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Esteban, O.

Publications and source records attributed to Esteban, O..

At least 19 recordsLinked to original sources
bioRxiv2026

Eye-Tracking-BIDS: the Brain Imaging Data Structure extended to gaze position and pupil data

The Brain Imaging Data Structure (BIDS) is a widely adopted, community-driven standard to organize neuroimaging data and metadata. Although numerous extensions have been developed to incrementally extend coverage to new modalities and data types, an unambiguous, granular specification for eye-tracking recordings is lacking. Here, we present how BIDS will structure data and metadata produced by eye-tracking devices, including gaze position and pupil data. In addition to prescribing the organization of the unprocessed (raw) recordings and associated metadata as produced by the device, BEP20 also resolves gaps in current BIDS specifications beyond the scope of eye tracking. In particular, it adds a mechanism for including asynchronous model parameters and messages, such as contextual information, statuses, and events, such as triggers, generated by the device. BEP20 includes examples that illustrate its applicability in various experimental settings. This BIDS extension provides a robust standard that supports the development of self-adaptive, open, and automated eye-tracking data structures, thereby bolstering transparency and reliability of results in this field.

neuroscience↗
bioRxiv2025

Evaluating the dependence of ADC-fMRI on haemodynamics inbreath-hold and resting-state conditions

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 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 interand 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.

neuroscience↗
bioRxiv2025

fMRIPrep Lifespan: Extending A Robust Pipeline for Functional MRI Preprocessing to Developmental Neuroimaging

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.

bioinformatics↗
bioRxiv2024

Quality assessment and control of unprocessed anatomical, functional, and diffusion MRI of the human brain using MRIQC

Quality control of MRI data prior to preprocessing is fundamental, as substandard data are known to increase variability spuriously. Currently, no automated or manual method reliably identifies subpar images, given pre-specified exclusion criteria. In this work, we propose a protocol describing how to carry out the visual assessment of T1-weighted, T2-weighted, functional, and diffusion MRI scans of the human brain with the visual reports generated by MRIQC. The protocol describes how to execute the software on all the images of the input dataset using typical research settings (i.e., a high-performance computing cluster). 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. Integrating proper quality control 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. RELATED LINKSO_ST_ABSKey reference(s) using this protocolC_ST_ABSEsteban, O. et al. (2017), PLoS ONE 12(9): e0184661. [10.1371/journal.pone.0184661] Esteban, O. et al. (2019), Sci Data 6, 30. [10.1038/s41597-019-0035-4] Esteban, O. et al. (2020), Nat Prot 15, 2186-2202. [10.1038/s41596-020-0327-3] Provins, C. et al. (2023), Front. Neuroinform. 1, 2813-1193. [10.3389/fnimg.2022.1073734] Bissett P. et al. (2024) Sci Data 11: 809. [10.1038/s41597-024-03636-y] Key data used in this protocolAmsterdam Open MRI Collection: Population Imaging of Psychology1 (AOMIC-PIOP1; ds002785 [https://openneuro.org/datasets/ds002785]).

neuroscience↗
bioRxiv2024

Defacing biases visual quality assessments of structural MRI

A critical step 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 modeling their ratings on the same set of images in two conditions: "nondefaced" (i.e., 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 MRIQCs quality metrics were mostly insensitive to defacing.

bioinformatics↗
bioRxiv2024

Eye-Opening Advances: Automated 3D Segmentation, Key Biomarkers Extraction, and the First Large-Scale MRI Eye Atlas.

In this study we introduce an automated 3D segmentation of the healthy human adult eye and orbit from Magnetic Resonance Images, to improve ophthalmic diagnostics and treatments. Past efforts 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 use of the deep learning-based nnU-Net for MR-Eye segmentation tasks. The results showcase robust and accurate 3D segmentations 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 major outcome we provide the first large-scale unbiased eye atlases (female, male and combined) towards standardization of spatial normalization tools for MR-Eye.

bioengineering↗
bioRxiv2024

Maturation-informed synthetic Magnetic Resonance Images of the Developing Human Fetal Brain

Magnetic resonance imaging is a powerful modality to investigate abnormal developmental patterns in utero. However, since it is not the first-line diagnostic tool in this sensitive population, data remain scarce and heterogeneous between scanners and centers. In addressing the data scarcity issue while generating data representative of real fetal brain MRI, we proposed FaBiAN, a Fetal Brain magnetic resonance Acquisition Numerical phantom. Here, we present a novel synthetic dataset of 594 two-dimensional, low-resolution series of T2-weighted images corresponding to 78 developing human fetal brains between 20.0 and 34.8 weeks of gestational age. Data are generated with substantive improvements from the original FaBiAN to account for local heterogeneities within white matter tissues throughout maturation. These synthetic-yet-highly-realistic images cover both healthy and pathological development trajectories simulated with standard clinical settings and anatomically informed by the Fetal Tissue Annotations (FeTA) dataset. Two independent radiologists qualitatively assessed the realism of the simulated images. We also quantitatively demonstrate the simulated datas increased fidelity to real data compared to the previous FaBiAN version. The reuse potential of the proposed dataset was also evaluated in the context of automated fetal brain tissue segmentation. Besides, our dataset that combines images generated from various clinical scenarios has been made publicly available to support the continuous endeavor of the community to develop advanced post-processing methods aswell as cutting-edge artificial intelligence models.

bioinformatics↗
bioRxiv2021

Moving Beyond Processing and Analysis-Related Variation in Neuroscience

When fields lack consensus standard methods and accessible ground truths, reproducibility can be more of an ideal than a reality. Such has been the case for functional neuroimaging, where there exists a sprawling space of tools and processing pipelines. We provide a critical evaluation of the impact of differences across five independently developed minimal preprocessing pipelines for functional MRI. We show that even when handling identical data, inter-pipeline agreement was only moderate, critically shedding light on a factor that limits cross-study reproducibility. We show that low inter-pipeline agreement mainly becomes appreciable when the reliability of the underlying data is high, which is increasingly the case as the field progresses. Crucially, we show that when inter-pipeline agreement is compromised, so too are the consistency of insights from brainwide association studies. We highlight the importance of comparing analytic configurations, as both widely discussed and commonly overlooked decisions can lead to marked variation.

neuroscience↗
bioRxiv2021

OpenNeuro: An open resource for sharing of neuroimaging data

The sharing of research data is essential to ensure reproducibility and maximize the impact of public investments in scientific research. Here we describe OpenNeuro, a BRAIN Initiative data archive that provides the ability to openly share data from a broad range of brain imaging data types following the FAIR principles for data sharing. We highlight the importance of the Brain Imaging Data Structure (BIDS) standard for enabling effective curation, sharing, and reuse of data. The archive presently shares more than 600 datasets including data from more than 20,000 participants, comprising multiple species and measurement modalities and a broad range of phenotypes. The impact of the shared data is evident in a growing number of published reuses, currently totalling more than 150 publications. We conclude by describing plans for future development and integration with other ongoing open science efforts.

neuroscience↗
bioRxiv2021

High-sensitivity detection of facial features on MRI brain scans with a convolutional network

Platforms and institutions that support MRI data sharing need to ensure that identifiable facial features are not present in shared images. Currently, this assessment requires manual effect as no auto-mated tools exist that can efficiently and accurately detect if an image has been "defaced". The scarcity of publicly available data with pre-served facial features, as well as the meager incentives to create such a cohort privately, have averted the development of face-detection models. Here, we introduce a framework to detect whether an input MRI brain scan has been defaced, with the ultimate goal of streamlining it within the submission protocols of MRI data archiving and sharing platforms. We present a binary (defaced/"nondefaced") classifier based on a custom convolutional neural network architecture. We train the model on 980 de-faced MRI scans from 36 different studies that are publicly available at OpenNeuro.org. To overcome the unavailability of nondefaced examples, we augment the dataset by inpainting synthetic faces into each training image. We show the adequacy of such a data augmentation in a cross-validation evaluation. We demonstrate the performance estimated with cross-validation matches that of an evaluation on a held-out dataset (N =581) preserving real faces, and obtain accuracy/sensitivity/speci-ficity scores of 0.978/0.983/0.972, respectively. Data augmentations are key to boosting the performance of models bounded by limited sample sizes and insufficient diversity. Our model contributes towards developing classifiers with[~] 100% sensitivity detecting faces, which is crucial to ensure that no identifiable data are inadvertently made public.

neuroscience↗
bioRxiv2021

TemplateFlow: a community archive of imaging templates and atlases for improved consistency in neuroimaging

Reference anatomies of the brain and corresponding atlases play a central role in experimental neuroimaging workflows and are the foundation for reporting standardized results. The choice of such references --i.e., templates-- and atlases is one relevant source of methodological variability across studies, which has recently been brought to attention as an important challenge to reproducibility in neuroscience. TemplateFlow is a publicly available framework for human and nonhuman brain models. The framework combines an open database with software for access, management, and vetting, allowing scientists to distribute their resources under FAIR --findable, accessible, interoperable, reusable-- principles. TemplateFlow supports a multifaceted insight into brains across species, and enables multiverse analyses testing whether results generalize across standard references, scales, and in the long term, species, thereby contributing to increasing the reliability of neuroimaging results.

neuroscience↗
bioRxiv2021

Intracranial electrical stimulation alters meso-scale network integration as a function of network topology

Human brain dynamics are organized into a multi-scale network structure that contains multiple tight-knit, meso-scale communities. Recent work has demonstrated that many psychological capacities, as well as impairments in cognitive function secondary to damage, can be mapped onto organizing principles at this mesoscopic scale. However, we still dont know the rules that govern the dynamic interactions between regions that are constrained by the topology of the broader network. In this preregistered study, we utilized a unique human dataset in which whole brain BOLD-fMRI activity was recorded simultaneously with intracranial electrical stimulation, to characterize the effects of direct neural stimulation on the dynamic reconfiguration of the broader network. Direct neural stimulation increased the extent to which the stimulation sites own mesoscale community integrated with the rest of the brain. Further, we found that these network changes depended on the topological role of the stimulation site itself: stimulating regions with high participation coefficients led to global integration, whereas stimulating sites with low participation coefficients integrated that regions own community with the rest of the brain. These findings provide direct causal evidence for how network topology shapes and constrains inter-regional coordination, and suggest applications for targeted therapeutic interventions in patients with deep-brain stimulation.

neuroscience↗
bioRxiv2020

Human es-fMRI Resource: Concurrent deep-brain stimulation and whole-brain functional MRI

Mapping the causal effects of one brain region on another (effective connectivity) is a challenging problem in neuroscience, since it requires invasive direct manipulation of brain function, together with whole-brain measurement of the effects produced. Here we establish a unique resource and present data from 26 human patients who underwent electrical stimulation during functional magnetic resonance imaging (es-fMRI). The patients had medically refractory epilepsy requiring surgically implanted intracranial electrodes in cortical and subcortical locations. One or multiple contacts on these electrodes were stimulated while simultaneously recording BOLD-fMRI activity in a block design. Multiple runs exist for patients with different stimulation sites. We describe the resource, data collection process, preprocessing using the fMRIPrep analysis pipeline and management of artifacts, and provide end-user analyses to visualize distal brain activation produced by site-specific electrical stimulation. The data are organized according to the brain imaging data structure (BIDS) specification, and are available for analysis or future dataset contributions on openneuro.org including both raw and preprocessed data.

neuroscience↗
bioRxiv2019

Analysis of task-based functional MRI data preprocessed with fMRIPrep

Functional magnetic resonance imaging (fMRI) is a standard tool to investigate the neural correlates of cognition. fMRI noninvasively measures brain activity, allowing identification of patterns evoked by tasks performed during scanning. Despite the long history of this technique, the idiosyncrasies of each dataset have led to the use of ad-hoc preprocessing protocols customized for nearly every different study. This approach is time-consuming, error-prone, and unsuitable for combining datasets from many sources. Here we showcase fMRIPrep (http://fmriprep.org), a robust tool to prepare human fMRI data for statistical analysis. This software instrument addresses the reproducibility concerns of the established protocols for fMRI preprocessing. By leveraging the Brain Imaging Data Structure (BIDS) to standardize both the input datasets --MRI data as stored by the scanner-- and the outputs --data ready for modeling and analysis--, fMRIPrep is capable of preprocessing a diversity of datasets without manual intervention. In support of the growing popularity of fMRIPrep, this protocol describes how to integrate the tool in a task-based fMRI investigation workflow.

bioinformatics↗
bioRxiv2018

Crowdsourced MRI quality metrics and expert quality annotations for training of humans and machines

The neuroimaging community is steering towards increasingly large sample sizes, which are highly heterogeneous because they can only be acquired by multi-site consortia. The visual assessment of every imaging scan is a necessary quality control step, yet arduous and time-consuming. A sizeable body of evidence shows that images of low quality are a source of variability that may be comparable to the effect size under study. We present the MRIQC Web-API, an open crowdsourced database that collects image quality metrics extracted from MR images and corresponding manual assessments by experts. The database is rapidly growing, and currently contains over 100,000 records of image quality metrics of functional and anatomical MRIs of the human brain, and over 200 expert ratings. The resource is designed for researchers to share image quality metrics and annotations that can readily be reused in training human experts and machine learning algorithms. The ultimate goal of the database is to allow the development of fully automated quality control tools that outperform expert ratings in identifying subpar images.

bioengineering↗
bioRxiv2018

FMRIPrep: a robust preprocessing pipeline for functional MRI

Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each new dataset, building upon a large inventory of tools available for each step. The complexity of these workflows has snowballed with rapid advances in MR data acquisition and image processing techniques. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for task-based and resting fMRI data. FMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing with no manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software-testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection comprising participants from 54 different studies in the OpenfMRI repository. We review the distinctive features of fMRIPrep in a qualitative comparison to other preprocessing workflows. We demonstrate that fMRIPrep achieves higher spatial accuracy as it introduces less uncontrolled spatial smoothness than commonly used preprocessing tools. FMRIPrep has the potential to transform fMRI research by equipping neuroscientists with a high-quality, robust, easy-to-use and transparent preprocessing workflow which can help ensure the validity of inference and the interpretability of their results.

bioinformatics↗
bioRxiv2017

MRIQC Web-API: Crowdsourcing image quality metrics and expert quality ratings of structural and functional MRI

SynopsisThe MRIQC Web-API is a resource for scientists to train new automatic quality classifiers. The MRIQC Web-API has collected more than 30K sets of image quality measures automatically extracted from BOLD and T1-weighted scans using MRIQC. MRIQC is an automated MRI Quality Control tool, and here we present an extension to crowdsource these quality metrics along with anonymized metadata and manual quality ratings. This new resource will allow a better understanding of the normative values and distributions of these quality metrics, help determine the relationships between image quality and metadata such as acquisition parameters and finally, provide a cost-effective, easy way to annotate the quality of a large number of cross-site MR scans.

bioinformatics↗