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Marinho LSR, Chiarantin GMD, Ikebara JM, Cardoso DS, de Lima-Vasconcellos TH, Higa GSV, Ferraz MSA, De Pasquale R, Takada SH, Papes F, Muotri AR, Kihara AH. The impact of antidepressants on human neurodevelopment: Brain organoids as experimental tools. Semin Cell Dev Biol 2023; 144:67-76. [PMID: 36115764 DOI: 10.1016/j.semcdb.2022.09.007] [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: 07/06/2022] [Revised: 09/10/2022] [Accepted: 09/10/2022] [Indexed: 11/23/2022]
Abstract
The use of antidepressants during pregnancy benefits the mother's well-being, but the effects of such substances on neurodevelopment remain poorly understood. Moreover, the consequences of early exposure to antidepressants may not be immediately apparent at birth. In utero exposure to selective serotonin reuptake inhibitors (SSRIs) has been related to developmental abnormalities, including a reduced white matter volume. Several reports have observed an increased incidence of autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) after prenatal exposure to SSRIs such as sertraline, the most widely prescribed SSRI. The advent of human-induced pluripotent stem cell (hiPSC) methods and assays now offers appropriate tools to test the consequences of such compounds for neurodevelopment in vitro. In particular, hiPSCs can be used to generate cerebral organoids - self-organized structures that recapitulate the morphology and complex physiology of the developing human brain, overcoming the limitations found in 2D cell culture and experimental animal models for testing drug efficacy and side effects. For example, single-cell RNA sequencing (scRNA-seq) and electrophysiological measurements on organoids can be used to evaluate the impact of antidepressants on the transcriptome and neuronal activity signatures in developing neurons. While the analysis of large-scale transcriptomic data depends on dimensionality reduction methods, electrophysiological recordings rely on temporal data series to discriminate statistical characteristics of neuronal activity, allowing for the rigorous analysis of the effects of antidepressants and other molecules that affect the developing nervous system, especially when applied in combination with relevant human cellular models such as brain organoids.
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Affiliation(s)
| | | | - Juliane Midori Ikebara
- Neurogenetics Laboratory, Universidade Federal do ABC, São Bernardo do Campo, SP 09606-045, Brazil
| | - Débora Sterzeck Cardoso
- Neurogenetics Laboratory, Universidade Federal do ABC, São Bernardo do Campo, SP 09606-045, Brazil
| | | | - Guilherme Shigueto Vilar Higa
- Neurogenetics Laboratory, Universidade Federal do ABC, São Bernardo do Campo, SP 09606-045, Brazil; Department of Physiology and Biophysics, Biomedical Sciences Institute I, São Paulo University, São Paulo, SP 05508-000, Brazil
| | | | - Roberto De Pasquale
- Department of Physiology and Biophysics, Biomedical Sciences Institute I, São Paulo University, São Paulo, SP 05508-000, Brazil
| | - Silvia Honda Takada
- Neurogenetics Laboratory, Universidade Federal do ABC, São Bernardo do Campo, SP 09606-045, Brazil
| | - Fabio Papes
- Department of Genetics, Evolution, Microbiology and Immunology, Institute of Biology, University of Campinas, Campinas, SP 13083-862, Brazil; Center for Medicinal Chemistry, University of Campinas, Campinas, SP 13083-875, Brazil; Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Alysson R Muotri
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Department of Cellular & Molecular Medicine, University of California San Diego, School of Medicine, Center for Academic Research and Training in Anthropogeny, Kavli Institute for Brain and Mind, Archealization Center (ArchC), La Jolla, CA 92037, USA.
| | - Alexandre Hiroaki Kihara
- Neurogenetics Laboratory, Universidade Federal do ABC, São Bernardo do Campo, SP 09606-045, Brazil.
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2
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Kim H, Min C, Jeong B, Lee KJ. Deciphering clock cell network morphology within the biological master clock, suprachiasmatic nucleus: From the perspective of circadian wave dynamics. PLoS Comput Biol 2022; 18:e1010213. [PMID: 35666776 PMCID: PMC9203024 DOI: 10.1371/journal.pcbi.1010213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 06/16/2022] [Accepted: 05/16/2022] [Indexed: 11/18/2022] Open
Abstract
The biological master clock, suprachiasmatic nucleus (of rat and mouse), is composed of ~10,000 clock cells which are heterogeneous with respect to their circadian periods. Despite this inhomogeneity, an intact SCN maintains a very good degree of circadian phase (time) coherence which is vital for sustaining various circadian rhythmic activities, and it is supposedly achieved by not just one but a few different cell-to-cell coupling mechanisms, among which action potential (AP)-mediated connectivity is known to be essential. But, due to technical difficulties and limitations in experiments, so far very little information is available about the morphology of the connectivity at a cellular scale. Building upon this limited amount of information, here we exhaustively and systematically explore a large pool (~25,000) of various network morphologies to come up with some plausible network features of SCN networks. All candidates under consideration reflect an experimentally obtained 'indegree distribution' as well as a 'physical range distribution of afferent clock cells.' Then, importantly, with a set of multitude criteria based on the properties of SCN circadian phase waves in extrinsically perturbed as well as in their natural states, we select out appropriate model networks: Some important measures are, 1) level of phase dispersal and direction of wave propagation, 2) phase-resetting ability of the model networks subject to external circadian forcing, and 3) decay rate of perturbation induced "phase-singularities." The successful, realistic networks have several common features: 1) "indegree" and "outdegree" should have a positive correlation; 2) the cells in the SCN ventrolateral region (core) have a much larger total degree than that of the dorsal medial region (shell); 3) The number of intra-core edges is about 7.5 times that of intra-shell edges; and 4) the distance probability density function for the afferent connections fits well to a beta function. We believe that these newly identified network features would be a useful guide for future explorations on the very much unknown AP-mediated clock cell connectome within the SCN.
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Affiliation(s)
- Hyun Kim
- Department of Physics, Korea University, Seoul, Korea
| | - Cheolhong Min
- Department of Physics, Korea University, Seoul, Korea
| | - Byeongha Jeong
- University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Kyoung J. Lee
- Department of Physics, Korea University, Seoul, Korea
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3
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O'Donovan B, Neugornet A, Neogi R, Xia M, Ortinski P. Cocaine experience induces functional adaptations in astrocytes: Implications for synaptic plasticity in the nucleus accumbens shell. Addict Biol 2021; 26:e13042. [PMID: 33864336 DOI: 10.1111/adb.13042] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 03/22/2021] [Accepted: 03/26/2021] [Indexed: 11/24/2022]
Abstract
Astrocytes have become established as an important regulator of neuronal activity in the brain. Accumulating literature demonstrates that cocaine self-administration in rodent models induces structural changes within astrocytes that may influence their interaction with the surrounding neurons. Here, we provide evidence that cocaine impacts astrocytes at the functional level and alters neuronal sensitivity to astrocyte-derived glutamate. We report that a 14-day period of short access to cocaine (2 h/day) decreases spontaneous astrocytic Ca2+ transients and precipitates changes in astrocyte network activity in the nucleus accumbens shell. This is accompanied by increased prevalence of slow inward currents, a physiological marker of neuronal activation by astrocytic glutamate, in a subset of medium spiny neurons. Within, but not outside, of this subset, we observe an increase in duration and frequency of N-methyl-D-aspartate (NMDA) receptor-mediated synaptic events. Additionally, we find that the link between synaptic NMDA receptor plasticity and neuron sensitivity to astrocytic glutamate is maintained independent of drug exposure and is observed in both cocaine and saline control animals. Imaging analyses of neuronal Ca2+ activity show no effect of cocaine self-administration on individual cells or on neuronal network activity in brain slices. Therefore, our data indicate that cocaine self-administration promotes astrocyte-specific functional changes that can be linked to increased glutamate-mediated coupling with principal neurons in the nucleus accumbens. Such coupling may be spatially restricted as it does not result in a broad impact on network structure of local neuronal circuits.
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Affiliation(s)
- Bernadette O'Donovan
- Department of Neuroscience, College of Medicine University of Kentucky Lexington Kentucky USA
| | - Austin Neugornet
- Department of Neuroscience, College of Medicine University of Kentucky Lexington Kentucky USA
| | - Richik Neogi
- Department of Neuroscience, College of Medicine University of Kentucky Lexington Kentucky USA
- Integrated Biomedical Sciences University of Kentucky Lexington Kentucky USA
| | - Mengfan Xia
- Department of Neuroscience, College of Medicine University of Kentucky Lexington Kentucky USA
| | - Pavel Ortinski
- Department of Neuroscience, College of Medicine University of Kentucky Lexington Kentucky USA
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4
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Laing CR. Effects of degree distributions in random networks of type-I neurons. Phys Rev E 2021; 103:052305. [PMID: 34134197 DOI: 10.1103/physreve.103.052305] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 04/28/2021] [Indexed: 11/07/2022]
Abstract
We consider large networks of theta neurons and use the Ott-Antonsen ansatz to derive degree-based mean-field equations governing the expected dynamics of the networks. Assuming random connectivity, we investigate the effects of varying the widths of the in- and out-degree distributions on the dynamics of excitatory or inhibitory synaptically coupled networks and gap junction coupled networks. For synaptically coupled networks, the dynamics are independent of the out-degree distribution. Broadening the in-degree distribution destroys oscillations in inhibitory networks and decreases the range of bistability in excitatory networks. For gap junction coupled neurons, broadening the degree distribution varies the values of parameters at which there is an onset of collective oscillations. Many of the results are shown to also occur in networks of more realistic neurons.
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Affiliation(s)
- Carlo R Laing
- School of Natural and Computational Sciences, Massey University, Private Bag 102-904 NSMC, Auckland, New Zealand
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5
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Knoll G, Lindner B. Recurrence-mediated suprathreshold stochastic resonance. J Comput Neurosci 2021; 49:407-418. [PMID: 34003421 PMCID: PMC8556192 DOI: 10.1007/s10827-021-00788-3] [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: 02/09/2021] [Revised: 04/21/2021] [Accepted: 04/26/2021] [Indexed: 11/29/2022]
Abstract
It has previously been shown that the encoding of time-dependent signals by feedforward networks (FFNs) of processing units exhibits suprathreshold stochastic resonance (SSR), which is an optimal signal transmission for a finite level of independent, individual stochasticity in the single units. In this study, a recurrent spiking network is simulated to demonstrate that SSR can be also caused by network noise in place of intrinsic noise. The level of autonomously generated fluctuations in the network can be controlled by the strength of synapses, and hence the coding fraction (our measure of information transmission) exhibits a maximum as a function of the synaptic coupling strength. The presence of a coding peak at an optimal coupling strength is robust over a wide range of individual, network, and signal parameters, although the optimal strength and peak magnitude depend on the parameter being varied. We also perform control experiments with an FFN illustrating that the optimized coding fraction is due to the change in noise level and not from other effects entailed when changing the coupling strength. These results also indicate that the non-white (temporally correlated) network noise in general provides an extra boost to encoding performance compared to the FFN driven by intrinsic white noise fluctuations.
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Affiliation(s)
- Gregory Knoll
- Bernstein Center for Computational Neuroscience Berlin, Philippstr. 13, Haus 2, Berlin, 10115, Germany. .,Physics Department of Humboldt University Berlin, Newtonstr. 15, 12489, Berlin, Germany.
| | - Benjamin Lindner
- Bernstein Center for Computational Neuroscience Berlin, Philippstr. 13, Haus 2, Berlin, 10115, Germany.,Physics Department of Humboldt University Berlin, Newtonstr. 15, 12489, Berlin, Germany
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6
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Laing CR, Bläsche C, Means S. Dynamics of Structured Networks of Winfree Oscillators. Front Syst Neurosci 2021; 15:631377. [PMID: 33643004 PMCID: PMC7902706 DOI: 10.3389/fnsys.2021.631377] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 01/18/2021] [Indexed: 01/01/2023] Open
Abstract
Winfree oscillators are phase oscillator models of neurons, characterized by their phase response curve and pulsatile interaction function. We use the Ott/Antonsen ansatz to study large heterogeneous networks of Winfree oscillators, deriving low-dimensional differential equations which describe the evolution of the expected state of networks of oscillators. We consider the effects of correlations between an oscillator's in-degree and out-degree, and between the in- and out-degrees of an “upstream” and a “downstream” oscillator (degree assortativity). We also consider correlated heterogeneity, where some property of an oscillator is correlated with a structural property such as degree. We finally consider networks with parameter assortativity, coupling oscillators according to their intrinsic frequencies. The results show how different types of network structure influence its overall dynamics.
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Affiliation(s)
- Carlo R Laing
- School of Natural and Computational Sciences, Massey University, Auckland, New Zealand
| | - Christian Bläsche
- School of Natural and Computational Sciences, Massey University, Auckland, New Zealand
| | - Shawn Means
- School of Natural and Computational Sciences, Massey University, Auckland, New Zealand
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7
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Laing CR, Bläsche C. The effects of within-neuron degree correlations in networks of spiking neurons. BIOLOGICAL CYBERNETICS 2020; 114:337-347. [PMID: 32124039 DOI: 10.1007/s00422-020-00822-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 02/15/2020] [Indexed: 05/20/2023]
Abstract
We consider the effects of correlations between the in- and out-degrees of individual neurons on the dynamics of a network of neurons. By using theta neurons, we can derive a set of coupled differential equations for the expected dynamics of neurons with the same in-degree. A Gaussian copula is used to introduce correlations between a neuron's in- and out-degree, and numerical bifurcation analysis is used determine the effects of these correlations on the network's dynamics. For excitatory coupling, we find that inducing positive correlations has a similar effect to increasing the coupling strength between neurons, while for inhibitory coupling it has the opposite effect. We also determine the propensity of various two- and three-neuron motifs to occur as correlations are varied and give a plausible explanation for the observed changes in dynamics.
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Affiliation(s)
- Carlo R Laing
- School of Natural and Computational Sciences, Massey University, NSMC, Private Bag 102-904, Auckland, New Zealand.
| | - Christian Bläsche
- School of Natural and Computational Sciences, Massey University, NSMC, Private Bag 102-904, Auckland, New Zealand
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8
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Vegué M, Roxin A. Firing rate distributions in spiking networks with heterogeneous connectivity. Phys Rev E 2019; 100:022208. [PMID: 31574753 DOI: 10.1103/physreve.100.022208] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Indexed: 11/07/2022]
Abstract
Mean-field theory for networks of spiking neurons based on the so-called diffusion approximation has been used to calculate certain measures of neuronal activity which can be compared with experimental data. This includes the distribution of firing rates across the network. However, the theory in its current form applies only to networks in which there is relatively little heterogeneity in the number of incoming and outgoing connections per neuron. Here we extend this theory to include networks with arbitrary degree distributions. Furthermore, the theory takes into account correlations in the in-degree and out-degree of neurons, which would arise, e.g., in the case of networks with hublike neurons. Finally, we show that networks with broad and positively correlated degrees can generate a large-amplitude sustained response to transient stimuli which does not occur in more homogeneous networks.
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Affiliation(s)
- Marina Vegué
- Centre de Recerca Matemàtica, Bellaterra, Spain and Departament de Matemàtiques, Universitat Politècnica de Catalunya, Barcelona, Spain and Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas y Universidad Miguel Hernández, Sant Joan d'Alacant, Alicante, Spain
| | - Alex Roxin
- Centre de Recerca Matemàtica, Bellaterra, Spain and Barcelona Graduate School of Mathematics, Barcelona, Spain
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9
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Zhao J, Qin YM, Che YQ. Effects of topologies on signal propagation in feedforward networks. CHAOS (WOODBURY, N.Y.) 2018; 28:013117. [PMID: 29390642 DOI: 10.1063/1.4999996] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
We systematically investigate the effects of topologies on signal propagation in feedforward networks (FFNs) based on the FitzHugh-Nagumo neuron model. FFNs with different topological structures are constructed with same number of both in-degrees and out-degrees in each layer and given the same input signal. The propagation of firing patterns and firing rates are found to be affected by the distribution of neuron connections in the FFNs. Synchronous firing patterns emerge in the later layers of FFNs with identical, uniform, and exponential degree distributions, but the number of synchronous spike trains in the output layers of the three topologies obviously differs from one another. The firing rates in the output layers of the three FFNs can be ordered from high to low according to their topological structures as exponential, uniform, and identical distributions, respectively. Interestingly, the sequence of spiking regularity in the output layers of the three FFNs is consistent with the firing rates, but their firing synchronization is in the opposite order. In summary, the node degree is an important factor that can dramatically influence the neuronal network activity.
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Affiliation(s)
- Jia Zhao
- Key Laboratory of Cognition and Personality (Ministry of Education) and Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Ying-Mei Qin
- Tianjin Key Laboratory of Information Sensing and Intelligent Control, Tianjin University of Technology and Education, Tianjin 300222, China
| | - Yan-Qiu Che
- Tianjin Key Laboratory of Information Sensing and Intelligent Control, Tianjin University of Technology and Education, Tianjin 300222, China
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10
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Kähne M, Sokolov IM, Rüdiger S. Population equations for degree-heterogenous neural networks. Phys Rev E 2017; 96:052306. [PMID: 29347732 DOI: 10.1103/physreve.96.052306] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Indexed: 11/07/2022]
Abstract
We develop a statistical framework for studying recurrent networks with broad distributions of the number of synaptic links per neuron. We treat each group of neurons with equal input degree as one population and derive a system of equations determining the population-averaged firing rates. The derivation rests on an assumption of a large number of neurons and, additionally, an assumption of a large number of synapses per neuron. For the case of binary neurons, analytical solutions can be constructed, which correspond to steps in the activity versus degree space. We apply this theory to networks with degree-correlated topology and show that complex, multi-stable regimes can result for increasing correlations. Our work is motivated by the recent finding of subnetworks of highly active neurons and the fact that these neurons tend to be connected to each other with higher probability.
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Affiliation(s)
- M Kähne
- Institut für Physik, Humboldt-Universität zu Berlin, Germany
| | - I M Sokolov
- Institut für Physik, Humboldt-Universität zu Berlin, Germany
| | - S Rüdiger
- Institut für Physik, Humboldt-Universität zu Berlin, Germany
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11
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Optimizing information processing in neuronal networks beyond critical states. PLoS One 2017; 12:e0184367. [PMID: 28922366 PMCID: PMC5603180 DOI: 10.1371/journal.pone.0184367] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2017] [Accepted: 08/22/2017] [Indexed: 11/19/2022] Open
Abstract
Critical dynamics have been postulated as an ideal regime for neuronal networks in the brain, considering optimal dynamic range and information processing. Herein, we focused on how information entropy encoded in spatiotemporal activity patterns may vary in critical networks. We employed branching process based models to investigate how entropy can be embedded in spatiotemporal patterns. We determined that the information capacity of critical networks may vary depending on the manipulation of microscopic parameters. Specifically, the mean number of connections governed the number of spatiotemporal patterns in the networks. These findings are compatible with those of the real neuronal networks observed in specific brain circuitries, where critical behavior is necessary for the optimal dynamic range response but the uncertainty provided by high entropy as coded by spatiotemporal patterns is not required. With this, we were able to reveal that information processing can be optimized in neuronal networks beyond critical states.
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12
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Kinjo ER, Rodríguez PXR, Dos Santos BA, Higa GSV, Ferraz MSA, Schmeltzer C, Rüdiger S, Kihara AH. New Insights on Temporal Lobe Epilepsy Based on Plasticity-Related Network Changes and High-Order Statistics. Mol Neurobiol 2017; 55:3990-3998. [PMID: 28555345 DOI: 10.1007/s12035-017-0623-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Accepted: 05/16/2017] [Indexed: 12/21/2022]
Abstract
Epilepsy is a disorder of the brain characterized by the predisposition to generate recurrent unprovoked seizures, which involves reshaping of neuronal circuitries based on intense neuronal activity. In this review, we first detailed the regulation of plasticity-associated genes, such as ARC, GAP-43, PSD-95, synapsin, and synaptophysin. Indeed, reshaping of neuronal connectivity after the primary, acute epileptogenesis event increases the excitability of the temporal lobe. Herein, we also discussed the heterogeneity of neuronal populations regarding the number of synaptic connections, which in the theoretical field is commonly referred as degree. Employing integrate-and-fire neuronal model, we determined that in addition to increased synaptic strength, degree correlations might play essential and unsuspected roles in the control of network activity. Indeed, assortativity, which can be described as a condition where high-degree correlations are observed, increases the excitability of neural networks. In this review, we summarized recent topics in the field, and data were discussed according to newly developed or unusual tools, as provided by mathematical graph analysis and high-order statistics. With this, we were able to present new foundations for the pathological activity observed in temporal lobe epilepsy.
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Affiliation(s)
- Erika Reime Kinjo
- Laboratório de Neurogenética, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil
| | - Pedro Xavier Royero Rodríguez
- Laboratório de Neurogenética, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil
| | - Bianca Araújo Dos Santos
- Laboratório de Neurogenética, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil
| | - Guilherme Shigueto Vilar Higa
- Laboratório de Neurogenética, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil
- Departamento de Fisiologia e Biofísica, Instituto de Ciências Biomédicas, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Mariana Sacrini Ayres Ferraz
- Laboratório de Neurogenética, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil
| | - Christian Schmeltzer
- Laboratório de Neurogenética, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil
- Institute of Physics, Humboldt University at Berlin, Berlin, Germany
| | - Sten Rüdiger
- Institute of Physics, Humboldt University at Berlin, Berlin, Germany
| | - Alexandre Hiroaki Kihara
- Laboratório de Neurogenética, Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil.
- Departamento de Fisiologia e Biofísica, Instituto de Ciências Biomédicas, Universidade de São Paulo, São Paulo, SP, Brazil.
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13
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Martens MB, Houweling AR, E Tiesinga PH. Anti-correlations in the degree distribution increase stimulus detection performance in noisy spiking neural networks. J Comput Neurosci 2016; 42:87-106. [PMID: 27812835 PMCID: PMC5250670 DOI: 10.1007/s10827-016-0629-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Revised: 09/27/2016] [Accepted: 10/03/2016] [Indexed: 01/10/2023]
Abstract
Neuronal circuits in the rodent barrel cortex are characterized by stable low firing rates. However, recent experiments show that short spike trains elicited by electrical stimulation in single neurons can induce behavioral responses. Hence, the underlying neural networks provide stability against internal fluctuations in the firing rate, while simultaneously making the circuits sensitive to small external perturbations. Here we studied whether stability and sensitivity are affected by the connectivity structure in recurrently connected spiking networks. We found that anti-correlation between the number of afferent (in-degree) and efferent (out-degree) synaptic connections of neurons increases stability against pathological bursting, relative to networks where the degrees were either positively correlated or uncorrelated. In the stable network state, stimulation of a few cells could lead to a detectable change in the firing rate. To quantify the ability of networks to detect the stimulation, we used a receiver operating characteristic (ROC) analysis. For a given level of background noise, networks with anti-correlated degrees displayed the lowest false positive rates, and consequently had the highest stimulus detection performance. We propose that anti-correlation in the degree distribution may be a computational strategy employed by sensory cortices to increase the detectability of external stimuli. We show that networks with anti-correlated degrees can in principle be formed by applying learning rules comprised of a combination of spike-timing dependent plasticity, homeostatic plasticity and pruning to networks with uncorrelated degrees. To test our prediction we suggest a novel experimental method to estimate correlations in the degree distribution.
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Affiliation(s)
- Marijn B Martens
- Department of Neuroinformatics, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.
| | - Arthur R Houweling
- Department of Neuroscience, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Paul H E Tiesinga
- Department of Neuroinformatics, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
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Gollo LL, Copelli M, Roberts JA. Diversity improves performance in excitable networks. PeerJ 2016; 4:e1912. [PMID: 27168961 PMCID: PMC4860327 DOI: 10.7717/peerj.1912] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2015] [Accepted: 03/17/2016] [Indexed: 11/20/2022] Open
Abstract
As few real systems comprise indistinguishable units, diversity is a hallmark of nature. Diversity among interacting units shapes properties of collective behavior such as synchronization and information transmission. However, the benefits of diversity on information processing at the edge of a phase transition, ordinarily assumed to emerge from identical elements, remain largely unexplored. Analyzing a general model of excitable systems with heterogeneous excitability, we find that diversity can greatly enhance optimal performance (by two orders of magnitude) when distinguishing incoming inputs. Heterogeneous systems possess a subset of specialized elements whose capability greatly exceeds that of the nonspecialized elements. We also find that diversity can yield multiple percolation, with performance optimized at tricriticality. Our results are robust in specific and more realistic neuronal systems comprising a combination of excitatory and inhibitory units, and indicate that diversity-induced amplification can be harnessed by neuronal systems for evaluating stimulus intensities.
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Affiliation(s)
- Leonardo L Gollo
- Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia; Centre for Integrative Brain Function, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Mauro Copelli
- Departamento de Física, Universidade Federal de Pernambuco , Recife PE , Brazil
| | - James A Roberts
- Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia; Centre for Integrative Brain Function, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
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Aljadeff J, Renfrew D, Vegué M, Sharpee TO. Low-dimensional dynamics of structured random networks. Phys Rev E 2016; 93:022302. [PMID: 26986347 DOI: 10.1103/physreve.93.022302] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Indexed: 01/12/2023]
Abstract
Using a generalized random recurrent neural network model, and by extending our recently developed mean-field approach [J. Aljadeff, M. Stern, and T. Sharpee, Phys. Rev. Lett. 114, 088101 (2015)], we study the relationship between the network connectivity structure and its low-dimensional dynamics. Each connection in the network is a random number with mean 0 and variance that depends on pre- and postsynaptic neurons through a sufficiently smooth function g of their identities. We find that these networks undergo a phase transition from a silent to a chaotic state at a critical point we derive as a function of g. Above the critical point, although unit activation levels are chaotic, their autocorrelation functions are restricted to a low-dimensional subspace. This provides a direct link between the network's structure and some of its functional characteristics. We discuss example applications of the general results to neuroscience where we derive the support of the spectrum of connectivity matrices with heterogeneous and possibly correlated degree distributions, and to ecology where we study the stability of the cascade model for food web structure.
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Affiliation(s)
- Johnatan Aljadeff
- Department of Neurobiology, University of Chicago, Chicago, Illinois, USA.,Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California, USA
| | - David Renfrew
- Department of Mathematics, University of California Los Angeles, Los Angeles, California, USA
| | - Marina Vegué
- Centre de Recerca Matemàtica, Campus de Bellaterra, Barcelona, Spain.,Departament de Matemàtiques, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Tatyana O Sharpee
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California, USA
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