Are EEG functional networks really describing the brain? A comparison with other information-processing complex systems

TL;DR

This paper compares EEG functional brain networks with other complex systems, revealing that they share many characteristics and may not uniquely represent brain-specific information processing.

Contribution

It demonstrates that EEG functional networks are highly similar to other complex systems, challenging their uniqueness in representing brain-specific computation.

Findings

Functional networks are similar across diverse complex systems.

Difficulty in distinguishing brain networks from other systems.

Implication that current methods may not capture brain-specific features.

Abstract

Functional networks representing human brain dynamics have become a standard tool in neuroscience, providing an accessible way of depicting the computation performed by the brain in healthy and pathological conditions. Yet, these networks share multiple characteristics with those representing other natural and man-made complex systems, leading to the question of whether they are actually capturing the uniqueness of the human brain. By resorting to a large set of data representing multiple financial, technological, social, and natural complex systems, and by relying on Deep Learning classification models, we show how they are highly similar. We specifically reach the conclusion that, under some general reconstruction methodological choices, it is as difficult to understand whether a network represents a human brain or a financial market, as to diagnose a major pathology. This suggests that functional networks are describing information processing mechanisms that are common across complex systems; but that are not currently defining the uniqueness of the human mind. We discuss the consequence of these findings for neuroscience and complexity science in general, and suggest future avenues for exploring this interesting topic.