BME MS Thesis Defense - Zhaojie Yao

Title: "Development of Analysis Approaches to Calcium-imaging Data of Hippocampal Neurons Associated with Classical Conditioning in Mice" Committee: Xue Han (Advisor, Chair) - BME Kamal Sen - BME Nancy Kopell - Mathematics & Statistics Abstract: Recent improvements in high performance fluorescent sensors and scientific CMOS cameras enables optical imaging of neural networks at a much larger scale. Our lab has demonstrated the ability of wide-field calcium-imaging (using GCaMP6f) to capture the concurrent dynamic activity from hundreds to thousands of neurons over millimeters of brain tissue in behaving mice. In the meantime, we developed software to facilitate rapid downstream data processing. However, the expansiveness of the neuronal network captured by the system requires innovation in analysis methods. This thesis explores the possibility of using dimension reduction methods, e.g. Gaussian Process Factor Analysis in displaying the low-dimension evolution of the neural trajectory of mouse hippocampal neuronal network associate in classical conditioning. Due to the slow exponential decay of GCaMP6f signal, Factor Analysis cannot be directly applied to the data set. Thus, a Fast Nonnegative Deconvolution method is used to estimate the spike train inference prior to application of dimension reduction. While the low-dimension presentation indicates intriguing features of the neural trajectories, traditional statistical analysis is then performed in the high-dimension space to manifest the relationship between learning and hippocampal network trajectory.