Statistical Methods for Neuroimaging

About the project

This research project developed statistical methods for analysing PET neuroimaging data with the aim of improving the early detection of neurodegenerative diseases, particularly Alzheimer’s disease.

The work explored Functional Data Analysis, simultaneous confidence corridors and statistical-learning methods as alternatives and complements to conventional mass-univariate neuroimaging approaches. It produced methodological publications, reproducible software and the neuroSCC R package.

The project culminated in the doctoral thesis Desarrollo de métodos estadísticos para el análisis de datos de neuroimagen de cara al diagnóstico precoz de enfermedades neurodegenerativas, defended in November 2025 with a Summa Cum Laude distinction.