Statistical modeling of adaptive neural networks explains coexistence of avalanches and oscillations in resting human brain

Abstract

Brain dynamics display collective phenomena as diverse as neuronal oscillations and avalanches. Oscillations are rhythmic, with fluctuations occurring at a characteristic scale, whereas avalanches are scale-free cascades of neural activity. Here we show that such antithetic features can coexist in a very generic class of adaptive neural networks. In the most simple yet fully microscopic model from this class we make direct contact with human brain resting-state activity recordings via tractable inference of the model's two essential parameters. The inferred model quantitatively captures the dynamics over a broad range of scales, from single sensor fluctuations, collective behaviors of nearly-synchronous extreme events on multiple sensors, to neuronal avalanches unfolding over multiple sensors across multiple timebins. Importantly, the inferred parameters correlate with model-independent signatures of "closeness to criticality", suggesting that the coexistence of scale-specific (neural oscillations) and scale-free (neuronal avalanches) dynamics in brain activity occurs close to a nonequilibrium critical point at the onset of self-sustained oscillations.

Beyond pulsed inhibition: Alpha oscillations modulate attenuation and amplification of neural activity in the awake resting-state

“…In this paper, we provided a description of the resting-state brain activity that uncovers the dynamic organization of neural activity cascades underlying brain oscillations, unifying two complementary approaches to neural synchronization—neuronal avalanches and oscillations [8, 22, 32, 39, 30, 34]. Our analysis shows that the collective neural dynamics underlying resting-state brain activity is governed by attenuation-amplification principles across timescales—which we link with prominent features of the alpha oscillations.…”

“…Indeed, simultaneous investigations of oscillations and avalanches in the human brain selectively focused on long-range temporal correlations in alpha amplitude fluctuations and on avalanche scaling features [43, 71]. At the same time, models capturing the simultaneous emergence of avalanches and oscillations concentrate on generic underlying mechanisms and signatures of criticality [48, 17, 24, 34]. In particular, a quantitative analysis of awake resting-state brain activity through a novel class of adaptive neural networks recently linked this coexistence to proximity to a non-equilibrium critical point at the onset of self-sustained oscillations [14, 34].…”

“…At the same time, models capturing the simultaneous emergence of avalanches and oscillations concentrate on generic underlying mechanisms and signatures of criticality [48, 17, 24, 34]. In particular, a quantitative analysis of awake resting-state brain activity through a novel class of adaptive neural networks recently linked this coexistence to proximity to a non-equilibrium critical point at the onset of self-sustained oscillations [14, 34]. In this context, the present study establishes the first functional and dynamical links between neural oscillations and avalanches in the awake resting-state, uncovering a deep relationship between two collective phenomena with antithetic features—scale-free avalanches and scale-specific brain rhythms.…”

“…On the other hand, neuronal avalanches exhibit scale-free, power-law statistical properties [4], and show a consistent spatio-temporal organization in terms of discrete events, from sequences of spiking neurons [44, 38, 49, 70, 18] to clusters of extreme amplitude fluctuations in LFP, EEG, and MEG sensor arrays [4, 45, 60, 35]. Such a coexistence of scale-free avalanches and scale-specific oscillations has been the focus of recent modelling attempts [48, 59, 58, 34].…”

Criticality of neuronal avalanches in human sleep and their relationship with sleep macro- and micro-architecture

“…In particular, recent studies suggest that criticality plays a key role in determining the temporal organization of sleep stage and arousal transitions (Lombardi et al, 2020a;Wang et al, 2019) The exponent k has been previously measured in the awake resting-state, from Zebrafish and rats to monkeys and humans (Ponce-Alvarez et al, 2018;Miller et al, 2019;Fontenele et al, 2019;Lombardi et al, 2021a;Mariani et al, 2021;Dalla Porta and Copelli, 2019). In line with our findings, Miller et al (Miller et al, 2019) found that, in awake monkeys, k ≃ 2 in the range corresponding to the power law regime of the size and duration distributions, while k ≃ 1 − 1.5 in the region that corresponds to the exponential cut-off of the distributions-where we found k ≈ 1.3.…”