Investigating Priming Effects of Physical Practice on Motor Imagery-Induced Event-Related Desynchronization

Abstract

For motor imagery (MI) to be effective, an internal representation of the to-be-imagined movement may be required. A representation can be achieved through prior motor execution (ME), but the neural correlates of MI that are primed by ME practice are currently unknown. In this study, young healthy adults performed MI practice of a unimanual visuomotor task (Group MI, n = 19) or ME practice combined with subsequent MI practice (Group ME&MI, n = 18) while electroencephalography (EEG) was recorded. Data analysis focused on the MI-induced event-related desynchronization (ERD). Specifically, changes in the ERD and movement times (MT) between a short familiarization block of ME (Block pre-ME), conducted before the MI or the ME combined with MI practice phase, and a short block of ME conducted after the practice phase (Block post-ME) were analyzed. Neither priming effects of ME practice on MI-induced ERD were found nor performanceenhancing effects of MI practice in general. We found enhancements of the ERD and MT in Block post-ME compared to Block pre-ME, but only for Group ME&MI. A comparison of ME performance measures before and after the MI phase indicated however that these changes could not be attributed to the combination of ME and MI practice. The mixed results of this study may be a consequence of the considerable intra-and inter-individual differences in the ERD, introduced by specifics of the experimental setup, in particular the individual and variable task duration, and suggest that task and experimental setup can affect the interplay of ME and MI.

The Impact of Context on EEG Motor Imagery Neurofeedback and Related Motor Domains

“…In a recent study, we aimed to evaluate the effect of extended ME practice in a visuo-motor task. We did not find evidence that ME practice leads to a stronger subsequent MI-induced ERD of the same movement in comparison to no prior ME practice [58]. This contrasts with previous studies reporting priming effects (ME on MI: [56]; MI on ME: [57,58]), which we currently interpret, as an indication that already rather small differences between setups can lead to deviant or opposing findings.…”

“…We did not find evidence that ME practice leads to a stronger subsequent MI-induced ERD of the same movement in comparison to no prior ME practice [58]. This contrasts with previous studies reporting priming effects (ME on MI: [56]; MI on ME: [57,58]), which we currently interpret, as an indication that already rather small differences between setups can lead to deviant or opposing findings. To only name two aspects which may have prevented measurable behavioural and neural gains following MI [58] in our study, we did not include a preparation phase before the beginning of each trial, and the motor task was slightly more complex than in other studies.…”

“…This contrasts with previous studies reporting priming effects (ME on MI: [56]; MI on ME: [57,58]), which we currently interpret, as an indication that already rather small differences between setups can lead to deviant or opposing findings. To only name two aspects which may have prevented measurable behavioural and neural gains following MI [58] in our study, we did not include a preparation phase before the beginning of each trial, and the motor task was slightly more complex than in other studies. Further research is necessary to shed light on the specific relevance of these and other factors for MI NF.…”

Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling

“…First, we did not check whether ME performance was predictable using our proposed coupling strength. ME and MI share a common mechanism and motor circuit-related motor network ( Lee et al, 2016 ; Daeglau et al, 2020 ). In this regard, it would have been more effective to examine the relationship with ME performance, to enable the wider use of the predictor in the future.…”

“…SMA, supplementary motor area; DLPFC, dorsolateral prefrontal cortex; CSP-cv, common spatial pattern with cross-validation; CSP, common spatial pattern; CSSP, common spatio-spectral pattern; FBCSP, filter bank common spatial pattern; BSSFO, Bayesian spatio-spectral filter optimization. Daeglau et al, 2020). In this regard, it would have been more effective to examine the relationship with ME performance, to enable the wider use of the predictor in the future.…”