The objective of our Mouse Connectome Project at USC (MCP) is to chart the long-range connectivity of ~800 delineated structures of the mouse brain in an effort to reveal its network organization. In Phase I (2009-2010), we established an efficient data production, collection, and image processing workflow dedicated to compiling connectomics data of the highest quality. We adopted an injection strategy that produced data most conducive for network analysis by simultaneously revealing, for any brain region (i.e. A), its (1) inputs (A?B); (2) outputs (A?B); (3) reciprocal or recurrent connections (A?B); and (4) intermediate stations, which bridge brain structures that are not directly connected (A?C?B). In Phase II (2011-2016), we traced ~2000 pathways from injections placed across the entire cerebral hemisphere and thalamus. As proposed, in Phase III (2017-2022) we will collect and analyze connections data for the hypothalamus, midbrain, pons, medulla, and cerebellum (~1400 additional pathways) (Specific Aim 1). Combined, these pathways will be used to construct the most comprehensive mesoscale connectome that charts all point-to-point connections of the entire mouse brain. Compiling these connectivity data sets however is only the first step in constructing the connectome. The ensuing challenge is to analyze the enormous data to extract information regarding network organization. Based on graph theoretical analysis of 600 manually annotated pathways, we assembled the global networks of the mammalian neocortex (Zingg et al., Cell, 2014). Although the gold standard, manual analysis was laborious, time consuming, and not efficient for our ultimate goal of generating brain-wide connectivity maps and networks. Therefore, in Phase II, we designed and created an innovative informatics workflow that efficiently and reliably registers, reconstructs, and annotates large-scale connections data. This workflow will be applied in Phase III to accelerate image processing, creation of connectivity maps, data annotation, and analysis. In Phase III, we will also initiate the first stage of constructing cell type specific neural networks (Specific Aim 2). Our connectivity-based cell type classification strategy will be used to identify all cell types of the medial prefrontal cortex and to gain a census of each cell type using 2D and 3D images. Novel rabies viral tracing will be employed to systematically reveal the neuronal inputs to these distinct cell populations. All of our data will be available as open resources (www.MouseConnctome.org) (Specific Aim 3): (1) the iConnectome viewer is the only visualization tool that allows users to view images of multiple fluorescently-labeled pathways within their own bright-field Nissl background and corresponding level of a standard mouse brain atlas; (2) the iConnectome Map Viewer allows access to connectivity maps, which feature hundreds of reconstructed pathways compiled atop a neuroanatomic frame; (3) the iConnectome Cell Type Viewer, which will feature images of all cell type circuits; (4) the Cell Type Map Viewer will host cell type specific connectivity maps; (5) the online Web Connectivity Matrix will present connections in a matrix; and (6) our 3D viewer will provide an overview of all connections in 3D.
The goal of the Mouse Connectome Project at USC (MCP) is to assemble the first comprehensive and reliable connectome of the mouse brain and to reveal the organization of its global networks. In Phases I and II, we traced pathways of nearly the entire cerebral hemisphere and thalamus, and created innovative informatics algorithms to process large-scale connectivity data. In Phase III, we propose to continue the collection, presentation, and analysis of connections data for the reminder of the brain in an effort to complete the connectome, to systematically classify all cell types of the medial prefrontal cortex and assemble their neural networks, and continuously process the data to make them openly available through our innovative iConnectome visualization tools (www.MouseConnectome.org).
|Zingg, Brian; Dong, Hong-Wei; Tao, Huizhong Whit et al. (2018) Input-output organization of the mouse claustrum. J Comp Neurol 526:2428-2443|
|Bienkowski, Michael S; Bowman, Ian; Song, Monica Y et al. (2018) Integration of gene expression and brain-wide connectivity reveals the multiscale organization of mouse hippocampal networks. Nat Neurosci 21:1628-1643|
|Hintiryan, Houri; Foster, Nicholas N; Bowman, Ian et al. (2016) The mouse cortico-striatal projectome. Nat Neurosci 19:1100-14|
|Wang, Nan; Gray, Michelle; Lu, Xiao-Hong et al. (2014) Neuronal targets for reducing mutant huntingtin expression to ameliorate disease in a mouse model of Huntington's disease. Nat Med 20:536-41|
|Bota, Mihail; Talpalaru, Stefan; Hintiryan, Houri et al. (2014) BAMS2 workspace: a comprehensive and versatile neuroinformatic platform for collating and processing neuroanatomical connections. J Comp Neurol 522:3160-76|
|Zingg, Brian; Hintiryan, Houri; Gou, Lin et al. (2014) Neural networks of the mouse neocortex. Cell 156:1096-111|
|Bota, Mihail; Dong, Hong-Wei; Swanson, Larry W (2012) Combining collation and annotation efforts toward completion of the rat and mouse connectomes in BAMS. Front Neuroinform 6:2|
|Hintiryan, Houri; Gou, Lin; Zingg, Brian et al. (2012) Comprehensive connectivity of the mouse main olfactory bulb: analysis and online digital atlas. Front Neuroanat 6:30|
|Biag, Jonathan; Huang, Yi; Gou, Lin et al. (2012) Cyto- and chemoarchitecture of the hypothalamic paraventricular nucleus in the C57BL/6J male mouse: a study of immunostaining and multiple fluorescent tract tracing. J Comp Neurol 520:6-33|