Development of analysis approaches to calcium-imaging data of hippocampal neurons associated with classical conditioning in mice
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Recent improvements in high performance fluorescent sensors and scientific CMOS cameras enable 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. The expansiveness of the neuronal network captured by the system requires innovation in data analysis methods. This thesis explores data analysis techniques to extract dynamics of hippocampal neural network containing a large number of individual neurons recorded using GCaMP6, while mice were learning a classical eye puff conditioning behavior. GCaMP6 fluorescence signals in each neuron is first considered one dimension, and each dataset thus contains hundreds to thousands dimensions. To understand the network structure, we first performed dimension reduction technique to examine the low-dimension evolution of the neural trajectory using Gaussian Process Factor Analysis, which smooths across dimensions, while extracting the low dimension representation. Because of the slow time course of GCaMP6 signals, the Factor Analysis was biased to the long lasting decay phase of the signal that does not represent neural activities. We found that it is critical to first estimate the spike train inference prior to application of dimension reduction, such as using the Fast Nonnegative Deconvolution method. While the low-dimension presentation described intriguing features in the neural trajectories that paralleled the learning behavior of the animal, to further quantify the network changes we directly examined the network in the high dimension space. We calculated the changes in the distance of the network trajectory over time in the high dimension space without any filtering, and compared across different phases of the behavioral states. We found that the speed of the trajectory in the high dimension space is significantly higher when animal learned the task, and the trajectory travelled much further away from baseline during the delay phase of the conditioning behavior. Together, these results demonstrate that dimension reduction analysis technique and the network trajectory within the non-reduced high dimension space can capture evolving features of neural networks recorded using calcium imaging. While this thesis concerns the hippocampal dynamics during learning, such data analysis techniques are expected to be broadly applicable to other behaviorally relevant networks.