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Interplay between periodic stimulation and GABAergic inhibition in striatal network oscillations

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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.


Sensitivity of the oscillations in the MSN population to the parameters of the FB inhibition.(A) OI of MSNs for different values of the maximal oscillatory amplitude onto FSIs when MSNs underwent ongoing (1.56 Hz) or evoked (8.59 Hz) activity. (B) Influence of the FB inhibition on the transfer of the oscillations in the MSN population (Amax = 250 pA). (C) The MSN population was split into two equal sized populations. All neurons received Poisson background inputs and, additionally, the first population (referred to as the source network) received asynchronous oscillatory inputs with the driving frequency set at 20 Hz, and the other population (referred to as the target network) also received asynchronous oscillatory inputs but with the driving frequency set at 30 Hz. For the high activity state of the source network, a statistically significant peak was present in the power spectrum of the target network at the oscillatory frequency of the source network (0.0192 vs 0.0752, p<0.01).
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pone.0175135.g006: Sensitivity of the oscillations in the MSN population to the parameters of the FB inhibition.(A) OI of MSNs for different values of the maximal oscillatory amplitude onto FSIs when MSNs underwent ongoing (1.56 Hz) or evoked (8.59 Hz) activity. (B) Influence of the FB inhibition on the transfer of the oscillations in the MSN population (Amax = 250 pA). (C) The MSN population was split into two equal sized populations. All neurons received Poisson background inputs and, additionally, the first population (referred to as the source network) received asynchronous oscillatory inputs with the driving frequency set at 20 Hz, and the other population (referred to as the target network) also received asynchronous oscillatory inputs but with the driving frequency set at 30 Hz. For the high activity state of the source network, a statistically significant peak was present in the power spectrum of the target network at the oscillatory frequency of the source network (0.0192 vs 0.0752, p<0.01).

Mentions: The oscillations when transferred with the aid of FSIs are robust to the firing rate of the MSNs. In fact, higher firing rate of MSNs (evoked activity) increased the power of oscillations compared to the low firing rate (ongoing activity) (Fig 6A). This suggests that increase in the firing rate of MSNs during a behavioral task will improve the transfer of cortical oscillations. When we completely removed FB inhibition, the change of the oscillatory peak in the power spectrum depended on the firing regime of the MSNs (Fig 6B). When MSNs fired with low frequency (< 2Hz) the peak increased as the firing of MSNs increased after removing FB inhibition (1.56 Hz vs 1.68 Hz). In contrast, when higher neural activity was imposed (>10 Hz) the peak decreased after removing FB inhibition due to additional desynchronization of MSNs that overrode the effect of increase in the firing rate. Finally, we wanted to see how selective stimulation of only MSNs with oscillatory currents lead to the transfer of oscillations. First, the stimulation was partial and only a portion of MSNs received oscillatory (Amax = 250pA) and Poisson-type synaptic background inputs while the rest of the MSN network received only Poisson-type synaptic background inputs and fired less than 1 Hz on average (see Materials and methods). For 6.25% of the oscillatory driven MSN population, the peak at driving frequency was not visible, while for 12.5% it was strong and stable (but could not propagate oscillations into unstimulated MSN pool even when oscillatory driven MSNs fired very high). Further we split MSNs into two populations. All neurons received Poisson background inputs and, additionally, the first half of MSNs received oscillatory inputs with the driving frequency set to 20 Hz, and the other half also received oscillatory inputs but with the driving frequency set to 30 Hz (in order to avoid interference of the first and second harmonics). We referred to the first half of the MSN population as the source network and the second half as the target network [58]. Here, we investigated how the source network affected the oscillatory activity in the target network. Fig 6C shows that the activity of the source network can impose the oscillatory activity in the target network, depending on the firing profile of the source network. When the average firing in the source network was ~ 3.6 Hz, the source network could not impose oscillatory activity at its driving frequency onto the target network (Fig 6C, blue line). We found that when the source network was in a state of high activity, a statistically significant peak was present in the power spectrum of the target network at the oscillatory frequency of the source network (Fig 6C, red line; 0.0192 vs 0.0752, p<0.01).


Interplay between periodic stimulation and GABAergic inhibition in striatal network oscillations
Sensitivity of the oscillations in the MSN population to the parameters of the FB inhibition.(A) OI of MSNs for different values of the maximal oscillatory amplitude onto FSIs when MSNs underwent ongoing (1.56 Hz) or evoked (8.59 Hz) activity. (B) Influence of the FB inhibition on the transfer of the oscillations in the MSN population (Amax = 250 pA). (C) The MSN population was split into two equal sized populations. All neurons received Poisson background inputs and, additionally, the first population (referred to as the source network) received asynchronous oscillatory inputs with the driving frequency set at 20 Hz, and the other population (referred to as the target network) also received asynchronous oscillatory inputs but with the driving frequency set at 30 Hz. For the high activity state of the source network, a statistically significant peak was present in the power spectrum of the target network at the oscillatory frequency of the source network (0.0192 vs 0.0752, p<0.01).
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pone.0175135.g006: Sensitivity of the oscillations in the MSN population to the parameters of the FB inhibition.(A) OI of MSNs for different values of the maximal oscillatory amplitude onto FSIs when MSNs underwent ongoing (1.56 Hz) or evoked (8.59 Hz) activity. (B) Influence of the FB inhibition on the transfer of the oscillations in the MSN population (Amax = 250 pA). (C) The MSN population was split into two equal sized populations. All neurons received Poisson background inputs and, additionally, the first population (referred to as the source network) received asynchronous oscillatory inputs with the driving frequency set at 20 Hz, and the other population (referred to as the target network) also received asynchronous oscillatory inputs but with the driving frequency set at 30 Hz. For the high activity state of the source network, a statistically significant peak was present in the power spectrum of the target network at the oscillatory frequency of the source network (0.0192 vs 0.0752, p<0.01).
Mentions: The oscillations when transferred with the aid of FSIs are robust to the firing rate of the MSNs. In fact, higher firing rate of MSNs (evoked activity) increased the power of oscillations compared to the low firing rate (ongoing activity) (Fig 6A). This suggests that increase in the firing rate of MSNs during a behavioral task will improve the transfer of cortical oscillations. When we completely removed FB inhibition, the change of the oscillatory peak in the power spectrum depended on the firing regime of the MSNs (Fig 6B). When MSNs fired with low frequency (< 2Hz) the peak increased as the firing of MSNs increased after removing FB inhibition (1.56 Hz vs 1.68 Hz). In contrast, when higher neural activity was imposed (>10 Hz) the peak decreased after removing FB inhibition due to additional desynchronization of MSNs that overrode the effect of increase in the firing rate. Finally, we wanted to see how selective stimulation of only MSNs with oscillatory currents lead to the transfer of oscillations. First, the stimulation was partial and only a portion of MSNs received oscillatory (Amax = 250pA) and Poisson-type synaptic background inputs while the rest of the MSN network received only Poisson-type synaptic background inputs and fired less than 1 Hz on average (see Materials and methods). For 6.25% of the oscillatory driven MSN population, the peak at driving frequency was not visible, while for 12.5% it was strong and stable (but could not propagate oscillations into unstimulated MSN pool even when oscillatory driven MSNs fired very high). Further we split MSNs into two populations. All neurons received Poisson background inputs and, additionally, the first half of MSNs received oscillatory inputs with the driving frequency set to 20 Hz, and the other half also received oscillatory inputs but with the driving frequency set to 30 Hz (in order to avoid interference of the first and second harmonics). We referred to the first half of the MSN population as the source network and the second half as the target network [58]. Here, we investigated how the source network affected the oscillatory activity in the target network. Fig 6C shows that the activity of the source network can impose the oscillatory activity in the target network, depending on the firing profile of the source network. When the average firing in the source network was ~ 3.6 Hz, the source network could not impose oscillatory activity at its driving frequency onto the target network (Fig 6C, blue line). We found that when the source network was in a state of high activity, a statistically significant peak was present in the power spectrum of the target network at the oscillatory frequency of the source network (Fig 6C, red line; 0.0192 vs 0.0752, p<0.01).

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.