Abstract
To explore the evaluation of white matter structural network analysis in the 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 underwent 3.0T MRI 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 vs. NC and SIVD vs. NC, whereas the Eg values of AD and SIVD were statistically decreased, and the lambda values were increased. The BC of the frontal cortex, left superior parietal gyrus, and left precuneus in AD patients were obviously reduced, while the BC of the prefrontal and subcortical regions were decreased in SIVD patients, compared with NC. SIVD patients had decreased structural connections in the frontal, prefrontal, and subcortical regions, while AD patients had decreased structural connections in the temporal and occipital regions and increased structural connections in the frontal and prefrontal regions. The highest area under curve (AUC) of BC was 0.946 in the right putamen for AD vs. 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 the right putamen, were valuable in differentiating AD and SIVD patients.
Highlights
According to a report from the World Health Organization in 2017, almost 50 million people have been diagnosed with dementia, and the number is said to increase to 82 million by 2030 (The Lancet Neurology, 2018)
We aim to explore brain structural network alterations in Alzheimer’s disease (AD) and subcortical ischemic vascular dementia (SIVD) patients, and to explore the value of brain structural network analysis in the differentiation of AD and SIVD
Episodic memory function was assessed by the auditory verbal learning test Huashan version (AVLT) including the auditory verbal learning test immediate recall (AVLT-IR) and the auditory verbal learning test delayed recall (AVLT-DR; Zhao et al, 2012)
Summary
According to a report from the World Health Organization in 2017, almost 50 million people have been diagnosed with dementia, and the number is said to increase to 82 million by 2030 (The Lancet Neurology, 2018). Dementia creates a heavy financial burden on society. Alzheimer’s disease (AD) is a progressive and degenerative disease resulting in cognitive impairment and behavior dysfunction. Vascular dementia (VaD) due to various vascular pathologies is the second most common cause of dementia after AD (Kang et al, 2016). AD and VaD accounts for approximately 60% and 20% of dementia, respectively (Rizzi et al, 2014). Subcortical ischemic vascular dementia (SIVD) accounts for a large part of VaD (Benjamin et al, 2016). SIVD has been focused on due to its high prevalence
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