Description du projet
Réseaux cérébraux et cartographie des troubles neurologiques
La science des réseaux a apporté de nouvelles opportunités pour comprendre le cerveau en tant que système complexe d’unités en interaction dans la santé et la maladie. Malgré les progrès rapides de la science interdisciplinaire des réseaux complexes, identifier les altérations cérébrales les plus représentatives et partagées causées par un trouble spécifique reste difficile, plus encore pour les réseaux cérébraux multimodaux où chacun est dérivé d’une modalité particulière de neuroimagerie. Le projet NormNets financé par l’UE développera une nouvelle technique tirant parti de la puissance de l’apprentissage profond géométrique pour relever les défis des neurosciences des réseaux en normalisant une population de réseaux cérébraux multimodaux. Les outils du projet feront considérablement progresser le domaine des neurosciences des réseaux en évaluant non seulement une cartographie intégrale mais aussi une cartographie prédictive des troubles neurologiques.
Objectif
Modern network science has introduced exciting new opportunities for understanding the brain as a complex system of interacting units in both health and disease and across the human lifespan. Despite the rapidly growing interdisciplinary science of complex networks, which spans the range from genetic and metabolic networks all the way up to social and economic systems, it remains a formidable challenge to identify the most representative and shared brain alterations caused by a specific disorder (e.g. Alzheimer’s disease), namely ‘disorder signature’, in a population of brain networks, let alone multi-modal brain networks where each brain network is derived from a particular neuroimaging modality (e.g. functional or diffusion magnetic resonance imaging (fMRI or dMRI)). Such integral signature can be revealed by what I name as a multimodal connectional brain template (CBT), which would constitute an unprecedented contribution to network neuroscience, rooted in firstly learning brain connectivity normalization and secondly foreseeing its evolution. During this fellowship, I will develop NormNets, a novel technique leveraging the power of geometric deep-learning to meet this challenge by normalizing a population of multimodal brain networks. Particularly, NormNets tools will substantially advance the field of network neuroscience by estimating not only an integral but also a predictive mapping of neurological disorders. In addition to the multi-disciplinary high-quality training I will receive at both host and secondment institutions, this fellowship will remarkably consolidate and accelerate my career on the international landscape scene in the new cross-disciplinary area of “geometric deep learning & integration connectomics” I will pioneer during this fellowship. With the endorsement of international multi-sectoral stakeholders, open NormNets resources will impact on and contribute towards the development of connectomics-rooted predictive precision medicine.
Champ scientifique
- natural sciencesbiological sciencesneurobiology
- medical and health sciencesbasic medicineneurologydementiaalzheimer
- medical and health scienceshealth sciencespersonalized medicine
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- engineering and technologymedical engineeringdiagnostic imagingmagnetic resonance imaging
Programme(s)
Thème(s)
Régime de financement
MSCA-IF-EF-ST - Standard EFCoordinateur
34469 Maslak, Istanbul
Turquie