1
|
Tejada J, Roque AC. Conductance-based models and the fragmentation problem: A case study based on hippocampal CA1 pyramidal cell models and epilepsy. Epilepsy Behav 2021; 121:106841. [PMID: 31864945 DOI: 10.1016/j.yebeh.2019.106841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 12/02/2019] [Accepted: 12/03/2019] [Indexed: 10/25/2022]
Abstract
Epilepsy has been a central topic in computational neuroscience, and in silico models have shown to be excellent tools to integrate and evaluate findings from animal and clinical settings. Among the different languages and tools for computational modeling development, NEURON stands out as one of the most used and mature neurosimulators. However, despite the vast quantity of models developed with NEURON, a fragmentation problem is evident in the great majority of models related to the same type of cell or cell properties. This fragmentation causes a lack of interoperability between the models because of differences in parameters. The problem is not related to the neurosimulator, which is prepared to reuse elements of other models, but related to decisions made during the model development, when it is not uncommon to adjust parameter values according to the necessities of the study. Here, this problem is presented by studying computational models related to temporal lobe epilepsy and the definitions of hippocampal CA1 pyramidal cells. The current assessment aims to highlight the implications of fragmentation for reliable modeling and the need to adopt a framework that allows a better interoperability between different models. This article is part of the Special Issue "NEWroscience 2018".
Collapse
Affiliation(s)
- Julian Tejada
- Departamento de Psicologia, DPS, Universidade Federal de Sergipe, SE 49100-000, Brazil; Facultad de Psicología, Fundación Universitaria Konrad Lorenz, Bogotá, Colombia.
| | - Antonio C Roque
- Departamento de Física, FFCLRP, Universidade de São Paulo, Ribeirão Preto, SP 14040-901, Brazil
| |
Collapse
|
2
|
Park SC, Chung CK. Postoperative seizure outcome-guided machine learning for interictal electrocorticography in neocortical epilepsy. J Neurophysiol 2018. [PMID: 29513147 DOI: 10.1152/jn.00225.2017] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
The objective of this study was to introduce a new machine learning guided by outcome of resective epilepsy surgery defined as the presence/absence of seizures to improve data mining for interictal pathological activities in neocortical epilepsy. Electrocorticographies for 39 patients with medically intractable neocortical epilepsy were analyzed. We separately analyzed 38 frequencies from 0.9 to 800 Hz including both high-frequency activities and low-frequency activities to select bands related to seizure outcome. An automatic detector using amplitude-duration-number thresholds was used. Interictal electrocorticography data sets of 8 min for each patient were selected. In the first training data set of 20 patients, the automatic detector was optimized to best differentiate the seizure-free group from not-seizure-free-group based on ranks of resection percentages of activities detected using a genetic algorithm. The optimization was validated in a different data set of 19 patients. There were 16 (41%) seizure-free patients. The mean follow-up duration was 21 ± 11 mo (range, 13-44 mo). After validation, frequencies significantly related to seizure outcome were 5.8, 8.4-25, 30, 36, 52, and 75 among low-frequency activities and 108 and 800 Hz among high-frequency activities. Resection for 5.8, 8.4-25, 108, and 800 Hz activities consistently improved seizure outcome. Resection effects of 17-36, 52, and 75 Hz activities on seizure outcome were variable according to thresholds. We developed and validated an automated detector for monitoring interictal pathological and inhibitory/physiological activities in neocortical epilepsy using a data-driven approach through outcome-guided machine learning. NEW & NOTEWORTHY Outcome-guided machine learning based on seizure outcome was used to improve detections for interictal electrocorticographic low- and high-frequency activities. This method resulted in better separation of seizure outcome groups than others reported in the literature. The automatic detector can be trained without human intervention and no prior information. It is based only on objective seizure outcome data without relying on an expert's manual annotations. Using the method, we could find and characterize pathological and inhibitory activities.
Collapse
Affiliation(s)
- Seong-Cheol Park
- Department of Neurosurgery, Gangneung Asan Hospital, University of Ulsan College of Medicine, Gangneung, Republic of Korea.,Department of Translational Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Chun Kee Chung
- Department of Translational Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Neurosurgery, Seoul National University College of Medicine, Seoul, Republic of Korea.,Neuroscience Research Institute, Seoul National University Medical Research Center, Seoul, Republic of Korea.,Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| |
Collapse
|
3
|
Messé A, Hütt MT, Hilgetag CC. Toward a theory of coactivation patterns in excitable neural networks. PLoS Comput Biol 2018; 14:e1006084. [PMID: 29630592 PMCID: PMC5908206 DOI: 10.1371/journal.pcbi.1006084] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 04/19/2018] [Accepted: 03/11/2018] [Indexed: 11/18/2022] Open
Abstract
The relationship between the structural connectivity (SC) and functional connectivity (FC) of neural systems is of central importance in brain network science. It is an open question, however, how the SC-FC relationship depends on specific topological features of brain networks or the models used for describing neural dynamics. Using a basic but general model of discrete excitable units that follow a susceptible—excited—refractory activity cycle (SER model), we here analyze how the network activity patterns underlying functional connectivity are shaped by the characteristic topological features of the network. We develop an analytical framework for describing the contribution of essential topological elements, such as common inputs and pacemakers, to the coactivation of nodes, and demonstrate the validity of the approach by comparison of the analytical predictions with numerical simulations of various exemplar networks. The present analytic framework may serve as an initial step for the mechanistic understanding of the contributions of brain network topology to brain dynamics. Functional connectivity, as reflected in the statistical dependencies of distributed activity, is widely used to probe the organization of complex systems such as the brain. While this measure has been helpful for characterizing brain states and highlighting alterations of brain dynamics in various diseases, the mechanisms underlying the generation of FC patterns remain poorly understood. One prominent factor shaping FC is the underlying neural network structure. Using a minimalist model of excitation, we investigate how the topology of excitable neural networks contributes to FC. Specifically, we show that FC can be analytically predicted from the way in which the nodes are embedded in the network and how they are related to basic self-organizing units of excitable dynamics, particularly, short pacemaker cycles. These insights are a step towards a mechanistic understanding of the activation patterns of complex neural networks.
Collapse
Affiliation(s)
- Arnaud Messé
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg, Germany
| | - Marc-Thorsten Hütt
- Department of Life Sciences and Chemistry, Jacobs University, Bremen, Germany
| | - Claus C. Hilgetag
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg, Germany
- Department of Health Sciences, Boston University, Boston, Massachusetts, United States of America
- * E-mail:
| |
Collapse
|
4
|
Bonneau H, Hassid A, Biham O, Kühn R, Katzav E. Distribution of shortest cycle lengths in random networks. Phys Rev E 2018; 96:062307. [PMID: 29347364 DOI: 10.1103/physreve.96.062307] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Indexed: 11/07/2022]
Abstract
We present analytical results for the distribution of shortest cycle lengths (DSCL) in random networks. The approach is based on the relation between the DSCL and the distribution of shortest path lengths (DSPL). We apply this approach to configuration model networks, for which analytical results for the DSPL were obtained before. We first calculate the fraction of nodes in the network which reside on at least one cycle. Conditioning on being on a cycle, we provide the DSCL over ensembles of configuration model networks with degree distributions which follow a Poisson distribution (Erdős-Rényi network), degenerate distribution (random regular graph), and a power-law distribution (scale-free network). The mean and variance of the DSCL are calculated. The analytical results are found to be in very good agreement with the results of computer simulations.
Collapse
Affiliation(s)
- Haggai Bonneau
- Racah Institute of Physics, The Hebrew University, Jerusalem 91904, Israel
| | - Aviv Hassid
- Racah Institute of Physics, The Hebrew University, Jerusalem 91904, Israel
| | - Ofer Biham
- Racah Institute of Physics, The Hebrew University, Jerusalem 91904, Israel
| | - Reimer Kühn
- Department of Mathematics, King's College London, Strand, London WC2R 2LS, United Kingdom
| | - Eytan Katzav
- Racah Institute of Physics, The Hebrew University, Jerusalem 91904, Israel
| |
Collapse
|
5
|
Turnbull L, Hütt MT, Ioannides AA, Kininmonth S, Poeppl R, Tockner K, Bracken LJ, Keesstra S, Liu L, Masselink R, Parsons AJ. Connectivity and complex systems: learning from a multi-disciplinary perspective. APPLIED NETWORK SCIENCE 2018; 3:11. [PMID: 30839779 PMCID: PMC6214298 DOI: 10.1007/s41109-018-0067-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Accepted: 05/29/2018] [Indexed: 05/05/2023]
Abstract
In recent years, parallel developments in disparate disciplines have focused on what has come to be termed connectivity; a concept used in understanding and describing complex systems. Conceptualisations and operationalisations of connectivity have evolved largely within their disciplinary boundaries, yet similarities in this concept and its application among disciplines are evident. However, any implementation of the concept of connectivity carries with it both ontological and epistemological constraints, which leads us to ask if there is one type or set of approach(es) to connectivity that might be applied to all disciplines. In this review we explore four ontological and epistemological challenges in using connectivity to understand complex systems from the standpoint of widely different disciplines. These are: (i) defining the fundamental unit for the study of connectivity; (ii) separating structural connectivity from functional connectivity; (iii) understanding emergent behaviour; and (iv) measuring connectivity. We draw upon discipline-specific insights from Computational Neuroscience, Ecology, Geomorphology, Neuroscience, Social Network Science and Systems Biology to explore the use of connectivity among these disciplines. We evaluate how a connectivity-based approach has generated new understanding of structural-functional relationships that characterise complex systems and propose a 'common toolbox' underpinned by network-based approaches that can advance connectivity studies by overcoming existing constraints.
Collapse
Affiliation(s)
| | | | | | - Stuart Kininmonth
- Stockholm Resilience Institute, Stockholm, Sweden
- The University of South Pacific, Suva, Fiji
| | | | - Klement Tockner
- Freie Universität Berlin, Berlin, Germany
- Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany
- Austrian Science Funds, Berlin, Germany
| | | | | | - Lichan Liu
- Laboratory for Human Brain Dynamics, Nicosia, Cyprus
| | | | | |
Collapse
|
6
|
Fretter C, Lesne A, Hilgetag CC, Hütt MT. Topological determinants of self-sustained activity in a simple model of excitable dynamics on graphs. Sci Rep 2017; 7:42340. [PMID: 28186182 PMCID: PMC5301238 DOI: 10.1038/srep42340] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2016] [Accepted: 01/09/2017] [Indexed: 11/08/2022] Open
Abstract
Simple models of excitable dynamics on graphs are an efficient framework for studying the interplay between network topology and dynamics. This topic is of practical relevance to diverse fields, ranging from neuroscience to engineering. Here we analyze how a single excitation propagates through a random network as a function of the excitation threshold, that is, the relative amount of activity in the neighborhood required for the excitation of a node. We observe that two sharp transitions delineate a region of sustained activity. Using analytical considerations and numerical simulation, we show that these transitions originate from the presence of barriers to propagation and the excitation of topological cycles, respectively, and can be predicted from the network topology. Our findings are interpreted in the context of network reverberations and self-sustained activity in neural systems, which is a question of long-standing interest in computational neuroscience.
Collapse
Affiliation(s)
- Christoph Fretter
- Department of Life Sciences and Chemistry, Jacobs University Bremen, D-28759 Bremen, Germany
- Department of Computational Neuroscience, Universitätsklinikum Hamburg-Eppendorf, D-20246 Hamburg, Germany
| | - Annick Lesne
- LPTMC, CNRS, UMR 7600, UPMC-Paris 6, Sorbonne Universités, 4 place Jussieu, F-75252, Paris, France
- Institut de Génétique Moléculaire de Montpellier, UMR 5535 CNRS, 1919 route de Mende, 34293 Montpellier cedex 5, France; Université de Montpellier, 163 rue Auguste Broussonnet, 34090 Montpellier, France
| | - Claus C. Hilgetag
- Department of Computational Neuroscience, Universitätsklinikum Hamburg-Eppendorf, D-20246 Hamburg, Germany
- Department of Health Sciences, Boston University, Boston, USA
| | - Marc-Thorsten Hütt
- Department of Life Sciences and Chemistry, Jacobs University Bremen, D-28759 Bremen, Germany
| |
Collapse
|
7
|
Fuertinger S, Simonyan K, Sperling MR, Sharan AD, Hamzei-Sichani F. High-frequency brain networks undergo modular breakdown during epileptic seizures. Epilepsia 2016; 57:1097-108. [PMID: 27221325 DOI: 10.1111/epi.13413] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/18/2016] [Indexed: 01/28/2023]
Abstract
OBJECTIVE Cortical high-frequency oscillations (HFOs; 100-500 Hz) play a critical role in the pathogenesis of epilepsy; however, whether they represent a true epileptogenic process remains largely unknown. HFOs have been recorded in the human cortex but their network dynamics during the transitional period from interictal to ictal phase remain largely unknown. We sought to determine the high-frequency network dynamics of these oscillations in patients with epilepsy who were undergoing intracranial electroencephalographic recording for seizure localization. METHODS We applied a graph theoretical analysis framework to high-resolution intracranial electroencephalographic recordings of 24 interictal and 24 seizure periods to identify the spatiotemporal evolution of community structure of high-frequency cortical networks at rest and during multiple seizure episodes in patients with intractable epilepsy. RESULTS Cortical networks at all examined frequencies showed temporally stable community architecture in all 24 interictal periods. During seizure periods, high-frequency networks showed a significant breakdown of their community structure, which was characterized by the emergence of numerous small nodal communities, not limited to seizure foci and encompassing the entire recorded network. Such network disorganization was observed on average 225 s before the electrographic seizure onset and extended on average 190 s after termination of the seizure. Gamma networks were characterized by stable community dynamics during resting and seizure periods. SIGNIFICANCE Our findings suggest that the modular breakdown of high-frequency cortical networks represents a distinct functional pathology that underlies epileptogenesis and corresponds to a cortical state of highest propensity to generate seizures.
Collapse
Affiliation(s)
- Stefan Fuertinger
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A
| | - Kristina Simonyan
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A.,Department of Otolaryngology, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A
| | - Michael R Sperling
- Department of Neurology, Sidney Kimmel College of Medicine, Thomas Jefferson University, Philadelphia, Pennsylvania, U.S.A
| | - Ashwini D Sharan
- Department of Neurosurgery, Sidney Kimmel College of Medicine, Thomas Jefferson University, Philadelphia, Pennsylvania, U.S.A
| | - Farid Hamzei-Sichani
- Department of Neurosurgery, Sidney Kimmel College of Medicine, Thomas Jefferson University, Philadelphia, Pennsylvania, U.S.A.,Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A
| |
Collapse
|
8
|
Jang HJ, Park K, Lee J, Kim H, Han KH, Kwag J. GABAA receptor-mediated feedforward and feedback inhibition differentially modulate the gain and the neural code transformation in hippocampal CA1 pyramidal cells. Neuropharmacology 2015; 99:177-86. [PMID: 26123028 DOI: 10.1016/j.neuropharm.2015.06.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2015] [Revised: 05/19/2015] [Accepted: 06/11/2015] [Indexed: 12/31/2022]
Abstract
Diverse variety of hippocampal interneurons exists in the CA1 area, which provides either feedforward (FF) or feedback (FB) inhibition to CA1 pyramidal cell (PC). However, how the two different inhibitory network architectures modulate the computational mode of CA1 PC is unknown. By investigating the CA3 PC rate-driven input-output function of CA1 PC using in vitro electrophysiology, in vitro-simulation of inhibitory network, and in silico computational modeling, we demonstrated for the first time that GABAA receptor-mediated FF and FB inhibition differentially modulate the gain, the spike precision, the neural code transformation and the information capacity of CA1 PC. Recruitment of FF inhibition buffered the CA1 PC spikes to theta-frequency regardless of the input frequency, abolishing the gain and making CA1 PC insensitive to its inputs. Instead, temporal variability of the CA1 PC spikes was increased, promoting the rate-to-temporal code transformation to enhance the information capacity of CA1 PC. In contrast, the recruitment of FB inhibition sub-linearly transformed the input rate to spike output rate with high gain and low spike temporal variability, promoting the rate-to-rate code transformation. These results suggest that GABAA receptor-mediated FF and FB inhibitory circuits could serve as network mechanisms for differentially modulating the gain of CA1 PC, allowing CA1 PC to switch between different computational modes using rate and temporal codes ad hoc. Such switch will allow CA1 PC to efficiently respond to spatio-temporally dynamic inputs and expand its computational capacity during different behavioral and neuromodulatory states in vivo.
Collapse
Affiliation(s)
- Hyun Jae Jang
- Neural Computation Laboratory, Department of Brain and Cognitive Engineering, Korea University, South Korea
| | - Kyerl Park
- Neural Computation Laboratory, Department of Brain and Cognitive Engineering, Korea University, South Korea; Division of Life Sciences, College of Life Sciences and Biotechnology, Korea University, South Korea
| | - Jaedong Lee
- Neural Computation Laboratory, Department of Brain and Cognitive Engineering, Korea University, South Korea
| | - Hyuncheol Kim
- Neural Computation Laboratory, Department of Brain and Cognitive Engineering, Korea University, South Korea
| | - Kyu Hun Han
- Neural Computation Laboratory, Department of Brain and Cognitive Engineering, Korea University, South Korea; Division of Biotechnology, College of Life Sciences and Biotechnology, Korea University, South Korea
| | - Jeehyun Kwag
- Neural Computation Laboratory, Department of Brain and Cognitive Engineering, Korea University, South Korea.
| |
Collapse
|
9
|
Garcia GC, Lesne A, Hilgetag CC, Hütt MT. Role of long cycles in excitable dynamics on graphs. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2014; 90:052805. [PMID: 25493832 DOI: 10.1103/physreve.90.052805] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2014] [Indexed: 06/04/2023]
Abstract
Topological cycles in excitable networks can play an important role in maintaining the network activity. When properly activated, cycles act as dynamic pacemakers, sustaining the activity of the whole network. Most previous research has focused on the contributions of short cycles to network dynamics. Here, we identify the specific cycles that are used during different runs of activation in sparse random graphs, as a basis of characterizing the contribution of cycles of any length. Both simulation and a refined mean-field approach evidence a decrease in the cycle usage when the cycle length increases, reflecting a trade-off between long time for recovery after excitation and low vulnerability to out-of-phase external excitations. In spite of this statistical observation, we find that the successful usage of long cycles, though rare, has important functional consequences for sustaining network activity: The average cycle length is the main feature of the cycle length distribution that affects the average lifetime of activity in the network. Particularly, use of long, rather than short, cycles correlates with higher lifetime, and cutting shortcuts in long cycles tends to increase the average lifetime of the activity. Our findings, thus, emphasize the essential, previously underrated role of long cycles in sustaining network activity. On a more general level, the findings underline the importance of network topology, particularly cycle structure, for self-sustained network dynamics.
Collapse
Affiliation(s)
- Guadalupe C Garcia
- School of Engineering and Science, Jacobs University Bremen, D-28759 Bremen, Germany
| | - Annick Lesne
- LPTMC, CNRS UMR 7600, Université Pierre et Marie Curie, Sorbonne Universités, 4 place Jussieu, F-75252, Paris, France and IGMM, CNRS UMR 5535, Université de Montpellier, 1919 route de Mende, F-34293, Montpellier, France
| | - Claus C Hilgetag
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, D-20148 Hamburg, Germany and Department of Health Sciences, Boston University, Boston, Massachusetts 02215, USA
| | - Marc-Thorsten Hütt
- School of Engineering and Science, Jacobs University Bremen, D-28759 Bremen, Germany
| |
Collapse
|
10
|
Khakhalin AS, Koren D, Gu J, Xu H, Aizenman CD. Excitation and inhibition in recurrent networks mediate collision avoidance in Xenopus tadpoles. Eur J Neurosci 2014; 40:2948-62. [PMID: 24995793 DOI: 10.1111/ejn.12664] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2014] [Revised: 04/23/2014] [Accepted: 05/28/2014] [Indexed: 01/24/2023]
Abstract
Information processing in the vertebrate brain is thought to be mediated through distributed neural networks, but it is still unclear how sensory stimuli are encoded and detected by these networks, and what role synaptic inhibition plays in this process. Here we used a collision avoidance behavior in Xenopus tadpoles as a model for stimulus discrimination and recognition. We showed that the visual system of the tadpole is selective for behaviorally relevant looming stimuli, and that the detection of these stimuli first occurs in the optic tectum. By comparing visually guided behavior, optic nerve recordings, excitatory and inhibitory synaptic currents, and the spike output of tectal neurons, we showed that collision detection in the tadpole relies on the emergent properties of distributed recurrent networks within the tectum. We found that synaptic inhibition was temporally correlated with excitation, and did not actively sculpt stimulus selectivity, but rather it regulated the amount of integration between direct inputs from the retina and recurrent inputs from the tectum. Both pharmacological suppression and enhancement of synaptic inhibition disrupted emergent selectivity for looming stimuli. Taken together these findings suggested that, by regulating the amount of network activity, inhibition plays a critical role in maintaining selective sensitivity to behaviorally-relevant visual stimuli.
Collapse
Affiliation(s)
- Arseny S Khakhalin
- Department of Neuroscience, Brown University, Box G-LN, Providence, RI, 02912, USA
| | | | | | | | | |
Collapse
|
11
|
Freeman K, Tao W, Sun H, Soonpaa MH, Rubart M. In situ three-dimensional reconstruction of mouse heart sympathetic innervation by two-photon excitation fluorescence imaging. J Neurosci Methods 2014; 221:48-61. [PMID: 24056230 PMCID: PMC3858460 DOI: 10.1016/j.jneumeth.2013.09.005] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2013] [Revised: 09/06/2013] [Accepted: 09/08/2013] [Indexed: 12/26/2022]
Abstract
BACKGROUND Sympathetic nerve wiring in the mammalian heart has remained largely unexplored. Resolving the wiring diagram of the cardiac sympathetic network would help establish the structural underpinnings of neurocardiac coupling. NEW METHOD We used two-photon excitation fluorescence microscopy, combined with a computer-assisted 3-D tracking algorithm, to map the local sympathetic circuits in living hearts from adult transgenic mice expressing enhanced green fluorescent protein (EGFP) in peripheral adrenergic neurons. RESULTS Quantitative co-localization analyses confirmed that the intramyocardial EGFP distribution recapitulated the anatomy of the sympathetic arbor. In the left ventricular subepicardium of the uninjured heart, the sympathetic network was composed of multiple subarbors, exhibiting variable branching and looping topology. Axonal branches did not overlap with each other within their respective parental subarbor nor with neurites of annexed subarbors. The sympathetic network in the border zone of a 2-week-old myocardial infarction was characterized by substantive rewiring, which included spatially heterogeneous loss and gain of sympathetic fibers and formation of multiple, predominately nested, axon loops of widely variable circumference and geometry. COMPARISON WITH EXISTING METHODS In contrast to mechanical tissue sectioning methods that may involve deformation of tissue and uncertainty in registration across sections, our approach preserves continuity of structure, which allows tracing of neurites over distances, and thus enables derivation of the three-dimensional and topological morphology of cardiac sympathetic nerves. CONCLUSIONS Our assay should be of general utility to unravel the mechanisms governing sympathetic axon spacing during development and disease.
Collapse
Affiliation(s)
- Kim Freeman
- Riley Heart Research Center, Wells Center for Pediatric Research, Indiana University School of Medicine, 1044 West Walnut Street, Indianapolis, IN 46202
| | - Wen Tao
- Riley Heart Research Center, Wells Center for Pediatric Research, Indiana University School of Medicine, 1044 West Walnut Street, Indianapolis, IN 46202
| | - Hongli Sun
- Riley Heart Research Center, Wells Center for Pediatric Research, Indiana University School of Medicine, 1044 West Walnut Street, Indianapolis, IN 46202
| | - Mark H. Soonpaa
- Riley Heart Research Center, Wells Center for Pediatric Research, Indiana University School of Medicine, 1044 West Walnut Street, Indianapolis, IN 46202
| | - Michael Rubart
- Riley Heart Research Center, Wells Center for Pediatric Research, Indiana University School of Medicine, 1044 West Walnut Street, Indianapolis, IN 46202
| |
Collapse
|
12
|
Simon A, Traub RD, Vladimirov N, Jenkins A, Nicholson C, Whittaker RG, Schofield I, Clowry GJ, Cunningham MO, Whittington MA. Gap junction networks can generate both ripple-like and fast ripple-like oscillations. Eur J Neurosci 2013; 39:46-60. [PMID: 24118191 DOI: 10.1111/ejn.12386] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2013] [Revised: 09/03/2013] [Accepted: 09/10/2013] [Indexed: 11/30/2022]
Abstract
Fast ripples (FRs) are network oscillations, defined variously as having frequencies of > 150 to > 250 Hz, with a controversial mechanism. FRs appear to indicate a propensity of cortical tissue to originate seizures. Here, we demonstrate field oscillations, at up to 400 Hz, in spontaneously epileptic human cortical tissue in vitro, and present a network model that could explain FRs themselves, and their relation to 'ordinary' (slower) ripples. We performed network simulations with model pyramidal neurons, having axons electrically coupled. Ripples (< 250 Hz) were favored when conduction of action potentials, axon to axon, was reliable. Whereas ripple population activity was periodic, firing of individual axons varied in relative phase. A switch from ripples to FRs took place when an ectopic spike occurred in a cell coupled to another cell, itself multiply coupled to others. Propagation could then start in one direction only, a condition suitable for re-entry. The resulting oscillations were > 250 Hz, were sustained or interrupted, and had little jitter in the firing of individual axons. The form of model FR was similar to spontaneously occurring FRs in excised human epileptic tissue. In vitro, FRs were suppressed by a gap junction blocker. Our data suggest that a given network can produce ripples, FRs, or both, via gap junctions, and that FRs are favored by clusters of axonal gap junctions. If axonal gap junctions indeed occur in epileptic tissue, and are mediated by connexin 26 (recently shown to mediate coupling between immature neocortical pyramidal cells), then this prediction is testable.
Collapse
Affiliation(s)
- Anna Simon
- Institute of Neuroscience, The Medical School, Newcastle University, Newcastle upon Tyne, UK
| | | | | | | | | | | | | | | | | | | |
Collapse
|
13
|
Vladimirov N, Tu Y, Traub RD. Synaptic gating at axonal branches, and sharp-wave ripples with replay: a simulation study. Eur J Neurosci 2013; 38:3435-47. [PMID: 23992155 DOI: 10.1111/ejn.12342] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2013] [Revised: 07/20/2013] [Accepted: 07/24/2013] [Indexed: 12/25/2022]
Abstract
Mechanisms of place cell replay occurring during sharp-wave ripples (SPW-Rs) remain obscure due to the fact that ripples in vitro depend on non-synaptic mechanisms, presumably via axo-axonal gap junctions between pyramidal cells. We suggest a model of in vivo SPW-Rs in which synaptic excitatory post-synaptic potentials (EPSPs) control the axonal spiking of cells in SPW-Rs: ripple activity remains hidden in the network of axonal collaterals (connected by gap junctions) due to conduction failures, unless there is a sufficient dendritic EPSP. The EPSP brings the axonal branching point to threshold, and action potentials from the collateral start to propagate to the soma and to the distal axon. The model coherently explains multiple experimental data on SPW-Rs, both in vitro and in vivo. The mechanism of synaptic gating leads to the following implication: a sequence of pyramidal cells can be replayed at ripple frequency by the superposition of subthreshold dendritic EPSPs and ripple activity in the axonal plexus. Replay is demonstrated in both forward and reverse directions. We discuss several testable predictions. In general, the mechanism of synaptic gating suggests that pyramidal cells under certain conditions can act like a transistor.
Collapse
|
14
|
Garcia GC, Lesne A, Hütt MT, Hilgetag CC. Building blocks of self-sustained activity in a simple deterministic model of excitable neural networks. Front Comput Neurosci 2012; 6:50. [PMID: 22888317 PMCID: PMC3412572 DOI: 10.3389/fncom.2012.00050] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2012] [Accepted: 07/01/2012] [Indexed: 12/04/2022] Open
Abstract
Understanding the interplay of topology and dynamics of excitable neural networks is one of the major challenges in computational neuroscience. Here we employ a simple deterministic excitable model to explore how network-wide activation patterns are shaped by network architecture. Our observables are co-activation patterns, together with the average activity of the network and the periodicities in the excitation density. Our main results are: (1) the dependence of the correlation between the adjacency matrix and the instantaneous (zero time delay) co-activation matrix on global network features (clustering, modularity, scale-free degree distribution), (2) a correlation between the average activity and the amount of small cycles in the graph, and (3) a microscopic understanding of the contributions by 3-node and 4-node cycles to sustained activity.
Collapse
Affiliation(s)
- Guadalupe C Garcia
- School of Engineering and Science, Jacobs University Bremen Bremen, Germany
| | | | | | | |
Collapse
|
15
|
Kammerer A, Tejero-Cantero A, Leibold C. Inhibition enhances memory capacity: optimal feedback, transient replay and oscillations. J Comput Neurosci 2012; 34:125-36. [PMID: 22782801 DOI: 10.1007/s10827-012-0410-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2012] [Revised: 05/18/2012] [Accepted: 06/21/2012] [Indexed: 11/25/2022]
Abstract
Recurring sequences of neuronal activation in the hippocampus are a candidate for a neurophysiological correlate of episodic memory. Here, we discuss a mean-field theory for such spike sequences in phase space and show how they become unstable when the neuronal network operates at maximum memory capacity. We find that inhibitory feedback rescues replay of the sequences, giving rise to oscillations and thereby enhancing the network's capacity. We further argue that transient sequences in an overloaded network with feedback inhibition may provide a mechanistic picture of memory-related neuronal activity during hippocampal sharp-wave ripple complexes.
Collapse
Affiliation(s)
- Axel Kammerer
- Department Biologie II, Ludwig-Maximilians University Munich, Großhadernerstrasse 2, 82152 Planegg, Germany.
| | | | | |
Collapse
|