Abstract
INTRODUCTION
Machine learning algorithms applied to neuroimaging may aid diagnosis and disease monitoring. This study aimed to derive neuroimaging signatures of brain aging (Spatial Pattern of Atrophy for Recognition–Brain Age [SPARE-BA]) and Alzheimer’s disease (SPARE-AD) in a diverse sample and to examine their associations with plasma biomarkers, diagnosis, and disease progression.
METHODS
SPARE-BA and SPARE-AD were calculated using T1-images from 1297 participants without dementia. Associations between neuroimaging signatures and plasma biomarkers were assessed with partial correlations. Logistic regression and cox proportional hazard models evaluated diagnostic accuracy and clinical progression.
RESULTS
SPARE-AD displayed the strongest association with plasma phosphorylated tau 217, whereas SPARE-BA correlated most strongly with plasma neurofilament light. The combination of neuroimaging signatures with plasma biomarkers achieved the highest diagnostic discriminability and strongest associations with clinical progression.
DISCUSSION
Neuroimaging signatures of aging and Alzheimer’s disease display distinct associations with plasma biomarkers. Neuroimaging signatures combined with plasma biomarkers may improve the detection and prediction of cognitive impairment.
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This post is Copyright: | October 17, 2026
Neuro-Dementia