Structural, static, and dynamic functional MRI predictors for conversion from mild cognitive impairment to Alzheimer's disease: Inter‐cohort validation of Shanghai Memory Study and ADNI

Abstract

AbstractMild cognitive impairment (MCI) is a critical prodromal stage of Alzheimer's disease (AD), and the mechanism underlying the conversion is not fully explored. Construction and inter‐cohort validation of imaging biomarkers for predicting MCI conversion is of great challenge at present, due to lack of longitudinal cohorts and poor reproducibility of various study‐specific imaging indices. We proposed a novel framework for inter‐cohort MCI conversion prediction, involving comparison of structural, static, and dynamic functional brain features from structural magnetic resonance imaging (sMRI) and resting‐state functional MRI (fMRI) between MCI converters (MCI_C) and non‐converters (MCI_NC), and support vector machine for construction of prediction models. A total of 218 MCI patients with 3‐year follow‐up outcome were selected from two independent cohorts: Shanghai Memory Study cohort for internal cross‐validation, and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort for external validation. In comparison with MCI_NC, MCI_C were mainly characterized by atrophy, regional hyperactivity and inter‐network hypo‐connectivity, and dynamic alterations characterized by regional and connectional instability, involving medial temporal lobe (MTL), posterior parietal cortex (PPC), and occipital cortex. All imaging‐based prediction models achieved an area under the curve (AUC) > 0.7 in both cohorts, with the multi‐modality MRI models as the best with excellent performances of AUC > 0.85. Notably, the combination of static and dynamic fMRI resulted in overall better performance as relative to static or dynamic fMRI solely, supporting the contribution of dynamic features. This inter‐cohort validation study provides a new insight into the mechanisms of MCI conversion involving brain dynamics, and paves a way for clinical use of structural and functional MRI biomarkers in future.