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. 2018 Jul 9;8:10307. doi: 10.1038/s41598-018-28680-8

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© The Author(s) 2018

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

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Motor pattern extraction from activity data of whole neurons. (a) An example of extracted motor patterns (bottom) from the activity data derived from pan-neuronally expressed GCaMP6f (top, grayscale image) (b) Mean of fictive motor pattern dervied from 3 samples aligned with the end point of each label. Average duration of each motor pattern, AT:1.8 ± 0.1 s, BW:2.4 ± 0.1 s, FW:1.7 ± 0.1 s, PT:1.6 ± 0.1 s, QS:2.3 ± 0.4 s (mean ± standard deviation). Note that unnormalized ΔF/F is shown since normalization obscured the motor pattern. (c) Motor pattern correlation mapping. Voxels with >0.1 Pearson’s r for each motor pattern, AT (c’), BW (c”), FW (c”’) and PT (c”’), are shown in colors. c shows the 18 ROIs used for the analyses (visual cues). Image baselines are derived from maximum GCaMP signal. (d) Neural activity in the structures identified by the correlation mapping. The average activity in the structures identified as specific to BW (top) and PT (middle) during each motor pattern (bottom). Scale bar 100 μm.