Brain Research, 548 (1991) 228-241 © 1991 Elsevier Science Publishers B.V. 0006-8993/91/$03.50 ADONIS 0006899391165794

228

BRES 16579

Cutaneous afferent activity in the median nerve during grasping in the primate Theodore E. Milner, Claude Dugas, Nathalie Picard and Allan M. Smith Centre de recherche en sciences neurologiques, Universit~ de Montrdal, Montrdal (Canada)

(Accepted 4 December 1990) Key words: Hand; Cutaneous; Grasping; Median nerve; Cuff electrode

Neural activity was recorded from the median nerve of a monkey during grasping and lifting, using a chronically implanted cuff electrode. At the onset of lifting, there was an initial dynamic response during which the intensity of the neural signal increased rapidly. This neural response attained its peak value well before the displacement, the load force or the grip force. The time course and peak of the rectified, integrated neurogram were best correlated with the rate of change of grip force. The neural activity declined exponentially to a steady value following the initial peak. During steady holding the mean amplitude of the neurogram was best correlated with the mean grip forcei At the end of the holding phase there was a short burst of neural activity as the monkey relaxed the grip force and released the object. During some blocks of trials pulse perturbations were applied to the object. When the monkey did not increase the grip force in advance of the perturbation, the perturbation produced a relatively large displacement of the object and a burst of neural activity whose onset coincided with the onset of displacement. When the monkey anticipated the perturbation by increasing the grip force during the holding period preceding the perturbation, the perturbation produced a relatively small displacement and relatively little increase in neural activity. INTRODUCTION The hand is often characterised as much by its sensory as its motor function. Cutaneous afferents in the glabrous skin of the hand provide detailed sensory information necessary for the precise manipulation of objects. This is clearly demonstrated by the loss of dexterity that accompanies cutaneous anesthesia s . The presence of cutaneous receptive fields in the hand representation areas of both the m o t o r cortex 16 and the cerebellum 5 confirms that such afferent information has the potential to play an important role in the modulation of motor commands. In studying neural activity in these motor centers, it is therefore important to have a means of monitoring the cutaneous sensory inputs. Furthermore, a knowledge of the task variables that correlate best with the cutaneous activity would provide an indication of what features of the task are encoded at the periphery and are therefore most likely to be represented centrally. It has been possible to study the information signalled by cutaneous mechanoreceptors since the advent of techniques for recording from single afferent fibers in peripheral nerves 19. Initially, these studies were very restrictive and examined only very simple tasks. Recently though, Westling and Johansson 21 developed a behav-

ioral paradigm that more fully exploited the richness of the motor and sensory capacity of the hand. They began by characterizing the mechanical and electromyographic events that comprise the grasp and transport of an object, then continued their investigations with microneurographic recordings of the associated activity of cutaneous afferents innervating the glabrous skin of the hand 9'22. In these studies they characterized the activity of 4 types of mechanoreceptors (FA I, FA II, SA I and SA II). These studies showed marked dynamic responses in F A I and SA I units at the onset and termination of grip, maintained discharge of SA I and SA II units during steady grip and transient responses of FA II units to vibratory stimuli. While microneurography is able to provide a detailed picture of single-unit activity in the peripheral nerves of human subjects, it has at least two significant drawbacks: the population of units that one can sample is relatively small and the recording electrode is easily dislodged if there is any significant m o v e m e n t of the surrounding tissue. The necessity of restricting movement constrains the range of behavior that can be investigated. Although these limitations are unavoidable in studies with human subjects, they can be circumvented in studies with behaving animals where one has the option of chronically

Correspondence: T.E. Milner, Institut de r6adaptation de Montr6al, Centre de recherche, 6300 Darlington Avenue, Montr6al, Qu6. H3S 2J4,

Canada.

229 i m p l a n t i n g r e c o r d i n g d e v i c e s such as n e r v e cuff electrodes

TM.T h e

n e r v e cuff gives a m o r e g l o b a l p i c t u r e of t h e

n e u r a l activity t h a n t h e m i c r o e l e c t r o d e , t r a d i n g r e c o r d i n g selectivity for g r e a t e r f r e e d o m o f m o v e m e n t w i t h t h e a d d i t i o n a l a d v a n t a g e o f p e r m a n e n c e . T h e cuff e l e c t r o d e r e c o r d s f r o m essentially the s a m e p o p u l a t i o n o f fibers as l o n g as it r e m a i n s s e r v i c e a b l e - - usually for a p e r i o d of several months. W e h a v e r e c e n t l y b e e n investigating the roles o f t h e m o t o r c o r t e x and c e r e b e l l u m in the c o n t r o l of p r e c i s i o n grip b y t r a i n i n g m o n k e y s to p e r f o r m a grasping a n d lifting task similar to that o f W e s t l i n g and J o h a n s s o n 21. In o r d e r to m o n i t o r t h e c u t a n e o u s a f f e r e n t activity a s s o c i a t e d with the task w e i m p l a n t e d a cuff e l e c t r o d e a r o u n d the m e d i a n n e r v e and r e c o r d e d activity o v e r a p e r i o d o f s e v e r a l m o n t h s . W e r e p o r t h e r e the m a i n f e a t u r e s o f t h e activity of t h e p o p u l a t i o n o f r e c o r d e d n e r v e fibers. O u r o b s e r v a t i o n s are c o n s i s t e n t with w h a t w o u l d be e x p e c t e d f r o m a s u m m a t i o n o f the r e s p o n s e s of t h e d i f f e r e n t classes o f cutaneous

afferents

documented

by

Westling

and

J o h a n s s o n 22. In e x a m i n i n g the c o r r e l a t i o n b e t w e e n the n e u r a l signal and v a r i o u s task v a r i a b l e s , we f o u n d that t h e n e u r a l signal was m o s t closely r e l a t e d to grip f o r c e d u r i n g t h e static h o l d i n g p h a s e , but was b e t t e r c o r r e l a t e d with t h e rate of c h a n g e of grip force during t h e d y n a m i c p h a s e of lifting. A p r e l i m i n a r y r e p o r t o f the results has a p p e a r e d in abstract f o r m 15.

MATERIALS AND METHODS One adolescent female Macaca fascicularis monkey weighing approximately 3 kg was used in this experiment. The monkey was the subject of two parallel studies: median nerve recording and single-unit recording in motor cortex. Only the former investigation is reported here.

Task and apparatus Briefly, the monkey was trained to pinch an object (a metal tab which resembled a clothespeg), lift it (together with an attached load) and hold it in a target window for 1 s. The object moved up and down a vertical rod on a set of bearings and was instrumented with transducers to measure the grip force, load (lifting) force and displacement 5. The load force transducer consisted of a strain gauge mounted on a thin strip of metal in series with the load. Because there was some free play in the bearings, it was possible for the monkey to exert a small bending moment on the load force transducer. In some data records there was a slight decline in load force during the holding period that resulted from unloading of the strain gauge by bending. Between trials the monkey was obliged to release the object and leave it in its initial resting position for 1.5 s. In general, the monkey released the object as soon as the end of the holding period was signalled by delivery of a juice reward. The monkey used a lateral pinch, gripping the object between the volar surface of the thumb and the lateral surface of the index finger. The surface area of the object (approximately 4 cm 2) was considerably larger than the contact area of the object and skin. The lifting movement consisted of a combination of wrist extension and radial flexion. The load and the texture of the gripping surface could be varied. Loads of 15 g, 65 g and 115 g were used. Four different gripping surfaces were

tested, of which two were used primarily: smooth metal (polished aluminum) and fine sandpaper. These are referred to as the smooth surface and rough surface, respectively throughout this report. Two other surfaces which were used on occasion were rough sandpaper and Velcro. The apparatus was also equipped with a solenoid that could be activated under computer control to produce a brief (100 ms) tug on the object, thus augmenting the load force. These perturbations were applied after 750 ms of holding. Blocks of perturbation trials were preceded and followed by blocks of trials without perturbations. The number of trials in each block depended on the monkey's success rate. At least 25 successful trials were normally recorded in one block before beginning the succeeding block.

Nerve cuff A nerve cuff for chronic implantation on the median nerve was fabricated using a technique similar to that of Jnlien and Rossignol 1°. The cuff consisted of a shell with a U-shaped cross-section and 3 equally spaced wires inside the shell, running transverse to its long axis. The shell was formed from a dental polymer, Reprosil. The inside diameter of the shell was 3 mm and its length was 15 mm. One wire was placed at the midpoint of the cuff and the others 5 mm to either side of the midpoint. Teflon-insulated multi-stranded stainless steel wire (120 ~m diameter) was used. The insulation was removed only from the portion of the wire inside the shell. Each wire had a 90* bend so that it ran along the outer surface, parallel to the long axis of the shell. The 3 wires were bonded to the outer surface of the shell with a thin layer of polymer.

Surgical preparation The monkey was anesthetized with sodium pentobarbital anesthesia (30 mg/kg) and the nerve cuff was surgically implanted around the median nerve under aseptic conditions. An incision was made along the midline of the inside surface of the forearm, several centimeters proximal to the radio-carpal joint of the wrist. The flexor digitorum superficialis muscle was retracted to expose the median nerve which runs along the underside of the muscle. The nerve was dissected free of the muscle, carefully laid into the cuff and covered with a layer of absorbable gelatin sponge, after which the cuff was sealed with a thin layer of polymer. In order to keep the wires leading from the cuff parallel to the nerve, they were loosely sutured to fascia at several points below the elbow. The cables were led under the skin and emerged at the head where they were soldered to a connector along with a ground wire. After insulating the connections in epoxy, the connector was fixed to the skull with cranioplastic cement. Recording from the nerve cuff began two days after surgery and continued daily for the entire 3-month period of motor cortical recording.

Recording procedures The nerve cuff was used in a tripolar configuration, recording differentially between the center contact and the two outer contacts, which were shorted together. The neural signal was fed into a preamplifier with a passband of 100 Hz-10 kHz, through a second-stage amplifier to a high-pass filter (8th-order Butterworth; cut-off 1500 Hz) then rectified and integrated (r = 10 ms). Position, load force, grip force and the rectified, integrated neurogram were digitized at 250 Hz. When the neurogram was digitized for spectral analysis, it was fed directly from the second-stage amplifier (passband 100 Hz-10 kHz) to the computer where it was sampled at 20 kHz.

Electromyographic recording On several occasions EMG was recorded from muscles innervated by the median nerve (iumbricals, opponens and abductor pollicis), using a pair of fine wires inserted percutaneously into the muscle. The location of the wires was verified by using them as stimulating electrodes and observing the resulting muscle contraction. The

230 EMG signals were amplified and filtered (10-3000 Hz) before being recorded on tape (bandwidth 0-5 kHz) together with the neurogram. The recorded signals were later digitized at 5 kHz for analysis.

v o l a r surface of the t h u m b and first t w o fingers while the

Data analysis

key was actively g r i p p i n g a n d lifting. T h e p o w e r s p e c t r u m

In order to establish which mechanical parameters were best correlated with the neural activity during the lifting phase and the holding phase, 10 recording sessions spanning approximately a two-month period were selected. The selection procedure consisted of first scanning the data to ensure that displacement, load force and grip force records were stationary within a 5% window during the holding phase. Enforcing such a criterion excluded any data records where the load force declined artificially due to bending of the strain gauge as described above (Task and Apparatus). If there were at least 8 trials per load condition that met the criterion during a recording session then it was included in the sample for regression analysis. The rectified, low-pass filtered neurogram was smoothed using a digital filter with a low-pass cut-off frequency of 20 Hz and the grip force and load force were differentiated using a digital filter with the same cut-off frequency. The data from each session were analyzed separately and linear regression coefficients were computed during both the lifting and holding phases for each of the following relations. During the lifting phase correlations were calculated between: (1) peak neural activity and peak grip force; (2) peak neural activity and peak load force; (3) peak neural activity and peak rate of change of grip force; (4) peak neural activity and peak rate of change of load force. A 400 ms period immediately preceding the reward was chosen during the holding period and correlations were calculated between: (1) mean neural activity and mean grip force; (2) mean neural activity and load force. This period comprises the interval between times 72 and 73, illustrated in Fig. 1 for the grip force and neurogram. Several additional analyses were used in order to establish which mechanical parameters most closely followed the neurogram during the lifting phase. First, a baseline value was computed during the period before the initiation of lifting, then a threshold was set and the time of threshold crossing was determined for each parameter. The threshold was taken as the baseline value plus 10% of the difference between the peak and baseline values, shown as time TO in Fig. 1. The time of occurrence of the neural peak and the peaks of the grip force, load force and their rates of change were also determined (time T1 in Fig. 1). Finally, the neurogram and the records of each of the mechanical parameters were normalized to their peak values and the area between the neurogram and the curve corresponding to each of the mechanical parameters was then computed over a fixed interval. The interval began at the threshold crossing in the neurogram and ended at the neurogram peak. A difference index was calculated from the area of the region between the curves, as illustrated in the case of the rate of change of grip force in Fig. 1. The more closely the neurogram tracked a particular mechanical parameter over the interval, the smaller the difference index.

muscles in the f o r e a r m a n d h a n d w e r e r e l a x e d . W e t h e n r e c o r d e d the E M G - c o n t a m i n a t e d

signal while the m o n -

m a g n i t u d e s of t h e two signals are s h o w n in Fig. 2. W h e n the signals are high-pass f i l t e r e d at 500 H z , the p e a k in the p o w e r s p e c t r u m o f the p u r e n e u r a l signal a p p e a r s at a b o u t 2000 H z w h e r e a s that of the c o n t a m i n a t e d signal is close to the c u t - o f f f r e q u e n c y with n o clear s e p a r a t i o n between EMG and neural components. However, when the signals are high-pass filtered at 1500 H z the p o w e r spectra are n e a r l y identical, e v e n w i t h o u t n o r m a l i z a t i o n (not s h o w n ) , indicating that t h e E M G c o n t a m i n a t i o n has b e e n a l m o s t c o m p l e t e l y r e m o v e d f r o m the n e r v e cuff signal. Since the E M G

c o n t a m i n a t e d signal, high-pass

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Fig.]. Gripforce,rateofchangeofgripforceandtherectified,

lifting, E M G

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s t u d i e d t h e f r e q u e n c y c o n t e n t of the signal r e c o r d e d by the n e r v e cuff in o r d e r to establish a high-pass cut-off f r e q u e n c y that w o u l d r e m o v e m o s t o f t h e l o w e r freq u e n c y E M G c o n t a m i n a t i o n w i t h o u t also r e m o v i n g a significant p o r t i o n of the n e u r a l signal. W e first r e c o r d e d a p u r e n e u r a l signal by s t r o k i n g the

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integrated neurogram are illustrated in the top 3 traces. The point at which each parameter crossed its 10% threshold is indicated by the time TO. The peak occurred at time TI. The 400 ms interval during the holding period chosen for the calculation of the static values is delimited by 1"2 and 73. The neurogram (thick line) and rate of change of grip force (thin line) are shown after normalization in the lower portion of the figure on an expanded time scale. The times TO and T1 refer to the onset and peak of the neurogram. The shaded area between the curves was calculated as a measure of their difference.

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Fig. 2. Comparison of the nerve cuff power spectra using 500 Hz (left) and 1500 Hz (right) high-pass cut-off frequencies. The sharp-peaked spectrum on the left side of the figure is that of a neural signal recorded while the monkey was exerting a steady grip force (forearm muscles active). The broader spectrum is that of a neural signal recorded while the fingers were passively stroked (forearm and hand muscles completely relaxed). Top: power spectra normalized so that total areas are equal (N.B. ordinate scales are x 10). Bottom: power spectra normalized so that peaks are equal to 1.

filtered at 1000 Hz, still had a considerable fraction of its power at lower frequencies than the pure neural signal, a high-pass cut-off of 1500 Hz was used in all subsequent recording. The neurogram recorded during gripping and lifting generally consists of both sensory and motor activity. By brushing the fingers and palm we mapped the cutaneous territory innervated by the median nerve distal to the

cuff. It consisted of the volar surface of the thumb, the first two fingers, the medial portion of the 4th finger and the portion of the palm medial to the 4th finger. We did not study the m o t o r innervation, but assumed that the innervated muscles were those commonly reported in anatomy textbooks, namely the first two lumbricals, the opponens, the abductor pollicis and the flexor pollicis brevis. The time course of the neural activity and the

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Fig. 3. Median nerve activity and EMG of intrinsic hand muscles. Displacement, load force and grip force are displayed in the top 3 traces. E M G from the opponens muscle (left) and first lumbrical (right) is shown in the 4th trace with the median nerve activity below.

EMG is compared in Fig. 3 for one of the lumbrical muscles and the opponens. The abductor pollicis, which is not shown, was activated earlier when the monkey opened the hand before grasping. For the two muscles shown in Fig. 3, the neural activity and the EMG tended to increase in parallel. However, when the monkey moved the fingers between trials without contacting an object (not shown) we detected no appreciable change in neural background activity, suggesting that most of the modulation in the neural signal was of cutaneous origin. Fig. 4 presents a general picture of the time course of neural activity and mechanical parameters obtained with 3 different loads and surface textures. The displayed records are the averages of 25 successful trials for each condition. There was a stereotyped sequence of events during execution of the task which we have grouped into 3 phases: lifting, holding and release. During lifting, the grip force intially rose as the monkey began to pinch the metal tab. When the grip force reached a value sufficient to prevent slipping, the

monkey began to generate a load force. Vertical movement of the object occurred as soon as the load force surpassed the weight of the load. The monkey stopped lifting when the target window was reached, indicated by a tone, and thereafter maintained the object at a constant position by exerting a steady load force. Maintenance of the target position corresponded to the onset of the holding phase. During this period the grip force sometimes declined. Finally, during the release phase at the end of the trial, the grip force was suddenly relaxed allowing the object to slip and fall back to its initial position. The sequence of changes in the mechanical parameters during the lifting phase is clearly indicated in Fig. 3, which shows the initial increase in the grip force followed in rapid succession by changes in the load force and the displacement. The time between the 10% threshold of the grip force and its peak was approximately 100 ms. Movement was carried out equally rapidly with the result that there was often a substantial overshoot in the load force as the object accelerated and then decelerated

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Fig. 4. Averaged records of displacement, load force, grip force and rectified integrated neural activity for 3 loads and surface textures. Each trace represents the mean of 25 successful trials. The thick line represents trials performed with a 15 g load, the intermediate line trials performed with a 65 g load and the thin line trials performed with a 115 g load. The loads are indicated with the corresponding neurograms (from the bottom up: 15 g, 65 g and 115 g, respectively). TABLE I

before arriving in the target window. Certain features consistently appeared in the rectified, integrated neurogram regardless of the surface texture of the grasped object or its weight. At the onset of lifting, the neurogram demonstrated an initial dynamic response, during which the intensity of the neural signal increased rapidly. The neural response attained its peak value well before any of the measured mechanical parameters. The median nerve activity then declined in an exponentiallike fashion to a steady value following the initial peak. The decline occurred over a longer time period than the initial rise. The neurogram achieved its steady value after the mechanical parameters had reached plateau values during the holding phase of the task. There was a short burst of neural activity during the release phase before the neurogram returned back to baseline level. The time at which the 10% threshold was crossed at the onset of the dynamic response during the lifting phase was compared with the threshold crossing for changes in the grip force, load force and their first derivatives. Since the initial change in neural activity was always detected before any change in the mechanical parameters, the

Relative timing of neural and mechanical events

R = rough surface, S = smooth surface, GF = grip force, LF = load force, RGF = rate of change of grip force, RFL = rate of change of load force. Surface texture

Mechanical parameter

Mean delay (SD) (ms)

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Delay between threshold crossing (TO in Fig. 1) for neurogram and the indicated mechanical parameter: R GF 117 (44.3) 355 S GF 119 (51.2) 270 R LF 140 (49.4) 355 146 (54.0) 270 s LF R RGF 75.5 (48.3) 355 S RGF 78.0 (51.9) 270 R RLF 109 (49.8) 355 116 (52.7) 270 S RLF Delay between peak (T1 in Fig. 1) in neurogram and the indicated mechanical parameter: R GF 361 (216) 355 s GF 351 (230) 270 81.8 (48.4) 355 R LF S LF 94.6 (51.8) 270 R RGF 10.1 (37.7) 355 S RGF 10.4 (35.8) 270 22.6 (49.8) 355 R RLF S RLF 24.7 (37.6) 270

234 time difference was expressed as a delay with respect to the detection of the neural threshold (Table I), The rate of change of grip force was the first parameter to increase following the onset of neural activity. The mean delay was approximately 75 ms. The time of peak neural activity during the lifting phase was also compared with the times of the peaks of the mechanical parameters. The peak rate of change of

grip force most nearly coincided with the neural peak, followed closely by the peak rate of change of load force (Table I). The delay between the neural peak and the peak rate of change of grip force was approximately 10 ms.

Linear regression analysis was carried out separately for trials performed with the smooth, polished metal surface and the rough, sandpaper surface. The 3 load

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Fig. 5. Top: regression lines computed for the relation between mean rectified, integrated neural activity and mean grip force during the holding phase (open circles: polished metal surface; filled circles: fine sandpaper surface). Bottom: regression lines computed for the relation between peak rectified, integrated neural activity and peak rate of change of grip force during the dynamic phase of lifting. Data on the left were taken from recording sessions approximately one month after cuff implantation, while data on the right were taken from recording sessions after 3 months of implantation. Note the different ordinate scales.

235 conditions (15 g, 65 g and 115 g) were combined for each selected recording session to give a total of 25-45 trials for the computation of each regression line. For the lifting phase, the neural peak was best correlated with the peak rate of change of grip force (mean regression coefficient i = 0.57 (S.D. = 0.12) for 8 recording sessions with the rough surface; f = 0.67 (S.D. = 0.067) for 6 recording sessions with the smooth surface). It was less correlated with the peak rate of change of load force and least correlated with the peak grip and load forces. The slopes and intercepts for the relation between the neural peak and the peak rate of change of grip force were determined to be significantly greater than zero (P < 0.01) on the basis of rejection of the null hypothesis using Student's t-distribution. However, there was a gradual decline in the amplitude of the neurogram over the course of the first two months following implantation which resulted in lower values for the slopes (P < 0.05) and intercepts (P < 0.001) for later recording sessions as shown in Fig. 5. The strong coupling between the neurogram and the rate of change of grip force is also indicated by the similarity of the time course of the neurogram over the interval between the 10% threshold and peak (Table II). The difference index was smallest for the rate of change of grip force, while the other parameters ranked in the same order as above for the linear regression analysis. The similarity of the neurogram and the rate of change of grip force is also illustrated in Fig. 1, where there are two distinct peaks in the neurogram during the lifting phase which correspond closely to peaks in the rate of change of grip force. Such a two burst structure was observed in a number of trials where there were two clear peaks in the rate of change of grip force.

The initial dynamic response during lifting was always strongly correlated with the rate of change of grip force, but following the initial response the neural activity could be influenced by the load force independently of the grip force, as illustrated in Fig. 6. On this particular trial, movement to the target occurred in 3 segments which are clearly evident as 3 distinct peaks in the neurogram. Although the segmentation is not apparent in the grip force, it can be seen as 3 distinct peaks in the load force. The first neural peak is associated with initial contact and gripping of the object while the second and third neural peaks are aligned with peaks in the load force which occur approximately at the midpoint of the first and second movement segments, respectively. There is also a clear burst of neural activity when the object is released. During the static holding phase of the trial, the grip force normally increased with the magnitude of the load: the greater the load, the greater the grip force. For the

DISPLACEMENT (mm) 10

0 LOAD FORCE (N)

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TABLE II Comparison of time course of neural activity and mechanical parameters

MEDIAN NERVE

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Mechanical parameter

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N

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236 same load, but different surface textures, the monkey tended to use a greater grip force for the smoother surface because the lower coefficient of friction of the smoother surface required the monkey to grip harder in order to prevent slipping. However, the grip force used for the same load and surface texture could vary considerably from trial to trial. Thus, the neural activity during the holding phase was well correlated with grip force (~ = 0.64 (S.D. = 0.12), N = 8, rough surface; f = 0.70 (S.D. = 0.15), N = 6, smooth surface), but poorly correlated with load force (f = 0.14 (S. D. = 0.36),

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0.5s PERTURBATION REWARD Fig. 7. Averaged records of displacement, load force, grip force and rectified integrated neural activity for blocks of trials before, during and after application of perturbations. Each trace represents the mean of 25 successful trials. The thick line represents an initial block of control trials where no perturbation was applied, the intermediate line represents trials where a perturbation was applied on each trial and the thin line represents a Second block of control trials performed immediatelyafter the block with perturbations. The first vertical line indicates the time at which the perturbation was applied. The corresponding neurograms are displayed in the lower half of the figure. Note the build up of grip force in anticipation of the perturbation and the grip force response following approximately 100 ms after the perturbation.

N = 8, rough surface; ~ = 0.36 (S.D. = 0.33), N = 6, smooth surface). Again the slopes and intercepts for the relation between the neural activity and the static grip force were always found to be significantly greater than zero (P < 0.0025), although the values were significantly lower (slopes P < 0.025; intercepts P < 0.001) for the later recording sessions (Fig. 5). In Fig. 7 we compare the neural activity in unperturbed trials and in trials in which a perturbation was applied during the holding phase. The perturbation consisted of a brief (100 ms) pulsatile increase in load force. The monkey was given a block of trials without perturbation followed by a block of trials where a perturbation was delivered on every trial and then a second block of trials without perturbation. Because the monkey could anticipate the perturbation, a strategy was eventually adopted in which the grip force was steadily increased during the holding phase until the perturbation occurred. This anticipatory activity also involved increased cocontraction of forearm muscles which stiffened the wrist. The preparatory strategy became more pronounced with experience over the 4 month recording period. Initially, the perturbation produced a relatively large displacement of the object and a concomitant burst of neural activity, followed by a rapid increase in grip force (Fig. 8, left). With practice, the monkey learned to anticipate the perturbation effectively. Consequently, the perturbation produced a relatively small displacement and there was relatively little increase in neural activity or grip force following the perturbation (Fig. 8, right). In Fig. 8 (right) the monkey briefly touched the object about 300 ms in advance of the onset of lifting. Although, barely perceptible in the grip force record, contact with the object produced a marked burst of neural activity which could be clearly distinguished from the main burst during lifting. With increased grip force, the mean level of neural activity during the holding phase was higher for the perturbed trials than for the unperturbed trials. The neural response to the perturbation consisted of a burst of activity whose onset coincided with the onset of displacement and increase in load force produced by the perturbation. The burst reached its peak at approximately the same time as the load force. DISCUSSION

Sensory nature of neurogram After high-pass filtering, the signal recorded by the nerve cuff appeared to represent neural activity almost exclusively. This conclusion is supported by two lines of evidence. First, when the signal was filtered its normalized power spectrum was nearly identical to that of a

237 DISPLACEMENT (mm)

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TIME (S) TIME (s) Fig. 8. Left: example of a trial where the perturbation produced a relatively large displacement of the object generating a large burst of neural activity followed by a large, rapid increase in grip force. Right: example of a trial where the perturbation was effectively anticipated. The perturbation produced a relatively small displacement of the object which generated very little neural activity or subsequent increase in grip force. Vertical lines indicate onset of perturbation. Note the small initial burst of activity that precedes the main neural burst (arrow).

neural signal uncontaminated by EMG. Second, the overall shape of the neurogram differs from what one would expect if there was significant E M G contamination. Espinoza and Smith 5 recorded E M G from a large number of hand and forearm muscles in 3 monkeys performing the same task as the monkey in this study. Among those muscles were the flexor digitorum super° ficialis and the flexor carpi radialis, which because of their proximity to the nerve cuff would be expected to be the major sources of E M G contamination in the neurogram. The envelope of activity of these muscles resembles the grip force more than the rate of change of grip force to which the neurogram is akin. Furthermore, the burst of activity that appears during the release phase must be purely neural, since these muscles are silent at that time. The amount of efferent neural activity during the release phase is undoubtedly less than during the lifting phase. The overall shape of the neurogram is as expected for neural activity that is primarily afferent in nature. Additional supporting evidence for the afferent nature of the neurogram comes from the time course of the

response to the perturbation. The neurogram rises sharply the instant that the perturbation is applied, whereas reflex efferent activity on its way to the intrinsic hand muscles would not be expected until approximately 40 ms following the onset of the perturbation 4. Therefore, any increase in neural activity during the intervening 40 ms must have been sensory in nature. Furthermore, we did not detect significant modulation of the neural background activity when the monkey moved the fingers freely between trials without touching an object, suggesting that efferent fibers were not strongly represented in the neural signal. The median nerve contains both sensory and motor fibers. We assumed that the muscles innervated distal to the cuff were the first two lumbricals, the opponens, the abductor pollicis and the flexor pollicis brevis. The large fiber sensory innervation consists of both cutaneous and muscle mechanoreceptors 7. We made no attempt to distinguish between sensory and motor contributions to the signal. Both components could be present. However, the evidence above and additional arguments presented below suggest that any motor component is relatively

238 insignificant. Gordon et al. 6 estimated the relative amount of sensory and motor activity in the tibial and common peroneal nerves of the cat during locomotion. They found that in these nerves, which contain large numbers of cutaneous afferents, there was 2-3 times as much sensory as motor activity. They attributed the greater sensory contribution to higher mean firing rates of sensory fibers and recruitment of relatively more sensory than motor fibers during the step cycle. The ratio of sensory to motor activity is likely somewhat higher than this for the monkey median nerve. Whereas the tibial and common peroneal nerves each innervate several relatively large muscles of the hindlimb, the intrinsic muscles of the hand innervated by the median nerve are by comparison quite small. Since the number of motor units that constitute a muscle is a function of its size, the proportion of motor axons in the median nerve will be less than in the tibial and common peroneal nerves. Darian-Smith and Kenins 2 estimated the relative number of rapidly adapting, slowly adapting and Pacinian fibers in the radial palmar digital nerve of the index finger in the monkey. Using the terminology of Vallbo and Johansson z°, these 3 groups would be FA I, SA and FA II, respectively. The FA I units accounted for 50.5%, SA units for 35.5% and FA II units for 5.0% of the large diameter fibers (>6/~m) in the nerve. The remaining 10% of the fibers could not be identified. The proportion of large diameter fibers in the median nerve that are motor fibers can be estimated from histological data. The recurrent branch of the median nerve which innervates the thenar muscles contains about 6% of the total number of myelinated fibers in the median nerve below the wrist 13. The only other muscles innervated by the median nerve at this level are the first two lumbricals. Based on their size in comparison to the thenar muscles it is unlikely that they are innervated by more than 5% of the remaining fibers. We can assume therefore, that no more than 11% of the fibers in the nerve cuff innervated muscles. A proportion of the 11% would have originated from muscle sensory receptors. Based on histological data from cat muscles 14 one can assume that the proportion is between one-quarter and one-third. Thus, muscle sensory receptors would account for about 2-4% of the total number of large diameter fibers in the nerve cuff and efferent fibers would constitute between 7 and 9% , i.e. more than 90% of the large diameter fibers in the nerve cuff would have originated from sensory receptors. The monkey often executed the task upwards of 1000 times in a period of several hours. Clearly, the activation of median innervated muscles could not have been anywhere near maximal or these muscles would have

soon fatigued. Therefore, it is most likely that the monkey recruited only a fraction of the total number of motor fibers in the nerve, namely those with the smallest diameters. Taken together with the fact that cutaneous afferents often fire at frequencies exceeding 100 Hz at the onset of grip 22, whereas motor unit firing rates generally do not exceed 30 Hz except during ballistic contractions 3, it is unlikely that efferent fibers contributed significantly to the neurogram. Johansson and Vallbo 7 estimated that about 50% of myelinated fibers in the human median nerve were A6 fibers, while the remaining 50% could be classified as large diameter fibers. Of the latter 5 0 % , it was estimated that 4-fifths originated from cutaneous mechanoreceptors. The amplitude of a single fiber action potential recorded with a cuff electrode is proportional to the square of the fiber diameter 17. Since the largest cutaneous afferents can be up to 4 times the diameter of the A6 fibers and muscle afferents even larger, the A6 fibers would not have made a significant contribution to the recorded signal. It is therefore clear that the overwhelming contribution to activity recorded by the cuff electrode must have come from cutaneous mechanoreceptors, the majority of which were FA I units, but also including a large proportion of SA units 2. We detected a significant increase in neural activity at least 75 ms before we could detect a corresponding change in the mechanical parameters or their first derivatives. One possibility is that the early neural activity was efferent, representing a command for muscle contraction well in advance of finger contact with the object. While we cannot rule out such an explanation completely, we have argued above that motor activity is unlikely to constitute a significant portion of the neurogram. The early activity is more likely afferent, arising when the fingers are initially placed in contact with the object before the lifting phase actually commences. Very light touch would not have caused enough of an increase in grip force to cross our 10% initiation threshold, but could have produced a substantial cutaneous response. The gradual decline in the amplitude of the neurogram over the first two months following implantation of the nerve cuff (pointed out in association with Fig. 5 above) was likely due to the loss of some axons near the periphery of the nerve. Although we have no histological data, other investigators have reported limited axon degeneration associated with confinement of a nerve within a cuff TM. Large diameter fibers closest to the electrode surfaces (i.e. near the periphery of the nerve) are most likely to be affected. Since these fibers make the largest contribution to the signal, their loss would result in a decline in signal amplitude. In addition, the growth of connective tissue within the cuff would increase the

239 separation between active fibers and recording electrodes, also contributing to the decline.

Patterns of neural activity The patterns of activity which we recorded correspond with what would be expected from the summation of activity of the various classes of cutaneous afferents described by Westling and Johansson 22. The large initial burst of activity during the dynamic lifting phase is characteristic of FA I and SA I units which originate from Meissner corpuscles and Merkel cell-neurite complexes, respectively. These units respond vigorously upon contact with the surface of the object, but decrease their discharge during the dynamic phase as skin indentation increases with grip force. Vibration sensitive FA II units (Pacinian corpuscles) could also contribute during this period, particularly during the initial movement of the object. We do not consider SA 1I units in the ensuing discussion because they have not been definitively identified in the monkey 2. Peak neural activity occurred considerably before either the peak grip or load force, but coincided approximately with the peak rate of change of grip or load force. Based on the results of Westling and Johansson 22, one can offer the following interpretation. The responses of the rapidly adapting FA I and SA I units seem to parallel the rate of skin displacement during grip. Because of the characteristics of skin compliance, rate of skin displacement is a decreasing function of grip force22. An afferent unit will have a high discharge rate when its receptive field is first stimulated by skin displacement, but the discharge rate will drop rapidly thereafter, since the rate of skin displacement decreases with increasing grip force. The area of skin in contact with the object increases with grip force resulting in an increase in the number of receptors stimulated and more active units. However, their discharge will always be maximal just after contact and rapidly decline thereafter. Furthermore, the contact area quickly reaches an asymptote at relatively low grip forces (about 2 N for humans). Taken together, the observations of Westling and Johansson suggest that the combined discharge of all active FA I and SA I units will be maximal at a point in the lifting phase when the grip force is still relatively low and its rate of increase is high. The peak rate of change of grip force meets this criterion. We were able to show that there was a significant correlation between the amplitude of the burst of neural activity during lifting and the amplitude of the rate of change of grip force. This relationship was similar for the smooth and rough surfaces. The increase in the amplitude of the neural peak with the rate of change of grip force probably arises from two effects. First, there

will be an increased temporal summation of FA I and SA I units at the onset of the lifting phase. The more rapidly the grip is exerted, the more quickly the number of stimulated receptive fields increases. Thus, the stimulus thresholds for more units would be reached in a shorter time, leading to their synchronous activation. Second, FA I and F A I I units have dynamic sensitivity to the velocity of indentation of their receptive fields u so their discharge rates will increase as the velocity of indentation increases as the rate of change of grip force increases. Westling and Johansson 22 showed that the firing rates of FA I and SA I units dropped significantly as the lifting phase progressed. By the onset of the holding phase, FA I units were relatively silent. Their discharge during the holding phase was usually sparse and intermittent. The SA I units continued firing, but at lower rates than at the onset of lifting. Because of the high density of type I receptors in the fingers, the marked drop in firing rate of FA I and SA I units would lead to a corresponding decline in the neural activity recorded by the cuff electrode. However, the continued tonic firing of SA I units would maintain the neural activity significantly higher than that recorded before the onset of the grip. Again there are two factors which contribute to the increase in neural activity with static grip force. The discharge rate of a mechanoreceptor increases and the number of stimulated mechanoreceptors increase with grip force. Knibestr112 showed that SA I units increase their firing rate as a function of the amplitude of maintained skin indentation. Since increased grip force will produce increased skin indentation, the mean firing rate of the SA units will increase during the holding phase. As discussed above, Westling and Johansson 22 showed that the contact area and hence number of stimulated receptive fields increases with grip force, but saturates at relatively low grip forces. While it is likely that both of these factors contribute to the correlation between neural activity and static grip force which we observed, rate coding probably predominates over recruitment. We observed a burst of neural activity when the object was released, which accords well with the findings of Westling and Johansson 22 who reported a strong response from most FA I units, about half the SA I units and a few F A I I units, when the object was released. Because fewer units are activated by the release than at the onset of grip, the neurogram peak is smaller on grasp release than during the lifting phase. The neural activity which was generated in response to the perturbation during the holding phase appears to be related to the slipping of the object. There is a brisk response that coincides with displacement of the object when the grip force is insufficient to prevent slipping.

240 However, when the monkey augments the grip force in advance of the perturbation and thereby prevents significant displacement of the object, there is often little modulation of the neural activity. Furthermore, with a high grip force any displacement produced by the perturbation will be taken up by a change in angle at the wrist rather than slip of the object. A n y sensory activity due to stretch of wrist muscles would not be recorded by the cuff-electrode because of its distal location on the nerve. Clearly, the grip response following the perturbation is related to the evoked cutaneous stimulation. When there is a relatively large displacement of the object (presumably due to slipping) there is a large burst of neural activity followed within 100 ms by a large increase in grip force. W h e n the grip force is already high in anticipation of the perturbation, the displacement, the neural response and the ensuing increase in grip force are all relatively small. This is consistent with the interpretation of Cole and Abbs 1 that the effective stimulus for the grip response was stretching of the skin from increased shear forces during object load perturbation. Implications f o r central control The cutaneous afferents of the glabrous skin of the hand relay several types of information that can be readily decoded from the overall activity of the median nerve during the dynamic phase of a grasping and lifting task. First, the neural signal has a phase lead with respect to any of the measured mechanical parameters which stems from the fact that the skin is considerably more compliant than the mechanical transducers. As a result,

REFERENCES 1 Cole, K.J. and Abbs, J.H., Grip force adjustments evoked by load force perturbations of a grasped object, J. Neurophysiol., 60 (1988) 1513-1522. 2 DarianoSmith, I. and Kenins, P., Innervation density of mechanoreceptive fibres supplying glabrous skin of the monkey's index finger, J. Physiol., 309 (1980) 147-155. 3 Desmedt, J.E., The size principle of motoneuron recruitment in ballistic or ramp voluntary contractions in man. In J.E. Desmedt (Ed.), Motor Units Types, Recruitment and Plasticity in Health and Disease, Progress in Clinical Neurophysiology, Vol. 9, Karger, Basel, 1981, pp. 97-136. 4 Dugas, C. and Smith, A.M., Induced slip of an object held in a precision grip produces compensatory responses in cerebeilar cortex and the muscles of prehension, Soc. Neurosci. Abstr., 14 (1988) 1239. 5 Espinoza, E. and Smith, A.M., Purkinje cell simple spike activity during grasping and lifting objects of different textures and weights, J. Neurophysiol., 64 (1990) 698-714. 6 Gordon, T., Hoffer, J.A., Jhamandas, J. and Stein, R.B., Long-term effects of axotomy on neural activity during cat locomotion, J. Physiol., 303 (1980) 243-263. 7 Johansson, R.S. and Vallbo, /~.B., Tactile sensibility in the human hand: relative and absolute densities of 4 types of mechanoreceptive units in glabrous skin, J. Physiol., 286 (1979)

afferent information about object contact is signalled in advance of any substantial increase in grip force. Second, the neural discharge appears to provide an instantaneous measure of the rate of change of grip force. By integrating this signal throughout the dynamic phase of grasping, an estimate of the grip force can also be obtained. During the holding phase of the task there is a steady discharge that is proportional to the magnitude of the grip force. Although this correlation is quite strong, the change in the neurogram per unit force is small when compared to the change that occurs during the dynamic phase of lifting. On the other hand, changes in load force that occur after the grip force is relatively steady, due to load perturbations or position adjustments, can produce relatively large dynamic neural responses. Similarly, during the release phase there is a strong dynamic neural response that signals loss of contact with the object. Not surprisingly, the central nervous system appears to be well apprised of dynamic events occurring peripherally. It is therefore in a position to respond rapidly to adjust grip force according to changing demands when manipulating an object. Acknowledgements. The authors would like to thank Dr. J.A. Hoffer for his contribution in a pilot experiment which provided incentive to proceed further. Technical assistance with the electronics was provided by J. Jodoin and much of the software was written by S. Doucet. Drs. J. Kalaska and T. Drew provided valuable constructive criticism of the manuscript. This work was supported by the Medical Research Council of Canada group grant in the neurological sciences to the Universit6 de Montreal and by a Natural Sciences and Engineering Research Council grant to Dr. T. Milner.

283-300. 8 Johansson, R.S. and Westling, G., Roles of glabrous skin receptors and sensorimotor memory in automatic control of precision grip when lifting rougher or more slippery objects, Exp. Brain Res., 56 (1984) 550-564. 9 Johansson, R.S. and Westling, G., Signals in tactile afferents from the fingers eliciting adaptive motor responses, Exp. Brain Res., 66 (1987) 141-154. 10 Julien, C. and Rossignol, S., Eiectroneurographic recordings with polymer cuff electrodes in paralyzed cats, J. Neurosci. Methods, 5 (1982) 267-272. 11 Knibest61, M., Stimulus-response functions of rapidly adapting mechanoreceptors in the human glabrous skin area, J. Physiol., 232 (1973) 427-452. 12 Knibest61, M., Stimulus-response functions of slowly adapting mechanoreceptors in the human glabrous skin area, J. Physiol., 245 (1975) 63-80. 13 Lee, R.G., Ashby, E, White, D.G. and Aguayo, A.J., Analysis of motor conduction velocity in the human median nerve by computer simulation of compound muscle action potentials, EEG Clin. Neurophysiol., 39 (1975) 225-237. 14 Lloyd, D.EC. and Chang, H.T., Afferent fibres in muscle nerves, J. Neurophysiol., 11 (1948) 199-207. 15 Milner, T.E., Dugas, C., Picard, N. and Smith, A.M., Median nerve recording during grasping, Soc. Neurosci. Abstr., 15 (1989) 52.

241 16 Ros6n, I. and Asanuma, H., Peripheral afferent inputs to the forelimb area of the monkey motor cortex: input-output relations, Exp. Brain Res., 14 (1972) 257-273. 17 Stein, R.B., Charles, D., Davis, L., Jhamandas, J., Mannard, A. and Nichols, T.R., Principles underlying new methods for chronic neural recording, Can. J. Neurol. Sci., 2 (1975) 235-244. 18 Stein, R.B., Nichols, T.R., Jhamandas, J., Davis, L. and Charles, D., Stable long-term recordings from cat peripheral nerves, Brain Research, 128 (1977) 21-38. 19 Vallbo, A.B. and Hagbarth, K.-E., Activity from skin mechanoreceptors recorded percutaneously in awake human subjects,

Exp. Neurol., 21 (1968) 270-289. 20 Vallbo, A.B. and Johansson, R.S., Properties of cutaneous mechanoreceptors in the human hand related to touch sensation, Human Neurobiol., 3 (1984) 3-14. 21 Westling, O. and Johansson, R.S., Factors influencing the force control during precision grip, Exp. Brain Res., 53 (1984) 277-284. 22 Westling, G. and Johansson, R.S., Responses in glabrous skin mechanoreceptors during precision grip in humans, Exp. Brain Res., 66 (1987) 128-140.