IDENTIFYING CONFIGURATIONAL ABNORMALITIES IN ALZHEIMER'S DISEASE PROGRESSION USING MULTI-VIEW STRUCTURE CONNECTOME

被引：0

作者：

Guo, Lei ^{[1
]}

Tang, Haoteng ^{[1
]}

Wang, Qi ^{[2
]}

Dennis, Emily ^{[3
]}

Zhu, Dajiang ^{[4
]}

Huang, Heng ^{[1
]}

Ajilore, Olusola ^{[5
]}

Leow, Alex D. ^{[5
,6
]}

Zhan, Liang ^{[1
]}

机构：

[1] Univ Pittsburgh, Dept Elect & Comp Engn, Pittsburgh, PA 15260 USA

[2] Michigan State Univ, Comp Sci & Engn, E Lansing, MI 48824 USA

[3] Harvard Med Sch, Brigham & Womens Hosp, Psychiat Neuroimaging Lab, Boston, MA 02115 USA

[4] Univ Texas Arlington, Dept Comp & Engn, Arlington, TX 76019 USA

[5] Univ Illinois, Dept Psychiat, Chicago, IL 60612 USA

[6] Univ Illinois, Dept Bioengn, Chicago, IL USA

来源：

2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019) | 2019年

关键词：

modular configuration; Alzheimer's disease; brain; structural connectome; diffusion MRI; TRACTOGRAPHY; BRAIN; CONNECTIVITY; NETWORKS; TRACKING;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Alzheimer's disease (AD) is the most common cause of dementia and while scientists know that AD involves progressive neuronal cell loss, the reason why this occurs is still not known. As AD exerts a systems-level impact on the brain, therefore the brain structural connectome, derived from whole-brain tractography using diffusion-weighted MRI, has the potential to study the systems-level changes associated with the AD progression. Traditionally, structural connectome is reconstructed based on one single tractography algorithm and commonly involves the comparison of summary graph-theoretical metrics, which could be biased and also discard important informative graph structure. In this paper, we proposed to study the AD effect on brain structural connectome using a multi-view approach. Our results supported multi-view structural connectomics improved power in detecting early changes associated with AD disease progression.

引用

页码：169 / 172

页数：4

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