Neural representation in M1 and S1 cortex of bilateral hand actions during prehension
Bimanual movements that require coordinated actions of the two hands may be coordinated by synchronous bilateral activation of somatosensory and motor cortical areas in both hemispheres, by enhanced activation of individual neurons specialized for bimanual actions, or by both mechanisms. To investigate cortical neural mechanisms that mediate unimanual and bimanual prehension, we compared actions of the left and right hands in a reach to grasp-and-pull instructed-delay task. Spike trains were recorded with multiple electrode arrays placed in the hand area of primary motor (M1) and somatosensory (S1) cortex of the right hemisphere in macaques, allowing us to measure and compare the relative timing, amplitude, and synchronization of cortical activity in these areas as animals grasped and manipulated objects that differed in shape and location. We report that neurons in the right hemisphere show common task-related firing patterns for the two hands but actions of the ipsilateral hand elicited weaker and shorter-duration responses than those of the contralateral hand. We report significant bimanual activation of neurons in M1 but not in S1 cortex when animals have free choice of hand use in prehension tasks. Population ensemble responses in M1 thereby provide an accurate depiction of hand actions during skilled manual tasks. These studies also demonstrate that somatosensory cortical areas serve important cognitive and motor functions in skilled hand actions. Bilateral representation of hand actions may serve an important role in "motor equivalence" when the same movements are performed by either hand and in transfer of skill learning between the hands.NEW & NOTEWORTHY Humans can manipulate small objects with the right or left hand but typically select the dominant hand to handle them. We trained monkeys to grasp and manipulate objects with either hand, while recording neural activity in primary motor (M1) and somatosensory (S1) cortex. Actions of both hands activate M1 neurons, but S1 neurons respond only to the contralateral hand. Bilateral sensitivity in M1 may aid skill transfer between hands after stroke or head injury.
A Quantitative Perceptual Model for Tactile Roughness
Everyone uses the sense of touch to explore the world, and roughness is one of the most important qualities in tactile perception. Roughness is a major identifier for judgments of material composition, comfort, and friction, and it is tied closely to manual dexterity. The advent of high-resolution 3D printing technology provides the ability to fabricate arbitrary 3D textures with surface geometry that confers haptic properties. In this work, we address the problem of mapping object geometry to tactile roughness. We fabricate a set of carefully designed stimuli and use them in experiments with human subjects to build a perceptual space for roughness. We then match this space to a quantitative model obtained from strain fields derived from elasticity simulations of the human skin contacting the texture geometry, drawing from past research in neuroscience and psychophysics. We demonstrate how this model can be applied to predict and alter surface roughness, and we show several applications in the context of fabrication.
Tactile Perception of the Roughness of 3D-Printed Textures
Surface roughness is one of the most important qualities in haptic perception. Roughness is a major identifier for judgments of material composition, comfort and friction, and is tied closely to manual dexterity. Some attention has been given to the study of roughness perception in the past, but it has typically focused on non-controllable natural materials or on a narrow range of artificial materials. The advent of high-resolution 3D printing technology provides the ability to fabricate arbitrary 3D textures with precise surface geometry to be used in tactile studies. We used parametric modeling and 3D printing to manufacture a set of textured plates with defined element spacing, shape, and arrangement. Using active touch and two-alternative forced choice protocols, we investigated the contributions of these surface parameters to roughness perception in human subjects. Results indicate that large spatial periods produce higher estimations of roughness (with Weber fraction = 0.19), small texture elements are perceived as rougher than large texture elements of the same wavelength, perceptual differences exist between textures with the same spacing but different arrangements, and roughness equivalencies exist between textures differing along different parameters. We posit that papillary ridges serve as tactile processing units, and neural ensembles encode the spatial profiles of the texture contact area to produce roughness estimates. The stimuli and the manufacturing process may be used in further studies of tactile roughness perception and in related neurophysiological applications.
Neural pathways for cognitive command and control of hand movements
Effect of blocking tactile information from the fingertips on adaptation and execution of grip forces to friction at the grasping surface
Adaptation of fingertip forces to friction at the grasping surface is necessary to prevent use of inadequate or excessive grip forces. Here we investigated the effect of blocking tactile information from the fingertips non-invasively on the adaptation and efficiency of grip forces to surface friction during precision grasp. Ten neurologically intact subjects grasped and lifted an instrumented grip device with 18 different frictional surfaces under three conditions: with bare hands, with a thin layer of plastic (Tegaderm), and with an additional layer of foam affixed to the fingertips. The coefficient of friction at the finger-object interface of each surface was obtained for each subject with bare hands and Tegaderm by measuring the slip ratio (grip force/ load force) at the moment of slip. We found that the foam layer reduced sensibility for two-point discrimination and pressure sensitivity at the fingertips, but Tegaderm did not. However, Tegaderm reduced static, but not dynamic, tactile discrimination. Adaptation of fingertip grip forces to surface friction measured by the rate of change of peak grip force, and grip force efficiency measured by the grip-load force ratio at lift, showed a proportional relationship with bare hands, but were impaired with Tegaderm and foam. Activation of muscles engaged in precision grip also varied with the frictional surface with bare hands, but not with Tegaderm and foam. The results suggest that sensitivity for static tactile discrimination is necessary for feedforward and feedback control of grip forces and for adaptive modulation of muscle activity during precision grasp.
Spike trains in posterior parietal and premotor cortex encode trained and natural grasping behaviors [Meeting Abstract]
To investigate the role of somatosensory and motor information during grasping behaviors, we used digital video and burst analysis of simultaneously recorded spike trains to define burst epochs when neuronal firing rates exceeded 1 SD above the mean. We reconstructed the trajectory of hand movements during each burst from successive digital video images as three macaques grasped and manipulated objects in a trained prehension task, and when engaged in natural grasping behaviors to acquire pieces of fruit. In the task, neurons in posterior parietal areas 5 and 7b/AIP and in ventral premotor cortex responded more vigorously during object acquisition than to manipulation. Firing rates rose 250-500 ms before touch, and peaked as the hand was preshaped during reach, or at initial contact with the object. Firing rates declined as grasp was secured, and returned to baseline or were inhibited during subsequent actions. Some neurons responded to grasping actions of the right and left hands (bilateral neurons), suggesting that their firing patterns reflect grasp intentions, or the internal motor commands for execution of these behaviors. Acquisition-sensitive firing patterns were also observed when the animal grasped food morsels at various workspace locations. Firing began as the animal projected the hand towards the food, and continued as the hand tracked it. Figure 1. Firing peaked as the fingertips contacted the food, and ended when it was secured in the hand. High firing was elicited when food morsels were plucked from a tray, with the fingers preshaped for precision grip, or during tracking actions when the fingers were spread apart to maximize surface area. As in the task, bilateral neurons responded to prehensile actions performed unilaterally by either hand. A second, weaker burst often occurred when food was placed in the mouth. Other neurons responded vigorously to acquisition by the contralateral hand, but fired at highest rates when bilateral actions were coordinated between the left and right hands, as when food morsels were transferred between them. These intrapersonalcoordinated neurons did not just encode equivalent tactile information from either side, but preferentially signaled coincident somatosensory data shared between hemispheres during synergistic hand actions. The two classes of bilateral neurons thus provide somesthetic feedback from both limbs, and encode whether they are acting independently or in concert. Our findings support hypotheses that firing patterns in posterior parietal and premotor cortex reflect the animal's intentions to accomplish task goals in motor coordinates. They suggest that actions preceding contact reinforce subsequent neural responses, allowing subjects to acquire and manipulate objects in a continuous, smooth sequence. (Figure presented)
Representation in somatosensory (SI) cortex of hand actions in prehension tasks [Meeting Abstract]
Representation in motor cortex (MI) of hand actions in a bimanual prehension task [Meeting Abstract]
Tangential torque tunes touch [Comment]
Spike trains in posterior parietal cortex (PPC) encode trained and natural grasping behaviors [Meeting Abstract]