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. 2022 Jul 26;13:973921. doi: 10.3389/fpsyt.2022.973921

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Copyright © 2022 Niu, Li, Wang, Hu, Hao, Li, Zhang and Cheng.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Static functional connectivity-based classification using fMRI data with global signal regression. (A) Scatter plot showing classification scores of all the subjects. (B) Receiver operating characteristic (ROC) curve of the classifier. (C) High-degree nodes (degree ≥ 8, larger spheres indicate nodes with higher degree) and their connections in the positive network. (D) High-degree nodes (degree ≥ 8) and their connections in the negative network. (E) Fingerprints of the 25 highest-degree nodes summarized by overlap with canonical neural networks.