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Bogguri C, George VK, Amiri B, Ladd A, Hum NR, Sebastian A, Enright HA, Valdez CA, Mundhenk TN, Cadena J, Lam D. Biphasic response of human iPSC-derived neural network activity following exposure to a sarin-surrogate nerve agent. Front Cell Neurosci 2024; 18:1378579. [PMID: 39301218 PMCID: PMC11410629 DOI: 10.3389/fncel.2024.1378579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 08/16/2024] [Indexed: 09/22/2024] Open
Abstract
Organophosphorus nerve agents (OPNA) are hazardous environmental exposures to the civilian population and have been historically weaponized as chemical warfare agents (CWA). OPNA exposure can lead to several neurological, sensory, and motor symptoms that can manifest into chronic neurological illnesses later in life. There is still a large need for technological advancement to better understand changes in brain function following OPNA exposure. The human-relevant in vitro multi-electrode array (MEA) system, which combines the MEA technology with human stem cell technology, has the potential to monitor the acute, sub-chronic, and chronic consequences of OPNA exposure on brain activity. However, the application of this system to assess OPNA hazards and risks to human brain function remains to be investigated. In a concentration-response study, we have employed a human-relevant MEA system to monitor and detect changes in the electrical activity of engineered neural networks to increasing concentrations of the sarin surrogate 4-nitrophenyl isopropyl methylphosphonate (NIMP). We report a biphasic response in the spiking (but not bursting) activity of neurons exposed to low (i.e., 0.4 and 4 μM) versus high concentrations (i.e., 40 and 100 μM) of NIMP, which was monitored during the exposure period and up to 6 days post-exposure. Regardless of the NIMP concentration, at a network level, communication or coordination of neuronal activity decreased as early as 60 min and persisted at 24 h of NIMP exposure. Once NIMP was removed, coordinated activity was no different than control (0 μM of NIMP). Interestingly, only in the high concentration of NIMP did coordination of activity at a network level begin to decrease again at 2 days post-exposure and persisted on day 6 post-exposure. Notably, cell viability was not affected during or after NIMP exposure. Also, while the catalytic activity of AChE decreased during NIMP exposure, its activity recovered once NIMP was removed. Gene expression analysis suggests that human iPSC-derived neurons and primary human astrocytes resulted in altered genes related to the cell's interaction with the extracellular environment, its intracellular calcium signaling pathways, and inflammation, which could have contributed to how neurons communicated at a network level.
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Affiliation(s)
- Chandrakumar Bogguri
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Vivek Kurien George
- Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Beheshta Amiri
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Alexander Ladd
- Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Nicholas R Hum
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Aimy Sebastian
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Heather A Enright
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Carlos A Valdez
- Global Security Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - T Nathan Mundhenk
- Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Jose Cadena
- Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Doris Lam
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
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2
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Kobayashi R, Shinomoto S. Inference of monosynaptic connections from parallel spike trains: A review. Neurosci Res 2024:S0168-0102(24)00097-X. [PMID: 39098768 DOI: 10.1016/j.neures.2024.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 07/12/2024] [Accepted: 07/19/2024] [Indexed: 08/06/2024]
Abstract
This article presents a mini-review about the progress in inferring monosynaptic connections from spike trains of multiple neurons over the past twenty years. First, we explain a variety of meanings of "neuronal connectivity" in different research areas of neuroscience, such as structural connectivity, monosynaptic connectivity, and functional connectivity. Among these, we focus on the methods used to infer the monosynaptic connectivity from spike data. We then summarize the inference methods based on two main approaches, i.e., correlation-based and model-based approaches. Finally, we describe available source codes for connectivity inference and future challenges. Although inference will never be perfect, the accuracy of identifying the monosynaptic connections has improved dramatically in recent years due to continuous efforts.
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Affiliation(s)
- Ryota Kobayashi
- Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8561, Japan; Mathematics and Informatics Center, The University of Tokyo, Tokyo 113-8656, Japan.
| | - Shigeru Shinomoto
- Graduate School of Biostudies, Kyoto University, Kyoto 606-8501, Japan; Research Organization of Open Innovation and Collaboration, Ritsumeikan University, Osaka 567-8570, Japan
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3
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Tang D, Zylberberg J, Jia X, Choi H. Stimulus type shapes the topology of cellular functional networks in mouse visual cortex. Nat Commun 2024; 15:5753. [PMID: 38982078 PMCID: PMC11233648 DOI: 10.1038/s41467-024-49704-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 06/13/2024] [Indexed: 07/11/2024] Open
Abstract
On the timescale of sensory processing, neuronal networks have relatively fixed anatomical connectivity, while functional interactions between neurons can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed single-cell resolution electrophysiological data from the Allen Institute, with simultaneous recordings of stimulus-evoked activity from neurons across 6 different regions of mouse visual cortex. Comparing the functional connectivity patterns during different stimulus types, we made several nontrivial observations: (1) while the frequencies of different functional motifs were preserved across stimuli, the identities of the neurons within those motifs changed; (2) the degree to which functional modules are contained within a single brain region increases with stimulus complexity. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.
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Affiliation(s)
- Disheng Tang
- School of Life Sciences, Tsinghua University, Beijing, 100084, PR China.
- Quantitative Biosciences Program, Georgia Institute of Technology, Atlanta, 30332, GA, USA.
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, 100084, PR China.
| | - Joel Zylberberg
- Department of Physics and Astronomy, and Centre for Vision Research, York University, Toronto, ON M3J 1P3, ON, Canada.
- Learning in Machines and Brains Program, CIFAR, Toronto, ON M5G 1M1, ON, Canada.
| | - Xiaoxuan Jia
- School of Life Sciences, Tsinghua University, Beijing, 100084, PR China.
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, 100084, PR China.
- Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, 100084, PR China.
| | - Hannah Choi
- Quantitative Biosciences Program, Georgia Institute of Technology, Atlanta, 30332, GA, USA.
- School of Mathematics, Georgia Institute of Technology, Atlanta, 30332, GA, USA.
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4
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Li X, Zhu H, Gu B, Yao C, Gu Y, Xu W, Zhang J, He J, Liu X, Li D. Advancing Intelligent Organ-on-a-Chip Systems with Comprehensive In Situ Bioanalysis. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2305268. [PMID: 37688520 DOI: 10.1002/adma.202305268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 08/03/2023] [Indexed: 09/11/2023]
Abstract
In vitro models are essential to a broad range of biomedical research, such as pathological studies, drug development, and personalized medicine. As a potentially transformative paradigm for 3D in vitro models, organ-on-a-chip (OOC) technology has been extensively developed to recapitulate sophisticated architectures and dynamic microenvironments of human organs by applying the principles of life sciences and leveraging micro- and nanoscale engineering capabilities. A pivotal function of OOC devices is to support multifaceted and timely characterization of cultured cells and their microenvironments. However, in-depth analysis of OOC models typically requires biomedical assay procedures that are labor-intensive and interruptive. Herein, the latest advances toward intelligent OOC (iOOC) systems, where sensors integrated with OOC devices continuously report cellular and microenvironmental information for comprehensive in situ bioanalysis, are examined. It is proposed that the multimodal data in iOOC systems can support closed-loop control of the in vitro models and offer holistic biomedical insights for diverse applications. Essential techniques for establishing iOOC systems are surveyed, encompassing in situ sensing, data processing, and dynamic modulation. Eventually, the future development of iOOC systems featuring cross-disciplinary strategies is discussed.
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Affiliation(s)
- Xiao Li
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Hui Zhu
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Bingsong Gu
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Cong Yao
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Yuyang Gu
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Wangkai Xu
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Jia Zhang
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Jiankang He
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Xinyu Liu
- Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, M5S 3G8, Canada
| | - Dichen Li
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
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5
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Simões TSAN, Filho CINS, Herrmann HJ, Andrade JS, de Arcangelis L. Thermodynamic analog of integrate-and-fire neuronal networks by maximum entropy modelling. Sci Rep 2024; 14:9480. [PMID: 38664504 PMCID: PMC11045794 DOI: 10.1038/s41598-024-60117-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 04/18/2024] [Indexed: 04/28/2024] Open
Abstract
Recent results have evidenced that spontaneous brain activity signals are organized in bursts with scale free features and long-range spatio-temporal correlations. These observations have stimulated a theoretical interpretation of results inspired in critical phenomena. In particular, relying on maximum entropy arguments, certain aspects of time-averaged experimental neuronal data have been recently described using Ising-like models, allowing the study of neuronal networks under an analogous thermodynamical framework. This method has been so far applied to a variety of experimental datasets, but never to a biologically inspired neuronal network with short and long-term plasticity. Here, we apply for the first time the Maximum Entropy method to an Integrate-and-fire (IF) model that can be tuned at criticality, offering a controlled setting for a systematic study of criticality and finite-size effects in spontaneous neuronal activity, as opposed to experiments. We consider generalized Ising Hamiltonians whose local magnetic fields and interaction parameters are assigned according to the average activity of single neurons and correlation functions between neurons of the IF networks in the critical state. We show that these Hamiltonians exhibit a spin glass phase for low temperatures, having mostly negative intrinsic fields and a bimodal distribution of interaction constants that tends to become unimodal for larger networks. Results evidence that the magnetization and the response functions exhibit the expected singular behavior near the critical point. Furthermore, we also found that networks with higher percentage of inhibitory neurons lead to Ising-like systems with reduced thermal fluctuations. Finally, considering only neuronal pairs associated with the largest correlation functions allows the study of larger system sizes.
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Affiliation(s)
- T S A N Simões
- Department of Mathematics and Physics, University of Campania "Luigi Vanvitelli", Viale Lincoln, 5, 81100, Caserta, Italy.
| | - C I N Sampaio Filho
- Departamento de Física, Fortaleza, Universidade Federal do Ceará, Ceará, 60451-970, Brazil
| | - H J Herrmann
- Departamento de Física, Fortaleza, Universidade Federal do Ceará, Ceará, 60451-970, Brazil
- ESPCI, PMMH, Paris, 7 quai St., 75005, Bernard, France
| | - J S Andrade
- Departamento de Física, Fortaleza, Universidade Federal do Ceará, Ceará, 60451-970, Brazil
| | - L de Arcangelis
- Department of Mathematics and Physics, University of Campania "Luigi Vanvitelli", Viale Lincoln, 5, 81100, Caserta, Italy
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Latifi S, Carmichael ST. The emergence of multiscale connectomics-based approaches in stroke recovery. Trends Neurosci 2024; 47:303-318. [PMID: 38402008 DOI: 10.1016/j.tins.2024.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 12/31/2023] [Accepted: 01/21/2024] [Indexed: 02/26/2024]
Abstract
Stroke is a leading cause of adult disability. Understanding stroke damage and recovery requires deciphering changes in complex brain networks across different spatiotemporal scales. While recent developments in brain readout technologies and progress in complex network modeling have revolutionized current understanding of the effects of stroke on brain networks at a macroscale, reorganization of smaller scale brain networks remains incompletely understood. In this review, we use a conceptual framework of graph theory to define brain networks from nano- to macroscales. Highlighting stroke-related brain connectivity studies at multiple scales, we argue that multiscale connectomics-based approaches may provide new routes to better evaluate brain structural and functional remapping after stroke and during recovery.
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Affiliation(s)
- Shahrzad Latifi
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA; Department of Neuroscience, Rockefeller Neuroscience Institute, West Virginia University, Morgantown, WV 26506, USA
| | - S Thomas Carmichael
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA.
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7
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Lam D, Enright HA, Cadena J, George VK, Soscia DA, Tooker AC, Triplett M, Peters SKG, Karande P, Ladd A, Bogguri C, Wheeler EK, Fischer NO. Spatiotemporal analysis of 3D human iPSC-derived neural networks using a 3D multi-electrode array. Front Cell Neurosci 2023; 17:1287089. [PMID: 38026689 PMCID: PMC10679684 DOI: 10.3389/fncel.2023.1287089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 10/19/2023] [Indexed: 12/01/2023] Open
Abstract
While there is a growing appreciation of three-dimensional (3D) neural tissues (i.e., hydrogel-based, organoids, and spheroids), shown to improve cellular health and network activity to mirror brain-like activity in vivo, functional assessment using current electrophysiology techniques (e.g., planar multi-electrode arrays or patch clamp) has been technically challenging and limited to surface measurements at the bottom or top of the 3D tissue. As next-generation MEAs, specifically 3D MEAs, are being developed to increase the spatial precision across all three dimensions (X, Y, Z), development of improved computational analytical tools to discern region-specific changes within the Z dimension of the 3D tissue is needed. In the present study, we introduce a novel computational analytical pipeline to analyze 3D neural network activity recorded from a "bottom-up" 3D MEA integrated with a 3D hydrogel-based tissue containing human iPSC-derived neurons and primary astrocytes. Over a period of ~6.5 weeks, we describe the development and maturation of 3D neural activity (i.e., features of spiking and bursting activity) within cross sections of the 3D tissue, based on the vertical position of the electrode on the 3D MEA probe, in addition to network activity (identified using synchrony analysis) within and between cross sections. Then, using the sequential addition of postsynaptic receptor antagonists, bicuculline (BIC), 2-amino-5-phosphonovaleric acid (AP-5), and 6-cyano-5-nitroquinoxaline-2,3-dione (CNQX), we demonstrate that networks within and between cross sections of the 3D hydrogel-based tissue show a preference for GABA and/or glutamate synaptic transmission, suggesting differences in the network composition throughout the neural tissue. The ability to monitor the functional dynamics of the entire 3D reconstructed neural tissue is a critical bottleneck; here we demonstrate a computational pipeline that can be implemented in studies to better interpret network activity within an engineered 3D neural tissue and have a better understanding of the modeled organ tissue.
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Affiliation(s)
- Doris Lam
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Heather A. Enright
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Jose Cadena
- Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Vivek Kurien George
- Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - David A. Soscia
- Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Angela C. Tooker
- Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Michael Triplett
- Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Sandra K. G. Peters
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Piyush Karande
- Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Alexander Ladd
- Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Chandrakumar Bogguri
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Elizabeth K. Wheeler
- Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Nicholas O. Fischer
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
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Parodi G, Brofiga M, Pastore VP, Chiappalone M, Martinoia S. Deepening the role of excitation/inhibition balance in human iPSCs-derived neuronal networks coupled to MEAs during long-term development. J Neural Eng 2023; 20:056011. [PMID: 37678214 DOI: 10.1088/1741-2552/acf78b] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 09/07/2023] [Indexed: 09/09/2023]
Abstract
Objective.The purpose of this study is to investigate whether and how the balance between excitation and inhibition ('E/I balance') influences the spontaneous development of human-derived neuronal networksin vitro. To achieve that goal, we performed a long-term (98 d) characterization of both homogeneous (only excitatory or inhibitory neurons) and heterogeneous (mixed neuronal types) cultures with controlled E/I ratios (i.e. E:I 0:100, 25:75, 50:50, 75:25, 100:0) by recording their electrophysiological activity using micro-electrode arrays.Approach.Excitatory and inhibitory neurons were derived from human induced pluripotent stem cells (hiPSCs). We realized five different configurations by systematically varying the glutamatergic and GABAergic percentages.Main results.We successfully built both homogeneous and heterogeneous neuronal cultures from hiPSCs finely controlling the E/I ratios; we were able to maintain them for up to 3 months. Homogeneity differentially impacted purely inhibitory (no bursts) and purely excitatory (few bursts) networks, deviating from the typical traits of heterogeneous cultures (burst dominated). Increased inhibition in heterogeneous cultures strongly affected the duration and organization of bursting and network bursting activity. Spike-based functional connectivity and image-based deep learning analysis further confirmed all the above.Significance.Healthy neuronal activity is controlled by a well-defined E/I balance whose alteration could lead to the onset of neurodevelopmental disorders like schizophrenia or epilepsy. Most of the commonly usedin vitromodels are animal-derived or too simplified and thus far from thein vivohuman condition. In this work, by performing a long-term study of hiPSCs-derived neuronal networks obtained from healthy human subjects, we demonstrated the feasibility of a robustin vitromodel which can be further exploited for investigating pathological conditions where the E/I balance is impaired.
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Affiliation(s)
- Giulia Parodi
- Department of Informatics, Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genova, Genova, Italy
| | - Martina Brofiga
- Department of Informatics, Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genova, Genova, Italy
- ScreenNeuroPharm s.r.l, Sanremo, Italy
- Neurofacility, Istituto Italiano di Tecnologia, Genova, Italy
| | - Vito Paolo Pastore
- Department of Informatics, Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genova, Genova, Italy
- Machine Learning Genoa Center (MaLGa), Department of Informatics, Bioengineering, Robotics, and Systems Engineering, University of Genova, Genova, Italy
| | - Michela Chiappalone
- Department of Informatics, Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genova, Genova, Italy
| | - Sergio Martinoia
- Department of Informatics, Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genova, Genova, Italy
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Tang D, Zylberberg J, Jia X, Choi H. Stimulus-dependent functional network topology in mouse visual cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.03.547364. [PMID: 37461471 PMCID: PMC10349950 DOI: 10.1101/2023.07.03.547364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/21/2023]
Abstract
Information is processed by networks of neurons in the brain. On the timescale of sensory processing, those neuronal networks have relatively fixed anatomical connectivity, while functional connectivity, which defines the interactions between neurons, can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli, which drive different neuronal activities in the network, could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed electrophysiological data from the Allen Brain Observatory, which utilized Neuropixels probes to simultaneously record stimulus-evoked activity from hundreds of neurons across 6 different regions of mouse visual cortex. The recordings had single-cell resolution and high temporal fidelity, enabling us to determine fine-scale functional connectivity. Comparing the functional connectivity patterns observed when different stimuli were presented to the mice, we made several nontrivial observations. First, while the frequencies of different connectivity motifs (i.e., the patterns of connectivity between triplets of neurons) were preserved across stimuli, the identities of the neurons within those motifs changed. This means that functional connectivity dynamically changes along with the input stimulus, but does so in a way that preserves the motif frequencies. Secondly, we found that the degree to which functional modules are contained within a single brain region (as opposed to being distributed between regions) increases with increasing stimulus complexity. This suggests a mechanism for how the brain could dynamically alter its computations based on its inputs. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.
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Affiliation(s)
- Disheng Tang
- School of Life Sciences, Tsinghua University
- Quantitative Biosciences Program, Georgia Institute of Technology
- IDG/McGovern Institute for Brain Research, Tsinghua University
| | - Joel Zylberberg
- Department of Physics and Astronomy, and Centre for Vision Research, York University
- Learning in Machines and Brains Program, CIFAR
- These authors jointly supervised this work: Joel Zylberberg, Xiaoxuan Jia, Hannah Choi
| | - Xiaoxuan Jia
- School of Life Sciences, Tsinghua University
- IDG/McGovern Institute for Brain Research, Tsinghua University
- Tsinghua–Peking Center for Life Sciences
- Allen Institute for Brain Science
- These authors jointly supervised this work: Joel Zylberberg, Xiaoxuan Jia, Hannah Choi
| | - Hannah Choi
- Quantitative Biosciences Program, Georgia Institute of Technology
- School of Mathematics, Georgia Institute of Technology
- These authors jointly supervised this work: Joel Zylberberg, Xiaoxuan Jia, Hannah Choi
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10
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Pastore VP, Parodi G, Brofiga M, Massobrio P, Chiappalone M, Odone F, Martinoia S. An efficient deep learning approach to identify dynamics in in vitro neural networks. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083487 DOI: 10.1109/embc40787.2023.10340862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Understanding and discriminating the spatiotemporal patterns of activity generated by in vitro and in vivo neuronal networks is a fundamental task in neuroscience and neuroengineering. The state-of-the-art algorithms to describe the neuronal activity mostly rely on global and local well-established spike and burst-related parameters. However, they are not able to capture slight differences in the activity patterns. In this work, we introduce a deep-learning-based algorithm to automatically infer the dynamics exhibited by different neuronal populations. Specifically, we demonstrate that our algorithm is able to discriminate with high accuracy the dynamics of five different populations of in vitro human-derived neural networks with an increasing inhibitory to excitatory neurons ratio.
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11
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Krishna S, Choudhury A, Keough MB, Seo K, Ni L, Kakaizada S, Lee A, Aabedi A, Popova G, Lipkin B, Cao C, Nava Gonzales C, Sudharshan R, Egladyous A, Almeida N, Zhang Y, Molinaro AM, Venkatesh HS, Daniel AGS, Shamardani K, Hyer J, Chang EF, Findlay A, Phillips JJ, Nagarajan S, Raleigh DR, Brang D, Monje M, Hervey-Jumper SL. Glioblastoma remodelling of human neural circuits decreases survival. Nature 2023; 617:599-607. [PMID: 37138086 PMCID: PMC10191851 DOI: 10.1038/s41586-023-06036-1] [Citation(s) in RCA: 96] [Impact Index Per Article: 96.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 03/31/2023] [Indexed: 05/05/2023]
Abstract
Gliomas synaptically integrate into neural circuits1,2. Previous research has demonstrated bidirectional interactions between neurons and glioma cells, with neuronal activity driving glioma growth1-4 and gliomas increasing neuronal excitability2,5-8. Here we sought to determine how glioma-induced neuronal changes influence neural circuits underlying cognition and whether these interactions influence patient survival. Using intracranial brain recordings during lexical retrieval language tasks in awake humans together with site-specific tumour tissue biopsies and cell biology experiments, we find that gliomas remodel functional neural circuitry such that task-relevant neural responses activate tumour-infiltrated cortex well beyond the cortical regions that are normally recruited in the healthy brain. Site-directed biopsies from regions within the tumour that exhibit high functional connectivity between the tumour and the rest of the brain are enriched for a glioblastoma subpopulation that exhibits a distinct synaptogenic and neuronotrophic phenotype. Tumour cells from functionally connected regions secrete the synaptogenic factor thrombospondin-1, which contributes to the differential neuron-glioma interactions observed in functionally connected tumour regions compared with tumour regions with less functional connectivity. Pharmacological inhibition of thrombospondin-1 using the FDA-approved drug gabapentin decreases glioblastoma proliferation. The degree of functional connectivity between glioblastoma and the normal brain negatively affects both patient survival and performance in language tasks. These data demonstrate that high-grade gliomas functionally remodel neural circuits in the human brain, which both promotes tumour progression and impairs cognition.
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Affiliation(s)
- Saritha Krishna
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Abrar Choudhury
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | | | - Kyounghee Seo
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Lijun Ni
- Department of Neurology, Stanford University, Stanford, CA, USA
| | - Sofia Kakaizada
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Anthony Lee
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Alexander Aabedi
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Galina Popova
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Benjamin Lipkin
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Caroline Cao
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Cesar Nava Gonzales
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Rasika Sudharshan
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Andrew Egladyous
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Nyle Almeida
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Yalan Zhang
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Annette M Molinaro
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | | | - Andy G S Daniel
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | | | - Jeanette Hyer
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Edward F Chang
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Anne Findlay
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Joanna J Phillips
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Department of Pathology, University of California, San Francisco, San Francisco, CA, USA
| | - Srikantan Nagarajan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - David R Raleigh
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Department of Pathology, University of California, San Francisco, San Francisco, CA, USA
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, USA
| | - David Brang
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Michelle Monje
- Department of Neurology, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford, CA, USA
| | - Shawn L Hervey-Jumper
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA.
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.
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12
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Adeyelu T, Ogundele OM. VTA multifaceted modulation of CA1 local circuits. Neurobiol Learn Mem 2023; 202:107760. [PMID: 37119849 DOI: 10.1016/j.nlm.2023.107760] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 04/18/2023] [Accepted: 04/22/2023] [Indexed: 05/01/2023]
Abstract
Excitatory pyramidal (PYR) cell activation of interneurons (INT) produces network oscillations that underlie cognitive processes in the CA1. Neural projections from the ventral tegmental area (VTA) to the hippocampus contribute to novelty detection by modulating CA1 PYR and INT activity. The role of the VTA in the VTA-hippocampus loop is mostly attributed to the dopamine neurons although the VTA glutamate-releasing terminals are dominant in the hippocampus. Because of the traditional focus on VTA dopamine circuits, how VTA glutamate inputs modulate PYR activation of INT in CA1 neuronal ensembles is poorly understood and has not been distinguished from the VTA dopamine inputs. By combining CA1 extracellular recording with VTA photostimulation in anesthetized mice, we compared the effects of VTA dopamine and glutamate input on CA1 PYR/INT connections. Stimulation of VTA glutamate neurons shortened PYR/INT connection time without altering the synchronization or connectivity strength. Conversely, activation of VTA dopamine inputs delayed CA1 PYR/INT connection time and increased the synchronization in putative pairs. Taken together, we conclude that VTA dopamine and glutamate projections produce tract-specific effects on CA1 PYR/INT connectivity and synchrony. As such, selective activation or co-activation of these systems will likely produce a range of modulatory effects on local CA1 circuits.
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Affiliation(s)
- Tolulope Adeyelu
- Department of Comparative Biomedical Sciences, Louisiana State University School of Veterinary Medicine. Baton Rouge, LA70803, Louisiana
| | - Olalekan M Ogundele
- Department of Comparative Biomedical Sciences, Louisiana State University School of Veterinary Medicine. Baton Rouge, LA70803, Louisiana.
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13
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Callegari F, Brofiga M, Massobrio P. Modeling the three-dimensional connectivity of in vitro cortical ensembles coupled to Micro-Electrode Arrays. PLoS Comput Biol 2023; 19:e1010825. [PMID: 36780570 PMCID: PMC9956882 DOI: 10.1371/journal.pcbi.1010825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 02/24/2023] [Accepted: 12/17/2022] [Indexed: 02/15/2023] Open
Abstract
Nowadays, in vitro three-dimensional (3D) neuronal networks are becoming a consolidated experimental model to overcome most of the intrinsic limitations of bi-dimensional (2D) assemblies. In the 3D environment, experimental evidence revealed a wider repertoire of activity patterns, characterized by a modulation of the bursting features, than the one observed in 2D cultures. However, it is not totally clear and understood what pushes the neuronal networks towards different dynamical regimes. One possible explanation could be the underlying connectivity, which could involve a larger number of neurons in a 3D rather than a 2D space and could organize following well-defined topological schemes. Driven by experimental findings, achieved by recording 3D cortical networks organized in multi-layered structures coupled to Micro-Electrode Arrays (MEAs), in the present work we developed a large-scale computational network model made up of leaky integrate-and-fire (LIF) neurons to investigate possible structural configurations able to sustain the emerging patterns of electrophysiological activity. In particular, we investigated the role of the number of layers defining a 3D assembly and the spatial distribution of the connections within and among the layers. These configurations give rise to different patterns of activity that could be compared to the ones emerging from real in vitro 3D neuronal populations. Our results suggest that the introduction of three-dimensionality induced a global reduction in both firing and bursting rates with respect to 2D models. In addition, we found that there is a minimum number of layers necessary to obtain a change in the dynamics of the network. However, the effects produced by a 3D organization of the cells is somewhat mitigated if a scale-free connectivity is implemented in either one or all the layers of the network. Finally, the best matching of the experimental data is achieved supposing a 3D connectivity organized in structured bundles of links located in different areas of the 2D network.
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Affiliation(s)
- Francesca Callegari
- Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, Genova, Italy
| | - Martina Brofiga
- Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, Genova, Italy
- ScreenNeuroPharm, Sanremo, Italy
| | - Paolo Massobrio
- Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, Genova, Italy
- National Institute for Nuclear Physics (INFN), Genova, Italy
- * E-mail:
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Liu Y, Xu S, Yang Y, Zhang K, He E, Liang W, Luo J, Wu Y, Cai X. Nanomaterial-based microelectrode arrays for in vitro bidirectional brain-computer interfaces: a review. MICROSYSTEMS & NANOENGINEERING 2023; 9:13. [PMID: 36726940 PMCID: PMC9884667 DOI: 10.1038/s41378-022-00479-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 10/04/2022] [Accepted: 10/21/2022] [Indexed: 06/18/2023]
Abstract
A bidirectional in vitro brain-computer interface (BCI) directly connects isolated brain cells with the surrounding environment, reads neural signals and inputs modulatory instructions. As a noninvasive BCI, it has clear advantages in understanding and exploiting advanced brain function due to the simplified structure and high controllability of ex vivo neural networks. However, the core of ex vivo BCIs, microelectrode arrays (MEAs), urgently need improvements in the strength of signal detection, precision of neural modulation and biocompatibility. Notably, nanomaterial-based MEAs cater to all the requirements by converging the multilevel neural signals and simultaneously applying stimuli at an excellent spatiotemporal resolution, as well as supporting long-term cultivation of neurons. This is enabled by the advantageous electrochemical characteristics of nanomaterials, such as their active atomic reactivity and outstanding charge conduction efficiency, improving the performance of MEAs. Here, we review the fabrication of nanomaterial-based MEAs applied to bidirectional in vitro BCIs from an interdisciplinary perspective. We also consider the decoding and coding of neural activity through the interface and highlight the various usages of MEAs coupled with the dissociated neural cultures to benefit future developments of BCIs.
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Affiliation(s)
- Yaoyao Liu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190 China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049 PR China
| | - Shihong Xu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190 China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049 PR China
| | - Yan Yang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190 China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049 PR China
| | - Kui Zhang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190 China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049 PR China
| | - Enhui He
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190 China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049 PR China
| | - Wei Liang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190 China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049 PR China
| | - Jinping Luo
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190 China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049 PR China
| | - Yirong Wu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190 China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049 PR China
| | - Xinxia Cai
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190 China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100049 PR China
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15
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Kim T, Chen D, Hornauer P, Emmenegger V, Bartram J, Ronchi S, Hierlemann A, Schröter M, Roqueiro D. Predicting in vitro single-neuron firing rates upon pharmacological perturbation using Graph Neural Networks. Front Neuroinform 2023; 16:1032538. [PMID: 36713289 PMCID: PMC9874697 DOI: 10.3389/fninf.2022.1032538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 12/13/2022] [Indexed: 01/12/2023] Open
Abstract
Modern Graph Neural Networks (GNNs) provide opportunities to study the determinants underlying the complex activity patterns of biological neuronal networks. In this study, we applied GNNs to a large-scale electrophysiological dataset of rodent primary neuronal networks obtained by means of high-density microelectrode arrays (HD-MEAs). HD-MEAs allow for long-term recording of extracellular spiking activity of individual neurons and networks and enable the extraction of physiologically relevant features at the single-neuron and population level. We employed established GNNs to generate a combined representation of single-neuron and connectivity features obtained from HD-MEA data, with the ultimate goal of predicting changes in single-neuron firing rate induced by a pharmacological perturbation. The aim of the main prediction task was to assess whether single-neuron and functional connectivity features, inferred under baseline conditions, were informative for predicting changes in neuronal activity in response to a perturbation with Bicuculline, a GABA A receptor antagonist. Our results suggest that the joint representation of node features and functional connectivity, extracted from a baseline recording, was informative for predicting firing rate changes of individual neurons after the perturbation. Specifically, our implementation of a GNN model with inductive learning capability (GraphSAGE) outperformed other prediction models that relied only on single-neuron features. We tested the generalizability of the results on two additional datasets of HD-MEA recordings-a second dataset with cultures perturbed with Bicuculline and a dataset perturbed with the GABA A receptor antagonist Gabazine. GraphSAGE models showed improved prediction accuracy over other prediction models. Our results demonstrate the added value of taking into account the functional connectivity between neurons and the potential of GNNs to study complex interactions between neurons.
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Affiliation(s)
- Taehoon Kim
- Bioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- Machine Learning and Computational Biology Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Dexiong Chen
- Machine Learning and Computational Biology Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Philipp Hornauer
- Bioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Vishalini Emmenegger
- Bioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Julian Bartram
- Bioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Silvia Ronchi
- Bioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Andreas Hierlemann
- Bioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Manuel Schröter
- Bioengineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Damian Roqueiro
- Machine Learning and Computational Biology Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
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16
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Celotto M, Lemke S, Panzeri S. Inferring the temporal evolution of synaptic weights from dynamic functional connectivity. Brain Inform 2022; 9:28. [PMID: 36480076 PMCID: PMC9732068 DOI: 10.1186/s40708-022-00178-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 11/14/2022] [Indexed: 12/13/2022] Open
Abstract
How to capture the temporal evolution of synaptic weights from measures of dynamic functional connectivity between the activity of different simultaneously recorded neurons is an important and open problem in systems neuroscience. Here, we report methodological progress to address this issue. We first simulated recurrent neural network models of spiking neurons with spike timing-dependent plasticity mechanisms that generate time-varying synaptic and functional coupling. We then used these simulations to test analytical approaches that infer fixed and time-varying properties of synaptic connectivity from directed functional connectivity measures, such as cross-covariance and transfer entropy. We found that, while both cross-covariance and transfer entropy provide robust estimates of which synapses are present in the network and their communication delays, dynamic functional connectivity measured via cross-covariance better captures the evolution of synaptic weights over time. We also established how measures of information transmission delays from static functional connectivity computed over long recording periods (i.e., several hours) can improve shorter time-scale estimates of the temporal evolution of synaptic weights from dynamic functional connectivity. These results provide useful information about how to accurately estimate the temporal variation of synaptic strength from spiking activity measures.
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Affiliation(s)
- Marco Celotto
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany.
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy.
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
| | - Stefan Lemke
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy
- Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, USA
| | - Stefano Panzeri
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany.
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto, Italy.
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Chiappalone M, Cota VR, Carè M, Di Florio M, Beaubois R, Buccelli S, Barban F, Brofiga M, Averna A, Bonacini F, Guggenmos DJ, Bornat Y, Massobrio P, Bonifazi P, Levi T. Neuromorphic-Based Neuroprostheses for Brain Rewiring: State-of-the-Art and Perspectives in Neuroengineering. Brain Sci 2022; 12:1578. [PMID: 36421904 PMCID: PMC9688667 DOI: 10.3390/brainsci12111578] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/09/2022] [Accepted: 11/17/2022] [Indexed: 08/27/2023] Open
Abstract
Neuroprostheses are neuroengineering devices that have an interface with the nervous system and supplement or substitute functionality in people with disabilities. In the collective imagination, neuroprostheses are mostly used to restore sensory or motor capabilities, but in recent years, new devices directly acting at the brain level have been proposed. In order to design the next-generation of neuroprosthetic devices for brain repair, we foresee the increasing exploitation of closed-loop systems enabled with neuromorphic elements due to their intrinsic energy efficiency, their capability to perform real-time data processing, and of mimicking neurobiological computation for an improved synergy between the technological and biological counterparts. In this manuscript, after providing definitions of key concepts, we reviewed the first exploitation of a real-time hardware neuromorphic prosthesis to restore the bidirectional communication between two neuronal populations in vitro. Starting from that 'case-study', we provide perspectives on the technological improvements for real-time interfacing and processing of neural signals and their potential usage for novel in vitro and in vivo experimental designs. The development of innovative neuroprosthetics for translational purposes is also presented and discussed. In our understanding, the pursuit of neuromorphic-based closed-loop neuroprostheses may spur the development of novel powerful technologies, such as 'brain-prostheses', capable of rewiring and/or substituting the injured nervous system.
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Affiliation(s)
- Michela Chiappalone
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Vinicius R. Cota
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Marta Carè
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Mattia Di Florio
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
| | - Romain Beaubois
- IMS Laboratory, CNRS UMR 5218, University of Bordeaux, 33405 Talence, France
| | - Stefano Buccelli
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Federico Barban
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Martina Brofiga
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
| | - Alberto Averna
- Department of Neurology, Bern University Hospital, University of Bern, 3012 Bern, Switzerland
| | - Francesco Bonacini
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
| | - David J. Guggenmos
- Department of Rehabilitation Medicine, University of Kansas Medical Center, Kansas City, KS 66103, USA
- Landon Center on Aging, University of Kansas Medical Center, Kansas City, KS 66103, USA
| | - Yannick Bornat
- IMS Laboratory, CNRS UMR 5218, University of Bordeaux, 33405 Talence, France
| | - Paolo Massobrio
- Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, 16145 Genova, Italy
- National Institute for Nuclear Physics (INFN), 16146 Genova, Italy
| | - Paolo Bonifazi
- IKERBASQUE, The Basque Fundation, 48009 Bilbao, Spain
- Biocruces Health Research Institute, 48903 Barakaldo, Spain
| | - Timothée Levi
- IMS Laboratory, CNRS UMR 5218, University of Bordeaux, 33405 Talence, France
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18
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Kress GT, Chan F, Garcia CA, Merrifield WS. Utilizing machine learning algorithms to predict subject genetic mutation class from in silico models of neuronal networks. BMC Med Inform Decis Mak 2022; 22:290. [DOI: 10.1186/s12911-022-02038-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 11/01/2022] [Indexed: 11/11/2022] Open
Abstract
Abstract
Background
Epilepsy is the fourth-most common neurological disorder, affecting an estimated 50 million patients globally. Nearly 40% of patients have uncontrolled seizures yet incur 80% of the cost. Anti-epileptic drugs commonly result in resistance and reversion to uncontrolled drug-resistant epilepsy and are often associated with significant adverse effects. This has led to a trial-and-error system in which physicians spend months to years attempting to identify the optimal therapeutic approach.
Objective
To investigate the potential clinical utility from the context of optimal therapeutic prediction of characterizing cellular electrophysiology. It is well-established that genomic data alone can sometimes be predictive of effective therapeutic approach. Thus, to assess the predictive power of electrophysiological data, machine learning strategies are implemented to predict a subject’s genetically defined class in an in silico model using brief electrophysiological recordings obtained from simulated neuronal networks.
Methods
A dynamic network of isogenic neurons is modeled in silico for 1-s for 228 dynamically modeled patients falling into one of three categories: healthy, general sodium channel gain of function, or inhibitory sodium channel loss of function. Data from previous studies investigating the electrophysiological and cellular properties of neurons in vitro are used to define the parameters governing said models. Ninety-two electrophysiological features defining the nature and consistency of network connectivity, activity, waveform shape, and complexity are extracted for each patient network and t-tests are used for feature selection for the following machine learning algorithms: Neural Network, Support Vector Machine, Gaussian Naïve Bayes Classifier, Decision Tree, and Gradient Boosting Decision Tree. Finally, their performance in accurately predicting which genetic category the subjects fall under is assessed.
Results
Several machine learning algorithms excel in using electrophysiological data from isogenic neurons to accurately predict genetic class with a Gaussian Naïve Bayes Classifier predicting healthy, gain of function, and overall, with the best accuracy, area under the curve, and F1. The Gradient Boosting Decision Tree performs the best for loss of function models indicated by the same metrics.
Conclusions
It is possible for machine learning algorithms to use electrophysiological data to predict clinically valuable metrics such as optimal therapeutic approach, especially when combining several models.
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Lam D, Sebastian A, Bogguri C, Hum NR, Ladd A, Cadena J, Valdez CA, Fischer NO, Loots GG, Enright HA. Dose-dependent consequences of sub-chronic fentanyl exposure on neuron and glial co-cultures. FRONTIERS IN TOXICOLOGY 2022; 4:983415. [PMID: 36032789 PMCID: PMC9403314 DOI: 10.3389/ftox.2022.983415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 07/20/2022] [Indexed: 11/13/2022] Open
Abstract
Fentanyl is one of the most common opioid analgesics administered to patients undergoing surgery or for chronic pain management. While the side effects of chronic fentanyl abuse are recognized (e.g., addiction, tolerance, impairment of cognitive functions, and inhibit nociception, arousal, and respiration), it remains poorly understood what and how changes in brain activity from chronic fentanyl use influences the respective behavioral outcome. Here, we examined the functional and molecular changes to cortical neural network activity following sub-chronic exposure to two fentanyl concentrations, a low (0.01 μM) and high (10 μM) dose. Primary rat co-cultures, containing cortical neurons, astrocytes, and oligodendrocyte precursor cells, were seeded in wells on either a 6-well multi-electrode array (MEA, for electrophysiology) or a 96-well tissue culture plate (for serial endpoint bulk RNA sequencing analysis). Once networks matured (at 28 days in vitro), co-cultures were treated with 0.01 or 10 μM of fentanyl for 4 days and monitored daily. Only high dose exposure to fentanyl resulted in a decline in features of spiking and bursting activity as early as 30 min post-exposure and sustained for 4 days in cultures. Transcriptomic analysis of the complex cultures after 4 days of fentanyl exposure revealed that both the low and high dose induced gene expression changes involved in synaptic transmission, inflammation, and organization of the extracellular matrix. Collectively, the findings of this in vitro study suggest that while neuroadaptive changes to neural network activity at a systems level was detected only at the high dose of fentanyl, transcriptomic changes were also detected at the low dose conditions, suggesting that fentanyl rapidly elicits changes in plasticity.
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Affiliation(s)
- Doris Lam
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Aimy Sebastian
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Chandrakumar Bogguri
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Nicholas R. Hum
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Alexander Ladd
- Computational Engineering Division, Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Jose Cadena
- Computational Engineering Division, Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Carlos A. Valdez
- Nuclear and Chemical Sciences Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Nicholas O. Fischer
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Gabriela G. Loots
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Heather A. Enright
- Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
- *Correspondence: Heather A. Enright,
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Callegari F, Brofiga M, Poggio F, Massobrio P. Stimulus-Evoked Activity Modulation of In Vitro Engineered Cortical and Hippocampal Networks. MICROMACHINES 2022; 13:mi13081212. [PMID: 36014137 PMCID: PMC9413227 DOI: 10.3390/mi13081212] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/25/2022] [Accepted: 07/28/2022] [Indexed: 11/21/2022]
Abstract
The delivery of electrical stimuli is crucial to shape the electrophysiological activity of neuronal populations and to appreciate the response of the different brain circuits involved. In the present work, we used dissociated cortical and hippocampal networks coupled to Micro-Electrode Arrays (MEAs) to investigate the features of their evoked response when a low-frequency (0.2 Hz) electrical stimulation protocol is delivered. In particular, cortical and hippocampal neurons were topologically organized to recreate interconnected sub-populations with a polydimethylsiloxane (PDMS) mask, which guaranteed the segregation of the cell bodies and the connections among the sub-regions through microchannels. We found that cortical assemblies were more reactive than hippocampal ones. Despite both configurations exhibiting a fast (<35 ms) response, this did not uniformly distribute over the MEA in the hippocampal networks. Moreover, the propagation of the stimuli-evoked activity within the networks showed a late (35−500 ms) response only in the cortical assemblies. The achieved results suggest the importance of the neuronal target when electrical stimulation experiments are performed. Not all neuronal types display the same response, and in light of transferring stimulation protocols to in vivo applications, it becomes fundamental to design realistic in vitro brain-on-a-chip devices to investigate the dynamical properties of complex neuronal circuits.
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Affiliation(s)
- Francesca Callegari
- Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, 16145 Genova, Italy; (F.C.); (M.B.); (F.P.)
| | - Martina Brofiga
- Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, 16145 Genova, Italy; (F.C.); (M.B.); (F.P.)
- ScreenNeuroPharm s.r.l., 18038 Sanremo, Italy
| | - Fabio Poggio
- Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, 16145 Genova, Italy; (F.C.); (M.B.); (F.P.)
| | - Paolo Massobrio
- Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, 16145 Genova, Italy; (F.C.); (M.B.); (F.P.)
- National Institute for Nuclear Physics (INFN), 16146 Genova, Italy
- Correspondence: ; Tel.: +39-010-335-2761
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21
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Maximum entropy models provide functional connectivity estimates in neural networks. Sci Rep 2022; 12:9656. [PMID: 35688933 PMCID: PMC9187636 DOI: 10.1038/s41598-022-13674-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 05/26/2022] [Indexed: 11/08/2022] Open
Abstract
Tools to estimate brain connectivity offer the potential to enhance our understanding of brain functioning. The behavior of neuronal networks, including functional connectivity and induced connectivity changes by external stimuli, can be studied using models of cultured neurons. Cultured neurons tend to be active in groups, and pairs of neurons are said to be functionally connected when their firing patterns show significant synchronicity. Methods to infer functional connections are often based on pair-wise cross-correlation between activity patterns of (small groups of) neurons. However, these methods are not very sensitive to detect inhibitory connections, and they were not designed for use during stimulation. Maximum Entropy (MaxEnt) models may provide a conceptually different method to infer functional connectivity. They have the potential benefit to estimate functional connectivity during stimulation, and to infer excitatory as well as inhibitory connections. MaxEnt models do not involve pairwise comparison, but aim to capture probability distributions of sets of neurons that are synchronously active in discrete time bins. We used electrophysiological recordings from in vitro neuronal cultures on micro electrode arrays to investigate the ability of MaxEnt models to infer functional connectivity. Connectivity estimates provided by MaxEnt models correlated well with those obtained by conditional firing probabilities (CFP), an established cross-correlation based method. In addition, stimulus-induced connectivity changes were detected by MaxEnt models, and were of the same magnitude as those detected by CFP. Thus, MaxEnt models provide a potentially powerful new tool to study functional connectivity in neuronal networks.
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22
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Harris MR, Wytock TP, Kovács IA. Computational Inference of Synaptic Polarities in Neuronal Networks. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2104906. [PMID: 35355451 PMCID: PMC9165506 DOI: 10.1002/advs.202104906] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 02/23/2022] [Indexed: 05/31/2023]
Abstract
Synaptic polarity, that is, whether synapses are inhibitory (-) or excitatory (+), is challenging to map, despite being a key to understand brain function. Here, synaptic polarity is inferred computationally considering three experimental scenarios, depending on the nature of available input data, using the Caenorhabditis elegans connectome as an example. First, the inputs consist of detailed neurotransmitter (NT) and receptor (R) gene expression, integrated through the connectome model (CM). The CM formulates the problem through a wiring rule network that summarizes how NT-R pairs govern synaptic polarity, and resolves 356 synaptic polarities in addition to the 1752 known polarities. Second, known synaptic polarities are considered as an input, in addition to the NT and R gene expression data, but without wiring rules. These data train the spatial connectome model, which infers the polarity of 81% of the CM-resolved connections at > 95 $>95$ % precision, while also inferring 147 of the remaining unknown polarities. Last, without known expression or wiring rules, polarities are inferred through a network sign prediction problem. As an illustration of high performance in this case, the generalized CM is introduced. These results address imminent challenges in unveiling large-scale synaptic polarities, an essential step toward more realistic brain models.
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Affiliation(s)
- Michael R. Harris
- Department of Physics and AstronomyNorthwestern UniversityEvanstonIL60208USA
- Department of PhysicsLoyola University ChicagoChicagoIL60660USA
| | - Thomas P. Wytock
- Department of Physics and AstronomyNorthwestern UniversityEvanstonIL60208USA
| | - István A. Kovács
- Department of Physics and AstronomyNorthwestern UniversityEvanstonIL60208USA
- Northwestern Institute on Complex SystemsNorthwestern UniversityEvanstonIL60208USA
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23
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Iyer RR, Liu YZ, Renteria CA, Tibble BE, Choi H, Žurauskas M, Boppart SA. Ultra-parallel label-free optophysiology of neural activity. iScience 2022; 25:104307. [PMID: 35602935 PMCID: PMC9114528 DOI: 10.1016/j.isci.2022.104307] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 02/18/2022] [Accepted: 04/22/2022] [Indexed: 01/21/2023] Open
Abstract
The electrical activity of neurons has a spatiotemporal footprint that spans three orders of magnitude. Traditional electrophysiology lacks the spatial throughput to image the activity of an entire neural network; besides, labeled optical imaging using voltage-sensitive dyes and tracking Ca2+ ion dynamics lack the versatility and speed to capture fast-spiking activity, respectively. We present a label-free optical imaging technique to image the changes to the optical path length and the local birefringence caused by neural activity, at 4,000 Hz, across a 200 × 200 μm2 region, and with micron-scale spatial resolution and 300-pm displacement sensitivity using Superfast Polarization-sensitive Off-axis Full-field Optical Coherence Microscopy (SPoOF OCM). The undulations in the optical responses from mammalian neuronal activity were matched with field-potential electrophysiology measurements and validated with channel blockers. By directly tracking the widefield neural activity at millisecond timescales and micrometer resolution, SPoOF OCM provides a framework to progress from low-throughput electrophysiology to high-throughput ultra-parallel label-free optophysiology.
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Affiliation(s)
- Rishyashring R. Iyer
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Yuan-Zhi Liu
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Carlos A. Renteria
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA,Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Brian E. Tibble
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Honggu Choi
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Mantas Žurauskas
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Stephen A. Boppart
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA,Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA,Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL, USA,Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL, USA,Corresponding author
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24
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Sun C, Lin KC, Yeung CY, Ching ESC, Huang YT, Lai PY, Chan CK. Revealing directed effective connectivity of cortical neuronal networks from measurements. Phys Rev E 2022; 105:044406. [PMID: 35590680 DOI: 10.1103/physreve.105.044406] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 03/23/2022] [Indexed: 06/15/2023]
Abstract
In the study of biological networks, one of the major challenges is to understand the relationships between network structure and dynamics. In this paper, we model in vitro cortical neuronal cultures as stochastic dynamical systems and apply a method that reconstructs directed networks from dynamics [Ching and Tam, Phys. Rev. E 95, 010301(R) (2017)2470-004510.1103/PhysRevE.95.010301] to reveal directed effective connectivity, namely, the directed links and synaptic weights, of the neuronal cultures from voltage measurements recorded by a multielectrode array. The effective connectivity so obtained reproduces several features of cortical regions in rats and monkeys and has similar network properties as the synaptic network of the nematode Caenorhabditis elegans, whose entire nervous system has been mapped out. The distribution of the incoming degree is bimodal and the distributions of the average incoming and outgoing synaptic strength are non-Gaussian with long tails. The effective connectivity captures different information from the commonly studied functional connectivity, estimated using statistical correlation between spiking activities. The average synaptic strengths of excitatory incoming and outgoing links are found to increase with the spiking activity in the estimated effective connectivity but not in the functional connectivity estimated using the same sets of voltage measurements. These results thus demonstrate that the reconstructed effective connectivity can capture the general properties of synaptic connections and better reveal relationships between network structure and dynamics.
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Affiliation(s)
- Chumin Sun
- Institute of Theoretical Physics and Department of Physics, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - K C Lin
- Institute of Theoretical Physics and Department of Physics, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - C Y Yeung
- Institute of Theoretical Physics and Department of Physics, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Emily S C Ching
- Institute of Theoretical Physics and Department of Physics, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Yu-Ting Huang
- Department of Physics and Center for Complex Systems, National Central University, Chungli, Taiwan 320, ROC
- Institute of Physics, Academia Sinica, Taipei, Taiwan 115, ROC
| | - Pik-Yin Lai
- Department of Physics and Center for Complex Systems, National Central University, Chungli, Taiwan 320, ROC
| | - C K Chan
- Institute of Physics, Academia Sinica, Taipei, Taiwan 115, ROC
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25
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Adeyelu T, Shrestha A, Adeniyi PA, Lee CC, Ogundele OM. CA1 Spike Timing is Impaired in the 129S Inbred Strain During Cognitive Tasks. Neuroscience 2022; 484:119-138. [PMID: 34800576 PMCID: PMC8844212 DOI: 10.1016/j.neuroscience.2021.11.021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 11/08/2021] [Accepted: 11/10/2021] [Indexed: 01/16/2023]
Abstract
A spontaneous mutation of the disrupted in schizophrenia 1 (Disc1) gene is carried by the 129S inbred mouse strain. Truncated DISC1 protein in 129S mouse synapses impairs the scaffolding of excitatory postsynaptic receptors and leads to progressive spine dysgenesis. In contrast, C57BL/6 inbred mice carry the wild-type Disc1 gene and exhibit more typical cognitive performance in spatial exploration and executive behavioral tests. Because of the innate Disc1 mutation, adult 129S inbred mice exhibit the behavioral phenotypes of outbred B6 Disc1 knockdown (Disc1-/-) or Disc1-L-100P mutant strains. Recent studies in Disc1-/- and L-100P mice have shown that impaired excitation-driven interneuron activity and low hippocampal theta power underlie the behavioral phenotypes that resemble human depression and schizophrenia. The current study compared the firing rate and connectivity profile of putative neurons in the CA1 of freely behaving inbred 129S and B6 mice, which have mutant and wild-type Disc1 genes, respectively. In cognitive behavioral tests, 129S mice had lower exploration scores than B6 mice. Furthermore, the mean firing rate for 129S putative pyramidal (pyr) cells and interneurons (int) was significantly lower than that for B6 CA1 neurons sampled during similar tasks. Analysis of pyr/int connectivity revealed a significant delay in synaptic transmission for 129S putative pairs. Sampled 129S pyr/int pairs also had lower detectability index scores than B6 putative pairs. Therefore, the spontaneous Disc1 mutation in the 129S strain attenuates the firing of putative pyr CA1 neurons and impairs spike timing fidelity during cognitive tasks.
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Affiliation(s)
- Tolulope Adeyelu
- Department of Comparative Biomedical Sciences, Louisiana State University School of Veterinary Medicine. Baton Rouge, LA70803, Louisiana
| | - Amita Shrestha
- Department of Comparative Biomedical Sciences, Louisiana State University School of Veterinary Medicine. Baton Rouge, LA70803, Louisiana
| | - Philip A. Adeniyi
- Department of Comparative Biomedical Sciences, Louisiana State University School of Veterinary Medicine. Baton Rouge, LA70803, Louisiana
| | - Charles C. Lee
- Department of Comparative Biomedical Sciences, Louisiana State University School of Veterinary Medicine. Baton Rouge, LA70803, Louisiana
| | - Olalekan M. Ogundele
- Department of Comparative Biomedical Sciences, Louisiana State University School of Veterinary Medicine. Baton Rouge, LA70803, Louisiana
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26
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van Albada SJ, Morales-Gregorio A, Dickscheid T, Goulas A, Bakker R, Bludau S, Palm G, Hilgetag CC, Diesmann M. Bringing Anatomical Information into Neuronal Network Models. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1359:201-234. [DOI: 10.1007/978-3-030-89439-9_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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27
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Estimating the Temporal Evolution of Synaptic Weights from Dynamic Functional Connectivity. Brain Inform 2022. [DOI: 10.1007/978-3-031-15037-1_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022] Open
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28
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Ghirga S, Chiodo L, Marrocchio R, Orlandi JG, Loppini A. Inferring Excitatory and Inhibitory Connections in Neuronal Networks. ENTROPY 2021; 23:e23091185. [PMID: 34573810 PMCID: PMC8465838 DOI: 10.3390/e23091185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/01/2021] [Accepted: 09/02/2021] [Indexed: 11/16/2022]
Abstract
The comprehension of neuronal network functioning, from most basic mechanisms of signal transmission to complex patterns of memory and decision making, is at the basis of the modern research in experimental and computational neurophysiology. While mechanistic knowledge of neurons and synapses structure increased, the study of functional and effective networks is more complex, involving emergent phenomena, nonlinear responses, collective waves, correlation and causal interactions. Refined data analysis may help in inferring functional/effective interactions and connectivity from neuronal activity. The Transfer Entropy (TE) technique is, among other things, well suited to predict structural interactions between neurons, and to infer both effective and structural connectivity in small- and large-scale networks. To efficiently disentangle the excitatory and inhibitory neural activities, in the article we present a revised version of TE, split in two contributions and characterized by a suited delay time. The method is tested on in silico small neuronal networks, built to simulate the calcium activity as measured via calcium imaging in two-dimensional neuronal cultures. The inhibitory connections are well characterized, still preserving a high accuracy for excitatory connections prediction. The method could be applied to study effective and structural interactions in systems of excitable cells, both in physiological and in pathological conditions.
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Affiliation(s)
- Silvia Ghirga
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia (IIT), Viale Regina Elena 291, 00161 Roma, Italy;
| | - Letizia Chiodo
- Engineering Department, Campus Bio-Medico University of Rome, Via Álvaro del Portillo 21, 00154 Roma, Italy;
| | - Riccardo Marrocchio
- Institute of Sound and Vibration Research, Highfield Campus, University of Southampton, Southampton SO17 1BJ, UK;
| | | | - Alessandro Loppini
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia (IIT), Viale Regina Elena 291, 00161 Roma, Italy;
- Engineering Department, Campus Bio-Medico University of Rome, Via Álvaro del Portillo 21, 00154 Roma, Italy;
- Correspondence:
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29
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Boschi A, Brofiga M, Massobrio P. Thresholding Functional Connectivity Matrices to Recover the Topological Properties of Large-Scale Neuronal Networks. Front Neurosci 2021; 15:705103. [PMID: 34483826 PMCID: PMC8415479 DOI: 10.3389/fnins.2021.705103] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 07/20/2021] [Indexed: 12/24/2022] Open
Abstract
The identification of the organization principles on the basis of the brain connectivity can be performed in terms of structural (i.e., morphological), functional (i.e., statistical), or effective (i.e., causal) connectivity. If structural connectivity is based on the detection of the morphological (synaptically mediated) links among neurons, functional and effective relationships derive from the recording of the patterns of electrophysiological activity (e.g., spikes, local field potentials). Correlation or information theory-based algorithms are typical routes pursued to find statistical dependencies and to build a functional connectivity matrix. As long as the matrix collects the possible associations among the network nodes, each interaction between the neuron i and j is different from zero, even though there was no morphological, statistical or causal connection between them. Hence, it becomes essential to find and identify only the significant functional connections that are predictive of the structural ones. For this reason, a robust, fast, and automatized procedure should be implemented to discard the “noisy” connections. In this work, we present a Double Threshold (DDT) algorithm based on the definition of two statistical thresholds. The main goal is not to lose weak but significant links, whose arbitrary exclusion could generate functional networks with a too small number of connections and altered topological properties. The algorithm allows overcoming the limits of the simplest threshold-based methods in terms of precision and guaranteeing excellent computational performances compared to shuffling-based approaches. The presented DDT algorithm was compared with other methods proposed in the literature by using a benchmarking procedure based on synthetic data coming from the simulations of large-scale neuronal networks with different structural topologies.
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Affiliation(s)
- Alessio Boschi
- Department of Informatics, Bioengineering, Robotics, Systems Engineering (DIBRIS), University of Genova, Genova, Italy
| | - Martina Brofiga
- Department of Informatics, Bioengineering, Robotics, Systems Engineering (DIBRIS), University of Genova, Genova, Italy
| | - Paolo Massobrio
- Department of Informatics, Bioengineering, Robotics, Systems Engineering (DIBRIS), University of Genova, Genova, Italy.,National Institute for Nuclear Physics (INFN), Genova, Italy
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30
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Puppo F, Pré D, Bang AG, Silva GA. Super-Selective Reconstruction of Causal and Direct Connectivity With Application to in vitro iPSC Neuronal Networks. Front Neurosci 2021; 15:647877. [PMID: 34335152 PMCID: PMC8323822 DOI: 10.3389/fnins.2021.647877] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 05/31/2021] [Indexed: 12/22/2022] Open
Abstract
Despite advancements in the development of cell-based in-vitro neuronal network models, the lack of appropriate computational tools limits their analyses. Methods aimed at deciphering the effective connections between neurons from extracellular spike recordings would increase utility of in vitro local neural circuits, especially for studies of human neural development and disease based on induced pluripotent stem cells (hiPSC). Current techniques allow statistical inference of functional couplings in the network but are fundamentally unable to correctly identify indirect and apparent connections between neurons, generating redundant maps with limited ability to model the causal dynamics of the network. In this paper, we describe a novel mathematically rigorous, model-free method to map effective-direct and causal-connectivity of neuronal networks from multi-electrode array data. The inference algorithm uses a combination of statistical and deterministic indicators which, first, enables identification of all existing functional links in the network and then reconstructs the directed and causal connection diagram via a super-selective rule enabling highly accurate classification of direct, indirect, and apparent links. Our method can be generally applied to the functional characterization of any in vitro neuronal networks. Here, we show that, given its accuracy, it can offer important insights into the functional development of in vitro hiPSC-derived neuronal cultures.
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Affiliation(s)
- Francesca Puppo
- BioCircuits Institute and Center for Engineered Natural Intelligence, University of California, San Diego, La Jolla, CA, United States
| | - Deborah Pré
- Conrad Prebys Center for Chemical Genomics, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, United States
| | - Anne G. Bang
- Conrad Prebys Center for Chemical Genomics, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, United States
| | - Gabriel A. Silva
- BioCircuits Institute, Center for Engineered Natural Intelligence, Department of Bioengineering, Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States
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31
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Inhibitory control in neuronal networks relies on the extracellular matrix integrity. Cell Mol Life Sci 2021; 78:5647-5663. [PMID: 34128077 PMCID: PMC8257544 DOI: 10.1007/s00018-021-03861-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 05/11/2021] [Accepted: 05/19/2021] [Indexed: 11/14/2022]
Abstract
Inhibitory control is essential for the regulation of neuronal network activity, where excitatory and inhibitory synapses can act synergistically, reciprocally, and antagonistically. Sustained excitation-inhibition (E-I) balance, therefore, relies on the orchestrated adjustment of excitatory and inhibitory synaptic strength. While growing evidence indicates that the brain’s extracellular matrix (ECM) is a crucial regulator of excitatory synapse plasticity, it remains unclear whether and how the ECM contributes to inhibitory control in neuronal networks. Here we studied the simultaneous changes in excitatory and inhibitory connectivity after ECM depletion. We demonstrate that the ECM supports the maintenance of E-I balance by retaining inhibitory connectivity. Quantification of synapses and super-resolution microscopy showed that depletion of the ECM in mature neuronal networks preferentially decreases the density of inhibitory synapses and the size of individual inhibitory postsynaptic scaffolds. The reduction of inhibitory synapse density is partially compensated by the homeostatically increasing synaptic strength via the reduction of presynaptic GABAB receptors, as indicated by patch-clamp measurements and GABAB receptor expression quantifications. However, both spiking and bursting activity in neuronal networks is increased after ECM depletion, as indicated by multi-electrode recordings. With computational modelling, we determined that ECM depletion reduces the inhibitory connectivity to an extent that the inhibitory synapse scaling does not fully compensate for the reduced inhibitory synapse density. Our results indicate that the brain’s ECM preserves the balanced state of neuronal networks by supporting inhibitory control via inhibitory synapse stabilization, which expands the current understanding of brain activity regulation. ![]()
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32
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McPherson JG, Bandres MF. Spontaneous neural synchrony links intrinsic spinal sensory and motor networks during unconsciousness. eLife 2021; 10:e66308. [PMID: 34042587 PMCID: PMC8177891 DOI: 10.7554/elife.66308] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 05/26/2021] [Indexed: 12/17/2022] Open
Abstract
Non-random functional connectivity during unconsciousness is a defining feature of supraspinal networks. However, its generalizability to intrinsic spinal networks remains incompletely understood. Previously, Barry et al., 2014 used fMRI to reveal bilateral resting state functional connectivity within sensory-dominant and, separately, motor-dominant regions of the spinal cord. Here, we record spike trains from large populations of spinal interneurons in vivo in rats and demonstrate that spontaneous functional connectivity also links sensory- and motor-dominant regions during unconsciousness. The spatiotemporal patterns of connectivity could not be explained by latent afferent activity or by populations of interconnected neurons spiking randomly. We also document connection latencies compatible with mono- and disynaptic interactions and putative excitatory and inhibitory connections. The observed activity is consistent with the hypothesis that salient, experience-dependent patterns of neural transmission introduced during behavior or by injury/disease are reactivated during unconsciousness. Such a spinal replay mechanism could shape circuit-level connectivity and ultimately behavior.
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Affiliation(s)
- Jacob Graves McPherson
- Program in Physical Therapy, Washington University School of MedicineSt. LouisUnited States
- Department of Anesthesiology, Washington University School of MedicineSt. LouisUnited States
- Washington University Pain Center, Washington University School of MedicineSt. LouisUnited States
- Program in Neurosciences, Washington University School of MedicineSt. LouisUnited States
| | - Maria F Bandres
- Program in Physical Therapy, Washington University School of MedicineSt. LouisUnited States
- Department of Biomedical Engineering, Washington University School of MedicineSt. LouisUnited States
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33
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Walker AS, Raliski BK, Nguyen DV, Zhang P, Sanders K, Karbasi K, Miller EW. Imaging Voltage in Complete Neuronal Networks Within Patterned Microislands Reveals Preferential Wiring of Excitatory Hippocampal Neurons. Front Neurosci 2021; 15:643868. [PMID: 34054406 PMCID: PMC8155642 DOI: 10.3389/fnins.2021.643868] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Accepted: 01/28/2021] [Indexed: 12/30/2022] Open
Abstract
Voltage imaging with fluorescent dyes affords the opportunity to map neuronal activity in both time and space. One limitation to imaging is the inability to image complete neuronal networks: some fraction of cells remains outside of the observation window. Here, we combine voltage imaging, post hoc immunocytochemistry, and patterned microisland hippocampal culture to provide imaging of complete neuronal ensembles. The patterned microislands completely fill the field of view of our high-speed (500 Hz) camera, enabling reconstruction of the spiking patterns of every single neuron in the network. Cultures raised on microislands are similar to neurons grown on coverslips, with parallel developmental trajectories and composition of inhibitory and excitatory cell types (CA1, CA3, and dentate granule cells, or DGC). We calculate the likelihood that action potential firing in one neuron triggers action potential firing in a downstream neuron in a spontaneously active network to construct a functional connection map of these neuronal ensembles. Importantly, this functional map indicates preferential connectivity between DGC and CA3 neurons and between CA3 and CA1 neurons, mimicking the neuronal circuitry of the intact hippocampus. We envision that patterned microislands, in combination with voltage imaging and methods to classify cell types, will be a powerful method for exploring neuronal function in both healthy and disease states. Additionally, because the entire neuronal network is sampled simultaneously, this strategy has the power to go further, revealing all functional connections between all cell types.
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Affiliation(s)
- Alison S. Walker
- Department of Chemistry, University of California, Berkeley, Berkeley, CA, United States
- Department of Molecular & Cell Biology, University of California, Berkeley, Berkeley, CA, United States
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
| | - Benjamin K. Raliski
- Department of Chemistry, University of California, Berkeley, Berkeley, CA, United States
| | - Dat Vinh Nguyen
- Department of Chemistry, University of California, Berkeley, Berkeley, CA, United States
| | - Patrick Zhang
- Department of Chemistry, University of California, Berkeley, Berkeley, CA, United States
| | - Kate Sanders
- Department of Chemistry, University of California, Berkeley, Berkeley, CA, United States
| | - Kaveh Karbasi
- Department of Chemistry, University of California, Berkeley, Berkeley, CA, United States
| | - Evan W. Miller
- Department of Chemistry, University of California, Berkeley, Berkeley, CA, United States
- Department of Molecular & Cell Biology, University of California, Berkeley, Berkeley, CA, United States
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
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Inhibitory neurons exhibit high controlling ability in the cortical microconnectome. PLoS Comput Biol 2021; 17:e1008846. [PMID: 33831009 PMCID: PMC8031186 DOI: 10.1371/journal.pcbi.1008846] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 03/01/2021] [Indexed: 02/08/2023] Open
Abstract
The brain is a network system in which excitatory and inhibitory neurons keep activity balanced in the highly non-random connectivity pattern of the microconnectome. It is well known that the relative percentage of inhibitory neurons is much smaller than excitatory neurons in the cortex. So, in general, how inhibitory neurons can keep the balance with the surrounding excitatory neurons is an important question. There is much accumulated knowledge about this fundamental question. This study quantitatively evaluated the relatively higher functional contribution of inhibitory neurons in terms of not only properties of individual neurons, such as firing rate, but also in terms of topological mechanisms and controlling ability on other excitatory neurons. We combined simultaneous electrical recording (~2.5 hours) of ~1000 neurons in vitro, and quantitative evaluation of neuronal interactions including excitatory-inhibitory categorization. This study accurately defined recording brain anatomical targets, such as brain regions and cortical layers, by inter-referring MRI and immunostaining recordings. The interaction networks enabled us to quantify topological influence of individual neurons, in terms of controlling ability to other neurons. Especially, the result indicated that highly influential inhibitory neurons show higher controlling ability of other neurons than excitatory neurons, and are relatively often distributed in deeper layers of the cortex. Furthermore, the neurons having high controlling ability are more effectively limited in number than central nodes of k-cores, and these neurons also participate in more clustered motifs. In summary, this study suggested that the high controlling ability of inhibitory neurons is a key mechanism to keep balance with a large number of other excitatory neurons beyond simple higher firing rate. Application of the selection method of limited important neurons would be also applicable for the ability to effectively and selectively stimulate E/I imbalanced disease states. How small numbers of inhibitory neurons functionally keep balance with large numbers of excitatory neurons in the brain by controlling each other is a fundamental question. Especially, this study quantitatively evaluated a topological mechanism of interaction networks in terms of controlling abilities of individual cortical neurons to other neurons. Combination of simultaneous electrical recording of ~1000 neurons and a quantitative evaluation method of neuronal interactions including excitatory-inhibitory categories, enabled us to evaluate the influence of individual neurons not only about firing rate but also about their relative positions in the networks and controllable ability of other neurons. Especially, the result showed that inhibitory neurons have more controlling ability than excitatory neurons, and such neurons were more often observed in deep layers. Because the limited number of neurons in terms controlling ability were much smaller than neurons based on centrality measure and, of course, more directly selected neurons based on their ability to control other neurons, the selection method of important neurons will help not only to produce realistic computational models but also will help to stimulate brain to effectively treat imbalanced disease states.
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Chicchi L, Cecchini G, Adam I, de Vito G, Livi R, Pavone FS, Silvestri L, Turrini L, Vanzi F, Fanelli D. Reconstruction scheme for excitatory and inhibitory dynamics with quenched disorder: application to zebrafish imaging. J Comput Neurosci 2021; 49:159-174. [PMID: 33826050 PMCID: PMC8046699 DOI: 10.1007/s10827-020-00774-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 12/01/2020] [Accepted: 12/07/2020] [Indexed: 11/30/2022]
Abstract
An inverse procedure is developed and tested to recover functional and structural information from global signals of brains activity. The method assumes a leaky-integrate and fire model with excitatory and inhibitory neurons, coupled via a directed network. Neurons are endowed with a heterogenous current value, which sets their associated dynamical regime. By making use of a heterogenous mean-field approximation, the method seeks to reconstructing from global activity patterns the distribution of in-coming degrees, for both excitatory and inhibitory neurons, as well as the distribution of the assigned currents. The proposed inverse scheme is first validated against synthetic data. Then, time-lapse acquisitions of a zebrafish larva recorded with a two-photon light sheet microscope are used as an input to the reconstruction algorithm. A power law distribution of the in-coming connectivity of the excitatory neurons is found. Local degree distributions are also computed by segmenting the whole brain in sub-regions traced from annotated atlas.
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Affiliation(s)
- Lorenzo Chicchi
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Florence, Italy.,CSDC, University of Florence, Sesto Fiorentino, Florence, Italy
| | - Gloria Cecchini
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Florence, Italy. .,CSDC, University of Florence, Sesto Fiorentino, Florence, Italy.
| | - Ihusan Adam
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Florence, Italy.,CSDC, University of Florence, Sesto Fiorentino, Florence, Italy.,Department of Information Engineering, University of Florence, Florence, Italy
| | - Giuseppe de Vito
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Florence, Italy.,Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy
| | - Roberto Livi
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Florence, Italy.,CSDC, University of Florence, Sesto Fiorentino, Florence, Italy.,INFN Sezione di Firenze, Sesto Fiorentino, Florence, Italy
| | - Francesco Saverio Pavone
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Florence, Italy.,European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Florence, Italy.,National Institute of Optics, National Research Councily, Sesto Fiorentino, Florence, Italy
| | - Ludovico Silvestri
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Florence, Italy.,European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Florence, Italy.,National Institute of Optics, National Research Councily, Sesto Fiorentino, Florence, Italy
| | - Lapo Turrini
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Florence, Italy.,European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Florence, Italy
| | - Francesco Vanzi
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Florence, Italy.,Department of Biology, University of Florence, Sesto Fiorentino, Florence, Italy
| | - Duccio Fanelli
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Florence, Italy.,CSDC, University of Florence, Sesto Fiorentino, Florence, Italy.,INFN Sezione di Firenze, Sesto Fiorentino, Florence, Italy
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36
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Pires Monteiro S, Voogd E, Muzzi L, De Vecchis G, Mossink B, Levers M, Hassink G, Van Putten M, Le Feber J, Hofmeijer J, Frega M. Neuroprotective effect of hypoxic preconditioning and neuronal activation in a in vitro human model of the ischemic penumbra. J Neural Eng 2021; 18:036016. [PMID: 33724235 DOI: 10.1088/1741-2552/abe68a] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVE In ischemic stroke, treatments to protect neurons from irreversible damage are urgently needed. Studies in animal models have shown that neuroprotective treatments targeting neuronal silencing improve brain recovery, but in clinical trials none of these were effective in patients. This failure of translation poses doubts on the real efficacy of treatments tested and on the validity of animal models for human stroke. Here, we established a human neuronal model of the ischemic penumbra by using human induced pluripotent stem cells and we provided an in-depth characterization of neuronal responses to hypoxia and treatment strategies at the network level. APPROACH We generated neurons from induced pluripotent stem cells derived from healthy donor and we cultured them on micro-electrode arrays. We measured the electrophysiological activity of human neuronal networks under controlled hypoxic conditions. We tested the effect of different treatment strategies on neuronal network functionality. MAIN RESULTS Human neuronal networks are vulnerable to hypoxia reflected by a decrease in activity and synchronicity under low oxygen conditions. We observe that full, partial or absent recovery depend on the timing of re-oxygenation and we provide a critical time threshold that, if crossed, is associated with irreversible impairments. We found that hypoxic preconditioning improves resistance to a second hypoxic insult. Finally, in contrast to previously tested, ineffective treatments, we show that stimulatory treatments counteracting neuronal silencing during hypoxia, such as optogenetic stimulation, are neuroprotective. SIGNIFICANCE We presented a human neuronal model of the ischemic penumbra and we provided insights that may offer the basis for novel therapeutic approaches for patients after stroke. The use of human neurons might improve drug discovery and translation of findings to patients and might open new perspectives for personalized investigations.
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Affiliation(s)
- Sara Pires Monteiro
- Department of Clinical Neurophysiology, University of Twente, 7522 NB Enschede, The Netherlands
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37
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Young J, Neveu CL, Byrne JH, Aazhang B. Inferring functional connectivity through graphical directed information. J Neural Eng 2021; 18. [PMID: 33684898 PMCID: PMC8600965 DOI: 10.1088/1741-2552/abecc6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 03/08/2021] [Indexed: 11/25/2022]
Abstract
Objective. Accurate inference of functional connectivity is critical for understanding brain function. Previous methods have limited ability distinguishing between direct and indirect connections because of inadequate scaling with dimensionality. This poor scaling performance reduces the number of nodes that can be included in conditioning. Our goal was to provide a technique that scales better and thereby enables minimization of indirect connections. Approach. Our major contribution is a powerful model-free framework, graphical directed information (GDI), that enables pairwise directed functional connections to be conditioned on the activity of substantially more nodes in a network, producing a more accurate graph of functional connectivity that reduces indirect connections. The key technology enabling this advancement is a recent advance in the estimation of mutual information (MI), which relies on multilayer perceptrons and exploiting an alternative representation of the Kullback–Leibler divergence definition of MI. Our second major contribution is the application of this technique to both discretely valued and continuously valued time series. Main results. GDI correctly inferred the circuitry of arbitrary Gaussian, nonlinear, and conductance-based networks. Furthermore, GDI inferred many of the connections of a model of a central pattern generator circuit in Aplysia, while also reducing many indirect connections. Significance. GDI is a general and model-free technique that can be used on a variety of scales and data types to provide accurate direct connectivity graphs and addresses the critical issue of indirect connections in neural data analysis.
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Affiliation(s)
- Joseph Young
- Department of Electrical & Computer Engineering, Rice University, 6100 Main St, Houston, Texas, 77005, UNITED STATES
| | - Curtis L Neveu
- Department of Neurobiology & Anatomy, The University of Texas Health Science Center at Houston John P and Katherine G McGovern Medical School, 6431 Fannin Street, Houston, Texas, 77030-1501, UNITED STATES
| | - John H Byrne
- Department of Neurobiology and Anatomy, The University of Texas Health Science Center at Houston John P and Katherine G McGovern Medical School, 6431 Fannin Street, Houston, Texas, 77030-1501, UNITED STATES
| | - Behnaam Aazhang
- Department of Electrical & Computer Engineering, Rice University, 6100 Main St, Houston, Texas, 77005, UNITED STATES
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38
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Olivares E, Izquierdo EJ, Beer RD. A Neuromechanical Model of Multiple Network Rhythmic Pattern Generators for Forward Locomotion in C. elegans. Front Comput Neurosci 2021; 15:572339. [PMID: 33679357 PMCID: PMC7930337 DOI: 10.3389/fncom.2021.572339] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Accepted: 01/21/2021] [Indexed: 12/04/2022] Open
Abstract
Multiple mechanisms contribute to the generation, propagation, and coordination of the rhythmic patterns necessary for locomotion in Caenorhabditis elegans. Current experiments have focused on two possibilities: pacemaker neurons and stretch-receptor feedback. Here, we focus on whether it is possible that a chain of multiple network rhythmic pattern generators in the ventral nerve cord also contribute to locomotion. We use a simulation model to search for parameters of the anatomically constrained ventral nerve cord circuit that, when embodied and situated, can drive forward locomotion on agar, in the absence of pacemaker neurons or stretch-receptor feedback. Systematic exploration of the space of possible solutions reveals that there are multiple configurations that result in locomotion that is consistent with certain aspects of the kinematics of worm locomotion on agar. Analysis of the best solutions reveals that gap junctions between different classes of motorneurons in the ventral nerve cord can play key roles in coordinating the multiple rhythmic pattern generators.
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Affiliation(s)
- Erick Olivares
- Cognitive Science Program, Indiana University Bloomington, Bloomington, IN, United States
| | - Eduardo J. Izquierdo
- Cognitive Science Program, Indiana University Bloomington, Bloomington, IN, United States
- Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, United States
| | - Randall D. Beer
- Cognitive Science Program, Indiana University Bloomington, Bloomington, IN, United States
- Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, United States
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39
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Juárez-Vidales JDJ, Pérez-Ortega J, Lorea-Hernández JJ, Méndez-Salcido F, Peña-Ortega F. Configuration and dynamics of dominant inspiratory multineuronal activity patterns during eupnea and gasping generation in vitro. J Neurophysiol 2021; 125:1289-1306. [PMID: 33502956 DOI: 10.1152/jn.00563.2020] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The pre-Bötzinger complex (preBötC), located within the ventral respiratory column, produces inspiratory bursts in varying degrees of synchronization/amplitude. This wide range of population burst patterns reflects the flexibility of the preBötC neurons, which is expressed in variations in the onset/offset times of their activations and their activity during the population bursts, with respiratory neurons exhibiting a large cycle-to-cycle timing jitter both at the population activity onset and at the population activity peak, suggesting that respiratory neurons are stochastically activated before and during the inspiratory bursts. However, it is still unknown whether this stochasticity is maintained while evaluating the coactivity of respiratory neuronal ensembles. Moreover, the preBötC topology also remains unknown. In this study, by simultaneously recording tens of preBötC neurons and using coactivation analysis during the inspiratory periods, we found that the preBötC has a scale-free configuration (mixture of not many highly connected nodes, hubs, with abundant poorly connected elements) exhibiting the rich-club phenomenon (hubs more likely interconnected with each other). PreBötC neurons also produce multineuronal activity patterns (MAPs) that are highly stable and change during the hypoxia-induced reconfiguration. Moreover, preBötC contains a coactivating core network shared by all its MAPs. Finally, we found a distinctive pattern of sequential coactivation of core network neurons at the beginning of the inspiratory periods, indicating that, when evaluated at the multicellular level, the coactivation of respiratory neurons seems not to be stochastic.NEW & NOTEWORTHY By means of multielectrode recordings of preBötC neurons, we evaluated their configuration in normoxia and hypoxia, finding that the preBötC exhibits a scale-free configuration with a rich-club phenomenon. preBötC neurons produce multineuronal activity patterns that are highly stable but change during hypoxia. The preBötC contains a coactivating core network that exhibit a distinctive pattern of coactivation at the beginning of inspirations. These results reveal some network basis of inspiratory rhythm generation and its reconfiguration during hypoxia.
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Affiliation(s)
- Josué de Jesús Juárez-Vidales
- Departamento de Neurobiología del Desarrollo y Neurofisiología, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Queretaro, Mexico
| | - Jesús Pérez-Ortega
- Departamento de Neurobiología del Desarrollo y Neurofisiología, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Queretaro, Mexico
| | - Jonathan Julio Lorea-Hernández
- Departamento de Neurobiología del Desarrollo y Neurofisiología, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Queretaro, Mexico
| | - Felipe Méndez-Salcido
- Departamento de Neurobiología del Desarrollo y Neurofisiología, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Queretaro, Mexico
| | - Fernando Peña-Ortega
- Departamento de Neurobiología del Desarrollo y Neurofisiología, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Queretaro, Mexico
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40
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Fenyves BG, Szilágyi GS, Vassy Z, Sőti C, Csermely P. Synaptic polarity and sign-balance prediction using gene expression data in the Caenorhabditis elegans chemical synapse neuronal connectome network. PLoS Comput Biol 2020; 16:e1007974. [PMID: 33347479 PMCID: PMC7785220 DOI: 10.1371/journal.pcbi.1007974] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 01/05/2021] [Accepted: 10/19/2020] [Indexed: 12/16/2022] Open
Abstract
Graph theoretical analyses of nervous systems usually omit the aspect of connection polarity, due to data insufficiency. The chemical synapse network of Caenorhabditis elegans is a well-reconstructed directed network, but the signs of its connections are yet to be elucidated. Here, we present the gene expression-based sign prediction of the ionotropic chemical synapse connectome of C. elegans (3,638 connections and 20,589 synapses total), incorporating available presynaptic neurotransmitter and postsynaptic receptor gene expression data for three major neurotransmitter systems. We made predictions for more than two-thirds of these chemical synapses and observed an excitatory-inhibitory (E:I) ratio close to 4:1 which was found similar to that observed in many real-world networks. Our open source tool (http://EleganSign.linkgroup.hu) is simple but efficient in predicting polarities by integrating neuronal connectome and gene expression data.
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Affiliation(s)
- Bánk G. Fenyves
- Department of Molecular Biology, Semmelweis University, Budapest, Hungary
- Department of Emergency Medicine, Semmelweis University, Budapest, Hungary
| | - Gábor S. Szilágyi
- Department of Molecular Biology, Semmelweis University, Budapest, Hungary
| | - Zsolt Vassy
- Department of Molecular Biology, Semmelweis University, Budapest, Hungary
| | - Csaba Sőti
- Department of Molecular Biology, Semmelweis University, Budapest, Hungary
| | - Peter Csermely
- Department of Molecular Biology, Semmelweis University, Budapest, Hungary
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41
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Ren N, Ito S, Hafizi H, Beggs JM, Stevenson IH. Model-based detection of putative synaptic connections from spike recordings with latency and type constraints. J Neurophysiol 2020; 124:1588-1604. [DOI: 10.1152/jn.00066.2020] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Detecting synaptic connections using large-scale extracellular spike recordings is a difficult statistical problem. Here, we develop an extension of a generalized linear model that explicitly separates fast synaptic effects and slow background fluctuations in cross-correlograms between pairs of neurons while incorporating circuit properties learned from the whole network. This model outperforms two previously developed synapse detection methods in the simulated networks and recovers plausible connections from hundreds of neurons in in vitro multielectrode array data.
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Affiliation(s)
- Naixin Ren
- Department of Psychological Sciences, University of Connecticut, Storrs, Connecticut
| | - Shinya Ito
- Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, California
| | - Hadi Hafizi
- Department of Physics, Indiana University, Bloomington, Indiana
| | - John M. Beggs
- Department of Physics, Indiana University, Bloomington, Indiana
| | - Ian H. Stevenson
- Department of Psychological Sciences, University of Connecticut, Storrs, Connecticut
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut
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42
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Ghosh S, Mondal A, Ji P, Mishra A, Dana SK, Antonopoulos CG, Hens C. Emergence of Mixed Mode Oscillations in Random Networks of Diverse Excitable Neurons: The Role of Neighbors and Electrical Coupling. Front Comput Neurosci 2020; 14:49. [PMID: 32581757 PMCID: PMC7294985 DOI: 10.3389/fncom.2020.00049] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 05/04/2020] [Indexed: 11/21/2022] Open
Abstract
In this paper, we focus on the emergence of diverse neuronal oscillations arising in a mixed population of neurons with different excitability properties. These properties produce mixed mode oscillations (MMOs) characterized by the combination of large amplitudes and alternate subthreshold or small amplitude oscillations. Considering the biophysically plausible, Izhikevich neuron model, we demonstrate that various MMOs, including MMBOs (mixed mode bursting oscillations) and synchronized tonic spiking appear in a randomly connected network of neurons, where a fraction of them is in a quiescent (silent) state and the rest in self-oscillatory (firing) states. We show that MMOs and other patterns of neural activity depend on the number of oscillatory neighbors of quiescent nodes and on electrical coupling strengths. Our results are verified by constructing a reduced-order network model and supported by systematic bifurcation diagrams as well as for a small-world network. Our results suggest that, for weak couplings, MMOs appear due to the de-synchronization of a large number of quiescent neurons in the networks. The quiescent neurons together with the firing neurons produce high frequency oscillations and bursting activity. The overarching goal is to uncover a favorable network architecture and suitable parameter spaces where Izhikevich model neurons generate diverse responses ranging from MMOs to tonic spiking.
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Affiliation(s)
- Subrata Ghosh
- Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata, India
| | - Argha Mondal
- Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata, India
| | - Peng Ji
- The Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Arindam Mishra
- Department of Mathematics, Centre for Mathematical Biology and Ecology, Jadavpur University, Kolkata, India
| | - Syamal K Dana
- Department of Mathematics, Centre for Mathematical Biology and Ecology, Jadavpur University, Kolkata, India.,Division of Dynamics, Faculty of Mechanical Engineering, Lodz University of Technology, Lodz, Poland
| | - Chris G Antonopoulos
- Department of Mathematical Sciences, University of Essex, Colchester, United Kingdom
| | - Chittaranjan Hens
- Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata, India
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43
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Gudowska-Nowak E, Nowak MA, Chialvo DR, Ochab JK, Tarnowski W. From Synaptic Interactions to Collective Dynamics in Random Neuronal Networks Models: Critical Role of Eigenvectors and Transient Behavior. Neural Comput 2019; 32:395-423. [PMID: 31835001 DOI: 10.1162/neco_a_01253] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
The study of neuronal interactions is at the center of several big collaborative neuroscience projects (including the Human Connectome Project, the Blue Brain Project, and the Brainome) that attempt to obtain a detailed map of the entire brain. Under certain constraints, mathematical theory can advance predictions of the expected neural dynamics based solely on the statistical properties of the synaptic interaction matrix. This work explores the application of free random variables to the study of large synaptic interaction matrices. Besides recovering in a straightforward way known results on eigenspectra in types of models of neural networks proposed by Rajan and Abbott (2006), we extend them to heavy-tailed distributions of interactions. More important, we analytically derive the behavior of eigenvector overlaps, which determine the stability of the spectra. We observe that on imposing the neuronal excitation/inhibition balance, despite the eigenvalues remaining unchanged, their stability dramatically decreases due to the strong nonorthogonality of associated eigenvectors. This leads us to the conclusion that understanding the temporal evolution of asymmetric neural networks requires considering the entangled dynamics of both eigenvectors and eigenvalues, which might bear consequences for learning and memory processes in these models. Considering the success of free random variables theory in a wide variety of disciplines, we hope that the results presented here foster the additional application of these ideas in the area of brain sciences.
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Affiliation(s)
- E Gudowska-Nowak
- Marian Smoluchowski Institute of Physics and Mark Kac Complex Systems Research Center, Jagiellonian University, PL 30-348 Kraków, Poland
| | - M A Nowak
- Marian Smoluchowski Institute of Physics and Mark Kac Complex Systems Research Center, Jagiellonian University, PL 30-348 Kraków, Poland
| | - D R Chialvo
- Center for Complex Systems and Brain Sciences, Escuela de Ciencia y Tecnología, Universidad Nacional de San Martín, San Martín, 1650 Buenos Aires, Argentina and Consejo Nacional de Investigaciones Científicas y Tecnológicas, 1650 Buenos Aires, Argentina
| | - J K Ochab
- Marian Smoluchowski Institute of Physics and Mark Kac Complex Systems Research Center, Jagiellonian University, PL 30-348 Kraków, Poland
| | - W Tarnowski
- Marian Smoluchowski Institute of Physics and Mark Kac Complex Systems Research Center, Jagiellonian University, PL 30-348 Kraków, Poland
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44
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López-Madrona VJ, Matias FS, Mirasso CR, Canals S, Pereda E. Inferring correlations associated to causal interactions in brain signals using autoregressive models. Sci Rep 2019; 9:17041. [PMID: 31745163 PMCID: PMC6863873 DOI: 10.1038/s41598-019-53453-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 10/26/2019] [Indexed: 12/22/2022] Open
Abstract
The specific connectivity of a neuronal network is reflected in the dynamics of the signals recorded on its nodes. The analysis of how the activity in one node predicts the behaviour of another gives the directionality in their relationship. However, each node is composed of many different elements which define the properties of the links. For instance, excitatory and inhibitory neuronal subtypes determine the functionality of the connection. Classic indexes such as the Granger causality (GC) quantifies these interactions, but they do not infer into the mechanism behind them. Here, we introduce an extension of the well-known GC that analyses the correlation associated to the specific influence that a transmitter node has over the receiver. This way, the G-causal link has a positive or negative effect if the predicted activity follows directly or inversely, respectively, the dynamics of the sender. The method is validated in a neuronal population model, testing the paradigm that excitatory and inhibitory neurons have a differential effect in the connectivity. Our approach correctly infers the positive or negative coupling produced by different types of neurons. Our results suggest that the proposed approach provides additional information on the characterization of G-causal connections, which is potentially relevant when it comes to understanding interactions in the brain circuits.
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Affiliation(s)
| | - Fernanda S Matias
- Cognitive Neuroimaging Unit, Commissariat à l'Energie Atomique (CEA), INSERM U992, NeuroSpin Center, 91191, Gif-sur-Yvete, France.,Instituto de Física, Universidade Federal de Alagoas, 57072-970, Maceió, Alagoas, Brazil
| | - Claudio R Mirasso
- Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (UIB-CSIC), Campus Universitat de les Illes Balears, E-07122, Palma de Mallorca, Spain
| | - Santiago Canals
- Instituto de Neurociencias, CSIC-UMH, Sant Joan d'Alacant, 03550, Spain
| | - Ernesto Pereda
- Departamento de Ingeniería Industrial, Escuela Superior de Ingeniería y Tecnología, IUNE, Universidad de La Laguna, Tenerife, 38205, Spain. .,Laboratory of Cognitive and Computational Neuroscience, CTB, UPM, Madrid, Spain.
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45
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Inferring and validating mechanistic models of neural microcircuits based on spike-train data. Nat Commun 2019; 10:4933. [PMID: 31666513 PMCID: PMC6821748 DOI: 10.1038/s41467-019-12572-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Accepted: 09/18/2019] [Indexed: 01/11/2023] Open
Abstract
The interpretation of neuronal spike train recordings often relies on abstract statistical models that allow for principled parameter estimation and model selection but provide only limited insights into underlying microcircuits. In contrast, mechanistic models are useful to interpret microcircuit dynamics, but are rarely quantitatively matched to experimental data due to methodological challenges. Here we present analytical methods to efficiently fit spiking circuit models to single-trial spike trains. Using derived likelihood functions, we statistically infer the mean and variance of hidden inputs, neuronal adaptation properties and connectivity for coupled integrate-and-fire neurons. Comprehensive evaluations on synthetic data, validations using ground truth in-vitro and in-vivo recordings, and comparisons with existing techniques demonstrate that parameter estimation is very accurate and efficient, even for highly subsampled networks. Our methods bridge statistical, data-driven and theoretical, model-based neurosciences at the level of spiking circuits, for the purpose of a quantitative, mechanistic interpretation of recorded neuronal population activity. It is difficult to fit mechanistic, biophysically constrained circuit models to spike train data from in vivo extracellular recordings. Here the authors present analytical methods that enable efficient parameter estimation for integrate-and-fire circuit models and inference of the underlying connectivity structure in subsampled networks.
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Buccelli S, Bornat Y, Colombi I, Ambroise M, Martines L, Pasquale V, Bisio M, Tessadori J, Nowak P, Grassia F, Averna A, Tedesco M, Bonifazi P, Difato F, Massobrio P, Levi T, Chiappalone M. A Neuromorphic Prosthesis to Restore Communication in Neuronal Networks. iScience 2019; 19:402-414. [PMID: 31421595 PMCID: PMC6706626 DOI: 10.1016/j.isci.2019.07.046] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 07/18/2019] [Accepted: 07/29/2019] [Indexed: 12/20/2022] Open
Abstract
Recent advances in bioelectronics and neural engineering allowed the development of brain machine interfaces and neuroprostheses, capable of facilitating or recovering functionality in people with neurological disability. To realize energy-efficient and real-time capable devices, neuromorphic computing systems are envisaged as the core of next-generation systems for brain repair. We demonstrate here a real-time hardware neuromorphic prosthesis to restore bidirectional interactions between two neuronal populations, even when one is damaged or missing. We used in vitro modular cell cultures to mimic the mutual interaction between neuronal assemblies and created a focal lesion to functionally disconnect the two populations. Then, we employed our neuromorphic prosthesis for bidirectional bridging to artificially reconnect two disconnected neuronal modules and for hybrid bidirectional bridging to replace the activity of one module with a real-time hardware neuromorphic Spiking Neural Network. Our neuroprosthetic system opens avenues for the exploitation of neuromorphic-based devices in bioelectrical therapeutics for health care.
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Affiliation(s)
- Stefano Buccelli
- Rehab Technologies IIT-INAIL Lab, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy; Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal and Child science (DINOGMI), University of Genova, L.go P. Daneo 3, 16132 Genova, Italy; Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Yannick Bornat
- Laboratoire de l'Intégration du Matériau au Système (IMS), University of Bordeaux, Bordeaux INP, CNRS UMR 5218, 351 Cours de la Libération, 33405 Talence Cedex, France
| | - Ilaria Colombi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal and Child science (DINOGMI), University of Genova, L.go P. Daneo 3, 16132 Genova, Italy; Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Matthieu Ambroise
- Laboratoire de l'Intégration du Matériau au Système (IMS), University of Bordeaux, Bordeaux INP, CNRS UMR 5218, 351 Cours de la Libération, 33405 Talence Cedex, France
| | - Laura Martines
- Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy; Department of Informatics, Bioengineering, Robotics, System Engineering (DIBRIS), University of Genova, Via all'Opera Pia 13, 16145 Genova, Italy
| | - Valentina Pasquale
- Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Marta Bisio
- Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy; Department of Neurosciences, University of Padova, Via Nicolò Giustiniani 5, 35128 Padova, Italy
| | - Jacopo Tessadori
- Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Przemysław Nowak
- Department of Informatics, Bioengineering, Robotics, System Engineering (DIBRIS), University of Genova, Via all'Opera Pia 13, 16145 Genova, Italy; Institute of Information Technology, Lodz University of Technology, ul. Wolczanska 215, 90-924 Lodz, Poland
| | - Filippo Grassia
- Laboratoire de l'Intégration du Matériau au Système (IMS), University of Bordeaux, Bordeaux INP, CNRS UMR 5218, 351 Cours de la Libération, 33405 Talence Cedex, France; University of Picardie Jules Verne, Laboratory of Innovative Technologies (LTI, EA 3899), Avenue des Facultés, Le Bailly, 80025 Amiens, France
| | - Alberto Averna
- Rehab Technologies IIT-INAIL Lab, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy; Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal and Child science (DINOGMI), University of Genova, L.go P. Daneo 3, 16132 Genova, Italy; Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Mariateresa Tedesco
- Department of Informatics, Bioengineering, Robotics, System Engineering (DIBRIS), University of Genova, Via all'Opera Pia 13, 16145 Genova, Italy
| | - Paolo Bonifazi
- School of Physics and Astronomy, Tel Aviv University, 69978 Tel Aviv, Israel; Computational Neuroimaging Laboratory, Biocruces Health Research Institute, Hospital Universitario Cruces, Baracaldo, Vizcaya 48903, Spain; Ikerbasque: The Basque Foundation for Science, Bilbao, Bizkaia 48013, Spain
| | - Francesco Difato
- Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Paolo Massobrio
- Department of Informatics, Bioengineering, Robotics, System Engineering (DIBRIS), University of Genova, Via all'Opera Pia 13, 16145 Genova, Italy
| | - Timothée Levi
- Laboratoire de l'Intégration du Matériau au Système (IMS), University of Bordeaux, Bordeaux INP, CNRS UMR 5218, 351 Cours de la Libération, 33405 Talence Cedex, France; LIMMS CNRS-IIS, The University of Tokyo, 153-8505 Tokyo, Japan.
| | - Michela Chiappalone
- Rehab Technologies IIT-INAIL Lab, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy; Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy.
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Active High-Density Electrode Arrays: Technology and Applications in Neuronal Cell Cultures. ADVANCES IN NEUROBIOLOGY 2019. [PMID: 31073940 DOI: 10.1007/978-3-030-11135-9_11] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Abstract
Active high-density electrode arrays realized with complementary metal-oxide-semiconductor (CMOS) technology provide electrophysiological recordings from several thousands of closely spaced microelectrodes. This has drastically advanced the spatiotemporal recording resolution of conventional multielectrode arrays (MEAs). Thus, today's electrophysiology in neuronal cultures can exploit label-free electrical readouts from a large number of single neurons within the same network. This provides advanced capabilities to investigate the properties of self-assembling neuronal networks, to advance studies on neurotoxicity and neurodevelopmental alterations associated with human brain diseases, and to develop cell culture models for testing drug- or cell-based strategies for therapies.Here, after introducing the reader to this neurotechnology, we summarize the results of different recent studies demonstrating the potential of active high-density electrode arrays for experimental applications. We also discuss ongoing and possible future research directions that might allow for moving these platforms forward for screening applications.
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De Blasi S, Ciba M, Bahmer A, Thielemann C. Total spiking probability edges: A cross-correlation based method for effective connectivity estimation of cortical spiking neurons. J Neurosci Methods 2019; 312:169-181. [DOI: 10.1016/j.jneumeth.2018.11.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 11/05/2018] [Accepted: 11/19/2018] [Indexed: 01/06/2023]
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Reconstruction of Functional Connectivity from Multielectrode Recordings and Calcium Imaging. ADVANCES IN NEUROBIOLOGY 2019; 22:207-231. [PMID: 31073938 DOI: 10.1007/978-3-030-11135-9_9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
In the last two decades, increasing research efforts in neuroscience have been focused on determining both structural and functional connectivity of brain circuits, with the main goal of relating the wiring diagram of neuronal systems to their emerging properties, from the microscale to the macroscale. While combining multisite parallel recordings with structural circuits' reconstruction in vivo is still very challenging, the reductionist in vitro approach based on neuronal cultures offers lower technical difficulties and is much more stable under control conditions. In this chapter, we present different approaches to infer the connectivity of cultured neuronal networks using multielectrode array or calcium imaging recordings. We first formally introduce the used methods, and then we will describe into details how those methods were applied in case studies. Since multielectrode array and calcium imaging recordings provide distinct and complementary spatiotemporal features of neuronal activity, in this chapter we present the strategies implemented with the two different methodologies in distinct sections.
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