在显微镜下逼真的大脑中建立电磁模型--对大脑刺激的影响。

Zhen Qi, Gregory M Noetscher, Alton Miles, Konstantin Weise, Thomas R Knösche, Cameron R Cadman, Alina R Potashinsky, Kelu Liu, William A Wartman, Guillermo Nunez Ponasso, Marom Bikson, Hanbing Lu, Zhi-De Deng, Aapo R Nummenmaa, Sergey N Makaroff
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引用次数: 0

摘要

在所有电刺激(神经调控)领域,细胞极化的传统分析包括两个不连续的步骤:i)预测宏观电场,忽略细胞的存在;ii)根据组织电场预测细胞极化。第一步假定电流流不会被密集曲折的细胞结构网络扭曲。这一假设的缺陷早已被认识到,但除了琐碎的几何结构外,由于它带来了难以解决的计算障碍而被忽视。我们利用:i) 最新的大脑电子显微镜图像,使在相对较大的体积上重建微观大脑网络成为可能;ii) 基于电荷的边界元快速多极法(BEM-FMM)公式,首次通过电刺激对现实的神经元极化进行多尺度刺激,其中考虑了微观结构对电流的扭曲。研究的数据集是小鼠 L2/L3 视觉皮层 250×140×90 μm 的切片,其中有 396 个紧密间隔的神经细胞和 34 个微毛细血管。我们对大脑微结构如何显著扭曲初级宏观电场进行了量化。尽管这种扭曲是非常局部的,但它会沿着神经元轴建设性地累积,与传统理论相比,可将神经元激活阈值降低 0.55-0.85 倍:经过后处理的细胞 CAD 模型(383 个)、微毛细血管 CAD 模型(34 个)、经过后处理的神经元形态(267 个)、不同极化时的细胞外场和电位分布(267×3)、*.用 Neuron 软件进行生物物理建模的 ses 项目文件(267×2),以及在不同条件下计算的神经元激活阈值(267×8):本研究介绍了一种新方法,用于在显微镜下逼真的脑体积(包括密集的神经元细胞和血液微毛细血管)内对印象电场扰动进行建模。它解决了数十年来用于电刺激的宏观级电磁模型的局限性。对于所研究的脑容量,与宏观方法相比,我们的模型预测神经激活阈值降低系数为 0.85-0.55。本研究开始弥合我们在生物电分析中长期认识到的差距,并为评估(和补偿)脑刺激和电生理学中宏观模型的适当性提供了一个框架。
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Enabling Electric Field Model of Microscopically Realistic Brain.

Background: Modeling brain stimulation at the microscopic scale may reveal new paradigms for a variety of stimulation modalities.

Objective: We present the largest map of distributions of the extracellular electric field to date within a layer L2/L3 mouse primary visual cortex brain sample, which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects (250×140×90 μm 3 volume).

Methods: We used the map to identify microscopic perturbations of the extracellular electric field and their effect on the activating thresholds of individual neurons. Previous relevant studies modeled a macroscopically homogeneous cortical volume. Result: Our immediate result is a reduction of the predicted stimulation field strength necessary for neuronal activation by a factor of approximately 0.7 (or by 30%) on average, due to microscopic perturbations of the extracellular electric field-an electric field "spatial noise" with a mean value of zero.

Conclusion: Although this result is largely sample-specific, it aligns with experimental data indicating that existing macroscopic theories substantially overestimate the electric fields necessary for brain stimulation.

Significance statement: Currently, there is a discrepancy between macroscopic volumetric brain modeling for brain stimulation and experimental results: experiments typically reveal lower electric intensities required for brain stimulation. This study is arguably the first attempt to model brain stimulation at the microscopic scale, enabled by automated analysis of modern scanning electron microscopy images of the brain. The immediate result is a prediction of lower electric field intensities necessary for brain stimulation, with an average reduction factor of 0.7.

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