We applied the network-identification approach to source-level coherence estimated from scalp-EEG as a function of time and frequency: In a first step, we computed coherence between all pairs of sources (400 × 400), at each point in time (n = 17; −0.8 to 0.8 in steps of 0.1) and frequency (n = 21; 4 to 128 Hz in steps of 0.25 octaves), and for each subject and condition. This results in an eight-dimensional space of connections (time × frequency × 3D space × 3D space). A single voxel in this space has a “volume” of 0.025 cm6 × s × oct (1 cm3 × 1 cm3

× 0.1 s × 0.25 octave). To compare coherence between conditions (bounce versus pass; stimulation versus baseline), we computed a t-statistic of the difference in z-transformed coherence between conditions across subjects (random effects statistic). We thresholded the

this website t-statistic at p = 0.01, resulting in a binary matrix with 0 for “smaller than threshold” (“no connection”) and 1 for “larger than threshold” (“connection”). We then performed a neighborhood filtering (filter parameter, learn more 0.5) by removing each connection that has a fraction of less than 0.5 directly neighboring connections (i.e., locations that differ by one unit in a single dimension, such as the same position and frequency but one time step difference). The neighborhood filtering results in a low-pass filtering of the connection-space and removes spurious bridges between connection clusters. We identified clusters in the eight-dimensional connection space as groups of connections that are linked through direct neighborhood relations (neighboring voxels with 1). Such a cluster corresponds to a network of cortical regions with different synchronization Rolziracetam between conditions that is continuous across time, frequency, and pairwise space. For each cluster, we defined its size as the integral of the t-scores (condition difference) across the volume of the cluster and tested its statistical significance using

a permutation statistic. We repeated the cluster identification 104 times (starting with the t-statistic between conditions) with shuffled condition labels to create an empirical distribution of cluster sizes under the null-hypothesis of no difference between conditions. The null-distribution was constructed from the largest clusters (two-tailed) of each resample therefore accounting for multiple comparisons (Nichols and Holmes, 2002). To optimize statistical sensitivity, we applied a Holm-correction (Holm, 1979): If a significant cluster was found, we removed the most significant cluster from the eight-dimensional space and repeated the analysis until no significant cluster remained.