Background:
Alzheimer's Disease (AD) is a progressive neurodegenerative disease with insidious
onset, which is difficult to be reversed and cured. Therefore, discovering more precise biological
information from neuroimaging biomarkers is crucial for accurate and automatic detection of AD.
Methods:
We innovatively used a Visibility Graph (VG) to construct the time-dependent brain networks
as well as functional connectivity network to investigate the underlying dynamics of AD brain based on
functional magnetic resonance imaging. There were 32 AD patients and 29 Normal Controls (NCs) from
the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. First, the VG method mapped the
time series of single brain region into networks. By extracting topological properties of the networks, the
most significant features were selected as discriminant features into a supporting vector machine for
classification. Furthermore, in order to detect abnormalities of these brain regions in the whole AD
brain, functional connectivity among different brain regions was calculated based on the correlation of
regional degree sequences.
Results:
According to the topology abnormalities exploration of local complex networks, we found several
abnormal brain regions, including left insular, right posterior cingulate gyrus and other cortical regions.
The accuracy of characteristics of the brain regions extracted from local complex networks was
88.52%. Association analysis demonstrated that the left inferior opercular part of frontal gyrus, right
middle occipital gyrus, right superior parietal gyrus and right precuneus played a tremendous role in
AD.
Conclusion:
These results would be helpful in revealing the underlying pathological mechanism of the
disease.
Complex networks of functional connectivity in a wetland reconnected to its floodplain
