Title:Disrupted Structural Brain Network in AD and aMCI: A Finding of Long Fiber Degeneration
Volume: 12
Issue: 6
Author(s): Rong Fang, Xiao-Xiao Yan, Zhi-Yuan Wu, Yu Sun, Qi-Hua Yin, Ying Wang, Hui-Dong Tang, Jun-Feng Sun, Fei Miao and Sheng-Di Chen
Affiliation:
Keywords:
Alzheimer’s disease (AD), amnestic mild cognitive impairment (aMCI), brain tissue, diffusion tensor imaging
(DTI), structural brain network, white matter connectivity.
Abstract: Although recent evidence has emerged that Alzheimer’s disease (AD) and amnestic mild
cognitive impairment (aMCI) patients show both regional brain abnormalities and topological degeneration
in brain networks, our understanding of the effects of white matter fiber aberrations on
brain network topology in AD and aMCI is still rudimentary. In this study, we investigated the regional volumetric aberrations
and the global topological abnormalities in AD and aMCI patients. The results showed a widely distributed
atrophy in both gray and white matters in the AD and aMCI groups. In particular, AD patients had weaker connectivity
with long fiber length than aMCI and normal control (NC) groups, as assessed by fractional anisotropy (FA). Furthermore,
the brain networks of all three groups exhibited prominent economical small-world properties. Interestingly, the
topological characteristics estimated from binary brain networks showed no significant group effect, indicating a tendency
of preserving an optimal topological architecture in AD and aMCI during degeneration. However, significantly
longer characteristic path length was observed in the FA weighted brain networks of AD and aMCI patients, suggesting
dysfunctional global integration. Moreover, the abnormality of the characteristic path length was negatively correlated
with the clinical ratings of cognitive impairment. Thus, the results therefore suggested that the topological alterations
in weighted brain networks of AD are induced by the loss of connectivity with long fiber lengths. Our findings
provide new insights into the alterations of the brain network in AD and may indicate the predictive value of the network
metrics as biomarkers of disease development.
