The relationship between network features (coreness of nodes, in-degrees, and out-degrees) and power of each was evaluated to understand how network features and power could be related. We conducted the following analyses, which are summarized in the results. We analyzed positive and negative correlations of network features and hγ power, results shown in Figure 6. We evaluated correlations between in-degrees, out-degrees with power in five frequency bands (hγ, γ, β, α, and θ), and demonstrated the relationships between the frequencies and the network features, results shown in Extended Data Figures 6-1, 6-2. An intuitive understanding of how in/out degrees correlate with power is shown in Extended Data Figure 6-3. Evaluated correlations between coreness of nodes, in-degrees, out-degrees with hγ power, for all time windows (SA + AA; results in Fig. 7). Evaluated correlations between coreness of nodes, in-degrees, out-degrees with hγ power, for time windows SA and AA separately, since the processes underlying SA and AA windows could be different (results in Extended Data Fig. 7-1).

With an understanding of how network features are related to power, we then evaluate whether network features provide additional information to the language process, compared with power alone, using data from DCS.

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