ArticleHuman brain mapping2019
A comparison of denoising pipelines in high temporal resolution task-based functional magnetic resonance imaging data.
Article in Human brain mapping, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Alterations of cerebrovascular reactivity following pediatric mild traumatic brain injury are independent of neurodevelopmental changes.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2025Article
- Multi-echo versus single-echo EPI sequences for task-fMRI: A comparative study.Imaging neuroscience (Cambridge, Mass.) · 2025Article
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- A practical guide for combining functional regions of interest and white matter bundles.Frontiers in neuroscience · 2024Article
- Multifaceted neural and vascular pathologies after pediatric mild traumatic brain injury.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2024Article
- Denoising task-related fMRI: Balancing noise reduction against signal loss.Human brain mapping · 2023Article
- Functional magnetic resonance imaging of headache: Issues, best-practices, and new directions, a narrative review.Headache · 2023Review
- Static and dynamic functional connectome reveals reconfiguration profiles of whole-brain network across cognitive states.Network neuroscience (Cambridge, Mass.) · 2023Article
- A methodological checklist for fMRI drug cue reactivity studies: development and expert consensus.Nature protocols · 2022Article
- A sub+cortical fMRI-based surface parcellation.Human brain mapping · 2022Article
- Is the prefrontal cortex organized by supramodal or modality-specific sensory demands during adolescence?Developmental cognitive neuroscience · 2021Article
- Persistent alterations in cerebrovascular reactivity in response to hypercapnia following pediatric mild traumatic brain injury.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2020Article
- Proactive inhibition deficits with normal perfusion after pediatric mild traumatic brain injury.Human brain mapping · 2019Article
- A comparison of denoising pipelines in high temporal resolution task-based functional magnetic resonance imaging data.Human brain mapping · 2019Article
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6 authors.
Funding
Abstract
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.
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