Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers
Open accessAbstract
Many sources of fluctuation contribute to the fMRI signal, and this makes identifying the effects that are truly related to the underlying neuronal activity difficult. Independent component analysis (ICA) - one of the most widely used techniques for the exploratory analysis of fMRI data - has shown to be a powerful technique in identifying various sources of neuronally-related and artefactual fluctuation in fMRI data (both with the application of external stimuli and with the subject "at rest"). ICA decomposes fMRI data into patterns of activity (a set of spatial maps and their corresponding time series) that are statistically independent and add linearly to explain voxel-wise time series. Given the set of ICA components, if the components representing "signal" (brain activity) can be distinguished form the "noise" components (effects of motion, non-neuronal physiology, scanner artefacts and other nuisance sources), the latter can then be removed from the data, providing an effective cleanup of structured noise. Manual classification of components is labour intensive and requires expertise; hence, a fully automatic noise detection algorithm that can reliably detect various types of noise sources (in both task and resting fMRI) is desirable. In this paper, we introduce FIX ("FMRIB's ICA-based X-noiseifier"), which provides an automatic solution for denoising fMRI data via accurate classification of ICA components. For each ICA component FIX generates a large number of distinct spatial and temporal features, each describing a different aspect of the data (e.g., what proportion of temporal fluctuations are at high frequencies). The set of features is then fed into a multi-level classifier (built around several different classifiers). Once trained through the hand-classification of a sufficient number of training datasets, the classifier can then automatically classify new datasets. The noise components can then be subtracted from (or regressed out of) the original data, to provide automated cleanup. On conventional resting-state fMRI (rfMRI) single-run datasets, FIX achieved about 95% overall accuracy. On high-quality rfMRI data from the Human Connectome Project, FIX achieves over 99% classification accuracy, and as a result is being used in the default rfMRI processing pipeline for generating HCP connectomes. FIX is publicly available as a plugin for FSL.
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Each of four 15-min resting state runs for each subject was cleaned (denoised) using 24-parameter motion regression followed by ICA+FIX denoising.
“Each of four 15-min resting state runs for each subject in the group was cleaned (denoised) using 24-parameter motion regression followed by ICA+FIX denoising30, 48.”
From Canonical genetic signatures of the adult human brain (2015) · cites this paper as [48]
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- Authors
- Salimi-Khorshidi, G., Douaud, G., Beckmann, C. F., Glasser, M. F., Griffanti, L., & Smith, S. M.
- Affiliations
- University of Oxford (GB)
- Radboud University Nijmegen (NL)
- Washington University in St. Louis (US)
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2014-04
- Volume
- 90
- Pages
- 449-468
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC4019210
- OpenAlex
- W2157446241
- Cited by
- 1,770
- References
- 38
- Citations by year
- 2026: 101 · 2025: 160 · 2024: 171 · 2023: 159 · 2022: 203 · 2021: 193 · 2020: 178 · 2019: 146 · 2018: 151 · 2017: 122 · 2016: 98 · 2015: 69 · 2014: 17 · 2013: 1
- FWCI
- 56.88
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- University of Twente Research Information (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion, other-oa)
- Radboud Repository (Radboud University) (submittedVersion)
- University of Twente Research Information (submittedVersion)
- Funders
- NIH (1U54MH091657-01, P30-NS057091, P41-RR08079/EB015894, F30-MH097312)
- UK Medical Research Council (MR/K006673/1)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Physical Sciences › Computer Science › Signal Processing › Blind Source Separation Techniques
- Keywords
- fMRI denoising, independent component analysis, resting-state fMRI, motion artifacts, Human Connectome Project
- MeSH terms
- Algorithms, Brain, Brain Mapping, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Artifacts, Principal Component Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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