Electroencephalographic cross-frequency coupling and multiplex brain network under manual acupuncture stimulation
Introduction
The modulatory effect induced by external stimulation on functions of neural system is becoming a focus in many neurological studies. As an external stimulus, acupuncture can provide significant symptom relief in many diseases, such as headache, neuropathic pain and chronic pain syndrome [1], [2], [3], [4]. Besides, it is indicated that acupuncture holds promise as a treatment for neurological disease, such as Alzheimer’s disease (AD) [5], depression during pregnancy [6], [7], and Parkinson’s disease (PD) [8]. It has been proved that the acupuncture can achieve clinical effects via modulating the cerebral cortex activity [9], [10]. In addition, the effect time of acupuncture has been studied. Recent research explored the changes in the brain activity after acupuncture and found that the effects of acupuncture on brain function persist for several minutes after acupuncture, which is known as the “post-effect” of acupuncture [11], [12], [13]. The therapeutic effectiveness of acupuncture has been proved by clinical practice. However, it is not completely understanding how acupuncture affects in different brain areas and different rhythms.
Electrical signals obtained from the outer surface of the head can reflect electrical activity in the brain, and these signals are recorded as brainwaves by EEG. EEG signals are multi-dimensional, time-domain biological signals, which externalize the information in the brain and reflect the activity level of each part of the cerebral cortex [14], [15]. Compared with fMRI, PET and other brain imaging technologies, EEG has lower cost while maintaining a very high temporal resolution [16], [17], [18]. These unique benefits make EEG a common method in the acupuncture research area. Using EEG, variations of the brain function under acupuncture can be analyzed. Recent EEG-based acupuncture studies suggested that acupuncture enabled the functional brain networks to obtain a stronger small-world property compared with pre-acupuncture state [19], and enhanced network connectivity and information transmission efficiency as well [20]. To further understand the changes of brain function during acupuncture and the therapeutic mechanism of practices, EEG is used to record brain activity under acupuncture.
Oscillation and synchronization take place concurrently in many distinct and same frequency rhythms, which are proved to play important roles in neuroscience research through the networks models consisting of both cross-frequency coupling (CFC) and within-frequency coupling (WFC). CFC, an information encoding strategy in the brain, to refers to the interplay between oscillations at distinct frequencies in physiological signals such as EEG [21] while WFC is the interplay between oscillations in the same frequency bands. Canolty and Knight suggested that CFC might serve as a mechanism to transfer information from large-scale brain networks to the local cortical processing, so as to achieve effective computing and functional integration across the space–time scale [22]. In addition, CFC has been reported to be a key mechanism in various cognitive tasks and play a critical role in multi-scale communication across the brain [23], [24], [25] although CFC is indeed much weaker than WFC. Several studies suggested that CFC may be the pathophysiological mechanism underlying many diseases such as epilepsy and AD [26]. Our previous study has shown that the coupling between the delta and other bands has a significant decrease in epileptic seizures, meanwhile the local efficiency in alpha bands is significantly reduced [27]. Due to the relieving effect of acupuncture on these diseases, we infer that the mechanism of acupuncture may be explained from the perspective of CFC. However, the effects of acupuncture on CFC are still unknown. Besides, considering the similar role of WFC, we study the coupling within same rhythms as well.
Complex network and graph theory provide powerful methods for studying functional brain networks. Unlike the single-channel analysis method, connectivity matrix analysis can describe the process of information exchange between brain regions. We can depict the brain as a complex functional network consisting of interacting subsystems, where the brain regions are considered of as nodes in a network, and the structural or functional connections between nodes serve as edges in the network. Additionally, multiplex network has become increasingly popular in neuroscience because it can capture complete information of multi-model, multi-scale and spatio-temporal data set [28], [29], [30], [31]. Compared with the functional network limited to a specific frequency band, cross-frequency network regards the information exchange of different regions as the result of the interaction of multiple layers of networks. Here, we utilize multiplex brain network to model function evolution under acupuncture, and extract the topological characteristic parameters of brain networks based on complex networks and graph theory to explore the functions of brain networks.
In this work, our objectives were to study the dynamic variation of functional brain networks reconstructed by the connectivity matrix within and cross frequency bands during manual acupuncture stimulation. Firstly, brain activities were directly collected by EEG device in three acupuncture states: pre-acupuncture, acupuncture, and post-acupuncture. Furthermore, the connectivity strength of each pair of EEG signal in four frequency bands was calculated via the phase synchronization index (PSI) method. Based on PSI matrices, the variations of WFC and CFC caused by acupuncture were analyzed via the ANOVA statistical method. The single layer and multiplex functional networks were reconstructed based on PSI matrix. To analyze the evolutionary dynamics of brain function under acupuncture, the characteristics of the brain network were calculated though complex network analysis methods. Finally, we further explained the effect of acupuncture on brain dynamics from the perspective of matrix eigenvalues.
Section snippets
Material and methods
To investigate the effects of acupuncture on brain networks and brain regions, we designed the acupuncture experiment as shown in Fig. 1. A and recorded the brain activity under acupuncture by EEG acquisition device. Based on the EEG signals, WFC and CFC networks were further reconstructed to analyze the variation of brain function under acupuncture (Fig. 1.B, C). All analyses in this paper were performed in MATLAB 2017b.
Results
To capture synchronization dynamics within and cross frequency bands under manual acupuncture stimulation, the PSI method was used to character the connection weights for each pair-wise channel. The cross-frequency synchronization matrices average across subjects in the pre-acupuncture, acupuncture and post-acupuncture states were shown in Fig. 2A. It was obviously that the synchronization strength observed within frequency bands was much higher than that between frequency bands. Moreover, it
Discussion
Except for being extensively used in Eastern medicine, acupuncture has also emerged as a crucial complementary and alternative therapy in Western medicine. It is used to treat diseases by acupuncture at special points on the body’s surface called acupoints. A large number of clinical researches have shown that “Zusanli” acupoint has the function of regulating energy, immunity and presentiment [38], [39]. Recent studies have shown that acupuncture can significantly improve brain cognitive
Conclusions
The paper proposed to explore CFC under manual acupuncture stimulation using multi-channel EEG signals evoked by acupuncture stimulation at “Zusanli” acupoint. In this paper, CFC and WFC were measured using PSI. Single-layer and multiplex brain networks were reconstructed based on functional connectivity matrix. Besides, to measure dynamic changes of brain functional network, a series of features were calculated by the graph theory method. The results showed that acupuncture can improve the
CRediT authorship contribution statement
Haitao Yu: Conceptualization, Methodology. Shanshan Li: Formal analysis, Software, Writing - original draft. Kai Li: Data curation, Writing - review & editing. Jiang Wang: Writing - review & editing. Jing Liu: Conceptualization, Supervision, Project administration. Fengqun Mu: Resources, Investigation.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This work was supported in part by the Tianjin Natural Science Foundation (Grant No. 19JCYBJC18800).
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