Evaluation of Diffusion Tensor White Matter Network of Alzheimer’s Disease

Alzheimer's disease (AD) would produce complicated changes of diffusion tensor white matter (WM) networks and its importance for AD progress indicators is still worth discussing. In our study, a cohort of 15 cases of subjects (5 AD, 5 mild cognitive impairment (MCI), and 5 normal controls (NC)) were chosen and the network characteristics of weighted characteristic path length (L_p), weighted clustering coefficient (C_p), global efficiency (E_g), local efficiency (E_l) and small-word (σ) were measured to observe the WM changes of AD. The statistical analysis of one-way analysis of variance (ANOVA) was used to compare the three groups via global and local analyses. The results showed that the global network characteristics of L_p and C_p gradually increased, while E_g and E_l gradually decreased for NC, MCI and AD. There were significant differences for bilateral central anterior gyrus and right superior temporal gyrus for local analyses with the same trend with global analyses. The proposed WM network characteristics could be used as imaging indicators for AD prediction.