Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota 55455
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ABSTRACT |
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Bosco, G. and R. E. Poppele. Reference Frames for Spinal Proprioception: Kinematics Based or Kinetics Based?. J. Neurophysiol. 83: 2946-2955, 2000. This second paper of the series deals with another issue regarding sensorimotor representations in the CNS that has received much attention, namely the relative weighting of kinematic and kinetic representations. The question we address here is the contribution of muscle tension afferent information in dorsal spinocerebellar tract (DSCT) sensory representations of foot position. In five anesthetized cats, we activated major hindlimb muscle groups using electrical stimulation of ventral root filaments while passively positioning of the left hind foot throughout its workspace. In general, as the parameters of the joint angle covariance planes indicated, muscle stimulation did not significantly change hindlimb geometry. We analyzed the effects of the muscle stimulation on DSCT neuronal activity within the framework of a kinematic-based representation of foot position. We used a multivariate regression model described in the companion paper, wherein indicators of the experimental condition were added as firing rate predictors along with the limb axis length and orientation to account for possible effects of muscle stimulation. The results indicated that the response gain of 35/59 neurons studied (59%) was not changed by the muscle activations, although most neurons showed some change in their overall firing level with stimulation of one or more muscles. Most of the neurons responded to pseudorandom stimulation of the same muscle groups with complex temporal patterns of activity. For a subpopulation of 42 neurons, we investigated the extent to which their representation of foot position was affected by a rigid constraint of the knee joint and at least one type of muscle stimulation. Although they could be divided into four subgroups based on significance level cutoffs for the constraint or stimulation effect, these effects were in fact quite distributed. However, when we examined the preferred directions of spatial tuning relative to the limb axis position, we found it was unchanged by muscle stimulation for most cells. Even in those cases in which response gain was altered by muscle stimulation, the cell's preferred direction generally was unaltered. The invariance of preferred direction with muscle stimulation lead us to the conclusion that the reference frame for DSCT coding may be based primarily on limb kinematics.
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INTRODUCTION |
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The results presented in the companion paper
(Bosco et al. 2000) suggested there may be at least two
groups of dorsal spinocerebellar tract (DSCT) neurons, those sensitive
to specific joint-angle configurations and those representing foot
position independently from the joint-angle configuration. We found
this duality by decoupling endpoint position from the joint-angle
configuration by means of external joint constraints. In doing so we
also imposed external forces on the constrained joints that not only
altered the joint angles and the signals from the various sensory
receptors measuring joint angles but also the internal joint forces and
the sensory signals from various force receptors as well. Because the
DSCT receives sensory input from both types of receptors, it seems even
more remarkable that foot-position representations of many neurons
remained essentially unaffected by the external joint constraints.
In contrast to the joint forces in the anesthetized cat, forces during
normal behavior are strongly effected by the activation of specific
muscle groups. Depending on the type of motor behavior, the muscle
forces may increase simply limb stiffness with little or no effect on
limb geometry or they may change the distribution of limb stiffness and
thereby change overall limb geometry. Thus proprioceptive signals from
muscle tension receptors could play a decisive role in the central
representations of limb geometry. For example, DSCT neurons have been
shown to receive strong inputs from Golgi tendon organs, which are
particularly sensitive to muscle contraction (Lundberg and
Winsbury 1960a,b
). The classical notion, based primarily on
monosynaptic responses to afferent nerve stimulation, was that only one
subgroup of DSCT neurons received such input (Lundberg and
Oscarsson 1960
). But later studies that focused on muscle
contractions and longer latency responses showed that the activity of
most DSCT neurons is influenced significantly by muscle contractions
that may exert both excitatory and inhibitory effects on the same
neurons (Osborn and Poppele 1989
). The lack of a solid
functional framework for the DSCT, however, made it difficult to
interpret these findings.
More recent findings showing that DSCT activity relates best to more global parameters of the hindlimb may now provide a plausible functional framework for interpreting the role of muscle contraction information in DSCT sensory coding. For example, within this more global framework we might relate the activity of individual neurons to the limb kinematics resulting from muscle activation rather than to the tension developed by individual muscles. Thus a question raised by these observations is the extent to which the proprioceptive representations within the spinocerebellar circuitry are kinematic or force (kinetic) based.
We investigated this issue here by comparing DSCT neuronal representations of foot position in the unconstrained limb with positions obtained when joint forces were perturbed by activating specific muscle groups. We found that a majority of neurons recorded under both conditions exhibited invariant endpoint representations even though the neurons clearly were affected by the muscle stimulation.
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METHODS |
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Experiments were carried out on five adult cats anesthetized
with barbiturate (Nembutal, Abbott Pharmaceuticals; 35 mg/kg ip,
supplemented by intravenous administration to maintain a surgical level
of anesthesia throughout the experiment). The experimental conditions
were basically identical to those in the companion paper (Bosco
et al. 2000), in fact all five animals were common to the two
sets of experiments.
Muscle stimulation
Hindlimb muscle groups were activated by means of electrical stimulation of dissected ventral roots. For this purpose, we performed an additional laminectomy at lower lumbar-sacral level to expose the ventral roots of segments L4-S2. For each experiment, we isolated two filaments that activated separate muscle groups in the anterior or posterior hindlimb, respectively. Usually, stimulating an individual dissected rootlet activated primarily one muscle and to a lesser extent, other, mostly functionally agonist muscles. In the rest of the article, we will refer to each muscle stimulation protocol with the name of the primary muscle that was activated. However, the reader should keep in mind that other muscles were activated as well. The dissected rootlets were drawn into bipolar cuff electrodes for stimulation and kept under mineral oil to prevent drying. We found that the stimulus parameters required for a visible muscle contraction remained fairly constant during the course of the experiment. Although we made no attempt to quantify the actual contractions, we did observe the contractions and verify that each cell responded to the contractions.
We used two stimulation paradigms. One was a pseudorandom activation at
a mean rate of eight per second maintained for 1 min. This was used
to determine the effect of the muscle twitches on DSCT activity
(Osborn and Poppele 1983
). The other paradigm was a 1-s
train (20 Hz) applied during static limb positioning. All stimuli were
above threshold for a visible contraction and 0.1-0.5 ms in duration.
We applied the stimulus train beginning 4 s after the onset of
each movement to determine its effect on position-related activity
rather than the movement-related activity that occurred in the earlier
postmovement interval (Bosco and Poppele 1997
).
Joint constraints
In many cases, we also applied joint constraints as described in
detail in the companion paper (Bosco et al. 2000). In
these experiments, we used only the rigid Plexiglas strip fixed between femoral and the tibial bone pins, which we referred to as a knee-fixed constraint.
Kinematic measurements
Limb kinematics are represented by the limb axis (the segment
joining the hip position with the foot position) or the joint angles as
illustrated in Fig. 1A and
described previously (Bosco and Poppele 1997;
Bosco et al. 2000
).
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Neuronal activity
We recorded unit activity from 61 DSCT axons identified by
antidromic activation and located in the dorsolateral funiculus at the
T10-T11 level of the
spinal cord using insulated tungsten electrodes (5 M, FHC,
Brunswick, ME). Neuronal activity was recorded continuously during two
to four series of passive limb movements through 20 positions in the
limb's parasagittal workspace (Fig. 1A) (Bosco et
al. 1996
). We aligned the neuronal activity to the movement
onset and averaged the activity recorded in the fifth second. Therefore
in the muscle stimulation trials this time interval included the entire
stimulation period.
Data analysis
KINEMATIC DATA.
We analyzed limb kinematics by fitting least-squares planes to the set
of joint angles determined for each foot position separately for each
experimental condition as described in detail in the companion paper
(Bosco et al. 2000). We then compared plane
orientations, defined by the direction cosines of the vector normal to
the plane, and the fraction of variance explained across experimental conditions.
NEURONAL DATA.
The neural data also were analyzed as we described in detail in the
companion paper. There were three separate sets of analysis, each
involving the use of multivariate regression models to relate firing
rates for each foot position to the limb kinematics. A linear
regression model was used to determine whether the average firing rate
(F) recorded in the fifth s after movement onset was significantly modulated by foot position expressed in the coordinates of limb axis length (L) and orientation (O)
(Bosco et al. 2000)
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(1) |
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(2) |
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(3) |
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RESULTS |
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Limb kinematics
Muscle stimulation had a slightly different effect on limb kinematics in each preparation. One example illustrated in Fig. 1A shows that "gastrocnemius" stimulation evoked a net flexion at the hip and the knee (B and C) and a net extension at the ankle (D). In this example "quadriceps" stimulation produced a slight extension at the hip and knee and a negligible effect at the ankle. The joint angle data for all five cats are plotted in a three-dimensional joint angle space in Fig. 2. We fit a least-squares plane through the data points representing joint angles for each of 20 foot positions. The plane parameters (percent of variance explained and direction cosines) for all five cats are summarized in Table 1. It can be noted that, except for the quadriceps stimulation (stim 1) in cat 3 (Fig. 2B), muscle stimulation generally had a modest effect on the joint angle covariance pattern.
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Neuronal data
We recorded the activity of 61 DSCT neurons we were able to study in the passive condition and with at least one ventral root stimulation series. The activity of 59 of these neurons (96.7%) was linearly related to passive hindfoot position (R2 > 0.4, P < 0.001; METHODS, Eq. 1).
MUSCLE STIMULATION. All the cells studied showed some response to muscle stimulation when tested with the pseudorandom stimulation, however, the stimulation did not consistently alter their positional response. The following examples illustrate the variety of behavior we observed.
The activity of cell 2667 showed a strong linear relationship to limb length and orientation under passive conditions (Fig. 3A). Pseudorandom stimulation (Fig. 3E) indicated that this neuron received strong inputs from receptors responding to the muscle activation (primarily the quadriceps and gastrocnemius muscles in this cat). However, despite a strong activation that might represent both excitatory and inhibitory influences from each muscle group, the mean firing rate of the cell was only slightly increased during stimulation, suggesting that the contractions had their main effect on the timing of DSCT spikes (Osborn and Poppele 1989
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JOINT CONSTRAINT AND MUSCLE STIMULATION.
The variability became more evident when we examined the responses of a
subset of 42 neurons that were recorded during the activation of at
least one muscle and also when the knee joint was immobilized by a
rigid constraint. For these neurons we could determine directly the
extent to which any alteration in joint forces, externally or
internally imposed, contributed to the neuronal representation of foot
position. Some typical examples are illustrated in Figs.
59.
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DIRECTION OF MAXIMAL ACTIVITY GRADIENT.
Slope changes in the relationship between firing rate and limb length
or orientation may not signify a change in a cell's preferred
direction (i.e., the direction of the maximal activity gradient in the
work space) because they may simply indicate comparable increases in
sensitivity to both parameters (see also Bosco et al.
2000). To distinguish this from changes in preferred direction, we used Eq. 3 (METHODS) to determine preferred
directions for each cell under each experimental condition. To quantify
any changes, we computed the cosine of the difference angles between a
cell's preferred direction in the passive control condition and those determined with muscle stimulation or joint constraint. The difference cosine for muscle stimulation versus the difference cosine for the knee
constraint is plotted in Fig. 9B for each of the 42 neurons illustrated in A (note that a cosine value of 1 indicates
identical preferred directions). As in A, we used the data
giving a maximal difference for neurons studied with two muscle
stimulation conditions.
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DISCUSSION |
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The principal result of this investigation is that limb forces may significantly modulate DSCT activity; nevertheless, the relationship between foot position and activity is often not affected. We showed this by examining the DSCT neuronal representation of limb position in the presence and absence of force perturbations imposed by electrical stimulation of hindlimb motor nerves.
Various studies have already pointed out that sensory receptors, like
the Golgi tendon organs that respond to muscle contraction, have a
complex somatotopic arrangement and heterogeneous effects on DSCT
neurons (Osborn and Poppele 1983; Zytnicki et al.
1995
). However, none of these studies examined the effects of
muscle contraction in a physiological context, so they may not be
expected to shed significant light on the functional role of such
receptors. Our current study is no exception to this because the muscle
activation patterns we achieved through direct electrical stimulation
of dissected ventral rootlets may have no physiological relevance. However, our main focus here was to study how muscle-tension
information may be integrated by spinal sensory circuitry into
higher-order representations of limb parameters like foot position.
From this perspective, a first consideration is that the cells we
studied all responded to pseudorandom muscle activation and usually to both muscles, consistent with our earlier study, which found 86% of
the DSCT responded specifically to muscle contraction (Osborn and Poppele 1989
). Thus it appears that nearly all the DSCT
cells are capable of responding to muscle activation throughout the hindlimb, so that like other sensory inputs to the DSCT circuitry, muscle force inputs also appear to be widely distributed. The question
then concerns the influence these inputs might have on the
representation of position we described for the passive limb.
The main issue we addressed in these papers was whether the observed
limb-centered reference frame for encoding by the DSCT results from
peripheral factors related to the biomechanics of the limb or to
central factors involving the spinal circuitry. In the companion paper
(Bosco et al. 2000), we disrupted the passive limb
biomechanical coupling across joints to rule it out as a major factor
in establishing a limb axis basis for the sensory representation.
Because about half of the cells were able to consistently represent the
foot position with this coupling disrupted, we concluded that the limb
axis representation is a property of the neuronal circuitry and not
entirely dependent on the biomechanics, at least for passive
manipulations of the limb.
This result also could be taken as evidence for a kinematic representation by the DSCT, and in some cases at least, for a kinematic representation of the endpoint. However, in this paper we presented evidence that DSCT representations may vary even when joint kinematics and endpoint kinematics are both controlled, suggesting that parameters not correlated with kinematics under some conditions also may be encoded by the DSCT. The question then becomes whether all DSCT information is encoded in a kinematic reference frame or whether there are other, perhaps force-based reference frames.
The simplest case to consider might be the existence of separate
channels for kinematic- and force-based information. Evidence from
behaving animals, for example, suggests that they may control limb
geometry and paw contact forces independently (Lacquaniti and
Maioli 1994), thereby suggesting a possible need for separate channels for kinematic- and force-related sensory feedback information. The results presented here for DSCT are not entirely inconsistent with
that notion. In fact, as we found with joint constraints, the
population of DSCT neurons could be divided roughly in halves on the
basis of the muscle stimulation experiments. One group of neurons,
accounting for 59% of the population, represented endpoint position
independently from the muscle contractions, suggesting a kinematic-
rather than force-related sensory coding. For the other 41%, the
cell's relation to limb endpoint changed significantly with muscle
stimulation, implying instead a force-related sensory coding.
Although the idea of separate sensory channels for limb kinematics and kinetics may be attractive, our data do not fully support it for the DSCT organization. In fact, analysis of the activity in the subset studied with both joint constraints and muscle stimulation suggested a much more distributed organization of the DSCT circuitry. Although four subgroups could be distinguished by setting significance level cutoffs, the distribution of t values for 42 neurons tested with joint constraint and muscle stimulation showed a continuum of response types, consistent with the idea of a distributed system.
A further analysis of the changes brought about by muscle contraction showed that they were nearly all changes in sensitivity that did not alter the cells' directional tuning. The direction of the spatial tuning thus remains invariant when limb forces are perturbed by isolated muscle contractions. That is, the activity of the cell continues to relate to the limb kinematics. Moreover, if this kinematic reference frame were the result of some artifact of the passive, anesthetized state of the animal, we could expect it might be significantly disrupted by muscle contractions. Instead the kinematic representation is robust in the presence of the contraction perturbations suggesting that it is likely to represent a fundamental property of the system, at least under the static conditions of these experiments.
Thus it seems from both sets of experiments that DSCT information could all be encoded in a limb-based kinematic reference frame. However, it is also clear that DSCT cells respond to muscle contraction, and they therefore are likely to encode some aspect of limb forces. Therefore one implication of our results is that whatever force information is encoded by the DSCT is encoded in a kinematic reference frame.
The results also suggest a clue about how muscle forces might be
encoded. The main effect of muscle activation we observed was a
modification of DSCT sensitivity to limb kinematics in a manner
resembling a gain field modulation (e.g., Andersen et al. 1985). That is, the magnitude of the changes in sensitivity
evoked by stimulation depended on foot position. Such gain fields may result from a multiplicative interaction between two types of signals,
and they represent a potential neural mechanism by which information
about multiple parameters may be compressed efficiently and combined in
a single unit's activity. In fact, we showed earlier that position and
movement signals may also be combined in this manner in DSCT
(Bosco and Poppele 1997
).
Concluding remarks
In the series of experiments described in these two papers, we finally departed from the totally passive hindlimb that had provided an extremely useful experimental model to study the functional characteristics of sensory processing in the spinal cord. Earlier, classical reductionist approaches provided some description of connectivity patterns within the DSCT circuitry, yet they lacked a functional framework for interpreting those findings. By imposing passive movements to the unconstrained limb, and relating the neuronal activity to various limb kinematic parameters we could, instead, examine the issue of possible functional frameworks. We rapidly came to the conclusion that DSCT best represented global parameters of the hindlimb rather than local parameters. Furthermore we indicated in the limb axis length and orientation a candidate for a possible reference frame.
The functional implications of this finding were twofold. First, a
representation of global limb parameters explained the extensive
convergence of sensory afferent information onto DSCT neurons pointed
out by earlier studies (Holmqvist et al. 1956). Second,
a limb-axis-based reference frame for sensory representations was an
attractive idea because postural studies in cats already had implicated
the same coordinate system in the control and maintenance of stable
posture (Lacquaniti et al. 1990
). Although the passive limb model has provided a reasonable functional framework, it nonetheless has its own limitations, most notably it fails to account
for muscle forces generated during normal behavior.
The working hypothesis that may be formulated from this current study, namely that whole-limb kinematics provide the basic framework for DSCT coding, makes a strong prediction about the behavior of the DSCT in more normal behavioral conditions. It predicts that a substantial fraction of the DSCT will encode information relative to the position and movement of the hindlimb endpoint even when proprioceptive feedback induced by the behavior becomes a major component of the sensory input.
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ACKNOWLEDGMENTS |
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The authors thank A. Rankin for help and assistance on this project and Drs. M. Flanders, J. Soechting, and S. Giszter for critical and helpful comments on the manuscript.
This research was supported by National Institute of Neurological Disorders and Stroke Grant NS-21143.
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FOOTNOTES |
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Address for reprint requests: R. E. Poppele, 6-145 Jackson Hall, University of Minnesota, 321 Church St. SE, Minneapolis, MN 55455.
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 30 August 1999; accepted in final form 7 February 2000.
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REFERENCES |
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