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 re...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2024-03-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011941&type=printable |
| _version_ | 1827301716061585408 |
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| author | Chaitanya Chintaluri Marta Bejtka Władysław Średniawa Michał Czerwiński Jakub M Dzik Joanna Jędrzejewska-Szmek Daniel K Wójcik |
| author_facet | Chaitanya Chintaluri Marta Bejtka Władysław Średniawa Michał Czerwiński Jakub M Dzik Joanna Jędrzejewska-Szmek Daniel K Wójcik |
| author_sort | Chaitanya Chintaluri |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T16:29:50Z |
| format | Article |
| id | doaj.art-e2d6c2fbb49f42508c9d27d90b49fed7 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-24T16:29:50Z |
| publishDate | 2024-03-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-e2d6c2fbb49f42508c9d27d90b49fed72024-03-30T05:31:53ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-03-01203e101194110.1371/journal.pcbi.1011941kCSD-python, reliable current source density estimation with quality control.Chaitanya ChintaluriMarta BejtkaWładysław ŚredniawaMichał CzerwińskiJakub M DzikJoanna Jędrzejewska-SzmekDaniel K WójcikInterpretation 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.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011941&type=printable |
| spellingShingle | Chaitanya Chintaluri Marta Bejtka Władysław Średniawa Michał Czerwiński Jakub M Dzik Joanna Jędrzejewska-Szmek Daniel K Wójcik kCSD-python, reliable current source density estimation with quality control. PLoS Computational Biology |
| title | kCSD-python, reliable current source density estimation with quality control. |
| title_full | kCSD-python, reliable current source density estimation with quality control. |
| title_fullStr | kCSD-python, reliable current source density estimation with quality control. |
| title_full_unstemmed | kCSD-python, reliable current source density estimation with quality control. |
| title_short | kCSD-python, reliable current source density estimation with quality control. |
| title_sort | kcsd python reliable current source density estimation with quality control |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011941&type=printable |
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