Recording sensory and motor information from peripheral nerves with Utah Slanted Electrode Arrays

Clark, Gregory A.; Ledbetter, Noah M.; Warren, David J.; Harrison, R.R.

doi:10.1109/iembs.2011.6091149

Cited by 51 publications

(36 citation statements)

References 11 publications

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“…Indeed, recording the responses of human or monkey afferents is technically challenging and only yields responses from a single fiber at a time. Even multielectrode arrays only yield responses from a sparse sample of afferents (53) or the aggregate activity of a large number of fibers (54,55). The model allows us to simulate the responses of entire populations of afferents to arbitrarily complex stimuli.…”

Section: Discussionmentioning

confidence: 99%

Simulating tactile signals from the whole hand with millisecond precision

Saal

Delhaye

Rayhaun

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

202

245

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When we grasp and manipulate an object, populations of tactile nerve fibers become activated and convey information about the shape, size, and texture of the object and its motion across the skin. The response properties of tactile fibers have been extensively characterized in single-unit recordings, yielding important insights into how individual fibers encode tactile information. A recurring finding in this extensive body of work is that stimulus information is distributed over many fibers. However, our understanding of population-level representations remains primitive. To fill this gap, we have developed a model to simulate the responses of all tactile fibers innervating the glabrous skin of the hand to any spatiotemporal stimulus applied to the skin. The model first reconstructs the stresses experienced by mechanoreceptors when the skin is deformed and then simulates the spiking response that would be produced in the nerve fiber innervating that receptor. By simulating skin deformations across the palmar surface of the hand and tiling it with receptors at their known densities, we reconstruct the responses of entire populations of nerve fibers. We show that the simulated responses closely match their measured counterparts, down to the precise timing of the evoked spikes, across a wide variety of experimental conditions sampled from the literature. We then conduct three virtual experiments to illustrate how the simulation can provide powerful insights into population coding in touch. Finally, we discuss how the model provides a means to establish naturalistic artificial touch in bionic hands.mechanoreceptor | tactile afferent | somatosensory periphery | skin mechanics | computational model T he human hand is endowed with thousands of mechanoreceptors of different types distributed across the skin, each innervated by one or more large myelinated nerve fibers (1). These fibers convey detailed information about contact events and provide us with an exquisite sensitivity to the form and surface properties of grasped objects (2, 3). During object manipulation and tactile exploration, the glabrous skin of the hand undergoes complex spatiotemporal mechanical deformations, which in turn, drive very precise spiking responses in individual afferents. Coarse object features, such as edges and corners, are reflected in spatial patterns of activation in slowly adapting type I (SA1) and rapidly adapting (RA) fibers, which are densely packed in the fingertip (3, 4). At the same time, interactions with objects and surfaces elicit high-frequency, low-amplitude surface waves that propagate across the skin of the finger and palm and excite vibration-sensitive Pacinian (PC) afferents all over the hand (5-8).Recording the activity of tactile nerve fibers in monkeys or humans is technically difficult, is slow, and generally yields responses from a single unit at a time (9, 10). Although such recordings have yielded powerful insights into the neural basis of touch, they provide a limited window into the information that the hand c...

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Section: Discussionmentioning

confidence: 99%

Simulating tactile signals from the whole hand with millisecond precision

Saal

Delhaye

Rayhaun

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

202

245

View full text Add to dashboard Cite

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“…This would permit identification of the localisation of function in peripheral nerve but also yield images of the envelopes of spiking activity which could provide a key to unlocking the neural code in nerve before it arrives in the CNS. It could also have practical application in reconstructive surgery of peripheral nerve after trauma, where it could be used to identify fascicle function and human robotics based on activity in extant peripheral nerve after trauma [13][14][15]. Further in the field of 'electroceutical' stimulation of autonomic nerve for treatment of disease, it could be used to identify the function of fascicles within autonomic nerve and so avoid off-target effects [16,17].…”

Section: Introductionmentioning

confidence: 99%

Imaging fast neural traffic at fascicular level with electrical impedance tomography: proof of principle in rat sciatic nerve

Donegá²,

et al. 2018

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Objective. Understanding the coding of neural activity in nerve fascicles is a high priority in computational neuroscience, electroceutical autonomic nerve stimulation and functional electrical stimulation for treatment of paraplegia. Unfortunately, it has been little studied as no technique has yet been available to permit imaging of neuronal depolarization within fascicles in peripheral nerve. Approach. We report a novel method for achieving this, using a flexible cylindrical multi-electrode cuff placed around nerve and the new medical imaging technique of fast neural electrical impedance tomography (EIT). In the rat sciatic nerve, it was possible to distinguish separate fascicles activated in response to direct electrical stimulation of the posterior tibial and common peroneal nerves. Main results. Reconstructed EIT images of fascicular activation corresponded with high spatial accuracy to the appropriate fascicles apparent in histology, as well as the inverse source analysis (ISA) of compound action potentials (CAP). With this method, a temporal resolution of 0.3 ms and spatial resolution of less than 100 µm was achieved. Significance. The method presented here is a potential solution for imaging activity within peripheral nerves with high spatial accuracy. It also provides a basis for imaging and selective neuromodulation to be incorporated in a single implantable nonpenetrating peri-neural device.

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“…3(a)). Other well studied peripheral nerve interfaces, such as the LIFE and USEA, have also demonstrated the utility of shielding for recording [29], [30]. Together these findings suggest that shielding should be an integral component of any chronic peripheral nerve recording interface.…”

Section: Discussionmentioning

confidence: 81%

Model-based Bayesian signal extraction algorithm for peripheral nerves

et al. 2017

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Objective Multi-channel cuff electrodes have recently been investigated for extracting fascicular-level motor commands from mixed neural recordings. Such signals could provide volitional, intuitive control over a robotic prosthesis for amputee patients. Recent work has demonstrated success in extracting these signals in acute and chronic preparations using spatial filtering techniques. These extracted signals, however, had low signal-to-noise ratios and thus limited their utility to binary classification. In this work a new algorithm is proposed which combines previous source localization approaches to create a model based method which operates in real time. Approach To validate this algorithm, a saline benchtop setup was created to allow the precise placement of artificial sources within a cuff and interference sources outside the cuff. The artificial source was taken from five seconds of chronic neural activity to replicate realistic recordings. The proposed algorithm, Hybrid Bayesian Signal Extraction (HBSE), is then compared to previous algorithms, beamforming and a Bayesian spatial filtering method, on this test data. An example chronic neural recording is also analyzed with all three algorithms. Main Results The proposed algorithm improved the signal to noise and signal to interference ratio of extracted test signals two to three fold, as well as increased the correlation coefficient between the original and recovered signals by 10–20%. These improvements translated to the chronic recording example and increased the calculated bit rate between the recovered signals and the recorded motor activity. Significance HBSE significantly outperforms previous algorithms in extracting realistic neural signals, even in the presence of external noise sources. These results demonstrate the feasibility of extracting dynamic motor signals from a multi-fascicled intact nerve trunk, which in turn could extract motor command signals from an amputee for the end goal of controlling a prosthetic limb.

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Recording sensory and motor information from peripheral nerves with Utah Slanted Electrode Arrays

Cited by 51 publications

References 11 publications

Simulating tactile signals from the whole hand with millisecond precision

Simulating tactile signals from the whole hand with millisecond precision

Imaging fast neural traffic at fascicular level with electrical impedance tomography: proof of principle in rat sciatic nerve

Model-based Bayesian signal extraction algorithm for peripheral nerves

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