Oscar Esteban is a Research and Teaching Ambizione FNS Fellow at the Service of Radiology of the Lausanne University Hospital (CHUV) and the University of Lausanne. Oscar’s research aims at pushing the boundaries of human and nonhuman neuroimaging —magnetic resonance imaging (MRI) most often,— and by that, help other researchers advance our understanding of the brain. In more specific terms, Oscar is currently developing tools that cater to researchers with “analysis-grade” data (see www.nipreps.org for more on this concept,) so they can focus on statistical modeling and inference. Perhaps, the flagship of these tools is fMRIPrep. Oscar actively investigates the reliability and validity of diffusion and functional MRI measurements. As a fundamental step of the neuroimaging workflow, Oscar wants to improve the computational reproducibility of our results and minimize this methodological variability in the preprocessing step by standardizing workflows and reaching consensus implementations. In the longer term, Oscar’s vision is to contribute to uncovering the interplay of structure, function, and dynamics of brain connectivity using MRI.
Céline’s background is in Physics. In her master’s, she gained plenty of experience in MRI research by contributing to four projects, ranging from semi-automatic segmentation of knee calcifications to analyzing recurrent brain co-activation patterns using clustering. In her PhD, she is working on standardizing the quality control and the preprocessing of both structural and functional MRI data. In particular, she is contributing to the development of easy-to-use toolboxes that automatize those processes. Examples of such toolboxes are MRIQC and fMRIPrep. She is also interested in supporting open science and reproducibility of MRI research. Her further goal is to determine the optimal preprocessing flow by performing tests on one densely-acquired patient. This will enable to focus on the methodological variability by removing inter-subject variability. On a third step, she will engage in bridging structure and function into a single framework and apply the methodology developed to the study of the integrative role of the Thalamus.
My background is in Astrophysics. During my PhD, I developed deep-learning tools to classify, segment and generate images. In addition to a machine learning core, my PhD research involved a substantial amount of image processing. In The AxonLab, I contribute to implementing preprocessing tools under the umbrella of the NiPreps ecosystem to improve computational reproducibility and reduce methodological variability in MRI research. In addition to this, I am strongly committed to promoting open science.
Alexandre Cionca is a research assistant in the Department of Radiology at the University Hospital of Lausanne (CHUV). He graduated from EPFL with a specialization in Computational Science and Engineering (CSE). He focused his studies on data science and statistical learning applied to the neuroimaging field. He then joined the Clinical and Experimental Neuropsychology lab (CENLab) at the University of Geneva, where he worked on all-around data processing and analysis for the COVID-COG project. His research included computational modeling of functional MRI in the unraveling of long-term neuropsychological effects following a SARS-CoV-2 infection. He is now fulfilling his civil service with the Axon Lab in an initiative to evaluate the reliability of the clinical MRI workflow that routinely aids medical decision at CHUV.
My PhD research used MRI to identify changes in brain networks related to healthy ageing. During this time, I realised my enthusiasm for solving data analysis problems and developing resources for image processing and analysis. In particular, I am interested in extending processing tools developed for human data to be species agnostic, and supporting open science and reproducibility in the preclinical MRI community. I continue to apply network-based analysis methods to MRI data with the aim of modelling brain changes over time.
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