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Dura-Bernal S, Neymotin SA, Suter BA, Dacre J, Moreira JVS, Urdapilleta E, Schiemann J, Duguid I, Shepherd GMG, Lytton WW. Multiscale model of primary motor cortex circuits predicts in vivo cell-type-specific, behavioral state-dependent dynamics. Cell Rep 2023; 42:112574. [PMID: 37300831 PMCID: PMC10592234 DOI: 10.1016/j.celrep.2023.112574] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 02/27/2023] [Accepted: 05/12/2023] [Indexed: 06/12/2023] Open
Abstract
Understanding cortical function requires studying multiple scales: molecular, cellular, circuit, and behavioral. We develop a multiscale, biophysically detailed model of mouse primary motor cortex (M1) with over 10,000 neurons and 30 million synapses. Neuron types, densities, spatial distributions, morphologies, biophysics, connectivity, and dendritic synapse locations are constrained by experimental data. The model includes long-range inputs from seven thalamic and cortical regions and noradrenergic inputs. Connectivity depends on cell class and cortical depth at sublaminar resolution. The model accurately predicts in vivo layer- and cell-type-specific responses (firing rates and LFP) associated with behavioral states (quiet wakefulness and movement) and experimental manipulations (noradrenaline receptor blockade and thalamus inactivation). We generate mechanistic hypotheses underlying the observed activity and analyzed low-dimensional population latent dynamics. This quantitative theoretical framework can be used to integrate and interpret M1 experimental data and sheds light on the cell-type-specific multiscale dynamics associated with several experimental conditions and behaviors.
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Affiliation(s)
- Salvador Dura-Bernal
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
| | - Samuel A Neymotin
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department of Psychiatry, Grossman School of Medicine, New York University (NYU), New York, NY, USA
| | - Benjamin A Suter
- Department of Physiology, Northwestern University, Evanston, IL, USA
| | - Joshua Dacre
- Centre for Discovery Brain Sciences, Edinburgh Medical School: Biomedical Sciences, University of Edinburgh, Edinburgh, UK
| | - Joao V S Moreira
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA
| | - Eugenio Urdapilleta
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA
| | - Julia Schiemann
- Centre for Discovery Brain Sciences, Edinburgh Medical School: Biomedical Sciences, University of Edinburgh, Edinburgh, UK; Center for Integrative Physiology and Molecular Medicine, Saarland University, Saarbrücken, Germany
| | - Ian Duguid
- Centre for Discovery Brain Sciences, Edinburgh Medical School: Biomedical Sciences, University of Edinburgh, Edinburgh, UK
| | | | - William W Lytton
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA; Department of Neurology, Kings County Hospital Center, Brooklyn, NY, USA
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2
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Stöber TM, Batulin D, Triesch J, Narayanan R, Jedlicka P. Degeneracy in epilepsy: multiple routes to hyperexcitable brain circuits and their repair. Commun Biol 2023; 6:479. [PMID: 37137938 PMCID: PMC10156698 DOI: 10.1038/s42003-023-04823-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 04/06/2023] [Indexed: 05/05/2023] Open
Abstract
Due to its complex and multifaceted nature, developing effective treatments for epilepsy is still a major challenge. To deal with this complexity we introduce the concept of degeneracy to the field of epilepsy research: the ability of disparate elements to cause an analogous function or malfunction. Here, we review examples of epilepsy-related degeneracy at multiple levels of brain organisation, ranging from the cellular to the network and systems level. Based on these insights, we outline new multiscale and population modelling approaches to disentangle the complex web of interactions underlying epilepsy and to design personalised multitarget therapies.
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Affiliation(s)
- Tristan Manfred Stöber
- Frankfurt Institute for Advanced Studies, 60438, Frankfurt am Main, Germany
- Institute for Neural Computation, Faculty of Computer Science, Ruhr University Bochum, 44801, Bochum, Germany
- Epilepsy Center Frankfurt Rhine-Main, Department of Neurology, Goethe University, 60590, Frankfurt, Germany
| | - Danylo Batulin
- Frankfurt Institute for Advanced Studies, 60438, Frankfurt am Main, Germany
- CePTER - Center for Personalized Translational Epilepsy Research, Goethe University, 60590, Frankfurt, Germany
- Faculty of Computer Science and Mathematics, Goethe University, 60486, Frankfurt, Germany
| | - Jochen Triesch
- Frankfurt Institute for Advanced Studies, 60438, Frankfurt am Main, Germany
| | - Rishikesh Narayanan
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, 560012, India
| | - Peter Jedlicka
- ICAR3R - Interdisciplinary Centre for 3Rs in Animal Research, Faculty of Medicine, Justus Liebig University Giessen, 35390, Giessen, Germany.
- Institute of Clinical Neuroanatomy, Neuroscience Center, Goethe University, 60590, Frankfurt am Main, Germany.
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3
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Cobb EAW, Petroccione MA, Scimemi A. NRN-EZ: an application to streamline biophysical modeling of synaptic integration using NEURON. Sci Rep 2023; 13:464. [PMID: 36627356 PMCID: PMC9832141 DOI: 10.1038/s41598-022-27302-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 12/29/2022] [Indexed: 01/12/2023] Open
Abstract
One of the fundamental goals in neuroscience is to determine how the brain processes information and ultimately controls the execution of complex behaviors. Over the past four decades, there has been a steady growth in our knowledge of the morphological and functional diversity of neurons, the building blocks of the brain. These cells clearly differ not only for their anatomy and ion channel distribution, but also for the type, strength, location, and temporal pattern of activity of the many synaptic inputs they receive. Compartmental modeling programs like NEURON have become widely used in the neuroscience community to address a broad range of research questions, including how neurons integrate synaptic inputs and propagate information through complex neural networks. One of the main strengths of NEURON is its ability to incorporate user-defined information about the realistic morphology and biophysical properties of different cell types. Although the graphical user interface of the program can be used to run initial exploratory simulations, introducing a stochastic representation of synaptic weights, locations and activation times typically requires users to develop their own codes, a task that can be overwhelming for some beginner users. Here we describe NRN-EZ, an interactive application that allows users to specify complex patterns of synaptic input activity that can be integrated as part of NEURON simulations. Through its graphical user interface, NRN-EZ aims to ease the learning curve to run computational models in NEURON, for users that do not necessarily have a computer science background.
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Affiliation(s)
- Evan A. W. Cobb
- grid.265850.c0000 0001 2151 7947Department of Biology, SUNY Albany, 1400 Washington Avenue, Albany, NY 12222-0100 USA ,grid.265850.c0000 0001 2151 7947Department of Computer Science, SUNY Albany, 1400 Washington Avenue, Albany, NY 12222-0100 USA
| | - Maurice A. Petroccione
- grid.265850.c0000 0001 2151 7947Department of Biology, SUNY Albany, 1400 Washington Avenue, Albany, NY 12222-0100 USA
| | - Annalisa Scimemi
- Department of Biology, SUNY Albany, 1400 Washington Avenue, Albany, NY, 12222-0100, USA.
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4
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Oláh VJ, Pedersen NP, Rowan MJM. Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons. eLife 2022; 11:e79535. [PMID: 36341568 PMCID: PMC9640191 DOI: 10.7554/elife.79535] [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: 04/16/2022] [Accepted: 10/23/2022] [Indexed: 11/09/2022] Open
Abstract
Understanding the activity of the mammalian brain requires an integrative knowledge of circuits at distinct scales, ranging from ion channel gating to circuit connectomics. Computational models are regularly employed to understand how multiple parameters contribute synergistically to circuit behavior. However, traditional models of anatomically and biophysically realistic neurons are computationally demanding, especially when scaled to model local circuits. To overcome this limitation, we trained several artificial neural network (ANN) architectures to model the activity of realistic multicompartmental cortical neurons. We identified an ANN architecture that accurately predicted subthreshold activity and action potential firing. The ANN could correctly generalize to previously unobserved synaptic input, including in models containing nonlinear dendritic properties. When scaled, processing times were orders of magnitude faster compared with traditional approaches, allowing for rapid parameter-space mapping in a circuit model of Rett syndrome. Thus, we present a novel ANN approach allowing for rapid, detailed network experiments using inexpensive and commonly available computational resources.
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Affiliation(s)
- Viktor J Oláh
- Department of Cell Biology, Emory University School of MedicineAtlantaUnited States
| | - Nigel P Pedersen
- Department of Neurology, Emory University School of MedicineAtlantaUnited States
| | - Matthew JM Rowan
- Department of Cell Biology, Emory University School of MedicineAtlantaUnited States
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5
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Yu Y, Han F, Wang Q. Exploring phase–amplitude coupling from primary motor cortex-basal ganglia-thalamus network model. Neural Netw 2022; 153:130-141. [DOI: 10.1016/j.neunet.2022.05.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 04/11/2022] [Accepted: 05/27/2022] [Indexed: 10/18/2022]
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6
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Kamaleddin MA. Degeneracy in the nervous system: from neuronal excitability to neural coding. Bioessays 2021; 44:e2100148. [PMID: 34791666 DOI: 10.1002/bies.202100148] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 09/26/2021] [Accepted: 09/28/2021] [Indexed: 02/04/2023]
Abstract
Degeneracy is ubiquitous across biological systems where structurally different elements can yield a similar outcome. Degeneracy is of particular interest in neuroscience too. On the one hand, degeneracy confers robustness to the nervous system and facilitates evolvability: Different elements provide a backup plan for the system in response to any perturbation or disturbance. On the other, a difficulty in the treatment of some neurological disorders such as chronic pain is explained in light of different elements all of which contribute to the pathological behavior of the system. Under these circumstances, targeting a specific element is ineffective because other elements can compensate for this modulation. Understanding degeneracy in the physiological context explains its beneficial role in the robustness of neural circuits. Likewise, understanding degeneracy in the pathological context opens new avenues of discovery to find more effective therapies.
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Affiliation(s)
- Mohammad Amin Kamaleddin
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.,Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, Canada
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7
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Romaro C, Najman FA, Lytton WW, Roque AC, Dura-Bernal S. NetPyNE Implementation and Scaling of the Potjans-Diesmann Cortical Microcircuit Model. Neural Comput 2021; 33:1993-2032. [PMID: 34411272 PMCID: PMC8382011 DOI: 10.1162/neco_a_01400] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 02/16/2021] [Indexed: 11/04/2022]
Abstract
The Potjans-Diesmann cortical microcircuit model is a widely used model originally implemented in NEST. Here, we reimplemented the model using NetPyNE, a high-level Python interface to the NEURON simulator, and reproduced the findings of the original publication. We also implemented a method for scaling the network size that preserves first- and second-order statistics, building on existing work on network theory. Our new implementation enabled the use of more detailed neuron models with multicompartmental morphologies and multiple biophysically realistic ion channels. This opens the model to new research, including the study of dendritic processing, the influence of individual channel parameters, the relation to local field potentials, and other multiscale interactions. The scaling method we used provides flexibility to increase or decrease the network size as needed when running these CPU-intensive detailed simulations. Finally, NetPyNE facilitates modifying or extending the model using its declarative language; optimizing model parameters; running efficient, large-scale parallelized simulations; and analyzing the model through built-in methods, including local field potential calculation and information flow measures.
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Affiliation(s)
- Cecilia Romaro
- Department of Physics, School of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP 14049, Brazil
| | - Fernando Araujo Najman
- Institute of Mathematics and Statistics, University of São Paulo, São Paulo, SP 05508, Brazil
| | - William W Lytton
- Department of Physiology and Pharmacology, State University of New York Downstate Health Sciences University, New York, NY 11203, U.S.A.
| | - Antonio C Roque
- Department of Physics, School of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP 14049, Brazil
| | - Salvador Dura-Bernal
- Department of Physiology and Pharmacology, State University of New York Downstate Health Sciences University, New York, NY 11203, U.S.A., and Nathan Kline Institute for Psychiatric Research, New York, NY 10962, U.S.A.
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8
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Kelley C, Dura-Bernal S, Neymotin SA, Antic SD, Carnevale NT, Migliore M, Lytton WW. Effects of Ih and TASK-like shunting current on dendritic impedance in layer 5 pyramidal-tract neurons. J Neurophysiol 2021; 125:1501-1516. [PMID: 33689489 PMCID: PMC8282219 DOI: 10.1152/jn.00015.2021] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 03/02/2021] [Accepted: 03/02/2021] [Indexed: 02/07/2023] Open
Abstract
Pyramidal neurons in neocortex have complex input-output relationships that depend on their morphologies, ion channel distributions, and the nature of their inputs, but which cannot be replicated by simple integrate-and-fire models. The impedance properties of their dendritic arbors, such as resonance and phase shift, shape neuronal responses to synaptic inputs and provide intraneuronal functional maps reflecting their intrinsic dynamics and excitability. Experimental studies of dendritic impedance have shown that neocortical pyramidal tract neurons exhibit distance-dependent changes in resonance and impedance phase with respect to the soma. We, therefore, investigated how well several biophysically detailed multicompartment models of neocortical layer 5 pyramidal tract neurons reproduce the location-dependent impedance profiles observed experimentally. Each model tested here exhibited location-dependent impedance profiles, but most captured either the observed impedance amplitude or phase, not both. The only model that captured features from both incorporates hyperpolarization-activated cyclic nucleotide-gated (HCN) channels and a shunting current, such as that produced by Twik-related acid-sensitive K+ (TASK) channels. TASK-like channel density in this model was proportional to local HCN channel density. We found that although this shunting current alone is insufficient to produce resonance or realistic phase response, it modulates all features of dendritic impedance, including resonance frequencies, resonance strength, synchronous frequencies, and total inductive phase. We also explored how the interaction of HCN channel current (Ih) and a TASK-like shunting current shape synaptic potentials and produce degeneracy in dendritic impedance profiles, wherein different combinations of Ih and shunting current can produce the same impedance profile.NEW & NOTEWORTHY We simulated chirp current stimulation in the apical dendrites of 5 biophysically detailed multicompartment models of neocortical pyramidal tract neurons and found that a combination of HCN channels and TASK-like channels produced the best fit to experimental measurements of dendritic impedance. We then explored how HCN and TASK-like channels can shape the dendritic impedance as well as the voltage response to synaptic currents.
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Affiliation(s)
- Craig Kelley
- Program in Biomedical Engineering, SUNY Downstate Health Sciences University and NYU Tandon School of Engineering, Brooklyn, New York
| | - Salvador Dura-Bernal
- Department of Physiology and Pharmacology, SUNY Downstate Health Sciences University, Brooklyn, New York
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York
| | - Samuel A Neymotin
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York
- Department of Psychiatry, NYU Grossman School of Medicine, New York City, New York
| | - Srdjan D Antic
- Neuroscience Department, Institute of Systems Genomics, University of Connecticut Health, Farmington, Connecticut
| | | | - Michele Migliore
- Institute of Biophysics, National Research Council, Palermo, Italy
| | - William W Lytton
- Program in Biomedical Engineering, SUNY Downstate Health Sciences University and NYU Tandon School of Engineering, Brooklyn, New York
- Department of Physiology and Pharmacology, SUNY Downstate Health Sciences University, Brooklyn, New York
- Department of Neurology, SUNY Downstate Health Sciences University, Brooklyn, New York
- Department of Neurology, Kings County Hospital Center, Brooklyn, New York
- The Robert F. Furchgott Center for Neural and Behavioral Science, Brooklyn, New York
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9
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van der Linden H, Silveira-Moriyama L, van der Linden V, Pessoa A, Valente K, Mink J, Paciorkowski A. Movement disorders in children with congenital Zika virus syndrome. Brain Dev 2020; 42:720-729. [PMID: 32682638 DOI: 10.1016/j.braindev.2020.06.016] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 06/25/2020] [Accepted: 06/30/2020] [Indexed: 01/19/2023]
Abstract
BACKGROUND Congenital Zika Virus Syndrome (CZVS) denotes the neurologic and developmental sequelae of congenital infection of the Zika virus. While prior studies have detailed the associated clinical phenotypes, new findings continue to be identified. Abnormal postures and movements have been previously described in children with CZVS, but not in detail. OBJECTIVE To examine a cohort of infants with CZVS and characterize the spectrum of motor abnormalities, especially movement disorders. DESIGN Cross-sectional prospective study of 21 infants with confirmed CZVS. SETTING Single-center cohort of 32 patients with serologically confirmed CZVS cared for in a referral center in Brazil. PARTICIPANTS 21 children (67% female), evaluated by two child neurologists and one movement disorders specialist, with clinical and laboratory diagnosis of CZVS aged between 16 and 30 months, with a mean age of 16 months at the time of the last examination. MAIN OUTCOME(S) AND MEASURE(S) Prospective neurologic examination by a team of three neurologists, including one movement disorders specialist. Sixteen (76.2%) children had a longitudinal evaluation with a six-month interval. The same team of experts analyzed recorded videos of all patients to characterize motor abnormalities and movement disorders. Neuroimaging findings were also analyzed to correlate with clinical findings. RESULTS Twenty (95.2%) patients presented with dystonic postures, including "125" posture of the fingers in 17 (80.1%), "swan neck" posture of the fingers in three (18.8%), oromandibular dystonia in nine (42.9%), extensor axial hypertonia in eight (38.1%) and internal rotation of the shoulder posture in two (9.5%). Four (19%) patients had tremor. All children had malformations of cortical development, and in 13 (61.9%), the pattern was consistent with a severe and diffuse gyral simplification. Seventeen children (81%) had calcification in the transition of grey and white matter, whereas 11 (52.4%) patients had basal ganglia calcifications. CONCLUSION AND RELEVANCE In our series, dystonic postures and other extrapyramidal signs were frequent and potentially disabling. Although children with CZVS are assessed and treated for spasticity, dystonia and other movement disorders remain neglected. This study emphasizes that extrapyramidal findings may potentially influence optimal strategies for rehabilitation and management.
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Affiliation(s)
- Hélio van der Linden
- Rehabilitation Center Dr. Henrique Santillo, Pediatric Neurology, Goiania, GO, Brazil; Neurology Institute, Goiania, GO, Brazil.
| | - Laura Silveira-Moriyama
- Fundação Espírita Américo Bairral, Itapira, SP, Brazil; Movement Disorder Unit, Department of Neurology, State University of Campinas, Sao Paulo, Brazil
| | | | - André Pessoa
- Hospital Infantil Albert Sabin, Fortaleza, CE, Brazil; State University of Ceará, Fortaleza, CE, Brazil
| | - Kette Valente
- Laboratory of Clinical Neurophysiology, Department of Psychiatry, Clinic Hospital - University of Sao Paulo (USP), Brazil
| | - Jonathan Mink
- Department of Neurology, Pediatrics, and Neuroscience, University of Rochester Medical Center, Rochester, NY, USA
| | - Alex Paciorkowski
- Deptartment of Neurology, Pediatrics, Biomedical Genetics, and Neuroscience, University of Rochester Medical Center, Rochester, NY, USA
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10
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Erdemir A, Mulugeta L, Ku JP, Drach A, Horner M, Morrison TM, Peng GCY, Vadigepalli R, Lytton WW, Myers JG. Credible practice of modeling and simulation in healthcare: ten rules from a multidisciplinary perspective. J Transl Med 2020; 18:369. [PMID: 32993675 PMCID: PMC7526418 DOI: 10.1186/s12967-020-02540-4] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 09/21/2020] [Indexed: 11/10/2022] Open
Abstract
The complexities of modern biomedicine are rapidly increasing. Thus, modeling and simulation have become increasingly important as a strategy to understand and predict the trajectory of pathophysiology, disease genesis, and disease spread in support of clinical and policy decisions. In such cases, inappropriate or ill-placed trust in the model and simulation outcomes may result in negative outcomes, and hence illustrate the need to formalize the execution and communication of modeling and simulation practices. Although verification and validation have been generally accepted as significant components of a model’s credibility, they cannot be assumed to equate to a holistic credible practice, which includes activities that can impact comprehension and in-depth examination inherent in the development and reuse of the models. For the past several years, the Committee on Credible Practice of Modeling and Simulation in Healthcare, an interdisciplinary group seeded from a U.S. interagency initiative, has worked to codify best practices. Here, we provide Ten Rules for credible practice of modeling and simulation in healthcare developed from a comparative analysis by the Committee’s multidisciplinary membership, followed by a large stakeholder community survey. These rules establish a unified conceptual framework for modeling and simulation design, implementation, evaluation, dissemination and usage across the modeling and simulation life-cycle. While biomedical science and clinical care domains have somewhat different requirements and expectations for credible practice, our study converged on rules that would be useful across a broad swath of model types. In brief, the rules are: (1) Define context clearly. (2) Use contextually appropriate data. (3) Evaluate within context. (4) List limitations explicitly. (5) Use version control. (6) Document appropriately. (7) Disseminate broadly. (8) Get independent reviews. (9) Test competing implementations. (10) Conform to standards. Although some of these are common sense guidelines, we have found that many are often missed or misconstrued, even by seasoned practitioners. Computational models are already widely used in basic science to generate new biomedical knowledge. As they penetrate clinical care and healthcare policy, contributing to personalized and precision medicine, clinical safety will require established guidelines for the credible practice of modeling and simulation in healthcare.
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Affiliation(s)
- Ahmet Erdemir
- Department of Biomedical Engineering and Computational Biomodeling (CoBi) Core, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue (ND20), Cleveland, OH, 44195, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - Lealem Mulugeta
- InSilico Labs LLC, 2617 Bissonnet St. Suite 435, Houston, TX, 77005, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - Joy P Ku
- Department of Bioengineering, Clark Center, Stanford University, 318 Campus Drive, Stanford, CA, 94305-5448, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - Andrew Drach
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, 201 E. 24th st, Austin, TX, 78712, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - Marc Horner
- ANSYS, Inc, 1007 Church Street, Suite 250, Evanston, IL, 60201, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - Tina M Morrison
- Division of Applied Mechanics, United States Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - Grace C Y Peng
- National Institute of Biomedical Imaging & Bioengineering, Suite 200, MSC 6707 Democracy Blvd5469, Bethesda, MD, 20892, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - Rajanikanth Vadigepalli
- Department of Pathology, Anatomy and Cell Biology, Daniel Baugh Institute for Functional Genomics/Computational Biology, Thomas Jefferson University, 1020 Locust St, Philadelphia, PA, 19107, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - William W Lytton
- State University of New York, Kings County Hospital, 450 Clarkson Ave., MSC 31, Brooklyn, NY, 11203, USA.,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA
| | - Jerry G Myers
- Human Research Program, Cross-Cutting Computational Modeling Project, National Aeronautics and Space Administration - John H. Glenn Research Center, 21000 Brookpark Road, Cleveland, OH, 44135, USA. .,Committee on Credible Practice of Modeling, & Simulation in Healthcare, Interagency Modeling and Analysis Group and Multiscale Modeling Consortium, Bethesda, MD, USA.
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11
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Sherif MA, Neymotin SA, Lytton WW. In silico hippocampal modeling for multi-target pharmacotherapy in schizophrenia. NPJ SCHIZOPHRENIA 2020; 6:25. [PMID: 32958782 PMCID: PMC7506542 DOI: 10.1038/s41537-020-00109-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Accepted: 06/23/2020] [Indexed: 02/08/2023]
Abstract
Treatment of schizophrenia has had limited success in treating core cognitive symptoms. The evidence of multi-gene involvement suggests that multi-target therapy may be needed. Meanwhile, the complexity of schizophrenia pathophysiology and psychopathology, coupled with the species-specificity of much of the symptomatology, places limits on analysis via animal models, in vitro assays, and patient assessment. Multiscale computer modeling complements these traditional modes of study. Using a hippocampal CA3 computer model with 1200 neurons, we examined the effects of alterations in NMDAR, HCN (Ih current), and GABAAR on information flow (measured with normalized transfer entropy), and in gamma activity in local field potential (LFP). We found that altering NMDARs, GABAAR, Ih, individually or in combination, modified information flow in an inverted-U shape manner, with information flow reduced at low and high levels of these parameters. Theta-gamma phase-amplitude coupling also had an inverted-U shape relationship with NMDAR augmentation. The strong information flow was associated with an intermediate level of synchrony, seen as an intermediate level of gamma activity in the LFP, and an intermediate level of pyramidal cell excitability. Our results are consistent with the idea that overly low or high gamma power is associated with pathological information flow and information processing. These data suggest the need for careful titration of schizophrenia pharmacotherapy to avoid extremes that alter information flow in different ways. These results also identify gamma power as a potential biomarker for monitoring pathology and multi-target pharmacotherapy.
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Affiliation(s)
- Mohamed A Sherif
- Department of Psychiatry, VA Connecticut Healthcare System, 950 Campbell Avenue, West Haven, CT, USA.
- Department of Psychiatry, Yale University, New Haven, CT, USA.
- Biomedical Engineering Graduate Program, SUNY Downstate Medical Center/NYU Tandon School of Engineering, Brooklyn, NY, USA.
| | - Samuel A Neymotin
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - William W Lytton
- Biomedical Engineering Graduate Program, SUNY Downstate Medical Center/NYU Tandon School of Engineering, Brooklyn, NY, USA
- Department of Physiology and Pharmacology, SUNY Downstate Medical Center, Brooklyn, NY, USA
- Department of Neurology, SUNY Downstate Medical Center, Brooklyn, NY, USA
- Department of Neurology, Kings County Hospital Center, Brooklyn, NY, USA
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12
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Neymotin SA, Daniels DS, Caldwell B, McDougal RA, Carnevale NT, Jas M, Moore CI, Hines ML, Hämäläinen M, Jones SR. Human Neocortical Neurosolver (HNN), a new software tool for interpreting the cellular and network origin of human MEG/EEG data. eLife 2020; 9:e51214. [PMID: 31967544 PMCID: PMC7018509 DOI: 10.7554/elife.51214] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 01/22/2020] [Indexed: 12/26/2022] Open
Abstract
Magneto- and electro-encephalography (MEG/EEG) non-invasively record human brain activity with millisecond resolution providing reliable markers of healthy and disease states. Relating these macroscopic signals to underlying cellular- and circuit-level generators is a limitation that constrains using MEG/EEG to reveal novel principles of information processing or to translate findings into new therapies for neuropathology. To address this problem, we built Human Neocortical Neurosolver (HNN, https://hnn.brown.edu) software. HNN has a graphical user interface designed to help researchers and clinicians interpret the neural origins of MEG/EEG. HNN's core is a neocortical circuit model that accounts for biophysical origins of electrical currents generating MEG/EEG. Data can be directly compared to simulated signals and parameters easily manipulated to develop/test hypotheses on a signal's origin. Tutorials teach users to simulate commonly measured signals, including event related potentials and brain rhythms. HNN's ability to associate signals across scales makes it a unique tool for translational neuroscience research.
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Affiliation(s)
- Samuel A Neymotin
- Department Neuroscience, Carney Institute for Brain SciencesBrown UniversityProvidenceUnited States
- Center for Biomedical Imaging and NeuromodulationNathan S. Kline Institute for Psychiatric ResearchOrangeburgUnited States
| | - Dylan S Daniels
- Department Neuroscience, Carney Institute for Brain SciencesBrown UniversityProvidenceUnited States
| | - Blake Caldwell
- Department Neuroscience, Carney Institute for Brain SciencesBrown UniversityProvidenceUnited States
| | - Robert A McDougal
- Department NeuroscienceYale UniversityNew HavenUnited States
- Department of BiostatisticsYale UniversityNew HavenUnited States
| | | | - Mainak Jas
- Athinoula A. Martinos Center for Biomedical ImagingMassachusetts General HospitalCharlestownUnited States
- Harvard Medical SchoolBostonUnited States
| | - Christopher I Moore
- Department Neuroscience, Carney Institute for Brain SciencesBrown UniversityProvidenceUnited States
| | - Michael L Hines
- Department NeuroscienceYale UniversityNew HavenUnited States
| | - Matti Hämäläinen
- Athinoula A. Martinos Center for Biomedical ImagingMassachusetts General HospitalCharlestownUnited States
- Harvard Medical SchoolBostonUnited States
| | - Stephanie R Jones
- Department Neuroscience, Carney Institute for Brain SciencesBrown UniversityProvidenceUnited States
- Center for Neurorestoration and NeurotechnologyProvidence VAMCProvidenceUnited States
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13
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Dura-Bernal S, Suter BA, Gleeson P, Cantarelli M, Quintana A, Rodriguez F, Kedziora DJ, Chadderdon GL, Kerr CC, Neymotin SA, McDougal RA, Hines M, Shepherd GMG, Lytton WW. NetPyNE, a tool for data-driven multiscale modeling of brain circuits. eLife 2019; 8:e44494. [PMID: 31025934 PMCID: PMC6534378 DOI: 10.7554/elife.44494] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 04/25/2019] [Indexed: 12/22/2022] Open
Abstract
Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, for example connectivity rules, to create millions of cell-to-cell connections. NetPyNE then enables users to generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis - connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate brain regions and phenomena.
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Affiliation(s)
- Salvador Dura-Bernal
- Department of Physiology & PharmacologyState University of New York Downstate Medical CenterBrooklynUnited States
| | - Benjamin A Suter
- Department of PhysiologyNorthwestern UniversityChicagoUnited States
| | - Padraig Gleeson
- Department of Neuroscience, Physiology and PharmacologyUniversity College LondonLondonUnited Kingdom
| | | | | | - Facundo Rodriguez
- Department of Physiology & PharmacologyState University of New York Downstate Medical CenterBrooklynUnited States
- MetaCell LLCBostonUnited States
| | - David J Kedziora
- Complex Systems Group, School of PhysicsUniversity of SydneySydneyAustralia
| | - George L Chadderdon
- Department of Physiology & PharmacologyState University of New York Downstate Medical CenterBrooklynUnited States
| | - Cliff C Kerr
- Complex Systems Group, School of PhysicsUniversity of SydneySydneyAustralia
| | - Samuel A Neymotin
- Department of Physiology & PharmacologyState University of New York Downstate Medical CenterBrooklynUnited States
- Nathan Kline Institute for Psychiatric ResearchOrangeburgUnited States
| | - Robert A McDougal
- Department of Neuroscience and School of MedicineYale UniversityNew HavenUnited States
- Center for Medical InformaticsYale UniversityNew HavenUnited States
| | - Michael Hines
- Department of Neuroscience and School of MedicineYale UniversityNew HavenUnited States
| | | | - William W Lytton
- Department of Physiology & PharmacologyState University of New York Downstate Medical CenterBrooklynUnited States
- Department of NeurologyKings County HospitalBrooklynUnited States
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14
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Migliore R, Lupascu CA, Bologna LL, Romani A, Courcol JD, Antonel S, Van Geit WAH, Thomson AM, Mercer A, Lange S, Falck J, Rössert CA, Shi Y, Hagens O, Pezzoli M, Freund TF, Kali S, Muller EB, Schürmann F, Markram H, Migliore M. The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow. PLoS Comput Biol 2018; 14:e1006423. [PMID: 30222740 PMCID: PMC6160220 DOI: 10.1371/journal.pcbi.1006423] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Revised: 09/27/2018] [Accepted: 08/08/2018] [Indexed: 11/19/2022] Open
Abstract
Every neuron is part of a network, exerting its function by transforming multiple spatiotemporal synaptic input patterns into a single spiking output. This function is specified by the particular shape and passive electrical properties of the neuronal membrane, and the composition and spatial distribution of ion channels across its processes. For a variety of physiological or pathological reasons, the intrinsic input/output function may change during a neuron's lifetime. This process results in high variability in the peak specific conductance of ion channels in individual neurons. The mechanisms responsible for this variability are not well understood, although there are clear indications from experiments and modeling that degeneracy and correlation among multiple channels may be involved. Here, we studied this issue in biophysical models of hippocampal CA1 pyramidal neurons and interneurons. Using a unified data-driven simulation workflow and starting from a set of experimental recordings and morphological reconstructions obtained from rats, we built and analyzed several ensembles of morphologically and biophysically accurate single cell models with intrinsic electrophysiological properties consistent with experimental findings. The results suggest that the set of conductances expressed in any given hippocampal neuron may be considered as belonging to two groups: one subset is responsible for the major characteristics of the firing behavior in each population and the other is responsible for a robust degeneracy. Analysis of the model neurons suggests several experimentally testable predictions related to the combination and relative proportion of the different conductances that should be expressed on the membrane of different types of neurons for them to fulfill their role in the hippocampus circuitry.
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Affiliation(s)
- Rosanna Migliore
- Institute of Biophysics, National Research Council, Palermo, Italy
| | | | - Luca L. Bologna
- Institute of Biophysics, National Research Council, Palermo, Italy
| | - Armando Romani
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Jean-Denis Courcol
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Stefano Antonel
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Werner A. H. Van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | | | | | - Sigrun Lange
- University College London, London, United Kingdom
- University of Westminster, London, United Kingdom
| | - Joanne Falck
- University College London, London, United Kingdom
| | - Christian A. Rössert
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Ying Shi
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Olivier Hagens
- Laboratory of Neural Microcircuitry (LNMC), Brain Mind Institute, EPFL, Lausanne, Switzerland
| | - Maurizio Pezzoli
- Laboratory of Neural Microcircuitry (LNMC), Brain Mind Institute, EPFL, Lausanne, Switzerland
| | - Tamas F. Freund
- Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Szabolcs Kali
- Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Eilif B. Muller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Felix Schürmann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Michele Migliore
- Institute of Biophysics, National Research Council, Palermo, Italy
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15
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Newton AJH, McDougal RA, Hines ML, Lytton WW. Using NEURON for Reaction-Diffusion Modeling of Extracellular Dynamics. Front Neuroinform 2018; 12:41. [PMID: 30042670 PMCID: PMC6049079 DOI: 10.3389/fninf.2018.00041] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2018] [Accepted: 06/12/2018] [Indexed: 11/13/2022] Open
Abstract
Development of credible clinically-relevant brain simulations has been slowed due to a focus on electrophysiology in computational neuroscience, neglecting the multiscale whole-tissue modeling approach used for simulation in most other organ systems. We have now begun to extend the NEURON simulation platform in this direction by adding extracellular modeling. The extracellular medium of neural tissue is an active medium of neuromodulators, ions, inflammatory cells, oxygen, NO and other gases, with additional physiological, pharmacological and pathological agents. These extracellular agents influence, and are influenced by, cellular electrophysiology, and cellular chemophysiology-the complex internal cellular milieu of second-messenger signaling and cascades. NEURON's extracellular reaction-diffusion is supported by an intuitive Python-based where/who/what command sequence, derived from that used for intracellular reaction diffusion, to support coarse-grained macroscopic extracellular models. This simulation specification separates the expression of the conceptual model and parameters from the underlying numerical methods. In the volume-averaging approach used, the macroscopic model of tissue is characterized by free volume fraction-the proportion of space in which species are able to diffuse, and tortuosity-the average increase in path length due to obstacles. These tissue characteristics can be defined within particular spatial regions, enabling the modeler to account for regional differences, due either to intrinsic organization, particularly gray vs. white matter, or to pathology such as edema. We illustrate simulation development using spreading depression, a pathological phenomenon thought to play roles in migraine, epilepsy and stroke. Simulation results were verified against analytic results and against the extracellular portion of the simulation run under FiPy. The creation of this NEURON interface provides a pathway for interoperability that can be used to automatically export this class of models into complex intracellular/extracellular simulations and future cross-simulator standardization.
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Affiliation(s)
- Adam J. H. Newton
- Department of Neuroscience, Yale University, New Haven, CT, United States
- SUNY Downstate Medical Center, The State University of New York, New York, NY, United States
| | - Robert A. McDougal
- Department of Neuroscience, Yale University, New Haven, CT, United States
- Center for Medical Informatics, Yale University, New Haven, CT, United States
| | - Michael L. Hines
- Department of Neuroscience, Yale University, New Haven, CT, United States
| | - William W. Lytton
- SUNY Downstate Medical Center, The State University of New York, New York, NY, United States
- Neurology, Kings County Hospital Center, Brooklyn, NY, United States
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16
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Mulugeta L, Drach A, Erdemir A, Hunt CA, Horner M, Ku JP, Myers JG, Vadigepalli R, Lytton WW. Credibility, Replicability, and Reproducibility in Simulation for Biomedicine and Clinical Applications in Neuroscience. Front Neuroinform 2018; 12:18. [PMID: 29713272 PMCID: PMC5911506 DOI: 10.3389/fninf.2018.00018] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Accepted: 03/29/2018] [Indexed: 12/27/2022] Open
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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Affiliation(s)
| | - Andrew Drach
- The Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States
| | - Ahmet Erdemir
- Department of Biomedical Engineering and Computational Biomodeling (CoBi) Core, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States
| | - C A Hunt
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, United States
| | | | - Joy P Ku
- Department of Bioengineering, Stanford University, Stanford, CA, United States
| | - Jerry G Myers
- NASA Glenn Research Center, Cleveland, OH, United States
| | - Rajanikanth Vadigepalli
- Department of Pathology, Anatomy and Cell Biology, Daniel Baugh Institute for Functional Genomics and Computational Biology, Thomas Jefferson University, Philadelphia, PA, United States
| | - William W Lytton
- Department of Neurology, SUNY Downstate Medical Center, The State University of New York, New York, NY, United States.,Department of Physiology and Pharmacology, SUNY Downstate Medical Center, The State University of New York, New York, NY, United States.,Department of Neurology, Kings County Hospital Center, New York, NY, United States
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17
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The antipsychotic drugs olanzapine and haloperidol modify network connectivity and spontaneous activity of neural networks in vitro. Sci Rep 2017; 7:11609. [PMID: 28912551 PMCID: PMC5599625 DOI: 10.1038/s41598-017-11944-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Accepted: 08/29/2017] [Indexed: 01/23/2023] Open
Abstract
Impaired neural synchronization is a hallmark of psychotic conditions such as schizophrenia. It has been proposed that schizophrenia-related cognitive deficits are caused by an unbalance of reciprocal inhibitory and stimulatory signaling. This supposedly leads to decreased power of induced gamma oscillations during the performance of cognitive tasks. In light of this hypothesis an efficient antipsychotic treatment should modify the connectivity and synchronization of local neural circuits. To address this issue, we investigated a model of hippocampal neuronal networks in vitro. Inhibitory and excitatory innervation of GABAergic and glutamatergic neurons was quantified using immunocytochemical markers and an automated routine to estimate network connectivity. The first generation (FGA) and second generation (SGA) antipsychotic drugs haloperidol and olanzapine, respectively, differentially modified the density of synaptic inputs. Based on the observed synapse density modifications, we developed a computational model that reliably predicted distinct changes in network activity patterns. The results of computational modeling were confirmed by spontaneous network activity measurements using the multiple electrode array (MEA) technique. When the cultures were treated with olanzapine, overall activity and synchronization were increased, whereas haloperidol had the opposite effect. We conclude that FGAs and SGAs differentially affect the balance between inhibition and excitation in hippocampal networks.
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18
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Neymotin SA, Dura-Bernal S, Moreno H, Lytton WW. Computer modeling for pharmacological treatments for dystonia. ACTA ACUST UNITED AC 2017; 19:51-57. [PMID: 28983321 DOI: 10.1016/j.ddmod.2017.02.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Dystonia is a movement disorder that produces involuntary muscle contractions. Current pharmacological treatments are of limited efficacy. Dystonia, like epilepsy is a disorder involving excessive activty of motor areas including motor cortex and several causal gene mutations have been identified. In order to evaluate potential novel agents for multitarget therapy for dystonia, we have developed a computer model of cortex that includes some of the complex array of molecular interactions that, along with membrane ion channels, control cell excitability.
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Affiliation(s)
| | | | - Herman Moreno
- Dept. Physiology & Pharmacology, SUNY Downstate, Brooklyn, NY.,Dept. Neurology, Kings County Hospital Center, Brooklyn, NY
| | - William W Lytton
- Dept. Physiology & Pharmacology, SUNY Downstate, Brooklyn, NY.,Dept. Neurology, Kings County Hospital Center, Brooklyn, NY
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19
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Newton AJH, Lytton WW. Computer modeling of ischemic stroke. DRUG DISCOVERY TODAY. DISEASE MODELS 2017; 19:77-83. [PMID: 28943884 PMCID: PMC5607016 DOI: 10.1016/j.ddmod.2017.01.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
The occlusion of a blood vessel in the brain causes an ischemic stroke. Current treatment relies restoration of blood flow within 3 hours. Substantial research has focused on neuroprotection to spare compromised neural tissue and extend the treatment time window. Despite success with animal models and extensive associated clinical testing, there are still no therapies of this kind. Ischemic stroke is fundamentally a multiscale phenomenon where a cascade of changes triggered by loss of blood flow involves processes at spatial scales from molecular to centimeters with damage occurring in milliseconds to days and recovery into years. Multiscale computational modeling is a technique to assist understanding of the many agents involved in these multitudinous interacting pathways to provide clues for in silico development of multi-target polypharmacy drug cocktails.
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Affiliation(s)
- Adam J H Newton
- Dept. Physiology & Pharmacology, SUNY Downstate, Brooklyn, NY
| | - William W Lytton
- Dept. Physiology & Pharmacology, SUNY Downstate, Brooklyn, NY
- Dept. Neurology, SUNY Downstate, Brooklyn, NY
- Dept. Neurology, Kings County Hospital Center, Brooklyn, NY
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20
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Neymotin SA, Suter BA, Dura-Bernal S, Shepherd GMG, Migliore M, Lytton WW. Optimizing computer models of corticospinal neurons to replicate in vitro dynamics. J Neurophysiol 2016; 117:148-162. [PMID: 27760819 DOI: 10.1152/jn.00570.2016] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2016] [Accepted: 10/13/2016] [Indexed: 11/22/2022] Open
Abstract
Corticospinal neurons (SPI), thick-tufted pyramidal neurons in motor cortex layer 5B that project caudally via the medullary pyramids, display distinct class-specific electrophysiological properties in vitro: strong sag with hyperpolarization, lack of adaptation, and a nearly linear frequency-current (F-I) relationship. We used our electrophysiological data to produce a pair of large archives of SPI neuron computer models in two model classes: 1) detailed models with full reconstruction; and 2) simplified models with six compartments. We used a PRAXIS and an evolutionary multiobjective optimization (EMO) in sequence to determine ion channel conductances. EMO selected good models from each of the two model classes to form the two model archives. Archived models showed tradeoffs across fitness functions. For example, parameters that produced excellent F-I fit produced a less-optimal fit for interspike voltage trajectory. Because of these tradeoffs, there was no single best model but rather models that would be best for particular usages for either single neuron or network explorations. Further exploration of exemplar models with strong F-I fit demonstrated that both the detailed and simple models produced excellent matches to the experimental data. Although dendritic ion identities and densities cannot yet be fully determined experimentally, we explored the consequences of a demonstrated proximal to distal density gradient of Ih, demonstrating that this would lead to a gradient of resonance properties with increased resonant frequencies more distally. We suggest that this dynamical feature could serve to make the cell particularly responsive to major frequency bands that differ by cortical layer. NEW & NOTEWORTHY We developed models of motor cortex corticospinal neurons that replicate in vitro dynamics, including hyperpolarization-induced sag and realistic firing patterns. Models demonstrated resonance in response to synaptic stimulation, with resonance frequency increasing in apical dendrites with increasing distance from soma, matching the increasing oscillation frequencies spanning deep to superficial cortical layers. This gradient may enable specific corticospinal neuron dendrites to entrain to relevant oscillations in different cortical layers, contributing to appropriate motor output commands.
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Affiliation(s)
- Samuel A Neymotin
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Medical Center, Brooklyn, New York;
| | - Benjamin A Suter
- Department of Physiology, Northwestern University, Chicago, Illinois
| | - Salvador Dura-Bernal
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Medical Center, Brooklyn, New York
| | | | - Michele Migliore
- Institute of Biophysics, National Research Council, Palermo, Italy
| | - William W Lytton
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Medical Center, Brooklyn, New York.,Department of Neurology, SUNY Downstate Medical Center, Brooklyn, New York.,Department of Neurology, Kings County Hospital Center, Brooklyn, New York; and.,The Robert F. Furchgott Center for Neural and Behavioral Science, Brooklyn, New York
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