Connectome-based prediction of future episodic memory performance for individual amnestic mild cognitive impairment patients.

Abstract

The amnestic mild cognitive impairment progression to probable Alzheimer's disease is a continuous phenomenon. Here we conduct a cohort study and apply machine learning to generate a model of predicting episodic memory development for individual amnestic mild cognitive impairment patient that incorporates whole-brain functional connectivity. Fifty amnestic mild cognitive impairment patients completed baseline and 3-year follow-up visits including episodic memory assessments (e.g. Rey Auditory Verbal Learning Test Delayed Recall) and resting-state functional MRI scanning. Using a multivariate analytical method known as relevance vector regression, we found that the baseline whole-brain functional connectivity features failed to predict the baseline Rey Auditory Verbal Learning Test Delayed Recall scores (r = 0.17, P = 0.082). Nonetheless, the baseline whole-brain functional connectivity pattern could predict the longitudinal Rey Auditory Verbal Learning Test Delayed Recall score with statistically significant accuracy (r = 0.50, P < 0.001). The connectivity that contributed most to the prediction (i.e. the top 1% connectivity) included within-default mode connections, within-limbic connections and the connections between default mode and limbic systems. More importantly, these connections with the highest absolute contribution weight mainly displayed long anatomical distances (i.e. Euclidean distance >75 mm). These 'neural fingerprints' may be appropriate biomarkers for amnestic mild cognitive impairment patients to optimize individual patient management and longitudinal evaluation in a timely fashion.