Alpha blocking and 1/fβ spectral scaling in resting EEG can be accounted for by a sum of damped alpha band oscillatory processes

Abstract

The dynamical and physiological basis of alpha band activity and 1/fβ noise in the EEG are the subject of continued speculation. Here we conjecture, on the basis of empirical data analysis, that both of these features may be economically accounted for through a single process if the resting EEG is conceived of being the sum of multiple stochastically perturbed alpha band damped linear oscillators with a distribution of dampings (relaxation rates). The modulation of alpha-band and 1/fβ noise activity by changes in damping is explored in eyes closed (EC) and eyes open (EO) resting state EEG. We aim to estimate the distribution of dampings by solving an inverse problem applied to EEG power spectra. The characteristics of the damping distribution are examined across subjects, sensors and recording condition (EC/EO). We find that there are robust changes in the damping distribution between EC and EO recording conditions across participants. The estimated damping distributions are found to be predominantly bimodal, with the number and position of the modes related to the sharpness of the alpha resonance and the scaling (β) of the power spectrum (1/fβ). The results suggest that there exists an intimate relationship between resting state alpha activity and 1/fβ noise with changes in both governed by changes to the damping of the underlying alpha oscillatory processes. In particular, alpha-blocking is observed to be the result of the most weakly damped distribution mode becoming more heavily damped. The results suggest a novel way of characterizing resting EEG power spectra and provides new insight into the central role that damped alpha-band activity may play in characterising the spatio-temporal features of resting state EEG.

The 1/f-like behavior of neural field spectra are a natural consequence of noise driven brain dynamics

“…The current noise 𝜖 𝐼 drives the voltage dynamics and, in response to this drive, the voltage oscillates (centered at frequency 𝑓 0 ) with activity damped by a resistive force (strength determined by 𝛿). Consistent with in vivo observations and more complex models of large-scale neural activity [35], [43]- [47], this generative model produces damped or persistent oscillations (depending on the magnitude of the damping coefficient 𝛿) with a broadband spectral peak centered at 𝑓 0 Hz.…”

“…However, many complex, specific mechanisms have been identified to generate this phenomenon. These include excitatory/inhibitory balance [28], low-pass frequency filtering by dendrites [29] or the extracellular medium [30], nonideal resistive components in the cell membrane [31], stochastic firing of neurons convolved with an exponential relaxation process [30], [32], [33], stochastic synaptic conductances [34], stochastically driven damped oscillators with different relaxation rates [35], local homogenous connectivity [36], combinations of many transient oscillations at different frequencies and amplitudes [37], or network mechanisms linking slower rhythms to broad neuronal recruitment and therefore larger amplitude field potentials [29]. Theoretically, scale-free phenomena – with 1/f-like behavior – have been linked to fractal properties [38], critical transitions [9], [39], and self-organized criticality [40], [41].…”

Aperiodic EEG predicts variability of visual temporal processing

“…Aperiodic exponents were also positively correlated with alpha power across participants, reflecting a trade-off between synchronous and asynchronous brain activity in the posterior region of the scalp (see Figure 3). While evidence for this relationship has already been reported both between and within subjects (Muthukumaraswamy & Liley, 2018), its underlying mechanism remains debated (Evertz et al, 2022; Muthukumaraswamy & Liley, 2018; Voytek & Knight, 2015).…”

“…While evidence for this relationship has already been reported both between and within subjects (Muthukumaraswamy & Liley, 2018), its underlying mechanism remains debated (Evertz et al, 2022;Muthukumaraswamy & Liley, 2018;.…”

Cross-attractor modeling of resting-state functional connectivity in psychiatric disorders

“…The attractor landscape was dominated by stable spirals or damped oscillations (see Supplemental Figures 1-8). While the reasoning for this observation may require further theoretical exploration, one possibility is that the ongoing activity of the resting state brain is a summation of damped oscillatory processes (Evertz et al, 2022), which can arise from spontaneous transitions between the available stable spiral attractors.…”