Statistical models of complex brain networks: a maximum entropy approach

TL;DR

This paper reviews maximum entropy models, specifically exponential random graph models, as tools to understand the complex network structure of the brain and identify biomarkers for neurological diseases.

Contribution

It introduces the application of ERGMs to brain networks, highlighting their potential for revealing local connection mechanisms and aiding in disease biomarker discovery.

Findings

ERGMs help identify local connection rules in brain networks.

Statistical models can predict biomarkers for neurological conditions.

Emerging tools improve probabilistic understanding of brain network complexity.

Abstract

The brain is a highly complex system. Most of such complexity stems from the intermingled connections between its parts, which give rise to rich dynamics and to the emergence of high-level cognitive functions. Disentangling the underlying network structure is crucial to understand the brain functioning under both healthy and pathological conditions. Yet, analyzing brain networks is challenging, in part because their structure represents only one possible realization of a generative stochastic process which is in general unknown. Having a formal way to cope with such intrinsic variability is therefore central for the characterization of brain network properties. Addressing this issue entails the development of appropriate tools mostly adapted from network science and statistics. Here, we focus on a particular class of maximum entropy models for networks, i.e. exponential random graph models (ERGMs), as a parsimonious approach to identify the local connection mechanisms behind observed global network structure. Efforts are reviewed on the quest for basic organizational properties of human brain networks, as well as on the identification of predictive biomarkers of neurological diseases such as stroke. We conclude with a discussion on how emerging results and tools from statistical graph modeling, associated with forthcoming improvements in experimental data acquisition, could lead to a finer probabilistic description of complex systems in network neuroscience.