Dynamic multilayer functional connectivity detects preclinical and clinical Alzheimer’s disease

Abstract

Abstract Background Increasing evidence suggests that patients with Alzheimer's disease (AD) present alterations in functional connectivity but previous results have not always been consistent. One of the reasons that may account for this inconsistency is the lack of consideration of temporal dynamics. To address this limitation, here we studied the dynamic modular organization on resting-state functional MRI (rs-fMRI) across different stages of AD using a novel multilayer brain network approach. Methods Participants from preclinical and clinical AD stages were included. Temporal multilayer networks were used to assess time-varying modular organization. Logistic regression models were employed for disease stage discrimination, and partial least squares analyses examined associations between dynamic measures with cognition and pathology. Results Temporal multilayer functional measures distinguished all groups, particularly preclinical stages, overcoming the discriminatory power of risk factors such as age, sex, and APOE 𝜀4 carriership. Dynamic functional measures exhibited strong associations with cognition as well as amyloid and tau pathology. Conclusions Dynamic multilayer functional connectivity shows promise as a functional imaging biomarker for both early and late stage AD diagnosis.