Disentangling Brain Graphs: A Note on the Conflation of Network and Connectivity Analyses

TL;DR

This paper clarifies the distinction between brain connectivity and network analyses, emphasizing that conflating these methods can hinder the development of more precise and meaningful neurobiological insights.

Contribution

It highlights the often-overlooked differences between connectivity and network analyses, advocating for their clear separation to improve analytical methods and biological interpretations.

Findings

Connectivity and network analyses are frequently conflated in literature.

Distinguishing these analyses enhances the biological interpretability of results.

Clarifying these concepts can lead to more precise neuroimaging methods.

Abstract

Understanding the human brain remains the Holy Grail in biomedical science, and arguably in all of the sciences. Our brains represent the most complex systems in the world (and some contend the universe) comprising nearly one hundred billion neurons with septillions of possible connections between them. The structure of these connections engenders an efficient hierarchical system capable of consciousness, as well as complex thoughts, feelings, and behaviors. Brain connectivity and network analyses have exploded over the last decade due to their potential in helping us understand both normal and abnormal brain function. Functional connectivity (FC) analysis examines functional associations between time series pairs in specified brain voxels or regions. Brain network analysis serves as a distinct subfield of connectivity analysis in which associations are quantified for all time series pairs to create an interconnected representation of the brain (a brain network), which allows studying its systemic properties. While connectivity analyses underlie network analyses, the subtle distinction between the two research areas has generally been overlooked in the literature, with them often being referred to synonymously. However, developing more useful analytic methods and allowing for more precise biological interpretations requires distinguishing these two complementary domains.