A comparison of denoising pipelines in high temporal resolution task‐based functional magnetic resonance imaging data Article Swipe
Andrew R. Mayer
,
Josef M. Ling
,
Andrew B. Dodd
,
Nicholas A. Shaff
,
Christopher J. Wertz
,
Faith M. Hanlon
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1002/hbm.24635
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1002/hbm.24635
It has been known for decades that head motion/other artifacts affect the blood oxygen level‐dependent signal. Recent recommendations predominantly focus on denoising resting state data, which may not apply to task data due to the different statistical relationships that exist between signal and noise sources. Several blind‐source denoising strategies (FIX and AROMA) and more standard motion parameter (MP) regression (0, 12, or 24 parameters) analyses were therefore compared across four sets of event‐related functional magnetic resonance imaging (erfMRI) and block‐design (bdfMRI) datasets collected with multiband 32‐ (repetition time [TR] = 460 ms) or older 12‐channel (TR = 2,000 ms) head coils. The amount of motion varied across coil designs and task types. Quality control plots indicated small to moderate relationships between head motion estimates and percent signal change in both signal and noise regions. Blind‐source denoising strategies eliminated signal as well as noise relative to MP24 regression; however, the undesired effects on signal depended both on algorithm (FIX > AROMA) and design (bdfMRI > erfMRI). Moreover, in contrast to previous results, there were minimal differences between MP12/24 and MP0 pipelines in both erfMRI and bdfMRI designs. MP12/24 pipelines were detrimental for a task with both longer block length (30 ± 5 s) and higher correlations between head MPs and design matrix. In summary, current results suggest that there does not appear to be a single denoising approach that is appropriate for all fMRI designs. However, even nonaggressive blind‐source denoising approaches appear to remove signal as well as noise from task‐related data at individual subject and group levels.
Related Topics
Concepts
Noise reduction
Noise (video)
SIGNAL (programming language)
Artificial intelligence
Functional magnetic resonance imaging
Computer science
Signal-to-noise ratio (imaging)
Pattern recognition (psychology)
Focus (optics)
Blind signal separation
Block (permutation group theory)
Computer vision
Speech recognition
Channel (broadcasting)
Mathematics
Psychology
Physics
Telecommunications
Image (mathematics)
Geometry
Programming language
Optics
Neuroscience
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/hbm.24635
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/hbm.24635
- OA Status
- bronze
- Cited By
- 23
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2945225126
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2945225126Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1002/hbm.24635Digital Object Identifier
- Title
-
A comparison of denoising pipelines in high temporal resolution task‐based functional magnetic resonance imaging dataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-05-22Full publication date if available
- Authors
-
Andrew R. Mayer, Josef M. Ling, Andrew B. Dodd, Nicholas A. Shaff, Christopher J. Wertz, Faith M. HanlonList of authors in order
- Landing page
-
https://doi.org/10.1002/hbm.24635Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/hbm.24635Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/hbm.24635Direct OA link when available
- Concepts
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Noise reduction, Noise (video), SIGNAL (programming language), Artificial intelligence, Functional magnetic resonance imaging, Computer science, Signal-to-noise ratio (imaging), Pattern recognition (psychology), Focus (optics), Blind signal separation, Block (permutation group theory), Computer vision, Speech recognition, Channel (broadcasting), Mathematics, Psychology, Physics, Telecommunications, Image (mathematics), Geometry, Programming language, Optics, NeuroscienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
23Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 5, 2023: 5, 2022: 4, 2021: 2Per-year citation counts (last 5 years)
- References (count)
-
56Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.effects | 151 |
| abstract_inverted_index.imaging | 77 |
| abstract_inverted_index.levels. | 257 |
| abstract_inverted_index.matrix. | 211 |
| abstract_inverted_index.minimal | 174 |
| abstract_inverted_index.percent | 126 |
| abstract_inverted_index.resting | 23 |
| abstract_inverted_index.results | 215 |
| abstract_inverted_index.signal. | 16 |
| abstract_inverted_index.subject | 254 |
| abstract_inverted_index.suggest | 216 |
| abstract_inverted_index.(bdfMRI) | 81 |
| abstract_inverted_index.(erfMRI) | 78 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 235 |
| abstract_inverted_index.analyses | 65 |
| abstract_inverted_index.approach | 227 |
| abstract_inverted_index.compared | 68 |
| abstract_inverted_index.contrast | 168 |
| abstract_inverted_index.datasets | 82 |
| abstract_inverted_index.depended | 154 |
| abstract_inverted_index.designs. | 186, 234 |
| abstract_inverted_index.erfMRI). | 165 |
| abstract_inverted_index.however, | 148 |
| abstract_inverted_index.magnetic | 75 |
| abstract_inverted_index.moderate | 119 |
| abstract_inverted_index.previous | 170 |
| abstract_inverted_index.regions. | 134 |
| abstract_inverted_index.relative | 144 |
| abstract_inverted_index.results, | 171 |
| abstract_inverted_index.sources. | 45 |
| abstract_inverted_index.standard | 55 |
| abstract_inverted_index.summary, | 213 |
| abstract_inverted_index.Moreover, | 166 |
| abstract_inverted_index.algorithm | 157 |
| abstract_inverted_index.artifacts | 10 |
| abstract_inverted_index.collected | 83 |
| abstract_inverted_index.denoising | 22, 48, 136, 226, 239 |
| abstract_inverted_index.different | 36 |
| abstract_inverted_index.estimates | 124 |
| abstract_inverted_index.indicated | 116 |
| abstract_inverted_index.multiband | 85 |
| abstract_inverted_index.parameter | 57 |
| abstract_inverted_index.pipelines | 180, 188 |
| abstract_inverted_index.resonance | 76 |
| abstract_inverted_index.therefore | 67 |
| abstract_inverted_index.undesired | 150 |
| abstract_inverted_index.approaches | 240 |
| abstract_inverted_index.eliminated | 138 |
| abstract_inverted_index.functional | 74 |
| abstract_inverted_index.individual | 253 |
| abstract_inverted_index.regression | 59 |
| abstract_inverted_index.strategies | 49, 137 |
| abstract_inverted_index.(repetition | 87 |
| abstract_inverted_index.appropriate | 230 |
| abstract_inverted_index.detrimental | 190 |
| abstract_inverted_index.differences | 175 |
| abstract_inverted_index.parameters) | 64 |
| abstract_inverted_index.regression; | 147 |
| abstract_inverted_index.statistical | 37 |
| abstract_inverted_index.12‐channel | 95 |
| abstract_inverted_index.correlations | 205 |
| abstract_inverted_index.motion/other | 9 |
| abstract_inverted_index.nonaggressive | 237 |
| abstract_inverted_index.predominantly | 19 |
| abstract_inverted_index.relationships | 38, 120 |
| abstract_inverted_index.Blind‐source | 135 |
| abstract_inverted_index.blind‐source | 47, 238 |
| abstract_inverted_index.block‐design | 80 |
| abstract_inverted_index.task‐related | 250 |
| abstract_inverted_index.event‐related | 73 |
| abstract_inverted_index.recommendations | 18 |
| abstract_inverted_index.level‐dependent | 15 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 93 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.80277088 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |