Predictions of Current and Future Episodic Memory Using Grey Matter Volume and Functional Connectome: A Longitudinal Study in Amnestic Mild Cognitive Impairment Patients
Background: Amnestic mild cognitive impairment (aMCI) patients are considered an at-risk group for progression to Alzheimer's dementia and accurate prediction of aMCI progression could facilitate the optimal decision-making for both clinicians and patients. Based on the baseline whole-brain grey-matter volume (GMV) and resting-state functional connectivity (FC), we used relevance vector regression to predict the baseline and longitudinal Rey's Auditory Verbal Learning Test Delayed Recall
... layed Recall (AVLT-DR) scores of individual aMCI patients.Methods: Fifty aMCI patients completed baseline and 3-year follow-up visits. All patients underwent comprehensive neuropsychological assessments and multimodal brain MRI scans.Results: We found that the GMV pattern predicted the baseline AVLT-DR score, while the pattern of FC predicted the longitudinal AVLT-DR score. In particular, GMV predicted the baseline AVLT-DR score with an accuracy of r = 0.54 (P < 0.001); the regions that contributed the most were within the default mode (e.g., the posterior cingulate gyrus, angular gyrus and middle temporal gyrus) and limbic systems (e.g., the hippocampus and parahippocampal gyrus). The FC predicted the longitudinal AVLT-DR score with an accuracy of r = 0.50 (P < 0.001), and the connections that contributed the most were the within- and between-system connectivity of the default mode and limbic systems. As a complement, we demonstrated that the GMV and FC patterns could also effectively predict the baseline and longitudinal composite episodic memory scores (calculated by averaging three well-known episodic memory test scores).Conclusions: Our results demonstrated the multimodal brain features in the individualized prediction of aMCI patients' current and future episodic memory performance. These "neural fingerprints" have the potential to be biomarkers for aMCI patients and can help medical professionals optimize individual patient management and longitudinal evaluation.