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      Towards a comprehensive atlas of cortical connections in a primate brain: Mapping tracer injection studies of the common marmoset into a reference digital template

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          Abstract

          The marmoset is an emerging animal model for large‐scale attempts to understand primate brain connectivity, but achieving this aim requires the development and validation of procedures for normalization and integration of results from many neuroanatomical experiments. Here we describe a computational pipeline for coregistration of retrograde tracing data on connections of cortical areas into a 3D marmoset brain template, generated from Nissl‐stained sections. The procedure results in a series of spatial transformations that are applied to the coordinates of labeled neurons in the different cases, bringing them into common stereotaxic space. We applied this procedure to 17 injections, placed in the frontal lobe of nine marmosets as part of earlier studies. Visualizations of cortical patterns of connections revealed by these injections are supplied as Supplementary Materials. Comparison between the results of the automated and human‐based processing of these cases reveals that the centers of injection sites can be reconstructed, on average, to within 0.6 mm of coordinates estimated by an experienced neuroanatomist. Moreover, cell counts obtained in different areas by the automated approach are highly correlated ( r = 0.83) with those obtained by an expert, who examined in detail histological sections for each individual. The present procedure enables comparison and visualization of large datasets, which in turn opens the way for integration and analysis of results from many animals. Its versatility, including applicability to archival materials, may reduce the number of additional experiments required to produce the first detailed cortical connectome of a primate brain. J. Comp. Neurol. 524:2161–2181, 2016. © 2016 The Authors The Journal of Comparative Neurology Published by Wiley Periodicals, Inc.

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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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            Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration.

            All fields of neuroscience that employ brain imaging need to communicate their results with reference to anatomical regions. In particular, comparative morphometry and group analysis of functional and physiological data require coregistration of brains to establish correspondences across brain structures. It is well established that linear registration of one brain to another is inadequate for aligning brain structures, so numerous algorithms have emerged to nonlinearly register brains to one another. This study is the largest evaluation of nonlinear deformation algorithms applied to brain image registration ever conducted. Fourteen algorithms from laboratories around the world are evaluated using 8 different error measures. More than 45,000 registrations between 80 manually labeled brains were performed by algorithms including: AIR, ANIMAL, ART, Diffeomorphic Demons, FNIRT, IRTK, JRD-fluid, ROMEO, SICLE, SyN, and four different SPM5 algorithms ("SPM2-type" and regular Normalization, Unified Segmentation, and the DARTEL Toolbox). All of these registrations were preceded by linear registration between the same image pairs using FLIRT. One of the most significant findings of this study is that the relative performances of the registration methods under comparison appear to be little affected by the choice of subject population, labeling protocol, and type of overlap measure. This is important because it suggests that the findings are generalizable to new subject populations that are labeled or evaluated using different labeling protocols. Furthermore, we ranked the 14 methods according to three completely independent analyses (permutation tests, one-way ANOVA tests, and indifference-zone ranking) and derived three almost identical top rankings of the methods. ART, SyN, IRTK, and SPM's DARTEL Toolbox gave the best results according to overlap and distance measures, with ART and SyN delivering the most consistently high accuracy across subjects and label sets. Updates will be published on the http://www.mindboggle.info/papers/ website.
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              BigBrain: an ultrahigh-resolution 3D human brain model.

              Reference brains are indispensable tools in human brain mapping, enabling integration of multimodal data into an anatomically realistic standard space. Available reference brains, however, are restricted to the macroscopic scale and do not provide information on the functionally important microscopic dimension. We created an ultrahigh-resolution three-dimensional (3D) model of a human brain at nearly cellular resolution of 20 micrometers, based on the reconstruction of 7404 histological sections. "BigBrain" is a free, publicly available tool that provides considerable neuroanatomical insight into the human brain, thereby allowing the extraction of microscopic data for modeling and simulation. BigBrain enables testing of hypotheses on optimal path lengths between interconnected cortical regions or on spatial organization of genetic patterning, redefining the traditional neuroanatomy maps such as those of Brodmann and von Economo.
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                Author and article information

                Journal
                J Comp Neurol
                J. Comp. Neurol
                10.1002/(ISSN)1096-9861
                CNE
                The Journal of Comparative Neurology
                John Wiley and Sons Inc. (Hoboken )
                0021-9967
                1096-9861
                03 June 2016
                01 August 2016
                : 524
                : 11 ( doiID: 10.1002/cne.v524.11 )
                : 2161-2181
                Affiliations
                [ 1 ] Neuroscience Program, Biomedicine Discovery Institute Monash University Clayton VIC Australia
                [ 2 ] Department of Physiology Monash University Clayton VIC Australia
                [ 3 ] Nencki Institute of Experimental Biology Warsaw Poland
                [ 4 ] Australian Research Council Centre of Excellence for Integrative Brain Function Monash University Node Clayton VIC Australia
                [ 5 ] Monash Vision Group Monash University Clayton VIC Australia
                [ 6 ] Cold Spring Harbor Laboratory Cold Spring Harbor New York USA
                Author notes
                [*] [* ]Correspondence to: Piotr Majka, Neuroscience Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia. E‐mail: Piotr.Majka@ 123456monash.edu
                Article
                CNE24023
                10.1002/cne.24023
                4892968
                27099164
                bf171b68-c692-4603-a93b-4a0993c6a4b3
                © 2016 The Authors The Journal of Comparative Neurology Published by Wiley Periodicals, Inc.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 30 January 2016
                : 11 April 2016
                : 18 April 2016
                Page count
                Pages: 22
                Funding
                Funded by: Australian Research Council
                Award ID: DP140101968
                Award ID: CE140100007
                Funded by: European Regional Development Fund under the Operational Programme Innovative Economy
                Award ID: POIG.02.03.00‐00‐003/09
                Funded by: National Institutes of Health
                Award ID: 1R01DA036400‐01
                Award ID: 1R01MH087988‐01
                Categories
                Toolbox
                Toolbox
                Custom metadata
                2.0
                cne24023
                01 August 2016
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.6.7 mode:remove_FC converted:05.08.2019

                Neurology
                digital atlas,marmoset,cerebral cortex,neuroanatomical tracing,image registration,nissl staining,brain template

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