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      Hierarchical organization of cortical and thalamic connectivity

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      * , 1 , 5 , 1 , 5 , 1 , 1 , 1 , 2 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 3 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 4 , 1 , 1 , 4 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1
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          Abstract

          The mammalian cortex is a laminar structure composed of many areas and cell types densely interconnected in complex ways, for which generalizable principles of organization remain mostly unknown. Here, we present a significant expansion of the Allen Mouse Brain Connectivity Atlas resource 1 , with ~1,000 new tracer experiments in cortex and its major satellite structure, the thalamus, using Cre driver lines to comprehensively and selectively label brain-wide connections by layer and projection neuron class. We derived a set of generalized anatomical rules describing corticocortical, thalamocortical and corticothalamic projections through observations of axon termination patterns. We built a model to assign connection patterns between areas as either feedforward or feedback, and generated testable predictions of hierarchical positions for individual cortical and thalamic areas and for cortical network modules. Our results reveal cell class-specific connections are organized in a shallow hierarchy within the mouse cortical thalamic network.

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          Most cited references62

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          Fast unfolding of communities in large networks

          Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008
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            Complex network measures of brain connectivity: uses and interpretations.

            Brain connectivity datasets comprise networks of brain regions connected by anatomical tracts or by functional associations. Complex network analysis-a new multidisciplinary approach to the study of complex systems-aims to characterize these brain networks with a small number of neurobiologically meaningful and easily computable measures. In this article, we discuss construction of brain networks from connectivity data and describe the most commonly used network measures of structural and functional connectivity. We describe measures that variously detect functional integration and segregation, quantify centrality of individual brain regions or pathways, characterize patterns of local anatomical circuitry, and test resilience of networks to insult. We discuss the issues surrounding comparison of structural and functional network connectivity, as well as comparison of networks across subjects. Finally, we describe a Matlab toolbox (http://www.brain-connectivity-toolbox.net) accompanying this article and containing a collection of complex network measures and large-scale neuroanatomical connectivity datasets. Copyright (c) 2009 Elsevier Inc. All rights reserved.
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              Complex brain networks: graph theoretical analysis of structural and functional systems.

              Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                8 October 2019
                30 October 2019
                November 2019
                11 September 2021
                : 575
                : 7781
                : 195-202
                Affiliations
                [1 ]Allen Institute for Brain Science
                [2 ]University of Washington, Dept. of Applied Mathematics
                [3 ]Laboratory of Systems Neuroscience, National Institute of Mental Health
                [4 ]Wenzhou Medical University, P.R. China
                [5 ]These authors contributed equally
                Author notes

                Author Contributions

                Conceptualization: H.Z., J.A.H., S.M. Supervision: H.Z., J.A.H., S.M., A.B., L.N., N.G., P.A.G., J.L., S.A.S, J.W.P., A.J., C.K. Project administration: S.M., S.W.O., W.W. Investigation, validation, methodology and formal analyses: J.A.H., S.M., K.E.H., H.C., J.D.W, J.K., P.B., S.C., L.C., A.C., A.F., N.G., N.G., C.G., P.A.G., A.M.H., A.H., R.H., L.K., X.K., J.L., J.L., P.L., Y.L., M.T.M., M.N., L.N., B.O., E.S., S.A.S., Q.W., A.W., Y.W. Data curation: J.A.H., K.E.H., J.D.W., P.B., S.C., A.M.H., B.O., W.W. Visualization: J.A.H., K.E.H., J.D.W., H.C., L.N., D.F., S.M., M.N., Y.W. The original draft was written by J.A.H., with input from K.E.H., J.D.W, S.M., H.C., Q.W., C.K., and H.Z. All co-authors reviewed the manuscript.

                [* ]Correspondence and requests for materials should be addressed to J.A.H. ( julieha@ 123456alleninstitute.org ).
                Article
                NIHMS1540627
                10.1038/s41586-019-1716-z
                8433044
                31666704
                f9c20f1f-9f67-404b-8544-52be9b3f18a7

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