Four male rhesus monkeys (Macaca mulatta, 6–10 kg) were trained to perform whole-limb visuomotor tasks while wearing KINARM (BKIN Technologies, Kingston, ON, Canada), a robotic exoskeleton that supports the arm, permits planar shoulder and elbow motion, and can apply mechanical torques at the shoulder and/or elbow (Scott, 1999; Figure 1A). A virtual reality system presented visual targets within the limb’s movement plane while permitting the monkeys to view their entire limb. The Queen’s University Animal Care Committee approved all procedures.

The monkeys performed a postural perturbation task (Herter et al., 2009). Mechanical perturbations were transiently applied to the monkeys’ right arms while they maintained their right hand at a visual target (6 mm radius) displayed near the center of the arm’s workspace (30° shoulder flexion, 90° elbow flexion) where passive viscoelastic forces are relatively small (Graham et al., 2003). The monkeys initiated each trial by moving their right hand to the visual target and holding it within an acceptance window (8 mm radius) for 1000–1500 ms. One of nine perturbations was then transiently applied to the monkeys’ arm for 300 ms. The nine perturbation conditions included four single-joint torques (shoulder flexion, SF; shoulder extension, SE; elbow flexion, EF; elbow extension, EE), four multi-joint torques (SF + EF, SF + EE, SE + EF, SE + EE), and an unloaded condition (Figure 1B). The magnitude of torque applied at each joint was fixed at either ±0.12 Nm (Monkeys A–C) or ±0.32 Nm (Monkey D), producing a uniform distribution in joint-torque space but a torque magnitude that was √2 greater in multi-joint than single-joint conditions. Each perturbation (except the unloaded condition) pushed the monkeys’ hand from the target’s acceptance window and the monkeys were required to return their hand to the visual target within 1500 ms and hold it there for another 1000–1500 ms to receive a liquid reward. The nine perturbation conditions were presented in a pseudo-random block design with each block repeated five times for a total of 45 trials.

Neural data was collected from the left RN of one monkey (Monkey D) using standard extracellular recording techniques developed for recording from brainstem structures (Marino et al., 2008). Microelectrodes were advanced through guide tubes that were placed inside a grid mounted within a stainless steel recording chamber (Crist Instruments, Hagerstown, MD) that was implanted over the stereotaxic coordinates of the RN. The recording chamber was centered on the midline and angled 35° posterior of vertical, which allowed us to identify the superior and inferior colliculus during penetrations into the rostral and caudal RN, respectively. Neural data was collected from the left M1 of all four monkeys using standard extracellular recording techniques for cortical neurons (Herter et al., 2007). However, M1 data from Monkey D only is presented in the current report because RN data was collected from Monkey D only. For penetrations into both RN and M1, microelectrodes were advanced until neural activity was observed in response to active or passive arm movements. Single neurons were then isolated and neural activity was recorded from all neurons that were related to active or passive movements of the shoulder and/or elbow, but not the wrist and/or fingers.

Electromyographic (EMG) activity was collected from proximal arm muscles involved in flexion or extension at the shoulder and/or elbow (Graham and Scott, 2003) using standard techniques (Loeb and Gans, 1986; Kurtzer et al., 2006a). Acute recordings were obtained from all four monkeys using pairs of single-strand wires that were percutaneously inserted approximately 5 mm apart in the muscle belly. Chronic recordings were attained from Monkeys A and C using bipolar multi-strand electrodes that were subcutaneously implanted within the superficial muscle belly. EMG activity was recorded from 11 different upper-limb muscles, including shoulder flexors (Anterior Deltoid, Pectoralis Major), shoulder extensors (Posterior Deltoid, Medial Deltoid), elbow flexors (Brachialis, Brachioradialis, Extensor Carpi Radialis Longus), elbow extensors (Triceps lateral head, Triceps medial head), biarticular flexors (Biceps long head, Biceps short head), and biarticular extensors (Triceps long head, Dorsoepitrochlearis). Electrode placement in each muscle was verified using micro-stimulation through the recording electrode.

During recording sessions, EMG signals were band-pass filtered (100–3,000 Hz) and recorded at 1 kHz (Monkeys A–C) or 4 kHz (Monkey D). During the subsequent offline analysis, signals were full-wave rectified and integrated into 5 ms bins. Muscles were only included in the analyses if they obtained a score of ≥3 on a subjective rating scale of signal quality (1 = poor, 5 = excellent; Kurtzer et al., 2006a). EMG data from the four monkeys was included in our analyses for the current report.

Joint angles, velocities, and applied torques were recorded at 1 kHz (Monkeys A–C) or 4 kHz (Monkey D). Cartesian hand positions and tangential hand speed were calculated from joint angles and velocities.

Although the applied loads were uniformly distributed in joint-torque space (Figure 1B), joint motion was highly nonuniform due to intersegmental dynamics (Figure 1C). Over the first 120 ms, each single-joint torque produced multi-joint motion (brown, green, orange and magenta lines) and two of the multi-joint torques generated single-joint motion (red and cyan lines). In addition, the magnitude of joint motion resulting from these two multi-joint torques was much smaller than the other two multi-joint perturbation conditions (blue and black lines).

We examined the activity of 49 neurons recorded in the upper-limb region of RN of Monkey D. After the recording sessions were completed, the monkey was euthanized and the brainstem was removed and sectioned for histological examination. Based on the location of the recording tracks, we confirmed that some penetrations targeted the RN. Over half of these neurons (n = 28, 57%) exhibited perturbation-related activity; i.e., their activity was significantly modulated at relatively short latencies (onset latency of 20–100 ms) and exhibited significant directional tuning in joint-torque space (bootstrap test, p < 0.01). Figures 2A,B, illustrate spike rasters and spike frequency histograms showing perturbation-related activity of two exemplar RN neurons. Both neurons showed markedly greater increases in activity for some perturbations conditions than others. The first neuron responded greatest to perturbations that required the monkey to generate an extensor torque at the shoulder and a flexor torque at the elbow (PTD = 139°, Figure 2C). The second neuron was most sensitive when the monkey produced extensor torques at the elbow and flexor torques at the shoulder (PTD = 290°, Figure 2D). Both neurons displayed large differences in modulation between preferred and non-preferred perturbation conditions, which resulted in substantial TSs of 120 and 115 (sp/s)/Nm, respectively (Figures 2C,D). Despite this similarity, the first neuron exhibited increases in activity for several perturbation conditions and decreases in a few directions, resulting in a tuning width that was slightly greater than cosine tuning (tuning width = 0.56, Figure 2C, right). In contrast, the second neuron showed large increases in activity for only a few perturbation conditions, resulting in narrow tuning relative to a cosine (tuning width = 0.26, Figure 2D, right). Relative to the unloaded baseline condition, the first neuron also showed similar increases (excitation) and deceases (inhibition) in activity in response to the various perturbations (excitation-inhibition ratio = 0.0, Figure 2C, left). The second neuron, however, exhibited excitation in response to each perturbation, though the extent of excitation differed between the perturbation directions (excitation-inhibition ratio = 1.66, Figure 2D, left).

We examined the activity of 109 neurons recorded in the shoulder-elbow region of M1 of Monkey D. Data from these neurons were presented in a previous publication that compared the activity of M1 neurons in the current task with their activity during static postural maintenance (Herter et al., 2009). Close to half of these neurons (n = 43, 39%) displayed perturbation-related activity (onset latency, 20–100 ms; bootstrap test, p < 0.01). Figures 3A,B, illustrate spike rasters and spike frequency histograms showing perturbation-related activity of two exemplar M1 neurons. Similar to the exemplar RN neurons seen previously, both exemplar M1 neurons showed large increases in activity for some perturbations. The first M1 neuron responded greatest to perturbations that required the monkey to generate an extensor torque at the shoulder and a flexor torque at the elbow (PTD = 114°, Figure 3C). The second neuron was most sensitive for loads that required production of extensor torques at the elbow (PTD = 283°, Figure 3D). Compared to the exemplar RN neurons, the first M1 neuron displayed smaller differences in modulation between preferred and non-preferred perturbation conditions (torque-slope = 62 (sp/s)/Nm, Figure 3C). The second M1 neuron was more sensitive to loads though still less sensitive than the two RN neurons (torque-slope = 91 (sp/s)/Nm, Figure 3D). Similar to the second RN neuron, both M1 neurons exhibited tuning widths that were slightly narrower than a cosine (tuning widths = 0.35 and 0.28, Figures 2C,D, right). Both M1 neurons showed similar diversity of excitation and inhibition that was seen in the exemplar RN neurons. Relative to baseline, the first M1 neuron showed increases and deceases in activity in response to the various perturbations, though excitation was greater than inhibition (excitation-inhibition ratio = 0.36, Figure 3C, left). Like the second RN neuron, the second M1 neuron exhibited excitation in response to each perturbation, though the extent of excitation varied across perturbation directions (excitation-inhibition ratio = 0.0, Figure 3D, left).

We examined the activity of 33 EMG samples recorded from 33 different sites (1 sample per site) in 11 proximal arm muscles of Monkeys A–D. We found that two thirds of the muscles (n = 22, 67%) exhibited perturbation-related activity (onset latency, 20–100 ms; bootstrap test, p < 0.01). Figures 4A,B, illustrate perturbation-related activity obtained from two exemplar upper-limb muscles. An EMG recording from a posterior deltoid sample showed increases in activity in response to loads that required production of extensor torques at the shoulder and flexor torques at the elbow (PTD = 321°, Figure 4C). Similarly, an exemplar EMG recording from brachioradialis showed increases in activity for responses requiring flexor torques at the elbow and extensor torques at the shoulder (PTD = 105°, Figure 4D). Both of these patterns are consistent with activities that would bring the hand back to the target in response to their stretch. Both muscles exhibited narrow tuning relative to a cosine (tuning widths = 0.16 and 0.25, Figures 4C,D, right). Both muscles also showed far greater excitation than inhibition, relative to their baseline activities (excitation-inhibition ratios = 1.06 and 0.62, Figures 4C,D, left).

We compared the response latencies of RN neurons, M1 neurons and upper limb muscles (Figure 5). As indicated above, many RN neurons (57%), M1 neurons (39%) and upper-limb muscles (67%) exhibited rapid responses (20–100 ms), suggesting that their activity is tightly coupled to the mechanical (sensory) stimulus. We found that the mean onset latencies of RN (44 ± 14 ms) and M1 (46 ± 19 ms) neurons were not significantly different from each other (Wilcoxon rank sum test, p > 0.1). By comparison, the mean onset latency of upper-limb muscles (55 ± 22 ms) was significantly longer than RN neurons (one-tailed t-test, p < 0.05) but did not differ significantly from M1 neurons, (Wilcoxon rank sum test, p > 0.1).

A common characteristic of M1 neurons is that their torque-related activity exhibits a bimodal distribution of PTDs that mirrors the distribution observed in upper-limb muscles (reviewed in Kurtzer and Scott, 2007). Specifically, PTDs of M1 neurons and upper-limb muscles are both biased towards whole-limb flexor (EF + SE) and whole-limb extensor (EE + SF) torques. Figure 6 investigates whether the RN neurons, M1 neurons and upper-limb muscles examined in the current study exhibit similar bimodal distributions of PTDs. Consistent with our previous studies, bimodal distributions of PTDs were seen in M1 neurons (Figure 6B, unimodal Rayleigh test, p > 0.1; bimodal Rayleigh test, p < 0.01, r = 0.53, PTD axes = 132–312°) and upper-limb muscles (Figure 6C, unimodal Rayleigh test, p > 0.1; bimodal Rayleigh test, p < 0.01, r = 0.66, PTD axes = 125–305°). In contrast, RN neurons exhibited similar results for unimodality and bimodality. RN neurons exhibited a unimodal distribution that was marginally insignificant (unimodal Rayleigh test, p = 0.06, r = 0.31, PTD = 288°), whereas the bimodal distribution was marginally significant (Figure 6A; bimodal Rayleigh test, p < 0.05, r = 0.33, PTD axes = 123–303°). Importantly, the distributions of RN neurons, M1 neurons and upper-limb muscles did not differ significantly from one another (t-tests, all p > 0.1).

In addition to comparing PTDs, we also examined whether the temporal evolution of other directional tuning properties in RN and M1 were similar to upper-limb muscles. Note that, because of the difference in units, TSs of neurons (discharge frequency) could not be directly compared with TSs obtained from EMG activity in muscles (arbitrary units). However, we found the mean TS in both brain regions and upper-limb muscles increased over time following perturbations (Figure 7A, linear regressions, all p < 0.05). Furthermore, the mean TS in RN was significantly greater than in M1 (Figure 7A, ANOVA, p < 0.01), indicating that firing frequencies of RN neurons increased more than M1 neurons in response to perturbations. Comparisons of tuning widths revealed that tuning widths were significantly narrower in upper-limb muscles than neurons in M1 and RN (ANOVA, p < 0.01), though the tuning widths were generally narrower than cosine tuning in all three (Figure 7B). Finally, we found that EIRs were broadly distributed in both RN and M1 (Figure 7C), including many neurons that exhibited reciprocal excitation and inhibition (–1 < EIR < 1) and many neurons that were excited by all perturbations but in differing amounts (EIR > 1). By comparison, most muscles exhibited reciprocal excitation and inhibition in which excitation was greater than inhibition (0 < EIR < 1). Despite these differences in the breadth of excitation and inhibition, we did not observe a significant difference in EIRs between RN neurons, M1 neurons and upper-limb muscles (Figure 7C, ANOVA, p > 0.1).