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      The hubs of the human connectome are generally implicated in the anatomy of brain disorders

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          Abstract

          See Sprons (doi: [Related article:]10.1093/brain/awu148) for a scientific commentary on this article.

          Brain networks contain a minority of highly connected hub nodes with high topological value and biological cost. Using network analysis of DTI data from healthy volunteers, and meta-analyses of published MRI studies in 26 brain disorders, Crossley et al. show that lesions across disorders tend to be concentrated at hubs.

          Abstract

          Brain networks or ‘connectomes’ include a minority of highly connected hub nodes that are functionally valuable, because their topological centrality supports integrative processing and adaptive behaviours. Recent studies also suggest that hubs have higher metabolic demands and longer-distance connections than other brain regions, and therefore could be considered biologically costly. Assuming that hubs thus normally combine both high topological value and high biological cost, we predicted that pathological brain lesions would be concentrated in hub regions. To test this general hypothesis, we first identified the hubs of brain anatomical networks estimated from diffusion tensor imaging data on healthy volunteers ( n = 56), and showed that computational attacks targeted on hubs disproportionally degraded the efficiency of brain networks compared to random attacks. We then prepared grey matter lesion maps, based on meta-analyses of published magnetic resonance imaging data on more than 20 000 subjects and 26 different brain disorders. Magnetic resonance imaging lesions that were common across all brain disorders were more likely to be located in hubs of the normal brain connectome ( P < 10 −4, permutation test). Specifically, nine brain disorders had lesions that were significantly more likely to be located in hubs ( P < 0.05, permutation test), including schizophrenia and Alzheimer’s disease. Both these disorders had significantly hub-concentrated lesion distributions, although (almost completely) distinct subsets of cortical hubs were lesioned in each disorder: temporal lobe hubs specifically were associated with higher lesion probability in Alzheimer’s disease, whereas in schizophrenia lesions were concentrated in both frontal and temporal cortical hubs. These results linking pathological lesions to the topological centrality of nodes in the normal diffusion tensor imaging connectome were generally replicated when hubs were defined instead by the meta-analysis of more than 1500 task-related functional neuroimaging studies of healthy volunteers to create a normative functional co-activation network. We conclude that the high cost/high value hubs of human brain networks are more likely to be anatomically abnormal than non-hubs in many (if not all) brain disorders.

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          Most cited references45

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          Modularity and community structure in networks

          M. Newman (2006)
          Many networks of interest in the sciences, including a variety of social and biological networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure has attracted considerable recent attention. One of the most sensitive detection methods is optimization of the quality function known as "modularity" over the possible divisions of a network, but direct application of this method using, for instance, simulated annealing is computationally costly. Here we show that the modularity can be reformulated in terms of the eigenvectors of a new characteristic matrix for the network, which we call the modularity matrix, and that this reformulation leads to a spectral algorithm for community detection that returns results of better quality than competing methods in noticeably shorter running times. We demonstrate the algorithm with applications to several network data sets.
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            Error and attack tolerance of complex networks

            Many complex systems, such as communication networks, display a surprising degree of robustness: while key components regularly malfunction, local failures rarely lead to the loss of the global information-carrying ability of the network. The stability of these complex systems is often attributed to the redundant wiring of the functional web defined by the systems' components. In this paper we demonstrate that error tolerance is not shared by all redundant systems, but it is displayed only by a class of inhomogeneously wired networks, called scale-free networks. We find that scale-free networks, describing a number of systems, such as the World Wide Web, Internet, social networks or a cell, display an unexpected degree of robustness, the ability of their nodes to communicate being unaffected by even unrealistically high failure rates. However, error tolerance comes at a high price: these networks are extremely vulnerable to attacks, i.e. to the selection and removal of a few nodes that play the most important role in assuring the network's connectivity.
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              Power-law distributions in empirical data

              Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution -- the part of the distribution representing large but rare events -- and by the difficulty of identifying the range over which power-law behavior holds. Commonly used methods for analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates of parameters for power-law distributions, and even in cases where such methods return accurate answers they are still unsatisfactory because they give no indication of whether the data obey a power law at all. Here we present a principled statistical framework for discerning and quantifying power-law behavior in empirical data. Our approach combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov statistic and likelihood ratios. We evaluate the effectiveness of the approach with tests on synthetic data and give critical comparisons to previous approaches. We also apply the proposed methods to twenty-four real-world data sets from a range of different disciplines, each of which has been conjectured to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data while in others the power law is ruled out.
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                Author and article information

                Journal
                Brain
                Brain
                brainj
                brain
                Brain
                Oxford University Press
                0006-8950
                1460-2156
                August 2014
                19 June 2014
                19 June 2014
                : 137
                : 8
                : 2382-2395
                Affiliations
                1 Department of Psychosis Studies, Institute of Psychiatry, King’s College London, London SE5 8AF, UK
                2 Research Imaging Institute and Department of Radiology, The University of Texas Health Science Centre at San Antonio, San Antonio, TX 78229, USA
                3 University of Cambridge, Behavioural & Clinical Neuroscience Institute, Department of Psychiatry, Cambridge CB2 0SZ, UK
                4 Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge CB21 5EF, UK
                5 GlaxoSmithKline, ImmunoPsychiatry, Alternative Discovery and Development, Stevenage SG1 2NY, UK
                Author notes
                Correspondence to: Nicolas A. Crossley, Department of Psychosis Studies, Institute of Psychiatry, King’s College London, London SE5 8AF, UK E-mail: nicolas.crossley@ 123456kcl.ac.uk

                *These authors contributed equally to this work.

                See doi: [Related article:]10.1093/brain/awu148 for the scientific commentary on this article.

                Article
                awu132
                10.1093/brain/awu132
                4107735
                25057133
                c53d28c2-4346-4216-a5b0-8ddb6370de3f
                © The Author (2014). Published by Oxford University Press on behalf of the Guarantors of Brain.

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

                History
                : 2 November 2013
                : 2 April 2014
                : 10 April 2014
                Page count
                Pages: 14
                Categories
                Dorsal Column
                Occasional Paper

                Neurosciences
                topology,vbm,graph analysis,rich club,tractography
                Neurosciences
                topology, vbm, graph analysis, rich club, tractography

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