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. 2022 Jul 7;12:11517. doi: 10.1038/s41598-022-15687-5

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© The Author(s) 2022

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 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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Representational similarity analysis. (A) Schematic of RSA processing pipeline. Participant-specific Ratings and task fMRI data are used to generate first-level representational dissimilarity matrices. First-Level RDMs are then used in 10,000-fold bootstrap resampling comparisons of models to functional RDMs for each participant. Resulting relatedness values are used in 50,000-fold t-max permutation testing (B) to determine significant (yellow) model-brain relationships at pre-intervention and post-intervention, as well as any significant effect of session (p < 0.05). First-Level RDMs are then averaged across participants to generate second-level RDMs, which are used to produce second order multidimensional scaling visualizations of RDM relatedness (C).