A phase-shifting anterior-posterior network organizes global phase relations

Abstract

Prior research has identified a variety of task-dependent networks that form through inter-regional phase-locking of oscillatory activity as neural correlates of specific behaviors. Despite ample knowledge of task-specific functional networks, general rules governing global phase relations have not been investigated. In order to discover such general rules, we focused on phase modularity, measured as the degree to which global phase relations in EEG comprised distinct synchronized clusters interacting with one another at large phase lags. Synchronized clusters were detected with a standard community-detection algorithm, and the level of phase modularity was quantified by the index q. Our findings suggest that phase modularity is functionally consequential since (1) temporal distribution of q was invariant across a broad range of frequencies (3-50 Hz examined) and behavioral conditions (resting with the eyes closed or watching a silent nature video), and (2) neural interactions (measured as power correlations) in beta-to-gamma bands consistently increased in high-modularity states. Notably, we found that the mechanism controlling phase modularity is remarkably simple. A network comprising anterior-posterior long-distance connectivity coherently shifted phase relations from low-angles (|Δθ| < π/4) in low-modularity states (bottom 5% in q) to high-angles (|Δθ| > 3π/4) in high-modularity states (top 5% in q), accounting for fluctuations in phase modularity. This anterior-posterior network likely plays a fundamental functional role as it controls phase modularity across a broad range of frequencies and behavioral conditions. These results may motivate future investigations into the functional roles of phase modularity as well as the anterior-posterior network that controls it.

Characteristics of spontaneous anterior-posterior oscillation-frequency convergences in the alpha band

“…Although taking the temporal derivative of EEG ( ∆,,-∆) , where ∆𝑡 is the temporal resolution, i.e., 1/512 sec here) would completely neutralize the 1/𝑓 * component only when 𝛽 = 1, the method worked well for our data (Menceloglu et al, 2020a(Menceloglu et al, , 2021a(Menceloglu et al, , 2021b(Menceloglu et al, , 2021c, suggesting that 𝛽~1 generally held for our data. As we discussed in our prior reports (Menceloglu et al, 2021a(Menceloglu et al, , 2021b(Menceloglu et al, , 2021c, taking EEG temporal derivative offers additional advantages. For example, EEG temporal derivatives are drift free.…”

“…Note that β in the 1/ f β spectral background varies around 1 (see [53] for a review of the various factors that influence β , and [54] for contributions of the excitatory and inhibitory neural dynamics to β ). Although taking the temporal derivative of EEG , where Δt is the temporal resolution, i.e., 1/512 sec here) would completely neutralize the 1/ f β component only when β = 1, the method worked well for our data [32,34-36], suggesting that β ∼1 generally held for our data. As we discussed in our prior reports [34-36], taking EEG temporal derivative offers additional advantages.…”

“…Although taking the temporal derivative of EEG , where Δt is the temporal resolution, i.e., 1/512 sec here) would completely neutralize the 1/ f β component only when β = 1, the method worked well for our data [32,34-36], suggesting that β ∼1 generally held for our data. As we discussed in our prior reports [34-36], taking EEG temporal derivative offers additional advantages. For example, EEG temporal derivatives are drift free.…”

“…The surface-Laplacian transform is expected to reduce volume-conduction effects from substantially greater than 5 cm in raw EEG to within 1-3 cm [36,40-41,46] which approximately corresponded to the average spacing of electrodes in our 64-channel montage. For our implementation of the surface-Laplacian transform, we used Perrin and colleagues’ algorithm [47-49] with a “smoothness” value, λ = 10 -5 (recommended for 64 channels [46]).…”

“…A generic nature video was presented on a 13-inch, 2017 MacBook Pro 2880(H)-by-1800(V)-pixelresolution LCD monitor with normal brightness and contrast settings, placed 100 cm in front of participants, subtending approximately 16° (H)-by-10° (V) of visual angle. Subsets of these data were previously analyzed for different purposes [32][33][34][35][36].…”