Scale-dependent brain age with cosmological higher-order statistics from structural magnetic resonance imaging.
Authors
Affiliations (6)
Affiliations (6)
- Instituto de Astrofísica de Canarias (IAC), C/ Vía Láctea, s/n, San Cristóbal de La Laguna, E-38205, Spain; Departamento de Astrofísica, Universidad de La Laguna (ULL), San Cristóbal de La Laguna, Tenerife, E-38206, Spain.
- Facultad de Psicología, Universidad de La Laguna (ULL), San Cristóbal de La Laguna, Tenerife, E-38200, Spain; Instituto de Tecnologías Biomédicas (ITB), Universidad de La Laguna (ULL), San Cristóbal de La Laguna, Tenerife, E-38200, Spain; Instituto Universitario de Neurociencias (IUNE), Universidad de La Laguna (ULL), San Cristóbal de La Laguna, Tenerife, E-38200, Spain.
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Via Consolare Valeria, Messina, I-98125, Italy.
- Instituto de Tecnologías Biomédicas (ITB), Universidad de La Laguna (ULL), San Cristóbal de La Laguna, Tenerife, E-38200, Spain; Instituto Universitario de Neurociencias (IUNE), Universidad de La Laguna (ULL), San Cristóbal de La Laguna, Tenerife, E-38200, Spain; Departamento de Ingeniería Industrial, Universidad de La Laguna (ULL), San Cristóbal de La Laguna, Tenerife, E-38200, Spain.
- Instituto de Astrofísica de Canarias (IAC), C/ Vía Láctea, s/n, San Cristóbal de La Laguna, E-38205, Spain; Departamento de Astrofísica, Universidad de La Laguna (ULL), San Cristóbal de La Laguna, Tenerife, E-38206, Spain; Observatoire de Paris, LERMA, PSL University, 61 avenue de l'Observatoire, Paris, F-75014, France; Université Paris-Cité, 5 Rue Thomas Mann, Paris, 75014, France.
- Instituto de Astrofísica de Canarias (IAC), C/ Vía Láctea, s/n, San Cristóbal de La Laguna, E-38205, Spain; Departamento de Astrofísica, Universidad de La Laguna (ULL), San Cristóbal de La Laguna, Tenerife, E-38206, Spain. Electronic address: [email protected].
Abstract
Inferring chronological age from magnetic resonance imaging (MRI) brain data has become a valuable tool for the early detection of neurodegenerative diseases. We present a method inspired by cosmological techniques for analyzing galaxy surveys, utilizing higher-order summary statistics with multivariate two- and three-point analyses in 3D Fourier space. This method offers physiological interpretability during the inference, allowing the detection of scales where brain anatomy differs across age groups, providing insights into brain aging processes. Similarly to the evolution of cosmic structures, the brain structure also evolves naturally but displays contrasting behaviors at different scales. On larger scales, structure loss occurs with age, possibly due to ventricular expansion, while smaller scales show increased structure, likely related to decreased cortical thickness and gray/white matter volume. Using MRI data from the OASIS-3 database of 869 sessions, our method predicts chronological age with a Mean Absolute Error (MAE) of 3.1 years, while providing information as a function of scale. A posterior density estimation shows that the 1-σ uncertainty for each individual varies between ∼2 and 8 years, suggesting that, beyond sample variance, complex genetic or lifestyle-related factors may influence brain aging. We perform a twofold validation of the method. First, we apply the method to the Cam-CAN dataset, yielding a MAE of ∼5.9 years for the age range from 18 to 88 years. Second, we apply the method to thousands of simulated MRI images generated with a state-of-the-art Latent Diffusion model. This work demonstrates the utility of interdisciplinary research, bridging cosmological methods and neuroscience.