Current brain-age models predict chronological age from neuroimaging but overlook the temporal complexity of brain signals, which captures diverse dynamical properties such as criticality, fractal structure, and excitation–inhibition balance. Recent findings show that complexity metrics, e.g. Lempel-Ziv, 1/f slope, are sensitive to age, cognition, and lifestyle factors like caffeine. This project aims to integrate, for individuals aged 18 to 87, structural MRI, fMRI, MEG, and complexity measures into BrainVital-AI, a multimodal, interpretable system that computes a “Brain Vitality Score.” We will link variability in this score to physical activity profiles, addressing a key gap in preventive neuroscience and personalised health.
Canada-Italy innovation award 2025 / Caputo, Arianna. - (2025).
Canada-Italy innovation award 2025
Arianna Caputo
2025
Abstract
Current brain-age models predict chronological age from neuroimaging but overlook the temporal complexity of brain signals, which captures diverse dynamical properties such as criticality, fractal structure, and excitation–inhibition balance. Recent findings show that complexity metrics, e.g. Lempel-Ziv, 1/f slope, are sensitive to age, cognition, and lifestyle factors like caffeine. This project aims to integrate, for individuals aged 18 to 87, structural MRI, fMRI, MEG, and complexity measures into BrainVital-AI, a multimodal, interpretable system that computes a “Brain Vitality Score.” We will link variability in this score to physical activity profiles, addressing a key gap in preventive neuroscience and personalised health.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
