Empenn (means “Brain” in Breton language) ERL U1228 research team is jointly affiliated with Inria, Inserm (National Institute of Health and Scientific Research), CNRS (INS2I institute), and University of Rennes I. It is a team of IRISA/UMR CNRS 6074. Empenn is based in Rennes, at both the medical and science campuses. The team follows the “VisAGeS” one that was created for 12 years in 2006 by Inria, As for “VisAGeS”, Empenn hosts the accreditation number U1228 renewed by Inserm in 2017, after a competitive evaluation conducted by both HCERES and Inserm.
Through this unique partnership, the ambition of Empenn is to establish a multidisciplinary team bringing together researchers in information sciences and medicine. Our medium- and long-term objective is to introduce our basic research to clinical practice, while maintaining the excellence of our methodological research.
Our goal is to foster research in medical imaging, neuroinformatics and population cohorts. In particular, the Empenn team targets the detection and development of imaging biomarkers for brain diseases and focus its efforts on translating this research to clinics and clinical neurosciences at large.
In particular, the objective of Empenn is to propose new statistical and computing methods, and to measure and model brain morphological, structural and functional states in order to better diagnose, monitor and deliver treatment for mental, neurological and substance use disorders. We propose combining advanced instrumental devices and new computational models to provide advanced diagnosis, therapeutic and neuro-rehabilitation solutions for some of the major disorders of the developing and aging brain.
Generic and challenging research topics in this broad domain include finding new ways to compare models and data, assist decisions and interpretation, and develop feedback from experiments. These activities are performed in close collaboration with the Neurinfo in vivo imaging platform, which is a critical environment for the experimental implementation of our research on challenging clinical research projects and the development of new clinical applications.
New practices in medicine bring new challenges in information sciences. This is acutely challenging for brain disorders,where the main challenges we facetoday include (1) improving the understanding of the brain (especially the brain in action), (2) undertaking more effective monitoring of therapeutic procedures, (3) modeling groups of normal and pathological individuals from signal and image descriptors, and (4) discovering new therapeutic and rehabilitation strategies for brain recovery. In addressing these challenges, current medicine lacks computational models able to align multimodal and multiscale observations ofpatients withunderlying pathological phenomena,as well as frameworks to validate these models in clinical settings. These issues pose new challenges in the field of digital sciences and require the development of new solutions for (1) mining descriptors from in vivo observations,(2) assimilating the large amount of data produced for each patient through compact and relevant mathematical representations, (3) learning the dynamics of spatiotemporal data to predict the course of the disease in individual patients, and (4) reconciling observations and treatment processes (the theragnostics concept).
In this context, some of our major developments and newly arising issues and challenges include:
- The generation of new descriptors to study brain structure and function (e.g. the combination of variations in brain perfusion with and without a contrast agent; changes in brain structure in relation to normal, pathological, functional or connectivity patterns; or the modeling of brain state during cognitive stimulation using neurofeedback).
- The integration of additional spatiotemporal and hybrid imaging sequences covering a larger range of observations, from the molecular level to the organ one, via the cellular level (arterial spin labeling, diffusion MRI, MR relaxometry, MR fingerprinting, MR cell labeling imaging, MR-PET molecular imaging, EEG-MRI-NIRS functional imaging, etc.).
- The creation of computational models through the data fusion of molecular, cellular (i.e. through dedicated ligands or nanocarriers), structural and functional image descriptors from group studies of normal and/or pathological subjects.
- The evaluation of these models in relation to acute pathologies, especially for the study of degenerative, psychiatric, traumatic or developmental brain diseases (primarily multiple sclerosis, stroke, traumatic brain injury (TBI) and depression, but applicable with a potential additional impact to epilepsy, Parkinson’s disease, dementia, PTSD, …) within a translational framework.
In terms of new major methodological challenges, we will address the development of models and algorithms to reconstruct, analyze and transform the images, and to manage the mass of data to store, distribute and “semanticize” (i.e. provide a logical division of the model’s components according to their meaning). As such, we expect to make methodological contributions in the fields of model inference; statistical analysis and modeling; the application of sparse representation (compressed sensing and dictionary learning) and machine learning (supervised/unsupervised classification and discrete model learning); data fusion (multimodal integration, registration, patch analysis, etc.); high-dimensional optimization; data integration; and brain-computer interfaces.
In summary, we expect to address the following major challenges:
- Developing new information processing methods able to detect imaging biomarkers in the context of mental, neurological, and substance use disorders.
- Providing new computational solutions, allowing a more appropriate representation of data for image analysis and the detection of biomarkers specific to a form or grade of pathology, or specific to a population of subjects.
- Providing new patient-adapted connectivity atlases for the study and characterization of diseases from quantitative MRI.
- Providing new analytical models of dynamic regional perfusion, and deriving indices of dynamic brain local perfusion from normal and pathological populations.
- Investigating whether the theragnostics paradigm of rehabilitation from hybrid neurofeedback can be effective in some behavioral and disability pathologies.
These major advances are primarily developed and validated in the context of several priority applications: multiple sclerosis, stroke rehabilitation, and the study and treatment of depression
In terms of scientific organization, our research project are organized around three major technological and basics cience topics (Population Imaging, Detection and Learning,and Quantitative Imaging) and three major translational axes (Behavior, Neuro-inflammationand Recovery) that are generic enough to address a large range of central nervous system diseases.
International and industrial relations
Inria associated Teams
- BARBANT (Boston and Rennes, Brain image Analysis) associated team
- In the period 2013 and 2017, this associated team is shared between INRIA Visages team and the Computational Radiology Laboratory of the Children’s hospital Boston at Harvard Medical School. It addresses the topic of better understanding the behavior and evolution of neurological pathologies (such as neurodevelopmental delay or multiple sclerosis) at the organ and local level, and the modeling of normal and pathological groups of individuals (cohorts) from image descriptors. After 6 years of official Inria associated team affiliation, BARBANT team will be transforms as an officila Inria Internationall partnership.
- We also have a strong collaboration under the International Neuroinformatics Coordinating Facility (INCF) on the development of standards and tools for neuroimaging data sharing