In a recent study published in Nature Mental Health , a group of researchers evaluated if a neurobiological model of the default-mode network (DMN) effective connectivity can predict future dementia diagnosis at the individual level. Study: Early detection of dementia with default-mode network effective connectivity . Image Credit: Komsan Loonprom/Shutterstock.
com There is a significant interest in reducing dementia's growing burden, with Alzheimer's disease (AD) as the leading cause. Early detection of neural changes could enable personalized prevention strategies. Resting-state functional magnetic resonance imaging (rs-fMRI) maps brain connectivity and shows altered patterns in AD, but traditional methods lack precision for individual risk prediction.
Effective connectivity analysis, modeling causal brain interactions, offers better detection. Early DMN dysconnectivity patterns are linked to genetic risk factors for AD and social isolation, suggesting their potential as preclinical biomarkers. Further research is needed to validate effective connectivity analysis for early dementia diagnosis and refine prevention strategies.
Controlling for confounders like age, sex, ethnicity, and head motion, the present study used data from the United Kingdom Biobank (UKB). An initial sample of 148 dementia cases was identified, with ten matched controls for each case. After preprocessing, the final sample included 103 cases and 1,030 controls, with 81 cases undiagnosed at the time of MR.