Descrizione del progetto
Reti cerebrali e mappatura dei disturbi neurologici
La teoria delle reti ha introdotto nuove opportunità per la comprensione del cervello in quanto sistema complesso di unità che interagiscono in salute e in malattia. Sebbene la teoria interdisciplinare delle reti complesse sia in rapida crescita, è ancora difficile identificare le alterazioni cerebrali più rappresentative e condivise causate da un disturbo specifico e lo è ancora di più per le reti cerebrali multimodali, in cui ciascuna proviene da una particolare modalità di neuroimaging. Il progetto NormNets, finanziato dall’UE, svilupperà una nuova tecnica sfruttando il potere dell’apprendimento profondo geometrico per raccogliere le sfide neuroscientifiche normalizzando una popolazione di reti cerebrali multimodali. Gli strumenti del progetto faranno progredire in modo sostanziale il campo della neuroscienza delle reti stimando non solo una mappatura integrale, ma anche predittiva, dei disturbi neurologici.
Obiettivo
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.
Campo scientifico
- 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
Programma(i)
Argomento(i)
Meccanismo di finanziamento
MSCA-IF-EF-ST - Standard EFCoordinatore
34469 Maslak, Istanbul
Turchia