Descripción del proyecto
Redes encefálicas y mapeo de trastornos neurológicos
La ciencia de redes ha generado nuevas oportunidades para comprender el encéfalo como un sistema complejo de unidades que interactúan entre sí en la salud y la enfermedad. A pesar de la ciencia interdisciplinaria de redes complejas, en rápido crecimiento, todavía resulta complicado identificar aquellas alteraciones encefálicas compartidas y más representativas causadas por un determinado trastorno, algo si cabe aún más difícil en el caso de las redes encefálicas multimodales, donde cada una procede de una modalidad de neuroimagenología específica. El proyecto financiado con fondos europeos NormNets desarrollará una novedosa técnica que aprovechará el poder del aprendizaje profundo geométrico para afrontar los desafíos de la neurociencia de redes mediante la normalización de una población de redes encefálicas multimodales. Las herramientas del proyecto darán un gran impulso al trabajo de la neurociencia de redes al calcular no solo un mapeo integral, sino también predictivo, de los trastornos neurológicos.
Objetivo
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.
Ámbito científico
- 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
Programa(s)
Convocatoria de propuestas
Consulte otros proyectos de esta convocatoriaConvocatoria de subcontratación
H2020-WF-02-2019
Régimen de financiación
MSCA-IF-EF-ST - Standard EFCoordinador
34469 Maslak, Istanbul
Turquía