P242 Dynamic time warping distance based connectivity: A new method for resting-state FMRI functional connectivity analysis

2017 ◽  
Vol 128 (9) ◽  
pp. e256
Author(s):  
Regina Meszlényi ◽  
Ladislav Peska ◽  
Petra Hermann ◽  
Krisztian Buza ◽  
Viktor Gál ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Dynamic Time Warping ◽  
Resting State Fmri ◽  
New Method ◽  
Connectivity Analysis ◽  
Time Warping ◽  
Functional Connectivity Analysis ◽  
Dynamic Time

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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