Analyzing complex functional brain networks: fusing statistics and network science to understand the brain

TL;DR

This paper reviews the integration of statistical methods with network science to enhance the analysis of complex functional brain networks, aiming to improve understanding of normal and disordered brain functions.

Contribution

It surveys existing tools and discusses challenges in combining statistics and network science for fMRI data analysis, highlighting potential for methodological advancements.

Findings

Fusion of statistical and network science methods can revolutionize brain function understanding.

Current challenges include methodological gaps in analyzing fMRI network data.

Proper application of these methods can lead to significant clinical insights.

Abstract

Complex functional brain network analyses have exploded over the last eight years, gaining traction due to their profound clinical implications. The application of network science (an interdisciplinary offshoot of graph theory) has facilitated these analyses and enabled examining the brain as an integrated system that produces complex behaviors. While the field of statistics has been integral in advancing activation analyses and some connectivity analyses in functional neuroimaging research, it has yet to play a commensurate role in complex network analyses. Fusing novel statistical methods with network-based functional neuroimage analysis will engender powerful analytical tools that will aid in our understanding of normal brain function as well as alterations due to various brain disorders. Here we survey widely used statistical and network science tools for analyzing fMRI network data and discuss the challenges faced in filling some of the remaining methodological gaps. When applied and interpreted correctly, the fusion of network scientific and statistical methods has a chance to revolutionize the understanding of brain function.