Suitability of the dorsal column nuclei for a neural prosthesis: functional considerations

The brainstem dorsal column nuclei (DCN) may be an ideal target for a future neural prosthesis to restore somatosensation in tetraplegic patients. We aimed to investigate the functional and structural characteristics of the DCN, with the overarching goal of determining their suitability as a somatosensory neural prosthetic target. First, we review the neuroanatomy of the DCN and surrounding nuclei, including the cuneate, gracile, external cuneate, X, and Z nuclei, which together comprise the DCN-complex. We reveal that the DCN are not organised to only process and relay tactile information, as is commonly thought, but instead are a complex sensorimotor integration and distribution hub, with diverse projection targets throughout the hindbrain and midbrain. Next, we sought to show that somatosensory signals arriving in the DCN are reproducible, and that they carry decodable information about the location and quality of somatosensory stimuli, which we propose are necessary conditions for a potential somatosensory neural prosthetic target. We record somatosensory-evoked signals from various locations across the surface of the DCN in 8-week-old anaesthetised male Wistar rats. We characterised somatosensory-evoked DCN surface signals and demonstrated that they have robust and reproducible high-frequency and low-frequency features within and across animals. Using a machine-learning approach, we developed a metric for evaluating the relevance of machine-learning inputs to target outputs, which we coined feature-learnability. Using feature-learnability allowed us to determine the DCN signal features that were most relevant to peripheral somatosensory events, which facilitated very high accuracy prediction of the location and quality of somatosensory events, from small numbers of features. This thesis supports the DCN as a potential somatosensory neural prosthetic target by: i) showing DCN connectivity with sensorimotor targets essential for movement modulation in conscious and non-conscious neural pathways; ii) determining DCN signal features that are most relevant to peripheral tactile and proprioceptive events. New knowledge about the most relevant DCN signal features may inform the development of biomimetic stimulus patterns designed to artificially activate the DCN in future neural prosthetic devices for restoring somatosensory feedback

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.AL was supported by the Australian Government Research Training Program. We are extremely gratefully to the Bootes Medical Research Foundation for funding this project

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

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.The authors are extremely grateful to the Bootes Medical Research Foundation which funded this project. AL was supported by the Australian Government Research Training Program

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 informatio

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

The brainstem dorsal column nuclei (DCN) play a role in early processing ofsomatosensory information arising from a variety of functionally distinct peripheral structures,before being transmitted to the cortex via the thalamus. To improve our understanding of howsensory 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. DCNsurface potentials were evoked by electrical stimulation of the left and right nerves innervatingcutaneous 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 responsesdemonstrated low-frequency events with significantly longer durations, more high-frequencyevents and larger magnitudes compared to responses evoked from sural nerve stimulation.Hotspots of low- and high-frequency DCN activity were found ipsilateral to stimulated nervesbut were not symmetrically organised. In conclusion, we find that sensory inputs from peripheralnerves evoke unique and characteristic DCN activity patterns that are highly reproducible bothwithin and across animals.The authors wish to thank the Gretel and Gordon BootesMedical Research Foundation for their generous donations which funded this project

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.Accepted Author ManuscriptHuman-Robot Interactio