Assessing High-Order Interdependencies Through Static O-Information Measures Computed on Resting State fMRI Intrinsic Component Networks

Resting state brain networks have reached a strong popularity in recent scientific endeavors due to their feasibility to characterize the metabolic mechanisms at the basis of neural control when the brain is not engaged in any task. The evaluation of these states, consisting in complex physiological processes employing a large amount of energy, is carried out from diagnostic images acquired through resting-state functionalmagnetic resonance (RS-fMRI) on different populations of subjects. In the present study, RS-fMRI signals from the WU-MinnHCP 1200 Subjects Data Release of the Human Connectome Project were studied with the aim of investigating the high order organizational structure of the…