Computation of the electroencephalogram (EEG) from network models of point neurons.
Computation of the electroencephalogram (EEG) from network models of point neurons.
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DOI：
10.1371/journal.pcbi.1008893
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发表时间：
2021-04
影响因子：
4.3
通讯作者：
Panzeri S
中科院分区：
文献类型：
--
作者：
Martínez-Cañada P;Ness TV;Einevoll GT;Fellin T;Panzeri S
脑电(EEG)是无创性研究脑功能障碍的主要工具。将实验记录的脑电与神经网络模型进行比较，对于更好地从神经机制的角度解释脑电是很重要的。目前大多数神经网络模型使用的是简单点神经元网络。它们捕捉了大脑皮层动力学的重要特性，并且在数值或分析上都很容易处理。然而，穴位神经元不能产生脑电，因为脑电的产生需要空间上分离的跨膜电流。在这里，我们探索了如何用点神经元网络模型中定义的量来计算啮齿动物脑电的准确近似值。我们构建了不同的脑电信号近似(或替代)，可以从泄漏积分与放电(LIF)点神经元网络计算出这些信号，例如放电率、膜电位和突触电流的组合。然后，我们评估了当LIF模型网络的突触电流被馈入具有真实形态的多室神经元的三维网络模型时，每个代理重建地面真实脑电的效果。基于AMPA和GABA电流的线性组合的代理比基于放电率或膜电位的代理表现更好。一种新的基于时移AMPA和GABA电流的优化线性组合的代用品，在广泛的网络状态下提供了最准确的脑电估计。新的线性指标解释了多种网络配置下地面真实脑电变化的85%-95%，这些网络配置包括不同的细胞形态、突触前输入的分布、记录电极的位置和网络的空间扩展。在突触电流上使用卷积神经网络(CNN)的非线性脑电代理将代理性能进一步提高2-8%。我们的代理可以很容易地直接从点神经元模拟计算出生物真实的脑电信号，从而便于在计算模型和实验脑电记录之间进行定量比较。点神经元网络被广泛用于神经动力学建模。然而，他们的输出不能直接与脑电(EEG)进行比较，脑电是非侵入性测量大脑活动的最常用工具之一。为了将神经网络理论和经验脑电数据直接结合起来，我们推导了一个新的数学表达式，称为EEG代理，它仅基于点-神经元网络模型的模拟得到的变量就可以高精度地估计EEG。为了比较和验证这些脑电指标，我们计算了一个真实的地面真实脑电，该脑电是由具有真实3D形态的模拟神经元网络产生的，这些神经元网络接收到与更简单的点状神经元网络相同的突触输入。新获得的脑电指标优于以前的方法，并且在不同的细胞形态、突触前输入的分布、记录电极的位置和网络的空间扩展的广泛网络配置下工作良好。新的代用品很好地逼近了脑电频谱和脑电诱发电位。我们的工作提供了重要的数学工具，可以根据大脑功能的神经模型更好地解释实验测量的脑电。
The electroencephalogram (EEG) is a major tool for non-invasively studying brain function and dysfunction. Comparing experimentally recorded EEGs with neural network models is important to better interpret EEGs in terms of neural mechanisms. Most current neural network models use networks of simple point neurons. They capture important properties of cortical dynamics, and are numerically or analytically tractable. However, point neurons cannot generate an EEG, as EEG generation requires spatially separated transmembrane currents. Here, we explored how to compute an accurate approximation of a rodent’s EEG with quantities defined in point-neuron network models. We constructed different approximations (or proxies) of the EEG signal that can be computed from networks of leaky integrate-and-fire (LIF) point neurons, such as firing rates, membrane potentials, and combinations of synaptic currents. We then evaluated how well each proxy reconstructed a ground-truth EEG obtained when the synaptic currents of the LIF model network were fed into a three-dimensional network model of multicompartmental neurons with realistic morphologies. Proxies based on linear combinations of AMPA and GABA currents performed better than proxies based on firing rates or membrane potentials. A new class of proxies, based on an optimized linear combination of time-shifted AMPA and GABA currents, provided the most accurate estimate of the EEG over a wide range of network states. The new linear proxies explained 85–95% of the variance of the ground-truth EEG for a wide range of network configurations including different cell morphologies, distributions of presynaptic inputs, positions of the recording electrode, and spatial extensions of the network. Non-linear EEG proxies using a convolutional neural network (CNN) on synaptic currents increased proxy performance by a further 2–8%. Our proxies can be used to easily calculate a biologically realistic EEG signal directly from point-neuron simulations thus facilitating a quantitative comparison between computational models and experimental EEG recordings. Networks of point neurons are widely used to model neural dynamics. Their output, however, cannot be directly compared to the electroencephalogram (EEG), which is one of the most used tools to non-invasively measure brain activity. To allow a direct integration between neural network theory and empirical EEG data, here we derived a new mathematical expression, termed EEG proxy, which estimates with high accuracy the EEG based simply on the variables available from simulations of point-neuron network models. To compare and validate these EEG proxies, we computed a realistic ground-truth EEG produced by a network of simulated neurons with realistic 3D morphologies that receive the same synaptic input of the simpler network of point neurons. The new obtained EEG proxies outperformed previous approaches and worked well under a wide range of network configurations with different cell morphologies, distribution of presynaptic inputs, position of the recording electrode and spatial extension of the network. The new proxies approximated well both EEG spectra and EEG evoked potentials. Our work provides important mathematical tools that allow a better interpretation of experimentally measured EEGs in terms of neural models of brain function.
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影响因子：
13.9
作者：
Buzsáki G;Wang XJ
通讯作者：
Wang XJ
细胞外田地和电流的起源-EEG，ECOG，LFP和尖峰。
DOI：
10.1038/nrn3241
发表时间：
2012-05-18
期刊：
Nature reviews. Neuroscience
影响因子：
--
作者：
Buzsáki G;Anastassiou CA;Koch C
通讯作者：
Koch C
影响因子：
2.5
作者：
Compte, A;Sanchez-Vives, MV;Wang, XJ
通讯作者：
Wang, XJ
影响因子：
4.3
作者：
Buehlmann, Andres;Deco, Gustavo
通讯作者：
Deco, Gustavo
影响因子：
4.3
作者：
Deco, Gustavo;Jirsa, Viktor K.;Robinson, Peter A.;Breakspear, Michael;Friston, Karl J.
通讯作者：
Friston, Karl J.