Parcels and particles: Markov blankets in the brain

TL;DR

This paper explores how Markov blankets and renormalisation group techniques can explain the emergence of intrinsic brain networks and critical dynamics from neuronal interactions, linking directed connectivity to observable phenomenology.

Contribution

It introduces a novel framework combining Markov blankets and renormalisation group methods to connect effective connectivity with brain network phenomenology.

Findings

Network phenomenology emerges from neuronal state partitions.

Markov blankets underpin self-organization in brain dynamics.

The approach links directed graphs to intrinsic brain network behavior.

Abstract

At the inception of human brain mapping, two principles of functional anatomy underwrote most conceptions - and analyses - of distributed brain responses: namely functional segregation and integration. There are currently two main approaches to characterising functional integration. The first is a mechanistic modelling of connectomics in terms of directed effective connectivity that mediates neuronal message passing and dynamics on neuronal circuits. The second phenomenological approach usually characterises undirected functional connectivity (i.e., measurable correlations), in terms of intrinsic brain networks, self-organised criticality, dynamical instability, etc. This paper describes a treatment of effective connectivity that speaks to the emergence of intrinsic brain networks and critical dynamics. It is predicated on the notion of Markov blankets that play a fundamental role in the self-organisation of far from equilibrium systems. Using the apparatus of the renormalisation group, we show that much of the phenomenology found in network neuroscience is an emergent property of a particular partition of neuronal states, over progressively larger scales. As such, it offers a way of linking dynamics on directed graphs to the phenomenology of intrinsic brain networks.