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Neuroimaging biomarkers that distinguish between typical brain aging andAlzheimer's disease (AD) are valuable for determining how much each contributesto cognitive decline. Machine learning models can derive multi-variate brainchange patterns related to the two processes, including the SPARE-AD (SpatialPatterns of Atrophy for Recognition of Alzheimer's Disease) and SPARE-BA (ofBrain Aging) investigated herein. However, substantial overlap between brainregions affected in the two processes confounds measuring them independently.We present a methodology toward disentangling the two. T1-weighted MRI imagesof 4,054 participants (48-95 years) with AD, mild cognitive impairment (MCI),or cognitively normal (CN) diagnoses from the iSTAGING (Imaging-basedcoordinate SysTem for AGIng and NeurodeGenerative diseases) consortium wereanalyzed. First, a subset of AD patients and CN adults were selected basedpurely on clinical diagnoses to train SPARE-BA1 (regression of age using CNindividuals) and SPARE-AD1 (classification of CN versus AD). Second, analogousgroups were selected based on clinical and molecular markers to train SPARE-BA2and SPARE-AD2: amyloid-positive (A+) AD continuum group (consisting of A+AD,A+MCI, and A+ and tau-positive CN individuals) and amyloid-negative (A-) CNgroup. Finally, the combined group of the AD continuum and A-/CN individualswas used to train SPARE-BA3, with the intention to estimate brain ageregardless of AD-related brain changes. Disentangled SPARE models derived brainpatterns that were more specific to the two types of the brain changes.Correlation between the SPARE-BA and SPARE-AD was significantly reduced.Correlation of disentangled SPARE-AD was non-inferior to the molecularmeasurements and to the number of APOE4 alleles, but was less to AD-relatedpsychometric test scores, suggesting contribution of advanced brain aging tothese scores.

Disentangling Alzheimer's disease neurodegeneration from typical brain aging using machine learning