Computational analysis of genesis of ERP and ERF

ERP 和 ERF 起源的计算分析

• 批准号: 6992719
• 负责人: SHINGO MURAKAMI
• 金额: $ 3.66万
• 依托单位: UNIVERSITY OF NEW MEXICO
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-12-10 至 2006-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6992719
• 关键词: cerebellum; computational neuroscience; dendrites; dipole moment; electroencephalography; evoked potentials; magnetic field; magnetoencephalography; mathematical model; model design /development; neocortex; neurons; pyramidal cells; single cell analysis; thalamocortical tract

DESCRIPTION (provided by applicant): We propose to develop mathematical network models of the mammalian neocortex in order to elucidate the genesis of event-related magnetic fields (ERFs) and electrical potentials (ERPs). We have previously provided a fundamental account of the genesis of ERF and ERP for the guinea pig hippocampus using a mathematical model based on RD Traub's 1991 model. We are proposing to extend this work to the neocortex in order to eventually help interpret magnetoencephalography (MEG) and electroencephalography (EEG) signals from the human brain. In this R03 application, we will limit our project to four specific aims that address a set of initial problems in developing mathematical models of the neocortex for interpretation of MEG and EEG. Aim1: We will first develop single-cell compartment models of the principal neurons of the neocortex based on Mainen's model. Our models will consist of the pyramidal cells in layers II/III and layer V, the spiny stellate cells in layer IIV and the inhibitory neurons (aspiny stellate cells) in layer III, based on real anatomical data and updated channel kinetics and distributions. The transmembrane potentials and cell properties (e.g. IV relation) will be 'computed and compared with published experimental data to validate the models. Aim 2: We will compute the intracellular current distribution in each cell type to infer the contributions of cells to MEG and EEG signal. The net current dipole moment in each cell will be computed at distances far from the cells in order to estimate the MEG and EEG signals per single neurons. We expect that the MEG and EEG signal will be primarily due to the pyramidal cells as conventionally assumed. Our new contribution will consist of quantitative estimates of the current dipole moments for realistically shaped cell models. Aim 3: We will determine the contributions of the distal and proximal apical dendrites, the perisomal region and basal dendrites to the ERF and ERP since they can be readily assessed with compartment models. This will be done for pyramidal cells with repetitive firing and bursting mode of firing. We predict that the currents in the perisomal region will dominate MEG and EEG signals for cells with repetitive firing and the currents in the distal as well as proximal trunk area of the apical dendrites to dominate for bursting cells, while the basal dendrites will have relatively small contributions due to their geometry. Aim 4: Once we develop and test single cell models in year 1, we will connect all cell types in a network, compute the ERF and ERP that would be produced by thalamocortical inputs into layer IV, and compare them with experimental waveforms.

描述（由申请人提供）：我们建议开发哺乳动物新皮层的数学网络模型，以阐明事件相关磁场（ERF）和电位（ERP）的起源。我们以前提供了一个基本帐户的起源ERF和ERP的豚鼠海马使用的数学模型的基础上RD特劳布的1991年模型。我们建议将这项工作扩展到新皮层，以便最终帮助解释来自人类大脑的脑磁图（MEG）和脑电图（EEG）信号。在这个R 03应用程序中，我们将把我们的项目限制在四个具体的目标，解决了一系列的初始问题，在开发数学模型的新皮层的MEG和EEG的解释。目标1：我们将首先开发基于Mainen模型的新皮层主要神经元的单细胞隔室模型。基于真实的解剖学数据和更新的通道动力学和分布，我们的模型将包括第II/III层和第V层的锥体细胞、第IIV层的棘状星状细胞和第III层的抑制性神经元（棘状星状细胞）。跨膜电位和细胞特性（例如IV关系）将被计算并与已发表的实验数据进行比较，以验证模型。目的二：计算每种细胞类型的细胞内电流分布，以推断细胞对脑磁信号和脑电信号的贡献。将在远离细胞的距离处计算每个细胞中的净电流偶极矩，以便估计每个单个神经元的MEG和EEG信号。我们预计，脑磁图和脑电图信号将主要是由于锥体细胞，如传统的假设。我们的新贡献将包括现实形状的细胞模型的电流偶极矩的定量估计。目标三：我们将确定的远端和近端的顶端树突，perisomal区域和基底树突的ERF和ERP的贡献，因为它们可以很容易地评估与房室模型。这将对具有重复放电和突发放电模式的锥体细胞进行。我们预测，在perisomal区域的电流将占主导地位的MEG和EEG信号的细胞与重复发射和电流的远端以及近端的主干区域的顶端树突占主导地位的爆裂细胞，而基底树突将有相对较小的贡献，由于其几何形状。目标4：一旦我们在第一年开发和测试单细胞模型，我们将连接网络中的所有细胞类型，计算丘脑皮层输入到第四层产生的ERF和ERP，并将它们与实验波形进行比较。