The Bermuda Triangle of d- and f-MRI sailors - software for susceptibility distortions (SDCFlows)

Estéban, Oscar; Adebimpe, Azeez; Markiewicz, Chris; Goncalves, Mathias; Blair, Ross; Cieslak, Matthew; Naveau, Mikaël; Sitek, Kevin; Sneve, Markus Handal; Provins, Céline; MacNicol, Eilidh; Satterthwaite, Theodore D.; Poldrack, Russell A.

doi:10.31219/osf.io/gy8nt

Cited by 8 publications

(7 citation statements)

References 7 publications

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“…First, quantitative metrics reported from MRIQC version 23.1.0 (Esteban et al, 2023) for the structural and BOLD data are evaluated to assess data quality and potentially problematic subjects. Second, behavioral data will be inspected to confirm that participants have the behavioral data for each run and that participants are performing at the targeted probe hit rate (e.g., at or near 60% overall probe hit rate, see supplemental Section 1.2).…”

Section: Data Quality Control and Preprocessingmentioning

confidence: 99%

Impact of analytic decisions on test-retest reliability of individual and group estimates in functional magnetic resonance imaging: a multiverse analysis using the monetary incentive delay task

Demidenko,

Mumford,

Poldrack

2024

Preprint

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Empirical studies reporting low test-retest reliability of individual blood oxygen-level dependent (BOLD) signal estimates in functional magnetic resonance imaging (fMRI) data have resurrected interest among cognitive neuroscientists in methods that may improve reliability in fMRI. Over the last decade, several individual studies have reported that modeling decisions, such as smoothing, motion correction and contrast selection, may improve estimates of test-retest reliability of BOLD signal estimates. However, it remains an empirical question whether certain analytic decisions consistently improve individual and group level reliability estimates in an fMRI task across multiple large, independent samples. This study used three independent samples (Ns: 60, 81, 120) that collected the same task (Monetary Incentive Delay task) across two runs and two sessions to evaluate the effects of analytic decisions on the individual (intraclass correlation coefficient [ICC(3,1)]) and group (Jaccard/Spearman rho) reliability estimates of BOLD activity of task fMRI data. The analytic decisions in this study vary across four categories: smoothing kernel (five options), motion correction (four options), task parameterizing (three options) and task contrasts (four options), totaling 240 different pipeline permutations. Across all 240 pipelines, the median ICC estimates are consistently low, with a maximum median ICC estimate of .44 - .55 across the three samples. The analytic decisions with the greatest impact on the median ICC and group similarity estimates are the Implicit Baseline contrast, Cue Model parameterization and a larger smoothing kernel. Using an Implicit Baseline in a contrast condition meaningfully increased group similarity and ICC estimates as compared to using the Neutral cue. This effect was largest for the Cue Model parameterization, however, improvements in reliability came at the cost of interpretability. This study illustrates that estimates of reliability in the MID task are consistently low and variable at small samples, and a higher test-retest reliability may not always improve interpretability of the estimated BOLD signal.

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Section: Data Quality Control and Preprocessingmentioning

confidence: 99%

Impact of analytic decisions on test-retest reliability of individual and group estimates in functional magnetic resonance imaging: a multiverse analysis using the monetary incentive delay task

Demidenko,

Mumford,

Poldrack

2024

Preprint

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“…Specifically, tissue boundaries produce steps of deviation from the nominal B 0 field, which are larger where the air is close to tissues. Because of these deviations, the signal is recorded at locations slightly displaced from the sampling grid along the phase encoding axis leading to susceptibility distortions (Esteban et al, 2021 ). Susceptibility distortions manifest in two different ways on the BOLD average panel of the MRIQC visual report ( Supplementary Figure 2 ): as signal drop-out, that is, a region where the signal vanishes (criterion BA), or as brain distortions (criterion BB).…”

Section: Methodsmentioning

confidence: 99%

Quality control in functional MRI studies with MRIQC and fMRIPrep

Provins

MacNicol

Seeley

et al. 2023

Front. Neuroimaging

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The implementation of adequate quality assessment (QA) and quality control (QC) protocols within the magnetic resonance imaging (MRI) research workflow is resource- and time-consuming and even more so is their execution. As a result, QA/QC practices highly vary across laboratories and “MRI schools”, ranging from highly specialized knowledge spots to environments where QA/QC is considered overly onerous and costly despite evidence showing that below-standard data increase the false positive and false negative rates of the final results. Here, we demonstrate a protocol based on the visual assessment of images one-by-one with reports generated by MRIQC and fMRIPrep, for the QC of data in functional (blood-oxygen dependent-level; BOLD) MRI analyses. We particularize the proposed, open-ended scope of application to whole-brain voxel-wise analyses of BOLD to correspondingly enumerate and define the exclusion criteria applied at the QC checkpoints. We apply our protocol on a composite dataset (n = 181 subjects) drawn from open fMRI studies, resulting in the exclusion of 97% of the data (176 subjects). This high exclusion rate was expected because subjects were selected to showcase artifacts. We describe the artifacts and defects more commonly found in the dataset that justified exclusion. We moreover release all the materials we generated in this assessment and document all the QC decisions with the expectation of contributing to the standardization of these procedures and engaging in the discussion of QA/QC by the community.

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“…BOLD fMRI is prone to geometric distortions in the phase-encoding direction which can be largely corrected using a variety of methods (Esteban et al ., 2021; Jezzard and Balaban, 1995). Adjacent to motion-sensitive cerebrospinal fluid (CSF), the brainstem is particularly susceptible to such geometric distortions.…”

Section: Methodsmentioning

confidence: 99%

Functional connectivity across the human subcortical auditory system using an autoregressive matrix-Gaussian copula graphical model approach with partial correlations

Sitek

Chandra

Sarkar

et al. 2022

Preprint

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Abstract/SummaryThe central auditory system is comprised of multiple subcortical brain structures that sequentially process and refine incoming acoustic signals along the primary auditory pathway. Due to the technical limitations of imaging small structures deep inside the brain, most of our knowledge of the subcortical auditory system is based on research in animal models using invasive methodologies. While recent advances in ultra-high field functional magnetic resonance imaging (fMRI) acquisition have enabled novel investigations of the human subcortex non-invasively, optimal approaches to assessing functional activation and connectivity are still being developed. Traditionally, functional connectivity using fMRI data is estimated with simple correlation matrices. Partial correlations however reveal the connectivity between two regions after removing the effects of all other regions and hence are often more meaningful. Partial correlation analysis is particularly promising in the subcortical auditory system, where sensory information is passed serially from nucleus to nucleus up the primary auditory pathway, providing redundant but also increasingly abstract representations of auditory stimuli (Chechik and Nelken, 2012). In this project, we developed and implemented a Gaussian copula graphical model (GCGM) approach to estimate the partial correlations and thereby infer the functional connectivity patterns within the auditory system. Given the paucity of non-invasive methods for human subcortical investigations, we aim to unveil novel information about the hierarchy and direct connections throughout the human subcortical auditory system. Our results show strong positive partial correlations between contralateral structures throughout the auditory system, particularly in the auditory midbrain and thalamus. We also found positive partial correlations between successive structures in the auditory pathway on each side (left and right), including between auditory midbrain and thalamus, and between primary and associative auditory cortex. Further, we confirmed that these connectivity estimates were unique to the auditory system, as non-auditory regions included as controls—namely, visual cortex and superior frontal cortex—were strongly connected to their contralateral homologues but only minimally connected with auditory brain regions. Additionally, these results were highly stable when splitting the data in half according to the acquisition schemes and computing partial correlations separately for each half of the data, as well as across cross-validation folds. Overall, these results demonstrate that unique functional connectivity patterns along the auditory pathway are recoverable using novel connectivity approaches and that our connectivity methods are reliable across multiple acquisitions.

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The Bermuda Triangle of d- and f-MRI sailors - software for susceptibility distortions (SDCFlows)

Cited by 8 publications

References 7 publications

Impact of analytic decisions on test-retest reliability of individual and group estimates in functional magnetic resonance imaging: a multiverse analysis using the monetary incentive delay task

Impact of analytic decisions on test-retest reliability of individual and group estimates in functional magnetic resonance imaging: a multiverse analysis using the monetary incentive delay task

Quality control in functional MRI studies with MRIQC and fMRIPrep

Functional connectivity across the human subcortical auditory system using an autoregressive matrix-Gaussian copula graphical model approach with partial correlations

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