Distinct profiles of functional connectivity density aberrance in Alzheimer's disease and mild cognitive impairment

Author:

Miao Dawei,Zhou Xiaoguang,Wu Xiaoyuan,Chen Chengdong,Tian Le

Abstract

IntroductionInvestigating the neuroimaging changes from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is of great significance. However, the details about the distinct functional characteristics of AD and MCI remain unknown.MethodsIn this study, we investigated distinct profiles of functional connectivity density (FCD) differences between AD and MCI compared with the normal population, aiming to depict the progressive brain changes from MCI to AD. As a data-driven method, FCD measures the profiles of FC for the given voxel at different scales. Resting-state functional magnetic resonance imaging (fMRI) images were obtained from patients with AD and MCI and matched healthy controls (HCs). One-way ANCOVA was used to investigate (global, long-range, and local) FCD differences among the three groups followed by post-hoc analysis controlling age, sex, and head motion.ResultsThe three groups exhibited significant global FCD differences in the superior frontal gyrus. The post-hoc results further showed that patients with AD had a significant increase in global FCD values than those with MCI and HCs. Patients with MCI exhibited an increased trend compared with HCs. We further identified brain regions contributing to the observed global FCD differences by conducting seed-based FC analysis. We also identified that the observed global FCD differences were the additive effects of altered FC between the superior frontal gyrus and the posterior default model network.DiscussionThese results depicted the global information communication capability impairment in AD and MCI providing a new insight into the progressive brain changes from MCI to AD.

Publisher

Frontiers Media SA

Subject

Psychiatry and Mental health

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Survey of Deep Learning for Alzheimer’s Disease;Machine Learning and Knowledge Extraction;2023-06-09

2. Functional networks in prolonged disorders of consciousness;Frontiers in Neuroscience;2023-02-17

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