一类连接分子尺度与脑尺度的平均场模型
A class of mean-field models to bridge molecular to brain scales
AI总结：
该研究综述一类可整合生物物理细节的平均场模型，用于连接分子与脑尺度，以麻醉为例说明其能评估微观变化对脑活动的影响，还可推广至脑疾病及药物研究，连接不同神经科学领域。
AI中文摘要：
预测分子变化如何影响大规模脑活动是一项困难任务，因为缺乏合适的方法来连接不同尺度。在本文中，我们综述了一类平均场模型，这类模型可整合突触受体、膜离子通道等生物物理细节，形成多尺度建模方法，用于评估微观变化对宏观脑活动的影响。本文以麻醉为例说明该方法：特定突触受体水平的变化可导致脑活动的全局改变及与外部输入的脱节，这仅能通过包含足够微观生物物理特性细节的平均场模型实现。这种基于生物物理的平均场方法可推广用于研究脑疾病的细胞或分子起源，或更好理解在微观尺度起作用的药物如何影响全局脑活动。此外，生物物理平均场模型还连接了从分子研究到脑成像的不同神经科学领域。
英文摘要：
Predicting how molecular changes affect large-scale brain activity is a difficult task because of the lack of appropriate methods to link scales. In this perspective, we review a class of mean-field models that can integrate biophysical details such as synaptic receptors or membrane ion channels. This leads to a multi-scale modeling approach that can be used to evaluate how microscopic changes can impact macroscopic brain activity. This approach is illustrated here for the case of anesthesia, where changes at the level of specific synaptic receptors can lead to a global change in brain activity and a disconnection from external inputs. This is only possible using mean-field models that can include enough detail about the microscopic biophysical properties. This biophysically-based mean-field approach could be generalized to study cellular or molecular origins of brain diseases, or to better understand how drugs acting at microscopic scales can influence global brain activity. Biophysical mean-field models also link different fields of neuroscience, from molecular studies to brain imaging.
