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. 2023 Nov 17;13:20162. doi: 10.1038/s41598-023-46991-3

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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 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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ConnICA FC-traits extraction process. (A) Multiband and monoband FC matrices were computed for each subject and the FC coefficients in the upper-triangular matrix were vectorized and concatenated into a group FC matrix. (B) The dimensionality of the group FC matrix was reduced to 65 components based on optimal subject identifiability. Then, the ConnICA decomposition was applied so that (C) the group FC matrix was decomposed into spatially independent functional connectivity components (a.k.a. FC-traits) and their corresponding weights for each FC matrix. (D) After testing for significant differences between groups, each functional trait was characterized by a circular graph, a nodal strength map and ANOVA regressions to neurophysiological assessments.