Activated Region Fitting: Extension to Functional Connectivity Analysis of Noisy fMRI data

NeuroImage ◽  
2009 ◽  
Vol 47 ◽  
pp. S170
Author(s):  
W.D. Weeda ◽  
L.J. Waldorp ◽  
H.M. Huizenga
Keyword(s):  
Functional Connectivity ◽  
Fmri Data ◽  
Connectivity Analysis ◽  
Functional Connectivity Analysis

Functional connectivity analysis of fMRI data using parameterized regions-of-interest

NeuroImage ◽  
2011 ◽  
Vol 54 (1) ◽  
pp. 410-416 ◽  
Author(s):  
Wouter D. Weeda ◽  
Lourens J. Waldorp ◽  
Raoul P.P.P. Grasman ◽  
Simon van Gaal ◽  
Hilde M. Huizenga
Keyword(s):  
Functional Connectivity ◽  
Regions Of Interest ◽  
Fmri Data ◽  
Connectivity Analysis ◽  
Functional Connectivity Analysis

scholarly journals An independent components and functional connectivity analysis of resting state fMRI data points to neural network dysregulation in adult ADHD

2013 ◽  
Vol 35 (4) ◽  
pp. 1261-1272 ◽  
Author(s):  
Elseline Hoekzema ◽  
Susana Carmona ◽  
J. Antoni Ramos-Quiroga ◽  
Vanesa Richarte Fernández ◽  
Rosa Bosch ◽  
...  
Keyword(s):  
Neural Network ◽  
Functional Connectivity ◽  
Resting State ◽  
Adult Adhd ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Connectivity Analysis ◽  
Independent Components ◽  
Functional Connectivity Analysis ◽  
Data Points

SINGULAR SPECTRUM ANALYSIS AND ADAPTIVE FILTERING ENHANCE THE FUNCTIONAL CONNECTIVITY ANALYSIS OF RESTING STATE fMRI DATA

2014 ◽  
Vol 24 (03) ◽  
pp. 1450010 ◽  
Author(s):  
PAOLO PIAGGI ◽  
DANILO MENICUCCI ◽  
CLAUDIO GENTILI ◽  
GIACOMO HANDJARAS ◽  
ANGELO GEMIGNANI ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Adaptive Filtering ◽  
Spectrum Analysis ◽  
Resting State ◽  
Singular Spectrum Analysis ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Connectivity Analysis ◽  
Singular Spectrum ◽  
Functional Connectivity Analysis

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.

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