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      Local vulnerability and global connectivity jointly shape neurodegenerative disease propagation

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          Abstract

          It is becoming increasingly clear that brain network organization shapes the course and expression of neurodegenerative diseases. Parkinson disease (PD) is marked by progressive spread of atrophy from the midbrain to subcortical structures and, eventually, to the cerebral cortex. Recent discoveries suggest that the neurodegenerative process involves the misfolding and prion-like propagation of endogenous α-synuclein via axonal projections. However, the mechanisms that translate local "synucleinopathy" to large-scale network dysfunction and atrophy remain unknown. Here, we use an agent-based epidemic spreading model to integrate structural connectivity, functional connectivity, and gene expression and to predict sequential volume loss due to neurodegeneration. The dynamic model replicates the spatial and temporal patterning of empirical atrophy in PD and implicates the substantia nigra as the disease epicenter. We reveal a significant role for both connectome topology and geometry in shaping the distribution of atrophy. The model also demonstrates that SNCA and GBA transcription influence α-synuclein concentration and local regional vulnerability. Functional coactivation further amplifies the course set by connectome architecture and gene expression. Altogether, these results support the theory that the progression of PD is a multifactorial process that depends on both cell-to-cell spreading of misfolded proteins and regional vulnerability.

          Abstract

          Many neurodegenerative diseases, including Parkinson's disease, may be prion illnesses characterized by propagation of abnormal proteins through the brain. An agent-based model informed by infectious disease epidemiology recapitulates the distribution of atrophy in Parkinson's disease, suggesting that connectomics and local gene expression interact to shape the spatial progression of the disease in the brain.

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          Most cited references74

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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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            Collective dynamics of 'small-world' networks.

            Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
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              FSL.

              FSL (the FMRIB Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion MRI brain imaging data, written mainly by members of the Analysis Group, FMRIB, Oxford. For this NeuroImage special issue on "20 years of fMRI" we have been asked to write about the history, developments and current status of FSL. We also include some descriptions of parts of FSL that are not well covered in the existing literature. We hope that some of this content might be of interest to users of FSL, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis. Copyright © 2011 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: Software
                Role: MethodologyRole: Writing – review & editing
                Role: Formal analysisRole: MethodologyRole: Software
                Role: MethodologyRole: Software
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                21 November 2019
                November 2019
                21 November 2019
                : 17
                : 11
                : e3000495
                Affiliations
                [1 ] McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
                [2 ] Centre de Recherche de I'Institut Universitaire de Gériatrie de Montréal, Montréal, Canada
                Inserm U1208, FRANCE
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-1236-0700
                http://orcid.org/0000-0001-7159-4561
                http://orcid.org/0000-0002-6095-7205
                http://orcid.org/0000-0002-0583-5811
                http://orcid.org/0000-0003-0307-2862
                http://orcid.org/0000-0002-0945-5779
                Article
                PBIOLOGY-D-19-02034
                10.1371/journal.pbio.3000495
                6894889
                31751329
                7552e301-b0c5-4a9f-a2a3-ab7e22aca1e5
                © 2019 Zheng et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 15 July 2019
                : 31 October 2019
                Page count
                Figures: 7, Tables: 0, Pages: 27
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100000024, Canadian Institutes of Health Research;
                Award ID: FDN-143242
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000864, Michael J. Fox Foundation for Parkinson's Research;
                Award ID: Biomarkers Across Neurodegenerative Diseases
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000957, Alzheimer's Association;
                Award ID: Biomarkers Across Neurodegenerative Diseases
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100012479, Weston Brain Institute;
                Award ID: Biomarkers Across Neurodegenerative Diseases
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100010785, Canada First Research Excellence Fund;
                Award ID: Healthy Brain for Healthy Lives
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000038, Natural Sciences and Engineering Research Council of Canada;
                Award ID: RGPIN #017-04265
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000156, Fonds de Recherche du Québec - Santé;
                Award ID: Chercheur Boursier
                Award Recipient :
                This research was undertaken thanks in part to funding from the Canada First Research Excellence Fund, awarded to McGill University for the Healthy Brains for Healthy Lives initiative. AD received funding from the Canadian Institutes for Health Research (grant number FDN-143242), Natural Sciences and Engineering Research Council of Canada, Michael J. Fox Foundation, Weston Brain Institute, and the Alzheimer Association. BM acknowledges support from the Natural Sciences and Engineering Research Council of Canada (NSERC Discovery Grant RGPIN 017-04265), the Fonds de recherche Qu ébec–Sant é (Chercheur Boursier), and the Canadian Institutes of Health Research (CIHR; Project Grant 391300). PPMI—a public-private partnership—is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including AbbVie, Avid, Biogen, Bristol-Myers Squibb, Covance, GE Healthcare, Genentech, GlaxoSmithKline, Lilly, Lundbeck, Merck, Meso Scale Discovery, Pfizer, Piramal, Roche, Sanofi Genzyme, Servier, Teva, and UCB. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Diagnostic Medicine
                Signs and Symptoms
                Atrophy
                Medicine and Health Sciences
                Pathology and Laboratory Medicine
                Signs and Symptoms
                Atrophy
                Biology and Life Sciences
                Genetics
                Gene Expression
                Research and Analysis Methods
                Simulation and Modeling
                Agent-Based Modeling
                Computer and Information Sciences
                Systems Science
                Agent-Based Modeling
                Physical Sciences
                Mathematics
                Systems Science
                Agent-Based Modeling
                Medicine and Health Sciences
                Neurology
                Neurodegenerative Diseases
                Movement Disorders
                Parkinson Disease
                Biology and Life Sciences
                Anatomy
                Brain
                Substantia Nigra
                Medicine and Health Sciences
                Anatomy
                Brain
                Substantia Nigra
                Biology and Life Sciences
                Neuroscience
                Brain Mapping
                Connectomics
                Biology and Life Sciences
                Anatomy
                Nervous System
                Neuroanatomy
                Connectomics
                Medicine and Health Sciences
                Anatomy
                Nervous System
                Neuroanatomy
                Connectomics
                Biology and Life Sciences
                Neuroscience
                Neuroanatomy
                Connectomics
                Medicine and Health Sciences
                Epidemiology
                Infectious Disease Epidemiology
                Medicine and Health Sciences
                Infectious Diseases
                Infectious Disease Epidemiology
                Medicine and Health Sciences
                Neurology
                Neurodegenerative Diseases
                Custom metadata
                vor-update-to-uncorrected-proof
                2019-12-05
                All public data used here are available to anyone and referenced in the manuscript with the current URLs. The code for running the agent-based model is available at https://github.com/yingqiuz/SIR_simulator. The deformation maps used to test the model are available at https://neurovault.org/collections/860/.

                Life sciences
                Life sciences

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