Exploring the power of MRI and clinical measures in predicting AD neuropathology

Predicting Alzheimer's disease (AD) pathology prior to clinical diagnosis is important for identifying individuals at high risk of developing AD dementia. However, there remains a gap in leveraging MRI and clinical data to predict AD pathology. This study examines a novel machine learning approach that integrates the combined vascular (white matter hyperintensities, WMHs) and structural brain changes (gray matter, GM) with clinical factors (cognitive scores) to predict post-mortem neuropathology. Participants from the Alzheimer's Disease Neuroimaging Initiative dataset (ADNI) and National Alzheimer's Coordinating Center (NACC) with both post-mortem neuropathology data and antemortem MRI and clinical data were included. Machine learning models were applied towards feature selection of the top seven MRI, clinical, and demographic data to identify the best performing set of variables that could predict postmortem neuropathology outcomes (i.e., neurofibrillary tangles, neuritic plaques, diffuse plaques, senile/amyloid plaques, and amyloid angiopathy). The best-performing neuropathology predictors from ADNI were then validated in NACC to compare results and ensure that the feature selection process did not lead to overfitting. In ADNI, the best-performing model included total and temporal lobe WMHs and achieved r = 0.87(RMSE = 0.62) during cross-validation for neuritic plaques. Overall, post-mortem neuropathology outcomes were predicted up to 14 years before death with high accuracies (~ 90%). Similar results were observed in the NACC dataset. These findings highlight that MRI features are critical to successfully predict AD-related pathology years in advance.