Multi-scAle high-order neTwork modelling in the stUdy of human aging: A data-dRiven perspectIve using Topology and geometrY

Descripción del proyecto

Utilización de neuroimágenes para cartografiar las vías del envejecimiento

Comprender el envejecimiento y las enfermedades asociadas a él es uno de los retos más complejos de la biología moderna. Las técnicas avanzadas de neuroimagen, como la resonancia magnética funcional y los electroencefalogramas, miden la actividad encefálica. Sin embargo, la creación de herramientas de diagnóstico precisas resulta compleja debido al ruido y a los problemas de calidad de los datos. Si bien el análisis de datos topológicos y geométricos es prometedor, la limitación de algoritmos dificulta el progreso. El equipo del proyecto MATURITY, que cuenta con el apoyo de las Acciones Marie Skłodowska-Curie, analizará datos de series temporales procedentes de bases de datos sobre el envejecimiento utilizando un planteamiento de red multicapa de orden superior. El equipo del proyecto tiene acceso exclusivo a datos de ciclo de vida a múltiples escalas, en particular datos multiómicos, de exposoma, microbioma y neuroimagen, así como datos clínicos de miles de pacientes. En resumen, explica cómo se produce el envejecimiento a través de vías biológicas.

Objetivo

A full understanding of ageing is still elusive, as well as the consequences in terms of age-related diseases. Several approaches have been proposed to overcome the challenges of cure and early diagnosis of brain diseases, such as Parkinson's and Alzheimer's. With the advent of technology, different approaches are used in order to measure brain activity, such as fMRI, PET scans, EEG/MEG scans, to name a few. However, in order to propose accurate tools for diagnosis, the choice of an appropriate data measurement that can be noise-free and endowed with the correct features is far from being an easy task. Topological and geometrical data analysis emerged in the last decade as a very promising tool. Yet, the lack of efficient approaches, as well as computational algorithms, prevents rapid advances in the field of big data. These limitations also prevent the analyses from going beyond low-dimensional data inspection, also limiting the information provided. The present project aims to propose systematic answers to some of these issues, with the goal of analysing and classifying (both topologically and geometrically) time-series from ageing databases in a multi-layer higher-order network. To this end, the RF has unique access to human lifespan (20 to over 100 years of age) multi-scale data (from multi-omics, exposome, microbiome, neuro-imaging to phenotypic) from the NIH/USA (National Institute on Aging) and multi-modal clinical data of thousands of patients. Being an expert in multivariate time series to generate higher-order information from, he will develop an efficient, optimized geometrical/topological tool for computing higher-order invariants. The RF hypothesizes a relation between the long genes and the susceptibility of the human brain to age-related disease. He anticipates that merging multi-omics and brain data, together with the proposed multi-layered topological workflow classification, will allow him to generate a proof-of-concept model for ageing pathways.