Scale-free behaviour and metastable brain-state switching driven by human cognition, an empirical approach

Abstract

We developed a framework to study brain dynamics under cognition. In particular, we investigated the spatiotemporal properties of brain state switches under cognition. The lack of electroencephalography stationarity is exploited as one of the signatures of the metastability of brain states. We correlated power law exponents in the variables that we proposed to describe brain states, and dynamical properties of non-stationarities with cognitive conditions. This framework was successfully tested with three different datasets: a working memory dataset, an Alzheimer disease dataset, and an emotions dataset. We discuss the temporal organization of switches between states, providing evidence suggesting the need to reconsider the piecewise model, in which switches appear at discrete times. Instead, we propose a more dynamically rich view, in which besides the seemingly discrete switches, switches between neighbouring states occur all the time. These micro switches are not (physical) noise, as their properties are also affected by cognition.

Keywords: Brain dynamics; Cognition; EEG non-stationarity; Machine learning; Metastability; Scale-free dynamics.

Fig. 1

a A synthetic signal. The underlying statistical distribution changes in the middle. b The time series of the evolution of the estimation of the two statistical moments over time, when using a window of 50 time points. c The process viewed as a trajectory in the space of states. Each dimension of this space is the estimation of a statistical moment

Fig. 2

A simplified illustration of the spatiotemporal organization that we expect to capture. There are three regions: $r_1$, $r_2$ and $r_3$. Each region has three available states. If we follow the state of r1 over time, we will observe that not only did the temporal behaviour change when condition 2 started, but also r1 engaged in joint activity with a different region

Fig. 3

WM dataset using only upper beta and lower gamma ranges. The mean speeds of the CSSs were used as features. To compute the mean, the smallest (blue) and largest (red) F fraction of amplitudes were used. The classification performance is studied as a function of F. (Color figure online)

Fig. 4

WM dataset. Performance of the classifier as a function of the fraction of transitions kept. In blue, keeping small transitions only, in red, keeping large transitions only. (Color figure online)

Fig. 5

AD dataset. Performance of the classifier as a function of the fraction of transitions kept. In blue, only the small transitions were kept, in red, only the large ones. (Color figure online)

Fig. 6

Emotions dataset. Performance of the classifier as a function of the fraction of transitions kept. In blue, small transitions, in red, large transitions. Due to the large size of the dataset, the performance of the classifier was computed for a smaller number of values of F as compared with the other datasets, and hence the figure is less smooth. (Color figure online)

Fig. 7

Power law fit of a randomly selected AD trial. The x values are the amplitudes of the PSD of the pCSS

Fig. 8

Visual representation of the most visited states of the (discretized) phase pace for each condition, for each dataset. The feature (a specific channel at a specific band) ranked first by OFR was selected to generate the image