Multi-scale analysis of acupuncture mechanisms for motor and sensory cortex activity based on SEEG data

Acupuncture, a traditional Chinese therapy, is gaining attention for its impact on the brain. While existing electroencephalogram and functional magnetic resonance image research has made significant contributions, this paper utilizes stereo-electroencephalography data for a comprehensive exploration of neurophysiological effects. Employing a multi-scale approach, channel-level analysis reveals notable $\delta $-band activity changes during acupuncture. At the brain region level, acupuncture modulated connectivity between the paracentral lobule and the precentral gyrus. Whole-brain analysis indicates acupuncture's influence on network organization, and enhancing $E_{glob}$ and increased interaction between the motor and sensory cortex. Brain functional reorganization is an important basis for functional recovery or compensation after central nervous system injury. The use of acupuncture to stimulate peripheral nerve trunks, muscle motor points, acupoints, etc., in clinical practice may contribute to the reorganization of brain function. This multi-scale perspective provides diverse insights into acupuncture's effects. Remarkably, this paper pioneers the introduction of stereo-electroencephalography data, advancing our understanding of acupuncture's mechanisms and potential therapeutic benefits in clinical settings.

A systematic review and coordinate-based meta-analysis of fMRI studies on acupuncture at LR 3

Objectives

The acupoint LR3 (Taichong) is frequently utilized in clinical acupuncture. However, its underlying neural mechanisms remain not fully elucidated, with speculations suggesting its close association with specific brain activity patterns.

Methods

A comprehensive literature search was undertaken across several online databases, such as PubMed, Web of Science, Embase, Cochrane Library, CNKI (China National Knowledge Infrastructure), Wanfang Database, VIP Database, and the Chinese Biomedical Database. Two independent researchers handled the study selection, quality assessment, and data extraction processes. Using the seed-based d-mapping meta-analysis approach, we evaluated the brain regions activated by LR3 acupuncture in healthy subjects. Subsequent subgroup analysis was stratified by fMRI types, and regression analyses were performed considering the duration of acupuncture, depth of needle insertion, and needle diameter. The identified active brain regions were then intricately projected onto large-scale functional networks.

Results

A total of 10 studies met the criteria for inclusion, encompassing 319 healthy right-handed participants. The meta-analysis indicates that acupuncture at the LR3 activates regions such as the right postcentral gyrus, left thalamus, left middle frontal gyrus, and right superior frontal gyrus. Additionally, meta-regression analysis highlights that increased acupuncture duration correlates with progressively intensified activation of the right superior frontal gyrus. Subgroup analysis posits that variations in the type of fMRI employed might account for heterogeneity in the pooled results. Concurrently, functional network analysis identifies the primary activated regions as aligning with the Basal ganglia network, Auditory network, Left executive control network, Posterior salience network, Right executive control network, and Sensorimotor networks.

Conclusion

Acupuncture at the LR3 in healthy subjects selectively activates brain regions linked to pain perception, emotional processing, and linguistic functions. Extending the needle retention duration intensifies the activation of the right superior frontal gyrus. These findings enrich our comprehension of the neurobiological underpinnings of acupuncture's role in pain mitigation and emotional regulation.

Electroacupuncture stimulation to modulate neural oscillations in promoting neurological rehabilitation

Electroacupuncture (EA) stimulation is a modern neuromodulation technique that integrates traditional Chinese acupuncture therapy with contemporary electrical stimulation. It involves the application of electrical currents to specific acupoints on the body following acupuncture. EA has been widely used in the treatment of various neurological disorders, including epilepsy, stroke, Parkinson's disease, and Alzheimer's disease. Recent research suggests that EA stimulation may modulate neural oscillations, correcting abnormal brain electrical activity, therefore promoting brain function and aiding in neurological rehabilitation. This paper conducted a comprehensive search in databases such as PubMed, Web of Science, and CNKI using keywords like "electroacupuncture," "neural oscillations," and "neurorehabilitation", covering the period from year 1980 to 2023. We provide a detailed overview of how electroacupuncture stimulation modulates neural oscillations, including maintaining neural activity homeostasis, influencing neurotransmitter release, improving cerebral hemodynamics, and enhancing specific neural functional networks. The paper also discusses the current state of research, limitations of electroacupuncture-induced neural oscillation techniques, and explores prospects for their combined application, aiming to offer broader insights for both basic and clinical research.

Epileptic seizures detection and the analysis of optimal seizure prediction horizon based on frequency and phase analysis

Changes in the frequency composition of the human electroencephalogram are associated with the transitions to epileptic seizures. Cross-frequency coupling (CFC) is a measure of neural oscillations in different frequency bands and brain areas, and specifically phase-amplitude coupling (PAC), a form of CFC, can be used to characterize these dynamic transitions. In this study, we propose a method for seizure detection and prediction based on frequency domain analysis and PAC combined with machine learning. We analyzed two databases, the Siena Scalp EEG database and the CHB-MIT database, and used the frequency features and modulation index (MI) for time-dependent quantification. The extracted features were fed to a random forest classifier for classification and prediction. The seizure prediction horizon (SPH) was also analyzed based on the highest-performing band to maximize the time for intervention and treatment while ensuring the accuracy of the prediction. Under comprehensive consideration, the results demonstrate that better performance could be achieved at an interval length of 5 min with an average accuracy of 85.71% and 95.87% for the Siena Scalp EEG database and the CHB-MIT database, respectively. As for the adult database, the combination of PAC analysis and classification can be of significant help for seizure detection and prediction. It suggests that the rarely used SPH also has a major impact on seizure detection and prediction and further explorations for the application of PAC are needed.

Altered brain functional networks after Quchi (LI 11) acupuncture: An EEG analysis

BACKGROUND

As a unique traditional Chinese medicine therapy, the central effect of acupuncture has received increasing attention. Functional brain networks can provide connectivity information among brain regions.

OBJECTIVE

The study goal is to explore the regulatory effect of acupuncture on the brain functional network.

METHODS

This paper analyzes the electroencephalography (EEG)-based power spectrum and brain functional network elicited by acupuncture at Quchi (LI 11).

RESULTS

The power spectrum results showed that acupuncture at LI 11 decreased the energy in the alpha frequency, mainly in the central region, left parietal lobe, left temporal lobe and left frontal lobe. Moreover, functional brain networks converted from the magnitude-squared coherence matrix in the alpha band are reconstructed. The results show that acupuncture did not alter the basic properties of the brain functional connection network. During acupuncture, the average node degree, average clustering coefficient, and small-world property of the brain functional connection network decreased after acupuncture compared with that before it. However, the average characteristic path length increased after acupuncture compared with before.

CONCLUSION

Acupuncture at LI 11 altered the brain's electrical activity. In the meantime, this acupuncture reduced the network's internal connectivity and information transfer efficiency.

Cerebral cortex Functional Networks of Transdermal Electrical Stimulation at Daling (PC7) Acupoint

The cerebral cortex functional network of Electroencephalogram (EEG) signals during transcutaneous electrical acupoint stimulation (TEAS) on 21 healthy subjects was constructed by using three modules: standard low-resolution brain electromagnetic tomography (sLORETA), phase-locking value (PLV), and complex network. We investigated the brain functional network triggered by PC7 stimulation by comparing with resting state and non-acupoint stimulation. The results showed that the PC7 stimulation mainly activated frontal lobe and temporal lobe including prefrontal cortex (BA10), insular lobe (BA13), temporal gyrus (BA22), anterior cingulate cortex (BA32), temporal pole (BA38), dorsolateral prefrontal cortex (BA46), and inferior frontal cortex (BA47), which are all closely linked to cognition, spirit, and emotion in brain. Furthermore, the degrees of node in frontal, temporal, and whole brain are increased significantly or extreme significantly with p < 0.05, p < 0.05, and p < 0.01, respectively; clustering coefficient in frontal, temporal, and whole brain are all statistically significant (p < 0.05). The information transmission efficiency of cerebral cortex has been greatly improved. During PC7 stimulation, the topological changes in the activation of cerebral regions and cortical functional networks are consistent with the therapeutic effect, which may provide theoretical support for acupoint stimulation to regulate nerve function.

The role of visual expectations in acupuncture analgesia: A quantitative electroencephalography study

Acupuncture is a complex treatment comprising multisensory stimulation, including visual and tactile sensations and experiences of body ownership. The purpose of this study was to investigate the role of these three components of acupuncture stimulation in acupuncture analgesia. 40 healthy volunteers participated in the study and received acupuncture treatment under three different conditions (real-hand, rubber-hand synchronous, and rubber-hand asynchronous). The tolerance for heat pain stimuli was measured before and after treatment. Brain oscillation changes were also measured using electroencephalography (EEG). The pain tolerance was significantly increased after acupuncture treatment under all three conditions. Noticeable deqi (needle) sensations in response to acupuncture stimulation of the rubber hand were found under both rubber-hand synchronous and rubber-hand asynchronous conditions. Deqi sensations were significantly correlated with acupuncture analgesia only under the rubber-hand synchronous condition. Increased delta and decreased theta, alpha, beta, and gamma waves were observed after acupuncture treatment under all three conditions. Our findings clarified the role of cognitive components of acupuncture treatment in acupuncture analgesia through the rubber-hand illusion. This study is a first step toward separating various components of acupuncture analgesia, i.e. visual, tactile, and body ownership, and utilizing those components to maximize analgesic effects.

Oscillatory Resonance and Dynamic Manifolds in Cortical Networks with Time Delay and Multiple External Stimuli

Rhythmic oscillation is crucial for information transmission and neural communication among different brain areas. Stochastic resonance (SR) can evoke different patterns of neural oscillation. However, the characteristics of network resonance and underlying dynamical mechanisms are still unclear. In this paper, a biological model of cortical network is established and its dynamical response to external periodic stimulation is investigated. We explore the oscillatory resonance of excitatory and inhibitory populations in cortical network. It is found that the intrinsic parameters of neural populations determine the extent of resonant activity, indicating that the firing rate exhibits coherent oscillation when the frequency of external stimulation is close to intrinsic frequency of neural population. In addition, the nonlinear dynamics of cortical network in oscillatory resonance can be represented by helical manifolds in low-dimensional state space. The geometry of neural manifolds reveals the periodic dynamics and state transition in oscillatory resonance. Moreover, time delay in chemical synapses can induce multiple resonances, which appear intermittently at integer multiples of the period of input signal. The dynamical response of neural population achieves maximal periodically, due to the transition of network states induced by time delay. Furthermore, mean-field theory is applied to analyze theoretical dynamic of cortical networks with time delay and demonstrate the effective transmission of stimulation information via oscillatory resonance in the brain. Consequently, the obtained results contribute to the improvement of neuromodulation for neurological disease from the viewpoint of the neural basis.

Cross-Cultural Brain Activity Differences Between True and Sham Acupuncture for Xerostomia During Head and Neck Cancer Radiotherapy

Background:

A prior phase III, multicenter (United States and China), clinical trial found true acupuncture (TA) resulted in lower xerostomia scores 12 months after radiotherapy than that of a standard care control group. This small pilot study examined brain function changes comparing TA to sham acupuncture (SA) in US and Fudan patients undergoing head and neck radiotherapy.

Methods:

To determine cerebral activity during TA versus SA acupuncture, patients underwent electroencephalogram evaluation (EEG) immediately prior, during and after both conditions. Acupuncture occurred during weeks 3 to 5 of radiotherapy, with patients receiving either TA or SA, followed 2 to 3 days later by the other treatment in a counterbalanced manner.

Results:

In the TA minus SA condition (N = 14 Fudan; N = 13 US), most changes were in the delta (0.5-3.5 Hz) and alpha (8-12 Hz) bandwidths. Delta was present in the frontal gyrus and parahippocampal gyrus. Alpha was present in the anterior and posterior cingulate, lingual gyrus, amygdala, precuneus, medial frontal gyrus, fusiform gyrus, and superior frontal gyrus. Maximal cortical differences in the Fudan cohort between TA and SA were in areas previously shown to be associated with (TA). In the US cohort, maximal differences between TA and SA were associated with areas which are usually decreased in TA conditions.

Conclusions:

There were distinct differences in brain function between those receiving TA and SA and there were clear differences between cultures, helping to explain the lack of placebo effect in the Fudan participants and strong placebo effect in the US patients.

Alternation of EEG Characteristics During Transcutaneous Acupoint Electrical Stimulation-Induced Sedation

Recent studies have shown that applying acupuncture during general anesthesia can reduce the dosage of anesthetics. Hence, it is speculated that acupuncture may have a sedative effect. However, existing studies employed acupuncture in combination with anesthetics, which makes determine acupuncture's role in producing sedation difficult. In this work, we investigated the sedative effect of acupuncture by using transcutaneous acupoint electrical stimulation (TAES) at bilateral Zusanli (ST36), Shenmen (HT7) and Sanyinjiao (SP6). Using a cross-over design, 2 separate sessions, that are, the resting and TAES sessions, were conducted for each subject. The sedative effect was quantified by using the bispectral index (BIS). The difference in brain activities between resting and TAES sessions was investigated by analyzing the simultaneously recorded EEG signals. Our results showed that a statistically significant difference in BIS values existed between resting and TAES sessions, which suggested that TAES alone was capable of inducing observable sedation. Using power spectrum analysis, we showed that TAES-induced sedation was accompanied by a reduction in alpha band power and an increment in delta band power. Permutation entropy was lower during the TAES session, which suggested that TAES reduced the complexity of the EEG signal. Moreover, a significant reduction in the global strength of brain functional connections was observed during TAES. These findings suggest that TAES alone can induce observable sedative effects, and this sedation effect is accompanied by changes in brain activities that have shown to be correlated with consciousness.

Modulation of Brain Rhythm Oscillations by Xingnao Kaiqiao Acupuncture Correlates with Stroke Recovery: A Randomized Control Trial

Objectives: In China, Xingnao Kaiqiao (XNKQ) acupuncture has been widely used for stroke treatment. However, its electrophysiological mechanism remains unclear. Hence, this study aims to study how XNKQ acupuncture modulates brain rhythm oscillations of stroke patients, and investigate its correlation with stroke recovery. Design: Randomized control trial. Subjects: Twenty (sub)acute ischemic stroke patients were enrolled and randomly assigned to two groups (an acupuncture group [AG] [n = 10] and a control group [CG] [n = 10]), and four patients (two patients in each group) dropped out of the study. Interventions: All patients received conventional treatments, and the patients in AG received additional XNKQ acupuncture treatment once a day for 10 consecutive days. Outcome measures: Before treatment, 14 days after, and 30 days after treatment onset, their movement impairments and neurologic deficits were measured using the National Institute of Health Stroke Scale (NIHSS), the Fugl-Meyer (FM) Scale, the Modified Rankin Scale (mRS), and the Modified Barthel Index (MBI), and their electroencephalogram data were recorded. Results: Compared with the CG, the AG showed more improvement in FM scores (p = 0.02), as well as decreased relative delta power and increased relative alpha power after 2 weeks' treatment. The decrease of the relative delta power and the increase of the relative alpha power in the ipsilesional frontal area were significantly correlated with the FM improvement (F5, F7, FC1, and Fz electrodes, all |r| > 0.517, p < 0.040). Conclusions: The curative effect of XNKQ acupuncture related to its electrophysiological modulation. This study was registered at the Chinese Clinical Trial Registry (ChiCTR2000038560).

Computer-aided Recognition Based on Decision-level Multimodal Fusion for Depression

Aiming at the problem of depression recognition, this paper proposes a computer-aided recognition framework based on decision-level multimodal fusion. In Song Dynasty of China, the idea of multimodal fusion was contained in "one gets different impressions of a mountain when viewing it from the front or sideways, at a close range or from afar" poetry. Objective and comprehensive analysis of depression can more accurately restore its essence, and multimodal can represent more information about depression compared to single modal. Linear electroencephalography (EEG) features based on adaptive auto regression (AR) model and typical nonlinear EEG features are extracted. EEG features related to depression and graph metric features in depression related brain regions are selected as the data basis of multimodal fusion to ensure data diversity. Based on the theory of multi-agent cooperation, the computer-aided depression recognition model of decision-level is realized. The experimental data comes from 24 depressed patients and 29 healthy controls (HC). The results of multi-group controlled trials show that compared with single modal or independent classifiers, the decision-level multimodal fusion method has a stronger ability to recognize depression, and the highest accuracy rate 92.13% was obtained. In addition, our results suggest that improving the brain region associated with information processing can help alleviate and treat depression. In the field of classification and recognition, our results clarify that there is no universal classifier suitable for any condition.

Electroencephalogram Analysis of Magnetic Stimulation at Different Acupoints

Magnetic stimulation has some similarities with acupuncture, and it has broad application prospects because of its non-invasiveness and easy quantification. This paper combines magnetic stimulation technology with electroencephalography to analyze the time-frequency and the brain functional network results elicited by magnetic stimulation at different acupoints. This paper hopes to observe the different effects of stimulating different acupoints on the brain from the perspective of EEG. The EEG signals during magnetic stimulation at ST36, ST40, and GB37 were recorded, respectively. The time-frequency results showed that the magnetic stimulation at ST36 and ST40 on the Foot Yangming Stomach Meridian increased the energy in the left parietal lobe and the right central region, and the energy increased mainly in the theta and alpha bands. However, during the magnetic stimulation at GB37 on the Foot Shaoyang Gallbladder Meridian, the energy in the central region and the frontal lobe increased, and the energy increased mainly in the delta, theta, and alpha bands. Moreover, the energy in the right parietal lobe decreased during magnetic stimulation at GB37. The results of brain functional network were also consistent with time-frequency results. The brain network connections of GB37 stimulation in the central region were significantly less than that of ST36 and ST40 (p < 0.01). In addition, the connections between central region and frontal lobe and the connections between central region and parietal lobe of GB37 stimulation were significantly different from that of ST36 and ST40 (p < 0.01). The above results indicate that ST36 and ST40 on the same meridian have similar effects on the brain, while GB37 on the other meridian has completely different effects from ST36 and ST40. The results of this paper explain the reason why stimulating ST36 and ST40 can treat similar diseases from the perspective of EEG, and also explain that stimulating GB37 has significantly different effects on the brain from that of ST36 and ST40.

Electroacupuncture Alters BCI-Based Brain Network in Stroke Patients

Goal. Stroke patients are usually accompanied by motor dysfunction, which greatly affects daily life. Electroacupuncture is a kind of nondrug therapy that can effectively improve motor function. However, the effect of electroacupuncture is hard to be measured immediately in clinic. This paper is aimed to reveal the instant changes in brain activity of three groups of stroke patients before, during, and after the electroacupuncture treatment by the EEG analysis in the alpha band and beta band. Methods. Seven different functional connectivity indicators including Pearson correlation coefficient, spectral coherence, mutual information, phase locking value, phase lag index, partial directed coherence, and directed transfer function were used to build the BCI-based brain network in stroke patients. Results and Conclusion. The results showed that the brain activity based on the alpha band of EEG decreased after the electroacupuncture treatment, while in the beta band of EEG, the brain activity decreased only in the first two groups. Significance. This method could be used to evaluate the effect of electroacupuncture instantly and quantitatively. The study will hopefully provide some neurophysiological evidence of the relationship between changes in brain activity and the effects of electroacupuncture. The study of BCI-based brain network changes in the alpha and beta bands before, during, and after electroacupuncture in stroke patients of different periods is helpful in adjusting and selecting the electroacupuncture regimens for different patients. The trial was registered on the Chinese clinical trial registry (ChiCTR2000036959).

Latent characteristics and neural manifold of brain functional network under acupuncture

Acupuncture can regulate the cognition of brain system, and different manipulations are the keys of realizing the curative effect of acupuncture on human body. Therefore, it is crucial to distinguish and monitor the different acupuncture manipulations automatically. In this brief, in order to enhance the robustness of electroencephalogram (EEG) detection against noise and interference, we propose an acupuncture manipulation detecting framework based on supervised ISOMAP and recurrent neural network (RNN). Primarily, the low-dimensional embedding neural manifold of brain dynamical functional network is extracted via the reconstructed geodetic distance. It is found that there exhibits stronger acupuncture-specific reconfiguration of brain network. Besides, we show that the distance travel along this manifold correlates strongly with changes of acupuncture manipulations. The low-dimensional brain topological structure of all subjects shows crescent-like feature when acupuncturing at Zusanli acupoints, and fixed-points are varying under diverse manipulation methods. Moreover, Takagi-Sugeno-Kang (TSK) classifier is adopted to identify acupuncture manipulations according to the nonlinear characteristics of neural manifolds. Compared with different classifier, TSK can further improve the accuracy of manipulation identification at 96.71%. The results demonstrate the effectiveness of our model in detecting the acupuncture manipulations, which may provide neural biomarkers for acupuncture physicians.

Gamma-band Enhancement of Functional Brain Connectivity Following Transcutaneous Electrical Nerve Stimulation

OBJECTIVE

Transcutaneous electrical nerve stimulation (TENS) has been suggested as a possible non-invasive pain treatment. However, the underlying mechanism of the analgesic effect of TENS and how brain network functional connectivity is affected following the use of TENS is not yet fully understood. The purpose of this study was to investigate the effect of high-frequency TENS on the alternation of functional brain network connectivity and the corresponding topographical changes, besides perceived sensations.

APPROACH

Forty healthy subjects participated in this study. EEG data and sensory profiles were recorded before and up to an hour following high-frequency TENS (100 Hz) in sham and intervention groups. Brain source activity from EEG data was estimated using the LORETA algorithm. In order to generate the brain connectivity network, the Phase lag index was calculated for all pair-wise connections of eight selected brain areas over six different frequency bands (i.e., δ, θ, α, β, γ, and 0.5-90 Hz).

MAIN RESULTS

The results suggested that the functional connectivity between the primary somatosensory cortex (SI) and the anterior cingulate cortex (ACC), in addition to functional connectivity between S1 and the medial prefrontal cortex (mPFC), were significantly increased in the gamma-band, following the TENS intervention. Additionally, using graph theory, several significant changes were observed in global and local characteristics of functional brain connectivity in gamma-band.

SIGNIFICANCE

Our observations in this paper open a neuropsychological window of understanding the underlying mechanism of TENS and the corresponding changes in functional brain connectivity, simultaneously with alternation in sensory perception.

Methodological issues of the central mechanism of two classic acupuncture manipulations based on fNIRS: suggestions for a pilot study

Background: Acupuncture reinforcing-reducing manipulation (ARRM) is a necessary procedure of traditional Chinese acupuncture and an essential factor affecting the therapeutic effect of acupuncture. Shaoshanhuo reinforcing method (SSH) and Toutianliang reducing method (TTL) are the most representative ARRMs. They integrate six single ARRMs and pose distinguished therapeutic effects of acupuncture. However, due to the complexity, diversity, and variation, investigating the mechanism of these two classic manipulations is insufficient. The neuroimaging technique is an important method to explore the central mechanism of SSH and TTL. This study attempted to design a randomized crossover trial based on functional near-infrared spectroscopy (fNIRS) to explore the mechanism of SSH and TTL, meanwhile, provide valuable methodological references for future studies. Methods: A total of 30 healthy subjects were finally included and analyzed in this study. fNIRS examination was performed to record the neural responses during the two most representative ARRMs. The cortical activation and the inter-network functional connectivity (FC) were explored. Results: The results found that SSH and TTL could elicit significant cerebral responses, respectively, but there was no difference between them. Conclusion: Neuroimaging techniques with a higher spatiotemporal resolution, combinations of therapeutic effects, and strict quality control are important to neuroimaging studies on SSH and TTL.

Breakdown of specific functional brain networks in clinical variants of Alzheimer's disease

Alzheimer's disease (AD) is characterized by different clinical entities. Although AD phenotypes share a common molecular substrate (i.e., amyloid beta and tau accumulation), several clinicopathological differences exist. Brain functional networks might provide a macro-scale scaffolding to explain this heterogeneity. In this review, we summarize the evidence linking different large-scale functional network abnormalities to distinct AD phenotypes. Specifically, executive deficits in early-onset AD link with the dysfunction of networks that support sustained attention and executive functions. Posterior cortical atrophy relates to the breakdown of visual and dorsal attentional circuits, while the primary progressive aphasia variant of AD may be associated with the dysfunction of the left-lateralized language network. Additionally, network abnormalities might provide in vivo signatures for distinguishing proteinopathies that mimic AD, such as TAR DNA binding protein 43 related pathologies. These network differences vis-a-vis clinical syndromes are more evident in the earliest stage of AD. Finally, we discuss how these findings might pave the way for new tailored interventions targeting the most vulnerable brain circuit at the optimal time window to maximize clinical benefits.

Low-Dimensional Dynamics of Brain Activity Associated with Manual Acupuncture in Healthy Subjects

Acupuncture is one of the oldest traditional medical treatments in Asian countries. However, the scientific explanation regarding the therapeutic effect of acupuncture is still unknown. The much-discussed hypothesis it that acupuncture’s effects are mediated via autonomic neural networks; nevertheless, dynamic brain activity involved in the acupuncture response has still not been elicited. In this work, we hypothesized that there exists a lower-dimensional subspace of dynamic brain activity across subjects, underpinning the brain’s response to manual acupuncture stimulation. To this end, we employed a variational auto-encoder to probe the latent variables from multichannel EEG signals associated with acupuncture stimulation at the ST36 acupoint. The experimental results demonstrate that manual acupuncture stimuli can reduce the dimensionality of brain activity, which results from the enhancement of oscillatory activity in the delta and alpha frequency bands induced by acupuncture. Moreover, it was found that large-scale brain activity could be constrained within a low-dimensional neural subspace, which is spanned by the “acupuncture mode”. In each neural subspace, the steady dynamics of the brain in response to acupuncture stimuli converge to topologically similar elliptic-shaped attractors across different subjects. The attractor morphology is closely related to the frequency of the acupuncture stimulation. These results shed light on probing the large-scale brain response to manual acupuncture stimuli.

The Temporal Dynamics of EEG Microstate Reveals the Neuromodulation Effect of Acupuncture With Deqi

The electroencephalography (EEG) microstate has recently emerged as a new whole-brain mapping tool for studying the temporal dynamics of the human brain. Meanwhile, the neuromodulation effect of external stimulation on the human brain is of increasing interest to neuroscientists. Acupuncture, which originated in ancient China, is recognized as an external neuromodulation method with therapeutic effects. Effective acupuncture could elicit the deqi effect, which is a combination of multiple sensations. However, whether the EEG microstate could be used to reveal the neuromodulation effect of acupuncture with deqi remains largely unclear. In this study, multichannel EEG data were recorded from 16 healthy subjects during acupuncture manipulation, as well as during pre- and post-manipulation tactile controls and pre- and post-acupuncture rest controls. As the basic acupuncture unit for regulating the central nervous system, the Hegu acupoint was used in this study, and each subject’s acupuncture deqi behavior scores were collected. To reveal the neuroimaging evidence of acupuncture with deqi, EEG microstate analysis was conducted to obtain the microstate maps and microstate parameters for different conditions. Furthermore, Pearson’s correlation was analyzed to investigate the correlation relationship between microstate parameters and deqi behavioral scores. Results showed that: (1) compared with tactile controls, acupuncture manipulation caused significantly increased deqi behavioral scores. (2) Acupuncture manipulation significantly increased the duration, occurrence, and contribution parameters of microstate C, whereas it decreased those parameters of microstate D. (3) Microstate C’s duration parameter showed a significantly positive correlation with acupuncture deqi behavior scores. (4) Acupuncture manipulation significantly increased the transition probabilities with microstate C as node, whereas it reduced the transition probabilities with microstate D as node. (5) Microstate B→C’s transition probability also showed a significantly positive correlation with acupuncture deqi behavior scores. Taken together, the temporal dynamic feature of EEG microstate could be used as objective neuroimaging evidence to reveal the neuromodulation effect of acupuncture with deqi.

Acupuncture With deqi Modulates the Hemodynamic Response and Functional Connectivity of the Prefrontal-Motor Cortical Network

As a world intangible cultural heritage, acupuncture is considered an essential modality of complementary and alternative therapy to Western medicine. Despite acupuncture’s long history and public acceptance, how the cortical network is modulated by acupuncture remains largely unclear. Moreover, as the basic acupuncture unit for regulating the central nervous system, how the cortical network is modulated during acupuncture at the Hegu acupoint is mostly unclear. Here, multi-channel functional near-infrared spectroscopy (fNIRS) data were recorded from twenty healthy subjects for acupuncture manipulation, pre- and post-manipulation tactile controls, and pre- and post-acupuncture rest controls. Results showed that: (1) acupuncture manipulation caused significantly increased acupuncture behavioral deqi performance compared with tactile controls. (2) The bilateral prefrontal cortex (PFC) and motor cortex were significantly inhibited during acupuncture manipulation than controls, which was evidenced by the decreased power of oxygenated hemoglobin (HbO) concentration. (3) The bilateral PFC’s hemodynamic responses showed a positive correlation trend with acupuncture behavioral performance. (4) The network connections with bilateral PFC as nodes showed significantly increased functional connectivity during acupuncture manipulation compared with controls. (5) Meanwhile, the network’s efficiency was improved by acupuncture manipulation, evidenced by the increased global efficiency and decreased shortest path length. Taken together, these results reveal that a cooperative PFC-Motor functional network could be modulated by acupuncture manipulation at the Hegu acupoint. This study provides neuroimaging evidence that explains acupuncture’s neuromodulation effects on the cortical network.

Feature Extraction and Identification of Alzheimer's Disease based on Latent Factor of Multi-Channel EEG

Alzheimer's disease is a neurodegenerative disease in old age, early diagnosis will help to delay the progression of the disease. Presently, the features of brain functional diseases can be obtained with EEG analysis, but the relationship between characteristics of EEG and Alzheimer's disease has not been clearly clarified. In this work, we hypothesize that there exist default brain variables (latent factors) across subjects in disease processes, decoding latent factor from brain activity contributes to the study of cognitive impairment. To that end, this work proposes to extract characteristics of Alzheimer's disease by combing latent factors of EEG with variational auto-encoder to realize disease identification. Primarily, power spectrum characteristics is investigated and it is found that the dominant frequency of two groups is different. Further analysis reveals that latent factor distribution of Alzheimer's disease exists obvious differences with normal group in the theta frequency band. Moreover, the latent factors are projected onto the three-dimensional state space and the transient rotation of neural state is found, which shows the dynamic characteristics of latent factors. In addition, Takagi-Sugeno-Kang classifier is adopted and multiple latent factors are fed into Takagi-Sugeno-Kang classifier for decoding. Compared with linear classifier, Takagi-Sugeno-Kang fuzzy classifier has better performance in classification of energy feature from sub-frequency bands of latent factors. The accuracy of identification could up to 98.10% when the combination of energy features of four frequency bands is used as model input. Collectively, this work provides a feasible tool for identification of neurological dysfunction from the view of latent factors, especially contributing to the diagnosis of Alzheimer's disease.

Brain variability in dynamic resting-state networks identified by fuzzy entropy: a scalp EEG study

Objective.Exploring the temporal variability in spatial topology during the resting state attracts growing interest and becomes increasingly useful to tackle the cognitive process of brain networks. In particular, the temporal brain dynamics during the resting state may be delineated and quantified aligning with cognitive performance, but few studies investigated the temporal variability in the electroencephalogram (EEG) network as well as its relationship with cognitive performance.Approach.In this study, we proposed an EEG-based protocol to measure the nonlinear complexity of the dynamic resting-state network by applying the fuzzy entropy. To further validate its applicability, the fuzzy entropy was applied into simulated and two independent datasets (i.e. decision-making and P300).Main results.The simulation study first proved that compared to the existing methods, this approach could not only exactly capture the pattern dynamics in time series but also overcame the magnitude effect of time series. Concerning the two EEG datasets, the flexible and robust network architectures of the brain cortex at rest were identified and distributed at the bilateral temporal lobe and frontal/occipital lobe, respectively, whose variability metrics were found to accurately classify different groups. Moreover, the temporal variability of resting-state network property was also either positively or negatively related to individual cognitive performance.Significance.This outcome suggested the potential of fuzzy entropy for evaluating the temporal variability of the dynamic resting-state brain networks, and the fuzzy entropy is also helpful for uncovering the fluctuating network variability that accounts for the individual decision differences.

Resting-State fMRI in Studies of Acupuncture

Research exploring the mechanism of acupuncture has been a hot topic in medicine. Resting-state functional magnetic resonance imaging (rs-fMRI) research is a noninvasive and extensive method, which is aimed at the research of the mechanism of acupuncture. Researchers use fMRI technologies to inspect the acupuncture process. The authors reviewed the application of rs-fMRI in acupuncture research in recent 10 years from the aspects of studying acupoints, subjects, acupuncture methods, and intensities. The results found that the application of rs-fMRI in acupuncture research mainly includes research on the onset mechanism of acupuncture treatment; visual evidence of diagnosis and treatment of dominant diseases; efficacy assessments; physiological mechanism of acupoint stimulation; and specific visualization of acupoints.

Robust Autoregression with Exogenous Input Model for System Identification and Predicting

Autoregression with exogenous input (ARX) is a widely used model to estimate the dynamic relationships between neurophysiological signals and other physiological parameters. Nevertheless, biological signals, such as electroencephalogram (EEG), arterial blood pressure (ABP), and intracranial pressure (ICP), are inevitably contaminated by unexpected artifacts, which may distort the parameter estimation due to the use of the L2 norm structure. In this paper, we defined the ARX in the Lp (p ≤ 1) norm space with the aim of resisting outlier influence and designed a feasible iteration procedure to estimate model parameters. A quantitative evaluation with various outlier conditions demonstrated that the proposed method could estimate ARX parameters more robustly than conventional methods. Testing with the resting-state EEG with ocular artifacts demonstrated that the proposed method could predict missing data with less influence from the artifacts. In addition, the results on ICP and ABP data further verified its efficiency for model fitting and system identification. The proposed Lp-ARX may help capture system parameters reliably with various input and output signals that are contaminated with artifacts.

Brain Functional Networks Based on Resting-State EEG Data for Major Depressive Disorder Analysis and Classification

If the brain is regarded as a system, it will be one of the most complex systems in the universe. Traditional analysis and classification methods of major depressive disorder (MDD) based on electroencephalography (EEG) feature-levels often regard electrode as isolated node and ignore the correlation between them, so it's difficult to find alters of abnormal topological architecture in brain. To solve this problem, we propose a brain functional network framework for MDD of analysis and classification based on resting state EEG. The phase lag index (PLI) was calculated based on the 64-channel resting state EEG to construct the function connection matrix to reduce and avoid the volume conductor effect. Then binarization of brain function network based on small world index was realized. Statistical analyses were performed on different EEG frequency band and different brain regions. The results showed that significant alterations of brain synchronization occurred in frontal, temporal, parietal-occipital regions of left brain and temporal region of right brain. And average shortest path length and clustering coefficient in left central region of theta band and node betweenness centrality in right parietal-occipital region were significantly correlated with PHQ-9 score of MDD, which indicates these three network metrics may be served as potential biomarkers to effectively distinguish MDD from controls and the highest classification accuracy can reach 93.31%. Our findings also point out that the brain function network of MDD patients shows a random trend, and small world characteristics appears to weaken.

Functional Organization of Brain Network in Peripheral Neural Anastomosis Rats after Electroacupuncture: An ICA and Connectome Analysis

Acupuncture is a mild therapy in rehabilitation practice of peripheral nerve injury. Previous studies confirmed the deep participation of brain plasticity in the process of functional restoration. The therapeutic effect of acupuncture is also believed to be closely associated with brain plasticity, especially in the hypothalamus and limbic system. But the fuzzy neural mechanism somehow limits the application or improvement of this therapy. There is little information about the effect of acupuncture on topological properties of brain networks. Instead of functional segregation approach, we utilized graph theory method to analyze the large-scale and distributed properties of information processing. We first established rat model of sciatic nerve injury and performed rehabilitation therapy of electroacupuncture for 120 days. Meanwhile, we used independent component analysis to extract seven sub-networks from the whole brain. Then measurements of graph theory were calculated in each sub-network as well as the whole brain network. We found no significant difference of any measurement in whole brain network among intervention group, model group and normal group. But the assortativity, hierarchy, small-world properties of sub-network displayed significant differences among three groups. It induces changes of neural plasticity in several sub-networks instead of whole brain network. We attributed the changes to the enhancement of the short-term compensatory adaptation and the reduction of the long-term overacting regional information transmission. The present study may shed light on the vague distinction of large-scale property of brain networks after electroacupuncture, which leads to a better understanding of this ancient traditional Chinese therapy.

Acupuncture Modulates Disrupted Whole-Brain Network after Ischemic Stroke: Evidence Based on Graph Theory Analysis

Background

Stroke can lead to disruption of the whole-brain network in patients. Acupuncture can modulate the functional network on a large-scale level in healthy individuals. However, whether and how acupuncture can make a potential impact on the disrupted whole-brain network after ischemic stroke remains elusive.

Methods

26 stroke patients with a right hemispheric subcortical infarct were recruited. We gathered the functional magnetic resonance imaging (fMRI) from patients with stroke and healthy controls in the resting state and after acupuncture intervention, to investigate the instant alterations of the large-scale functional networks. The graph theory analysis was applied using the GRETNA and SPM12 software to construct the whole-brain network and yield the small-world parameters and network efficiency.

Results

Compared with the healthy subjects, the stroke patients had a decreased normalized small-worldness (σ), global efficiency (E _g), and the mean local efficiency (E _loc) of the whole-brain network in the resting state. There was a correlation between the duration after stroke onset and E _loc. Acupuncture improved the patients' clustering coefficient (C _p) and E _loc but did not make a significant impact on the σ and E _g. The postacupuncture variables of the whole-brain network had no association with the time of onset.

Conclusion

The poststroke whole-brain network tended to a random network with reduced network efficiency. Acupuncture was able to modulate the disrupted patterns of the whole-brain network following the subcortical ischemic stroke. Our findings shed light on the potential mechanisms of the functional reorganization on poststroke brain networks involving acupuncture intervention from a large-scale perspective.

Bidimensional Multiscale Fuzzy Entropy and Its Application to Pseudoxanthoma Elasticum

OBJECTIVE

We propose a new bidimensional entropy measure and its multiscale form and evaluate their behavior using various synthetic and real images. The bidimensional multiscale measure finds application in helping clinicians for pseudoxanthoma elasticum (PXE) detection in dermoscopic images.

METHOD

We developed bidimensional fuzzy entropy ( FuzEn_2D) and its multiscale extension ( MSF_2D) and then evaluated them on a set of synthetic images and texture datasets. Afterwards, we applied MSF_2D to dermoscopic PXE images and compared the results to those obtained by bidimensional multiscale sample entropy ( MSE_2D).

RESULTS

The results for the synthetic images illustrate that FuzEn_2D has the ability to quantify images irregularity. Moreover, FuzEn_2D, compared with bidimensional sample entropy ( SampEn_2D), leads to more stable results. The tests with the multiscale version show that MSF_2D is a proper image complexity measure. When applied to the dermoscopic PXE images, the paired t-test illustrates a significant statistical difference between MSF_2D of neck images with papules and normal skin images at a couple of scale factors.

CONCLUSION

The results for the synthetic data illustrate that FuzEn_2D is an image irregularity measure that overcomes SampEn_2D in terms of reliability, especially for small-sized images, and stability of results. The results for the PXE dermoscopic images demonstrate the ability of MSF_2D to recognize dermoscopic images of normal zones from PXE papules zones with a large effect size.

SIGNIFICANCE

This work introduces new image irregularity and complexity measures and shows the potential for MSF_2D to serve as a possible tool helping medical doctors in PXE diagnosis.

Functional Integration and Segregation in Multiplex Brain Networks for Alzheimer's Disease

Growing evidence links impairment of brain functions in Alzheimer's disease (AD) with disruptions of brain functional connectivity. However, whether the AD brain shows similar changes from a dynamic or cross-frequency view remains poorly explored. This paper provides an effective framework to investigate the properties of multiplex brain networks in AD considering inter-frequency and temporal dynamics. Using resting-state EEG signals, two types of multiplex networks were reconstructed separately considering the network interactions between different frequency bands or time points. We further applied multiplex network features to characterize functional integration and segregation of the cross-frequency or time-varying networks. Finally, machine learning methods were employed to evaluate the performance of multiplex-network-based indexes for detection of AD. Results revealed that the brain networks of AD patients are disrupted with reduced segregation particularly in the left occipital area for both cross-frequency and time-varying networks. However, the alteration of integration differs among brain regions and may show an increasing trend in the frontal area of AD brain. By combining the features of integration and segregation in time-varying networks, the best classification performance was achieved with an accuracy of 92.5%. These findings suggest that our multiplex framework can be applied to explore functional integration and segregation of brain networks and characterize the abnormalities of brain function. This may shed new light on the brain network analysis and extend our understanding of brain function in patients with neurological diseases.

Modulation Effect of Acupuncture on Functional Brain Networks and Classification of Its Manipulation With EEG Signals

Acupuncture manipulation is the key of Chinese medicine acupuncture therapy. In clinical practice, different acupuncture manipulations are required to achieve different therapeutic effects, which means it is crucial to distinguish different acupuncture manipulations. In this paper, we proposed a classification framework for different acupuncture manipulations, which employed the graph theory and machine learning method. Multichannel EEG signals evoked by acupuncture at "Zusanli" acupoint were recorded from healthy humans by two acupuncture manipulations: twirling-rotating (TR) and lifting-thrusting (LT). Phase locking value was used to estimate the phase synchronization of pair-wise EEG channels. It was found that acupunctured by TR manipulation exhibit significantly higher synchronization degree than acupunctured by LT manipulation. With the construction of functional brain network, the topological features of graph theory were extracted. Taken the network features as inputs, machine learning classifiers were established to classify acupuncture manipulations. The highest accuracy can achieve 92.14% with support vector machine. By further optimizing the network features utilized in machine learning classifiers, it was found that the combination of node betweenness and small world network index is the most effective factor for acupuncture manipulations classification. These findings suggested that our approach provides new ideas for automatically identify acupuncture manipulations from the perspective of functional brain networks and machine learning methods.

Neural Activities Classification of Human Inhibitory Control Using Hierarchical Model

Human inhibitory control refers to the suppression of behavioral response in real environments, such as when driving a car or riding a motorcycle, playing a game and operating a machine. The P300 wave is a neural marker of human inhibitory control, and it can be used to recognize the symptoms of attention deficit hyperactivity disorder (ADHD) in human. In addition, the P300 neural marker can be considered as a stop command in the brain-computer interface (BCI) technologies. Therefore, the present study of electroencephalography (EEG) recognizes the mindset of human inhibition by observing the brain dynamics, like P300 wave in the frontal lobe, supplementary motor area, and in the right temporoparietal junction of the brain, all of them have been associated with response inhibition. Our work developed a hierarchical classification model to identify the neural activities of human inhibition. To accomplish this goal phase-locking value (PLV) method was used to select coupled brain regions related to inhibition because this method has demonstrated the best performance of the classification system. The PLVs were used with pattern recognition algorithms to classify a successful-stop versus a failed-stop in left-and right-hand inhibitions. The results demonstrate that quadratic discriminant analysis (QDA) yielded an average classification accuracy of 94.44%. These findings implicate the neural activities of human inhibition can be utilized as a stop command in BCI technologies, as well as to identify the symptoms of ADHD patients in clinical research.

Graph Theory Analysis of Functional Connectivity in Major Depression Disorder With High-Density Resting State EEG Data

Existing studies have shown functional brain networks in patients with major depressive disorder (MDD) have abnormal network topology structure. But the methods to construct brain network still exist some issues to be solved. This paper is to explore reliable and robust construction methods of functional brain network using different coupling methods and binarization approaches, based on high-density 128-channel resting state EEG recordings from 16 MDD patients and 16 normal controls (NC). It was found that the combination of imaginary part of coherence and cluster-span threshold outperformed other methods. Based on this combination, right hemisphere function deficiency, symmetry breaking and randomized network structure were found in MDD, which confirmed that MDD had aberrant cognitive processing. Furthermore, clustering coefficient in left central region in theta band and node betweenness centrality in right temporal region in alpha band were significantly negatively correlated with depressive level. And these network metrics had the ability to discriminate MDD from NC, which indicated that these network metrics might be served as the electrophysiological characteristics for probable MDD identification. Hence, this paper may provide reliable methods to construct functional brain network and offer potential biomarkers in MDD.

Cortical Network Synchrony Under Applied Electrical Field in vitro

Synchronous network activity plays a crucial role in complex brain functions. Stimulating the nervous system with applied electric field (EF) is a common tool for probing network responses. We used a gold wire-embedded silk protein film-based interface culture to investigate the effects of applied EFs on random cortical networks of in vitro cultures. Two-week-old cultures were exposed to EF of 27 mV/mm for <1 h and monitored by time-lapse calcium imaging. Network activity was represented by calcium signal time series mapped to source neurons and analyzed by using a community detection algorithm. Cortical cultures exhibited large scale, synchronized oscillations under alternating EF of changing frequencies. Field polarity and frequency change were both found to be necessary for network synchrony, as monophasic pulses of similar frequency changes or EF of a constant frequency failed to induce correlated activities of neurons. Group-specific oscillatory patterns were entrained by network-level synchronous oscillations when the alternating EF frequency was increased from 0.2 Hz to 200 kHz. Binary responses of either activity increase or decrease contributed to the opposite phase patterns of different sub-populations. Conversely, when the EF frequency decreased over the same range span, more complex behavior emerged showing group-specific amplitude and phase patterns. These findings formed the basis of a hypothesized network control mechanism for temporal coordination of distributed neuronal activity, involving coordinated stimulation by alternating polarity, and time delay by change of frequency. These novel EF effects on random neural networks have important implications for brain functional studies and neuromodulation applications.

EEG characteristic analysis of coach bus drivers based on brain connectivity as revealed via a graph theoretical network

This study describes the detection of driving fatigue using the characteristics of brain networks in a real driving environment. First, the θ, β and 36–44 Hz rhythm from the EEG signals of drivers were extracted using wavelet packet decomposition (WPD). The correlation between EEG channels was calculated using a Pearson correlation coefficient and subsequently, the brain networks were built. Furthermore, the clustering coefficient (C) and global efficiency (G) of the complex brain networks were calculated to analyze the functional differences in the brains of drivers over time. Combined with the relative power spectrum ratio (β/θ) of EEG signals and the mean value from questionnaires, the correlation of data characteristics between brain networks and subjective and objective data was analyzed. The results show that changes in the fatigue state of drivers can be effectively detected by calculating the data characteristics of brain networks in a real driving environment.

This study describes the detection of driving fatigue using the characteristics of brain networks in a real driving environment.