Chinese Journal of Lasers, Volume. 45, Issue 6, 0607001(2018)
Optical Phase Characterization Method for Dynamic Characteristics of Neuronal Discharge
As basic units of structure and function of the biological nervous system, neurons encode, transmit, and integrate information through discharge activities neuron, plays an important role in life activities. Based on the dynamic nature of neuronal discharge activity and phase imaging technique, we discuss nondestructive and label free imaging method for living cells and their inner substructure. According to phase information of neuron model, we study the characteristics of static morphology and dynamic discharge activities. The optical imaging simulation technique is used to establish the neuron phase model and obtain the information of its phase distribution. Starting from the physical meaning of the phase function, the substructure of the model is analyzed. Considering dynamic effect of the change of intracellular ion concentration in discharge activities on refractive index and phase information, the method of visualizing the dynamic process with phase information is discussed preliminarily. For the complex heterogeneous phase volume model, the local static morphological information on the substructure and phase characterization result of dynamic discharge activity on the sample are obtained without phase decoupling by introducing the heterogeneous contrastive compensation idea. The availability of the method is verified by simulation analysis. The results show that the optical phase characterization method for neuronal discharge characteristics and morphology is nondestructive, label-free, and quantifiable.
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Zhiya Chen, Ying Ji, Wenbo Tang, Mingming Zhang, Yuanyuan Xu, Yawei Wang. Optical Phase Characterization Method for Dynamic Characteristics of Neuronal Discharge[J]. Chinese Journal of Lasers, 2018, 45(6): 0607001
Category: biomedical photonics and laser medicine
Received: Sep. 19, 2017
Accepted: --
Published Online: Jul. 5, 2018
The Author Email: Ji Ying (jy@ujs.edu.cn)