A new causal centrality measure reveals the prominent role of subcortical structures in the causal architecture of the extended default mode network

Abstract

AbstractNetwork representation has been a groundbreaking concept for understanding the behavior of complex systems in social sciences, biology, neuroscience, and beyond. Network science is mathematically founded on graph theory, where nodal importance is gauged using measures ofcentrality. Notably, recent work suggests that the topological centrality of a node should not be over-interpreted as its dynamical or causal importance in the network. Hence, identifying the influential nodes in dynamic causal models (DCM) remains an open question. This paper introducescausal centralityfor DCM, a dynamics-sensitive and causally-founded centrality measure based on the notion ofinterventionin graphical models. Operationally, this measure simplifies to an identifiable expression using Bayesian model reduction. As a proof of concept, the average DCM of the extended default mode network (eDMN) was computed in 74 healthy subjects. Next, causal centralities of different regions were computed for this causal graph, and compared against major graph-theoretical centralities. The results showed that thesubcorticalstructures of the eDMN are more causally central than thecorticalregions, even though the (dynamics-free) graph-theoretical centralities unanimously favor the latter. Importantly, model comparison revealed that only the pattern of causal centrality wascausally relevant. These results are consistent with the crucial role of the subcortical structures in the neuromodulatory systems of the brain, and highlight their contribution to the organization of large-scale networks. Potential applications of causal centrality - to study other neurotypical and pathological functional networks – are discussed, and some future lines of research are outlined.