Charactering Neural Spiking Activity Evoked by Acupuncture Through Coupling Generalized Linear Model

Acupuncturing the ST36 acupoint can evoke a responding activity in the spinal dorsal root ganglia and generate spikes. In order to identify the responding mechanism of different acupuncture manipulations, in this paper the spike history of neurons is taken as the starting point and the coupling gene...

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Main Authors: Qing Qin, Kaiyue Zhang, Yanqiu Che, Chunxiao Han, Yingmei Qin, Shanshan Li
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Entropy
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Online Access:https://www.mdpi.com/1099-4300/26/12/1088
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author Qing Qin
Kaiyue Zhang
Yanqiu Che
Chunxiao Han
Yingmei Qin
Shanshan Li
author_facet Qing Qin
Kaiyue Zhang
Yanqiu Che
Chunxiao Han
Yingmei Qin
Shanshan Li
author_sort Qing Qin
collection DOAJ
description Acupuncturing the ST36 acupoint can evoke a responding activity in the spinal dorsal root ganglia and generate spikes. In order to identify the responding mechanism of different acupuncture manipulations, in this paper the spike history of neurons is taken as the starting point and the coupling generalized linear model is adopted to encode the neuronal spiking activity evoked by different acupuncture manipulations. Then, maximum likelihood estimation is used to fit the model parameters and estimate the coupling parameters of stimulus, the self-coupling parameters of the neuron’s own spike history and the cross-coupling parameters of other neurons’ spike history. We use simulation data to test the estimation algorithm’s effectiveness and analyze the main factors that evoke neuronal responding activity. Finally, we use the coupling generalized linear model to encode neuronal spiking activity evoked by two acupuncture manipulations, and estimate the coupling parameters of stimulus, the self-coupling parameters and the cross-coupling parameters. The results show that in acupuncture experiments, acupuncture stimulus is the inducing factor of neuronal spiking activity, and the cross-coupling of other neurons’ spike history is the main factor of neuronal spiking activity. Additionally, the higher the amplitude of the neuronal spiking waveform, the greater the cross-coupling parameter. This lays a theoretical foundation for the scientific application of acupuncture therapy.
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spelling doaj-art-0ef8fe00ee5344d59acb7c2c54525bc62025-08-20T02:00:27ZengMDPI AGEntropy1099-43002024-12-012612108810.3390/e26121088Charactering Neural Spiking Activity Evoked by Acupuncture Through Coupling Generalized Linear ModelQing Qin0Kaiyue Zhang1Yanqiu Che2Chunxiao Han3Yingmei Qin4Shanshan Li5Tianjin Key Laboratory of Information Sensing & Intelligent Control, Tianjin University of Technology and Education, Tianjin 300222, ChinaTianjin Key Laboratory of Information Sensing & Intelligent Control, Tianjin University of Technology and Education, Tianjin 300222, ChinaTianjin Key Laboratory of Information Sensing & Intelligent Control, Tianjin University of Technology and Education, Tianjin 300222, ChinaTianjin Key Laboratory of Information Sensing & Intelligent Control, Tianjin University of Technology and Education, Tianjin 300222, ChinaTianjin Key Laboratory of Information Sensing & Intelligent Control, Tianjin University of Technology and Education, Tianjin 300222, ChinaTianjin Key Laboratory of Information Sensing & Intelligent Control, Tianjin University of Technology and Education, Tianjin 300222, ChinaAcupuncturing the ST36 acupoint can evoke a responding activity in the spinal dorsal root ganglia and generate spikes. In order to identify the responding mechanism of different acupuncture manipulations, in this paper the spike history of neurons is taken as the starting point and the coupling generalized linear model is adopted to encode the neuronal spiking activity evoked by different acupuncture manipulations. Then, maximum likelihood estimation is used to fit the model parameters and estimate the coupling parameters of stimulus, the self-coupling parameters of the neuron’s own spike history and the cross-coupling parameters of other neurons’ spike history. We use simulation data to test the estimation algorithm’s effectiveness and analyze the main factors that evoke neuronal responding activity. Finally, we use the coupling generalized linear model to encode neuronal spiking activity evoked by two acupuncture manipulations, and estimate the coupling parameters of stimulus, the self-coupling parameters and the cross-coupling parameters. The results show that in acupuncture experiments, acupuncture stimulus is the inducing factor of neuronal spiking activity, and the cross-coupling of other neurons’ spike history is the main factor of neuronal spiking activity. Additionally, the higher the amplitude of the neuronal spiking waveform, the greater the cross-coupling parameter. This lays a theoretical foundation for the scientific application of acupuncture therapy.https://www.mdpi.com/1099-4300/26/12/1088acupuncturespike historycouplinggeneralized linear modelmaximum likelihood estimation
spellingShingle Qing Qin
Kaiyue Zhang
Yanqiu Che
Chunxiao Han
Yingmei Qin
Shanshan Li
Charactering Neural Spiking Activity Evoked by Acupuncture Through Coupling Generalized Linear Model
Entropy
acupuncture
spike history
coupling
generalized linear model
maximum likelihood estimation
title Charactering Neural Spiking Activity Evoked by Acupuncture Through Coupling Generalized Linear Model
title_full Charactering Neural Spiking Activity Evoked by Acupuncture Through Coupling Generalized Linear Model
title_fullStr Charactering Neural Spiking Activity Evoked by Acupuncture Through Coupling Generalized Linear Model
title_full_unstemmed Charactering Neural Spiking Activity Evoked by Acupuncture Through Coupling Generalized Linear Model
title_short Charactering Neural Spiking Activity Evoked by Acupuncture Through Coupling Generalized Linear Model
title_sort charactering neural spiking activity evoked by acupuncture through coupling generalized linear model
topic acupuncture
spike history
coupling
generalized linear model
maximum likelihood estimation
url https://www.mdpi.com/1099-4300/26/12/1088
work_keys_str_mv AT qingqin characteringneuralspikingactivityevokedbyacupuncturethroughcouplinggeneralizedlinearmodel
AT kaiyuezhang characteringneuralspikingactivityevokedbyacupuncturethroughcouplinggeneralizedlinearmodel
AT yanqiuche characteringneuralspikingactivityevokedbyacupuncturethroughcouplinggeneralizedlinearmodel
AT chunxiaohan characteringneuralspikingactivityevokedbyacupuncturethroughcouplinggeneralizedlinearmodel
AT yingmeiqin characteringneuralspikingactivityevokedbyacupuncturethroughcouplinggeneralizedlinearmodel
AT shanshanli characteringneuralspikingactivityevokedbyacupuncturethroughcouplinggeneralizedlinearmodel