Encoding of forelimb forces by corticospinal tract activity in the rat

Yi Guo, Richard A. Foulds, Sergei V. Adamovich, Mesut Sahin

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

In search of a solution to the long standing problems encountered in traditional brain computer interfaces (BCI), the lateral descending tracts of the spinal cord present an alternative site for taping into the volitional motor signals. Due to the convergence of the cortical outputs into a final common pathway in the descending tracts of the spinal cord, neural interfaces with the spinal cord can potentially acquire signals richer with volitional information in a smaller anatomical region. The main objective of this study was to evaluate the feasibility of extracting motor control signals from the corticospinal tract (CST) of the rat spinal cord. Flexible substrate, multi-electrode arrays (MEA) were implanted in the CST of rats trained for a lever pressing task. This novel use of flexible substrate MEAs allowed recording of CST activity in behaving animals for up to three weeks with the current implantation technique. Time-frequency and principal component analyses (PCA) were applied to the neural signals to reconstruct isometric forelimb forces. Computed regression coefficients were then used to predict isometric forces in additional trials. The correlation between measured and predicted forces in the vertical direction averaged across six animals was 0.67 and R2 value was 0.44. Force regression in the horizontal directions was less successful, possibly due to the small amplitude of forces. Neural signals above and near the high gamma band made the largest contributions to prediction of forces. The results of this study support the feasibility of a spinal cord computer interface (SCCI) for generation of command signals in paralyzed individuals.

Original languageEnglish (US)
Article numberArticle 62
JournalFrontiers in Neuroscience
Issue number8 MAY
DOIs
StatePublished - 2014

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)

Keywords

  • Brain computer interfaces
  • Corticospinal tract
  • Time-frequency analysis

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