Invited speaker 2020

Dr Oscar Esteban

 · 3 mins read

We are delighted to introduce another guest speaker, Dr Oscar Esteban. He will be giving a talk on Tuesday 10th November about niPreps, an open-source neuroimaging preprocessing framework. Together with Michael Joseph, he will also be part of the team behind the tutorial on how to contribute to an open-source project on Thursday 12th November.

A bit about Dr Esteban

Dr Esteban is a Research and Teaching Ambizione FNS Fellow at the Service of Radiology of the Lausanne University Hospital (CHUV) and University of Lausanne. Dr Esteban’s research aims to push the boundaries of neuroimaging —magnetic resonance imaging (MRI) mostly— and through this, to help other researchers advance our understanding of the human brain. More specifically, Dr Esteban is currently developing tools that cater for researchers with “analysis-grade” data (see nipreps.org for more information on this concept) so they can focus on the statistical modeling and inference. Perhaps, the flagship of these tools is fMRIPrep. This drive for a preprocessing step of the neuroimaging research workflow is justified by the concerning methodological variability that negatively contributes to reproducibility in the field. In particular, Dr Esteban wants to improve the computational reproducibility of our results and minimize this methodological variability in the preprocessing step, by standardizing methodologies and reaching a consensus on how preprocessing is implemented. In the longer term, Dr Esteban’s vision is to contribute to uncovering the interplay between structure, function, and dynamics of brain connectivity using MRI.

There’s a bit more about Oscar’s talk in Brainhack Donostia in his abstract below!

The current neuroimaging workflow has matured into a large chain of processing and analysis steps involving a large number of experts, across imaging modalities and applications. Moreover, the availability of a comprehensive portfolio of software libraries and tools has also resulted in a concerning degree of analytical variability. Generalizing the preprocessing —that is, the intermediate step between data generation by the measurement device and the subsequent statistical modeling and analysis— beyond fMRIPrep, we propose a framework called NiPreps (NeuroImaging Preprocessing toolS) that we envision as a workbench for the development of such pipelines. By exclusively addressing the preprocessing of the data, fMRIPrep has successfully allowed researchers to focus their effort and expertise on the portion most relevant to scientific inference (in other words, statistical and computational analyses) and reduce methodological variability. Moreover, the development and fast adoption of fMRIPrep have revealed that neuroscientists need tools that simplify their research workflow, provide visual reports and checkpoints, and engender trust in the tool itself. NiPreps expands fMRIPrep to operate on new imaging modalities (for instance, diffusion MRI) and disciplines (for instance, preclinical imaging). In short, NiPreps paves the way towards next-generation imaging, ultimately allowing neuroscientists to seek a unified statistical framework capable of rigorously integrating cross-application and cross-species data analysis.

Don’t miss the dMRIprep tutorial by Michael Joseph!

The development and fast adoption of fMRIPrep have revealed that neuroscientists need tools that simplify their research workflow, provide visual reports and checkpoints, and engender trust in the tool itself. dMRIPrep extends fMRIPrep ‘s approach and principles to diffusion MRI (dMRI). The preprocessing of dMRI involves numerous steps to clean and standardize the data before fitting a particular model or carrying out tractography. Generally, researchers create ad-hoc preprocessing workflows for each dataset, building upon a large inventory of available tools. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. dMRIPrep is an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for whole-brain dMRI data. In this tutorial they will demonstrate how to engage as a new contributor to dMRIPrep with the implementation of a longstanding request for a new feature in the software (https://github.com/nipreps/dmriprep/issues/64). They will describe the overall process of contributing to an open-source project step-by-step, particularizing steps to the neuroimaging field whenever possible.