The brain is a complex organ. In this era of aging clocks, a number of approaches have been developed to assess the biological age of the brain from neuroimaging and other data. Is one metric for brain age sufficient, however? Here researchers present evidence for different portions of the brain to undergo age-related change at different rates. The researchers correlate their novel imaging-based models of brain aging to measures of cognitive performance in their study population, which gives more weight to the work than would have been the case for modeling alone.
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. For example, frontal association cortices exhibited accelerated brain aging relative to sensorimotor cortices in older adults. 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.
Link: https://doi.org/10.1...003-025-08228-z
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