title: The (in)stability of functional brain network measures across thresholds.
abstract: The large-scale organization of the brain has features of complex networks that can be quantified using network measures from graph theory. However, many network measures were designed to be calculated on binary graphs, whereas functional brain organization is typically inferred from a continuous measure of correlations in temporal signal between brain regions. Thresholding is a necessary step to use binary graphs derived from functional connectivity data. However, there is no current consensus on what threshold to use, and network measures and group contrasts may be unstable across thresholds. Nevertheless, whole-brain network analyses are being applied widely with findings typically reported at an arbitrary threshold or range of thresholds. This study sought to evaluate the stability of network measures across thresholds in a large resting state functional connectivity dataset. Network measures were evaluated across absolute (correlation-based) and proportional (sparsity-based) thresholds, and compared between sex and age groups. Overall, network measures were found to be unstable across absolute thresholds. For example, the direction of group differences in a given network measure may change depending on the threshold. Network measures were found to be more stable across proportional thresholds. These results demonstrate that caution should be used when applying thresholds to functional connectivity data and when interpreting results from binary graph models.
authors.affiliation: Department of Psychiatry, Yale School of Medicine, USA. Electronic address: kathleen.garrison@yale.edu.
authors.forename: Kathleen A
authors.initials: KA
authors.lastname: Garrison
authors.affiliation: Department of Diagnostic Radiology, Yale School of Medicine, USA.
authors.forename: Dustin
authors.initials: D
authors.lastname: Scheinost
authors.affiliation: Interdepartmental Neuroscience Program, Yale University, USA.
authors.forename: Emily S
authors.initials: ES
authors.lastname: Finn
authors.affiliation: Department of Diagnostic Radiology, Yale School of Medicine, USA.
authors.forename: Xilin
authors.initials: X
authors.lastname: Shen
authors.affiliation: Department of Diagnostic Radiology, Yale School of Medicine, USA; Department of Neurosurgery, Yale School of Medicine, USA.
authors.forename: R Todd
authors.initials: RT
authors.lastname: Constable
mesh_terms: D000328:Adult; D000368:Aged; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D059906:Neuroimaging; D055815:Young Adult
keywords: Functional connectivity; Graph theory; Network analysis; Resting state; Threshold