Information content and interpretation of CSD non-trivially depends on array density

Abstract Objective. We aim to determine whether commonly-used current source density (CSD) estimation techniques provide valid results when used with recordings from the latest generation of high-density arrays and how the density affects signal interpretation. Approach. We first develop an analytic formulation for the contributions of Gaussian current sources to an estimated CSD as a function of current source position and array density. We then simulate local field potential recordings in a biophysically detailed model of a subvolume of rat somatosensory cortex, and compare estimated current source densities to the ground truth current densities averaged over relevant tissue volumes. Main results. Commonly used methods to estimate the CSD can produce spurious results when the inter-electrode spacing is small relative to the width of the current source, confusing sources and sinks. For high-density recording electrodes, the estimated CSD diverges from the volume-averaged ground-truth current distribution that the CSD estimation method attempts to reconstruct. Both confusion of sinks and sources and amplification of local current fluctuations play a role in this divergence. Careful choice of CSD reconstruction parameters can be used to preferentially reveal the information content on a local or higher-level scale. Significance. Our results shed light on high-spatial-frequency oscillations observed in estimated current source densities from in vivo experiments. Care must be taken when choosing a reconstruction method and interpreting the results from high density arrays, but proper parameter tuning allows to focus on specific information-of-interest. In addition, it was found that detailed network models, including active conductances and background noise, can be necessary to accurately simulate high-frequency current fluctuations.

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Publication Details

Journal
Journal of Neural Engineering
Published
2026-09-15
DOI
https://doi.org/10.1088/1741-2552/ae93f9
Citations
1
Primary Topic
Neural dynamics and brain function
Type
article
Field-Weighted Citation Impact
6.20

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article

Information content and interpretation of CSD non-trivially depends on array density

Joseph Tharayil, Esra Neufeld, Michael Reimann
1 citations
Journal of Neural Engineering
Neural dynamics and brain function
6.20
article

Information content and interpretation of CSD non-trivially depends on array density

Joseph Tharayil, Esra Neufeld, Michael Reimann
article en
1 citations

Abstract

Abstract Objective. We aim to determine whether commonly-used current source density (CSD) estimation techniques provide valid results when used with recordings from the latest generation of high-density arrays and how the density affects signal interpretation. Approach. We first develop an analytic formulation for the contributions of Gaussian current sources to an estimated CSD as a function of current source position and array density. We then simulate local field potential recordings in a biophysically detailed model of a subvolume of rat somatosensory cortex, and compare estimated current source densities to the ground truth current densities averaged over relevant tissue volumes. Main results. Commonly used methods to estimate the CSD can produce spurious results when the inter-electrode spacing is small relative to the width of the current source, confusing sources and sinks. For high-density recording electrodes, the estimated CSD diverges from the volume-averaged ground-truth current distribution that the CSD estimation method attempts to reconstruct. Both confusion of sinks and sources and amplification of local current fluctuations play a role in this divergence. Careful choice of CSD reconstruction parameters can be used to preferentially reveal the information content on a local or higher-level scale. Significance. Our results shed light on high-spatial-frequency oscillations observed in estimated current source densities from in vivo experiments. Care must be taken when choosing a reconstruction method and interpreting the results from high density arrays, but proper parameter tuning allows to focus on specific information-of-interest. In addition, it was found that detailed network models, including active conductances and background noise, can be necessary to accurately simulate high-frequency current fluctuations.

Journal of Neural EngineeringVol. 23(5)
Allen Institute for Brain Science (US), École Polytechnique Fédérale de Lausanne (CH)
Board of the Swiss Federal Institutes of Technology
Openalex Percentile: Top 3%
Neural dynamics and brain function
6.20
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