Multilevel Deficiency of White Matter Connectivity Networks in Alzheimer’s Disease: A Diffusion MRI Study with DTI and HARDI Models
Year: 2016
Type: article
Study design: Observational Study
Abstract: Alzheimer's disease (AD) is the most common form of dementia in elderly people. It is an irreversible and progressive brain disease. In this paper, we utilized diffusion-weighted imaging (DWI) to detect abnormal topological organization of white matter (WM) structural networks. We compared the differences between WM connectivity characteristics at global, regional, and local levels in 26 patients with probable AD and 16 normal control (NC) elderly subjects, using connectivity networks constructed with the diffusion tensor imaging (DTI) model and the high angular resolution diffusion imaging (HARDI) model, respectively. At the global level, we found that the WM structural networks of both AD and NC groups had a small-world topology; however, the AD group showed a significant decrease in both global and local efficiency, but an increase in clustering coefficient and the average shortest path length. We further found that the AD patients had significantly decreased nodal efficiency at the regional level, as well as weaker connections in multiple local cortical and subcortical regions, such as precuneus, temporal lobe, hippocampus, and thalamus. The HARDI model was found to be more advantageous than the DTI model, as it was more sensitive to the deficiencies in AD at all of the three levels.
Source: Neural Plasticity
DOI: 10.1155/2016/2947136
Authors: Tao Wang, Feng Shi, Yan Jin, Pew-Thian Yap, Chong-Yaw Wee, Jianye Zhang, Cece Yang, Xia Li, Shifu Xiao, Dinggang Shen
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,830
Funders: National Natural Science Foundation of China, Ministry of Science and Technology of the People's Republic of China, Science and Technology Commission of Shanghai Municipality, Shanghai Jiao Tong University, National Institutes of Health, National Institute on Aging, National Institute of Biomedical Imaging and Bioengineering
Awards: G1129848973, G118294567, G1361364689, G1416738819, G1436888976, G1480856418, G1600120695, G1610907132, G1622706024, G1686812770, G1762541377, G2094358411, G2318686993, G2417991531, G2435513456, G244047279, G2467744433, G2621044299, G2686953907, G3139536118, G3226223673, G3258782064, G3273278936, G3630199162, G3720663128, G4457244792, G4635614389, G4667471329, G4732766361, G4739438220, G4798393751, Quantifying Brain Abnormality by Multimodality Neuroimage Analysis, G5037463811, Longitudinal Mapping of Human Brain Development in the First Years of Life, G5810097220, Fast, Robust Analysis of Large Population Data, G5824648808, G5848304090, G625467705, G6397127351, G6748413423, G6822685783, G6908867551, G7223378191, Development of Robust Brain Measurement Tools Informed by Ultrahigh Field 7T MRI, G7286763381, G7311086192, G7491769530, G7576627941, G7630908750, G7709099665, G7758590858, G8380473181, G8610910102, G8939655454, G8956925711, G93323634
OpenAlex ID: W2235332512