多参数频谱建模从无创电生理记录中分离突触动力学与神经兴奋性
Multi-parametric spectral modeling disentangles synaptic kinetics and neural excitability from non-invasive electrophysiological recordings
一项发表于 bioRxiv 的预印本提出一种基于生物机制的多参数频谱分析方法，试图从无创电生理记录中同时分离神经兴奋性与突触动力学，而非依赖单一频谱指标。作者以阿尔茨海默病为概念验证，报告该方法能捕捉到过度兴奋与谷氨酸再摄取功能障碍的特征。该研究尚未完成同行评审，结论基于摘要层面的概念验证，样本与验证细节未在材料中报告，能否用于临床评估仍需进一步研究。
Spectral modeling of electrophysiological recordings enables non-invasive characterization of neural excitability. However, current approaches rely on a single spectral index, limiting insight into the multiple biophysical processes underlying excitation-inhibition balance. Here, we introduce a biologically informed, multi-parametric spectral analysis that disentangles neural excitability and synaptic kinetics from recordings. As a proof of concept, we demonstrate that this approach captures signatures of hyperexcitability and glutamate reuptake dysfunction characteristic of Alzheimer's disease.
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来源：bioRxiv · Neuroscience · biorxiv.org