Reliability characterization of MRI measurements for analyses of brain networks on a single human

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’

Q’kay: a manager for the quality assessment of large neuroimaging studies

Despite substantial efforts toward improving the tools to carry out the visual assessment of quality, as well as automation, the quality control (QC) of imaging data remains an onerous, yet critical step of analysis workflows, especially within large-scale studies. Indeed, the reliability and reproducibility of results can be improved by implementing QC checkpoints throughout the workflow (Niso et al. 2022, Provins et al. 2023). Here, we introduce Q’kay, a web service to deploy rigorous QC protocols on large datasets leveraging the individual reports generated by tools like MRIQC (Esteban et al., 2017) and fMRIPrep (Esteban et al., 2019)

Effects of phase-encoding on BOLD data with a positive control task

Quality assessment and quality control (QA/QC) checkpoints layered throughout the dataflow are essential to ensure the reliability of neuroimaging analyses. In the case of functional MRI, best practices recommend collecting a ‘positive control’ task with which the different layers of QA/QC can be validated. These are short and simple tasks designed to elicit robust and precisely located brain activation patterns, permitting the diagnosis of potential issues in the workflow. Here, we examine how the phase-encoding direction (PE) choice in echo-planar imaging (EPI) blood-oxygen-level-dependent (BOLD) fMRI influences the resulting activation maps using a positive control task that includes visual and motor paradigms

Assessment of B1 field dynamics of rats BOLD fMRI using the wavelet transform

Supplementary material to the abstract entitled "Assessment of B1 field dynamics of rats BOLD fMRI using the wavelet transform"

Assessment of B1 field dynamics of rats BOLD fMRI using the wavelet transform

Magnetic resonance imaging (MRI) generates a radiofrequency field (B1) to frequency encode the object being imaged. Deviations from the nominal B1 field produce artifactual intensity nonuniformity (INU) across the image, which is problematic, especially for automated analyses that assume a tissue is represented by voxels of similar intensity throughout the image (Belaroussi et al. 2006). These artifacts are particularly exacerbated by receiver coil failures. Such events are difficult to capture as they tend to be short-lived and sporadic. In brain blood-oxygen-level-dependent (BOLD) functional MRI (fMRI), B1 field dynamics is usually visualized with a video of the scan to spot signal intensity changes, but this method is time-consuming and error-prone, as the human observer needs to keep focus during the whole video. Here, we showcase a visualization tool to assess B1 field dynamics and a derived summary metric to efficiently detect low spatial-frequency artifacts, such as transient INU

Effects of phase-encoding on BOLD data with a positive control task

Quality assessment and quality control (QA/QC) checkpoints layered throughout the dataflow are essential to ensure the reliability of neuroimaging analyses. In the case of functional MRI, best practices recommend collecting a ‘positive control’ task with which the different layers of QA/QC can be validated. These are short and simple tasks designed to elicit robust and precisely located brain activation patterns, permitting the diagnosis of potential issues in the workflow. Here, we examine how the phase-encoding direction (PE) choice in echo-planar imaging (EPI) blood-oxygen-level-dependent (BOLD) fMRI influences the resulting activation maps using a positive control task that includes visual and motor paradigms

An open-source implementation of estimation and correction of head-motion and eddy-current distortions generalizable across diffusion MRI signal models

We develop an open-source tool for the retrospective estimation of inter-volume head-motion and eddy-current distortions, typically found in diffusion MRI (dMRI) data acquired with echo-planar imaging schemes. The implementation is “open-since-inception” to ensure transparency. By leveraging the widely used DIPY package and a user-friendly interface, researchers have at their disposal an implementation combining state-of-art approaches with substantial improvements that can efficiently leverage any compliant diffusion model(s) while simultaneously accounting for susceptibility distortions

Defacing biases in manual and automated quality assessments of structural MRI with MRIQC

A critical requirement prior to data-sharing of human neuroimaging is the removal of 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 may introduce undesired variability into downstream analysis and interpretation. Here, we pre-register a study design to investigate the degree to which the so-called defacing alters 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 will be investigated on a single-site subset of the dataset (N=185). By means of repeated-measures analysis of variance (rm-ANOVA), or linear mixed-effects models in case data do not meet rm-ANOVA’s assumptions, we will determine whether four trained human raters’ perception of quality is significantly influenced by defacing by comparing their ratings on the same set of images in two conditions: “non-defaced” (i.e preserving facial features) and “defaced”. Relatedly, we will also verify that defaced set is being assigned higher quality grades on average. In addition, we will also investigate these biases on automated quality assessments by applying multivariate rm-ANOVA (rm-MANOVA) on the image quality metrics extracted with MRIQC on the full IXI dataset (N=580; three acquisition sites). The analysis code, tested on simulated data, is made openly available with this pre-registration report. This study seeks evidence of the deleterious effects of defacing on data quality assessments by humans and machine agents

A high definition anatomical brain template of one individual healthy subject

We propose a high-definition template of a single healthy brain built using multimodal registration. 35 T1-weighted (T1w) and 35 T2-weighted (T2w) anatomical brain images of one individual healthy human male (aged 40) were retrieved from the Human Connectome Phantom (HCPh) dataset, an ongoing Stage 1 Registered Report