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      Multi-scale brain networks

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          Abstract

          The network architecture of the human brain has become a feature of increasing interest to the neuroscientific community, largely because of its potential to illuminate human cognition, its variation over development and aging, and its alteration in disease or injury. Traditional tools and approaches to study this architecture have largely focused on single scales—of topology, time, and space. Expanding beyond this narrow view, we focus this review on pertinent questions and novel methodological advances for the multi-scale brain. We separate our exposition into content related to multi-scale topological structure, multi-scale temporal structure, and multi-scale spatial structure. In each case, we recount empirical evidence for such structures, survey network-based methodological approaches to reveal these structures, and outline current frontiers and open questions. Although predominantly peppered with examples from human neuroimaging, we hope that this account will offer an accessible guide to any neuroscientist aiming to measure, characterize, and understand the full richness of the brain’s multiscale network structure—irrespective of species, imaging modality, or spatial resolution.

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          Most cited references139

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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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              The organization of the human cerebral cortex estimated by intrinsic functional connectivity.

              Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.
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                Author and article information

                Journal
                9215515
                20498
                Neuroimage
                Neuroimage
                NeuroImage
                1053-8119
                1095-9572
                15 November 2017
                11 November 2016
                15 October 2017
                20 November 2017
                : 160
                : 73-83
                Affiliations
                [a ]School of Engineering and Applied Science, Department of Bioengineering, USA
                [b ]Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
                Author notes
                [* ]Corresponding author at: School of Engineering and Applied Science, Department of Bioengineering, USA. dsb@ 123456seas.upenn.edu (D.S. Bassett)
                Article
                NIHMS919671
                10.1016/j.neuroimage.2016.11.006
                5695236
                27845257
                2f14130a-9697-4ac6-8618-6952d7a8a6aa

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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                Categories
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                Neurosciences
                complex networks,brain networks,multi-scale,network neuroscience,multi-resolution,multi-layer,graph theory

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