Firing rate and pattern heterogeneity in the globus pallidus arise from a single neuron population
Friday, November 15, 2013
Deister CA, Dodla R, Barraza D, Kita H, and Wilson C.J. J Neurophysiol.109:497-506.
Intrinsic heterogeneity in networks of interconnected cells types has profound effects on synchrony and spike-time reliability of network responses. Projection neurons of the globus pallidus (GPe) are interconnected by GABAergic inhibitory synapses and in vivo fire continuously, but display significant rate and firing pattern heterogeneity. Despite being deprived of most of their synaptic inputs, GPe neurons in slices also fire continuously, and vary greatly in their firing rate (1-70 spikes/s) and in regularity of their firing. We asked if this rate and pattern heterogeneity arises from separate cell types differing in rate, local synaptic interconnections or variability of intrinsic properties. We recorded the resting discharge of GPe neurons using extracellular methods both in vivo and in vitro. Spike-to-spike variability (jitter) was measured as the standard deviation of interspike-intervals. Firing rate and jitter covaried continuously, with slow firing being associated with higher variability than faster firing, as would be expected from heterogeneity arising from a single physiologically-distinct cell type. The relationship between rate and jitter was unaffected by blockade of GABA and glutamate receptors. When the firing rate of individual neurons was altered with constant current, jitter changed to maintain the rate-jitter relationship seen across neurons. Long duration (30-60 min) recordings showed slow and spontaneous bidirectional drift in rate similar to the across-cell heterogeneity. Paired recordings in vivo and in vitro showed that individual cells wandered in rate independently of each other. Input conductance and rate wandered together, in a manner suggestive that both were due to fluctuations of an inward current.
Figure 1. Heterogeneity of GPe neuron firing rate in-vivo.
A. Example ISI histogram from a GPe neuron that fired at an average rate of 85.2 spikes/s. Inset, plot of instantaneous firing rate against preceding spike time for this example cell. B. Example ISI histogram from a GPe neuron that fired at an average rate of 16.1 spikes/s. Inset, plot of instantaneous firing rate time for the one minute sample of the firing of this example cell. C. Plot of the standard deviation (σ) of ISIs against mean firing rate for a one-minute sample of firing from each neuron in our sample.
Figure 2. Firing patterns of GPe neurons from in-vitro cell-attached recordings.
A. Portion of a spike train recorded from a neuron firing in a regular fashion. B. Example ISI histogram from the same neuron in A. C. A portion of a spike train recorded from a more irregularly firing neuron. D. ISI histogram of the irregular neuron shown in C. E. Plot of the standard deviation (σ) of ISIs against mean firing rate for each neuron in our sample. The distribution was well described at rates above 1 Hz, by a simple hyperbolic function. F. Scatter-plot of average firing rate plotted against the age of the animal from which the neuron was recorded from.
Figure 3. Synaptic inputs do not contribute to the distribution of GPe neuron firing rates and patterns.
A: plot of SD of ISIs plotted as a function of mean firing rate for cells recorded in control artificial cerebrospinal fluid (black dots), CGP-55845 (blue dots), and a cocktail of gabazine (GBZ), MK-801, and NBQX (red dots). Solid continuous line is the same hyperbolic function plotted in Fig. 1. B: sample distributions of mean firing rates across conditions. C: sample distributions of standard deviation in ISIs across conditions.
Figure 4. Individual GPe neurons can fire over the entire continuum of rate and pattern.
A. Portions of spike trains recorded from an example GPe neuron recorded in the perforated patch configuration with varying amplitudes of hyperpolarizing current injections. Note the significantly different time-scale for the -48 pA current injection. B. Frequency-current relationship for the neuron in A. C. Plot of σ of intervals against mean firing rate, along with the same hyperbolic function shown in Figures 1&2, for the neuron shown in A. Current injection amplitude is denoted above each symbol. Inset shows the compiled results from 13 neurons subjected to hyperpolarizing current injections.
Figure 5. Spontaneous rate fluctuations in GPe neurons.
A. ISI plotted as a function of spike time for three examples cell-attached recordings from long duration (35-90 min) recordings. The examples were chosen to represent the range of firing rates observed. B. ISI histograms for the three examples shown in A, and color coded in the same way. C. Plot of SD against mean firing rate for the three examples shown in A. Each point represents the SD and mean firing rate calculated in a bin of 100 adjacent spikes.
Figure 6. GPe neurons fluctuate in an independent fashion, in vivo and in vitro.
A. Examples of instantaneous firing rate evolution in time for pairs recorded in vitro (i, top) and in vivo (ii, bottom). The two cells are arbitrarily color-coded (black and blue). B. Plot of SD against mean firing rate for the examples shown in A. In vivo and in vitro neuronal pairs fluctuated in rate and regularity much like individual neurons in preceding figures. Insets show the non-normalized cross correlogram for each pair. C. Box and whisker plots showing the distribution of correlation coefficients (r) for differences in adjacent ISIs, which reflect the direction of rate change for all neuronal pairs in vitro (gray; n=12) and in vivo (white; n=18). Samples were not significantly different (p>0.05; Mann-Whitney U Test; W = 122). D. Box and whisker plots showing the distribution of mean firing rate differences calculated across the entire recording period for all neuronal pairs in vitro (gray; n=12) and in vivo (white; n=18). Samples were not significantly different (p > 0.05; Mann-Whitney U Test; W = 81).
Figure 7. Fluctuations in autonomous rate correlate with fluctuations in input resistance.
A. Neurons were recorded in current clamp and allowed to fire spontaneously. Every 30 seconds a hyperpolarizing current was injected for 1.5 seconds. Instantaneous firing rate is plotted as a function of spike time for this example cell. B. Portions of the 30 minute voltage trace showing the neuron’s response to hyperpolarization for two different mean firing rates. Spikes are truncated by ~10 mV. The mean subthreshold Vm and minimum Vm were used to calculate input resistance. C. Left, average firing rate was calculated for 100 spikes preceding each current injection and correlated with the neuron’s input resistance. For this example, the correlation coefficient (r) was -0.54 (p < 0.0005). The blue and red dots correspond to the blue and red traces shown in B. Right, subthreshold Vm and minimum Vm were plotted as a function of firing rate to determine which component showed more covariation with firing rate. Each component was fit with the best linear fit. In this example, the subthreshold Vm vs. rate plot had a slope of 0.02, and the r was 0.15 (p > 0.2). The slope value for minimum Vm plotted with rate was 0.28, and the r was 0.61 (p < 0.00005). D. Mean regression slope values, as in C, for our sample (n = 6). The regression slope for minimum Vm was significantly higher than of the subthreshold Vm (p < 0.05). Extrapolation of the best fit line predicts a reversal voltage value of -29 mV.