Structural Interactions within the Default Mode Network Identified by Bayesian Network Analysis in Alzheimer’s Disease

Abstract

Alzheimer’s disease (AD) is a well-known neurodegenerative disease that is associated with dramatic morphological abnormalities. The default mode network (DMN) is one of the most frequently studied resting-state networks. However, less is known about specific structural dependency or interactions among brain regions within the DMN in AD. In this study, we performed a Bayesian network (BN) analysis based on regional grey matter volumes to identify differences in structural interactions among core DMN regions in structural MRI data from 80 AD patients and 101 normal controls (NC). Compared to NC, the structural interactions between the medial prefrontal cortex (mPFC) and other brain regions, including the left inferior parietal cortex (IPC), the left inferior temporal cortex (ITC) and the right hippocampus (HP), were significantly reduced in the AD group. In addition, the AD group showed prominent increases in structural interactions from the left ITC to the left HP, the left HP to the right ITC, the right HP to the right ITC, and the right IPC to the posterior cingulate cortex (PCC). The BN models significantly distinguished AD patients from NC with 87.12% specificity and 81.25% sensitivity. We then used the derived BN models to examine the replicability and stability of AD-associated BN models in an independent dataset and the results indicated discriminability with 83.64% specificity and 80.49% sensitivity. The results revealed that the BN analysis was effective for characterising regional structure interactions and the AD-related BN models could be considered as valid and predictive structural brain biomarker models for AD. Therefore, our study can assist in further understanding the pathological mechanism of AD, based on the view of the structural network, and may provide new insights into classification and clinical application in the study of AD in the future.

The Pathoconnectivity Profile of Alzheimer’s Disease: A Morphometric Coalteration Network Analysis

“…A second interesting aspect is the relationship between the left hippocampus and right inferior temporal gyrus, which was highlighted by k -core decomposition. This result is in agreement with the study of Wang et al ( 132 ), which found that the interaction between these two areas is typical of AD. Of note, Wang et al ( 132 ) examined 80 pathological subjects using Bayesian network analysis and prior-defined regions of interest, while the present study has applied a meta-analytical approach on a substantially bigger VBM database of 883 patients diagnosed with AD.…”

Bayesian Graphical Network Analyses Reveal Complex Biological Interactions Specific to Alzheimer's Disease

“…Similarly, Wang and colleagues recently used Bayesian network classifiers to integrate data from multiple platforms to identify biomarkers confirming previously published results [31]. Previous research using Bayesian networks to define marker connections in AD has primarily been performed using imaging data [28, 29], however these methods have not defined the posterior probability of both within and between group connections.…”

Voxel-based meta-analysis of grey matter changes in Alzheimer’s disease

“…In addition, Jacobs et al demonstrated that grey matter atrophy in inferior parietal lobule was connected to the prefrontal cortices [ 46 ]. Recently, Wang and colleagues employing Bayesian network models and integrating grey matter volume information from multiple brain regions found increased correlations from Left inferior temporal cortex to Left hippocampus, Left hippocampus to Right inferior temporal cortex, Right hippocampus to Right inferior temporal cortex, and Right inferior parietal cortex to Posterior cingulate cortex in AD patients [ 47 ]. The morphological changes in the grey matter in different brain regions abide by covariance pattern, reflecting the DMN network attributes of the human brain, and also suggest that the atrophy of these structures is not independent, but that primary neurodegeneration in one of the structures that could lead to secondary degeneration of regions connected to it.…”