CAREER: Modeling the Structure and Dynamics of Neuronal Circuits in the Drosophila larvae using Image Analytics
ABI Innovation: Modeling the Drosophila Brain with Single Neuron Resolution using Computer Vision Approaches
Today, there are efforts towards the reconstruction of neuronal circuitries in the adult fruit fly brain. They are mainly based on semi-automatic mapping for visualization purposes. Essentially, they focus on 3D image acquisition techniques, image processing for neuron localization, manual neuron trace corrections, and manual identification or no identification of the neurons within the brain (based on topology). They are indicative of what exists today, what we call "top-down" approaches: they study directly the neuronal wiring, ignoring the individual neuron morphology, since neuron identification is done manually.
Our principle research approach is what we describe as "bottom-up": we aim at building necessary knowledge about the individual essential components of the brain wiring, the neuronal types and sub-types, and then investigate the wiring, i.e., the synaptic map. The figure illustrates the similarities and differences of our approach with today's state-of-the- art.
This approach is essentially the first computational effort to achieve automated, high-throughput processing of the brain. This work also serves as a neuron-morphology "dictionary" for the identification of neuron types/sub-types in the existing top-down approaches.
We focus on the automated quantitative analysis of the individual neuron structure, automated identification of the neuron compartments (soma, axon, dendrites), and automated numerical modeling of the morphology of motor neuron sub-types. The principle of our work is that neuron (sub-)type morphology prototypes can be inferred to the solution of constructing neuronal circuitries. Therefore, this project aims at providing knowledge and tools for fully automated reconstruction of neuronal circuitries with simultaneous identification of individual neurons in complex brain imagery.