Advancing motion denoising of multiband resting-state functional connectivity fMRI data

Williams, John C.; Tubiolo, Philip N; Luceno, Jacob R; Snellenberg, Jared X. Van

doi:10.1016/j.neuroimage.2022.118907

Cited by 7 publications

(16 citation statements)

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“…Here, we leverage clean runs as an approximation of a within-subject ground truth. This approach avoids an important confound in between-subject approaches to probing artifacts: certain populations, e.g., neuropsychiatric populations, may move more on average and also have true neural differences with respect to other populations (Williams et al, 2022). The between-subject approach risks assuming all fMRI changes in high- motion individuals are artifactual and can therefore miss true neural differences.…”

Section: Discussionmentioning

confidence: 99%

“…These effects tend to be immediate and short-lasting, and scale with the degree of head displacement (Satterthwaite et al, 2013). Head motion can also bias functional connectivity results because certain populations tend to exhibit higher head motion than healthy individuals, including older individuals and those with severe psychopathology (Huijbers et al, 2017; Martin et al, 2018; Power et al, 2020; Williams et al, 2022). As such, head motion must be effectively managed to minimize artifacts that can confound individual- and group-level results.…”

Section: Introductionmentioning

confidence: 99%

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Strategies for motion- and respiration-robust estimation of fMRI intrinsic neural timescales

Goldberg,

Rosario,

Power

et al. 2024

Preprint

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Intrinsic neural timescale (INT) is a resting-state fMRI (rs-fMRI) measure that reflects the time window of neural integration within a brain region. Despite the potential relevance of INT to cognition, brain organization, and neuropsychiatric illness, the influences of physiological artifacts on INT have not been systematically considered. Two artifacts, head motion and respiration, pose serious issues in rs-fMRI studies. Here, we described their impact on INT estimation and tested the ability of two denoising strategies for mitigating these artifacts, high-motion frame censoring and global signal regression (GSR). We used a subset of the HCP Young Adult dataset with runs annotated for breathing patterns (Lynch et al., 2020) and at least one "clean" (reference) run that had minimal head motion and no respiration artifacts; other runs from the same participants (n = 46) were labeled as "non-clean." We found that non-clean runs exhibited brain-wide increases in INT compared to their respective clean runs and the magnitude of error in INT between non-clean and clean runs correlated with the amount of head motion. Importantly, effect sizes were comparable to INT effects reported in the clinical literature. GSR and high-motion frame censoring improved the similarity between INT maps from non-clean runs and their respective clean run. Using a pseudo-random frame-censoring approach, there was a relationship between the amount of censored frames and both the mean INT and mean error, suggesting that frame censoring itself biases INT estimation. A group-level correction procedure reduced this bias and improved similarity between non-clean runs and their respective clean run. Based on our findings, we offer recommendations for rs-fMRI INT studies, which include implementing GSR and high-motion frame censoring with Lomb-Scargle interpolation of censored data, and performing group-level correction of the bias introduced by frame censoring.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Strategies for motion- and respiration-robust estimation of fMRI intrinsic neural timescales

Goldberg,

Rosario,

Power

et al. 2024

Preprint

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“…Volumes acquired during periods of excess participant motion were removed using volume censoring. Volume censoring (Power et al, 2012; Power et al, 2014; Power et al, 2015; Smyser et al, 2011; Williams et al, 2022) was performed using study-wide thresholds for participant motion, measured by low-pass-filtered framewise displacement (LPF-FD), and run-wise thresholds for whole-brain signal fluctuation, using generalized extreme value low-pass filtered temporal derivative root-mean-squared over voxels (GEV-DV) thresholding, as has been described in detail elsewhere (Williams et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

“…Censoring thresholds were determined using resting-state data available for participants with usable TL MGN and LGN fROIs using the Multiband Censoring Optimization Tool (Williams et al, 2022) (https://github.com/CNaP-Lab/MCOT/), resulting in an LPF-FD threshold ( ΦF ) of 0.07587 mm and GEV-DV parameter ( dG ) of 3.105 (arbitrary units).…”

Section: Methodsmentioning

confidence: 99%

Functional Localization of the Human Auditory and Visual Thalamus Using a Thalamic Localizer Functional Magnetic Resonance Imaging Task

Williams,

Tubiolo,

Zheng

et al. 2024

Preprint

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Functional magnetic resonance imaging (fMRI) of the auditory and visual sensory systems of the human brain is an active area of investigation in the study of human health and disease. The medial geniculate nucleus (MGN) and lateral geniculate nucleus (LGN) are key thalamic nuclei involved in the processing and relay of auditory and visual information, respectively, and are the subject of blood-oxygen-level-dependent (BOLD) fMRI studies of neural activation and functional connectivity in human participants. However, localization of BOLD fMRI signal originating from neural activity in MGN and LGN remains a technical challenge, due in part to the poor definition of boundaries of these thalamic nuclei in standard T1-weighted and T2-weighted magnetic resonance imaging sequences. Here, we report the development and evaluation of an auditory and visual sensory thalamic localizer (TL) fMRI task that produces participant-specific functionally-defined regions of interest (fROIs) of both MGN and LGN, using 3 Tesla multiband fMRI and a clustered-sparse temporal acquisition sequence, in less than 16 minutes of scan time. We demonstrate the use of MGN and LGN fROIs obtained from the TL fMRI task in standard resting-state functional connectivity (RSFC) fMRI analyses in the same participants. In RSFC analyses, we validated the specificity of MGN and LGN fROIs for signals obtained from primary auditory and visual cortex, respectively, and benchmark their performance against alternative atlas- and segmentation-based localization methods. The TL fMRI task and analysis code (written in Presentation and MATLAB, respectively) have been made freely available to the wider research community.

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“…Nuisance parameters included six motion parameters, their squares, derivatives, and squared derivatives, as well as spike regressors to remove high motion volumes, without being convolved with the HRF. Spike regressors were identified using run-adaptive, generalized extreme value, low-pass filtered DVARS (GEV-DV) thresholds and chosen to identify approximately 3% of total volumes across each dataset 18 . The GEV-DV parameters used for each dataset were 28 in NYSPI-GE, 21 in SBU-Siemens, and 42 in HCP-Siemens.…”

Section: Methodsmentioning

confidence: 99%

Characterization and Mitigation of a Simultaneous Multi-Slice fMRI Artifact: Multiband Artifact Regression in Simultaneous Slices

Tubiolo,

Williams,

Van Snellenberg

2023

Preprint

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Simultaneous multi-slice (multiband) acceleration in fMRI has become widespread, but may be affected by novel forms of signal artifact. Here, we demonstrate a previously unreported artifact manifesting as a shared signal between simultaneously acquired slices in all resting-state and task-based multiband fMRI datasets we investigated, including publicly available consortium data. We propose Multiband Artifact Regression in Simultaneous Slices (MARSS), a regression-based detection and correction technique that successfully mitigates this shared signal in unprocessed data. We demonstrate that the signal isolated by MARSS correction is likely non-neural, appearing stronger in neurovasculature than grey matter. We show that MARSS correction leads to study-wide increases in signal-to-noise ratio, decreases in cortical coefficient of variation, and mitigation of systematic artefactual spatial patterns in participant-level task betas. Finally, we demonstrate that MARSS correction has substantive effects on second-level t-statistics in analyses of task-evoked activation. We recommend that investigators apply MARSS to all multiband fMRI datasets.

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Advancing motion denoising of multiband resting-state functional connectivity fMRI data

Cited by 7 publications

References 50 publications

Strategies for motion- and respiration-robust estimation of fMRI intrinsic neural timescales

Strategies for motion- and respiration-robust estimation of fMRI intrinsic neural timescales

Functional Localization of the Human Auditory and Visual Thalamus Using a Thalamic Localizer Functional Magnetic Resonance Imaging Task

Characterization and Mitigation of a Simultaneous Multi-Slice fMRI Artifact: Multiband Artifact Regression in Simultaneous Slices

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