EEG Spectral Generators Involved in Motor Imagery: A swLORETA Study.

Ana-Maria Cebolla,Guy Cheron,Axelle Leroy,Ernesto Palmero-Soler

doi:10.3389/fpsyg.2017.02133

Abstract

In order to characterize the neural generators of the brain oscillations related to motor imagery (MI), we investigated the cortical, subcortical, and cerebellar localizations of their respective electroencephalogram (EEG) spectral power and phase locking modulations. The MI task consisted in throwing a ball with the dominant upper limb while in a standing posture, within an ecological virtual reality (VR) environment (tennis court). The MI was triggered by the visual cues common to the control condition, during which the participant remained mentally passive. As previously developed, our paradigm considers the confounding problem that the reference condition allows two complementary analyses: one which uses the baseline before the occurrence of the visual cues in the MI and control resting conditions respectively; and the other which compares the analog periods between the MI and the control resting-state conditions. We demonstrate that MI activates specific, complex brain networks for the power and phase modulations of the EEG oscillations. An early (225 ms) delta phase-locking related to MI was generated in the thalamus and cerebellum and was followed (480 ms) by phase-locking in theta and alpha oscillations, generated in specific cortical areas and the cerebellum. Phase-locking preceded the power modulations (mainly alpha–beta ERD), whose cortical generators were situated in the frontal BA45, BA11, BA10, central BA6, lateral BA13, and posterior cortex BA2. Cerebellar-thalamic involvement through phase-locking is discussed as an underlying mechanism for recruiting at later stages the cortical areas involved in a cognitive role during MI.

Highlights

Motor imagery (MI), or the mental simulation of specific actions (Jeannerod, 1994; Jeannerod and Decety, 1995), has increasingly been used as a research paradigm in cognitive neuroscience, both as a type of neurological therapy (Cramer et al, 2007; Grangeon et al, 2012), and as a technique in sports training (Wang et al, 2014; Di Rienzo et al, 2015)
We provide evidence that: (i) no matter which perspective used, the early phase locking (225 ms after visual cues) in delta oscillation related to MI was generated in the thalamus and the cerebellum; (ii) this delta wave was followed (480 ms) by concomitant phase locking in theta and alpha oscillations, generated in well-defined cortical areas and cerebellum depending of the perspective used; (iii) these phaselocked generators preceded the contribution of eventrelated spectral perturbation (ERSP) cortical generators from anterior in the frontal cortex BA45, BA11, BA10 to central BA6, lateral BA13 and posterior cortex BA2 related to MI
Independent of the control condition, an early delta phase locking was generated in the thalamus and cerebellum

Summary

Introduction

Motor imagery (MI), or the mental simulation of specific actions (Jeannerod, 1994; Jeannerod and Decety, 1995), has increasingly been used as a research paradigm in cognitive neuroscience, both as a type of neurological therapy (Cramer et al, 2007; Grangeon et al, 2012), and as a technique in sports training (Wang et al, 2014; Di Rienzo et al, 2015). Because of the complexity of the brain’s resting state dynamics (Wang et al, 2009; Ferrarini et al, 2011) out of which MI operation emerges (Zhang et al, 2015; Saiote et al, 2016), the choice of a control condition remains a challenge (Gusnard et al, 2001) Confronted with this complex situation, which involves a dynamic interplay between conscious and unconscious processes (Cheron et al, 2009; Jerath et al, 2015) an ecological virtual reality (VR) environment seems appropriate for the study of brain oscillations related to MI, as it was demonstrated that ecological visual scenes produced similar brain activation patterns in human individuals (Hasson and Malach, 2006). As the source modeling used here does not underestimate deep sources (Trujillo-Barreto et al, 2004), we expect it will reveal the cortical, subcortical and cerebellar networks involved

Methods

Results

Discussion

Conclusion