ArticleFrontiers in aging neuroscience2026
Ultra-fast MRI for brain-age prediction in a real-world cognitive disorders clinic.
Article in Frontiers in aging neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Alzheimer's trials and memory-clinic workflows require frequent structural MRI, but standard 3D-T1 MPRAGE can be burdensome and motion-prone. The Wave-CAIPI sequence offers major time savings, yet it is unclear whether these ultra-fast scans can be used to derive dementia-related biomarkers from models that have been trained on standard scans. Methods: We acquired paired scans from the standard and Wave-CAIPI MPRAGE protocols in 147 patients from a cognitive disorders clinic and generated measures of the brain's biological age. We applied six public brain-age pipelines (brainageR, DeepBrainNet, PyBrainAge, ENIGMA, pyment, MCCQR-MLP) to assess variability across software packages. We evaluated accuracy, interchangeability, cross-protocol agreement and clinical discrimination (subjective memory complaints versus neurodegenerative disorders), and tested effects of acquisition, diagnosis, and its interaction in a mixed-effects model. Results: Cross-protocol agreement was excellent across brain-age pipelines (intraclass correlation coefficient: ICC ≳ 0.90). Clinical discrimination was comparable between protocols, with effect sizes varying modestly by model-protocol combinations. Small, model-specific offsets and significant acquisition-by-diagnosis interactions were seen for some pipelines, consistent with a calibratable protocol effect; test-retest reliability was high and quality control measures were similar across protocols. Discussion: The ultra-fast Wave-CAIPI protocol could generate robust brain-age estimates in memory clinic patients, while markedly reducing scan time. When mixing ultra-fast and standard scans, a harmonization or transfer learning step is advisable to remove model-dependent offsets.
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