Cortical activity during motor execution, motor imagery, and imagery-based online feedback

Abstract

Imagery of motor movement plays an important role in learning of complex motor skills, from learning to serve in tennis to perfecting a pirouette in ballet. What and where are the neural substrates that underlie motor imagery-based learning? We measured electrocorticographic cortical surface potentials in eight human subjects during overt action and kinesthetic imagery of the same movement, focusing on power in "high frequency" (76-100 Hz) and "low frequency" (8-32 Hz) ranges. We quantitatively establish that the spatial distribution of local neuronal population activity during motor imagery mimics the spatial distribution of activity during actual motor movement. By comparing responses to electrocortical stimulation with imagery-induced cortical surface activity, we demonstrate the role of primary motor areas in movement imagery. The magnitude of imagery-induced cortical activity change was approximately 25% of that associated with actual movement. However, when subjects learned to use this imagery to control a computer cursor in a simple feedback task, the imagery-induced activity change was significantly augmented, even exceeding that of overt movement.

Fig. 1.

Spectral changes in cortical surface potentials during hand and tongue movement and imagery in subject 1. (A) On the left, a characteristic example of the cortical potential power spectral density (PSD) for hand movement (red) and rest (blue) is shown. On the right, the same is seen between hand imagery (red) and rest (blue). The PSDs are from a primary motor electrode (Brodmann area 4, Talairach coordinate [−43, −14, 56], circled in B), referenced to the common average. Power at low frequencies (“LFB,” 8–32 Hz, green) decreases with movement/imagery, and power at high frequencies (“HFB,” 76–100 Hz, orange) increases during movement/imagery. In this electrode, the HFB increase with imagery is 32% that of movement (comparing orange areas). For the LFB, it is 90% (green areas). (B) The electrode positions are shown along with the electrodes in which stimulation produced movement of the hand (light blue) or tongue (light pink). The PSD in A is from the circled electrode. (C) Interpolated HFB activation maps for hand and tongue movement and imagery are shown on the left. Each is scaled to the maximum absolute value of activation (indicated by the number above each cortical map). On the right, the overlap is quantified between hand and tongue movement (yellow), hand movement and imagery (light blue), and tongue movement and imagery (light pink). ø, significance, P > 0.01 (by reshuffling, in this case P = 0.27). (D) As in C, for the LFB. All but the HFB hand movement vs. tongue movement comparison were significantly overlapping, with P < 10^–4.

Fig. 2.

Comparison of cortical activity during movement and imagery. (A) The plot shows the ratio of shift in power during imagery to that during movement for electrodes in which activity between movement and rest was significantly different. Each white dot indicates the ratio at an individual electrode. The geometric mean of the imagery:movement ratios for the HFB was 0.26. For the LFB it was 0.49 (LFB ratio was significantly larger than HFB ratio, P = 0.005 by permutation resampling, 10⁵ iterations). (B) For subjects 2–5, the overlap is quantified between hand and tongue movement (yellow), hand movement and imagery (light blue), and tongue movement and imagery (light pink). ø, significance, P > 0.01 (by reshuffling).

Fig. 3.

Changes in cortical activity during feedback control of a cursor using ECoG in subject 1. (A) A specific electrode-frequency combination was identified from an initial motor task (gold electrode, 79–95 Hz; ECS-identified primary tongue cortex; see Fig. 1). The power, P(t), in this feature was used to control the velocity of a computer cursor following the simple linear equation shown. The cursor velocity was derived every 40 ms from the power P(t) at the selected channel and frequency during the previous 280 ms (with respect to mean power, P₀). The subject was instructed to imagine saying the word ‘move’ to move the cursor toward one target (“active” target) and to rest (or “idle”) to move the cursor to the other target (“passive” target). (B) The power at the chosen electrode-frequency combination is shown during four consecutive experimental runs of the cursor feedback task. Red dots indicate the mean power during active target trials, and blue dots indicate the mean power during passive target trials (datum noted with a cross represents an outlier lying beyond the upper edge of the plot). The green line denotes P₀, the mean power across passive/active trials. The black line indicates a “discriminative index”; i.e., the smoothed difference between the mean power during the previous three active target trials and the previous three passive target trials. This index demonstrates that target accuracies (shown in C) were highest when the subject found a middle dynamic range. After the third run, the subject reported having ceased to perform imagery, and instead “thought about the cursor moving up or down to get it to move” at some point during the run. (C) Distribution of HFB (upper brain plots) and LFB activations, as well as target hit accuracies (% next to run number), during each of the four experimental runs. All activation maps are to the same scale (indicated by the color bar). The final activations are most prominent at the electrode that was used for cursor control. The number flanking each brain plot is the maximum (absolute value) activation.

Fig. 4.

Augmentation of cortical activity during learning in subject 2. (A) Electrocortical stimulation sites that produced face/hand movement (not further specified by neurologist) are shown. One of these (gold-circled) was selected from the motor/imagery tasks for feedback (using power from 37 to 43 Hz). (B) ECoG-based brain activation maps for tongue movement, imagined movement, and feedback-based BCI control of cursor, in the HFB and LFB ranges. Activation in each map is scaled to the maximum absolute activation (noted by flanking number). The feedback electrode is noted in each by an enlarged black dot. (C) Relative activation in the electrode-frequency combination for each of the conditions, computed by dividing the imagery- and feedback-related activation by the activation for actual movement, and normalizing the magnitude of activation for actual movement to 1 for all plots.

Fig. 5.

Relative activation for movement, imagery, and feedback in subjects S1, S2, S6, and S8, as in Fig. 3C. Subject 1 did not perform a speech (word repetition) imagery task. Together with the result of Fig. 3, the bar plots show that the magnitude of feedback imagery-related activation after learning is augmented, and, in four of five cases, exceeds the magnitude of activation for actual movement.