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Huang X, Wei X, Wang J, Yi G. Frequency-dependent membrane polarization across neocortical cell types and subcellular elements by transcranial alternating current stimulation. J Neural Eng 2024; 21:016034. [PMID: 38382101 DOI: 10.1088/1741-2552/ad2b8a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 02/21/2024] [Indexed: 02/23/2024]
Abstract
Objective.Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation technique that directly interacts with ongoing brain oscillations in a frequency-dependent manner. However, it remains largely unclear how the cellular effects of tACS vary between cell types and subcellular elements.Approach.In this study, we use a set of morphologically realistic models of neocortical neurons to simulate the cellular response to uniform oscillating electric fields (EFs). We systematically characterize the membrane polarization in the soma, axons, and dendrites with varying field directions, intensities, and frequencies.Main results.Pyramidal cells are more sensitive to axial EF that is roughly parallel to the cortical column, while interneurons are sensitive to axial EF and transverse EF that is tangent to the cortical surface. Membrane polarization in each subcellular element increases linearly with EF intensity, and its slope, i.e. polarization length, highly depends on the stimulation frequency. At each frequency, pyramidal cells are more polarized than interneurons. Axons usually experience the highest polarization, followed by the dendrites and soma. Moreover, a visible frequency resonance presents in the apical dendrites of pyramidal cells, while the other subcellular elements primarily exhibit low-pass filtering properties. In contrast, each subcellular element of interneurons exhibits complex frequency-dependent polarization. Polarization phase in each subcellular element of cortical neurons lags that of field and exhibits high-pass filtering properties. These results demonstrate that the membrane polarization is not only frequency-dependent, but also cell type- and subcellular element-specific. Through relating effective length and ion mechanism with polarization, we emphasize the crucial role of cell morphology and biophysics in determining the frequency-dependent membrane polarization.Significance.Our findings highlight the diverse polarization patterns across cell types as well as subcellular elements, which provide some insights into the tACS cellular effects and should be considered when understanding the neural spiking activity by tACS.
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Affiliation(s)
- Xuelin Huang
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Xile Wei
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Jiang Wang
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Guosheng Yi
- School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, People's Republic of China
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2
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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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3
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Haynes VR, Zhou Y, Crook SM. Discovering optimal features for neuron-type identification from extracellular recordings. Front Neuroinform 2024; 18:1303993. [PMID: 38371496 PMCID: PMC10869512 DOI: 10.3389/fninf.2024.1303993] [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: 09/28/2023] [Accepted: 01/15/2024] [Indexed: 02/20/2024] Open
Abstract
Advancements in multichannel recordings of single-unit activity (SUA) in vivo present an opportunity to discover novel features of spatially-varying extracellularly-recorded action potentials (EAPs) that are useful for identifying neuron-types. Traditional approaches to classifying neuron-types often rely on computing EAP waveform features based on conventions of single-channel recordings and thus inherit their limitations. However, spatiotemporal EAP waveforms are the product of signals from underlying current sources being mixed within the extracellular space. We introduce a machine learning approach to demix the underlying sources of spatiotemporal EAP waveforms. Using biophysically realistic computational models, we simulate EAP waveforms and characterize them by the relative prevalence of these sources, which we use as features for identifying the neuron-types corresponding to recorded single units. These EAP sources have distinct spatial and multi-resolution temporal patterns that are robust to various sampling biases. EAP sources also are shared across many neuron-types, are predictive of gross morphological features, and expose underlying morphological domains. We then organize known neuron-types into a hierarchy of latent morpho-electrophysiological types based on differences in the source prevalences, which provides a multi-level classification scheme. We validate the robustness, accuracy, and interpretations of our demixing approach by analyzing simulated EAPs from morphologically detailed models with classification and clustering methods. This simulation-based approach provides a machine learning strategy for neuron-type identification.
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Affiliation(s)
- Vergil R. Haynes
- Laboratory for Auditory Computation and Neurophysiology, College of Health Solutions, Arizona State University, Tempe, AZ, United States
- Laboratory for Informatics and Computation in Open Neuroscience, School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, United States
| | - Yi Zhou
- Laboratory for Auditory Computation and Neurophysiology, College of Health Solutions, Arizona State University, Tempe, AZ, United States
| | - Sharon M. Crook
- Laboratory for Informatics and Computation in Open Neuroscience, School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, United States
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4
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Garcia S, Windolf C, Boussard J, Dichter B, Buccino AP, Yger P. A Modular Implementation to Handle and Benchmark Drift Correction for High-Density Extracellular Recordings. eNeuro 2024; 11:ENEURO.0229-23.2023. [PMID: 38238082 PMCID: PMC10897502 DOI: 10.1523/eneuro.0229-23.2023] [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: 06/29/2023] [Revised: 12/20/2023] [Accepted: 12/21/2023] [Indexed: 02/28/2024] Open
Abstract
High-density neural devices are now offering the possibility to record from neuronal populations in vivo at unprecedented scale. However, the mechanical drifts often observed in these recordings are currently a major issue for "spike sorting," an essential analysis step to identify the activity of single neurons from extracellular signals. Although several strategies have been proposed to compensate for such drifts, the lack of proper benchmarks makes it hard to assess the quality and effectiveness of motion correction. In this paper, we present a benchmark study to precisely and quantitatively evaluate the performance of several state-of-the-art motion correction algorithms introduced in the literature. Using simulated recordings with induced drifts, we dissect the origins of the errors performed while applying a motion correction algorithm as a preprocessing step in the spike sorting pipeline. We show how important it is to properly estimate the positions of the neurons from extracellular traces in order to correctly estimate the probe motion, compare several interpolation procedures, and highlight what are the current limits for motion correction approaches.
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Affiliation(s)
- Samuel Garcia
- Centre de Recherche en Neuroscience de Lyon, CNRS, Lyon 69675, France
| | | | | | | | - Alessio P Buccino
- CatalystNeuro, Benicia, California 94510
- Allen Institute for Neural Dynamics, Seattle, Washington 98109
| | - Pierre Yger
- Institut de la Vision, Sorbonne Université, INSERM, Paris 75012, France
- Lille Neurosciences & Cognition (lilNCog)-U1172 (INSERM, Lille), Univ Lille, Centre Hospitalier Universitaire de Lille, Lille 59800, France
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5
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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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Varghese N, Moscoso B, Chavez A, Springer K, Ortiz E, Soh H, Santaniello S, Maheshwari A, Tzingounis AV. KCNQ2/3 Gain-of-Function Variants and Cell Excitability: Differential Effects in CA1 versus L2/3 Pyramidal Neurons. J Neurosci 2023; 43:6479-6494. [PMID: 37607817 PMCID: PMC10513074 DOI: 10.1523/jneurosci.0980-23.2023] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 08/09/2023] [Accepted: 08/15/2023] [Indexed: 08/24/2023] Open
Abstract
Gain-of-function (GOF) pathogenic variants in the potassium channels KCNQ2 and KCNQ3 lead to hyperexcitability disorders such as epilepsy and autism spectrum disorders. However, the underlying cellular mechanisms of how these variants impair forebrain function are unclear. Here, we show that the R201C variant in KCNQ2 has opposite effects on the excitability of two types of mouse pyramidal neurons of either sex, causing hyperexcitability in layer 2/3 (L2/3) pyramidal neurons and hypoexcitability in CA1 pyramidal neurons. Similarly, the homologous R231C variant in KCNQ3 leads to hyperexcitability in L2/3 pyramidal neurons and hypoexcitability in CA1 pyramidal neurons. However, the effects of KCNQ3 gain-of-function on excitability are specific to superficial CA1 pyramidal neurons. These findings reveal a new level of complexity in the function of KCNQ2 and KCNQ3 channels in the forebrain and provide a framework for understanding the effects of gain-of-function variants and potassium channels in the brain.SIGNIFICANCE STATEMENT KCNQ2/3 gain-of-function (GOF) variants lead to severe forms of neurodevelopmental disorders, but the mechanisms by which these channels affect neuronal activity are poorly understood. In this study, using a series of transgenic mice we demonstrate that the same KCNQ2/3 GOF variants can lead to either hyperexcitability or hypoexcitability in different types of pyramidal neurons [CA1 vs layer (L)2/3]. Additionally, we show that expression of the recurrent KCNQ2 GOF variant R201C in forebrain pyramidal neurons could lead to seizures and SUDEP. Our data suggest that the effects of KCNQ2/3 GOF variants depend on specific cell types and brain regions, possibly accounting for the diverse range of phenotypes observed in individuals with KCNQ2/3 GOF variants.
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Affiliation(s)
- Nissi Varghese
- Department of Physiology and Neurobiology, University of Connecticut, Storrs, Connecticut 06269
| | - Bruno Moscoso
- Department of Neurology, Baylor College of Medicine, Houston, Texas 77030
| | - Ana Chavez
- Department of Neurology, Baylor College of Medicine, Houston, Texas 77030
| | - Kristen Springer
- Department of Physiology and Neurobiology, University of Connecticut, Storrs, Connecticut 06269
| | - Erika Ortiz
- Department of Neurology, Baylor College of Medicine, Houston, Texas 77030
| | - Heun Soh
- Department of Physiology and Neurobiology, University of Connecticut, Storrs, Connecticut 06269
| | - Sabato Santaniello
- Department of Biomedical Engineering and Connecticut Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, Connecticut 06269
| | - Atul Maheshwari
- Department of Neurology, Baylor College of Medicine, Houston, Texas 77030
| | - Anastasios V Tzingounis
- Department of Physiology and Neurobiology, University of Connecticut, Storrs, Connecticut 06269
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7
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Aberra AS, Wang R, Grill WM, Peterchev AV. Multi-scale model of axonal and dendritic polarization by transcranial direct current stimulation in realistic head geometry. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.23.554447. [PMID: 37767087 PMCID: PMC10522328 DOI: 10.1101/2023.08.23.554447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
Background Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation modality that can alter cortical excitability. However, it remains unclear how the subcellular elements of different neuron types are polarized by specific electric field (E-field) distributions. Objective To quantify neuronal polarization generated by tDCS in a multi-scale computational model. Methods We embedded layer-specific, morphologically-realistic cortical neuron models in a finite element model of the E-field in a human head and simulated steady-state polarization generated by conventional primary-motor-cortex-supraorbital (M1-SO) and 4×1 high-definition (HD) tDCS. We quantified somatic, axonal, and dendritic polarization of excitatory pyramidal cells in layers 2/3, 5, and 6, as well as inhibitory interneurons in layers 1 and 4 of the hand knob. Results Axonal and dendritic terminals were polarized more than the soma in all neurons, with peak axonal and dendritic polarization of 0.92 mV and 0.21 mV, respectively, compared to peak somatic polarization of 0.07 mV for 1.8 mA M1-SO stimulation. Both montages generated regions of depolarization and hyperpolarization beneath the M1 anode; M1-SO produced slightly stronger, more diffuse polarization peaking in the central sulcus, while 4×1 HD produced higher peak polarization in the gyral crown. Simulating polarization by uniform local E-field approximated the spatial distribution of tDCS polarization but produced large errors in some regions. Conclusions Polarization of pre- and postsynaptic compartments of excitatory and inhibitory cortical neurons may play a significant role in tDCS neuromodulation. These effects cannot be predicted from the E-field distribution alone but rather require calculation of the neuronal response.
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Aberra AS, Lopez A, Grill WM, Peterchev AV. Rapid estimation of cortical neuron activation thresholds by transcranial magnetic stimulation using convolutional neural networks. Neuroimage 2023; 275:120184. [PMID: 37230204 PMCID: PMC10281353 DOI: 10.1016/j.neuroimage.2023.120184] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 04/13/2023] [Accepted: 05/22/2023] [Indexed: 05/27/2023] Open
Abstract
BACKGROUND Transcranial magnetic stimulation (TMS) can modulate neural activity by evoking action potentials in cortical neurons. TMS neural activation can be predicted by coupling subject-specific head models of the TMS-induced electric field (E-field) to populations of biophysically realistic neuron models; however, the significant computational cost associated with these models limits their utility and eventual translation to clinically relevant applications. OBJECTIVE To develop computationally efficient estimators of the activation thresholds of multi-compartmental cortical neuron models in response to TMS-induced E-field distributions. METHODS Multi-scale models combining anatomically accurate finite element method (FEM) simulations of the TMS E-field with layer-specific representations of cortical neurons were used to generate a large dataset of activation thresholds. 3D convolutional neural networks (CNNs) were trained on these data to predict thresholds of model neurons given their local E-field distribution. The CNN estimator was compared to an approach using the uniform E-field approximation to estimate thresholds in the non-uniform TMS-induced E-field. RESULTS The 3D CNNs estimated thresholds with mean absolute percent error (MAPE) on the test dataset below 2.5% and strong correlation between the CNN predicted and actual thresholds for all cell types (R2 > 0.96). The CNNs estimated thresholds with a 2-4 orders of magnitude reduction in the computational cost of the multi-compartmental neuron models. The CNNs were also trained to predict the median threshold of populations of neurons, speeding up computation further. CONCLUSION 3D CNNs can estimate rapidly and accurately the TMS activation thresholds of biophysically realistic neuron models using sparse samples of the local E-field, enabling simulating responses of large neuron populations or parameter space exploration on a personal computer.
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Affiliation(s)
- Aman S Aberra
- Department of Biomedical Engineering, School of Engineering, Duke University, NC, USA
| | - Adrian Lopez
- Department of Electrical and Computer Engineering, School of Engineering, Duke University, NC, USA; Department of Mathematics, College of Arts and Sciences, Duke University, NC, USA
| | - Warren M Grill
- Department of Biomedical Engineering, School of Engineering, Duke University, NC, USA; Department of Electrical and Computer Engineering, School of Engineering, Duke University, NC, USA; Department of Neurobiology, School of Medicine, Duke University, NC, USA; Department of Neurosurgery, School of Medicine, Duke University, NC, USA
| | - Angel V Peterchev
- Department of Biomedical Engineering, School of Engineering, Duke University, NC, USA; Department of Electrical and Computer Engineering, School of Engineering, Duke University, NC, USA; Department of Neurosurgery, School of Medicine, Duke University, NC, USA; Department of Psychiatry and Behavioral Sciences, School of Medicine, Duke University, NC, USA.
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Dura-Bernal S, Neymotin SA, Suter BA, Dacre J, Moreira JVS, Urdapilleta E, Schiemann J, Duguid I, Shepherd GMG, Lytton WW. Multiscale model of primary motor cortex circuits predicts in vivo cell-type-specific, behavioral state-dependent dynamics. Cell Rep 2023; 42:112574. [PMID: 37300831 PMCID: PMC10592234 DOI: 10.1016/j.celrep.2023.112574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 02/27/2023] [Accepted: 05/12/2023] [Indexed: 06/12/2023] Open
Abstract
Understanding cortical function requires studying multiple scales: molecular, cellular, circuit, and behavioral. We develop a multiscale, biophysically detailed model of mouse primary motor cortex (M1) with over 10,000 neurons and 30 million synapses. Neuron types, densities, spatial distributions, morphologies, biophysics, connectivity, and dendritic synapse locations are constrained by experimental data. The model includes long-range inputs from seven thalamic and cortical regions and noradrenergic inputs. Connectivity depends on cell class and cortical depth at sublaminar resolution. The model accurately predicts in vivo layer- and cell-type-specific responses (firing rates and LFP) associated with behavioral states (quiet wakefulness and movement) and experimental manipulations (noradrenaline receptor blockade and thalamus inactivation). We generate mechanistic hypotheses underlying the observed activity and analyzed low-dimensional population latent dynamics. This quantitative theoretical framework can be used to integrate and interpret M1 experimental data and sheds light on the cell-type-specific multiscale dynamics associated with several experimental conditions and behaviors.
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Affiliation(s)
- Salvador Dura-Bernal
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
| | - Samuel A Neymotin
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department of Psychiatry, Grossman School of Medicine, New York University (NYU), New York, NY, USA
| | - Benjamin A Suter
- Department of Physiology, Northwestern University, Evanston, IL, USA
| | - Joshua Dacre
- Centre for Discovery Brain Sciences, Edinburgh Medical School: Biomedical Sciences, University of Edinburgh, Edinburgh, UK
| | - Joao V S Moreira
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA
| | - Eugenio Urdapilleta
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA
| | - Julia Schiemann
- Centre for Discovery Brain Sciences, Edinburgh Medical School: Biomedical Sciences, University of Edinburgh, Edinburgh, UK; Center for Integrative Physiology and Molecular Medicine, Saarland University, Saarbrücken, Germany
| | - Ian Duguid
- Centre for Discovery Brain Sciences, Edinburgh Medical School: Biomedical Sciences, University of Edinburgh, Edinburgh, UK
| | | | - William W Lytton
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA; Department of Neurology, Kings County Hospital Center, Brooklyn, NY, USA
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Shomali SR, Rasuli SN, Ahmadabadi MN, Shimazaki H. Uncovering hidden network architecture from spiking activities using an exact statistical input-output relation of neurons. Commun Biol 2023; 6:169. [PMID: 36792689 PMCID: PMC9932086 DOI: 10.1038/s42003-023-04511-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 01/20/2023] [Indexed: 02/17/2023] Open
Abstract
Identifying network architecture from observed neural activities is crucial in neuroscience studies. A key requirement is knowledge of the statistical input-output relation of single neurons in vivo. By utilizing an exact analytical solution of the spike-timing for leaky integrate-and-fire neurons under noisy inputs balanced near the threshold, we construct a framework that links synaptic type, strength, and spiking nonlinearity with the statistics of neuronal population activity. The framework explains structured pairwise and higher-order interactions of neurons receiving common inputs under different architectures. We compared the theoretical predictions with the activity of monkey and mouse V1 neurons and found that excitatory inputs given to pairs explained the observed sparse activity characterized by strong negative triple-wise interactions, thereby ruling out the alternative explanation by shared inhibition. Moreover, we showed that the strong interactions are a signature of excitatory rather than inhibitory inputs whenever the spontaneous rate is low. We present a guide map of neural interactions that help researchers to specify the hidden neuronal motifs underlying observed interactions found in empirical data.
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Affiliation(s)
- Safura Rashid Shomali
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, 19395-5746, Iran.
| | - Seyyed Nader Rasuli
- grid.418744.a0000 0000 8841 7951School of Physics, Institute for Research in Fundamental Sciences (IPM), Tehran, 19395-5531 Iran ,grid.411872.90000 0001 2087 2250Department of Physics, University of Guilan, Rasht, 41335-1914 Iran
| | - Majid Nili Ahmadabadi
- grid.46072.370000 0004 0612 7950Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, 14395-515 Iran
| | - Hideaki Shimazaki
- Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan. .,Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN), Hokkaido University, Hokkaido, 060-0812, Japan.
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Wang B, Aberra AS, Grill WM, Peterchev AV. Responses of model cortical neurons to temporal interference stimulation and related transcranial alternating current stimulation modalities. J Neural Eng 2023; 19:10.1088/1741-2552/acab30. [PMID: 36594634 PMCID: PMC9942661 DOI: 10.1088/1741-2552/acab30] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 12/13/2022] [Indexed: 12/15/2022]
Abstract
Objective.Temporal interference stimulation (TIS) was proposed as a non-invasive, focal, and steerable deep brain stimulation method. However, the mechanisms underlying experimentally-observed suprathreshold TIS effects are unknown, and prior simulation studies had limitations in the representations of the TIS electric field (E-field) and cerebral neurons. We examined the E-field and neural response characteristics for TIS and related transcranial alternating current stimulation modalities.Approach.Using the uniform-field approximation, we simulated a range of stimulation parameters in biophysically realistic model cortical neurons, including different orientations, frequencies, amplitude ratios, amplitude modulation, and phase difference of the E-fields, and obtained thresholds for both activation and conduction block.Main results. For two E-fields with similar amplitudes (representative of E-field distributions at the target region), TIS generated an amplitude-modulated (AM) total E-field. Due to the phase difference of the individual E-fields, the total TIS E-field vector also exhibited rotation where the orientations of the two E-fields were not aligned (generally also at the target region). TIS activation thresholds (75-230 V m-1) were similar to those of high-frequency stimulation with or without modulation and/or rotation. For E-field dominated by the high-frequency carrier and with minimal amplitude modulation and/or rotation (typically outside the target region), TIS was less effective at activation and more effective at block. Unlike AM high-frequency stimulation, TIS generated conduction block with some orientations and amplitude ratios of individual E-fields at very high amplitudes of the total E-field (>1700 V m-1).Significance. The complex 3D properties of the TIS E-fields should be accounted for in computational and experimental studies. The mechanisms of suprathreshold cortical TIS appear to involve neural activity block and periodic activation or onset response, consistent with computational studies of peripheral axons. These phenomena occur at E-field strengths too high to be delivered tolerably through scalp electrodes and may inhibit endogenous activity in off-target regions, suggesting limited significance of suprathreshold TIS.
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Affiliation(s)
- Boshuo Wang
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Aman S. Aberra
- Department of Biomedical Engineering, School of Engineering, Duke University, Durham, NC 27708, USA
| | - Warren M. Grill
- Department of Biomedical Engineering, School of Engineering, Duke University, Durham, NC 27708, USA
- Department of Electrical and Computer Engineering, School of Engineering, Duke University, Durham, NC 27708, USA
- Department of Neurobiology, School of Medicine, Duke University, Durham, NC 27710, USA
- Department of Neurosurgery, School of Medicine, Duke University, Durham, NC 27710, USA
| | - Angel V. Peterchev
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Duke University, Durham, NC 27710, USA
- Department of Biomedical Engineering, School of Engineering, Duke University, Durham, NC 27708, USA
- Department of Electrical and Computer Engineering, School of Engineering, Duke University, Durham, NC 27708, USA
- Department of Neurosurgery, School of Medicine, Duke University, Durham, NC 27710, USA
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12
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Kang W, Ju C, Joo J, Lee J, Shon YM, Park SM. Closed-loop direct control of seizure focus in a rodent model of temporal lobe epilepsy via localized electric fields applied sequentially. Nat Commun 2022; 13:7805. [PMID: 36528681 PMCID: PMC9759546 DOI: 10.1038/s41467-022-35540-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 12/09/2022] [Indexed: 12/23/2022] Open
Abstract
Direct electrical stimulation of the seizure focus can achieve the early termination of epileptic oscillations. However, direct intervention of the hippocampus, the most prevalent seizure focus in temporal lobe epilepsy is thought to be not practicable due to its large size and elongated shape. Here, in a rat model, we report a sequential narrow-field stimulation method for terminating seizures, while focusing stimulus energy at the spatially extensive hippocampal structure. The effects and regional specificity of this method were demonstrated via electrophysiological and biological responses. Our proposed modality demonstrates spatiotemporal preciseness and selectiveness for modulating the pathological target region which may have potential for further investigation as a therapeutic approach.
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Affiliation(s)
- Wonok Kang
- grid.49100.3c0000 0001 0742 4007School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, Pohang, 37673 Republic of Korea ,grid.49100.3c0000 0001 0742 4007Medical Device Innovation Center, Pohang University of Science and Technology, Pohang, 37673 Republic of Korea
| | - Chanyang Ju
- grid.49100.3c0000 0001 0742 4007Medical Device Innovation Center, Pohang University of Science and Technology, Pohang, 37673 Republic of Korea ,grid.49100.3c0000 0001 0742 4007Department of Convergence IT Engineering, Pohang University of Science and Technology, Pohang, 37673 Republic of Korea
| | - Jaesoon Joo
- grid.264381.a0000 0001 2181 989XBiomedical Engineering Research Center, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, 06351 South Korea
| | - Jiho Lee
- grid.49100.3c0000 0001 0742 4007Medical Device Innovation Center, Pohang University of Science and Technology, Pohang, 37673 Republic of Korea ,grid.49100.3c0000 0001 0742 4007Department of Convergence IT Engineering, Pohang University of Science and Technology, Pohang, 37673 Republic of Korea
| | - Young-Min Shon
- grid.264381.a0000 0001 2181 989XBiomedical Engineering Research Center, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, 06351 South Korea ,grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, 06351 Republic of Korea
| | - Sung-Min Park
- grid.49100.3c0000 0001 0742 4007School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, Pohang, 37673 Republic of Korea ,grid.49100.3c0000 0001 0742 4007Medical Device Innovation Center, Pohang University of Science and Technology, Pohang, 37673 Republic of Korea ,grid.49100.3c0000 0001 0742 4007Department of Convergence IT Engineering, Pohang University of Science and Technology, Pohang, 37673 Republic of Korea ,grid.49100.3c0000 0001 0742 4007Department of Electrical Engineering, Pohang University of Science and Technology, Pohang, 37673 Republic of Korea ,grid.49100.3c0000 0001 0742 4007Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, 37673 Republic of Korea ,grid.15444.300000 0004 0470 5454Institute of Convergence Science, Yonsei University, Seoul, 03722 Republic of Korea
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13
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Abdellah M, Cantero JJG, Guerrero NR, Foni A, Coggan JS, Calì C, Agus M, Zisis E, Keller D, Hadwiger M, Magistretti PJ, Markram H, Schürmann F. Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience. Brief Bioinform 2022; 24:6847753. [PMID: 36434788 PMCID: PMC9851302 DOI: 10.1093/bib/bbac491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/27/2022] [Accepted: 10/14/2022] [Indexed: 11/27/2022] Open
Abstract
Ultraliser is a neuroscience-specific software framework capable of creating accurate and biologically realistic 3D models of complex neuroscientific structures at intracellular (e.g. mitochondria and endoplasmic reticula), cellular (e.g. neurons and glia) and even multicellular scales of resolution (e.g. cerebral vasculature and minicolumns). Resulting models are exported as triangulated surface meshes and annotated volumes for multiple applications in in silico neuroscience, allowing scalable supercomputer simulations that can unravel intricate cellular structure-function relationships. Ultraliser implements a high-performance and unconditionally robust voxelization engine adapted to create optimized watertight surface meshes and annotated voxel grids from arbitrary non-watertight triangular soups, digitized morphological skeletons or binary volumetric masks. The framework represents a major leap forward in simulation-based neuroscience, making it possible to employ high-resolution 3D structural models for quantification of surface areas and volumes, which are of the utmost importance for cellular and system simulations. The power of Ultraliser is demonstrated with several use cases in which hundreds of models are created for potential application in diverse types of simulations. Ultraliser is publicly released under the GNU GPL3 license on GitHub (BlueBrain/Ultraliser). SIGNIFICANCE There is crystal clear evidence on the impact of cell shape on its signaling mechanisms. Structural models can therefore be insightful to realize the function; the more realistic the structure can be, the further we get insights into the function. Creating realistic structural models from existing ones is challenging, particularly when needed for detailed subcellular simulations. We present Ultraliser, a neuroscience-dedicated framework capable of building these structural models with realistic and detailed cellular geometries that can be used for simulations.
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Affiliation(s)
- Marwan Abdellah
- Corresponding authors. Marwan Abdellah, Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland. E-mail: ; Felix Schürmann, Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland. E-mail:
| | | | - Nadir Román Guerrero
- Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland
| | - Alessandro Foni
- Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland
| | - Jay S Coggan
- Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland
| | - Corrado Calì
- Biological and Environmental Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal, Saudi Arabia,Neuroscience Institute Cavalieri Ottolenghi (NICO) Orbassano, Italy,Department of Neuroscience, University of Torino Torino, Italy
| | - Marco Agus
- Visual Computing Center King Abdullah University of Science and Technology (KAUST) Thuwal, Saudi Arabia,College of Science and Engineering Hamad Bin Khalifa University Doha, Qatar
| | - Eleftherios Zisis
- Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland
| | - Daniel Keller
- Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland
| | - Markus Hadwiger
- Visual Computing Center King Abdullah University of Science and Technology (KAUST) Thuwal, Saudi Arabia
| | - Pierre J Magistretti
- Biological and Environmental Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal, Saudi Arabia
| | - Henry Markram
- Blue Brain Project (BBP) École Polytecnique Fédérale de Lausanne (EPFL) Geneva, Switzerland
| | - Felix Schürmann
- Corresponding authors. Marwan Abdellah, Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland. E-mail: ; Felix Schürmann, Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland. E-mail:
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14
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How Do Spike Collisions Affect Spike Sorting Performance? eNeuro 2022; 9:ENEURO.0105-22.2022. [PMID: 36171060 PMCID: PMC9532018 DOI: 10.1523/eneuro.0105-22.2022] [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: 03/11/2022] [Revised: 06/15/2022] [Accepted: 06/23/2022] [Indexed: 12/15/2022] Open
Abstract
Recently, a new generation of devices have been developed to record neural activity simultaneously from hundreds of electrodes with a very high spatial density, both for in vitro and in vivo applications. While these advances enable to record from many more cells, they also challenge the already complicated process of spike sorting (i.e., extracting isolated single-neuron activity from extracellular signals). In this work, we used synthetic ground-truth recordings with controlled levels of correlations among neurons to quantitatively benchmark the performance of state-of-the-art spike sorters focusing specifically on spike collisions. Our results show that while modern template-matching-based algorithms are more accurate than density-based approaches, all methods, to some extent, failed to detect synchronous spike events of neurons with similar extracellular signals. Interestingly, the performance of the sorters is not largely affected by the spiking activity in the recordings, with respect to average firing rates and spike-train correlation levels. Since the performances of all modern spike sorting algorithms can be affected as function of the activity of the recorded neurons, scientific claims on correlations and synchrony should be carefully assessed based on the analysis provided in this paper.
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15
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Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks. Molecules 2022; 27:molecules27196256. [PMID: 36234792 PMCID: PMC9573053 DOI: 10.3390/molecules27196256] [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: 08/01/2022] [Revised: 08/29/2022] [Accepted: 09/15/2022] [Indexed: 11/16/2022] Open
Abstract
The classification of biological neuron types and networks poses challenges to the full understanding of the human brain’s organisation and functioning. In this paper, we develop a novel objective classification model of biological neuronal morphology and electrical types and their networks, based on the attributes of neuronal communication using supervised machine learning solutions. This presents advantages compared to the existing approaches in neuroinformatics since the data related to mutual information or delay between neurons obtained from spike trains are more abundant than conventional morphological data. We constructed two open-access computational platforms of various neuronal circuits from the Blue Brain Project realistic models, named Neurpy and Neurgen. Then, we investigated how we could perform network tomography with cortical neuronal circuits for the morphological, topological and electrical classification of neurons. We extracted the simulated data of 10,000 network topology combinations with five layers, 25 morphological type (m-type) cells, and 14 electrical type (e-type) cells. We applied the data to several different classifiers (including Support Vector Machine (SVM), Decision Trees, Random Forest, and Artificial Neural Networks). We achieved accuracies of up to 70%, and the inference of biological network structures using network tomography reached up to 65% of accuracy. Objective classification of biological networks can be achieved with cascaded machine learning methods using neuron communication data. SVM methods seem to perform better amongst used techniques. Our research not only contributes to existing classification efforts but sets the road-map for future usage of brain–machine interfaces towards an in vivo objective classification of neurons as a sensing mechanism of the brain’s structure.
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16
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Borges FS, Moreira JVS, Takarabe LM, Lytton WW, Dura-Bernal S. Large-scale biophysically detailed model of somatosensory thalamocortical circuits in NetPyNE. Front Neuroinform 2022; 16:884245. [PMID: 36213546 PMCID: PMC9536213 DOI: 10.3389/fninf.2022.884245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 07/27/2022] [Indexed: 11/13/2022] Open
Abstract
The primary somatosensory cortex (S1) of mammals is critically important in the perception of touch and related sensorimotor behaviors. In 2015, the Blue Brain Project (BBP) developed a groundbreaking rat S1 microcircuit simulation with over 31,000 neurons with 207 morpho-electrical neuron types, and 37 million synapses, incorporating anatomical and physiological information from a wide range of experimental studies. We have implemented this highly detailed and complex S1 model in NetPyNE, using the data available in the Neocortical Microcircuit Collaboration Portal. NetPyNE provides a Python high-level interface to NEURON and allows defining complicated multiscale models using an intuitive declarative standardized language. It also facilitates running parallel simulations, automates the optimization and exploration of parameters using supercomputers, and provides a wide range of built-in analysis functions. This will make the S1 model more accessible and simpler to scale, modify and extend in order to explore research questions or interconnect to other existing models. Despite some implementation differences, the NetPyNE model preserved the original cell morphologies, electrophysiological responses and spatial distribution for all 207 cell types; and the connectivity properties of all 1941 pathways, including synaptic dynamics and short-term plasticity (STP). The NetPyNE S1 simulations produced reasonable physiological firing rates and activity patterns across all populations. When STP was included, the network generated a 1 Hz oscillation comparable to the original model in vitro-like state. By then reducing the extracellular calcium concentration, the model reproduced the original S1 in vivo-like states with asynchronous activity. These results validate the original study using a new modeling tool. Simulated local field potentials (LFPs) exhibited realistic oscillatory patterns and features, including distance- and frequency-dependent attenuation. The model was extended by adding thalamic circuits, including 6 distinct thalamic populations with intrathalamic, thalamocortical (TC) and corticothalamic connectivity derived from experimental data. The thalamic model reproduced single known cell and circuit-level dynamics, including burst and tonic firing modes and oscillatory patterns, providing a more realistic input to cortex and enabling study of TC interactions. Overall, our work provides a widely accessible, data-driven and biophysically-detailed model of the somatosensory TC circuits that can be employed as a community tool for researchers to study neural dynamics, function and disease.
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Affiliation(s)
- Fernando S. Borges
- Department of Physiology and Pharmacology, State University of New York Downstate Health Sciences University, Brooklyn, NY, United States
- Center for Mathematics, Computation, and Cognition, Federal University of ABC, São Paulo, Brazil
| | - Joao V. S. Moreira
- Department of Physiology and Pharmacology, State University of New York Downstate Health Sciences University, Brooklyn, NY, United States
| | - Lavinia M. Takarabe
- Center for Mathematics, Computation, and Cognition, Federal University of ABC, São Paulo, Brazil
| | - William W. Lytton
- Department of Physiology and Pharmacology, State University of New York Downstate Health Sciences University, Brooklyn, NY, United States
- Department of Neurology, Kings County Hospital Center, Brooklyn, NY, United States
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, United States
| | - Salvador Dura-Bernal
- Department of Physiology and Pharmacology, State University of New York Downstate Health Sciences University, Brooklyn, NY, United States
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
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17
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Duggins P, Eliasmith C. Constructing functional models from biophysically-detailed neurons. PLoS Comput Biol 2022; 18:e1010461. [PMID: 36074765 PMCID: PMC9455888 DOI: 10.1371/journal.pcbi.1010461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 07/30/2022] [Indexed: 11/25/2022] Open
Abstract
Improving biological plausibility and functional capacity are two important goals for brain models that connect low-level neural details to high-level behavioral phenomena. We develop a method called “oracle-supervised Neural Engineering Framework” (osNEF) to train biologically-detailed spiking neural networks that realize a variety of cognitively-relevant dynamical systems. Specifically, we train networks to perform computations that are commonly found in cognitive systems (communication, multiplication, harmonic oscillation, and gated working memory) using four distinct neuron models (leaky-integrate-and-fire neurons, Izhikevich neurons, 4-dimensional nonlinear point neurons, and 4-compartment, 6-ion-channel layer-V pyramidal cell reconstructions) connected with various synaptic models (current-based synapses, conductance-based synapses, and voltage-gated synapses). We show that osNEF networks exhibit the target dynamics by accounting for nonlinearities present within the neuron models: performance is comparable across all four systems and all four neuron models, with variance proportional to task and neuron model complexity. We also apply osNEF to build a model of working memory that performs a delayed response task using a combination of pyramidal cells and inhibitory interneurons connected with NMDA and GABA synapses. The baseline performance and forgetting rate of the model are consistent with animal data from delayed match-to-sample tasks (DMTST): we observe a baseline performance of 95% and exponential forgetting with time constant τ = 8.5s, while a recent meta-analysis of DMTST performance across species observed baseline performances of 58 − 99% and exponential forgetting with time constants of τ = 2.4 − 71s. These results demonstrate that osNEF can train functional brain models using biologically-detailed components and open new avenues for investigating the relationship between biophysical mechanisms and functional capabilities. Computational models of biologically realistic neural networks help scientists understand and recreate a wide variety of brain processes, responsible for everything from fish locomotion to human cognition. To be useful, these models must both recreate features of the brain, such as the electrical, chemical, and geometric properties of neurons, and perform useful functional operations, such as storing and retrieving information from a short term memory. Here, we develop a new method for training networks built from biologically detailed components. We simulate networks that contain a variety of complex neurons and synapses, then show that our method successfully trains them to perform a variety of cognitive operations. Most notably, we train a working memory model that contains detailed reconstructions of cortical neurons, and demonstrate that it performs a memory task with performance that is comparable to simple animals. Researchers can use our method to train detailed brain models and investigate how biological features (or deficits thereof) relate to cognition, which may provide insights into the biological basis of mental disorders such as Parkinson’s disease.
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Affiliation(s)
- Peter Duggins
- Computational Neuroscience Research Group, Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada
- * E-mail:
| | - Chris Eliasmith
- Computational Neuroscience Research Group, Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada
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18
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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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19
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Schürmann F, Courcol JD, Ramaswamy S. Computational Concepts for Reconstructing and Simulating Brain Tissue. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1359:237-259. [PMID: 35471542 DOI: 10.1007/978-3-030-89439-9_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
It has previously been shown that it is possible to derive a new class of biophysically detailed brain tissue models when one computationally analyzes and exploits the interdependencies or the multi-modal and multi-scale organization of the brain. These reconstructions, sometimes referred to as digital twins, enable a spectrum of scientific investigations. Building such models has become possible because of increase in quantitative data but also advances in computational capabilities, algorithmic and methodological innovations. This chapter presents the computational science concepts that provide the foundation to the data-driven approach to reconstructing and simulating brain tissue as developed by the EPFL Blue Brain Project, which was originally applied to neocortical microcircuitry and extended to other brain regions. Accordingly, the chapter covers aspects such as a knowledge graph-based data organization and the importance of the concept of a dataset release. We illustrate algorithmic advances in finding suitable parameters for electrical models of neurons or how spatial constraints can be exploited for predicting synaptic connections. Furthermore, we explain how in silico experimentation with such models necessitates specific addressing schemes or requires strategies for an efficient simulation. The entire data-driven approach relies on the systematic validation of the model. We conclude by discussing complementary strategies that not only enable judging the fidelity of the model but also form the basis for its systematic refinements.
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Affiliation(s)
- Felix Schürmann
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland.
| | - Jean-Denis Courcol
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Srikanth Ramaswamy
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
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20
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Computational synthesis of cortical dendritic morphologies. Cell Rep 2022; 39:110586. [PMID: 35385736 DOI: 10.1016/j.celrep.2022.110586] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 07/22/2021] [Accepted: 03/08/2022] [Indexed: 12/30/2022] Open
Abstract
Neuronal morphologies provide the foundation for the electrical behavior of neurons, the connectomes they form, and the dynamical properties of the brain. Comprehensive neuron models are essential for defining cell types, discerning their functional roles, and investigating brain-disease-related dendritic alterations. However, a lack of understanding of the principles underlying neuron morphologies has hindered attempts to computationally synthesize morphologies for decades. We introduce a synthesis algorithm based on a topological descriptor of neurons, which enables the rapid digital reconstruction of entire brain regions from few reference cells. This topology-guided synthesis generates dendrites that are statistically similar to biological reconstructions in terms of morpho-electrical and connectivity properties and offers a significant opportunity to investigate the links between neuronal morphology and brain function across different spatiotemporal scales. Synthesized cortical networks based on structurally altered dendrites associated with diverse brain pathologies revealed principles linking branching properties to the structure of large-scale networks.
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21
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Kaneko K, Currin CB, Goff KM, Wengert ER, Somarowthu A, Vogels TP, Goldberg EM. Developmentally regulated impairment of parvalbumin interneuron synaptic transmission in an experimental model of Dravet syndrome. Cell Rep 2022; 38:110580. [PMID: 35354025 PMCID: PMC9003081 DOI: 10.1016/j.celrep.2022.110580] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 01/09/2022] [Accepted: 03/06/2022] [Indexed: 11/25/2022] Open
Abstract
Dravet syndrome is a neurodevelopmental disorder characterized by epilepsy, intellectual disability, and sudden death due to pathogenic variants in SCN1A with loss of function of the sodium channel subunit Nav1.1. Nav1.1-expressing parvalbumin GABAergic interneurons (PV-INs) from young Scn1a+/− mice show impaired action potential generation. An approach assessing PV-IN function in the same mice at two time points shows impaired spike generation in all Scn1a+/− mice at postnatal days (P) 16–21, whether deceased prior or surviving to P35, with normalization by P35 in surviving mice. However, PV-IN synaptic transmission is dysfunctional in young Scn1a+/− mice that did not survive and in Scn1a+/− mice ≥ P35. Modeling confirms that PV-IN axonal propagation is more sensitive to decreased sodium conductance than spike generation. These results demonstrate dynamic dysfunction in Dravet syndrome: combined abnormalities of PV-IN spike generation and propagation drives early disease severity, while ongoing dysfunction of synaptic transmission contributes to chronic pathology. Dravet syndrome is caused by variants in SCN1A with loss of function of Nav1.1 sodium channels. Kaneko et al. use the “mini-slice” to record at two developmental time points. Impaired spike generation of Nav1.1-expressing PV interneurons in Scn1a+/− mice is transient, while abnormalities of PV interneuron synaptic transmission persist.
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Affiliation(s)
- Keisuke Kaneko
- Division of Neurology, Department of Pediatrics, The Children's Hospital of Philadelphia, Abramson Research Center, Philadelphia, PA 19104, USA
| | - Christopher B Currin
- The Institute of Science and Technology Austria, Am Campus 1, Klosterneuburg, Austria
| | - Kevin M Goff
- Medical Scientist Training Program (MSTP), The University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Neuroscience Graduate Group, The University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Eric R Wengert
- Division of Neurology, Department of Pediatrics, The Children's Hospital of Philadelphia, Abramson Research Center, Philadelphia, PA 19104, USA
| | - Ala Somarowthu
- Division of Neurology, Department of Pediatrics, The Children's Hospital of Philadelphia, Abramson Research Center, Philadelphia, PA 19104, USA
| | - Tim P Vogels
- The Institute of Science and Technology Austria, Am Campus 1, Klosterneuburg, Austria
| | - Ethan M Goldberg
- Division of Neurology, Department of Pediatrics, The Children's Hospital of Philadelphia, Abramson Research Center, Philadelphia, PA 19104, USA; Neuroscience Graduate Group, The University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Neurology, The University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Neuroscience, The University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
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22
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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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23
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Reimann MW, Riihimäki H, Smith JP, Lazovskis J, Pokorny C, Levi R. Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies. PLoS One 2022; 17:e0261702. [PMID: 35020728 PMCID: PMC8754339 DOI: 10.1371/journal.pone.0261702] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 12/07/2021] [Indexed: 11/18/2022] Open
Abstract
In motor-related brain regions, movement intention has been successfully decoded from in-vivo spike train by isolating a lower-dimension manifold that the high-dimensional spiking activity is constrained to. The mechanism enforcing this constraint remains unclear, although it has been hypothesized to be implemented by the connectivity of the sampled neurons. We test this idea and explore the interactions between local synaptic connectivity and its ability to encode information in a lower dimensional manifold through simulations of a detailed microcircuit model with realistic sources of noise. We confirm that even in isolation such a model can encode the identity of different stimuli in a lower-dimensional space. We then demonstrate that the reliability of the encoding depends on the connectivity between the sampled neurons by specifically sampling populations whose connectivity maximizes certain topological metrics. Finally, we developed an alternative method for determining stimulus identity from the activity of neurons by combining their spike trains with their recurrent connectivity. We found that this method performs better for sampled groups of neurons that perform worse under the classical approach, predicting the possibility of two separate encoding strategies in a single microcircuit.
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Affiliation(s)
- Michael W. Reimann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | | | - Jason P. Smith
- University of Aberdeen, Aberdeen, United Kingdom
- Nottingham Trent University, Nottingham, United Kingdom
| | - Jānis Lazovskis
- University of Aberdeen, Aberdeen, United Kingdom
- University of Latvia, Rīga, Latvia
| | - Christoph Pokorny
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Ran Levi
- University of Aberdeen, Aberdeen, United Kingdom
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24
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Reconstruction of the Hippocampus. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1359:261-283. [DOI: 10.1007/978-3-030-89439-9_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
AbstractThe hippocampus is a widely studied brain region thought to play an important role in higher cognitive functions such as learning, memory, and navigation. The amount of data on this region increases every day and delineates a complex and fragmented picture, but an integrated understanding of hippocampal function remains elusive. Computational methods can help to move the research forward, and reconstructing a full-scale model of the hippocampus is a challenging yet feasible task that the research community should undertake.In this chapter, we present strategies for reconstructing a large-scale model of the hippocampus. Based on a previously published approach to reconstruct and simulate brain tissue, which is also explained in Chap. 10, we discuss the characteristics of the hippocampus in the light of its special anatomical and physiological features, data availability, and existing large-scale hippocampus models. A large-scale model of the hippocampus is a compound model of several building blocks: ion channels, morphologies, single cell models, connections, synapses. We discuss each of those building blocks separately and discuss how to merge them back and simulate the resulting network model.
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25
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Accelerating Allen Brain Institute’s Large-Scale Computational Model of Mice Primary Visual Cortex. ARTIF INTELL 2022. [DOI: 10.1007/978-3-031-20503-3_57] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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26
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Wouters J, Kloosterman F, Bertrand A. A data-driven spike sorting feature map for resolving spike overlap in the feature space. J Neural Eng 2021; 18. [PMID: 34181592 DOI: 10.1088/1741-2552/ac0f4a] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 06/28/2021] [Indexed: 11/12/2022]
Abstract
Objective.Spike sorting is the process of extracting neuronal action potentials, or spikes, from an extracellular brain recording, and assigning each spike to its putative source neuron. Spike sorting is usually treated as a clustering problem. However, this clustering process is known to be affected by overlapping spikes. Existing methods for resolving spike overlap typically require an expensive post-processing of the clustering results. In this paper, we propose the design of a domain-specific feature map, which enables the resolution of spike overlap directly in the feature space.Approach.The proposed domain-specific feature map is based on a neural network architecture that is trained to simultaneously perform spike sorting and spike overlap resolution. Overlapping spikes clusters can be identified in the feature space through a linear relation with the single-neuron clusters for which the neurons contribute to the overlapping spikes. To aid the feature map training, a data augmentation procedure is presented that is based on biophysical simulations.Main results.We demonstrate the potential of our method on independent and realistic test data. We show that our novel approach for resolving spike overlap generalizes to unseen and realistic test data. Furthermore, the sorting performance of our method is shown to be similar to the state-of-the-art, but our method does not assume the availability of spike templates for resolving spike overlap.Significance.Resolving spike overlap directly in the feature space, results in an overall simplified spike sorting pipeline compared to the state-of-the-art. For the state-of-the-art, the overlapping spike snippets exhibit a large spread in the feature space and do not appear as concentrated clusters. This can lead to biased spike template estimates which affect the sorting performance of the state-of-the-art. In our proposed approach, overlapping spikes form concentrated clusters and spike overlap resolution does not depend on the availability of spike templates.
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Affiliation(s)
- J Wouters
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics and Leuven., Leuven, Belgium
| | - F Kloosterman
- Neuro-Electronics Research Flanders (NERF), Leuven, Belgium.,KU Leuven, Brain & Cognition Research Unit, Leuven, Belgium.,VIB, Leuven, Belgium
| | - A Bertrand
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics and Leuven., Leuven, Belgium
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27
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Abdellah M, Foni A, Zisis E, Guerrero NR, Lapere S, Coggan JS, Keller D, Markram H, Schürmann F. Metaball skinning of synthetic astroglial morphologies into realistic mesh models for visual analytics and in silico simulations. Bioinformatics 2021; 37:i426-i433. [PMID: 34252950 PMCID: PMC8275327 DOI: 10.1093/bioinformatics/btab280] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Motivation Astrocytes, the most abundant glial cells in the mammalian brain, have an instrumental role in developing neuronal circuits. They contribute to the physical structuring of the brain, modulating synaptic activity and maintaining the blood–brain barrier in addition to other significant aspects that impact brain function. Biophysically, detailed astrocytic models are key to unraveling their functional mechanisms via molecular simulations at microscopic scales. Detailed, and complete, biological reconstructions of astrocytic cells are sparse. Nonetheless, data-driven digital reconstruction of astroglial morphologies that are statistically identical to biological counterparts are becoming available. We use those synthetic morphologies to generate astrocytic meshes with realistic geometries, making it possible to perform these simulations. Results We present an unconditionally robust method capable of reconstructing high fidelity polygonal meshes of astroglial cells from algorithmically-synthesized morphologies. Our method uses implicit surfaces, or metaballs, to skin the different structural components of astrocytes and then blend them in a seamless fashion. We also provide an end-to-end pipeline to produce optimized two- and three-dimensional meshes for visual analytics and simulations, respectively. The performance of our pipeline has been assessed with a group of 5000 astroglial morphologies and the geometric metrics of the resulting meshes are evaluated. The usability of the meshes is then demonstrated with different use cases. Availability and implementation Our metaball skinning algorithm is implemented in Blender 2.82 relying on its Python API (Application Programming Interface). To make it accessible to computational biologists and neuroscientists, the implementation has been integrated into NeuroMorphoVis, an open source and domain specific package that is primarily designed for neuronal morphology visualization and meshing. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Marwan Abdellah
- Blue Brain Project (BBP), École polytechnique fédérale de Lausanne (EPFL), Geneva 1202, Switzerland
| | - Alessandro Foni
- Blue Brain Project (BBP), École polytechnique fédérale de Lausanne (EPFL), Geneva 1202, Switzerland
| | - Eleftherios Zisis
- Blue Brain Project (BBP), École polytechnique fédérale de Lausanne (EPFL), Geneva 1202, Switzerland
| | - Nadir Román Guerrero
- Blue Brain Project (BBP), École polytechnique fédérale de Lausanne (EPFL), Geneva 1202, Switzerland
| | - Samuel Lapere
- Blue Brain Project (BBP), École polytechnique fédérale de Lausanne (EPFL), Geneva 1202, Switzerland
| | - Jay S Coggan
- Blue Brain Project (BBP), École polytechnique fédérale de Lausanne (EPFL), Geneva 1202, Switzerland
| | - Daniel Keller
- Blue Brain Project (BBP), École polytechnique fédérale de Lausanne (EPFL), Geneva 1202, Switzerland
| | - Henry Markram
- Blue Brain Project (BBP), École polytechnique fédérale de Lausanne (EPFL), Geneva 1202, Switzerland
| | - Felix Schürmann
- Blue Brain Project (BBP), École polytechnique fédérale de Lausanne (EPFL), Geneva 1202, Switzerland
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28
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Peach RL, Arnaudon A, Schmidt JA, Palasciano HA, Bernier NR, Jelfs KE, Yaliraki SN, Barahona M. HCGA: Highly comparative graph analysis for network phenotyping. PATTERNS (NEW YORK, N.Y.) 2021; 2:100227. [PMID: 33982022 PMCID: PMC8085611 DOI: 10.1016/j.patter.2021.100227] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Revised: 02/02/2021] [Accepted: 03/03/2021] [Indexed: 11/30/2022]
Abstract
Networks are widely used as mathematical models of complex systems across many scientific disciplines. Decades of work have produced a vast corpus of research characterizing the topological, combinatorial, statistical, and spectral properties of graphs. Each graph property can be thought of as a feature that captures important (and sometimes overlapping) characteristics of a network. In this paper, we introduce HCGA, a framework for highly comparative analysis of graph datasets that computes several thousands of graph features from any given network. HCGA also offers a suite of statistical learning and data analysis tools for automated identification and selection of important and interpretable features underpinning the characterization of graph datasets. We show that HCGA outperforms other methodologies on supervised classification tasks on benchmark datasets while retaining the interpretability of network features. We exemplify HCGA by predicting the charge transfer in organic semiconductors and clustering a dataset of neuronal morphology images.
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Affiliation(s)
- Robert L. Peach
- Department of Mathematics, Imperial College London, SW7 2AZ London, UK
| | - Alexis Arnaudon
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| | - Julia A. Schmidt
- Department of Chemistry, Imperial College London, SW7 2AZ London, UK
| | | | | | - Kim E. Jelfs
- Department of Chemistry, Imperial College London, SW7 2AZ London, UK
| | | | - Mauricio Barahona
- Department of Mathematics, Imperial College London, SW7 2AZ London, UK
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29
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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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30
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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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31
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Martínez-Cañada P, Ness TV, Einevoll GT, Fellin T, Panzeri S. Computation of the electroencephalogram (EEG) from network models of point neurons. PLoS Comput Biol 2021; 17:e1008893. [PMID: 33798190 PMCID: PMC8046357 DOI: 10.1371/journal.pcbi.1008893] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 04/14/2021] [Accepted: 03/18/2021] [Indexed: 12/28/2022] Open
Abstract
The electroencephalogram (EEG) is a major tool for non-invasively studying brain function and dysfunction. Comparing experimentally recorded EEGs with neural network models is important to better interpret EEGs in terms of neural mechanisms. Most current neural network models use networks of simple point neurons. They capture important properties of cortical dynamics, and are numerically or analytically tractable. However, point neurons cannot generate an EEG, as EEG generation requires spatially separated transmembrane currents. Here, we explored how to compute an accurate approximation of a rodent's EEG with quantities defined in point-neuron network models. We constructed different approximations (or proxies) of the EEG signal that can be computed from networks of leaky integrate-and-fire (LIF) point neurons, such as firing rates, membrane potentials, and combinations of synaptic currents. We then evaluated how well each proxy reconstructed a ground-truth EEG obtained when the synaptic currents of the LIF model network were fed into a three-dimensional network model of multicompartmental neurons with realistic morphologies. Proxies based on linear combinations of AMPA and GABA currents performed better than proxies based on firing rates or membrane potentials. A new class of proxies, based on an optimized linear combination of time-shifted AMPA and GABA currents, provided the most accurate estimate of the EEG over a wide range of network states. The new linear proxies explained 85-95% of the variance of the ground-truth EEG for a wide range of network configurations including different cell morphologies, distributions of presynaptic inputs, positions of the recording electrode, and spatial extensions of the network. Non-linear EEG proxies using a convolutional neural network (CNN) on synaptic currents increased proxy performance by a further 2-8%. Our proxies can be used to easily calculate a biologically realistic EEG signal directly from point-neuron simulations thus facilitating a quantitative comparison between computational models and experimental EEG recordings.
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Affiliation(s)
- Pablo Martínez-Cañada
- Neural Coding Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
- Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
| | - Torbjørn V. Ness
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Gaute T. Einevoll
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
- Department of Physics, University of Oslo, Oslo, Norway
| | - Tommaso Fellin
- Neural Coding Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
- Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
| | - Stefano Panzeri
- Neural Coding Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
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Abstract
The recent trend toward an industrialization of brain exploration and the technological prowess of artificial intelligence algorithms and high-performance computing has caught the imagination of the public. These impressive advances are fueling an uncontrolled societal hype, the more amplified, the more "Blue Sky" the claim is. Will we ever be able to simulate a brain in silico? Will "it" (the digital avatar) be conscious? The Blue Brain Project (BBP) and the European flagship the Human Brain Project (HBP) have surfed on this wave for the past 10 years. Their already significant lifetimes now offer new case studies for neuroscience sociology and epistemology, as the projects mature. Their distinctive "Blue Sky" flavor has been a key feature in securing unprecedented funding (more than one billion Euros) mostly through supranational institutions. The longitudinal analysis of these ventures provides clues to how the neuromyth they propagate sells science, in a scientific world based on an economy of promises.
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Affiliation(s)
- Yves Frégnac
- UNIC-NeuroPSI, Institut des Neurosciences Paris-Saclay, Centre National de la Recherche Scientifique, Gif-sur-Yvette 91190, France
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33
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Sáray S, Rössert CA, Appukuttan S, Migliore R, Vitale P, Lupascu CA, Bologna LL, Van Geit W, Romani A, Davison AP, Muller E, Freund TF, Káli S. HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data. PLoS Comput Biol 2021; 17:e1008114. [PMID: 33513130 PMCID: PMC7875359 DOI: 10.1371/journal.pcbi.1008114] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 02/10/2021] [Accepted: 12/24/2020] [Indexed: 11/19/2022] Open
Abstract
Anatomically and biophysically detailed data-driven neuronal models have become widely used tools for understanding and predicting the behavior and function of neurons. Due to the increasing availability of experimental data from anatomical and electrophysiological measurements as well as the growing number of computational and software tools that enable accurate neuronal modeling, there are now a large number of different models of many cell types available in the literature. These models were usually built to capture a few important or interesting properties of the given neuron type, and it is often unknown how they would behave outside their original context. In addition, there is currently no simple way of quantitatively comparing different models regarding how closely they match specific experimental observations. This limits the evaluation, re-use and further development of the existing models. Further, the development of new models could also be significantly facilitated by the ability to rapidly test the behavior of model candidates against the relevant collection of experimental data. We address these problems for the representative case of the CA1 pyramidal cell of the rat hippocampus by developing an open-source Python test suite, which makes it possible to automatically and systematically test multiple properties of models by making quantitative comparisons between the models and electrophysiological data. The tests cover various aspects of somatic behavior, and signal propagation and integration in apical dendrites. To demonstrate the utility of our approach, we applied our tests to compare the behavior of several different rat hippocampal CA1 pyramidal cell models from the ModelDB database against electrophysiological data available in the literature, and evaluated how well these models match experimental observations in different domains. We also show how we employed the test suite to aid the development of models within the European Human Brain Project (HBP), and describe the integration of the tests into the validation framework developed in the HBP, with the aim of facilitating more reproducible and transparent model building in the neuroscience community.
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Affiliation(s)
- Sára Sáray
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
- Institute of Experimental Medicine, Budapest, Hungary
- * E-mail: (SS); (SK)
| | - Christian A. Rössert
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Shailesh Appukuttan
- Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique/Université Paris-Saclay, Gif-sur-Yvette, France
| | - Rosanna Migliore
- Institute of Biophysics, National Research Council, Palermo, Italy
| | - Paola Vitale
- Institute of Biophysics, National Research Council, Palermo, Italy
| | | | - Luca L. Bologna
- Institute of Biophysics, National Research Council, Palermo, Italy
| | - Werner Van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Armando Romani
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Andrew P. Davison
- Paris-Saclay Institute of Neuroscience, Centre National de la Recherche Scientifique/Université Paris-Saclay, Gif-sur-Yvette, France
| | - Eilif Muller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
- Department of Neurosciences, Faculty of Medicine, University of Montreal, Montreal, Canada
- CHU Sainte-Justine Research Center, Montreal, Canada
- Quebec Artificial Intelligence Institute (Mila), Montreal, Canada
| | - Tamás F. Freund
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
- Institute of Experimental Medicine, Budapest, Hungary
| | - Szabolcs Káli
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
- Institute of Experimental Medicine, Budapest, Hungary
- * E-mail: (SS); (SK)
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34
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Buccino AP, Hurwitz CL, Garcia S, Magland J, Siegle JH, Hurwitz R, Hennig MH. SpikeInterface, a unified framework for spike sorting. eLife 2020; 9:e61834. [PMID: 33170122 PMCID: PMC7704107 DOI: 10.7554/elife.61834] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 11/09/2020] [Indexed: 12/21/2022] Open
Abstract
Much development has been directed toward improving the performance and automation of spike sorting. This continuous development, while essential, has contributed to an over-saturation of new, incompatible tools that hinders rigorous benchmarking and complicates reproducible analysis. To address these limitations, we developed SpikeInterface, a Python framework designed to unify preexisting spike sorting technologies into a single codebase and to facilitate straightforward comparison and adoption of different approaches. With a few lines of code, researchers can reproducibly run, compare, and benchmark most modern spike sorting algorithms; pre-process, post-process, and visualize extracellular datasets; validate, curate, and export sorting outputs; and more. In this paper, we provide an overview of SpikeInterface and, with applications to real and simulated datasets, demonstrate how it can be utilized to reduce the burden of manual curation and to more comprehensively benchmark automated spike sorters.
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Affiliation(s)
- Alessio P Buccino
- Department of Biosystems Science and Engineering, ETH ZurichZürichSwitzerland
- Centre for Integrative Neuroplasticity (CINPLA), University of OsloOsloNorway
| | - Cole L Hurwitz
- School of Informatics, University of EdinburghEdinburghUnited Kingdom
| | - Samuel Garcia
- Centre de Recherche en Neuroscience de Lyon, CNRSLyonFrance
| | | | | | | | - Matthias H Hennig
- School of Informatics, University of EdinburghEdinburghUnited Kingdom
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35
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Ecker A, Romani A, Sáray S, Káli S, Migliore M, Falck J, Lange S, Mercer A, Thomson AM, Muller E, Reimann MW, Ramaswamy S. Data-driven integration of hippocampal CA1 synaptic physiology in silico. Hippocampus 2020; 30:1129-1145. [PMID: 32520422 PMCID: PMC7687201 DOI: 10.1002/hipo.23220] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 04/20/2020] [Accepted: 05/07/2020] [Indexed: 12/31/2022]
Abstract
The anatomy and physiology of monosynaptic connections in rodent hippocampal CA1 have been extensively studied in recent decades. Yet, the resulting knowledge remains disparate and difficult to reconcile. Here, we present a data‐driven approach to integrate the current state‐of‐the‐art knowledge on the synaptic anatomy and physiology of rodent hippocampal CA1, including axo‐dendritic innervation patterns, number of synapses per connection, quantal conductances, neurotransmitter release probability, and short‐term plasticity into a single coherent resource. First, we undertook an extensive literature review of paired recordings of hippocampal neurons and compiled experimental data on their synaptic anatomy and physiology. The data collected in this manner is sparse and inhomogeneous due to the diversity of experimental techniques used by different groups, which necessitates the need for an integrative framework to unify these data. To this end, we extended a previously developed workflow for the neocortex to constrain a unifying in silico reconstruction of the synaptic physiology of CA1 connections. Our work identifies gaps in the existing knowledge and provides a complementary resource toward a more complete quantification of synaptic anatomy and physiology in the rodent hippocampal CA1 region.
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Affiliation(s)
- András Ecker
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Armando Romani
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Sára Sáray
- Institute of Experimental Medicine, Budapest, Hungary.,Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Szabolcs Káli
- Institute of Experimental Medicine, Budapest, Hungary.,Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Michele Migliore
- Institute of Biophysics, National Research Council, Palermo, Italy
| | - Joanne Falck
- UCL School of Pharmacy, University College London, London, UK
| | - Sigrun Lange
- UCL School of Pharmacy, University College London, London, UK.,School of Life Sciences, University of Westminster, London, UK
| | - Audrey Mercer
- UCL School of Pharmacy, University College London, London, UK
| | - Alex M Thomson
- UCL School of Pharmacy, University College London, London, UK
| | - Eilif Muller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Michael W Reimann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Srikanth Ramaswamy
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
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36
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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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37
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Matzner A, Gorodetski L, Korngreen A, Bar-Gad I. Dynamic input-dependent encoding of individual basal ganglia neurons. Sci Rep 2020; 10:5833. [PMID: 32242059 PMCID: PMC7118110 DOI: 10.1038/s41598-020-62750-0] [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: 11/28/2019] [Accepted: 03/16/2020] [Indexed: 11/09/2022] Open
Abstract
Computational models are crucial to studying the encoding of individual neurons. Static models are composed of a fixed set of parameters, thus resulting in static encoding properties that do not change under different inputs. Here, we challenge this basic concept which underlies these models. Using generalized linear models, we quantify the encoding and information processing properties of basal ganglia neurons recorded in-vitro. These properties are highly sensitive to the internal state of the neuron due to factors such as dependency on the baseline firing rate. Verification of these experimental results with simulations provides insights into the mechanisms underlying this input-dependent encoding. Thus, static models, which are not context dependent, represent only part of the neuronal encoding capabilities, and are not sufficient to represent the dynamics of a neuron over varying inputs. Input-dependent encoding is crucial for expanding our understanding of neuronal behavior in health and disease and underscores the need for a new generation of dynamic neuronal models.
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Affiliation(s)
- Ayala Matzner
- The Leslie & Susan Goldschmied (Gonda) Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
| | - Lilach Gorodetski
- Goodman Faculty of life sciences, Bar-Ilan University, Ramat-Gan, Israel
| | - Alon Korngreen
- The Leslie & Susan Goldschmied (Gonda) Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel.,Goodman Faculty of life sciences, Bar-Ilan University, Ramat-Gan, Israel
| | - Izhar Bar-Gad
- The Leslie & Susan Goldschmied (Gonda) Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel.
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38
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Nolte M, Gal E, Markram H, Reimann MW. Impact of higher order network structure on emergent cortical activity. Netw Neurosci 2020; 4:292-314. [PMID: 32181420 PMCID: PMC7069066 DOI: 10.1162/netn_a_00124] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Accepted: 12/23/2019] [Indexed: 11/04/2022] Open
Abstract
Synaptic connectivity between neocortical neurons is highly structured. The network structure of synaptic connectivity includes first-order properties that can be described by pairwise statistics, such as strengths of connections between different neuron types and distance-dependent connectivity, and higher order properties, such as an abundance of cliques of all-to-all connected neurons. The relative impact of first- and higher order structure on emergent cortical network activity is unknown. Here, we compare network structure and emergent activity in two neocortical microcircuit models with different synaptic connectivity. Both models have a similar first-order structure, but only one model includes higher order structure arising from morphological diversity within neuronal types. We find that such morphological diversity leads to more heterogeneous degree distributions, increases the number of cliques, and contributes to a small-world topology. The increase in higher order network structure is accompanied by more nuanced changes in neuronal firing patterns, such as an increased dependence of pairwise correlations on the positions of neurons in cliques. Our study shows that circuit models with very similar first-order structure of synaptic connectivity can have a drastically different higher order network structure, and suggests that the higher order structure imposed by morphological diversity within neuronal types has an impact on emergent cortical activity.
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Affiliation(s)
- Max Nolte
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Eyal Gal
- Edmond and Lily Safra Center for Brain Sciences, The Hebrew University, Jerusalem, Israel
- Department of Neurobiology, The Hebrew University, Jerusalem, Israel
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
- Laboratory of Neural Microcircuitry, Brain Mind Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Michael W. Reimann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
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39
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GABA-mediated tonic inhibition differentially modulates gain in functional subtypes of cortical interneurons. Proc Natl Acad Sci U S A 2020; 117:3192-3202. [PMID: 31974304 DOI: 10.1073/pnas.1906369117] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
The binding of GABA (γ-aminobutyric acid) to extrasynaptic GABAA receptors generates tonic inhibition that acts as a powerful modulator of cortical network activity. Despite GABA being present throughout the extracellular space of the brain, previous work has shown that GABA may differentially modulate the excitability of neuron subtypes according to variation in chloride gradient. Here, using biophysically detailed neuron models, we predict that tonic inhibition can differentially modulate the excitability of neuron subtypes according to variation in electrophysiological properties. Surprisingly, tonic inhibition increased the responsiveness (or gain) in models with features typical for somatostatin interneurons but decreased gain in models with features typical for parvalbumin interneurons. Patch-clamp recordings from cortical interneurons supported these predictions, and further in silico analysis was then performed to seek a putative mechanism underlying gain modulation. We found that gain modulation in models was dependent upon the magnitude of tonic current generated at depolarized membrane potential-a property associated with outward rectifying GABAA receptors. Furthermore, tonic inhibition produced two biophysical changes in models of relevance to neuronal excitability: 1) enhanced action potential repolarization via increased current flow into the dendritic compartment, and 2) reduced activation of voltage-dependent potassium channels. Finally, we show theoretically that reduced potassium channel activation selectively increases gain in models possessing action potential dynamics typical for somatostatin interneurons. Potassium channels in parvalbumin-type models deactivate rapidly and are unavailable for further modulation. These findings show that GABA can differentially modulate interneuron excitability and suggest a mechanism through which this occurs in silico via differences of intrinsic electrophysiological properties.
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40
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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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41
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Buccino AP, Hagen E, Einevoll GT, Hafliger PD, Cauwenbergh G. Independent Component Analysis for Fully Automated Multi-Electrode Array Spike Sorting. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:2627-2630. [PMID: 30440947 DOI: 10.1109/embc.2018.8512788] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
In neural electrophysiology, spike sorting allows to separate different neurons from extracellularly measured recordings. It is an essential processing step in order to understand neural activity and it is an unsupervised problem in nature, since no ground truth information is available. There are several available spike sorting packages, but many of them require a manual intervention to curate the results, which makes the process time consuming and hard to reproduce. Here, we focus on high-density Multi-Electrode Array (MEA) recordings and we present a fully automated pipeline based on Independent Component Analysis (ICA). While ICA has been previously investigated for spike sorting, it has never been compared with fully automated state-of-the-art algorithms. We use realistic simulated datasets to compare the spike sorting performance in terms of complexity, signal-to-noise ratio, and recording duration. We show that an ICA-based fully automated spike sorting approach can be a viable alternative approach due to its precision and robustness, but it needs to be optimized for time constraints and requires sufficient density of electrodes to cover active neurons in the proximity of the MEA.
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42
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Buccino AP, Ness TV, Einevoll GT, Cauwenberghs G, Hafliger PD. A Deep Learning Approach for the Classification of Neuronal Cell Types. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:999-1002. [PMID: 30440559 DOI: 10.1109/embc.2018.8512498] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Classification of neurons from extracellular recordings is mainly limited to putatively excitatory or inhibitory units based on the spike shape and firing patterns. Narrow waveforms are considered to be fast spiking inhibitory neurons and broad waveforms excitatory neurons. The aim of this work is twofold. First, we intend to use the rich spatial information from high-density Multi-Electrode Arrays (MEAs) to make classification more robust; second, we hope to be able to classify sub-types of excitatory and inhibitory neurons. We first built, in simulation, a large dataset of action potentials from detailed neural models. Then, we extracted spike features from the simulated recordings on a high-density Multi-Electrode Array model. Finally, we used a Convolutional Neural Networks (CNN), to classify the different cell types. Compared with the ground truth data from the simulated dataset, the results show that this forward modelling/machine learning approach is very robust in recognizing excitatory and inhibitory spikes (accuracy $\ge 92.15$%). Additionally, the approach can, to a certain extent, correctly classify different cell sub-types. As the detail and fidelity of neural models increase and high-density recordings become available, this approach could become a viable and robust alternative for classification of neural cell types from in-vivo extracellular recordings.
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43
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Barros-Zulaica N, Rahmon J, Chindemi G, Perin R, Markram H, Muller E, Ramaswamy S. Estimating the Readily-Releasable Vesicle Pool Size at Synaptic Connections in the Neocortex. Front Synaptic Neurosci 2019; 11:29. [PMID: 31680928 PMCID: PMC6813366 DOI: 10.3389/fnsyn.2019.00029] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 09/30/2019] [Indexed: 12/21/2022] Open
Abstract
Previous studies based on the 'Quantal Model' for synaptic transmission suggest that neurotransmitter release is mediated by a single release site at individual synaptic contacts in the neocortex. However, recent studies seem to contradict this hypothesis and indicate that multi-vesicular release (MVR) could better explain the synaptic response variability observed in vitro. In this study we present a novel method to estimate the number of release sites per synapse, also known as the size of the readily releasable pool (NRRP), from paired whole-cell recordings of connections between layer 5 thick tufted pyramidal cell (L5_TTPC) in the juvenile rat somatosensory cortex. Our approach extends the work of Loebel et al. (2009) by leveraging a recently published data-driven biophysical model of neocortical tissue. Using this approach, we estimated NRRP to be between two to three for synaptic connections between L5_TTPCs. To constrain NRRP values for other connections in the microcircuit, we developed and validated a generalization approach using published data on the coefficient of variation (CV) of the amplitudes of post-synaptic potentials (PSPs) from literature and comparing them against in silico experiments. Our study predicts that transmitter release at synaptic connections in the neocortex could be mediated by MVR and provides a data-driven approach to constrain the MVR model parameters in the microcircuit.
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Affiliation(s)
| | - John Rahmon
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Giuseppe Chindemi
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Rodrigo Perin
- Laboratory of Neural Microcircuitry, Brain Mind Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Laboratory of Neural Microcircuitry, Brain Mind Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Eilif Muller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Srikanth Ramaswamy
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
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44
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Simulation of transcranial magnetic stimulation in head model with morphologically-realistic cortical neurons. Brain Stimul 2019; 13:175-189. [PMID: 31611014 PMCID: PMC6889021 DOI: 10.1016/j.brs.2019.10.002] [Citation(s) in RCA: 141] [Impact Index Per Article: 28.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 08/30/2019] [Accepted: 10/03/2019] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Transcranial magnetic stimulation (TMS) enables non-invasive modulation of brain activity with both clinical and research applications, but fundamental questions remain about the neural types and elements TMS activates and how stimulation parameters affect the neural response. OBJECTIVE To develop a multi-scale computational model to quantify the effect of TMS parameters on the direct response of individual neurons. METHODS We integrated morphologically-realistic neuronal models with TMS-induced electric fields computed in a finite element model of a human head to quantify the cortical response to TMS with several combinations of pulse waveforms and current directions. RESULTS TMS activated with lowest intensity intracortical axonal terminations in the superficial gyral crown and lip regions. Layer 5 pyramidal cells had the lowest thresholds, but layer 2/3 pyramidal cells and inhibitory basket cells were also activated at most intensities. Direct activation of layers 1 and 6 was unlikely. Neural activation was largely driven by the field magnitude, rather than the field component normal to the cortical surface. Varying the induced current direction caused a waveform-dependent shift in the activation site and provided a potential mechanism for experimentally observed differences in thresholds and latencies of muscle responses. CONCLUSIONS This biophysically-based simulation provides a novel method to elucidate mechanisms and inform parameter selection of TMS and other cortical stimulation modalities. It also serves as a foundation for more detailed network models of the response to TMS, which may include endogenous activity, synaptic connectivity, inputs from intrinsic and extrinsic axonal projections, and corticofugal axons in white matter.
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45
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Nolte M, Reimann MW, King JG, Markram H, Muller EB. Cortical reliability amid noise and chaos. Nat Commun 2019; 10:3792. [PMID: 31439838 PMCID: PMC6706377 DOI: 10.1038/s41467-019-11633-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2018] [Accepted: 07/23/2019] [Indexed: 02/01/2023] Open
Abstract
Typical responses of cortical neurons to identical sensory stimuli appear highly variable. It has thus been proposed that the cortex primarily uses a rate code. However, other studies have argued for spike-time coding under certain conditions. The potential role of spike-time coding is directly limited by the internally generated variability of cortical circuits, which remains largely unexplored. Here, we quantify this internally generated variability using a biophysical model of rat neocortical microcircuitry with biologically realistic noise sources. We find that stochastic neurotransmitter release is a critical component of internally generated variability, causing rapidly diverging, chaotic recurrent network dynamics. Surprisingly, the same nonlinear recurrent network dynamics can transiently overcome the chaos in response to weak feed-forward thalamocortical inputs, and support reliable spike times with millisecond precision. Our model shows that the noisy and chaotic network dynamics of recurrent cortical microcircuitry are compatible with stimulus-evoked, millisecond spike-time reliability, resolving a long-standing debate.
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Affiliation(s)
- Max Nolte
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, 1202, Geneva, Switzerland.
| | - Michael W Reimann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, 1202, Geneva, Switzerland
| | - James G King
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, 1202, Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, 1202, Geneva, Switzerland
- Laboratory of Neural Microcircuitry, Brain Mind Institute, École Polytechnique Fédérale de Lausanne, 1015, Lausanne, Switzerland
| | - Eilif B Muller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, 1202, Geneva, Switzerland.
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46
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Gleeson P, Cantarelli M, Marin B, Quintana A, Earnshaw M, Sadeh S, Piasini E, Birgiolas J, Cannon RC, Cayco-Gajic NA, Crook S, Davison AP, Dura-Bernal S, Ecker A, Hines ML, Idili G, Lanore F, Larson SD, Lytton WW, Majumdar A, McDougal RA, Sivagnanam S, Solinas S, Stanislovas R, van Albada SJ, van Geit W, Silver RA. Open Source Brain: A Collaborative Resource for Visualizing, Analyzing, Simulating, and Developing Standardized Models of Neurons and Circuits. Neuron 2019; 103:395-411.e5. [PMID: 31201122 PMCID: PMC6693896 DOI: 10.1016/j.neuron.2019.05.019] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Revised: 03/04/2019] [Accepted: 05/09/2019] [Indexed: 02/07/2023]
Abstract
Computational models are powerful tools for exploring the properties of complex biological systems. In neuroscience, data-driven models of neural circuits that span multiple scales are increasingly being used to understand brain function in health and disease. But their adoption and reuse has been limited by the specialist knowledge required to evaluate and use them. To address this, we have developed Open Source Brain, a platform for sharing, viewing, analyzing, and simulating standardized models from different brain regions and species. Model structure and parameters can be automatically visualized and their dynamical properties explored through browser-based simulations. Infrastructure and tools for collaborative interaction, development, and testing are also provided. We demonstrate how existing components can be reused by constructing new models of inhibition-stabilized cortical networks that match recent experimental results. These features of Open Source Brain improve the accessibility, transparency, and reproducibility of models and facilitate their reuse by the wider community.
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Affiliation(s)
- Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Matteo Cantarelli
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK; MetaCell Limited, Oxford, UK
| | - Boris Marin
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK; Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, Santo André, Brazil
| | - Adrian Quintana
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Matt Earnshaw
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Sadra Sadeh
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Eugenio Piasini
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK; Computational Neuroscience Initiative and Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA
| | - Justas Birgiolas
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | | | - N Alex Cayco-Gajic
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Sharon Crook
- School of Life Sciences, Arizona State University, Tempe, AZ, USA; School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, USA
| | - Andrew P Davison
- Unité de Neuroscience, Information et Complexité, Centre National de la Recherche Scientifique, Paris, France
| | | | - András Ecker
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK; Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Michael L Hines
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | | | - Frederic Lanore
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | | | - William W Lytton
- SUNY Downstate Medical Center and Kings County Hospital, Brooklyn, NY, USA
| | | | - Robert A McDougal
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA; Center for Medical Informatics, Yale University, New Haven, CT, USA
| | | | - Sergio Solinas
- Department of Biomedical Science, University of Sassari, Sassari, Italy; Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Rokas Stanislovas
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Sacha J van Albada
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6) and JARA-Institut Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany
| | - Werner van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - R Angus Silver
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK.
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Abdellah M, Hernando J, Eilemann S, Lapere S, Antille N, Markram H, Schürmann F. NeuroMorphoVis: a collaborative framework for analysis and visualization of neuronal morphology skeletons reconstructed from microscopy stacks. Bioinformatics 2019; 34:i574-i582. [PMID: 29949998 PMCID: PMC6022592 DOI: 10.1093/bioinformatics/bty231] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
Motivation From image stacks to computational models, processing digital representations of neuronal morphologies is essential to neuroscientific research. Workflows involve various techniques and tools, leading in certain cases to convoluted and fragmented pipelines. The existence of an integrated, extensible and free framework for processing, analysis and visualization of those morphologies is a challenge that is still largely unfulfilled. Results We present NeuroMorphoVis, an interactive, extensible and cross-platform framework for building, visualizing and analyzing digital reconstructions of neuronal morphology skeletons extracted from microscopy stacks. Our framework is capable of detecting and repairing tracing artifacts, allowing the generation of high fidelity surface meshes and high resolution volumetric models for simulation and in silico imaging studies. The applicability of NeuroMorphoVis is demonstrated with two case studies. The first simulates the construction of three-dimensional profiles of neuronal somata and the other highlights how the framework is leveraged to create volumetric models of neuronal circuits for simulating different types of in vitro imaging experiments. Availability and implementation The source code and documentation are freely available on https://github.com/BlueBrain/NeuroMorphoVis under the GNU public license. The morphological analysis, visualization and surface meshing are implemented as an extensible Python API (Application Programming Interface) based on Blender, and the volume reconstruction and analysis code is written in C++ and parallelized using OpenMP. The framework features are accessible from a user-friendly GUI (Graphical User Interface) and a rich CLI (Command Line Interface). Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Marwan Abdellah
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Juan Hernando
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Stefan Eilemann
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Samuel Lapere
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Nicolas Antille
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Felix Schürmann
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
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Duarte R, Morrison A. Leveraging heterogeneity for neural computation with fading memory in layer 2/3 cortical microcircuits. PLoS Comput Biol 2019; 15:e1006781. [PMID: 31022182 PMCID: PMC6504118 DOI: 10.1371/journal.pcbi.1006781] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 05/07/2019] [Accepted: 01/09/2019] [Indexed: 11/24/2022] Open
Abstract
Complexity and heterogeneity are intrinsic to neurobiological systems, manifest in every process, at every scale, and are inextricably linked to the systems’ emergent collective behaviours and function. However, the majority of studies addressing the dynamics and computational properties of biologically inspired cortical microcircuits tend to assume (often for the sake of analytical tractability) a great degree of homogeneity in both neuronal and synaptic/connectivity parameters. While simplification and reductionism are necessary to understand the brain’s functional principles, disregarding the existence of the multiple heterogeneities in the cortical composition, which may be at the core of its computational proficiency, will inevitably fail to account for important phenomena and limit the scope and generalizability of cortical models. We address these issues by studying the individual and composite functional roles of heterogeneities in neuronal, synaptic and structural properties in a biophysically plausible layer 2/3 microcircuit model, built and constrained by multiple sources of empirical data. This approach was made possible by the emergence of large-scale, well curated databases, as well as the substantial improvements in experimental methodologies achieved over the last few years. Our results show that variability in single neuron parameters is the dominant source of functional specialization, leading to highly proficient microcircuits with much higher computational power than their homogeneous counterparts. We further show that fully heterogeneous circuits, which are closest to the biophysical reality, owe their response properties to the differential contribution of different sources of heterogeneity. Cortical microcircuits are highly inhomogeneous dynamical systems whose information processing capacity is determined by the characteristics of its heterogeneous components and their complex interactions. The high degree of variability that characterizes macroscopic population dynamics, both during ongoing, spontaneous activity and active processing states reflects the underlying complexity and heterogeneity which has the potential to dramatically constrain the space of functions that any given circuit can compute, leading to richer and more expressive information processing systems. In this study, we identify different tentative sources of heterogeneity and assess their differential and cumulative contribution to the microcircuit’s dynamics and information processing capacity. We study these properties in a generic Layer 2/3 cortical microcircuit model, built and constrained by multiple sources of experimental data, and demonstrate that heterogeneity in neuronal properties and microconnectivity structure are important sources of functional specialization, greatly improving the circuit’s processing capacity, while capturing various important features of cortical physiology.
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Affiliation(s)
- Renato Duarte
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (JBI-1 / INM-10), Jülich Research Centre, Jülich, Germany
- Bernstein Center Freiburg, Albert-Ludwig University of Freiburg, Freiburg im Breisgau, Germany
- Faculty of Biology, Albert-Ludwig University of Freiburg, Freiburg im Breisgau, Germany
- Institute of Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
- * E-mail:
| | - Abigail Morrison
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (JBI-1 / INM-10), Jülich Research Centre, Jülich, Germany
- Bernstein Center Freiburg, Albert-Ludwig University of Freiburg, Freiburg im Breisgau, Germany
- Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr-University Bochum, Bochum, Germany
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Berecki G, Bryson A, Terhag J, Maljevic S, Gazina EV, Hill SL, Petrou S. SCN1A
gain of function in early infantile encephalopathy. Ann Neurol 2019; 85:514-525. [DOI: 10.1002/ana.25438] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2018] [Revised: 02/14/2019] [Accepted: 02/15/2019] [Indexed: 02/01/2023]
Affiliation(s)
- Géza Berecki
- Ion Channels and Disease Group, Florey Institute of Neuroscience and Mental HealthUniversity of Melbourne Parkville Victoria Australia
| | - Alexander Bryson
- Ion Channels and Disease Group, Florey Institute of Neuroscience and Mental HealthUniversity of Melbourne Parkville Victoria Australia
| | - Jan Terhag
- Ion Channels and Disease Group, Florey Institute of Neuroscience and Mental HealthUniversity of Melbourne Parkville Victoria Australia
| | - Snezana Maljevic
- Ion Channels and Disease Group, Florey Institute of Neuroscience and Mental HealthUniversity of Melbourne Parkville Victoria Australia
| | - Elena V. Gazina
- Ion Channels and Disease Group, Florey Institute of Neuroscience and Mental HealthUniversity of Melbourne Parkville Victoria Australia
| | - Sean L. Hill
- Blue Brain ProjectSwiss Federal Institute of Technology in Lausanne Geneva Switzerland
| | - Steven Petrou
- Ion Channels and Disease Group, Florey Institute of Neuroscience and Mental HealthUniversity of Melbourne Parkville Victoria Australia
- Department of MedicineRoyal Melbourne Hospital, University of Melbourne Parkville Victoria Australia
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Thornton C, Hutchings F, Kaiser M. The Virtual Electrode Recording Tool for EXtracellular Potentials (VERTEX) Version 2.0: Modelling in vitro electrical stimulation of brain tissue. Wellcome Open Res 2019; 4:20. [PMID: 30984877 PMCID: PMC6439485 DOI: 10.12688/wellcomeopenres.15058.1] [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] [Accepted: 01/21/2019] [Indexed: 11/20/2022] Open
Abstract
Neuronal circuits can be modelled in detail allowing us to predict the effects of stimulation on individual neurons. Electrical stimulation of neuronal circuits in vitro and in vivo excites a range of neurons within the tissue and measurements of neural activity, e.g the local field potential (LFP), are again an aggregate of a large pool of cells. The previous version of our Virtual Electrode Recording Tool for EXtracellular Potentials (VERTEX) allowed for the simulation of the LFP generated by a patch of brain tissue. Here, we extend VERTEX to simulate the effect of electrical stimulation through a focal electric field. We observe both direct changes in neural activity and changes in synaptic plasticity. Testing our software in a model of a rat neocortical slice, we determine the currents contributing to the LFP, the effects of paired pulse stimulation to induce short term plasticity (STP), and the effect of theta burst stimulation (TBS) to induce long term potentiation (LTP).
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Affiliation(s)
- Christopher Thornton
- Interdisciplinary Computing and Complex bioSystems (ICOS) Research Group, Newcastle University, UK, Newcastle upon Tyne, NE4 5TG, UK
| | - Frances Hutchings
- Interdisciplinary Computing and Complex bioSystems (ICOS) Research Group, Newcastle University, UK, Newcastle upon Tyne, NE4 5TG, UK
| | - Marcus Kaiser
- Interdisciplinary Computing and Complex bioSystems (ICOS) Research Group, Newcastle University, UK, Newcastle upon Tyne, NE4 5TG, UK
- Institute of Neuroscience, Newcastle University, UK, Newcastle upon Tyne, NE2 4HH, UK
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