We propose to develop NeuroAutomatica, an innovative, automated neuron reconstruction software platform that provides analysis tools for automated quantitative analysis of neuronal structure. The software will provide multi-scalar information about multiple individual neurons in close proximity, including dendritic spines. Providing this innovative functionality will help research in important fields such as neurodegenerative diseases, learning, and memory. NeuroAutomatica will enable sophisticated and detailed analysis of neuronal structure that is currently unavailable. Furthermore, it will increase the pace of research technology by automating some analyses which are now painstakingly manually performed.
Recent research has pointed to the importance of the smallest element of a neuron's architecture-the dendritic spine-where it is hypothesized that critical structural changes occur to underlie changes in cellular function responsible for processing a continuous influx and storage of information. Thus, dendritic spines have become a central target in research focusing on learning and memory and, in particular, on the prevention of cognitive decline in neurodegenerative diseases. Furthermore, new advances in imaging and physiological experiments have led to investigations into neuronal circuits. Our proposed software will enable the structural analysis of these networks. NeuroAutomatica will accelerate the pace of discovery and understanding in basic neuroscience research by providing an observer independent software system capable of accurately and efficiently reconstructing neurons, including dendritic spines, in dense networks.