WAVELET-BASED STATISTICAL ANALYSIS FOR OPTICAL IMAGING IN MOUSE OLFACTORY BULB
Optical imaging is a powerful technique to map brain function in animals. In this study, we consider in vivo optical imaging of the murine olfactory bulb, using an intrinsic signal and a genetically expressed activity reporter fluorescent protein (synaptopHfluorin). The aim is to detect odor-evoked activations that occur in small spherical structures of the olfactory bulb called glomeruli. We propose a new way of analyzing this kind of data that combines a linear model (LM) fitting along the temporal dimension, together with a discrete wavelet transform (DWT) along the spatial dimensions. We show that relevant regressors for the LM are available for both types of optical signals. In addition, the spatial wavelet transform allows us to exploit spatial correlation at different scales, and in particular to extract activation patterns at the expected size of glomeruli. Our framework also provides a statistical significance for every pixel in the activation maps and it has strong type I error control.