Adaptation rate is similar across steps
As in previous experiments (Huber et al., 2004
; Landsness et al., 2009
; Perfetti et al., 2011
), movements in both RAN and ROT tasks were straight with sharp reversals and overlapping strokes. Velocity profiles were bell-shaped with clear acceleration and deceleration phases. Reaction time, movement duration and peak velocity were stable across the entire session ().
The directional accuracy at the peak velocity in fast reaching movements is a good proxy for planning accuracy. Thus, its changes in the course of adaptation reflect the switch from feedback to feedforward mechanisms: a higher accuracy at that point indicates a better plan, a greater degree of learning, and thus, a lesser need for feedback corrections. Indeed, during ROT, subjects progressively decreased the directional error at peak velocity across runs and steps (run: F(2,32)=55.01, p < 0.0001; step: F(3,48) = 39.04, p < 0.0001; run X step: F(6,96) = 0.75, p =0. 610, ). This result was confirmed by the finding of lower directional errors in the last 30 movements compared to the first 30 movements of each step (blocks: F(1,16) = 141.93, p < 0.0001; Step: F(3,48) = 29.26 p<0.0001; run X step: F(6,96) =1.7, p=0.18; ). Importantly, adaptation rates (i.e., the difference of directional error between the first and last 30 movements) were similar across steps (F(3,48) = 1.71, p = 0.20, ). Thus, for each subject, we computed an average index of early adaptation (EARLY ROT) and one of late adaptation (LATE ROT) by averaging the corresponding directional errors across the four steps. These indices were used for correlations with EEG-derived indices (see below).
Finally, as previously reported (Huber et al., 2004
; Landsness et al., 2009
), adaptation rates were faster when tested the following day, (t(12)
= 2.44, p = 0.031; Day1 = 11.3 ± 1.2; Day2 = 9.02 ± 1), suggesting the occurrence of retention and enhancement in this visuo-motor skill. Notably, when subjects were asked whether they have noticed anything during the task execution, reported that something “strange” was going on with the mouse, but nobody ever mentioned explicitly that the cursor on the screen was rotated.
Modulation of gamma and theta activity in a right parietal cortical region
To find the EEG correlates of the switch from feedback to feedforward mechanisms, and thus the development of learning, we first used power analysis to compare RAN (baseline motor task) and EARLY ROT, RAN and LATE ROT as well as between EARLY ROT and LATE ROT for all the frequency bands, in both the motor planning and execution temporal windows (250 ms each, see methods). shows the significant results. EARLY ROT and RAN did not significantly differ in the motor planning window in any of the frequency bands. However, during movement execution, we found a selective increase of gamma in a small set of electrodes over the right posterior parietal scalp region (). In the same area, when we compared LATE ROT and EARLY ROT, we found a significant increase of theta oscillatory activity during movement planning, while there was a decrease of gamma activity during movement execution. Finally, during both movement planning and execution, a significant increase of gamma activity was present over frontal sites. Altogether, these results suggest that the right parietal region plays an important role in the switch from feedback to feedforward mechanisms.
These findings were confirmed and further characterized with the direct comparison of the mean spectral estimates in the three conditions, RAN, EARLY ROT and LATE ROT. These analyses were performed for the two regions of interest (ROIs), the right parietal (RP) and the frontal (FR, ), obtained by pooling the electrodes with significant power variation. The results are summarized in (bars). Briefly, in right parietal ROI, the tree conditions differed in gamma activity during movement execution (F(2,32) = 10.93, p < .001), with higher values in EARLY ROT as compared to RAN (p < .001) and to LATE ROT (p = .016). Also, theta activity during movement planning showed an effect of condition (F(2,32) = 3.8, p = .033), with higher values in the LATE ROT compared to EARLY (p < .001). For the frontal ROI (, bars), gamma activity did not show any difference between the two temporal windows: in both, there was a significant effect of condition (F(2,32) = 5.1, p = .012), with a significant decrease in EARLY ROT as compared to RAN (p = .025) and to LATE ROT (EARLY vs. LATE: p = .018). Importantly, the changes in both gamma and theta oscillatory activity between EARLY and LATE ROT were present for all the four rotation steps, as illustrated in the bottom diagrams of . Even if adequate procedures were used to reduce artifacts associated with eye movements, muscle and eye activities might have partially affected the results obtained in the frontal ROI.
We then ascertained whether the gamma and theta differences between EARLY and LATE ROT in right parietal ROI could predict the degree of adaptation and retention in our task. We found that the higher was the increase in right parietal ROI theta oscillatory activity in LATE as compared to EARLY ROT during movement planning, the larger was the degree of adaptation (r = 0.5; p = .04; N = 17). No correlation was found with any of the other kinematics indices (all r < 0.14; p > 0.57), suggesting a specificity of theta modulation in memory processes. In addition, in the subset of 13 subjects in which retention was tested the following day, we found that the same change in theta power showed a correlational trend with the degree of retention (r = 0.5 p = .08; N = 13). No relevant correlations were found between gamma band and behavioral indices of learning or retention, suggesting that theta activity in right parietal ROI plays a specific role in the promotion and maintenance of new motor memory.
In summary, our results suggest that learning might be related to a local switch, in the right parietal regions, from gamma activity during movement execution in EARLY adaptation to theta oscillatory activity during movement planning in the LATE stages of adaptation. It is worth noting that, in order to estimate the contribution of the phase locked oscillatory activity to our findings, we computed the time/frequency transformations on the averaged signal and performed the same analysis as described above. Interestingly, the oscillatory activity phase locked to the movement onset did not show any significant difference between the conditions of interests in any frequency band (data not shown).
During adaptation, gamma/theta phase coupling is enhanced in motor planning
Analysis of the local dynamics of this phenomenon in right parietal ROI should indeed provide important insights on how the brain dynamically forms new and, possibly, more efficient internal models during adaptation. In agreement with the present and earlier results that the right posterior parietal cortex plays an important role in visuo-motor adaptation (Ghilardi et al., 2000
; Huber et al., 2004
), we focused our attention on the right parietal ROI. In particular, we investigated the functional connectivity across gamma and theta frequencies, which has been recently suggested as a marker of memory formation (Lisman and Idiart, 1995
; Sauseng et al., 2010
). The findings of the cross-FrPC are reported in . The mean gamma/theta coherence values over the planning and execution of reaching plateaued in the gamma range between 35 and 50 Hz (). EARLY ROT compared to RAN yielded a general increase of gamma/theta phase coupling before the movement onset (), reaching statistical significance during movement planning just after stimulus presentation. In the LATE ROT, the increased gamma/theta phase coherence was still evident before the movement onset. In summary, compared to RAN, movement planning during adaptation is accompanied by an enhancement of gamma/theta phase coupling, which is more evident in the EARLY ROT (). Direct comparison between LATE and EARLY ROT revealed a significant decrease of gamma/theta phase synchronization during movement execution ().
Figure 4 Cross Frequency Phase Coherence (Cross-FrPC) between gamma and theta frequencies within the Right Parietal ROI. A: top: Mean theta/gamma Cross-FrPC values for RAN, EARLY ROT and LAT ROT. The y-axis represents gamma frequency in bins; the x-axis represents (more ...)
The higher values of gamma/theta phase coupling in the right parietal region during the adaptation process might reflect the dynamic changes related to synaptic plasticity -and thus, to the formation of new memories-in the posterior cortex (Wespatat et al., 2004
; Jensen et al., 2007
). In this context, the connectivity of the right parietal ROI with other regions acquires particular importance, as it might reveal the formation of transient neural assemblies important for motor learning.
Enhanced gamma band connectivity between centro-posterior regions occurs during early adaptation
To determine the functional connectivity of the right parietal ROI with the other sites of the scalp, we computed cross channel phase coherence between the right parietal ROI and all the 181 electrodes. The obtained topographies in the planning and execution intervals were similar between conditions for all the frequency bands. Task-related coherence variation for all frequency bands was tested separately for planning and execution with the SnPM approach. The significant findings are shown in . Briefly, the only significant differences were found in the gamma band (25-55 Hz) between EARLY ROT and RAN (p < 0.05). Specifically, in the EARLY ROT and only during movement planning, there was a significant enhancement of phase coupling of the right parietal area with the contralateral homologous region. During movement execution, this phase synchronization spread widely over the left hemisphere, reaching significance over the left parieto-occipital region. These results suggest that the early stages of adaptation are associated with enhanced connectivity in the gamma band between the centro-posterior regions of the two hemispheres. However, we must acknowledge that the use of reference-dependend EEG signal for the phase coherence analysis could have biased our results.
Figure 5 Cross Channel Phase Coherence (Cross-ChPC) for the four frequency bands between the Right Parietal ROI and all the remaining electrodes. A: The maps show the T-scores obtained from the comparisons of Cross-ChPC across RAN, EARLY ROT and LATE ROT during (more ...)