6
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Prion-like spreading of Alzheimer’s disease within the brain’s connectome

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The prion hypothesis states that misfolded proteins can act as infectious agents that template the misfolding and aggregation of healthy proteins to transmit a disease. Increasing evidence suggests that pathological proteins in neurodegenerative diseases adopt prion-like mechanisms and spread across the brain along anatomically connected networks. Local kinetic models of protein misfolding and global network models of protein spreading provide valuable insight into several aspects of prion-like diseases. Yet, to date, these models have not been combined to simulate how pathological proteins multiply and spread across the human brain. Here, we create an efficient and robust tool to simulate the spreading of misfolded protein using three classes of kinetic models, the Fisher–Kolmogorov model, the Heterodimer model and the Smoluchowski model. We discretize their governing equations using a human brain network model, which we represent as a weighted Laplacian graph generated from 418 brains from the Human Connectome Project. Its nodes represent the anatomic regions of interest and its edges are weighted by the mean fibre number divided by the mean fibre length between any two regions. We demonstrate that our brain network model can predict the histopathological patterns of Alzheimer’s disease and capture the key characteristic features of finite-element brain models at a fraction of their computational cost: simulating the spatio-temporal evolution of aggregate size distributions across the human brain throughout a period of 40 years takes less than 7 s on a standard laptop computer. Our model has the potential to predict biomarker curves, aggregate size distributions, infection times, and the effects of therapeutic strategies including reduced production and increased clearance of misfolded protein.

          Related collections

          Most cited references28

          • Record: found
          • Abstract: not found
          • Article: not found

          THE WAVE OF ADVANCE OF ADVANTAGEOUS GENES

          R Fisher (1937)
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Small-world brain networks.

            Many complex networks have a small-world topology characterized by dense local clustering or cliquishness of connections between neighboring nodes yet a short path length between any (distant) pair of nodes due to the existence of relatively few long-range connections. This is an attractive model for the organization of brain anatomical and functional networks because a small-world topology can support both segregated/specialized and distributed/integrated information processing. Moreover, small-world networks are economical, tending to minimize wiring costs while supporting high dynamical complexity. The authors introduce some of the key mathematical concepts in graph theory required for small-world analysis and review how these methods have been applied to quantification of cortical connectivity matrices derived from anatomical tract-tracing studies in the macaque monkey and the cat. The evolution of small-world networks is discussed in terms of a selection pressure to deliver cost-effective information-processing systems. The authors illustrate how these techniques and concepts are increasingly being applied to the analysis of human brain functional networks derived from electroencephalography/magnetoencephalography and fMRI experiments. Finally, the authors consider the relevance of small-world models for understanding the emergence of complex behaviors and the resilience of brain systems to pathological attack by disease or aberrant development. They conclude that small-world models provide a powerful and versatile approach to understanding the structure and function of human brain systems.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              An analytical solution to the kinetics of breakable filament assembly.

              We present an analytical treatment of a set of coupled kinetic equations that governs the self-assembly of filamentous molecular structures. Application to the case of protein aggregation demonstrates that the kinetics of amyloid growth can often be dominated by secondary rather than by primary nucleation events. Our results further reveal a range of general features of the growth kinetics of fragmenting filamentous structures, including the existence of generic scaling laws that provide mechanistic information in contexts ranging from in vitro amyloid growth to the in vivo development of mammalian prion diseases.
                Bookmark

                Author and article information

                Journal
                J R Soc Interface
                J R Soc Interface
                RSIF
                royinterface
                Journal of the Royal Society Interface
                The Royal Society
                1742-5689
                1742-5662
                October 2019
                16 October 2019
                16 October 2019
                : 16
                : 159
                : 20190356
                Affiliations
                [1 ]Living Matter Laboratory, Stanford University , Stanford, CA, USA
                [2 ]Hertie-Institute for Clinical Brain Research/German Center for Neurodegenerative Diseases , Tübingen, Germany
                [3 ]Mathematical Institute, University of Oxford , Oxford, UK
                Author notes
                Author information
                http://orcid.org/0000-0002-6283-935X
                Article
                rsif20190356
                10.1098/rsif.2019.0356
                6833337
                31615329
                26ec19a9-6522-4489-9a71-62efb6ff87cb
                © 2019 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
                : 22 May 2019
                : 23 September 2019
                Funding
                Funded by: Engineering and Physical Sciences Research Council, http://dx.doi.org/10.13039/501100000266;
                Award ID: EP/R020205/1
                Funded by: Division of Civil, Mechanical and Manufacturing Innovation, http://dx.doi.org/10.13039/100000147;
                Award ID: CMMI 1727268
                Categories
                1004
                24
                30
                Life Sciences–Physics interface
                Research Article
                Custom metadata
                October, 2019

                Life sciences
                neurodegeneration,alzheimer’s disease,prion,network,connectome,graph laplacian
                Life sciences
                neurodegeneration, alzheimer’s disease, prion, network, connectome, graph laplacian

                Comments

                Comment on this article