Comprehensive evaluation of AT(N) imaging biomarkers for predicting cognitive decline

Few studies have investigated how different Alzheimer's disease biomarker definitions affect predictions of cognitive decline.

We predicted cognitive decline using 42 biomarker definitions spanning amyloid beta, tau, and neurodegeneration in 383 ADNI participants with baseline neuroimaging and longitudinal cognitive assessments. We identified optimal predictors for each pathology and variable type (continuous, binary, non-binary categorical) using nested cross-validation. Models were compared against support vector machines (SVMs) incorporating brain-wide pathology. In a subsample, we compared imaging-based biomarkers with biofluids (cerebrospinal fluid and plasma).

We observed substantial variability in accuracy, even for measures of the same pathology. Tau biomarkers were most accurate, performing comparably to models incorporating all three pathologies. Binarization of biomarkers mostly preserved accuracy, while SVMs trained on brain-wide pathology data generally did not improve performance. Imaging-based biomarkers consistently outperformed biofluid ones.

Imaging-based biomarker definitions are not interchangeable and can influence the assessment of cognitive decline.