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OBJECTIVES: To investigate the clinimetric properties and clinical utility of the AsTex((R)), a new clinical tool for evaluation of hand sensation following stroke. DESIGN: The AsTex((R)) was administered on two occasions separated by a week to appraise test-retest reliability, and by three assessors on single occasion to establish inter-rater reliability. Pilot normative values were collected in an age-stratified sample. Clinical utility was evaluated based on ease of administration, ceiling and floor effects, and responsiveness to sensory recovery. PARTICIPANTS: Test-retest (n = 31) and inter-rater (n = 31) reliability and normative values (n = 95) for the AsTex((R)) were established in neurologically normal participants aged 18-85 years. Test-retest reliability was investigated in 22 individuals a mean of 46 months (range 12-125) post stroke and clinical utility was evaluated in an additional 24 subacute stroke participants a mean of 29.4 days (range 12-41) post stroke. MAIN MEASURE: The AsTex((R)). RESULTS: The AsTex((R)) demonstrated excellent test-retest (intraclass correlation coefficient (ICC) = 0.98, 95% confidence interval (95% CI) = 0.97-0.99) and inter-rater reliability (ICC = 0.81, 95% CI = 0.73-0.87) in neurologically normal participants. Test-retest reliability of the AsTex((R)) in individuals following stroke was excellent (ICC = 0.86, 95% CI = 0.68-0.94). The AsTex((R)) was simple to administer, demonstrated small standard error of measurement (0.14 mm), minimal floor and ceiling effects (12.5% and 8.3%) and excellent responsiveness (standardized response mean = 0.57) in subacute stroke participants. CONCLUSION: The AsTex((R)) is a reliable, clinically useful and responsive tool for evaluating hand sensation following stroke. [ABSTRACT FROM AUTHOR]

Dexterous object manipulation depends critically on information about forces normal and tangential to the fingerpads, and also on torque associated with object orientation at grip surfaces. We investigated how torque information is encoded by human tactile afferents in the fingerpads and compared them to 97 afferents recorded in monkeys ( n = 3; 2 females) in our previous study. Human data included slowly-adapting Type-II (SA-II) afferents, which are absent in the glabrous skin of monkeys. Torques of different magnitudes (3.5-7.5 mNm) were applied in clockwise and anticlockwise directions to a standard central site on the fingerpads of 34 human subjects (19 females). Torques were superimposed on a 2, 3, or 4 N background normal force. Unitary recordings were made from fast-adapting Type-I (FA-I, n = 39), and slowly-adapting Type-I (SA-I, n = 31) and Type-II (SA-II, n = 13) afferents supplying the fingerpads via microelectrodes inserted into the median nerve. All three afferent types encoded torque magnitude and direction, with torque sensitivity being higher with smaller normal forces. SA-I afferent responses to static torque were inferior to dynamic stimuli in humans, while in monkeys the opposite was true. In humans this might be compensated by the addition of sustained SA-II afferent input, and their capacity to increase or decrease firing rates with direction of rotation. We conclude that the discrimination capacity of individual afferents of each type was inferior in humans than monkeys which could be because of differences in fingertip tissue compliance and skin friction. SIGNIFICANCE STATEMENT We investigated how individual human tactile nerve fibers encode rotational forces (torques) and compared them to their monkey counterparts. Human hands, but not monkey hands, are innervated by a tactile neuron type (SA-II afferents) specialized to encode directional skin strain yet, so far, torque encoding has only been studied in monkeys. We find that human SA-I afferents were generally less sensitive and less able to discriminate torque magnitude and direction than their monkey counterparts, especially during the static phase of torque loading. However, this shortfall in humans could be compensated by SA-II afferent input. This indicates that variation in afferent types might complement each other signaling different stimulus features possibly providing computational advantage to discriminate stimuli., (Copyright © 2023 the authors.)

Torsional loads are ubiquitous during everyday dextrous manipulations. We examined how information about torque is provided to the sensorimotor control system by populations of tactile afferents. Torsional loads of different magnitudes were applied in clockwise and anticlockwise directions to a standard central site on the fingertip. Three different background levels of contact (grip) force were used. The median nerve was exposed in anaesthetized monkeys and single unit responses recorded from 66 slowly adapting type-I (SA-I) and 31 fast adapting type-I (FA-I) afferents innervating the distal segments of the fingertips. Most afferents were excited by torque but some were suppressed. Responses of the majority of both afferent types were scaled by torque magnitude applied in one or other direction, with the majority of FA-I afferent responses and about half of SA-I afferent responses scaled in both directions. Torque direction affected responses in both afferent types, but more so for the SA-I afferents. Latencies of the first spike in FA-I afferent responses depended on the parameters of the torque. We used a Parzen window classifier to assess the capacity of the SA-I and FA-I afferent populations to discriminate, concurrently and in real-time, the three stimulus parameters, namely background normal force, torque magnitude and direction. Despite the potentially confounding interactions between stimulus parameters, both the SA-I and the FA-I populations could extract torque magnitude accurately. The FA-I afferents signalled torque magnitude earlier than did the SA-I afferents, but torque direction was extracted more rapidly and more accurately by the SA-I afferent population.

Control of tangential force plays a key role in everyday manipulations. In anesthetized monkeys, forces tangential to the skin were applied at a range of magnitudes comparable to those used in routine manipulations and in eight different directions. The paradigm used enabled separation of responses to tangential force from responses to the background normal force. For slowly adapting type I (SAI) afferents, tangential force responses ranged from excitatory through no response to suppression, with both a static and dynamic component. For fast adapting type I (FAI) afferents, responses were dynamic and excitatory only. Responses of both afferent types were scaled by tangential force magnitude, elucidating the neural basis for previous human psychophysical scaling data. Most afferents were direction selective with a range of preferred directions and a range in sharpness of tuning. Both the preferred direction and the degree of tuning were independent of the background normal force. Preferred directions were distributed uniformly over 360 degrees for SAI afferents, but for FAI afferents they were biased toward the proximo-ulnar direction. Afferents from all over the glabrous skin of the distal segment of the finger responded; there was no evident relationship between the position of an afferent's receptive field on the finger and its preferred direction or its degree of tuning. Nor were preferred directions biased either toward or away from the receptive field center. In response to the relatively large normal forces, some afferents saturated and others did not, regardless of the positions of their receptive fields. Total afferent response matched human psychophysical scaling functions for normal force.

When humans manipulate objects, the sensorimotor system coordinates three-dimensional forces to optimize and maintain grasp stability. To do this, the CNS requires precise information about the magnitude and direction of load force (tangential to skin surface) plus feedback about grip force (normal to skin). Previous studies have shown that there is rapid, precise coordination between grip and load forces that deteriorates with digital nerve block. Obviously, mechanoreceptive afferents innervating fingerpad skin contribute essential information. We quantify human capacity to scale tangential and normal forces using only cutaneous information. Our paradigm simulated natural manipulations (a force tangential to the skin superimposed on an indenting force normal to the skin). Precisely controlled forces were applied by a custom-built stimulator to an immobilized fingerpad. Using magnitude estimation, subjects (n = 8) scaled the magnitude of tangential force (0.25-2.8 N) in two experiments (normal force, 2.5 and 4 N, respectively). Performance was unaffected by normal force magnitude and tangential force direction. Moreover, when both normal (2-4 N) and tangential forces were varied in a randomized-block factorial design, the relationship between applied and perceived tangential force remained near linear, with a minor but statistically significant nonlinearity. Our subjects could also discriminate small differences in tangential force, and this was the case for two different reference stimuli. In both cases, the Weber fraction was 0.16. Finally, scaling functions for magnitude estimates of normal force (1-5 N) were also approximately linear. These data show that the cutaneous afferents provide a wealth of precise information about both normal and tangential force.

For humans to manipulate an object successfully, the motor control system must have accurate information about parameters such as the shape of the stimulus, its position of contact on the skin, and the magnitude and direction of contact force. The same information is required for perception during haptic exploration of an object. Much of these data are relayed by the mechanoreceptive afferents innervating the glabrous skin of the digits. Single afferent responses are modulated by all the relevant stimulus parameters. Thus, only in complete population reconstructions is it clear how each of the parameters can be signaled to the brain independently when many are changing simultaneously, as occurs in most normal movements or haptic exploration. Modeling population responses reveals how resolution is affected by neural noise and intrinsic properties of the population such as the pattern and density of innervation and the covariance of response variability.

Analysis of population responses in the tactile system requires a step beyond the isomorphic representations that are commonly presented. Using a simple model based on our data for spheres contacting the fingerpad, we illustrate how the parameters of the population itself have a profound effect on the fidelity of neural representations or codes. The effects of these parameters, such as innervation density, variability of sensitivity, type and covariance of noise are not apparent from single unit responses and, at least at present, require a theoretical or modeling approach of some sort.

When the flat faces of a coin are grasped between thumb and index finger, a "curved edge" is felt. Analogous curved edges were generated by our stimuli, which comprised the flat face of segments of annuli applied passively to immobilized fingers. Humans could scale the curvature of the annulus and could discriminate changes in curvature of approximately 20 m(-1). The responses of single slowly adapting type I afferents (SAIs) recorded in anesthetized monkeys could be quantified by the product of two factors: their sensitivity and a spatial profile dependent only on the radius of the annulus. This allowed us to reconstruct realistic SAI population responses that included noise, variation in fiber sensitivity, and varying innervation patterns. The critical question was how relatively small populations ( approximately 70 active fibers) can encode edge curvature with such precision. A template-matching approach was used to establish the accuracy of edge representation in the population. The known large interfiber variability in sensitivity had no effect on curvature resolution. Neural resolution was superior to human performance until large levels of central noise were present showing that, unlike simple detection, spatial processing is limited centrally. In contrast to the behavior of mean response codes, neural resolution improved with increasing covariance in noise. Surprisingly, resolution for any single population varied considerably with small changes in the position of the stimulus relative to the SAI matrix. Overall innervation density was not as critical as the spacing of receptive fields at right angles to the edge.

The aim of this study was to determine the acuity of the peripheral tactile system for gaps and to determine how stimulus orientation may impact on this. We quantified the ability of humans to discriminate small differences in gap width using a forced-choice task. Stimuli were presented passively to the distal fingerpad in a region where the skin ridges all run approximately in the same direction. Two standard gap widths were used (2 and 2.9 mm), and the comparison gap widths were larger than the standard gaps. With the gap axis parallel to the skin ridges, the average difference limen was approximately 0.2 mm for both standards. We examined the effect of stimulus orientation by asking subjects to discriminate between a smooth surface and a grating (ridge width, 1.5 mm; groove width, 0. 75 mm). They were able to discriminate the two surfaces when the axis of the grooves was parallel to the skin ridges, but performance was below threshold in the orthogonal orientation. The underlying neural mechanisms were investigated using the gap stimuli to activate single slowly adapting type I mechanoreceptive afferents (SAIs) innervating the fingerpads of anesthetized monkeys. The edges of the gap produced response peaks, and the gap resulted in a trough in the receptive field profiles. The response magnitude at the peaks was greater, and at the troughs was smaller, for larger gap widths and also when the axis of the gap was parallel to the skin ridges as compared with the orthogonal orientation. Simulated SAI population responses showed that response profiles were distorted by variation in afferent sensitivity and by neural noise. Using signal detection theory, based on a neural measure of the gaps computed over the active population, the acuity of the SAIs under realistic population conditions was compared with human performance. These analyses showed how parameters like afferent sensitivity, the pattern and density of innervation, and noise impact on performance and why their impact is different for the two stimulus orientations investigated. The greatest limitation was imposed by noise that is independent of response magnitude, and this effect was greater for stimuli oriented orthogonal to the skin ridges than for the parallel orientation.

The central part of the fingerpad in anesthetized monkeys was stimulated by spheres varying in curvature indented into the skin. Responses were recorded from single slowly adapting type I primary afferent fibers (SAIs) innervating the sides and end of the distal segment of the stimulated finger. Although these afferents had receptive field centers that were remote from the stimulus, their responses were substantial. Increasing the curvature of the stimulus resulted in an increased response for most afferents. In general, responses increased most between stimuli with curvatures of 0 (flat) and 80.6 m(-1), with further increases in curvature having progressively smaller effects on the response. We calculated an index of sensitivity to changes in curvature; this index varied widely among the afferents but for most it was less than the index calculated for afferents innervating the fingerpad in the vicinity of the stimulus. Responses of all the SAIs increased when the contact force of the stimulus increased. An index of sensitivity to changes in contact force varied widely among the afferents but in all cases was greater than the index calculated for SAIs from the fingerpad itself. Neither the curvature sensitivity nor the force sensitivity of an afferent was related in any obvious way to the location of its receptive field center on the digit. There was only a minor correspondence between an afferent's sensitivity to force and its sensitivity to curvature. The large number of afferents innervating the border regions of the digit do respond to stimuli contacting the central fingerpad; they convey some information about the curvature of the stimulus and substantial information about contact force.

The capacity of a population of primary afferent fibers to signal information about a sphere indenting the fingerpad is limited by factors such as the inhomogeneity of sensitivity among the afferents, the pattern and density of innervation, and the effects of noise (response variability). Using experimental data recorded from single slowly adapting type I afferents (SAIs), we simulated the response of the SAI population to such a stimulus. The human ability to discriminate stimulus curvature, location, and force has been quantified previously. We devised three neural measures, treating them as surrogates for the real neural measures underlying human performance, and explored how population parameters usually overlooked in neural coding studies affect such measures. Variation in sensitivity among SAIs is large; this distorts population response profiles markedly but has no significant impact on the neural measures. Two classes of noise were introduced, one dependent on and the other independent of the level of neural activity. Resolution of the model was compared with discrimination in humans. Correlation of noise among neurons had different effects for the different measures. An increase in correlation decreased resolution in the measure for force but improved resolution in the measure for position. Increasing innervation density (1) always increased resolution for position and (2) increased resolution for force if noise was uncorrelated but had diminishing effects as correlation increased. Correlation and innervation density had complex effects on the measure for curvature, depending on the class of noise. Nonuniformity in the pattern of innervation had negligible effects on resolution.

A forced choice paradigm was used to measure the capacity of human subjects to discriminate the curvature of stimuli applied passively to the skin of an immobilized finger. The stimuli consisted of spherically curved segments with a base radius of 2.5 mm; thus the area of contact with the fingerpad skin was approximately 19.6 mm2 for all stimuli. There were 2 series of experiments. In series 1, the standard surface had a curvature of 286 m-1 (radius of curvature 3.5 mm); subjects were able to discriminate an increase in curvature of about 13%. In series 2, the standard had a curvature of 154 m-1 (radius 6.5 mm); subjects were able to discriminate an increase in curvature of about 18%. Thus, even when the contact area between the surface and the skin was invariant, humans were able to discriminate small changes in curvature using only information from the cutaneous receptors.

Stimuli with spherically curved surfaces were presented passively to the fingerpads of human subjects. There were 28 stimuli, consisting of all combinations of 4 different curvatures and 7 different contact forces; these were presented in random order. Subjects scaled their perceived magnitude of the contact force using magnitude estimation. Perceived force increased markedly with an increase in experimentally applied contact force. An increase in curvature resulted in a slight increase in perceived contact force. Thus, when humans are passively presented with objects changing in both shape and contact force, they are able to extract information about the force. Because of the passive nature of the task, all such information must be conveyed to the brain by the cutaneous mechanoreceptors.