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1Movement Neuroscience Laboratory, University of Auckland; 2Health and Rehabilitation Research Centre, Auckland University of Technology, Auckland, New Zealand; 3Department of Physical Medicine and Rehabilitation, Northwestern University; and 4Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, Illinois
Submitted 21 May 2008; accepted in final form 12 August 2008
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ABSTRACT |
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INTRODUCTION |
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). The contribution of these premotoneurons in the control of the upper limb in higher primates has been under debate (Lemon and Griffiths 2005; Maier et al. 1998; Nakajima et al. 2000). Recent experiments in the macaque monkey have revealed the propriospinal system is subject to robust inhibitory control (Alstermark et al. 1999; Isa et al. 2006). In humans the disynaptic pathway is also constrained by inhibitory interneurons, in which the function is unclear (Iglesias et al. 2007; Nicolas et al. 2001). Indirect evidence suggests the cervical propriospinal system may coordinate muscle synergies necessary for upper limb reaching in humans (Gracies et al. 1991; Pauvert et al. 1998; see Pierrot-Deseilligny and Burke 2005). Sensory inputs are integrated with descending cortical outputs via feedback loops that may update the motor command en route to motoneurons. Sensory feedback serves to focus corticomotor drive to propriospinal premotoneurons appropriate for the motor synergy (Burke et al. 1992; Mazevet and Pierrot-Deseilligny 1994). The gain of these reflexes relies on the balance of excitation between propriospinal premotoneurons and inhibitory interneurons (Iglesias et al. 2007; Nicolas et al. 2001).
The propriospinal system can be studied indirectly in humans using transcranial magnetic stimulation (TMS) conditioned by peripheral nerve stimulation. With appropriate interstimulus intervals (ISIs), paired TMS and peripheral stimulation of propriospinally linked muscles leads to a facilitation of the motor-evoked potential (MEP). Two characteristics identify the propriospinal neurons as the putative site of summation. First, the ISI between cortical and peripheral stimulation that gives rise to facilitation is longer than that expected for a monosynaptic reflex. This is thought to reflect the additional distance for afferent input from peripheral stimulation to reach the more superiorly located premotoneurons in the spinal cord (Gracies et al. 1991; Pauvert et al. 1998). Second, peripheral conditioning stimuli facilitate MEPs elicited by weak TMS intensities, but suppress those evoked by stronger TMS intensities. This implies that weak cortical and weak peripheral inputs summate at propriospinal neurons to facilitate MEPs, whereas strong cortical or peripheral inputs may converge on inhibitory interneurons that serve to suppress MEPs through a disynaptic pathway (Iglesias et al. 2007; Nicolas et al. 2001).
Using these techniques, excitability of the propriospinal system linking forearm and arm muscles has been shown to be task dependent. Transmission through propriospinal neurons is increased under muscle-fatiguing conditions (Martin et al. 2007), during bilateral movement tasks (Stinear and Byblow 2004b) and when making dexterous movements with the hand (Iglesias et al. 2007; Marchand-Pauvert and Iglesias 2008). Altered propriospinal activity has been implicated in abnormal muscle synergies in patients who have experienced stroke (Mazevet et al. 2003; Stinear and Byblow 2004a). To date, there is no evidence of propriospinal input to muscles of the rotator cuff complex. Since gripping and precise use of the hand in humans are accompanied by changes in the excitability of the rotator cuff muscles (Laursen et al. 1998; Sporrong and Styf 1999; Sporrong et al. 1995, 1996), sensory feedback from contracting hand muscles may project to the rotator cuff via propriospinal premotoneurons. The aim of this experiment was to assess the role of the putative propriospinal system in controlling infraspinatus (INF) and to assess whether task differences in MEP ratios suggest task-dependent modulation of the propriospinal premotoneurons. INF was chosen because it is an important muscle in the rotator cuff group. It contracts during shoulder flexion and abduction to control the head of the humerus in the glenoid socket (Kronberg et al. 1990; Palmerud et al. 2000) and is routinely activated during upper-limb reaching movements. INF was paired with flexor carpi ulnaris (FCU), a wrist flexor muscle that is also commonly activated during reaching and grasping. Our hypothesis was that sensory feedback from the ulnar nerve affects transmission of descending drive to INF via the cervical propriospinal system and, furthermore, is modulated by task.
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METHODS |
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Fourteen healthy adults (age range 21–47 yr, mean age 29 yr, three males) with no history of upper-limb musculoskeletal or neurological disorder completed the study. Subjects provided informed consent in accordance with the Declaration of Helsinki and the study was approved by the local ethics committee.
Electromyography
Subjects were seated and surface electromyography (EMG) was recorded from the right INF muscle by adhesive electrodes (Ambu, Ballerup, Denmark) positioned 3 cm below the midpoint of the spine of the scapula and aligned with the direction of the underlying muscle fibers. Surface EMG was recorded from the right FCU using a belly-tendon montage. EMG signals were amplified (Grass P511AC, Grass Instrument Division, West Warwick, RI), filtered with a bandwidth of 30–1,000 Hz, sampled at a frequency of 2 kHz (National Instruments, Austin, TX), and displayed on a computer by custom LabVIEW software.
Transcranial magnetic stimulation
Single-pulse TMS (Magstim 200, Dyfed, Wales) was applied over the left hemisphere to elicit MEPs in the right INF. A figure-of-eight coil (90-mm wing diameter) induced a current directed posterior to anterior in the underlying tissue. The stimulation site evoking the largest-amplitude MEP in the right INF was located and marked on the scalp. Active motor threshold (AMT) for each task (described in the following text) was determined as the minimum stimulus intensity that elicited a 100-µV MEP in the INF in 5 of 10 stimuli. Five stimulus intensities were then used during task performance, in increments of 2% maximal stimulator output (MSO) starting from task AMT. The order of stimulus intensity was randomized. At each intensity, 12 nonconditioned and 12 peripheral nerve conditioned trials were collected at a rate of 0.2 Hz.
Conditioning peripheral nerve stimulation
A Digitimer DS7A constant current stimulator (Digitimer, Hertfordshire, UK) delivered a square wave, l-ms duration pulse every 5 s via an adhesive electrode (Ambu) to the ulnar nerve at the elbow. The anode was placed over the medial aspect of the arm just above the elbow. The intensity of the stimulus was set to 0.8 x motor threshold to preferentially stimulate group I sensory afferents (Nicolas et al. 2001).
The ISIs between peripheral nerve stimulation and TMS were calculated using indirect methods (Alexander and Harrison 2003; Roberts et al. 2008) and confirmed in pilot experiments. The calculations were as follows (rounded to nearest millisecond for variations in height/distance). Efferent conduction time from M1 to INF is 11 ms, indicative of MEP latency. Based on distance (
0.24 m), efferent conduction time from INF alpha motoneurons (
MNs) to surface EMG electrodes is 5 ms, accounting for delays at the neuromuscular junction. Therefore conduction time from M1 to INF MN is 6 ms. Based on distance (0.5 m), afferent conduction time from peripheral stimulation at the elbow to INF MN is 10 ms, accounting for a 1-ms synapse onto MN. Thus for peripheral stimulus and TMS to interact at the INF MN an ISI of 4 ms would be required (10 – 6 = 4 ms). Since we are interested in the interaction of the afference of the premotoneuronal propriospinal circuits, we have added an additional 3 ms for central conduction time (see Pierrot-Deseilligny and Burke 2005) and estimated an ISI of about 7 ms between the peripheral stimulus and TMS to facilitate summation at the level of propriospinal neurons. Therefore at the beginning of each experiment ISIs of 6, 7, and 8 ms were explored with a TMS intensity of task AMT + 2%. The ISI that produced the largest MEP facilitation was considered optimal and used for the remainder of the experiment.
Motor tasks
Two motor tasks were used to investigate the modulation of propriospinal premotoneurons. The first was a simple cocontraction task. Participants were seated with their hands on their lap. When verbally cued they flexed their right shoulder to an angle of 90° with the elbow straight, activating the INF, and then ulnar deviated the wrist. FCU EMG activity during ulnar deviation triggered the stimulators when a threshold of 0.1 mV was reached. After 5 s they returned the hand to their lap.
The second task was a grip-lift task. Participants were again seated with their hands on their lap. From this starting position, they were asked to reach forward, grasp, and lift a purpose-built device from a table directly in front of them, using the thumb and fourth and fifth digits of their right hand. Participants held the device at a constant height for about 3 s and then returned it to the table. Stimulators were triggered 400 ms following the onset of vertical lift so that stimuli were delivered near the lift endpoint. At this time point, both INF and FCU were typically activated, and participants were lifting with shoulder flexion, while the elbow remained extended. For both tasks, trials were rejected on-line if INF root mean square (rms)EMG was outside the range 0.01–0.15 mV in the 100 ms preceding stimulation.
Data analysis
EMG data were full-wave rectified off-line using custom software. The area of the rectified MEP in INF EMG was measured from the onset latency (11–18 ms depending on the individual) using the same analysis window for all trials in each participant. For each participant, the stimulus intensity that produced the greatest facilitation was identified for each task. Responses to four stimulus intensities were analyzed: maximal facilitation (F), F – 2%, F + 2%, and F + 4%. Mean MEP area was expressed as a ratio (conditioned/nonconditioned) at each of these stimulus intensities. MEP ratio was analyzed using a 2 x 2 repeated-measures ANOVA with factors Task (cocontraction, grip-lift) and Intensity (F – 2%, F, F + 2%, F + 4%). Facilitation or suppression with respect to nonconditioned trials was assessed with one-sample t-test. Paired t-test assessed the effect of task on facilitation and suppression phases. Post hoc tests were adjusted for multiple comparisons using standard procedures (Rom 1990). Background rmsEMG was calculated from 100 to 10 ms prior to the stimulus and averaged across stimulus intensities for both nonconditioned and conditioned responses. The relationship between background EMG and MEP ratio was assessed by regression analysis to determine whether the level of prestimulus EMG influenced MEP modulation. Task-dependent modulation of corticomotor excitability was assessed by plotting stimulus–response curves of INF MEP area from nonconditioned trials at F – 2%, F, F + 2%, and F + 4%. For each task, the slope of the line of best fit from linear regression of MEP area was determined for each participant and the effect of task assessed using a paired t-test. Regression analyses were performed to explore any relationship between background EMG nonconditioned MEP size and MEP ratio. Significance level was set at P = 0.05. Means ± SE are reported in the text.
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RESULTS |
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Effects of TMS intensity and task on MEP ratio
Figure 1 illustrates rectified EMG traces from a typical subject. TMS intensity-dependent facilitation and suppression of INF MEPs for the cocontraction and grip-lift tasks can be seen. MEP ratios for the group (conditioned/nonconditioned expressed as percentage change) are shown in Fig. 2.
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Rectified traces were superimposed and visually inspected. There was no obvious deviation in the initial rising phase of the MEP between nonconditioned and conditioned traces.
Effects of nonconditioned MEP area and background EMG on MEP ratio
Regression analysis revealed no effect of nonconditioned MEP area on MEP ratio for either task (Cocontraction, r2 = 0.04, P = 0.12; Grip-Lift, r2 = 0.009, P = 0.63). Therefore nonconditioned MEP area did not significantly influence the size of the MEP ratio. Similarly, background EMG was not significantly correlated to MEP ratio for either task (Cocontraction, r2 = 0.018 or Grip-Lift, r2 = 0.003, both P > 0.3). Background EMG rms values were 0.030 ± 0.003 mV for cocontraction and 0.043 ± 0.005 mV for grip-lift. Changes in INF MEP ratios most likely reflect changes in excitability due to conditioning rather than fluctuations in background EMG.
Corticospinal excitability
A stimulus–response curve of the mean nonconditioned INF MEPs from a typical subject is shown in Fig. 3. This demonstrates larger MEPs during the grip-lift task and a steeper recruitment slope. For the group data, the slope was steeper during grip-lift (0.25 ± 0.03 mV) than that during cocontraction (0.15 ± 0.18 mV) [F(1,13) = 7.07, P < 0.05]. Regression analyses revealed no significant correlation between nonconditioned MEP amplitude and background EMG. For cocontraction r2 = 0.07, and for grip-lift r2 = 0.03 (all P > 0.67). The steeper slope of nonconditioned MEPs is not a function of increased EMG, but a task-related increase in corticomotor drive.
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DISCUSSION |
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Propriospinal modulation between hand and shoulder
During cocontraction of the INF and FCU, INF MEPs elicited by weak TMS intensities were facilitated by peripheral input, whereas INF MEPs elicited by stronger TMS intensities were suppressed. INF MEP area was unaffected by further changes in TMS output. This putative propriospinally mediated response pattern is in line with previous studies of propriospinal control of more distal muscles using similar techniques (Iglesias et al. 2007; Nicolas et al. 2001; Stinear and Byblow 2004b). These results support our hypothesis that the INF muscle may be controlled by propriospinal networks and can respond to feedback from afferents within the ulnar nerve. This sensory information probably serves to fine-tune the activity of INF for shoulder stability during upper-limb reaching.
The observed facilitation and suppression of MEPs likely occur at the premotoneuronal level because the ISI used is too long to effect a monosynaptic response. ISIs of 4 ms for monosynaptic and 7 ms for propriospinal summation were estimated based on the work of others and tested in pilot studies (Alexander and Harrison 2003; Roberts et al. 2008). Our average ISI of 7 ms evoking maximal INF MEP facilitation was consistent with a propriospinal pathway. Furthermore, at this ISI, the low-threshold pathway subserving facilitation gives way to inhibition with higher TMS intensities. This effect seems most likely if responses are mediated at the same interneuron (Nicolas et al. 2001). Facilitation and suppression in the current study were evoked at 6, 7, or 8 ms depending on the individual, which are similar to those reported for other peripheral nerve and muscle combinations (Iglesias et al. 2007; Nicolas et al. 2001; Stinear and Byblow 2004b). Finally, the initial portion of the rising MEP slope did not appear altered by conditioning similar to earlier studies (Mazevet et al. 1996; Nicolas et al. 2001), although this was not measured quantitatively in the present study. Overall the data support the idea that the observed facilitation and inhibition at F and F + 2%, respectively, are not the result of monosynaptic inputs to the INF motoneurons but, rather, are mediated via interposed interneurons.
Ulnar nerve afferents provide feedback to other shoulder muscles besides INF. Deltoid, triceps, and biceps also share propriospinally mediated connections (Gracies et al. 1991). Electrical stimulation of the ulnar nerve evokes long-latency, possibly transcortical, reflexes in shoulder girdle muscles (Alexander and Harrison 2003; Alexander et al. 2005) and in INF and deltoid (Roberts et al. 2008). The lack of monosynaptic feedback projections from the ulnar nerve to deltoid (Creange et al. 1992), INF (Roberts et al. 2008), or the shoulder girdle muscles (Alexander and Harrison 2003; Alexander et al. 2005) lends further support to the idea of a propriospinally mediated site of summation and underscores the important role of descending corticomotor commands in modulating feedback from the hand.
The pathways mediating peripheral afference from the hand were not examined directly. However, we consider it likely that the peripheral stimulation acted on large-diameter fibers, due to the low stimulus intensity used for evoking responses (Lourenco et al. 2006). Although the grip-lift task stimulated cutaneous receptors in the hand, whereas the cocontraction task did not, this is unlikely to have influenced INF MEPs since cutaneous stimulation to the palmar side of the fingers does not alter responses evoked by ulnar nerve stimulation (Iglesias et al. 2007).
Task-dependent modulation of propriospinal premotoneurons
When performing the grip-lift task, both facilitatory and inhibitory effects of the conditioning stimulus were attenuated. MEPs were less facilitated at F, although this did not differ significantly from the cocontraction task. In contrast, inhibition at F + 2% was significantly different between tasks, with no inhibition in the grip-lift task. The contrasting responses between cocontraction and grip-lift tasks at F + 2% suggest differential modulation via the propriospinal system. Background EMG activity or nonconditioned MEP size are unlikely explanatory variables for effects due to task, since neither correlated with MEP ratio. Less INF suppression at stronger TMS intensities during grip-lift suggests that descending drive to propriospinal premotoneurons may be altered by task. Task-related changes in propriospinal transmission were recently described between hand and wrist muscles (Iglesias et al. 2007), where flexor carpi radialis (FCR) MEPs were recorded during voluntary contractions, gripping and pointing using methods similar to those of the present study. Task-related modulations were assessed by increasing peripheral stimulus intensity rather than TMS intensity, inducing the typical pattern of facilitation followed by suppression of MEPs during voluntary contractions. Differential effects of task were observed only at intensities >0.8 x MT, where FCR MEPs were facilitated during gripping compared with voluntary contractions and pointing (Iglesias et al. 2007). These findings, combined with those of the present study, suggest that forearm and hand muscle afferents have strong divergent projections to more proximal joints via propriospinal premotoneurons and that these projections are modulated during functional activities.
Task-dependent modulation of corticomotor excitability
The INF recruitment curves derived from nonconditioned trials indicate greater excitability during the grip-lift task. Difference in background EMG between tasks may have contributed in some part to this difference, but is unlikely to be the sole contributor since there was no correlation between EMG level at the time of stimulation and nonconditioned INF MEP size. The greater excitability of direct corticomotor projections during grip-lift may serve to stabilize the shoulder for this task. Task-related increases in INF excitability during gripping appear to occur in parallel with decreased corticospinal drive to inhibitory interneurons. Disfacilitation of the inhibitory pathway would allow peripheral afferents to excite propriospinal neurons more than inhibitory interneurons (Fig. 4). Alternatively, the difference in INF MEPs between the two tasks may be due to different proportions of cortical command relayed via propriospinal interneurons. If this were the case, however, greater facilitation during grip-lift should have been apparent at both F and F + 2%, although this was not observed. Therefore we propose that disfacilitation of inhibitory interneurons is a more likely mechanism contributing to the effect of task. Greater sensitivity to afference from the hand may allow feedback to increase indirect corticospinal drive to INF. Any change in the external environment signaled by hand afferents can then adjust coordination to maintain task performance. Concurrent activation of indirect propriospinal pathways in this manner likely provides adaptable movement control, allowing integration of sensory information from the forearm and hand during purposeful movement.
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GRANTS |
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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Address for reprint requests and other correspondence: W. D. Byblow, Movement Neuroscience Laboratory, University of Auckland, Auckland, New Zealand (E-mail: w.byblow{at}auckland.ac.nz)
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ABSTRACT
INTRODUCTION
