Connectome-based predictive modeling of brain pathology and cognition in autosomal dominant Alzheimer's disease

基于连接组的常染色体显性阿尔茨海默病脑病理和认知预测模型

阅读:1

Abstract

INTRODUCTION: Autosomal dominant Alzheimer's disease (ADAD) through genetic mutations can result in near complete expression of the disease. Tracking AD pathology development in an ADAD cohort of Presenilin-1 (PSEN1) E280A carriers' mutation has allowed us to observe incipient tau tangles accumulation as early as 6 years prior to symptom onset. METHODS: Resting-state functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) scans were acquired in a group of PSEN1 carriers (n = 32) and non-carrier family members (n = 35). We applied connectome-based predictive modeling (CPM) to examine the relationship between the participant's functional connectome and their respective tau/amyloid-β levels and cognitive scores (word list recall). RESULTS: CPM models strongly predicted tau concentrations and cognitive scores within the carrier group. The connectivity patterns between the temporal cortex, default mode network, and other memory networks were the most informative of tau burden. DISCUSSION: These results indicate that resting-state functional magnetic resonance imaging (fMRI) methods can complement PET methods in early detection and monitoring of disease progression in ADAD. HIGHLIGHTS: Connectivity-based predictive modeling of tau and amyloid-β in ADAD carriers. Strong predictions for tau deposition; weaker predictions for amyloid-β. Cognitive scores for memory and mental state are predicted strongly. Connectivity between IPL, DAN, DMN, temporal cortex most predictive.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。