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Shin-Yi Lin C, Howells J, Rutkove S, Nandedkar S, Neuwirth C, Noto YI, Shahrizaila N, Whittaker RG, Bostock H, Burke D, Tankisi H. Neurophysiological and imaging biomarkers of lower motor neuron dysfunction in motor neuron diseases/amyotrophic lateral sclerosis: IFCN handbook chapter. Clin Neurophysiol 2024; 162:91-120. [PMID: 38603949 DOI: 10.1016/j.clinph.2024.03.015] [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] [Received: 10/03/2023] [Revised: 02/07/2024] [Accepted: 03/12/2024] [Indexed: 04/13/2024]
Abstract
This chapter discusses comprehensive neurophysiological biomarkers utilised in motor neuron disease (MND) and, in particular, its commonest form, amyotrophic lateral sclerosis (ALS). These encompass the conventional techniques including nerve conduction studies (NCS), needle and high-density surface electromyography (EMG) and H-reflex studies as well as novel techniques. In the last two decades, new methods of assessing the loss of motor units in a muscle have been developed, that are more convenient than earlier methods of motor unit number estimation (MUNE),and may use either electrical stimulation (e.g. MScanFit MUNE) or voluntary activation (MUNIX). Electrical impedance myography (EIM) is another novel approach for the evaluation that relies upon the application and measurement of high-frequency, low-intensity electrical current. Nerve excitability techniques (NET) also provide insights into the function of an axon and reflect the changes in resting membrane potential, ion channel dysfunction and the structural integrity of the axon and myelin sheath. Furthermore, imaging ultrasound techniques as well as magnetic resonance imaging are capable of detecting the constituents of morphological changes in the nerve and muscle. The chapter provides a critical description of the ability of each technique to provide neurophysiological insight into the complex pathophysiology of MND/ALS. However, it is important to recognise the strengths and limitations of each approach in order to clarify utility. These neurophysiological biomarkers have demonstrated reliability, specificity and provide additional information to validate and assess lower motor neuron dysfunction. Their use has expanded the knowledge about MND/ALS and enhanced our understanding of the relationship between motor units, axons, reflexes and other neural circuits in relation to clinical features of patients with MND/ALS at different stages of the disease. Taken together, the ultimate goal is to aid early diagnosis, distinguish potential disease mimics, monitor and stage disease progression, quantify response to treatment and develop potential therapeutic interventions.
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Affiliation(s)
- Cindy Shin-Yi Lin
- Faculty of Medicine and Health, Central Clinical School, Brain and Mind Centre, University of Sydney, Sydney 2006, Australia.
| | - James Howells
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Seward Rutkove
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Sanjeev Nandedkar
- Natus Medical Inc, Middleton, Wisconsin, USA and Medical College of Wisconsin, Milwaukee, WI, USA
| | - Christoph Neuwirth
- Neuromuscular Diseases Unit/ALS Clinic, Kantonsspital, St. Gallen, Switzerland
| | - Yu-Ichi Noto
- Department of Neurology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Nortina Shahrizaila
- Division of Neurology, Department of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia
| | - Roger G Whittaker
- Newcastle University Translational and Clinical Research Institute (NUTCRI), Newcastle University., Newcastle Upon Tyne, United Kingdom
| | - Hugh Bostock
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, Queen Square, WC1N 3BG, London, United Kingdom
| | - David Burke
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Hatice Tankisi
- Department of Clinical Neurophysiology, Aarhus University Hospital and Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
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Barendregt NW, Webb EG, Kilpatrick ZP. Adaptive Bayesian inference of Markov transition rates. Proc Math Phys Eng Sci 2023. [DOI: 10.1098/rspa.2022.0453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023] Open
Abstract
Optimal designs minimize the number of experimental runs (samples) needed to accurately estimate model parameters, resulting in algorithms that, for instance, efficiently minimize parameter estimate variance. Governed by knowledge of past observations, adaptive approaches adjust sampling constraints online as model parameter estimates are refined, continually maximizing expected information gained or variance reduced. We apply adaptive Bayesian inference to estimate transition rates of Markov chains, a common class of models for stochastic processes in nature. Unlike most previous studies, our sequential Bayesian optimal design is updated with each observation and can be simply extended beyond two-state models to birth–death processes and multistate models. By iteratively finding the best time to obtain each sample, our adaptive algorithm maximally reduces variance, resulting in lower overall error in ground truth parameter estimates across a wide range of Markov chain parameterizations and conformations.
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Affiliation(s)
- Nicholas W. Barendregt
- Department of Applied Mathematics, University of Colorado Boulder, 1111 Engineering Center, ECOT 225, 526 UCB, Boulder, CO 80309, USA
| | - Emily G. Webb
- Applied Physics Laboratory, Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD 20723, USA
| | - Zachary P. Kilpatrick
- Department of Applied Mathematics, University of Colorado Boulder, 1111 Engineering Center, ECOT 225, 526 UCB, Boulder, CO 80309, USA
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Using Experimental Data and Information Criteria to Guide Model Selection for Reaction–Diffusion Problems in Mathematical Biology. Bull Math Biol 2019; 81:1760-1804. [DOI: 10.1007/s11538-019-00589-x] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Accepted: 02/20/2019] [Indexed: 12/20/2022]
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Price DJ, Bean NG, Ross JV, Tuke J. An induced natural selection heuristic for finding optimal Bayesian experimental designs. Comput Stat Data Anal 2018. [DOI: 10.1016/j.csda.2018.04.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Dehideniya MB, Drovandi CC, McGree JM. Optimal Bayesian design for discriminating between models with intractable likelihoods in epidemiology. Comput Stat Data Anal 2018. [DOI: 10.1016/j.csda.2018.03.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Price DJ, Bean NG, Ross JV, Tuke J. Designing group dose-response studies in the presence of transmission. Math Biosci 2018; 304:62-78. [PMID: 30055213 DOI: 10.1016/j.mbs.2018.07.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Revised: 05/24/2018] [Accepted: 07/17/2018] [Indexed: 10/28/2022]
Abstract
Dose-response studies are used throughout pharmacology, toxicology and in clinical research to determine safe, effective, or hazardous doses of a substance. When involving animals, the subjects are often housed in groups; this is in fact mandatory in many countries for social animals, on ethical grounds. An issue that may consequently arise is that of unregulated between-subject dosing (transmission), where a subject may transmit the substance to another subject. Transmission will obviously impact the assessment of the dose-response relationship, and will lead to biases if not properly modelled. Here we present a method for determining the optimal design - pertaining to the size of groups, the doses, and the killing times - for such group dose-response experiments, in a Bayesian framework. Our results are of importance to minimising the number of animals required in order to accurately determine dose-response relationships. Furthermore, we additionally consider scenarios in which the estimation of the amount of transmission is also of interest. A particular motivating example is that of Campylobacter jejuni in chickens. Code is provided so that practitioners may determine the optimal design for their own studies.
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Affiliation(s)
- David J Price
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, VIC 3010, Australia; Victorian Infectious Diseases Reference Laboratory Epidemiology Unit, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Royal Melbourne Hospital, VIC 3000, Australia.
| | - Nigel G Bean
- School of Mathematical Sciences, University of Adelaide, SA 5005, Australia; ARC Centre of Excellence for Mathematical & Statistical Frontiers, School of Mathematical Sciences, University of Adelaide, SA 5005, Australia
| | - Joshua V Ross
- School of Mathematical Sciences, University of Adelaide, SA 5005, Australia; ARC Centre of Excellence for Mathematical & Statistical Frontiers, School of Mathematical Sciences, University of Adelaide, SA 5005, Australia
| | - Jonathan Tuke
- School of Mathematical Sciences, University of Adelaide, SA 5005, Australia; ARC Centre of Excellence for Mathematical & Statistical Frontiers, School of Mathematical Sciences, University of Adelaide, SA 5005, Australia
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7
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Karabatsos G, Leisen F. An approximate likelihood perspective on ABC methods. STATISTICS SURVEYS 2018. [DOI: 10.1214/18-ss120] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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8
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McGree J. Developments of the total entropy utility function for the dual purpose of model discrimination and parameter estimation in Bayesian design. Comput Stat Data Anal 2017. [DOI: 10.1016/j.csda.2016.05.020] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Saa PA, Nielsen LK. Construction of feasible and accurate kinetic models of metabolism: A Bayesian approach. Sci Rep 2016; 6:29635. [PMID: 27417285 PMCID: PMC4945864 DOI: 10.1038/srep29635] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2016] [Accepted: 06/20/2016] [Indexed: 12/24/2022] Open
Abstract
Kinetic models are essential to quantitatively understand and predict the behaviour of metabolic networks. Detailed and thermodynamically feasible kinetic models of metabolism are inherently difficult to formulate and fit. They have a large number of heterogeneous parameters, are non-linear and have complex interactions. Many powerful fitting strategies are ruled out by the intractability of the likelihood function. Here, we have developed a computational framework capable of fitting feasible and accurate kinetic models using Approximate Bayesian Computation. This framework readily supports advanced modelling features such as model selection and model-based experimental design. We illustrate this approach on the tightly-regulated mammalian methionine cycle. Sampling from the posterior distribution, the proposed framework generated thermodynamically feasible parameter samples that converged on the true values, and displayed remarkable prediction accuracy in several validation tests. Furthermore, a posteriori analysis of the parameter distributions enabled appraisal of the systems properties of the network (e.g., control structure) and key metabolic regulations. Finally, the framework was used to predict missing allosteric interactions.
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Affiliation(s)
- Pedro A. Saa
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia
| | - Lars K. Nielsen
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia
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Price DJ, Bean NG, Ross JV, Tuke J. On the efficient determination of optimal Bayesian experimental designs using ABC: A case study in optimal observation of epidemics. J Stat Plan Inference 2016. [DOI: 10.1016/j.jspi.2015.12.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Ryan EG, Drovandi CC, Pettitt AN. Simulation-based fully Bayesian experimental design for mixed effects models. Comput Stat Data Anal 2015. [DOI: 10.1016/j.csda.2015.06.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Ryan EG, Drovandi CC, McGree JM, Pettitt AN. A Review of Modern Computational Algorithms for Bayesian Optimal Design. Int Stat Rev 2015. [DOI: 10.1111/insr.12107] [Citation(s) in RCA: 105] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Elizabeth G. Ryan
- School of Mathematical Sciences; Queensland University of Technology; Brisbane Australia
- Biostatistics Department, Institute of Psychiatry, Psychology and Neuroscience; King's College London; London UK
| | - Christopher C. Drovandi
- School of Mathematical Sciences; Queensland University of Technology; Brisbane Australia
- ARC Centre of Excellence for Mathematical and Statistical Frontiers; Queensland University of Technology; Brisbane Australia
| | - James M. McGree
- School of Mathematical Sciences; Queensland University of Technology; Brisbane Australia
- ARC Centre of Excellence for Mathematical and Statistical Frontiers; Queensland University of Technology; Brisbane Australia
| | - Anthony N. Pettitt
- School of Mathematical Sciences; Queensland University of Technology; Brisbane Australia
- ARC Centre of Excellence for Mathematical and Statistical Frontiers; Queensland University of Technology; Brisbane Australia
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Vo BN, Drovandi CC, Pettitt AN, Simpson MJ. Quantifying uncertainty in parameter estimates for stochastic models of collective cell spreading using approximate Bayesian computation. Math Biosci 2015; 263:133-42. [DOI: 10.1016/j.mbs.2015.02.010] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2014] [Revised: 01/09/2015] [Accepted: 02/25/2015] [Indexed: 02/02/2023]
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Hainy M, Müller WG, Wagner H. Likelihood-free simulation-based optimal design with an application to spatial extremes. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT : RESEARCH JOURNAL 2015; 30:481-492. [PMID: 27563280 PMCID: PMC4981187 DOI: 10.1007/s00477-015-1067-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
In this paper we employ a novel method to find the optimal design for problems where the likelihood is not available analytically, but simulation from the likelihood is feasible. To approximate the expected utility we make use of approximate Bayesian computation methods. We detail the approach for a model on spatial extremes, where the goal is to find the optimal design for efficiently estimating the parameters determining the dependence structure. The method is applied to determine the optimal design of weather stations for modeling maximum annual summer temperatures.
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Affiliation(s)
- Markus Hainy
- Department of Applied Statistics, Johannes Kepler University, Altenberger Strasse 69, 4040 Linz, Austria
| | - Werner G. Müller
- Department of Applied Statistics, Johannes Kepler University, Altenberger Strasse 69, 4040 Linz, Austria
| | - Helga Wagner
- Department of Applied Statistics, Johannes Kepler University, Altenberger Strasse 69, 4040 Linz, Austria
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Abstract
Mathematical models have been central to ecology for nearly a century. Simple models
of population dynamics have allowed us to understand fundamental aspects underlying
the dynamics and stability of ecological systems. What has remained a challenge,
however, is to meaningfully interpret experimental or observational data in light of
mathematical models. Here, we review recent developments, notably in the growing
field of approximate Bayesian computation (ABC), that allow us to calibrate
mathematical models against available data. Estimating the population demographic
parameters from data remains a formidable statistical challenge. Here, we attempt to
give a flavor and overview of ABC and its applications in population biology and
ecology and eschew a detailed technical discussion in favor of a general discussion
of the advantages and potential pitfalls this framework offers to population
biologists.
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18
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Liepe J, Holzhütter HG, Kloetzel PM, Stumpf MPH, Mishto M. Modelling proteasome and proteasome regulator activities. Biomolecules 2014; 4:585-99. [PMID: 24970232 PMCID: PMC4101499 DOI: 10.3390/biom4020585] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2014] [Revised: 05/28/2014] [Accepted: 05/30/2014] [Indexed: 02/07/2023] Open
Abstract
Proteasomes are key proteases involved in a variety of processes ranging from the clearance of damaged proteins to the presentation of antigens to CD8+ T-lymphocytes. Which cleavage sites are used within the target proteins and how fast these proteins are degraded have a profound impact on immune system function and many cellular metabolic processes. The regulation of proteasome activity involves different mechanisms, such as the substitution of the catalytic subunits, the binding of regulatory complexes to proteasome gates and the proteasome conformational modifications triggered by the target protein itself. Mathematical models are invaluable in the analysis; and potentially allow us to predict the complex interactions of proteasome regulatory mechanisms and the final outcomes of the protein degradation rate and MHC class I epitope generation. The pioneering attempts that have been made to mathematically model proteasome activity, cleavage preference variation and their modification by one of the regulatory mechanisms are reviewed here.
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Affiliation(s)
- Juliane Liepe
- Theoretical Systems Biology, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, UK.
| | | | - Peter M Kloetzel
- Institute of Biochemistry, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany.
| | - Michael P H Stumpf
- Theoretical Systems Biology, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, UK.
| | - Michele Mishto
- Institute of Biochemistry, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany.
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Ryan EG, Drovandi CC, Thompson MH, Pettitt AN. Towards Bayesian experimental design for nonlinear models that require a large number of sampling times. Comput Stat Data Anal 2014. [DOI: 10.1016/j.csda.2013.08.017] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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20
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Hainy M, Müller WG, Wagner H. Likelihood-Free Simulation-Based Optimal Design: An Introduction. SPRINGER PROCEEDINGS IN MATHEMATICS & STATISTICS 2014. [DOI: 10.1007/978-1-4939-2104-1_26] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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