Differences in Aβ brain networks in Alzheimer's disease and healthy controls
Abstract The prevailing β-amyloid (Aβ)-cascade hypothesis is the most classical Alzheimer's disease (AD) pathogenesis. In this hypothesis, excessive Aβ plaque deposition in human brain is considered to be the cause of AD. Carbon 11-labeled Pittsburgh compound B Positron emission tomography (11C...
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Veröffentlicht in: | Brain research 2017-01, Vol.1655, p.77-89 |
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Sprache: | eng |
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Zusammenfassung: | Abstract The prevailing β-amyloid (Aβ)-cascade hypothesis is the most classical Alzheimer's disease (AD) pathogenesis. In this hypothesis, excessive Aβ plaque deposition in human brain is considered to be the cause of AD. Carbon 11-labeled Pittsburgh compound B Positron emission tomography (11C-PiB PET) is the latest technology to detect Aβ plaques in vivo . Thus, it is possible to investigate the difference of Aβ brain networks between AD patients and Health Controls (HC) by analyzing 11C-PiB PET images. In this study, a graph-theoretical method was employed to investigate the topological properties of Aβ networks in 18 Chinese AD patients and 16 HC subjects from Huashan Hospital, Shanghai. The results showed that both groups demonstrated small-world property, and this property was more obvious in AD group. Additionally, the clustering coefficients and path lengths were significantly lower in AD group. The global efficiency was larger in AD than in HC. A direct comparison between with and without regression found that sex, age and weight had no significant effect on the Aβ network. Moreover, three altered regions in AD group were identified, including left cuneus (CUN.L), right caudate nucleus (CAU.R) and left superior frontal gyrus (SFGdor.L). A voxel-wise correlation analysis showed that in AD patients, the regions of strengthened connection with CUN.L were mainly located in frontal cortex and parietal cortex, the regions of strengthen connection with CAU.R were mainly located in temporal cortex. Finally, a machine learning based analysis demonstrated that the three regions could be better biomarkers than the whole brain for AD classification. |
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ISSN: | 0006-8993 1872-6240 |
DOI: | 10.1016/j.brainres.2016.11.019 |