4.1.1 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.

4.1.2 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:

• Provide a computational environment that combines data-driven (machine learning) and Bayesian solutions to improve the detection of abnormal patterns in images through decision or evidence theory data fusion strategies. The major initial application is multiple sclerosis. Over the longer term, we also expect to adapt these methods to address a wider range of neurological diseases (epilepsy, stroke, tumors, etc.) in neonate and adult brains.
• 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).

4.1.3 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.

4.2.1 Behavior

Advances in the field of in vivo imaging offer new opportunities for addressing the management of resistant affective disorders and their consequences (suicide risk and socio-professional impact), and the management of spatial cognition disorders after stroke and their consequences (postural perturbations and the loss of autonomy). Our objective, and the main challenge in this context, is to introduce medical image computing methods to the multidisciplinary field of behavioral disorders (cognitive disorders, particularly spatial and postural control disorders or anterograde memory impairment, mood disorders, notably resistant depression, schizophrenic disorders, pervasive developmental disorders, attention disorders, etc.) in order to gain a better understanding of the pathology and devise innovative therapeutic approaches.

We also expect to become a major player in the future and make important contributions with significant impacts, primarily in drug-resistant depression in young and old populations. In particular, we expect to provide new image-related metrics combining perfusion, metabolism and microstructural information regarding the brain in order to better characterize pathologies, provide prospective evolution values and potentially provide new brain stimulation targets that could be used in neurofeedback rehabilitation protocols or other types of brain stimulation procedures.

We aim to provide new imaging markers of mental diseases, especially in the context of mood disorders. The new biomarkers are derived from the metabolic (ASL and later ASL+PET) point of view as well as from the microstructural point of view (multicompartment diffusion MRI and relaxometry). Similarly, we expect to exhibit imaging biomarker regularities combining metabolic and structural information. Over the longer term, we expect these biomarkers to be the target of neurofeedback rehabilitation procedures. Also, over the longer term, we expect to supplement the MRI markers with molecular markers coming from new PET tracers, especially those associated with serotonin intake, at one time point or during a rehabilitation protocol under hybrid PET-EEG-MRI neurofeedback procedures.

4.2.2 Neuroinflammation

Some of the major ongoing research issues regarding neuroimaging of neuro-inflammatory diseases concern the definition of new biomarkers to track the development of the pathology using high- dimensional data (e.g. nD+t MRI). This includes the use of white matter-specific imaging, such as magnetization transfer MRI, relaxometry and diffusion-weighted imaging (DW-MRI). Our objective is (1) to develop information-processing tools to tag the spatiotemporal evolutions of Multiple Sclerosis patterns at the brain parenchyma and spinal cord levels from their different signatures (inflammatory cells visible with USPIO or Gd contrast agents on MRI, persistent black holes, eloquent regional atrophy and microstructure signatures); and (2) to test these new tools on new imaging cohorts. In this respect, we for instance conduct studies on brain and spinal cord imaging, continuing on from the PHRC multicentric EMISEP project (PI: G. Edan), as it is very likely that lesions in the spine will directly affect the ambulatory ability of the patient (and thereby the clinical scores). In order to extend this experiment to a larger MS population, based on our expertise from the OFSEP cohort, we also plan to improve the MS therapeutic decision process through the MUSIC project (Multiple Sclerosis Imaging Check out, a public/private project) and, notably, the RHU PRIMUS project. Our goal is to develop and assess a standardized monitoring tool that provides a robust, long-term computerized MRI follow-up that will become the gold standard in clinical practice for therapeutic decisions in MS treatment. As part of this project, Empenn will share its expertise in data management systems (Shanoir and FLI-IAM) and automatic processing tools (through the medInria and Anima software repositories) to extract quantitative indices from the images.

4.2.3 Recovery

Mental and neurological disorders are the leading cause of years lived with a disability. Treatment-resistant depression affects approximately 2% of the European population. Meanwhile, in the case of brain disorders, almost 1.5 million Europeans (15 million people worldwide) suffer a stroke event each year. Current recovery methods for brain disorders and traumatic brain injuries remain limited, preventing many from achieving full recuperation. We propose to address the issue of brain recovery by introducing new advances from recent breakthroughs in computational medical imaging, data processing and human-machine interfaces, and demonstrate how these new concepts can be used, in particular for the treatment of stroke and major depressive disorders.

We ambition to combine advanced instrumental devices (hybrid EEG, NIRS and MRI platforms), with new hybrid brain computer interface paradigms and new computational models to provide neurofeedback-based therapeutic and neuro-rehabilitation paradigms in some of the major mental and neurological disorders of the developmental and aging brain.

Neurofeedback involves using a brain-computer interface that provides an individual with real-time biofeedback about his or her brain activity in the form of sensory feedback. It enables individuals to learn to better control their brain activity, which can be measured in real time using various non-invasive sensors as described above. Although EEG is currently the only modality used by clinical practitioners in that context, it lacks specificity due to its low spatial resolution. Dynamic research into fMRI-neurofeedback has held promise for treating depression, chronic pain and stroke, since it offers the prospect of real-time imagery of the activity in deep brain structures with high spatial resolution. However, the low temporal resolution and high cost of fMRI-neurofeedback has hampered the development of many applications. We believe that the future belongs to hybrid responses that combine multimodal sensors and intend to demonstrate this in the Empenn project.

7.1.1 Anima

• Keywords:
  Filtering, Medical imaging, Diffusion imaging, Registration, Relaxometry
• 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:
  Olivier Commowick
• Participants:
  Aymeric Stamm, Fang Cao, Florent Leray, Guillaume Pasquier, Laurence Catanese, Olivier Commowick, Renaud Hedouin, René-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:
  Olivier Commowick
• Participants:
  Maxime Sermesant, Olivier Commowick, Théodore Papadopoulo
• 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-NG (SHAring NeurOImaging Resources - Next Generation)
• Keywords:
  Neuroimaging, DICOM, Nifti
• Functional Description:

  Shanoir-NG (SHAring NeurOImaging Resources - Next Generation) is an open-source web platform designed to share, archive, search and visualize medical imaging data. "Next Generation" stands for a complete technological remake released in 2020 with a new technical architecture based on microservices. It provides a user-friendly secure web access and offers an intuitive workflow to facilitate the collecting and retrieving of imaging data from multiple sources. It supports the following formats: DICOM classic/enhanced (MR, CT, PT, NM), BIDS, processed datasets (NIfTI), Bruker, EEG(BrainVision/EDF).

  Shanoir-NG comes along many features such as pseudonymization of data (based on standard profiles), support for multi-centric clinical studies on subjects or group of subjects. Shanoir-NG 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-NG allows researchers, clinicians, PhD students and engineers to undertake quality research projects with an emphasis on remote collaboration.

• News of the Year:
  Shanoir-NG is a complete technological remake of the first version of the Shanoir application, but maintaining the key concepts of Shanoir.
• 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
• Partners:
  CHU Grenoble, INSERM, CNRS, Université Grenoble Alpes, Université de Strasbourg

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
• Authors:
  Arthur Masson, Brandon Le Bon, Benoit Combes
• 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:
  Olivier Commowick
• Participants:
  Olivier Commowick, Florent Leray, René-Paul Debroize, Guillaume Pasquier

7.2.1 The Neurinfo Platform

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

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 studied how analytical variability can impact fMRI results and proposed recommendations and neuroinformatics models to describe the data. We also maintained our clinical interest regarding several pathologies by exploring brain function and connectivity. Also, technical recommendations regarding multicentric imaging protocols were proposed.

Towards efficient fMRI data re-use: can we run between-group analyses with datasets processed differently?

Participants: Pierre Maurel, Camille Maumet, Xavier Rolland.

In recent years, the lack of reproducibility of research findings has become an important source of concerns in many scientific fields, including functional Magnetic Resonance Imaging (fMRI). The low statistical power often observed in fMRI studies was identified as one of the leading causes of irreproducibility. The development of data sharing in the field of neuroimaging opens up new opportunities to achieve larger sample sizes by reusing existing data. FMRI studies use subject data processed with pipelines, and although most shared datasets currently include raw data, we may expect to see an increasing proportion of processed data among shared subject data in the future, for privacy and sustainability reasons. Pipelines consist of multiple steps, each with multiple possible methodological choices: this existing variability in terms of processing and analysis (analytical variability) has an impact on the results. We investigated the impact of analytical variability when combining subject data processed differently in between-group analyses. We created a set of pipelines for subject-level processing that we applied on data from the Human Connectome Project (n=1080). We then performed between-group analyses with subject data processed with different pipelines, under the null hypothesis (making any detection a false positive) using two different software packages: SPM and FSL. We compared the estimated false positive rates obtained to the nominal false positive rate. We found that the analytical variability induced by the studied parameters was found to be acceptable for some of these analyses and redhibitory for others. We concluded that different processed subject data cannot be combined without taking into account the processing applied on these data. Associated publication: 38.

Figure from Rolland et al. ISBI

8.1.2 Detection and learning

In this section, we summarize our contributions that focus on information extraction from medical imaging data. Machine learning methods have been proposed to detect brain abnormalities, in order to assist and improve the quality of a diagnosis. We also contributed to the field of bimodal EEG-fMRI neurofeedback, crucial for a better understanding of brain mechanism.

8.1.3 Quantitative imaging

Quantitative imaging is essential for an accurate and specific characterisation of tissue integrity or neural activity, among others. We contributed to methods in fiber tractography, and specifically tractograms evaluation measures.

8.2.1 Behavior

8.2.2 Neuro-inflammation

9.1.1 Siemens

Participants: Elise Bannier, Emmanuel Caruyer, Olivier Commowick, Isabelle Corouge, Jean-Christophe Ferré, 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).

10.1.1 Associate Teams in the framework of an Inria International Lab or in the framework of an Inria International Program

10.1.2 Visits of international scientists

10.2.1 Other european programs/initiatives

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

Participants: Francesca Galassi, Ricky Walsh, Benoît Combès, Olivier Commowick, Ricky Walsh.

• Funding:
  Co-funding for a PhD thesis in AI - Duration: 2022-2025.
• Summary:
  Co-funding (50% with UR1) 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 RHU PRIMUS: Transforming the care of patients with Multiple Sclerosis using a multidimensional data-driven clinical decision support system

Participants: Elise Bannier, Benoît Combès, Olivier Commowick, 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.

10.3.3 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.

10.3.4 GRASP: Generalizing Results Across Scientific Pipelines

Participants: Camille Maumet, Boris Clenet.

• 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.

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

Participants: Julie Coloigner.

• 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. Mathematical modelling using graph theory has recently played a significant role in analyzing complex cerebral networks and exhibiting connectivity disruptions that are characteristic of specific brain disorders. However, 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 is particular promising to shed new light on the complex interplay between brain function and structure, by jointly analyzing functional activity and the underlying structural connectome. 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 and clinical symptoms. In the PRESCO project, we will develop a new multimodal and multi-stage approach using innovative machine learning (ML) 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 and, then, 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 the diseases.

10.3.6 Rapid Neocortical Declarative Learning in normal aging and memory disorders

Participants: Pierre-Yves Jonin, Julie Coloigner.

• Funding:
  Fondation de l'Avenir - Duration: 2020-2022 - Budget: 40k€.
• Summary:
  Our project aims at making the case for the existence of a rapid declarative learning system largely independent from the extended hippocampal system and characterizing its neural bases, by use of experimental psychology, cognitive neuropsychology and neuroimaging methods. This project is led in collaboration with Dr Audrey Noël, Assistant Prof., University of Rennes 2, France, with Dr Gabriel Besson, Associate Researcher, University of Coimbra, Portugal, with Dr Ann-Kathrin Zaiser, Associate Researcher, University of Heidelberg, Germany, with Dr Serge Belliard, PhD, MD, Rennes University Hospital, Neurology Dept., France, and with Dr Anca Pasnicu, MD, Rennes University Hospital, Neurology Dept., France.

10.3.7 Connectivity of the amygdala in depression

Participants: Olivier Commowick, Emmanuel Caruyer, Julie Coloigner, Claire Cury.

• Funding:
  Fondation de France + INCR – Institut des Neurosciences Cliniques de Rennes - Duration: 2019-2023 - Budget: 250k€
• Summary:
  The onset of depression in teenagers and young adults increases the risk to develop a drug-resistant depression in the adulthood. This project aims at evaluating the role of early changes in the microstructure and connectivity of the amygdala. Using a cohort of drug-resistant patients (N=30), non drug-resistant patients (N=30) and controls (N=30), the aim is to identify imaging biomarkers of the pathology and to compare these with emotional and cognitive phenotypes in this population, searching for early differences in the development of the amygdala connectivity. Inclusions are ongoing. This is a colloborative project with M.-L. Paillère Martinot from Paris-Descartes University, as Principal Investigator.

10.3.8 Hybrid EEG/MRI Neurofeedback for rehabilitation of brain pathologies

Participants: Elise Bannier, Isabelle Bonan, Isabelle Corouge, Jean-Christophe Ferré, Jean-Yves Gauvrit, Pierre Maurel, Mathis Fleury.

• Funding:
  Fondation pour la recherche médicale (FRM) - Duration: 2017-2022 - Budget: 370k€
• Summary:
  This project is a continuation of the HEMISFER project ("Hybrid Eeg-MrI and Simultaneous neuro-FEedback for brain Rehabilitation") conducted at Inria Rennes with the support of the Labex "CominLabs". The goal of this project is to make full use of neurofeedback (NF) paradigm in the context of brain rehabilitation. The major breakthrough will come from the coupling associating functional and metabolic information from Magnetic Resonance Imaging (fMRI) to Electro-encephalography (EEG) to “optimize” the neurofeedback protocol. We propose to combine advanced instrumental devices (Hybrid EEG and MRI platforms), with new hybrid Brain computer interface (BCI) paradigms and new computational models to provide novel therapeutic and neuro-rehabilitation paradigms in some of the major mental and neurological disorders of the developmental and the aging brain (e.g. stroke, language disorders, Mood Depressive Disorder (MDD)). Though the concept of using neurofeedback paradigms for brain therapy has somehow been experimented recently (mostly through case studies), performing neurofeedback through simultaneous fMRI and EEG has almost never been done before so far (two teams in the world including us within the HEMISFER CominLabs project). This project will be conducted through a very complementary set of competences over the different involved teams: Empenn U1228, HYBRID and PANAMA Teams from Inria/Irisa Rennes and EA 4712 team from University of Rennes 1.

10.3.9 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.

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

Participants: Elise Bannier, Emmanuel Caruyer, Benoit Combès, Olivier Commowick, 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.

10.3.11 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.

10.3.12 France Life Imaging (FLI)

Participants: Olivier Commowick, 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. Olivier Commowick is participating in the working group workflow and image processing and Michael Kain is the technical manager. Apart from the team members, software solutions like MedInria and Shanoir are part of the software platform.

10.3.13 OFSEP: French Multiple Sclerosis Observatory

Participants: Elise Bannier, Olivier Commowick, Gilles Edan, Jean-Christophe Ferré, Francesca Galassi, Arthur Masson, 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.

10.4.1 PEPERONI : Portable and Personalized Neurofeedback for Stroke Rehabilitation

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

• Funding:
  Labex CominLabs : from Sept. 2022 to end of 2024 - Budget: 290k€
• Summary:
  Neurofeedback (NF) consists in presenting a person with a stimulus directly related to his or her ongoing brain activity. NF can be used to teach subjects how to regulate their own brain functions by providing real-time sensory feedback of the brain “in action”. Recent studies showed that NF is promising for the treatment of various neuronal pathologies. Electroencephalography (EEG), which has historically been the preferred modality for NF, suffers from a lack of specificity, preventing the transfer of this treatment to clinical use. On the other hand functional Magnetic Resonance Imaging (fMRI) has a good specificity, but it is a cumbersome and expensive modality, making it difficult to develop personalized protocols. In this project, we aim to develop a methodological and experimental framework opening the door to a more portable and personalized NF, for easier and effective clinical use, with a focus on post-stroke motor rehabilitation. We propose to organize the project in four work packages, grouped in two axes. The following figure summarizes the organization of the project.

10.4.2 Région Bretagne: SAD

Participants: Francesca Galassi, Benoit Combès, Burhan Rashid Hussein.

• Funding:
  SAD 2022 - PRIMUS : RHU PRIMUS - Duration: 48 months (start: December 2022) - Budget: 75k€
• Summary:
  Complementary funding for a post-doctoral position (Burhan Rashid Hussein) within the RHU PRIMUS project.

10.4.3 Région Bretagne: ARED MAPPIS

Participants: Camille Maumet, Elodie Germani.

• Funding:
  Budget: 0.5 PhD thesis
• Summary:
  The goal of this thesis is to build a "map" of the neuroimaging pipeline space that will be used to study one or more of the following challenges: 1) Debugging pipelines; 2) Identifying cases where pipelines suffer from violations of their underlying assumptions; 3) Eliminating unsuitable pipelines based on the input dataset. In all of the above cases, a systematic exploration of the processing chain space is impossible. Furthermore, there is generally no ground truth that can be used to compare pipeline results in the neuroimaging setting. In this thesis, we propose to provide a representation of fMRI statistics in a lower dimensional space and use this space to estimate the proximity or distance of different pipelines. By using this map of distances between pipelines in different contexts, we will find a solution to the problems outlined above.

10.4.4 Région Bretagne: Boost Europe MIND

Participants: Camille Maumet.

• Funding:
  Budget: 20k EUR
• Summary:
  The goal of the Boost'Europe MIND project is to implement and share an open dataset including several different treatments of the same neuroimaging study.

10.4.5 Région Bretagne: Boost Europe

Participants: Fanny Dégeilh.

• Funding:
  Budget: 20k EUR
• Summary:
  The main objective of this action is to conduct a pilot study allowing to initiate the implementation of the specific infrastructure for acquiring non-sedated MRI in young children with and without traumatic brain injury at the Neurinfo platform.

11.1.1 Scientific events: organisation

11.1.2 Scientific events: selection

11.1.3 Journal

11.1.4 Invited talks

• Elise Bannier, SOFMER 2022, congress dedicated to physical medecine and rehabilitation : "BOLD functional MRI".
• Isabelle Corouge, "Arterial Spin Labeling (ASL): data processing and quantification", REMI network, January 2022.
• Benoit Combès, Arthur Masson: Assise de l’OFSEP 2022: Challenge nouvelles lésions : perspectives d’évaluation.
• Elodie Germani, "L Codent, L Créent : s'initier à l'informatique par la programmation créative", Colloque Femmes & Sciences, November 2022.
• Nolwenn Jégou, "Reproductibilité d'une tâche motrice en NIRS et comparaison avec l'IRM sur des sujets sains", Workshop Acquisition multimodale et analyse des données - 15-16/12/2022, Rennes.
• Camille Maumet, "An introduction to neuroinformatics" Joint meeting of R Ladies Gaborone and PyData Botswana, Online, May 2022.
• Camille Maumet, "Sharing research products FAIRly", OHBM 2022 educational course: Cultivating open science practices in academic research and culture, Glasgow, UK, June 2022.
• Camille Maumet, "Reviewing neuroimaging flexibility: Components and records of provenance". OHBM 2022: Symposium: What should we do with neuroimaging analytical flexibility?, Glasgow, UK, June 2022.
• Camille Maumet, "Open science: A journey from sharing research artefacts to collaborative research". Rigor and Reproducibility Seminar Series, UF Interdisciplinary T32 in Movement Disorders and Neurorestoration, Gainsville (Online), US, July 2022.
• Camille Maumet, "MRI Post-processing Platforms and How they Compare", MRI Together Global workshop on open, reproducible, and inclusive MR research, session Juggling with data, December 2022.

11.1.5 Leadership within the scientific community

• 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 and oversees the activities of the working group "national and international" with Dr. Nicolas Rougier.
• Elise Bannier is member of the organization committee of the ARSEP MRI Workshop
• Elise Bannier is board member of the REMI Network and took part in the organization of 2 virtual meetings (January 20th and November 7th) and one physical meeting (May 30th at ICM) on methodological and pratical aspects of MRI in clinical research
• Elise Bannier is board member (by election) of the French society for MRI (SFRMBM) in Biology and Medecine

11.1.6 Research administration

• Anne Kerbrat, Francesca Galassi and Benoit Combès are involved in the research administration (Accord de Consortium, Analyse de risque) of the RHU Primus project.

11.2.1 Teaching

• ESIR, École Supérieure d’Ingénieur de Rennes:
  • Pierre Maurel is the 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).
  • Julie Coloigner, "Analyse Multimodale de Signaux Biomédicaux" (12h).
  • Claire Cury, "Traitement avancé des images" (Plenary : 12h).
  • Claire Cury, "Statistiques" (TD : 8h).
  • Claire Cury, "Base de données" (TP : 32h).
  • Francesca Galassi, co-responsable of the "Apprentissage artificiel" course, ESIR2 INFO (30h).
  • Francesca Galassi, responsable of the "Algoritmique des graphes" course, ESIR1 (24h).
  • Francesca Galassi, responsable of the "Base de données" course, ESIR2 SNR (24h).
  • Francesca Galassi, "Imagerie médicale" (TP: 15h).
  • Cédric Meurée, "Algorithmique et complexité" (TD: 14h, TP: 14h)
  • Caroline Pinte, "Mathématiques appliquées au traitement d'images" (TP: 18h)
  • Caroline Pinte, "Projets d'imagerie médicale" (TP: 10h)
  • Caroline Pinte, "Neurofeedback (NF) et Brain Computer Interface (BCI) : Une Introduction" (Plenary: 4h)
  • Sébastien Dam, "Algorithmique et complexité" (TP: 28h).
• 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”.
  • Camille Maumet is co-head of the UE “Gestion de données massives et complexes”.
  • 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, Camille Maumet, “Soutenance de présentations critiques d'articles scientifiques” (TD: 3h).
  • Isabelle Corouge, "IRM de perfusion par Arterial Spin Labeling (ASL)" (Plenary: 3h).
  • Camille Maumet, "Workflows de traitements d'images" (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).
• ENS Rennes/UR1:
  • Emmanuel Caruyer, "Méthodes numériques pour le traitement d'images", L3 SIF (Plenary: 20h).
  • Pierre Maurel, "Introduction to image processing" (20h).
• Master Informatique, ISTIC, UR1:
  • Julie Coloigner, "Computer Vision" (Plenary: 10h)parcours Science informatique (SIF), M2.
  • Elodie Germani, "Option Machine Learning" (TP: 10h), specialités IL (Ingenierie Logiciel) et CCN (Competences Complémentaires Numériques).
• Licence BECV (Biologie, Environnement, Chimie du Vivant), University of Rennes 1: Elodie Germani, "Mathématiques pour la biologie" (TD: 27h).
• Bachelor for speech therapy, L3, University of Rennes: Elise Bannier, "Imagerie fonctionnelle cérébrale du langage" (Plenary: 2h).
• Diplome Universitaire MERC (Manipulateur en Recherche Clinique), University of Montpellier: Elise Bannier, "Spécificités de la recherche clinique en imagerie" (Plenary: 7h).
• Master Physique Médicale, M2, University of Rennes:
  • Elise Bannier, "Imagerie par Résonance Magnétique " (TD: 2h).
• L2, MR Technologists, University Hospital of Rennes:
  • Elise Bannier, "Imagerie par Résonance Magnétique " (Plenary: 6h).
• 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 1: Pierre-Yves Jonin, "Neurosciences cognitives et cliniques de la mémoire humaine" (Plenary: 3h).

11.2.2 Supervision

11.2.3 Juries

• Francesca Galassi: member of Ghiles Reguig's PhD thesis committee, "Contrôle qualité rétrospectif pour l'IRM quantitative", Institut du Cerveau, Sorbonne Université.
• Camille Maumet: member of Sahar Allouch's PhD thesis committee, "La variabilité analytique dans l’estimation des réseaux cérébraux fonctionnels à partir de l’électroencéphalographie", Université Libanaise et Laboratoire Traitement du Signal et de l’Image (LTSI – UMR 1099)
• Elise Bannier was panel expert for two HCERES evaluations
• Julie Coloigner was in the jury of Moyen incitatif Inria 2022, Rennes.
• Claire Cury and Julie Coloigner were in the jury of Ecole Doctorale Biologie Santé Rennes.

11.3.1 Internal or external Inria responsibilities

• Claire Cury, Scientific mediation Officer of the Inria Rennes Scientific mediation team.
• Camille Maumet is a co-organizer of the local version of the program "L codent L créent", an outreach program to send PhD students to teach Python to middle school students in 8 sessions of 45 minutes. It was initiated in Lille, with Anne-Cécile Orgerie and Tassadit Bouadi. The program is currently supported by: Fondation Blaise Pascal, ED MathSTIC, Inria and Fondation Rennes 1.

11.3.2 Articles and contents

• Book "Le corps en images : les nouvelles imagerie pour la santé", CNRS Editions, may 2022. Co-directed by Laure Blanc-Féraud, Emmanuel Caruyer, Christian Jutten, Hervé Liebgott.
  • Olivier Commowick: "L'imagerie cérébrale néonatale".
  • Elise Bannier, Camille Maumet with Anne Hespel: "L'imagerie au service de la santé : questions éthiques et sociétales".
  • Claire Cury, Isabelle Bonan, with Anatole Lécuyer (Inria), Giulia Lioi (IMT): "L'imagerie cérébrale au service de la rééducation" 41.

11.3.3 Education

• Elodie Germani, Caroline Pinte. L codent L créent - Teaching middle schoolers at Collège Le Landry and Les Gayeulles, Rennes. Animation of practical session that aim to initiate young girls to programming and to show them that careers in computer science are accessible.
• Camille Maumet, "L codent, L créent, Rennes Initiation à l’informatique via la programmation créative", Training for teachers in secondary and high schools of the Colloque Femmes & Sciences, November 2022.

11.3.4 Interventions

• Quentin Duché, Hachim Bani, Elise Bannier, Isabelle Corouge, Emmanuel Caruyer, Pierre Maurel. Semaine du cerveau 2022, 14 mars 2022 : Visite de la plateforme Neurinfo.
• Calire Cury, Quentin Duché, Fanny Dégeilh, Elodie Germani, Malo Gaubert, Nolwenn Jégou, Caroline Pinte. Semaine du cerveau 2022, 16 mars 2022 : présentation du quiz grand public "À la découverte des métiers de la recherche en neuroimagerie".
• Claire Cury, Caroline Pinte, Lou Scotto Di Covella, "Le neurofeedback, ou comment peut-on rééduquer notre cerveau ?". Animation, Espace des sciences de Rennes, 14 juin 2022.
• Elise Bannier, Pierre Maurel, "Numérique et Cerveau : ce que nous apporte l'imagerie IRM". Animation, Espace des sciences de Rennes, 21 juin 2022.
• Pierre Maurel, "1 scientifique, 1 classe : Chiche !". Présentation des thématiques et du métier à une classe de terminale et 3 classes de seconde. 17 novembre 2022.
• Elise Bannier, Claire Cury, Fanny Dégeilh, Elodie Germani, Pierre Maurel, participating to the "Speed searching" activity for the Festival des Sciences at Les Champs Libres, October 2022
• Quentin Duché, Elise Bannier - October 2022 : Formation des salariés et bénévoles de l’association Nozambule à Rennes. Présentation des résultats de l'étude sur le marketing de l'alcool.

International journals

• 11 articleF.Freya Acar, C.Camille Maumet, T.Talia Heuten, M.Maya Vervoort, H.Han Bossier, R.Ruth Seurinck and B.Beatrijs Moerkerke. Review Paper: Reporting Practices for Task fMRI Studies.NeuroinformaticsOctober 2022
  HALDOIback to text
• 12 articleJ.-M.Jean-Marie Batail, I.Isabelle Corouge, B.Benoit Combès, C.Camille Conan, M.Muriel Guillery-Sollier, M.Marc Vérin, P.Paul Sauleau, F.Florence Le Jeune, J.-Y.Jean-Yves Gauvrit, J.-Y.Jen-Yves Gauvrit, G.Gabriel Robert, C.Christian Barillot, J.-C.Jean-Christophe Ferré and D.Dominique Drapier. Apathy in depression: An arterial spin labeling perfusion MRI study.Journal of Psychiatric Research157January 2023, 7-16
  HALDOI
• 13 articleM.Marine Cacioppo, M.Mathieu Lempereur, A.Antoine Marin, H.Helene Rauscente, A.Armel Cretual, S.Sylvain Brochard and I.Isabelle Bonan. Motor patterns of the impaired upper limb in children with unilateral cerebral palsy performing bimanual tasks.Clinical Biomechanics972022, 105710
  HALDOI
• 14 articleK.-S.Kwok-Shing Chan, R.Renaud Hédouin, J.Jeroen Mollink, J.Jenni Schulz, A.-M.Anne-Marie van Cappellen van Walsum and J. P.José P Marques. Imaging white matter microstructure with gradient-echo phase imaging: Is ex vivo imaging with formalin-fixed tissue a good approximation of the in vivo brain?Magnetic Resonance in MedicineMarch 2022, 1-11
  HALDOI
• 15 articleN.Nicolas Coquery, Y.Yentl Gautier, Y.Y. Serrand, P. P.Paul P. Meurice, E.Elise Bannier, R.Ronan Thibault, A.Aymery Constant, R.Romain Moirand and D.David Val-Laillet. Brain responses to food choices and decisions depend on individual hedonic profiles and eating habits in healthy young women.Frontiers in nutrition92022, 920170
  HALDOIback to text
• 16 articleL.Léa Domain, M.Murielle Guillery, N.Nicklas Linz, A.Alexandra König, J.-M.Jean-Marie Batail, R.Renaud David, I.Isabelle Corouge, E.Elise Bannier, J.-C.Jean-Christophe Ferré, T.Thibaut Dondaine, D.Dominique Drapier and G.Gabriel Robert. Multimodal MRI cerebral correlates of verbal fluency switching and its impairment in women with depression.Neuroimage-Clinical332022, 102910
  HALDOIback to text
• 17 articleC.Chadia Ed-driouch, F.Florent Chéneau, F.Françoise Simon, G.Guillaume Pasquier, B.Benoit Combès, A.Anne Kerbrat, E.Emmanuelle Le Page, S.Sophie Limou, N.Nicolas Vince, D.David‐axel Laplaud, F.Franck Mars, C.Cédric Dumas, G.Gilles Edan and P.Pierre‐antoine Gourraud. Multiple sclerosis clinical decision support system based on projection to reference datasets.Annals of Clinical and Translational Neurology912December 2022, 1863-1873
  HALDOI
• 18 articleM.Marwane Ghemame, C.C. Cathelineau, B.Béatrice Carsin-Nicol, P.-A.Pierre-Antoine Eliat, H.Hervé Saint-Jalmes, J.-C.Jean-Christophe Ferré and F.Frédéric Mouriaux. Ex vivo porcine model for eye, eyelid, and orbit movement analysis of 4-mm ferromagnetic foreign bodies in MRI.Graefe's Archive for Clinical and Experimental Ophthalmology2601January 2022, 311-318
  HALDOI
• 19 articleA.Ambre Godet, A.Alexandra Fortier, E.Elise Bannier, N.Nicolas Coquery and D.David Val-Laillet. Interactions between emotions and eating behaviors: Main issues, neuroimaging contributions, and innovative preventive or corrective strategies.Reviews in Endocrine and Metabolic Disorders232022, 807-831
  HALDOIback to text
• 20 articleK.Karim Jamal, S.Sébastien Cordillet, S.Stephanie Leplaideur, H.Hélène Rauscent, M.Melanie Cogne and I.Isabelle Bonan. Reliability and minimal detectable change of body-weight distribution and body sway between right and left brain-damaged patients at a chronic stage.Disability and Rehabilitation2022
  HALDOI
• 21 articleP.-Y.Pierre-Yves Jonin, Q.Quentin Duché, E.Elise Bannier, I.Isabelle Corouge, J.-C.Jean-Christophe Ferré, S.Serge Belliard, C.Christian Barillot and E.Emmanuel Barbeau. Building memories on prior knowledge: behavioral and fMRI evidence of impairment in early Alzheimer's disease.Neurobiology of Aging110February 2022, 1-12
  HALDOIback to text
• 22 articleS.Salomé Le Franc, G.Gabriela Herrera Altamira, M.Maud Guillen, S.Simon Butet, S.Stéphanie Fleck, A.Anatole Lécuyer, L.Laurent Bougrain and I.Isabelle Bonan. Toward an Adapted Neurofeedback for Post-stroke Motor Rehabilitation: State of the Art and Perspectives.Frontiers in Human Neuroscience16July 2022, 1-21
  HALDOI
• 23 articleA.Antoine Legouhy, F.François Rousseau, C.Christian Barillot and O.Olivier Commowick. An iterative centroid approach for diffeomorphic online atlasing.IEEE Transactions on Medical Imaging4192022, 2521-2531
  HALDOIback to text
• 24 articleG.Guiomar Niso, R.Rotem Botvinik-Nezer, S.Stefan Appelhoff, A.Alejandro de La Vega, O.Oscar Esteban, J.Joset Etzel, K.Karolina Finc, M.Melanie Ganz, R.Remi Gau, Y.Yaroslav Halchenko, P.Peer Herholz, A.Agah Karakuzu, D.David Keator, C. J.Christopher Johnson Markiewicz, C.Camille Maumet, C.Cyril Pernet, F.Franco Pestilli, N.Nazek Queder, T.Tina Schmitt, W.Weronika Sójka, A. S.Adina Svenja Wagner, K. J.Kirstie Jane Whitaker and J.Jochem Rieger. Open and reproducible neuroimaging: from study inception to publication.NeuroImageSeptember 2022
  HALDOI
• 25 articleM. M.Max M Owens, M. D.Matthew D Albaugh, N.Nicholas Allgaier, D.Dekang Yuan, G.Gabriel Robert, R. B.Renata Basso Cupertino, P. A.Philip A. Spechler, A.Anthony Juliano, S.Sage Hahn, T.Tobias Banaschewski, A. L.Arun L.W. Bokde, S.Sylvane Desrivières, H.Herta Flor, A.Antoine Grigis, P. A.Penny A Gowland, A.Andreas Heinzel, R.Rüdiger Brühl, J.-L.Jean-Luc Martinot, M.-L.Marie-Laure Paillere-Martinot, E.Eric Artiges, F.Frauke Nees, D.Dimitri Papadopoulos-Orfanos, H.Herve Lemaitre, T.Tomas Paus, L.Luise Poustka, S.Sabina Millenet, J. H.Juliane H Fröhner, M. N.Michael N Smolka, H.Henrik Walter, R.Robert Whelan, S.Scott Mackey, G.Gunter Schumann and H.Hugh Garavan. Bayesian causal network modeling suggests adolescent cannabis use accelerates prefrontal cortical thinning.Translational Psychiatry121December 2022, 188
  HALDOI
• 26 articleJ.Justine Saint-Aubert, J.Julien Manson, I.Isabelle Bonan, Y.Yoann Launey, A.Anatole Lécuyer and M.Mélanie Cogné. Effect of Vibrations on Impression of Walking and Embodiment With First- and Third-Person Avatar.IEEE Transactions on Visualization and Computer Graphics2022, 1-8
  HALDOI
• 27 articleR.Ricky Walsh and M.Mickael Tardy. A Comparison of Techniques for Class Imbalance in Deep Learning Classification of Breast Cancer.Diagnostics131December 2022, 67
  HALDOI

International peer-reviewed conferences

• 28 inproceedingsM.Manuel Abbas, J.J.C. Roy, G.Gabriel Robert and R.Régine Le Bouquin Jeannès. Utility of Actimetry to Detect Apathy in Old-Age Depression: a Pilot Study.EUSIPCO 2022 - 30th European Signal Processing Conference2022-AugustEuropean Signal Processing ConferenceBelgrade, Serbia2022, 1203-1207
  HAL
• 29 inproceedingsC.Claire Cury, J.-M.Jean-Marie Batail and J.Julie Coloigner. Shape-based features of white matter fiber-tracts associated with outcome in Major Depression Disorder.MICCAI 2022 - International Conference on Medical Image Computing and Computer-Assisted InterventionLNCS-13431Medical Image Computing and Computer Assisted InterventionSingapour, SingaporeSpringer Nature SwitzerlandSeptember 2022, 163-173
  HALDOIback to text
• 30 inproceedingsF.Fanny Dégeilh, T.Tilmann von Soest, L.Lia Ferschmann, J.Joanne Beer, M.Malo Gaubert, I.Inga Koerte and C.Christian Tamnes. Social problems and brain structure trajectories following pediatric mild traumatic brain injury.OHBM 2022 - Organisation of Human Brain Mapping Annual MeetingGlasgow, United KingdomJune 2022, 1-3
  HAL
• 31 inproceedingsF.Fanny Dégeilh, T.Tilmann von Soest, L.Lia Ferschmann and C. K.Christian K Tamnes. Changes in emotional and behavior problems, and brain morphometry following mild traumatic brain injury in early adolescence: A pre-post study design.Flux Congress 2022Paris, FranceSeptember 2022
  HALback to text
• 32 inproceedingsT.Thomas Durantel, J.Julie Coloigner and O.Olivier Commowick. A convolutional Wasserstein distance for tractography evaluation : complementary study to state-of-the-art measures.ISBI 2022 - IEEE International Symposium on Biomedical ImagingCalcutta, IndiaIEEEMarch 2022
  HALDOIback to text

Conferences without proceedings

• 33 inproceedingsK.Karine Gallopel-Morvan, Q.Quentin Duché, E.Elise Bannier, J.-F.Jacques-François Diouf, O.Olivier Droulers, S.Sophie Lacoste-Badie and R.Romain Moirand. Using neuroimaging to evaluate the impact of alcohol ads on young drinkers.Journées Neuroscience Psychiatrie NeurologieParis, FranceJune 2022
  HAL
• 34 inproceedingsE.Elodie Germani and C.Camille Maumet. FMRI data analysis: How does analytical variability vary with sample size?OHBM 2022 - 28th Annual Meeting of the Organization for Human Brain MappingGlasgow, United KingdomJune 2022, 1-5
  HALback to text
• 35 inproceedingsV.Vincent Goupil, V.V Gouranton, A.-S.Anne-Solène Michaud, J.-Y.Jean-Yves Gauvrit and B.B Arnaldi. A BIM-based model to study wayfinding signage using virtual reality.WBC 2022 - CIB World Building CongressMelbourne, AustraliaJune 2022, 1-10
  HALDOI
• 36 inproceedingsR.Renaud Hedouin, O.Olivier Commowick and J.Julie Coloigner. Imaging biomarker discovery in major depressive disorder with diffusion MRI multi-compartment models.OHBM 2022 - 28th Annual Meeting of the Organization for Human Brain MappingGlasgow, United KingdomJune 2022
  HAL
• 37 inproceedingsN.Nolwenn Jégou, E.Elise Bannier, N.Nora Erhart, H. H.Hector H Garcia, E.Emmanuel Caruyer and I.Isabelle Corouge. Reproducibility of motor task-based fNIRS and comparison with functional MRI in healthy adults.Biennial meeting of the Society for functional Near-Infrared Spectroscopy (fNIRS 2022)Boston, MA, United StatesOctober 2022
  HALback to text
• 38 inproceedingsX.Xavier Rolland, P.Pierre Maurel and C.Camille Maumet. Towards efficient fmri data re-use: can we run between-group analyses with datasets processed differently with spm ?ISBI 2022 - IEEE International Symposium on Biomedical ImagingCalcutta, IndiaIEEEMarch 2022, 1-4
  HALback to text
• 39 inproceedingsH.Haykel Snoussi, B.Benoit Combès, O.Olivier Commowick, E.Elise Bannier, A.Anne Kerbrat, J.Julien Cohen-Adad, C.Christian Barillot and E.Emmanuel Caruyer. Reproducibility and evolution of diffusion MRI measurements within the cervical spinal cord in multiple sclerosis.ISBI 2022 - IEEE International Symposium on Biomedical ImagingKolkata, IndiaIEEEMarch 2022, 1-5
  HAL

Scientific books

• 40 bookH.Helene Rey-Valette, C.Chady Jabbour, P.Pierre Maurel and J.-M. A.Jean-Michel A Salles. Infrastructures de données spatiales: Évaluation socio-économique: retours d'expérience, concepts et méthodes.July 2022, 1-123
  HAL

Scientific book chapters

• 41 inbookC.Claire Cury, I.Isabelle Bonan, A.Anatole Lécuyer and G.Giulia Lioi. L'imagerie cérébrale au service de la rééducation.Le corps en images : Les nouvelles imagerie pour la santé2022, 1-7
  HALback to text

Doctoral dissertations and habilitation theses

• 42 thesisE.Emmanuel Caruyer. Numerical methods for white matter microstructure and brain structural connectivity in diffusion MRI.Université de Rennes 1December 2022
  HAL
• 43 thesisS.Stéphanie Challois-Leplaideur. The neural basis of egocentric spatial representation andneck muscle vibration effects on spatial cognition, in healthy subjects and patients who had a right stroke.Université Rennes 1January 2022
  HAL

Reports & preprints

• 44 miscD.Dani Beck, L.Lia Ferschmann, N.Niamh Macsweeney, L.Linn Norbom, T.Thea Wiker, E.Eira Aksnes, V.Valerie Karl, F.Fanny Dégeilh, M.Madelene Holm, K.Kathryn Mills, O.Ole Andreassen, I.Ingrid Agartz, L.Lars Westlye, T.Tilmann von Soest and C.Christian Tamnes. Puberty differentially predicts brain maturation in males and females during early adolescence: A longitudinal ABCD Study.December 2022
  HALDOI
• 45 miscF.Fanny Dégeilh, T. V.Tilmann Von Soest, L.Lia Ferschmann, J.Joanne Beer, M.Malo Gaubert, I.Inga Koerte and C.Christian Tamnes. Social problems and brain structure development following childhood mild traumatic brain injury.September 2022
  HALDOI
• 46 miscL.Lia Ferschmann, I.Ingrid Overweg, F.Fanny Dégeilh, M.Mona Bekkhus, A.Alexandra Havdahl, T.Tilmann von Soest and C.Christian Tamnes. Development of prosocial behavior and inhibitory control in late childhood: A longitudinal exploration of sex differences and reciprocal relations.December 2022
  HALDOI
• 47 miscE.Elodie Germani, E.Elisa Fromont and C.Camille Maumet. On the benefits of self-taught learning for brain decoding.2022
  HALback to text
• 48 miscB. R.Burhan Rashid Hussein, C.Cédric Meurée, M.Malo Gaubert, A.Arthur Masson, A.Anne Kerbrat, B.Benoît Combès and F.Francesca Galassi. A study on loss functions and decision thresholds for the segmentation of multiple sclerosis lesions on spinal cord MRI.November 2022
  HALback to text
• 49 miscA.Aya Kabbara, N.Nina Forde, C.Camille Maumet and M.Mahmoud Hassan. Successful reproduction of a large EEG study across software packages.August 2022
  HALDOIback to text
• 50 reportG.Geoffroy Oudoumanessah, M.Michel Dojat and F.Florence Forbes. Unsupervised scalable anomaly detection: application to medical imaging.Inria Grenoble - Rhône-Alpes; Grenoble Institut des neurosciencesOctober 2022, 1-43
  HAL

Other scientific publications

• 51 inproceedingsM.Malo Gaubert, E.Elise Bannier, R.Raphael Chouteau, V.Virginie Callot, J.-C.Jean-Christophe Ferré, G.Guillaume Hamon, A.Anne Kerbrat and B.Benoit Combès. Magnetization transfer imaging of the whole spinal cord in multiple sclerosis patients.ECTRIMS 2022 - 38th Congress of the European Committee for Treatment and Research in Multiple SclerosisAmsterdam, NetherlandsOctober 2022, 1-1
  HALback to text
• 52 inproceedingsM.Malo Gaubert, B.Benoit Combès, E.Elise Bannier, V.Virginie Callot, J.-C.Jean-Christophe Ferré, G.Guillaume Hamon, R.Raphael Chouteau and A.Anne Kerbrat. Impact of lesion damages along the whole motor pathways on disability in multiple sclerosis.ECTRIMS 2022 - 38th Congress of the European Committee for Treatment and Research in Multiple SclerosisAmsterdam, NetherlandsOctober 2022, 1-1
  HALback to text
• 53 inproceedingsR.Renaud Hedouin, O.Olivier Commowick, G.Gabriel Robert and J.Julie Coloigner. Imaging biomarker discovery in major depressive disorder with diffusion MRI multi-compartment models.OHBM 2022 - 28th Annual Meeting of the Organization of Human Brain MappingGlasgow, United KingdomJune 2022, 1-1
  HAL
• 54 miscC.Camille Maumet. Open science: A journey from sharing research artefacts to collaborative research.Gainesville (Online), United StatesJuly 2022
  HAL
• 55 miscC.Camille Maumet. Reviewing neuroimaging flexibility: Components and records of provenance.Glasgow, United KingdomJune 2022
  HAL
• 56 inproceedingsS.Sandesh Patil, A.Axel Bonnet, S.Sorina Camarasu-Pop and T.Tristan Glatard. Deploying container-based applications on EGI with VIP.EGI2022 - EGI conferencePrague, Czech RepublicSeptember 2022, 1-1
  HAL
• 57 inproceedingsC.Caroline Pinte, C.Claire Cury and P.Pierre Maurel. RNN-LSTM neural network for predicting fMRI neurofeedback scores from EEG signals.rtFIN 2022 - Real-Time Functional Imaging and Neurofeedback meetingNew Haven, United StatesOctober 2022
  HALback to text

Scientific popularization

• 58 bookL.Laure Blanc-Féraud, E.Emmanuel Caruyer, C.Christian Jutten and H.Herve Liebgott. Le corps en images: Les nouvelles imageries pour la santé.2022
  HAL
• 59 inbookE.Emmanuel Caruyer. Imagerie par résonance magnétique de diffusion.Le corps en images2022, 1-8
  HAL
• 60 inbookF.François Rousseau, O.Olivier Commowick, J.Julien Jomier, N.Nicolas Passat and F.Fabrice Wallois. L’imagerie cérébrale néonatale.Le corps en images. Les nouvelles technologies d'imagerie pour la santé2022, 146-159
  HAL

Educational activities

• 61 unpublishedC.Camille Maumet. An introduction to neuroinformatics.May 2022, LicenceBotswana
  HAL
• 62 unpublishedC.Camille Maumet. Sharing research products FAIRly.June 2022, DoctoralUnited Kingdom
  HAL