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J Alzheimers Dis. 2015;44(3):963-75. doi: 10.3233/JAD-141623.

Effects of amyloid and small vessel disease on white matter network disruption.

Author information

1
Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
2
Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
3
Department of Biomedical Engineering, Hanyang University, Seoul, Korea.
4
Department of Neurology, Yonser University College of Medicine, Seoul, Korea.
5
Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
6
Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
7
Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
8
Department of Nuclear Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
9
Department of Neurology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.

Abstract

There is growing evidence that the human brain is a large scale complex network. The structural network is reported to be disrupted in cognitively impaired patients. However, there have been few studies evaluating the effects of amyloid and small vessel disease (SVD) markers, the common causes of cognitive impairment, on structural networks. Thus, we evaluated the association between amyloid and SVD burdens and structural networks using diffusion tensor imaging (DTI). Furthermore, we determined if network parameters predict cognitive impairments. Graph theoretical analysis was applied to DTI data from 232 cognitively impaired patients with varying degrees of amyloid and SVD burdens. All patients underwent Pittsburgh compound-B (PiB) PET to detect amyloid burden, MRI to detect markers of SVD, including the volume of white matter hyperintensities and the number of lacunes, and detailed neuropsychological testing. The whole-brain network was assessed by network parameters of integration (shortest path length, global efficiency) and segregation (clustering coefficient, transitivity, modularity). PiB retention ratio was not associated with any white matter network parameters. Greater white matter hyperintensity volumes or lacunae numbers were significantly associated with decreased network integration (increased shortest path length, decreased global efficiency) and increased network segregation (increased clustering coefficient, increased transitivity, increased modularity). Decreased network integration or increased network segregation were associated with poor performances in attention, language, visuospatial, memory, and frontal-executive functions. Our results suggest that SVD alters white matter network integration and segregation, which further predicts cognitive dysfunction.

KEYWORDS:

Amyloid; diffusion tensor imaging; graph theory; small vessel disease; white matter network

PMID:
25374100
DOI:
10.3233/JAD-141623
[Indexed for MEDLINE]

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