肌萎缩侧索硬化
医学
海马体
额叶
正电子发射断层摄影术
神经科学
额内侧回
颞叶
额上回
扣带回前部
脑回
海马硬化
内科学
磁共振成像
核医学
放射科
癫痫
认知
心理学
精神科
疾病
作者
Yongxiang Tang,Pan Liu,Wanzhen Li,Zhen Liu,Ming Zhou,Jian Li,Yunchang Yuan,Liangjuan Fang,Mengli Wang,Lu Shen,Yiyun Huang,Beisha Tang,Junling Wang,Shuo Hu
摘要
Background and purpose Synaptic loss is well established as the major correlate of characteristic and consistent pathology in amyotrophic lateral sclerosis (ALS). We aimed to assess the possible discriminant diagnostic value of 18F-SynVesT-1 positron emission tomography (PET) as a marker of ALS pathology and investigate whether specific synaptic density signatures are present in ALS with different subtypes. Methods Twenty-one patients with ALS and 25 healthy controls (HCs) were recruited. All participants underwent 18F-SynVesT-1-PET. Synaptic density between ALS and HCs and between different ALS subgroups were compared. Correlation between synaptic density and clinical features in ALS was also analyzed. Results Low uptake distribution was found in the group comprising 21 ALS patients as compared with HCs in the right temporal lobe and the bilateral inferior frontal gyrus, anterior cingulate, and hippocampus–insula region. We also found low uptake in the bilateral superior temporal gyrus, hippocampus–insula, anterior cingulate, and left inferior frontal gyrus in ALS patients with cognitive impairment compared to HCs. Furthermore, compared to spinal onset ALS, bulbar onset ALS showed low uptake in the bilateral cingulate gyrus and high uptake in the bilateral superior temporal gyrus and left occipital lobe. No significant result was found in correlation analysis. Conclusions This approach may provide a direct measure of synaptic density, and it therefore might represent a potentially useful biomarker for ALS diagnosis, as well as for estimating the cognitive decline and site of onset in ALS. 18F-SynVesT-1-PET is presently not justified as a routine investigation to detect evidence of brain dysfunction leading to progression in ALS.
科研通智能强力驱动
Strongly Powered by AbleSci AI