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Title: Peripheral Nerve Activation Evokes Machine-Learnable Signals in the Dorsal Column Nuclei
Authors: Loutit, Alastair
Shivdasani, Mohit
Maddess, Ted
Redmond, Stephen
Morley, John
Stuart, Greg
Birznieks, Ingvars
Vickery, Richard
Potas, Jason
Keywords: Machine learing
Tactile
Proprioception
Somatosensory
Lateralization
Neural prosthesis
Gracile nuclei
Issue Date: Apr-2019
Publisher: Frontiers in Systems Neuroscience
Citation: Loutit, A. J., M. N. Shivdasani, T. Maddess, S. J. Redmond, J. W. Morley, G. J. Stuart, I. Birznieks, R. M. Vickery, and J. R. Potas. 2019. Peripheral Nerve Activation Evokes Machine-Learnable Signals in the Dorsal Column Nuclei. Front Syst Neurosci. 13: 11.
Abstract: 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.
URI: http://repository.bionicsinstitute.org:8080/handle/123456789/344
ISSN: 1662-5137 (Print) 1662-5137
Appears in Collections:Bionic Hearing Research Publications

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