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      Genetic, cellular, and connectomic characterization of the brain regions commonly plagued by glioma

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          Abstract

          Using neuroimaging and genetic methods, Mandal et al. show that gliomas localize to brain regions characterized by functional hubness, stem-like cells, and transcription of genes involved in tumour development. The results illustrate how factors of diverse scale can collectively determine the location of brain dysfunction.

          Abstract

          For decades, it has been known that gliomas follow a non-random spatial distribution, appearing more often in some brain regions (e.g. the insula) compared to others (e.g. the occipital lobe). A better understanding of the localization patterns of gliomas could provide clues to the origins of these types of tumours, and consequently inform treatment targets. Following hypotheses derived from prior research into neuropsychiatric disease and cancer, gliomas may be expected to localize to brain regions characterized by functional hubness, stem-like cells, and transcription of genetic drivers of gliomagenesis. We combined neuroimaging data from 335 adult patients with high- and low-grade glioma to form a replicable tumour frequency map. Using this map, we demonstrated that glioma frequency is elevated in association cortex and correlated with multiple graph-theoretical metrics of high functional connectedness. Brain regions populated with putative cells of origin for glioma, neural stem cells and oligodendrocyte precursor cells, exhibited a high glioma frequency. Leveraging a human brain atlas of post-mortem gene expression, we found that gliomas were localized to brain regions enriched with expression of genes associated with chromatin organization and synaptic signalling. A set of glioma proto-oncogenes was enriched among the transcriptomic correlates of glioma distribution. Finally, a regression model incorporating connectomic, cellular, and genetic factors explained 58% of the variance in glioma frequency. These results add to previous literature reporting the vulnerability of hub regions to neurological disease, as well as provide support for cancer stem cell theories of glioma. Our findings illustrate how factors of diverse scale, from genetic to connectomic, can independently influence the anatomic localization of brain dysfunction.

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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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              REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms

              Outcomes of high-throughput biological experiments are typically interpreted by statistical testing for enriched gene functional categories defined by the Gene Ontology (GO). The resulting lists of GO terms may be large and highly redundant, and thus difficult to interpret. REVIGO is a Web server that summarizes long, unintelligible lists of GO terms by finding a representative subset of the terms using a simple clustering algorithm that relies on semantic similarity measures. Furthermore, REVIGO visualizes this non-redundant GO term set in multiple ways to assist in interpretation: multidimensional scaling and graph-based visualizations accurately render the subdivisions and the semantic relationships in the data, while treemaps and tag clouds are also offered as alternative views. REVIGO is freely available at http://revigo.irb.hr/.
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                Author and article information

                Journal
                Brain
                Brain
                brainj
                Brain
                Oxford University Press
                0006-8950
                1460-2156
                November 2020
                04 December 2020
                04 December 2020
                : 143
                : 11
                : 3294-3307
                Affiliations
                [1 ] Brain Mapping Unit, Department of Psychiatry, University of Cambridge , Cambridge, UK
                [2 ] Academic Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge , Cambridge, UK
                Author notes
                Correspondence to: Ayan Mandal University of Cambridge, Department of Psychiatry, Herchel Smith Building, Robinson Way Cambridge, UK CB2 0SZ E-mail: asm82@ 123456cam.ac.uk
                Article
                awaa277
                10.1093/brain/awaa277
                7891236
                33278823
                845dbb44-3cc8-44c6-bd02-0764881b5a36
                © The Author(s) (2020). 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/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 21 April 2020
                : 26 June 2020
                : 12 July 2020
                Page count
                Pages: 14
                Funding
                Funded by: Gates Cambridge Scholarship;
                Funded by: High Performance Hub for Clinical Informatics;
                Funded by: Medical Research Council, DOI 10.13039/501100000265;
                Funded by: MRC, DOI 10.13039/501100000265;
                Award ID: MR/M009041/1
                Funded by: Bill and Melinda Gates Foundation, DOI 10.13039/100000865;
                Award ID: OPP1144
                Categories
                Original Articles
                AcademicSubjects/MED00310
                AcademicSubjects/SCI01870

                Neurosciences
                neuro-oncology,gliomagenesis,connectomics,imaging-transcriptomics
                Neurosciences
                neuro-oncology, gliomagenesis, connectomics, imaging-transcriptomics

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