Fig 1.
Summary of the framework for the computation of the brain transition cost from EEG data.
(a) The EEG activity of 44 participants was acquired at rest and while performing a spatial Stroop task. The participants were presented either with a congruent (C) or incongruent (I) stimulus. The proportion of congruency (PC) was modulated within three blocks (PC25: 25% C, 75% I; PC50: 50% C, 50% I; PC75: 75% C, 25% I). (b) EEG activity was characterized by employing a microstate analysis. The modified k-means clustering found seven most representative topologies, which we named from A to G. (c) Schrödinger bridge framework for computing brain transition cost. Given the microstate probability distribution at rest (π0) and while performing a task (πT), the cost is computed as the Kullback-Leibler divergence between the spontaneous (resting) dynamics, described by joint probability for two consecutive steps (Qij), and the Schrödinger bridge, i.e., the most probable path that links the resting and task distribution, subject to the given constraints.
Fig 2.
Microstate distributions distinguish tasks from resting.
The boxplots show the microstate distributions at rest (white boxplot) and during the experimental conditions. The saturation of the blue (orange) scale represents the PC level (and, thus, the level of control demands).
Fig 3.
Estimating the transportation cost matrix from microstate joint probability of consecutive timesteps at rest.
(a) Transportation cost matrix, averaged over the 44 participants, representing the cost for the brain to transition from state i to state j. (b) Network describing the transitions among microstates during resting. We show only the significant asymmetric transitions (t-test, p<0.05).
Fig 4.
Brain transition cost correlates with task demand and performance.
(a) The boxplots show the distribution of transition costs as a function of both the stimulus congruency, that is congruent (C) vs. incongruent (I) and the PC level (PC25, PC50, PC75;), as indicated by their significant interaction in the linear mixed effects analysis. (b) The plot shows the participants’ Stroop effects in transition costs (i.e., the difference in transition costs between incongruent and congruent trials: Δ Cost) as a function of their Stroop effects in response times (Δ RT), as indicated by the random effects revealed by the linear mixed effects model (see main text).