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

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.Author summary: Recording electric potential with wires inserted in the brain is a standard method in neuroscientist’s arsenal for a century. This extracellular potential reflects electric activity of billions of neural cells processing incoming information and our deepest thoughts. It is difficult to interpret because the effect of every single cell activity propagates in space like a wave in a lake after a stone is thrown. Therefore, every electric recording is a sum of billions of stronger and weaker contributions from close and remote cells, respectively. It is easier to understand the local activity of the brain when we reconstruct the distribution of the sources contributing to the measurements. This is similar to reconstructing the information on where and when stones where thrown into the lake from wiggling of multiple buoys placed on the water surface. The techniques for source reconstruction are called the Current Source Density methods. Early techniques required distributions of electrodes on regular grids. Our kernel Current Source Density method can reconstruct this activity from arbitrary distribution of electrodes and can take into account measurement noise. Here we present our software toolbox, kCSD-python, which implements this method. It comes with a number of tools for efficient estimation, for testing reliability of the results, and with several tutorials showing how to use it.