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      A Weighted and Directed Interareal Connectivity Matrix for Macaque Cerebral Cortex

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          Abstract

          Retrograde tracer injections in 29 of the 91 areas of the macaque cerebral cortex revealed 1,615 interareal pathways, a third of which have not previously been reported. A weight index (extrinsic fraction of labeled neurons [FLNe]) was determined for each area-to-area pathway. Newly found projections were weaker on average compared with the known projections; nevertheless, the 2 sets of pathways had extensively overlapping weight distributions. Repeat injections across individuals revealed modest FLNe variability given the range of FLNe values (standard deviation <1 log unit, range 5 log units). The connectivity profile for each area conformed to a lognormal distribution, where a majority of projections are moderate or weak in strength. In the G 29 × 29 interareal subgraph, two-thirds of the connections that can exist do exist. Analysis of the smallest set of areas that collects links from all 91 nodes of the G 29 × 91 subgraph (dominating set analysis) confirms the dense (66%) structure of the cortical matrix. The G 29 × 29 subgraph suggests an unexpectedly high incidence of unidirectional links. The directed and weighted G 29 × 91 connectivity matrix for the macaque will be valuable for comparison with connectivity analyses in other species, including humans. It will also inform future modeling studies that explore the regularities of cortical networks.

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          Most cited references95

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          Emergence of scaling in random networks

          Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
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            Specification of cerebral cortical areas.

            P Rakic (1988)
            How the immense population of neurons that constitute the human cerebral neocortex is generated from progenitors lining the cerebral ventricle and then distributed to appropriate layers of distinctive cytoarchitectonic areas can be explained by the radial unit hypothesis. According to this hypothesis, the ependymal layer of the embryonic cerebral ventricle consists of proliferative units that provide a proto-map of prospective cytoarchitectonic areas. The output of the proliferative units is translated via glial guides to the expanding cortex in the form of ontogenetic columns, whose final number for each area can be modified through interaction with afferent input. Data obtained through various advanced neurobiological techniques, including electron microscopy, immunocytochemistry, [3H]thymidine and receptor autoradiography, retrovirus gene transfer, neural transplants, and surgical or genetic manipulation of cortical development, furnish new details about the kinetics of cell proliferation, their lineage relationships, and phenotypic expression that favor this hypothesis. The radial unit model provides a framework for understanding cerebral evolution, epigenetic regulation of the parcellation of cytoarchitectonic areas, and insight into the pathogenesis of certain cortical disorders in humans.
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              Small-world anatomical networks in the human brain revealed by cortical thickness from MRI.

              An important issue in neuroscience is the characterization for the underlying architectures of complex brain networks. However, little is known about the network of anatomical connections in the human brain. Here, we investigated large-scale anatomical connection patterns of the human cerebral cortex using cortical thickness measurements from magnetic resonance images. Two areas were considered anatomically connected if they showed statistically significant correlations in cortical thickness and we constructed the network of such connections using 124 brains from the International Consortium for Brain Mapping database. Significant short- and long-range connections were found in both intra- and interhemispheric regions, many of which were consistent with known neuroanatomical pathways measured by human diffusion imaging. More importantly, we showed that the human brain anatomical network had robust small-world properties with cohesive neighborhoods and short mean distances between regions that were insensitive to the selection of correlation thresholds. Additionally, we also found that this network and the probability of finding a connection between 2 regions for a given anatomical distance had both exponentially truncated power-law distributions. Our results demonstrated the basic organizational principles for the anatomical network in the human brain compatible with previous functional networks studies, which provides important implications of how functional brain states originate from their structural underpinnings. To our knowledge, this study provides the first report of small-world properties and degree distribution of anatomical networks in the human brain using cortical thickness measurements.
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                Author and article information

                Journal
                Cereb Cortex
                Cereb. Cortex
                cercor
                cercor
                Cerebral Cortex (New York, NY)
                Oxford University Press
                1047-3211
                1460-2199
                January 2014
                25 September 2012
                25 September 2012
                : 24
                : 1
                : 17-36
                Affiliations
                [1 ]Stem cell and Brain Research Institute, INSERM U846 , 69500 Bron, France
                [2 ]Université de Lyon, Université Lyon I , 69003, Lyon, France
                [3 ]Department of Neurobiology, University of Yale , New Haven, CT 06520, USA
                [4 ]Department of Physics, Interdisciplinary Center for Network Science and Applications, University of Notre Dame , Notre Dame, IN 46556, USA
                [5 ]CERMEP, Hôpital Neurologique , 69394 Lyon Cedex 03, France
                [6 ]Washington University School of Medicine , St Louis, MO, USA
                [7 ]Current address: Physics Department, Babes-Bolyai University , Cluj-Napoca, Romania
                [8 ]Current address:Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with the Max Planck Society , Frankfurt, Germany
                [9 ]Current address: Nathan S. Kline Institute for Psychiatric Research , Orangeburg, NY, USA
                [10 ]Current address: Escuela de Medicina, Departamento de Pre-clínicas, Universidad de Valparaíso , Valparaíso, Chile
                [11 ]Current address: Department of Vision and Cognition, Netherlands Institute for Neuroscience , Amsterdam, The Netherlands
                [12 ]Current address: Decision and Action Laboratory, University of Oxford , Oxford, UK
                [13 ]Current address: Service de gynécologie-obstétrique, hospices civils de Lyon , France
                [14 ]Current address: McGill Vision Research , Montreal, Canada
                [15 ]Current address: Cerveau et Cognition, UMR 5549 , Toulouse, France
                Author notes
                Address correspondence to Henry Kennedy. Email: henry.kennedy@ 123456inserm.fr
                Article
                bhs270
                10.1093/cercor/bhs270
                3862262
                23010748
                2732823a-afef-4091-a873-cc7f479e7d49
                © The Author 2012. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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                Page count
                Pages: 20
                Categories
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                Neurology
                connection,cortex,graph,monkey,network
                Neurology
                connection, cortex, graph, monkey, network

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