Model of Brain Activation Predicts the Neural Collective Influence Map of the Brain

TL;DR

This paper introduces a novel network model of brain activation that predicts influential neural nodes, aiming to enhance understanding of brain robustness and inform targeted interventions.

Contribution

It presents a new model of brain network interactions that predicts neural collective influencers, improving upon fragile dependency models and offering potential for brain activity control.

Findings

Predicted neural collective influencer map of the brain.

Identified minimal influential nodes for broadcasting information.

Proposed intervention protocols targeting influential neural nodes.

Abstract

Efficient complex systems have a modular structure, but modularity does not guarantee robustness, because efficiency also requires an ingenious interplay of the interacting modular components. The human brain is the elemental paradigm of an efficient robust modular system interconnected as a network of networks (NoN). Understanding the emergence of robustness in such modular architectures from the interconnections of its parts is a long-standing challenge that has concerned many scientists. Current models of dependencies in NoN inspired by the power grid express interactions among modules with fragile couplings that amplify even small shocks, thus preventing functionality. Therefore, we introduce a model of NoN to shape the pattern of brain activations to form a modular environment that is robust. The model predicts the map of neural collective influencers (NCIs) in the brain, through the optimization of the influence of the minimal set of essential nodes responsible for broadcasting information to the whole-brain NoN. Our results suggest new intervention protocols to control brain activity by targeting influential neural nodes predicted by network theory.