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. © 2014 World Scientific Publishing Company.

Singular spectrum analysis and adaptive filtering enhance the functional connectivity analysis of resting state fMRI data

Handjaras G.;
2014-01-01

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. © 2014 World Scientific Publishing Company.
2014
Adaptive filtering; functional magnetic resonance imaging; physiological noise; resting state; RV coefficient; singular spectrum analysis; Algorithms; Brain; Humans; Oxygen; Rest; Brain Mapping; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Spectrum Analysis
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11771/12929
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