An Algorithm Based on a Cable-Nernst Planck Model Predicting Synaptic Activity throughout the Dendritic Arbor with Micron Specificity.

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Claire Guerrier, Tristan Dellazizzo Toth, Nicolas Galtier, Kurt Haas
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Abstract

Recent technological advances have enabled the recording of neurons in intact circuits with a high spatial and temporal resolution, creating the need for modeling with the same precision. In particular, the development of ultra-fast two-photon microscopy combined with fluorescence-based genetically-encoded Ca2+-indicators allows capture of full-dendritic arbor and somatic responses associated with synaptic input and action potential output. The complexity of dendritic arbor structures and distributed patterns of activity over time results in the generation of incredibly rich 4D datasets that are challenging to analyze (Sakaki et al. in Frontiers in Neural Circuits 14:33, 2020). Interpreting neural activity from fluorescence-based Ca2+ biosensors is challenging due to non-linear interactions between several factors influencing intracellular calcium ion concentration and its binding to sensors, including the ionic dynamics driven by diffusion, electrical gradients and voltage-gated conductances. To investigate those dynamics, we designed a model based on a Cable-like equation coupled to the Nernst-Planck equations for ionic fluxes in electrolytes. We employ this model to simulate signal propagation and ionic electrodiffusion across a dendritic arbor. Using these simulation results, we then designed an algorithm to detect synapses from Ca2+ imaging datasets. We finally apply this algorithm to experimental Ca2+-indicator datasets from neurons expressing jGCaMP7s (Dana et al. in Nature Methods 16:649-657, 2019), using full-dendritic arbor sampling in vivo in the Xenopus laevis optic tectum using fast random-access two-photon microscopy. Our model reproduces the dynamics of visual stimulus-evoked jGCaMP7s-mediated calcium signals observed experimentally, and the resulting algorithm allows prediction of the location of synapses across the dendritic arbor. Our study provides a way to predict synaptic activity and location on dendritic arbors, from fluorescence data in the full dendritic arbor of a neuron recorded in the intact and awake developing vertebrate brain.

Abstract Image

基于Cable-Nernst Planck模型预测树突乔木突触活动的微米特异性算法。
最近的技术进步使完整的神经元电路的记录具有高的空间和时间分辨率,创造了对相同精度的建模的需求。特别是,超快速双光子显微镜结合基于荧光的遗传编码Ca2+指示器的发展,可以捕获与突触输入和动作电位输出相关的全树突乔木和体细胞反应。随着时间的推移,树突树梢结构的复杂性和活动的分布模式导致生成非常丰富的4D数据集,这些数据集具有挑战性(Sakaki等人在Frontiers in Neural Circuits 14:33, 2020)。由于影响细胞内钙离子浓度及其与传感器结合的几个因素之间的非线性相互作用,包括由扩散、电梯度和电压门控电导驱动的离子动力学,从基于荧光的Ca2+生物传感器解释神经活动具有挑战性。为了研究这些动力学,我们设计了一个基于Cable-like方程与电解质中离子通量的能思-普朗克方程耦合的模型。我们使用这个模型来模拟信号的传播和离子的电扩散。利用这些模拟结果,我们设计了一种从Ca2+成像数据集检测突触的算法。最后,我们将该算法应用于来自表达jGCaMP7s的神经元的实验Ca2+指标数据集(Dana等人在Nature Methods 16:649-657, 2019),使用快速随机访问双光子显微镜在非洲爪猴的光学顶部进行全树突乔木采样。我们的模型再现了视觉刺激引起的jgcamp7s介导的钙信号的动态,实验观察到,所得算法可以预测树突乔木上突触的位置。我们的研究提供了一种预测突触活动和树突树突上的位置的方法,从完整的和清醒的发育中的脊椎动物大脑中记录的神经元的完整树突树突的荧光数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
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