Distinct brain age gradients across the adult lifespan reflect diverse neurobiological hierarchies
Nicholas Riccardi, Alex Teghipco, Sarah Newman‐Norlund, Roger Newman-Norlund, Ida Rangus, Chris Rorden, Julius Fridriksson, Leonardo Bonilha
Abstract
‘Brain age’ is a biological clock typically used to describe brain health with one number, but its relationship with established gradients of cortical organization remains unclear. We address this gap by leveraging a data-driven, region-specific brain age approach in 335 neurologically intact adults, using a convolutional neural network (volBrain) to estimate regional brain ages directly from structural MRI without a predefined set of morphometric properties. Six distinct gradients of brain aging are replicated in two independent cohorts. Spatial patterns of accelerated brain aging in older adults quantitatively align with the archetypal sensorimotor-to-association axis of cortical organization. Other brain aging gradients reflect neurobiological hierarchies such as gene expression and externopyramidization. Participant-level correspondences to brain age gradients are associated with cognitive and sensorimotor performance and explained behavioral variance more effectively than global brain age. These results suggest that regional brain age patterns reflect fundamental principles of cortical organization and behavior. Using deep learning-based regional brain age estimates in 335 adults, we show that spatial patterns of brain aging align with established cortical gradients such as gene expression and outperform ‘global’ brain age estimates in explaining behavior.