kCSD-python, reliable current source density estimation with quality control. - Chaitanya Chintaluri; Marta Bejtka; Władysław Średniawa; Michał Czerwiński; Jakub M Dzik; Joanna Jędrzejewska-Szmek; Daniel K Wójcik (PLoS Computational Biology, 2024)
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.