Biology subjects

Loutit, A. J.

Publications and source records attributed to Loutit, A. J..

3 recordsLinked to original sources

bioRxiv2024

Lamellar Schwann cells in the Pacinian corpuscle potentiate vibration perception

Pacinian corpuscles are among the most sensitive mechanoreceptors found in vertebrates and they are tuned to vibrations in the highest perceptible frequency range (100-2000Hz). One of their anatomical hallmarks is the onion-like cell layers surrounding the central axon. The innermost layers consist of [~]60 densely packed lamellar Schwann cells (LSCs), whose function remains largely unknown. Using high-resolution 3D electron microscopy we found that LSCs in Pacinian corpuscles of the mouse hindlimb do not form concentric rings, but complex, multilayered and intertwining assemblies that are connected via an estimated 5805.1 desmosomes and 4142.5 gap-junctions. LSCs make multiple converging contacts with the afferent axon and its protrusions with desmosomes. Using optogenetic manipulations of LSCs we demonstrate that their activation does not only drive reliable time-locked spiking in the axon, but that their inactivation significantly elevates the thresholds in-situ and increases perceptual thresholds behaviorally. Together these findings provide evidence that LSCs are a key element of somatosensory processing, actively potentiating mechanosensitivity in Pacinian corpuscles. HighlightsO_LIHigh-resolution electron microscopy reveals details of the Pacinian corpuscle C_LIO_LILamellar Schwann cells form claw-like structures with converging axonal contacts C_LIO_LISchwann-cell modulation bidirectionally affects neural coding of Pacinian afferent C_LIO_LIInactivation of lamellar Schwann-cells increases perceptual thresholds C_LI

neuroscience↗

bioRxiv2022

How tactile afferents in the human fingerpad encode tangential torques associated with manipulation: are we better than monkeys?

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. In this study we investigated how torque information is encoded by human tactile afferents, including slowly adapting type-II (SA-II) afferents, in the fingerpads. SA-II afferent properties seemed perfectly suited for torque encoding but could not be previously investigated as they 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 participants. Torques were superimposed on a 2 N, 3 N, or 4 N background normal force. Unitary microneurography recordings were made from fast adapting type-I (FA-I, n=39), slowly adapting type-I (SA-I, n=31), and type-II (SA-II, n=13) afferents supplying the fingerpads. All three afferent types encoded torque magnitude and direction, with SA-II afferents showing excitatory and inhibitory modulation depending on torque direction. Most afferents of all three types had higher torque sensitivity with smaller normal force. FA-I afferents showed the best torque magnitude and worst directional discrimination abilities in both species. Human SA-I afferent response to static torque was inferior to dynamic stimuli, while in monkeys the opposite was true. In humans this might be compensated by the addition of a sustained SA-II afferent input. In comparison to monkeys the performance of each afferent type was inferior in humans, likely due to differences in fingertip tissue compliance and skin friction. [currently 246 words; 250 words max] Significance StatementWe investigate how individual human tactile nerve fibres encode rotational forces (torques) and compare them to their monkey counterparts. Human hands, but not monkey hands, are innervated by a tactile neuron type (SA-II afferents) specialised 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 the addition of SA-II afferent input. This indicates that variation in afferent types might compliment their specialisation for stimulus features, by slight differences between encoding in the two species.

neuroscience↗

bioRxiv2019

Novel neural signal features permit robust machine-learning of natural tactile- and proprioception-dominated dorsal column nuclei signals

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, restoration of somatosensory feedback and advances in neural decoding of motor control-related brain signals are required. We used 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 in urethane-anaesthetised rats. We determined signal features that are highly informative for decoding somatosensory stimuli, yet these appear underutilised in neuroprosthetic applications. We found that proprioception-dominated stimuli generalise across animals better than tactile-dominated stimuli, and we demonstrate how information that signal features contribute to neural decoding changes over a time-course of dynamic somatosensory events. These findings may improve neural decoding for various applications including novel neuroprosthetic design.

neuroscience↗