Functional brain networks in healthy subjects under acupuncture stimulation: An EEG study based on nonlinear synchronization likelihood analysis

Physica A 468 (2017) 566–577

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Physica A journal homepage: www.elsevier.com/locate/physa

Functional brain networks in healthy subjects under acupuncture stimulation: An EEG study based on nonlinear synchronization likelihood analysis Haitao Yu a,∗ , Jing Liu b , Lihui Cai a , Jiang Wang a , Yibin Cao b , Chongqing Hao c a

School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China

b

Department of Neurology, Tangshan Gongren Hospital, Tangshan, Hebei 063000, China

c

School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei 050018, China

highlights • • • •

Modulatory effect of manual acupuncture on functional activity of the brain is studied. Effects of acupuncture on the EEG power are different in distinct frequency bands. Synchronization likelihood is used to estimate the correlation between pairwise EEG signals. Acupuncture can improve functional brain connectivity and facilitate information transmission.

article

info

Article history: Received 27 July 2016 Received in revised form 4 October 2016 Available online 27 October 2016 Keywords: Acupuncture Electroencephalogram Power spectral density Functional brain network Synchronization likelihood

abstract Electroencephalogram (EEG) signal evoked by acupuncture stimulation at ’’Zusanli’’ acupoint is analyzed to investigate the modulatory effect of manual acupuncture on the functional brain activity. Power spectral density of EEG signal is first calculated based on the autoregressive Burg method. It is shown that the EEG power is significantly increased during and after acupuncture in delta and theta bands, but decreased in alpha band. Furthermore, synchronization likelihood is used to estimate the nonlinear correlation between each pairwise EEG signals. By applying a threshold to resulting synchronization matrices, functional networks for each band are reconstructed and further quantitatively analyzed to study the impact of acupuncture on network structure. Graph theoretical analysis demonstrates that the functional connectivity of the brain undergoes obvious change under different conditions: pre-acupuncture, acupuncture, and post-acupuncture. The minimum path length is largely decreased and the clustering coefficient keeps increasing during and after acupuncture in delta and theta bands. It is indicated that acupuncture can significantly modulate the functional activity of the brain, and facilitate the information transmission within different brain areas. The obtained results may facilitate our understanding of the long-lasting effect of acupuncture on the brain function. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Understanding the modulatory effect of external stimulation on dynamics and functions of nerve system is one of main goals of neuroscience. Acupuncture, one of the most important parts of traditional Chinese medicine (TCM), is widely

∗

Corresponding author. E-mail address: [email protected] (H. Yu).

http://dx.doi.org/10.1016/j.physa.2016.10.068 0378-4371/© 2016 Elsevier B.V. All rights reserved.

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applied in the treatment of various diseases, especially for pain relief and stress control [1]. The therapeutic effectiveness of acupuncture has been established in long-term clinical studies [2,3]. It has been suggested that acupuncture works better than a placebo for several kinds of pain, and its effective rate in the treatment of chronic pain is comparable with that of morphine [4,5]. In addition, clinical experience and controlled studies have provided further evidences for the efficacy of acupuncture treatment for neurological disorders, such as epilepsy, Alzheimer’s disease (AD), and Parkinson’s disease [6–8]. Acupuncture can evoke substantial responses of central nervous system as well as cerebral areas, and has a significant influence on the neuronal circuit dynamics [9–11]. Brain imaging findings show that acupuncture, taken as an external stimulus, is capable to trigger a modulation of brain activity [12–14]. However, an explanation of the mechanism involved in acupuncture treatment is barely reported. Acupuncture achieves treatment effectiveness and physiological modulation by a combination effect of induced electrical signals and chemical signals [15,16]. In particular, electrical signals evoked by acupuncture manipulation are significantly contributed to its therapeutic efficacy [16]. Their transmission and function processes are being extensively explored by analytically investigating the activated neural signals. Han et al. demonstrated that the neural electrical signals in spinal dorsal root and spinal dorsal horn induced by manual acupuncture at ‘‘Zusanli’’ acupoint show chaotic features [17]. Men et al. further indicated that different manipulations of acupuncture can evoke distinct types of neuronal firing [18]. Moreover, Pei et al. found that acupuncture can improve the functional connectivity structure and information processing efficiency of the brain [19]. Despite all this, effect of acupuncture on brain activity and function still remains unclear. Recently, specific brain responses to acupuncture stimulation attracted much attention. Different brain imaging techniques have been used to investigate acupuncture mechanisms. By functional magnetic resonance imaging (fMRI) method, modulation effects of acupuncture on the brain activity, various acupoint-specific brain patterns, and different acupuncture effects between acupoint and sham point are reported [20–24]. Additionally, Electroencephalogram (EEG) is applied to record acupuncture-evoked neural signals in different brain areas [25]. Approaches to EEG analysis, such as coherence, entropy, and power spectral density (PSD), have been applied to assess the responses of cerebral cortex [13,26–30]. Among these analytical methods, the spectral analysis is especially important, due to the dependence of frequency components of brain waveform on the function determined by physiological conditions [31,32]. Through quantitatively characterizing EEG signals, various effects of acupuncture stimulation on the brain activity have been reported. Frequency analyses of EEG data indicated that acupuncture manipulation non-specifically increased the power of all spectral bands except the gamma band [27]. Similarly, Tanaka et al. reported that the EEG power was increased in all frequency bands after acupuncture [28], while Rosted et al. demonstrated that there were no changes brought by acupuncture in the resting EEG [29]. Chen et al. further found that acupuncture with different frequencies imposes different impacts on EEG power distribution [30]. Although spectral analysis could characterize acupuncture effect in frequency domain, it just focuses on linear features of EEG signals [33]. In fact, EEG signals are naturally nonlinear due to the nonlinearity of brain activity at neuronal level. Previous studies have shown that nonlinear EEG analysis is more effective in characterizing different brain states [34–36]. This gives rise to the possibility of revealing acupuncture effect on brain function with nonlinear methods. Synchronization likelihood is an unbiased measure of generalized synchronization between pairs of signals and can be used to perform nonlinear and non-stationary time series analysis [37]. Compared with linear methods like the traditional coherence and cross-correlation, synchronization likelihood is able to measure linear and nonlinear synchronization between pair-wise signals at the same time [37,38]. Recently, this method has been widely applied in the study of normal and disturbed brain functions [39–41]. It is shown that the synchronization likelihood was able to distinguish between neonatal EEG epochs with and without epileptic seizures [42]. Pijnenburg et al. studied the EEG synchronization likelihood in Alzheimer’s disease, and showed that a decrease of beta band synchronization occurs both in a resting condition and during a working memory task [43]. Moreover, Stam et al. further revealed the reduction of synchronization likelihood on AD patients at high-frequency bands [38]. Here, synchronization likelihood is applied to measure nonlinear correlation between each pair EEG signals in different frequency bands. Recent developments in the quantitative analysis of EEG data have been rapidly transferred to functional brain networks. The brain is constituted by numerous neurons within different cerebral areas. The corresponding synchronized evolutionary dynamics of brain regions could result in a dynamical functional connectivity in extensive temporal and spatial scale [44,45]. Extensive fMRI studies have demonstrated that acupuncture stimulation can activate extensive brain regions from the view of functional connectivity [46]. By quantitatively analyzing fMRI data before and after acupuncture, Dhond et al. showed that verum acupuncture can increase the functional connectivity of the default mode network and sensorimotor network in the post stimulus resting brain [47]. Zhong et al. evaluated the effective connectivity within auditory network and found that acupuncture at different acupoints could exert different modulatory effects on resting-state networks [48]. Bai et al. demonstrated that acupuncture can change the brain network into a functional state underlying both pain perception and modulation, exhibited by significant changes in the functional connectivity of local brain regions [46]. Yin et al. further reported that the properties of functional network were dynamically changing at different states of acupoint magnetic stimulation [49]. So the reconstruction of functional connectivity of the brain, based on recorded multi-channel EEG signals, could shed light on the physiological mechanisms underlying acupuncture treatment. In order to investigate the modulatory effect of acupuncture on functional brain activities, we record the EEG signals activated by manual acupuncture at ‘‘Zusanli’’ acupoint of human subjects in different states: pre-acupuncture, acupuncture, and post-acupuncture. The EEG power is first analyzed with the PSD method in three acupuncture states and different

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Fig. 1. Design of acupuncture experiment: (a) ‘‘Zusanli’’ acupoint; (b) position of EEG electrodes and division of 5 brain areas; (c) acupuncture process; (d) EEG signals for Pre-Acupuncture (Pre-Acup), Acupuncture (Acup): 100 times/min, and Post-Acupuncture (Post-Acup) states (1 min for each state).

frequency bands. Then, synchronization likelihood approach is applied to measure the nonlinear synchrony and functional correlation between each pair-wise EEG signals. Functional brain networks will be further reconstructed based on the resulting synchronization matrices and their topological characteristics are quantitatively analyzed to investigate the impact of acupuncture on the functional connectivity of the whole brain. In the end, the promising results related to modulatory mechanisms of acupuncture on the brain activity are discussed and concluded. 2. Experiment design Experiments are performed in 19 healthy volunteers (with age ranging 23–27, 10 males and 9 females) with no experience of acupuncture treatment. The subjects are acupunctured at ‘‘Zusanli’’ acupoint of the right leg (Fig. 1(a)) by the same licensed acupuncturist. Clinical studies show that acupuncture at ‘‘Zusanli’’ acupoint has many the rapeutical benefits, such as treating sleep disorders, controlling stress, handling dizziness, and so on [50]. All subjects are required to stay quiet, awake with eyes closed to remove the obvious interference of myoelectricity. Experimental procedure is shown in Fig. 1(c). After 5 min relaxing, the stainless steel needle is inserted into ‘‘Zusanli’’ acupoint. In the first 2 min, the needle is kept in a resting state, and then the subjects are acupunctured by using the twirling method for 2 min. The twisting is mainly manipulated by the thumb to push forward with force, which is within a range of 90°–180° with a rate of 100 times/min. After acupuncture, subjects are kept in a resting state for 6 min. The experimental electrodes’ position is placed according to 10–20 standard system. The bilateral ear is regarded as the reference ground (the reference electrode is placed between A1 and A2), and the 19 Ag–AgCl scalp electrodes are channels

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Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T3, T4, T5, T6, Fz, Cz, Pz, as shown in Fig. 1(b). Thus, a dataset includes 19-channel EEG signals is obtained. We divide 19 channels into 5 areas (Fig. 1(b)), i.e. frontal area (F), left temporal area (LT), central area (CP), occipital area (O), and right temporal area (RT). The sampling rate of experimental device is 256 Hz, and the hardware filter is 0.5–100 Hz. During the experiment, EEG data is recorded continuously which lasts 10 min for each subject. Then, three middle segments of 1 min’s EEG series are extracted from the recorded EEG data for each state (PreAcupuncture; Acupuncture: 100 times/min; Post-Acupuncture, Fig. 1(d)), with the aim to remove the impact of insertion of the needle, adaptation to the acupuncture and other possible factors. The data is preprocessed to filter out the noise and draw useful information by a band-passed finite impulse digital filter of 0.5–30 Hz. 3. Analysis methods Power spectral density (PSD) is first applied to estimate the power of EEG signal in five brain regions, and its change during acupuncture process is analyzed. On this basis, the band-limited EEG signal is decomposed into four sub-bands by wavelet transformation: delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–15 Hz), and beta (15–30 Hz) [51,52]. The effect of acupuncture on power distribution over brain regions in each frequency band is further analyzed. Frequency bands which show significant power changes during acupuncture treatment are selected for further investigation. On the other hand, synchronization likelihood method is adopted to estimate the synchrony degree between each pair of EEG signals in different frequency bands. By applying a threshold to the resulting synchronization matrix, functional brain network for each sub-band can be reconstructed. The topological parameters of brain networks are extracted and corresponding structural changes between different states (Pre-acupuncture, Acupuncture, and Post-acupuncture) are investigated to reveal the effect of acupuncture on the functional connectivity of the brain. 3.1. Power spectral density The PSD for each channel EEG signal is estimated using autoregressive (AR) Burg method, which is one of the most frequently used parametric methods [53]. The AR method is based on the hypothesis that the given signal x(n) is the output sequence of a linear system whose input is white noise, and can be expressed as follows x(n) = −

p 

ak x(n − k) + u(n)

(1)

k=1

where ak are the AR coefficients, p is the order of the AR model, u(n) is the white noise with variance σ 2 . The AR method of order p can be characterized by AR parameters {a1 , a1 , . . . , an , σ 2 }. Then PSD is defined as follows

σ2

2 . p   1 +  ak e−j2π fk   

Pˆ AR (f ) = 

(2)

k=1

In this work, AR coefficients are estimated by the recursive Burg method, which is based on minimizing the forward and backward prediction errors [54]. With the estimates of AR parameters by the Burg algorithm, PSD estimation is formed as [55] Pˆ BURG (f ) = 

eˆ p

2   1 +  aˆ p e−j2π fk    p

(3)

k=1

where eˆ p is the total least squares error. The order p of AR method is determined by Akaike information criterion (AIC) [56]. Here, the model order is taken as p = 10. In order to observe the topographical distribution of the power of different rhythms, 19-channel EEG signals of all subjects are analyzed respectively in four frequency bands. 3.2. Synchronization likelihood Synchronization likelihood is applied to measure nonlinear synchronization between each pair of EEG signals. The synchrony degree is determined by the resemblance of time series in shape, which is measured by tightness of the dots in their corresponding trajectories obtained after phase space reconstruction [37]. Given M simultaneously recorded EEG signals with a length N for each one, the embedded vectors Xk,i are reconstructed according to Takens embedding theorem: Xk,i = (xk,i , xk,i+l , xk,i+2l , . . . , xk,i+(m−1)l ) k = 1, 2, . . . , M , i = 1, 2, . . . , N where l is the lag and m is the embedding dimension.

(4)

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For each channel signal k and each time point i, we define Pkε,i as the probability that the distance of embedded vectors is smaller than ε : Pkε,i =

N 

1 2(ω2 − ω1 )

   φ ε − Xk,i − Xk,j 

(5)

j=1 ω1 <|i−j|<ω2

where the |·| is the Euclidean distance and φ is the Heaviside step function, ω1 and ω2 are width of two time windows. For each time series k, the critical distance εk,i is determined by setting Pkε,i = pref , where pref ≪ 1. Then for each discrete time pair (i, j) within the considered window (ω1 < |i − j| < ω2 ), we can determine the number Hi,j of channels, for which the distance of embedded vectors Xk,i and Xk,j is smaller than εk,i : Hi,j =

M     θ εk,i − Xk,i − Xk,j  .

(6)

k=1

This number lies in a range between 0 and M, and reflects how many of the embedded signals ‘resemble’ each other. The synchronization likelihood Sk,i,j for each channel k and each discrete time pair (i, j) can be defined as Hi,j − 1

if Xk.i − Xk,j  < εk,i ,

Sk,i,j =

  if Xk.i − Xk,j  ≥ εk,i ,

Sk,i,j = 0.





M −1

(7) (8)

By averaging over all j, the synchronization likelihood Sk,i , which describes how strongly channel k at time i is synchronized to all other M − 1 channels, can be obtained as Sk,i =

1 2(ω2 − ω1 )

N 

Sk,i,j .

(9)

j=1 ω1 <|j−i|<ω2

The value of Sk,i increases with the enhancement of synchronization between pair of signals, which is 1 when they are completely synchronized and is correspond to 0 when out of synchrony. The synchronization likelihood between each pair of EEG channels (brain regions) can be calculated. Then, a 19 × 19 matrix is obtained and can be taken as a correlation matrix of the whole brain. By threshold processing, the element in the matrix larger than the threshold is set to be 1 and denotes a connection between corresponding brain areas, while less than the threshold is 0 and indicates no connection. Based on this, a functional brain network under acupuncture is reconstructed. Its structural features will be further quantitatively analyzed by extracting the topological parameters using graph theoretical methods. 3.3. Topological parameters of functional network 3.3.1. Clustering coefficient Clustering coefficient is a useful measure to assess the functional segregation of a network. The clustering coefficient ci of a node i is defined as the number of edges ei between its direct neighbors divided by the total number of all possible edges, ki (ki − 1)/2, i.e. ci = 2ei /ki (ki − 1). The clustering coefficient of the network is the average clustering coefficient over all N nodes C = i=1 ci /N. Degree k of node i is the number of edges directly connected to the node. 3.3.2. Minimum path length Minimum path length is the optimal path length from one node to another and reflects the efficiency of information transfer within the network. The shortest path length lij between nodes i and j is the minimum number of edges that needs through node to node j. The minimum path length is the average of its shortest path length over all node pairs: L=



lij /N (N − 1).

(10)

i,j∈N ,i̸=j

3.4. Statistical analysis One-way Analysis of Variance (ANOVA) test is used to assess significant changes in the EEG features for different acupuncture states. A smaller p-value indicates a higher group difference, and vice versa. Generally, p < 0.05 is considered as the significance level in statistics. Additionally, as the multi-comparisons are conducted, Bonferroni correction is applied to avoid spurious rejections. Therefore, the significance level is set as p < 0.05/5 = 0.01 for the comparison in five brain regions and p < 0.05/4 = 0.0125 for the comparison in four frequency bands.

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4. Results 4.1. Power spectrum density estimation The AR-Burg algorithm is applied to estimate the power spectrum density (PSD) of recorded EEG signal. Fig. 2 shows the PSD as a function of frequency for five brain areas in three acupuncture states. The results are averaged over 19 subjects. In F region (Fig. 2(a)–(c)), the power is mainly distributed within the range 0.5–4 Hz. The PSD gets a peak at f = 1.5 Hz, and the maximum for Post-Acupuncture state is much higher than those for Pre-Acupuncture and Acupuncture states. For other four regions (LT, CP, O, and RT), the EEG power is mainly distributed within the range 0.5–4 Hz and 8–15 Hz. The PSD gets two local peaks at around f = 1.5 Hz and f = 11.5 Hz, respectively. The maximum at low frequency increases in Acupuncture state and decreases in Post-Acupuncture state, while the maximum at high frequency decreases gradually during and after acupuncture. Hence, the EEG signal is decomposed into four sub-bands by wavelet transformation: delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–15 Hz), and beta (15–30 Hz). Based on previous studies, the EEG signal in different frequency bands is related to distinct physiological functions of the brain [57–59]. The power distribution of 19-channel EEG signals in four sub-bands for three acupuncture states is investigated and topographic maps of power distribution over all brain regions is shown in Fig. 3. In delta band, the power of Acupuncture state is prominently enhanced compared with Pre-Acupuncture state. The delta power is weakened in Post-Acupuncture state, but still higher than that in Pre- Acupuncture state. In theta band, the PSD of EEG signal is slightly decreased during acupuncture, but largely increased after acupuncture, especially around Fz and Cz channels. However, in alpha band, the EEG power reduces continuously under the acupuncture treatment and the brain activity is weakened. Comparatively, the PSD of EEG signals in beta band has no obvious change, indicating that acupuncture stimulation has little impact on the power distribution in high frequency band (larger than 15 Hz). The obtained results demonstrate that the EEG power is strongly changed by the acupuncture at ‘‘Zusanli’’ acupoint and the trends are different in distinct frequency bands. Particularity, the power is increased in delta and theta frequency bands, but decreased in alpha band. 4.2. Functional brain network analysis In this section, we reconstruct functional connectivity of the brain by using synchronization likelihood method in three acupuncture states and four frequency bands. For different acupuncture states, the threshold value remains the same. Fig. 4 shows synchronization likelihood matrices and functional brain networks of delta band in three states, where green line denotes the connections within left brain (LL), red line the connections between left and right brain (LR), blue line the connections within the right brain (RR), and black line the connections within the midbrain (Fz, Cz, and Oz). It is found that the connection between F with other brain areas is few, where FP1 and FP2 have no connection with other electrodes. Nevertheless, the functional connections among LT, RT, CP and O areas are relatively tight, which indicates that acupunctureevoked information exchange mainly concentrates within these brain regions. In addition, connectivity is sparse in PreAcupuncture state, but obviously increases during Acupuncture, indicating the enhancement of synchronization among these brain areas. Importantly, the increased connections are mainly long-distance links between left and right brain regions. In Post-Acupuncture state, the number of functional connectivity is decreased compared with that in Acupuncture state. It can be thus concluded that acupuncture can largely improve the complexity of brain network, which may enhance the brain activity. The number of functional connections between different brain areas is shown in Fig. 5. Obviously, connections of the functional network are generally increased during acupuncture and decreased after acupuncture, especially in delta and theta frequency bands. Moreover, the connection of between left and right hemispheres is less than that within left or right hemisphere, especially in beta band. Acupuncture can significantly enhance the long-distance connections (green bars) between left and right hemispheres, while has less impact on the connections within the right or left hemisphere. Similar results are also illustrated in Fig. 4 for delta band. Graph theoretical method is applied to analyze topological properties of functional brain networks in different frequency bands. Fig. 6 shows statistical results of minimum path length and clustering coefficient in different acupuncture states. For delta and theta bands, the minimum path length is largely decreased during and after acupuncture which are significantly different from that before acupuncture. It is demonstrated that acupuncture could accelerate the efficiency of information transmission within different brain areas. Moreover, the clustering coefficients keep increasing following acupuncture, which indicates acupuncture could improve the degree of the network integration. Comparatively, two characteristics have less difference among different acupuncture states in alpha and beta bands, which is in accordance with the result obtained from Fig. 5. The numbers of functional connections change little in these frequency bands. 5. Discussions In this study we investigate how acupuncture modulates the functional activity of the brain by quantitatively analyzing the evoked multi-channel EEG signals of human subjects. The main methods we adopted are power spectrum density and functional connectivity network. Our results show that the EEG signals are strongly affected by the acupuncture at

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Fig. 2. Power spectrum density vs. frequency for five brain areas in three acupuncture states: Pre-Acupuncture (left panel), Acupuncture (middle panel), and Post-Acupuncture (right panel). (a)–(c) frontal area (F); (d)–(f) left temporal area (LT); (g)–(i) central area (CP); (j)–(l) occipital area (O); (m)–(o) right temporal area (RT). The dashed lines indicate stand deviations across all subjects.

‘‘Zusanli’’ acupoint. During and after acupuncture, the power spectrum of EEG signal is significantly increased in the delta and theta bands, while decreased in alpha band. Furthermore, synchronization likelihood is used to estimate the nonlinear

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a

573

c

b

0.8 0.7 0.6

delta

0.5 0.4 0.3 0.2 0.15

theta

0.1 0.05 0 0.5 0.4 0.3

alpha

0.2 0.1 0 0.3

0.2

beta 0.1

0

Fig. 3. Distribution of power spectrum density in different acupuncture states: (a) Pre-Acupuncture; (b) Acupuncture; (c) Post-Acupuncture. The color scale indicates the relative PSD in each frequency band. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

a

b

c

1

15

15

15

10

10

10

0.8 0.6 0.4

5

5

d

5

5

10

15

5

e

10

0.2

5

15

10

15

0

f

Fig. 4. Synchronization likelihood matrices (upper) and functional brain networks (lower) of delta band in three acupuncture states: (a) and (d) PreAcupuncture, (b) and (e) Acupuncture, (c) and (f) Post-Acupuncture. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Fig. 5. Number of functional connections within the left hemisphere (LL), between left and right hemispheres (LR), and within the right hemisphere (RR) of the brain for three states: Pre-Acupuncture (Pre-Acup), Acupuncture (Acup), and Post-Acupuncture (Post-Acup). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 6. The minimum path length (a) and clustering coefficients (b) of the functional brain network in different frequency bands and different states: Pre-Acupuncture (Pre-Acup), Acupuncture (Acup), and Post-Acupuncture (Post-Acup).

synchrony between each pair of EEG signals. On this basis, functional brain networks for each band are reconstructed and topological parameters are extracted to study the change of functional connectivity in different states: pre-acupuncture, during-acupuncture, and post-acupuncture. It is shown that acupuncture can significantly modulate the organization of

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functional brain network by adding the connections between left and right brain hemispheres. The functional brain network attains a smaller path length and a larger clustering coefficients under acupuncture treatment, indicating an improvement of information transmission within different brain areas. Power spectral analysis is a well-established approach for EEG analysis. The resulting power spectrum shows the energy distribution of EEG signal in the frequency domain, which can clearly show cortical neural dynamics and functional brain activity. Here, we calculate the power spectral density based on the autoregressive Burg method [53], and revealed that the acupuncture at ‘‘Zusanli’’ acupoint can strongly affect the EEG power. Compared with resting state, the PSD of EEG signals in post-acupuncture state is highly increased in the delta and theta bands, but substantially decreases in alpha band. It has been suggested that the EEG power is closely related to brain functions. For example, the capacity of working memory is closely related to the power of theta and alpha bands of EEG signals [57]. Alzheimer’s EEG always shows an increase of delta and theta spectrum and a decrease of alpha and beta spectrum [58]. Moreover, an increase of EEG power in delta and theta bands is observed for Parkinson’s disease [59]. The obtained PSD variations of EEG signal under acupuncture treatment may show its modulation mechanisms for brain functions. Functional connective network is an efficient method to study the collective dynamics of the brain. It allows us to quantitatively characterize the global organization and topological reconfiguration of the whole brain in response to acupuncture stimulation. By applying synchronization likelihood method to determine the correlation between all pairwise of EEG signals, we found that acupuncture can increase the nonlinear synchrony between different brain areas. Then, the functional connectivity matrices of the synchronization are converted into brain networks and analyzed by a network model from graph theory. Topological parameters of the functional brain network are extracted, such as the minimum path length and clustering coefficient. The obtained results show that acupuncture can substantially change the functional connectivity of the brain. The number of connections is largely increased in the acupuncture and post-acupuncture states. Importantly, these increased connections are mainly shortcuts between the left and right brain hemispheres, which indicates that acupuncture can facilitate long-distance communication between remote brain areas and improve its cognitive function. Similar results are also obtained by fMRI studies [46–48]. The properties of functional network dynamically change at different states of stimulation. Statistical results show that the minimum path length of the functional brain network is largely decreased and the clustering coefficients keep increasing during and after acupuncture in delta and theta bands. That is to say, the functional regions of brain network become much closer, implying an enhancement of information transmission and processing within different brain areas, which is in accordance with Liu’s findings with fMRI study [60]. Acupuncture can convert the brain network from resting state to a functional one. Experimental evidences have shown that the reconstructed brain networks exhibit abnormal topological properties under pathological conditions. For example, Micheloyannis et al. showed that the functional connectivity of schizophrenia patient shows a lower clustering coefficients and higher path length than that of healthy subjects [61]. Ponten et al. revealed that the brain network has a prominent increase of clustering coefficient and path length during and after the seizure [62]. Stam et al. also proposed that functional brain networks in Alzheimer’s disease are abnormally organized with a longer characteristic path length [63]. Thus, this analysis may provide a potential explanation for the efficacy of acupuncture treatment for neurological disorders. 6. Conclusions This work is set to study the effect of acupuncture stimulation at ‘‘Zusanli’’ Acupoint on functional activity of human brain. Methods of power spectral density and complex network analysis are applied to assess property changes in EEG signals among three states: before, during, and after acupuncture. Power spectral analysis shows the power distribution of EEG in the frequency domain, which is largely increased during and after acupuncture in the delta and theta frequency bands, but decreased in alpha band. By applying synchronization likelihood method to determine the correlation between all pairwise of EEG signals, it is found that acupuncture can increase the nonlinear synchrony between different brain areas. Complex network analysis further reveals that the properties of functional network dynamically change at different states of stimulation. A more efficiency functional connectivity of the brain is observed under acupuncture treatment, induced by the increase of long-distance connections between left and right brain regions. These results may provide a new perspective for us to understand the modulatory effect of acupuncture on the brain function, as well as the potential benefits of clinical treatments. Acknowledgments This work was supported by the National Natural Science Foundation of China (Grant No. 61302002), Natural Science Foundation of Tianjin City (Grant No. 14JCQNJC01200), Tangshan Technology Research and Development Program (Grant No. 14130223B), and Natural Science Foundation of Hebei Province (Grant No. F2014208013). References [1] K. Vanderploeg, X. Yi, Acupuncture in modern society, J. Acupunct. Meridian Stud. 2 (2009) 26–63. [2] T.J. Kaptchuk, Acupuncture: theory, efficacy, and practice, Ann. Intern. Med. 136 (2002) 374–383.

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[3] G. Stux, R. Hammerschlag, Clinical Acupuncture: Scientific Basis, Springer, Heidelberg, 2001. [4] J. Ezzo, B. Berman, V.A. Hadhazy, A.R. Jadad, L. Lao, B.B. Singh, Is acupuncture effective for the treatment of chronic pain? A systematic review, Pain 86 (2000) 217–225. [5] P.M. Richardson, C.A. Vincent, Acupuncture for the treatment of pain: a review of evaluative research, Pain 24 (1986) 15–40. [6] A.A. Rabinstein, L.M. Shulman, Acupuncture in clinical neurology, Neurologist 9 (2003) 137–148. [7] Y. Feng, L. Bai, Y. Ren, S. Chen, H. Wang, W. Zhang, J. Tian, FMRI connectivity analysis of acupuncture effects on the whole brain network in mild cognitive impairment patients, Magn. Reson. Imaging 30 (2012) 672–682. [8] H. Lee, H.J. Park, J. Park, M.J. Kim, M. Hong, J. Yang, S. Choi, H. Lee, Acupuncture application for neurological disorders, Neurol. Res. 29 (2007) S49–S54. [9] S.S. Yoo, E.K. Teh, R.A. Blinder, F.A. Jolesz, Modulation of cerebellar activities by acupuncture stimulation: evidence from fMRI study, Neuroimage 22 (2004) 932–940. [10] W. Cai, Acupuncture and the nervous system, Am. J. Chin. Med. 20 (1992) 331–337. [11] A.K. Chan, A. Vujnovich, L. Bradnam-Roberts, The effect of acupuncture on alpha-motoneuron excitability, Acupunct. Electrother. Res. 29 (2004) 53–72. [12] L. Bai, W. Qin, J. Tian, M. Dong, X. Pan, P. Chen, J. Dai, W. Yang, Y. Liu, Acupuncture modulates spontaneous activities in the anticorrelated resting brain networks, Brain Res. 1279 (2009) 37–49. [13] G. Yi, J. Wang, H. Bian, C. Han, B. Deng, X. Wei, H. Li, Multi-scale order recurrence quantification analysis of EEG signals evoked by manual acupuncture in healthy subjects, Cogn. Neurodyn. 7 (2013) 79–88. [14] W. Qin, J. Tian, L. Bai, X. Pan, L. Yang, P. Chen, J. Dai, L. Ai, B. Zhao, Q. Gong, W. Wang, K.M. Deneen, Y. Liu, FMRI connectivity analysis of acupuncture effects on an amygdala-associated brain network, Mol. Pain 4 (2008) 55. [15] J. Shen, Research on the neurophysiological mechanisms of acupuncture: review of selected studies and methodological issues, J. Altern. Complement. Med. 7 (2001) S121–S127. [16] M.T. Wu, J.C. Hsieh, J. Xiong, C.F. Yang, H.B. Pan, Y.C. Chen, G. Tsai, B.R. Rosen, K.K. Kwong, Central nervous pathway for acupuncture stimulation: localization of processing with functional MR imaging of the brain–preliminary experience, Radiology 212 (1999) 133–141. [17] C.X. Han, J. Wang, Y.Q. Che, B. Deng, Y. Guo, Y.M. Guo, Y.Y. Liu, Nonlinear characteristics extraction from electrical signals of dorsal spinal nerve root evoked by acupuncture at Zusanli point, Acta Phys. Sinica 59 (2010) 5880–5887. [18] C. Men, J. Wang, Y.M. Qin, B. Deng, X.L. Wei, Characterizing electrical signals evoked by acupuncture through complex network mapping: a new perspective on acupuncture, Comput. Methods Programs Biomed. 104 (2011) 498–504. [19] X. Pei, J. Wang, B. Deng, X. Wei, H. Yu, WLPVG approach to the analysis of EEG-based functional brain network under manual acupuncture, Cogn. Neurodyn. 8 (2014) 417–428. [20] W. Huang, D. Pach, V. Napadow, K. Park, X. Long, J. Neumann, Y. Maeda, T. Nierhaus, F. Liang, C.M. Witt, Characterizing acupuncture stimuli using brain imaging with FMRI–a systematic review and meta-analysis of the literature, PLoS One 7 (2012) e32960. [21] B. Yan, K. Li, J. Xu, W. Wang, K. Li, H. Liu, B. Shan, X. Tang, Acupoint-specific fMRI patterns in human brain, Neurosci. Lett. 383 (2005) 236–240. [22] V. Napadow, R. Dhond, K. Park, J. Kim, N. Makris, K.K. Kwong, R.E. Harris, P.L. Purdon, N. Kettner, K.K. Hui, Time-variant fMRI activity in the brainstem and higher structures in response to acupuncture, Neuroimage 47 (2009) 289–301. [23] V. Napadow, R.P. Dhond, J. Kim, L. LaCount, M. Vangel, R.E. Harris, N. Kettner, K. Park, Brain encoding of acupuncture sensation–coupling on-line rating with fMRI, Neuroimage 47 (2009) 1055–1065. [24] J.L. Fang, T. Krings, J. Weidemann, I.G. Meister, A. Thron, Functional MRI in healthy subjects during acupuncture: different effects of needle rotation in real and false acupoints, Neuroradiology 46 (2004) 359–362. [25] E. Hori, K. Takamoto, S. Urakawa, T. Ono, H. Nishijo, Effects of acupuncture on the brain hemodynamics, Auton. Neurosci. 157 (2010) 74–80. [26] L. Guo, Y. Wang, H. Yu, N. Yin, Y. Li, Study of brain functional network based on sample entropy of EEG under magnetic stimulation at PC6 acupoint, Biomed. Mater. Eng. 24 (2014) 1063–1069. [27] S. Sakai, E. Hori, K. Umeno, N. Kitabayashi, T. Ono, H. Nishijo, Specific acupuncture sensation correlates with EEGs and autonomic changes in human subjects, Auton. Neurosci. 133 (2007) 158–169. [28] Y. Tanaka, Y. Koyama, E. Jodo, Y. Kyama, A. Kawauchi, O. Ukimura, T. Miki, Effects of acupuncture to the sacral segment on the bladder activity and electroencephalogram, Psychiatry Clin. Neurosci. 56 (2002) 249–250. [29] P. Rosted, P.A. Griffiths, P. Bacon, N. Gravill, Is there an effect of acupuncture on the resting EEG? Complement. Ther. Med. 9 (2001) 77–81. [30] A.C. Chen, F.J. Liu, L. Wang, L. Arendt-Nielsen, Mode and site of acupuncture modulation in the human brain: 3D (124-ch) EEG power spectrum mapping and source imaging, Neuroimage 29 (2006) 1080–1091. [31] M. Näpflin, M. Wildi, J. Sarnthein, Test-retest reliability of EEG spectra during a working memory task, Neuroimage 43 (2008) 687–693. [32] R. Wang, J. Wang, H. Yu, X. Wei, C. Yang, B. Deng, Power spectral density and coherence analysis of Alzheimer’s EEG, Cogn. Neurodyn. 9 (2015) 291–304. [33] S.M. Zhou, J.Q. Gan, F. Sepulveda, Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface, Inform. Sci. 178 (2008) 1629–1640. [34] K. Gadhoumi, J.M. Lina, F. Mormann, J. Gotman, Seizure prediction for therapeutic devices: A review, J. Neurosci. Methods 260 (2016) 270–282. [35] J. Roschke, J. Fell, P. Beckmann, Nonlinear analysis of sleep EEG data in schizophrenia: calculation of the principal Lyapunov exponent, Psychiatry Res. 56 (1995) 257–269. [36] C.J. Stam, B. Jellesa, H.A. Achtereektea, S.A. Romboutsa, J.P. Slaetsb, R.W. Keunena, Investigation of EEG non-linearity in dementia and Parkinson’s disease, Electroencephalogr. Clin. Neurophysiol. 95 (1995) 309–317. [37] C.J. Stam, B.W. van Dijk, Synchronization likelihood: an unbiased measure of generalized synchronization in multivariate data sets, Physica D 163 (2002) 236–251. [38] C.J. Stam, A.M. van Cappellen van Walsum, Y.A. Pijnenburg, H.W. Berendse, J.C. de Munck, P. Scheltens, B.W. van Dijk, Generalized synchronization of MEG recordings in Alzheimer’s Disease: evidence for involvement of the gamma band, J. Clin. Neurophysiol. 19 (2002) 562–574. [39] P. Sauseng, W. Klimesch, M. Doppelmayr, T. Pecherstorfer, R. Freunberger, S. Hanslmayr, Hum. EEG alpha synchronization and functional coupling during top-down processing in a working memory task, Brain Mapp. 26 (2005) 148–155. [40] C.J. Stam, A.M. van Cappellen van Walsum, S. Micheloyannis, Variability of EEG synchronization during a working memory task in healthy subjects, Int. J. Psychophysiol. 46 (2002) 53–66. [41] J. Altenburg, R.J. Vermeulen, R.L. Strijers, W.P. Fetter, C.J. Stam, Seizure detection in the neonatal EEG with synchronization likelihood, Clin. Neruophysiol. 114 (2003) 50–55. [42] L.S. Smit, R.J. Vermeulen, W.P. Fetter, R.L. Strijers, C.J. Stam, Neonatal seizure monitoring using non-linear EEG analysis, Neuropediatrics 35 (2004) 329–335. [43] Y.A. Pijnenburg, Y. vd Made, A.M. van Cappellen van Walsum, D.L. Knol, P. Scheltens, C.J. Stam, EEG synchronization likelihood in mild cognitive impairment and Alzheimer’s disease during a working memory task, Clin. Neruophysiol. 115 (2004) 1332–1339. [44] E. Bullmore, O. Sporns, Complex brain networks: graph theoretical analysis of structural and functional systems, Nat. Rev. Neurosci. 10 (2009) 186–198. [45] C.J. Stam, J.C. Reijneveld, Graph theoretical analysis of complex networks in the brain, Nonlinear Biomed. Phys. 1 (2007) 3. [46] L. Bai, W. Qin, J. Tian, J. Dai, W. Yang, Detection of dynamic brain networks modulated by acupuncture using a graph theory model, Prog. Nat. Sci. 19 (2009) 827–835. [47] R.P. Dhond, C. Yeh, K. Park, N. Kettner, V. Napadow, Acupuncture modulates resting state connectivity in default and sensorimotor brain networks, Pain 136 (2008) 407–418. [48] C. Zhong, L. Bai, R. Dai, T. Xue, H. Wang, Y. Feng, Z. Liu, Y. You, S. Chen, J. Tian, Modulatory effects of acupuncture on resting-state networks: a functional MRI study combining independent component analysis and multivariate Granger causality analysis, J. Magn. Reson. Imaging 35 (2012) 572–581. [49] N. Yin, G.Z. Xu, Q. Zhou, Construction and analysis of complex brain functional network under acupoint magnetic stimulation, Acta Phys. Sinica 62 (2013) 118704.

H. Yu et al. / Physica A 468 (2017) 566–577

577

[50] J.S. Han, Acupuncture and endorphins, Neurosci. Lett. 361 (2004) 258–261. [51] M. Ahmadlou, H. Adeli, A. Adeli, New diagnostic EEG markers of the Alzheimer’s disease using visibility graph, J. Neural. Transm. 117 (2010) 1099–1109. [52] H. Adeli, S. Ghosh-Dastidar, N. Dadmehr, A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy, IEEE Trans. Biomed. Eng. 54 (2007) 205–211. [53] M. Akin, M.K. Kiymik, Application of periodogram and AR spectral analysis to EEG signals, J. Med. Syst. 24 (2000) 247–256. [54] S.M. Kay, S.L. Marple Jr., Spectrum analysis—A modern perspective, Proc. IEEE 69 (1981) 1380–1419. [55] S.M. Kay, Modern Spectral Estimation: Theory and Application, Prentice-Hall, New Jersey, 1988. [56] H. Akaike, A new look at the statistical model identification, IEEE Trans. Automat. Control 19 (1974) 716–723. [57] A. Gevins, M.E. Smith, L. McEvoy, D. Yu, High-resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice, Cereb. Cortex 7 (1997) 374–385. [58] J. Dauwels, F. Vialatte, A. Cichocki, Diagnosis of Alzheimer’s disease from EEG signals: where are we standing? Curr. Alzheimer Res. 7 (2010) 487–505. [59] C.X. Han, J. Wang, G.S. Yi, Y.Q. Che, Investigation of EEG abnormalities in the early stage of Parkinson’s disease, Cogn. Neurodyn. 7 (2013) 351–359. [60] B. Liu, J. Chen, J. Wang, X. Liu, X. Duan, X. Shang, Y. Long, Z. Chen, X. Li, Y. Huang, Y. He, Altered small-world efficiency of brain functional networks in acupuncture at ST36: a functional MRI study, PLoS One 7 (2012) e39342. [61] S. Micheloyannis, E. Pachou, C.J. Stam, M. Breakspear, P. Bitsios, M. Vourkas, S. Erimaki, M. Zervakis, Small-world networks and disturbed functional connectivity in schizophrenia, Schizophr. Res. 87 (2006) 60–66. [62] S.C. Ponten, F. Bartolomei, C.J. Stam, Small-world networks and epilepsy: graph theoretical analysis of intracerebrally recorded mesial temporal lobe seizures, Clin. Neurophysiol. 118 (2007) 918–927. [63] C.J. Stam, B.F. Jones, G. Nolte, M. Breakspear, P. Scheltens, Small-world networks and functional connectivity in Alzheimer’s disease, Cereb. Cortex 17 (2007) 92–99.