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. 2022 Jul 21;12:12023. doi: 10.1038/s41598-022-15539-2

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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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(A) Full FNC for patients computed using most 5% of the salient data selected based on feature attribution values for different disorders. (B) Static FNC (i.e., using 100% data) matrices for patients of different disorders. The FNC based on 5% salient data (A) does indeed convey the same focused dynamic information as currently assessed in FNC matrices based on 100% data (B). It is thus apparent that the proposed model can capture the focused information aligned with the current domain knowledge. (C) Pairwise difference of FNC matrices based on 5% salient data. The difference FNC matrices based on focused data indicate that each disorder has a uniquely distinguishable association with brain dynamics.