Multimodal Modeling of Neural Network Activity: Computing LFP, ECoG, EEG, and MEG Signals With LFPy 2.0
Multimodal Modeling of Neural Network Activity: Computing LFP, ECoG, EEG, and MEG Signals With LFPy 2.0
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DOI：
10.3389/fninf.2018.00092
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发表时间：
2018-12-18
影响因子：
3.5
通讯作者：
Einevoll, Gaute T.
中科院分区：
文献类型：
--
作者：
Hagen, Espen;Naess, Solveig;Einevoll, Gaute T.
几十年来，记录细胞外的脑电信号以及后来的脑磁信号一直是测量大脑活动的主要技术。然而，这种信号的解释是不平凡的，因为测量的信号来自本地和远程神经元活动。在体积导体理论中，细胞外电位可以从神经元的跨膜电流的贡献的距离加权和计算。给定相同的跨膜电流，也可以计算大脑内外记录的磁场贡献。这使得计算工具的发展，实现接地的生物物理学基础的电气和磁性测量方式的前向模型。LFPy（LFPy.readthedocs.io）结合了一个成熟的方案，用于预测具有任意生物细节水平的单个神经元的细胞外电位。它依赖于NEURON（neuron.yale.edu）来计算多室神经元的跨膜电流，然后将其与静电前向模型结合使用。它的功能现在被扩展到允许建模的多室神经元网络的细胞外电位和电流偶极矩的并发计算。然后，电流偶极矩与合适的体积导体头部模型相结合，用于计算神经元活动的非侵入性测量，如头皮电位（脑电图记录; EEG）和头部外的磁场（脑磁图记录; MEG）。一种这样的内置头部模型是四球头部模型，其结合了大脑、脑脊液、颅骨和头皮的不同电导率。我们证明了新的功能的软件，通过构建一个网络的生物解剖学详细的多室神经元模型从新皮层微电路合作（NMC）门户网站（bbp.epfl.ch/nmc-portal）与相应的统计连接和突触，并计算在体内样细胞外电位（局部场电位，LFP;皮层电信号，ECoG）和相应的电流偶极矩。从当前的偶极矩，我们估计相应的EEG和MEG信号使用四球头模型。我们还显示了强大的缩放性能的LFPy与不同数量的消息传递接口（MPI）进程，并为不同的网络规模与不同密度的连接。开源软件LFPy同样适合在笔记本电脑上执行，也适合在高性能计算（HPC）设施上并行执行，并且可以在GitHub.com上公开获得。
Recordings of extracellular electrical, and later also magnetic, brain signals have been the dominant technique for measuring brain activity for decades. The interpretation of such signals is however nontrivial, as the measured signals result from both local and distant neuronal activity. In volume-conductor theory the extracellular potentials can be calculated from a distance-weighted sum of contributions from transmembrane currents of neurons. Given the same transmembrane currents, the contributions to the magnetic field recorded both inside and outside the brain can also be computed. This allows for the development of computational tools implementing forward models grounded in the biophysics underlying electrical and magnetic measurement modalities. LFPy (LFPy.readthedocs.io) incorporated a well-established scheme for predicting extracellular potentials of individual neurons with arbitrary levels of biological detail. It relies on NEURON (neuron.yale.edu) to compute transmembrane currents of multicompartment neurons which is then used in combination with an electrostatic forward model. Its functionality is now extended to allow for modeling of networks of multicompartment neurons with concurrent calculations of extracellular potentials and current dipole moments. The current dipole moments are then, in combination with suitable volume-conductor head models, used to compute non-invasive measures of neuronal activity, like scalp potentials (electroencephalographic recordings; EEG) and magnetic fields outside the head (magnetoencephalographic recordings; MEG). One such built-in head model is the four-sphere head model incorporating the different electric conductivities of brain, cerebrospinal fluid, skull and scalp. We demonstrate the new functionality of the software by constructing a network of biophysically detailed multicompartment neuron models from the Neocortical Microcircuit Collaboration (NMC) Portal (bbp.epfl.ch/nmc-portal) with corresponding statistics of connections and synapses, and compute in vivo-like extracellular potentials (local field potentials, LFP; electrocorticographical signals, ECoG) and corresponding current dipole moments. From the current dipole moments we estimate corresponding EEG and MEG signals using the four-sphere head model. We also show strong scaling performance of LFPy with different numbers of message-passing interface (MPI) processes, and for different network sizes with different density of connections. The open-source software LFPy is equally suitable for execution on laptops and in parallel on high-performance computing (HPC) facilities and is publicly available on GitHub.com.