Probabilistic, entropy-maximizing control of large-scale neural synchronization

Abstract

Oscillatory neural activity is dynamically controlled to coordinate perceptual, attentional and cognitive processes. On the macroscopic scale, this control is reflected in the U-shaped deviations of EEG spectral-power dynamics from stochastic dynamics, characterized by disproportionately elevated occurrences of the lowest and highest ranges of power. To understand the mechanisms that generate these low- and high-power states, we fit a simple mathematical model of synchronization of oscillatory activity to human EEG data. The results consistently indicated that the majority (~95%) of synchronization dynamics is controlled by slowly adjusting the probability of synchronization while maintaining maximum entropy within the timescale of a few seconds. This strategy appears to be universal as the results generalized across oscillation frequencies, EEG current sources, and participants (N = 52) whether they rested with their eyes closed, rested with their eyes open in a darkened room, or viewed a silent nature video. Given that precisely coordinated behavior requires tightly controlled oscillatory dynamics, the current results suggest that the large-scale spatial synchronization of oscillatory activity is controlled by the relatively slow, entropy-maximizing adjustments of synchronization probability (demonstrated here) in combination with temporally precise phase adjustments (e.g., phase resetting generated by sensorimotor interactions). Interestingly, we observed a modest but consistent spatial pattern of deviations from the maximum-entropy rule, potentially suggesting that the mid-central-posterior region serves as an "entropy dump" to facilitate the temporally precise control of spectral-power dynamics in the surrounding regions.

Spatiotemporal dynamics of maximal and minimal EEG spectral power

“…Participants p29-p52 ( N = 24) participated in the replication of the rest-with-eyes-closed condition and subsequently participated in a rest-with-eyes-open-in-dark condition which was the same as the former except that the room was darkened and participants kept their eyes open while blinking naturally. Subsets of these EEG data were previously analyzed for a different purpose [29–31].…”

“…We refer to the surface-Laplacian transformed EEG signals that represent the macroscopic current source/sink densities under the 60 scalp sites (with the 4 noise-prone sites removed from analyses; see above) simply as EEG signals. These EEG-recording and pre-processing procedures were identical to those used in our prior study [29].…”

“…We used the temporal derivative of EEG as in our prior studies that examined all [29] or a subset [31] of the current EEG data for different purposes. While the rationale for taking the temporal derivative of EEG is detailed in [29], it offers the following advantages.…”

“…To focus on oscillatory neural activity, we used the temporal derivative of EEG (macroscopically estimating the underlying electrical currents) that virtually eliminated the nonoscillatory 1/f b spectral backgrounds [27,[39][40] without having to estimate and discount them in sliding-time windows. We generated phase-scrambled controls whose spectral power fluctuated stochastically (i.e., unpredictably in a memory-free manner) while maintaining the time-averaged spectral-amplitude profiles of the actual EEG data.…”

“…This line of investigation has revealed frequency-specific hierarchical networks (including networks and sub-networks) whose structures and activities correlate with a variety of behavioral functions [10][11][12][13][14][15][16][17][18][19][20][21][22] and mental states [23][24][25][26]. While prior studies examined intricate amplitude, phase, and phase-amplitude relations underlying specific behavioral functions and mental states, we have focused on identifying general rules governing the spontaneous spatiotemporal dynamics of EEG and EEG spectral-power activity [27,[39][40].…”