Provins Céline

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.

Network-based approaches are widely adopted to model functional and structural ‘connectivity’ of the living brain, extracted noninvasively with magnetic resonance imaging (MRI). However, these analyses —on functional and structural networks— render unreliable at the finer temporal, spatial, and brain-parcellation scales. Consequently, the clinical translation of these analyses has yet to materialize meaningfully, and interpretation of the skyrocketing production of scientific literature requires caution. We will characterize relevant sources of variability and assess the reliability of structural and functional networks extracted from MRI with the repeated acquisition of a single, healthy individual, whom we regard as the ‘Human Connectome Phantom’. Two comprehensive MRI protocols will be executed across three different devices ( 48, 12, and 12 sessions, respectively) while recording a wealth of physiological signals to help model corresponding spurious effects on brain networks. To maximize reuse, e.g., as a benchmark reference, a baseline for machine learning models, or a source of prior knowledge, we will openly share all data and their derivatives. By systematically assessing spurious sources of variability throughout the neuroimaging workflow, we will deliver reliability margins of brain networks that inform future research and contribute to the standardization of ‘connectivity measurement’.

A critical requirement before data-sharing of human neuroimaging is removing facial features to protect individuals’ privacy. However, not only does this process redact identifiable information about individuals, but it also removes non-identifiable information. This introduces undesired variability into downstream analysis and interpretation. This registered report investigated the degree to which the so-called defacing altered the quality assessment of T1-weighted images of the human brain from the openly available “IXI dataset”. The effect of defacing on manual quality assessment was investigated on a single-site subset of the dataset (N=185). By comparing two linear mixed-effects models, we determined that four trained human raters’ perception of quality was significantly influenced by defacing by comparing their ratings on the same set of images in two conditions: “nondefaced” (that is, preserving facial features) and “defaced”. In addition, we investigated these biases on automated quality assessments by applying repeated-measures multivariate ANOVA (rm-MANOVA) on the image quality metrics extracted with MRIQC on the full IXI dataset (N=581; three acquisition sites). This study found that defacing altered the quality assessments by humans and showed that MRIQC’s quality metrics were mostly insensitive to defacing.