title: Analyzing complex functional brain networks: Fusing statistics and network science to understand the brain*†
abstract: Complex functional brain network analyses have exploded over the last decade, 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.
authors.affiliation: Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC.
authors.forename: Sean L
authors.initials: SL
authors.lastname: Simpson
authors.affiliation: Department of Biostatistics and Bioinformatics, The Rollins School of Public Health, Emory University, Atlanta, GA.
authors.forename: F DuBois
authors.initials: FD
authors.lastname: Bowman
authors.affiliation: Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC.
authors.forename: Paul J
authors.initials: PJ
authors.lastname: Laurienti
keywords: Graph theory; connectivity; fMRI; network model; neuroimaging; small-world