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Interplay between periodic stimulation and GABAergic inhibition in striatal network oscillations

View Article: PubMed Central - PubMed

ABSTRACT

Network oscillations are ubiquitous across many brain regions. In the basal ganglia, oscillations are also present at many levels and a wide range of characteristic frequencies have been reported to occur during both health and disease. The striatum, the main input nucleus of the basal ganglia, receives massive glutamatergic inputs from the cortex and is highly susceptible to external oscillations. However, there is limited knowledge about the exact nature of this routing process and therefore, it is of key importance to understand how time-dependent, external stimuli propagate through the striatal circuitry. Using a network model of the striatum and corticostriatal projections, we try to elucidate the importance of specific GABAergic neurons and their interactions in shaping striatal oscillatory activity. Here, we propose that fast-spiking interneurons can perform an important role in transferring cortical oscillations to the striatum especially to those medium spiny neurons that are not directly driven by the cortical oscillations. We show how the activity levels of different populations, the strengths of different inhibitory synapses, degree of outgoing projections of striatal cells, ongoing activity and synchronicity of inputs can influence network activity. These results suggest that the propagation of oscillatory inputs into the medium spiny neuron population is most efficient, if conveyed via the fast-spiking interneurons. Therefore, pharmaceuticals that target fast-spiking interneurons may provide a novel treatment for regaining the spectral characteristics of striatal activity that correspond to the healthy state.

No MeSH data available.


Input-output transfer functions of MSNs and FSIs in a network of striatum.(A) Schematic of the network model of striatum with MSNs receiving inhibitory inputs from FSIs and other MSNs. The selected populations, which depend on the particular studied setup, of FSIs and MSNs receive sinusoidal current and background activity. The rest of the neurons (that do not receive oscillatory inputs) receive only excitatory and uncorrelated Poisson synaptic inputs to achieve the different realistic firing rates of MSNs and FSIs. (B) Change in the firing rate of MSNs as a function of the strength of excitatory inputs (left panel). The neurons were driven by Poisson type spiking inputs (600 Hz). The effect of feedforward and feedback inhibition in reducing the MSNs firing rate increased with an increase in the firing rate of MSNs. Change in the firing rate of FSIs as a function of the strength of excitatory input when FSIs were driven by Poisson type spiking input (600 Hz; right panel). (C) Effect of the amplitude and frequency of sinusoidal current on the output firing rate of MSNs (left panel) and FSIs (right panel). The MSNs and FSIs received sinusoidal current inputs at different amplitudes and frequencies in addition to a fixed Poisson type spiking inputs that set the baseline firing rate of the MSNs (<1 Hz) and FSIs (<10 Hz).
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pone.0175135.g001: Input-output transfer functions of MSNs and FSIs in a network of striatum.(A) Schematic of the network model of striatum with MSNs receiving inhibitory inputs from FSIs and other MSNs. The selected populations, which depend on the particular studied setup, of FSIs and MSNs receive sinusoidal current and background activity. The rest of the neurons (that do not receive oscillatory inputs) receive only excitatory and uncorrelated Poisson synaptic inputs to achieve the different realistic firing rates of MSNs and FSIs. (B) Change in the firing rate of MSNs as a function of the strength of excitatory inputs (left panel). The neurons were driven by Poisson type spiking inputs (600 Hz). The effect of feedforward and feedback inhibition in reducing the MSNs firing rate increased with an increase in the firing rate of MSNs. Change in the firing rate of FSIs as a function of the strength of excitatory input when FSIs were driven by Poisson type spiking input (600 Hz; right panel). (C) Effect of the amplitude and frequency of sinusoidal current on the output firing rate of MSNs (left panel) and FSIs (right panel). The MSNs and FSIs received sinusoidal current inputs at different amplitudes and frequencies in addition to a fixed Poisson type spiking inputs that set the baseline firing rate of the MSNs (<1 Hz) and FSIs (<10 Hz).

Mentions: It has been estimated that there are around 2800 MSNs located within the volume of the dendrites of one medium spiny cell. MSNs are the dominant neuron type in the striatum (up to 95% in rodents [49]), and the radius of their axonal and dendritic arborisations are both around 200 μm. There are at least two main inhibitory circuits in the striatum that are activated by cortical inputs and that control firing in MSNs. The first is FF inhibition via the small population of FSIs, and the second is FB inhibition from the axon collaterals of the MSNs themselves. Therefore, we simulated a network of two types of GABAergic neurons, 2800 MSNs and 56 FSIs, which was driven with external inputs (see section 1.4) corresponding to an external stimulation from the cortex. A scheme of the striatal network model is shown in Fig 1A. The connection probability between the MSNs was equal to 0.18 [50]. Each MSN received inhibitory inputs from on average 11 FSIs, resulting in 20% connectivity from FSIs to MSNs [30].


Interplay between periodic stimulation and GABAergic inhibition in striatal network oscillations
Input-output transfer functions of MSNs and FSIs in a network of striatum.(A) Schematic of the network model of striatum with MSNs receiving inhibitory inputs from FSIs and other MSNs. The selected populations, which depend on the particular studied setup, of FSIs and MSNs receive sinusoidal current and background activity. The rest of the neurons (that do not receive oscillatory inputs) receive only excitatory and uncorrelated Poisson synaptic inputs to achieve the different realistic firing rates of MSNs and FSIs. (B) Change in the firing rate of MSNs as a function of the strength of excitatory inputs (left panel). The neurons were driven by Poisson type spiking inputs (600 Hz). The effect of feedforward and feedback inhibition in reducing the MSNs firing rate increased with an increase in the firing rate of MSNs. Change in the firing rate of FSIs as a function of the strength of excitatory input when FSIs were driven by Poisson type spiking input (600 Hz; right panel). (C) Effect of the amplitude and frequency of sinusoidal current on the output firing rate of MSNs (left panel) and FSIs (right panel). The MSNs and FSIs received sinusoidal current inputs at different amplitudes and frequencies in addition to a fixed Poisson type spiking inputs that set the baseline firing rate of the MSNs (<1 Hz) and FSIs (<10 Hz).
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC5383243&req=5

pone.0175135.g001: Input-output transfer functions of MSNs and FSIs in a network of striatum.(A) Schematic of the network model of striatum with MSNs receiving inhibitory inputs from FSIs and other MSNs. The selected populations, which depend on the particular studied setup, of FSIs and MSNs receive sinusoidal current and background activity. The rest of the neurons (that do not receive oscillatory inputs) receive only excitatory and uncorrelated Poisson synaptic inputs to achieve the different realistic firing rates of MSNs and FSIs. (B) Change in the firing rate of MSNs as a function of the strength of excitatory inputs (left panel). The neurons were driven by Poisson type spiking inputs (600 Hz). The effect of feedforward and feedback inhibition in reducing the MSNs firing rate increased with an increase in the firing rate of MSNs. Change in the firing rate of FSIs as a function of the strength of excitatory input when FSIs were driven by Poisson type spiking input (600 Hz; right panel). (C) Effect of the amplitude and frequency of sinusoidal current on the output firing rate of MSNs (left panel) and FSIs (right panel). The MSNs and FSIs received sinusoidal current inputs at different amplitudes and frequencies in addition to a fixed Poisson type spiking inputs that set the baseline firing rate of the MSNs (<1 Hz) and FSIs (<10 Hz).
Mentions: It has been estimated that there are around 2800 MSNs located within the volume of the dendrites of one medium spiny cell. MSNs are the dominant neuron type in the striatum (up to 95% in rodents [49]), and the radius of their axonal and dendritic arborisations are both around 200 μm. There are at least two main inhibitory circuits in the striatum that are activated by cortical inputs and that control firing in MSNs. The first is FF inhibition via the small population of FSIs, and the second is FB inhibition from the axon collaterals of the MSNs themselves. Therefore, we simulated a network of two types of GABAergic neurons, 2800 MSNs and 56 FSIs, which was driven with external inputs (see section 1.4) corresponding to an external stimulation from the cortex. A scheme of the striatal network model is shown in Fig 1A. The connection probability between the MSNs was equal to 0.18 [50]. Each MSN received inhibitory inputs from on average 11 FSIs, resulting in 20% connectivity from FSIs to MSNs [30].

View Article: PubMed Central - PubMed

ABSTRACT

Network oscillations are ubiquitous across many brain regions. In the basal ganglia, oscillations are also present at many levels and a wide range of characteristic frequencies have been reported to occur during both health and disease. The striatum, the main input nucleus of the basal ganglia, receives massive glutamatergic inputs from the cortex and is highly susceptible to external oscillations. However, there is limited knowledge about the exact nature of this routing process and therefore, it is of key importance to understand how time-dependent, external stimuli propagate through the striatal circuitry. Using a network model of the striatum and corticostriatal projections, we try to elucidate the importance of specific GABAergic neurons and their interactions in shaping striatal oscillatory activity. Here, we propose that fast-spiking interneurons can perform an important role in transferring cortical oscillations to the striatum especially to those medium spiny neurons that are not directly driven by the cortical oscillations. We show how the activity levels of different populations, the strengths of different inhibitory synapses, degree of outgoing projections of striatal cells, ongoing activity and synchronicity of inputs can influence network activity. These results suggest that the propagation of oscillatory inputs into the medium spiny neuron population is most efficient, if conveyed via the fast-spiking interneurons. Therefore, pharmaceuticals that target fast-spiking interneurons may provide a novel treatment for regaining the spectral characteristics of striatal activity that correspond to the healthy state.

No MeSH data available.