A Mutual Information Approach to Automate Identification of Neuronal Clusters in Drosophila Brain Images - Masse, Nicolas Y.; Cachero, Sebastian; Ostrovsky, Aaron; Jefferis, Gregory S. (Frontiers in Neuroinformatics, 2012)
Mapping neural circuits can be accomplished by labeling a small number of neural structures per brain, and then combining these structures across multiple brains. This sparse labeling method has been particularly effective in Drosophila melanogaster, where clonally related clusters of neurons derived from the same neural stem cell (neuroblast clones) are functionally related and morphologically highly stereotyped across animals. However identifying these neuroblast clones (approximately 180 per central brain hemisphere) manually remains challenging and time consuming. Here, we take advantage of the stereotyped nature of neural circuits in Drosophila to automatically identify clones, requiring manual annotation of only an initial, smaller set of images. Our procedure starts by filtering the images to accentuate neural projections and cell bodies, and then skeletonises the projections with a dimension reduction algorithm. Images are then registered onto a common template brain, allowing us to determine which projections and cell bodies are shared across different brains. We then determine whether the presence of a cell body or projection is associated with the presence of a clone, allowing us identify the neural structures that can reliably indicate whether a brain contains a specific clone. This enables us to detect the presence of clones in novel images by mapping their cell bodies and projections and matching them against these informative neural structures. The approach is not limited to a specific labeling strategy and can be used to identify partial (e.g. individual neurons) as well as complete matches. Furthermore this approach could be generalised to studies of neural circuits in other organisms.