PPubMed14 Mar 2024Cited 7×

kCSD-python, reliable current source density estimation with quality control kCSD-python

Chaitanya Chintaluri, Marta Bejtka, Władysław Średniawa, Michał Czerwiński, Jakub M. Dzik, Joanna Jędrzejewska-Szmek, Daniel K. Wójcik, Daniele Marinazzo

Abstract

Interpretation of extracellular recordings can be challenging due to the long range of electric field. This challenge can be mitigated by estimating the current source density (CSD). Here we introduce kCSD-python, an open Python package implementing Kernel Current Source Density (kCSD) method and related tools to facilitate CSD analysis of experimental data and the interpretation of results. We show how to counter the limitations imposed by noise and assumptions in the method itself. kCSD-python allows CSD estimation for an arbitrary distribution of electrodes in 1D, 2D, and 3D, assuming distributions of sources in tissue, a slice, or in a single cell, and includes a range of diagnostic aids. We demonstrate its features in a Jupyter Notebook tutorial which illustrates a typical analytical workflow and main functionalities useful in validating analysis results.

A figure from kCSD-python, reliable current source density estimation with quality control kCSD-python — fig. from the paper

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kCSD-python provides an open‑source, well‑documented Python toolbox for reliable kernel current source density estimation with built‑in quality‑control and extensive tutorials.

What’s new

i.Open‑source implementation of kernel CSD and its two variants under a BSD license
ii.Integrated quality‑control tools (cross‑validation, error‑propagation maps, handling of noisy or broken electrodes)
iii.Extensive, runnable Jupyter‑Notebook tutorials and Google Colab access for zero‑install use
iv.Support for Python 3.8‑3.11 with minimal dependencies (numpy, scipy, matplotlib)
v.Pre‑computed example datasets (100 2D and 3D reconstructions for small and large sources)

How they did it

The authors re‑implemented the kernel CSD algorithm and its variants in Python, built a library of basis sources, and validated it using synthetic data with known ground truth. They evaluated performance on regular, noisy, and broken‑electrode grids, generated error‑propagation maps, and provided tutorials that reproduce all figures.

What they found

Average error quantified for 10×10 electrode grids with up to 20 broken contacts (Fig 7)
100 example 2D and 3D reconstructions for small and large sources made publicly available
Cross‑validation reliably selects the regularization parameter R in noise‑free scenarios
Error maps show robustness of kCSD to noise and electrode loss

Caveats

✕Assumes purely ohmic, isotropic, homogeneous tissue conductivity (except MoI variant)
✕Quality of reconstruction depends on appropriate placement of basis sources; poor placement yields meaningless results
✕Cannot separate overlapping cell‑population activity without additional analysis (e.g., ICA)
✕Model‑based approach may over‑fit noise if regularization is not properly tuned

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