3.3.1 Population imaging

One major objective of neuroimaging researchers and clinicians is to be able to stratify brain imaging data in order to derive new and more specific population models. In practice, this requires to set up large-scale experiments that, due to the lack of resources and capabilities to recruit locally subjects who meet specific inclusion criteria, motivates the need for sharing the load.

However, building and using multi-site large-scale resources pose specific challenges to deal with the huge quantity of data produced and their diversity. Empenn focuses on two challenges in particular:

• Providing computational environments for the computation and use of imaging biomarkers in the targeted brain diseases, a solution to be used by radiologists and neurologists/psychiatrists for the clinical follow-up of a large patient population.
• Modeling analytic variability of image processing pipelines to better understand and predict the behaviour of imaging biomarker detection solutions and improve reproducibility and productivity in clinical neuroimaging research.

3.3.2 Detection and learning

We intend to make significant contributions with major impacts in learning coupling models between functional recordings during neurofeedback procedures. These advances will provide a breakthrough in brain-computer interfaces for rehabilitation protocols. Our aim is to:

• Our research employs data-driven approaches, encompassing machine learning and deep learning, to enhance the detection and segmentation of abnormal patterns in medical images. Our primary focus is on multiple sclerosis (MS) and, more recently, on stroke. The findings from our studies indicate promising outcomes in automated tools for accurate disease activity assessment and lesion segmentation within large MRI databases. Special attention is given to the integration of multimodal information and the utilization of labeled and unlabeled data. As we progress, our aim is to adapt these methods to address a broader range of neurological diseases, including epilepsy, tumors, etc., in both neonate and adult brains. This research contributes to advancing diagnostic tools and methodologies in the field of medical imaging.
• Develop solutions for combining brain state measurements from multimodal sensors or sequences (e.g. fMRI, ASL, EEG, NIRS, etc.) with applications in the spatiotemporal reconstruction of brain activity from MRI-EEG or the combined detection of the endogenous hemodynamic and resting state network of the brain from ASL and NIRS. Over the longer term, the advent of new hybrid brain imaging sensors (e.g. PET-MRI) will require these methods to be extended to a larger spectrum of information combining structural, morphological, metabolic, electrophysiological and cellular/molecular information (e.g. through the use of specific ligands/nanocarriers).

3.3.3 Quantitative imaging

The Empenn research group focuses on the development of several quantitative techniques in magnetic resonance imaging of the brain. These methods allow for characterization of both the function and the structure of the brain with high precision. Arterial spin labelling (ASL) is a contrast agent-free imaging technique which labels arterial blood water as an endogenous tracer for perfusion and can measure resting-state cerebral blood flow. We are interested in estimating multiparametric hemodynamics using ASL, such as combined cerebral blood flow and arterial transit times, and derive statistical descriptors to represent significant differences between groups. In addition to quantitative perfusion parameters, our contributions on tissue compartment imaging aim at delineating neural circuits and characterize their microstructure properties, using both diffusion MRI and relaxometry. In diffusion MRI, arbitrary gradient waveforms were shown to exhibit higher sensitivity to microstructure parameters than standard pulsed gradients. We work on the optimization of sampling protocols in this domain, with the objective to propose sequences compatible with in vivo acquisition. Complementary to diffusion MRI, we develop methods for the reconstruction of myelin-bound, extra-axonal and cerebrospinal fluid water using multi-compartment modelling of the T2-relaxometry signal. We combine these techniques with tractography to identify trajectories of pathologies associated to the evolution of these microstructural parameters along specific fiber bundles in the brain white matter. Finally, we are also focusing on assessing the characteristics (repeatability, reproducibility and sensitivity) of several quantitative metrics variability (e.g. MTR, T1 relaxometry) in the spinal cord of patients with MS.

3.3.4 Translational research

The three translational axes focus on the central nervous system and are presented in details in the following section.

7.1.1 Anima

• Keywords:
  Medical imaging, Neuroimaging, Image processing
• Scientific Description:
  Anima is a set of libraries and tools developed by the team as a common repository of research algorithms. As of now, it contains tools for image registration, statistical analysis (group comparison, patient to group comparison), diffusion imaging (model estimation, tractography, etc.), quantitative MRI processing (quantitative relaxation times estimation, MR simulation), image denoising and filtering, and segmentation tools. All of these tools are based on stable libraries (ITK, VTK), making it simple to maintain.
• Functional Description:
  Anima is a set of libraries and tools in command line mode for processing and analysing medical images.
• URL:
  https://­anima.­irisa.­fr
• Contact:
  Julie Coloigner
• Participants:
  Aymeric Stamm, Fang Cao, Florent Leray, Guillaume Pasquier, Laurence Catanese, Olivier Commowick, Renaud Hedouin, Rene-Paul Debroize

7.1.2 MedINRIA

• Keywords:
  Visualization, DWI, Health, Segmentation, Medical imaging
• Scientific Description:
  MedInria aims at creating an easily extensible platform for the distribution of research algorithms developed at Inria for medical image processing. This project has been funded by the D2T (ADT MedInria-NT) in 2010, renewed in 2012. A fast-track ADT was awarded in 2017 to transition the software core to more recent dependencies and study the possibility of a consortium creation.The Empenn team leads this Inria national project and participates in the development of the common core architecture and features of the software as well as in the development of specific plugins for the team's algorithm.
• Functional Description:
  medInria is a free software platform dedicated to medical data visualization and processing.
• URL:
  https://­med.­inria.­fr
• Contact:
  Florent Leray
• Participants:
  Maxime Sermesant, Olivier Commowick
• Partners:
  HARVARD Medical School, IHU - LIRYC, NIH

7.1.3 autoMRI

• Keywords:
  FMRI, MRI, ASL, FASL, SPM, Automation
• Scientific Description:
  This software is highly configurable in order to fit a wide range of needs. Pre-processing includes segmentation of anatomical data, as well as co-registration, spatial normalization and atlas building of all data types. The analysis pipelines perform either within-group analysis or between-group or one subject-versus-group comparison, and produce statistical maps of regions with significant differences. These pipelines can be applied to structural data to exhibit patterns of atrophy or lesions, to ASL (both pulsed or pseudo-continuous sequences) data to detect perfusion abnormalities, to functional data - either BOLD or ASL - to outline brain activations related to block or event-related paradigms. New functionalities have been implemented to facilitate the management and processing of data coming from complex projects.
• Functional Description:
  AutoMRI is based on MATLAB and the SPM12 toolbox and provides complete pipelines to pre-process and analyze various types of images (anatomical, functional, perfusion).
• URL:
  https://­team.­inria.­fr/­visages/­software/
• Contact:
  Isabelle Corouge
• Participants:
  Camille Maumet, Elise Bannier, Isabelle Corouge, Pierre Maurel, Quentin Duché, Julie Coloigner

7.1.4 ShanoirUploader

• Name:
  ShanoirUploader (SHAring NeurOImaging Resources Uploader)
• Keywords:
  Webservices, PACS, Medical imaging, Neuroimaging, DICOM, Health, Biology, Java, Shanoir
• Scientific Description:
  ShanoirUploader is a desktop application on base of JavaWebStart (JWS). The application can be downloaded and installed using an internet browser. It interacts with a PACS to query and retrieve the data stored on it. After this ShanoirUploader sends the data to a Shanoir server instance in order to import these data. This application bypasses the situation, that in most of the clinical network infrastructures a server to server connection is complicated to set up between the PACS and a Shanoir server instance.
• Functional Description:
  ShanoirUploader is a Java desktop application that transfers data securely between a PACS and a Shanoir server instance (e.g., within a hospital). It uses either a DICOM query/retrieve connection or a local CD/DVD access to search and access images from a local PACS or the local CD/DVD. After having retrieved the data, the DICOM files are locally anonymized and then uploaded to the Shanoir server. A possible integration of a hash creation application for patient identifiers is provided as well. The primary goals of that application are to enable mass data transfers between different remote server instances and therefore reduce the waiting time of the users, when importing data into Shanoir. Most of the time during import is spent with data transfers.
• URL:
  https://­github.­com/­fli-iam/­shanoir-ng/­wiki/­ShanoirUploader
• Contact:
  Michael Kain
• Participants:
  Christian Barillot, Inès Fakhfakh, Justine Guillaumont, Michael Kain, Yao Chi

7.1.5 Shanoir-NG

• Name:
  Shanoir (SHAring iN vivO Imaging Resources)
• Keyword:
  Medical imaging
• Functional Description:

  Shanoir (SHAring iN vivO Imaging Resources) is an open-source web platform designed to share, archive, search and visualize medical imaging data. It provides an user-friendly secure web access and offers an intuitive workflow to facilitate the collecting and retrieving of imaging data from multiple sources. Quality control can be applied on imported data. Mass data can be downloaded in multiple ways, via the web interface and via a Python script.

  It supports the following formats: DICOM classic/enhanced (MR, CT, PT, NM), BIDS, processed datasets (NIfTI), Bruker, EEG(BrainVision/EDF).

  Shanoir comes along many features such as pseudonymization of data (based on DICOM standard profiles), support for multi-centric clinical studies on subjects. Shanoir offers an ontology-based data organization (OntoNeuroLOG). Among other things, this facilitates the reuse of data and metadata, the integration of processed data and provides traceability trough an evolutionary approach. Shanoir allows researchers, clinicians, PhD students and engineers to undertake quality research projects with an emphasis on remote collaboration. Data user agreements (DUA) can be configured by study to be accepted by each accessing users and access requests can be initated to study administrators.

• Release Contributions:
  - New tree view - New version of OHIF, with annotations and segmentations - Mass processing integration with VIP - Java 21 - Spring Boot 3.2
• URL:
  https://­github.­com/­fli-iam/­shanoir-ng
• Contact:
  Michael Kain
• Participants:
  Michael Kain, Anthony Baire, Julien Louis, Jean-Côme Douteau, Pierre-Henri Dauvergne, Arthur Masson, Youenn Merel

7.1.6 LongiSeg4MS

• Name:
  Longitudinal Segmentation For Multiple Sclerosis
• Keywords:
  3D, Brain MRI, Deep learning, Detection
• Functional Description:
  LongiSeg4MS is an automatic new multiple sclerosis (MS) lesion detection tool based on longitudinal data and using deep learning. The system uses FLAIR, T1 or T2 modalities, or a combination of those. The input is 2, 4 or 6 images (2 FLAIR, 2 FLAIR and 2 T1, etc.), a set of modalities for each time point, and outputs a segmentation map describing the location of new MS lesions.
• URL:
  https://­gitlab.­inria.­fr/­amasson/­longiseg4ms
• Contact:
  Arthur Masson
• Partner:
  OFSEP

7.1.7 Anima medInria plugins

• Keywords:
  IRM, Medical imaging, Diffusion imaging
• Functional Description:
  Plugins for the medInria software based on the open source software Anima developed in the Visages / Empenn team. These plugins are interfaces between anima and medinria allowing to use Anima functionalities within the clinical user interface provided by medInria. The current functionalities included in the plugins are right now: image registration, denoising, quantitative imaging (relaxometry), and model estimation and visualization from diffusion imaging.
• URL:
  https://­github.­com/­medInria/­medInria-visages
• Contact:
  Florent Leray
• Participants:
  Olivier Commowick, Florent Leray, Rene-Paul Debroize, Guillaume Pasquier

7.1.8 MS_SC_lesions_seg_t2_stir

• Keywords:
  Segmentation, Multimodality, Python, Docker, MRI
• Functional Description:
  The software provides segmentation of multiple sclerosis lesions from a pair of T2-weighted and STIR MRI images of the spinal cord.
• Contact:
  Benoit Combes

7.1.9 MS_SC_lesions_seg

• Keywords:
  Segmentation, MRI, Multiple Sclerosis
• Functional Description:
  The software provides segmentation of multiple sclerosis lesions in T2-weighted MRI images of patients' spinal cords.
• Contact:
  Benoit Combes

7.1.10 NARPS Open Pipelines

• Name:
  NARPS Open Pipelines
• Keywords:
  Functional MRI, FMRI, Variability, Statistical analysis, Reproducibility
• Scientific Description:

  A codebase reproducing the 70 pipelines of the NARPS study (Botvinik-Nezer et al., 2020) shared as an open resource for the community.

  NARPS Open Pipelines is developed in the Empenn team by Boris Clénet, Elodie Germani, Jeremy Lefort-Besnard and Camille Maumet with contributions by Rémi Gau. In addition, this project was presented and received contributions during multiple hackathons, for a complete list see: https://github.com/Inria-Empenn/narps_open_pipelines?tab=readme-ov-file#credits

• Functional Description:
  We believe the NARPS Open Pipelines codebase will help analysing and understanding variability of fMRI analysis workflows, hence participating in the reproducible research movement.
• URL:
  https://­github.­com/­Inria-Empenn/­narps_open_pipelines
• Publication:
  hal-04286609
• Contact:
  Camille Maumet
• Participants:
  Boris Clenet, Elodie Germani, Jeremy Lefort-Besnard, Camille Maumet, Remi Gau
• Partner:
  Région Bretagne

7.1.11 shanoir downloader

• Name:
  Shanoir Downloader
• Keywords:
  Medical imaging, Data management, Big data, Python
• Scientific Description:
  Shanoir Downloader enables large volumes of imaging data stored on the Shanoir software platform to be downloaded via a python interface. Data can be retrieved in DICOM or NIFTI formats. The integrity of downloaded data is verified. Shanoir Downloader also enables downloaded data to be pseudonymised and organised according to the BIDS standard.
• Functional Description:
  Shanoir Downloader enables large volumes of imaging data stored on the Shanoir software platform to be downloaded via a python interface. Data can be retrieved in DICOM or NIFTI formats. The integrity of downloaded data is verified. Shanoir Downloader also enables downloaded data to be pseudonymised and organised according to the BIDS standard.
• News of the Year:
  In the context of the FAIR data access principles, appropriate support for the BIDS data standard based on the heudiconv software has been implemented, support for distributed data management and versioning has also been included via the datalad software. Finally, a deletion of large volumes of data stored on Shanoir was also implemented in order to consolidate the existing databases of the Shanoir platform.
• URL:
  https://­github.­com/­Inria-Empenn/­shanoir_downloader
• Contact:
  Michael Kain
• Participants:
  Arthur Masson, Quentin Duché, Malo Gaubert, Alexandre Pron, Jean-Côme Douteau, Michael Kain

7.2.1 The Neurinfo Platform

Participants: Elise Bannier, Emmanuel Caruyer, Isabelle Corouge, Quentin Duché, Jean-Christophe Ferré, Jean-Yves Gauvrit.

Empenn is the founding actor of an experimental research platform which was installed in August 2009 at the University Hospital of Rennes. The University of Rennes, Inria, CNRS for the academic side, and the University Hospital of Rennes and the Cancer Institute “Eugene Marquis” for the clinical side, are partners of this neuroinformatics platform called Neurinfo (Neurinfo website). Concerning the Neurinfo Platform, the activity domain is a continuum between methodological and technological research built around specific clinical research projects. On the medical field, the translational research domain mainly concerns medical imaging and more specifically the clinical neurosciences. Among them are multiple sclerosis, epilepsy, neurodegenerative, neurodevelopmental and psychiatric diseases, surgical procedures of brain lesions, neuro-oncology and radiotherapy planning. Beyond these central nervous system applications, the platform is also open to alternative applications. Neurinfo ambitions to support the emergence of research projects based on their level of innovation, their pluri-disciplinarity and their ability to foster collaborations between different actors (public and private research entities, different medical specialties, different scientific profiles). In this context, a research 3T MRI system (Siemens Verio) was acquired in summer 2009 in order to develop the clinical research in the domain of morphological, functional, structural and cellular in-vivo imaging. A new 3T Siemens Prisma MRI scanner was installed at the Neuroinfo platform in February 2018. In 2014, an equipment for simultaneous recording of EEG and MRI images was acquired from Brain Product. In 2015, a mock scanner for experimental set-up was acquired as well as a High Performance Computing environment made of one large computing cluster and a data center that is shared and operated by the Inria center and IRISA (UMR CNRS 6074). The computation cluster (480 cores) and the data center (up to 150 TB) are dedicated to host and process imaging data produced by the Neurinfo platform, but also by other research partners that share their protocols on the Neurinfo neuroinformatics system (currently more than 60 sites). In 2019, an MRI and EEG-compatible fNIRS system was acquired through a co-funding from the INS2I institute of CNRS and FEDER. At the end of 2019, GIS IBISA awarded the Neurinfo platform with a complementary funding that will be dedicated to supplement the current system with additional sensors (from 8x8 optodes to 16x16 optodes). In 2022, the Regional Council of Britanny funding was renewed to provide engineer support for another year to develop and integrate this new imaging system.

7.3.1 The HCP multi-pipeline dataset: an opportunity to investigate analytical variability in fMRI data analysis

Participants: Elodie Germani, Pierre Maurel, Camille Maumet.

Results of functional Magnetic Resonance Imaging (fMRI) studies can be impacted by many sources of variability including differences due to: the sampling of the participants, differences in acquisition protocols and material but also due to different analytical choices in the processing of the fMRI data. While variability across participants or across acquisition instruments have been extensively studied in the neuroimaging literature the root causes of analytical variability remain an open question. In 58, we share the HCP multi-pipeline dataset, including the resulting statistic maps for 24 typical fMRI pipelines on 1,080 participants of the HCP-Young Adults dataset. We share both individual and group results - for 1,000 groups of 50 participants - over 5 motor contrasts. We hope that this large dataset covering a wide range of analysis conditions will provide new opportunities to study analytical variability in fMRI.

8.1.1 Population imaging

Population imaging is fundamental when it comes to evaluate clinical biomarkers. In this section we summarise our contributions over the last year to this theme. We proposed a new fMRI preprocessing pipeline adapted for stroke patients. We studied how analytical variability impacts fMRI results and mega-analyses and proposed solutions to derive valid multiverse analyses and correct for pipeline-induced variance. We continued to contribute to the "Brain Imaging Data Structure", a widespread standardization effort in the brain imaging community.

8.1.2 Detection and learning

In this section, we summarize our contributions that focus on information extraction from medical imaging data. We proposed new segmentation methods for multiple sclerosis lesions in the spinal cord and for post-stroke lesions. We developed a new approach for source reconstruction in EEG. We applied a multicompartment approach to measure brain microstructure in late-life depression. We predicted fMRI neurofeedback scores based on EEG.

8.1.3 Quantitative imaging

Quantitative imaging methods can provide access to imaging metrics which can help characterize tissue integrity or neural activity. These methods can be used to assess tissue impairment, lesion severity and follow disease evolution. This year we proposed to estimate orientation dispersion in diffusion MRI using a multidimensional approach and to take into account anatomical priors to guide tractography. We investigated the reproducibility of T1 measures as well as quantitative susceptibility mapping in the cervical spine.

8.2.1 Behavior

This year, we evaluated the effect of a dorso-lateral prefrontal cortex targeted FNIRS NF training on functional connectivity, we studied the impact of a strong warning label on alcohol ads on the reward system, we evaluated diffusion MRI and fMRI derived features of anxiety and depression and brain patterns of treatment-resistant mood disorders.

8.2.2 Neuro-inflammation

This year, we evaluated the use of quantitative MRI imaging to assess brain and spinal cord lesions severity in MS and studied the impact of microstrctural damage in the spinal cord at 5 years.

8.2.3 Recovery

This year, we pursued our work regarding the evaluation of neurofeedback as a recovery therapy. We studied the use of a multimodal neurofeedback for motor imagery, we evaluated a different kind of feedback : a thermal neurofeedback. We proposed a method to explore neurodevelopmental trajectories and evaluated a treatment for recovering balance in stroke patients.

8.2.4 Psychiatry

8.2.5 Other

9.1.1 Siemens

Participants: Elise Bannier, Emmanuel Caruyer, Isabelle Corouge, Jean-Christophe Ferré, Francesca Galassi, Jean-Yves Gauvrit.

A collaboration between Siemens, Empenn and the Neurinfo platform is in place and formalized by a research contract. Thanks to this agreement, the Neurinfo platform has received the object code of MRI sequences under development at Siemens for evaluation in clinical research. In addition, the Neurinfo platform has received the source code of selected MRI sequences. As a result, MRI sequences can be developed on site by our team. For example, an MRI diffusion sequence was modified to load arbitrarly diffusion gradient waveforms for the FastMicroDiff project (led by E. Caruyer).

Additionally, Siemens supports the CIFRE thesis of Youwan Mahé (2024-2027). This PhD project focuses on developing advanced AI-driven techniques for the automatic detection of post-stroke anomalies from multi-modal MRI.

DECRYPT

• Title:
  Diffusion simulation for tissuE miCrostructure and bRain connectivitY with oPtimized acquisiTions
• Duration:
  2024 ->
• Coordinator:
  Jean-Philippe Thiran (jean-philippe.thiran@epfl.ch)
• Partners:
  • Ecole Polytechnique Fédérale de Lausanne Lausanne (Suisse)
• Inria contact:
  Emmanuel Caruyer
• Summary:

  The reconstruction of microscopic-scale information using magnetic resonance and its application to biological tissue in vivo with magnetic resonance imaging (MRI) has boosted our understanding of the organization of organs, in health and pathology (Alexander et al., 2019). In particular, neuroimaging with diffusion MRI has unveiled unprecedented details on the brain architecture with white matter tractography and the analysis of the brain connectome. In neuro-degenerative diseases, microstructural alterations usually occur at a relatively early stage, their detection could provide unique insight for the diagnosis, prognosis and monitoring of a number of pathologies, including but not limited to multiple sclerosis, Alzheimer’s disease, stroke or patients in a coma.

  A number of challenges have been highlighted in this quest for a microstructure-informed connectome reconstruction (Maier-Hein et al., 2019). In particular, current methods lack specificity and sensitivity to parameters of interest. These parameters are either local descriptors of the microstructure (cellular density, shape/caliber parameters) or macroscopic descriptors of the brain connectivity (detection and quantification of the “strength” of the connectivity between two interconnected brain regions). By developing numerical substrate, Monte-Carlo simulation methods, inverse problems solving with fingerprinting and machine learning, and the design of data acquisition methods tailored for specific tasks in microstructure characterization, we will contribute to this developing field. We will also develop methods for the integrated statistical analysis of the microstructure-informed brain connectome, building on recent development of graph-based signal processing and statistics on functional data.

10.2.1 Visits of international scientists

10.2.2 Visits to international teams

10.3.1 ANR-20-THIA-0018: programme Contrats doctoraux en intelligence artificielle

Participants: Francesca Galassi, Ricky Walsh, Benoît Combès.

• Funding:
  Co-funding for a PhD thesis in AI - Duration: 2022-2025.
• Summary:
  Co-funding (50% with Univ. Rennes) for a doctoral program in Artificial Intelligence. The PhD concerns the automatic segmentation of MS lesions in spinal cord MRI by means of AI-based solutions.

10.3.2 ANR-20-THIA-0018: programme Contrats doctoraux en intelligence artificielle

Participants: Camille Maumet, Elodie Germani.

• Funding:
  Co-funding for a PhD thesis in AI - Duration: 2021-2024.
• Summary:
  Co-funding (50% with Univ. Rennes) for a doctoral program in Artificial Intelligence. The PhD concerns representation learning for reproducible neuroimaging.

10.3.3 RHU PRIMUS: Transforming the care of patients with Multiple Sclerosis using a multidimensional data-driven clinical decision support system

Participants: Elise Bannier, Benoît Combès, Gilles Edan, Jean-Christophe Ferré, Francesca Galassi, Anne Kerbrat.

• Funding:
  RHU - Duration: 2022-2026 - Budget: 8272kC
• Partners:
  Observatoire Français de la Sclérose en Plaques (OFSEP), France Life Imaging (FLI), Pixyl.
• Summary:
  The overall objective of PRIMUS is to develop and validate a CE-marked data-driven clinical decision support system (CDSS) for multiple sclerosis (MS). The CDSS will support clinical decision-making by providing easily interpretable information on treatment options. MS is a complex disease, with different phenotypes and heterogeneous progression patterns. Over the past two decades, MS practice has been flooded with data and the number of available treatments has considerably increased. Although clinical, biological and imaging information is now being generated on a massive scale, it contributes to clinical decision-making in a rather haphazard, siloed and non-standardised fashion, so that selecting the most appropriate therapeutic option remains hard. PRIMUS contributes to data-driven homogenization of shared decision practices with and for patients with MS. To achieve this goal, the project will develop advanced artificial intelligence solutions, for a patient- and physician-centred CDSS.

10.3.4 EyeSkin-NF : Eye-tracking and skin conductance measures for neurofeedback analysis and validation

Participants: Claire Cury, Elise Bannier, Pierre Maurel, Hachim Bani, Rene-Paul Debroize, Agustina Fragueido.

• Funding:
  Exploratory action Inria - Duration: 2021 - 2024.
• Summary:
  Neurofeedback techniques (NF) or restorative brain computer interfaces (BCI) consist in providing a subject with real-time feedback about its own brain activity, in order to learn self-regulate specific brain regions during NF training. Brain activity can be measured by various techniques such as EEG and/or fMRI. However, analysis of NF sessions is limited due to the difficulty at identifying the origin of failed training. To enhance and monitor participant's motivation in real-time during EEG-fMRI recording, bio-signal can be measured via eye-tracking (ET) or skin conductance (SC) devices. For a precise evaluation of the motivation mental states of interest such as focus, arousal, mind wandering or mental load can be analysed. The main objective of this project is to investigate measures from eye-tracking and skin conductance signals to evaluate in real-time subject’s motivation during NF training.

10.3.5 GRASP: Generalizing Results Across Scientific Pipelines

Participants: Camille Maumet, Boris Clenet, Jeremy Lefort-Besnard.

• Funding:
  Exploratory action Inria - Duration: 2022 - 2024.
• Summary:
  Scientific pipelines are at the heart of modern experimental sciences. But practionners face a highly complex pipeline landscape – different tools, algorithms, parameters – in which different pipelines can lead to contradictory research findings. GRASP will model pipeline-induced variability to derive valid and generalizable results in the field of brain imaging.

10.3.6 ANR-NODAL: Identification de biomarqueurs de maladies neurodégénératives par l'analyse de la connectivité multimodale.

Participants: Julie Coloigner, Carlo Ferritto.

• Funding:
  Appel à projets générique 2022 - Duration: 2022 - 2026.
• Summary:
  The neurodegenerative diseases like Alzheimer’s (AD) and Parkinson’s (PD) disease are the consequences of pathological processes that begin decades before the onset of the typical clinical symptoms. However, current diagnosis comes quite late in the course of the disease, while evidences underline the multiple benefits that would be associated with earlier diagnosis. An outstanding challenge for clinical neurosciences is therefore to provide reliable, non-invasive, affordable and easy-to-track biomarkers able to improve both the early detection and the monitoring of neurodegenerative diseases. Recent advances in non-invasive connectome mapping techniques offer great hope for significant progress in taking up this challenge by investigating cerebral organization. Indeed, it is well acknowledged that AD and PD display a progressive multifactorial disruption of functional and structural cerebral networks, all along the course of the diseases. A recent framework called Graph Signal Processing (GSP) is particular promising to shed new light on the complex interplay between brain function and structure. For the first time, GSP will be extended to the development of more sensitive metric of AD and PD progression, taking into account the cerebral functional-structural coupling, contrary to the classical biomarkers using a single-modality data or clinical assessment. In the PRESCO project, we will develop a new multimodal and multi-stage approach using innovative machine learning methods, adapted for GSP-based features, to provide non-invasive, reliable and easy-to-track candidate biomarkers for each stage of AD and PD diseases. We will apply this approach on two large patients’ cohorts. Then, we will assess the effectiveness of candidate disease-specific biomarkers on a new innovative local multimodal cohorts including patients with and without cognitive impairment, at various stages of AD and PD. At the end of 2023, we began the acquisition of the cohort including the MRI data and neurocognitve assessment. Carlo Ferritto, a PhD student, works on this project from October 2023. This is a collaborative project with Pierre-Yves Jonin, CHU Rennes and Giulia Lioi, researcher, IMT, Brest.

10.3.7 ANR-PASTRAMI: Patient-specific statistics for microstructure-augmented connectomics

Participants: Élise Bannier, Emmanuel Caruyer, Julie Coloigner, Claire Cury, Marie Poirier.

• Funding:
  Appel à projets générique 2023 - Duration: 2023 - 2028.
• Summary:
  The PASTRAMI project proposes to promote the use of diffusion magnetic resonance imaging (MRI) to derive biomarkers of axonal injury along white matter (WM) fascicles as prognostic factors of functional recovery after severe traumatic brain injury (TBI). We propose to develop statistical methods for patient-specific localization of abnormalities in microstructure and/or structural connectivity, along specific WM fascicles and/or on the full connectome. In a clinical study, the objective will be to assess the predictive accuracy of the proposed model evaluated in the 10 days period following TBI to predict unfavourable outcome at 1-year after the first injury in patients admitted in intensive care for severe TBI. This project is a collaboration with the Laboratoire de mathématiques Jean Leray (Nantes), CHU Rennes and the HIA Sainte-Anne (Toulon).

10.3.8 ANR-JCJC-VICUNA: Exploring the variabiIity induced by different configurations in the neuroimaging analytical space

Participants: Camille Maumet, Boris Clenet, Youenn Merel.

• Funding:
  Appel à projets générique 2022 - Duration: 2022 - 2026.
• Summary:
  Using the same data to answer the same scientific question, researchers may reach contradictory conclusions depending on the analytical pipeline they choose. For many years this problem has been rampant in experimental sciences and recent studies stemming from many fields have brought scientific evidence of this issue. Overall this phenomenon has reduced confidence in research findings and is effectively an important remaining driver of the reproducibility crisis. Software is central to modern scientific research and with the development of data science and its subfields (such as bioinformatics or neuroinformatics) the different tools and approaches available to study a dataset have multiplied. Those software have been very valuable to practitioners and brought the capacity to process more data in a shorter amount of time. But overall, they also provide a large number of possible analysis paths that can be used in order to address a scientific question. With VICUNA, we will provide a proof-of-concept explorat analytical variability in brain imaging. We will navigate in the pipeline space at large and understand which parts of the space are effectively in-use. We will explore and look into how results vary in 3 large open datasets (NARPS, UK Biobank and HCP). This is a collaborative project with Mathieu Acher, INSA Rennes. Boris Clenet is a research engineer (since May 2024) and Youenn Merel is a PhD student (since October 2024).

10.3.9 ANR-JCJC-CoYoKi: Commotion cérébrale chez le jeune enfant : altération cérébrale aiguë et neurodéveloppement

Participants: Fanny Dégeilh, Claire Cury, Pierre Maurel.

• Funding:
  Appel à projets générique 2024 - Duration: 2024 - 2028.
• Summary:
  CoYoKi combines cutting-edge advanced neuroimaging, computational models and longitudinal statistics to provide new methods and knowledge of high research quality and clinical applicability to improve the health of young children with a concussion.

10.3.10 ANR-JCJC-NIRVAVA: Unravelling bimodal neurofeedback efficiency for dynamic non-invasive brain rehabilitation

Participants: Claire Cury, Elise Bannier.

• Funding:
  Appel à projets générique 2024 - Duration: 2024 - 2028. Budget: 320k€
• Summary:
  Neurofeedback approaches (NF) provide real-time feedback to a patient or participant about his or her own brain activity to self-regulate brain areas or networks, targeted by a neural rehabilitation. The estimation of neurofeedback information is done through online brain functional feature extraction. This is a very promising brain rehabilitation technique for major depression, stroke and other neurological pathologies. NF is based on real-time measures of brain activity usually using a single brain imaging modality, with the majority relying on electroencephalography (EEG) and some recent ones employing functional magnetic resonance imaging (fMRI). Both EEG and fMRI are non-invasive methods, indirectly coupled, that measure complementary aspects of brain activity. Simultaneous EEG-fMRI recording has been used to understand the links between EEG and fMRI, and is recognised as a promising multi-modal measurement of brain activity. Moreover, recent studies have shown the high potential of combining EEG and fMRI in a bimodal NF training (i.e., NF scores estimated from EEG and fMRI) to achieve advanced self-regulation, by providing a more specific estimation of the underlying neural activity. This recent technology combined withthe use of Carbon Wire Loops (CWL) - implemented in few labs in the world - that synchronises both signals for real-time NF, has the potential to bring synergy between both signals during NF task. Yet, NF approaches suffer from an inefficiency problem. Indeed, between 30-50% of the participants undergoing a NF training fail to regulate their brain activity. The origin of this problem can come from (i) signal quality, (ii) task difficulty, (iii) participant’s inability to learn, or (iv) lack of participant’s task engagement. With NIRVANA, at the interface between signal processing and neuroscience, I propose to address the origins of NF inefficiency problem and to provide dynamic NF protocols adapted to each participant. Results from NIRVANA will contribute to boost the potential of NF as a rehabilitation technique, and thus, to reduce the burden of brain disorders.

10.3.11 INCLUDE: Integrating fuNctional MRI and EEG with Carbon-wire Loops : towards the characterization of mUltimoDal functional biomarkErs

Participants: Elise Bannier, Julie Coloigner, Claire Cury, Mathis Piquet.

• Funding:
  Exploratory action Inria - Duration: 2024 - 2028. Budget: 250k€
• Summary:
  Simultaneous EEG-fMRI combines two complementary neuroimaging techniques, that could allow to establish an enhanced high-resolution spatiotemporal connectivity imaging technique. However, the EEG signals acquired under MRI are usually contaminated by many artifacts hampering the estimation of the connectivity. To overcome this issue, we propose to develop an innovative multimodal connectivity estimation using an accurate denoising method with carbon-wire loops. This will be done in collaboration with Frédéric Grouiller, University of Geneva, Geneva. The research engineer, Mathis Piquet was recruited at the beginning of the project. He or she is working on Aim 1 to implement offline and online pre- processing of EEG data using the CWL. The post-doctoral researcher will be recruited in 2025. He or she will work on the Aim 2.

10.3.12 ACTIDIFF: Apathy in Late Life Depression: New Biomarkers Using Actimetry and Diffusion Imaging

Participants: Gabriel Robert, Julie Coloigner.

• Funding:
  Institut des Neurosciences Cliniques de Rennes (INCR) - 50k€
• Summary:
  A better understanding of the apathy in late-life depression would help predict the pejorative course of this disorder, such as dementia or persistent depression. Actimetry allows an objective and ecological assessment of motor activity, but still needs to be evaluated in the depression of the elderly. To better understand the pathophysiology of apathy, a study linking measurements of apathy, motor activity and functional connectivity was conducted. Previous results showed that apathy is associated with changes in brain connectivity at rest in regions of the brain involved in goal-directed behaviors. The objective of this ACTIDIFF project is to study the neuronal fibers between these regions in order to better understand the pathophysiology of apathy in depression in elderly people. To do this, we propose to develop an innovative method for studying microstructure changes, based on MRI-estimated metrics of diffusion along fibers in relation to apathy. The joint exploration of actimetry, apathy and brain imaging opens new hopes in preventing cognitive impairment and identifying novel biomarkers for the early stages of dementia.

10.3.13 INCA: Individual attention monitoring during Neurofeedback for Clinical Applications

Participants: Claire Cury, Julie Fournier.

• Funding:
  Institut des Neurosciences Cliniques de Rennes (INCR) - 50k€
• Summary:
  il existe une grande variabilité dans la réussite du neurofeedback (NF), puisqu’un tiers des participants suivant un entrainement neurofeedback n’apprennent pas à réguler leur propre activité cérébrale. Les facteurs motivationnels et attentionnels sont les principaux responsables de la réussite du NF. La motivation est susceptible d’influencer l’attention, car de mauvaises performances peuvent accroître la peur de l’incompétence et réduire la confiance en soi, ce qui peut conduire à un désengagement envers la tâche et à une potentielle étiquette de « non-répondant » pour les individus au fil du temps. Nous savons que maintenir son attention sur une tâche durant une période prolongée est crucial pour une bonne performance, mais cela est exigeant et conduit à des pertes d’attention. L’état d’alerte physiologique est appelé arousal. Lorsque l'arousal est trop faible ou trop élevé, le participant a des pertes d'attention conduisant à de moins bons résultats. En effet, lorsque nous sommes trop détendus, notre esprit peut vagabonder, et lorsque le niveau d'arousal est élevé il peut être lié au stress ou à une distraction provoquée par des pensées intrusives. Seul un niveau d’arousal intermédiaire est lié à la concentration sur la tâche et à une bonne performance. En neurosciences comportementales, le suivi oculaire et la conductance cutanée, ont été largement utilisés pour mesurer les caractéristiques psychophysiologiques liées à l'arousal et à la concentration. En utilisant ces capteurs psychophysiologiques, l’objectif d’INCA est de développer une méthode permettant de suivre le niveau d’engagement des patients lors d’un entrainement neurofeedback. Nous validerons cette méthode sur des données cliniques de patients post-AVC aillant suivi un entraînement neurofeedback. Les résultats d’INCA permettront par la suite d’optimiser individuellement les séances de NF en estimant avec précision et en temps réel l’engagement des patients dans la tâche afin d’adapter la séance en conséquence. L’adaptation individuelle des procédures NF et la réduction conséquente de la proportion de « non-répondeurs » augmenteront l’efficacité du NF et renforceront sa capacité de rééducation cérébrale. INCA a le potentiel d’impacter positivement la qualité de vie d’une grande variété de patients.

10.3.14 Knowledge addition through Neuroimaging of Alcohol consumption in healthy young Volunteers, causes or consequences

Participants: Elise Bannier, Quentin Duché, Gabriel Robert.

• Funding:
  Funding: INCR - Duration: 2020-2023 - Budget: 45k€
• Summary:
  Alcohol consumption is responsible for 3 million annual deaths worldwide (5.1 percent of the global burden of disease). It causes disease (liver cirrhosis, cancers, etc.) and other social costs (injuries, road accidents, alcohol dependence, etc.). Excessive alcohol consumption grows through adolescence. This type of behavior has also been shown to have subtle but significant deleterious effects on cognitive function in adolescents. Advances in the field of neuroimaging make it possible to characterize anatomical changes and the evolution of neuropsychological deficits. Besides, focusing on the societal causes of alcohol abuse, a large body of studies show that exposure to alcohol advertising through media bootstraps early consumption initiation, greater desire to drink, increased alcohol use and binge drinking patterns among young people, especially minors. We aim to combine the analysis of the locally acquired IMAJ dataset (PI Karine Gallopel-Morvan, INCA Funding) and data from the european consortium IMAGEN datasets to determine whether there are functional characteristics and external factors that can explain behavior towards alcohol and to extract biomarkers capable of predicting excessive behavior. Relying on the IMAJ dataset, we will analyze whether, depending on warning formats displayed on ads (small and text-only vs. larger, shock-inducing and pictorial), health messages can influence brain activity by decreasing the effect of attractive alcohol content ads on the reward system area and on behavioral responses. Relying on the already effective collaboration of Dr Robert with Prof Schumann, we will explore the longitudinal anatomical and functional data from the IMAGEN cohort to extract biomarkers of alcohol consumption evolution and complement the analysis with the results obtained from the IMAJ dataset.

10.3.15 PHRC EMISEP: Evaluation of early spinal cord injury and late physical disability in Relapsing Remitting Multiple Sclerosis

Participants: Elise Bannier, Emmanuel Caruyer, Benoit Combès, Gilles Edan, Jean-Christophe Ferré, Anne Kerbrat.

• Funding:
  PHRC - Duration: 2016-2023 - Budget: 200k€
• Summary:
  Multiple Sclerosis (MS) is the most frequent acquired neurological disease affecting young adults (1 over 1000 inhabitants in France) and leading to impairment. Early and well adapted treatment is essential for patients presenting aggressive forms of MS. This PHRC (Programme hospitalier de recherche clinique) project focuses on physical impairment and especially on the ability to walk. Several studies, whether epidemiologic or based on brain MRI, have shown that several factors are likely to announce aggressive development of the disease, such as age, number of focal lesions on baseline MRI, clinical activity. However, these factors only partially explain physical impairment progression, preventing their use at the individual level. Spinal cord is often affected in MS, as demonstrated in postmortem or imaging studies. Yet, early radiological depiction of spinal cord lesions is not always correlated with clinical symptoms. Preliminary data, on reduced number of patients, and only investigating the cervical spinal cord, have shown that diffuse spinal cord injury, observed via diffusion or magnetisation transfer imaging, would be correlated with physical impairment as evaluated by the (EDSS) Expanded Disability Status Scale score. Besides, the role of early spinal cord affection (first two years) in the evolution of physical impairment remains unknown. In this project, we propose to address these different issues and perform a longitudinal study on Relapsing Remitting Multiple Sclerosis (RRMS) patients, recruited in the first year of the disease. Our goal is to show that diffuse and focal lesions detected spinal cord MRI in the first two years can be used to predict disease evolution and physical impairment at 5 years. Twelve centers are involved in the study to include 80 patients. To date, all subjects have been included and the last visit of the last patient is scheduled early 2023. The EMISEP data consists of brain and spinal cord structural and quantitative MR images of early MS patients followed over 5 years. Four papers have been published so far on data acquired at baseline on healthy controls and patients. Three papers were co-authored in the context of international collaborations. Additional papers are in preparation.

10.3.16 Estimating the impact of multiple sclerosis lesions in motor and proprioceptive tracts, from the brain to the thoracic spinal cord, on their functions, assessed from clinical tests (MS-TRACTS and MAP-MS)

Participants: Elise Bannier, Benoit Combès, Malo Gaubert, Anne Kerbrat.

• Funding:
  ARSEP, COREC and INCR - Duration: 2020-2023 - Budget: 200k
• Summary:
  Previous studies, whether epidemiologic or based on brain MRI, have shown that several factors were likely to announce aggressive development of the disease, such as age, clinical relapses, number of focal lesions on baseline MRI. However, these factors only partially explain physical disability progression, preventing their use at the individual level. We hypothesize that a fine assessment of damage on specific networks, from the brain to the thoracic cord, offers a relevant biomarker of disability progression in MS. Such damage assessments must take into account both lesion location, assessed on structural brain and cord MR images and lesion severity, assessed using advanced brain and cord imaging through quantitative MRI.We propose to test this hypothesis by combining assessments of lesion location and severity on corticospinal and proprioceptive tracts from the brain to the thoracic cord with clinical and () electrophysiological measurements. The MS-TRACTS study involves two French centers (Rennes,Marseille) and includes a total of 60 relapsing remitting MS patients. The expected outcome is to obtain early biomarkers of physical impairment evolution in RRMS patients, first treated with immunomodulatory treatment. The long-term goal is to provide the clinician with biomarkers able to anticipate therapeutic decisions and support the switch to alternative more aggressive treatment. Inclusions are ongoing. The MAP-MS study involves the same two French centers and will nclude 40 progressive MS patients. The investigation will focus on motor asymetry in these more advanced patients. This study includes two French centers (Rennes, Marseille) and includes a total of 60 patients. The expected outcome is to obtain early biomarkers of physical impairment evolution in RRMS patients, first treated with immunomodulatory treatment. The long-term goal is to provide the clinician with biomarkers able to anticipate therapeutic decisions and support the switch to alternative more aggressive treatment. Inclusions are ongoing.

Show image description

Estimating the impact of multiple sclerosis lesions in motor and proprioceptive tracts, from the brain to the thoracic spinal cord, on their functions, assessed from clinical tests (MS-TRACTS and MAP-MS): An example of Magnetization Transfer Ratio (MTR) mapping of the whole spinal cord acquired from the MS-TRACTS imaging protocol.

10.3.17 France Life Imaging (FLI)

Participants: Michael Kain, Camille Maumet, Jean-Christophe Ferré.

• Funding:
  Funding: FLI - Duration: 2012-2023 - Total budget: 2000k€ (phase 1) + 1200k€ (phase 2) + 800k€ (phase 3)
• Summary:
  France Life Imaging (FLI) is a large-scale research infrastructure project to establish a coordinated and harmonized network of biomedical imaging in France. This project was selected by the call “Investissements d’Avenir - Infrastructure en Biologie et Santé”. One node of this project is the node Information Analysis and Management (IAM), a transversal node built by a consortium of teams that contribute to the construction of a network for data storage and information processing. Instead of building yet other dedicated facilities, the IAM node use already existing data storage and information processing facilities (LaTIM Brest; CREATIS Lyon; CIC-IT Nancy; Empenn U1228 Inria Rennes; CATI CEA Saclay; ICube Strasbourg) that increase their capacities for the FLI infrastructure. Inter-connections and access to services are achieved through a dedicated software platform that is developed based on the expertise gained through successful existing developments. The IAM node has several goals. It is building a versatile facility for data management that inter-connects the data production sites and data processing for which state-of-the-art solutions, hardware and software, are available to infrastructure users. Modular solutions are preferred to accommodate the large variety of modalities acquisitions, scientific problems, data size, and to be adapted for future challenges. Second, it offers the latest development that are made available to image processing research teams. The team Empenn fulfills multiple roles in this nation-wide project. Michael Kain is the technical manager, Camille Maumet is part of the steering committee. Apart from the team members, software solutions like MedInria and Shanoir are part of the software platform.

10.3.18 OFSEP: French Multiple Sclerosis Observatory

Participants: Elise Bannier, Gilles Edan, Jean-Christophe Ferré, Francesca Galassi, Benoît Combès, Anne Kerbrat.

• Funding:
  ANR-PIA - Duration: since 2017 - Budget: 175k€
• Summary:
  The French Observatory of Multiple Sclerosis (OFSEP) is one of ten projects selected in January 2011 in response to the call for proposal in the "Investissements d’Avenir - Cohorts 2010" program aunched by the French Government. It allows support from the National Agency for Research (ANR) of approximately 10 million € for 10 years. It is coordinated by the Department of Neurology at the Neurological Hospital Pierre Wertheimer in Lyon (Professor Christian Confavreux), and it is supported by the EDMUS Foundation against multiple sclerosis, the University Claude Bernard Lyon 1 and the Hospices Civils de Lyon. OFSEP is based on a network of neurologists and radiologists distributed throughout the French territory and linked to 61 centers. OFSEP national cohort includes more than 50,000 people with Multiple Sclerosis, approximately half of the patients residing in France. The generalization of longitudinal monitoring and systematic association of clinical data and neuroimaging data is one of the objectives of OFSEP in order to improve the quality, efficiency and safety of care and promote clinical, basic and translational research in MS. For the concern of data management, the Shanoir platform of Inria has been retained to manage the imaging data of the National OFSEP cohort in multiple sclerosis. One long term objective of the OFSEP project is to identify prognostic factors of the evolution of Multiple Sclerosis. The HD Cohort is an enhanced cohort specifically designed for this purpose in which some patients are followed-up on a yearly basis. Additional clinical, quality of life and other patient-reported data is also collected. This study aims at developing personalized predictive tools to improve patient care management, and help in making decision to start, maintain or adapt medical care. Collected data will be processed to extract valuable information enabling to determine specific biomarkers of the evolution of the disease. Multiple Sclerosis brain lesions are of particular interest, hence the need for a careful comparison of lesion segmentation methods. A litterature review enabled to gather most promising cross-sectionnal methods, designed to identify and localize lesions with precise measurement of the lesion load at one particular point in time ; and longitudinal methods which gives more insight on the evolution of those lesions over the different time points. Those later methods are particularly interesting for clinicians for whom the type of lesion evolution is of foremost importance. A cross-sectionnal method and a longitudinal method were trained and evaluated to select the ones which will be used to analyze the entire HD Cohort dataset. Moreover, an experimental and a statistical design to compare the accuracy, sensitivity and specificity of the active/inactive classification of MS patients based on brain MRI as assessed using the analysis of brain was proposed. These designs will allow to assess the interest of re-analyzing the MRI data to improve the quality of the standardized reports used in most epidemiologic studies from the OFSEP cohorts. The collaboration has recently been extended until end of 2025 with a particular focus on spinal cord imaging and slowly evolving lesions.

10.3.19 QSM-SPICO: Quantitative Susceptibility Mapping for Spinal Cord

Participants: Elise Bannier, Benjamin Streichenberger, Anne Kerbrat, Benoit Combès.

• Fundings:
  FLI-RE4 - 20k€, ARSEP - 60 k€, Fondation de l'Avenir 40 k€
• Summary:
  Quantitative Susceptibility Mapping (QSM) is a promissing quantative imaging technique for the characterization of lesions in Multiple Sclerosis (MS). QSM provides a novel type of contrast linked to the tissue magnetic susceptibility. The latter is sensitive to iron accumulation and myelin content, which are both important metrics when studying MS lesions. As part of a research expertise transfer sponsored by France Life Imaging, in collaboratin with Mathieu Santin, at ICM/CENIR, Ludovic de Rochefort and Stéphane Roche from the Ventio Startup in Marseille, we started exploring the possibility to perform QSM in the spinal cord. This is challenging because of size of the cord and the presence of fat in the spine. To tackle this challenge, we implemented the IDEAL algorithm - iterative decomposition of water and fat with echo asymmetry and least-squares estimation. The aim is be able to characterize spinal MS lesions using QSM. The first results are encouraging.Additional fundings from France SEP (PostDoc) and Fondation de l'Avenir (HR and Licence) were obtained at the end of 2024 to continue the projet in 2025.

10.3.20 PEPR ShareFAIR

Participants: Camille Maumet, Elise Bannier, Melvin Atay.

• Funding:
  PEPR Santé numérique.
• Summary:

  Access to a wide variety of complementary, multi-scale and massive data collections offers unprecedented opportunities for healthcare research. A large number of analyses can be performed on these datasets, for scientific advances and discoveries to emerge. The national 'Digital Health'Acceleration Strategy ambitions to boost digital health innovation which includes designing innovative health data analysis approaches.

  Importantly, such data analyses are complex, they rely on various computational tools that have to be parametrized and chained together. There is now compelling evidence that many scientific discoveries will not stand the test of time: increasing the reproducibility of computed results is of paramount importance, especially in the healthcare domain.

  Sharing of health data is often hampered by personal data protection requirements and comes up against technical constraints (security, volume). These constraints can however be limited when the protocols and the workflows implementing analyses are sufficiently reusable to reproduce analyses in situ.

  Additionally, when designed to be reusable, protocols and their implementations - workflows - provide the provenance traces of the analyzed data, describing how data results have been obtained and thus increasing scientists’ confidence in the results produced.

  This calls for innovative solutions for the annotation of biomedical and clinical datasets and extraction of provenance. Protocols and their implementation as workflows using and generating datasets should be elevated to first-class objects and the inherent dual relationship between datasets and protocols/workflows should be better exploited.

  Challenges thus include standardization and annotation for datasets and protocols, extracting protocols and workflows from text and other datasets, and synthesizing them into interoperable, yet shareable protocols.

  The originality of ShareFAIR lies in tackling both the reliability of datasets and analysis protocols and in harnessing the dual relationship between datasets and protocols. Specifically, ShareFAIR will provide:

  (i) standards to uniformly represent datasets, ontologies/common vocabularies to annotate datasets and protocols/workflows, and provenance to trace the origin of datasets,

  (ii) an interoperable framework for the design, annotation and reuse of reliable and shareable protocols,

  (iii) approaches to extract protocols from textual data to enrich the set of protocols and workflows and better document the provenance of datasets, and approaches to learn protocols from biomedical and clinical datasets.

  This project is led by Sarah Boulakia-Cohen from Univ Paris Saclay.

10.4.1 PEPERONI : Portable and Personalized Neurofeedback for Stroke Rehabilitation

Participants: Elise Bannier, Isabelle Bonan, Julie Coloigner, Isabelle Corouge, Claire Cury, Pierre Maurel, Camille Muller, Caroline Pinte.

11.1.1 Scientific events: organisation

11.1.2 Scientific events: selection

11.1.3 Journal

11.1.4 Invited talks

• Ricky Walsh, "Automated segmentation of spinal cord lesions using multiple MR sequences", Les Assises de l'Ofsep, March 2024.
• Francesca Galassi, "Deep Learning in Medical Imaging: What's Needed for Training Data?", PDIA 2024-AFIA, April 2024.
• Camille Maumet, "Data sharing: towards large open datasets in brain imaging", IABM 2024 67, Mar 2024, Grenoble, France.
• Camille Maumet "Towards reproducible neuroimaging - across different analysis pipelines", QLS 2024 - McGill Seminar Series in Quantitative Life Sciences and Medicine, Feb 2024, Montreal (Online), Canada.
• Camille Maumet, "L'ouverture des codes source et logiciels: état des lieux et enjeux", ARDOISE 2024 - Journée du réseau de l'atelier rennais de la donnée, Jun 2024, Rennes, France.
• Camille Maumet, panelist to the roundtable "Women and minorities in research", MIDL 2024 - Medical Imaging with Deep Learning Conference - Doctoral Symposium 78, Jul 2024, Paris, France."
• Camille Maumet, "From NIDM to BIDS-Prov: A personal perspective on 10 years of metadata standardization towards neuroimaging data sharing", Workshop on the future of FAIR data management and engineering for open and reproducible neuroimaging", Jul 2024, Oldenburg, Germany.
• Camille Maumet, "Towards reproducible neuroimaging across different analysis pipelines", INT 2024 - Seminar at Institut de Neurosciences de la Timone, Oct 2024, Marseille, France.
• Camille Maumet, Speaker (videorecorded) in the D3A 2.0 Session on Reproducibility - Conference on Danish Digitization, Data Science and AI, Oct 2024, Nyborg, Denmark (Online).
• Camille Maumet, "Increasing computational reproducibility or how-to share your research code", MRI Together 2024 - Global workshop on open, reproducible, and inclusive MR, Dec 2024, Online.
• Elise Bannier, "Spinal Cord imaging recommandations using MRI" on May 27th at the REMI meeting, Paris + Online.
• Elise Bannier "T2 relaxometry and Myelin Water Fraction measurement" at the FLI régional days on November 27th.
• Elise Bannier "Multicenter use of the MP2RAGE sequence in the brain and spinal cord and its application to study multiple sclérosis" at the FLI national days on December 10th.
• Benjamin Streichenberger "Spinal Cord Quantitative Susceptibility Mapping and application to multiple sclérosis" at the FLI régional days on November 27th.
• Benjamin Streichenberger "Spinal Cord Quantitative Susceptibility Mapping and application to multiple sclerosis" at the FLI national days on December 11th.
• Quentin Duché "Étude en IRM fonctionnelle de l'impact des publicités pour l'alcool sur des jeunes buveurs" at the "Après-midi des utilisateurs de Neurinfo" on October 14th.

11.1.5 Leadership within the scientific community

• Pierre Maurel is elected member of the administration board of the "Institut des Neurosciences Cliniques de Rennes" (INCR)
• Camille Maumet is member (elected) of the steering committee of the Brain Imaging Data Structure (BIDS).
• Camille Maumet is member (by selection) of the national committee on Open Science, Working group "open software" led by Roberto Di Cosmo and François Pellegrini.
• Emmanuel Caruyer is member (by selection) of the scientific committee of the "Institut des Neurosciences Cliniques de Rennes" (INCR)
• Elise Bannier is board member (elected) of the SFRMBM society
• Elise Bannier is presiding the organization of the national MR imaging conference for Biology and Médecine (SFRMBM 2025) to be held in Saint-Malo on March 24-26th.

11.1.6 Scientific expertise

• Pierre Maurel, expert in the evaluation process of the Inserm MESSIDORE Call 2024
• Pierre Maurel, expert for the "Appel à projet générique" 2024 of ANR

11.1.7 Research administration

Camille Maumet, member of the Inria Evaluation Committee ("commission d'évaluation") since October 2023.

11.2.1 Teaching

• ESIR, École Supérieure d'Ingénieur de Rennes:
  • Pierre Maurel is co-head of the Master program "imagerie numérique" (two last year of the Engineering School)
  • Pierre Maurel, "General image processing" (30h).
  • Pierre Maurel, "Algorithmique et complexité" (30h).
  • Pierre Maurel, "Imagerie médicale" (30h).
  • Francesca Galassi is co-head of the Master program "Systemes d'Information" (two last year of the Engineering School)
  • Francesca Galassi, "Apprentissage Automatique" (Plenary: 12h).
  • Francesca Galassi, "Algoritmique des graphes" (Plenary: 12h, TD: 6h, TP: 6h).
  • Julie Coloigner, "Analyse avancé de Signaux et images" (35h).
  • Julie Coloigner, "Mathématiques appliqués" (25h).
  • Claire Cury, "Traitement avancé des images" (Plenary : 12h).
  • Cédric Meurée, "Apprentissage artificiel" (TP: 12h).
  • Caroline Pinte, "Mathématiques appliquées au traitement d'images" (TP: 18h)
  • Caroline Pinte, "Projets d'imagerie médicale" (TP: 12h)
  • Caroline Pinte, "Neurofeedback (NF) et Brain Computer Interface (BCI) : Une Introduction" (Plenary: 3h)
  • Sébastien Dam, "Algorithmique et complexité" (TP: 28h).
  • Sébastien Dam, "Algoritmique des graphes" (TD: 6h, TP: 12h).
  • Carla Joud, ESIR 1, Maths-S5/S4, Rennes Engineering school (TD: 10h, TP:10h)
  • Carla Joud, CUPGE2, "Mathématiques appliqués" (TP: 24h).
  • Jérémy Lefort-Besnard, "Algorithmique et complexité" (Master ESIR, TD:14h et TP:14h)
  • Grégoire Ville, "Algorithmique et Complexité" (ESIR 2 - Informatics, TD: 13.5 h)
  • Jérémy Lefort-Besnard, "Apprentissage Automatique" (Master ESIR, TP:12h)
  • Alexandre Pron, "Statistiques" (L3 ESIR, TD: 3h, TP: 6h).
• Master SIBM, M2, University of Angers-Brest-Rennes:
  • Jean-Christophe Ferré is head of the master.
  • Benoît Combès is co-head of the UE “Modélisation et Apprentissage Automatique pour le Traitement des Images Médicales”.
  • Emmanuel Caruyer, "Méthodes d'analyse d'IRM de diffusion" (Plenary: 3h).
  • Julie Coloigner, "Méthode d'analyse de la connectivité cérébrale" (Plenary: 3h).
  • Benoit Combès, "Méthodes de segmentation pour l'imagerie médicale" (Plenary: 3h).
  • Benoit Combès, “Méthodes de recalage linéaire et non-linéaires des images médicales” (Plenary: 6h).
  • Benoit Combès, “Applications des méthodes de traitement des images médicales” (Plenary: 3h).
  • Benoit Combès, “Eléments de statistiques pour l’induction scientifique” (Plenary: 4.5h).
  • Benoit Combès, “Soutenance de présentations critiques d'articles scientifiques” (TD: 3h).
  • Isabelle Corouge, "IRM de perfusion par Arterial Spin Labeling (ASL)" (Plenary: 3h).
  • Elise Bannier, "Imagerie fonctionnelle cérébrale" (Plenary: 1h).
  • Quentin Duché, "Traitement des données d'IRM fonctionnelle" (Plenary: 1h).
  • Elise Bannier, “Utilisation et réutilisation des données d'imagerie” (Plenary: 1h).
  • Camille Maumet, "Workflows de traitement d'images" (Plenary: 3h)
• ENS Rennes/Univ. Rennes:
  • Emmanuel Caruyer, "Méthodes numériques pour le traitement d'images", L3 SIF (Plenary: 20h).
  • Francesca Galassi, "Traitement d'Images", M2 Informatique (Plenary: 20h).
• L2 INSA:
  • Nolwenn Jégou, "Cours Java" (Plenary: 20h).
  • Nolwenn Jégou, "TP Java" (Plenary: 18h).
• Master EIT Data Science, M2, Univ. Rennes:
  • Francesca Galassi, "Deep Learning" (Plenary: 12h, TP: 12h).
• Master Informatique, ISTIC, Univ. Rennes:
  • Julie Coloigner, "Computer Vision" (Plenary: 10h)parcours Science informatique (SIF), M2.
• Bachelor for speech therapy, L3, University of Rennes: Elise Bannier, "Imagerie fonctionnelle cérébrale du langage" (Plenary: 2h).
• Master Physique Médicale, M2, University of Rennes:
  • Elise Bannier, "Imagerie par Résonance Magnétique " (TD: 4h).
  • Elise Bannier, Participation in the Master's defense jury (1h)".
• L2, IFMEM, MR Technologists, University Hospital of Rennes:
  • Elise Bannier, "Imagerie par Résonance Magnétique " (Plenary: 10h).
• Master Neuropsychologie, M2, University of Savoie: Pierre-Yves Jonin, "Limites méthodologiques du bilan neuropsychologique à visée diagnostique" (Plenary: 3h30).
• Master Psychologie et neuropsychologie de l’enfant et de l’adulte : langage, cognition et apprentissage, M2, University of Poitiers: Pierre-Yves Jonin, "Méthodologie de l'étude de cas" (Plenary: 3h).
• Master Biologie et Santé, M1, University of Bretagne Occidentale: Pierre-Yves Jonin, "Explorations neuropsychologiques des maladies neurologiques et psychiatriques" (Plenary: 4h).
• Master Psychologie Clinique, Psychopathologie et Psychologie de la Santé, M2, University of Rennes 2:
  • Pierre-Yves Jonin, "Neuropsychologie clinique des pathologies neurodégénératives" (Plenary: 4h).
  • Pierre-Yves Jonin, "Méthodologie de l'étude de cas" (Plenary: 4h).
• Licence Psychologie, L3, University of Rennes 2:
  • Pierre-Yves Jonin, "Les syndromes neuropsychologiques" (TD: 16h).
  • Pierre-Yves Jonin, "Approche neuropsychologique du handicap" (TD: 4h).
• Master Neurosciences Cliniques, M2, University of Rennes:
  • Pierre-Yves Jonin, "Neurosciences cognitives et cliniques de la mémoire humaine" (Plenary: 3h).
• DEUST Métiers de la Forme, D1 STAPS, Univeristy of Rennes 2:
  • Camille Muller, "Neurosciences" (Plenary: 12h).
  • Camille Muller, “Biomécanique” Plenary: 20h)
• Licence APAS-ESPM, L3 STAPS, Univeristy of Rennes 2:
  • Camille Muller, "Biomécanique de l'avance en âge" (Plenary: 6h).
  • Camille Muller, “Biomécanique de l'avance en âge” TD: 8h)
• L2 ISTN - Data science, ISTIC, University of Rennes:
  • Elodie Germani, "Initiation to data management, visualization, and modelization (linear regression, statistical testing, time series, etc.) using R" (TP: 24h).
• Summer School on Affordable Artificial Intelligence (SAAI), AGYA, Bonn, Germany
  • Elodie Germani, "Practical sessions in Python programming for data science and machine learning, statistics, and mathematics." (TP: 10h).

11.2.2 Supervision

11.2.3 Juries

• Emmanuel Caruyer, reviewer of the PhD of Juan Luis Villareal Haro, EPFL, entitled "Simulation-based Microstructure Imaging: Towards Realistic White Matter Modeling using Monte Carlo Methods in DW-MRI", defended Nov. 12th, Lausanne, Switzerland.
• Emmanuel Caruyer, expert for the candidacy exam of Ekin Taskin, PhD student, EPFL.
• Pierre Maurel, member of the selection committee for an ATER position at ESIR/IRISA
• Pierre Maurel, president of the doctoral defence Jury of Claude Petit, IRISA, Rennes.
• Pierre Maurel, president of the HDR defence Jury of Fanny Dégeilh, Inserm-Empenn, Rennes.
• Pierre Maurel, president of the HDR defence Jury of Julie Coloigner, IRISA, Rennes.
• Francesca Galassi, member of the PhD committees of:
  • T. Mayet, Domains to Domains Translations: Application to Semantic Segmentation, INSA Rouen, Dec 2024.
  • T. Soulier, Tracking Lesion Genesis and Lesional Myelin Dynamics in Multiple Sclerosis using Deep Learning Approaches with Biomedical Insights and Advanced Imaging, Institut du Cerveau et de la Moelle Épinière, Sorbonne Université, Dec 2024.
  • L. Benjamin, Quantification and Characterization of Segmentation Uncertainty in Medical Images by Deep Networks, CIFRE: Pixyl and Université Grenoble Alpes, March 2024.
• Camille Maumet, reviewer of the PhD of Eduarda Gervini Zampieri Centeno entitled "Etude des dynamiques neurales cortico-ganglions de la base pendant le sommeil chez les oiseaux chanteurs avec une methodologie inspiré par la Science Ouverte", Uni. Bordeaux, France.
• Elise Bannier, reviewer of the PhD of Nathalie Barrau entitled "3D MR Spirometry", Uni. Paris-Saclay, France.
• Claire Cury, member of the PhD defense jury of Maxime Dieudonné entitled "Modélisation du développement et de la maturation du plissement cortical chez l'homme", INT, Marseille, France.
• Claire Cury, member of the PhD defense jury of Jakez Rolland entitled "Datascape: un framework numérique pour l'abstraction et l'exploration de données hétérogènes et multidimensionnelles.", LS2N, Nantes, France.
• Claire Cury was member for the Jury admission CRCN Inria 2024.
• Camille Maumet was member for the Jury of selectrion for CRCN Inria 2024 in Lyon and in Bordeaux.

11.3.1 Specific official responsibilities in science outreach structures

• Claire Cury, mediation Officer of the Inria Rennes Scientific mediation team.
• Claire Cury is the regional representant of the Fondation Blaise Pascal
• Elodie Germani was organisor of a session for Pint of Science.
• Elise Bannier, Isabelle Corouge organized a visit for scholars of the Neurinfo facility on March 11th in the content of the brain awareness week. Four classes of 20-30 scholars each took part in the visit with 4 topics : MRI, functional MRI, NIRS/EEG neurofeedback, MR image processing

11.3.2 Participation in Live events

• Élise Bannier, Emmanuel Caruyer, Isabelle Corouge, Nolwenn Jégou, Malo Gaubert, Pierre Maurel, Camille Muller, Alexandre Pron, Pierre Maurel, Quentin Duché, Solène Painchaud: "Semaine du cerveau": Visit of the Neurinfo plateform for pupils (March 11).
• Fanny Dégeilh, Elodie Germani, Nolwenn Jégou, Carla Joud, Jérémy Lefort-Besnard, Camille Muller, "À la découverte des métiers de la recherche en imagerie cérébrale." 26e édition de la Semaine du Cerveau, Rennes, March 2024.
• Talk at Pint of Science: Jeremy Lefort-Besnard and Camille Muller "Le neurofeedback débarque et l’IA s’emballe", Elise Bannier "Faites connaissance avec votre cerveau", Pint Of science (May 15)
• Camille Muller, "Le cerveau en images. Faites connaissance avec votre cerveau!". Pint of Science 2024, Rennes, May 2024
• Quentin Duché, Emmanuel Caruyer, Isabelle Corouge and Elise Bannier organised and participated at "Après-midi des Utilisateurs de Neurinfo" (October 14).

International journals

• 14 articleS.Sandra Abou Kassm, L.Louise Gratecap, E.Evan Ragey, F.Francois Alexis Richard, D.Dominique Somme, F.Florian Naudet and G.Gabriel Hadrien Robert. Does physical disability bias clinical general impression of psychiatrists evaluating older adults with moderate to severe depressive symptoms? A randomized controlled trial.General Hospital Psychiatry2024, Online ahead of printHALDOI
• 15 articleN.Nail Benallegue, F.Fabien Rollot, S.Sandrine Wiertlewski, R.Romain Casey, M.Marc Debouverie, A.Anne Kerbrat, J.Jérôme de Seze, J.Jonathan Ciron, A.Aurelie Ruet, P.Pierre Labauge, E.Elisabeth Maillart, H.Helene Zephir, C.Caroline Papeix, G.Gilles Defer, C.Christine Lebrun-Frenay, T.Thibault Moreau, E.Eric Berger, B.Bruno Stankoff, P.Pierre Clavelou, O.Olivier Heinzlef, J.Jean Pelletier, E.Eric Thouvenot, A.Abdullatif Al Khedr, B.Bertrand Bourre, O.Olivier Casez, P.Philippe Cabre, A.Abir Wahab, L.Laurent Magy, S.Sandra Vukusic and D.-A.David-Axel Laplaud. Highly Effective Therapies as First-Line Treatment for Pediatric-Onset Multiple Sclerosis.JAMA neurology813March 2024, 273-282HALDOI
• 16 articleA.-C.Anne-Claire Binter, L.Laura Granés, E.Elise Bannier, M.Montserrat de Castro, S.Sami Petricola, S.Serena Fossati, M.Martine Vrijheid, C.Cécile Chevrier, H.Hanan El Marroun, M.Mark Nieuwenhuijsen, D.Dave Saint-Amour, H.Henning Tiemeier and M.Mònica Guxens. Urban environment during pregnancy and childhood and white matter microstructure in preadolescence in two European birth cohorts.Environmental Pollution346April 2024, 123612HALDOI
• 17 articleN.Nicolas Collongues, F.Françoise Durand-Dubief, C.Christine Lebrun-Frénay, B.Bertrand Audoin, X.Xavier Ayrignac, C.Caroline Bensa, K.Kevin Bigaut, B.Bertrand Bourre, C.Clarisse Carra Dallière, J.Jonathan Ciron, G.Gilles Defer, A.Arnaud Kwiatkowski, E.Emmanuelle Leray, E.Elisabeth Maillart, R.Romain Marignier, G.Guillaume Mathey, N.Nathalie Morel, E.Eric Thouvenot, H.Helene Zephir, J.Julie Boucher, C.Clémence Boutière, P.Pierre Branger, A.Angélique da Silva, S.Sarah Demortière, M.Maxime Guillaume, B.Benjamin Hebant, E.Edouard Januel, A.Anne Kerbrat, E.Eric Manchon, A.Adil Maarouf, A.Alexis Montcuquet, C.Chloe Pierret, J.Julie Pique, J.Julien Poupart, C.Chloé Prunis, T.Thomas Roux, P.Perrine Schmitt, G.Géraldine Androdias and M.Mikael Cohen. Cancer and multiple sclerosis: 2023 recommendations from the French Multiple Sclerosis Society.Multiple Sclerosis Journal307June 2024, 899-924HALDOI
• 18 articleS.Sébastien Dam, J.-M.Jean-Marie Batail, G.Gabriel Robert, D.Dominique Drapier, P.Pierre Maurel and J.Julie Coloigner. Structural Brain Connectivity and Treatment Improvement in Mood Disorder.Brain connectivity1442024, 239-251HALDOIback to text
• 19 articleD.Dominique Dupont, K.Ken Tang, C.Cindy Beaudoin, F.Fanny Dégeilh, I.Isabelle Gagnon, K. O.Keith Owen Yeates, S.Sean Rose, J.Jocelyn Gravel, B.Brett Burstein, A.Antonia Stang, R.Rachel Stanley, R.Roger Zemek and M.Miriam Beauchamp. Postconcussive Symptoms After Early Childhood Concussion.JAMA Network Open73March 2024, e243182HALDOI
• 20 articleT.Thomas Durantel, G.Gabriel Girard, E.Emmanuel Caruyer, O.Olivier Commowick and J.Julie Coloigner. A Riemannian framework for incorporating white matter bundle priors in ODF-based tractography algorithms..PLoS ONE2024, 1-10In press. HALback to text
• 21 articleA.Agustina Fragueiro, C.Claire Cury, F.Federica Santacroce, F.Ford Burles, G.Giuseppe Iaria and G.Giorgia Committeri. Medial positioning of the hippocampus and hippocampal fissure volume in Developmental Topographical Disorientation.Hippocampus344January 2024, 204-216HALDOIback to text
• 22 articleK.Karine Gallopel-Morvan, Q.Quentin Duché, J.-F.Jacques-François Diouf, S.Sophie Lacoste-Badie, O.Olivier Droulers, R.Romain Moirand and E.Elise Bannier. Impact of text‐only versus large text‐and‐picture alcohol warning formats: A functional magnetic resonance imaging study in French young male drinkers.Alcohol, Clinical and Experimental Research4882024, 1610-1620HALDOIback to text
• 23 articleM.Malo Gaubert, B.Benoit Combès, E.Elise Bannier, A.Arthur Masson, V.Vivien Caron, G.Gaëlle Baudron, J.-C.Jean-Christophe Ferré, L.Laure Michel, E.Emmanuelle Le Page, B.Bruno Stankoff, G.Gilles Edan, B.Benedetta Bodini and A.Anne Kerbrat. Microstructural damage and repair in the spinal cord of patients with early multiple sclerosis and association with disability at 5 years.Neurology Neuroimmunology & Neuroinflammation1212025, e200333HALDOI
• 24 articleA.Ambre Godet, Y.Yann Serrand, B.B. Léger, R.Romain Moirand, E.Elise Bannier, D.David Val-Laillet and N.Nicolas Coquery. Functional near-infrared spectroscopy-based neurofeedback training targeting the dorsolateral prefrontal cortex induces changes in cortico-striatal functional connectivity.Scientific Reports1412024, 20025HALDOIback to text
• 25 articleR.Renaud Hédouin, J.-C.Jean-Charles Roy, T.Thomas Desmidt, G.Gabriel Robert and J.Julie Coloigner. Microstructural brain assessment in late-life depression and apathy using diffusion MRI multi-compartments models and tractometry.Scientific Reports1412024, 18193HALDOIback to text
• 26 articleL.Lisa Hemforth, B.Baptiste Couvy-Duchesne, K.Kevin de Matos, C.Camille Brianceau, M.Matthieu Joulot, T.Tobias Banaschewski, A. L.Arun L W Bokde, S.Sylvane Desrivières, H.Herta Flor, A.Antoine Grigis, H.Hugh Garavan, P.Penny Gowland, A.Andreas Heinz, R.Rüdiger Brühl, J.-L.Jean-Luc Martinot, M.-L.Marie-Laure Paillère Martinot, E.Eric Artiges, D.Dimitri Papadopoulos, H.Herve Lemaitre, T.Tomas Paus, L.Luise Poustka, S.Sarah Hohman, N.Nathalie Holz, J. H.Juliane H. Fröhner, M. N.Michael N Smolka, N.Nilakshi Vaidya, H.Henrik Walter, R.Robert Whelan, G.Gunter Schumann, C.Christian Büchel, J.-B.Jean-Baptiste Poline, B.Bernd Itterman, V.Vincent Frouin, A.Alexandre Martin, C.Claire Cury and O.Olivier Colliot. Automatic rating of incomplete hippocampal inversions evaluated across multiple cohorts.Journal of Machine Learning for Biomedical Imaging22024, 1-26HALDOIback to text
• 27 articleG.Guillaume Jouvenot, G.Guilhem Courbon, M.Mathilde Lefort, F.Fabien Rollot, R.Romain Casey, E.Emmanuelle Le Page, L.Laure Michel, G.Gilles Edan, J.Jérome de Seze, L.Laurent Kremer, K.Kevin Bigaut, S.Sandra Vukusic, G.Guillaume Mathey, J.Jonathan Ciron, A.Aurélie Ruet, E.Elisabeth Maillart, P.Pierre Labauge, H.Hélène Zephir, C.Caroline Papeix, G.Gilles Defer, C.Christine Lebrun-Frenay, T.Thibault Moreau, D. A.David Axel Laplaud, E.Eric Berger, B.Bruno Stankoff, P.Pierre Clavelou, E.Eric Thouvenot, O.Olivier Heinzlef, J.Jean Pelletier, A.Abdullatif Al-Khedr, O.Olivier Casez, B.Bertrand Bourre, P.Philippe Cabre, A.Abir Wahab, L.Laurent Magy, J.-P.Jean-Philippe Camdessanché, I.Ines Doghri, S.Solène Moulin, H.Haifa Ben-Nasr, C.Céline Labeyrie, K.Karolina Hankiewicz, J.-P.Jean-Philippe Neau, C.Corinne Pottier, C.Chantal Nifle, N.Nicolas Collongues, A.Anne Kerbrat, F.François Cotton, P.Pascal Douek, F.Francis Guillememin, A.Alexandre Pachot, J.Javier Olaiz, C.Claire Rigaud-Bully, R.Romain Marignier, M.Marc Debouverie, C.Catherine Lubetzki, M.Mikaël Cohen, A.Agnès Fromont, S.Sandrine Wiertlewsky, B.Bertrand Audoin, C.Claire Giannesini, O.Olivier Gout, A.Alexis Montcuquet, S.Serge Bakchine, A.Aude Maurousset and N.Nicolas Maubeuge. High-Efficacy Therapy Discontinuation vs Continuation in Patients 50 Years and Older With Nonactive MS.JAMA neurologyMarch 2024, e240395HALDOI
• 28 articleA.Alix Lamouroux, G.Giulia Lioi, J.Julie Coloigner, P.Pierre Maurel and N.Nicolas Farrugia. fMRIStroke: A preprocessing pipeline for fMRI Data from Stroke patients.Journal of Open Source Software9103November 2024, 6636HALDOIback to text
• 29 articleS.Stephanie Leplaideur, É.Étienne Allart, L.Lucie Chochina, D.Dominic Pérennou, G.Gilles Rode, F. C.François Constant Boyer, J.Jean Paysant, A.Alain Yelnik, K.Karim Jamal, Q.Quentin Duché, J.-F.Jean-François Morcet, B.Bruno Laviolle, B.Benoit Combès, E.Elise Bannier and I.Isabelle Bonan. Neck muscle vibration and prism adaptation fail to improve balance disturbances after stroke: A multicentre randomised controlled study.Annals of Physical and Rehabilitation Medicine6772024, 101871HALDOIback to text
• 30 articleS.Stefano Moia, H.-T.Hao-Ting Wang, A. S.Anibal S. Heinsfeld, D.Dorota Jarecka, Y. F.Yu Fang Yang, S.Stephan Heunis, M.Michele Svanera, B. D.Benjamin De Leener, A.Andrea Gondová, S.Sin Kim, A.Arshitha Basavaraj, J. M.Johanna M. M. Bayer, R. G.Roza G. Bayrak, P.-L.Pierre-Louis Bazin, I. P.Isil Poyraz Bilgin, S.Steffen Bollmann, D.Daniel Borek, V.Valentina Borghesani, T.Trang Cao, G.Gang Chen, A. d.Alejandro de La Vega, S.Sebastian Dresbach, P.Philipp Ehses, J.Jan Ernsting, I.Inês Esteves, O.Oscar Ferrante, K. G.Kelly G. Garner, R.Rémi Gau, E.Elodie Germani, T.Tara Ghafari, S. S.Satrajit S. Ghosh, S. E.Sarah E. Goodale, C. D.Cassandra D. Gould Van Praag, S.Samuel Guay, O. F.Omer Faruk Gulban, Y. O.Yaroslav O. Halchenko, M.Michael Hanke, P.Peer Herholz, K.Katja Heuer, F.Felix Hoffstaedter, R.Ruoqi Huang, R.Renzo Huber, O.Ole Jensen, K.Kan Keeratimahat, J. Q.Julian Q. Kosciessa, S.Sladjana Lukic, N.Neville Magielse, C. J.Christopher J. Markiewicz, C. G.Caroline G. Martin, C.Camille Maumet, A.Anna Menacher, J.Jeff Mentch, C.Christian Mönch, S.Shammi More, L.Leonardo Muller, L.Leonardo Muller-Rodriguez, S. A.Samuel A. Nastase, E.Eliana Nicolaisen-Sobesky, D. M.Dylan M. Nielson, C. R.Christopher R. Nolan, F.François Paugam, P.Pedro Pinheiro-Chagas, A. L.Ana Luísa Pinho, A.Alessandra Pizzuti, B.Benjamin Poldrack, B. A.Benedikt A. Poser, R.Roberta Rocca, J.Jacob Sanz-Robinson, K.Kelvin Sarink, K. R.Kevin R Sitek, N.Nadine Spychala, R.Rüdiger Stirnberg, M.Michał Szczepanik, M.Mohammad Torabi, R.Roberto Toro, S. G.Sebastian G. W. Urchs, S. L.Sofie L. Valk, A. S.Adina S Wagner, L. K.Laura K. Waite, A. Q.Alexander Q. Waite, L.Lea Waller, T. J.Tyler J. Wishard, J.Jianxiao Wu, Y.Yuchen Zhou and J. D.Janine D. Bijsterbosch. Proceedings of the OHBM Brainhack 2022.Aperture NeuroMarch 2024HALDOI
• 31 articleM.Marine Mondino, C.Cécilia Neige, J.-M.Jean-Marie Batail, N.Noomane Bouaziz, M.Maxime Bubrovszky, S.Samuel Bulteau, A.Anastasia Demina, L.Ludovic Dormegny-Jeanjean, G.Ghina Harika-Germaneau, D.Dominique Januel, C.Charles Laidi, V.Virginie Moulier, M.Marion Plaze, A.Arnaud Pouchon, E.Emmanuel Poulet, M.Maud Rothärmel, A.Anne Sauvaget, A.Antoine Yrondi, D.David Szekely and J.Jérôme Brunelin. Shaping tomorrow: how the STEP training course pioneered noninvasive brain stimulation training for psychiatry in France.Frontiers in Psychiatry15November 2024, 1-10HALDOI
• 32 articleL.Laurent Pierot, A.Aymeric Rouchaud, E.Emmanuel Chabert, H.Hubert Desal, F.Frédéric Ricolfi, C.Coralie Barbe, S.Sébastien Soize, A.Adberrahim Zerroug, F.Francois Eugène, F.Frédéric Clarençon and J.-C.Jean-Christophe Ferré. Endovascular treatment of unruptured intracranial aneurysms: Rate of thromboembolic events depicted by diffusion-weighted imaging in relation to different techniques.Journal of Neurointerventional SurgeryAugust 2024, jnis-2024-022051HALDOI
• 33 articleG.Giuseppe Placidi, L.Luigi Cinque, G. L.Gian Luca Foresti, F.Francesca Galassi, F.Filippo Mignosi, M.Michele Nappi and M.Matteo Polsinelli. A Context-Dependent CNN-Based Framework for Multiple Sclerosis Segmentation in MRI.International Journal of Neural SystemsDecember 2024HALDOI
• 34 articleR.Russell Poldrack, C.Christopher Markiewicz, S.Stefan Appelhoff, Y.Yoni Ashar, T.Tibor Auer, S.Sylvain Baillet, S.Shashank Bansal, L.Leandro Beltrachini, C.Christian Benar, G.Giacomo Bertazzoli, S.Suyash Bhogawar, R.Ross Blair, M.Marta Bortoletto, M.Mathieu Boudreau, T.Teon Brooks, V.Vince Calhoun, F. M.Filippo Maria Castelli, P.Patricia Clement, A.Alexander Cohen, J.Julien Cohen-Adad, S.Sasha d'Ambrosio, G.Gilles de Hollander, M.María de la Iglesia-Vayá, A.Alejandro de la Vega, A.Arnaud Delorme, O.Orrin Devinsky, D.Dejan Draschkow, E. P.Eugene Paul Duff, E.Elizabeth Dupre, E.Eric Earl, O.Oscar Esteban, F.Franklin Feingold, G.Guillaume Flandin, A.Anthony Galassi, G.Giuseppe Gallitto, M.Melanie Ganz, R.Rémi Gau, J.James Gholam, S.Satrajit Ghosh, A.Alessio Giacomel, A.Ashley Gillman, P.Padraig Gleeson, A.Alexandre Gramfort, S.Samuel Guay, G.Giacomo Guidali, Y.Yaroslav Halchenko, D.Daniel Handwerker, N.Nell Hardcastle, P.Peer Herholz, D.Dora Hermes, C.Christopher Honey, R.Robert Innis, H.-I.Horea-Ioan Ioanas, A.Andrew Jahn, A.Agah Karakuzu, D.David Keator, G.Gregory Kiar, B.Balint Kincses, A.Angela Laird, J.Jonathan Lau, A.Alberto Lazari, J. H.Jon Haitz Legarreta, A.Adam Li, X.Xiangrui Li, B.Bradley Love, H.Hanzhang Lu, E.Eleonora Marcantoni, C.Camille Maumet, G.Giacomo Mazzamuto, S.Steven Meisler, M.Mark Mikkelsen, H.Henk Mutsaerts, T.Thomas Nichols, A.Aki Nikolaidis, G.Gustav Nilsonne, G.Guiomar Niso, M.Martin Norgaard, T.Thomas Okell, R.Robert Oostenveld, E.Eduard Ort, P.Patrick Park, M.Mateusz Pawlik, C.Cyril Pernet, F.Franco Pestilli, J.Jan Petr, C.Christophe Phillips, J.-B.Jean-Baptiste Poline, L.Luca Pollonini, P. R.Pradeep Reddy Raamana, P.Petra Ritter, G.Gaia Rizzo, K.Kay Robbins, A.Alexander Rockhill, C.Christine Rogers, A.Ariel Rokem, C.Chris Rorden, A. M.Alexandre M Routier, J. M.Jose Manuel Saborit-Torres, T.Taylor Salo, M.Michael Schirner, R.Robert Smith, T.Tamas Spisak, J.Julia Sprenger, N.Nicole Swann, M.Martin Szinte, S.Sylvain Takerkart, B.Bertrand Thirion, A.Adam Thomas, S.Sajjad Torabian, G.Gaël Varoquaux, B.Bradley Voytek, J.Julius Welzel, M.Martin Wilson, T.Tal Yarkoni and K.Krzysztof Gorgolewski. The Past, Present, and Future of the Brain Imaging Data Structure (BIDS).Imaging Neuroscience22024, 1-19HALDOIback to text
• 35 articleJ.-C.Jean-Charles Roy, R.Renaud Hédouin, T.Thomas Desmidt, S.Sébastien Dam, I.Iris Miréa-Grivel, W.Weyl Louise, E.Elise Bannier, L.Laurent Barantin, D.Dominique Drapier, J.-M.Jean-Marie Batail, R.Renaud David, J.Julie Coloigner and G. H.Gabriel H. Robert. Quantifying Apathy in Late-Life Depression: Unraveling Neurobehavioral Links through Daily Activity Patterns and Brain Connectivity Analysis.Biological Psychiatry: Cognitive Neuroscience and Neuroimaging2024, 1-30HALDOIback to text

International peer-reviewed conferences

• 36 inproceedingsS.Sébastien Dam, P.Pierre Maurel and J.Julie Coloigner. GSP-DERIVED BRAIN FEATURES FOR CLASSIFICATION IN ANXIETY AND DEPRESSION.ISBI 2024 - 21st IEEE International Symposium on Biomedical ImagingAthens, GreeceIEEE2024HALback to text
• 37 inproceedingsS.Sébastien Dam, P.Pierre Maurel and J.Julie Coloigner. Graph Wavelet Packets for the Classification of Brain Data in Anxiety and Depression.Proceedings EUSIPCO 2024EUSIPCO 2024 - 32nd European conference on signal processingLyon, France2024, 1-5HALback to text
• 38 inproceedingsA.Agustina Fragueiro, R.-P.René-Paul Debroize, E.Elise Bannier and C.Claire Cury. Eye-tracking and skin conductance to monitor task engagement during neurofeedback sessions.BCI 2024 - 9th Graz Brain-Computer Interface ConferenceGraz, Austria2024, 1-6HALback to text
• 39 inproceedingsE.Elodie Germani, E.Elisa Fromont and C.Camille Maumet. Uncovering communities of pipelines in the task-fMRI analytical space.IEEE International Conference on Image ProcessingAbu Dhabi, United Arab EmiratesOctober 2024HALDOIback to text
• 40 inproceedingsC.Carla Joud, L.Laurent Albera, I.Isabelle Merlet and J.Julie Coloigner. SOS-MUSIC, a subspace approach for EEG source imaging promoting sparsity of active sources.EUSIPCO 2024 - 32nd European signal processing conferenceLyon, France2024, 1-5HALback to text
• 41 inproceedingsR.Ricky Walsh, M.Malo Gaubert, C.Cédric Meurée, B. R.Burhan Rashid Hussein, A.Anne Kerbrat, R.Romain Casey, B.Benoît Combès and F.Francesca Galassi. Multi-sequence learning for multiple sclerosis lesion segmentation in spinal cord MRI.MICCAI 2024 - 27th International Conference on Medical Image Computing and Computer-Assisted InterventionMarrakech, Morocco2024, 1-10HALDOIback to text

Conferences without proceedings

• 42 inproceedingsA.Andjela Dimitrijevic, F.Fanny Dégeilh and B.Benjamin de Leener. Can We Encode Intra-and Inter-Variability with Log Jacobian Maps Derived from Brain Morphological Deformations Using Pediatric MRI Scans?MIDL 2024 - Medical Imaging with Deep LearningParis, France2024, 1-5HALback to text
• 43 inproceedingsD.Dominique Dupont, C.Cindy Beaudoin, K.Ken Tang, F.Fanny Dégeilh, I.Isabelle Gagnon, K.Keith Yeates, S.Sean Rose, J.Jocelyn Gravel, B.Brett Burstein, A.Antonia Stang, R.Rachel Stanley and M.Miriam Beauchamp. Developmental Approach to Identifying Post-Concussive Symptoms in Early Childhood Concussion.INS 2024 - International Neuropsychological Society Annual North American MeetingNew York, United States2024HAL
• 44 inproceedingsE.Elodie Germani, N.Nikhil Baghwat, M.Mathieu Dugré, R.Remi Gau, A.Andrzej Sokolowski, M.Madeleine Sharp, J.-B.Jean-Baptiste Poline and T.Tristan Glatard. Prédire l'évolution de la maladie de Parkinson à l'aide de données cliniques et d'IRM fonctionnelles: reproduction et robustesse d'une étude.IABM 2024 - 2ème édition du Colloque Français d'Intelligence Artificielle en Imagerie BiomédicaleGrenoble, France2024, 1-4HAL
• 45 inproceedingsF.François Le Jeune, E.Emile Savalle, A.Anatole Lécuyer, M.-M. J.Marc J.-M. Macé, P.Pierre Maurel and L.Léa Pillette. Exploring Thermal Feedback in Brain-Computer Interface User Training.CORTICO 2024 - Journées du COllectif pour la Recherche Transdisciplinaire sur les Interfaces Cerveau-OrdinateurNancy, France2024, 1-3HALback to text
• 46 inproceedingsF.François Le Jeune, E.Emile Savalle, A.Anatole Lécuyer, M.-M. J.Marc J.-M. Macé, P.Pierre Maurel and L.Léa Pillette. Introducing the Use of Thermal Neurofeedback.BCI 2024 - 9th Graz Brain-Computer Interface ConferenceGraz, Austria2024, 1-6HALback to text
• 47 inproceedingsJ.Jeremy Lefort-Besnard, T. E.Thomas E. Nichols and C.Camille Maumet. Review and evaluation of meta-analysis methods on correlated data for multiverse analysis.IABM 2024Grenoble, FranceMarch 2024HALback to text
• 48 inproceedingsJ.Jeremy Lefort-Besnard, T. E.Thomas E. Nichols and C.Camille Maumet. Same Data Meta Analysis for Neurimaging Multiverse Data.INCF Neuroinformatics Assembly 2024Austin (TX), United States2024HALback to text
• 49 inproceedingsJ.Jeremy Lefort-Besnard, T. E.Thomas E. Nichols and C.Camille Maumet. Systematic review and evaluation of meta-analysis methods for same data meta-analyses in a multiverse setting.OHBM 2024 - Annual Meeting on Organization for Human Brain MappingSeoul, South Korea2024, Selected for oral presentationHALback to text
• 50 inproceedingsL.Lounès Meddahi, S. s.Stéphanie s Leplaideur, A.Arthur Masson, I.Isabelle Bonan, E.Elise Bannier and F.Francesca Galassi. Enhancing stroke lesion detection and segmentation through nnU-net and multi-modal MRI Analysis.WCNR 2024 - 13th World Congress for NeurorehabilitationVancouver, Canada2024, 1-1HALback to text
• 51 inproceedingsC. O.Camille O Muller, T.Thomas Prampart, E.Elise Bannier, I.Isabelle Corouge and P.Pierre Maurel. Evaluation of multimodal EEG-fNIRS neurofeedback for motor imagery.rtFIN 2024 - Real-Time Functional Imaging and Neurofeedback meetingHeidelberg, GermanyNovember 2024HALback to text
• 52 inproceedingsM.-L.Marie-Laure Paillère Martinot, J.-C.Jean-Charles Roy, J.Julie Coloigner, R.Renaud Hédouin, J.-L.Jean-Luc Martinot, E.Eric Artiges and G.Gabriel Robert. 263. Treatment Resistant Versus Non-Resistant Depression: A Whole-Brain Morphometry Replication Study in Young Adults.2024 - Society of Biological Psychiatry’s Meeting9510Austin (TX), United States2024, S207-S207HALDOI

Doctoral dissertations and habilitation theses

• 53 thesisÉ.Élodie Germani. Exploring and mitigating analytical variability in fMRI results using representation learning.Université de RennesSeptember 2024HALback to text
• 54 thesisC.Camille Maumet. Towards reproducible neuroimaging: Solutions for sharing and re-using brain imaging data.University of RennesFebruary 2024HALback to text

Reports & preprints

• 55 reportE.Elise Bannier, S.Simon Castellan, S.Steven Derrien, F.Francesca Galassi, L.Laurent Garnier, L.Ludovic Hoyet, A.Antoine l'Azou, N.Noé Lahaye, M.-M. J.Marc J.-M. Macé, O.Olivier Martineau, A.Arthur Masson, T.Thomas Maugey, B.Benjamin Ninassi, E.Erven Rohou, M.Matthieu Simonin and F.François Taïani. Reducing GHG emissions from business travel: A collaborative approach at IRISA/Inria.Groupe de travail « missions » IRISA / Centre Inria de l’Université de RennesMarch 2024, 1-16HALback to text
• 56 miscS.Sébastien Dam, J.-M.Jean-Marie Batail, P.Pierre Maurel and J.Julie Coloigner. Multimodal Graph Convolutional Network on Brain Structure and Function in Adolescent Anxiety and Depression.October 2024HALback to text
• 57 miscE.Elodie Germani, N.Nikhil Baghwat, M.Mathieu Dugré, R.Rémi Gau, A.Albert Montillo, K.Kevin Nguyen, A.Andrzej Sokolowski, M.Madeleine Sharp, J.-B.Jean-Baptiste Poline and T.Tristan Glatard. Predicting Parkinson's disease trajectory using clinical and functional MRI features: a reproduction and replication study.September 2024HAL
• 58 miscE.Elodie Germani, E.Elisa Fromont, P.Pierre Maurel and C.Camille Maumet. The HCP multi-pipeline dataset: an opportunity to investigate analytical variability in fMRI data analysis.2024HALback to text
• 59 miscE.Elodie Germani, C.Camille Maumet and E.Elisa Fromont. Mitigating analytical variability in fMRI results with style transfer.2024HALback to text
• 60 miscE.Elodie Germani, X.Xavier Rolland, P.Pierre Maurel and C.Camille Maumet. On the validity of fMRI mega-analyses using data processed with different pipelines.2024HALback to text
• 61 miscJ.Jeremy Lefort-Besnard, T. E.Thomas E. Nichols and C.Camille Maumet. Statistical Inference for Same Data Meta-Analysis in Neuroimaging Multiverse Analyzes.2024HALback to text
• 62 miscL.Lounès Meddahi, S. s.Stéphanie s Leplaideur, A.Arthur Masson, I.Isabelle Bonan, E.Elise Bannier and F.Francesca Galassi. Deep Learning and Multi-Modal MRI for the Segmentation of Sub-Acute and Chronic Stroke Lesions.2024HALback to text
• 63 miscC.Caroline Pinte, C.Claire Cury and P.Pierre Maurel. Investigating fMRI neurofeedback score prediction from EEG signals: genetic algorithm applied to hyperparameter selection.2024HALback to text

Other scientific publications

• 64 inproceedingsC.Constance Bocquillon, J. L.Juan Luis Villarreal Haro, J. R.Jonathan Rafael Patino Lopez, J.-P.Jean-Philippe Thiran, I.Isabelle Corouge and E.Emmanuel Caruyer. Orientation dispersion estimated from multidimensional diffusion MRI: does it match simulated realistic microstructure?ESMRMB 2024 - 40th Annual Meeting cientific European Society for Magnetic Resonance in Medecine and BiologyBarcelone, Spain2024, 1-5HALback to text
• 65 inproceedings A.Andjela Dimitrijevic, F.Fanny Dégeilh and B.Benjamin de Leener. Can we distinguish intra- and inter-variability with log Jacobian maps derived from brain morphological deformations using pediatric MRI scans? ISMRM 2024 Singapour, Singapore 2024 HAL
• 66 inproceedingsC.Carla Joud, L.Laurent Albera, J.Julie Coloigner and I.Isabelle Merlet. SOS-MUSIC: A subspace approach for EEG source imaging promoting sparsity of active sources.EUSIPCO 2024 - 32nd European signal processing conferenceLyon, France2024, 1-1HAL
• 67 miscC.Camille Maumet. Data sharing: towards large open datasets in brain imaging.Grenoble, France2024, 1-42HALback to text
• 68 miscC.Camille Maumet and M.Michel Dojat. Neuroimaging data sharing with FLI-IAM.Marseille, France2024HAL
• 69 miscC.Camille Maumet. Flash Talks/Poster session: GDPR-compliant human neuroimaging data sharing using Shanoir.Paris, France2024HAL
• 70 miscC.Camille Maumet. From NIDM to BIDS-Prov: A personal perspective on 10 years of metadata standardization towards neuroimaging data sharing.Oldenburg, GermanyJuly 2024HAL
• 71 inproceedingsC.Camille Maumet. GDPR-compliant sharing of human neuroimaging data using Shanoir.EOSC 2024 - France Tripartite Event42Paris, FranceFebruary 2021, 1945 - 1951HAL
• 72 miscC.Camille Maumet. Increasing computational reproducibility or how-to share *your* research code.Online, AustraliaDecember 2024HAL
• 73 miscC.Camille Maumet. L'ouverture des codes source et logiciels: état des lieux et enjeux.Rennes, France2024, 1-32HAL
• 74 miscC.Camille Maumet. Speaker in the D3A 2.0 Session on Reproducibility.Nyborg, Denmark2024, https://youtu.be/WQf_GMB-ckY?si=yDtu1kKPc8DhALXZHAL
• 75 miscC.Camille Maumet. Towards reproducible neuroimaging - across different analysis pipelines.Montreal (Online), CanadaFebruary 2024HAL
• 76 miscC.Camille Maumet. Towards reproducible neuroimaging across different analysis pipelines.Marseille, France2024, 1-72HAL
• 77 inproceedingsC.Caroline Pinte, C.Claire Cury and P.Pierre Maurel. Genetic algorithm applied to hyperparameter selection for fMRI neurofeedback scores prediction from EEG signals.IABM 2024 - Colloque Français d'Intelligence Artificielle en Imagerie BiomédicaleGrenoble, France2024, 1-1HALback to text
• 78 miscR.Rachel Torchet, E.Emre Özel, E.Enka Blanchard and C.Camille Maumet. Women and minorities in research.Paris, France2024HALback to text

Scientific popularization

• 79 miscF.Fanny Dégeilh, E.Elodie Germani, N.Nolwenn Jégou, C.Carla Joud, J.Jeremy Lefort-Besnard and C. O.Camille O Muller. À la découverte des métiers de la recherche en imagerie cérébrale.Rennes, FranceMarch 2024, 1-89HAL
• 80 misc F.Francesca Galassi. Deep Learning in Medical Imaging: What's Needed for Training Data? Bordeaux, France 2024 HAL