Oscar Estéban

This study introduces an automated 3D MRI segmentation method for the human eye and orbit, providing large-scale unbiased atlases for clinical research.

The paper introduces a protocol for assessing and controlling MRI data quality using MRIQC software, focusing on anatomical, functional, and diffusion MRI scans of the human brain.

This paper extends the BIDS standard to include eye-tracking data and metadata, improving organization and transparency for gaze position and pupil recordings.

The paper extends fMRIPrep to handle MRI data from infancy to old age, demonstrating high-quality preprocessing across a wide age range.

This study evaluates ADC-fMRI's dependence on haemodynamics during breath-hold and resting-state conditions, finding it less affected by vascular contributions compared to dfMRI and BOLD.

This study assesses how defacing structural MRI images affects quality assessments by humans and automated tools, finding significant human bias and minimal impact on MRIQC metrics.

The paper evaluates the impact of different preprocessing pipelines on functional MRI reproducibility and shows moderate inter-pipeline agreement, affecting brainwide association study consistency.

The paper introduces a synthetic fetal brain MRI dataset generated with improved realism and used to evaluate automated segmentation and AI model development.

TemplateFlow provides a FAIR framework for sharing and managing multi-scale, multi-species brain models to improve reproducibility in neuroimaging.

OpenNeuro is an open archive for sharing brain imaging data following FAIR principles, hosting over 600 datasets from 20,000 participants across species and modalities, with over 150 publications citing its data.

The paper introduces a convolutional neural network to detect defaced facial features in MRI brain scans, achieving high accuracy and sensitivity using data augmentation.

This study shows that intracranial electrical stimulation alters meso-scale network integration depending on the topological role of the stimulated brain regions.

This paper introduces fMRIPrep, a tool for preprocessing task-based fMRI data, and demonstrates its integration into a workflow for statistical analysis.

The paper presents a human es-fMRI resource with data from 26 patients undergoing deep-brain stimulation and whole-brain functional MRI, providing raw and preprocessed data for analysis.

fMRIPrep, a BIDS-driven, self-adapting fMRI preprocessing pipeline, earned above-acceptable expert quality ratings on 50 of 54 OpenfMRI datasets and introduced less smoothing than FSL feat.

The paper introduces MRIQC Web-API, a resource for training automatic quality classifiers by crowdsourcing image quality metrics and expert ratings from MRI scans.

The paper evaluates tractography methods by analyzing a simulated dataset, finding that most algorithms generate many false-positive connections.

MRIQC is a tool for predicting MRI image quality that generalizes across sites with 76% accuracy but struggles with site-specific artifacts.

This paper introduces BIDS Apps, a framework for creating portable neuroimaging analysis tools compatible with BIDS data, improving reproducibility and ease of use.