scholarly journals A dynamic role for dopamine receptors in the control of mammalian spinal networks

2019 ◽  
Author(s):  
Simon A. Sharples ◽  
Nicole E. Burma ◽  
Joanna Borowska-Fielding ◽  
Charlie H.T. Kwok ◽  
Shane E.A. Eaton ◽  
...  
Keyword(s):  
Neural Networks ◽  
Spinal Cord ◽  
Dopamine Receptors ◽  
Spontaneous Activity ◽  
Network Activity ◽  
Network State ◽  
Endogenous Dopamine ◽  
Network Output ◽  
Spinal Network

AbstractDopamine is well known to regulate movement through the differential control of direct and indirect pathways in the striatum that express D1 and D2 receptors respectively. The spinal cord also expresses all dopamine receptors however; how the specific receptors regulate spinal network output in mammals is poorly understood. We explore the receptor-specific mechanisms that underlie dopaminergic control of spinal network output of neonatal mice during changes in spinal network excitability. During spontaneous activity, which is a characteristic of developing spinal networks operating in a low excitability state, we found that dopamine is primarily inhibitory. We uncover an excitatory D1-mediated effect of dopamine on motoneurons and network output that also involves co-activation with D2 receptors. Critically, these excitatory actions require higher concentrations of dopamine; however, analysis of dopamine concentrations of neonates indicates that endogenous levels of spinal dopamine are low. Because endogenous levels of spinal dopamine are low, this excitatory dopaminergic pathway is likely physiologically-silent at this stage in development. In contrast, the inhibitory effect of dopamine, at low physiological concentrations is mediated by parallel activation of D2, D3, D4 and α2 receptors which is reproduced when endogenous dopamine levels are increased by blocking dopamine reuptake and metabolism. We provide evidence in support of dedicated spinal network components that are controlled by excitatory D1 and inhibitory D2 receptors that is reminiscent of the classic dopaminergic indirect and direct pathway within the striatum. These results indicate that network state is an important factor that dictates receptor-specific and therefore dose-dependent control of neuromodulators on spinal network output and advances our understanding of how neuromodulators regulate neural networks under dynamically changing excitability.Significance statementMonoaminergic neuromodulation of neural networks is dependent not only on target receptors but also on network state. We studied the concentration-dependent control of spinal networks of the neonatal mouse, in vitro, during a low excitability state characterized by spontaneous network activity. Spontaneous activity is an essential element for the development of networks. Under these conditions, we defined converging receptor and cellular mechanisms that contribute to the diverse, concentration-dependent control of spinal motor networks by dopamine, in vitro. These experiments advance understanding of how monoamines modulate neuronal networks under dynamically changing excitability conditions and provide evidence of dedicated D1 and D2 regulated network components in the spinal cord that are consistent with those reported in the striatum.

scholarly journals A dynamic role for dopamine receptors in the control of mammalian spinal networks

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Simon A. Sharples ◽  
Nicole E. Burma ◽  
Joanna Borowska-Fielding ◽  
Charlie H. T. Kwok ◽  
Shane E. A. Eaton ◽  
...  
Keyword(s):  
Dopamine Receptors ◽  
Inhibitory Effect ◽  
Endogenous Dopamine ◽  
Co Activation ◽  
Dopaminergic Pathway ◽  
Network Output ◽  
Spinal Network ◽  
Specific Receptors ◽  
Differential Control ◽  
Parallel Activation

Abstract Dopamine is well known to regulate movement through the differential control of direct and indirect pathways in the striatum that express D1 and D2 receptors respectively. The spinal cord also expresses all dopamine receptors; however, how the specific receptors regulate spinal network output in mammals is poorly understood. We explore the receptor-specific mechanisms that underlie dopaminergic control of spinal network output of neonatal mice during changes in spinal network excitability. During spontaneous activity, which is a characteristic of developing spinal networks operating in a low excitability state, we found that dopamine is primarily inhibitory. We uncover an excitatory D1-mediated effect of dopamine on motoneurons and network output that also involves co-activation with D2 receptors. Critically, these excitatory actions require higher concentrations of dopamine; however, analysis of dopamine concentrations of neonates indicates that endogenous levels of spinal dopamine are low. Because endogenous levels of spinal dopamine are low, this excitatory dopaminergic pathway is likely physiologically-silent at this stage in development. In contrast, the inhibitory effect of dopamine, at low physiological concentrations is mediated by parallel activation of D2, D3, D4 and α2 receptors which is reproduced when endogenous dopamine levels are increased by blocking dopamine reuptake and metabolism. We provide evidence in support of dedicated spinal network components that are controlled by excitatory D1 and inhibitory D2 receptors that is reminiscent of the classic dopaminergic indirect and direct pathway within the striatum. These results indicate that network state is an important factor that dictates receptor-specific and therefore dose-dependent control of neuromodulators on spinal network output and advances our understanding of how neuromodulators regulate neural networks under dynamically changing excitability.

scholarly journals A supervised machine learning approach to characterize spinal network function

2019 ◽  
Vol 121 (6) ◽  
pp. 2001-2012 ◽  
Author(s):  
A. N. Dalrymple ◽  
S. A. Sharples ◽  
N. Osachoff ◽  
A. P. Lognon ◽  
P. J. Whelan
Keyword(s):  
Machine Learning ◽  
Spinal Cord ◽  
Nervous System ◽  
Spontaneous Activity ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Network Activity ◽  
Multilayer Perceptrons ◽  
Network Development ◽  
Newborn Mice

Spontaneous activity is a common feature of immature neuronal networks throughout the central nervous system and plays an important role in network development and consolidation. In postnatal rodents, spontaneous activity in the spinal cord exhibits complex, stochastic patterns that have historically proven challenging to characterize. We developed a software tool for quickly and automatically characterizing and classifying episodes of spontaneous activity generated from developing spinal networks. We recorded spontaneous activity from in vitro lumbar ventral roots of 16 neonatal [postnatal day (P)0–P3] mice. Recordings were DC coupled and detrended, and episodes were separated for analysis. Amplitude-, duration-, and frequency-related features were extracted from each episode and organized into five classes. Paired classes and features were used to train and test supervised machine learning algorithms. Multilayer perceptrons were used to classify episodes as rhythmic or multiburst. We increased network excitability with potassium chloride and tested the utility of the tool to detect changes in features and episode class. We also demonstrate usability by having a novel experimenter use the program to classify episodes collected at a later time point (P5). Supervised machine learning-based classification of episodes accounted for changes that traditional approaches cannot detect. Our tool, named SpontaneousClassification, advances the detail in which we can study not only developing spinal networks, but also spontaneous networks in other areas of the nervous system.NEW & NOTEWORTHY Spontaneous activity is important for nervous system network development and consolidation. Our software uses machine learning to automatically and quickly characterize and classify episodes of spontaneous activity in the spinal cord of newborn mice. It detected changes in network activity following KCl-enhanced excitation. Using our software to classify spontaneous activity throughout development, in pathological models, or with neuromodulation, may offer insight into the development and organization of spinal circuits.

scholarly journals Oxytocin shapes spontaneous activity patterns in the developing visual cortex by activating somatostatin interneurons

2019 ◽  
Author(s):  
Paloma P Maldonado ◽  
Alvaro Nuno-Perez ◽  
Jan Kirchner ◽  
Elizabeth Hammock ◽  
Julijana Gjorgjieva ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Spontaneous Activity ◽  
Sensory Processing ◽  
Activity Patterns ◽  
Network Activity ◽  
Synaptic Connections ◽  
Pairwise Correlations ◽  
Early Brain Development

SummarySpontaneous network activity shapes emerging neuronal circuits during early brain development, however how neuromodulation influences this activity is not fully understood. Here, we report that the neuromodulator oxytocin powerfully shapes spontaneous activity patterns. In vivo, oxytocin strongly decreased the frequency and pairwise correlations of spontaneous activity events in visual cortex (V1), but not in somatosensory cortex (S1). This differential effect was a consequence of oxytocin only increasing inhibition in V1 and increasing both inhibition and excitation in S1. The increase in inhibition was mediated by the depolarization and increase in excitability of somatostatin+ (SST) interneurons specifically. Accordingly, silencing SST+ neurons pharmacogenetically fully blocked oxytocin’s effect on inhibition in vitro as well its effect on spontaneous activity patterns in vivo. Thus, oxytocin decreases the excitatory/inhibitory ratio and modulates specific features of V1 spontaneous activity patterns that are crucial for refining developing synaptic connections and sensory processing later in life.

In vitro screening of silver nanoparticles and ionic silver using neural networks yields differential effects on spontaneous activity and pharmacological responses

Toxicology ◽  
2016 ◽  
Vol 355-356 ◽  
pp. 1-8 ◽  
Author(s):  
Jenna D. Strickland ◽  
William R. LeFew ◽  
James Crooks ◽  
Diana Hall ◽  
Jayna N.R. Ortenzio ◽  
...  
Keyword(s):  
Neural Networks ◽  
Silver Nanoparticles ◽  
Spontaneous Activity ◽  
In Vitro Screening ◽  
Differential Effects ◽  
Ionic Silver

scholarly journals Two-Photon Calcium Imaging of Network Activity in XFP-Expressing Neurons in the Mouse

2007 ◽  
Vol 97 (4) ◽  
pp. 3118-3125 ◽  
Author(s):  
Jennifer M. Wilson ◽  
Daniel A. Dombeck ◽  
Manuel Díaz-Ríos ◽  
Ronald M. Harris-Warrick ◽  
Robert M. Brownstone
Keyword(s):  
Spinal Cord ◽  
Laser Scanning ◽  
Fluorescent Protein ◽  
Laser Scanning Microscopy ◽  
Network Activity ◽  
Spatiotemporal Pattern ◽  
Two Photon ◽  
Physiological Investigation ◽  
Photon Excitation

Fluorescent protein (XFP) expression in postnatal neurons allows the anatomical and physiological investigation of identified subpopulations of interneurons with established techniques. However, the spatiotemporal pattern of activity of these XFP neurons within a network and their role in the functional output of the network are more challenging issues to investigate. Here we apply two-photon excitation laser scanning microscopy to mouse spinal cord locomotor networks and present the methodology by which calcium activity can be recorded in XFP-expressing neurons. Such activity can be studied both in relation to neighboring non-XFP neurons in a spinal cord slice preparation and in relation to functional locomotor output monitored by ventral root activity in the intact in vitro spinal cord. Thus the network properties and functional correlates with locomotion of identified populations of interneurons can be studied simultaneously.

scholarly journals Formation of neural networks with structural and functional features consistent with small-world network topology on surface-grafted polymer particles

2019 ◽  
Vol 6 (10) ◽  
pp. 191086 ◽  
Author(s):  
Vibeke Devold Valderhaug ◽  
Wilhelm Robert Glomm ◽  
Eugenia Mariana Sandru ◽  
Masahiro Yasuda ◽  
Axel Sandvig ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Topology ◽  
Small World ◽  
Network Activity ◽  
Small World Network ◽  
Grafted Polymer ◽  
Functional Features ◽  
The Brain

In vitro electrophysiological investigation of neural activity at a network level holds tremendous potential for elucidating underlying features of brain function (and dysfunction). In standard neural network modelling systems, however, the fundamental three-dimensional (3D) character of the brain is a largely disregarded feature. This widely applied neuroscientific strategy affects several aspects of the structure–function relationships of the resulting networks, altering network connectivity and topology, ultimately reducing the translatability of the results obtained. As these model systems increase in popularity, it becomes imperative that they capture, as accurately as possible, fundamental features of neural networks in the brain, such as small-worldness. In this report, we combine in vitro neural cell culture with a biologically compatible scaffolding substrate, surface-grafted polymer particles (PPs), to develop neural networks with 3D topology. Furthermore, we investigate their electrophysiological network activity through the use of 3D multielectrode arrays. The resulting neural network activity shows emergent behaviour consistent with maturing neural networks capable of performing computations, i.e. activity patterns suggestive of both information segregation (desynchronized single spikes and local bursts) and information integration (network spikes). Importantly, we demonstrate that the resulting PP-structured neural networks show both structural and functional features consistent with small-world network topology.

scholarly journals Chronic electrical stimulation homeostatically decreases spontaneous activity, but paradoxically increases evoked network activity

2013 ◽  
Vol 109 (7) ◽  
pp. 1824-1836 ◽  
Author(s):  
Anubhuti Goel ◽  
Dean V. Buonomano
Keyword(s):  
Electrical Stimulation ◽  
Spontaneous Activity ◽  
External Input ◽  
Homeostatic Plasticity ◽  
Network Activity ◽  
Chronic Stimulation ◽  
Learning Rules ◽  
Chronic Electrical Stimulation ◽  
Evoked Activity

Neural dynamics generated within cortical networks play a fundamental role in brain function. However, the learning rules that allow recurrent networks to generate functional dynamic regimes, and the degree to which these regimes are themselves plastic, are not known. In this study we examined plasticity of network dynamics in cortical organotypic slices in response to chronic changes in activity. Studies have typically manipulated network activity pharmacologically; we used chronic electrical stimulation to increase activity in in vitro cortical circuits in a more physiological manner. Slices were stimulated with “implanted” electrodes for 4 days. Chronic electrical stimulation or treatment with bicuculline decreased spontaneous activity as predicted by homeostatic learning rules. Paradoxically, however, whereas bicuculline decreased evoked network activity, chronic stimulation actually increased the likelihood that evoked stimulation elicited polysynaptic activity, despite a decrease in evoked monosynaptic strength. Furthermore, there was an inverse correlation between spontaneous and evoked activity, suggesting a homeostatic tradeoff between spontaneous and evoked activity. Within-slice experiments revealed that cells close to the stimulated electrode exhibited more evoked polysynaptic activity and less spontaneous activity than cells close to a control electrode. Collectively, our results establish that chronic stimulation changes the dynamic regimes of networks. In vitro studies of homeostatic plasticity typically lack any external input, and thus neurons must rely on “spontaneous” activity to reach homeostatic “set points.” However, in the presence of external input we propose that homeostatic learning rules seem to shift networks from spontaneous to evoked regimes.

Ontogeny of Rhythmic Motor Patterns Generated in the Embryonic Rat Spinal Cord

2003 ◽  
Vol 89 (3) ◽  
pp. 1187-1195 ◽  
Author(s):  
Jun Ren ◽  
John J. Greer
Keyword(s):  
Spinal Cord ◽  
Spontaneous Activity ◽  
Developmental Stages ◽  
Rhythmic Activity ◽  
Late Gestation ◽  
Spatiotemporal Pattern ◽  
Rat Spinal Cord ◽  
Motor Patterns ◽  
Rhythmic Motor

Patterned spontaneous activity is generated in developing neuronal circuits throughout the CNS including the spinal cord. This activity is thought to be important for activity-dependent neuronal growth, synapse formation, and the establishment of neuronal networks. In this study, we examine the spatiotemporal distribution of motor patterns generated by rat spinal cord and medullary circuits from the time of initial axon outgrowth through to the inception of organized respiratory and locomotor rhythmogenesis during late gestation. This includes an analysis of the neuropharmacological control of spontaneous rhythms generated within the spinal cord at different developmental stages. In vitro spinal cord and medullary-spinal cord preparations isolated from rats at embryonic ages (E)13.5–E21.5 were studied. We found age-dependent changes in the spatiotemporal pattern, neurotransmitter control, and propensity for the generation of spontaneous rhythmic motor discharge during the prenatal period. The developmental profile of the neuropharmacological control of rhythmic bursting can be divided into three periods. At E13.5–E15.5, the spinal networks comprising cholinergic and glycinergic synaptic interconnections are capable of generating rhythmic activity, while GABAergic synapses play a role in supporting the spontaneous activity. At late stages (E18.5–E21.5), glutamate drive acting via non- N-methyl-d-aspartate (non-NMDA) receptors is primarily responsible for the rhythmic activity. During the middle stage (E16.5–E17.5), the spontaneous activity results from the combination of synaptic drive acting via non-NMDA glutamatergic, nicotinic acetylcholine, glycine, and GABAA receptors. The modulatory actions of chloride-mediated conductances shifts from predominantly excitatory to inhibitory late in gestation.

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