2022 journal article

Estimating effective connectivity in Alzheimer's disease progression: A dynamic causal modeling study


By: J. Huang, J. Jung* & C. Nam

author keywords: Alzheimer's disease (AD); mild cognitive impairment (MCI); effective connectivity; dynamic causal modeling; resting-state fMRI
Source: Web Of Science
Added: January 30, 2023

Alzheimer's disease (AD) affects the whole brain from the cellular level to the entire brain network structure. The causal relationship among brain regions concerning the different AD stages is not yet investigated. This study used Dynamic Causal Modeling (DCM) method to assess effective connectivity (EC) and investigate the changes that accompany AD progression.We included the resting-state fMRI data of 34 AD patients, 31 late mild cognitive impairment (LMCI) patients, 34 early MCI (EMCI) patients, and 31 cognitive normal (CN) subjects selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The parametric Empirical Bayes (PEB) method was used to infer the effective connectivities and the corresponding probabilities. A linear regression analysis was carried out to test if the connection strengths could predict subjects' cognitive scores.The results showed that the connections reduced from full connection in the CN group to no connection in the AD group. Statistical analysis showed the connectivity strengths were lower for later-stage patients. Linear regression analysis showed that the connection strengths were partially predictive of the cognitive scores.Our results demonstrated the dwindling connectivity accompanying AD progression on causal relationships among brain regions and indicated the potential of EC as a loyal biomarker in AD progression.