White matter aging in late midlife women: Validation and association with neurodegenerative biomarkers.
Abstract
Brain age (BA) prediction models have emerged as valuable tools for understanding individual differences in trajectories of brain aging. These models aim to estimate overall brain health by predicting BA based on structural MRI data. To enhance the specificity of existing BA models, we introduce a deep learning‐based BA prediction model. The model focuses on the cerebral white matter to assess the prominent cerebrovascular AD risk pathway for women. Here we validate our deep learning‐derived white matter brain age (WMBA) in a sample of late midlife women enrolled in MsBrain. We also examine WMBA in relation to plasma neurodegenerative biomarkers in this sample.
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Research summary
The paper focuses on brain aging and the development of a deep learning model to predict brain age based on white matter analysis, which is relevant to understanding the aging process. However, it primarily addresses the association of brain age with neurodegenerative biomarkers rather than tackling the root causes of aging or proposing interventions for lifespan extension. Thus, while it contributes to the field of aging research, its impact is limited and more incremental than transformative.
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