Dynamic causal modeling of the cortical responses to wrist perturbations

Yuan Yang, Bekir Guliyev, Alfred Schouten

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)
39 Downloads (Pure)

Abstract

Mechanical perturbations applied to the wrist joint typically evoke a stereotypical sequence of cortical and muscle responses. The early cortical responses (<100 ms) are thought be involved in the “rapid” transcortical reaction to the perturbation while the late cortical responses (>100 ms) are related to the “slow” transcortical reaction. Although previous studies indicated that both responses involve the primary motor cortex, it remains unclear if both responses are engaged by the same effective connectivity in the cortical network. To answer this question, we investigated the effective connectivity cortical network after a “ramp-and-hold” mechanical perturbation, in both the early (<100 ms) and late (>100 ms) periods, using dynamic causal modeling. Ramp-and-hold perturbations were applied to the wrist joint while the subject maintained an isometric wrist flexion. Cortical activity was recorded using a 128-channel electroencephalogram (EEG). We investigated how the perturbation modulated the effective connectivity for the early and late periods. Bayesian model comparisons suggested that different effective connectivity networks are engaged in these two periods. For the early period, we found that only a few cortico-cortical connections were modulated, while more complicated connectivity was identified in the cortical network during the late period with multiple modulated cortico-cortical connections. The limited early cortical network likely allows for a rapid muscle response without involving high-level cognitive processes, while the complexity of the late network may facilitate coordinated responses.
Original languageEnglish
Article number518
Number of pages8
JournalFrontiers in Neuroscience
Volume11
DOIs
Publication statusPublished - 2017

Keywords

  • sensory feedback
  • stretch response
  • dynamic causal modeling
  • sensorimotor network
  • EEG
  • effective connectivity
  • OA-Fund TU Delft

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