Abstract
Acupuncturing the ST36 acupoint can evoke the response of the sensory nervous system, which is translated into output electrical signals in the spinal dorsal root. Neural response activities, especially synchronous spike events, evoked by different acupuncture manipulations have remarkable differences. In order to identify these network collaborative activities, we analyze the underlying spike correlation in the synchronous spike event. In this paper, we adopt a log-linear model to describe network response activities evoked by different acupuncture manipulations. Then the state-space model and Bayesian theory are used to estimate network spike correlations. Two sets of simulation data are used to test the effectiveness of the estimation algorithm and the model goodness-of-fit. In addition, simulation data are also used to analyze the relationship between spike correlations and synchronous spike events. Finally, we use this method to identify network spike correlations evoked by four different acupuncture manipulations. Results show that reinforcing manipulations (twirling reinforcing and lifting-thrusting reinforcing) can evoke the third-order spike correlation but reducing manipulations (twirling reducing and lifting-thrusting reducing) does not. This is the main reason why synchronous spikes evoked by reinforcing manipulations are more abundant than reducing manipulations.
Highlights
Different acupuncturing manipulations can evoke different rapid and immediate concentrated effects in the corresponding target organ (Ezzo et al, 2000)
This paper introduces the concept of spike correlation and builds a log-linear model to describe ensemble spike activities evoked by four acupuncture manipulations
According to the idea of the state-space model, ensemble spike trains are defined as observation variables and spike correlations are defined as unknown state variables, which are estimated by the Bayesian theory
Summary
Different acupuncturing manipulations can evoke different rapid and immediate concentrated effects in the corresponding target organ (Ezzo et al, 2000). Some studies have shown that high-order dependencies cannot be neglected in ensemble spike activities and a log-linear model containing only up to pair-wise interactions cannot account for stimulus encoding (Montani et al, 2009; Roudi et al, 2009; Ohiorhenuan et al, 2010; Santos et al, 2010; Yu et al, 2011). This method is used to ensemble spike trains evoked by different acupuncture manipulations. Based on the optimal model, ensemble spike correlations evoked by different acupuncture manipulations are estimated
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