2.9. Source level

To identify the sources of the obtained brain activity, the MRI scans of participants' heads were realigned with the coordinates of the MEG data by marking the fiducials in both ear canals and the nasion. Then, they were resliced and segmented into brain, scalp, and skull using SPM12, to obtain realistic single‐shell models of the inside of the skull with a resolution of 10 mm, serving as individual volume conduction models of participants’ heads (Nolte, 2003). Next, the realigned MRI scans were warped to the Montreal Neurological Institute (MNI) space template to obtain subject‐specific source model grids in normalized space, so they could be compared across participants. Using the volume conduction models, lead field matrices were computed for each grid point per participant. Then, a frequency domain beamforming technique (DICS) was applied to estimate the activity at the source level (Gross et al., 2001). The cross‐spectral density matrix of the sensor level data for both conditions combined was computed at 15 Hz. Spectral smoothing of 5 Hz yielded a cross‐spectral density matrix between 10 and 20 Hz. This frequency range was based on previous findings resulting from the same task and analysis techniques (Piai et al., 2015) as well as the present sensor level findings (see below). As the transition from alpha to beta activity is usually considered around 12–15 Hz, this frequency range is referred to as alpha–beta power in the present report. Together with the lead field matrices, the cross‐spectral density matrices were used to calculate a common spatial filter for each grid point. These filters were then applied to the Fourier transformed sensor data per condition to estimate source level power for each grid point. Then, the power estimates for constrained and unconstrained trials were averaged for each participant. The difference in source power between the constrained and unconstrained conditions was evaluated on the group level using the same non‐parametric cluster‐based permutation test as for the sensor level data mentioned above. A dependent‐sample t test thresholded at an alpha level of 0.05 served to identify the biggest cluster of neighboring grid points showing a difference between the two conditions. The Monte Carlo p‐value of this cluster was calculated as the amount out of 5,000 random permutations that yielded a more extreme effect than the observed one evaluated at an alpha level of 0.05 (two‐tailed).

To gain a similar measure of the effect size at both time points, we also calculated Cohen's d (Cohen, 1988) over the whole brain for each session. This was done for each point in the grid by taking the alpha–beta power differences between the two experimental conditions and calculating the standard deviation over all participants of these non‐standardized effect sizes. Then, Cohen's d maps were computed by dividing the grand average of the non‐standardized effect sizes by this standard deviation obtained from the previous step. This yielded an effect size measure for each grid point.

To investigate the robustness of our effect for an MEG experiment of shorter duration, the first 112 trials (out of 224 trials in total) were selected from each session. This served as a representation of half a session and was analyzed in the same way as the full sessions specified above. Importantly, the trial selection for half a session was performed only after the preprocessing step, meaning that incorrect, noisy, and blinking trials were discarded previously. Thus, every representation of half a session consisted of 112 clean and correct trials for all participants, with an average of 55 unconstrained trials (SD = 4) and 57 constrained trials (SD = 4). To compare the effect size of both sessions, we also calculated Cohen's d for the half session, in the same way as for the full session described above.