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

摘要

Complex functional brain network analyses have exploded over the last eightyears, gaining traction due to their profound clinical implications. Theapplication of network science (an interdisciplinary offshoot of graph theory)has facilitated these analyses and enabled examining the brain as an integratedsystem that produces complex behaviors. While the field of statistics has beenintegral in advancing activation analyses and some connectivity analyses infunctional neuroimaging research, it has yet to play a commensurate role incomplex network analyses. Fusing novel statistical methods with network-basedfunctional neuroimage analysis will engender powerful analytical tools thatwill aid in our understanding of normal brain function as well as alterationsdue to various brain disorders. Here we survey widely used statistical andnetwork science tools for analyzing fMRI network data and discuss thechallenges faced in filling some of the remaining methodological gaps. Whenapplied and interpreted correctly, the fusion of network scientific andstatistical methods has a chance to revolutionize the understanding of brainfunction.