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

Area of Science:

• Neuroscience
• Medical Imaging
• Biomarkers

Background:

• Alzheimer's disease (AD) impacts brain-wide networks, but causal relationships across disease stages remain unclear.
• Understanding these changes is crucial for early diagnosis and treatment development.

Purpose of the Study:

• To investigate changes in effective connectivity (EC) between brain regions during Alzheimer's disease progression.
• To assess the potential of EC as a biomarker for AD.

Main Methods:

• Utilized resting-state fMRI data from Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
• Applied Dynamic Causal Modeling (DCM) and Parametric Empirical Bayes (PEB) for EC inference.
• Correlated EC strengths with cognitive scores using linear regression.

Main Results:

• Effective connectivity significantly decreased from cognitive normal (CN) to early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer's disease (AD) groups.
• Connectivity strengths were lower in later disease stages and partially predicted cognitive scores.

Conclusions:

• Demonstrated dwindling brain connectivity as a hallmark of Alzheimer's disease progression.
• Effective connectivity shows promise as a reliable biomarker for tracking AD progression and its impact on brain networks.