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. 2022 May 6;227(6):2087–2102. doi: 10.1007/s00429-022-02496-9

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© The Author(s) 2022

Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 4 — Results of group classification during meditation and resting-state. The performance of 1NN and MLR in discriminating the two groups are shown for resting-state condition (A) and meditation task (B). Significant differences in performance depending on the type of features are shown in black for the contrast EC-FC features and in purple for the contrast EC-SC features. Violin plots colored in gray represent accuracy distributions of surrogate data. EC features led to the highest average accuracies with either the MLR or the 1NN, depending on the condition. While the 1NN yielded the best performance for the resting-state sessions (average accuracy of 67%), the MLR outperformed the 1NN when comparing the two groups during the meditation task (average accuracy of 68% against 55%, respectively)