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      Quantifying Mesoscale Neuroanatomy Using X-Ray Microtomography

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          Abstract

          Methods for resolving the three-dimensional (3D) microstructure of the brain typically start by thinly slicing and staining the brain, followed by imaging numerous individual sections with visible light photons or electrons. In contrast, X-rays can be used to image thick samples, providing a rapid approach for producing large 3D brain maps without sectioning. Here we demonstrate the use of synchrotron X-ray microtomography (µCT) for producing mesoscale (∼1 µm 3 resolution) brain maps from millimeter-scale volumes of mouse brain. We introduce a pipeline for µCT-based brain mapping that develops and integrates methods for sample preparation, imaging, and automated segmentation of cells, blood vessels, and myelinated axons, in addition to statistical analyses of these brain structures. Our results demonstrate that X-ray tomography achieves rapid quantification of large brain volumes, complementing other brain mapping and connectomics efforts.

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          Most cited references 48

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          A mesoscale connectome of the mouse brain.

          Comprehensive knowledge of the brain's wiring diagram is fundamental for understanding how the nervous system processes information at both local and global scales. However, with the singular exception of the C. elegans microscale connectome, there are no complete connectivity data sets in other species. Here we report a brain-wide, cellular-level, mesoscale connectome for the mouse. The Allen Mouse Brain Connectivity Atlas uses enhanced green fluorescent protein (EGFP)-expressing adeno-associated viral vectors to trace axonal projections from defined regions and cell types, and high-throughput serial two-photon tomography to image the EGFP-labelled axons throughout the brain. This systematic and standardized approach allows spatial registration of individual experiments into a common three dimensional (3D) reference space, resulting in a whole-brain connectivity matrix. A computational model yields insights into connectional strength distribution, symmetry and other network properties. Virtual tractography illustrates 3D topography among interconnected regions. Cortico-thalamic pathway analysis demonstrates segregation and integration of parallel pathways. The Allen Mouse Brain Connectivity Atlas is a freely available, foundational resource for structural and functional investigations into the neural circuits that support behavioural and cognitive processes in health and disease.
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            Adaptive greedy approximations

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              Correlations of neuronal and microvascular densities in murine cortex revealed by direct counting and colocalization of nuclei and vessels.

              It is well known that the density of neurons varies within the adult brain. In neocortex, this includes variations in neuronal density between different lamina as well as between different regions. Yet the concomitant variation of the microvessels is largely uncharted. Here, we present automated histological, imaging, and analysis tools to simultaneously map the locations of all neuronal and non-neuronal nuclei and the centerlines and diameters of all blood vessels within thick slabs of neocortex from mice. Based on total inventory measurements of different cortical regions ( approximately 10(7) cells vectorized across brains), these methods revealed: (1) In three dimensions, the mean distance of the center of neuronal somata to the closest microvessel was 15 mum. (2) Volume samples within lamina of a given region show that the density of microvessels does not match the strong laminar variation in neuronal density. This holds for both agranular and granular cortex. (3) Volume samples in successive radii from the midline to the ventral-lateral edge, where each volume summed the number of cells and microvessels from the pia to the white matter, show a significant correlation between neuronal and microvessel densities. These data show that while neuronal and vascular densities do not track each other on the 100 mum scale of cortical lamina, they do track each other on the 1-10 mm scale of the cortical mantle. The absence of a disproportionate density of blood vessels in granular lamina is argued to be consistent with the initial locus of functional brain imaging signals.
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                Author and article information

                Journal
                eNeuro
                eNeuro
                eneuro
                eneuro
                eNeuro
                eNeuro
                Society for Neuroscience
                2373-2822
                23 September 2017
                16 October 2017
                Sep-Oct 2017
                : 4
                : 5
                Affiliations
                [1 ]Department of Biomedical Engineering, Georgia Institute of Technology and Emory University , Atlanta, GA, 30332
                [2 ]The Johns Hopkins University Applied Physics Laboratory , Laurel, MD, 20723
                [3 ]Dept. of Computer Science, The Johns Hopkins University , Baltimore, MD, 21218
                [4 ]Dept. of Neurobiology, University of Chicago , Chicago, IL, 60637
                [5 ]Dept. of Physical Medicine and Rehabilitation, Northwestern University , Chicago, IL, 60611
                [6 ]Sensory Motor Performance Program, Rehabilitation Institute of Chicago , Chicago, IL, 60611
                [7 ]Advanced Photon Source, Argonne National Laboratory , Lemont, IL, 60439
                [8 ]Department of Biomedical Engineering, The Johns Hopkins University , Baltimore, MD, 21205
                [9 ]Institute of Computational Medicine, The Johns Hopkins University , Baltimore, MD, 21218
                [10 ]Department of Physics and Astronomy, Northwestern University , Chicago, IL, 60208
                [11 ]Department of Biomedical Engineering, University of Pennsylvania , Philadelphia, PA, 19104
                [12 ]Center for Nanoscale Materials, Argonne National Laboratory , Lemont, IL, 60439
                Author notes

                Authors report no conflict of interest.

                Author contributions: E.L.D., C.J., K.P.K., and N.K. designed research; E.L.D., W.G.R., D.G., X.X., and N.K. performed research; E.L.D., W.G.R., J.A.P., H.F., V.d.A., K.F., J.T.V., and N.K. contributed unpublished reagents/analytic tools; E.L.D. and D.G. analyzed data; E.L.D., J.A.P., K.P.K., and N.K. wrote the paper.

                This research used resources from the US Department of Energy (DOE) Office of Science User Facilities operated for the DOE Office of Science by Argonne National Laboratory under contract no. DE-AC02-06CH11357. Support was provided by NIH U01MH109100 (E.L.D., H.L.F., D.G., X.X., C.J., N.K., and K.P.K.), the IARPA MICRONS project under IARPA Contract D16PC0002 (N.K.), an educational Fellowship from the Johns Hopkins University Applied Physics Laboratory (W.G.R.), the Defense Advanced Research Projects Agency (DARPA) SIMPLEX program through SPAWAR contract N66001-15-C-4041, and DARPA GRAPHS N66001-14-1-4028.

                [*]

                K.P.K. and N.K. contributed equally to this paper.

                Correspondence should be addressed to either of the following: Eva L. Dyer, Georgia Institute of Technology 313 Ferst Drive NW Atlanta, GA 30332. E-mail: evadyer@ 123456gatech.edu ; or Narayanan Kasthuri. E-mail: bobbykasthuri@ 123456anl.gov .
                Article
                eN-MNT-0195-17
                10.1523/ENEURO.0195-17.2017
                5659258
                Copyright © 2017 Dyer et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

                Page count
                Figures: 8, Tables: 1, Equations: 16, References: 64, Pages: 0, Words: 12651
                Product
                Funding
                Funded by: http://doi.org/10.13039/100000002 HHS | National Institutes of Health (NIH)
                Award ID: NIH U01MH109100
                Funded by: http://doi.org/10.13039/100000185 DOD | Defense Advanced Research Projects Agency (DARPA)
                Award ID: N66001-15-C-4041
                Funded by: http://doi.org/10.13039/100000185 DOD | Defense Advanced Research Projects Agency (DARPA)
                Award ID: N66001-14-1-4028
                Categories
                7
                7.2
                Methods/New Tools
                Novel Tools and Methods
                Custom metadata
                September/October 2017

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