A physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordings.
A physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordings.
复制标题
用于从层流电生理记录中分析振荡发生器的物理神经质量模型框架。
DOI：
10.1016/j.neuroimage.2023.119938
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发表时间：
2023
期刊：
影响因子：
5.7
通讯作者：
Ruffini,Giulio
中科院分区：
文献类型：
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作者：
Sanchez-Todo,Roser;Bastos,AndréM;Lopez-Sola,Edmundo;Mercadal,Borja;Santarnecchi,Emiliano;Miller,EarlK;Deco,Gustavo;Ruffini,Giulio
皮层功能产生于多尺度网络的相互作用，可以使用神经质量模型(NMM)在高水平上进行研究，该模型代表了大量神经元的平均活动。在这里，我们首先提供了一个新的框架，称为层流NMM，或简称LaNMM，其中我们结合了传导物理和NMM来模拟电生理测量。然后，我们利用这个框架从猕猴的前额叶皮质收集的层流分辨数据中推断出振荡发生器的位置。我们定义了一个能够产生慢和快耦合振荡的最小模型，并优化了LaNMM特定的参数以适应多接触记录。我们使用一个优化函数对候选模型进行排名，该函数评估模型的功能连接性(FC)与数据之间的匹配，其中FC由不同皮质深度的双极电压测量之间的协方差定义。最好的解决方案家族通过选择锥体细胞及其突触的位置再现了观察到的电生理学的FC，这些位置导致浅层的快速活动和大部分深度的慢活动的产生，与最近的文献建议一致。最后，我们讨论了如何更普遍地使用这种混合建模框架来推断大脑皮层回路。
Cortical function emerges from the interactions of multi-scale networks that may be studied at a high level using neural mass models (NMM) that represent the mean activity of large numbers of neurons. Here, we provide first a new framework called laminar NMM, or LaNMM for short, where we combine conduction physics with NMMs to simulate electrophysiological measurements. Then, we employ this framework to infer the location of oscillatory generators from laminar-resolved data collected from the prefrontal cortex in the macaque monkey. We define a minimal model capable of generating coupled slow and fast oscillations, and we optimize LaNMM-specific parameters to fit multi-contact recordings. We rank the candidate models using an optimization function that evaluates the match between the functional connectivity (FC) of the model and data, where FC is defined by the covariance between bipolar voltage measurements at different cortical depths. The family of best solutions reproduces the FC of the observed electrophysiology by selecting locations of pyramidal cells and their synapses that result in the generation of fast activity at superficial layers and slow activity across most depths, in line with recent literature proposals. In closing, we discuss how this hybrid modeling framework can be more generally used to infer cortical circuitry.