High-resolution analysis of the brain's connectivity, which reveals the actual wiring diagram connecting nerve cells of the brain, provides insights unattainable any other way into the way the healthy brain works and what goes awry in diseases and disorders of the nervous system. The primary challenge of this approach is that at present there are no reliable, robust and powerful computer-based techniques to analyze the extraordinarily large and vastly complicated networks of brain cells to detect connectional motifs in their highly branching and connected structure. Nor are there visualization tools that allow neuroscientists to explore the brain network patterns effectively. This work will analyze large brain networks from electron microscopy datasets in young and old mammalian brain samples. These data sets each contains hundreds of thousands of nerve cells and billions of synapses that interconnect them. The proposal aims to develop new methods and tools to analyze these vast brain networks at the synapse, motif, and network levels. If successful, the project will provide data and analysis tools for the development of new theories of how the brain works.
Recent advances in image acquisition using multi-beam serial-section electron microscopy (sSEM) and automated segmentation methods have enabled data collection for large tissue samples in a variety of animals. These data will be used to curate large-scale datasets with one million labeled synapses with synaptic cleft locations, pre- and postsynaptic polarity predictions, and excitatory and inhibitory type predictions. This has not been accomplished previously given the enormous amount of data. The aim is to discover synaptic motifs by subdividing complex neural networks into quantifiable and meaningful subgraphs. Automatic generation of candidates for motifs will be created by developing an efficient neurite-centric wiring-diagram reconstruction method and subgraph detection algorithm to find common patterns. These data will be used to quantify and compare reconstructed neural networks from different specimens at different spatial and temporal scales and build a visualization platform to assist neuroscientists to analyze these networks as they seek to ask and answer fundamental questions related to neural circuits in the brain.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
