疾病
正电子发射断层摄影术
痴呆
医学
心理学
阿尔茨海默病
病理
神经影像学
病态的
神经科学
作者
A.C. Macedo,D.F.P.A. Durço,Cécile Tissot,J. Therriault,Andréia V. Faria,Étienne Aumont,Stijn Servaes,Nesrine Rahmouni,Jaime Fernández Arias,Yu Tian Wang,Firoza Z Lussier,A. Bieger,Eduardo R. Zimmer,Tharick A. Pascoal,Serge Gauthier,Pedro Rosa‐Neto
出处
期刊:JPAD
[SERDI]
日期:2024-01-01
被引量:3
标识
DOI:10.14283/jpad.2024.15
摘要
In vivo Alzheimer's disease diagnosis and staging is traditionally based on clinical features. However, the agreement between clinical and pathological Alzheimer's disease diagnosis, whose diagnosis assessment includes amyloid and Braak histopathological tau staging, is not completely convergent. The development of positron emission tomography (PET) tracers targeting neurofibrillary tangles offers prospects for advancing the staging of Alzheimer's disease from both biological and clinical perspectives. Recent advances in radiochemistry made it possible to apply the postmortem Braak staging framework to tau-PET images obtained in vivo. Here, our aim is to provide a narrative review of the current literature on the relationship between Alzheimer's disease clinical features and the PET-based Braak staging framework. Overall, the available studies support the stepwise increase in disease severity following the advance of PET-based Braak stages, with later stages being associated with worse cognitive and clinical symptoms. In line with this, there is a trend for unimpaired cognition, mild cognitive impairment, and Alzheimer's disease dementia to be compatible with early, intermediate, and late patterns of tau deposition based on PET-based Braak stages. Moreover, neuropsychiatric symptom severity seems to be linked to the extent of tau-PET signal across Braak areas. In sum, this framework seems to correspond well with the clinical progression of Alzheimer's disease, which is an indication of its potential utility in research and clinical practice, especially for detecting preclinical tau levels in individuals without symptoms. However, further research is needed to improve the generalizability of these findings and to better understand the applications of this staging framework.
科研通智能强力驱动
Strongly Powered by AbleSci AI