Structural interactions within the default mode network identified by Bayesian network analysis in Alzheimer's...

Area of Science:

• Neuroimaging
• Neuroscience
• Medical Imaging

Background:

• Alzheimer's disease (AD) is a neurodegenerative disorder characterized by significant brain abnormalities.
• The default mode network (DMN) is crucial for brain function, but its structural interactions in AD are not well understood.

Purpose of the Study:

• To investigate structural interactions among core default mode network regions in Alzheimer's disease using Bayesian network analysis.
• To identify potential structural brain biomarkers for AD classification and understanding its pathological mechanisms.

Main Methods:

• Bayesian network (BN) analysis was applied to structural MRI data from 80 AD patients and 101 normal controls (NC).
• Regional grey matter volumes were used to model structural interactions within the DMN.
• The derived BN models were validated on an independent dataset for replicability and stability.

Main Results:

• AD patients exhibited significantly reduced structural interactions between the medial prefrontal cortex (mPFC) and regions like the inferior parietal cortex (IPC), inferior temporal cortex (ITC), and hippocampus (HP).
• Increased structural interactions were observed in AD from the left ITC to the left HP, and between the hippocampus and ITC.
• The BN models achieved high accuracy in distinguishing AD patients from NC (87.12% specificity, 81.25% sensitivity) and showed similar performance in the independent dataset.

Conclusions:

• Bayesian network analysis effectively characterizes regional structural interactions in the brain.
• AD-associated BN models serve as valid and predictive structural brain biomarker candidates.
• This approach provides new insights into AD's pathological mechanisms and potential clinical applications.