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. 2023 Aug 28;26(9):1584–1594. doi: 10.1038/s41593-023-01413-5

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© The Author(s) 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. 9 — The stability of the results shown in Fig. 5 with respect to the choice of the number of latent factors is explored (dashed vertical line denotes the 5 latent factors we used in Fig. 5). The first row (a, b) explores the hit-miss difference for the mean off-diagonal correlation; shown are (a) the effect size (dashed horizontal line denotes nil effect) and (b) the p-value of the observed effect (dashed horizontal lines denote p = 0.05, p = 0.01, and p = 0.001; two-sided Wilcoxon signed-rank test without correction multiple comparison), for all activity (black diamond), shared activity (purple crosses) and non-shared activity (pink crosses). The second row (c, d) explores the hit-miss difference for the mean on-diagonal covariance μ_V. The third row (e, f) explores the hit-miss difference for the standard deviation of the off-diagonal covariance σ_CC. The fourth row (g, h) explores the hit-miss difference for the ratio of the standard deviation of the off-diagonal covariance and the mean on-diagonal covariance σ_CC/μ_V. The fourth row (i, j) explores the hit-miss difference for the recurrence R. The fifth row (k, l) shows the same hit-miss difference for R, but uses Principal Component Analysis (PCA) instead of LFA to estimate shared activity.