to understand the effects of Tuina on pain‐related circuits and motor‐related cortex function regulation, and to further understand the central neural plasticity mechanisms of Tuina in the recovery of peripheral nerve injury.

2. MATERIALS AND METHODS

2.1. Animals

A total of 16 male Sprague‐Dawley rats weighted from 200 to 250 g and aged from 6 to 8 weeks were used in the study. These rats were randomly divided into the experimental and control groups (eight rats in each group). All of the rats were obtained from Shanghai Slack Laboratory Animal Company. They were housed under a condition of 12 h dark/12 h light cycle, with unrestricted food and water supply. They were kept for 1 week before any intervention started.

2.2. Peripheral nerve injury model

The peripheral nerve injury model was established by cutting and immediately repairing the sciatic nerve (Figure 1). Specifically, after an intraperitoneal injection of 0.2% pentobarbital, the sciatic nerve on the right side was completely transected at midthigh level. Epineurium of the sciatic nerve was immediately anastomosed with 12‐0 sutures (Ren et al., 2016). During the surgery, bipolar coagulation was utilized for hemostasis, in order to reduce postsurgical mortality. The incision area was treated with antibiotic powder and sealed with Michel clips.

FIGURE 1.

FIGURE 1

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Peripheral nerve injury model. The peripheral nerve injury model was established by cutting and immediately repairing the sciatic nerve of the rat.

2.3. Tuina therapy

A customized Tuina manipulation emulator (patent No. ZL201420482075.3, State Intellectual Property Office) was used in the present study. The emulator's key structural elements consisted of a controller, a microengine, a pair of mobile arms, several pressure transducers, and a silicone tube (Figure 2). The mobile arms with periodic movement were designed to simulate manipulation by human hands. The intervention parameters including pressure, frequency, and duration were set before the procedure started. During the procedure, the pressure could be regulated by the tension adjusting screw, the tension spring, and sensor be monitored on the screen, and the frequency be controlled by the adjusting knob. The emulator was used to simulate twirling and kneading manipulation on the gastrocnemius muscle of the injured hindlimb. The force was set at 0.45 N, and the frequency was 60 times/min, 10 min a day. Tuina manipulation started on the seventh postoperative day.

FIGURE 2.

FIGURE 2

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Illustration of the Tuina manipulation emulator. The photograph shows the Tuina manipulation emulator and its application on the rat. The emulator's key structural elements consisted of a controller, a microengine, a pair of mobile arms, several pressure transducers, and a silicone tube. The mobile arms with periodic movement were designed to simulate manipulation by human hands. The intervention parameters including pressure, frequency, and duration were set before the procedure started.

To guarantee the consistency of models, one specific technician performed all the surgery and Tuina intervention.

2.4. Behavior tests

2.4.1. Thermal withdrawal latency

Thermal withdrawal latency (TWL) of the injured hindpaw was measured using the Hargreaves method for mice and rats by a plantar test apparatus (IITC Life Science Inc., Woodland Hills, CA) every 1 month after the surgery. In the test, the rat was placed quietly on the plexiglass platform for 30 min to adapt to the environment. Thermal stimulation was given by a moveable thermal radiant heat source to the lateral plantar surface of the right (injured) hindpaw. The time from the onset of heat to the withdrawal of the hindpaw was recorded as the TWL. In order to avoid tissue injury, a maximal automatic cutoff latency was set to 20 s. Each test was repeated three times with at least a 5‐min interval between two tests.

2.4.2. CatWalk gait analysis

The animal gait analysis system is a popular tool for the quantitative assessment of footsteps and natural gait in animal models of nerve disease, neurotic atrophy, nerve trauma, and pain symptom groups. In the present study, the Catwalk XT animal gait analysis system (Noldus Information Technology, Wageningen, The Netherlands) was used to evaluate motor function in rats. The system included a 1.5‐m long walking platform with a glass walkway at the bottom. The light‐emitting diode emitted light scattering into the glass plate of the walkway. When the rat passed the walkway, the footprints were captured by a high‐speed camera placed underneath and the speed and intensity were recorded.

Two weeks before the surgery, adaptive training was carried out every day in the morning and afternoon. During the training, 12 g/day of food was given daily to cause slight starvation. An eligible training requires at least three consecutive uninterrupted runs. The conditional parameters for a qualified run were as follows: passing time 1.00–8.00 s, speed variation rate less than 60%. The gait CatWalk analysis was carried out every month for 4 months after the surgery. The maximum contact mean intensity (MCMI), stride length (SL), and swing speed (SS) were recorded.

2.4.3. Statistical analysis of behavioral tests

The behavioral results were expressed as mean ± standard deviation (mean ± SD) and analyzed using SPSS 22.0 software package (SPSS Inc., Chicago, IL). A multifactor repeated measures analysis of variance was performed to compare the behavioral data of the control group and intervention group at the four time points for TWL, MCMI, SL, and SS. Correlation analysis was performed between the behavioral data and fMRI results separately. Values of p < .05 were considered to be statistically significant.

2.5. Functional MRI scan

All the fMRI scans were performed in a 7.0‐T horizontal‐bore Bruker scanner (Bruker Corporation, Mannheim, Germany) with a gradient system of 116 mm inner diameter, of which the maximum gradient strength achieved 400 mT/m. A single transmit and receive surface coil which involved a single copper‐wire loop was utilized. During the scan, rats were anesthetized by sevoflurane, fixed on the scanner, and supported with an artificial ventilator. Their heartbeat and respiratory rate were monitored during the scans. The fMRI scans were performed at the first and fourth postoperative months. The parameters of the functional image were listed as followed, an interleaved single‐shot Echo‐planar imaging (EPI) sequence, flip angle 90°, slice thickness 0.5 mm, repetition time 3000 ms, echo time 20 ms, number of averages 1, Field of view (FOV) 32 × 32 mm with 64 × 64 points. The localization scan and the functional imaging covered a limited area that centered on the bregma point.

2.5.1. Stimulation tasks

A dummy scan lasted for 8 s in the time series was applied before any task was performed. The dummy scan was performed for magnetic equilibrium and the initial 8 s of data would not be included in the analysis. A “boxcar” model for the stimulation paradigm was used, which sequentially contained an epoch of ON and an epoch of OFF. Both “ON” and “OFF” epochs lasted for 30 s and these two epochs formed one cycle. In order to acquire significantly positive results, a total of eight cycles were arranged in one stimulation session. During each session, only one side hindpaw was stimulated. The electrical stimulation was applied with needles inserted beneath the skin of each hindpaw. One needle electrode was located between the first and second digits, while the other one was located between the third and fourth digits. In order to avoid habituation of sensory stimulation, the stimulus was performed in a pseudorandom pattern. The fMRI scans were performed at the first and fourth months after surgery.

2.5.2. Data processing and statistical analysis

The preprocessing and statistical analysis were mainly performed by a toolbox, SPM 8 (Statistical Parametric Mapping 8, http://www.fil.ion.ucl.ac.uk/spm/) based on the Matlab platform. First, the Bruker format data were transformed to the Nifti (Neuroimaging Informatics Technology Initiative) format by the Bru2nii toolbox. Then all the images transformed were scaled up by 10 times of the original size, which was close to the size of the human brain. This upscaling procedure only altered the dimension descriptor fields in the file header and enabled possible usage of processing algorithms originally created for the human brain (Pallares et al., 2015; Tambalo et al., 2015). Preprocessing procedures include the following steps: (1) The nonbrain tissue was stripped manually before further analysis; (2) slice timing: it was performed to remove the temporal bias caused by the different slice acquisition time; (3) realign: images were realigned with rigid‐body transformation and the voxel‐misplacement caused by subjects’ slight head motion were corrected. Through this realignment, a mean volume of functional images was generated; (4) normalization: in order to transform the images from the individual space into the standard space, a transformation matrix was estimated utilizing the mean volume and a standard T2‐weighted template. The estimated ind‐std matrix was then written into every functional volume; (5) smooth: finally, the normalized images were smoothed by a kernel of twice of the voxel size (full width at half maximum, FWHM = [6 6 6]).

In the first‐level analysis, a general linear model was determined according to the stimulation paradigm and the parameters were estimated through a Bayesian approach by SPM8. The contrast images containing information on β1–β2 were then acquired. In the second‐level analysis, a two‐sample t‐test was performed between the experimental and the control groups at the same time point. In addition, paired t‐tests were conducted to compare the brain activity between the fourth month and the first month within each group. After the t‐value map was generated with contrast vectors, the threshold was set as p < .05 and the results were then interrogated with the false discovery rate (FDR) correction. The statistical map was visualized by projecting to a standard rodent template. The analysis and results were reported based on the Montreal Neurological Institute (MNI) atlas standard.

3. RESULTS

3.1. Behavioral analysis

There was a significant main effect of the group on the TWL, MCMI, SL, and SS ((F(1,14) = 12.400, p = .003 for the TWL; F(1,14) = 21.187, p < .001 for the MCMI; F(1,14) = 26.060, p < .001 for Sthe L; F(1,14) = 13.077, p = .003 for the SS). Additional two‐sample t‐tests were performed between two groups per time point. From the first to fourth postoperative months, the TWL of the two groups both decreased gradually. There was no significant difference between the two groups at first month after surgery (p > .05). From the second month, the TWL of the Tuina group was significantly lower than the control group (p < .05) (Figure 3a and Table 1). MCMI, SL, and SS of the two groups increased gradually from the first to fourth month. For the MCMI, there was no significant difference between the two groups from the first to third month (p > .05). At the fourth month, the MCMI of the Tuina group was significantly higher than the control group (p < .05) (Figure 3b and Table 1). For the SL, there was no significant difference between the two groups at the first and the second month (p > .05). The SL of the Tuina group was significantly higher than the control group at the third and fourth month (p < .05) (Figure 2 and Table 1; Figure 3c and Table 1). For the SS, there was also no significant difference between the two groups at the first and the second month (p > .05). At the third and fourth months, the SS of the Tuina group was significantly higher than the control group (p < .05) (Figure 3d and Table 1).

FIGURE 3.

FIGURE 3

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Comparison of behavioral tests between the intervention group and control group. Thermal withdrawal latency (TWL), maximum contact mean intensity (MCMI), stride length(SL), and swing speed (SS) of rats of the intervention group and control group at four time points. *p < .05 (at individual time points between the intervention group and control group).

TABLE 1.

Results of behavioral tests and statistical analysis between groups.

Group Time point Thermal withdrawal latency (s) Maximum contact mean intensity (arbitrary unit) Stride length (cm) Swing speed (cm/s)
Control group 1 month 11.9 ± 3.42 68.25 ± 6.45 4.55 ± 4.80 26.06 ± 3.21
2 month 11.27 ± 1.41 88.36 ± 7.20 7.45 ± 1.29 69.90 ± 7.39
3 month 9.57 ± 1.20 97.54 ± 11.00 8.48 ± 1.16 75.12 ± 13.73
4 month 9.56 ± 1.60 109.13 ± 9.31 9.86 ± 0.70 82.25 ± 15.64
Tuina group 1 month 12.8 ± 1.88 68.65 ± 9.67 4.80 ± 0.33 30.63 ± 5.70
2 month 8.99 ± 0.99 97.07 ± 10.72 8.73 ± 1.61 73.65 ± 11.60
3 month 7.41 ± 0.94 106.29 ± 10.98 12.42 ± 2.35 97.56 ± 16.33
4 month 6.12 ± 1.08 132.05 ± 5.89 14.36 ± 2.54 101.39 ± 19.14

Control versus Tuina

p‐value

 1 month .523 .923 .107 .068
2 month .002 .077 .101 .454
3 month .001 .133 .001 .010
4 month .000 .000 .000 .046
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3.2. fMRI results

The two‐sample T‐test was performed between the two groups at each time point. In addition, paired t‐tests were conducted to compare the brain activity between the fourth month and the first month within each group. And the threshold of statistical analysis was set at p < .05 (FDR correction).

3.2.1. Right (injured) hindpaw stimulation task (intervention group–control group)

At the first postoperative month, multiple areas including limbic/paralimbic areas, pain‐regulating areas, and basal ganglia presented lower activation in the intervention group, compared with the control group. Specifically, subregions of the hippocampus, retrosplenial cortex, medial prefrontal cortex, orbitofrontal cortex, hypothalamus, cingulated cortex, piriform cortex, insular, and subregions of thalamus presented significantly lower activation (Figure 4 and Table 2).

FIGURE 4.

FIGURE 4

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Comparison of cortical activation between the experimental and control groups during the right (injured) hindpaw stimulation task. Each column in the figure displays the difference in the limbic/paralimbic system, pain‐related brain regions, and somatosensory cortex between experimental and control groups. Each row in the figure displays the difference in activation between experimental and control groups at two observation points. The warm tone represents higher activation in the intervention group than that in the control group, while the cold tone represents lower activation.

TABLE 2.

Difference of activation between the experimental and control groups during the right (injured) hindpaw stimulation task at the first postoperative month.

Cluster centroid (MNI)
Brain regions Extent x y z t‐value
Intervention < Control group
Cortex_Somatosensory
(L)cluster1 192 −30 20 −33 −6.323
(L)cluster2 106 −38 15 −43 −7.012
(L)cluster3 15 −63 13 −29 −3.200
Limbic system
(L)Hippocampus_Subiculum 313 −40 9 27 −15.087
(L)Hippocampus_Postero_Dorsal 313 −34 13 5 −10.148
(L)Cortex_Retrosplenial 244 −3 30 29 −4.037
(L)Hypothalamus_Lateral 61 −1 −32 −1 −5.121
(L)Hippocampus_Antero_Dorsal 46 −3 13 −15 −3.726
(L)Hippocampus_Ventral 41 −50 −20 5 −3.704
(L)Cortex_Medial_Prefrontal 30 −5 5 −83 −4.065
(L)Cortex_Retrosplenial 20 −13 30 9 −3.187
(L)Cortex_Orbitofrontal 17 −40 −7 −79 −2.537
(L)Cortex_Orbitofrontal 10 −3 11 −91 −2.532
(R)Cortex_Retrosplenial 244 16 30 11 −12.421
(R)Hippocampus_Ventral 75 49 −18 7 −3.719
(R)Hippocampus_Ventral 75 57 −20 −5 −4.546
(R)Cortex_Retrosplenial 36 14 30 29 −4.221
(R)Hippocampus_Subiculum 22 22 13 9 −3.446
(R)Hypothalamus_Lateral 21 13 −36 −1 −3.335
(R)Cortex_Piriform 21 36 −18 −79 −3.344
(R)Hippocampus_Postero_Dorsal 16 20 17 −7 −3.710
Pain‐related brain areas
(L)Cortex_Insular 258 −54 −26 −55 −6.133
(L)Thalamus_Midline_Dorsal 120 −11 −11 −3 −2.446
(L)Thalamus_Dorsolateral 23 −32 −18 1 −4.883
(L)Thalamus_Midline_Dorsal 17 −1 −3 −25 −2.994
(L)Thalamus_Dorsolateral 541 26 −20 −11 −17.900
(L)Thalamus_Midline_Dorsal 118 3 −9 −29 −5.465
(R)Cortex_Cingulate 16 3 17 −37 −5.662
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At the fourth postoperative month, the activation pattern was similar to that of the first postoperative month. Areas including the limbic/paralimbic areas, pain‐regulating areas, and basal ganglia presented lower activation in the intervention group. Specific regions of the somatosensory cortex, subregions of the hippocampus, amygdala, AcbC, AcbSh, cingulate cortex, subregions of thalamus, pallidum, and putamen were involved. Additionally, activation in the prefrontal area was higher in the intervention group compared with the control group (Figure 4 and Table 3).

TABLE 3.

Difference of activation between the experimental and control groups during the right (injured) hindpaw stimulation task at the fourth postoperative month.

Cluster centroid (MNI)
Brain regions Extent x y z t‐value
Intervention > Control group
(L)Cortex_Orbitofrontal 107 −24 −3 −85 3.709
(L)Cortex_Medial_Prefrontal 27 −11 7 −73 2.480
(R)Cortex_Medial_Prefrontal 69 5 3 −83 4.583
(R)Cortex_Frontal_Association 58 30 15 −95 3.649
(R)Cortex_Medial_Prefrontal 37 5 −1 −67 3.159
Intervention < Control group
Cortex_Somatosensory
(L)cluster1 24 −61 9 −17 −3.158
(L)cluster2 20 −52 11 −23 −3.577
(L)cluster3 10 −50 24 −27 −2.147
(R)cluster4 50 38 22 −53 −3.107
(R)cluster5 39 49 17 −13 −3.926
(R)cluster6 35 42 21 −35 −3.040
(R)cluster7 30 65 7 −51 −2.664
(R)cluster8 17 61 15 −21 −2.683
(R)cluster9 16 34 24 −25 −2.381
Limbic system
(L)Hippocampus_Subiculum 96 −50 −13 19 −2.250
(L)Cortex_Entorhinal 18 −24 −22 −77 −2.911
(L)Hippocampus_Postero_Dorsal 17 −42 13 −5 −4.283
(L)Hippocampus_Postero_Dorsal 14 −13 7 7 −2.436
(L)Hypothalamus_Medial 13 −11 −42 −21 −2.772
(L)Amygdala 10 −48 −34 −15 −2.859
(L)Amygdala 10 −36 −42 −29 −2.561
(R)Hippocampus_Postero_Dorsal 316 28 21 −7 −4.395
(R)Hypothalamus_Medial 106 7 −38 −21 −3.902
(R)Hippocampus_Antero_Dorsal 48 16 15 −23 −4.160
(R)Amygdala 22 46 −32 −19 −4.215
(R)Hippocampus_Postero_Dorsal 22 53 −5 −3 −3.490
(R)AcbC 21 18 −20 −61 −3.018
(R)Hippocampus_Subiculum 21 24 21 27 −3.374
(R)Hippocampus_Postero_Dorsal 20 3 11 3 −2.710
(R)Hippocampus_Subiculum 12 40 1 25 −2.462
(R)AcbSh 10 7 −30 −59 −2.793
Pain‐related brain areas and basal ganglia
(L)Cortex_Cingulate 601 −7 24 −41 −6.596
(L)Thalamus_Dorsolateral 82 −24 −11 −1 −2.800
(L)Thalamus_Dorsolateral 49 −24 −3 −31 −2.869
(L)Cortex_Insular 19 −63 −26 −31 −2.971
(L)Thalamus_Midline_Dorsal 19 −1 −14 −9 −2.917
(L)Thalamus_Midline_Dorsal 18 −17 −30 −33 −3.721
(L)Thalamus_Dorsolateral 13 −24 −13 −21 −3.165
(R)Thalamus_Midline_Dorsal 102 16 −9 −5 −3.900
(R)Thalamus_Dorsolateral 48 34 −12 −21 −3.444
(L)Ventral_Pallidum 117 −17 −38 −51 −4.252
(R)Globus_Pallidus 90 36 −30 −27 −3.890
(R)Caudate_Putamen 49 22 −38 −51 −3.399
(R)Caudate_Putamen 37 42 3 −31 −3.283
(L)Caudate_Putamen 18 −13 3 −51 −3.198
(R)Caudate_Putamen 42 34 −14 −41 −2.681
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3.2.2. Left (intact)hindpaw stimulation task (intervention group—control group)

At the first postoperative month, activation in multiple areas was lower in the intervention group, including the limbic/paralimbic areas, pain‐regulating areas, and basal ganglia, compared with the control group. Specifically, subregions of the hypothalamus, prefrontal cortex, piriform cortex, entorhinal cortex, cingulate cortex, subregions of the thalamus, pallidum, putamen, and subregions of the hippocampus were involved (Figure 5 and Table 4).

FIGURE 5.

FIGURE 5

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Comparison of cortical activation between the experimental and control groups during the left (intact) hindpaw stimulation task. Each column in the figure displays different activated brain areas between experimental and control groups. Each row in the figure displays the difference in activation between two observation points in the same group. The warm tone represents higher activation in the intervention group than that in the control group, while the cold tone represents lower activation.

TABLE 4.

Difference of activation between the experimental and control groups during the left (intact) hindpaw stimulation task at the first postoperative month.

Cluster centroid (MNI)
Brain regions Extent x y z t‐value
Intervention < Control group
Limbic system
(L)Hypothalamus_Medial 608 −5 −40 −13 −5.763
(L)Cortex_Entorhinal 22 −59 −34 −27 −4.951
(L)Hippocampus_Antero_Dorsal 15 −30 9 −11 −3.645
(L)Hippocampus_Antero_Dorsal 14 −7 15 −25 −2.923
(L)Hippocampus_Postero_Dorsal 13 −44 3 −3 −4.637
(L)Cortex_Retrosplenial 13 −7 32 −23 −2.276
(R)Hippocampus_Antero_Dorsal 93 1 13 −17 −4.089
(R)Cortex_Medial_Prefrontal 81 7 −1 −67 −2.823
(R)Cortex_Piriform 71 61 −38 −33 −3.100
(R)Hippocampus_Subiculum 52 47 13 15 −3.319
(R)Cortex_Medial_Prefrontal 38 7 −7 −77 −4.016
(R)Hippocampus_Antero_Dorsal 32 30 11 −17 −4.366
(R)Hypothalamus_Medial 12 3 −38 −35 −3.328
(R)Hippocampus_Subiculum 11 34 9 27 −3.489
Pain‐related brain areas and basal ganglia
(L)Cortex_Cingulate 81 −3 15 −61 −4.181
(L)Thalamus_Ventromedial 35 −3 −28 −33 −5.730
(L)Cortex_Insular 15 −59 −15 −55 −3.184
(R)Thalamus_Dorsolateral 69 14 −20 −19 −3.481
(R)Thalamus_Dorsolateral 29 30 −20 13 −5.650
(R)Ventral_Pallidum 28 24 −30 −45 −6.986
(R)Substantia_Nigra 23 18 −34 9 −4.735
(R)Caudate_Putamen 784 44 −14 −47 −3.346
(R)Ventral_Pallidum 6 34 −40 −43 −2.739
(R)Cortex_Medial_Prefrontal 81 7 −1 −67 −2.823
(R)Cortex_Medial_Prefrontal 38 7 −7 −77 −4.016
(R)Cortex_Parietal_Association 187 42 23 −1 −2.681
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At the fourth postoperative month, activation in brain regions in the intervention group was also lower, involving the limbic/paralimbic areas, pain‐regulating areas, and basal ganglia, compared with the control group. Specifically, the retrosplenial cortex, AcbSh, subregions of the hypothalamus, insular cortex, globus pallidus, cingulate cortex, subregions of thalamus, pallidum, and putamen were involved (Figure 5 and Table 5).

TABLE 5.

Difference of activation between the experimental and control groups during the left (intact) hindpaw stimulation task at the fourth postoperative month.

Cluster centroid (MNI)
Brain regions Extent x y z t‐value
Intervention < Control group
Limbic system
(L)Cortex_Retrosplenial 30 −1 38 −19 −2.766
(L)Cortex_Retrosplenial 20 −19 24 27 −3.919
(L)AcbSh 20 −15 −32 −67 −3.309
(L)Cortex_Retrosplenial 13 −17 28 11 −3.391
(L)Hypothalamus_Medial 13 −3 −26 −7 −2.294
(L)Cortex_Retrosplenial 10 −3 22 −21 −2.497
(R)Hippocampus_Ventral 37 61 −18 1 −5.351
(R)Hippocampus_Antero_Dorsal 36 12 19 −15 −3.697
(R)Hippocampus_Postero_Dorsal 36 26 26 −7 −3.405
(R)Hypothalamus_Medial 17 3 −44 −9 −2.648
Pain‐related brain areas and basal ganglia
(L)Thalamus_Dorsolateral 26 −19 −18 −11 −4.064
(L)Cortex_Insular 23 −57 −26 −45 −3.107
(L)Thalamus_Midline_Dorsal 20 −7 −3 −23 −2.374
(L)Cortex_Insular 10 −61 −9 −57 ‐2.893
(R)Thalamus_Midline_Dorsal 39 9 −7 −17 −2.761
(R)Thalamus_Dorsolateral 25 38 3 −5 −2.603
(R)Thalamus_Dorsolateral 14 32 −16 −13 −2.574
(L)Globus_Pallidus 139 −32 −13 −39 −3.514
(L)Caudate_Putamen 43 −15 −1 −49 −2.565
(R)Caudate_Putamen 15 16 −13 −49 −3.006
(R)Caudate_Putamen 13 16 7 −43 −2.391
(R)Caudate_Putamen 55 38 −11 −57 −4.088
(L)Cortex_Frontal_Association 76 −34 16 −95 −2.828
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3.2.3. Right (injured) hindpaw stimulation task (the first postoperative month—the fourth postoperative month)

In the control group, compared to the first month, the following brain regions showed increased activity in the fourth month: right posterodorsal hippocampus, the right interstitial nucleus of the posterior limb of the anterior commissure (IPAC); The following brain regions showed decreased activity: left retrosplenial cortex, right entorhinal cortex, left superior colliculus, left motor cortex, right mesencephalic region (Table 6).

TABLE 6.

Difference of activation between the fourth month and the first month during the right (injured) hindpaw stimulation task in the brain regions.

Cluster centroid (MNI)
Brain regions Extent x y z t‐value
Control group
Fourth month > First month
(R)Hippocampus_Postero_Dorsal 51 51 1 −3 23
(R)IPAC 63 28 −42 −47 63
Fourth month < First month
L_Cortex_Retrosplenial 56 −3 26 −9 −44
R_Cortex_Entorhinal 69 53 −30 −39 −42
L_Superior_Colliculus 47 −24 −7 19 −32
L_Cortex_Motor 51 −13 30 −49 −20
R_Mesencephalic_Region 39 7 −26 23 −19
Intervention
Fourth month < First month
Frontal_Sup_2_R 20 34 −10 71 −16
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In the intervention group, the following brain region showed decreased activity in the fourth month compared to the first month: right superior frontal (Table 6).

3.2.4. Motor‐related brain regions during right (injured) hindpaw stimulation task (intervention group–control group)

At the first postoperative month, the control group showed increased activity in the primary motor cortex and somatosensory cortex of the right hemisphere compared to the intervention group (Table 7), p < .001.

TABLE 7.

Difference of activation between the experimental and control groups during right (injured) hindpaw stimulation task in the motor‐related brain regions.

Cluster centroid (MNI)
Brain regions Extent x y z t‐value
First month
Intervention < Control group
(R)Cortex_Motor 12 12 34 −37 −2.524
(R)Cortex_Somatosensory 69 59 −16 −3 −4.029
Fourth month
Intervention > Control group
(L)Cortex_Motor 15 −32 24 −89 2.598
(R)Cortex_Motor 24 30 20 −83 2.944
(R)Cortex_Somatosensory 26 61 −16 7 2.818
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At the fourth postoperative month, the intervention group showed increased activity in both primary motor cortices and the somatosensory cortex of the right hemisphere compared to the control group (Table 7), p < .001.

3.3. Correlation

At the first postoperative month, the Pearson correlation coefficients between the TWL of the injured hindpaw and the functional activity of the left insular cortex was –.03210 (p > .05), indicating no statistical significance; the Pearson correlation coefficients between the right motor cortex and the MCMI, SL, and SS of the injured limb were .1602, −.01238, −.01712 (p > .05), indicating no statistical significance.

At the fourth postoperative month, the Pearson correlation coefficients between the right motor cortex and the MCMI, SL, and SS of the injured limb were .5326, −.1351, −.2623 (p > .05), indicating no statistical significance; the Pearson correlation coefficients between the left motor cortex and the MCMI, SL, and SS of the injured limb were .2446, .2945, −.02689 (p > .05), indicating no statistical significance.

4. DISCUSSION

The sciatic nerve is a mixed nerve that composes of sensory and motor fibers. After injury, both motor and sensory functions of the hindlimb are partially or completely lost except for the saphenous innervations area (Shin & Howard, 2012).

Several measures could be used to evaluate sensorimotor function recovery following sciatic nerve injury repair. The TWL is one of the classic methods to evaluate the recovery of sensory function after sciatic nerve injury in rats. It can quantitatively evaluate the sensory function recovery of injured hindlimbs (da Silva et al., 2022). In the present study, the sensory function gradually recovered in both the experimental and control groups, indicated by decreased TWL. Tuina could significantly promote the final outcome of the sensory function following nerve repair.

Gait analysis is an automated behavior testing method, which is easy to control the speed of animal movement and allows the evaluation of animal locomotion function in free status (Bozkurt et al., 2008; Xu et al., 2019) and has been widely used in animal models of peripheral nervous diseases. It is also an important method for evaluating static and dynamic functions after nerve injury (Deumens et al., 2007; Gabriel et al., 2007; Kappos et al., 2017; Wu, Lu, Hua, Ma, Shan, et al., 2018; Wu, Lu, Hua, Ma, & Xu, 2018). In the present study, gait analysis parameters, MCMI, SL, and SS, were used to evaluate the motor function after sciatic nerve injury repair. The motor function recovered to various degrees in both groups. The MCMI, SL, and SS were significantly better in the Tuina group. Therefore, Tuina is also related to better motor recovery after nerve repair. The effect was more obvious at the third and fourth months after the surgery.

Previous studies have demonstrated that peripheral nerve injuries would result in changes not only at the local site of injury but also in long‐lasting cortical plasticity. These changes are caused by permanent loss of sensation and the misdirection of the newly formed axons after the nerve repair, the newly formed sensory and motor axons reinnervated do not match their original organs (Darian‐Smith, 1994; Lundborg, 2007; Rosen & Lundborg, 2004; Yamahachi et al., 2009). Reports have suggested that Tuina would improve functional outcomes following peripheral nerve injury repair (Z. Liu et al., 2021; T. T. Lv, Mo, Yu, Shao, et al., 2020). However, it is not well understood whether the long‐lasting cortical plasticity is involved in the improvement of function after Tuina therapy. To our knowledge, the present study is the first longitudinal study on cortical plasticity in a rat model of peripheral nerve injury treated by Tuina therapy.

Our study found that 1 month after modeling, compared to the intervention group, the primary motor cortex activity in the right hemisphere was enhanced in the control group, possibly due to the compensatory movement of the contralateral limb caused by pain in the affected limb of the control group, which promoted the activation of the right motor cortex. Interestingly, after 4 months of modeling, the activity of the bilateral motor cortex was higher in the intervention group than in the control group. While the longitudinal comparison of the model group showed a decrease in activity in the left primary motor cortex, there was no significant change in the intervention group. Behavioral results also indicated that the intervention group outperformed the control group, which may reflect the preservation effect of Tuina on the limb motor function after peripheral nerve injury, avoiding a decline in the motor function caused by nerve damage.

In the cortical activation regions during right (injured) hindpaw stimulation tasks, several brain areas were less activated in the intervention group than that in the control group. As the somatosensory cortex mainly receives a projection of sensory fibers from the peripheral, and the enhancement of afferent signal is a key factor that impacts the excitability of the somatosensory cortex. A study on monkeys has proved this view. They found that high‐intensity stimulation to the monkey`s fingers can make the somatosensory cortex more activated (Qi et al., 2016). It was also reported that Tuina therapy may decrease the activation level of peripheral nociceptive C‐fiber, which might reduce the sensory afferentation signal (Jiang et al., 2016). Therefore, the somatosensory cortex of the intervention group showed lower activation during the peripheral stimulating task.

Neuropathies following transection usually induce paresthesia or pain before reinnervation of targets (Alexander et al., 2019). Tuina therapy would alleviate paresthesia or pain after the peripheral neural pathway was reconnected. This reduction of sensory afferents following Tuina therapy consequently led to a reduction in the extent and strength of activation in the sensory cortex. Therefore, the somatosensory cortex in the intervention group was less activated.

In addition, the pain‐related areas, basal ganglia, and limbic/paralimbic areas also presented lower activation in the Tuina group. Previous studies have reported regions of modules in the “pain network,” including the primary somatosensory cortex, medial frontal cortical structures (such as the ACC, middle cingulate cortex (MCC), and supplementary motor area (SMA)), and the parasylvian cortical structures (such as the insula and opercular) (C. C. Liu et al., 2011). The insula is most strongly associated with other structures in the brain network, the anterior insula is connected with ACC and the frontal cortices, while the posterior insula is associated with the primary somatosensory cortex, motor cortex, and temporal cortex (Abram et al., 2015). The ACC is a key structure that processes information relating to pain‐induced unpleasantness. The thalamus is an important sensory relay station that transmits sensory signals to the cerebral cortex and is highly associated with pain (Zitnik et al., 2014). The basal ganglia (such as caudate, putamen, and pallidum) is a major site for adaptive plasticity in the brain, affecting the normal state in a broad range of behaviors as well as neurological and psychiatric conditions including pain (Graybiel, 2004). The limbic system includes multiple brain regions involving memory, emotion, and sensation. The hippocampus and amygdala transmit cortical signals to the hypothalamus and brainstem to regulate the arousal and motivational state of the whole neuraxis (Cardinal, 2002). It is recognized that the hippocampus, amygdala, and parahippocampus play an important role in regulating motivation, emotions, memory, and pain (Kirby et al., 2013; Y. Yang et al., 2016; Zubieta et al., 2001). Moreover, the inactivation of the hippocampus and amygdala was found to be related to the regulation of pain threshold (W. T. Zhang et al., 2003). In the present study, the activation pattern of pain‐related areas, basal ganglia, and limbic/paralimbic areas was quite synchronized. Therefore, these areas also played an important role in Tuina therapy‐induced brain plasticity.

The prefrontal cortex has been suggested to participate in controlling functional interactions between areas of the pain‐related brain regions (Tracey & Mantyh, 2007). Along with the basal ganglia, the prefrontal cortex is also closely associated with attention and executive during tasks (Yuan & Raz, 2014). Considering the integration of sensory and motor functions, the sensory input is essential for motor initialization and adjustment in walking. After 4 months of Tuina therapy, the higher activation of the prefrontal cortex implied restoration of the motor regulating function. Rather than reactivating some isolated areas alone, it demonstrated a gradual recovery of more advanced brain function.

According to the brain activation regions during the left (intact) hindpaw stimulation task, lower activation of pain‐related areas, basal ganglia, and limbic/paralimbic was observed in the intervention group, which was similar to the right (injured)hindpaw stimulation task. We assumed that lower activation in pain‐related areas and advanced modulation network was related to relieving effects of Tuina therapy on decreasing the excitability of sensory, pain, and emotion‐related areas. This suggested that Tuina therapy potentially leads to more extended changes in the brain other than the local effect.

5. CONCLUSION

Tuina therapy on the gastrocnemius muscle of rats with sciatic nerve injury can effectively alleviate pain and maintain the motor function of the affected limb, thus avoiding the decline in activity caused by nerve injury. In addition, Tuina therapy changes the activation level of pain‐related brain regions and the decreased activity of the motor cortex caused by nerve injury, providing a central nervous system mechanism for Tuina therapy in peripheral nerve injury and reflecting the impact of peripheral stimulation on brain plasticity.

5.1. Limitation

Further investigations should be conducted before extrapolating the present conclusions due to the differences between humans and rodents. More investment is needed to study the central nervous system mechanisms of Tuina therapy.

AUTHOR CONTRIBUTIONS

S‐JM, J‐JW: conceptualization and writing–original draft. X‐YH, M‐XZ: methodology. S‐JM, J‐PZ, and X‐YH: validation. J‐JW and M‐XZ: formal analysis. J‐GX and J‐PZ: writing, reviewing, and editing. All authors read and approved the final manuscript.

CONFLICT OF INTEREST STATEMENT

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

PEER REVIEW

The peer review history for this article is available at https://publons.com/publon/10.1002/brb3.3174.

ACKNOWLEDGMENTS

We thank all contributors and participants for their contribution to this study. This study was supported by the National Key R&D Program of China (Grant No. 2018YFC2001600), the Medical Key Specialized Project of Baoshan District(Grant No. BSZK‐2023‐BZ12, BSZK‐2023‐BP08), the Science and Technology Foundation Project of Baoshan District (Grant No. 20‐E‐43), and the National Natural Science Foundation of China (Grant No 81603713).a

Ma, S.‐J. , Zhang, J.‐P. , Hua, Xu‐Y. , Wu, J.‐J. , Zheng, M.‐X. , & Xu, J.‐G. (2023). Tuina therapy promotes behavioral improvement and brain plasticity in rats with peripheral nerve injury and repair. Brain and Behavior, 13, e3174. 10.1002/brb3.3174

Shu‐Jie Ma and Jun‐Peng Zhang have contributed equally to this work

Contributor Information

Jia‐Jia Wu, Email: wujiajia@shutcm.edu.cn.

Mou‐Xiong Zheng, Email: zhengmouxiong@shutcm.edu.cn.

Jian‐Guang Xu, Email: xjg@shutcm.edu.cn.

DATA AVAILABILITY STATEMENT

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

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