1
|
Arnaudon A, Reva M, Zbili M, Markram H, Van Geit W, Kanari L. Controlling morpho-electrophysiological variability of neurons with detailed biophysical models. iScience 2023; 26:108222. [PMID: 37953946 PMCID: PMC10638024 DOI: 10.1016/j.isci.2023.108222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 07/21/2023] [Accepted: 10/12/2023] [Indexed: 11/14/2023] Open
Abstract
Variability, which is known to be a universal feature among biological units such as neuronal cells, holds significant importance, as, for example, it enables a robust encoding of a high volume of information in neuronal circuits and prevents hypersynchronizations. While most computational studies on electrophysiological variability in neuronal circuits were done with single-compartment neuron models, we instead focus on the variability of detailed biophysical models of neuron multi-compartmental morphologies. We leverage a Markov chain Monte Carlo method to generate populations of electrical models reproducing the variability of experimental recordings while being compatible with a set of morphologies to faithfully represent specifi morpho-electrical type. We demonstrate our approach on layer 5 pyramidal cells and study the morpho-electrical variability and in particular, find that morphological variability alone is insufficient to reproduce electrical variability. Overall, this approach provides a strong statistical basis to create detailed models of neurons with controlled variability.
Collapse
Affiliation(s)
- Alexis Arnaudon
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Maria Reva
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Mickael Zbili
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Werner Van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Lida Kanari
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| |
Collapse
|
2
|
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 (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] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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
| |
Collapse
|
3
|
Rosenberg N, Reva M, Binda F, Restivo L, Depierre P, Puyal J, Briquet M, Bernardinelli Y, Rocher AB, Markram H, Chatton JY. Overexpression of UCP4 in astrocytic mitochondria prevents multilevel dysfunctions in a mouse model of Alzheimer's disease. Glia 2023; 71:957-973. [PMID: 36537556 DOI: 10.1002/glia.24317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 10/31/2022] [Accepted: 11/25/2022] [Indexed: 02/16/2023]
Abstract
Alzheimer's disease (AD) is becoming increasingly prevalent worldwide. It represents one of the greatest medical challenges as no pharmacologic treatments are available to prevent disease progression. Astrocytes play crucial functions within neuronal circuits by providing metabolic and functional support, regulating interstitial solute composition, and modulating synaptic transmission. In addition to these physiological functions, growing evidence points to an essential role of astrocytes in neurodegenerative diseases like AD. Early-stage AD is associated with hypometabolism and oxidative stress. Contrary to neurons that are vulnerable to oxidative stress, astrocytes are particularly resistant to mitochondrial dysfunction and are therefore more resilient cells. In our study, we leveraged astrocytic mitochondrial uncoupling and examined neuronal function in the 3xTg AD mouse model. We overexpressed the mitochondrial uncoupling protein 4 (UCP4), which has been shown to improve neuronal survival in vitro. We found that this treatment efficiently prevented alterations of hippocampal metabolite levels observed in AD mice, along with hippocampal atrophy and reduction of basal dendrite arborization of subicular neurons. This approach also averted aberrant neuronal excitability observed in AD subicular neurons and preserved episodic-like memory in AD mice assessed in a spatial recognition task. These findings show that targeting astrocytes and their mitochondria is an effective strategy to prevent the decline of neurons facing AD-related stress at the early stages of the disease.
Collapse
Affiliation(s)
- Nadia Rosenberg
- Department of Fundamental Neurosciences, University of Lausanne, Lausanne, Switzerland
| | - Maria Reva
- Blue Brain Project (BBP), École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Francesca Binda
- Department of Fundamental Neurosciences, University of Lausanne, Lausanne, Switzerland
| | - Leonardo Restivo
- Department of Fundamental Neurosciences, University of Lausanne, Lausanne, Switzerland
| | - Pauline Depierre
- Department of Fundamental Neurosciences, University of Lausanne, Lausanne, Switzerland
| | - Julien Puyal
- Department of Fundamental Neurosciences, University of Lausanne, Lausanne, Switzerland
| | - Marc Briquet
- Department of Fundamental Neurosciences, University of Lausanne, Lausanne, Switzerland
| | | | - Anne-Bérengère Rocher
- Department of Fundamental Neurosciences, University of Lausanne, Lausanne, Switzerland
| | - Henry Markram
- Blue Brain Project (BBP), École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Jean-Yves Chatton
- Department of Fundamental Neurosciences, University of Lausanne, Lausanne, Switzerland.,Cellular Imaging Facility, University of Lausanne, Lausanne, Switzerland
| |
Collapse
|
4
|
Hunt S, Leibner Y, Mertens EJ, Barros-Zulaica N, Kanari L, Heistek TS, Karnani MM, Aardse R, Wilbers R, Heyer DB, Goriounova NA, Verhoog MB, Testa-Silva G, Obermayer J, Versluis T, Benavides-Piccione R, de Witt-Hamer P, Idema S, Noske DP, Baayen JC, Lein ES, DeFelipe J, Markram H, Mansvelder HD, Schürmann F, Segev I, de Kock CPJ. Strong and reliable synaptic communication between pyramidal neurons in adult human cerebral cortex. Cereb Cortex 2023; 33:2857-2878. [PMID: 35802476 PMCID: PMC10016070 DOI: 10.1093/cercor/bhac246] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 12/25/2022] Open
Abstract
Synaptic transmission constitutes the primary mode of communication between neurons. It is extensively studied in rodent but not human neocortex. We characterized synaptic transmission between pyramidal neurons in layers 2 and 3 using neurosurgically resected human middle temporal gyrus (MTG, Brodmann area 21), which is part of the distributed language circuitry. We find that local connectivity is comparable with mouse layer 2/3 connections in the anatomical homologue (temporal association area), but synaptic connections in human are 3-fold stronger and more reliable (0% vs 25% failure rates, respectively). We developed a theoretical approach to quantify properties of spinous synapses showing that synaptic conductance and voltage change in human dendritic spines are 3-4-folds larger compared with mouse, leading to significant NMDA receptor activation in human unitary connections. This model prediction was validated experimentally by showing that NMDA receptor activation increases the amplitude and prolongs decay of unitary excitatory postsynaptic potentials in human but not in mouse connections. Since NMDA-dependent recurrent excitation facilitates persistent activity (supporting working memory), our data uncovers cortical microcircuit properties in human that may contribute to language processing in MTG.
Collapse
Affiliation(s)
| | | | - Eline J Mertens
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
| | - Natalí Barros-Zulaica
- Blue Brain Project, Ecole polytechnique fédérale de Lausanne, Campus Biotech, Geneva 1202, Switzerland
| | - Lida Kanari
- Blue Brain Project, Ecole polytechnique fédérale de Lausanne, Campus Biotech, Geneva 1202, Switzerland
| | - Tim S Heistek
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
| | - Mahesh M Karnani
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
| | - Romy Aardse
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
| | - René Wilbers
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
| | - Djai B Heyer
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
| | - Natalia A Goriounova
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
| | | | | | - Joshua Obermayer
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
| | - Tamara Versluis
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
| | - Ruth Benavides-Piccione
- Laboratorio Cajal de Circuitos Corticales, Universidad Politécnica de Madrid and Instituto Cajal (CSIC), Pozuelo de Alarcón, Madrid 28223, Spain
| | - Philip de Witt-Hamer
- Neurosurgery Department, Amsterdam Universitair Medische Centra, location VUmc, 1081 HV Amsterdam, the Netherlands
| | - Sander Idema
- Neurosurgery Department, Amsterdam Universitair Medische Centra, location VUmc, 1081 HV Amsterdam, the Netherlands
| | - David P Noske
- Neurosurgery Department, Amsterdam Universitair Medische Centra, location VUmc, 1081 HV Amsterdam, the Netherlands
| | - Johannes C Baayen
- Neurosurgery Department, Amsterdam Universitair Medische Centra, location VUmc, 1081 HV Amsterdam, the Netherlands
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Javier DeFelipe
- Laboratorio Cajal de Circuitos Corticales, Universidad Politécnica de Madrid and Instituto Cajal (CSIC), Pozuelo de Alarcón, Madrid 28223, Spain
| | - Henry Markram
- Blue Brain Project, Ecole polytechnique fédérale de Lausanne, Campus Biotech, Geneva 1202, Switzerland
| | - Huibert D Mansvelder
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, the Netherlands
| | - Felix Schürmann
- Blue Brain Project, Ecole polytechnique fédérale de Lausanne, Campus Biotech, Geneva 1202, Switzerland
| | - Idan Segev
- Department of Neurobiology and Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, 9190501 Jerusalem, Israel
| | | |
Collapse
|
5
|
Keller D, Verasztó C, Markram H. Cell-type-specific densities in mouse somatosensory cortex derived from scRNA-seq and in situ RNA hybridization. Front Neuroanat 2023; 17:1118170. [PMID: 37007642 PMCID: PMC10055737 DOI: 10.3389/fnana.2023.1118170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 01/30/2023] [Indexed: 03/17/2023] Open
Abstract
Cells in the mammalian cerebral cortex exhibit layer-dependent patterns in their distribution. Classical methods of determining cell type distributions typically employ a painstaking process of large-scale sampling and characterization of cellular composition. We found that by combining in situ hybridization (ISH) images with cell-type-specific transcriptomes, position-dependent cortical composition in P56 mouse could be estimated in the somatosensory cortex. The method uses ISH images from the Allen Institute for Brain Science. There are two novel aspects of the methodology. First, it is not necessary to select a subset of genes that are particular for a cell type of interest, nor is it necessary to only use ISH images with low variability among samples. Second, the method also compensated for differences in soma size and incompleteness of the transcriptomes. The soma size compensation is particularly important in order to obtain quantitative estimates since relying on bulk expression alone would overestimate the contribution of larger cells. Predicted distributions of broader classes of cell types agreed with literature distributions. The primary result is that there is a high degree of substructure in the distribution of transcriptomic types beyond the resolution of layers. Furthermore, transcriptomic cell types each exhibited characteristic soma size distributions. Results suggest that the method could also be employed to assign transcriptomic cell types to well-aligned image sets in the entire brain.
Collapse
|
6
|
Roussel Y, Verasztó C, Rodarie D, Damart T, Reimann M, Ramaswamy S, Markram H, Keller D. Mapping of morpho-electric features to molecular identity of cortical inhibitory neurons. PLoS Comput Biol 2023; 19:e1010058. [PMID: 36602951 DOI: 10.1371/journal.pcbi.1010058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 03/26/2022] [Indexed: 01/06/2023] Open
Abstract
Knowledge of the cell-type-specific composition of the brain is useful in order to understand the role of each cell type as part of the network. Here, we estimated the composition of the whole cortex in terms of well characterized morphological and electrophysiological inhibitory neuron types (me-types). We derived probabilistic me-type densities from an existing atlas of molecularly defined cell-type densities in the mouse cortex. We used a well-established me-type classification from rat somatosensory cortex to populate the cortex. These me-types were well characterized morphologically and electrophysiologically but they lacked molecular marker identity labels. To extrapolate this missing information, we employed an additional dataset from the Allen Institute for Brain Science containing molecular identity as well as morphological and electrophysiological data for mouse cortical neurons. We first built a latent space based on a number of comparable morphological and electrical features common to both data sources. We then identified 19 morpho-electrical clusters that merged neurons from both datasets while being molecularly homogeneous. The resulting clusters best mirror the molecular identity classification solely using available morpho-electrical features. Finally, we stochastically assigned a molecular identity to a me-type neuron based on the latent space cluster it was assigned to. The resulting mapping was used to derive inhibitory me-types densities in the cortex.
Collapse
Affiliation(s)
- Yann Roussel
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Csaba Verasztó
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Dimitri Rodarie
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Tanguy Damart
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Michael Reimann
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Srikanth Ramaswamy
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Daniel Keller
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
| |
Collapse
|
7
|
Denizdurduran B, Markram H, Gewaltig MO. Optimum trajectory learning in musculoskeletal systems with model predictive control and deep reinforcement learning. Biol Cybern 2022; 116:711-726. [PMID: 35951117 PMCID: PMC9691497 DOI: 10.1007/s00422-022-00940-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 07/04/2022] [Indexed: 05/13/2023]
Abstract
From the computational point of view, musculoskeletal control is the problem of controlling high degrees of freedom and dynamic multi-body system that is driven by redundant muscle units. A critical challenge in the control perspective of skeletal joints with antagonistic muscle pairs is finding methods robust to address this ill-posed nonlinear problem. To address this computational problem, we implemented a twofold optimization and learning framework to be specialized in addressing the redundancies in the muscle control . In the first part, we used model predictive control to obtain energy efficient skeletal trajectories to mimick human movements. The second part is to use deep reinforcement learning to obtain a sequence of stimulus to be given to muscles in order to obtain the skeletal trajectories with muscle control. We observed that the desired stimulus to muscles is only efficiently constructed by integrating the state and control input in a closed-loop setting as it resembles the proprioceptive integration in the spinal cord circuits. In this work, we showed how a variety of different reference trajectories can be obtained with optimal control and how these reference trajectories are mapped to the musculoskeletal control with deep reinforcement learning. Starting from the characteristics of human arm movement to obstacle avoidance experiment, our simulation results confirm the capabilities of our optimization and learning framework for a variety of dynamic movement trajectories. In summary, the proposed framework is offering a pipeline to complement the lack of experiments to record human motion-capture data as well as study the activation range of muscles to replicate the specific trajectory of interest. Using the trajectories from optimal control as a reference signal for reinforcement learning implementation has allowed us to acquire optimum and human-like behaviour of the musculoskeletal system which provides a framework to study human movement in-silico experiments. The present framework can also allow studying upper-arm rehabilitation with assistive robots given that one can use healthy subject movement recordings as reference to work on the control architecture of assistive robotics in order to compensate behavioural deficiencies. Hence, the framework opens to possibility of replicating or complementing labour-intensive, time-consuming and costly experiments with human subjects in the field of movement studies and digital twin of rehabilitation.
Collapse
Affiliation(s)
- Berat Denizdurduran
- Alpine Intuition Sarl, Route de Crochy 20, 1024 Ecublens, Switzerland
- 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
| | - Marc-Oliver Gewaltig
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| |
Collapse
|
8
|
Denizdurduran B, Markram H, Gewaltig MO. Correction: Optimum trajectory learning in musculoskeletal systems with model predictive control and deep reinforcement learning. Biol Cybern 2022; 116:727. [PMID: 36224401 DOI: 10.1007/s00422-022-00947-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Affiliation(s)
- Berat Denizdurduran
- Alpine Intuition Sarl, Route de Crochy 20, 1024, Ecublens, Switzerland.
- 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
| | - Marc-Oliver Gewaltig
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202, Geneva, Switzerland
| |
Collapse
|
9
|
Denizdurduran B, Markram H, Gewaltig MO. Correction: Optimum trajectory learning in musculoskeletal systems with model predictive control and deep reinforcement learning. Biol Cybern 2022; 116:729. [PMID: 36255485 PMCID: PMC9691477 DOI: 10.1007/s00422-022-00949-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Affiliation(s)
- Berat Denizdurduran
- Alpine Intuition Sarl, Route de Crochy 20, 1024 Ecublens, Switzerland
- 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
| | - Marc-Oliver Gewaltig
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| |
Collapse
|
10
|
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] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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:
| |
Collapse
|
11
|
Chindemi G, Abdellah M, Amsalem O, Benavides-Piccione R, Delattre V, Doron M, Ecker A, Jaquier AT, King J, Kumbhar P, Monney C, Perin R, Rössert C, Tuncel AM, Van Geit W, DeFelipe J, Graupner M, Segev I, Markram H, Muller EB. A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex. Nat Commun 2022; 13:3038. [PMID: 35650191 PMCID: PMC9160074 DOI: 10.1038/s41467-022-30214-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 04/19/2022] [Indexed: 01/14/2023] Open
Abstract
Pyramidal cells (PCs) form the backbone of the layered structure of the neocortex, and plasticity of their synapses is thought to underlie learning in the brain. However, such long-term synaptic changes have been experimentally characterized between only a few types of PCs, posing a significant barrier for studying neocortical learning mechanisms. Here we introduce a model of synaptic plasticity based on data-constrained postsynaptic calcium dynamics, and show in a neocortical microcircuit model that a single parameter set is sufficient to unify the available experimental findings on long-term potentiation (LTP) and long-term depression (LTD) of PC connections. In particular, we find that the diverse plasticity outcomes across the different PC types can be explained by cell-type-specific synaptic physiology, cell morphology and innervation patterns, without requiring type-specific plasticity. Generalizing the model to in vivo extracellular calcium concentrations, we predict qualitatively different plasticity dynamics from those observed in vitro. This work provides a first comprehensive null model for LTP/LTD between neocortical PC types in vivo, and an open framework for further developing models of cortical synaptic plasticity.
Collapse
Affiliation(s)
- Giuseppe Chindemi
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland.
| | - Marwan Abdellah
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Oren Amsalem
- Department of Neurobiology, the Hebrew University of Jerusalem, Jerusalem, Israel.,Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
| | - Ruth Benavides-Piccione
- Instituto Cajal, Consejo Superior de Investigaciones Científicas, Madrid, Spain.,Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
| | - Vincent Delattre
- Laboratory of Neural Microcircuitry, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Michael Doron
- Edmond and Lily Safra Center for Brain Sciences, the Hebrew University of Jerusalem, Jerusalem, Israel
| | - András Ecker
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Aurélien T Jaquier
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - James King
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Pramod Kumbhar
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Caitlin Monney
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Rodrigo Perin
- Laboratory of Neural Microcircuitry, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Christian Rössert
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Anil M Tuncel
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Werner Van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Javier DeFelipe
- Instituto Cajal, Consejo Superior de Investigaciones Científicas, Madrid, Spain.,Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid, Spain
| | - Michael Graupner
- Université de Paris, SPPIN - Saints-Pères Paris Institute for the Neurosciences, CNRS, Paris, France
| | - Idan Segev
- Department of Neurobiology, the Hebrew University of Jerusalem, Jerusalem, Israel.,Edmond and Lily Safra Center for Brain Sciences, the Hebrew University of Jerusalem, Jerusalem, Israel
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Laboratory of Neural Microcircuitry, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Eilif B Muller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland. .,Department of Neurosciences, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada. .,CHU Sainte-Justine Research Center, Montréal, QC, Canada. .,Quebec Artificial Intelligence Institute (Mila), Montréal, Canada.
| |
Collapse
|
12
|
Coggan JS, Keller D, Markram H, Schürmann F, Magistretti PJ. Representing Stimulus Information in an Energy Metabolism Pathway. J Theor Biol 2022; 540:111090. [PMID: 35271865 DOI: 10.1016/j.jtbi.2022.111090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 02/21/2022] [Accepted: 03/01/2022] [Indexed: 10/18/2022]
Abstract
We explored a computational model of astrocytic energy metabolism and demonstrated the theoretical plausibility that this type of pathway might be capable of coding information about stimuli in addition to its known functions in cellular energy and carbon budgets. Simulation results indicate that glycogenolytic glycolysis triggered by activation of adrenergic receptors can capture the intensity and duration features of a neuromodulator waveform and can respond in a dose-dependent manner, including non-linear state changes that are analogous to action potentials. We show how this metabolic pathway can translate information about external stimuli to production profiles of energy-carrying molecules such as lactate with a precision beyond simple signal transduction or non-linear amplification. The results suggest the operation of a metabolic state-machine from the spatially discontiguous yet interdependent metabolite elements. Such metabolic pathways might be well-positioned to code an additional level of salient information about a cell's environmental demands to impact its function. Our hypothesis has implications for the computational power and energy efficiency of the brain.
Collapse
Affiliation(s)
- Jay S Coggan
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, CH-1202, Switzerland.
| | - Daniel Keller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, CH-1202, Switzerland
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, CH-1202, Switzerland
| | - Felix Schürmann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, CH-1202, Switzerland
| | - Pierre J Magistretti
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| |
Collapse
|
13
|
Shichkova P, Coggan JS, Markram H, Keller D. A Standardized Brain Molecular Atlas: A Resource for Systems Modeling and Simulation. Front Mol Neurosci 2021; 14:604559. [PMID: 34858137 PMCID: PMC8631404 DOI: 10.3389/fnmol.2021.604559] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 10/05/2021] [Indexed: 12/12/2022] Open
Abstract
Accurate molecular concentrations are essential for reliable analyses of biochemical networks and the creation of predictive models for molecular and systems biology, yet protein and metabolite concentrations used in such models are often poorly constrained or irreproducible. Challenges of using data from different sources include conflicts in nomenclature and units, as well as discrepancies in experimental procedures, data processing and implementation of the model. To obtain a consistent estimate of protein and metabolite levels, we integrated and normalized data from a large variety of sources to calculate Adjusted Molecular Concentrations. We found a high degree of reproducibility and consistency of many molecular species across brain regions and cell types, consistent with tight homeostatic regulation. We demonstrated the value of this normalization with differential protein expression analyses related to neurodegenerative diseases, brain regions and cell types. We also used the results in proof-of-concept simulations of brain energy metabolism. The standardized Brain Molecular Atlas overcomes the obstacles of missing or inconsistent data to support systems biology research and is provided as a resource for biomolecular modeling.
Collapse
Affiliation(s)
- Polina Shichkova
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Jay S Coggan
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, 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
| | - Daniel Keller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| |
Collapse
|
14
|
Zisis E, Keller D, Kanari L, Arnaudon A, Gevaert M, Delemontex T, Coste B, Foni A, Abdellah M, Calì C, Hess K, Magistretti PJ, Schürmann F, Markram H. Digital Reconstruction of the Neuro-Glia-Vascular Architecture. Cereb Cortex 2021; 31:5686-5703. [PMID: 34387659 PMCID: PMC8568010 DOI: 10.1093/cercor/bhab254] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 07/05/2021] [Accepted: 07/06/2021] [Indexed: 01/21/2023] Open
Abstract
Astrocytes connect the vasculature to neurons mediating the supply of nutrients and biochemicals. They are involved in a growing number of physiological and pathophysiological processes that result from biophysical, physiological, and molecular interactions in this neuro-glia-vascular ensemble (NGV). The lack of a detailed cytoarchitecture severely restricts the understanding of how they support brain function. To address this problem, we used data from multiple sources to create a data-driven digital reconstruction of the NGV at micrometer anatomical resolution. We reconstructed 0.2 mm3 of the rat somatosensory cortex with 16 000 morphologically detailed neurons, 2500 protoplasmic astrocytes, and its microvasculature. The consistency of the reconstruction with a wide array of experimental measurements allows novel predictions of the NGV organization, allowing the anatomical reconstruction of overlapping astrocytic microdomains and the quantification of endfeet connecting each astrocyte to the vasculature, as well as the extent to which they cover the latter. Structural analysis showed that astrocytes optimize their positions to provide uniform vascular coverage for trophic support and signaling. However, this optimal organization rapidly declines as their density increases. The NGV digital reconstruction is a resource that will enable a better understanding of the anatomical principles and geometric constraints, which govern how astrocytes support brain function.
Collapse
Affiliation(s)
- Eleftherios Zisis
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva 1202, Switzerland
| | - Daniel Keller
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva 1202, Switzerland
| | - Lida Kanari
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva 1202, Switzerland
| | - Alexis Arnaudon
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva 1202, Switzerland
| | - Michael Gevaert
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva 1202, Switzerland
| | - Thomas Delemontex
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva 1202, Switzerland
| | - Benoît Coste
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva 1202, Switzerland
| | - Alessandro Foni
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva 1202, Switzerland
| | - Marwan Abdellah
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva 1202, Switzerland
| | - Corrado Calì
- Neuroscience Institute Cavalieri Ottolenghi, Orbassano, Turin 10043, Italy
- Department of Neuroscience, University of Torino, Torino 10126, Italy
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| | - Kathryn Hess
- Laboratory for Topology and Neuroscience, Brain Mind Institute, École polytechnique fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland
| | - Pierre Julius Magistretti
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| | - Felix Schürmann
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva 1202, Switzerland
| | - Henry Markram
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva 1202, Switzerland
| |
Collapse
|
15
|
Gal E, Amsalem O, Schindel A, London M, Schürmann F, Markram H, Segev I. The Role of Hub Neurons in Modulating Cortical Dynamics. Front Neural Circuits 2021; 15:718270. [PMID: 34630046 PMCID: PMC8500625 DOI: 10.3389/fncir.2021.718270] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 08/24/2021] [Indexed: 12/03/2022] Open
Abstract
Many neurodegenerative diseases are associated with the death of specific neuron types in particular brain regions. What makes the death of specific neuron types particularly harmful for the integrity and dynamics of the respective network is not well understood. To start addressing this question we used the most up-to-date biologically realistic dense neocortical microcircuit (NMC) of the rodent, which has reconstructed a volume of 0.3 mm3 and containing 31,000 neurons, ∼37 million synapses, and 55 morphological cell types arranged in six cortical layers. Using modern network science tools, we identified hub neurons in the NMC, that are connected synaptically to a large number of their neighbors and systematically examined the impact of abolishing these cells. In general, the structural integrity of the network is robust to cells’ attack; yet, attacking hub neurons strongly impacted the small-world topology of the network, whereas similar attacks on random neurons have a negligible effect. Such hub-specific attacks are also impactful on the network dynamics, both when the network is at its spontaneous synchronous state and when it was presented with synchronized thalamo-cortical visual-like input. We found that attacking layer 5 hub neurons is most harmful to the structural and functional integrity of the NMC. The significance of our results for understanding the role of specific neuron types and cortical layers for disease manifestation is discussed.
Collapse
Affiliation(s)
- Eyal Gal
- The Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.,Department of Neurobiology, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Oren Amsalem
- Department of Neurobiology, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Alon Schindel
- Department of Neurobiology, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Michael London
- The Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.,Department of Neurobiology, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Felix Schürmann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Idan Segev
- The Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.,Department of Neurobiology, The Hebrew University of Jerusalem, Jerusalem, Israel
| |
Collapse
|
16
|
Simko J, Markram H. Morphology, physiology and synaptic connectivity of local interneurons in the mouse somatosensory thalamus. J Physiol 2021; 599:5085-5101. [PMID: 34591324 PMCID: PMC9298088 DOI: 10.1113/jp281711] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 09/28/2021] [Indexed: 01/03/2023] Open
Abstract
Abstract The thalamic reticular nucleus (TRN) neurons, projecting across the external medullary lamina, have long been considered to be the only significant source of inhibition of the somatosensory ventral posterior (VP) nuclei of the thalamus. Here we report for the first time effective local inhibition and disinhibition in the VP. Inhibitory interneurons were found in GAD67–GFP‐expressing mice and studied using in vitro multiple patch clamp. Inhibitory interneurons have expansive bipolar or tripolar morphologies, reach across most of the VP nucleus and display low threshold bursting behaviour. They form triadic and non‐triadic synaptic connections onto thalamocortical relay neurons and other interneurons, mediating feedforward inhibition and disinhibition. Synaptic inputs arrive before those expected from the TRN neurons, suggesting that local inhibition plays an early and significant role in the functioning of the somatosensory thalamus.
![]() Key points The physiology and structure of local interneurons in the mouse somatosensory thalamus is described for the first time. Inhibitory interneurons have extensive dendritic arborization providing significant local dendro‐dendritic inhibition in the somatosensory thalamus. Triadic and non‐triadic synaptic connectivity onto thalamic relay neurons and other interneurons provides both local feedforward inhibition and disinhibition. Interneurons of the somatosensory thalamus provide inhibition before the thalamic reticular nucleus, suggesting they play an important role in sensory perception.
Collapse
Affiliation(s)
- Jane Simko
- Laboratory of Neural Microcircuitry, Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Henry Markram
- Laboratory of Neural Microcircuitry, Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.,Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| |
Collapse
|
17
|
Krepl J, Casalegno F, Delattre E, Erö C, Lu H, Keller D, Rodarie D, Markram H, Schürmann F. Supervised Learning With Perceptual Similarity for Multimodal Gene Expression Registration of a Mouse Brain Atlas. Front Neuroinform 2021; 15:691918. [PMID: 34393747 PMCID: PMC8355627 DOI: 10.3389/fninf.2021.691918] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 07/02/2021] [Indexed: 11/30/2022] Open
Abstract
The acquisition of high quality maps of gene expression in the rodent brain is of fundamental importance to the neuroscience community. The generation of such datasets relies on registering individual gene expression images to a reference volume, a task encumbered by the diversity of staining techniques employed, and by deformations and artifacts in the soft tissue. Recently, deep learning models have garnered particular interest as a viable alternative to traditional intensity-based algorithms for image registration. In this work, we propose a supervised learning model for general multimodal 2D registration tasks, trained with a perceptual similarity loss on a dataset labeled by a human expert and augmented by synthetic local deformations. We demonstrate the results of our approach on the Allen Mouse Brain Atlas (AMBA), comprising whole brain Nissl and gene expression stains. We show that our framework and design of the loss function result in accurate and smooth predictions. Our model is able to generalize to unseen gene expressions and coronal sections, outperforming traditional intensity-based approaches in aligning complex brain structures.
Collapse
Affiliation(s)
- Jan Krepl
- Blue Brain Project, Ecole polytechnique fédérale de Lausanne, Genève, Switzerland
| | - Francesco Casalegno
- Blue Brain Project, Ecole polytechnique fédérale de Lausanne, Genève, Switzerland
| | - Emilie Delattre
- Blue Brain Project, Ecole polytechnique fédérale de Lausanne, Genève, Switzerland
| | - Csaba Erö
- Blue Brain Project, Ecole polytechnique fédérale de Lausanne, Genève, Switzerland
| | - Huanxiang Lu
- Blue Brain Project, Ecole polytechnique fédérale de Lausanne, Genève, Switzerland
| | - Daniel Keller
- Blue Brain Project, Ecole polytechnique fédérale de Lausanne, Genève, Switzerland
| | - Dimitri Rodarie
- Blue Brain Project, Ecole polytechnique fédérale de Lausanne, Genève, Switzerland
| | - Henry Markram
- Blue Brain Project, Ecole polytechnique fédérale de Lausanne, Genève, Switzerland
| | - Felix Schürmann
- Blue Brain Project, Ecole polytechnique fédérale de Lausanne, Genève, Switzerland
| |
Collapse
|
18
|
Logette E, Lorin C, Favreau C, Oshurko E, Coggan JS, Casalegno F, Sy MF, Monney C, Bertschy M, Delattre E, Fonta PA, Krepl J, Schmidt S, Keller D, Kerrien S, Scantamburlo E, Kaufmann AK, Markram H. A Machine-Generated View of the Role of Blood Glucose Levels in the Severity of COVID-19. Front Public Health 2021; 9:695139. [PMID: 34395368 PMCID: PMC8356061 DOI: 10.3389/fpubh.2021.695139] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 06/30/2021] [Indexed: 01/08/2023] Open
Abstract
SARS-CoV-2 started spreading toward the end of 2019 causing COVID-19, a disease that reached pandemic proportions among the human population within months. The reasons for the spectrum of differences in the severity of the disease across the population, and in particular why the disease affects more severely the aging population and those with specific preconditions are unclear. We developed machine learning models to mine 240,000 scientific articles openly accessible in the CORD-19 database, and constructed knowledge graphs to synthesize the extracted information and navigate the collective knowledge in an attempt to search for a potential common underlying reason for disease severity. The machine-driven framework we developed repeatedly pointed to elevated blood glucose as a key facilitator in the progression of COVID-19. Indeed, when we systematically retraced the steps of the SARS-CoV-2 infection, we found evidence linking elevated glucose to each major step of the life-cycle of the virus, progression of the disease, and presentation of symptoms. Specifically, elevations of glucose provide ideal conditions for the virus to evade and weaken the first level of the immune defense system in the lungs, gain access to deep alveolar cells, bind to the ACE2 receptor and enter the pulmonary cells, accelerate replication of the virus within cells increasing cell death and inducing an pulmonary inflammatory response, which overwhelms an already weakened innate immune system to trigger an avalanche of systemic infections, inflammation and cell damage, a cytokine storm and thrombotic events. We tested the feasibility of the hypothesis by manually reviewing the literature referenced by the machine-generated synthesis, reconstructing atomistically the virus at the surface of the pulmonary airways, and performing quantitative computational modeling of the effects of glucose levels on the infection process. We conclude that elevation in glucose levels can facilitate the progression of the disease through multiple mechanisms and can explain much of the differences in disease severity seen across the population. The study provides diagnostic considerations, new areas of research and potential treatments, and cautions on treatment strategies and critical care conditions that induce elevations in blood glucose levels.
Collapse
Affiliation(s)
- Emmanuelle Logette
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Henry Markram
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
| |
Collapse
|
19
|
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] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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
| |
Collapse
|
20
|
Newton TH, Reimann MW, Abdellah M, Chevtchenko G, Muller EB, Markram H. In silico voltage-sensitive dye imaging reveals the emergent dynamics of cortical populations. Nat Commun 2021; 12:3630. [PMID: 34131136 PMCID: PMC8206372 DOI: 10.1038/s41467-021-23901-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 05/19/2021] [Indexed: 11/08/2022] Open
Abstract
Voltage-sensitive dye imaging (VSDI) is a powerful technique for interrogating membrane potential dynamics in assemblies of cortical neurons, but with effective resolution limits that confound interpretation. To address this limitation, we developed an in silico model of VSDI in a biologically faithful digital reconstruction of rodent neocortical microcircuitry. Using this model, we extend previous experimental observations regarding the cellular origins of VSDI, finding that the signal is driven primarily by neurons in layers 2/3 and 5, and that VSDI measurements do not capture individual spikes. Furthermore, we test the capacity of VSD image sequences to discriminate between afferent thalamic inputs at various spatial locations to estimate a lower bound on the functional resolution of VSDI. Our approach underscores the power of a bottom-up computational approach for relating scales of cortical processing.
Collapse
Affiliation(s)
- Taylor H Newton
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland.
- IT'IS Foundation for Research on Information Technologies in Society, Zurich, Switzerland.
| | - Michael W Reimann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Marwan Abdellah
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Grigori Chevtchenko
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Eilif B Muller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Neurosciences, Faculty of Medicine, University of Montreal, Montreal, QC, Canada
- CHU Sainte-Justine Research Center, Montreal, QC, Canada
- Quebec Artificial Intelligence Institute (Mila), Montreal, QC, Canada
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, Lausanne, Switzerland
| |
Collapse
|
21
|
Courcol JD, Invernizzi CF, Landry ZC, Minisini M, Baumgartner DA, Bonhoeffer S, Chabriw B, Clerc EE, Daniels M, Getta P, Girod M, Kazala K, Markram H, Pasqualini A, Martínez-Pérez C, Peaudecerf FJ, Peaudecerf MS, Pfreundt U, Roller BRK, Słomka J, Vasse M, Wheeler JD, Metzger CMJA, Stocker R, Schürmann F. ARC: An Open Web-Platform for Request/Supply Matching for a Prioritized and Controlled COVID-19 Response. Front Public Health 2021; 9:607677. [PMID: 33665184 PMCID: PMC7921781 DOI: 10.3389/fpubh.2021.607677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 01/18/2021] [Indexed: 11/30/2022] Open
Abstract
In 2020 the world was hit by the COVID-19 pandemic putting entire governments and civil societies in crisis mode. Around the globe unprecedented shortages of equipment and qualified personnel were reported in hospitals and diagnostic laboratories. When a crisis is global, supply chains are strained worldwide and external help may not be readily available. In Switzerland, as part of the efforts of the Swiss National COVID-19 Science Task Force, we developed a tailor-made web-based tool where needs and offers for critical laboratory equipment and expertise can be brought together, coordinated, prioritized, and validated. This Academic Resources for COVID-19 (ARC) Platform presents the specialized needs of diagnostic laboratories to academic research groups at universities, allowing the sourcing of said needs from unconventional supply channels, while keeping the entities tasked with coordination of the crisis response in control of each part of the process. An instance of the ARC Platform is operated in Switzerland (arc.epfl.ch) catering to the diagnostic efforts in Switzerland and sourcing from the Swiss academic sector. The underlying technology has been released as open source so that others can adopt the customizable web-platform for need/supply match-making in their own relief efforts, during the COVID-19 pandemic or any future disaster.
Collapse
Affiliation(s)
- Jean-Denis Courcol
- Blue Brain Project, Ecole polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Cédric F Invernizzi
- Spiez Laboratory, Federal Office for Civil Protection FOCP, Spiez, Switzerland
| | - Zachary C Landry
- Environmental Microfluidics Group, Institute of Environmental Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zurich, Switzerland
| | | | - Dieter A Baumgartner
- Environmental Microfluidics Group, Institute of Environmental Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zurich, Switzerland
| | - Sebastian Bonhoeffer
- Institute for Integrative Biology, Department of Environmental Systems Science, ETH Zürich, Zurich, Switzerland
| | | | - Estelle E Clerc
- Environmental Microfluidics Group, Institute of Environmental Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zurich, Switzerland
| | - Michael Daniels
- Institute of Biogeochemistry and Pollutant Dynamics, Department of Environmental Systems Science, ETH Zürich, Zurich, Switzerland.,Department of Environmental Microbiology, Eawag, Dübendorf, Switzerland
| | - Pavlo Getta
- Blue Brain Project, Ecole polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
| | | | - Kinga Kazala
- Section Software Services, Department of IT Services, ETH Zürich, Zurich, Switzerland
| | - Henry Markram
- Blue Brain Project, Ecole polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
| | | | - Clara Martínez-Pérez
- Environmental Microfluidics Group, Institute of Environmental Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zurich, Switzerland
| | - François J Peaudecerf
- Environmental Microfluidics Group, Institute of Environmental Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zurich, Switzerland
| | - Margit S Peaudecerf
- Institute for Biochemistry, Department of Biology, ETH Zürich, Zurich, Switzerland
| | - Ulrike Pfreundt
- Environmental Microfluidics Group, Institute of Environmental Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zurich, Switzerland
| | - Benjamin R K Roller
- Institute of Biogeochemistry and Pollutant Dynamics, Department of Environmental Systems Science, ETH Zürich, Zurich, Switzerland.,Department of Environmental Microbiology, Eawag, Dübendorf, Switzerland
| | - Jonasz Słomka
- Environmental Microfluidics Group, Institute of Environmental Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zurich, Switzerland
| | - Marie Vasse
- Institute for Integrative Biology, Department of Environmental Systems Science, ETH Zürich, Zurich, Switzerland
| | - Jeanette D Wheeler
- Environmental Microfluidics Group, Institute of Environmental Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zurich, Switzerland
| | - César M J A Metzger
- Spiez Laboratory, Federal Office for Civil Protection FOCP, Spiez, Switzerland
| | - Roman Stocker
- Environmental Microfluidics Group, Institute of Environmental Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zurich, Switzerland
| | - Felix Schürmann
- Blue Brain Project, Ecole polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
| |
Collapse
|
22
|
Abdellah M, Guerrero NR, Lapere S, Coggan JS, Keller D, Coste B, Dagar S, Courcol JD, Markram H, Schürmann F. Interactive visualization and analysis of morphological skeletons of brain vasculature networks with VessMorphoVis. Bioinformatics 2020; 36:i534-i541. [PMID: 32657395 PMCID: PMC7355309 DOI: 10.1093/bioinformatics/btaa461] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
MOTIVATION Accurate morphological models of brain vasculature are key to modeling and simulating cerebral blood flow in realistic vascular networks. This in silico approach is fundamental to revealing the principles of neurovascular coupling. Validating those vascular morphologies entails performing certain visual analysis tasks that cannot be accomplished with generic visualization frameworks. This limitation has a substantial impact on the accuracy of the vascular models employed in the simulation. RESULTS We present VessMorphoVis, an integrated suite of toolboxes for interactive visualization and analysis of vast brain vascular networks represented by morphological graphs segmented originally from imaging or microscopy stacks. Our workflow leverages the outstanding potentials of Blender, aiming to establish an integrated, extensible and domain-specific framework capable of interactive visualization, analysis, repair, high-fidelity meshing and high-quality rendering of vascular morphologies. Based on the initial feedback of the users, we anticipate that our framework will be an essential component in vascular modeling and simulation in the future, filling a gap that is at present largely unfulfilled. AVAILABILITY AND IMPLEMENTATION VessMorphoVis is freely available under the GNU public license on Github at https://github.com/BlueBrain/VessMorphoVis. The morphology analysis, visualization, meshing and rendering modules are implemented as an add-on for Blender 2.8 based on its Python API (application programming interface). The add-on functionality is made available to users through an intuitive graphical user interface, as well as through exhaustive configuration files calling the API via a feature-rich command line interface running Blender in background mode. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Marwan Abdellah
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| | - Nadir Román Guerrero
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| | - Samuel Lapere
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| | - Jay S Coggan
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| | - Daniel Keller
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| | - Benoit Coste
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| | - Snigdha Dagar
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| | - Jean-Denis Courcol
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| | - Felix Schürmann
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland
| |
Collapse
|
23
|
Kanari L, Ramaswamy S, Shi Y, Morand S, Meystre J, Perin R, Abdellah M, Wang Y, Hess K, Markram H. Objective Morphological Classification of Neocortical Pyramidal Cells. Cereb Cortex 2020; 29:1719-1735. [PMID: 30715238 PMCID: PMC6418396 DOI: 10.1093/cercor/bhy339] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 11/20/2018] [Indexed: 12/22/2022] Open
Abstract
A consensus on the number of morphologically different types of pyramidal cells (PCs) in the neocortex has not yet been reached, despite over a century of anatomical studies, due to the lack of agreement on the subjective classifications of neuron types, which is based on expert analyses of neuronal morphologies. Even for neurons that are visually distinguishable, there is no common ground to consistently define morphological types. The objective classification of PCs can be achieved with methods from algebraic topology, and the dendritic arborization is sufficient for the reliable identification of distinct types of cortical PCs. Therefore, we objectively identify 17 types of PCs in the rat somatosensory cortex. In addition, we provide a solution to the challenging problem of whether 2 similar neurons belong to different types or to a continuum of the same type. Our topological classification does not require expert input, is stable, and helps settle the long-standing debate on whether cell-types are discrete or continuous morphological variations of each other.
Collapse
Affiliation(s)
- Lida Kanari
- Blue Brain Project, Brain and Mind Institute, EPFL, Campus Biotech: CH 1202, Geneva, Switzerland
| | - Srikanth Ramaswamy
- Blue Brain Project, Brain and Mind Institute, EPFL, Campus Biotech: CH 1202, Geneva, Switzerland
| | - Ying Shi
- Blue Brain Project, Brain and Mind Institute, EPFL, Campus Biotech: CH 1202, Geneva, Switzerland
| | - Sebastien Morand
- Laboratory for Topology and Neuroscience, Brain Mind Institute, EPFL, CH 1015, Lausanne, Switzerland
| | - Julie Meystre
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, CH 1015, Lausanne, Switzerland
| | - Rodrigo Perin
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, CH 1015, Lausanne, Switzerland
| | - Marwan Abdellah
- Blue Brain Project, Brain and Mind Institute, EPFL, Campus Biotech: CH 1202, Geneva, Switzerland
| | - Yun Wang
- School of Optometry and Ophthalmology, Wenzhou Medical College, Wenzhou, Zhejiang, PR China.,Allen Institute for Brain Science, Seattle, WA, USA
| | - Kathryn Hess
- Laboratory for Topology and Neuroscience, Brain Mind Institute, EPFL, CH 1015, Lausanne, Switzerland
| | - Henry Markram
- Blue Brain Project, Brain and Mind Institute, EPFL, Campus Biotech: CH 1202, Geneva, Switzerland.,Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, CH 1015, Lausanne, Switzerland
| |
Collapse
|
24
|
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] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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
| |
Collapse
|
25
|
Coggan JS, Keller D, Markram H, Schürmann F, Magistretti PJ. Excitation states of metabolic networks predict dose-response fingerprinting and ligand pulse phase signalling. J Theor Biol 2020; 487:110123. [PMID: 31866398 DOI: 10.1016/j.jtbi.2019.110123] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 11/08/2019] [Accepted: 12/16/2019] [Indexed: 12/30/2022]
Abstract
With a computational model of energy metabolism in an astrocyte, we show how a system of enzymes in a cascade can act as a functional unit of interdependent reactions, rather than merely a series of independent reactions. These systems may exist in multiple states, depending on the level of stimulation, and the effects of substrates at any point will depend on those states. Response trajectories of metabolites downstream from cAMP-stimulated glycogenolysis exhibit a host of non-linear dynamical response characteristics including hysteresis and response envelopes. Dose-dependent phase transitions predict a novel intracellular signalling mechanism and suggest a theoretical framework that could be relevant to single cell information processing, drug discovery or synthetic biology. Ligands may produce unique dose-response fingerprints depending on the state of the system, allowing selective output tuning. We conclude with the observation that state- and dose-dependent phase transitions, what we dub "ligand pulses" (LPs), may carry information and resemble action potentials (APs) generated from excitatory postsynaptic potentials. In our model, the relevant information from a cAMP-dependent glycolytic cascade in astrocytes could reflect the level of neuromodulatory input that signals an energy demand threshold. We propose that both APs and LPs represent specialized cases of molecular phase signalling with a common evolutionary root.
Collapse
Affiliation(s)
- Jay S Coggan
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva CH-1202, Switzerland.
| | - Daniel Keller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva CH-1202, Switzerland.
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva CH-1202, Switzerland.
| | - Felix Schürmann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva CH-1202, Switzerland.
| | - Pierre J Magistretti
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia.
| |
Collapse
|
26
|
Ranjan R, Logette E, Marani M, Herzog M, Tache V, Scantamburlo E, Buchillier V, Markram H. A Kinetic Map of the Homomeric Voltage-gated Potassium Channel (Kv) Family. Biophys J 2020. [DOI: 10.1016/j.bpj.2019.11.739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
|
27
|
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] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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
| |
Collapse
|
28
|
Casalegno F, Newton T, Daher R, Abdelaziz M, Lodi-Rizzini A, Schürmann F, Krejci I, Markram H. Caries Detection with Near-Infrared Transillumination Using Deep Learning. J Dent Res 2019; 98:1227-1233. [PMID: 31449759 PMCID: PMC6761787 DOI: 10.1177/0022034519871884] [Citation(s) in RCA: 106] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Dental caries is the most prevalent chronic condition worldwide. Early detection can significantly improve treatment outcomes and reduce the need for invasive procedures. Recently, near-infrared transillumination (TI) imaging has been shown to be effective for the detection of early stage lesions. In this work, we present a deep learning model for the automated detection and localization of dental lesions in TI images. Our method is based on a convolutional neural network (CNN) trained on a semantic segmentation task. We use various strategies to mitigate issues related to training data scarcity, class imbalance, and overfitting. With only 185 training samples, our model achieved an overall mean intersection-over-union (IOU) score of 72.7% on a 5-class segmentation task and specifically an IOU score of 49.5% and 49.0% for proximal and occlusal carious lesions, respectively. In addition, we constructed a simplified task, in which regions of interest were evaluated for the binary presence or absence of carious lesions. For this task, our model achieved an area under the receiver operating characteristic curve of 83.6% and 85.6% for occlusal and proximal lesions, respectively. Our work demonstrates that a deep learning approach for the analysis of dental images holds promise for increasing the speed and accuracy of caries detection, supporting the diagnoses of dental practitioners, and improving patient outcomes.
Collapse
Affiliation(s)
- F. Casalegno
- Blue Brain Project, École polytechnique fédérale de Lausanne, Genève, Switzerland
| | - T. Newton
- Blue Brain Project, École polytechnique fédérale de Lausanne, Genève, Switzerland
| | - R. Daher
- Clinique universitaire de médecine dentaire, Université de Genève, Genève, Switzerland
| | - M. Abdelaziz
- Clinique universitaire de médecine dentaire, Université de Genève, Genève, Switzerland
| | - A. Lodi-Rizzini
- Clinique universitaire de médecine dentaire, Université de Genève, Genève, Switzerland
| | - F. Schürmann
- Blue Brain Project, École polytechnique fédérale de Lausanne, Genève, Switzerland
| | - I. Krejci
- Clinique universitaire de médecine dentaire, Université de Genève, Genève, Switzerland
| | - H. Markram
- Blue Brain Project, École polytechnique fédérale de Lausanne, Genève, Switzerland
| |
Collapse
|
29
|
Abstract
In connectomics, the study of the network structure of connected neurons, great advances are being made on two different scales: that of macro- and meso-scale connectomics, studying the connectivity between populations of neurons, and that of micro-scale connectomics, studying connectivity between individual neurons. We combine these two complementary views of connectomics to build a first draft statistical model of the micro-connectome of a whole mouse neocortex based on available data on region-to-region connectivity and individual whole-brain axon reconstructions. This process reveals a targeting principle that allows us to predict the innervation logic of individual axons from meso-scale data. The resulting connectome recreates biological trends of targeting on all scales and predicts that an established principle of scale invariant topological organization of connectivity can be extended down to the level of individual neurons. It can serve as a powerful null model and as a substrate for whole-brain simulations.
Collapse
Affiliation(s)
- Michael W Reimann
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland.
| | - Michael Gevaert
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Ying Shi
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Huanxiang Lu
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Henry Markram
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| | - Eilif Muller
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland
| |
Collapse
|
30
|
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.
Collapse
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.
| |
Collapse
|
31
|
Ranjan R, Logette E, Marani M, Herzog M, Tâche V, Scantamburlo E, Buchillier V, Markram H. A Kinetic Map of the Homomeric Voltage-Gated Potassium Channel (Kv) Family. Front Cell Neurosci 2019; 13:358. [PMID: 31481875 PMCID: PMC6710402 DOI: 10.3389/fncel.2019.00358] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Accepted: 07/19/2019] [Indexed: 11/13/2022] Open
Abstract
The voltage-gated potassium (Kv) channels, encoded by 40 genes, repolarize all electrically excitable cells, including plant, cardiac, and neuronal cells. Although these genes were fully sequenced decades ago, a comprehensive kinetic characterization of all Kv channels is still missing, especially near physiological temperature. Here, we present a standardized kinetic map of the 40 homomeric Kv channels systematically characterized at 15, 25, and 35°C. Importantly, the Kv kinetics at 35°C differ significantly from commonly reported kinetics, usually performed at room temperature. We observed voltage-dependent Q10 for all active Kv channels and inherent heterogeneity in kinetics for some of them. Kinetic properties are consistent across different host cell lines and conserved across mouse, rat, and human. All electrophysiology data from all Kv channels are made available through a public website (Channelpedia). This dataset provides a solid foundation for exploring kinetics of heteromeric channels, roles of auxiliary subunits, kinetic modulation, and for building accurate Kv models.
Collapse
Affiliation(s)
- Rajnish Ranjan
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Emmanuelle Logette
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Laboratory of Neural Microcircuitry, Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Michela Marani
- Laboratory of Neural Microcircuitry, Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Mirjia Herzog
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Laboratory of Neural Microcircuitry, Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Valérie Tâche
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Enrico Scantamburlo
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Valérie Buchillier
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Laboratory of Neural Microcircuitry, Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| |
Collapse
|
32
|
Keller D, Meystre J, Veettil RV, Burri O, Guiet R, Schürmann F, Markram H. A Derived Positional Mapping of Inhibitory Subtypes in the Somatosensory Cortex. Front Neuroanat 2019; 13:78. [PMID: 31447655 PMCID: PMC6691028 DOI: 10.3389/fnana.2019.00078] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Accepted: 07/18/2019] [Indexed: 12/31/2022] Open
Abstract
Obtaining a catalog of cell types is a fundamental building block for understanding the brain. The ideal classification of cell-types is based on the profile of molecules expressed by a cell, in particular, the profile of genes expressed. One strategy is, therefore, to obtain as many single-cell transcriptomes as possible and isolate clusters of neurons with similar gene expression profiles. In this study, we explored an alternative strategy. We explored whether cell-types can be algorithmically derived by combining protein tissue stains with transcript expression profiles. We developed an algorithm that aims to distribute cell-types in the different layers of somatosensory cortex of the developing rat constrained by the tissue- and cellular level data. We found that the spatial distribution of major inhibitory cell types can be approximated using the available data. The result is a depth-wise atlas of inhibitory cell-types of the rat somatosensory cortex. In principle, any data that constrains what can occur in a particular part of the brain can also strongly constrain the derivation of cell-types. This draft inhibitory cell-type mapping is therefore dynamic and can iteratively converge towards the ground truth as further data is integrated.
Collapse
Affiliation(s)
- Daniel Keller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Julie Meystre
- Laboratory of Neural Microcircuitry, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Rahul V Veettil
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Olivier Burri
- Bioimaging and Optics Platform, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Romain Guiet
- Bioimaging and Optics Platform, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Felix Schürmann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Laboratory of Neural Microcircuitry, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| |
Collapse
|
33
|
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] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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
| |
Collapse
|
34
|
Abstract
Our knowledge of the brain has evolved over millennia in philosophical, experimental and theoretical phases. We suggest that the next phase is simulation neuroscience. The main drivers of simulation neuroscience are big data generated at multiple levels of brain organization and the need to integrate these data to trace the causal chain of interactions within and across all these levels. Simulation neuroscience is currently the only methodology for systematically approaching the multiscale brain. In this review, we attempt to reconstruct the deep historical paths leading to simulation neuroscience, from the first observations of the nerve cell to modern efforts to digitally reconstruct and simulate the brain. Neuroscience began with the identification of the neuron as the fundamental unit of brain structure and function and has evolved towards understanding the role of each cell type in the brain, how brain cells are connected to each other, and how the seemingly infinite networks they form give rise to the vast diversity of brain functions. Neuronal mapping is evolving from subjective descriptions of cell types towards objective classes, subclasses and types. Connectivity mapping is evolving from loose topographic maps between brain regions towards dense anatomical and physiological maps of connections between individual genetically distinct neurons. Functional mapping is evolving from psychological and behavioral stereotypes towards a map of behaviors emerging from structural and functional connectomes. We show how industrialization of neuroscience and the resulting large disconnected datasets are generating demand for integrative neuroscience, how the scale of neuronal and connectivity maps is driving digital atlasing and digital reconstruction to piece together the multiple levels of brain organization, and how the complexity of the interactions between molecules, neurons, microcircuits and brain regions is driving brain simulation to understand the interactions in the multiscale brain.
Collapse
Affiliation(s)
- Xue Fan
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| |
Collapse
|
35
|
Colangelo C, Shichkova P, Keller D, Markram H, Ramaswamy S. Cellular, Synaptic and Network Effects of Acetylcholine in the Neocortex. Front Neural Circuits 2019; 13:24. [PMID: 31031601 PMCID: PMC6473068 DOI: 10.3389/fncir.2019.00024] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Accepted: 03/22/2019] [Indexed: 12/17/2022] Open
Abstract
The neocortex is densely innervated by basal forebrain (BF) cholinergic neurons. Long-range axons of cholinergic neurons regulate higher-order cognitive function and dysfunction in the neocortex by releasing acetylcholine (ACh). ACh release dynamically reconfigures neocortical microcircuitry through differential spatiotemporal actions on cell-types and their synaptic connections. At the cellular level, ACh release controls neuronal excitability and firing rate, by hyperpolarizing or depolarizing target neurons. At the synaptic level, ACh impacts transmission dynamics not only by altering the presynaptic probability of release, but also the magnitude of the postsynaptic response. Despite the crucial role of ACh release in physiology and pathophysiology, a comprehensive understanding of the way it regulates the activity of diverse neocortical cell-types and synaptic connections has remained elusive. This review aims to summarize the state-of-the-art anatomical and physiological data to develop a functional map of the cellular, synaptic and microcircuit effects of ACh in the neocortex of rodents and non-human primates, and to serve as a quantitative reference for those intending to build data-driven computational models on the role of ACh in governing brain states.
Collapse
Affiliation(s)
- Cristina Colangelo
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | | | | | | | - Srikanth Ramaswamy
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| |
Collapse
|
36
|
Abstract
In this commentary, we join Ward (this issue) in the usefulness of conceptualizing neural output in terms of signal and noise relationships, to create the missing links between neural, behavioral and subjective sensory sensitivity. We draw from our work in the Intense World Theory of Autism and the valproic acid rodent model, to complement the discussion with the consideration of developmental time and function of the system, for a neural output to serve as a predictor of atypical outcome in sensory sensitivity, and guide personalized therapies.
Collapse
Affiliation(s)
- Mônica Regina Favre
- a Blue Brain Project, Brain and Mind Institute , EPFL, Campus Biotech , Geneva , Switzerland.,b Laboratory of Neural Microcircuitry, Brain Mind Institute, Department of Life Sciences , EPFL , Lausanne , Switzerland
| | - Henry Markram
- a Blue Brain Project, Brain and Mind Institute , EPFL, Campus Biotech , Geneva , Switzerland.,b Laboratory of Neural Microcircuitry, Brain Mind Institute, Department of Life Sciences , EPFL , Lausanne , Switzerland
| | - Kamila Markram
- b Laboratory of Neural Microcircuitry, Brain Mind Institute, Department of Life Sciences , EPFL , Lausanne , Switzerland
| |
Collapse
|
37
|
Erö C, Gewaltig MO, Keller D, Markram H. Corrigendum: A Cell Atlas for the Mouse Brain. Front Neuroinform 2019; 13:7. [PMID: 30837861 PMCID: PMC6390506 DOI: 10.3389/fninf.2019.00007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 02/01/2019] [Indexed: 11/17/2022] Open
Affiliation(s)
- Csaba Erö
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Marc-Oliver Gewaltig
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Daniel Keller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| |
Collapse
|
38
|
Abstract
Many biological systems consist of branching structures that exhibit a wide variety of shapes. Our understanding of their systematic roles is hampered from the start by the lack of a fundamental means of standardizing the description of complex branching patterns, such as those of neuronal trees. To solve this problem, we have invented the Topological Morphology Descriptor (TMD), a method for encoding the spatial structure of any tree as a "barcode", a unique topological signature. As opposed to traditional morphometrics, the TMD couples the topology of the branches with their spatial extents by tracking their topological evolution in 3-dimensional space. We prove that neuronal trees, as well as stochastically generated trees, can be accurately categorized based on their TMD profiles. The TMD retains sufficient global and local information to create an unbiased benchmark test for their categorization and is able to quantify and characterize the structural differences between distinct morphological groups. The use of this mathematically rigorous method will advance our understanding of the anatomy and diversity of branching morphologies.
Collapse
Affiliation(s)
- Lida Kanari
- Blue Brain Project-EPFL, Geneva, Switzerland.
| | - Paweł Dłotko
- Department of Mathematics, Swansea University, Swansea, UK
| | - Martina Scolamiero
- Laboratory for Topology and Neuroscience at the Brain Mind Institute, EPFL, Geneva, Switzerland
| | - Ran Levi
- Institute of Mathematics, University of Aberdeen, Aberdeen, UK
| | | | - Kathryn Hess
- Laboratory for Topology and Neuroscience at the Brain Mind Institute, EPFL, Geneva, Switzerland
| | | |
Collapse
|
39
|
Abstract
Despite vast numbers of studies of stained cells in the mouse brain, no current brain atlas provides region-by-region neuron counts. In fact, neuron numbers are only available for about 4% of brain of regions and estimates often vary by as much as 3-fold. Here we provide a first 3D cell atlas for the whole mouse brain, showing cell positions constructed algorithmically from whole brain Nissl and gene expression stains, and compared against values from the literature. The atlas provides the densities and positions of all excitatory and inhibitory neurons, astrocytes, oligodendrocytes, and microglia in each of the 737 brain regions defined in the AMBA. The atlas is dynamic, allowing comparison with previously reported numbers, addition of cell types, and improvement of estimates as new data is integrated. The atlas also provides insights into cellular organization only possible at this whole brain scale, and is publicly available.
Collapse
Affiliation(s)
- Csaba Erö
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Marc-Oliver Gewaltig
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Daniel Keller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| |
Collapse
|
40
|
Abstract
The mouse brain is the most extensively studied brain of all species. We performed an exhaustive review of the literature to establish our current state of knowledge on cell numbers in mouse brain regions, arguably the most fundamental property to measure when attempting to understand a brain. The synthesized information, collected in one place, can be used by both theorists and experimentalists. Although for commonly-studied regions cell densities could be obtained for principal cell types, overall we know very little about how many cells are present in most brain regions and even less about cell-type specific densities. There is also substantial variation in cell density values obtained from different sources. This suggests that we need a new approach to obtain cell density datasets for the mouse brain.
Collapse
Affiliation(s)
- Daniel Keller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | | | | |
Collapse
|
41
|
Ramaswamy S, Colangelo C, Markram H. Data-Driven Modeling of Cholinergic Modulation of Neural Microcircuits: Bridging Neurons, Synapses and Network Activity. Front Neural Circuits 2018; 12:77. [PMID: 30356701 PMCID: PMC6189313 DOI: 10.3389/fncir.2018.00077] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 09/10/2018] [Indexed: 01/26/2023] Open
Abstract
Neuromodulators, such as acetylcholine (ACh), control information processing in neural microcircuits by regulating neuronal and synaptic physiology. Computational models and simulations enable predictions on the potential role of ACh in reconfiguring network activity. As a prelude into investigating how the cellular and synaptic effects of ACh collectively influence emergent network dynamics, we developed a data-driven framework incorporating phenomenological models of the physiology of cholinergic modulation of neocortical cells and synapses. The first-draft models were integrated into a biologically detailed tissue model of neocortical microcircuitry to investigate the effects of levels of ACh on diverse neuron types and synapses, and consequently on emergent network activity. Preliminary simulations from the framework, which was not tuned to reproduce any specific ACh-induced network effects, not only corroborate the long-standing notion that ACh desynchronizes spontaneous network activity, but also predict that a dose-dependent activation of ACh gives rise to a spectrum of neocortical network activity. We show that low levels of ACh, such as during non-rapid eye movement (nREM) sleep, drive microcircuit activity into slow oscillations and network synchrony, whereas high ACh concentrations, such as during wakefulness and REM sleep, govern fast oscillations and network asynchrony. In addition, spontaneous network activity modulated by ACh levels shape spike-time cross-correlations across distinct neuronal populations in strikingly different ways. These effects are likely due to the regulation of neurons and synapses caused by increasing levels of ACh, which enhances cellular excitability and decreases the efficacy of local synaptic transmission. We conclude by discussing future directions to refine the biological accuracy of the framework, which will extend its utility and foster the development of hypotheses to investigate the role of neuromodulators in neural information processing.
Collapse
Affiliation(s)
- Srikanth Ramaswamy
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL) Biotech Campus, Geneva, Switzerland
| | - Cristina Colangelo
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL) Biotech Campus, Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL) Biotech Campus, Geneva, Switzerland
| |
Collapse
|
42
|
Coggan JS, Calì C, Keller D, Agus M, Boges D, Abdellah M, Kare K, Lehväslaiho H, Eilemann S, Jolivet RB, Hadwiger M, Markram H, Schürmann F, Magistretti PJ. A Process for Digitizing and Simulating Biologically Realistic Oligocellular Networks Demonstrated for the Neuro-Glio-Vascular Ensemble. Front Neurosci 2018; 12:664. [PMID: 30319342 PMCID: PMC6171468 DOI: 10.3389/fnins.2018.00664] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 09/04/2018] [Indexed: 01/01/2023] Open
Abstract
One will not understand the brain without an integrated exploration of structure and function, these attributes being two sides of the same coin: together they form the currency of biological computation. Accordingly, biologically realistic models require the re-creation of the architecture of the cellular components in which biochemical reactions are contained. We describe here a process of reconstructing a functional oligocellular assembly that is responsible for energy supply management in the brain and creating a computational model of the associated biochemical and biophysical processes. The reactions that underwrite thought are both constrained by and take advantage of brain morphologies pertaining to neurons, astrocytes and the blood vessels that deliver oxygen, glucose and other nutrients. Each component of this neuro-glio-vasculature ensemble (NGV) carries-out delegated tasks, as the dynamics of this system provide for each cell-type its own energy requirements while including mechanisms that allow cooperative energy transfers. Our process for recreating the ultrastructure of cellular components and modeling the reactions that describe energy flow uses an amalgam of state-of the-art techniques, including digital reconstructions of electron micrographs, advanced data analysis tools, computational simulations and in silico visualization software. While we demonstrate this process with the NGV, it is equally well adapted to any cellular system for integrating multimodal cellular data in a coherent framework.
Collapse
Affiliation(s)
- Jay S Coggan
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Corrado Calì
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Daniel Keller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Marco Agus
- Visual Computing Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.,CRS4, Center of Research and Advanced Studies in Sardinia, Visual Computing, Pula, Italy
| | - Daniya Boges
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Marwan Abdellah
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Kalpana Kare
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Heikki Lehväslaiho
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.,CSC - IT Center for Science, Espoo, Finland
| | - Stefan Eilemann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Renaud Blaise Jolivet
- Département de Physique Nucléaire et Corpusculaire, University of Geneva, Geneva, Switzerland.,The European Organization for Nuclear Research, Geneva, Switzerland
| | - Markus Hadwiger
- Visual Computing Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Felix Schürmann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Pierre J Magistretti
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| |
Collapse
|
43
|
Migliore R, Lupascu CA, Bologna LL, Romani A, Courcol JD, Antonel S, Van Geit WAH, Thomson AM, Mercer A, Lange S, Falck J, Rössert CA, Shi Y, Hagens O, Pezzoli M, Freund TF, Kali S, Muller EB, Schürmann F, Markram H, Migliore M. The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow. PLoS Comput Biol 2018; 14:e1006423. [PMID: 30222740 PMCID: PMC6160220 DOI: 10.1371/journal.pcbi.1006423] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Revised: 09/27/2018] [Accepted: 08/08/2018] [Indexed: 11/19/2022] Open
Abstract
Every neuron is part of a network, exerting its function by transforming multiple spatiotemporal synaptic input patterns into a single spiking output. This function is specified by the particular shape and passive electrical properties of the neuronal membrane, and the composition and spatial distribution of ion channels across its processes. For a variety of physiological or pathological reasons, the intrinsic input/output function may change during a neuron's lifetime. This process results in high variability in the peak specific conductance of ion channels in individual neurons. The mechanisms responsible for this variability are not well understood, although there are clear indications from experiments and modeling that degeneracy and correlation among multiple channels may be involved. Here, we studied this issue in biophysical models of hippocampal CA1 pyramidal neurons and interneurons. Using a unified data-driven simulation workflow and starting from a set of experimental recordings and morphological reconstructions obtained from rats, we built and analyzed several ensembles of morphologically and biophysically accurate single cell models with intrinsic electrophysiological properties consistent with experimental findings. The results suggest that the set of conductances expressed in any given hippocampal neuron may be considered as belonging to two groups: one subset is responsible for the major characteristics of the firing behavior in each population and the other is responsible for a robust degeneracy. Analysis of the model neurons suggests several experimentally testable predictions related to the combination and relative proportion of the different conductances that should be expressed on the membrane of different types of neurons for them to fulfill their role in the hippocampus circuitry.
Collapse
Affiliation(s)
- Rosanna Migliore
- Institute of Biophysics, National Research Council, Palermo, Italy
| | | | - Luca L. Bologna
- Institute of Biophysics, National Research Council, Palermo, Italy
| | - Armando Romani
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Jean-Denis Courcol
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Stefano Antonel
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Werner A. H. Van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | | | | | - Sigrun Lange
- University College London, London, United Kingdom
- University of Westminster, London, United Kingdom
| | - Joanne Falck
- University College London, London, United Kingdom
| | - Christian A. Rössert
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Ying Shi
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Olivier Hagens
- Laboratory of Neural Microcircuitry (LNMC), Brain Mind Institute, EPFL, Lausanne, Switzerland
| | - Maurizio Pezzoli
- Laboratory of Neural Microcircuitry (LNMC), Brain Mind Institute, EPFL, Lausanne, Switzerland
| | - Tamas F. Freund
- Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Szabolcs Kali
- Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Eilif B. Muller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Felix Schürmann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Michele Migliore
- Institute of Biophysics, National Research Council, Palermo, Italy
| |
Collapse
|
44
|
Coggan JS, Keller D, Calì C, Lehväslaiho H, Markram H, Schürmann F, Magistretti PJ. Norepinephrine stimulates glycogenolysis in astrocytes to fuel neurons with lactate. PLoS Comput Biol 2018; 14:e1006392. [PMID: 30161133 PMCID: PMC6160207 DOI: 10.1371/journal.pcbi.1006392] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Revised: 09/27/2018] [Accepted: 07/24/2018] [Indexed: 12/20/2022] Open
Abstract
The mechanism of rapid energy supply to the brain, especially to accommodate the heightened metabolic activity of excited states, is not well-understood. We explored the role of glycogen as a fuel source for neuromodulation using the noradrenergic stimulation of glia in a computational model of the neural-glial-vasculature ensemble (NGV). The detection of norepinephrine (NE) by the astrocyte and the coupled cAMP signal are rapid and largely insensitive to the distance of the locus coeruleus projection release sites from the glia, implying a diminished impact for volume transmission in high affinity receptor transduction systems. Glucosyl-conjugated units liberated from glial glycogen by NE-elicited cAMP second messenger transduction winds sequentially through the glycolytic cascade, generating robust increases in NADH and ATP before pyruvate is finally transformed into lactate. This astrocytic lactate is rapidly exported by monocarboxylate transporters to the associated neuron, demonstrating that the astrocyte-to-neuron lactate shuttle activated by glycogenolysis is a likely fuel source for neuromodulation and enhanced neural activity. Altogether, the energy supply for both astrocytes and neurons can be supplied rapidly by glycogenolysis upon neuromodulatory stimulus. Although efficient compared to computers, the human brain utilizes energy at 10-fold the rate of other organs by mass. How the brain is supplied with sufficient on-demand energy to support its activity in the absence of neuronal storage capacity remains unknown. Neurons are not capable of meeting their own energy requirements, instead energy supply in the brain is managed by an oligocellular cartel composed of neurons, glia and the local vasculature (NGV), wherein glia can provide the ergogenic metabolite lactate to the neuron in a process called the astrocyte-to-neuron shuttle (ANLS). The only means of energy storage in the brain is glycogen, a polymerized form of glucose that is localized largely to astrocytes, but its exact role and conditions of use are not clear. In this computational model we show that neuromodulatory stimulation by norepinephrine induces astrocytes to recover glucosyl subunits from glycogen for use in a glycolytic process that favors the production of lactate. The ATP and NADH produced support metabolism in the astrocyte while the lactate is exported to feed the neuron. Thus, rapid energy demands by both neurons and glia in a stimulated brain can be met by glycogen mobilization.
Collapse
Affiliation(s)
- Jay S. Coggan
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- * E-mail: (JSC); (PJM)
| | - Daniel Keller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Corrado Calì
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
| | - Heikki Lehväslaiho
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Felix Schürmann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Pierre J. Magistretti
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
- * E-mail: (JSC); (PJM)
| |
Collapse
|
45
|
Doron M, Chindemi G, Muller E, Markram H, Segev I. Timed Synaptic Inhibition Shapes NMDA Spikes, Influencing Local Dendritic Processing and Global I/O Properties of Cortical Neurons. Cell Rep 2018; 21:1550-1561. [PMID: 29117560 DOI: 10.1016/j.celrep.2017.10.035] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Revised: 08/17/2017] [Accepted: 10/08/2017] [Indexed: 10/18/2022] Open
Abstract
The NMDA spike is a long-lasting nonlinear phenomenon initiated locally in the dendritic branches of a variety of cortical neurons. It plays a key role in synaptic plasticity and in single-neuron computations. Combining dynamic system theory and computational approaches, we now explore how the timing of synaptic inhibition affects the NMDA spike and its associated membrane current. When impinging on its early phase, individual inhibitory synapses strongly, but transiently, dampen the NMDA spike; later inhibition prematurely terminates it. A single inhibitory synapse reduces the NMDA-mediated Ca2+ current, a key player in plasticity, by up to 45%. NMDA spikes in distal dendritic branches/spines are longer-lasting and more resilient to inhibition, enhancing synaptic plasticity at these branches. We conclude that NMDA spikes are highly sensitive to dendritic inhibition; sparse weak inhibition can finely tune synaptic plasticity both locally at the dendritic branch level and globally at the level of the neuron's output.
Collapse
Affiliation(s)
- Michael Doron
- Edmond and Lily Safra Center for Brain Sciences, the Hebrew University of Jerusalem, Jerusalem 91904, Israel.
| | - Giuseppe Chindemi
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Biotech Campus, 1202 Geneva, Switzerland
| | - Eilif Muller
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Biotech Campus, 1202 Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Biotech Campus, 1202 Geneva, Switzerland
| | - Idan Segev
- Edmond and Lily Safra Center for Brain Sciences, the Hebrew University of Jerusalem, Jerusalem 91904, Israel; Department of Neurobiology, the Hebrew University of Jerusalem, Jerusalem 91904, Israel
| |
Collapse
|
46
|
Abstract
Synaptic connectivity between neurons is naturally constrained by the anatomical overlap of neuronal arbors, the space on the axon available for synapses, and by physiological mechanisms that form synapses at a subset of potential synapse locations. What is not known is how these constraints impact emergent connectivity in a circuit with diverse morphologies. We investigated the role of morphological diversity within and across neuronal types on emergent connectivity in a model of neocortical microcircuitry. We found that the average overlap between the dendritic and axonal arbors of different types of neurons determines neuron-type specific patterns of distance-dependent connectivity, severely constraining the space of possible connectomes. However, higher order connectivity motifs depend on the diverse branching patterns of individual arbors of neurons belonging to the same type. Morphological diversity across neuronal types, therefore, imposes a specific structure on first order connectivity, and morphological diversity within neuronal types imposes a higher order structure of connectivity. We estimate that the morphological constraints resulting from diversity within and across neuron types together lead to a 10-fold reduction of the entropy of possible connectivity configurations, revealing an upper bound on the space explored by structural plasticity.
Collapse
Affiliation(s)
- Michael W Reimann
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Anna-Lena Horlemann
- Faculty of Mathematics and Statistics, University of St. Gallen, Bodanstrasse 6, CH-9000 St. Gallen, Switzerland
| | - Srikanth Ramaswamy
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Eilif B Muller
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Henry Markram
- Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| |
Collapse
|
47
|
Rubchinsky LL, Ahn S, Klijn W, Cumming B, Yates S, Karakasis V, Peyser A, Woodman M, Diaz-Pier S, Deraeve J, Vassena E, Alexander W, Beeman D, Kudela P, Boatman-Reich D, Anderson WS, Luque NR, Naveros F, Carrillo RR, Ros E, Arleo A, Huth J, Ichinose K, Park J, Kawai Y, Suzuki J, Mori H, Asada M, Oprisan SA, Dave AI, Babaie T, Robinson P, Tabas A, Andermann M, Rupp A, Balaguer-Ballester E, Lindén H, Christensen RK, Nakamura M, Barkat TR, Tosi Z, Beggs J, Lonardoni D, Boi F, Di Marco S, Maccione A, Berdondini L, Jędrzejewska-Szmek J, Dorman DB, Blackwell KT, Bauermeister C, Keren H, Braun J, Dornas JV, Mavritsaki E, Aldrovandi S, Bridger E, Lim S, Brunel N, Buchin A, Kerr CC, Chizhov A, Huberfeld G, Miles R, Gutkin B, Spencer MJ, Meffin H, Grayden DB, Burkitt AN, Davey CE, Tao L, Tiruvadi V, Ali R, Mayberg H, Butera R, Gunay C, Lamb D, Calabrese RL, Doloc-Mihu A, López-Madrona VJ, Matias FS, Pereda E, Mirasso CR, Canals S, Geminiani A, Pedrocchi A, D’Angelo E, Casellato C, Chauhan A, Soman K, Srinivasa Chakravarthy V, Muddapu VR, Chuang CC, Chen NY, Bayati M, Melchior J, Wiskott L, Azizi AH, Diba K, Cheng S, Smirnova EY, Yakimova EG, Chizhov AV, Chen NY, Shih CT, Florescu D, Coca D, Courtiol J, Jirsa VK, Covolan RJM, Teleńczuk B, Kempter R, Curio G, Destexhe A, Parker J, Klishko AN, Prilutsky BI, Cymbalyuk G, Franke F, Hierlemann A, da Silveira RA, Casali S, Masoli S, Rizza M, Rizza MF, Masoli S, Sun Y, Wong W, Farzan F, Blumberger DM, Daskalakis ZJ, Popovych S, Viswanathan S, Rosjat N, Grefkes C, Daun S, Gentiletti D, Suffczynski P, Gnatkovski V, De Curtis M, Lee H, Paik SB, Choi W, Jang J, Park Y, Song JH, Song M, Pallarés V, Gilson M, Kühn S, Insabato A, Deco G, Glomb K, Ponce-Alvarez A, Ritter P, Gilson M, Campo AT, Thiele A, Deeba F, Robinson PA, van Albada SJ, Rowley A, Hopkins M, Schmidt M, Stokes AB, Lester DR, Furber S, Diesmann M, Barri A, Wiechert MT, DiGregorio DA, Dimitrov AG, Vich C, Berg RW, Guillamon A, Ditlevsen S, Cazé RD, Girard B, Doncieux S, Doyon N, Boahen F, Desrosiers P, Laurence E, Doyon N, Dubé LJ, Eleonora R, Durstewitz D, Schmidt D, Mäki-Marttunen T, Krull F, Bettella F, Metzner C, Devor A, Djurovic S, Dale AM, Andreassen OA, Einevoll GT, Næss S, Ness TV, Halnes G, Halgren E, Halnes G, Mäki-Marttunen T, Pettersen KH, Andreassen OA, Sætra MJ, Hagen E, Schiffer A, Grzymisch A, Persike M, Ernst U, Harnack D, Ernst UA, Tomen N, Zucca S, Pasquale V, Pica G, Molano-Mazón M, Chiappalone M, Panzeri S, Fellin T, Oie KS, Boothe DL, Crone JC, Yu AB, Felton MA, Zulfiqar I, Moerel M, De Weerd P, Formisano E, Boothe DL, Crone JC, Felton MA, Oie K, Franaszczuk P, Diggelmann R, Fiscella M, Hierlemann A, Franke F, Guarino D, Antolík J, Davison AP, Frègnac Y, Etienne BX, Frohlich F, Lefebvre J, Marcos E, Mattia M, Genovesio A, Fedorov LA, Dijkstra TM, Sting L, Hock H, Giese MA, Buhry L, Langlet C, Giovannini F, Verbist C, Salvadé S, Giugliano M, Henderson JA, Wernecke H, Sándor B, Gros C, Voges N, Dabrovska P, Riehle A, Brochier T, Grün S, Gu Y, Gong P, Dumont G, Novikov NA, Gutkin BS, Tewatia P, Eriksson O, Kramer A, Santos J, Jauhiainen A, Kotaleski JH, Belić JJ, Kumar A, Kotaleski JH, Shimono M, Hatano N, Ahmad S, Cui Y, Hawkins J, Senk J, Korvasová K, Tetzlaff T, Helias M, Kühn T, Denker M, Mana P, Grün S, Dahmen D, Schuecker J, Goedeke S, Keup C, Goedeke S, Heuer K, Bakker R, Tiesinga P, Toro R, Qin W, Hadjinicolaou A, Grayden DB, Ibbotson MR, Kameneva T, Lytton WW, Mulugeta L, Drach A, Myers JG, Horner M, Vadigepalli R, Morrison T, Walton M, Steele M, Anthony Hunt C, Tam N, Amaducci R, Muñiz C, Reyes-Sánchez M, Rodríguez FB, Varona P, Cronin JT, Hennig MH, Iavarone E, Yi J, Shi Y, Zandt BJ, Van Geit W, Rössert C, Markram H, Hill S, O’Reilly C, Iavarone E, Shi Y, Perin R, Lu H, Zandt BJ, Bryson A, Rössert C, Hadrava M, Hlinka J, Hosaka R, Olenik M, Houghton C, Iannella N, Launey T, Kameneva T, Kotsakidis R, Meffin H, Soriano J, Kubo T, Inoue T, Kida H, Yamakawa T, Suzuki M, Ikeda K, Abbasi S, Hudson AE, Heck DH, Jaeger D, Lee J, Abbasi S, Janušonis S, Saggio ML, Spiegler A, Stacey WC, Bernard C, Lillo D, Bernard C, Petkoski S, Spiegler A, Drakesmith M, Jones DK, Zadeh AS, Kambhampati C, Karbowski J, Kaya ZG, Lakretz Y, Treves A, Li LW, Lizier J, Kerr CC, Masquelier T, Kheradpisheh SR, Kim H, Kim CS, Marakshina JA, Vartanov AV, Neklyudova AA, Kozlovskiy SA, Kiselnikov AA, Taniguchi K, Kitano K, Schmitt O, Lessmann F, Schwanke S, Eipert P, Meinhardt J, Beier J, Kadir K, Karnitzki A, Sellner L, Klünker AC, Kuch L, Ruß F, Jenssen J, Wree A, Sanz-Leon P, Knock SA, Chien SC, Maess B, Knösche TR, Cohen CC, Popovic MA, Klooster J, Kole MH, Roberts EA, Kopell NJ, Kepple D, Giaffar H, Rinberg D, Koulakov A, Forlim CG, Klock L, Bächle J, Stoll L, Giemsa P, Fuchs M, Schoofs N, Montag C, Gallinat J, Lee RX, Stephens GJ, Kuhn B, Tauffer L, Isope P, Inoue K, Ohmura Y, Yonekura S, Kuniyoshi Y, Jang HJ, Kwag J, de Kamps M, Lai YM, dos Santos F, Lam KP, Andras P, Imperatore J, Helms J, Tompa T, Lavin A, Inkpen FH, Ashby MC, Lepora NF, Shifman AR, Lewis JE, Zhang Z, Feng Y, Tetzlaff C, Kulvicius T, Li Y, Pena RFO, Bernardi D, Roque AC, Lindner B, Bernardi D, Vellmer S, Saudargiene A, Maninen T, Havela R, Linne ML, Powanwe A, Longtin A, Naveros F, Garrido JA, Graham JW, Dura-Bernal S, Angulo SL, Neymotin SA, Antic SD. 26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2. BMC Neurosci 2017. [PMCID: PMC5592442 DOI: 10.1186/s12868-017-0371-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
|
48
|
Newton AJH, Seidenstein AH, McDougal RA, Pérez-Cervera A, Huguet G, M-Seara T, Haimerl C, Angulo-Garcia D, Torcini A, Cossart R, Malvache A, Skiker K, Maouene M, Ragognetti G, Lorusso L, Viggiano A, Marcelli A, Senatore R, Parziale A, Stramaglia S, Pellicoro M, Angelini L, Amico E, Aerts H, Cortés J, Laureys S, Marinazzo D, Stramaglia S, Bassez I, Faes L, Almgren H, Razi A, Van de Steen F, Krebs R, Aerts H, Kanari L, Dlotko P, Scolamiero M, Levi R, Shillcock J, de Kock CP, Hess K, Markram H, Ly C, Marsat G, Gillespie T, Sandström M, Abrams M, Grethe JS, Martone M, De Gernier R, Solinas S, Rössert C, Haelterman M, Massar S, Pasquale V, Pastore VP, Martinoia S, Massobrio P, Capone C, Tort-Colet N, Sanchez-Vives MV, Mattia M, Almasi A, Cloherty SL, Grayden DB, Wong YT, Ibbotson MR, Meffin H, Prince LY, Tsaneva-Atanasova K, Mellor JR, Mazzoni A, Rosa M, Carpaneto J, Romito LM, Priori A, Micera S, Migliore R, Lupascu CA, Franchina F, Bologna LL, Romani A, Saray S, Van Geit W, Káli S, Thomson A, Mercer A, Lange S, Falck J, Muller E, Schürmann F, Todorov D, Capps R, Barnett W, Molkov Y, Devalle F, Pazó D, Montbrió E, Mochol G, Azab H, Hayden BY, Moreno-Bote R, Balasubramani PP, Chakravarthy SV, Muddapu VR, Gheorghiu MD, Mimica B, Withlock J, Mureșan RC, Zick JL, Schultz K, Blackman RK, Chafee MV, Netoff TI, Roberts N, Nagaraj V, Lamperski A, Netoff TI, Grado LL, Johnson MD, Darrow DP, Lonardoni D, Amin H, Di Marco S, Maccione A, Berdondini L, Nieus T, Stimberg M, Goodman DFM, Nowotny T, Koren V, Dragoi V, Obermayer K, Castro S, Fernandez M, El-Deredy W, Xu K, Maidana JP, Orio P, Chen W, Hepburn I, Casalegno F, Devresse A, Ovcharenko A, Pereira F, Delalondre F, De Schutter E, Bratby P, Gallimore AR, Klingbeil G, Zamora C, Zang Y, Crotty P, Palmerduca E, Antonietti A, Casellato C, Erö C, D’Angelo E, Gewaltig MO, Pedrocchi A, Bytschok I, Dold D, Schemmel J, Meier K, Petrovici MA, Shen HA, Surace SC, Pfister JP, Lefebvre B, Marre O, Yger P, Papoutsi A, Park J, Ash R, Smirnakis S, Poirazi P, Felix RA, Dimitrov AG, Portfors C, Daun S, Toth TI, Jędrzejewska-Szmek J, Kabbani N, Blackwel KT, Moezzi B, Schaworonkow N, Plogmacher L, Goldsworthy MR, Hordacre B, McDonnell MD, Iannella N, Ridding MC, Triesch J, Maex R, Safaryan K, Steuber V, Tang R, Tang YY, Verveyko DV, Brazhe AR, Verisokin AY, Postnov DE, Günay C, Panuccio G, Giugliano M, Prinz AA, Varona P, Rabinovich MI, Denham J, Ranner T, Cohen N, Reva M, Rebola N, Kirizs T, Nusser Z, DiGregorio D, Mavritsaki E, Rentzelas P, Ukani NH, Tomkins A, Yeh CH, Bruning W, Fenichel AL, Zhou Y, Huang YC, Florescu D, Ortiz CL, Richmond P, Lo CC, Coca D, Chiang AS, Lazar AA, Moezzi B, Creaser JL, Lin C, Ashwin P, Brown JT, Ridler T, Levenstein D, Watson BO, Buzsáki G, Rinzel J, Curtu R, Nguyen A, Assadzadeh S, Robinson PA, Sanz-Leon P, Forlim CG, de Almeida LOB, Pinto RD, Rodríguez FB, Lareo Á, Forlim CG, Rodríguez FB, Montero A, Mosqueiro T, Huerta R, Rodriguez FB, Changoluisa V, Rodriguez FB, Cordeiro VL, Ceballos CC, Kamiji NL, Roque AC, Lytton WW, Knox A, Rosenthal JJC, Daun S, Popovych S, Liu L, Wang BA, Tóth TI, Grefkes C, Fink GR, Rosjat N, Perez-Trujillo A, Espinal A, Sotelo-Figueroa MA, Cruz-Aceves I, Rostro-Gonzalez H, Zapotocky M, Hoskovcová M, Kopecká J, Ulmanová O, Růžička E, Gärtner M, Duvarci S, Roeper J, Schneider G, Albert S, Schmack K, Remme M, Schreiber S, Migliore M, Lupascu CA, Bologna LL, Antonel SM, Courcol JD, Schürmann F, Çelikok SU, Navarro-López EM, Şengör NS, Elibol R, Sengor NS, Özdemir MY, Li T, Arleo A, Sheynikhovich D, Nakamura A, Shimono M, Song Y, Park S, Choi I, Jeong J, Shin HS, Sadeh S, Gleeson P, Angus Silver R, Chatzikalymniou AP, Skinner FK, Sanchez-Rodriguez LM, Sotero RC, Hertäg L, Mackwood O, Sprekeler H, Puhlmann S, Weber SN, Higgins D, Naumann LB, Weber SN, Iyer R, Mihalas S, Ticcinelli V, Stankovski T, McClintock PVE, Stefanovska A, Janjić P, Solev D, Seifert G, Kocarev L, Steinhäuser C, Salmasi M, Glasauer S, Stemmler M, Zhang D, Zhang C, Stepanyants A, Goncharenko J, Kros L, Davey N, de Zeeuw C, Hoebeek F, Sinha A, Adams R, Schmuker M, Psarrou M, Schilstra M, Torben-Nielsen B, Metzner C, Schweikard A, Mäki-Marttunen T, Zurowski B, Marinazzo D, Faes L, Stramaglia S, Jordan HOC, Stringer SM, Gajewska-Dendek E, Suffczyński P, Tam N, Zouridakis G, Pollonini L, Tang YY, Asl MM, Valizadeh A, Tass PA, Nold A, Fan W, Konrad S, Endle H, Vogt J, Tchumatchenko T, Herpich J, Tetzlaff C, Luboeinski J, Nachstedt T, Ciba M, Bahmer A, Thielemann C, Kuebler ES, Tauskela JS, Thivierge JP, Bakker R, García-Amado M, Evangelio M, Clascá F, Tiesinga P, Buckley CL, Toyoizumi T, Dubreuil AM, Monasson R, Treves A, Spalla D, Rosay S, Kleberg FI, Wong W, de Oliveira Floriano B, Matsuo T, Uchida T, Dibenedetto D, Uludağ K, Goodarzinick A, Schmidt M, Hilgetag CC, Diesmann M, van Albada SJ, Fauth M, van Rossum M, Reyes-Sánchez M, Amaducci R, Muñiz C, Varona P, Elices I, Arroyo D, Levi R, Cohen B, Chow C, Vattikuti S, Bertolotti E, Burioni R, di Volo M, Vezzani A, Menzat B, Vogels TP, Wagatsuma N, Saha S, Kapoor R, Kerr R, Wagner J, del Molino LCG, Yang GR, Mejias JF, Wang XJ, Song H, Goodliffe J, Luebke J, Weaver CM, Thomas J, Sinha N, Shaju N, Maszczyk T, Jin J, Cash SS, Dauwels J, Brandon Westover M, Karimian M, Moerel M, De Weerd P, Burwick T, Westra RL, Abeysuriya R, Hadida J, Sotiropoulos S, Jbabdi S, Woolrich M, Bensmail C, Wrobel B, Zhou X, Ji Z, Liu X, Xia Y, Wu S, Wang X, Zhang M, Wu S, Ofer N, Shefi O, Yaari G, Carnevale T, Majumdar A, Sivagnanam S, Yoshimoto K, Smirnova EY, Amakhin DV, Malkin SL, Zaitsev AV, Chizhov AV, Zaleshina M, Zaleshin A, Barranca VJ, Zhu G, Skilling QM, Maruyama D, Ognjanovski N, Aton SJ, Zochowski M, Wu J, Aton S, Rich S, Booth V, Budak M, Dura-Bernal S, Neymotin SA, Suter BA, Shepherd GMG, Felton MA, Yu AB, Boothe DL, Oie KS, Franaszczuk PJ, Shuvaev SA, Başerdem B, Zador A, Koulakov AA, López-Madrona VJ, Pereda E, Mirasso CR, Canals S, Masoli S, Rongala UB, Mazzoni A, Spanne A, Jorntell H, Oddo CM, Vartanov AV, Neklyudova AK, Kozlovskiy SA, Kiselnikov AA, Marakshina JA, Teleńczuk M, Teleńczuk B, Destexhe A, Kuokkanen PT, Kraemer A, McColgan T, Carr CE, Kempter R. 26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3. BMC Neurosci 2017. [PMCID: PMC5592441 DOI: 10.1186/s12868-017-0372-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
|
49
|
Reimann MW, Nolte M, Scolamiero M, Turner K, Perin R, Chindemi G, Dłotko P, Levi R, Hess K, Markram H. Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function. Front Comput Neurosci 2017; 11:48. [PMID: 28659782 PMCID: PMC5467434 DOI: 10.3389/fncom.2017.00048] [Citation(s) in RCA: 112] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2017] [Accepted: 05/18/2017] [Indexed: 01/21/2023] Open
Abstract
The lack of a formal link between neural network structure and its emergent function has hampered our understanding of how the brain processes information. We have now come closer to describing such a link by taking the direction of synaptic transmission into account, constructing graphs of a network that reflect the direction of information flow, and analyzing these directed graphs using algebraic topology. Applying this approach to a local network of neurons in the neocortex revealed a remarkably intricate and previously unseen topology of synaptic connectivity. The synaptic network contains an abundance of cliques of neurons bound into cavities that guide the emergence of correlated activity. In response to stimuli, correlated activity binds synaptically connected neurons into functional cliques and cavities that evolve in a stereotypical sequence toward peak complexity. We propose that the brain processes stimuli by forming increasingly complex functional cliques and cavities.
Collapse
Affiliation(s)
- Michael W Reimann
- Blue Brain Project, École Polytechnique Fédérale de LausanneGeneva, Switzerland
| | - Max Nolte
- Blue Brain Project, École Polytechnique Fédérale de LausanneGeneva, Switzerland
| | - Martina Scolamiero
- Laboratory for Topology and Neuroscience, Brain Mind Institute, École Polytechnique Fédérale de LausanneLausanne, Switzerland
| | - Katharine Turner
- Laboratory for Topology and Neuroscience, Brain Mind Institute, École Polytechnique Fédérale de LausanneLausanne, Switzerland
| | - Rodrigo Perin
- Laboratory of Neural Microcircuitry, Brain Mind Institute, École Polytechnique Fédérale de LausanneLausanne, Switzerland
| | - Giuseppe Chindemi
- Blue Brain Project, École Polytechnique Fédérale de LausanneGeneva, Switzerland
| | | | - Ran Levi
- Institute of Mathematics, University of AberdeenAberdeen, United Kingdom
| | - Kathryn Hess
- Laboratory for Topology and Neuroscience, Brain Mind Institute, École Polytechnique Fédérale de LausanneLausanne, Switzerland
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de LausanneGeneva, Switzerland.,Laboratory of Neural Microcircuitry, Brain Mind Institute, École Polytechnique Fédérale de LausanneLausanne, Switzerland
| |
Collapse
|
50
|
Gal E, London M, Globerson A, Ramaswamy S, Reimann MW, Muller E, Markram H, Segev I. Rich cell-type-specific network topology in neocortical microcircuitry. Nat Neurosci 2017; 20:1004-1013. [DOI: 10.1038/nn.4576] [Citation(s) in RCA: 87] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Accepted: 05/03/2017] [Indexed: 12/14/2022]
|