Oscar Esteban

By rewarding speed, volume, and coherent positive narratives, traditional publishing incentivizes questionable scientific practices and analytic flexibility. The latter especially contributes to blurring the distinction between exploration and confirmation, which inflates the apparent strength of confirmatory claims. This systemic issue has gone unchecked for decades, leaving the scientific community with a body of scholarly literature that is unreliable and lacking validity. Registered Reports (RRs) address problematic publishing practices by requiring a priori registration of confirmatory tests, which is peer-reviewed before the research begins (stage 1). The editorial acceptance of a stage 1 plan establishes a commitment to publication regardless of research outcomes. Publication remains, nonetheless, contingent on a second peer review and editorial scrutiny (stage 2) to verify that the approved plan was followed, all deviations were transparently disclosed and justified, confirmatory and exploratory claims are clearly delineated, and the integrity of the design was maintained. Meta-scientific comparisons indicate that RRs reduce outcome-based publication bias and improve methodological rigor without compromising perceived novelty. By making accepted RR protocols and their review history citable research outputs, RRs also strengthen recognition for peer-review work. In this perspective, we argue that RRs should become the primary mode of confirmatory publication moving forward.

In this study we introduce automated 3D segmentation of the healthy human adult eye and orbit from Magnetic Resonance Images, to improve ophthalmic diagnostics and treatments. Past efforts have primarily focused on small sample sizes and varied imaging modalities. Here, we leverage a large-scale dataset of T1-weighted MRI of 1245 subjects and the deep learning-based nnU-Net for MR-Eye segmentation tasks. The results showcase robust and accurate 3D segmentation of lens, globe, optic nerve, rectus muscles, and orbital fat. We also present the automated estimation of key ophthalmic morphometry biomarkers such as axial length and volumetry, while benchmarking correlations between body mass index and eye structure volumes. Quality control protocols are introduced through the pipeline to ensure the reliability of the segmented large-scale data, further enhancing the applicability of our algorithm in clinical research. As a major outcome we provide the first large-scale unbiased eye atlases (female, male, and combined) towards standardization of spatial normalization tools for MR-Eye.

Quality control (QC) of magnetic resonance imaging (MRI) data before preprocessing is fundamental, because substandard data are known to introduce additional variability in the form of noise to subsequent analyses. This can result in spurious results of a false effect or the obstruction of a true effect. Consequently, there is a need for a reliable and robust method to identify subpar images, given pre-specified exclusion criteria. Here, we describe how to carry out the visual assessment of T1-weighted, T2-weighted, functional and diffusion MRI scans of the human brain with visual reports generated by MRIQC (https://mriqc.readthedocs.io/en/stable/). We provide guidance and instructions for using the MRIQC software on all the images of the input dataset using typical research settings (i.e., a high-performance computing cluster). This includes installing MRIQC, configuring datasets (30–45 min active, plus 10–15 min of compute time per scan) and executing MRIQC (10–15 min compute time per scan). We then describe how to screen the visual reports generated with MRIQC to identify artifacts and potential quality issues and annotate the latter with the ‘rating widget’, a utility that enables rapid annotation and minimizes bookkeeping errors (1–5 min per participant). Integrating proper QC checks on the unprocessed data is fundamental to producing reliable statistical results and crucial to identifying faults in the scanning settings, preempting the acquisition of large datasets with persistent artifacts that should have been addressed as they emerged.

The field of neuroimaging has witnessed an exponential increase in data production and availability, fueled by technical advances that have enabled large-scale data collection, as well as by changes in policies and funding initiatives that support open data-sharing programs. As the number of openly available datasets grows larger, the need for efficient, standardized, and flexible tools to index, inspect, and fetch neuroimaging data is becoming a pressing need. OpenNeuro is a popular data hosting service in neuroimaging research; it allows to share publicly neuroimaging data stored following the Brain Imaging Data Structure (BIDS) standard. Although tools such as bids2table (https://childmindresearch.github.io/bids2table/bids2table.html) or Neurobagel Query enable interrogating BIDS datasets (including those on OpenNeuro) and the associated metadata, researchers that require using a subset of one or multiple datasets stored in data repositories typically need to fetch the entire dataset, and to use dedicated tools to interrogate and select the relevant data, hindering interoperability and scalability. Furthermore, existing tools to interrogate BIDS datasets only enable interrogating the metadata, and miss other features that can only be obtained by inspecting the actual volumetric data. Conversely, command-line tools allowing to fetch datasets from remote servers (e.g., Cohort Creator) do not provide data inspection or metadata-based filtering capabilities. Thus, a tool to efficiently interrogate and filter neuroimaging data hosted on public repositories is missing.We present NiQuery , a principled, open-source Python library designed to address these challenges by providing a robust framework for querying neuroimaging datasets, inspecting metadata, performing subset selection, and aggregating results. NiQuery seeks to facilitate reproducible research by enabling transparent access to the content of neuroimaging datasets, including volume-specific metadata.

Apparent diffusion coefficient (ADC)-fMRI offers a promising functional contrast, capable of mapping neuronal activity directly in both grey and white matter. However, previous studies have shown that diffusion-weighted fMRI (dfMRI), from which ADC-fMRI derives, is influenced by blood-oxygen level dependent (BOLD) effects, leading to a concern that the dfMRI contrast is still rooted in neurovascular rather than neuromorphological coupling. Mitigation strategies have been proposed to remove vascular contributions while retaining neuromorphological coupling, by: i) analysing ADC timecourses calculated from two interleaved diffusion-weightings, known as ADC-fMRI; ii) using b-values of at least 200 s mm−2; and iii) using a sequence compensated for cross-terms with fluctuating background field gradients associated with blood oxygenation. Respiration-induced haemodynamic fluctuations, which are dissociated from neural activity, are an excellent test-bed for the robustness of ADC-fMRI to vascular contributions. In this study, we investigate the association between end-tidal CO2 and ADC-fMRI, in comparison with dfMRI and BOLD, in both breath-hold and resting-state paradigms in the human brain. We confirm a strong dependence of the BOLD signal on respiration, and a pattern of delayed haemodynamic response in white matter. While dfMRI mitigates much of the vascular contribution, it retains some association with respiration, as expected. Conversely, ADC-fMRI is mostly unaffected by vascular contribution, exhibiting minimal correlation between expired CO2 and ADC timeseries, as well as low inter- and intra-subject reproducibility in correlation maps. These findings validate ADC-fMRI as a predominantly non-vascular contrast sensitive to microstructural dynamics, enabling whole-brain functional imaging unconstrained by vascular confounds.

The Organisation for Human Brain Mapping (OHBM) Open Science Room (OSR) is one of two principal events coordinated by the Open Science Special Interest Group (OSSIG). While the OHBM Brainhack occurs before the main OHBM conference, the OSR runs concurrently with the main program, offering a varied schedule parallel to other sessions. The OSR focuses on the opportunities, advancements, and challenges presented by open science to the neuroscience community. Sessions in the OSR emphasize interactive discussions aimed at advancing neuroscience research through open science practices. This report summarizes the events that took place in the OSR during the OHBM conference in June 2024, in Seoul, Korea.

SynopsisMotivation: Create an open-source alternative to FSL’s eddy without commercial limitations thatgeneralizes over problems beyond eddy current distortions of diffusion MRI.Goals: Evaluate the tool’s performance vis-à-vis with corresponding tools.Approach: We implement a Gaussian Process regressor model and a Bayesian optimizationlayer on an existing registration framework, and estimate the alignment of neuroimaging dataenabled by the eddymotion framework.Results: Using publicly available neuroimaging data, we provide evidence about theeffectiveness of our volume-to-volume modeling framework for generalized artifact identificationand correction in neuroimaging.ImpactWe present eddymotion, an open-source framework for volume-to-volume artifact estimationinspired by FSL eddy. Our tool allows for easy alternative model implementation, it can be usedfor non-dMRI imaging modalities and does not have a restrictive license.

Apparent diffusion coefficient (ADC)-fMRI offers a promising functional contrast, capable of map-ping neuronal activity directly in both grey and white matter. However, previous studies have shown that diffusion-weighted fMRI (dfMRI), from which ADC-fMRI derives, is influenced by BOLD effects, leading to a concern that the dfMRI contrast is still rooted in neurovascular rather than neuromor-phological coupling. Mitigation strategies have been proposed to remove vascular contributions while retaining neuromorphological coupling, by: i) analysing ADC timecourses calculated from two inter-leaved diffusion-weightings, known as ADC-fMRI; ii) using b-values of at least 200 s mm^-2; and iii) using a sequence compensated for cross-terms with fluctuating background field gradients associated with blood oxygenation. Respiration-induced haemodynamic fluctuations, which are dissociated from neural activity, are an excellent test-bed for the robustness of ADC-fMRI to vascular contributions. In this study, we investigate the association between end-tidal CO₂ and ADC-fMRI, in comparison with dfMRI and BOLD, in both breath-hold and resting-state paradigms in the human brain. We confirm a strong dependence of the BOLD signal on respiration, and find a pattern of delayed haemodynamic response to respiration in regions comprising the default mode network. While dfMRI mitigates much of the vascular contribution, it retains some association with respiration, as expected. Conversely, ADC-fMRI is mostly unaffected by vascular contribution, exhibiting minimal correlation between ex-pired CO₂ and ADC timeseries, as well as low inter-and intra-subject reproducibility in correlation maps. These findings validate ADC-fMRI as a predominantly non-vascular contrast sensitive to mi-crostructural dynamics, enabling whole-brain functional imaging unconstrained by vascular confounds. Significance statement Apparent diffusion coefficient (ADC)-fMRI offers a unique neuromorpho-logical contrast, enabling functional imaging of both grey and white matter with greater spatial and tem-poral specificity than BOLD, as demonstrated in task and resting-state studies. Recent methodological advances have isolated ADC-fMRI from confounding haemodynamic influences, increasing specificity to microstructural neuronal dynamics. Here, we provide the first evaluation of these strategies in breath-hold and resting-state conditions, monitoring respiration as a proxy for haemodynamic fluctuations dissoci-ated from neural activity. Our results show minimal association between ADC-fMRI and respiration, confirming that ADC-fMRI is largely immune to haemodynamic fluctuations. It provides crucial vali-dation that ADC-fMRI reflects a non-vascular functional contrast distinct from and complementary to BOLD, supporting its utility in mapping activity across the human brain.

The adoption of a standardized preprocessing workflow is vital for fostering community, sharing, and reproducibility. fMRIPrep has been a critical advancement towards this end, however, it is limited in its capacity to be applied to data across the lifespan, starting from infancy. Here, we introduce fMRIPrep Lifespan, an extension of fMRIPrep that extends the standardized processing from childhood to senescence to include neonatal, infant, and toddler structural and functional MRI data preprocessing. This effort involves a NiPreps integration of 1) a workflow akin to fMRIPrep optimized for MRI data in the first years of life (previously NiBabies) and 2) upstream enhancements to the entire NiPreps suite, including multi-echo data processing, modularization of workflow components, and convergence of processing with other popular workflows (ABCD-BIDS, Human Connectome Project Pipelines). Using data from the Baby Connectome Project (participants 1-43 months of age), we demonstrate that fMRIPrep Lifespan produces high-quality outputs across a wide age range. Moving forward, the scalable, modular infrastructure of fMRIPrep Lifespan will ensure adaptability to data from birth to old age while maintaining robust and reproducible frameworks for functional MRI research across the lifespan.