title: Singular spectrum analysis and adaptive filtering enhance the functional connectivity analysis of resting state fMRI data.
abstract: Sources of noise in resting-state fMRI experiments include instrumental and physiological noises, which need to be filtered before a functional connectivity analysis of brain regions is performed. These noisy components show autocorrelated and nonstationary properties that limit the efficacy of standard techniques (i.e. time filtering and general linear model). Herein we describe a novel approach based on the combination of singular spectrum analysis and adaptive filtering, which allows a greater noise reduction and yields better connectivity estimates between regions at rest, providing a new feasible procedure to analyze fMRI data.
authors.affiliation: Department of Energy and Systems Engineering, University of Pisa, Largo Lucio Lazzarino, Pisa, 56122, Italy.
authors.forename: Paolo
authors.initials: P
authors.lastname: Piaggi
authors.forename: Danilo
authors.initials: D
authors.lastname: Menicucci
authors.forename: Claudio
authors.initials: C
authors.lastname: Gentili
authors.forename: Giacomo
authors.initials: G
authors.lastname: Handjaras
authors.forename: Angelo
authors.initials: A
authors.lastname: Gemignani
authors.forename: Alberto
authors.initials: A
authors.lastname: Landi
mesh_terms: D000465:Algorithms; D001921:Brain / Q000098:blood supply*; D001931:Brain Mapping; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D010100:Oxygen / Q000097:blood; D012146:Rest / Q000502:physiology*; D013057:Spectrum Analysis
chemical_list: D010100:Oxygen