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Chu C, Low YLC, Ma L, Wang Y, Cox T, Doré V, Masters CL, Goudey B, Jin L, Pan Y. How Can We Use Mathematical Modeling of Amyloid-β in Alzheimer's Disease Research and Clinical Practices? J Alzheimers Dis 2024; 97:89-100. [PMID: 38007665 DOI: 10.3233/jad-230938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2023]
Abstract
The accumulation of amyloid-β (Aβ) plaques in the brain is considered a hallmark of Alzheimer's disease (AD). Mathematical modeling, capable of predicting the motion and accumulation of Aβ, has obtained increasing interest as a potential alternative to aid the diagnosis of AD and predict disease prognosis. These mathematical models have provided insights into the pathogenesis and progression of AD that are difficult to obtain through experimental studies alone. Mathematical modeling can also simulate the effects of therapeutics on brain Aβ levels, thereby holding potential for drug efficacy simulation and the optimization of personalized treatment approaches. In this review, we provide an overview of the mathematical models that have been used to simulate brain levels of Aβ (oligomers, protofibrils, and/or plaques). We classify the models into five categories: the general ordinary differential equation models, the general partial differential equation models, the network models, the linear optimal ordinary differential equation models, and the modified partial differential equation models (i.e., Smoluchowski equation models). The assumptions, advantages and limitations of these models are discussed. Given the popularity of using the Smoluchowski equation models to simulate brain levels of Aβ, our review summarizes the history and major advancements in these models (e.g., their application to predict the onset of AD and their combined use with network models). This review is intended to bring mathematical modeling to the attention of more scientists and clinical researchers working on AD to promote cross-disciplinary research.
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Affiliation(s)
- Chenyin Chu
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Yi Ling Clare Low
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Liwei Ma
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Yihan Wang
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Timothy Cox
- The Australian e-Health Research Centre, CSIRO, Parkville, Victoria, Australia
| | - Vincent Doré
- The Australian e-Health Research Centre, CSIRO, Parkville, Victoria, Australia
| | - Colin L Masters
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Benjamin Goudey
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, Victoria, Australia
| | - Liang Jin
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
- Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
| | - Yijun Pan
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
- Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, Australia
- Department of Organ Anatomy, Graduate School of Medicine, Tohoku University, Sendai, Miyagi, Japan
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Bertsch M, Franchi B, Tesi MC, Tora V. The role of A[Formula: see text] and Tau proteins in Alzheimer's disease: a mathematical model on graphs. J Math Biol 2023; 87:49. [PMID: 37646953 PMCID: PMC10468937 DOI: 10.1007/s00285-023-01985-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 06/25/2023] [Accepted: 08/13/2023] [Indexed: 09/01/2023]
Abstract
In this Note we study a mathematical model for the progression of Alzheimer's Disease in the human brain. The novelty of our approach consists in the representation of the brain as two superposed graphs where toxic proteins diffuse, the connectivity graph which represents the neural network, and the proximity graph which takes into account the extracellular space. Toxic proteins such as [Formula: see text] amyloid and Tau play in fact a crucial role in the development of Alzheimer's disease and, separately, have been targets of medical treatments. Recent biomedical literature stresses the potential impact of the synergetic action of these proteins. We numerically test various modelling hypotheses which confirm the relevance of this synergy.
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Affiliation(s)
- Michiel Bertsch
- Department of Mathematics, University of Roma “Tor Vergata”, Rome, Italy
- Istituto per le Applicazioni del Calcolo “M. Picone”, Consiglio Nazionale delle Ricerche, Rome, Italy
| | - Bruno Franchi
- Department of Mathematics, Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Maria Carla Tesi
- Department of Mathematics, Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Veronica Tora
- Department of Mathematics, University of Roma “Tor Vergata”, Rome, Italy
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Ramirez DM, Whitesell JD, Bhagwat N, Thomas TL, Ajay AD, Nawaby A, Delatour B, Bay S, LaFaye P, Knox JE, Harris JA, Meeks JP, Diamond MI. Endogenous pathology in tauopathy mice progresses via brain networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.23.541792. [PMID: 37293074 PMCID: PMC10245958 DOI: 10.1101/2023.05.23.541792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Neurodegenerative tauopathies are hypothesized to propagate via brain networks. This is uncertain because we have lacked precise network resolution of pathology. We therefore developed whole-brain staining methods with anti-p-tau nanobodies and imaged in 3D PS19 tauopathy mice, which have pan-neuronal expression of full-length human tau containing the P301S mutation. We analyzed patterns of p-tau deposition across established brain networks at multiple ages, testing the relationship between structural connectivity and patterns of progressive pathology. We identified core regions with early tau deposition, and used network propagation modeling to determine the link between tau pathology and connectivity strength. We discovered a bias towards retrograde network-based propagation of tau. This novel approach establishes a fundamental role for brain networks in tau propagation, with implications for human disease.
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Affiliation(s)
- Denise M.O. Ramirez
- Department of Neurology, Peter O’Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center; Dallas, TX, USA
| | - Jennifer D. Whitesell
- Allen Institute for Brain Science; Seattle, WA, USA
- Cajal Neuroscience; Seattle, WA, USA
| | - Nikhil Bhagwat
- Allen Institute for Brain Science; Seattle, WA, USA
- McConnell Brain Imaging Centre, The Neuro (Montreal Neurological Institute-Hospital), McGill University; Montreal, Quebec, Canada
| | - Talitha L. Thomas
- Center for Alzheimer’s and Neurodegenerative Diseases, Peter O’Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center; Dallas, TX, USA
| | - Apoorva D. Ajay
- Department of Neurology, Peter O’Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center; Dallas, TX, USA
| | - Ariana Nawaby
- Department of Neurology, Peter O’Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center; Dallas, TX, USA
| | - Benoît Delatour
- Paris Brain Institute (ICM), CNRS UMR 7225, INSERM U1127, Sorbonne Université, Hôpital de la Pitié-Salpêtrière; Paris, France
| | - Sylvie Bay
- Unité de Chimie des Biomolécules, Institut Pasteur, Université Paris Cité, CNRS UMR 3523; Paris, France
| | - Pierre LaFaye
- Antibody Engineering Platform, Institut Pasteur, Université Paris Cité, CNRS UMR 3528; Paris, France
| | | | | | - Julian P. Meeks
- Department of Neuroscience, University of Rochester Medical School; Rochester, NY, USA
| | - Marc I. Diamond
- Center for Alzheimer’s and Neurodegenerative Diseases, Peter O’Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center; Dallas, TX, USA
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Torok J, Anand C, Verma P, Raj A. Connectome-based biophysics models of Alzheimer's disease diagnosis and prognosis. Transl Res 2023; 254:13-23. [PMID: 36031051 PMCID: PMC11019890 DOI: 10.1016/j.trsl.2022.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 08/08/2022] [Indexed: 11/22/2022]
Abstract
With the increasing prevalence of Alzheimer's disease (AD) among aging populations and the limited therapeutic options available to slow or reverse its progression, the need has never been greater for improved diagnostic tools for identifying patients in the preclinical and prodomal phases of AD. Biophysics models of the connectome-based spread of amyloid-beta (Aβ) and microtubule-associated protein tau (τ) have enjoyed recent success as tools for predicting the time course of AD-related pathological changes. However, given the complex etiology of AD, which involves not only connectome-based spread of protein pathology but also the interactions of many molecular and cellular players over multiple spatiotemporal scales, more robust, complete biophysics models are needed to better understand AD pathophysiology and ultimately provide accurate patient-specific diagnoses and prognoses. Here we discuss several areas of active research in AD whose insights can be used to enhance the mathematical modeling of AD pathology as well as recent attempts at developing improved connectome-based biophysics models. These efforts toward a comprehensive yet parsimonious mathematical description of AD hold great promise for improving both the diagnosis of patients at risk for AD and our mechanistic understanding of how AD progresses.
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Affiliation(s)
- Justin Torok
- Department of Radiology, University of California, San Francisco, San Francisco, California.
| | - Chaitali Anand
- Department of Radiology, University of California, San Francisco, San Francisco, California
| | - Parul Verma
- Department of Radiology, University of California, San Francisco, San Francisco, California
| | - Ashish Raj
- Department of Radiology, University of California, San Francisco, San Francisco, California; Department of Bioengineering, University of California, Berkeley and University of California, San Francisco, Berkeley, California; Department of Radiology, Weill Cornell Medicine, New York, New York.
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Montal V, Diez I, Kim CM, Orwig W, Bueichekú E, Gutiérrez-Zúñiga R, Bejanin A, Pegueroles J, Dols-Icardo O, Vannini P, El-Fakhri G, Johnson KA, Sperling RA, Fortea J, Sepulcre J. Network Tau spreading is vulnerable to the expression gradients of APOE and glutamatergic-related genes. Sci Transl Med 2022; 14:eabn7273. [PMID: 35895837 PMCID: PMC9942690 DOI: 10.1126/scitranslmed.abn7273] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
A key hallmark of Alzheimer's disease (AD) pathology is the intracellular accumulation of tau protein in the form of neurofibrillary tangles across large-scale networks of the human brain cortex. Currently, it is still unclear how tau accumulates within specific cortical systems and whether in situ genetic traits play a role in this circuit-based propagation progression. In this study, using two independent cohorts of cognitively normal older participants, we reveal the brain network foundation of tau spreading and its association with using high-resolution transcriptomic genetic data. We observed that specific connectomic and genetic gradients exist along the tau spreading network. In particular, we identified 577 genes whose expression is associated with the spatial spreading of tau. Within this set of genes, APOE and glutamatergic synaptic genes, such as SLC1A2, play a central role. Thus, our study characterizes neurogenetic topological vulnerabilities in distinctive brain circuits of tau spreading and suggests that drug development strategies targeting the gradient expression of this set of genes should be explored to help reduce or prevent pathological tau accumulation.
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Affiliation(s)
- Victor Montal
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School; Boston, Massachusetts, USA.,Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autonoma de Barcelona; Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED); Madrid, Spain
| | - Ibai Diez
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School; Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School; Charlestown, Massachusetts, USA
| | - Chan-Mi Kim
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School; Boston, Massachusetts, USA
| | - William Orwig
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School; Boston, Massachusetts, USA
| | - Elisenda Bueichekú
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School; Boston, Massachusetts, USA
| | - Raquel Gutiérrez-Zúñiga
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School; Boston, Massachusetts, USA
| | - Alexandre Bejanin
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autonoma de Barcelona; Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED); Madrid, Spain
| | - Jordi Pegueroles
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autonoma de Barcelona; Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED); Madrid, Spain
| | - Oriol Dols-Icardo
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autonoma de Barcelona; Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED); Madrid, Spain
| | - Patrizia Vannini
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School; Charlestown, Massachusetts, USA.,Center for Alzheimer research and treatment, Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School; Boston, MA
| | - Georges El-Fakhri
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School; Boston, Massachusetts, USA
| | - Keith A. Johnson
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School; Boston, Massachusetts, USA
| | - Reisa A. Sperling
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School; Charlestown, Massachusetts, USA
| | - Juan Fortea
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autonoma de Barcelona; Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED); Madrid, Spain
| | - Jorge Sepulcre
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School; Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School; Charlestown, Massachusetts, USA.,Correspondence should be addressed to Jorge Sepulcre, 149 13th St, Office 5.209, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts; ; +1 617 726 2899
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Coupled Neural–Glial Dynamics and the Role of Astrocytes in Alzheimer’s Disease. MATHEMATICAL AND COMPUTATIONAL APPLICATIONS 2022. [DOI: 10.3390/mca27030033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Neurodegenerative diseases such as Alzheimer’s (AD) are associated with the propagation and aggregation of toxic proteins. In the case of AD, it was Alzheimer himself who showed the importance of both amyloid beta (Aβ) plaques and tau protein neurofibrillary tangles (NFTs) in what he called the “disease of forgetfulness”. The amyloid beta forms extracellular aggregates and plaques, whereas tau proteins are intracellular proteins that stabilize axons by cross-linking microtubules that can form largely messy tangles. On the other hand, astrocytes and microglial cells constantly clear these plaques and NFTs from the brain. Astrocytes transport nutrients from the blood to neurons. Activated astrocytes produce monocyte chemoattractant protein-1 (MCP-1), which attracts anti-inflammatory macrophages and clears Aβ. At the same time, the microglia cells are poorly phagocytic for Aβ compared to proinflammatory and anti-inflammatory macrophages. In addition to such distinctive neuropathological features of AD as amyloid beta and tau proteins, neuroinflammation has to be brought into the picture as well. Taking advantage of a coupled mathematical modelling framework, we formulate a network model, accounting for the coupling between neurons and astroglia and integrating all three main neuropathological features with the brain connectome data. We provide details on the coupled dynamics involving cytokines, astrocytes, and microglia. Further, we apply the tumour necrosis factor alpha (TNF-α) inhibitor and anti-Aβ drug and analyze their influence on the brain cells, suggesting conditions under which the drug can prevent cell damage. The important role of astrocytes and TNF-α inhibitors in AD pathophysiology is emphasized, along with potentially promising pathways for developing new AD therapies.
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Bertsch M, Franchi B, Raj A, Tesi MC. Macroscopic modelling of Alzheimer’s disease: difficulties and challenges. BRAIN MULTIPHYSICS 2021. [DOI: 10.1016/j.brain.2021.100040] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
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