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

Waveform similarity analysis: A simple template comparing approach for detecting and quantifying noisy evoked compound action potentials

Experimental electrophysiological assessment of evoked responses from regenerating nerves is challenging due to the typical complex response of events dispersed over various latencies and poor signal-to-noise ratio. Our objective was to automate the detection of compound action potential events and derive their latencies and magnitudes using a simple cross-correlation template comparison approach. For this, we developed an algorithm called Waveform Similarity Analysis. To test the algorithm, challenging signals were generated in vivo by stimulating sural and sciatic nerves, whilst recording evoked potentials at the sciatic nerve and tibialis anterior muscle, respectively, in animals recovering from sciatic nerve transection. Our template for the algorithm was generated based on responses evoked from the intact side. We also simulated noisy signals and examined the output of the Waveform Similarity Analysis algorithm with imperfect templates. Signals were detected and quantified using Waveform Similarity Analysis, which was compared to event detection, latency and magnitude measurements of the same signals performed by a trained observer, a process we called Trained Eye Analysis. The Waveform Similarity Analysis algorithm could successfully detect and quantify simple or complex responses from nerve and muscle compound action potentials of intact or regenerated nerves. Incorrectly specifying the template outperformed Trained Eye Analysis for predicting signal amplitude, but produced consistent latency errors for the simulated signals examined. Compared to the trained eye, Waveform Similarity Analysis is automatic, objective, does not rely on the observer to identify and/or measure peaks, and can detect small clustered events even when signal-tonoise ratio is poor. Waveform Similarity Analysis provides a simple, reliable and convenient approach to quantify latencies and magnitudes of complex waveforms and therefore serves as a useful tool for studying evoked compound action potentials in neural regeneration studies. Copyright

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

Tapping Into the Language of Touch: Using Non-invasive Stimulation to Specify Tactile Afferent Firing Patterns

The temporal pattern of action potentials can convey rich information in a variety of sensory systems. We describe a new non-invasive technique that enables precise, reliable generation of action potential patterns in tactile peripheral afferent neurons by brief taps on the skin. Using this technique, we demonstrate sophisticated coding of temporal information in the somatosensory system, that shows that perceived vibration frequency is not encoded in peripheral afferents as was expected by either their firing rate or the underlying periodicity of the stimulus. Instead, a burst gap or silent gap between trains of action potentials conveys frequency information. This opens the possibility of new encoding strategies that could be deployed to convey sensory information using mechanical or electrical stimulation in neural prostheses and brain-machine interfaces, and may extend to senses beyond artificial encoding of aspects of touch. We argue that a focus on appropriate use of effective temporal coding offers more prospects for rapid improvement in the function of these interfaces than attempts to scale-up existing devices

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

Induction of muscle-regenerative multipotent stem cells from human adipocytes by PDGF-AB and 5-azacytidine

Terminally differentiated murine osteocytes and adipocytes can be reprogrammed using platelet-derived growth factor-AB and 5-azacytidine into multipotent stem cells with stromal cell characteristics. We have now optimized culture conditions to reprogram human adipocytes into induced multipotent stem (iMS) cells and characterized their molecular and functional properties. Although the basal transcriptomes of adipocyte-derived iMS cells and adipose tissue-derived mesenchymal stem cells were similar, there were changes in histone modifications and CpG methylation at cis-regulatory regions consistent with an epigenetic landscape that was primed for tissue development and differentiation. In a non-specific tissue injury xenograft model, iMS cells contributed directly to muscle, bone, cartilage, and blood vessels, with no evidence of teratogenic potential. In a cardiotoxin muscle injury model, iMS cells contributed specifically to satellite cells and myofibers without ectopic tissue formation. Together, human adipocyte-derived iMS cells regenerate tissues in a context-dependent manner without ectopic or neoplastic growth