ADAPTIVE FILTERING AND RANDOM VARIABLES COEFFICIENT FOR ANALYZING FUNCTIONAL MAGNETIC RESONANCE IMAGING DATA-Reference-Cited by-同舟云学术

ADAPTIVE FILTERING AND RANDOM VARIABLES COEFFICIENT FOR ANALYZING FUNCTIONAL MAGNETIC RESONANCE IMAGING DATA

Published:2013-04-29 Issue:03 Volume:23 Page:1350011
ISSN:0129-0657
Container-title:International Journal of Neural Systems
language:en
Short-container-title:Int. J. Neur. Syst.

Author:

PIAGGI PAOLO¹,MENICUCCI DANILO²,GENTILI CLAUDIO³,HANDJARAS GIACOMO⁴,GEMIGNANI ANGELO⁵,LANDI ALBERTO⁶

Affiliation:

1. Department of Energy and Systems Engineering, University of Pisa, Largo Lucio Lazzarino, Pisa, 56122, Italy

2. EXTREME Centre, Scuola Superiore Sant'Anna – Institute of Clinical Physiology, National Research Council (CNR) Via G. Moruzzi 1, Pisa, 56124, Italy

3. Department of Surgery, Medical, Molecular and Critical Area Pathology, University of Pisa, Via Paradisa 2, Pisa, 56124, Italy

4. Laboratory of Clinical Biochemistry and Molecular Biology, University of Pisa Via Roma 55, Pisa, 56126, Italy

5. EXTREME Centre, Department of Surgery, Medical, Molecular and Critical Area Pathology, University of Pisa, Via Paradisa 2, Pisa, 56124, Italy

6. Department of Information Engineering, University of Pisa, Largo Lucio Lazzarino, Pisa, 56122, Italy

Abstract

Functional magnetic resonance imaging (fMRI) is used to study brain functional connectivity (FC) after filtering the physiological noise (PN). Herein, we employ: adaptive filtering for removing nonstationary PN; random variables (RV) coefficient for FC analysis. Comparisons with standard techniques were performed by quantifying PN filtering and FC in neural vs. non-neural regions. As a result, adaptive filtering plus RV coefficient showed a greater suppression of PN and higher connectivity in neural regions, representing a novel effective approach to analyze fMRI data.

Publisher

World Scientific Pub Co Pte Lt

Subject

Computer Networks and Communications,General Medicine

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0129065713500111

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