Parametric Modeling Analysis of Optical Imaging Data on Neuronal Activities in the Brain

Abstract

An optical imaging technique using a voltage-sensitive dye (voltage imaging) has been widely applied to the analyses of various brain functions. Because optical signals in voltage imaging are small and require several kinds of preprocessing, researchers who use voltage imaging often conduct signal averaging of multiple trials and correction of signals by cutting the noise near the baseline in order to improve the apparent signal–noise ratio. However, a noise cutting threshold level that is usually set arbitrarily largely affects the analyzed results. Therefore, we aimed to develop a new method to objectively evaluate optical imaging data on neuronal activities. We constructed a parametric model to analyze optical time series data. We have chosen the respiratory neuronal network in the brainstem as a representative system to test our method. In our parametric model we assumed an optical signal of each pixel as the input and the inspiratory motor nerve activity of the spinal cord as the output. The model consisted of a threshold function and a delay transfer function. Although it was a simple nonlinear dynamic model, it could provide precise estimation of the respiratory motor output. By classifying each pixel into five types based on our model parameter values and the estimation error ratio, we obtained detailed classification of neuronal activities. The parametric modeling approach can be effectively employed for the evaluation of voltage-imaging data and thus for the analysis of the brain function.