Joint cross-correlation analysis reveals complex, time-dependent functional relationship between cortical neurons and arm electromyograms.

Published

Journal Article

Correlation between cortical activity and electromyographic (EMG) activity of limb muscles has long been a subject of neurophysiological studies, especially in terms of corticospinal connectivity. Interest in this issue has recently increased due to the development of brain-machine interfaces with output signals that mimic muscle force. For this study, three monkeys were implanted with multielectrode arrays in multiple cortical areas. One monkey performed self-timed touch pad presses, whereas the other two executed arm reaching movements. We analyzed the dynamic relationship between cortical neuronal activity and arm EMGs using a joint cross-correlation (JCC) analysis that evaluated trial-by-trial correlation as a function of time intervals within a trial. JCCs revealed transient correlations between the EMGs of multiple muscles and neural activity in motor, premotor and somatosensory cortical areas. Matching results were obtained using spike-triggered averages corrected by subtracting trial-shuffled data. Compared with spike-triggered averages, JCCs more readily revealed dynamic changes in cortico-EMG correlations. JCCs showed that correlation peaks often sharpened around movement times and broadened during delay intervals. Furthermore, JCC patterns were directionally selective for the arm-reaching task. We propose that such highly dynamic, task-dependent and distributed relationships between cortical activity and EMGs should be taken into consideration for future brain-machine interfaces that generate EMG-like signals.

Full Text

Duke Authors

Cited Authors

  • Zhuang, KZ; Lebedev, MA; Nicolelis, MAL

Published Date

  • December 1, 2014

Published In

Volume / Issue

  • 112 / 11

Start / End Page

  • 2865 - 2887

PubMed ID

  • 25210153

Pubmed Central ID

  • 25210153

Electronic International Standard Serial Number (EISSN)

  • 1522-1598

Digital Object Identifier (DOI)

  • 10.1152/jn.00031.2013

Language

  • eng

Conference Location

  • United States