Visual inspection of independent components: defining a procedure for artifact removal from fMRI data
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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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Two raters blind to induction independently inspected the spatial patterns and timecourses of the 26 extracted components and determined that 24 were nonartifactual, with 100% inter-rater agreement.
“Two raters blind to induction visually inspected the spatial patterns and timecourses of the 26 extracted components independently (see Kelly et al. 2010) and determined that 24 of the 26 components were nonartifactual (inter-rater agreement was 100% on which components were nonartifact and artifact).”
From Neural Mechanisms of Episodic Retrieval Support Divergent Creative Thinking (2019) · cites this paper as Kelly et al. 2010
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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
- Venue
- Journal of Neuroscience Methods
- Publisher
- Elsevier BV
- Published
- 2010-06
- Volume
- 189
- Issue
- 2
- Pages
- 233-245
- ISSN
- 0165-0270 (ISSN-L)
- 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
- 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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