White Matter Structural Network Analysis to Differentiate Alzheimer’s Disease and Subcortical Ischemic Vascular Dementia

To explore the evaluation of white matter structural network analysis in differentiation of Alzheimer’s disease (AD) and subcortical ischemic vascular dementia (SIVD). 67 participants (31 AD patients, 19 SIVD patients and 19 normal control (NC)) were enrolled in this study. Each participant performed 3.0T MR scanning. Diffusion tensor imaging (DTI) data were analyzed by graph theory (GRETNA toolbox). Statistical analyses of global parameters (Gamma, Sigma, Lambda, global shortest path length (Lp), global efficiency (Eg) and local efficiency (Eloc)) and nodal parameters (betweenness centrality (BC)) were obtained. Network based statistical analysis (NBS) was employed to analyze the group differences of structural connections. The diagnosis efficiency of nodal BC in identifying different types of dementia was assessed by receiver operating characteristic (ROC) analysis. There were no significant differences of gender and years of education among the groups. There were no significant differences of Sigma and Gamma in AD versus NC and SIVD versus NC, whereas the Eg values of AD and SIVD were statistically decreased and Lambda values of them were increased. The BC of frontal cortex, left superior parietal gyrus and left precuneus in AD patients were reduced obviously, while the BC of prefrontal and subcortical regions were decreased in SIVD patients, compared with NC. SIVD patients were with decreased structural connections in frontal, prefrontal and subcortical regions, while AD patients decreased in temporal and occipital regions and increased in frontal and prefrontal regions. The highest area under curve (AUC) of BC was 0.946 in right putamen when AD versus SIVD. White matter structural network analysis may be a potential and promising method, and the topological changes of the network, especially the BC change in right putamen, were valuable in differentiating AD and SIVD patients.