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      Gene transcription profiles associated with inter-modular hubs and connection distance in human functional magnetic resonance imaging networks

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          Abstract

          Human functional magnetic resonance imaging (fMRI) brain networks have a complex topology comprising integrative components, e.g. long-distance inter-modular edges, that are theoretically associated with higher biological cost. Here, we estimated intra-modular degree, inter-modular degree and connection distance for each of 285 cortical nodes in multi-echo fMRI data from 38 healthy adults. We used the multivariate technique of partial least squares (PLS) to reduce the dimensionality of the relationships between these three nodal network parameters and prior microarray data on regional expression of 20 737 genes. The first PLS component defined a transcriptional profile associated with high intra-modular degree and short connection distance, whereas the second PLS component was associated with high inter-modular degree and long connection distance. Nodes in superior and lateral cortex with high inter-modular degree and long connection distance had local transcriptional profiles enriched for oxidative metabolism and mitochondria, and for genes specific to supragranular layers of human cortex. In contrast, primary and secondary sensory cortical nodes in posterior cortex with high intra-modular degree and short connection distance had transcriptional profiles enriched for RNA translation and nuclear components. We conclude that, as predicted, topologically integrative hubs, mediating long-distance connections between modules, are more costly in terms of mitochondrial glucose metabolism.

          This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

            In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.
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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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                Author and article information

                Journal
                Philos Trans R Soc Lond B Biol Sci
                Philos. Trans. R. Soc. Lond., B, Biol. Sci
                RSTB
                royptb
                Philosophical Transactions of the Royal Society B: Biological Sciences
                The Royal Society
                0962-8436
                1471-2970
                5 October 2016
                5 October 2016
                : 371
                : 1705 , Theo Murphy meeting issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’ organized and edited by Anusha Mishra, Zebulun Kurth-Nelson, Catherine Hall and Clare Howarth
                : 20150362
                Affiliations
                [1 ]Department of Psychiatry, University of Cambridge , Cambridge CB2 0SZ, UK
                [2 ]Department of Clinical Neurosciences, University of Cambridge , Cambridge CB2 0SZ, UK
                [3 ]MRC/Wellcome Trust Behavioural and Clinical Neuroscience Institute, University of Cambridge , Cambridge CB2 0SZ, UK
                [4 ]Research Department of Clinical, Educational and Health Psychology, University College London , London WC1E 6BT, UK
                [5 ]Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London , London WC1N 3BG, UK
                [6 ]Max Planck UCL Centre for Computational Psychiatry and Ageing Research , London WC1B 5EH, UK
                [7 ]Cambridgeshire and Peterborough NHS Foundation Trust , Huntingdon PE29 3RJ, UK
                [8 ]Immuno-Psychiatry, Immuno-Inflammation Therapeutic Area Unit, GlaxoSmithKline R&D , Stevenage SG1 2NY, UK
                Author notes
                [†]

                Full member list is available in the electronic supplementary material.

                One contribution of 15 to a Theo Murphy meeting issue ‘ Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.

                Author information
                http://orcid.org/0000-0002-4494-0753
                http://orcid.org/0000-0002-8955-8283
                Article
                rstb20150362
                10.1098/rstb.2015.0362
                5003862
                27574314
                8fdf5d81-a939-43f4-81ad-548d268e078c
                © 2016 The Authors.

                Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

                History
                : 31 May 2016
                Funding
                Funded by: Wellcome Trust, http://dx.doi.org/10.13039/100004440;
                Funded by: Medical Research Council, http://dx.doi.org/10.13039/501100000265;
                Funded by: National Institute of Health Research;
                Categories
                1001
                133
                22
                Articles
                Research Article
                Custom metadata
                October 5, 2016

                Philosophy of science
                economy,graph theory,hub,allen institute for brain sciences,transcriptome,community structure

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