Automatic classification and removal of structured physiological noise for resting state functional connectivity MRI analysis-Reference-Cited by-同舟云学术

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Automatic classification and removal of structured physiological noise for resting state functional connectivity MRI analysis

Published:2019-05 Issue: Volume:58 Page:97-107
ISSN:0730-725X
Container-title:Magnetic Resonance Imaging
language:en
Short-container-title:Magnetic Resonance Imaging

Author:

Lee Kangjoo^ORCID,Khoo Hui Ming,Fourcade Constance,Gotman Jean,Grova Christophe

Funder

Canadian Institutes of Health Research

Natural Sciences and Engineering Research Council of Canada

Max E. Binz Fellowship

Mark Rayport and Shirley Ferguson Rayport fellowship

Preston Robb fellowship

Uehara Memorial Foundation

Publisher

Elsevier BV

Subject

Radiology, Nuclear Medicine and imaging,Biomedical Engineering,Biophysics

Reference26 articles.

1. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging;Fox;Nat Rev Neurosci,2007

2. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis;Greicius;Proc Natl Acad Sci,2003

3. Connectivity-based parcellation: critique and implications;Eickhoff;Hum Brain Mapp,2015

4. A data-driven sparse GLM for fMRI analysis using sparse dictionary learning with MDL criterion;Lee;IEEE Trans Med Imaging,2011

5. SPARK: sparsity-based analysis of reliable k-hubness and overlapping network structure in brain functional connectivity;Lee;NeuroImage,2016

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3. Sparse representation of complex-valued fMRI data based on spatiotemporal concatenation of real and imaginary parts;Journal of Neuroscience Methods;2021-03

4. Heart Rate and Respiration Affect the Functional Connectivity of Default Mode Network in Resting-State Functional Magnetic Resonance Imaging;Frontiers in Neuroscience;2020-06-30

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