Registration to a neuroanatomical reference atlas - identifying glomeruli in optical recordings of the honeybee brain

Abstract

An odorant stimulus given to a bee elicits a characteristic combinatorial pattern of activity in neuronal units called glomeruli. These patterns can be measured by optical imaging, however detecting and identifying the glomeruli is a laborious task and prone to errors. Here, we present an image analysis pipeline for the automatic detection and identification of glomeruli. It involves Independent Component Analysis (ICA) to detect glomeruli in CCD camera data, a filtering step to exclude non- glomerulus objects and a graph-matching approach to find the best projection of the observed brain region onto a reference atlas. We evaluate our method against a manual glomerulus identification performed by a human expert and show that we achieve reliable results. Employing our method, we are now able to screen multiple recordings with the same accuracy, yielding a homogeneous collection of glomerulus identity mappings. These will subsequently be used to extract activity patterns that can be compared between individuals.