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. 2020 Oct 13;10:17149. doi: 10.1038/s41598-020-74054-4

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© The Author(s) 2020

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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Overview of bundle analytics (BUAN) framework. (A) shows the process of bundle recognition. (B) shows the process of creating assignment maps (segments) on each extracted bundle. (C) shows the bundle profile analysis concept where groups (red and green) with assignment maps can be used to find statistically significant group differences at specific locations in the bundles. Notice in (C.c) how multiple metrics can be significant in one area (shown with red) and others can be significant in other areas (shown with yellow). Finally (D), shows an example of the network-based bundle shape similarity analysis method of BUAN. In (D.c), a single scalar value [0–1] summarizes the shape differences between the two bundles. The matrix (D.b) provides the complete information of all shape differences in the populations for the the left arcuate fasciculus.