Peripheral Nerve Activation Evokes Machine-Learnable Signals in the Dorsal Column Nuclei

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dc.contributor.authorLoutit, Alastair
dc.contributor.authorShivdasani, Mohit
dc.contributor.authorMaddess, Ted
dc.contributor.authorRedmond, Stephen
dc.contributor.authorMorley, John
dc.contributor.authorStuart, Greg
dc.contributor.authorBirznieks, Ingvars
dc.contributor.authorVickery, Richard
dc.contributor.authorPotas, Jason
dc.date.accessioned2019-04-23T04:02:29Z
dc.date.available2019-04-23T04:02:29Z
dc.date.issued2019-04
dc.description.abstractThe 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.en_US
dc.description.sponsorshipThe authors are extremely grateful to the Bootes Medical Research Foundation which funded this project. AL was supported by the Australian Government Research Training Program.en_US
dc.identifier.citationLoutit, 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.en_US
dc.identifier.issn1662-5137 (Print) 1662-5137
dc.identifier.urihttp://repository.bionicsinstitute.org:8080/handle/123456789/344
dc.language.isoenen_US
dc.publisherFrontiers in Systems Neuroscienceen_US
dc.subjectMachine learingen_US
dc.subjectTactileen_US
dc.subjectProprioceptionen_US
dc.subjectSomatosensoryen_US
dc.subjectLateralizationen_US
dc.subjectNeural prosthesisen_US
dc.subjectGracile nucleien_US
dc.titlePeripheral Nerve Activation Evokes Machine-Learnable Signals in the Dorsal Column Nucleien_US
dc.typeArticleen_US
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