Nonlinear coupling between occipital and motor cortex during motor imagery: a dynamic causal modeling study.

We demonstrate the capacity of dynamic causal modeling to characterize the nonlinear coupling among cortical sources that underlie time-frequency modulations in MEG data. Our experimental task involved the mental rotation of hand drawings that ten subjects used to decide if it was a right or left hand. Reaction times were shorter when the stimuli were presented with a small rotation angle (fast responses) compared to a large rotation angle (slow responses). The grand-averaged data showed that in both cases performance was accompanied by a marked increase in gamma activity in occipital areas and a concomitant decrease in alpha and beta power in occipital and motor regions. Modeling directed (cross) frequency interactions between the two regions revealed that after the stimulus induced a gamma increase and beta decrease in occipital regions, interactions with the motor area served to attenuate these modulations. The difference between fast and slow behavioral responses was manifest as an altered coupling strength in both forward and backward connections, which led to a less pronounced attenuation for more difficult (slow reaction time) trials. This was mediated by a (backwards) beta to gamma coupling from motor till occipital sources, whereas other interactions were mainly within the same frequency. Results are consistent with the theory of predictive coding and suggest that during motor imagery, the influence of motor areas on activity in occipital cortex co-determines performance. Our study illustrates the benefit of modeling experimental responses in terms of a generative model that can disentangle the contributions of intra-areal vis-à-vis inter-areal connections to time-frequency modulations during task performance.