Functional hierarchies in brain dynamics characterized by signal reversibility in ferret cortex

Brain signal irreversibility has been shown to be a promising approach to study neural dynamics. Nevertheless, the relation with cortical hierarchy and the influence of different electrophysiological features is not completely understood. In this study, we recorded local field potentials (LFPs) during spontaneous behavior, including awake and sleep periods, using custom micro-electrocorticographic (mu ECoG) arrays implanted in ferrets. In contrast to humans, ferrets remain less time in each state across the sleep-wake cycle. We deployed a diverse set of metrics in order to measure the levels of complexity of the different behavioral states. In particular, brain irreversibility, which is a signature of non-equilibrium dynamics, captured by the arrow of time of the signal, revealed the hierarchical organization of the ferret's cortex. We found different signatures of irreversibility and functional hierarchy of large-scale dynamics in three different brain states (active awake, quiet awake, and deep sleep), showing a lower level of irreversibility in the deep sleep stage, compared to the other. Irreversibility also allowed us to disentangle the influence of different cortical areas and frequency bands in this process, showing a predominance of the parietal cortex and the theta band. Furthermore, when inspecting the embedded dynamic through a Hidden Markov Model, the deep sleep stage was revealed to have a lower switching rate and lower entropy production. These results suggest functional hierarchies in organization that can be revealed through thermodynamic features and information theory metrics. Understanding the brain functioning and the mechanisms underlying the transition between different brain states has been a key goal of modern clinical neuroscience. Global balance of the brain could be assessed by measuring spontaneous changes in brain dynamics. However, the subtlety of the effects demands advanced computational methods to extract the relevant dynamical information from neuroimaging recordings. To this aim, electrocorticographic measurements of ferrets provide a unique opportunity for inspecting these transitions in a long fluctuating recording. Our findings demonstrate the large, and still underexploited potential of several methods in the study of large-scale brain dynamics. We think that our approach can be fruitfully applied to a wide array of brain disorders, subserving both the theoretical goal of a clearer understanding of these diseases, and at the same time, the clinical goal of maximizing patients' classification, diagnosis, and prognosis. Finally, by providing detailed insight into the role different regions and states in global brain dynamics, our approach may inform external stimulation therapies that, combining with traditional behavioral therapies, may significantly accelerate recovery in different brain disorders.