Motor Inhibition during Overt and Covert Actions: An Electrical Neuroimaging Study

Abstract

Given ample evidence for shared cortical structures involved in encoding actions, whether or not subsequently executed, a still unsolved problem is the identification of neural mechanisms of motor inhibition, preventing "covert actions" as motor imagery from being performed, in spite of the activation of the motor system. The principal aims of the present study were the evaluation of: 1) the presence in covert actions as motor imagery of putative motor inhibitory mechanisms; 2) their underlying cerebral sources; 3) their differences or similarities with respect to cerebral networks underpinning the inhibition of overt actions during a Go/NoGo task. For these purposes, we performed a high density EEG study evaluating the cerebral microstates and their related sources elicited during two types of Go/NoGo tasks, requiring the execution or withholding of an overt or a covert imagined action, respectively. Our results show for the first time the engagement during motor imagery of key nodes of a putative inhibitory network (including pre-supplementary motor area and right inferior frontal gyrus) partially overlapping with those activated for the inhibition of an overt action during the overt NoGo condition. At the same time, different patterns of temporal recruitment in these shared neural inhibitory substrates are shown, in accord with the intended overt or covert modality of action performance. The evidence that apparently divergent mechanisms such as controlled inhibition of overt actions and contingent automatic inhibition of covert actions do indeed share partially overlapping neural substrates, further challenges the rigid dichotomy between conscious, explicit, flexible and unconscious, implicit, inflexible forms of motor behavioral control.

Fig 1. Experimental paradigm and stimuli.

(A) Session A: Go and NoGo conditions. (B) Session B: Motor Imagery and NoGo Motor Imagery conditions.

Fig 2. Event related potential (ERP) waveforms.

Group-averaged (n = 15) stimulus-locked ERP waveforms (plotted as voltage in μV in function of time in ms, stimulus onset: 0 ms) for the four experimental conditions from Fz, Cz and Pz electrodes. MI: Motor Imagery; NoGoMI: NoGo Motor Imagery.

Fig 3. Electrophysiological results over the 700 ms post-stimulus period (stimulus onset: 0 ms) of session A.

(A1 and C1) Group-averaged (n = 15) ERP waveforms for Go (A1) and NoGo (C1) conditions, superimposed across the 110 recording channels (e1–e110). (A2 and C2) Microstate segmentation results for Go (A2) and NoGo (C2) conditions. The temporal distribution of the microstates in each condition revealed by the spatio-temporal segmentation analysis applied on session A dataset is reported on the curve of the global field power (GFP) (i.e., the variance of the 110 channels over the whole scalp at a given time point). Each microstate and its temporal window are indicated by different colors; the same color indicates the same microstate. (B) Mean topographic maps and related mean LAURA source estimations (in red panels) corresponding to each microstate for the group-averaged ERP data. All topographic maps are plotted with nasion upward and left scalp leftward; each map is scaled separately with respect to its maximum and minimum values to optimise the contrast. The current density maxima resulting from source estimations (green: low current density; red: high current density) are rendered on horizontal slices of MNI152 template brain (left hemisphere on the left side); source estimation for each microstate is independently scaled with respect to its maximum value.

Fig 4. Electrophysiological results over the 700 ms post-stimulus period (stimulus onset: 0 ms) of session B.

(A1 and C1) Group-averaged (n = 15) ERP waveforms for Motor Imagery (MI) (A1) and NoGo Motor Imagery (NoGoMI) (C1) conditions, superimposed across the 110 recording channels (e1–e110). (A2 and C2) Microstate segmentation results for MI (A2) and NoGoMI (C2) conditions. (B) Mean topographic maps and related mean LAURA source estimations (in red panels) corresponding to each microstate for group-averaged ERP data. All other conventions as in Fig 3.

Fig 5. Statistical comparisons of LAURA source estimations between condition-specific microstates.

NoGo vs. Go conditions. (A) NoGo-Map 5 vs. Go-Map 6. (B) NoGo-Map 8 vs. Go-Map 7. (C) NoGo-Map 10 vs. Go-Map 9. All significant voxels are colored (t ₍₁₄₎ > 2.14 / < -2.14, P < 0.05): positive t-values (red color) indicate higher current source densities in NoGo than in Go condition; negative t-values (blue color) indicate higher current source densities in Go than in NoGo condition. LAURA solutions are rendered on MNI152 template brain.

Fig 6. Statistical comparisons of LAURA source estimations between condition-specific microstates.

MI vs.: NoGoMI, Go, NoGo conditions. (A) MI-Map 5 vs. NoGoMI-Map 4. (B) MI-Map 5 vs. Go-Map 6. (C) MI-Map 5 vs. NoGo-Map 5. Positive t-values (red color) indicate higher current source densities in MI than in the compared condition; negative t-values (blue color) indicate higher current source densities in the compared condition than in MI condition. All other conventions as in Fig 5.