Information Dynamics of the Brain, Cardiovascular and Respiratory Network during Different Levels of Mental Stress

Abstract

In this study, an analysis of brain, cardiovascular and respiratory dynamics was conducted combining information-theoretic measures with the Network Physiology paradigm during different levels of mental stress. Starting from low invasive recordings of electroencephalographic, electrocardiographic, respiratory, and blood volume pulse signals, the dynamical activity of seven physiological systems was probed with one-second time resolution measuring the time series of the δ , θ , α and β brain wave amplitudes, the cardiac period (RR interval), the respiratory amplitude, and the duration of blood pressure wave propagation (pulse arrival time, PAT). Synchronous 5-min windows of these time series, obtained from 18 subjects during resting wakefulness (REST), mental stress induced by mental arithmetic (MA) and sustained attention induced by serious game (SG), were taken to describe the dynamics of the nodes composing the observed physiological network. Network activity and connectivity were then assessed in the framework of information dynamics computing the new information generated by each node, the information dynamically stored in it, and the information transferred to it from the other network nodes. Moreover, the network topology was investigated using directed measures of conditional information transfer and assessing their statistical significance. We found that all network nodes dynamically produce and store significant amounts of information, with the new information being prevalent in the brain systems and the information storage being prevalent in the peripheral systems. The transition from REST to MA was associated with an increase of the new information produced by the respiratory signal time series (RESP), and that from MA to SG with a decrease of the new information produced by PAT. Each network node received a significant amount of information from the other nodes, with the highest amount transferred to RR and the lowest transferred to δ , θ , α and β . The topology of the physiological network underlying such information transfer was node- and state-dependent, with the peripheral subnetwork showing interactions from RR to PAT and between RESP and RR, PAT consistently across states, the brain subnetwork resulting more connected during MA, and the subnetwork of brain-peripheral interactions involving different brain rhythms in the three states and resulting primarily activated during MA. These results have both physiological relevance as regards the interpretation of central and autonomic effects on cardiovascular and respiratory variability, and practical relevance as regards the identification of features useful for the automatic distinction of different mental states.

Keywords: information dynamics; network physiology; stress assessment; wearable devices.

Figure 1

Schematic representation of the experimental protocol adopted in this study.

Figure 2

ECG, respiratory signal and BVP acquired from the wearable sensors. The red dots indicates what concerns the ECG, the detection of the R picks; for the respiratory signal, the corresponding value; and for the BVP the point of maximum derivative.

Figure 3

RR interval, respiratory and PAT time series measured for a representative subject during the resting phase (REST), the serious game test (SG) and the mental arithmetic test (MA).

Figure 4

Brain wave amplitude (PSD of a sliding window of 2 $\mathrm{s}$ of duration with 50% overlap) time series measured for a representative subject as the time course of the $\delta$, $\theta$, $\alpha$, $\beta$ EEG power during the resting phase (REST), the serious game test (SG) and the mental arithmetic test (MA).

Figure 5

Boxplots of the information storage $S_j$ (p < 0.05) for the seven time series under consideration during rest (REST), mental arithmetic (MA), and serious game (SG). The lines under the boxplots indicate significant differences between the linked mental states as determined by the ANOVA test; moreover, the names of the time series that are significantly different from the one under consideration for any assigned mental state, listed above each boxplot.

Figure 6

Boxplots of the new information $N_j$ (p < 0.05) for the seven time series under consideration during rest (REST), mental arithmetic (MA), and serious game (SG). The lines under the boxplots indicate significant differences between the linked mental states as determined by the ANOVA test; moreover, the names of the time series that are significantly different from the one under consideration for any assigned mental state, listed above each boxplot.

Figure 7

Boxplots of the total information transfer $T_j$ (p < 0.05) for the seven time series under consideration during rest (REST), mental arithmetic (MA), and serious game (SG). The lines under the boxplots indicate significant differences between the linked mental states as determined by the ANOVA test; moreover, the names of the time series that are significantly different from the one under consideration for any assigned mental state, listed above each boxplot.

Figure 8

Information transfer for the cardiorespiratory-brain network using the conditional information transfer $T_{i\to j|k}$. The arrows thickness is proportional to the number of subjects for which that link is statistically significant (p < 0.05) using an F-test. The magnitude of $T_j$ for each node is coded accordingly to the colorbar on the left.