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. 2023 Mar 30;26(4):579–593. doi: 10.1038/s41593-023-01284-w

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© The Author(s) 2023, corrected publication 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Extended Data Fig. 4 — (a) Left: A Random Forest Regression model was trained (80% of data, 2.3e⁴ samples) to predict (20% of data, 5.6³ samples) average astrocyte Ca²⁺ fluorescence accurately (mean r² = 0.85 ± 4.5e^-3 std, n = 10 cross-validations). Right: Relative feature importance based on permutation testing of each predictor. Randomly generated data was included as a negative control and did not inform model predictions. (b) Same analysis as in (a), using the neuronal arousal PC rather than average neuronal activity. Random Forest Regression using the neuronal arousal PC showed a similar accuracy to that using mean neuronal fluorescence (mean r² = 0.87 ± 6.4e^-3 std, n = 10 cross-validations). Boxplots show median and IQR with whiskers to 1.5 * IQR.