NeuralCipher Research overview
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Abstract Background The brain age gap (BAG), a neuroimaging-derived biomarker of accelerated brain aging, faces translational challenges due to model inaccuracies and unclear disease-mechanism linkages. We systematically evaluated BAG's clinical relevance across neuropsychiatric disorders, cognitive trajectories, mortality, and lifestyle interventions. Methods Using multi-cohort data (UK Biobank [n = 38,967], Alzheimer’s Disease Neuroimaging Initiative [ADNI; n = 1,402], Parkinson’s Progression Markers Initiative [PPMI; n = 1,182]), we developed a 3D Vision Transformer (3D-ViT) model for whole-brain age estimation. Survival analyses, restricted cubic splines, and stratified regressions assessed BAG’s associations with cognition, 16 neuropsychiatric disorders, and mortality. Lifestyle modulation effects were quantified through longitudinal BAG progression. Results The 3D Vision Transformer demonstrated robust predictive accuracy, achieving a mean absolute error (MAE) of 2.68 years in the UK Biobank cohort and 2.99–3.20 years in external validation cohorts (ADNI/PPMI). Per 1-year increment in BAG was linearly associated with elevated risks of Alzheimer's disease (HR = 1.165, 95% CI = 1.086–1.249; +16.5% risk/year), mild cognitive impairment (HR = 1.040, 95% CI = 1.030–1.050; +4.0%), and all-cause mortality (HR = 1.12, 1.09–1.15; +12%; all p p p = 0.154). Cognitive decline followed nonlinear trajectories, with critical thresholds for domain-specific cognitive decline emerging at Q4 (BAG > 2.48 years). Lifestyle interventions synergistically attenuated BAG progression in advanced neurodegeneration (Q3–Q4; p Interpretation : BAG robustly predicts accelerated brain aging, neuropsychiatric multimorbidity, and mortality. Its nonlinear cognitive thresholds and stage-dependent lifestyle modifiability underscore clinical utility for risk stratification and personalized prevention strategies.
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