Abstract #1458

Resting-State Brain Networks Dysfunction Associated with Beta-Amyloid Burden in Alzheimer's Disease: A Multimodal Neuroimaging Study

Wanwan Guo^1,2,3,4, Hongda Shao⁵, Yan Zhang⁵, Hairong Zheng⁴, Dong Liang⁴, Jianjun Liu⁵, Lingyan Zhang^1,2,3, and Zhanli Hu^4,6

¹Lab of Molecular Imaging and Medical Intelligence, Department of Radiology, Longgang Central Hospital of Shenzhen, Shenzhen, China, ²Shenzhen Clinical Medical College, Guangzhou University of Chinese Medicine, Shenzhen, China, ³Longgang Clinical Institute of Shantou University Medical College, Shenzhen, China, ⁴Research Center for Medical AI, Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, ⁵Department of Nuclear Medicine, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, ⁶The Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, Shenzhen, China

Synopsis

Keywords: Functional Connectivity, Alzheimer's Disease, functional connectivity, network analysis, resting-state fMRI, beta-amyloid PET

Motivation: Although beta-amyloid (Aβ) deposition is a hallmark pathological trait of Alzheimer’s disease (AD), simply knowing levels of Aβ does not sufficiently predict progression of AD. As an additional biomarker, functional brain networks can help better predict risk for future brain function decline in the context of elevated Aβ burden.

Goal(s): Delve into the Associations between functional brain networks and Aβ burden in AD patients.

Approach: In this study, we used resting-state functional MRI and amyloid PET to investigate the abnormalities of functional brain networks and their associations with Aβ pathology in AD patients.

Results: Anticipate results concerning network disruptions associated with Aβ burden.

Impact: This study investigated the associations between functional brain networks and beta-amyloid in AD, furthering our comprehension of this brain disorder. Moreover, the results proposed that functional connnectivity and functional brain networks had the potential to detect early changes of AD.

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