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

Yu, Haitao; Liu, Jing; Cai, Lihui; Wang, Jiang; Cao, Yibin; Chen, Hao

doi:10.1016/j.physa.2016.10.068

Cited by 41 publications

(33 citation statements)

References 59 publications

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“…Studies ( Lafon et al, 2017 ; Hutt et al, 2018 ) have recently shown that high-frequency periodic stimulation can lead to waveform-dependent changes in the oscillatory dynamics of neurons while randomly fluctuating stimulation linearizes the neuron response function. Other studies, instead, have shown that the neuron’s response change as a function of the intensity, duration, and polarity of the stimulus ( McIntyre and Grill, 2002 ; Wagenaar et al, 2004 ) and these changes are noticed at multiple spatial scales, from small neuronal clusters to large populations ( Yoo et al, 2004 ; Yu et al, 2017 , 2018 ). Neuronal sensitivity depends on the cell’s channel protein and receptor composition, synapse maturate state, and cell morphology ( O’Brien et al, 1998 ; Mathie et al, 2003 ; Holtmaat and Svoboda, 2009 ; Rebola et al, 2010 ; Bagley and Westbrook, 2012 ; Yi et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Cortical Network Synchrony Under Applied Electrical Field in vitro

2018

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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.

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Section: Introductionmentioning

confidence: 99%

Cortical Network Synchrony Under Applied Electrical Field in vitro

2018

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“…Electroencephalogram (EEG) is a common tool to capture the changes of electrical activity caused by acupuncture [35]. Specifically, in the central nervous system of the human brain, there are many neurons.…”

Section: Introductionmentioning

confidence: 99%

Predicting Prognostic Effects of Acupuncture for Depression Using Electroencephalogram

Fan

Huang

Zhao

et al. 2020

Preprint

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Background: Depression is considered to be a major public health problem with significant implications for individuals and society. Patients with depression can be with complementary therapies such as acupuncture. Predicting the prognostic effects of acupuncture has a big significance of helping physicians to take early interventions for patients with depression and avoid malignant events.Methods: In this work, a novel framework of predicting prognostic effects of acupuncture for depression based on electroencephalogram (EEG) recordings is presented. Specifically, EEG is utilized for predicting prognostic effects of acupuncture. Max-relevance and min-redundancy (mRMR), with merits of removing redundant information among selected features and remaining high relevance between selected features and response variable, is employed to select important lead-rhythm features extracted from EEG recordings. Then, according to the subjects’ HAMD scores before and after acupuncture for 8 weeks, the reduction rate of HAMD score is calculated as a measure of the prognostic effects of acupuncture. Finally, five widely used machine learning methods are utilized for building the predicting models of prognostic effects of acupuncture for depression.Results: Experimental results show that non-linear machine learning methods have better performance than linear ones on predicting prognostic effects of acupuncture using EEG recordings. Especially, the support vector machine with Gaussian kernel (SVM-RBF) can achieve the best and stable performance using the mRMR with both evaluating criteria of FCD and FCQ for feature selection. Both mRMR-FCD and mRMR-FCQ obtain the same best performance, where the accuracy and F1 score are 84.61 % and 86.67 %, respectively. What’s more, lead-rhythm features selected by mRMR-FCD and mRMR-FCQ are analyzed. Top seven selected lead-rhythm features have much higher mRMR evaluating scores, which guarantee the good predicting performance for machine learning methods to some degree.Conclusion: The presented framework in this work is effective in predicting prognostic effects of acupuncture for depression. It can be integrated into an intelligent medical system and provide the information of prognostic effects of acupuncture for physicians. Informed prognostic effects of acupuncture for depression in advance and taking interventions can greatly reduce the risk of malignant events for patients with mental disorders.

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“…[23][24][25][26] In addition, research based on brain networks in the eld of human brain cognition has also been carried out. [27][28][29] Researches have shown that the methods using the main brain region enable the study the characteristics of brain function have been gradually adopted in research. channel signals of EEG to analyze the characteristics of Alzheimer's disease.…”

Section: Introductionmentioning

confidence: 99%