Responses of Rapidly Adapting Afferent Neurons to Dynamic
Stretch of Rat Hairy Skin
Zaccaria Del Prete1 and
Peter Grigg2
1 Department of Mechanical and Aeronautical Engineering, University of Rome la Sapienza, 00184 Rome, Italy; and 2 Department of Physiology, University of Massachusetts Medical School, Worcester, Massachusetts 01655
 |
ABSTRACT |
Del Prete, Zaccaria and Peter Grigg. Responses of rapidly adapting afferent neurons to dynamic stretch of rat hairy skin. J. Neurophysiol. 80: 745-754, 1998. Twenty-four rapidly adapting (RA) cutaneous afferents were recorded from a preparation of isolated, innervated hairy skin from the rat hindlimb for the purpose of identifying the mechanical variables associated with the initiation of afferent discharge. Neurons were studied while the skin was stretched dynamically along a single direction with the use of a linear actuator and a feedback controller. Input signals were load- or displacement-controlled stretches that followed either periodic or pseudorandom Gaussian noise control signals. When the tissue was actuated, loads and displacements were measured along the direction of stretch and neuronal responses were recorded. All RA afferents were activated by dynamic stretching. None had a sustained response to static stretch. Cross-correlation products, calculated between neuronal responses and either stress- or strain-related variables observed at the time of the spike, revealed a strong relationship between neuronal responses and tensile stress. Neuronal responses were observed at rates of change of stress between +1,000 and
800 kPa/s. Neuronal responses were poorly related to skin strain. Two loading conditions were used along the direction transverse to the stretch. In one condition the sides were unconstrained, so that on axial loading there was zero stress and negative strain along the transverse axis. In the other condition the sides were constrained so that when the tissue was loaded axially there was zero strain and positive stress along the transverse axis. In these two conditions the same level of axial stress was associated with two levels of axial strain. The neuronal responses were determined by the stress and not the strain. Neuronal responses were observed at stresses >5 kPa. It appears that RA afferents make little contribution to signaling limb movements or position in rat hindlimb on the basis of the behavior of rat hindlimb skin, as observed when the limb is rotated.
 |
INTRODUCTION |
Sensitivity to stretch is a pervasive phenomenon in cutaneous mechanoreceptor neurons. Many afferents in both glabrous (Burke et al. 1988
; Hulliger et al. 1979
) and hairy (Burgess et al. 1968
; Edin and Abbs 1991
; Grigg 1996
; Khalsa et al. 1997
; Kumazawa and Perl 1977
; Nordin 1994
; Nordin and Hagbarth 1989
; Nordin and Thomander 1989
; Perl 1968
; Shea and Perl 1985
) skin are activated by stretching the skin or by rotations of nearby joints. When skin is stretched, afferent neurons respond to local mechanical states internal to the skin. A major goal of this study was to determine the relationship between activity in cutaneous rapidly adapting (RA) afferents and the mechanical states in the skin that are caused by stretching. Identifying the mechanical states in skin that are caused by stretch and that are associated with activation of mechanoreceptor neurons was the focus of several recent publications from this laboratory (Grigg 1996
; Khalsa et al. 1996
). Those studies were based on a preparation of isolated, innervated skin from the rat hindlimb. Isolated skin was used so that tensile forces applied to the skin could be measured, allowing stresses to be known. The aforementioned studies used skin samples of known geometry, studied in an apparatus in which both the forces applied to the margins of the skin and the resulting deformations of the skin could be measured. Because both the applied forces and the displacements were known it was possible to characterize the stresses and strains in the skin when it was stretched. With the use of this method, SA2, C, and A
afferents were shown to be stretch sensitive; they preferentially signaled tissue stress rather than tissue strain (Grigg 1996
; Khalsa et al. 1997
). In the course of doing the aforementioned studies, RA afferents were observed to also be activated during stretch stimuli (Grigg 1996
). However, the apparatus used in those studies applied stretches so slowly that RA afferents were very poorly activated and it was not possible to systematically study them. In the present communication we describe experiments in which RA afferents were studied by using an apparatus capable of applying rapid stretches that allowed for appropriately activating them. The goals of these experiments were to characterize the responses of RA cutaneous afferents from hairy skin and to relate responses to the stresses and strains caused by dynamic stretching.
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METHODS |
Strips of skin with their sensory innervation intact were surgically removed from the hindlimb of rats and studied as an in vitro preparation. RA afferents were recorded while the skin was subjected to uniaxial stretching with the use of either periodic (sine or triangle) waveforms or pseudorandom Gaussian noise (PGN) stimuli (Looft 1994
, 1996a
,b
; Rogers et al. 1996
).
Adult rats of either sex were anesthetized with pentobarbital sodium (Nembutal; 35 mg/kg ip). The skin on the inner side of one hindlimb was clipped and depilated. The boundaries of the specimen of skin to be excised were marked with a pen (Fig. 1). The excised section was ~20 × 14 mm; marks were placed on the skin that defined its in situ dimensions so that the geometry could be reproduced in vitro. The long axis of the sample, along which it would be stretched, was parallel to the long axis of the femur. The sample had a large aspect ratio (length:width) so that stresses would be uniform in the area where neurons were sampled. The edges of the skin section were fashioned into six tabs that were subsequently used to apply loads to the sample. The tabs at the ends were 5 mm wide; the tissue was actuated by clamps that attached to these tabs. The tabs along the sides were ~7 mm wide at their base (Fig. 2B); they were used to apply loading along the transverse direction of the tissue. The geometry of the tabs was designed so that stresses would be uniform at the base of each. The cutaneous nerve innervating this region of skin was dissected from the skin to the saphenous nerve where it was cut.

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| FIG. 1.
Location of the skin patch excised for study. Shaded areas represent the bones of the leg. The long axis of the sample (Y) was parallel to the femur. R, rostral; C, caudal.
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| FIG. 2.
Panel A: apparatus used for uniaxial stretching. A, Ling linear actuator; B, LVDT; C, Burleigh linear actuator for static length offsets; D, load cell; E, chamber; M, masses hanging on strings to exert side loads on tissue sample. Panel B: skin mounted in clamps. G1 and G2, clamps that couple skin to actuator and to load cell; F, polished steel rods at edge of chamber over which the strings pass; N, nerve, shown drawn into oil-filled chamber; R, recording electrode.
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Apparatus
The skin sample was maintained in a plastic chamber (Fig. 2A) that was circulated with artificial interstitial fluid (Bretag 1969
), gassed with 95% O2-5% CO2, and kept at room temperature (20°C). It was mounted by gripping the tabs at the ends of the long axis in two 5-mm-wide clamps. One clamp was coupled to a Ling 203 linear actuator and the other to a Sensotec model 31 load cell. The actuator shaft passed through an LVDT (Macro Devices DC 750) so that actuator displacements could be measured. The actuator was feedback controlled and could be operated in either position- or force-controlled mode. The system had a mechanical bandwidth of 0-20 Hz; the Ling actuator had a displacement limit of ~5 mm. In some experiments, particularly those in which the sides of the sample were unconstrained, very large axial strains were required to activate neurons. The Ling actuator could not by itself generate sufficiently large displacements. Therefore a means was employed to impose additional, static (offset) changes in the length of the sample. The load cell was mounted on the shaft of a second linear actuator (Burleigh PZ-550) so that static changes in length (offsets) could be imposed. When static offsets were used they were imposed before the dynamic stimuli began. The controlled variable for all inputs was either tensile load or displacement along the long axis of the skin sample. We used two loading conditions along the transverse (orthogonal) axis of the sample. In initial experiments the sides of the sample were unconstrained. Thus when the skin was stretched uniaxially, the axial strains (i.e., along the direction of stretch) were very large and there were negative strains along the orthogonal direction. Because stress is present only along one direction, we refer to this stimulus condition as uniaxial stress. Because skin is under biaxial tension in its natural state, we also studied neurons using a second boundary condition in which the tissue was loaded along the orthogonal direction. In this condition static loads were applied to the tabs along the sides. The loads were applied by coupling each of the side tabs to a 2-g mass with the use of a length of suture that was led over a polished steel rod at the edge of the chamber (Fig. 2B) so the mass could hang freely. The suture was coupled to the skin using a small fish hook that was inserted into a hole in the end of each tab. The tabs were ~7 mm wide so the resulting (orthogonal) stress was on the order of 8 kPa. When biaxially loaded in this fashion, the width of the sample approximated its in situ width. When the tissue was actuated axially, increments in force were developed along the orthogonal direction. However, these forces were small enough that they did not overcome the frictional forces between the suture and the rod over which it was hung. Thus the lengths of the tabs did not change when the sample was stretched along its long axis. In this loading condition uniaxial stretching caused positive increments in stress (of unknown magnitude) and zero strain along the transverse direction. Because there were stresses along both directions, we refer to this loading as biaxial stress. In the interval between runs the side tabs were unloaded by lifting the hanging masses onto a small platform under the apparatus.

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| FIG. 3.
Power spectrum of pseudorandom Gaussian noise (PGN) control signal, showing that the upper cutoff frequency is ~20 Hz.
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| FIG. 4.
Histogram of stresses and strains observed in 2 data collection runs. A: actuator was operated in displacement control so that strains are normally distributed whereas stresses are skewed. B: actuator was operated in force control so that strains are skewed.
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| FIG. 5.
Relationship between uniaxial strain and stress. Uniaxial stress condition (i.e., sides of the sample were not constrained). Collected by a 1-Hz triangle wave input under displacement control. Data from a run in which neuronal responses were collected (same experiment as Fig. 6). The strain did not cycle through 0 in each cycle because part of the tissue displacement was a static offset.
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Nerve recording
The nerve was drawn into a small, oil-filled chamber for recording (Fig. 2B). It was dissected into small filaments that were placed on a gold wire electrode for recording. The indifferent electrode was placed in the bath. Neuronal signals were conventionally amplified and filtered; the responses of single afferents were discriminated with a template-matching algorithm (SPS Systems, Prospect, South Australia). Afferents were classified as RA if they had only transient responses to manual stroking, indenting, and stretching of the skin. Afferent neurons were sought whose receptive fields were in the 5-mm-wide strip of tissue extending between the clamps and in the 14-mm area between the side tabs. Neurons with receptive fields outside of this area (i.e., close to the clamps or in the tabs) were not studied because of concerns that the stresses might not be uniform in those locations.
Conduction velocities were measured in a small number of neurons by electrically stimulating the surface of the skin and recording the latency of the evoked response. It was possible to estimate conduction velocity in all neurons by calculating the cross-correlation product between stress and neuronal action potentials. Stress was recorded instantaneously, whereas action potentials were recorded after a delay that resulted from conduction along the axon. For neurons in which the conduction time was known, the conduction time was subtracted from the time of occurrence of each spike. When the cross-correlation product was computed between stress and neuronal responses, it was found that the product peaked at 0 ms. In neurons in which the conduction velocity was not measured, the cross-correlation product was computed and the location of the peak was determined. The time between the peak and 0 ms was taken as an estimate of conduction time. Most conduction times were estimated from cross correlograms rather than being measured directly. However, conduction times estimated with this method were not different from those measured directly and resulted in calculated conduction velocities of 20-30 m/s. If these are increased by 45% to correct for the low temperature of the bath (Petajan 1968
), it can be concluded that all afferents were myelinated A fibers.
Stimuli were presented and data were collected on a PC with the use of LabView software. Analogs (loads and displacements) were sampled at 500/s and stored in files. Action potentials were recorded in a third file as either 0 (no action potential) or 1 (action potential).
Mechanical stimuli
The skin was stimulated by stretching it uniaxially. Thus depending on whether load or displacement was the controlled variable, tissue stress or strain (respectively) along the long axis of the sample was forced to follow the input waveform. The input signal to the controller was either periodic sine or triangle waves or a PGN waveform. In initial experiments both periodic stimuli (sine and triangle waves) and PGN waveforms were used as inputs. However, periodic stimuli were discontinued in later experiments in favor of the PGN for the following reasons. First, periodic stimuli elicited strongly nonlinear behavior in the form of phase locking of the response to the input stimulus. Second, we wished to explore the response properties of RA afferents over a wide range of magnitudes and rates of change in both stress and strain (Looft 1994
; Rogers et al. 1996
). Periodic stimuli represented a small subset of the points in the space defined by the full range of those variables, whereas PGN stimuli presented a wide range of combinations (see Responses to PGN inputs) in a relatively short period.

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| FIG. 6.
Sample records of raw data (A) and cycle histograms (B) showing mechanical and neuronal responses to periodic stretching. All collected with a 1-Hz displacement-controlled triangle wave input. A: raw data showing stress (thin line), strain (heavy line), and spike potentials in 3 successive cycles. The minimum value of both stress and strain (note axis offset) is >0 because of displacement offsets during this run. B: representation of stimulus for 1 cycle. C: cycle histogram showing the location of occurrence of action potentials during stimulus cycle. Bin size, 10 ms.
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| FIG. 7.
Sample records of raw data showing mechanical and neuronal responses of a skin sample to stretching with the use of a PGN input. Thick line, strain; thin line, stress. Spike potentials are represented along the X axis. The segments that are displayed (2-s duration) are taken from different parts of the PGN sequence; time 0 on the X axis does not refer to the beginning of the run. A: force-controlled run; B: displacement-controlled run.
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| FIG. 8.
A: 3-D histograms drawn as surfaces, depicting the stimuli presented in single, data collection runs using displacement control (left panels; strain) and force control (right panels; stress). Each histogram shows the number of times (vertical axis) a given combination of strain (e) and de/dt (left panel) or stress (S) and dS/dt (right panel) were presented in 120 s. B: neuronal responses observed during the same run as in A, presented as color overlays on the histograms of part A. Colors represent the magnitude of neuronal response, which is expressed as the number of spikes associated with each stimulus combination during the run.
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PGN stimuli were used to characterize the properties of all afferents. The PGN stimulus waveform had a bandwidth of 0-20 Hz (Fig. 3). Frequencies between 10 and 20 Hz were less well represented than lower frequencies. Data collection runs were 120 s in duration. The PGN sequence had a duration of 30 s and it was repeated four times in each run. The magnitude of the variable that was under control was distributed normally, whereas the uncontrolled variable was skewed (Fig. 4) because of the nonlinearity of the stress-strain relationship for the skin.
Experimental procedure
The search stimuli for afferents were stroking the skin, stretching the skin, or actuating hairs (if present) on the skin surface. Units were sought in the central region of the tissue sample (Fig. 2B). When a suitable afferent was isolated, it was studied while the skin was subjected to stretching. All neurons were studied by using PGN inputs. The tissue was subjected to stretching along its long axis, with increments in either force or length tracking the PGN input signal. Intertrial intervals were 4 min, based on preliminary observations that showed the neuron response to be stable at that interval. Most neurons were studied with the use of both position- and force-controlled stimuli the biaxial stress loading condition along the transverse boundary.
Loads were converted to stresses by using measures of the clamp width (5 mm) and by using 0.3 mm as tissue thickness (Grigg 1996
). Displacements were converted to strains (
) with a Lagrangian formulation (
=
L/L0).
L was taken as the sum of the actuator displacement and the static offset displacement; L0 was the initial length of the sample (between the clamps). Rates of change of both stress and strain were determined by differentiating the stress and strain data files by using the LabView routine, Derivative.VI. Because there was significant noise in the load data for some runs, we filtered those load records with a moving average filter of rank 3 before differentiating.
The dependence of neuronal responses on either stress or strain was explored by calculating the cross-correlation product among the neuronal response record and normalized values of the corresponding stress and strain and their rates of change. The variables were normalized because the numerical magnitude of the cross-correlation product reflects the magnitude of the variables used in the calculation. In this application stresses were numbers on the order of 5-30 kPa and strains were numbers on the order of 0.0-0.4. Thus the magnitudes of the resulting cross correlations were not directly comparable. Consequently, stress and strain distributions were both normalized by using the expression x = (x
µ)/
. This normalization results in both distributions having the same mean value and the same variance so comparisons may be made between them. The normalized values were cross correlated with the neuronal spike record, which was assigned the value 1.0 (2-ms duration) when a spike occurred and 0 during interspike intervals (Looft 1994
). Thus the cross correlation yields a value that is proportional to the average magnitude of the stress or strain variable in relation to the time of occurrence of spikes.
 |
RESULTS |
Twenty-four RA afferents were recorded in six successful experiments. Locations of receptive fields of representative neurons are shown in Fig. 2B. Nine neurons were studied by using periodic stimuli, and all 24 were studied by using PGN stimuli. When stretched axially, skin displayed strongly nonlinear and time-dependant behavior. The relationship between stress and strain was evaluated during the application of triangle stimuli (Fig. 5). The general shape of the relationship shows the nonlinearity of the skin, and the hysteresis loop reflects the viscous component of tissue behavior. Stresses were greater during the loading than during the unloading hemicycle. There was evidence that the skin displayed stress relaxation in displacement-controlled runs and creep in force-controlled runs, as would be expected of a viscoelastic material. However, this complex mechanical behavior was not systematically studied.
Responses to periodic inputs
The typical response of an afferent when driven by a periodic input is shown in Fig. 6. In this example displacement was the controlled variable and the input waveform was a 1-Hz triangle wave. The nonlinear relationship between strain and stress causes the stress tracing (Fig. 6, A and B) to be strongly nonlinear. Neuronal responses were observed primarily on the upswing of load or position, when d
/dt and d
/dt were positive. However, some spikes did occur after the peak of the stimulus, as can be seen in the cycle histogram (Fig. 6B). The cycle histogram also shows that the two spikes observed in each cycle were phase locked to the stimulus cycle.
Responses to PGN inputs
The responses of neurons to the PGN stimuli were much more complex than responses to periodic stimuli. The response of one neuron to two segments of PGN sequences is shown in Fig. 7. Figure 7A was taken from a run conducted under force control; Fig. 7B was collected with the use of displacement control. These sequences illustrate the aperiodicity of the input waveform, the juxtaposition of stresses with different rates of change of stress, and the relationship of neuronal action potentials to the stimuli.
It is difficult to summarize the relationship between input stimuli and neuronal responses in the PGN runs because of the complexity of the PGN waveform. For example, during a stress-controlled run both stress and its rate of change vary constantly. Strain and its rate of change are also constantly varying and are nonlinearly related to the stress input. All combinations of stress and rates of change of stress and strain and also rates of change of strain are not equally likely during a run. Figure 8 is a graphical representation of the distribution of the magnitudes of stresses, strains, and their rates of change during a force-controlled data collection run lasting 120 s. The afferent discharge evoked by the stimuli in Fig. 8A is shown in Fig. 8B as a colored overlay (Rogers et al. 1996
). Neuronal responses are plotted on the actual stimulus distribution, because all combinations of stimulus variables are not equally likely. The height of the surface represents the stimulus distribution, and the colors represent the level of neuronal activity and indicate preferred stimulus combinations. The following several features of the neuronal response are of interest. First, neuronal responses were observed mostly when the rate of change of the stimulus was positive, but there was a significant response associated with 0 or negative rates of change. This effect is in contrast to the results from periodic stimuli, where responses were almost solely confined to the loading hemicycles. Observing a response when d
/dt or d
/dt is zero does not imply that the neuron had a response to static loading; it simply means that neural responses were observed around the peaks of dynamic stimuli and fell before, after, or at the peak of the stimulus. Static loading of the skin resulted in zero discharge from these afferents. Second, it is not possible to judge threshold relationships between stimulus intensity and neuronal response from a plot like Fig. 8. For example, in Fig. 8 few neuronal responses are observed below stresses of 15 kPa or >40 kPa. However, beyond those values few stimuli are presented, so there is limited opportunity for the neuron to respond. Furthermore, responses observed during low-stress stimuli appear to be affected by previous exposure to higher-stress stimuli. During the run represented by Fig. 8, the mean stress was ~24 kPa. If there are interactive effects between stimuli, then neuronal responses to low-stress stimuli could be suppressed by previous exposure to larger stimuli.
We undertook separate observations to explore responses to low-intensity stimuli. The goal was to test for responses to low-intensity stimuli in the absence of potential interaction effects caused by previous exposure to large stimuli. In these observations we studied single afferents in successive stress-controlled runs of 30-s duration in which the applied stresses differed between runs. Stimulus values in successive runs ranged from those low enough to evoke only a few responses to others that were high enough to evoke large numbers of responses. The results of one such experiment are shown in Fig. 9. In this figure the same afferent was studied in four PGN runs in which an identical input waveform was scaled to produce different mean stresses. The responses observed in three runs with mean intensities of 4, 7.5, and 15 kPa are shown (Fig. 9, top, A-C). At high-stimulus intensity (Fig. 9C) no responses are observed at stresses <12 kPa. Yet, when the magnitude of the stimulus was smaller (Fig. 9, A and B), many responses were observed at stresses between 5 and 12 kPa. At the lowest level of stimulation, in which the mean value of the stress input was 4 kPa, neuronal responses are observed at ~5 kPa. There was no consistent relationship between the appearance of threshold responses and any rate of change of stress. It is difficult to identify a threshold from these data, because it is unclear what criterion to use to define a threshold in the context of a PGN stimulus. However, the lowest stress at which action potentials were reliably elicited was ~5 kPa (Fig. 9). The number of neuronal responses observed during a single 30-s repeat of the input signal at different intensities for this neuron is shown in Fig. 9D. Stresses of 5 kPa were associated with axial strains in the range of 0.05-0.10. Experiments of this sort were done with five neurons, each having similar results.

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| FIG. 9.
A: 3-D stimulus histograms with neuronal responses depicted as colored overlays, similar to those in Fig. 8. A-C: mechanical and neuronal responses resulting from 3 force-controlled PGN runs of different stimulus intensity. Each run was 30-s duration. The PGN stimulus was scaled to a mean stress of 4 (A), 7.5 (B), and 15 kPa (C). D: number of action potentials recorded during data collection runs, plotted vs. the mean stimulus intensity during the run. Same experiment as A. * Data points that are represented by the histograms in A.
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Slow changes in the response of neurons were also difficult to study, owing to the constantly changing nature of the PGN stimulus. However, the PGN input signal repeated every 30 s, so it was possible to track long-term adaptation behavior of afferents by measuring the number of action potentials evoked during each repeat of the PGN input signal (Fig. 10). Every afferent displayed behavior similar to this. This adaptation behavior was not related to tissue stress relaxation because it was observed in both force- and displacement-controlled runs.

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| FIG. 10.
Slow adaptation of responses of RA afferents. Vertical axis, average number of action potentials evoked during successive 30-s presentations of the same PGN stimulus during a single run of 120-s duration. Two runs (force- and displacement-controlled) are illustrated.
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One reason for undertaking these experiments was to explore the relationship between neuronal responses and tissue stress and strain variables. This relationship was analyzed by computing the cross-correlation products between neuronal responses and various candidate input variables. Cross-correlations were calculated by using data only from the last 30 s of each run, when the creep or stress relaxation behavior would be at a minimum. We computed the cross-correlation product between neuronal response and the normalized values of
, d
/dt,
, and d
/dt for each run for each neuron. Figure 11 shows the cross-correlation product computed among neuronal response and stress and strain for a typical neuron. The peak occurs around 0 ms, indicating that the strongest relationship is between the spikes and the value of the input variables at the time of occurrence of the spikes. The peak values of the cross-correlation products were averaged across all neurons (Fig. 12). Spike responses were strongly associated with stress (
) and d
/dt, and more poorly associated with strain (
) and d
/dt. Each stress variable was significantly greater than either strain variable (t-tests; P < 0.001).

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| FIG. 11.
Cross-correlation products calculated between the neural response record and strain (left panel) and stress (right panel). Horizontal axis, location of data value with relation to spike. Positive values represent stresses or strains before the spike; negative values represent stresses or strains after the spike.
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| FIG. 12.
Mean value (n = 24 neurons) of the peak value of X correlation products, calculated between the spike train and strain (E), dE/dt, stress (S), and E, dE/dt, S, and dS/dt. Values for both S and dS/dt are significantly greater than for either strain variable (P < 0.001).
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The relationship among stress, strain, and neuronal responses was also explored in the experiments in which the edges of the tissue were constrained. Constraining the sides of the tissue sample had, expectedly, a strong effect on the relationship between stress and strain along the direction of stretch (Grigg 1996
). With the edges constrained, a given axial stress caused a much smaller axial strain. Figure 13 depicts the responses of an RA afferent studied under force control with both uniaxial and biaxial stress paradigms. The axial stresses expectedly were equal in the two cases because force was the controlled variable in both runs. In contrast, the axial strain was much greater in the uniaxial stress experiment. Neuronal responses were observed at approximately the same axial stress in the two experiments but at different axial strains depending on whether the run was done with the uniaxial or biaxial stress paradigm. Thus these experiments support the contention that RA afferents are stress sensors. Similar trials were done with five afferents and all displayed the same behavior.

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| FIG. 13.
Responses of a single afferent during 2 runs (both force-controlled) with different loading conditions along the transverse axis. Biaxial stress, sides constrained; uniaxial stress, sides unconstrained during axial stretching. Total number of action potentials elicited from the neuron was approximately equal in both cases. A: data from both runs, plotted vs. axial stress. B: same neuronal response data, plotted vs. axial strain.
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DISCUSSION |
One goal of these studies was to work out the relationship between the responses of RA afferents in hairy skin and the magnitude of mechanical states caused by controlled dynamic stretch stimuli. A unique relationship between neuronal response and either stress or strain can be established only if stress and strain are somehow decoupled from each other in the experimental design. In these experiments, stress and strain were decoupled by viscoelastic effects associated with dynamic stretching; i.e., a given strain would be associated with different stresses depending on the strain rate. In addition, stresses and strains were decoupled from each other by long-term relaxation processes related to tissue viscoelasticity. Relaxation processes decouple stress and strain because in force-controlled runs, stresses are controlled while the tissue creeps (i.e., it elongates throughout the run). In displacement-controlled runs the strains are controlled while the stress relaxes throughout the run. In both stress- and strain-controlled experiments we found that neuronal responses were more strongly associated with stress than with strain. The dependence of neural responses on stress rather than strain is also strongly suggested by the results of the experiments in which the transverse loading of the sample was varied. Constraining the sides of the tissue while stretching it axially allowed a particular level of axial stress to be associated with two very different values of axial strain. The results showed that the neuron's response tracked the stress and not the strain. However, because the magnitudes of all of the components of stress and strain were not known in those experiments, the latter results can only be taken as suggestive.
Although the role of axial stress in determining the discharge of RA afferents is strongly suggested by the results of the biaxial experiments, the resolution of the role of transverse stresses and strains must await experiments in which those variables are directly measured and controlled.
It has been shown (Edin 1992
) that the responses of cutaneous RA afferents are related to skin strains caused by moving fingers. When skin is stretched, however, both stresses and strains are produced; although neuronal activation is closely related to the strains, it may still be driven by stress. An advantage of the in vitro preparation used here is that the stresses can be measured as well as the strains.
A caveat concerning the association of neuronal responses with mechanical variables is that the association with stress is based partly on cross-correlation measures. Cross correlation is based on the assumption that there is a linear relationship between the variables that are cross correlated. There is ample evidence, relating to interactive effects among stimuli, that the current system has nonlinear components. A full description of the relationship between neuronal responses and mechanical variables awaits analyses with suitable nonlinear methods (e.g., Fuller et al. 1991
). These caveats notwithstanding, the findings that RA afferents sense stress rather than strain is consistent with a number of other observations from this laboratory (Fuller et al. 1991
; Grigg 1996
; Khalsa et al. 1997
), determining that stretch-evoked neural afferent activity is associated with stress rather than strain variables. Although the mechanism underlying stress sensing is unknown, it appears to be a pervasive phenomenon in stretch-sensitive afferents.
In characterizing the properties of afferent neurons we relied primarily on experiments that used PGN stimuli. It might be argued that periodic stimuli are more appropriate for characterizing afferent neurons in this system. After all, periodic stimuli can be appropriate for systems that experience periodic inputs (Morley and Goodwin 1987
) and skin is subjected to periodic stretching during locomotion. However, high frequencies were required to activate RAs, and the stimuli that were effective seemed to have little relationship to locomotion. Furthermore, the experiments in which we used sine and triangle stimuli showed strong phase locking in the neural responses, typical of that shown by others (Harrington and Merzenich 1970
). By using PGN stimuli we were able to explore relationships among neuronal responses and stress and strain and also their rates of change without nonlinear, phase-locking behavior, without performing large numbers of trials in which the amplitude and frequency of periodic stimuli were varied.
Knowing the skin stresses and strains that can evoke activity in RA afferents, it is possible to use data published elsewhere to estimate responses that would result from leg rotations. The magnitudes of strains that are produced in this region of skin when the hindlimb is rotated are known (Grigg 1996
). It is a limitation that whereas stress appears to be the key variable for activating afferents, only strains can be measured in the intact skin. Nonetheless, it is possible to use skin strain measures to infer whether limb movements might activate RAs and thus infer what sensory role RA afferents in this region of the skin might play in the rat. Rotating the leg from neutral to full extension caused a strain of 13% along the long axis of the leg and a negative strain of 14% along the orthogonal direction (Grigg 1996
). This is very similar to the pattern of strains that we observed in the uniaxial stress experiments (i.e., axially stretching the sample with its sides unconstrained). In those experiments there were positive strains along the Y direction (conforming to the long axis of the leg) and negative strains along the X direction. In the uniaxial stress experiments, runs in which strains were on the order of 15% resulted in poor or nonactivation of afferents. In the example of Fig. 13, a uniaxial stress stimulus that effectively activated an afferent was associated with an axial strain of ~40%, which is far greater than strains caused by limb rotation. In addition, RA activation was generally associated with high rates of change of stress or strain. Casual observation of rat locomotion suggests that such rapid movements are infrequent. Thus on balance it seems that in rat skin, RA afferents probably play little if any sensory role in signaling skin stretch associated with limb movements. This is in sharp contrast to the properties of RA afferents in hairy skin in the dorsum of the hand (Edin 1992
; Edin and Abbs 1991
) and in the face of humans (Nordin 1994
; Nordin and Hagbarth 1989
; Nordin and Thomander 1989
). In both of these locations, hairy skin RA afferents have an RA response to stretch caused by small, local traction stimuli or by local movements that would stretch the skin. It is clear that in the hand, cutaneous afferents (including RAs) in hairy skin play a role in signaling movements (Edin and Johansson 1995
). It has been suggested that afferent neurons in the face and in the hand's dorsum may not be totally representative of afferents in hairy skin elsewhere on the body (Vallbo et al. 1995
). Although that may be, we emphatically point out that the stretch sensitivity of afferents in rat hairy skin are most certainly different from those in the above cases, and we urge caution in extrapolating from our results to other species. In the human face and hand, for example, the material properties of the skin are undoubtedly different from the very soft skin found in rats. It has been suggested (Pubols and Benkich 1986
) that mechanical properties of the skin shape the response of cutaneous afferents. For example, the stiffer the skin, the smaller would be the strains required to reach a threshold stress value.
We found that RA afferents have a slow component of adaptation in addition to their well-known rapid adaptation behavior. When the same PGN stimulus sequence was repeated sequentially, the neuronal response declined with successive repeats. This behavior was not due to long-term mechanical responses of the skin, such as creep or stress relaxation, because it was observed equally in force- and displacement-controlled runs. Thus the mechanisms underlying this behavior, although unknown, appear to be based on neuronal rather than mechanical mechanisms.
The skin was depilated in all experiments. Depilation was necessary because in this in vitro preparation any hair left on the skin formed a wet mat that precluded either identifying or adequately stimulating any units. One concern was that the depilatory could change the properties of the units. However, others (Aoki and Yamamura 1977
; Burgess et al. 1968
) studied hair afferents after depilatory treatment and did not report any effect of the depilatory. Furthermore, we studied some units with and without hairs and found no difference in their properties. A second concern regarding depilation was that it made it impossible to determine the absolute identity of any afferents. It has been reported (Burgess et al. 1968
) that stretch sensitivity was observed in G2 and D hair units as well as in Field units, all of which were RAs when studied with indentations or stroking. Furthermore, it has been reported (Burgess et al. 1974
) that G1 and G2 units could be activated by rapid movements (indentations) of the skin. Thus our sample could contain D, G1, or G2 hair units as well as field units. Even in the several experiments where some afferents were activated by moving residual hairs, we cannot rule out the possibility that the units were field units that are sensitive to hair movements (Burgess et al. 1974
). Therefore all units were classified solely on the basis of their RA response.
 |
ACKNOWLEDGEMENTS |
We are grateful to P. Tilander for technical assistance in constructing the apparatus.
This work was supported by National Institute of Neurological Disorders and Stroke Grant NS-10783. Z. Del Prete was supported in part by the Italian Consiglio Nazionale delle Ricerche.
 |
FOOTNOTES |
Address for reprint requests: P. Grigg, Dept. of Physiology S4-245, University of Massachusetts Medical School, 55 Lake Ave. N., Worcester, MA 01655.
Received 3 November 1997; accepted in final form 5 May 1998.
 |
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