Project

Medical imaging is a powerful tool for the understanding of brain mechanisms underlying the onset and progression of cognitive disorders in neurological diseases. We have developped transversal markers using MRI and PET data, through geometrically approaches to cortex analysis, brain connectivity analysis, and quantification of metabolism, perfusion and iron load. We are now working on the integration of these markers in order to exploit their multimodal and complementary information.

The aim of the team’s project is to develop measures of brain multimodal connectivity for the study of the pathophysiological mechanisms of cognitive disorders observed in neurological diseases and animal models. These tools will allow a personalized follow-up using AI and will be based on the integration of information from multimodal medical data.

Team: UMR-S 1172 : Renaud Lopes (PI), Grégory Kuchcinski (co-PI), Régis Bordet, Jean-Pierre Pruvo, Xavier Leclerc, Olivier Outteryck, Cécile Bordier, Frank Semah, Kathy Dujardin, Florence Pasquier, Charlotte Cordonnier, Luc Defebvre, David Devos, Caroline Moreau, Etienne Allart, Maxime Bertoux, Xavier Delbeuck, Nacim Betrouni, Thibaut Dondaine, Martin Bretzner (doctorant), Quentin Vannod-Michel (doctorant), Morgan Gautherot (doctorant), Vincent Roca (doctorant), Jean-Baptiste Davion (doctorant), Guillaume Carey (doctorant) ;

UMS2014 – US41 : Damien Huglo, Romain Viard, Florent Auger, Clémence Leboullenger

Highlights

1. Study of post-stroke cognitive impairment using MRI

Since 2015, we have been working on the study of post-stroke cognitive impairment. We have identified an altered functional network on MRI specific to cognitive impairment. This network was then used and coupled with an AI algorithm in the long-term personalized prediction of cognitive impairment through a collaboration with the Prs. M. Dichgans and M. Duering from the Institute for stroke and dementia research in Munich. Two publications in Neurology have resulted from this work.

 

Etude en imagerie des troubles cognitifs post-AVC

 

2. Contribution of Artificial Intelligence in Neurosciences

Since 2018, a scientific partnership with General Electric is in place on the prediction of brain age in MRI, by deep learning, as a marker of clinical subtypes in early onset Alzheimer disease (M. Gautherot's PhD). Since 2020, a partnership with Philips is in place on AI harmonization of MRI images in multicenter studies (V. Roca’s PhD). In addition, M. Bretzner (Ph.D. student) is carrying out a mobility at the MGH in Boston on the use of machine learning on a cohort of 2000 ischemic strokes.

 

Partenariats scientifiques avec General Electric et Philips

3. Identification of MR biomarkers in neurodegenerative and psychiatric diseases – ARIANES project

The aim of the ARIANES project (arianes.fr) is to network the various MRI technologies in the Hauts-de-France region in order to improve the screening, early diagnosis and follow-up of patients with neurological and psychiatric diseases. The acquisition of a future 7T MRI is prioritized for the definition of new markers of these pathologies that will resonate throughout the Region thanks to AI techniques. The project has been selected for the next CPER 2021-2027.

 

Association pour la recherche en imagerie avancée en neurosciences et santé mentale – ARIANES

Publications

Lopes, R ; Bournonville, C ; Kuchcinski, G ; Dondaine, T ; Mendyk, A-M ; Viard, R ; Pruvo, J-P ; Hénon, H ; Geogarkis, M ;Duering, M ; Dichgans, M ; Cordonnier, C ; Leclerc, X ; Bordet, R ; Prediction of Long-term Cognitive Functions after Minor Stroke, Using Functional Connectivity, Neurology, 2021, early view. DOI: 1212/WNL.0000000000011452

Vanhoutte, M ; Semah, F ; Leclerc, X ; Sillaire, AR ; Jaillard, A ; Kuchcinski, G ; Pasquier, F ; Lopes, R, Three-year changes of cortical 18F-FDG in amnestic vs. non-amnestic sporadic early-onset Alzheimer's disease., Eur J Nucl Med Mol Imaging, 2020, 47, 304-318. DOI: 1007/s00259-019-04519-w

Kuchcinski, G.; Munsch, F.; Lopes, R.; Bigourdan, A.; Su, J.; Sagnier, S.; Renou, P.; Pruvo, J.; Rutt, B.; Dousset, V.; Sibon, I.; Tourdias, T. Thalamic Alterations Remote to Infarct Appear as Focal Iron Accumulation and Impact Clinical Outcome. Brain 2017, 140, 1932–1946. DOI: 1093/brain/awx114