Weak modulation of thalamic discharge by basal ganglia output in association with a reaching task

Task-related activity in the ventral thalamus, a major target of basal ganglia (BG) output, is often assumed to be permitted or triggered by changes in BG activity through gating- or rebound-like mechanisms. To test those hypothesized relationships, we sampled single-unit activity from connected BG-output and thalamic nuclei (globus pallidus-internus, GPi, and ventrolateral-anterior nucleus, VLa) in non-human primates performing a reaching task. Increases in firing were the most common peri-movement change in both nuclei. Moreover, peri-movement changes generally began earlier in VLa than in GPi. Simultaneously-recorded GPi-VLa pairs rarely showed short-timescale spike-to-spike correlations or slow across-trials covariations and the latter were equally positive and negative. Finally, spontaneous bursts and pauses in GPi activity were both followed by small yet significant reductions in mean VLa rate. These results appear incompatible with gating and rebound models. Thus, BG-thalamic communication is more subtle than expected and may be dominated by a common external source.

Task-related activity in the ventral thalamus, a major target of basal ganglia (BG) output, is often assumed to be permitted or triggered by changes in BG activity through gating-or rebound-like mechanisms. To test those hypothesized relationships, we sampled single-unit activity from connected BG-output and thalamic nuclei (globus pallidus-internus, GPi, and ventrolateral-anterior nucleus, VLa) in non-human primates performing a reaching task. Increases in firing were the most common peri-movement change in both nuclei. Moreover, peri-movement changes generally began earlier in VLa than in GPi. Simultaneously-recorded GPi-VLa pairs rarely showed short-timescale spike-to-spike correlations or slow across-trials covariations and the latter were equally positive and negative. Finally, spontaneous bursts and pauses in GPi activity were both followed by small yet significant reductions in mean VLa rate. These results appear incompatible with gating and rebound models. Thus, BG-thalamic communication is more subtle than expected and may be dominated by a common external source.
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The copyright holder for this preprint . http://dx.doi.org/10.1101/546598 doi: bioRxiv preprint first posted online Feb. 11, 2019; The connection between the basal ganglia (BG) and its major downstream target, the thalamus, has received increased attention recently [Kammermeier et al. 2016;Goldberg et al. 2013;Anderson et al. 2015;Bosch-Bouju et al. 2014a] due to its role as a key pathway by which the BG can influence cortical function. 1 It is well established that the BG-thalamic projection is composed of GABAergic neurons [Ueki 1983;Penny & Young 1981;Kuramoto et al. 2010] that fire at high tonic rates of about 60 spikes/sec in animals at rest [DeLong 1971] and that this projection terminates densely on thalamocortical relay neurons, as well as on GABAergic thalamic interneurons (in species in which these exist) [Bodor et al. 2008;Ilinsky et al. 1997]. The fundamental mechanism by which the BGthalamic pathway communicates task-related information, however, remains uncertain [Goldberg et al. 2013]. A long-standing and widely accepted theory states that pauses in BG output activity "gate" the task-related activation of thalamus via disinhibition [Albin et al. 1989;Hikosaka 2007;Chevalier & Deniau 1990;Deniau & Chevalier 1985;Nambu 2004;Nambu et al. 1991]. More specifically, the high tonic firing rate of BG output neurons normally prevents thalamic neurons from responding to excitatory inputs (e.g., from cortex), but a task-related pause in BG output, which typically extends over one hundred milliseconds or more, would act as a disinhibitory opening of the gate allowing a temporally-coordinated task-related activation of thalamus. A competing theory hypothesizes that BG output may promote subsequent thalamic activation.
Specifically, the low threshold spike (LTS) mechanism common to thalamocortical neurons [Person & Perkel 2007;Bosch-Bouju et al. 2014a;Bosch-Bouju et al. 2014b;Person & Perkel 2005] may produce rebound bursts of thalamic activity following a cessation of transiently elevated inhibition from the BG (e.g., following task-related increases in BG output activity) [Bosch-Bouju et al. 2014a, Kim et al. 2017]. Both of these theories predict a tight temporal control of thalamic task-related responses by changes in BG output; more specifically, the latency of task-related changes in BG output activity should lead by a short time interval the resulting responses in thalamus. The gating hypothesis, in addition, predicts an inverse relationship in the signs of taskrelated changes in BG and BG-recipient thalamus, with the prevalence of task-related increases in BG output associated with a proportional prevalence of decreases in BG-recipient thalamus activity.
Others have suggested that the influence of BG output on thalamus is subtler, primarily consisting of a modulation of thalamic activity, while cortex is the primary driver. This idea arose initially from the observation that inactivation of BG output does not abolish task-related activity in the BG-recipient thalamus [Anderson et al. 1993;Inase et al. 1996]. That result was corroborated recently by  using the songbird model.  also substantiated two insights suggested previously from between-studies comparisons [Nambu 2008;]: first, that task-related increases in firing are far more prevalent than decreases both in BG-output neurons [Anderson & Horak 1985;Brotchie et al. 1991;Mitchell et al. 1987;Mink & Thach 1991;Turner & Anderson 1997] and in BG-recipient thalamus [van Donkelaar et al. 1999;Nambu et al. 1991;Anderson & Turner 1991]; and second, that the latencies of task-related activity in BG output neurons [Anderson & Horak 1985;Turner & Anderson 1997;Mink & Thach 1991] do not lead those of the BG-recipient thalamus, but may in fact lag behind them [Anderson & Turner 1991;Nambu et al. 1991;van Donkelaar et al. 1999].  did observe strong inhibitory effects of individual BG efferents onto thalamic neurons. In that study, the primary mode of BG-thalamic communication was an entrainment of thalamic spiking to the interspike intervals of BG efferents. A similar mechanism, if present in mammals, would provide a way for BG output to modulate the timing of thalamic spiking without requiring, as the gating and rebound models do, strict relationships in the timing and sign of task-related changes in firing. However, the high strength of the entrainment observed by  is likely a product of the unique synaptic anatomy of the birdsong circuit.
The BG-thalamic loop circuit devoted to skeletomotor function provides a well-defined anatomically-segregated substrate for testing the theories outlined above. This circuit receives convergent input from motor and somatosensory cortical areas and projects back to the motor cortices by way of a monosynaptic projection from the internal globus pallidus (GPi) to the anterior ventrolateral thalamus (VLa) [Graybiel et al. 1994;Alexander et al. 1991]. Experimentally-induced perturbations of this circuit are known to impair the vigor, timing and initiation of voluntary movement [Buhusi & Meck 2005;Mink 1996]. A surprising diversity of opinion remains, however, about the specific contributions that this circuit makes to motor control under normal physiologic conditions [Redgrave et al. 1999;Graybiel et al. 2014; Barter et al. 2015;DeLong & Wichmann 2007;Yttri & Dudman 2016;Thura & Cisek 2017]. A better understanding of the mechanisms that govern GPi-to-VLa communication is likely to reduce that uncertainty.
Here, for the first time in non-human primates (NHPs), we studied single-unit activity sampled simultaneously from connected regions of GPi and VLa while animals performed a highly standardized reaching task. Contrary to predictions of the gating theory, movement-related increases in discharge were common in both GPi and VLa. Furthermore, VLa task-related changes tended to begin earlier than GPi changes, inconsistent with ideas of gating and rebound. The firing of simultaneously recorded cell pairs in GPi and VLa was seldom correlated, in contrast to what would be expected from gating or rebound models; bursts and pauses in GPi spontaneous activity had similar weak effects on VLa firing. Hence, at least during performance of a well-learned reaching task, our results challenge the view that task-related activity in the BG-recipient thalamus arises primarily from gating or rebound-inducing signals transmitted from the BG. Our results also do not show evidence of the strong direct inhibitory effects or tight temporal entrainment seen in songbirds. Rather they suggest that temporal influences of GPi outputs on VLa activity are more subtle, and that both pallidal and thalamic discharge may be dominated by other, possibly cortical, We studied the single-unit activity of neurons sampled from connected regions of the GPi and VLa.
The spiking activity of isolated single-units was sampled from both areas simultaneously in macaque monkeys while the animals performed a two-choice reaction time reaching task for food reward [Franco & Turner 2012;Zimnik et al. 2015]. Multiple microelectrodes or 16-contact linear probes were positioned acutely in the arm-related regions of both nuclei [Hoover & Strick 1993].
We used a combination of electrophysiologic techniques to ensure that recordings were obtained from anatomically connected regions of GPi and VLa. Consistent with previous observations [Anderson & Turner 1991;Nambu et al. 1988] the location of the VLa nucleus was distinguished from surrounding thalamic regions -and from the cerebellar-recipient posterior ventrolateral nucleus (VLp) in particular -by the presence of a short-latency inhibition of spiking in response to electrical stimulation of the GPi and the absence of a short-latency excitation in response to stimulation of the superior cerebellar peduncle (SCP, Fig. 1B). This electrophysiologic localization of the VLa/VLp border was performed during initial mapping studies prior to formal data collection. Across multiple parallel electrode tracks, thalamic neurons inhibited by GPi stimulation (yellow tick marks in Fig. 1A, examples 1 and 2 in Fig. 1B) were always encountered at locations that were dorsal and anterior to neurons excited by SCP stimulation (green tick marks in  Fig. 1B). In most cases a narrow border (<0.5mm) separated the deepest GPiinhibited neuron and the dorsal-most SCP-excited cell (Fig. 1A) and the location of this border aligned closely across adjacent electrode tracks. Coincident inhibition from GPi stimulation and excitation from SCP (e.g., Fig. 1B3) was observed only at a small number of thalamic locations and those were located exclusively in the narrow border between GPi-inhibited and SCP-excited regions (red tick mark in Fig. 1A, example 3 in Fig. 1B).
The sub-regions of GPi and VLa that compose the arm-related BG-thalamic circuit were identified by testing for short-latency effects of electrical stimulation in arm-related areas of primary motor cortex (Fig. 1C). Previous studies have shown that stimulation of cortex can elicit complex triphasic responses in GPi neurons, via two-and three-synapse arcs through the BG [Nambu et al. 1988;Tachibana et al. 2008] (Fig. 1C top), and that excitation of corticothalamic projections can elicit excitatory and inhibitory responses in VLa neurons [Galvan et al. 2016 i  s  t  i  m  u  l  a  t  i  o  n  e  v  o  k  e  d  a  p  a  u  s  e  i  n  n  e  u  r  o  n  a  l  a  c  t  i  v  i  t  y  a  n  d  S  C  P  s  t  i  m  u  l  a  t  i  o  n  h  a  d  n  o  e  f  f  e  c  t  .  N  e  u  r  o  n  s  i  n  V  L  w  e  r  e  c  l  a  s  s  i  f  i  e  d  a  s  V  L  p  n  e  u  r  o  n  s  i  f  t  h  e  y  w  e  r  e  e  x  c  i  t  e  d  b  y  s  t  i  m  u  l  a  t  i  o  n  o  f  S  C  P  a  n  d  d  i  d  n  o  t  r  e  s  p  o  n  d  t  o  s  t  i  m  u  l  a  t  i  o  n  o  f  t  h  e All rights reserved. No reuse allowed without permission.
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. . N  e  u  r  o  n  s  l  o  c  a  t  e  d  a  t  t  h  e  b  o  r  d  e  r  b  e  t  w  e  e  n  V  L  a  a  n  d  V  L  p  o  c  c  a  s  i  o  n  a  l  l  y  r  e  s  p  o  n  d  e  d  t  o  s  t  i  m  u  l  a  t  i  o  n  o  f  b  o  t  h  G  P  i  a  n  d  S  C  P  (  r  e  d  t  i  c  k  m  a  r  k  ,  l  o  c  a  t  i  o  n  3  i  n  F  i  g  s  .  1  A  a  n  d  B  ) . N  e  u  r  o  n  s  t  h  a  t  d  i  d  n  o  t  r  e  s  p  o  n  d  t  o  s  t  i  m  u  l  a  t  i  o  n  w  e  r  e  c  l  a  s  s  i  f  i  e  d  a  s  V  L  a  n  e  u  r  o  n  s  (  b  l  a  c  k  t  i  c  k  m  a  r  k  s  F  i  g  .  1  A  )  o  n  l  y  i  f  t  h  e  y  w  e  r  e  l  o  c  a  t  e  d  a  n  t  e  r  o  d  o  r  s  a  l  t  o  t  h  e  V  L  a  /  V  L  p  b  o  u  n  d  a  r  y  a  n  d  p  o  s  t  e  r  o  v  e  n  t  r  a  l  t  o  t  h  e  r  e  t  i  c  u  l  a  r  n  u  c  l  e  u  s  o  f  t  h  e  t  h  a  l  a  m  u  s  .   C  .  R  e  g  i  o  n  s  o  f  t  h  e  G  P  i  a  n  d  V  L  a  t  h  a  t  b  e  l  o  n  g  t  o  t  h  e  a  r  m  -r  e  l  a  t  e  d  B  G  -t  h  a  l  a  m  i  c  c  i  r  c  u  i  t  w  e  r  e  i  d  e  n  t  i  f  i  e  d  b  y  t  e  s  t  i  n  g  f  o  r  s  h  o  r  t  -l  a  t  e  n  c  y  e  f  f  e  c  t  s  o  f  e  l  e  c  t  r  i  c  a  Turner 1991], and those rates and the differences between neural populations were highly consistent for the two animals (Table 1; p=1, ranksum test). Also, as expected, the action potentials All rights reserved. No reuse allowed without permission.
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The mean firing rate of most single-units showed small but significant ramps during the start position hold period (i.e., before presentation of the task's go cue; p<0.05, linear regression; 97% and 98% of GPi and VLa cells, respectively). This phenomenon is illustrated for exemplar GPi and VLa units in Fig. 2A. The GPi unit's firing rate declined slowly (−1.3 spikes/s 2 , p<0.001) during the ~1.2 s hold period before appearance of the go cue (red tick marks in rasters, Fig. 2A) while that of the VLa unit increased slowly (1.28 spikes/s 2 , p<0.001; Fig. 2A). For both GPi and VLa unit populations, the observed slopes were distributed symmetrically around zero (means: −0.05 and −0.14 spikes/s 2 respectively; p=0.29, ranksum test). Positively-and negatively-sloped ramps in activity were equally common (49% and 51%, respectively) and those fractions did not differ significantly between GPi and VLa neurons (p=0.27, chi-square test). These linear trends in delay period activity were taken into account by the algorithm used to detect peri-movement changes in firing rate, as described below.  (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint . http://dx.doi.org/10.1101/546598 doi: bioRxiv preprint first posted online Feb. 11, 2019; occurred at low rates in both animals [4.8±1.2% and 3.4±0.7% of trials in animals G and I, respectively; mean ± standard error of the mean ( We examined the peri-movement activity of neurons in GPi and VLa to test for evidence of an inverse relationship in their rate changes, as predicted by the gating hypothesis. Large proportions of both neural populations modulated their firing rates around the onset of reaches to the left or right target (Table 1). We constructed mean spike-density and interspike-interval functions for each single-unit separately for movements to left and right targets and then tested for significant changes in firing rate relative to that unit's baseline activity (i.e., linear trend ±SD of activity prior to go-cue presentation, yellow horizontal lines in Fig. 2A and Fig. 2 - Figure Supplement 1). To avoid a bias toward detecting rate increases relative to rate decreases that is inherent to spike density functions, we tested for peri-movement decreases in firing as increases in the mean interspike-interval (see  All rights reserved. No reuse allowed without permission. (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.  e  d  e  n  s  i  t  y  f  u  n  c  t  i  o  n  s  w  e  r  e  z  -s  c  o  r  e  d  r  e  l  a  t  i  v  e  t  o  m  e  a  n  r  a  t  e  p  r  i  o  r  t  o  g  o  c  u  e  p  r  e  s  e  n  t  a  t  i  o  n  a  n  d  d  i  s  p  l  a  y  e  d  o  n  a  c  o  l  o  r  s  c  a  l  e  . C . responses according to movement direction, however, will not be addressed in this manuscript. We considered neuronal responses independently for each movement direction and tested for differences between GPi and VLa populations in the incidence of different forms of peri-movement activity (see Table 1 and Fig. 2B). Monophasic changes in firing (composed of a simple increase or decrease in firing) were the most common change detected in both GPi and VLa, amounting to 62% and 85% of all responses detected, respectively. Monophasic responses were more common in VLa than in GPi (p=2.8×10 -12 , chi-square test). Conversely, polyphasic responses -a series of one All rights reserved. No reuse allowed without permission.
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The copyright holder for this preprint . http://dx.doi.org/10.1101/546598 doi: bioRxiv preprint first posted online Feb. 11, 2019; or more increase and decrease in firing -were more common in GPi than in VLa Monophasic increases were the most common form of response both in GPi and in VLa (46% and 63% of responses detected, respectively; Table 1). Increases in firing were also the earliest change in firing in 60% of all polyphasic responses (p=8.4×10 -13 , chi-square test) and this was equally true for GPi and VLa (p=0.65, chi-square). When the individual phases of polyphasic responses were considered independently, increases in firing were also more common than decreases both in GPi and in VLa (61% and 68% of changes, respectively; p=1.2×10 -17 , chi-square test) with increases being nominally more common in VLa than in GPi (p=0.03, chi-square test).
Another way to compare the balance of task-related increases and decreases between GPi and VLa is to examine the mean firing rate across the two populations. Population averages combined across all responses types (All in Fig. 2C) showed increases in firing rate during the perimovement period for both cell types. The inclination toward increases was confirmed quantitatively by integrating changes in firing rate from baseline across the peri-movement epoch individually for each neuron. The mean of this integrated change was positive both for GPi and VLa (z=7.37, p=1.7×10 -13 , and z=6.59, p=4.7×10 -11 for GPi and VLa, respectively, rank sum test; Table 1).
In summary, the general distribution of different response types and the skew toward rate increases were similar in GPi and VLa (see The gating theory predicts that decreases in GPi activity should precede and permit increases in VLa, while a rebound mechanism would also feature changes in GPi activity that precede those in All rights reserved. No reuse allowed without permission. (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint . http://dx.doi.org/10.1101/546598 doi: bioRxiv preprint first posted online Feb. 11, 2019; VLa. To address these predictions, we compared the times of onset for all individual changes in firing detected in GPi and VLa single units. As is evident in both Fig. 2B and Fig. 3A, response onset times were distributed widely across the peri-movement period and that was equally true for neural responses in GPi and VLa. Quantitative comparison, however, showed that, on average, GPi changes in discharge began later than those in VLa (median onset times: −53 and −90 ms for GPi and VLa populations, respectively; z=3.28, p=1.4×10 -3 , rank sum test; Fig. 3A), in contrast to the relationship posited in the gating and rebound frameworks. The more specific gating prediction states that decreases in GPi activity should precede VLa increases and, similarly, GPi increases precede VLa decreases. In contrast to that postulated relationship, the distribution of GPi decreases lagged in time behind that of VLa increases (median onset times: −31.5 and −88 ms for GPi and VLa populations, respectively; z=3.74, p=1.8×10 -4 , rank sum test; Fig. 3B). Similarly, VLa decreases preceded GPi increases, although the timing difference in this case did not reach significance (median onset times: −76 and −95.5 ms for GPi and VLa populations, respectively; z=1.71, p=0.08, rank sum test; Fig. 3C). Thus, the timing of changes in peri-movement discharge is also not consistent with the idea that GPi activity triggers or gates task-related activity in VLa.  All rights reserved. No reuse allowed without permission.
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It is possible that the large differences between GPi and VLa populations in baseline firing rate and rate variability could introduce biases in the latencies estimated using the standard method. To test the reliability of the latency results presented above, we re-estimated response onset latencies using an alternate approach based on the time at which an activity function crosses a fraction of the peak change (see Methods). That approach resulted in slightly earlier onset latencies overall (median latency shift: -14 ms), but the differences in latencies between GPi and VLa populations were fully consistent with those described above for the standard analysis (see Fig. 3 - Figure Supplement 1).
We next investigated the possibility that GPi to VLa communication might be evident in the activity of cell pairs sampled simultaneously from the two structures.
Both gating and rebound triggering of thalamic activity by the BG should produce strong correlations in the precise timing of spikes in GPi and VLa. Contrary to that expectation, we found little evidence for short time-scale spike-to-spike synchrony between GPi and VLa unit pairs ( Fig.   4 A-F). When we computed cross-correlation functions (CCFs) for pairs of spike trains sampled simultaneously from GPi and VLa, only 5.1% of the CCFs showed any statistically significant modulation (orange lines in Fig. 4A; 23 of 449 cell pairs; p<0.05 relative to a 20 ms jittered control; Table 2; note that the number of pairs included differs between comparisons due to the strict selection criteria used to ensure adequate statistical power, as described in more detail in All rights reserved. No reuse allowed without permission. (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint . http://dx.doi.org/10.1101/546598 doi: bioRxiv preprint first posted online Feb. 11, 2019; Materials and methods). Similarly, small fractions of CCFs reached significance when the analysis was restricted to a rest period during the start-position hold period (5.8% of pairs; 25 out of 430), or to the peri-movement period (4.2% of pairs; 18 of 430; Table 2). Furthermore, the distribution of those peak correlation values did not differ from the distribution of control peak correlations taken from CCFs generated after jittering spike times within 20 ms time windows (Fig. 4 A-C, p=0.51 for whole recordings, p=0.40 for rest, and p=0.08 for movement periods; permutation test). More broadly, population averages of the CCFs did not extend significantly beyond the confidence intervals from the control data ( Fig. 4 D -F, p>0.05 relative to shuffled control). Therefore, short latency cell-to-cell interactions were rare in our sample of GPi-VLa pairs and, when present, small in magnitude. These observations bring into question the standard gating and rebound hypotheses.
Next, we tested for slow trial-to-trial correlations in firing rate ('noise correlations') between simultaneously-recorded GPi-VLa cell pairs. This analysis was performed separately for rest and peri-movement periods. If gating was a common mechanism in GPi-VLa communication, then the majority of significant noise correlations would be expected to be negative whereas some versions of the rebound mechanism predict more frequent positive correlations due to the capacity of brief increases in GPi activity to effectively recruit prolonged rebound-supporting currents in VLa [Person & Perkel 2007;Bosch-Bouju et al. 2014a;Bosch-Bouju et al. 2014b;Person & Perkel 2005]. We found that the overall distribution of noise correlations was not significantly different from the control distribution ( Fig. 4 G-H; p=0.07 during rest, p=0.388 during movement; permutation test). Nevertheless, noise correlations did reach significance during rest for 5.4% of cell pairs (18 of 332 pairs; p<0.05 relative to shuffled controls; Table 2) and during movement for 7.2% of cell pairs (27 of 372 pairs). Roughly equal fractions of those significant correlations were positive (orange lines, Fig. 4 G-H) and negative (magenta lines; 44% versus 55% during rest and 41% versus 59% during movement, respectively; Table 2). Examples of the largest three noise correlations during movement are shown in Fig. 4-Figure Supplement 1. There was no evidence All rights reserved. No reuse allowed without permission.
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. Bursts and pauses are common features of GPi activity that could facilitate the transmission of information to VLa. The post-synaptic effects in VLa of GPi bursts and pauses could also be larger in magnitude and easier to detect than effects produced by single GPi spikes. We therefore estimated the influences of bursts and pauses in GPi unit activity on the firing rate of VLa neurons during the rest period (Fig. 5). The gating hypothesis predicts that GPi bursts should be associated closely in time with reductions in VLa firing rate and GPi pauses with VLa increases. The rebound hypothesis predicts that GPi burst offsets should be followed by increases in VLa firing. We detected the occurrences of bursts and pauses in a GPi unit's rest period activity separately using standard methods [Wichmann & Soares 2006] and then averaged the firing rate of simultaneouslyrecorded VLa neurons around the times of GPi burst onset, burst offset, and pause onset. The All rights reserved. No reuse allowed without permission.
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint . http://dx.doi.org/10.1101/546598 doi: bioRxiv preprint first posted online Feb. 11, 2019; resulting burst-triggered, burst offset-triggered, and pause-triggered averages of VLa activity were averaged across the populations of qualifying GPi-VLa pairs (Fig. 5 A-C; Table 2;  Surprisingly, a deceleration in VLa activity was also associated with pauses in GPi activity ( Fig. 5 C). The VLa decrease reached significance 50 ms after GPi pause onset. Given that GPi pauses had a mean duration of 195 ms, the decrease in VLa firing rate occurred during GPi pauses, not following them. Population averaged cross-correlations of the times of pauses in GPi unit activity relative to burst offsets for the same unit (Fig. 5 D) showed a broad (>200 ms) period of negative correlation (i.e., reduced likelihood of a pause) at negative time lags, as would be expected because pauses in firing are unlikely to occur during bursts. The probability of a pause swung sharply to positive values at the time of burst offset and remained positive for >500 ms thereafter. Thus, bursts and pauses in GPi activity tended to occur together in that order, with decreases in VLa firing rates occurring following GPi bursts, during GPi pauses.
An analysis of individual GPi-VLa cell pairs provided results consistent with the population-level burst/pause analysis. Small fractions of GPi-VLa pairs showed any significant change in VLa activity following GPi burst onsets, burst offsets, or pause onsets (Burst/pause influences; Table 2). Among those significant effects, decreases in VLa firing rate were far more common than increases, composing more than two-thirds of the significant effects for all alignment events (Burst/pause influences; Table 2). Due to the small number of cases, however, those All rights reserved. No reuse allowed without permission.
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint . http://dx.doi.org/10.1101/546598 doi: bioRxiv preprint first posted online Feb. 11, 2019; differences in prevalence were only nominally significant and only so for burst onset and offset (p=0.04, 0.05 and 0.16 for burst onset, burst offset and pause onset, respectively; chi-square test).
Together, these results suggest that the activity of some GPi-VLa neuron pairs is coordinated such that burst-pause complexes in the GPi neuron's activity are associated with longlasting reductions in the firing rate of the VLa neuron. The characteristics of this phenomenon, however, are not consistent with predictions of either gating or rebound models.  (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint . http://dx.doi.org/10.1101/546598 doi: bioRxiv preprint first posted online Feb. 11, 2019; models have been tested before in the NHP. Here, we sampled single-unit activity simultaneously from connected regions of GPi and VLa thalamus during performance of a reaching task. We searched for evidence consistent with a gating or rebound sculpting of thalamic activity by BG output. Some of our results also bear on the entrainment model.
We found that peri-movement modulations in discharge were very common in GPi and VLa. Critically, those modulations consisted of increases in discharge more often than decreases both in GPi and in VLa. This finding was supported by two independent analyses: first of the signs (increase vs. decrease) of individual response profiles, and second of integrated changes in firing across the movement period. It is difficult to reconcile these results with the gating hypothesis without invoking some yet undiscovered mechanism that would make decrease-type responses in GPi, which were in the minority in our observations, more effective at eliciting VLa spikes than increase-type responses are at inhibiting them (e.g., Goldberg et al.'s [2013] "different motor channels" idea).
Both gating and rebound hypotheses predict that task-related changes in GPi activity should begin earlier in time than the neuronal responses they are hypothesized to elicit in thalamus.
Contrary to those predictions, we found that onset latencies of GPi responses lagged in time behind those of VLa responses. That was true for a comparison of all responses (median lag: 37 ms) and, most directly relevant to the hypotheses, for a comparison of GPi decreases versus VLa increases (median lag 56 ms). GPi increases also tended to lag behind VLa decreases, but by a shorter (nonsignificant) time interval. Together, these results bring into question the idea that task-related activity in VLa is generated or permitted by changes in GPi activity. Instead, they buttress previous suggestions ] that task-related activity in BG-recipient regions of thalamus is generated primarily by some non-BG source (e.g., by glutamatergic inputs All rights reserved. No reuse allowed without permission. (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint . http://dx.doi.org/10.1101/546598 doi: bioRxiv preprint first posted online Feb. 11, 2019; from cortex [Rouiller et al. 1998;McFarland & Haber 2002]) and that BG inputs have a more subtle influence on thalamic activity than often assumed. Another approach to test for possible influences of GPi input on VLa activity is in the pattern of correlated activity observed in simultaneously-recorded GPi-VLa cell pairs. This approach has the potential to elucidate the nature of cell-to-cell communication and how it differs between task conditions [Palm G. et al. 1988]. (See below for caveats concerning this approach.) It is revealing that very few GPi-VLa cell pairs (<6%) showed significant spike-to-spike correlations, and in those few, the correlations were small in magnitude (correlation coefficients <0.05) and independent of task period (i.e., during rest or movement periods). Moreover, the whole population of cross-correlations did not differ from a control distribution. That result differs markedly from the common assumption, as predicted by gating and rebound hypotheses, that cross-correlations for connected GPi-VLa cell pairs will be strongly negative. It also differs from the strong negative Proper evaluation of the correlation results discussed above requires a consideration of how likely it was for our recordings to encounter synaptically-connected GPi-VLa cell pairs. Even though recordings were restricted to regions of the GPi and VLa that were likely to be connected (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint . http://dx.doi.org/10.1101/546598 doi: bioRxiv preprint first posted online Feb. 11, 2019; & Parent 2004;Parent et al. 2001;Ilinsky et al. 1997]. Within each cluster, large multi-synapse boutons contact primarily the somata and proximal dendrites of multiple thalamocortical projection neurons [Bodor et al. 2008;Ilinsky et al. 1997;Parent et al 2001]. Thus, although exact quantification of the degree of GPi-to-VLa divergence has yet to be performed, it is clear that individual GPi neurons diverge to contact numerous thalamic neurons distributed across the VLa.
This anatomic arrangement should markedly improve our chances of encountering connected GPi-VLa pairs by random sampling. The absence of evidence for connected GPi-VLa cell pairs in our cross-correlation results implies either that the degree of GPi-to-VLa divergence is more sparse than what the anatomy suggests or that the influence of individual GPi cell firing on the recipient VLa neuron was far more subtle in our paradigm than what current theories would predict.
An influence of GPi inputs on VLa activity might also be evident in slow trial-to-trial covariations in the firing rates observed within GPi-VLa cell pairs (noise correlations). If a gating mechanism dominated GPi-VLa communication, then the majority of significant noise correlations would be expected to be negative and/or the overall distribution might be biased toward negative correlations. Noise correlations in our data were occasionally significant (5% of pairs at rest and 7% of pairs during movement), but these were composed of balanced proportions of positive and negative correlations (Fig. 4G-H) and the overall distribution of noise correlations did not differ from a shuffled control. Significant noise correlations can be produced by a variety of mechanisms other than direct monosynaptic connectivity, which include, most obviously, co-modulation of both Bursts and pauses in GPi activity are prolonged neurophysiologic events likely to have more profound effects on post-synaptic neurons than the effects of single spikes [Wichmann & Soares 2005;Chan et al. 2011]. Most important here, a burst of inhibitory GPi input to a thalamic neuron followed by a pause in firing should be an ideal stimulus to trigger rebound-type spikingif, that is, the rebound mechanism is in effect. As others have described previously [Wichmann & All rights reserved. No reuse allowed without permission.
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The copyright holder for this preprint . http://dx.doi.org/10.1101/546598 doi: bioRxiv preprint first posted online Feb. 11, 2019; Soares 2005], we found that spontaneous bursts in GPi firing during periods of attentive rest are often followed by pauses. However, these burst-pause events in GPi neurons were coupled with small yet sustained reductions in mean VLa firing rate. The VLa rate reductions began after the offset of GPi bursts and during the pause, which was appropriate timing for a rebound-like effect.
However, the sign of the VLa rate changes was the opposite of what the rebound mechanism predicts. Moreover, both the timing and sign of the observed VLa rate changes were inconsistent with what the gating hypothesis predicts. The observed co-occurrence of VLa firing rate reductions with GPi burst-pause complexes may reflect large-scale properties of the BG-thalamo-cortical network, similar to those invoked previously to explain the detailed structure of bursts and pauses in pallidal activity [Wichmann & Soares 2005;Elias et al. 2007]. Regardless of that, our results are not consistent with straightforward interpretations of gating or rebound models, both of which hypothesize that thalamic activity is strongly determined by BG output.
To our knowledge, this is the first study of single-unit activity sampled simultaneously from the GPi and VLa. These results, though novel, are consistent with many previous observations. Past between-studies comparisons observed that task-related increases in firing are more prevalent than decreases both in BG-output neurons [Anderson & Horak 1985;Brotchie et al. 1991;Mitchell et al. 1987;Mink & Thach 1991;Turner & Anderson 1997] and in VLa thalamus [van Donkelaar et al. 1999 (69%); Nambu et al. 1991 (83%); Anderson & Turner 1991]. The latencies of task-related activity in BG output neurons [Anderson & Horak 1985;Turner & Anderson 1997;Mink & Thach 1991] also appeared to lag in time behind those in VLa [Anderson & Turner 1991;Nambu et al. 1991;van Donkelaar et al. 1999]. In addition, task-related changes in VLa activity were unaffected by temporary inactivations of the GPi , even though the background firing rate of VLa neurons increased during those inactivations. More recently,  confirmed in the songbird BG-thalamic circuit the paradoxical presence of task-related increases in activity both in BG output neurons and in BG-recipient thalamus and the persistence of task-related All rights reserved. No reuse allowed without permission.
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The copyright holder for this preprint . http://dx.doi.org/10.1101/546598 doi: bioRxiv preprint first posted online Feb. 11, 2019; activity in the BG-recipient thalamus following ablation of the BG. The present results are also consistent with the more general observation that inactivation or ablation of BG outputs have, at most, minor detrimental effects on the performance of familiar motor tasks both in human patients [Svennilson et al. 1960;Baron et al. 1996;Cersosimo et al. 2008;Obeso et al. 2009] and in neurologically-normal non-human animals [Desmurget & Turner 2008;Piron et al. 2016;Horak & Anderson 1984;Inase et al. 1996].
How do we bring the current results into coherence with other studies that demonstrated strong BG-thalamic effects? For example, a recent study showed that optogenetic stimulation of BG-thalamic projections was followed (at a lag of ~70 ms) by a sharp rebound-like increase in thalamic spiking accompanied by muscle contractions [Kim et al. 2017]. Based on the timing reported there, any similar post-inhibitory rebound in our data would have been apparent in the GPi burst-pause analysis (Fig. 5), yet we saw a decrease rather than an increase in thalamic firing. The most likely explanation for the disparate results is that the degree of synchronization in spiking between BG output neurons matters tremendously for the impact of that spiking on recipient thalamic neurons. Obviously, massed stimulation of BG efferent terminals (e.g., using optogenetic methods) will induce a synchronized volley of action potentials in a large fraction of the BG output neurons. Such a synchronized population volley will have a much larger post-synaptic influence on thalamic neurons than that of the highly de-synchronized population spiking that is typical for nonperturbed BG output neurons [Bar-Gad et al. 2003a;Nini et al. 1995;Wilson 2013]. Note that very low levels of between-neuron synchrony are a common feature of BG output populations in neurologically-normal animals [Bar-Gad et al. 2003b;Wilson 2013], even during performance of standard behavioral tasks [Nini et al. 1995]. As discussed above, our results describe the dynamics of randomly sampled pairs of neurons in GPi and VLa and they do not rule out the possibility that strong interactions exist within tightly All rights reserved. No reuse allowed without permission.
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The copyright holder for this preprint . http://dx.doi.org/10.1101/546598 doi: bioRxiv preprint first posted online Feb. 11, 2019; focused sub-circuits connecting those nuclei. For example, it would be nearly impossible to detect by random sampling the very strong entrainment-like interactions observed in the hyperfocused pallido-thalamic circuit of the songbird [Luo & Perkel 1999;.
The anatomy of the mammalian GPi-VLa projection, however, suggests a far more branched organization containing a great deal of divergence and convergence [Bodor et al. 2008;Ilinsky et al. 1997] in which it should be possible to study connected cell pairs by random sampling from GPi and VLa. At minimum, our results put a low upper limit (i.e., less than 23 in 449, see Table 2) on the probability of finding strongly correlated activity, if any exists, in randomly selected GPi-VLa cell pairs. In addition, our results are inconsistent with the classic gating idea in which a coordinated drop in GPi activity is required to release thalamic activity and subsequent selection of action [Redgrave et al. 1999;Mink 1996].
It is also important to acknowledge that we studied GPi-VLa communication during performance of a simple well-learned reaching task. It is possible that the influences of BG output on thalamic spiking could be stronger under more demanding or less-stereotyped behavioral contexts. For example, several lines of evidence suggest that BG-thalamic pathways drive behavioral variability or exploration during motor learning [Kao et al. 2005;Kojima et al. 2018;Sheth et al. 2011]. Other studies suggest BG involvement in the on-line modulation of movement vigor [Yttri & Dudman 2016;Desmurget & Turner 2008] or the urgency to move [Thura & Chisek 2017]. A growing number of studies have concluded that the influence of BG output on a behavior becomes less important the more well-learned the behavior becomes [Ashby et al. 2010;Piron et al 2016;. It is possible that under one or more of those less-stereotyped behavioral contexts task-related activity in the GPi may adopt characteristics more capable of influencing VLa activity (e.g., larger magnitudes, earlier onset latencies, or more synchronization of spiking between GPi single-units).
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The copyright holder for this preprint . http://dx.doi.org/10.1101/546598 doi: bioRxiv preprint first posted online Feb. 11, 2019; C o n c l u s i o n In conclusion, we found no evidence consistent with the idea that BG output discharge gates thalamic discharge ('classic gating hypothesis ', Albin et al. [1989]). The most likely alternative is that both pallidal and thalamic discharge may be driven by a third source Inase et al. 1996  reaching task that has been described in detail previously [Franco et al. 2012;Zimnick et al. 2015].
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The copyright holder for this preprint . http://dx.doi.org/10.1101/546598 doi: bioRxiv preprint first posted online Feb. 11, 2019; In brief, the animal faced a vertical response panel that contained two target LEDs, positioned 7 cm to the left and right of midline, and associated infrared proximity sensors. The animal's left hand rested at a "home-position" at waist height and equipped with a proximity sensor. The animal was trained to hold the home-position (1-2s, uniform random distribution) until the right or left LED was lit as a directional "Go" signal (selected in pseudo-random order). The animal was given 1s to move its hand from the home-position to the indicated target. Once the correct target was contacted, the animal was required to hold its hand at the target for 0.5-1.0s (randomized) before food reward was delivered via a sipper tube and computer-controlled peristaltic pump. The animal was then allowed to return its hand to the home-position with no time limit. S u r g e r y General surgical procedures have been described previously [Desmurget et al. 2008;Zimnick et al. 2015]. The chamber implantation surgery was performed under sterile conditions with ketamine induction followed by Isoflurane anesthesia. Vital signs (i.e. pulse rate, blood pressure, respiration, end-tidal pCO 2 , and EKG) were monitored continuously to ensure proper anesthesia. A cylindrical titanium recording chamber was affixed to the skull at stereotaxic coordinates to allow access to the right globus pallidus and ventrolateral thalamus via a parasagittal approach.  To guide an electrical stimulation-based localization of the region of GPi devoted to arm motor control [Yoshida et al. 1993] and of the connected region of VLa [Anderson & Turner 1991] we implanted stimulation electrodes in the arm-related region of primary motor cortex and in the SCP All rights reserved. No reuse allowed without permission.
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The copyright holder for this preprint . http://dx.doi.org/10.1101/546598 doi: bioRxiv preprint first posted online Feb. 11, 2019; at its decussation (Fig. 1). The anatomic locations of sites for implantation were estimated initially from structural MRI scans (Siemens 3T Allegra Scanner, voxel size of 0.6mm) using an interactive 3D software system (Cicerone) to visualize MRI images and predict trajectories for microelectrode penetrations [Miocinovic et al. 2007]. Subsequent microelectrode mapping methods were used to identify the precise chamber coordinates for the implantation.
Custom-built stimulating electrodes were implanted at these sites using methods described previously [Turner & DeLong 2000]. Macroelectrodes consisted of two Teflon-insulated Pt-Ir microwires (50µm) glued inside a short stainless steel cannula with ~0.5mm separation between the distal ends of the microwires. Insulation was stripped from ~0.2mm of the distal ends of the microwire to achieve an impedance of ~10kΩ. The electrode assembly was implanted transdurally via the coronal chamber using a protective guide cannula and stylus mounted in the microdrive. In the months following implantation, the location and integrity of macroelectrodes were monitored by comparing the muscle contractions evoked by stimulation through the electrode against what was observed during microelectrode mapping. Anderson 1997] and to electrical stimulation in the arm region of primary motor cortex [Yoshida et al. 1993] (Fig. 1C). During localization of the target region in VLa, a macroelectrode was positioned acutely in the GPi. The target region for recording in VLa was identified by the presence of typical thalamic neuronal discharge that: a) responded to GPi stimulation with a short latency All rights reserved. No reuse allowed without permission.
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The copyright holder for this preprint . http://dx.doi.org/10.1101/546598 doi: bioRxiv preprint first posted online Feb. 11, 2019; pause in firing [Anderson & Turner 1991] often followed by a rebound increase in firing probability; and b) did not respond to SCP stimulation, which would be indicative of a neuron in VLp, the cerebellar-recipient portion of motor thalamus located immediately posterior to VLa (Fig.   1B). Many VLa neurons also responded at short latency to stimulation in primary motor cortex. All subsequent data collection was directed to these target regions of GPi and VLa.
We also performed microstimulation mapping of VLa and VLp (biphasic pulses <200μA, 0.2msduration at 300Hz; Model 2100, A-M Systems). Consistent with previous reports, stimulation in putative VLa rarely evoked movement whereas stimulation in putative VLp evoked movement often and at low threshold Vitek et al. 1996]. However, it was possible to evoke movement from some locations close to VLp but identified as VLa according to the localization criteria described above. Thus, results from microstimulation mapping of the thalamus were not used as primary criteria for identification of the VLa/VLp border.
All recordings were performed with at least one electrode positioned in each of GPi and VLa. When stable single-unit isolation was available from one or more single-units in both GPi and VLa, as judged by online spike sorting, neuronal data and behavioral event codes were collected while the animal performed the behavioral task.
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The copyright holder for this preprint . for VLa units.
We tested for peri-movement changes in single-unit spike rate using a standard method [Zimnik et al. 2015] that was modified to improve the sensitivity to firing rate decreases through use of different estimates of unit activity for the detection of increases and decreases in discharge.
For increases, we used a standard spike density function (SDF), which correlates directly with a neuron's mean instantaneous firing rate. For decreases, however, we used a function that reflects a All rights reserved. No reuse allowed without permission.
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The copyright holder for this preprint . http://dx.doi.org/10.1101/546598 doi: bioRxiv preprint first posted online Feb. 11, 2019; unit's instantaneous inter-spike interval (ISI) [Alexander & Crutcher 1990] which scales with the reciprocal of a neuron's instantaneous spike rate. Use of the ISI function avoided a potential insensitivity for the detection of decreases in SDFs due to floor effects, which would be particularly problematic for low firing-rate neurons such as those in VLa. (By definition, the minimum value for an SDF is zero spikes/s regardless of the duration of pause in firing, whereas an ISI function can reliably represent arbitrarily long pauses in firing.) SDFs were constructed by convolving a unit's spike time stamps (1 kHz resolution) with a Gaussian kernel (σ = 25 ms). ISI functions were calculated as a millisecond-by-millisecond representation of the current time interval between successive single-unit spikes. Across-trial mean SDF and ISI functions aligned on the time of movement onset were constructed separately for valid behavioral trials to left and right targets.
The detection algorithm then tested both SDF and ISI activity functions for significant positive deviations from a control rate within a 700 ms window that started at the median time of target LED onset (i.e., within a time period that encompassed both reaction time and movement duration for our animals). The threshold for significance was defined relative to the mean and SD of values from a pre-trigger control period (a 700 ms window that ended at the median time of target LED onset) after any linear trend in the mean activity function from that period was subtracted. A movement-related change in firing rate was defined as a significant elevation from the control mean activity that lasted at least 70 ms (e.g., Fig. 2 A, solid vertical lines; t-test; one sample vs. control period mean; omnibus p < 0.001 after Bonferroni correction for multiple comparisons). Any such elevations in the SDF were classified as increases in discharge whereas elevations in the ISI function were classified as decreases in discharge. Note that this approach enabled detection of biphasic changes (e.g., an increase followed by a decrease).
For each significant movement-related change detected, we used two independent approaches to estimate the time of onset (i.e., the latency). The first standard approach [Zimnik et al. 2015;Turner & DeLong 2000] simply took the earliest significant time bin yielded by the All rights reserved. No reuse allowed without permission.
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The copyright holder for this preprint . http://dx.doi.org/10.1101/546598 doi: bioRxiv preprint first posted online Feb. 11, 2019; detection algorithm described above. To ensure that the standard approach did not provide biased results, we also applied a second approach which defined onset as the time at which the mean activity function crossed a threshold corresponding to 10% of the maximum change in rate relative to the control rate (as defined above).  Next, we tested whether correlations in trial-by-trial variations in firing rates ('noise correlations') between GPi and VLa discharge were present. We computed spike counts in 500 ms All rights reserved. No reuse allowed without permission.
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The copyright holder for this preprint . http://dx.doi.org/10.1101/546598 doi: bioRxiv preprint first posted online Feb. 11, 2019; bins within the same rest and movement periods as used for CCFs. If the total spike count in a time bin across all trials of a unit was lower than 10, the bin of this unit was excluded. Spike counts were z-scored and trials with a score > 3 were removed from further analysis, as described by Liu et al. [2013]. Separately for each pair and each of the two targets, correlations were then computed across bins of spike counts. Simultaneous modulations of firing rates that occur consistently across movements are thus not reflected in the noise correlations. Instead, only trial-by-trial variations of rate contribute. Randomly shuffling trials within each recording served as surrogate data. The classic gating hypothesis states that any increased BG output, regardless of its relation to movement timing, can attenuate thalamic spiking [Hikosaka 2007]. Here, we investigated the influence of GPi bursts and pauses on VLa spiking during rest. Bursts were detected with a 'surprise' method developed by Legéndy & Salcman [1985] and implemented by Wichmann & Soares [2006]. The surprise value was defined as S=-log(p), where p is the probability that the distribution of inter-spike-intervals within the candidate burst is from a Poisson distribution. Only bursts with a surprise value of 5 or larger, with at least three spikes and an intra-burst firing rate of at least twice the baseline firing rate, were considered. Likewise, pauses were defined as interspike-intervals of at least 100ms with a minimum surprise value of 5. All bursts and pauses that were detected during the time period when the task was performed (from 0.4 s before detected movement onset until 0.8 s after return to the home key) were excluded. As movement periods were associated with strong modulations in firing rate (see Fig. 2), reliable burst and pause detection during these periods was not possible.
VLa spike trains were convolved with a Gaussian density of standard deviation ߪ ൌ 10 ms.
We then averaged all epochs of VLa spiking from 0.8 s before to 0.8 s after GPi burst onset and called this average spike train a burst-triggered average (BTA). Each simultaneously recorded GPi All rights reserved. No reuse allowed without permission.
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The copyright holder for this preprint . http://dx.doi.org/10.1101/546598 doi: bioRxiv preprint first posted online Feb. 11, 2019; -VLa pair thus led to one BTA. We used the same analysis with alignment to burst end to determine burst-offset-triggered averages (BOTAs) and with alignment to pause onsets for pausetriggered averages (PTAs). Some pairs included few GPi bursts or low baseline firing rates in VLa, impeding the detection of burst or pause influences. To avoid including such noisy data, we excluded GPi-VLa pairs with noisy pre-burst baselines, defined as the average VLa activity 800 -50 ms prior to the respective event (GPi burst onset, offset, or pause): If the difference between the 2.5 th and the 97.5 th percentile of this baseline was larger than 60% of the absolute average baseline activity, the respective GPi-VLa pair was neglected. Hence, only pairs with a rather constant, predictable baseline were included in the analysis.
All obtained TAs of each type (BTAs, BOTAs, and PTAs) were averaged to compute a population average TA of that type. We also evaluated whether some VLa units showed a significantly high modulation after simultaneously recorded GPi bursts or pauses. Detection thresholds were set to the 2.5 th and 97.5 th percentile of the baseline before each event. If the average TA within 0-100 ms after the event crossed one of the thresholds, the TA was assigned to be 'decreasing' or 'increasing', respectively. Finally, we computed a population average of all cross-correlations between GPi pause and GPi burst offset times, both smoothed with a Gaussian density of standard deviation ߪ ൌ 10 ms.
S t a t i s t i c a l t e s t i n g For each analysis relating to cell-pair interactions ( Fig. 4 and 5, Table 2), we computed surrogate data as described above. Permutations were done 400 times and each set of shuffled data was processed identically to unshuffled data. The resulting 400 surrogate data sets were then used as a control distribution of which the 2.5 th and the 97.5 th percentile were taken as limits of the 95% confidence interval. For analyses that involve multiple comparisons, the confidence intervals were shifted such that in total, 5% of shuffled controls became significant for any comparison.
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The copyright holder for this preprint . http://dx.doi.org/10.1101/546598 doi: bioRxiv preprint first posted online Feb. 11, 2019; Differences in distributions were tested by comparison of Kolmogorov-Smirnov (KS)statistics. We computed the KS-statistic comparing the empirically obtained distribution to 399 control distributions ('test statistic') as well as the KS-statistic of each of 400 the control distributions compared to the remaining 399 control distributions (400 'control statistics'). If the test statistic was larger than the 95 th percentile of the control statistics, we concluded that the obtained distribution was significantly different from the control distribution. All rights reserved. No reuse allowed without permission.
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