Cited by 345Blind Source Separation TechniquesAdvanced MRI Techniques and ApplicationsFunctional Brain Connectivity Studies

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Visual inspection of independent components: Defining a procedure for artifact removal from fMRI data

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Kelly, Robert E., Alexopoulos, George S., Wang, Zhishun, Gunning, Faith M., Murphy, Christopher F., Morimoto, Sarah Shizuko, Kanellopoulos, Dora, Jia, Zhiru, Lim, Kelvin O., Hoptman, Matthew J.·2010·Journal of Neuroscience Methods

DOI 10.1016/j.jneumeth.2010.03.028PMC PMC3299198

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Abstract

Artifacts in functional magnetic resonance imaging (fMRI) data, primarily those related to motion and physiological sources, negatively impact the functional signal-to-noise ratio in fMRI studies, even after conventional fMRI preprocessing. Independent component analysis' demonstrated capacity to separate sources of neural signal, structured noise, and random noise into separate components might be utilized in improved procedures to remove artifacts from fMRI data. Such procedures require a method for labeling independent components (ICs) as representing artifacts to be removed or neural signals of interest to be spared. Visual inspection is often considered an accurate method for such labeling as well as a standard to which automated labeling methods are compared. However, detailed descriptions of methods for visual inspection of ICs are lacking in the literature. Here we describe the details of, and the rationale for, an operationalized fMRI data denoising procedure that involves visual inspection of ICs (96% inter-rater agreement). We estimate that dozens of subjects/sessions can be processed within a few hours using the described method of visual inspection. Our hope is that continued scientific discussion of and testing of visual inspection methods will lead to the development of improved, cost-effective fMRI denoising procedures.

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Authors: Kelly, R. E., Alexopoulos, G. S., Wang, Z., Gunning, F. M., Murphy, C. F., Morimoto, S. S., Kanellopoulos, D., Jia, Z., Lim, K. O., & Hoptman, M. J.
Affiliations: Cornell University (US)
New York State Psychiatric Institute (US)
University of Minnesota (US)
Nathan Kline Institute for Psychiatric Research (US)
Corresponding author: Kelly, Robert E.
ORCID: Alexopoulos, George S.
Wang, Zhishun
Gunning, Faith M.
Murphy, Christopher F.
Morimoto, Sarah Shizuko
Kanellopoulos, Dora
Lim, Kelvin O.
Hoptman, Matthew J.
Venue: Journal of Neuroscience Methods
Publisher: Elsevier BV
Published: 2010-06
Volume: 189
Issue: 2
Pages: 233-245
ISSN: 0165-0270 (ISSN-L)
DOI: 10.1016/j.jneumeth.2010.03.028
PMC: PMC3299198
OpenAlex: W2067963436
Cited by: 345
References: 65
Citations by year: 2026: 3 · 2025: 6 · 2024: 13 · 2023: 7 · 2022: 15 · 2021: 27 · 2020: 25 · 2019: 31 · 2018: 37 · 2017: 49 · 2016: 35 · 2015: 33 · 2014: 22 · 2013: 23 · 2012: 12
FWCI: 20.32
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Journal of Neuroscience Methods (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: Eli Lilly and Company
National Institute of Mental Health (K23MH067702, P30MH085943, R01MH065653, T32MH019132, K23MH074818)
Topics: Physical Sciences › Computer Science › Signal Processing › Blind Source Separation Techniques
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords: independent component analysis, visual inspection, fMRI denoising, motion artifacts, physiological noise, interobserver agreement, connected component labeling, signal-to-noise ratio
MeSH terms: Brain, Brain Mapping, Computer Simulation, Data Interpretation, Statistical, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Mental Processes, Middle Aged, Neuropsychological Tests, Rest, Time Factors, Visual Perception, Observer Variation, Artifacts, Databases as Topic

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Visual inspection of independent components: Defining a procedure for artifact removal from fMRI data

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