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      Differences in network controllability and regional gene expression underlie hallucinations in Parkinson’s disease

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          Abstract

          Visual hallucinations in Parkinson’s disease are associated with connectivity loss, but the factors underlying selective regional vulnerability are unknown. Zarkali et al. show that hallucinations are associated with the breakdown of a structural subnetwork critical for brain integration, with distinct gene expression patterns.

          Abstract

          Visual hallucinations are common in Parkinson’s disease and are associated with poorer prognosis. Imaging studies show white matter loss and functional connectivity changes with Parkinson’s visual hallucinations, but the biological factors underlying selective vulnerability of affected parts of the brain network are unknown. Recent models for Parkinson’s disease hallucinations suggest they arise due to a shift in the relative effects of different networks. Understanding how structural connectivity affects the interplay between networks will provide important mechanistic insights. To address this, we investigated the structural connectivity changes that accompany visual hallucinations in Parkinson’s disease and the organizational and gene expression characteristics of the preferentially affected areas of the network. We performed diffusion-weighted imaging in 100 patients with Parkinson’s disease (81 without hallucinations, 19 with visual hallucinations) and 34 healthy age-matched controls. We used network-based statistics to identify changes in structural connectivity in Parkinson’s disease patients with hallucinations and performed an analysis of controllability, an emerging technique that allows quantification of the influence a brain region has across the rest of the network. Using these techniques, we identified a subnetwork of reduced connectivity in Parkinson’s disease hallucinations. We then used the Allen Institute for Brain Sciences human transcriptome atlas to identify regional gene expression patterns associated with affected areas of the network. Within this network, Parkinson’s disease patients with hallucinations showed reduced controllability (less influence over other brain regions), than Parkinson’s disease patients without hallucinations and controls. This subnetwork appears to be critical for overall brain integration, as even in controls, nodes with high controllability were more likely to be within the subnetwork. Gene expression analysis of gene modules related to the affected subnetwork revealed that down-weighted genes were most significantly enriched in genes related to mRNA and chromosome metabolic processes (with enrichment in oligodendrocytes) and upweighted genes to protein localization (with enrichment in neuronal cells). Our findings provide insights into how hallucinations are generated, with breakdown of a key structural subnetwork that exerts control across distributed brain regions. Expression of genes related to mRNA metabolism and membrane localization may be implicated, providing potential therapeutic targets.

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          WGCNA: an R package for weighted correlation network analysis

          Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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            The hospital anxiety and depression scale.

            A self-assessment scale has been developed and found to be a reliable instrument for detecting states of depression and anxiety in the setting of an hospital medical outpatient clinic. The anxiety and depressive subscales are also valid measures of severity of the emotional disorder. It is suggested that the introduction of the scales into general hospital practice would facilitate the large task of detection and management of emotional disorder in patients under investigation and treatment in medical and surgical departments.
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              The Hospital Anxiety and Depression Scale

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                Author and article information

                Journal
                Brain
                Brain
                brainj
                Brain
                Oxford University Press
                0006-8950
                1460-2156
                November 2020
                29 October 2020
                29 October 2020
                : 143
                : 11
                : 3435-3448
                Affiliations
                [a1 ] Dementia Research Centre, University College London , 8-11 Queen Square, London, WC1N 3AR, UK
                [a2 ] Huntington’s Disease Centre, University College London , Russell Square House, London, WC1B 5EH, UK
                [a3 ] Department of Neurodegenerative Disease, UCL Institute of Neurology , 10-12 Russell Square House, London, UK
                [a4 ] Reta Lila Weston Institute of Neurological Studies , 1 Wakefield Street, London, WC1N 1PJ, UK
                [a5 ] Institute of Cognitive Neuroscience, University College London , 17-19 Queen Square, London, WC1N 3AR, UK
                [a6 ] Wellcome Centre for Human Neuroimaging, University College London , 12 Queen Square, London, WC1N 3AR, UK
                [a7 ] Movement Disorders Consortium, University College London , London WC1N 3BG, UK
                Author notes
                Correspondence to: Dr Angeliki Zarkali Dementia Research Centre, University College London, 8-11 Queen Square, WC1N 3AR, UK E-mail: a.zarkali@ 123456ucl.ac.uk
                Author information
                http://orcid.org/0000-0003-2808-072X
                http://orcid.org/0000-0001-6470-7919
                http://orcid.org/0000-0002-2476-4385
                http://orcid.org/0000-0002-5092-6325
                Article
                awaa270
                10.1093/brain/awaa270
                7719028
                33118028
                f81ce3de-abaa-41c6-b082-513b7e038154
                © 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
                : 6 December 2019
                : 29 June 2020
                : 2 July 2020
                Page count
                Pages: 14
                Funding
                Funded by: Alzheimer’s Research UK Clinical Research Fellowship;
                Award ID: 2018B-001
                Funded by: National Institute for Health Research, DOI 10.13039/501100000272;
                Funded by: Wellcome Clinical Research Career Development Fellowship;
                Award ID: 201567/Z/16/Z
                Categories
                Original Articles
                AcademicSubjects/MED00310
                AcademicSubjects/SCI01870

                Neurosciences
                parkinson’s disease,visual hallucinations,diffusion weighted imaging,regional gene expression,controllability

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