BrainPath: Generating Subject-Specific Brain Aging Trajectories
This work addresses the need for personalized brain aging models to support precision research into neurodegeneration and aging interventions, representing a novel method for a known bottleneck.
The authors tackled the problem of predicting individual brain aging trajectories from a single baseline MRI, which is crucial for understanding neurodegenerative diseases and aging heterogeneity. They introduced BrainPath, a 3D generative framework that outperformed state-of-the-art models in structural similarity, error metrics, and age-difference accuracy on ADNI and NACC datasets.
Quantifying and forecasting individual brain aging trajectories is critical for understanding neurodegenerative disease and the heterogeneity of aging, yet current approaches remain limited. Most models predict chronological age, an imperfect surrogate for biological aging, or generate synthetic MRIs that enhance data diversity but fail to capture subject-specific trajectories. Here, we present BrainPath, a 3D generative framework that learns longitudinal brain aging dynamics during training and, at inference, predicts anatomically faithful MRIs at arbitrary timepoints from a single baseline scan. BrainPath integrates an age calibration loss, a swap learning strategy, and an age perceptual loss to preserve subtle, biologically meaningful variations. Across held-out ADNI and an independent NACC dataset, BrainPath outperforms state-of-the-art reference models in structural similarity (SSIM), mean squared error (MSE), peak signal-to-noise ratio (PSNR), and MRI age-difference accuracy, while capturing realistic and temporally consistent aging patterns. Beyond methodological innovation, BrainPath enables personalized mapping of brain aging, synthetic follow-up scan prediction, and trajectory-based analyses, providing a foundation for precision modeling of brain aging and supporting research into neurodegeneration and aging interventions.