This 2-year postdoctoral position is part of the exploratory action INCLUDE (Integrating fuNctional MRI and EEG with Carbon-wire Loops: towards the characterization of mUltimoDal functional biomarkErs), funded by Inria (PI: Julie Coloigner and Claire Cury, researchers at IRISA/Inria, Univ Rennes, Empenn team, julie.coloigner@irisa.fr, claire.cury@inria.fr)
https://jobs.inria.fr/public/classic/en/offres/2025-09390
Context
The human brain is organized as a complex network of billions of neurons, each connected to about 100,000 others through axons forming bundles of white matter fibers. Mapping these neuronal connections is crucial to study the neural foundations of both the healthy and pathological brain. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are the main techniques used to assess brain function. EEG measures scalp-level signals that directly reflect neuronal activity with high temporal precision. fMRI, on the other hand, is sensitive to hemodynamic changes and provides an indirect measure of neuronal activity, but with poor temporal precision.
The INCLUDE Project
Simultaneous EEG-fMRI acquisition combines two complementary neuroimaging techniques, potentially enabling the development of an improved imaging approach with high spatio-temporal connectivity resolution. However, EEG signals acquired during fMRI are typically contaminated by numerous artifacts, which hinder connectivity estimation. Recent work has proposed recording these artifacts using a set of carbon-wire loops (CWL) attached to the EEG cap. Denoising with these reference signals makes it possible, for the first time, to obtain EEG data of sufficient quality to allow for robust EEG connectivity estimation.
The goal of the INCLUDE project is to accurately estimate connectivity using simultaneous EEG-fMRI recordings. The main challenges of the project are:
(i) effectively removing EEG artifacts using carbon-wire loops, and
(ii) integrating EEG and fMRI data.
Assignment
Postdoctoral Objective
The aim is to develop a method for connectome estimation from simultaneous EEG-fMRI recordings. Differences in connectivity matrices measured by EEG and fMRI are expected due to differences in spatial and temporal resolution, as well as because they capture different mechanisms of brain activity. A strong intermodal correlation has been observed in the EEG β-frequency band.
The recruited candidate will estimate both common and complementary connectivity information, as well as the relationship between EEG and fMRI connectivity organization across different frequency bands, using dynamic connectivity [5]. This work will be carried out in collaboration with Jonathan Wirsich, University of Geneva, who will provide expertise in EEG dynamic connectivity estimation. The postdoctoral researcher will be expected to develop a novel approach.
Exploiting both complementary and shared features with an appropriate fusion strategy is crucial for improving connectome estimation and identifying novel disease biomarkers. In this context, the postdoctoral researcher will construct multilayer graphs containing both fMRI and EEG connectivity matrices.
Main activities
Responsibilities
The candidate will work under the supervision of Julie Coloigner, in collaboration with a postdoctoral researcher (fixed-term contract) also recruited for the project.
The recruited person will be responsible for setting up the EEG-fMRI platform with carbon-wire loops, enabling the estimation of connectivity with high spatio-temporal resolution.
For further understanding of the proposed research topic:
A state of the art, bibliography, and scientific references are available at the following URL. Please feel free to consult it or contact us for more details: INCLUDE.
Collaboration
The recruited candidate will collaborate with:
- Julie Coloigner, researcher at IRISA, expert in connectivity
- Elise Bannier, Research Engineer in MR Physics
- Claire Cury, INRIA researcher , expert in EEG and in neurofeedback
- Mathis Piquet, engineer working on the project
- Jonathan Wirsich, researcher in University of Geneva
Supervision/Management
The recruited candidate will be responsible for implementing the EEG-fMRI platform and developing EEG data preprocessing methods.
The candidate will also have the opportunity to write a scientific article, as first author (if sufficiently comfortable in English), presenting and detailing the denoising method based on carbon-wire loops.
How to Apply
Please apply on https://jobs.inria.fr/public/classic/en/offres/2025-09390 and send us the following information and documents:
- CV
- Your PhD defense report (if the thesis has already been defended)
- A motivation letter
- A recommendation letter, or the contact details of a PhD supervisor who could provide a reference for your application.
References
[1] Arthur W Toga et al. “Mapping the human connectome”. In: Neurosurgery 71.1 (2012), p. 1.
[2] Toni Ihalainen et al. “Data quality in fMRI and simultaneous EEG–fMRI”. In: Magnetic Resonance Materials in Physics, Biology and Medicine 28.1 (2015), pp. 23–31.
[3] Frédéric Grouiller et al. “A comparative study of different artefact removal algorithms for EEG signals acquired during functional MRI”. In: Neuroimage 38.1 (2007), pp. 124–137.
[4] Johan N van der Meer et al. “Carbon-wire loop based artifact correction outperforms post-processing EEG/fMRI corrections—A validation of a real-time simultaneous EEG/fMRI correction method”. In: Neuroimage 125 (2016), pp. 880–894.
[5] Jonathan Wirsich et al. “The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5 T to 7T”. In: NeuroImage 231 (2021), p. 117864.
Skills
Technical skills and required level:
- Development (MATLAB/Python): Very good level
- Signal processing: Good level
- Electroencephalography: Strong interest; prior experience desirable
- Medical imaging: Strong interest; prior experience desirable
- Data acquisition: Strong interest; experience is a plus
Interpersonal skills:
- Autonomous
- Excellent communication skills (to seek support and present project progress)
- Pedagogical, patient, and dynamic (for data acquisition with healthy participants)
