Reference Frames for Spinal Proprioception: Kinematics Based or Kinetics Based?

G. Bosco and R. E. Poppele

Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota 55455


    ABSTRACT
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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.


    INTRODUCTION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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.


    METHODS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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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Fig. 1. Hindlimb geometry. Stick figures represent the cat hindlimb geometry. The hip joint (h) is fixed by bone pins in the iliac and the foot (f) is attached to the platform arm of a robot that places it in 1 of 20 locations in a parasagittal plane (gray dots). Arrow indicates the reference foot position. Joint angles are defined as shown for hip (h), knee (k) and ankle (a). Limb axis (not shown) connects hip (h) to foot (f). Thin line stick diagrams illustrate passive limb geometry for 3 foot positions and thick lines show effect of gastrocnemius (gray) and quadriceps (black) stimulation for cat 5. Plots in B-D show differences in individual joint angles between the control passive condition and gastrocnemius (gray circles) or quadriceps (black triangles) stimulation. Extension and flexion directions from the identity line are labeled for each joint.

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 MOmega , 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)
<IT>F</IT><IT>=&bgr;<SUB>0</SUB>+&bgr;<SUP>*</SUP><SUB>1</SUB></IT><IT>L</IT><IT>+&bgr;<SUP>*</SUP><SUB>2</SUB></IT><IT>O</IT><IT>+&egr;</IT> (1)
For the neurons that were found to be significantly modulated (P < 0.001), we determined further whether the representation was invariant in the passive and muscle stimulation conditions. For this purpose we added the binary variables M and S, each associated with the stimulation of one of the muscles, to the model in Eq. 1
<IT>F</IT><IT>=&bgr;<SUB>0</SUB>+&bgr;<SUP>*</SUP><SUB>1</SUB></IT><IT>L</IT><IT>+&bgr;<SUP>*</SUP><SUB>2</SUB></IT><IT>O</IT><IT>+&bgr;<SUP>*</SUP><SUB>3</SUB></IT><IT>M</IT><IT>+&bgr;<SUP>*</SUP><SUB>4</SUB></IT><IT>M</IT><IT>*</IT><IT>L</IT><IT>+&bgr;<SUP>*</SUP><SUB>5</SUB></IT><IT>M</IT><IT>*</IT><IT>O</IT> (2)

<IT>+&bgr;<SUP>*</SUP><SUB>6</SUB></IT><IT>S</IT><IT>+&bgr;<SUP>*</SUP><SUB>7</SUB></IT><IT>S</IT><IT>*</IT><IT>L</IT><IT>+&bgr;<SUP>*</SUP><SUB>8</SUB></IT><IT>S</IT><IT>*</IT><IT>O</IT><IT>+&egr;</IT>
The terms M, S, and their interactions with the positional terms L and O in Eq. 2 measure differences in overall firing level and the sensitivities to kinematic parameters that depend on whether the limb was passive or under stimulation. We set a cutoff P < 0.01 for significant differences across experimental conditions. For the subset of data common to the previous paper, the binary variable K was added to Eq. 2 to account for the knee-constraint condition. A fuller description of this analysis is presented in the companion paper (Bosco et al. 2000).

The neuronal spatial tuning gradient (preferred direction) was determined by using the following positional gradient function (Bosco and Poppele 1997; Kettner et al. 1988)
<IT>F</IT>(<B>S</B>)<IT>=</IT><IT>f</IT><SUB><IT>0</IT></SUB><IT>+</IT><IT>h</IT><IT>*‖</IT><B>S</B><IT>‖* cos </IT>(<IT>arg </IT><B>S</B><IT>−</IT><IT>G</IT>) (3)
where F represents the firing rate estimated from Eq. 1 for each foot position (L, O). Each foot position is re-expressed in the coordinates of a vector S having its origin at the reference foot position illustrated in Fig. 1, and pointing to the foot position (L, O). The distance from the reference position to the foot position is |S|, and the direction is arg S (directions defined with 0° back and increasing counterclockwise). The mean firing rate is f0, and h is the rate of change of discharge rate with distance from the origin in the direction of the maximal activity gradient, G (the preferred direction).

We determined the preferred direction, G, separately for each experimental condition and compared them by computing the cosine of their difference between either the muscle stimulation or knee-constraint conditions and the control passive condition.


    RESULTS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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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Fig. 2. Joint-angle covariance planes. Each joint angle for a given foot position is plotted as the difference from the mean angle across the 20 positions in a 3-dimensional joint space representation. Each plane is plotted from 2 perspectives, 1 showing the plane orientation with respect to the axes of the 3 joints, the other edge-on view shows the scatter of points off the plane. A: passive unstimulated limb. B: stim 1 muscle group. C: stim 2 muscle group; see legends on the right side of graphs and Table 1 for muscle stimulation identification for each cat.


                              
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Table 1. Least-square joint-angle covariance planes

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). The overall effect of muscle activation on the foot position response of this neuron was also rather weak. Activation of the gastrocnemius caused no change in the cell's mean firing levels over the workspace (Fig. 3C), whereas stimulation of the quadriceps increased the overall neuronal firing without changing the sensitivity of its relative relationship to foot position (B).



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Fig. 3. Effect of muscle stimulation on the relation between firing rates and the length and orientation of the hindlimb axis for cell 2667. A-C: The firing rate measured in each foot position (<= 4 trials per position) is plotted against the length and orientation of the limb axis. The plane is the least-squares regression fit to the data. A: unstimulated, B: quadriceps stimulation, C: gastrocnemius stimulation. D: the relation between actual firing rates (real) and the rates predicted from the regression model (predicted; Eq. 2) for the unstimulated (gray triangles), gastrocnemius (open circles) and quadriceps (black circles) stimulation. E: poststimulus firing rate densit after a series of single shock stimuli (pseudorandom sequence, 8 per second) to the quadriceps group (bold) and gastrocnemius group (fine). Mean firing rates in the unstimulated (dotted) and stimulated conditions are indicated by horizontal lines.

We quantified any response differences across experimental conditions by means of a multivariate regression model that included indicators for the experimental condition (METHODS, Eq. 2). The result of this analysis, graphed in Fig. 3D for cell 2667, shows the predicted values of firing activity plotted against the firing rates actually recorded along with separate regression lines fit to each experimental condition. It is evident that although gastrocnemius stimulation evoked no change in firing rate (gray triangles) with respect to the control condition, quadriceps activation (black circles) raised the overall level of activity significantly but did not change the slope or gain of the relationship.

A different example is represented by cell 2688 from the same animal (Fig. 4). Pseudorandom stimulation of either muscle evoked a similar strong level of inhibition of this cell's firing rate followed by an excitatory rebound (Fig. 4E). Once again, though, the mean firing rate under stimulation was only slightly higher than in the control condition. Unlike cell 2667, however, the relationships between firing rates and foot position were changed significantly by activation of both muscle groups. The regression lines in Fig. 4D show that the intercepts and slopes were both similarly altered by muscle activation. It is also worth mentioning that this cell was recorded in the same cat and 26 h after cell 2667 because it indicates there was probably no significant loss of stimulus effectiveness over the course a long experiment, which could last 36 h.



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Fig. 4. Effect of muscle stimulation on the relation between firing rates and the length and orientation of the hindlimb axis for cell 2688. Format is the same as for Fig. 3. A: unstimulated, B: quadriceps stimulation, C: gastrocnemius stimulation.

As with the joint constraints, we found that the slope of the relationship between firing rate and foot position for over half the cells was unaffected by muscle stimulation. Neither the slopes nor the overall activity levels were altered by stimulation for 7/59 cells (12%), and only overall activity levels were affected by at least one muscle stimulation for another 28 neurons (47%). The remaining 24 neurons (41%) all showed significant changes in slope with at least one type of muscle stimulation. In principle, we could distinguish two neuronal populations from this analysis. One, accounting for ~60% of the total population, represented limb endpoint position regardless of the force perturbations, whereas the other represented endpoint position differently depending on the muscle activation. The distinction between the two groups was not as clear-cut as the statistics might imply, however, because the magnitude of the effect of muscle stimulation was quite variable among neurons.

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. 5-9.



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Fig. 5. Effect of muscle stimulation and joint constraint on the relation between firing rates and the length and orientation of the hindlimb axis for cell 2710. Format for A-D is the same as for Fig. 3A. A: unstimulated, unconstrained, B: knee-angle constrained, C: biceps stimulation, D: quadriceps stimulation, E: pseudorandom stimulation of biceps (bold lines) and quadriceps (fine lines); same format as Fig. 3E.

Cell 2710 (Fig. 5) represented an extreme in which the spatial tuning was essentially invariant across all conditions. The only significant activity change for this neuron was an overall increase in firing level with quadriceps muscle activation (D). Once again, the lack of effect could not be simply explained by a lack of responsiveness to muscle stimulation (E). In fact, the changes in mean firing rate during pseudorandom stimulation were about the same as those observed during the static foot placements with 20-per-second stimulation. Overall, 12/42 neurons (29%) in this subset showed a similar behavior.

Cell 2669 (Fig. 6) represented the opposite end of the spectrum. Slope changes were evident with gastrocnemius stimulation (D) and also with the knee constrained (B). Note again that the relative lack of effect on response gain observed with quadriceps stimulation (C) was not simply due to a lack of input from this muscle (E). In fact, pseudorandom stimulation of either muscle evoked a clear excitatory response and a slight decrease in the mean firing rate. A total of 13/42 (31%) of the cells exhibited this type of behavior.



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Fig. 6. Effect of muscle stimulation and joint constraint on the relation between firing rates and the length and orientation of the hindlimb axis for cell 2669. Format for A-D is the same as for Fig. 3A. A: unstimulated, unconstrained, B: knee-angle constrained, C: quadriceps stimulation, D: gastrocnemius stimulation, E: pseudorandom stimulation of quadriceps (bold lines) and gastrocnemius (fine lines); same format as Fig. 3E.

Cell 2708 (Fig. 7) showed significant slope changes only for the knee-constraint condition. Stimulation of either the biceps or the quadriceps muscles evoked complex patterns of poststimulus activity (E) but did not produce significant slope changes (C and D). In this case, quadriceps stimulation did produce a significant increase in firing level, however (D). Ten other neurons or a total of 11/42 (24%) presented similar features.



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Fig. 7. Effect of muscle stimulation and joint constraint on the relation between firing rates and the length and orientation of the hindlimb axis for cell 2708. Format for A-D is the same as for Fig. 3A. A: unstimulated, unconstrained, B: knee-angle constrained, C: biceps stimulation, D: quadriceps stimulation, E: pseudorandom stimulation of biceps (bold lines) and quadriceps (fine lines); same format as Fig. 3E.

Finally, a subgroup of eight neurons (19%) did not show any significant change in the relationship between activity and foot position for the knee-constraint condition but did for muscle stimulation. An example is cell 2726 (Fig. 8), which was most strongly affected by gastrocnemius stimulation (C). Pseudorandom stimulation of either quadriceps or gastrocnemius evoked a short-latency inhibition followed by an excitatory rebound and an overall increase in firing rate (E), similar to overall firing rate increases observed throughout the workspace [highly significant for gastrocnemius (C), almost significant for quadriceps (D)]. Only gastrocnemius activation affected the gain of this cell's response though by increasing the slope of the relationship between activity and foot position.



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Fig. 8. Effect of muscle stimulation and joint constraint on the relation between firing rates and the length and orientation of the hindlimb axis for cell 2726. Format for A-D is the same as for Fig. 3A. A: unstimulated, unconstrained, B: knee-angle constrained, C: quadriceps stimulation, D: gastrocnemius stimulation, E: pseudorandom stimulation of quadriceps (bold lines) and gastrocnemius (fine lines); same format as Fig. 3E.

On the basis of this analysis, we can distinguish four groups of cells according to the probability that their activity gradient remains invariant along at least one kinematic dimension (i.e., limb axis length or orientation) during muscle stimulation and/or knee constraint. Once again, however, there is a significant variability among neurons in the degree to which this criterion is satisfied as illustrated when the corresponding t values are considered in each case (Fig. 9A). The four neuronal subgroups we described in the preceding text are each contained in a quadrant delimited by the lines passing through t values of 2.67, corresponding to P values of 0.01. Most of the data points in this scatter plot lie very close to the borders between quadrants, suggesting that the distribution of t values may in fact be continuous.



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Fig. 9. Comparison of effects of constraining the knee joint and muscle stimulation. A: level of significance, given by the t value for the maximum change in a coefficient value between the unconstrained and knee-constrained condition for the relation of firing rate to limb axis position (t value joint constraint) versus the same value determined for muscle stimulation (t value muscle stimulation). Lines are drawn at the values of 2.67 on each axis, corresponding to a P value of 0.01. Cells the behavior of which is documented in Figs. 5-8 are indicated on the plot. B: The cosine of differences between each cell's preferred direction angle for muscle stimulation [cosine (stimulated - unstimulated)] and joint constraint [cosine (constrained - unconstrained)]. The lines are drawn at cosine values of 0.8, corresponding to a 37° difference.

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.

The cluster of data points in the top right corner of plot shows that the preferred directions for half of the cells (21/42) were within 36.4° (i.e. ±18°) under all conditions tested. For example, cells 2710 and 2669, which represented the extreme cases of the muscle stimulation effect, are both found in this cluster because neither stimulation nor constraint affected the preferred direction of either cell even though both conditions altered the sensitivity of cell 2669. In fact the muscle stimulation changed the preferred directions in only 8/42 by more than 37°. The most extreme example is cell 2726, which had about a 90° change in preferred direction with gastrocnemius stimulation. In contrast, the preferred directions of 18/42 cells were significantly altered by the joint constraint, for example cell 2708. Thus about half the cells did not change their preferred directions in either condition and very few cells changed preferred directions in both.


    DISCUSSION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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.


    ACKNOWLEDGMENTS

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.


    FOOTNOTES

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.


    REFERENCES
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
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