Brain pace estimates reveal early brain aging linked to impairment
Brain-PACE: A Deep Siamese MRI Framework for Modelling Longitudinal Brain Acceleration
Computer Vision and Pattern Recognition
Summary
Brain health can be estimated by predicting brain age from MRI scans. The authors developed Brain-PACE, a method that looks at how fast the brain ages over time by comparing pairs of MRI scans. They found that a faster brain aging pace was linked to worse thinking and daily functioning in people with mild cognitive problems, and to higher levels of a brain protein involved in Alzheimer's disease. Their method improved accuracy over previous approaches by learning from brain images in a smarter way.
What this means in practice
- •For clinical imaging teams: Identify individuals with accelerated brain aging by analyzing paired MRI scans to inform early diagnosis of cognitive decline.
- •For medical device developers: Develop MRI analysis tools that incorporate Brain-PACE’s improved deep learning framework for better tracking of brain health progression.$Commercial implications: Enables creation of enhanced diagnostic software for neurodegenerative diseases to sell to hospitals and clinics.
Authors
Samuel Maddox, Jacob Newman, Saber Sami, Michal Mackiewicz, for the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers, Lifestyle flagship study of ageing
Abstract
Brain age estimation has become a popular research proxy for assessing brain health and disease, yet longitudinal trajectories of brain ageing are still poorly defined, and clinical use is limited. Building on existing Siamese longitudinal frameworks, we develop Brain-Predicted Age Acceleration (Brain-PACE) to directly estimate the pace of structural brain ageing from paired T1-weighted MRI. Brain-PACE identified accelerated ageing in $42.6$% of participants with mild cognitive impairment. Faster Brain-PACE was associated with greater functional and cognitive impairment (FAQ; $r=0.35$, ADAS13; $r=0.30$, CDR-SB; $r=0.32$) and greater regional tau burden in the posterior cingulate ($r=0.59$), precuneus ($r=0.47$), and entorhinal cortex ($r=0.37$). These associations were stronger than those observed when pace was calculated indirectly from repeated cross-sectional brain age estimates, suggesting that direct longitudinal modelling captures complementary information relevant to ongoing pathological change. Methodologically, Brain-PACE extends the LILAC framework by combining spatial attention with soft label distribution learning and a Cramér distance objective, improving probabilistic performance and reducing prediction bias while providing measures of predictive uncertainty. Together, these findings support Brain-PACE as a complementary longitudinal imaging phenotype with sensitivity to relevant clinical and biological changes in early neurodegeneration.