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Ramezani Z, André V, Khizroev S. Modeling the effect of magnetoelectric nanoparticles on neuronal electrical activity: An analog circuit approach. Biointerphases 2024; 19:031001. [PMID: 38738941 DOI: 10.1116/5.0199163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 04/22/2024] [Indexed: 05/14/2024] Open
Abstract
This paper introduces a physical neuron model that incorporates magnetoelectric nanoparticles (MENPs) as an essential electrical circuit component to wirelessly control local neural activity. Availability of such a model is important as MENPs, due to their magnetoelectric effect, can wirelessly and noninvasively modulate neural activity, which, in turn, has implications for both finding cures for neurological diseases and creating a wireless noninvasive high-resolution brain-machine interface. When placed on a neuronal membrane, MENPs act as magnetic-field-controlled finite-size electric dipoles that generate local electric fields across the membrane in response to magnetic fields, thus allowing to controllably activate local ion channels and locally initiate an action potential. Herein, the neuronal electrical characteristic description is based on ion channel activation and inhibition mechanisms. A MENP-based memristive Hodgkin-Huxley circuit model is extracted by combining the Hodgkin-Huxley model and an equivalent circuit model for a single MENP. In this model, each MENP becomes an integral part of the neuron, thus enabling wireless local control of the neuron's electric circuit itself. Furthermore, the model is expanded to include multiple MENPs to describe collective effects in neural systems.
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Affiliation(s)
- Zeinab Ramezani
- Department of Electrical and Computer Engineering, College of Engineering, University of Miami, Miami, Florida 33146
| | - Victoria André
- Department of Biomedical Engineering, College of Engineering, University of Miami, Miami, Florida 33146
| | - Sakhrat Khizroev
- Department of Electrical and Computer Engineering, College of Engineering, University of Miami, Miami, Florida 33146
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2
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Marasco A, Tribuzi C, Iuorio A, Migliore M. Mathematical generation of data-driven hippocampal CA1 pyramidal neurons and interneurons copies via A-GLIF models for large-scale networks covering the experimental variability range. Math Biosci 2024; 371:109179. [PMID: 38521453 DOI: 10.1016/j.mbs.2024.109179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 11/10/2023] [Accepted: 03/13/2024] [Indexed: 03/25/2024]
Abstract
Efficient and accurate large-scale networks are a fundamental tool in modeling brain areas, to advance our understanding of neuronal dynamics. However, their implementation faces two key issues: computational efficiency and heterogeneity. Computational efficiency is achieved using simplified neurons, whereas there are no practical solutions available to solve the problem of reproducing in a large-scale network the experimentally observed heterogeneity of the intrinsic properties of neurons. This is important, because the use of identical nodes in a network can generate artifacts which can hinder an adequate representation of the properties of a real network. To this aim, we introduce a mathematical procedure to generate an arbitrary large number of copies of simplified hippocampal CA1 pyramidal neurons and interneurons models, which exhibit the full range of firing dynamics observed in these cells - including adapting, non-adapting and bursting. For this purpose, we rely on a recently published adaptive generalized leaky integrate-and-fire (A-GLIF) modeling approach, leveraging on its ability to reproduce the rich set of electrophysiological behaviors of these types of neurons under a variety of different stimulation currents. The generation procedure is based on a perturbation of model's parameters related to the initial data, firing block, and internal dynamics, and suitably validated against experimental data to ensure that the firing dynamics of any given cell copy remains within the experimental range. A classification procedure confirmed that the firing behavior of most of the pyramidal/interneuron copies was consistent with the experimental data. This approach allows to obtain heterogeneous copies with mathematically controlled firing properties. A full set of heterogeneous neurons composing the CA1 region of a rat hippocampus (approximately 1.2 million neurons), are provided in a database freely available in the live paper section of the EBRAINS platform. By adapting the underlying A-GLIF framework, it will be possible to extend the numerical approach presented here to create, in a mathematically controlled manner, an arbitrarily large number of non-identical copies of cell populations with firing properties related to other brain areas.
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Affiliation(s)
- A Marasco
- Department of Mathematics and Applications, University of Naples Federico II, Naples, Italy; Institute of Biophysics, National Research Council, Palermo, Italy.
| | - C Tribuzi
- Department of Mathematics and Applications, University of Naples Federico II, Naples, Italy
| | - A Iuorio
- University of Vienna, Faculty of Mathematics, Vienna, Austria; Department of Engineering, Parthenope University of Naples, Naples, Italy
| | - M Migliore
- Institute of Biophysics, National Research Council, Palermo, Italy
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3
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Takács V, Bardóczi Z, Orosz Á, Major A, Tar L, Berki P, Papp P, Mayer MI, Sebők H, Zsolt L, Sos KE, Káli S, Freund TF, Nyiri G. Synaptic and dendritic architecture of different types of hippocampal somatostatin interneurons. PLoS Biol 2024; 22:e3002539. [PMID: 38470935 PMCID: PMC10959371 DOI: 10.1371/journal.pbio.3002539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 03/22/2024] [Accepted: 02/06/2024] [Indexed: 03/14/2024] Open
Abstract
GABAergic inhibitory neurons fundamentally shape the activity and plasticity of cortical circuits. A major subset of these neurons contains somatostatin (SOM); these cells play crucial roles in neuroplasticity, learning, and memory in many brain areas including the hippocampus, and are implicated in several neuropsychiatric diseases and neurodegenerative disorders. Two main types of SOM-containing cells in area CA1 of the hippocampus are oriens-lacunosum-moleculare (OLM) cells and hippocampo-septal (HS) cells. These cell types show many similarities in their soma-dendritic architecture, but they have different axonal targets, display different activity patterns in vivo, and are thought to have distinct network functions. However, a complete understanding of the functional roles of these interneurons requires a precise description of their intrinsic computational properties and their synaptic interactions. In the current study we generated, analyzed, and make available several key data sets that enable a quantitative comparison of various anatomical and physiological properties of OLM and HS cells in mouse. The data set includes detailed scanning electron microscopy (SEM)-based 3D reconstructions of OLM and HS cells along with their excitatory and inhibitory synaptic inputs. Combining this core data set with other anatomical data, patch-clamp electrophysiology, and compartmental modeling, we examined the precise morphological structure, inputs, outputs, and basic physiological properties of these cells. Our results highlight key differences between OLM and HS cells, particularly regarding the density and distribution of their synaptic inputs and mitochondria. For example, we estimated that an OLM cell receives about 8,400, whereas an HS cell about 15,600 synaptic inputs, about 16% of which are GABAergic. Our data and models provide insight into the possible basis of the different functionality of OLM and HS cell types and supply essential information for more detailed functional models of these neurons and the hippocampal network.
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Affiliation(s)
- Virág Takács
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
| | - Zsuzsanna Bardóczi
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
| | - Áron Orosz
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
- János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Budapest, Hungary
| | - Abel Major
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
| | - Luca Tar
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
- Roska Tamás Doctoral School of Sciences and Technology, Pázmány Péter Catholic University, Budapest, Hungary
| | - Péter Berki
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
- János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Budapest, Hungary
| | - Péter Papp
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
| | - Márton I. Mayer
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
- János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Budapest, Hungary
| | - Hunor Sebők
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
| | - Luca Zsolt
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
| | - Katalin E. Sos
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
- János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Budapest, Hungary
| | - Szabolcs Káli
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
| | - Tamás F. Freund
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
| | - Gábor Nyiri
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
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Guet-McCreight A, Chameh HM, Mazza F, Prevot TD, Valiante TA, Sibille E, Hay E. In-silico testing of new pharmacology for restoring inhibition and human cortical function in depression. Commun Biol 2024; 7:225. [PMID: 38396202 PMCID: PMC10891083 DOI: 10.1038/s42003-024-05907-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 02/09/2024] [Indexed: 02/25/2024] Open
Abstract
Reduced inhibition by somatostatin-expressing interneurons is associated with depression. Administration of positive allosteric modulators of α5 subunit-containing GABAA receptor (α5-PAM) that selectively target this lost inhibition exhibit antidepressant and pro-cognitive effects in rodent models of chronic stress. However, the functional effects of α5-PAM on the human brain in vivo are unknown, and currently cannot be assessed experimentally. We modeled the effects of α5-PAM on tonic inhibition as measured in human neurons, and tested in silico α5-PAM effects on detailed models of human cortical microcircuits in health and depression. We found that α5-PAM effectively recovered impaired cortical processing as quantified by stimulus detection metrics, and also recovered the power spectral density profile of the microcircuit EEG signals. We performed an α5-PAM dose-response and identified simulated EEG biomarker candidates. Our results serve to de-risk and facilitate α5-PAM translation and provide biomarkers in non-invasive brain signals for monitoring target engagement and drug efficacy.
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Affiliation(s)
- Alexandre Guet-McCreight
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
| | | | - Frank Mazza
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - Thomas D Prevot
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Taufik A Valiante
- Krembil Brain Institute, University Health Network, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Department of Surgery, University of Toronto, Toronto, ON, Canada
- Center for Advancing Neurotechnological Innovation to Application, Toronto, ON, Canada
- Max Planck-University of Toronto Center for Neural Science and Technology, Toronto, ON, Canada
| | - Etienne Sibille
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
| | - Etay Hay
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Department of Physiology, University of Toronto, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
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Baruzzi V, Lodi M, Storace M. Optimization strategies to obtain smooth gait transitions through biologically plausible central pattern generators. Phys Rev E 2024; 109:014404. [PMID: 38366407 DOI: 10.1103/physreve.109.014404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 12/07/2023] [Indexed: 02/18/2024]
Abstract
Central pattern generators are small networks that contribute to generating animal locomotion. The models used to study gait generation and gait transition mechanisms often require biologically accurate neuron and synapse models, with high dimensionality and complex dynamics. Tuning the parameters of these models to elicit network dynamics compatible with gait features is not a trivial task, due to the impossibility of inferring a priori the effects of each parameter on the nonlinear system's emergent dynamics. In this paper we explore the use of global optimization strategies for parameter optimization in multigait central pattern generator (CPG) models with complex cell dynamics and minimal topology. We first consider an existing quadruped CPG model as a test bed for the objective function formulation, then proceed to optimize the parameters of a newly proposed multigait, interlimb hexapod CPG model. We successfully obtain hexapod gaits and prompt gait transitions by varying only control currents, while all CPG parameters, once optimized, are kept fixed. This mechanism of gait transitions is compatible with short-term synaptic plasticity.
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Affiliation(s)
- V Baruzzi
- Department of Electrical, Electronics and Telecommunication Engineering and Naval Architecture, University of Genoa, 16145 Genoa, Italy
| | - M Lodi
- Department of Electrical, Electronics and Telecommunication Engineering and Naval Architecture, University of Genoa, 16145 Genoa, Italy
| | - M Storace
- Department of Electrical, Electronics and Telecommunication Engineering and Naval Architecture, University of Genoa, 16145 Genoa, Italy
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Reva M, Rössert C, Arnaudon A, Damart T, Mandge D, Tuncel A, Ramaswamy S, Markram H, Van Geit W. A universal workflow for creation, validation, and generalization of detailed neuronal models. PATTERNS (NEW YORK, N.Y.) 2023; 4:100855. [PMID: 38035193 PMCID: PMC10682753 DOI: 10.1016/j.patter.2023.100855] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 03/24/2023] [Accepted: 09/12/2023] [Indexed: 12/02/2023]
Abstract
Detailed single-neuron modeling is widely used to study neuronal functions. While cellular and functional diversity across the mammalian cortex is vast, most of the available computational tools focus on a limited set of specific features characteristic of a single neuron. Here, we present a generalized automated workflow for the creation of robust electrical models and illustrate its performance by building cell models for the rat somatosensory cortex. Each model is based on a 3D morphological reconstruction and a set of ionic mechanisms. We use an evolutionary algorithm to optimize neuronal parameters to match the electrophysiological features extracted from experimental data. Then we validate the optimized models against additional stimuli and assess their generalizability on a population of similar morphologies. Compared to the state-of-the-art canonical models, our models show 5-fold improved generalizability. This versatile approach can be used to build robust models of any neuronal type.
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Affiliation(s)
- Maria Reva
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| | - Christian Rössert
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| | - Alexis Arnaudon
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| | - Tanguy Damart
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| | - Darshan Mandge
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| | - Anıl Tuncel
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| | - Srikanth Ramaswamy
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
- Laboratory of Neural Microcircuitry (LNMC), Brain Mind Institute, School of Life Sciences, École polytechnique fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Werner Van Geit
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
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7
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Srikanth S, Narayanan R. Heterogeneous off-target impact of ion-channel deletion on intrinsic properties of hippocampal model neurons that self-regulate calcium. Front Cell Neurosci 2023; 17:1241450. [PMID: 37904732 PMCID: PMC10613471 DOI: 10.3389/fncel.2023.1241450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 09/20/2023] [Indexed: 11/01/2023] Open
Abstract
How do neurons that implement cell-autonomous self-regulation of calcium react to knockout of individual ion-channel conductances? To address this question, we used a heterogeneous population of 78 conductance-based models of hippocampal pyramidal neurons that maintained cell-autonomous calcium homeostasis while receiving theta-frequency inputs. At calcium steady-state, we individually deleted each of the 11 active ion-channel conductances from each model. We measured the acute impact of deleting each conductance (one at a time) by comparing intrinsic electrophysiological properties before and immediately after channel deletion. The acute impact of deleting individual conductances on physiological properties (including calcium homeostasis) was heterogeneous, depending on the property, the specific model, and the deleted channel. The underlying many-to-many mapping between ion channels and properties pointed to ion-channel degeneracy. Next, we allowed the other conductances (barring the deleted conductance) to evolve towards achieving calcium homeostasis during theta-frequency activity. When calcium homeostasis was perturbed by ion-channel deletion, post-knockout plasticity in other conductances ensured resilience of calcium homeostasis to ion-channel deletion. These results demonstrate degeneracy in calcium homeostasis, as calcium homeostasis in knockout models was implemented in the absence of a channel that was earlier involved in the homeostatic process. Importantly, in reacquiring homeostasis, ion-channel conductances and physiological properties underwent heterogenous plasticity (dependent on the model, the property, and the deleted channel), even introducing changes in properties that were not directly connected to the deleted channel. Together, post-knockout plasticity geared towards maintaining homeostasis introduced heterogenous off-target effects on several channels and properties, suggesting that extreme caution be exercised in interpreting experimental outcomes involving channel knockouts.
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Affiliation(s)
- Sunandha Srikanth
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
- Undergraduate Program, Indian Institute of Science, Bangalore, India
| | - Rishikesh Narayanan
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
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Lameu EL, Rasiah NP, Baimoukhametova DV, Loewen SP, Bains JS, Nicola W. Particle-swarm based modelling reveals two distinct classes of CRH PVN neurons. J Physiol 2023; 601:3151-3171. [PMID: 36223200 DOI: 10.1113/jp283133] [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: 03/28/2022] [Accepted: 09/28/2022] [Indexed: 11/08/2022] Open
Abstract
Electrophysiological recordings can provide detailed information of single neurons' dynamical features and shed light on their response to stimuli. Unfortunately, rapidly modelling electrophysiological data for inferring network-level behaviours remains challenging. Here, we investigate how modelled single neuron dynamics leads to network-level responses in the paraventricular nucleus of the hypothalamus (PVN), a critical nucleus for the mammalian stress response. Recordings of corticotropin releasing hormone neurons from the PVN (CRHPVN ) were performed using whole-cell current-clamp. These, neurons, which initiate the endocrine response to stress, were rapidly and automatically fit to a modified adaptive exponential integrate-and-fire model (AdEx) with particle swarm optimization (PSO). All CRHPVN neurons were accurately fit by the AdEx model with PSO. Multiple sets of parameters were found that reliably reproduced current-clamp traces for any single neuron. Despite multiple solutions, the dynamical features of the models such as the rheobase, fixed points, and bifurcations, were shown to be stable across fits. We found that CRHPVN neurons can be divided into two subtypes according to their bifurcation at the onset of firing: CRHPVN -integrators and CRHPVN -resonators. The existence of CRHPVN -resonators was then directly confirmed in a follow-up patch-clamp hyperpolarization protocol which readily induced post-inhibitory rebound spiking in 33% of patched neurons. We constructed networks of CRHPVN model neurons to investigate the network level responses of CRHPVN neurons. We found that CRHPVN -resonators maintain baseline firing in networks even when all inputs are inhibitory. The dynamics of a small subset of CRHPVN neurons may be critical to maintaining a baseline firing tone in the PVN. KEY POINTS: Corticotropin-releasing hormone neurons (CRHPVN ) in the paraventricular nucleus of the hypothalamus act as the final neural controllers of the stress response. We developed a computational modelling platform that uses particle swarm optimization to rapidly and accurately fit biophysical neuron models to patched CRHPVN neurons. A model was fitted to each patched neuron without the use of dynamic clamping, or other procedures requiring sophisticated inputs and fitting algorithms. Any neuron undergoing standard current clamp step protocols for a few minutes can be fitted by this procedure The dynamical analysis of the modelled neurons shows that CRHPVN neurons come in two specific 'flavours': CRHPVN -resonators and CRHPVN -integrators. We directly confirmed the existence of these two classes of CRHPVN neurons in subsequent experiments. Network simulations show that CRHPVN -resonators are critical to retaining the baseline firing rate of the entire network of CRHPVN neurons as these cells can fire rebound spikes and bursts in the presence of strong inhibitory synaptic input.
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Affiliation(s)
- Ewandson L Lameu
- Cell Biology and Anatomy Department, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Neilen P Rasiah
- Department of Physiology and Pharmacology, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Dinara V Baimoukhametova
- Department of Physiology and Pharmacology, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Spencer P Loewen
- Department of Physiology and Pharmacology, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Jaideep S Bains
- Department of Physiology and Pharmacology, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Wilten Nicola
- Cell Biology and Anatomy Department, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
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9
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Rimehaug AE, Stasik AJ, Hagen E, Billeh YN, Siegle JH, Dai K, Olsen SR, Koch C, Einevoll GT, Arkhipov A. Uncovering circuit mechanisms of current sinks and sources with biophysical simulations of primary visual cortex. eLife 2023; 12:e87169. [PMID: 37486105 PMCID: PMC10393295 DOI: 10.7554/elife.87169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 07/10/2023] [Indexed: 07/25/2023] Open
Abstract
Local field potential (LFP) recordings reflect the dynamics of the current source density (CSD) in brain tissue. The synaptic, cellular, and circuit contributions to current sinks and sources are ill-understood. We investigated these in mouse primary visual cortex using public Neuropixels recordings and a detailed circuit model based on simulating the Hodgkin-Huxley dynamics of >50,000 neurons belonging to 17 cell types. The model simultaneously captured spiking and CSD responses and demonstrated a two-way dissociation: firing rates are altered with minor effects on the CSD pattern by adjusting synaptic weights, and CSD is altered with minor effects on firing rates by adjusting synaptic placement on the dendrites. We describe how thalamocortical inputs and recurrent connections sculpt specific sinks and sources early in the visual response, whereas cortical feedback crucially alters them in later stages. These results establish quantitative links between macroscopic brain measurements (LFP/CSD) and microscopic biophysics-based understanding of neuron dynamics and show that CSD analysis provides powerful constraints for modeling beyond those from considering spikes.
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Affiliation(s)
| | | | - Espen Hagen
- Department of Physics, University of OsloOsloNorway
- Department of Data Science, Norwegian University of Life SciencesÅsNorway
| | | | - Josh H Siegle
- MindScope Program, Allen InstituteSeattleUnited States
| | - Kael Dai
- MindScope Program, Allen InstituteSeattleUnited States
| | - Shawn R Olsen
- MindScope Program, Allen InstituteSeattleUnited States
| | - Christof Koch
- MindScope Program, Allen InstituteSeattleUnited States
| | - Gaute T Einevoll
- Department of Physics, University of OsloOsloNorway
- Department of Physics, Norwegian University of Life SciencesÅsNorway
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10
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Chapman DP, Vicini S, Burns MP, Evans R. Single Neuron Modeling Identifies Potassium Channel Modulation as Potential Target for Repetitive Head Impacts. Neuroinformatics 2023; 21:501-516. [PMID: 37294503 PMCID: PMC10833395 DOI: 10.1007/s12021-023-09633-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/17/2023] [Indexed: 06/10/2023]
Abstract
Traumatic brain injury (TBI) and repetitive head impacts can result in a wide range of neurological symptoms. Despite being the most common neurological disorder in the world, repeat head impacts and TBI do not have any FDA-approved treatments. Single neuron modeling allows researchers to extrapolate cellular changes in individual neurons based on experimental data. We recently characterized a model of high frequency head impact (HFHI) with a phenotype of cognitive deficits associated with decreases in neuronal excitability of CA1 neurons and synaptic changes. While the synaptic changes have been interrogated in vivo, the cause and potential therapeutic targets of hypoexcitability following repetitive head impacts are unknown. Here, we generated in silico models of CA1 pyramidal neurons from current clamp data of control mice and mice that sustained HFHI. We use a directed evolution algorithm with a crowding penalty to generate a large and unbiased population of plausible models for each group that approximated the experimental features. The HFHI neuron model population showed decreased voltage gated sodium conductance and a general increase in potassium channel conductance. We used partial least squares regression analysis to identify combinations of channels that may account for CA1 hypoexcitability after HFHI. The hypoexcitability phenotype in models was linked to A- and M-type potassium channels in combination, but not by any single channel correlations. We provide an open access set of CA1 pyramidal neuron models for both control and HFHI conditions that can be used to predict the effects of pharmacological interventions in TBI models.
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Affiliation(s)
- Daniel P Chapman
- Interdisciplinary Program in Neuroscience, Georgetown University Medical Center, Washington, DC, USA
| | - Stefano Vicini
- Interdisciplinary Program in Neuroscience, Georgetown University Medical Center, Washington, DC, USA
- Department of Pharmacology and Physiology, Georgetown University Medical Center, Washington, DC, USA
| | - Mark P Burns
- Interdisciplinary Program in Neuroscience, Georgetown University Medical Center, Washington, DC, USA.
- Department of Neuroscience, Georgetown University Medical Center, New Research Building-EG11, 3970 Reservoir Rd, NW, Washington, DC, 20057, USA.
| | - Rebekah Evans
- Interdisciplinary Program in Neuroscience, Georgetown University Medical Center, Washington, DC, USA.
- Department of Neuroscience, Georgetown University Medical Center, New Research Building-EG11, 3970 Reservoir Rd, NW, Washington, DC, 20057, USA.
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11
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Koch NA, Sonnenberg L, Hedrich UBS, Lauxmann S, Benda J. Loss or gain of function? Effects of ion channel mutations on neuronal firing depend on the neuron type. Front Neurol 2023; 14:1194811. [PMID: 37292138 PMCID: PMC10244640 DOI: 10.3389/fneur.2023.1194811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 05/03/2023] [Indexed: 06/10/2023] Open
Abstract
Introduction Clinically relevant mutations to voltage-gated ion channels, called channelopathies, alter ion channel function, properties of ionic currents, and neuronal firing. The effects of ion channel mutations are routinely assessed and characterized as loss of function (LOF) or gain of function (GOF) at the level of ionic currents. However, emerging personalized medicine approaches based on LOF/GOF characterization have limited therapeutic success. Potential reasons are among others that the translation from this binary characterization to neuronal firing is currently not well-understood-especially when considering different neuronal cell types. In this study, we investigate the impact of neuronal cell type on the firing outcome of ion channel mutations. Methods To this end, we simulated a diverse collection of single-compartment, conductance-based neuron models that differed in their composition of ionic currents. We systematically analyzed the effects of changes in ion current properties on firing in different neuronal types. Additionally, we simulated the effects of known mutations in KCNA1 gene encoding the KV1.1 potassium channel subtype associated with episodic ataxia type 1 (EA1). Results These simulations revealed that the outcome of a given change in ion channel properties on neuronal excitability depends on neuron type, i.e., the properties and expression levels of the unaffected ionic currents. Discussion Consequently, neuron-type specific effects are vital to a full understanding of the effects of channelopathies on neuronal excitability and are an important step toward improving the efficacy and precision of personalized medicine approaches.
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Affiliation(s)
- Nils A. Koch
- Institute of Neurobiology, Faculty of Mathematics and Natural Sciences, University of Tübingen, Tübingen, Germany
- Bernstein Center for Computational Neuroscience Tübingen, Tübingen, Germany
| | - Lukas Sonnenberg
- Institute of Neurobiology, Faculty of Mathematics and Natural Sciences, University of Tübingen, Tübingen, Germany
- Bernstein Center for Computational Neuroscience Tübingen, Tübingen, Germany
| | - Ulrike B. S. Hedrich
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Stephan Lauxmann
- Institute of Neurobiology, Faculty of Mathematics and Natural Sciences, University of Tübingen, Tübingen, Germany
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Jan Benda
- Institute of Neurobiology, Faculty of Mathematics and Natural Sciences, University of Tübingen, Tübingen, Germany
- Bernstein Center for Computational Neuroscience Tübingen, Tübingen, Germany
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12
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Schneider M, Tzanou A, Uran C, Vinck M. Cell-type-specific propagation of visual flicker. Cell Rep 2023; 42:112492. [PMID: 37195864 DOI: 10.1016/j.celrep.2023.112492] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 03/10/2023] [Accepted: 04/24/2023] [Indexed: 05/19/2023] Open
Abstract
Rhythmic flicker stimulation has gained interest as a treatment for neurodegenerative diseases and as a method for frequency tagging neural activity. Yet, little is known about the way in which flicker-induced synchronization propagates across cortical levels and impacts different cell types. Here, we use Neuropixels to record from the lateral geniculate nucleus (LGN), the primary visual cortex (V1), and CA1 in mice while presenting visual flicker stimuli. LGN neurons show strong phase locking up to 40 Hz, whereas phase locking is substantially weaker in V1 and is absent in CA1. Laminar analyses reveal an attenuation of phase locking at 40 Hz for each processing stage. Gamma-rhythmic flicker predominantly entrains fast-spiking interneurons. Optotagging experiments show that these neurons correspond to either parvalbumin (PV+) or narrow-waveform somatostatin (Sst+) neurons. A computational model can explain the observed differences based on the neurons' capacitative low-pass filtering properties. In summary, the propagation of synchronized activity and its effect on distinct cell types strongly depend on its frequency.
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Affiliation(s)
- Marius Schneider
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt am Main, Germany; Donders Centre for Neuroscience, Department of Neuroinformatics, Radboud University Nijmegen, 6525 Nijmegen, the Netherlands.
| | - Athanasia Tzanou
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt am Main, Germany
| | - Cem Uran
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt am Main, Germany; Donders Centre for Neuroscience, Department of Neuroinformatics, Radboud University Nijmegen, 6525 Nijmegen, the Netherlands
| | - Martin Vinck
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt am Main, Germany; Donders Centre for Neuroscience, Department of Neuroinformatics, Radboud University Nijmegen, 6525 Nijmegen, the Netherlands.
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13
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Glasgow NG, Chen Y, Korngreen A, Kass RE, Urban NN. A biophysical and statistical modeling paradigm for connecting neural physiology and function. J Comput Neurosci 2023; 51:263-282. [PMID: 37140691 PMCID: PMC10182162 DOI: 10.1007/s10827-023-00847-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 02/10/2023] [Accepted: 02/16/2023] [Indexed: 05/05/2023]
Abstract
To understand single neuron computation, it is necessary to know how specific physiological parameters affect neural spiking patterns that emerge in response to specific stimuli. Here we present a computational pipeline combining biophysical and statistical models that provides a link between variation in functional ion channel expression and changes in single neuron stimulus encoding. More specifically, we create a mapping from biophysical model parameters to stimulus encoding statistical model parameters. Biophysical models provide mechanistic insight, whereas statistical models can identify associations between spiking patterns and the stimuli they encode. We used public biophysical models of two morphologically and functionally distinct projection neuron cell types: mitral cells (MCs) of the main olfactory bulb, and layer V cortical pyramidal cells (PCs). We first simulated sequences of action potentials according to certain stimuli while scaling individual ion channel conductances. We then fitted point process generalized linear models (PP-GLMs), and we constructed a mapping between the parameters in the two types of models. This framework lets us detect effects on stimulus encoding of changing an ion channel conductance. The computational pipeline combines models across scales and can be applied as a screen of channels, in any cell type of interest, to identify ways that channel properties influence single neuron computation.
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Affiliation(s)
- Nathan G Glasgow
- Department of Neurobiology and Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Yu Chen
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Alon Korngreen
- The Leslie and Susan Gonda Interdisciplinary Brain Research Centre, Bar-Ilan University, Ramat Gan, Israel
- The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel
| | - Robert E Kass
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Nathan N Urban
- Department of Biological Sciences, Lehigh University, Bethlehem, PA, USA
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14
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Naudin L. Different parameter solutions of a conductance-based model that behave identically are not necessarily degenerate. J Comput Neurosci 2023; 51:201-206. [PMID: 36905484 DOI: 10.1007/s10827-023-00848-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 02/13/2023] [Accepted: 02/22/2023] [Indexed: 03/12/2023]
Affiliation(s)
- Loïs Naudin
- Laboratoire Lorrain de Recherche en Informatique et ses Applications, CNRS, Université de Lorraine, Nancy, France. .,Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, F-75012, France.
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15
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Birgiolas J, Haynes V, Gleeson P, Gerkin RC, Dietrich SW, Crook S. NeuroML-DB: Sharing and characterizing data-driven neuroscience models described in NeuroML. PLoS Comput Biol 2023; 19:e1010941. [PMID: 36867658 PMCID: PMC10016719 DOI: 10.1371/journal.pcbi.1010941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 03/15/2023] [Accepted: 02/12/2023] [Indexed: 03/04/2023] Open
Abstract
As researchers develop computational models of neural systems with increasing sophistication and scale, it is often the case that fully de novo model development is impractical and inefficient. Thus arises a critical need to quickly find, evaluate, re-use, and build upon models and model components developed by other researchers. We introduce the NeuroML Database (NeuroML-DB.org), which has been developed to address this need and to complement other model sharing resources. NeuroML-DB stores over 1,500 previously published models of ion channels, cells, and networks that have been translated to the modular NeuroML model description language. The database also provides reciprocal links to other neuroscience model databases (ModelDB, Open Source Brain) as well as access to the original model publications (PubMed). These links along with Neuroscience Information Framework (NIF) search functionality provide deep integration with other neuroscience community modeling resources and greatly facilitate the task of finding suitable models for reuse. Serving as an intermediate language, NeuroML and its tooling ecosystem enable efficient translation of models to other popular simulator formats. The modular nature also enables efficient analysis of a large number of models and inspection of their properties. Search capabilities of the database, together with web-based, programmable online interfaces, allow the community of researchers to rapidly assess stored model electrophysiology, morphology, and computational complexity properties. We use these capabilities to perform a database-scale analysis of neuron and ion channel models and describe a novel tetrahedral structure formed by cell model clusters in the space of model properties and features. This analysis provides further information about model similarity to enrich database search.
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Affiliation(s)
- Justas Birgiolas
- Ronin Institute, Montclair, New Jersey, United States of America
| | - Vergil Haynes
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, Arizona, United States of America
- College of Health Solutions, Arizona State University, Phoenix, Arizona, United States of America
| | - Padraig Gleeson
- Department of Neuroscience, Physiology, and Pharmacology, University College London, London, United Kingdom
| | - Richard C. Gerkin
- School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
| | - Suzanne W. Dietrich
- School of Mathematical and Natural Sciences, Arizona State University, Tempe, Arizona, United States of America
| | - Sharon Crook
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, Arizona, United States of America
- * E-mail:
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16
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Haufler D, Ito S, Koch C, Arkhipov A. Simulations of cortical networks using spatially extended conductance-based neuronal models. J Physiol 2022. [PMID: 36567262 PMCID: PMC10290729 DOI: 10.1113/jp284030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 12/19/2022] [Indexed: 12/27/2022] Open
Abstract
The Hodgkin-Huxley model of action potential generation and propagation, published in the Journal of Physiology in 1952, initiated the field of biophysically detailed computational modelling in neuroscience, which has expanded to encompass a variety of species and components of the nervous system. Here we review the developments in this area with a focus on efforts in the community towards modelling the mammalian neocortex using spatially extended conductance-based neuronal models. The Hodgkin-Huxley formalism and related foundational contributions, such as Rall's cable theory, remain widely used in these efforts to the current day. We argue that at present the field is undergoing a qualitative change due to new very rich datasets describing the composition, connectivity and functional activity of cortical circuits, which are being integrated systematically into large-scale network models. This trend, combined with the accelerating development of convenient software tools supporting such complex modelling projects, is giving rise to highly detailed models of the cortex that are extensively constrained by the data, enabling computational investigation of a multitude of questions about cortical structure and function.
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Affiliation(s)
- Darrell Haufler
- Mindscope Program, Allen Institute, Seattle, Washington, USA
| | - Shinya Ito
- Mindscope Program, Allen Institute, Seattle, Washington, USA
| | - Christof Koch
- Mindscope Program, Allen Institute, Seattle, Washington, USA
| | - Anton Arkhipov
- Mindscope Program, Allen Institute, Seattle, Washington, USA
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17
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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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18
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Microglial Extracellular Vesicles as Modulators of Brain Microenvironment in Glioma. Int J Mol Sci 2022; 23:ijms232113165. [PMID: 36361947 PMCID: PMC9656645 DOI: 10.3390/ijms232113165] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 10/25/2022] [Accepted: 10/26/2022] [Indexed: 11/22/2022] Open
Abstract
Microglial cells represent the resident immune elements of the central nervous system, where they exert constant monitoring and contribute to preserving neuronal activity and function. In the context of glioblastoma (GBM), a common type of tumor originating in the brain, microglial cells deeply modify their phenotype, lose their homeostatic functions, invade the tumoral mass and support the growth and further invasion of the tumoral cells into the surrounding brain parenchyma. These modifications are, at least in part, induced by bidirectional communication among microglial and tumoral cells through the release of soluble molecules and extracellular vesicles (EVs). EVs produced by GBM and microglial cells transfer different kinds of biological information to receiving cells, deeply modifying their phenotype and activity and could represent important diagnostic markers and therapeutic targets. Recent evidence demonstrates that in GBM, microglial-derived EVs contribute to the immune suppression of the tumor microenvironment (TME), thus favoring GBM immune escape. In this review, we report the current knowledge on EV formation, biogenesis, cargo and functions, with a focus on the effects of microglia-derived EVs in GBM. What clearly emerges from this analysis is that we are at the beginning of a full understanding of the complete picture of the biological effects of microglial-derived EVs and that further investigations using multidisciplinary approaches are necessary to validate their use in GBM diagnosis and therapy.
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19
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Guet-McCreight A, Chameh HM, Mahallati S, Wishart M, Tripathy SJ, Valiante TA, Hay E. Age-dependent increased sag amplitude in human pyramidal neurons dampens baseline cortical activity. Cereb Cortex 2022; 33:4360-4373. [PMID: 36124673 DOI: 10.1093/cercor/bhac348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 08/05/2022] [Accepted: 08/06/2022] [Indexed: 11/14/2022] Open
Abstract
Aging involves various neurobiological changes, although their effect on brain function in humans remains poorly understood. The growing availability of human neuronal and circuit data provides opportunities for uncovering age-dependent changes of brain networks and for constraining models to predict consequences on brain activity. Here we found increased sag voltage amplitude in human middle temporal gyrus layer 5 pyramidal neurons from older subjects and captured this effect in biophysical models of younger and older pyramidal neurons. We used these models to simulate detailed layer 5 microcircuits and found lower baseline firing in older pyramidal neuron microcircuits, with minimal effect on response. We then validated the predicted reduced baseline firing using extracellular multielectrode recordings from human brain slices of different ages. Our results thus report changes in human pyramidal neuron input integration properties and provide fundamental insights into the neuronal mechanisms of altered cortical excitability and resting-state activity in human aging.
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Affiliation(s)
- Alexandre Guet-McCreight
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, 250 College St, Toronto, ON M5T 1R8, Canada
| | | | - Sara Mahallati
- Krembil Brain Institute, University Health Network, Toronto, ON M5T1M8, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada
| | - Margaret Wishart
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, 250 College St, Toronto, ON M5T 1R8, Canada
| | - Shreejoy J Tripathy
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, 250 College St, Toronto, ON M5T 1R8, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario M5T 1R8, Canada.,Institute of Medical Sciences, University of Toronto, Toronto, ON M5S 1A8, Canada.,Department of Physiology, University of Toronto, Toronto, ON M5S1A8, Canada
| | - Taufik A Valiante
- Krembil Brain Institute, University Health Network, Toronto, ON M5T1M8, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada.,Institute of Medical Sciences, University of Toronto, Toronto, ON M5S 1A8, Canada.,Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada.,Department of Surgery, University of Toronto, Toronto, ON M5T 1P5, Canada.,Center for Advancing Neurotechnological Innovation to Application, University of Toronto, Toronto, ON M5G 2A2, Canada.,Max Planck-University of Toronto Center for Neural Science and Technology, Toronto, ON, Canada
| | - Etay Hay
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, 250 College St, Toronto, ON M5T 1R8, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario M5T 1R8, Canada.,Department of Physiology, University of Toronto, Toronto, ON M5S1A8, Canada
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20
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Panarese A, Vissani M, Meneghetti N, Vannini E, Cracchiolo M, Micera S, Caleo M, Mazzoni A, Restani L. Disruption of layer-specific visual processing in a model of focal neocortical epilepsy. Cereb Cortex 2022; 33:4173-4187. [PMID: 36089833 DOI: 10.1093/cercor/bhac335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 07/25/2022] [Accepted: 07/26/2022] [Indexed: 11/12/2022] Open
Abstract
The epileptic brain is the result of a sequence of events transforming normal neuronal populations into hyperexcitable networks supporting recurrent seizure generation. These modifications are known to induce fundamental alterations of circuit function and, ultimately, of behavior. However, how hyperexcitability affects information processing in cortical sensory circuits is not yet fully understood. Here, we investigated interlaminar alterations in sensory processing of the visual cortex in a mouse model of focal epilepsy. We found three main circuit dynamics alterations in epileptic mice: (i) a spreading of visual contrast-driven gamma modulation across layers, (ii) an increase in firing rate that is layer-unspecific for excitatory units and localized in infragranular layers for inhibitory neurons, and (iii) a strong and contrast-dependent locking of firing units to network activity. Altogether, our data show that epileptic circuits display a functional disruption of layer-specific organization of visual sensory processing, which could account for visual dysfunction observed in epileptic subjects. Understanding these mechanisms paves the way to circuital therapeutic interventions for epilepsy.
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Affiliation(s)
- Alessandro Panarese
- The Biorobotics Institute, Scuola Superiore Sant'Anna, viale Rinaldo Piaggio 34, 56025 Pontedera, Italy.,Department of Excellence in Robotics and Artificial Intelligence, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, 56127 Pisa, Italy
| | - Matteo Vissani
- The Biorobotics Institute, Scuola Superiore Sant'Anna, viale Rinaldo Piaggio 34, 56025 Pontedera, Italy.,Department of Excellence in Robotics and Artificial Intelligence, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, 56127 Pisa, Italy
| | - Nicolò Meneghetti
- The Biorobotics Institute, Scuola Superiore Sant'Anna, viale Rinaldo Piaggio 34, 56025 Pontedera, Italy.,Department of Excellence in Robotics and Artificial Intelligence, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, 56127 Pisa, Italy
| | - Eleonora Vannini
- Neuroscience Institute, National Research Council (CNR), via G. Moruzzi 1, 56124 Pisa, Italy
| | - Marina Cracchiolo
- The Biorobotics Institute, Scuola Superiore Sant'Anna, viale Rinaldo Piaggio 34, 56025 Pontedera, Italy.,Department of Excellence in Robotics and Artificial Intelligence, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, 56127 Pisa, Italy
| | - Silvestro Micera
- The Biorobotics Institute, Scuola Superiore Sant'Anna, viale Rinaldo Piaggio 34, 56025 Pontedera, Italy.,Department of Excellence in Robotics and Artificial Intelligence, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, 56127 Pisa, Italy.,Bertarelli Foundation Chair in Translational Neuroengineering, Centre for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Chemin des Mines 9, 1202 Geneva, Switzerland
| | - Matteo Caleo
- Neuroscience Institute, National Research Council (CNR), via G. Moruzzi 1, 56124 Pisa, Italy.,Department of Biomedical Sciences, University of Padua, via G. Colombo 3, 35121 Padua, Italy
| | - Alberto Mazzoni
- The Biorobotics Institute, Scuola Superiore Sant'Anna, viale Rinaldo Piaggio 34, 56025 Pontedera, Italy.,Department of Excellence in Robotics and Artificial Intelligence, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, 56127 Pisa, Italy
| | - Laura Restani
- Neuroscience Institute, National Research Council (CNR), via G. Moruzzi 1, 56124 Pisa, Italy
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21
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Nandi A, Chartrand T, Van Geit W, Buchin A, Yao Z, Lee SY, Wei Y, Kalmbach B, Lee B, Lein E, Berg J, Sümbül U, Koch C, Tasic B, Anastassiou CA. Single-neuron models linking electrophysiology, morphology, and transcriptomics across cortical cell types. Cell Rep 2022; 40:111176. [PMID: 35947954 PMCID: PMC9793758 DOI: 10.1016/j.celrep.2022.111176] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 01/28/2022] [Accepted: 07/18/2022] [Indexed: 12/30/2022] Open
Abstract
Which cell types constitute brain circuits is a fundamental question, but establishing the correspondence across cellular data modalities is challenging. Bio-realistic models allow probing cause-and-effect and linking seemingly disparate modalities. Here, we introduce a computational optimization workflow to generate 9,200 single-neuron models with active conductances. These models are based on 230 in vitro electrophysiological experiments followed by morphological reconstruction from the mouse visual cortex. We show that, in contrast to current belief, the generated models are robust representations of individual experiments and cortical cell types as defined via cellular electrophysiology or transcriptomics. Next, we show that differences in specific conductances predicted from the models reflect differences in gene expression supported by single-cell transcriptomics. The differences in model conductances, in turn, explain electrophysiological differences observed between the cortical subclasses. Our computational effort reconciles single-cell modalities that define cell types and enables causal relationships to be examined.
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Affiliation(s)
- Anirban Nandi
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Thomas Chartrand
- Allen Institute for Brain Science, Seattle, WA 98109, USA,These authors contributed equally
| | - Werner Van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva 1202, Switzerland,These authors contributed equally
| | - Anatoly Buchin
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Soo Yeun Lee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Yina Wei
- Allen Institute for Brain Science, Seattle, WA 98109, USA,Zhejiang Lab, Hangzhou City, Zhejiang Province 311121, China
| | - Brian Kalmbach
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Brian Lee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Ed Lein
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jim Berg
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Uygar Sümbül
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Christof Koch
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Bosiljka Tasic
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Costas A. Anastassiou
- Allen Institute for Brain Science, Seattle, WA 98109, USA,Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA,Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA,Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA,Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA,Lead contact,Correspondence:
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22
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Ladd A, Kim KG, Balewski J, Bouchard K, Ben-Shalom R. Scaling and Benchmarking an Evolutionary Algorithm for Constructing Biophysical Neuronal Models. Front Neuroinform 2022; 16:882552. [PMID: 35784184 PMCID: PMC9248031 DOI: 10.3389/fninf.2022.882552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 05/18/2022] [Indexed: 11/28/2022] Open
Abstract
Single neuron models are fundamental for computational modeling of the brain's neuronal networks, and understanding how ion channel dynamics mediate neural function. A challenge in defining such models is determining biophysically realistic channel distributions. Here, we present an efficient, highly parallel evolutionary algorithm for developing such models, named NeuroGPU-EA. NeuroGPU-EA uses CPUs and GPUs concurrently to simulate and evaluate neuron membrane potentials with respect to multiple stimuli. We demonstrate a logarithmic cost for scaling the stimuli used in the fitting procedure. NeuroGPU-EA outperforms the typically used CPU based evolutionary algorithm by a factor of 10 on a series of scaling benchmarks. We report observed performance bottlenecks and propose mitigation strategies. Finally, we also discuss the potential of this method for efficient simulation and evaluation of electrophysiological waveforms.
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Affiliation(s)
- Alexander Ladd
- Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States
- *Correspondence: Alexander Ladd
| | - Kyung Geun Kim
- Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States
| | - Jan Balewski
- NERSC, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Kristofer Bouchard
- Helen Wills Neuroscience Institute & Redwood Center for Theoretical Neuroscience, University of California, Berkeley, Berkeley, CA, United States
- Scientific Data Division and Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Roy Ben-Shalom
- Neurology Department, MIND Institute, University of California, Davis, Sacramento, CA, United States
- Roy Ben-Shalom
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23
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Yegenoglu A, Subramoney A, Hater T, Jimenez-Romero C, Klijn W, Pérez Martín A, van der Vlag M, Herty M, Morrison A, Diaz-Pier S. Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn. Front Comput Neurosci 2022; 16:885207. [PMID: 35720775 PMCID: PMC9199579 DOI: 10.3389/fncom.2022.885207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 04/13/2022] [Indexed: 11/13/2022] Open
Abstract
Neuroscience models commonly have a high number of degrees of freedom and only specific regions within the parameter space are able to produce dynamics of interest. This makes the development of tools and strategies to efficiently find these regions of high importance to advance brain research. Exploring the high dimensional parameter space using numerical simulations has been a frequently used technique in the last years in many areas of computational neuroscience. Today, high performance computing (HPC) can provide a powerful infrastructure to speed up explorations and increase our general understanding of the behavior of the model in reasonable times. Learning to learn (L2L) is a well-known concept in machine learning (ML) and a specific method for acquiring constraints to improve learning performance. This concept can be decomposed into a two loop optimization process where the target of optimization can consist of any program such as an artificial neural network, a spiking network, a single cell model, or a whole brain simulation. In this work, we present L2L as an easy to use and flexible framework to perform parameter and hyper-parameter space exploration of neuroscience models on HPC infrastructure. Learning to learn is an implementation of the L2L concept written in Python. This open-source software allows several instances of an optimization target to be executed with different parameters in an embarrassingly parallel fashion on HPC. L2L provides a set of built-in optimizer algorithms, which make adaptive and efficient exploration of parameter spaces possible. Different from other optimization toolboxes, L2L provides maximum flexibility for the way the optimization target can be executed. In this paper, we show a variety of examples of neuroscience models being optimized within the L2L framework to execute different types of tasks. The tasks used to illustrate the concept go from reproducing empirical data to learning how to solve a problem in a dynamic environment. We particularly focus on simulations with models ranging from the single cell to the whole brain and using a variety of simulation engines like NEST, Arbor, TVB, OpenAIGym, and NetLogo.
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Affiliation(s)
- Alper Yegenoglu
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
- Department of Mathematics, Institute of Geometry and Applied Mathematics, RWTH Aachen University, Aachen, Germany
| | - Anand Subramoney
- Institute of Neural Computation, Ruhr University Bochum, Bochum, Germany
| | - Thorsten Hater
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Cristian Jimenez-Romero
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Wouter Klijn
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Aarón Pérez Martín
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Michiel van der Vlag
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Michael Herty
- Department of Mathematics, Institute of Geometry and Applied Mathematics, RWTH Aachen University, Aachen, Germany
| | - Abigail Morrison
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany
- Computer Science 3-Software Engineering, RWTH Aachen University, Aachen, Germany
| | - Sandra Diaz-Pier
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
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24
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Linaro D, Levy MJ, Hunt DL. Cell type-specific mechanisms of information transfer in data-driven biophysical models of hippocampal CA3 principal neurons. PLoS Comput Biol 2022; 18:e1010071. [PMID: 35452457 PMCID: PMC9089861 DOI: 10.1371/journal.pcbi.1010071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 05/10/2022] [Accepted: 03/31/2022] [Indexed: 11/19/2022] Open
Abstract
The transformation of synaptic input into action potential output is a fundamental single-cell computation resulting from the complex interaction of distinct cellular morphology and the unique expression profile of ion channels that define the cellular phenotype. Experimental studies aimed at uncovering the mechanisms of the transfer function have led to important insights, yet are limited in scope by technical feasibility, making biophysical simulations an attractive complementary approach to push the boundaries in our understanding of cellular computation. Here we take a data-driven approach by utilizing high-resolution morphological reconstructions and patch-clamp electrophysiology data together with a multi-objective optimization algorithm to build two populations of biophysically detailed models of murine hippocampal CA3 pyramidal neurons based on the two principal cell types that comprise this region. We evaluated the performance of these models and find that our approach quantitatively matches the cell type-specific firing phenotypes and recapitulate the intrinsic population-level variability in the data. Moreover, we confirm that the conductance values found by the optimization algorithm are consistent with differentially expressed ion channel genes in single-cell transcriptomic data for the two cell types. We then use these models to investigate the cell type-specific biophysical properties involved in the generation of complex-spiking output driven by synaptic input through an information-theoretic treatment of their respective transfer functions. Our simulations identify a host of cell type-specific biophysical mechanisms that define the morpho-functional phenotype to shape the cellular transfer function and place these findings in the context of a role for bursting in CA3 recurrent network synchronization dynamics. The hippocampus is comprised of numerous types of neurons, which constitute the cellular substrate for its rich repertoire of network dynamics. Among these are sharp waves, sequential activations of ensembles of neurons that have been shown to be crucially involved in learning and memory. In the CA3 area of the hippocampus, two types of excitatory cells, thorny and a-thorny neurons, are preferentially active during distinct phases of a sharp wave, suggesting a differential role for these cell types in phenomena such as memory consolidation. Using a strictly data-driven approach, we built biophysically realistic models of both thorny and a-thorny cells and used them to investigate the integrative differences between these two cell types. We found that both neuron classes have the capability of integrating incoming synaptic inputs in a supralinear fashion, although only a-thorny cells respond with bursts of action potentials to spatially and temporally clustered synaptic inputs. Additionally, by using a computational approach based on information theory, we show that, owing to this propensity for bursting, a-thorny cells can encode more information in their spiking output than their thorny counterpart. These results shed new light on the computational capabilities of two types of excitatory neurons and suggest that thorny and a-thorny cells may play distinct roles in the generation of hippocampal network synchronization.
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Affiliation(s)
- Daniele Linaro
- Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, Milan, Italy
- * E-mail: (DL); (DLH)
| | - Matthew J. Levy
- Center for Neural Science and Medicine, Cedars-Sinai Medical Center, Los Angeles, California, United State of America
| | - David L. Hunt
- Center for Neural Science and Medicine, Cedars-Sinai Medical Center, Los Angeles, California, United State of America
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California, United State of America
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, California, United State of America
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, United State of America
- * E-mail: (DL); (DLH)
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25
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Pfeiffer P, Barreda Tomás FJ, Wu J, Schleimer JH, Vida I, Schreiber S. A dynamic clamp protocol to artificially modify cell capacitance. eLife 2022; 11:75517. [PMID: 35362411 PMCID: PMC9135398 DOI: 10.7554/elife.75517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 03/17/2022] [Indexed: 11/13/2022] Open
Abstract
Dynamics of excitable cells and networks depend on the membrane time constant, set by membrane resistance and capacitance. Whereas pharmacological and genetic manipulations of ionic conductances of excitable membranes are routine in electrophysiology, experimental control over capacitance remains a challenge. Here, we present capacitance clamp, an approach that allows electrophysiologists to mimic a modified capacitance in biological neurons via an unconventional application of the dynamic clamp technique. We first demonstrate the feasibility to quantitatively modulate capacitance in a mathematical neuron model and then confirm the functionality of capacitance clamp in in vitro experiments in granule cells of rodent dentate gyrus with up to threefold virtual capacitance changes. Clamping of capacitance thus constitutes a novel technique to probe and decipher mechanisms of neuronal signaling in ways that were so far inaccessible to experimental electrophysiology.
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Affiliation(s)
- Paul Pfeiffer
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | | | - Jiameng Wu
- Institute for Integrative Neuroanatomy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Jan-Hendrik Schleimer
- Institute of Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Imre Vida
- Institute for Integrative Neuroanatomy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Susanne Schreiber
- Institute of Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
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26
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Buccino AP, Yuan X, Emmenegger V, Xue X, Gänswein T, Hierlemann A. An automated method for precise axon reconstruction from recordings of high-density micro-electrode arrays. J Neural Eng 2022; 19. [PMID: 35234667 PMCID: PMC7612575 DOI: 10.1088/1741-2552/ac59a2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 03/01/2022] [Indexed: 11/13/2022]
Abstract
Objective Neurons communicate with each other by sending action potentials through their axons. The velocity of axonal signal propagation describes how fast electrical action potentials can travel. This velocity can be affected in a human brain by several pathologies, including multiple sclerosis, traumatic brain injury and channelopathies. High-density microelectrode arrays (HD-MEAs) provide unprecedented spatio-temporal resolution to extracellularly record neural electrical activity. The high density of the recording electrodes enables to image the activity of individual neurons down to subcellular resolution, which includes the propagation of axonal signals. However, axon reconstruction, to date, mainly relies on manual approaches to select the electrodes and channels that seemingly record the signals along a specific axon, while an automated approach to track multiple axonal branches in extracellular action-potential recordings is still missing. Approach In this article, we propose a fully automated approach to reconstruct axons from extracellular electrical-potential landscapes, so-called “electrical footprints” of neurons. After an initial electrode and channel selection, the proposed method first constructs a graph based on the voltage signal amplitudes and latencies. Then, the graph is interrogated to extract possible axonal branches. Finally, the axonal branches are pruned, and axonal action-potential propagation velocities are computed. Main results We first validate our method using simulated data from detailed reconstructions of neurons, showing that our approach is capable of accurately reconstructing axonal branches. We then apply the reconstruction algorithm to experimental recordings of HD-MEAs and show that it can be used to determine axonal morphologies and signal-propagation velocities at high throughput. Significance We introduce a fully automated method to reconstruct axonal branches and estimate axonal action-potential propagation velocities using HD-MEA recordings. Our method yields highly reliable and reproducible velocity estimations, which constitute an important electrophysiological feature of neuronal preparations.
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Affiliation(s)
| | - Xinyue Yuan
- D-BSSE, ETH Zürich, Basel, Zurich, 8092, SWITZERLAND
| | | | - Xiaohan Xue
- D-BSSE, ETH Zürich, Basel, Zurich, 8092, SWITZERLAND
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27
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Ben-Shalom R, Ladd A, Artherya NS, Cross C, Kim KG, Sanghevi H, Korngreen A, Bouchard KE, Bender KJ. NeuroGPU: Accelerating multi-compartment, biophysically detailed neuron simulations on GPUs. J Neurosci Methods 2022; 366:109400. [PMID: 34728257 PMCID: PMC9887806 DOI: 10.1016/j.jneumeth.2021.109400] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 10/09/2021] [Accepted: 10/27/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND The membrane potential of individual neurons depends on a large number of interacting biophysical processes operating on spatial-temporal scales spanning several orders of magnitude. The multi-scale nature of these processes dictates that accurate prediction of membrane potentials in specific neurons requires the utilization of detailed simulations. Unfortunately, constraining parameters within biologically detailed neuron models can be difficult, leading to poor model fits. This obstacle can be overcome partially by numerical optimization or detailed exploration of parameter space. However, these processes, which currently rely on central processing unit (CPU) computation, often incur orders of magnitude increases in computing time for marginal improvements in model behavior. As a result, model quality is often compromised to accommodate compute resources. NEW METHOD Here, we present a simulation environment, NeuroGPU, that takes advantage of the inherent parallelized structure of the graphics processing unit (GPU) to accelerate neuronal simulation. RESULTS & COMPARISON WITH EXISTING METHODS NeuroGPU can simulate most biologically detailed models 10-200 times faster than NEURON simulation running on a single core and 5 times faster than GPU simulators (CoreNEURON). NeuroGPU is designed for model parameter tuning and best performs when the GPU is fully utilized by running multiple (> 100) instances of the same model with different parameters. When using multiple GPUs, NeuroGPU can reach to a speed-up of 800 fold compared to single core simulations, especially when simulating the same model morphology with different parameters. We demonstrate the power of NeuoGPU through large-scale parameter exploration to reveal the response landscape of a neuron. Finally, we accelerate numerical optimization of biophysically detailed neuron models to achieve highly accurate fitting of models to simulation and experimental data. CONCLUSIONS Thus, NeuroGPU is the fastest available platform that enables rapid simulation of multi-compartment, biophysically detailed neuron models on commonly used computing systems accessible by many scientists.
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Affiliation(s)
- Roy Ben-Shalom
- Weill Institute for Neurosciences, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States,Department of Neurology, University of California, San Francisco, San Francisco, CA, United States,MIND Institute University of California, Davis, CA, United States,Computational Research Division, Lawrence Berkeley National Lab, Berkeley, CA, United States,Correspondence to: University of California, Davis MIND Institute Wet Lab 2805 50th Street, Room 2460 Sacramento, CA 95817, United States., (R. Ben-Shalom), (K.J. Bender)
| | - Alexander Ladd
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, United States
| | - Nikhil S. Artherya
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, United States
| | - Christopher Cross
- Weill Institute for Neurosciences, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Kyung Geun Kim
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, United States
| | - Hersh Sanghevi
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, United States
| | - Alon Korngreen
- The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel,The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel
| | - Kristofer E. Bouchard
- Computational Research Division, Lawrence Berkeley National Lab, Berkeley, CA, United States,Hellen Wills Neuroscience Institute & Redwood Center for Theoretical Neuroscience, University of California, Berkeley, Berkeley, CA, United States,Biological Systems and Engineering Division, Lawrence Berkeley National Lab, Berkeley, CA, United States
| | - Kevin J. Bender
- Weill Institute for Neurosciences, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States,Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
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28
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Yao HK, Guet-McCreight A, Mazza F, Moradi Chameh H, Prevot TD, Griffiths JD, Tripathy SJ, Valiante TA, Sibille E, Hay E. Reduced inhibition in depression impairs stimulus processing in human cortical microcircuits. Cell Rep 2022; 38:110232. [PMID: 35021088 DOI: 10.1016/j.celrep.2021.110232] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 10/07/2021] [Accepted: 12/16/2021] [Indexed: 12/01/2022] Open
Abstract
Cortical processing depends on finely tuned excitatory and inhibitory connections in neuronal microcircuits. Reduced inhibition by somatostatin-expressing interneurons is a key component of altered inhibition associated with treatment-resistant major depressive disorder (depression), which is implicated in cognitive deficits and rumination, but the link remains to be better established mechanistically in humans. Here we test the effect of reduced somatostatin interneuron-mediated inhibition on cortical processing in human neuronal microcircuits using a data-driven computational approach. We integrate human cellular, circuit, and gene expression data to generate detailed models of human cortical microcircuits in health and depression. We simulate microcircuit baseline and response activity and find a reduced signal-to-noise ratio and increased false/failed detection of stimuli due to a higher baseline activity in depression. We thus apply models of human cortical microcircuits to demonstrate mechanistically how reduced inhibition impairs cortical processing in depression, providing quantitative links between altered inhibition and cognitive deficits.
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Affiliation(s)
- Heng Kang Yao
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON M5T 1R7, Canada; Department of Physiology, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Alexandre Guet-McCreight
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON M5T 1R7, Canada
| | - Frank Mazza
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON M5T 1R7, Canada; Department of Physiology, University of Toronto, Toronto, ON M5S 1A1, Canada
| | | | - Thomas D Prevot
- Department of Psychiatry, University of Toronto, Toronto, ON M5S 1A1, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R7, Canada
| | - John D Griffiths
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON M5T 1R7, Canada; Department of Psychiatry, University of Toronto, Toronto, ON M5S 1A1, Canada; Institute of Medical Sciences, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Shreejoy J Tripathy
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON M5T 1R7, Canada; Department of Physiology, University of Toronto, Toronto, ON M5S 1A1, Canada; Department of Psychiatry, University of Toronto, Toronto, ON M5S 1A1, Canada; Institute of Medical Sciences, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Taufik A Valiante
- Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada; Institute of Medical Sciences, University of Toronto, Toronto, ON M5S 1A1, Canada; Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M5S 1A1; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON M5S 1A1, Canada; Department of Surgery, University of Toronto, Toronto, ON M5S 1A1, Canada; Max Planck-University of Toronto Center for Neural Science and Technology, University of Toronto, Toronto, ON M5S 1A1, Canada; Center for Advancing Neurotechnological Innovation to Application, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Etienne Sibille
- Department of Psychiatry, University of Toronto, Toronto, ON M5S 1A1, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M5T 1R7, Canada; Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Etay Hay
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON M5T 1R7, Canada; Department of Physiology, University of Toronto, Toronto, ON M5S 1A1, Canada; Department of Psychiatry, University of Toronto, Toronto, ON M5S 1A1, Canada.
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29
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Allam SL, Rumbell TH, Hoang-Trong T, Parikh J, Kozloski JR. Neuronal population models reveal specific linear conductance controllers sufficient to rescue preclinical disease phenotypes. iScience 2021; 24:103279. [PMID: 34778727 PMCID: PMC8577087 DOI: 10.1016/j.isci.2021.103279] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 08/05/2021] [Accepted: 10/13/2021] [Indexed: 12/23/2022] Open
Abstract
Preclinical drug candidates are screened for their ability to ameliorate in vitro neuronal electrophysiology, and go/no-go decisions progress drugs to clinical trials based on population means across cells and animals. However, these measures do not mitigate clinical endpoint risk. Population-based modeling captures variability across multiple electrophysiological measures from healthy, disease, and drug phenotypes. We pursued optimizing therapeutic targets by identifying coherent sets of ion channel target modulations for recovering heterogeneous wild-type (WT) population excitability profiles from a heterogeneous Huntington's disease (HD) population. Our approach combines mechanistic simulations with population modeling of striatal neurons using evolutionary optimization algorithms to design 'virtual drugs'. We introduce efficacy metrics to score populations and rank virtual drug candidates. We found virtual drugs using heuristic approaches that performed better than single target modulators and standard classification methods. We compare a real drug to virtual candidates and demonstrate a novel in silico triaging method.
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Affiliation(s)
- Sushmita L. Allam
- IBM T.J. Watson Research Center, 13-158B, P.O. Box 218, 1101 Kitchawan Road, Yorktown Heights, NY 10598, USA
| | - Timothy H. Rumbell
- IBM T.J. Watson Research Center, 13-158B, P.O. Box 218, 1101 Kitchawan Road, Yorktown Heights, NY 10598, USA
| | - Tuan Hoang-Trong
- IBM T.J. Watson Research Center, 13-158B, P.O. Box 218, 1101 Kitchawan Road, Yorktown Heights, NY 10598, USA
| | - Jaimit Parikh
- IBM T.J. Watson Research Center, 13-158B, P.O. Box 218, 1101 Kitchawan Road, Yorktown Heights, NY 10598, USA
| | - James R. Kozloski
- IBM T.J. Watson Research Center, 13-158B, P.O. Box 218, 1101 Kitchawan Road, Yorktown Heights, NY 10598, USA
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30
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Wildenberg GA, Rosen MR, Lundell J, Paukner D, Freedman DJ, Kasthuri N. Primate neuronal connections are sparse in cortex as compared to mouse. Cell Rep 2021; 36:109709. [PMID: 34525373 DOI: 10.1016/j.celrep.2021.109709] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 07/30/2021] [Accepted: 08/20/2021] [Indexed: 12/29/2022] Open
Abstract
Detailing how primate and mouse neurons differ is critical for creating generalized models of how neurons process information. We reconstruct 15,748 synapses in adult Rhesus macaques and mice and ask how connectivity differs on identified cell types in layer 2/3 of primary visual cortex. Primate excitatory and inhibitory neurons receive 2-5 times fewer excitatory and inhibitory synapses than similar mouse neurons. Primate excitatory neurons have lower excitatory-to-inhibitory (E/I) ratios than mouse but similar E/I ratios in inhibitory neurons. In both species, properties of inhibitory axons such as synapse size and frequency are unchanged, and inhibitory innervation of excitatory neurons is local and specific. Using artificial recurrent neural networks (RNNs) optimized for different cognitive tasks, we find that penalizing networks for creating and maintaining synapses, as opposed to neuronal firing, reduces the number of connections per node as the number of nodes increases, similar to primate neurons compared with mice.
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Affiliation(s)
- Gregg A Wildenberg
- Department of Neurobiology, University of Chicago, Chicago, IL 60637, USA; Argonne National Laboratory, Lemont, IL 60439, USA.
| | - Matt R Rosen
- Department of Neurobiology, University of Chicago, Chicago, IL 60637, USA
| | - Jack Lundell
- Department of Neurobiology, University of Chicago, Chicago, IL 60637, USA
| | - Dawn Paukner
- Department of Neurobiology, University of Chicago, Chicago, IL 60637, USA
| | - David J Freedman
- Department of Neurobiology, University of Chicago, Chicago, IL 60637, USA
| | - Narayanan Kasthuri
- Department of Neurobiology, University of Chicago, Chicago, IL 60637, USA; Argonne National Laboratory, Lemont, IL 60439, USA.
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31
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Sinha M, Narayanan R. Active Dendrites and Local Field Potentials: Biophysical Mechanisms and Computational Explorations. Neuroscience 2021; 489:111-142. [PMID: 34506834 PMCID: PMC7612676 DOI: 10.1016/j.neuroscience.2021.08.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 08/30/2021] [Accepted: 08/31/2021] [Indexed: 10/27/2022]
Abstract
Neurons and glial cells are endowed with membranes that express a rich repertoire of ion channels, transporters, and receptors. The constant flux of ions across the neuronal and glial membranes results in voltage fluctuations that can be recorded from the extracellular matrix. The high frequency components of this voltage signal contain information about the spiking activity, reflecting the output from the neurons surrounding the recording location. The low frequency components of the signal, referred to as the local field potential (LFP), have been traditionally thought to provide information about the synaptic inputs that impinge on the large dendritic trees of various neurons. In this review, we discuss recent computational and experimental studies pointing to a critical role of several active dendritic mechanisms that can influence the genesis and the location-dependent spectro-temporal dynamics of LFPs, spanning different brain regions. We strongly emphasize the need to account for the several fast and slow dendritic events and associated active mechanisms - including gradients in their expression profiles, inter- and intra-cellular spatio-temporal interactions spanning neurons and glia, heterogeneities and degeneracy across scales, neuromodulatory influences, and activitydependent plasticity - towards gaining important insights about the origins of LFP under different behavioral states in health and disease. We provide simple but essential guidelines on how to model LFPs taking into account these dendritic mechanisms, with detailed methodology on how to account for various heterogeneities and electrophysiological properties of neurons and synapses while studying LFPs.
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Affiliation(s)
- Manisha Sinha
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka 560012, India
| | - Rishikesh Narayanan
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka 560012, India.
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32
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Lee EK, Balasubramanian H, Tsolias A, Anakwe SU, Medalla M, Shenoy KV, Chandrasekaran C. Non-linear dimensionality reduction on extracellular waveforms reveals cell type diversity in premotor cortex. eLife 2021; 10:e67490. [PMID: 34355695 PMCID: PMC8452311 DOI: 10.7554/elife.67490] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 08/04/2021] [Indexed: 11/13/2022] Open
Abstract
Cortical circuits are thought to contain a large number of cell types that coordinate to produce behavior. Current in vivo methods rely on clustering of specified features of extracellular waveforms to identify putative cell types, but these capture only a small amount of variation. Here, we develop a new method (WaveMAP) that combines non-linear dimensionality reduction with graph clustering to identify putative cell types. We apply WaveMAP to extracellular waveforms recorded from dorsal premotor cortex of macaque monkeys performing a decision-making task. Using WaveMAP, we robustly establish eight waveform clusters and show that these clusters recapitulate previously identified narrow- and broad-spiking types while revealing previously unknown diversity within these subtypes. The eight clusters exhibited distinct laminar distributions, characteristic firing rate patterns, and decision-related dynamics. Such insights were weaker when using feature-based approaches. WaveMAP therefore provides a more nuanced understanding of the dynamics of cell types in cortical circuits.
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Affiliation(s)
- Eric Kenji Lee
- Psychological and Brain Sciences, Boston UniversityBostonUnited States
| | - Hymavathy Balasubramanian
- Bernstein Center for Computational Neuroscience, Bernstein Center for Computational NeuroscienceBerlinGermany
| | - Alexandra Tsolias
- Department of Anatomy and Neurobiology, Boston UniversityBostonUnited States
| | | | - Maria Medalla
- Department of Anatomy and Neurobiology, Boston UniversityBostonUnited States
| | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford UniversityStanfordUnited States
- Department of Bioengineering, Stanford UniversityStanfordUnited States
- Department of Neurobiology, Stanford UniversityStanfordUnited States
- Wu Tsai Neurosciences Institute, Stanford UniversityStanfordUnited States
- Bio-X Institute, Stanford UniversityStanfordUnited States
- Howard Hughes Medical Institute, Stanford UniversityStanfordUnited States
| | - Chandramouli Chandrasekaran
- Psychological and Brain Sciences, Boston UniversityBostonUnited States
- Department of Anatomy and Neurobiology, Boston UniversityBostonUnited States
- Center for Systems Neuroscience, Boston UniversityBostonUnited States
- Department of Biomedical Engineering, Boston UniversityBostonUnited States
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33
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Rodríguez-Collado A, Rueda C. Electrophysiological and Transcriptomic Features Reveal a Circular Taxonomy of Cortical Neurons. Front Hum Neurosci 2021; 15:684950. [PMID: 34381341 PMCID: PMC8350032 DOI: 10.3389/fnhum.2021.684950] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 06/28/2021] [Indexed: 11/24/2022] Open
Abstract
The complete understanding of the mammalian brain requires exact knowledge of the function of each neuron subpopulation composing its parts. To achieve this goal, an exhaustive, precise, reproducible, and robust neuronal taxonomy should be defined. In this paper, a new circular taxonomy based on transcriptomic features and novel electrophysiological features is proposed. The approach is validated by analysing more than 1850 electrophysiological signals of different mouse visual cortex neurons proceeding from the Allen Cell Types database. The study is conducted on two different levels: neurons and their cell-type aggregation into Cre lines. At the neuronal level, electrophysiological features have been extracted with a promising model that has already proved its worth in neuronal dynamics. At the Cre line level, electrophysiological and transcriptomic features are joined on cell types with available genetic information. A taxonomy with a circular order is revealed by a simple transformation of the first two principal components that allow the characterization of the different Cre lines. Moreover, the proposed methodology locates other Cre lines in the taxonomy that do not have transcriptomic features available. Finally, the taxonomy is validated by Machine Learning methods which are able to discriminate the different neuron types with the proposed electrophysiological features.
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34
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Sætra MJ, Einevoll GT, Halnes G. An electrodiffusive neuron-extracellular-glia model for exploring the genesis of slow potentials in the brain. PLoS Comput Biol 2021; 17:e1008143. [PMID: 34270543 PMCID: PMC8318289 DOI: 10.1371/journal.pcbi.1008143] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 07/28/2021] [Accepted: 06/28/2021] [Indexed: 11/29/2022] Open
Abstract
Within the computational neuroscience community, there has been a focus on simulating the electrical activity of neurons, while other components of brain tissue, such as glia cells and the extracellular space, are often neglected. Standard models of extracellular potentials are based on a combination of multicompartmental models describing neural electrodynamics and volume conductor theory. Such models cannot be used to simulate the slow components of extracellular potentials, which depend on ion concentration dynamics, and the effect that this has on extracellular diffusion potentials and glial buffering currents. We here present the electrodiffusive neuron-extracellular-glia (edNEG) model, which we believe is the first model to combine compartmental neuron modeling with an electrodiffusive framework for intra- and extracellular ion concentration dynamics in a local piece of neuro-glial brain tissue. The edNEG model (i) keeps track of all intraneuronal, intraglial, and extracellular ion concentrations and electrical potentials, (ii) accounts for action potentials and dendritic calcium spikes in neurons, (iii) contains a neuronal and glial homeostatic machinery that gives physiologically realistic ion concentration dynamics, (iv) accounts for electrodiffusive transmembrane, intracellular, and extracellular ionic movements, and (v) accounts for glial and neuronal swelling caused by osmotic transmembrane pressure gradients. The edNEG model accounts for the concentration-dependent effects on ECS potentials that the standard models neglect. Using the edNEG model, we analyze these effects by splitting the extracellular potential into three components: one due to neural sink/source configurations, one due to glial sink/source configurations, and one due to extracellular diffusive currents. Through a series of simulations, we analyze the roles played by the various components and how they interact in generating the total slow potential. We conclude that the three components are of comparable magnitude and that the stimulus conditions determine which of the components that dominate.
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Affiliation(s)
- Marte J. Sætra
- Department of Numerical Analysis and Scientific Computing, Simula Research Laboratory, Oslo, Norway
| | - Gaute T. Einevoll
- Centre for Integrative Neuroplasticity, University of Oslo, Oslo, Norway
- Department of Physics, University of Oslo, Oslo, Norway
- Department of Physics, Norwegian University of Life Sciences, Ås, Norway
| | - Geir Halnes
- Centre for Integrative Neuroplasticity, University of Oslo, Oslo, Norway
- Department of Physics, Norwegian University of Life Sciences, Ås, Norway
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35
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Wouters J, Kloosterman F, Bertrand A. SHYBRID: A Graphical Tool for Generating Hybrid Ground-Truth Spiking Data for Evaluating Spike Sorting Performance. Neuroinformatics 2021; 19:141-158. [PMID: 32617751 DOI: 10.1007/s12021-020-09474-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Spike sorting is the process of retrieving the spike times of individual neurons that are present in an extracellular neural recording. Over the last decades, many spike sorting algorithms have been published. In an effort to guide a user towards a specific spike sorting algorithm, given a specific recording setting (i.e., brain region and recording device), we provide an open-source graphical tool for the generation of hybrid ground-truth data in Python. Hybrid ground-truth data is a data-driven modelling paradigm in which spikes from a single unit are moved to a different location on the recording probe, thereby generating a virtual unit of which the spike times are known. The tool enables a user to efficiently generate hybrid ground-truth datasets and make informed decisions between spike sorting algorithms, fine-tune the algorithm parameters towards the used recording setting, or get a deeper understanding of those algorithms.
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Affiliation(s)
- Jasper Wouters
- Department of Electrical Engineering (ESAT), Stadius Center for Dynamical Systems, Signal Processing, and Data Analytics, KU Leuven, Leuven, Belgium.
| | - Fabian Kloosterman
- Neuro-Electronics Research Flanders (NERF), Leuven, Belgium
- Brain & Cognition Research Unit, KU Leuven, Leuven, Belgium
- VIB, Leuven, Belgium
| | - Alexander Bertrand
- Department of Electrical Engineering (ESAT), Stadius Center for Dynamical Systems, Signal Processing, and Data Analytics, KU Leuven, Leuven, Belgium
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36
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Buccino AP, Einevoll GT. MEArec: A Fast and Customizable Testbench Simulator for Ground-truth Extracellular Spiking Activity. Neuroinformatics 2021; 19:185-204. [PMID: 32648042 PMCID: PMC7782412 DOI: 10.1007/s12021-020-09467-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
When recording neural activity from extracellular electrodes, both in vivo and in vitro, spike sorting is a required and very important processing step that allows for identification of single neurons’ activity. Spike sorting is a complex algorithmic procedure, and in recent years many groups have attempted to tackle this problem, resulting in numerous methods and software packages. However, validation of spike sorting techniques is complicated. It is an inherently unsupervised problem and it is hard to find universal metrics to evaluate performance. Simultaneous recordings that combine extracellular and patch-clamp or juxtacellular techniques can provide ground-truth data to evaluate spike sorting methods. However, their utility is limited by the fact that only a few cells can be measured at the same time. Simulated ground-truth recordings can provide a powerful alternative mean to rank the performance of spike sorters. We present here MEArec, a Python-based software which permits flexible and fast simulation of extracellular recordings. MEArec allows users to generate extracellular signals on various customizable electrode designs and can replicate various problematic aspects for spike sorting, such as bursting, spatio-temporal overlapping events, and drifts. We expect MEArec will provide a common testbench for spike sorting development and evaluation, in which spike sorting developers can rapidly generate and evaluate the performance of their algorithms.
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Affiliation(s)
- Alessio Paolo Buccino
- Centre for Integrative Neuroplasticity (CINPLA), University of Oslo, Oslo, Norway. .,Bio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zürich, Zürich, Switzerland.
| | - Gaute Tomas Einevoll
- Centre for Integrative Neuroplasticity (CINPLA), University of Oslo, Oslo, Norway.,Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
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37
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Goff KM, Goldberg EM. A Role for Vasoactive Intestinal Peptide Interneurons in Neurodevelopmental Disorders. Dev Neurosci 2021; 43:168-180. [PMID: 33794534 PMCID: PMC8440337 DOI: 10.1159/000515264] [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: 12/15/2020] [Accepted: 02/10/2021] [Indexed: 11/19/2022] Open
Abstract
GABAergic inhibitory interneurons of the cerebral cortex expressing vasoactive intestinal peptide (VIP-INs) are rapidly emerging as important regulators of network dynamics and normal circuit development. Several recent studies have also identified VIP-IN dysfunction in models of genetically determined neurodevelopmental disorders (NDDs). In this article, we review the known circuit functions of VIP-INs and how they may relate to accumulating evidence implicating VIP-INs in the mechanisms of prominent NDDs. We highlight recurring VIP-IN-mediated circuit motifs that are shared across cerebral cortical areas and how VIP-IN activity can shape sensory input, development, and behavior. Ultimately, we extract a set of themes that inform our understanding of how VIP-INs influence pathogenesis of NDDs. Using publicly available single-cell RNA sequencing data from the Allen Institute, we also identify several underexplored disease-associated genes that are highly expressed in VIP-INs. We survey these genes and their shared related disease phenotypes that may broadly implicate VIP-INs in autism spectrum disorder and intellectual disability rather than epileptic encephalopathy. Finally, we conclude with a discussion of the relevance of cell type-specific investigations and therapeutics in the age of genomic diagnosis and targeted therapeutics.
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Affiliation(s)
- Kevin M Goff
- Medical Scientist Training Program (MSTP), The University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Neuroscience Graduate Group, The University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Ethan M Goldberg
- Neuroscience Graduate Group, The University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Division of Neurology, Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- The Epilepsy NeuroGenetics Initiative, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Departments of Neurology, The University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Departments of Neuroscience, The University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
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38
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Zhang T, Zeng Y, Zhang Y, Zhang X, Shi M, Tang L, Zhang D, Xu B. Neuron type classification in rat brain based on integrative convolutional and tree-based recurrent neural networks. Sci Rep 2021; 11:7291. [PMID: 33790380 PMCID: PMC8012629 DOI: 10.1038/s41598-021-86780-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 03/17/2021] [Indexed: 11/24/2022] Open
Abstract
The study of cellular complexity in the nervous system based on anatomy has shown more practical and objective advantages in morphology than other perspectives on molecular, physiological, and evolutionary aspects. However, morphology-based neuron type classification in the whole rat brain is challenging, given the significant number of neuron types, limited reconstructed neuron samples, and diverse data formats. Here, we report that different types of deep neural network modules may well process different kinds of features and that the integration of these submodules will show power on the representation and classification of neuron types. For SWC-format data, which are compressed but unstructured, we construct a tree-based recurrent neural network (Tree-RNN) module. For 2D or 3D slice-format data, which are structured but with large volumes of pixels, we construct a convolutional neural network (CNN) module. We also generate a virtually simulated dataset with two classes, reconstruct a CASIA rat-neuron dataset with 2.6 million neurons without labels, and select the NeuroMorpho-rat dataset with 35,000 neurons containing hierarchical labels. In the twelve-class classification task, the proposed model achieves state-of-the-art performance compared with other models, e.g., the CNN, RNN, and support vector machine based on hand-designed features.
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Affiliation(s)
- Tielin Zhang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Yi Zeng
- Institute of Automation, Chinese Academy of Sciences, Beijing, China. .,University of Chinese Academy of Sciences, Beijing, China. .,Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
| | - Yue Zhang
- Electronics and Communication Engineering, Peking University, Beijing, China
| | - Xinhe Zhang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Mengting Shi
- Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Likai Tang
- Department of Automation, Tsinghua University, Beijing, China
| | - Duzhen Zhang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Bo Xu
- Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China.,Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
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39
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Rich S, Moradi Chameh H, Sekulic V, Valiante TA, Skinner FK. Modeling Reveals Human-Rodent Differences in H-Current Kinetics Influencing Resonance in Cortical Layer 5 Neurons. Cereb Cortex 2021; 31:845-872. [PMID: 33068000 PMCID: PMC7906797 DOI: 10.1093/cercor/bhaa261] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 08/18/2020] [Accepted: 08/18/2020] [Indexed: 01/01/2023] Open
Abstract
While our understanding of human neurons is often inferred from rodent data, inter-species differences between neurons can be captured by building cellular models specifically from human data. This includes understanding differences at the level of ion channels and their implications for human brain function. Thus, we here present a full spiking, biophysically detailed multi-compartment model of a human layer 5 (L5) cortical pyramidal cell. Model development was primarily based on morphological and electrophysiological data from the same human L5 neuron, avoiding confounds of experimental variability. Focus was placed on describing the behavior of the hyperpolarization-activated cation (h-) channel, given increasing interest in this channel due to its role in pacemaking and differentiating cell types. We ensured that the model exhibited post-inhibitory rebound spiking considering its relationship with the h-current, along with other general spiking characteristics. The model was validated against data not used in its development, which highlighted distinctly slower kinetics of the human h-current relative to the rodent setting. We linked the lack of subthreshold resonance observed in human L5 neurons to these human-specific h-current kinetics. This work shows that it is possible and necessary to build human-specific biophysical neuron models in order to understand human brain dynamics.
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Affiliation(s)
- Scott Rich
- Division of Clinical and Computational Neuroscience, Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Homeira Moradi Chameh
- Division of Clinical and Computational Neuroscience, Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Vladislav Sekulic
- Division of Clinical and Computational Neuroscience, Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Taufik A Valiante
- Division of Clinical and Computational Neuroscience, Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON M5S 1A1, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A1, Canada
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON M5S 1A1, Canada
- Electrical and Computer Engineering, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Frances K Skinner
- Division of Clinical and Computational Neuroscience, Krembil Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
- Departments of Medicine (Neurology) and Physiology, University of Toronto, Toronto, ON M5S 1A1, Canada
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40
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Dai K, Gratiy SL, Billeh YN, Xu R, Cai B, Cain N, Rimehaug AE, Stasik AJ, Einevoll GT, Mihalas S, Koch C, Arkhipov A. Brain Modeling ToolKit: An open source software suite for multiscale modeling of brain circuits. PLoS Comput Biol 2020; 16:e1008386. [PMID: 33253147 PMCID: PMC7728187 DOI: 10.1371/journal.pcbi.1008386] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 12/10/2020] [Accepted: 09/16/2020] [Indexed: 11/26/2022] Open
Abstract
Experimental studies in neuroscience are producing data at a rapidly increasing rate, providing exciting opportunities and formidable challenges to existing theoretical and modeling approaches. To turn massive datasets into predictive quantitative frameworks, the field needs software solutions for systematic integration of data into realistic, multiscale models. Here we describe the Brain Modeling ToolKit (BMTK), a software suite for building models and performing simulations at multiple levels of resolution, from biophysically detailed multi-compartmental, to point-neuron, to population-statistical approaches. Leveraging the SONATA file format and existing software such as NEURON, NEST, and others, BMTK offers a consistent user experience across multiple levels of resolution. It permits highly sophisticated simulations to be set up with little coding required, thus lowering entry barriers to new users. We illustrate successful applications of BMTK to large-scale simulations of a cortical area. BMTK is an open-source package provided as a resource supporting modeling-based discovery in the community.
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Affiliation(s)
- Kael Dai
- Allen Institute, Seattle, Washington, United States of America
| | | | - Yazan N. Billeh
- Allen Institute, Seattle, Washington, United States of America
| | - Richard Xu
- Allen Institute, Seattle, Washington, United States of America
| | - Binghuang Cai
- Allen Institute, Seattle, Washington, United States of America
| | - Nicholas Cain
- Allen Institute, Seattle, Washington, United States of America
| | - Atle E. Rimehaug
- Norwegian University of Life Sciences & University of Oslo, Oslo, Norway
| | | | - Gaute T. Einevoll
- Norwegian University of Life Sciences & University of Oslo, Oslo, Norway
| | - Stefan Mihalas
- Allen Institute, Seattle, Washington, United States of America
| | - Christof Koch
- Allen Institute, Seattle, Washington, United States of America
| | - Anton Arkhipov
- Allen Institute, Seattle, Washington, United States of America
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41
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Simulations of Myenteric Neuron Dynamics in Response to Mechanical Stretch. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2020:8834651. [PMID: 33123188 PMCID: PMC7582074 DOI: 10.1155/2020/8834651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 08/20/2020] [Accepted: 09/25/2020] [Indexed: 12/02/2022]
Abstract
Background Intestinal sensitivity to mechanical stimuli has been studied intensively in visceral pain studies. The ability to sense different stimuli in the gut and translate these to physiological outcomes relies on the mechanosensory and transductive capacity of intrinsic intestinal nerves. However, the nature of the mechanosensitive channels and principal mechanical stimulus for mechanosensitive receptors are unknown. To be able to characterize intestinal mechanoelectrical transduction, that is, the molecular basis of mechanosensation, comprehensive mathematical models to predict responses of the sensory neurons to controlled mechanical stimuli are needed. This study aims to develop a biophysically based mathematical model of the myenteric neuron with the parameters constrained by learning from existing experimental data. Findings. The conductance-based single-compartment model was selected. The parameters in the model were optimized by using a combination of hand tuning and automated estimation. Using the optimized parameters, the model successfully predicted the electrophysiological features of the myenteric neurons with and without mechanical stimulation. Conclusions The model provides a method to predict features and levels of detail of the underlying physiological system in generating myenteric neuron responses. The model could be used as building blocks in future large-scale network simulations of intrinsic primary afferent neurons and their network.
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42
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Gonçalves PJ, Lueckmann JM, Deistler M, Nonnenmacher M, Öcal K, Bassetto G, Chintaluri C, Podlaski WF, Haddad SA, Vogels TP, Greenberg DS, Macke JH. Training deep neural density estimators to identify mechanistic models of neural dynamics. eLife 2020; 9:e56261. [PMID: 32940606 PMCID: PMC7581433 DOI: 10.7554/elife.56261] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 09/16/2020] [Indexed: 01/27/2023] Open
Abstract
Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators-trained using model simulations-to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin-Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics.
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Affiliation(s)
- Pedro J Gonçalves
- Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of MunichMunichGermany
- Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar)BonnGermany
| | - Jan-Matthis Lueckmann
- Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of MunichMunichGermany
- Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar)BonnGermany
| | - Michael Deistler
- Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of MunichMunichGermany
- Machine Learning in Science, Excellence Cluster Machine Learning, Tübingen UniversityTübingenGermany
| | - Marcel Nonnenmacher
- Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of MunichMunichGermany
- Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar)BonnGermany
- Model-Driven Machine Learning, Institute of Coastal Research, Helmholtz Centre GeesthachtGeesthachtGermany
| | - Kaan Öcal
- Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar)BonnGermany
- Mathematical Institute, University of BonnBonnGermany
| | - Giacomo Bassetto
- Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of MunichMunichGermany
- Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar)BonnGermany
| | - Chaitanya Chintaluri
- Centre for Neural Circuits and Behaviour, University of OxfordOxfordUnited Kingdom
- Institute of Science and Technology AustriaKlosterneuburgAustria
| | - William F Podlaski
- Centre for Neural Circuits and Behaviour, University of OxfordOxfordUnited Kingdom
| | - Sara A Haddad
- Max Planck Institute for Brain ResearchFrankfurtGermany
| | - Tim P Vogels
- Centre for Neural Circuits and Behaviour, University of OxfordOxfordUnited Kingdom
- Institute of Science and Technology AustriaKlosterneuburgAustria
| | - David S Greenberg
- Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of MunichMunichGermany
- Model-Driven Machine Learning, Institute of Coastal Research, Helmholtz Centre GeesthachtGeesthachtGermany
| | - Jakob H Macke
- Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of MunichMunichGermany
- Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar)BonnGermany
- Machine Learning in Science, Excellence Cluster Machine Learning, Tübingen UniversityTübingenGermany
- Max Planck Institute for Intelligent SystemsTübingenGermany
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43
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Systematic Integration of Structural and Functional Data into Multi-scale Models of Mouse Primary Visual Cortex. Neuron 2020; 106:388-403.e18. [DOI: 10.1016/j.neuron.2020.01.040] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 10/17/2019] [Accepted: 01/27/2020] [Indexed: 01/08/2023]
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44
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Ofer N, Shefi O, Yaari G. Axonal Tree Morphology and Signal Propagation Dynamics Improve Interneuron Classification. Neuroinformatics 2020; 18:581-590. [PMID: 32346847 DOI: 10.1007/s12021-020-09466-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Neurons are diverse and can be differentiated by their morphological, electrophysiological, and molecular properties. Current morphology-based classification approaches largely rely on the dendritic tree structure or on the overall axonal projection layout. Here, we use data from public databases of neuronal reconstructions and membrane properties to study the characteristics of the axonal and dendritic trees for interneuron classification. We show that combining signal propagation patterns observed by biophysical simulations of the activity along ramified axonal trees with morphological parameters of the axonal and dendritic trees, significantly improve classification results compared to previous approaches. The classification schemes introduced here can be utilized for robust neuronal classification. Our work paves the way for understanding and utilizing form-function principles in realistic neuronal reconstructions.
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Affiliation(s)
- Netanel Ofer
- Faculty of Engineering, Bar Ilan University, Ramat Gan, 5290002, Israel.,Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, 5290002, Israel
| | - Orit Shefi
- Faculty of Engineering, Bar Ilan University, Ramat Gan, 5290002, Israel. .,Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, 5290002, Israel.
| | - Gur Yaari
- Faculty of Engineering, Bar Ilan University, Ramat Gan, 5290002, Israel.
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45
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Dai K, Hernando J, Billeh YN, Gratiy SL, Planas J, Davison AP, Dura-Bernal S, Gleeson P, Devresse A, Dichter BK, Gevaert M, King JG, Van Geit WAH, Povolotsky AV, Muller E, Courcol JD, Arkhipov A. The SONATA data format for efficient description of large-scale network models. PLoS Comput Biol 2020; 16:e1007696. [PMID: 32092054 PMCID: PMC7058350 DOI: 10.1371/journal.pcbi.1007696] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 03/05/2020] [Accepted: 01/28/2020] [Indexed: 12/04/2022] Open
Abstract
Increasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simulation, as well as sharing of such large-scale models, a broadly applicable, flexible, and high-performance data format is necessary. To address this need, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. It is designed for memory and computational efficiency and works across multiple platforms. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is used in multiple modeling and visualization tools, and we also provide reference Application Programming Interfaces and model examples to catalyze further adoption. SONATA format is free and open for the community to use and build upon with the goal of enabling efficient model building, sharing, and reproducibility. Neuroscience is experiencing a rapid growth of data streams characterizing composition, connectivity, and activity of brain networks in ever increasing details. Data-driven modeling will be essential to integrate these multimodal and complex data into predictive simulations to advance our understanding of brain function and mechanisms. To enable efficient development and sharing of such large-scale models utilizing diverse data types, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is already supported by several popular tools for model building, simulations, and visualization. It is free and open for everyone to use and build upon and will enable increased efficiency, reproducibility, and scientific exchange in the community.
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Affiliation(s)
- Kael Dai
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Juan Hernando
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Yazan N. Billeh
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Sergey L. Gratiy
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Judit Planas
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Andrew P. Davison
- Paris-Saclay Institute of Neuroscience UMR, Centre National de la Recherche Scientifique/Université Paris-Saclay, Gif-sur-Yvette, France
| | - Salvador Dura-Bernal
- State University of New York Downstate Medical Center, Brooklyn, New York, United States of America
- Nathan Kline Institute for Psychiatric Research, Orangeburg, New York, United States of America
| | - Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom
| | - Adrien Devresse
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Benjamin K. Dichter
- Department of Neurosurgery, Stanford University, Stanford, California, United States of America
- Biological Systems and Engineering, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
| | - Michael Gevaert
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - James G. King
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Werner A. H. Van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Arseny V. Povolotsky
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Eilif Muller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Jean-Denis Courcol
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Anton Arkhipov
- Allen Institute for Brain Science, Seattle, Washington, United States of America
- * E-mail:
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Michiels M. Electrophysiology prediction of single neurons based on their morphology.. [DOI: 10.1101/2020.02.04.933697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
AbstractElectrophysiology data acquisition of single neurons represents a key factor for the understanding of neuronal dynamics. However, the traditional method to acquire this data is through patch-clamp technology, which presents serious scalability flaws due to its slowness and complexity to record at fine-grained spatial precision (dendrites and axon).In silico biophysical models are therefore created for simulating hundreds of experiments that would be impractical to recreate in vitro. The optimal way to create these models is based on the knowledge of the morphological and electrical features for each neuron. Since large-scale data acquisition is often unfeasible for electrical data, previous expert knowledge can be used but it must have an acceptable degree of similarity with the type of neurons that we are trying to model.Here, we present a data-driven machine learning approach to predict the electrophysiological features of single neurons in case of only having their morphology available. To solve this multi-output regression problem, we use an artificial neural network that has the particularity of providing a probability distribution for every output feature, to incorporate uncertainty. Input data to train the model is obtained from from the Allen Cell Types database. The electrical properties can depend on the morphology, whose acquisition technology is highly automated and scalable so there exist large data sets of them. We also provide integrations with the BluePyOpt library to create a biophysical model using the original morphology and the predicted electrical features. Finally, we connect the resulting biophysical model with the Geppetto UI software to run all the simulations in a sophisticated user interface.
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Amsalem O, Eyal G, Rogozinski N, Gevaert M, Kumbhar P, Schürmann F, Segev I. An efficient analytical reduction of detailed nonlinear neuron models. Nat Commun 2020; 11:288. [PMID: 31941884 PMCID: PMC6962154 DOI: 10.1038/s41467-019-13932-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 12/09/2019] [Indexed: 12/31/2022] Open
Abstract
Detailed conductance-based nonlinear neuron models consisting of thousands of synapses are key for understanding of the computational properties of single neurons and large neuronal networks, and for interpreting experimental results. Simulations of these models are computationally expensive, considerably curtailing their utility. Neuron_Reduce is a new analytical approach to reduce the morphological complexity and computational time of nonlinear neuron models. Synapses and active membrane channels are mapped to the reduced model preserving their transfer impedance to the soma; synapses with identical transfer impedance are merged into one NEURON process still retaining their individual activation times. Neuron_Reduce accelerates the simulations by 40-250 folds for a variety of cell types and realistic number (10,000-100,000) of synapses while closely replicating voltage dynamics and specific dendritic computations. The reduced neuron-models will enable realistic simulations of neural networks at unprecedented scale, including networks emerging from micro-connectomics efforts and biologically-inspired "deep networks". Neuron_Reduce is publicly available and is straightforward to implement.
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Affiliation(s)
- Oren Amsalem
- Department of Neurobiology, Hebrew University of Jerusalem, 9190401, Jerusalem, Israel.
| | - Guy Eyal
- Department of Neurobiology, Hebrew University of Jerusalem, 9190401, Jerusalem, Israel
| | - Noa Rogozinski
- Department of Neurobiology, Hebrew University of Jerusalem, 9190401, Jerusalem, Israel
| | - Michael Gevaert
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202, Geneva, Switzerland
| | - Pramod Kumbhar
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202, Geneva, Switzerland
| | - Felix Schürmann
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202, Geneva, Switzerland
| | - Idan Segev
- Department of Neurobiology, Hebrew University of Jerusalem, 9190401, Jerusalem, Israel
- Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, 9190401, Jerusalem, Israel
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Malik R, Pai ELL, Rubin AN, Stafford AM, Angara K, Minasi P, Rubenstein JL, Sohal VS, Vogt D. Tsc1 represses parvalbumin expression and fast-spiking properties in somatostatin lineage cortical interneurons. Nat Commun 2019; 10:4994. [PMID: 31676823 PMCID: PMC6825152 DOI: 10.1038/s41467-019-12962-4] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Accepted: 10/04/2019] [Indexed: 12/21/2022] Open
Abstract
Medial ganglionic eminence (MGE)-derived somatostatin (SST)+ and parvalbumin (PV)+ cortical interneurons (CINs), have characteristic molecular, anatomical and physiological properties. However, mechanisms regulating their diversity remain poorly understood. Here, we show that conditional loss of the Tuberous Sclerosis Complex (TSC) gene, Tsc1, which inhibits the mammalian target of rapamycin (MTOR), causes a subset of SST+ CINs, to express PV and adopt fast-spiking (FS) properties, characteristic of PV+ CINs. Milder intermediate phenotypes also occur when only one allele of Tsc1 is deleted. Notably, treatment of adult mice with rapamycin, which inhibits MTOR, reverses the phenotypes. These data reveal novel functions of MTOR signaling in regulating PV expression and FS properties, which may contribute to TSC neuropsychiatric symptoms. Moreover, they suggest that CINs can exhibit properties intermediate between those classically associated with PV+ or SST+ CINs, which may be dynamically regulated by the MTOR signaling.
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Affiliation(s)
- Ruchi Malik
- Department of Psychiatry and UCSF Weill Institute for Neurosciences, 675 Nelson Rising Ln, San Francisco, CA, 94158, USA
- Center for Integrative Neuroscience, University of California San Francisco, 1550 4th St., San Francisco, CA, 94158, USA
- Sloan-Swartz Center for Theoretical Neurobiology, University of California San Francisco, 1550 4th St., San Francisco, CA, 94158, USA
| | - Emily Ling-Lin Pai
- Department of Psychiatry and UCSF Weill Institute for Neurosciences, 675 Nelson Rising Ln, San Francisco, CA, 94158, USA
- Neuroscience Program, UCSF, University of California San Francisco, 1550 4th St., San Francisco, CA, 94158, USA
- Nina Ireland Laboratory of Developmental Neurobiology, University of California San Francisco, 1550 4th St., San Francisco, CA, 94158, USA
| | - Anna N Rubin
- Department of Psychiatry and UCSF Weill Institute for Neurosciences, 675 Nelson Rising Ln, San Francisco, CA, 94158, USA
- Nina Ireland Laboratory of Developmental Neurobiology, University of California San Francisco, 1550 4th St., San Francisco, CA, 94158, USA
| | - April M Stafford
- Department of Pediatrics and Human Development, 400 Monroe Ave. NW, Grand Rapids, MI, 49503, USA
| | - Kartik Angara
- Department of Pediatrics and Human Development, 400 Monroe Ave. NW, Grand Rapids, MI, 49503, USA
| | - Petros Minasi
- Department of Psychiatry and UCSF Weill Institute for Neurosciences, 675 Nelson Rising Ln, San Francisco, CA, 94158, USA
| | - John L Rubenstein
- Department of Psychiatry and UCSF Weill Institute for Neurosciences, 675 Nelson Rising Ln, San Francisco, CA, 94158, USA
- Neuroscience Program, UCSF, University of California San Francisco, 1550 4th St., San Francisco, CA, 94158, USA
- Nina Ireland Laboratory of Developmental Neurobiology, University of California San Francisco, 1550 4th St., San Francisco, CA, 94158, USA
| | - Vikaas S Sohal
- Department of Psychiatry and UCSF Weill Institute for Neurosciences, 675 Nelson Rising Ln, San Francisco, CA, 94158, USA.
- Center for Integrative Neuroscience, University of California San Francisco, 1550 4th St., San Francisco, CA, 94158, USA.
- Sloan-Swartz Center for Theoretical Neurobiology, University of California San Francisco, 1550 4th St., San Francisco, CA, 94158, USA.
| | - Daniel Vogt
- Department of Pediatrics and Human Development, 400 Monroe Ave. NW, Grand Rapids, MI, 49503, USA.
- Neuroscience Program, Michigan State University, East Lansing, MI, USA.
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Inferring and validating mechanistic models of neural microcircuits based on spike-train data. Nat Commun 2019; 10:4933. [PMID: 31666513 PMCID: PMC6821748 DOI: 10.1038/s41467-019-12572-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Accepted: 09/18/2019] [Indexed: 01/11/2023] Open
Abstract
The interpretation of neuronal spike train recordings often relies on abstract statistical models that allow for principled parameter estimation and model selection but provide only limited insights into underlying microcircuits. In contrast, mechanistic models are useful to interpret microcircuit dynamics, but are rarely quantitatively matched to experimental data due to methodological challenges. Here we present analytical methods to efficiently fit spiking circuit models to single-trial spike trains. Using derived likelihood functions, we statistically infer the mean and variance of hidden inputs, neuronal adaptation properties and connectivity for coupled integrate-and-fire neurons. Comprehensive evaluations on synthetic data, validations using ground truth in-vitro and in-vivo recordings, and comparisons with existing techniques demonstrate that parameter estimation is very accurate and efficient, even for highly subsampled networks. Our methods bridge statistical, data-driven and theoretical, model-based neurosciences at the level of spiking circuits, for the purpose of a quantitative, mechanistic interpretation of recorded neuronal population activity. It is difficult to fit mechanistic, biophysically constrained circuit models to spike train data from in vivo extracellular recordings. Here the authors present analytical methods that enable efficient parameter estimation for integrate-and-fire circuit models and inference of the underlying connectivity structure in subsampled networks.
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Rumbell T, Kozloski J. Dimensions of control for subthreshold oscillations and spontaneous firing in dopamine neurons. PLoS Comput Biol 2019; 15:e1007375. [PMID: 31545787 PMCID: PMC6776370 DOI: 10.1371/journal.pcbi.1007375] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 10/03/2019] [Accepted: 09/04/2019] [Indexed: 11/20/2022] Open
Abstract
Dopaminergic neurons (DAs) of the rodent substantia nigra pars compacta (SNc) display varied electrophysiological properties in vitro. Despite this, projection patterns and functional inputs from DAs to other structures are conserved, so in vivo delivery of consistent, well-timed dopamine modulation to downstream circuits must be coordinated. Here we show robust coordination by linear parameter controllers, discovered through powerful mathematical analyses of data and models, and from which consistent control of DA subthreshold oscillations (STOs) and spontaneous firing emerges. These units of control represent coordinated intracellular variables, sufficient to regulate complex cellular properties with radical simplicity. Using an evolutionary algorithm and dimensionality reduction, we discovered metaparameters, which when regressed against STO features, revealed a 2-dimensional control plane for the neuron’s 22-dimensional parameter space that fully maps the natural range of DA subthreshold electrophysiology. This plane provided a basis for spiking currents to reproduce a large range of the naturally occurring spontaneous firing characteristics of SNc DAs. From it we easily produced a unique population of models, derived using unbiased parameter search, that show good generalization to channel blockade and compensatory intracellular mechanisms. From this population of models, we then discovered low-dimensional controllers for regulating spontaneous firing properties, and gain insight into how currents active in different voltage regimes interact to produce the emergent activity of SNc DAs. Our methods therefore reveal simple regulators of neuronal function lurking in the complexity of combined ion channel dynamics. Electrophysiological activity of the neuronal membrane and concomitant ion channel properties are highly variable within groups of neurons of the same type from the same brain region. Reconciliation of the mechanisms generating neuronal activity is challenging due to the complexity of the interactions between the channel currents involved. Here we present a set of mathematical analyses that uncover the low-dimensional intracellular parameter combinations capable of regulating features of subthreshold oscillations and spontaneous firing in empirically constrained models of nigral dopaminergic neurons. This method generates, from a naive starting point, linear combinations of ion channel properties that are surprisingly capable of reliably controlling a wide variety of emergent electrophysiological activity, thereby predicting drug effects and shedding light on unsuspected compensatory mechanisms that contribute to neuronal function.
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Affiliation(s)
- Timothy Rumbell
- IBM Research, Computational Biology Center, Thomas J. Watson Research Laboratories, Yorktown Heights, New York, United States of America
- * E-mail:
| | - James Kozloski
- IBM Research, Computational Biology Center, Thomas J. Watson Research Laboratories, Yorktown Heights, New York, United States of America
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