Altered default mode network connectivity in Alzheimer's disease--a resting functional MRI and Bayesian network study

Area of Science:

• Neuroscience
• Medical Imaging
• Computational Biology

Background:

• Functional magnetic resonance imaging (fMRI) studies have identified the default mode network (DMN).
• Disruptions in DMN connectivity are associated with neurodegenerative diseases like Alzheimer's disease (AD).
• Previous research indicates altered functional connectivity in the DMN of AD patients.

Purpose of the Study:

• To confirm DMN differences between AD patients and normal controls (NC) using Independent Component Analysis (ICA).
• To investigate and compare the effective connectivity of the DMN between AD patients and NC using Bayesian Networks (BN).
• To identify potential imaging biomarkers for AD based on altered DMN effective connectivity.

Main Methods:

• Independent Component Analysis (ICA) was used to analyze resting-state fMRI data.
• Bayesian Network (BN) modeling was applied to assess effective connectivity within the DMN.
• Nonparametric random permutation testing was employed to compare BN models between AD and NC groups.

Main Results:

• Decreased DMN functional connectivity was observed in AD patients within regions including the posterior cingulate cortex (PCC), medial prefrontal cortex (MPFC), inferior parietal cortex (IPC), inferior temporal cortex (ITC), and hippocampus (HC).
• Significant alterations in DMN effective connectivity were found in AD, including lost connections and reversed connection directions.
• The right hippocampus showed a statistically indistinguishable connection to other regions (except the left HC), suggesting increased pathological and functional burden in AD.

Conclusions:

• Altered DMN effective connectivity patterns are characteristic of Alzheimer's disease.
• The identified changes in DMN connectivity may serve as potential imaging biomarkers for AD.
• Bayesian network analysis provides novel insights into the disrupted network dynamics in AD.