Detailed network connectivity maps do not exist at present for any mammalian brain circuit. This project will provide the first empirical map of mouse brain network connectivity, focusing on the individual cerebral cortical microcolumn (as determined by Serial Block Face Scanning Electron Microscope; SBF-SEM), and on the entire mouse cortex by integrating light microscopy data (from our Knife-Edge Scanning Microscope; KESM) with that of the SBF-SEM. The project will chart brain networks in the mouse at multiple scales of spatial resolution, and develop interfaces between these levels of description. The combined use of large-scale 3D microscopes, SBF-SEM and KESM, together with automated image analysis and reconstruction methods, will open the internal connectivity of brains of all species to measurement and modeling of brain architecture at a neuronal and subneuronal level of detail. The project will recruit mouse brain data from three sources at three scales: nanoscale (from SBF-SEM, Stanford University); microscale (from KESM, Texas A&M University); and macroscale (from the Mouse Atlas Project, UCLA). A key objective of this project is to develop seamless interfaces across the three levels. Staining, imaging, image processing, and reconstruction methods will be developed to interoperate across these multiple levels. At nanoscale: Stanford is developing high-contrast heavy-element staining methods to be used with a new, automated SBF-SEM for tracing small and tightly packed axons and dendrites over the entire volume of a functional microcircuit. At microscale: We will use the KESM to scan the mouse brain at 300 nm resolution and create an aligned volume data set of select cortical areas. For these two levels, a common heavy element stain will be used. The KESM, in turn, can image conventionally stained tissue to compare to the pre-existing UCLA 3D mouse brain atlas (MAP). The data will be cast into the Mouse Brain Web (MBW), a web-based representation of the mouse brain network which will make possible multi-scale integration of circuitry information across these levels. The availability of such multi-scale information for the mouse will be strongly beneficial to our understanding of human brain function and development. Such information will also contribute to discovering better treatment of disorders, such as epilepsy, and potential regenerative abilities. Data from this project will be made public, along with the software for its integrative storage, retrieval, and analysis.
