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. 2019 Oct 29;9:15486. doi: 10.1038/s41598-019-51990-4

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© The Author(s) 2019

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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Pain matrix rs-fMRI seed-based correlation analysis. (a) 3D image of the pain matrix: ACC, anterior cingulate cortex; PCC, posterior cingulate cortex; BA41, primary auditory cortex; AMYG, amygdala; AnG, angular gyrus; CAU, caudate; GP, globus pallidus; PUT, putamen; INS, insular cortex; mPFC, medial prefrontal cortex; PAG, periaqueductal gray matter; M1, primary motor cortex; SI, primary somatosensory cortex; SII, secondary somatosensory cortex; SMA, supplementary motor area; STS, superior temporal sulcus; THA, thalamus. (b) Connectivity maps of the pain matrix for FM (left) and HC (right) respectively. We calculated the BOLD mean time-series of all 34 seeds into one single a priori brain mask, and correlated it with the rest of the whole brain. Colors show either positive (red–yellow) or negative (blue–light blue) correlations of the pain matrix. Image shows positive correlations of the pain matrix with the Default Mode Network (DMN), with areas such as mPFC, PCC, PCN, and AnG.