Current Source Density Analisis for HD-MEA

For the preliminary version of this code, please refer to https://github.com/kzapfe/CSDA

A figure that represents detection of centers of mass and tracing of center of mass trajectories.

This methodology provides a means to define the center of mass of sinks and sources in time in high spatiotemporal resolution in brain slices, which can aid to infer information transmission. Using CSD analysis, a disjoint component analysis permits to dissect restricted synaptic activation that is obscured by high voltage electrographic elements, as well as subthreshold activity, not overtly detected with voltage recordings, can be better defined and traced in a quantitative manner.

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First Version, September 2024

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The following codes contain the methods described in the following article:

• Zapfe, K.W.P., Romero-Maldonado, I., Gutiérrez, R., High Resolution Detection of Stationary and Evolving 2D-Current Source Density within Neuronal Microcircuits, 2024. (under review for publication)

Created with data extracted from High Density Multielectrode Arrays (HD-MEAs, 3Brain) using slices of rodent hippocampal tissue in mind.

• Step 00: data preprocessing. Conversion from HDF5 (brw) to jld (native julia) format and segmentation to improve data manageability.

• Step 01: Detection of recording errors and debugging of discontinuities in the signals.

• Step 02: Voltage to current flow conversion ( sink and sources ). Detection of centers of mass and tracing of center-of-mass trajectories.

• ACD: Detection of channels corresponding to viable tissue. Separation of channels providing physiological signal from noise.

This code is released under GPL-3.0.

Supported systems

• This software was developed specifically for high density multielectrode arrays 3Brain BIOCAM X

Quick Start

These notebooks and modules were developed and tested in Julia (versions 1.7.2 and 1.10.2) using Jupyter Notebooks and text editors, across Linux distributions and Windows 10. We recommend using Julia 1.10.x to ensure compatibility with the latest dependencies.

All the packages and functions are properly described at the beginning of each module or notebook

Get Started

• Install Julia 1.10.x

Visit julialang.org/download to download and install Julia.

Note

You can also check out Julia's previous versions at julialang.org/downloads/oldreleases.

• Clone the repository

git clone https://github.com/LBitn/Hippocampus-HDMEA-CSDA.git

• Install the dependencies

Open Julia and run the following commands to install the Julia kernel package for Jupyter Notebooks:

using Pkg
Pkg.add("IJulia");

If you already have Jupyter on your computer, this process will add a Julia kernel for Jupyter. Then, you can start Jupyter Notebook as usual by running jupyter notebook in the terminal.

Alternatively, you can let IJulia install and manage its own Jupyter setup. To do this, type the following at the Julia

using IJulia
notebook();

Contributors, alphabetical

• Angel Vazquez Flores: Parameter optimisation, Test
• Francisco Victorio Santiago: Test and sugestions
• Isabel Romero Maldonado: Codes and Data Analysis
• Jorge Antonio Hernández García: Test and sugestions
• Karel Zapfe Aguilar: Author of the concept and source codes
• Luis Reynaldo Ramos González: Test and Optimisation
• Rafael Gutiérrez Aguilar: Conceived and designed research

Contact

Dr. R. Gutiérrez is based at Department of Pharmacobiology, Centro de Investigación y Estudios Avanzados del Instituto Politécnico Nacional. Please contact the following e-mail: [email protected]

References