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NeuropsychBrainAge: a biomarker for conversion from mild cognitive impairment to Alzheimer's disease.

Background BrainAge models based on neuroimaging data have shown good accuracy for diagnostic classification. However, they have replicability issues due to site and patient variability intrinsic to neuroimaging techniques. We aimed to develop a BrainAge model trained on neuropsychological tests to identify a biomarker to distinguish stable mild cognitive impairment (sMCI) from progressive mild cognitive impairment (pMCI) to Alzheimer's disease (AD). Methods Using a linear regressor, a BrainAge model was trained on healthy controls (CN) based on neuropsychological tests. The model was applied to sMCI and pMCI subjects to obtain predicted ages. The BrainAge delta, the predicted age minus the chronological age, was used as a biomarker to distinguish between sMCI and pMCI. We compared the model to one trained on neuroimaging features. Findings The AUC of the ROC curve for differentiating sMCI from pMCI was 0.91. It greatly outperforms the model trained on neuroimaging features which only obtains an AUC of 0.681. The AUC achieved is at par with the State-of-the-Art BrainAge models that use Deep Learning. The BrainAge delta was correlated with the time to conversion, the time taken for a pMCI subject to convert to AD. Interpretation We suggest that the BrainAge delta trained only with neuropsychological tests is a good biomarker to distinguish between sMCI and pMCI. This opens up the possibility to study other neurological and psychiatric disorders using this technique but with different neuropsychological tests.