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      Credibility, Replicability, and Reproducibility in Simulation for Biomedicine and Clinical Applications in Neuroscience

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          Abstract

          Modeling and simulation in computational neuroscience is currently a research enterprise to better understand neural systems. It is not yet directly applicable to the problems of patients with brain disease. To be used for clinical applications, there must not only be considerable progress in the field but also a concerted effort to use best practices in order to demonstrate model credibility to regulatory bodies, to clinics and hospitals, to doctors, and to patients. In doing this for neuroscience, we can learn lessons from long-standing practices in other areas of simulation (aircraft, computer chips), from software engineering, and from other biomedical disciplines. In this manuscript, we introduce some basic concepts that will be important in the development of credible clinical neuroscience models: reproducibility and replicability; verification and validation; model configuration; and procedures and processes for credible mechanistic multiscale modeling. We also discuss how garnering strong community involvement can promote model credibility. Finally, in addition to direct usage with patients, we note the potential for simulation usage in the area of Simulation-Based Medical Education, an area which to date has been primarily reliant on physical models (mannequins) and scenario-based simulations rather than on numerical simulations.

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          Most cited references77

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          Impulses and Physiological States in Theoretical Models of Nerve Membrane

          Van der Pol's equation for a relaxation oscillator is generalized by the addition of terms to produce a pair of non-linear differential equations with either a stable singular point or a limit cycle. The resulting "BVP model" has two variables of state, representing excitability and refractoriness, and qualitatively resembles Bonhoeffer's theoretical model for the iron wire model of nerve. This BVP model serves as a simple representative of a class of excitable-oscillatory systems including the Hodgkin-Huxley (HH) model of the squid giant axon. The BVP phase plane can be divided into regions corresponding to the physiological states of nerve fiber (resting, active, refractory, enhanced, depressed, etc.) to form a "physiological state diagram," with the help of which many physiological phenomena can be summarized. A properly chosen projection from the 4-dimensional HH phase space onto a plane produces a similar diagram which shows the underlying relationship between the two models. Impulse trains occur in the BVP and HH models for a range of constant applied currents which make the singular point representing the resting state unstable.
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            NEST (NEural Simulation Tool)

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              Cortical control of arm movements: a dynamical systems perspective.

              Our ability to move is central to everyday life. Investigating the neural control of movement in general, and the cortical control of volitional arm movements in particular, has been a major research focus in recent decades. Studies have involved primarily either attempts to account for single-neuron responses in terms of tuning for movement parameters or attempts to decode movement parameters from populations of tuned neurons. Even though this focus on encoding and decoding has led to many seminal advances, it has not produced an agreed-upon conceptual framework. Interest in understanding the underlying neural dynamics has recently increased, leading to questions such as how does the current population response determine the future population response, and to what purpose? We review how a dynamical systems perspective may help us understand why neural activity evolves the way it does, how neural activity relates to movement parameters, and how a unified conceptual framework may result.
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                Author and article information

                Contributors
                Journal
                Front Neuroinform
                Front Neuroinform
                Front. Neuroinform.
                Frontiers in Neuroinformatics
                Frontiers Media S.A.
                1662-5196
                16 April 2018
                2018
                : 12
                : 18
                Affiliations
                [1] 1InSilico Labs LLC , Houston, TX, United States
                [2] 2The Institute for Computational Engineering and Sciences, The University of Texas at Austin , Austin, TX, United States
                [3] 3Department of Biomedical Engineering and Computational Biomodeling (CoBi) Core, Lerner Research Institute, Cleveland Clinic , Cleveland, OH, United States
                [4] 4Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco , San Francisco, CA, United States
                [5] 5ANSYS, Inc. , Evanston, IL, United States
                [6] 6Department of Bioengineering, Stanford University , Stanford, CA, United States
                [7] 7NASA Glenn Research Center , Cleveland, OH, United States
                [8] 8Department of Pathology, Anatomy and Cell Biology, Daniel Baugh Institute for Functional Genomics and Computational Biology, Thomas Jefferson University , Philadelphia, PA, United States
                [9] 9Department of Neurology, SUNY Downstate Medical Center, The State University of New York , New York, NY, United States
                [10] 10Department of Physiology and Pharmacology, SUNY Downstate Medical Center, The State University of New York , New York, NY, United States
                [11] 11Department of Neurology, Kings County Hospital Center , New York, NY, United States
                Author notes

                Edited by: Sharon Crook, Arizona State University, United States

                Reviewed by: Georg Hinkel, FZI Forschungszentrum Informatik, Germany; Upinder S. Bhalla, National Centre for Biological Sciences, India

                *Correspondence: Lealem Mulugeta, lealem@ 123456insilico-labs.com William W. Lytton, bill.lytton@ 123456downstate.edu
                Article
                10.3389/fninf.2018.00018
                5911506
                29713272
                287cc7bc-06b7-434f-a6db-7f03d92eca1c
                Copyright © 2018 Mulugeta, Drach, Erdemir, Hunt, Horner, Ku, Myers, Vadigepalli and Lytton.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 01 February 2018
                : 29 March 2018
                Page count
                Figures: 3, Tables: 0, Equations: 0, References: 106, Pages: 16, Words: 0
                Funding
                Funded by: National Institutes of Health 10.13039/100000002
                Award ID: R01GM104139
                Award ID: R01EB024573
                Award ID: R01 GM107340
                Award ID: U54 EB020405
                Award ID: P2C HD065690
                Award ID: U01 EB023224
                Award ID: U01 HL133360
                Award ID: OT2 OD023848
                Award ID: R01-EB022903
                Award ID: U01-EB017695
                Award ID: R01-MH086638
                Funded by: U.S. Army 10.13039/100006751
                Award ID: W81XWH-15-1-0232
                Funded by: New York State Department of Health 10.13039/100004856
                Award ID: DOH01-C32250GG-3450000
                Categories
                Neuroscience
                Review

                Neurosciences
                computational neuroscience,verification and validation,model sharing,modeling and simulations,simulation-based medical education,multiscale modeling,personalized and precision medicine,mechanistic modeling

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