A comparison of denoising pipelines in high temporal resolution task-based functional magnetic resonance imaging data.
Adolescent
Adult
Artifacts
Brain
/ diagnostic imaging
Female
Functional Neuroimaging
/ methods
Head Movements
Humans
Image Processing, Computer-Assisted
/ methods
Magnetic Resonance Imaging
/ methods
Male
Pattern Recognition, Visual
/ physiology
Psychomotor Performance
/ physiology
Research Design
Young Adult
artifacts
denoising
fMRI
head motion
signal-to-noise
Journal
Human brain mapping
ISSN: 1097-0193
Titre abrégé: Hum Brain Mapp
Pays: United States
ID NLM: 9419065
Informations de publication
Date de publication:
09 2019
09 2019
Historique:
received:
22
08
2018
revised:
15
03
2019
accepted:
06
05
2019
pubmed:
24
5
2019
medline:
14
4
2020
entrez:
24
5
2019
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 31119818
doi: 10.1002/hbm.24635
pmc: PMC6865567
doi:
Types de publication
Comparative Study
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
3843-3859Subventions
Organisme : NIMH NIH HHS
ID : R01 MH101512
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS098494
Pays : United States
Organisme : NIH HHS
ID : 1R01NS098494-01A1
Pays : United States
Organisme : NIH HHS
ID : 1R01MH101512-01A1
Pays : United States
Informations de copyright
© 2019 Wiley Periodicals, Inc.
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