Scaling of whole-brain dynamics reproduced by high-order moments of turbulence indicators

We investigate how brain activity can be supported by a turbulent regime based on the deviations of a self-similar scaling of high-order structure functions within the phenomenological Kolmogorov's theory. By analyzing a large neuroimaging data set, we establish the relationship between scaling exponents and their order, showing that brain activity has more than one invariant scale, and thus orders higher than 2 are needed to accurately describe its underlying statistical properties. Furthermore, we build whole-brain models of coupled oscillators to show that high-order information allows for a better description of the brain's empirical information transmission and reactivity.