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. 2021 Jun 29;12:3314. doi: 10.1038/s41467-021-23509-x

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© The Author(s) 2021

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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Fig. 1 — A Salience Network (SN), Frontal-Parietal Network (FPN), and Default Mode network (DMN) ROIs: (1) left anterior insula (lAI); (2) right anterior insula (rAI); (3) dorsomedial prefrontal cortex (DMPFC); (4) left middle frontal gyrus (lMFG); (5) right middle frontal gyrus (rMFG); (6) left frontal eye field (lFEF); (7) right frontal eye field (rFEF); (8) left intraparietal lobule (lIPL); (9) right intraparietal lobule (rIPL); (10) posterior cingulate cortex (PCC), and (11) ventromedial prefrontal cortex (VMPFC). B General linear model analysis revealed significant working-memory-load-related activation in SN and FPN (task-positive nodes), and deactivation in the DMN (task-negative nodes) (p < 0.01, FDR-corrected, two-sided t-test). N = 737 participants. Data are presented as mean ± SEM. C Overview of data analysis pipeline. We first screened the HCP n-back working-memory dataset based on head motion, behavioral performance, and participant handedness. We then extracted time series from each of the 11 ROIs and applied MDSI to determine working-memory load-specific dynamic causal interactions from each participant in the 2-back and 0-back task conditions. MDSI-derived causal influences were then used to investigate (1) whether multivariate dynamic causal interaction patterns distinguished 2-back versus 0-back task conditions, (2) task-dependent causal outflow from each ROI, (3) network controllability as a function of working-memory load, and (4) the relationship between the strength of dynamic causal interactions and behavioral performance. HCP human connectome project, MDSI multivariate dynamic state-space systems identification, ROI region of interest. Source data are provided as a Source data file.