Role of arm reaching movement kinematics in friction perception at initial contact with smooth surfaces.

When manipulating objects, humans begin adjusting their grip force to friction within 100 ms of contact. During motor adaptation, subjects become aware of the slipperiness of touched surfaces. Previously, we have demonstrated that humans cannot perceive frictional differences when surfaces are brought in contact with an immobilised finger, but can do so when there is submillimeter lateral displacement or subjects actively make the contact movement. Similarly, in, we investigated how humans perceive friction in the absence of intentional exploratory sliding or rubbing movements, to mimic object manipulation interactions. We used a two-alternative forced-choice paradigm in which subjects had to reach and touch one surface followed by another, and then indicate which felt more slippery. Subjects correctly identified the more slippery surface in 87 ± 8% of cases (mean ± SD; n = 12). Biomechanical analysis of finger pad skin displacement patterns revealed the presence of tiny (<1 mm) localised slips, known to be sufficient to perceive frictional differences. We tested whether these skin movements arise as a result of natural hand reaching kinematics. The task was repeated with the introduction of a hand support, eliminating the hand reaching movement and minimising fingertip movement deviations from a straight path. As a result, our subjects' performance significantly declined (66 ± 12% correct, mean ± SD; n = 12), suggesting that unrestricted reaching movement kinematics and factors such as physiological tremor, play a crucial role in enhancing or enabling friction perception upon initial contact. KEY POINTS: More slippery objects require a stronger grip to prevent them from slipping out of hands. Grip force adjustments to friction driven by tactile sensory signals are largely automatic and do not necessitate cognitive involvement; nevertheless, some associated awareness of grip surface slipperiness under such sensory conditions is present and helps to select a safe and appropriate movement plan. When gripping an object, tactile receptors provide frictional information without intentional rubbing or sliding fingers over the surface. However, we have discovered that submillimeter range lateral displacement might be required to enhance or enable friction sensing. The present study provides evidence that such small lateral movements causing localised partial slips arise and are an inherent part of natural reaching movement kinematics.

How Tactile Afferents in the Human Fingerpad Encode Tangential Torques Associated with Manipulation: Are Monkeys Better than Us?

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.

Submillimeter Lateral Displacement Enables Friction Sensing and Awareness of Surface Slipperiness.

Human tactile perception and motor control rely on the frictional estimates that stem from the deformation of the skin and slip events. However, it is not clear how exactly these mechanical events relate to the perception of friction. This study aims to quantify how minor lateral displacement and speed enables subjects to feel frictional differences. In a 2-alternative forced-choice protocol, an ultrasonic friction-reduction device was brought in contact perpendicular to the skin surface of an immobilized index finger; after reaching 1N normal force, the plate was moved laterally. A combination of four displacement magnitudes (0.2, 0.5, 1.2 and 2 mm), two levels of friction (high, low) and three displacement speeds (1, 5 and 10 mm/s) were tested. We found that the perception of frictional difference was enabled by submillimeter range lateral displacement. Friction discrimination thresholds were reached with lateral displacements ranging from 0.2 to 0.5 mm and surprisingly speed had only a marginal effect. These results demonstrate that partial slips are sufficient to cause awareness of surface slipperiness. These quantitative data are crucial for designing haptic devices that render slipperiness. The results also show the importance of subtle lateral finger movements present during dexterous manipulation tasks.

Functional organization and connectivity of the dorsal column nuclei complex reveals a sensorimotor integration and distribution hub.

The dorsal column nuclei complex (DCN-complex) includes the dorsal column nuclei (DCN, referring to the gracile and cuneate nuclei collectively), external cuneate, X, and Z nuclei, and the median accessory nucleus. The DCN are organized by both somatotopy and modality, and have a diverse range of afferent inputs and projection targets. The functional organization and connectivity of the DCN implicate them in a variety of sensorimotor functions, beyond their commonly accepted role in processing and transmitting somatosensory information to the thalamus, yet this is largely underappreciated in the literature. To consolidate insights into their sensorimotor functions, this review examines the morphology, organization, and connectivity of the DCN and their associated nuclei. First, we briefly discuss the receptors, afferent fibers, and pathways involved in conveying tactile and proprioceptive information to the DCN. Next, we review the modality and somatotopic arrangements of the remaining constituents of the DCN-complex. Finally, we examine and discuss the functional implications of the myriad of DCN-complex projection targets throughout the diencephalon, midbrain, and hindbrain, in addition to their modulatory inputs from the cortex. The organization and connectivity of the DCN-complex suggest that these nuclei should be considered a complex integration and distribution hub for sensorimotor information.

Dorsal Column Nuclei Neural Signal Features Permit Robust Machine-Learning of Natural Tactile- and Proprioception-Dominated Stimuli.

Neural prostheses enable users to effect movement through a variety of actuators by translating brain signals into movement control signals. However, to achieve more natural limb movements from these devices, the restoration of somatosensory feedback is required. We used feature-learnability, a machine-learning approach, to assess signal features for their capacity to enhance decoding performance of neural signals evoked by natural tactile and proprioceptive somatosensory stimuli, recorded from the surface of the dorsal column nuclei (DCN) in urethane-anesthetized rats. The highest performing individual feature, spike amplitude, classified somatosensory DCN signals with 70% accuracy. The highest accuracy achieved was 87% using 13 features that were extracted from both high and low-frequency (LF) bands of DCN signals. In general, high-frequency (HF) features contained the most information about peripheral somatosensory events, but when features were acquired from short time-windows, classification accuracy was significantly improved by adding LF features to the feature set. We found that proprioception-dominated stimuli generalize across animals better than tactile-dominated stimuli, and we demonstrate how information that signal features contribute to neural decoding changes over the time-course of dynamic somatosensory events. These findings may inform the biomimetic design of artificial stimuli that can activate the DCN to substitute somatosensory feedback. Although, we investigated somatosensory structures, the feature set we investigated may also prove useful for decoding other (e.g., motor) neural signals.

Restoring Somatosensation: Advantages and Current Limitations of Targeting the Brainstem Dorsal Column Nuclei Complex.

Current neural prostheses can restore limb movement to tetraplegic patients by translating brain signals coding movements to control a variety of actuators. Fast and accurate somatosensory feedback is essential for normal movement, particularly dexterous tasks, but is currently lacking in motor neural prostheses. Attempts to restore somatosensory feedback have largely focused on cortical stimulation which, thus far, have succeeded in eliciting minimal naturalistic sensations. Yet, a question that deserves more attention is whether the cortex is the best place to activate the central nervous system to restore somatosensation. Here, we propose that the brainstem dorsal column nuclei are an ideal alternative target to restore somatosensation. We review some of the recent literature investigating the dorsal column nuclei functional organization and neurophysiology and highlight some of the advantages and limitations of the dorsal column nuclei as a future neural prosthetic target. Recent evidence supports the dorsal column nuclei as a potential neural prosthetic target, but also identifies several gaps in our knowledge as well as potential limitations which need to be addressed before such a goal can become reality.

Peripheral Nerve Activation Evokes Machine-Learnable Signals in the Dorsal Column Nuclei.

The brainstem dorsal column nuclei (DCN) are essential to inform the brain of tactile and proprioceptive events experienced by the body. However, little is known about how ascending somatosensory information is represented in the DCN. Our objective was to investigate the usefulness of high-frequency (HF) and low-frequency (LF) DCN signal features (SFs) in predicting the nerve from which signals were evoked. We also aimed to explore the robustness of DCN SFs and map their relative information content across the brainstem surface. DCN surface potentials were recorded from urethane-anesthetized Wistar rats during sural and peroneal nerve electrical stimulation. Five salient SFs were extracted from each recording electrode of a seven-electrode array. We used a machine learning approach to quantify and rank information content contained within DCN surface-potential signals following peripheral nerve activation. Machine-learning of SF and electrode position combinations was quantified to determine a hierarchy of information importance for resolving the peripheral origin of nerve activation. A supervised back-propagation artificial neural network (ANN) could predict the nerve from which a response was evoked with up to 96.8 ± 0.8% accuracy. Guided by feature-learnability, we maintained high prediction accuracy after reducing ANN algorithm inputs from 35 (5 SFs from 7 electrodes) to 6 (4 SFs from one electrode and 2 SFs from a second electrode). When the number of input features were reduced, the best performing input combinations included HF and LF features. Feature-learnability also revealed that signals recorded from the same midline electrode can be accurately classified when evoked from bilateral nerve pairs, suggesting DCN surface activity asymmetry. Here we demonstrate a novel method for mapping the information content of signal patterns across the DCN surface and show that DCN SFs are robust across a population. Finally, we also show that the DCN is functionally asymmetrically organized, which challenges our current understanding of somatotopic symmetry across the midline at sub-cortical levels.

Characterisation and functional mapping of surface potentials in the rat dorsal column nuclei.

KEY POINTS: The brainstem dorsal column nuclei (DCN) process sensory information arising from the body before it reaches the brain and becomes conscious. Despite significant investigations into sensory coding in peripheral nerves and the somatosensory cortex, little is known about how sensory information arising from the periphery is represented in the DCN. Following stimulation of hind-limb nerves, we mapped and characterised the evoked electrical signatures across the DCN surface. We show that evoked responses recorded from the DCN surface are highly reproducible and are unique to nerves carrying specific sensory information. ABSTRACT: The brainstem dorsal column nuclei (DCN) play a role in early processing of somatosensory information arising from a variety of functionally distinct peripheral structures, before being transmitted to the cortex via the thalamus. To improve our understanding of how sensory information is represented by the DCN, we characterised and mapped low- (<200 Hz) and high-frequency (550-3300 Hz) components of nerve-evoked DCN surface potentials. DCN surface potentials were evoked by electrical stimulation of the left and right nerves innervating cutaneous structures (sural nerve), or a mix of cutaneous and deep structures (peroneal nerve), in 8-week-old urethane-anaesthetised male Wistar rats. Peroneal nerve-evoked DCN responses demonstrated low-frequency events with significantly longer durations, more high-frequency events and larger magnitudes compared to responses evoked from sural nerve stimulation. Hotspots of low- and high-frequency DCN activity were found ipsilateral to stimulated nerves but were not symmetrically organised. In conclusion, we find that sensory inputs from peripheral nerves evoke unique and characteristic DCN activity patterns that are highly reproducible both within and across animals.