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Piette C, Tin SNW, Liège AD, Bloch-Queyrat C, Degos B, Venance L, Touboul J. Deep Brain Stimulation restores information processing in parkinsonian cortical networks. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.25.24310748. [PMID: 39252923 PMCID: PMC11383511 DOI: 10.1101/2024.08.25.24310748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder associated with alterations of neural activity and information processing primarily in the basal ganglia and cerebral cortex. Deep brain stimulation (DBS) of the subthalamic nucleus (STN-DBS) is the most effective therapy when patients experience levodopa-induced motor complications. A growing body of evidence points towards a cortical effect of STN-DBS, restoring key electrophysiological markers, such as excessive beta band oscillations, commonly observed in PD. However, the mechanisms of STN-DBS remain elusive. Here, we aim to better characterize the cortical substrates underlying STN-DBS-induced improvement in motor symptoms. We recorded electroencephalograms (EEG) from PD patients and found that, although apparent EEG features were not different with or without therapy, EEG signals could more accurately predict limb movements under STN-DBS. To understand the origins of this enhanced information transmission under STN-DBS in the human EEG data, we investigated the information capacity and dynamics of a variety of computational models of cortical networks. The extent of improvement in decoding accuracy of complex naturalistic inputs under STN-DBS depended on the synaptic parameters of the network as well as its excitability and synchronization levels. Additionally, decoding accuracy could be optimized by adjusting STN-DBS parameters. Altogether, this work draws a comprehensive link between known alterations in cortical activity and the degradation of information processing capacity, as well as its restoration under DBS. These results also offer new perspectives for optimizing STN-DBS parameters based on clinically accessible measures of cortical information processing capacity.
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Affiliation(s)
- Charlotte Piette
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology, Collège de France, CNRS, INSERM, PSL University, 75005 Paris, France
- Department of Mathematics and Volen National Center for Complex Systems, Brandeis University, MA Waltham, USA
| | - Sophie Ng Wing Tin
- Service de Physiologie, Explorations Fonctionnelles et Médecine du Sport, Assistance Publique-Hôpitaux de Paris (AP-HP), Avicenne University Hospital, Sorbonne Paris Nord University, 93009 Bobigny, France
- Inserm UMR 1272, Sorbonne Paris Nord University, 93009 Bobigny, France
| | - Astrid De Liège
- Department of Neurology, Avicenne University Hospital, Sorbonne Paris Nord University, 93009 Bobigny, France
| | - Coralie Bloch-Queyrat
- Department of Clinical Research, Avicenne University Hospital, Assistance Publique-Hôpitaux de Paris (AP-HP), 93009, Bobigny, France
| | - Bertrand Degos
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology, Collège de France, CNRS, INSERM, PSL University, 75005 Paris, France
- Department of Neurology, Avicenne University Hospital, Sorbonne Paris Nord University, 93009 Bobigny, France
| | - Laurent Venance
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology, Collège de France, CNRS, INSERM, PSL University, 75005 Paris, France
| | - Jonathan Touboul
- Department of Mathematics and Volen National Center for Complex Systems, Brandeis University, MA Waltham, USA
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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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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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Valverde S, Vandecasteele M, Piette C, Derousseaux W, Gangarossa G, Aristieta Arbelaiz A, Touboul J, Degos B, Venance L. Deep brain stimulation-guided optogenetic rescue of parkinsonian symptoms. Nat Commun 2020; 11:2388. [PMID: 32404907 PMCID: PMC7220902 DOI: 10.1038/s41467-020-16046-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Accepted: 04/08/2020] [Indexed: 11/09/2022] Open
Abstract
Deep brain stimulation (DBS) of the subthalamic nucleus is a symptomatic treatment of Parkinson’s disease but benefits only to a minority of patients due to stringent eligibility criteria. To investigate new targets for less invasive therapies, we aimed at elucidating key mechanisms supporting deep brain stimulation efficiency. Here, using in vivo electrophysiology, optogenetics, behavioral tasks and mathematical modeling, we found that subthalamic stimulation normalizes pathological hyperactivity of motor cortex pyramidal cells, while concurrently activating somatostatin and inhibiting parvalbumin interneurons. In vivo opto-activation of cortical somatostatin interneurons alleviates motor symptoms in a parkinsonian mouse model. A computational model highlights that a decrease in pyramidal neuron activity induced by DBS or by a stimulation of cortical somatostatin interneurons can restore information processing capabilities. Overall, these results demonstrate that activation of cortical somatostatin interneurons may constitute a less invasive alternative than subthalamic stimulation. Deep brain stimulation (DBS) is a symptomatic treatment of Parkinson’s disease (PD) that benefits only a minority of patients. Here, the authors show that activation of cortical somatostatin interneurons alleviates motor symptoms in a mouse model of PD and may constitute a less invasive alternative than DBS.
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Affiliation(s)
- Sébastien Valverde
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology, Collège de France, CNRS UMR7241/INSERM U1050, MemoLife Labex, 75005, Paris, France
| | - Marie Vandecasteele
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology, Collège de France, CNRS UMR7241/INSERM U1050, MemoLife Labex, 75005, Paris, France
| | - Charlotte Piette
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology, Collège de France, CNRS UMR7241/INSERM U1050, MemoLife Labex, 75005, Paris, France.,Department of Mathematics and Volen National Center for Complex Systems, Brandeis University, Waltham, MA, USA
| | - Willy Derousseaux
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology, Collège de France, CNRS UMR7241/INSERM U1050, MemoLife Labex, 75005, Paris, France
| | - Giuseppe Gangarossa
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology, Collège de France, CNRS UMR7241/INSERM U1050, MemoLife Labex, 75005, Paris, France
| | - Asier Aristieta Arbelaiz
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology, Collège de France, CNRS UMR7241/INSERM U1050, MemoLife Labex, 75005, Paris, France
| | - Jonathan Touboul
- Department of Mathematics and Volen National Center for Complex Systems, Brandeis University, Waltham, MA, USA
| | - Bertrand Degos
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology, Collège de France, CNRS UMR7241/INSERM U1050, MemoLife Labex, 75005, Paris, France.,Department of Neurology, Avicenne University Hospital, Sorbonne Paris Nord University, 93009, Bobigny, France
| | - Laurent Venance
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology, Collège de France, CNRS UMR7241/INSERM U1050, MemoLife Labex, 75005, Paris, France.
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Curto C, Morrison K. Relating network connectivity to dynamics: opportunities and challenges for theoretical neuroscience. Curr Opin Neurobiol 2019; 58:11-20. [PMID: 31319287 DOI: 10.1016/j.conb.2019.06.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 06/22/2019] [Indexed: 11/29/2022]
Abstract
We review recent work relating network connectivity to the dynamics of neural activity. While concepts stemming from network science provide a valuable starting point, the interpretation of graph-theoretic structures and measures can be highly dependent on the dynamics associated to the network. Properties that are quite meaningful for linear dynamics, such as random walk and network flow models, may be of limited relevance in the neuroscience setting. Theoretical and computational neuroscience are playing a vital role in understanding the relationship between network connectivity and the nonlinear dynamics associated to neural networks.
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Affiliation(s)
- Carina Curto
- The Pennsylvania State University, PA 16802, United States.
| | - Katherine Morrison
- School of Mathematical Sciences, University of Northern Colorado, Greeley, CO 80639, USA
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Dura-Bernal S, Neymotin SA, Kerr CC, Sivagnanam S, Majumdar A, Francis JT, Lytton WW. Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis. IBM JOURNAL OF RESEARCH AND DEVELOPMENT 2017; 61:6.1-6.14. [PMID: 29200477 PMCID: PMC5708558 DOI: 10.1147/jrd.2017.2656758] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Biomimetic simulation permits neuroscientists to better understand the complex neuronal dynamics of the brain. Embedding a biomimetic simulation in a closed-loop neuroprosthesis, which can read and write signals from the brain, will permit applications for amelioration of motor, psychiatric, and memory-related brain disorders. Biomimetic neuroprostheses require real-time adaptation to changes in the external environment, thus constituting an example of a dynamic data-driven application system. As model fidelity increases, so does the number of parameters and the complexity of finding appropriate parameter configurations. Instead of adapting synaptic weights via machine learning, we employed major biological learning methods: spike-timing dependent plasticity and reinforcement learning. We optimized the learning metaparameters using evolutionary algorithms, which were implemented in parallel and which used an island model approach to obtain sufficient speed. We employed these methods to train a cortical spiking model to utilize macaque brain activity, indicating a selected target, to drive a virtual musculoskeletal arm with realistic anatomical and biomechanical properties to reach to that target. The optimized system was able to reproduce macaque data from a comparable experimental motor task. These techniques can be used to efficiently tune the parameters of multiscale systems, linking realistic neuronal dynamics to behavior, and thus providing a useful tool for neuroscience and neuroprosthetics.
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Neymotin SA, Dura-Bernal S, Lakatos P, Sanger TD, Lytton WW. Multitarget Multiscale Simulation for Pharmacological Treatment of Dystonia in Motor Cortex. Front Pharmacol 2016; 7:157. [PMID: 27378922 PMCID: PMC4906029 DOI: 10.3389/fphar.2016.00157] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Accepted: 05/30/2016] [Indexed: 12/20/2022] Open
Abstract
A large number of physiomic pathologies can produce hyperexcitability in cortex. Depending on severity, cortical hyperexcitability may manifest clinically as a hyperkinetic movement disorder or as epilpesy. We focus here on dystonia, a movement disorder that produces involuntary muscle contractions and involves pathology in multiple brain areas including basal ganglia, thalamus, cerebellum, and sensory and motor cortices. Most research in dystonia has focused on basal ganglia, while much pharmacological treatment is provided directly at muscles to prevent contraction. Motor cortex is another potential target for therapy that exhibits pathological dynamics in dystonia, including heightened activity and altered beta oscillations. We developed a multiscale model of primary motor cortex, ranging from molecular, up to cellular, and network levels, containing 1715 compartmental model neurons with multiple ion channels and intracellular molecular dynamics. We wired the model based on electrophysiological data obtained from mouse motor cortex circuit mapping experiments. We used the model to reproduce patterns of heightened activity seen in dystonia by applying independent random variations in parameters to identify pathological parameter sets. These models demonstrated degeneracy, meaning that there were many ways of obtaining the pathological syndrome. There was no single parameter alteration which would consistently distinguish pathological from physiological dynamics. At higher dimensions in parameter space, we were able to use support vector machines to distinguish the two patterns in different regions of space and thereby trace multitarget routes from dystonic to physiological dynamics. These results suggest the use of in silico models for discovery of multitarget drug cocktails.
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Affiliation(s)
- Samuel A Neymotin
- Department Physiology and Pharmacology, SUNY Downstate Medical Center, State University of New YorkBrooklyn, NY, USA; Department Neuroscience, Yale University School of MedicineNew Haven, CT, USA
| | - Salvador Dura-Bernal
- Department Physiology and Pharmacology, SUNY Downstate Medical Center, State University of New York Brooklyn, NY, USA
| | - Peter Lakatos
- Nathan S. Kline Institute for Psychiatric Research Orangeburg, NY, USA
| | - Terence D Sanger
- Department Biomedical Engineering, University of Southern CaliforniaLos Angeles, CA, USA; Division Neurology, Child Neurology and Movement Disorders, Children's Hospital Los AngelesLos Angeles, CA, USA
| | - William W Lytton
- Department Physiology and Pharmacology, SUNY Downstate Medical Center, State University of New YorkBrooklyn, NY, USA; Department Neurology, SUNY Downstate Medical CenterBrooklyn, NY, USA; Department Neurology, Kings County Hospital CenterBrooklyn, NY, USA; The Robert F. Furchgott Center for Neural and Behavioral ScienceBrooklyn, NY, US
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Neymotin SA, McDougal RA, Bulanova AS, Zeki M, Lakatos P, Terman D, Hines ML, Lytton WW. Calcium regulation of HCN channels supports persistent activity in a multiscale model of neocortex. Neuroscience 2016; 316:344-66. [PMID: 26746357 PMCID: PMC4724569 DOI: 10.1016/j.neuroscience.2015.12.043] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2015] [Revised: 12/16/2015] [Accepted: 12/21/2015] [Indexed: 01/08/2023]
Abstract
Neuronal persistent activity has been primarily assessed in terms of electrical mechanisms, without attention to the complex array of molecular events that also control cell excitability. We developed a multiscale neocortical model proceeding from the molecular to the network level to assess the contributions of calcium (Ca(2+)) regulation of hyperpolarization-activated cyclic nucleotide-gated (HCN) channels in providing additional and complementary support of continuing activation in the network. The network contained 776 compartmental neurons arranged in the cortical layers, connected using synapses containing AMPA/NMDA/GABAA/GABAB receptors. Metabotropic glutamate receptors (mGluR) produced inositol triphosphate (IP3) which caused the release of Ca(2+) from endoplasmic reticulum (ER) stores, with reuptake by sarco/ER Ca(2+)-ATP-ase pumps (SERCA), and influence on HCN channels. Stimulus-induced depolarization led to Ca(2+) influx via NMDA and voltage-gated Ca(2+) channels (VGCCs). After a delay, mGluR activation led to ER Ca(2+) release via IP3 receptors. These factors increased HCN channel conductance and produced firing lasting for ∼1min. The model displayed inter-scale synergies among synaptic weights, excitation/inhibition balance, firing rates, membrane depolarization, Ca(2+) levels, regulation of HCN channels, and induction of persistent activity. The interaction between inhibition and Ca(2+) at the HCN channel nexus determined a limited range of inhibition strengths for which intracellular Ca(2+) could prepare population-specific persistent activity. Interactions between metabotropic and ionotropic inputs to the neuron demonstrated how multiple pathways could contribute in a complementary manner to persistent activity. Such redundancy and complementarity via multiple pathways is a critical feature of biological systems. Mediation of activation at different time scales, and through different pathways, would be expected to protect against disruption, in this case providing stability for persistent activity.
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Affiliation(s)
- S A Neymotin
- Department of Physiology & Pharmacology, SUNY Downstate, 450 Clarkson Avenue, Brooklyn, NY 11203, USA; Department of Neuroscience, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA.
| | - R A McDougal
- Department of Neuroscience, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA.
| | - A S Bulanova
- Department of Physiology & Pharmacology, SUNY Downstate, 450 Clarkson Avenue, Brooklyn, NY 11203, USA; Department of Neuroscience, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA.
| | - M Zeki
- Department of Mathematics, Zirve University, 27260 Gaziantep, Turkey.
| | - P Lakatos
- Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY 10962, USA.
| | - D Terman
- Department of Mathematics, The Ohio State University, 231 W 18th Avenue, Columbus, OH 43210, USA.
| | - M L Hines
- Department of Neuroscience, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA.
| | - W W Lytton
- Department of Physiology & Pharmacology, SUNY Downstate, 450 Clarkson Avenue, Brooklyn, NY 11203, USA; Department of Neurology, SUNY Downstate, 450 Clarkson Avenue, Brooklyn, NY 11203, USA; Department Neurology, Kings County Hospital Center, 451 Clarkson Avenue, Brooklyn, NY 11203, USA.
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Dura-Bernal S, Li K, Neymotin SA, Francis JT, Principe JC, Lytton WW. Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm. Front Neurosci 2016; 10:28. [PMID: 26903796 PMCID: PMC4746359 DOI: 10.3389/fnins.2016.00028] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Accepted: 01/25/2016] [Indexed: 01/08/2023] Open
Abstract
Neural stimulation can be used as a tool to elicit natural sensations or behaviors by modulating neural activity. This can be potentially used to mitigate the damage of brain lesions or neural disorders. However, in order to obtain the optimal stimulation sequences, it is necessary to develop neural control methods, for example by constructing an inverse model of the target system. For real brains, this can be very challenging, and often unfeasible, as it requires repeatedly stimulating the neural system to obtain enough probing data, and depends on an unwarranted assumption of stationarity. By contrast, detailed brain simulations may provide an alternative testbed for understanding the interactions between ongoing neural activity and external stimulation. Unlike real brains, the artificial system can be probed extensively and precisely, and detailed output information is readily available. Here we employed a spiking network model of sensorimotor cortex trained to drive a realistic virtual musculoskeletal arm to reach a target. The network was then perturbed, in order to simulate a lesion, by either silencing neurons or removing synaptic connections. All lesions led to significant behvaioral impairments during the reaching task. The remaining cells were then systematically probed with a set of single and multiple-cell stimulations, and results were used to build an inverse model of the neural system. The inverse model was constructed using a kernel adaptive filtering method, and was used to predict the neural stimulation pattern required to recover the pre-lesion neural activity. Applying the derived neurostimulation to the lesioned network improved the reaching behavior performance. This work proposes a novel neurocontrol method, and provides theoretical groundwork on the use biomimetic brain models to develop and evaluate neurocontrollers that restore the function of damaged brain regions and the corresponding motor behaviors.
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Affiliation(s)
- Salvador Dura-Bernal
- Department of Physiology and Pharmacology, State University of New York Downstate Medical Center Brooklyn, NY, USA
| | - Kan Li
- Department of Electrical and Computer Engineering, University of Florida Gainesville, FL, USA
| | - Samuel A Neymotin
- Department of Physiology and Pharmacology, State University of New York Downstate Medical Center Brooklyn, NY, USA
| | - Joseph T Francis
- Department of Physiology and Pharmacology, State University of New York Downstate Medical CenterBrooklyn, NY, USA; BME Cullen College of Engineering, University of HoustonHouston, TX, USA
| | - Jose C Principe
- Department of Electrical and Computer Engineering, University of Florida Gainesville, FL, USA
| | - William W Lytton
- Department of Physiology and Pharmacology, State University of New York Downstate Medical CenterBrooklyn, NY, USA; Department of Neurology, State University of New York Downstate Medical CenterBrooklyn, NY, USA; Department of Neurology, Kings County Hospital CenterBrooklyn, NY, USA
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Lee G, Matsunaga A, Dura-Bernal S, Zhang W, Lytton WW, Francis JT, Fortes JA. Towards real-time communication between in vivo neurophysiological data sources and simulator-based brain biomimetic models. ACTA ACUST UNITED AC 2015; 3:1-23. [PMID: 26702394 PMCID: PMC4685709 DOI: 10.1186/s40244-014-0012-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Development of more sophisticated implantable brain-machine interface (BMI) will require both interpretation of the neurophysiological data being measured and subsequent determination of signals to be delivered back to the brain. Computational models are the heart of the machine of BMI and therefore an essential tool in both of these processes. One approach is to utilize brain biomimetic models (BMMs) to develop and instantiate these algorithms. These then must be connected as hybrid systems in order to interface the BMM with in vivo data acquisition devices and prosthetic devices. The combined system then provides a test bed for neuroprosthetic rehabilitative solutions and medical devices for the repair and enhancement of damaged brain. We propose here a computer network-based design for this purpose, detailing its internal modules and data flows. We describe a prototype implementation of the design, enabling interaction between the Plexon Multichannel Acquisition Processor (MAP) server, a commercial tool to collect signals from microelectrodes implanted in a live subject and a BMM, a NEURON-based model of sensorimotor cortex capable of controlling a virtual arm. The prototype implementation supports an online mode for real-time simulations, as well as an offline mode for data analysis and simulations without real-time constraints, and provides binning operations to discretize continuous input to the BMM and filtering operations for dealing with noise. Evaluation demonstrated that the implementation successfully delivered monkey spiking activity to the BMM through LAN environments, respecting real-time constraints.
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Affiliation(s)
- Giljae Lee
- Department of Electrical and Computer Engineering, University of Florida, P.O. Box 116200, 216 Larsen Hall, Gainesville 32611, FL, USA
| | - Andréa Matsunaga
- Department of Electrical and Computer Engineering, University of Florida, P.O. Box 116200, 216 Larsen Hall, Gainesville 32611, FL, USA
| | - Salvador Dura-Bernal
- Department of Physiology and Pharmacology, State University of New York Downstate Medical Center, 450 Clarkson Avenue, Brooklyn 11203, NY, USA
| | - Wenjie Zhang
- Department of Electrical and Computer Engineering, University of Florida, P.O. Box 116200, 216 Larsen Hall, Gainesville 32611, FL, USA
| | - William W Lytton
- Department of Physiology and Pharmacology, State University of New York Downstate Medical Center, 450 Clarkson Avenue, Brooklyn 11203, NY, USA ; Department of Neurology, State University New York Downstate Medical Center, 450 Clarkson Avenue, Brooklyn 11203, NY, USA ; Department of Neurology, Kings County Hospital, 450 Clarkson Avenue, Brooklyn 11203, NY, USA ; Joint Program in Biomedical Engineering at Polytechnic Institute of New York University and State University of New York Downstate, 450 Clarkson Avenue, Brooklyn 11203, NY, USA ; Program in Neural and Behavioral Science at State University of New York Downstate, 450 Clarkson Avenue, Brooklyn, NY, 11203, USA ; The Robert F. Furchgott Center for Neural & Behavioral Science, State University of New York Downstate Medical Center, 450 Clarkson Avenue, Brooklyn, NY, 11203, USA
| | - Joseph T Francis
- Department of Physiology and Pharmacology, State University of New York Downstate Medical Center, 450 Clarkson Avenue, Brooklyn 11203, NY, USA ; Joint Program in Biomedical Engineering at Polytechnic Institute of New York University and State University of New York Downstate, 450 Clarkson Avenue, Brooklyn 11203, NY, USA ; Program in Neural and Behavioral Science at State University of New York Downstate, 450 Clarkson Avenue, Brooklyn, NY, 11203, USA ; The Robert F. Furchgott Center for Neural & Behavioral Science, State University of New York Downstate Medical Center, 450 Clarkson Avenue, Brooklyn, NY, 11203, USA
| | - José Ab Fortes
- Department of Electrical and Computer Engineering, University of Florida, P.O. Box 116200, 216 Larsen Hall, Gainesville 32611, FL, USA ; Department of Computer and Information Science and Engineering, University of Florida, P.O. Box 116120, E301 CSE Building, Gainesville 32611, FL, USA
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Loeb GE, Tsianos GA. Major remaining gaps in models of sensorimotor systems. Front Comput Neurosci 2015; 9:70. [PMID: 26089795 PMCID: PMC4454839 DOI: 10.3389/fncom.2015.00070] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Accepted: 05/21/2015] [Indexed: 01/01/2023] Open
Abstract
Experimental descriptions of the anatomy and physiology of individual components of sensorimotor systems have revealed substantial complexity, making it difficult to intuit how complete systems might work. This has led to increasing efforts to develop and employ mathematical models to study the emergent properties of such systems. Conversely, the development of such models tends to reveal shortcomings in the experimental database upon which models must be constructed and validated. In both cases models are most useful when they point up discrepancies between what we think we know and possibilities that we may have overlooked. This overview considers those components of complete sensorimotor systems that currently appear to be potentially important but poorly understood. These are generally omitted completely from modeled systems or buried in implicit assumptions that underlie the design of the model.
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Affiliation(s)
- Gerald E Loeb
- Department of Biomedical Engineering, University of Southern California Los Angeles, CA, USA
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