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W4230670690 abstract "Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods References Decision letter Author response Article and author information Metrics Abstract Dysfunction of the basal ganglia produces severe deficits in the timing, initiation, and vigor of movement. These diverse impairments suggest a control system gone awry. In engineered systems, feedback is critical for control. By contrast, models of the basal ganglia highlight feedforward circuitry and ignore intrinsic feedback circuits. In this study, we show that feedback via axon collaterals of substantia nigra projection neurons control the gain of the basal ganglia output. Through a combination of physiology, optogenetics, anatomy, and circuit mapping, we elaborate a general circuit mechanism for gain control in a microcircuit lacking interneurons. Our data suggest that diverse tonic firing rates, weak unitary connections and a spatially diffuse collateral circuit with distinct topography and kinetics from feedforward input is sufficient to implement divisive feedback inhibition. The importance of feedback for engineered systems implies that the intranigral microcircuit, despite its absence from canonical models, could be essential to basal ganglia function. https://doi.org/10.7554/eLife.02397.001 eLife digest The basal ganglia are a group of nuclei located deep within the brain that are involved in the control of movement. The death of neurons in one particular nucleus—known as the substantia nigra—gives rise to a range of symptoms that are characteristic of Parkinson’s disease, including slowness of movement and tremors. Although the basic anatomy and circuitry of the basal ganglia were worked out many years ago, it is not clear how these structures control voluntary movement. Based on insights from engineering, Brown et al. propose a model in which negative feedback within the substantia nigra—largely overlooked by previous models—regulates the output of the basal ganglia and thus contributes to the control of movement. Most areas of the brain contain projection neurons, which connect to other areas of the brain, and interneurons, which do not form connections beyond the nucleus in which they reside. In these areas, dedicated networks of interneurons use feedback to exert control over the signals that the projection neurons carry to other areas of the brain. However, it is thought that the substantia nigra does not contain interneurons. This led Brown et al. to propose that structures called axon collaterals form a microcircuit that can instead supply such feedback in the substantia nigra. Axons are the nerve fibres that carry signals away from the cell body of a neuron, and axon collaterals are branches of those axons. Data obtained by recording and manipulating electrical activity in the substantia nigra were consistent with this model and further experiments allowed this microcircuit to be mapped in detail. By revealing the circuit mechanisms of negative feedback within the substantia nigra, the work of Brown et al. changes our understanding of the basal ganglia and could have implications for understanding the mechanisms and ultimately the treatment of disorders such as Parkinson’s disease. https://doi.org/10.7554/eLife.02397.002 Introduction The basal ganglia are a collection of interconnected subcortical regions of the vertebrate brain (DeLong, 2000). Pathological disruptions of basal ganglia signaling produce profound deficits in the timing (Buhusi and Meck, 2005), vigor (Turner and Desmurget, 2010), and initiation (Mink, 1996) of voluntary movements. While it is thus clear that the basal ganglia are critical for voluntary movement, the specific mechanisms by which movement is controlled by basal ganglia activity remain unclear (Turner and Desmurget, 2010). Voluntary control of movement can be explained in terms of optimal feedback control theory (Diedrichsen et al., 2010). The basal ganglia circuit can be described as an extended loop that begins with projections from deep layer cortical neurons and ultimately returns to the cortex via projections from the basal ganglia to the ventral thalamus (Haber, 2003). However, the basal ganglia circuit also contains intrinsic feedback projections (Gerfen, 2004). In engineered control systems, feedback is critical for stable performance (Astrom and Murray, 2008). The substantia nigra (SN) pars reticulata (SNr) is the primary source of output from the sensorimotor basal ganglia in rodents (Gerfen, 2004). The vast majority of neurons in the SNr are projection neurons that synthesize and release the neurotransmitter ϒ-aminobutryic acid (GABA). Projection neurons of the SNr target pre-motor areas in the ventral thalamus, dorsal midbrain, and tegmentum (Parent, 1990; Hikosaka, 2007). In addition to these long range targets, nigral projection neurons also elaborate axon collaterals within the SN (Mailly et al., 2003; Cebrián et al., 2005; Deniau et al., 2007a). There are no known interneurons in the SNr (Deniau et al., 2007a), and thus collaterals of projection neurons are the sole source of intrinsic feedback for the basal ganglia output. Anatomical reconstructions have indicated that the axon collaterals of SNr projection neurons are sparse (Mailly et al., 2003). The functional impact of this intranigral microcircuit remains unclear. Antidromic activation of SNr projection neurons in anesthetized animals has been used to infer the presence of inhibition via projection neurons collaterals (Tepper and Lee, 2007; Brazhnik et al., 2008); however, the relative impact, spatial organization and temporal properties of recruitment of feedback inhibition via SNr collateral inhibition remains largely unknown. In this study, we explore the hypothesis that the microcircuit formed by SNr collaterals could implement a critical negative feedback node in the context of a control system for voluntary behavior that is implemented in the extended cortico-basal ganglia circuit. In engineered systems, the functional impact of a negative feedback can be difficult to detect and characterize (Astrom and Murray, 2008). For example, at steady state or in the absence of change in the state of the system, there may be no obvious effect of appropriately functioning negative feedback. However, sudden transitions in the state of the system can reveal the contribution of negative feedback—altering, for example, the gain and/or the time course of settling around transitions. By analogy to an engineered system, the SNr microcircuit may have little apparent impact in the absence of sudden changes in the state of the population activity. However, changes in behavior and receipt of sensory stimuli are accompanied by phasic transitions, both increases and decreases, in the activity of the SNr population. We thus reasoned that the role of negative feedback, implemented by the SNr microcircuit, could become apparent under such conditions. Recent work has shown that a salient or conditioned stimulus (CS), for example an auditory tone, can lead to phasic changes in the activity of SNr neurons in rodents (Schmidt et al., 2013). Moreover, these short-latency modulations of activity are predictive of the initiation of conditioned behavioral responses—that is action selection (Pan et al., 2013; Schmidt et al., 2013). If the basal ganglia act as a control system for behavior, then we would predict that control over the gain or dynamic range of these phasic modulations should be critical for normal voluntary actions. Detailed study of the local inhibitory connections within the SNr has been hampered by the difficulty in isolating and specifically exciting the SNr collaterals independent of afferent inhibitory and excitatory projections (Hammond et al., 1983). We overcame this challenge by using cell-type specific expression of channelrhodopsin-2 (ChR2), a light-gated cation channel (Boyden et al., 2005; Zhang et al., 2006), in SNr GABA neurons. This optogenetic approach allowed us to stimulate SNr GABA neurons with high temporal and spatial resolution without contamination from excitatory afferents, dopaminergic transmission, or afferent inhibitory input from the striatonigral projection both in vitro and in vivo. Consistent with the prediction from anatomical data, we show that inhibition derived from the collaterals of projection neurons in the SNr is sparse and has little to no effect on tonic baseline firing. However, we also observed that activation of the SNr projection neuron population could elicit a large and potent feedback inhibition capable of shaping output activity. Here, we show that this unique combination of effects is the result of a number of distinctive features of the SN microcircuit: (1) postsynaptic currents resulting from collateral synaptic input provided robust inhibition with a rapid onset during strong activation of the network; (2) unitary connections are weak with sufficiently low release probability to sustain release during repetitive stimulation; (3) asynchronous basal inhibition in the tonically active network is effectively compensated for by intrinsic conductances that sustain tonic spiking; (4) the potency of feedback inhibition is proportional to total activation of the microcircuit due to a sparse, spatially diffuse connectivity. Together, these properties of the intrinsic microcircuit of the SNr implement a robust inhibition that is rapidly and stably recruited in proportion to the sustained activation of the projection neuron population with little effect in the absence of stimulation—in other words, the inhibitory microcircuit of the SNr mediates a divisive gain control on the basal ganglia output. Results If collateral, feedback projections of SNr neurons provides a source of negative feedback, then we would expect that the level of activation of the population immediately prior to a stimulus should be inversely correlated with the modulation of the response of an individual neuron to that stimulus. In other words, if there were more activity producing collateral inhibition, the phasic response to a stimulus could be blunted. To test this possibility, we examined a dataset of 599 single units recorded from the ventral midbrain of mice (n = 5, strongly biased towards recordings from GABAergic neurons in the SN, ‘Materials and methods’) performing a classical trace conditioning task described previously (Pan et al., 2013). For each individual unit, we computed the normalized response to a salient stimulus (an auditory tone that predicted a delayed reward) as a function of the normalized activity of the simultaneously recorded population of neurons for 32 recording sessions in which at least eight neurons were recorded simultaneously (median = 12; maximum = 21) for each trial in the recording session (median = 71 trials; range = 42:132) (Figure 1A–B). This yielded a data set of 28,277 comparisons from which we estimated the correlation between the activation of the population at baseline to the activation of each individual neuron in response to the conditioned stimulus (CS). We found that both the population (−0.1; p<0.001 permutation test) and 22/32 individual sessions exhibited significant negative correlations (−0.12 ± 0.05 SD; p<0.05, permutation test; Figure 1C). Figure 1 Download asset Open asset Transient changes in the basal ganglia output are reduced by ongoing population activity. A data set of 599 single units was isolated from recording sessions with at least eight simultaneously recorded units (Pan et al., 2013). Electrodes were targeted to the substantia nigra and ventral tegmental region of mice trained to perform an auditory trace conditioning paradigm. (A) Spiking activity was z-scored, aligned to the onset of the conditioned stimulus (CS), and averaged for all units. (B) For each recorded unit the mean subtracted response (RESPsingle) was computed as a function of the mean normalized activity prior to CS onset for the rest of the simultaneously recorded population (PREpopulation; 7–20 units). Population data were binned, averaged, and fit with a sigmoid function (cyan line). (C) The correlation coefficient between RESPsingle and PREpopulation was computed for each session (n = 32). A histogram of all correlation scores is drawn with significant correlations (permutation test) indicated by filled gray bars. The correlation score of the entire population (−0.1) is indicated by a cyan triangle. https://doi.org/10.7554/eLife.02397.003 These data thus suggest that the activation of the SNr population indeed provides a negative feedback to limit the phasic response of the population to salient stimuli. However, these data also imply a surprising structure to the SNr microcircuit—namely, even a relatively poor estimate of population activity (7–20 simultaneously recorded units across electrodes spread over hundreds of microns) is sufficient to provide predictive power for the response of individual units. However, significant individual pairwise correlations were very rarely observed in these recordings (Pan et al., 2013) consistent with prior work (Nevet et al., 2007). This would suggest that either the negative correlation observed is a consequence of correlations in activity due to extended feedback projections or that individual units receive relatively weak and diffuse input from many SN neurons rather than strong feedback inhibition from proximally located neurons. To distinguish between these possibilities, we first sought to test whether this negative feedback property of the SN microcircuit could be recapitulated in an in vitro preparation where extrinsic, multisynaptic sources of feedback are eliminated. The functional properties of feedback inhibition to the basal ganglia were assessed by channelrhodopsin-2 (ChR2) mediated stimulation of SNr GABA neurons. In one set of experiments an adeno-associated virus (AAV) that expressed a cre-dependent ChR2 transgene (Atasoy et al., 2008) was injected into the SNr of a mouse line in which cre-recombinase was expressed under the glutamic acid decarboxylase (Gad2) promoter to target expression to SNr GABA neurons (hereafter referred to as Gad2-ChR2; Figure 2A,C). In the other, we exploited a transgenic mouse line which has strong expression of ChR2 under the control of the thymus cell antigen 1 (Thy1) promoter (Arenkiel et al., 2007), (hereafter referred to as Thy1-ChR2; Figure 2B,D). In this transgenic line ChR2 is robustly expressed in SNr GABA neurons, but not in SN dopaminergic neurons (Pan et al., 2013;) or in upstream projection neurons of the dorsal striatum (data available from the authors on request). Both approaches thus provide a method to specifically excite SNr GABA neurons with high reliability and fine temporal resolution (Figure 2E–J). Figure 2 Download asset Open asset Light evoked activity of ChR2-expressing SNr GABA neurons in vitro. ChR2 was selectively expressed in SNr GABA neurons via two methods. Viral injection of AAV expressing cre-dependent ChR2-GFP transgene into SNr of a mouse line in which cre-recombinase was expressed under the glutamic acid decarboxylase (Gad2) promoter (A, Gad2-ChR2) and transgenic mouse line (Thy1 Line18) which has ChR2 expression under the control of Thy1 promoter (B, Thy1-ChR2), (A–B) left: schematic of midbrain region with SN labeled and pipette representing injection target in (A), middle: midbrain coronal sections showing ChR2-GFP expression (green), right: two-photon image of ChR2-GFP positive SNr GABA neurons. In vitro wide-field illumination of midbrain slice (0.5 ms light pulse, 10 Hz, cyan arrows) reliably evoked action potentials in SNr GABA neurons in Gad2-ChR2 (C; n = 12/20 cells) and Thy1-ChR2 (D; n = 21/21 cells) mice, (C and D rater plot [upper] and cell-attached recording [lower] of a representative neuron from each mouse line showing evoked spiking over five trials repeating the same light stimulus. Quantification of light evoked spiking probability (E), latency (F) and standard deviation of the latency (jitter) (G) for a range of photostimulation durations recorded from both Gad2-ChR2 (red; n = 5 cells) and Thy1-ChR2 (green; n = 7 cells) mice. Representative light evoked ChR2-mediated inward current recorded at a range of membrane voltages (from −80 mV to +40 mV) from a single neuron recorded in either the Gad2-ChR2 (H) or Thy1-ChR2 (I) mouse. (J) Current-voltage relationship of light-evoked currents recorded in either Gad2-ChR2 (red; n = 8 cells) or Thy1-ChR2 (green; n = 8 cells) mice. https://doi.org/10.7554/eLife.02397.004 Local SNr inhibition is sufficient to modify basal ganglia output during phasic activation Local axon collaterals of projection neurons provide a source of feedback inhibition proportional to the output of the SNr. For this inhibition to regulate the output of the SNr it must be sufficient to suppress activity even in the presence of strong, phasic activation of the projection neurons. Phasic activation of the SNr population occurs, for example, at the onset of salient sensory cues (Pan et al., 2013; Schmidt et al., 2013; Figure 3—figure supplement 1). Thus, to determine whether local inhibition was sufficient to regulate the gain of the SNr network, we used ChR2 stimulation to drive repetitive somatic spiking in the projection neuron network. This recruits a population of SNr neurons with a time course and distribution of responses similar to that evoked by conditioned stimuli (Pan et al., 2013; Figure 3—figure supplement 1). We determined the consequences of local inhibition by comparing activity evoked when inhibition was intact with activity evoked following pharmacological blockade of inhibition. Whole cell current-clamp recordings from individual SNr projection neurons were obtained from brain slices of Thy1-ChR2 mice in the presence of excitatory synaptic transmission blockers (D-AP5 and NBQX; Figure 3A). Wide-field illumination through a 10X objective was used to stimulate activity throughout the SNr network. Direct light-evoked spiking in the recorded neuron was substantially, or in some cases completely, suppressed under control conditions (Figure 3B,C). However, reliable light-evoked spiking was always present following application of the GABAA receptor antagonist gabazine (Gbz) to block local inhibition (Figure 3D). The suppression of evoked spiking was consistent across stimulus durations within a cell (Figure 3E), whereas the magnitude of suppression was more idiosyncratic across cells for a given stimulus condition (Figure 3F). Figure 3 with 2 supplements see all Download asset Open asset The local inhibitory microcircuit of the SNr provides feedback gain control. (A) Schematic of the experimental configuration. 1-2 SNr GABA neurons were recorded from in the whole-cell current clamp configuration. Wide-field illumination of the slice (indicated by cyan circle) was used to photostimulate the SNr network. (B) Example recording from an individual SNr GABA neuron during light stimulation (upper cyan trace). Note the stereotyped membrane potential fluctuations during photostimulation (10 trials). (C and D) Example recordings from the same neuron recorded during 10 trials of stimulation (‘Stim’; upper cyan trace) under control conditions (C; Cntrl; black) and following pharmacological blockage of inhibition via gabazine application (D; +Gbz; red) aligned to stimulus onset. Tick marks indicate spike times for 10 repetitions of the same light stimulus. Lower traces show the intracellular recording from the same neuron overlaid for all trials. (E) Raster plot of evoked spiking in control conditions (left) and in the presence of Gbz (right) for 4 blocks of 10 trails of increasing stimulus durations (4 ms, 8 ms, 12 ms, 20 ms; top to bottom) for a single neuron. (F) Raster plots of evoked spiking for 10 trials aligned to the onset of an 8 ms light stimulus for the population of neurons under control conditions (left) and in the presence of Gbz (right). (G) Normalized response across the population of neurons binned by stimulus duration and grouped by treatment (black, Cntrl and red, +Gbz). Significant effects on both stimulus duration and treatment condition were observed (Two-way ANOVA, p<0.05). Zero stimulus responses (open symbols) were estimated from the background firing rate. No significant difference was observed. (H) Full width half maximum (FWHM) of the peristimulus time histogram (PSTH) for evoked spiking in control and following Gbz (p<0.01). (I) For paired recordings, the percent inhibition of one neuron in the pair was plotted as a function of the percent inhibition of the other neuron for all stimulus conditions (black circles). A significant positive correlation was found and indicated by the solid black line (p<0.01; two tailed t test). https://doi.org/10.7554/eLife.02397.005 The ability of feedback inhibition to suppress spiking more effectively with increasing stimulus duration (Figure 3E) implies a divisive gain control. To quantify the gain effect across the population, we compared the normalized response to photostimulation of increasing duration both in the presence and absence of inhibitory synaptic transmission (Figure 3G). We found that the response of the population showed a significant increase as a function of stimulus duration and the magnitude of the increase was significantly reduced by the presence of feedback inhibition (two-factor ANOVA; p<0.05). Divisive gain control is characterized by a suppression of spiking at large stimuli but little to no effect on the response to weak or absent stimuli. Consistent with a divisive gain control, we found that baseline firing was unaffected by removal of inhibition (Figure 3G, open circles). To contrast a feedback gain control with the effect predicted for subtractive inhibition, we examined recordings from dopamine neurons of the SN that do not express ChR2, but are strongly inhibited by ChR2 expressing projection neurons (Pan et al., 2013). For dopamine neurons, we observed a constant suppression of spiking across the range of stimuli used (Pan et al., 2013). If the reduction in spiking observed during stimulation was the result of feedback inhibition one would predict that the inhibition should onset after the onset of the population response and truncate the response present in the absence of feedback. Consistent with this prediction we found that suppression of spiking was characterized by a significant decrease in the duration of the evoked spiking (Figure 3H). This effect on the duration could be observed in many individual neuron responses (Figure 3F). Our in vivo results suggested that the extent of suppression of transient activation in SNr neurons is proportional to the estimated activation of the network (Figure 1). Anatomical studies indicate that the vast majority of projection neurons elaborate collaterals within the SNr, however, these collaterals can form relatively few (∼10) putative synaptic contacts (Mailly et al., 2003). Moreover, we found that unitary release events produced relatively weak mIPSCs (∼150 pS) (Figure 3—figure supplement 2). Taken together, these data imply that the inhibition observed results from the activation of approximately 50–100 presynaptic inputs. Given that the GABAergic neurons in the SNr are thought to be exclusively projection neurons (Deniau et al., 2007b), this is consistent with the finding that projection neuron collaterals form 79.4 ± 96.1 (SD) boutons per neuron within the SNr of the rat (Mailly et al., 2003). Assuming a modest or low probability of paired connections, our connectivity estimates imply that the extent of activation across a large population of SNr neurons would determine the extent of feedback inhibition consistent with our observation in vivo. While we do not have a direct measure of the total extent of activation of the SNr by our photostimulation, we note that neurons recorded simultaneously experience the same activation state of the network. Thus, we reasoned that the extent of feedback inhibition in a pair of recorded neurons should be correlated if feedback inhibition is proportional to the total activation of the network. Consistent with this prediction, we found that there was a significant correlation (Pearson's correlation, p<0.01 permutation test) in paired recordings (Figure 3I). These results suggest that a given SNr projection neuron receives input from a spatially diffuse collection of other SNr projection neurons. The data above are consistent with the claim that a large population of SNr projection neurons must be recruited to fire within a relatively small time window (5–20 ms) in order to achieve robust feedback inhibition and divisive gain effects (Figure 3G). These results were obtained in the Thy1-ChR2 mouse where all neurons in the SNr express ChR2 (Figure 2). This would suggest that if a local subset of the SNr was expressing ChR2, the divisive gain effect should be present, but reduced in magnitude analogous to the smaller effects observed when less of the network was recruited in the Thy1-ChR2 preparation (Figure 3G). Indeed, we found that when the same experiment was repeated in ChR2+ nigral neurons from virally infected Gad2-ChR2 mice a divisive gain effect was observed, but reduced in magnitude (p<0.05; two-factor ANOVA; 15% reduction in the saturated response). Intrinsic properties that produce tonic spiking effectively counteract transient inhibition To alter the gain of a response to activation of the network requires a change in the slope of the curve. As described above, we observed that there was a substantial effect of feedback inhibition in the strongly activated SNr circuit, but no effect in the absence of stimulation—resulting in a change in the slope of the response to stimulation. However, it is confusing how a strongly coupled inhibitory network of tonically active neurons could exhibit no effect of feedback even in the absence of stimulation. We first asked whether the rate of spontaneous IPSCs was consistent with our estimate, and a prior anatomical estimate (Mailly et al., 2003), of >50 inputs from other SNr projection neurons. The expected rate of spontaneous IPSCs would thus be approximately: (1) RuIPSCs=Npre×Rpre×Prelease where, RuIPSCs is the predicted rate of unitary IPSCs (uIPSCs), Npre is the number of presynaptic inputs (release sites), Rpre is the mean firing rate of presynaptic inputs, and Prelease is the effective release probability across all release sites. Thus, with a relatively low release probability (<0.5), we would predict 75–300 Hz of uIPSCs. This corresponds well to the rate of uIPSCs estimated directly from voltage clamp recordings (Figure 4A–C). Consistent with this estimate, we also show that repetitive stimulation of SNr collaterals fails to completely depress transmission (Figure 4—figure supplement 2) consistent with a vesicular release probability low enough to allow vesicle recycling to keep pace with release. Such a mechanism has been described in detail for Purkinje cell synapses (Telgkamp et al., 2004). These observations suggest that there is indeed a substantial background rate of IPSCs that, when pharmacologically blocked, has no significant effect on the tonic firing of SNr projection neurons (Atherton and Bevan, 2005). Figure 4 with 2 supplements see all Download asset Open asset High background inhibition has little affect on tonic activity of SNr neurons. (A) Whole-cell recording of spontaneous IPSCs (sIPSCs) onto SNr neurons in control conditions (Cntrl; black trace) and following addition of tetrodotoxin (TTX) to isolate miniature events (+TTX; red trace, Vh 0 mV). (B) Cumulative histogram of IPSC amplitude in control conditions and following addition of TTX (n = 4 cells). (C) Box and whisker plot of IPSC amplitude for control and following addition of TTX. (D) Spiking output of SNr neurons following addition of high background excitation (upper) or inhibition (lower) via the dynamic clamp. (E) Summary data of change in firing rate of SNr neurons (n = 11 cells) following an increasing the relative frequency of inhibitory (red) or excitatory conductances (blue). (F) The slope of the change in firing rate as a function of change in conductance was significantly greater following increases in excitatory conductance compared to inhibitory conductance. https://doi.org/10.7554/eLife.02397.008 The question as we posed it—how can a strongly coupled inhibitory network of tonically active neurons exhibit no effect of feedback under basal conditions?—implies that tonic spiking is the problem. Alternatively, tonic spiking could be the solution. For a neuron to repetitively fire it must, upon the return from a spike, exhibit a net membrane current that is inward and thus drives the membrane towards spike threshold (Raman and Bean, 1999). This implies a positive slope conductance combined with a net inward current below threshold (Nolan et al., 2003)—in other words, the conductances that drive repetitive firing oppose hyperpolarizing currents in the perithreshold regime. Combined with a reduced driving force of inhibition near threshold, these biophysical features suggest that SNr neurons are much less sensitive to inhibition than to excitation in this regime. To test this hypothesis explicitly, we performed dynamic clamp experiments in which we systematically varied the balance between a high background rate of IPSCs and EPSCs (Figure 4D–F). Indeed, we found that the sensitivity of the spike rate to increasing inhibition was much reduced compared to the se" @default.
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W4230670690 title "Decision letter: The inhibitory microcircuit of the substantia nigra provides feedback gain control of the basal ganglia output" @default.
W4230670690 doi "https://doi.org/10.7554/elife.02397.021" @default.
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