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

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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.

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Abbreviations

NMDA:

N-methyl D-aspartate

VGCC:

Voltage-gated calcium channels

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Funding

Financial support from CIHR grant #FDN-148468, AccelNet International Network for Bio-Inspired Computing and the Fyssen foundation is gratefully acknowledged.

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Author notes

  1. Claire Guerrier and Tristan Dellazizzo Toth contributed equally to this work.

Authors and Affiliations

  1. Université Côte d’azur, LJAD, CNRS UMR7351, Nice, France

    Claire Guerrier

  2. CNRS - IRL3457, CRM, Université de Montréal, Montréal, Canada

    Claire Guerrier

  3. Djavad Mowafaghian Centre for Brain Health, UBC - Vancouver, Vancouver, Canada

    Tristan Dellazizzo Toth & Kurt Haas

  4. Independent researcher, Montréal, Canada

    Nicolas Galtier

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  1. Claire Guerrier
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  2. Tristan Dellazizzo Toth
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  4. Kurt Haas
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Contributions

KH designed the project. CG and NG built the model and did the simulations. TDT performed experiments. CG, TDT and KH wrote the main manuscript text. TDT and CG prepared Fig. 1. NG prepared Figs. 2, 3, 4, and 5. All authors reviewed the manuscript.

Corresponding author

Correspondence to Claire Guerrier.

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The animal study was reviewed and approved by the UBC Animal Care Committee and was in accordance with the Canadian Council on Animal Care (CCAC) guidelines. Animal Care Number: A190297.

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Guerrier, C., Dellazizzo Toth, T., Galtier, N. et al. An Algorithm Based on a Cable-Nernst Planck Model Predicting Synaptic Activity throughout the Dendritic Arbor with Micron Specificity. Neuroinform 21, 207–220 (2023). https://doi.org/10.1007/s12021-022-09609-z

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