The statistical analyses were performed in SPSS (release Normality was checked using histogram inspection and the Kolmogorov-Smirnov test. Post-hoc pairwise comparisons were performed using estimated marginal means. False discovery rate (fdr) adjusted p-values were calculated with p.adjust in R (version 3.6.1) to correct for testing across multiple networks, and values below 0.05 were considered significant.

One-way ANOVAs and chi-squared tests were used to compare participant characteristics and the average movement in the MRI scanner between groups for each study, as well as to compare HC groups of both studies. Subsequently, cortisol- and subjective-AUCi levels compared using linear mixed models (LMMs) with a 2 × 3 design per study to compare groups (condition × group) and with a 2 × 2 design to compare HCs per study (condition × study).

To identify network changes related to stress in healthy controls, discovery analyses were run per network using LMMs on network centrality z-scores by pooling HCs from both studies to increase statistical power. An interaction effect was indicative of a relation to the stress response. The study of acquisition had been added as covariate. Firstly, analyses were run using a 2 × 3 design (condition × time) for each network, with a significant interaction effect (using uncorrected p-values) being used to identify networks that were related to the stress exposure. Subsequently, LMMs were used on these networks at RS1 (2 × 1, condition × time) to compare groups before stress exposure, and at RS1-RS2 and RS2-RS3 (2 × 2, condition × time) to evaluate change related to the initial stress response and to stress recovery, respectively. Subsequently, the HCs were investigated per study, to see whether effects related to the initial stress response or stress recovery were consistent across both studies.

To better understand connectivity changes observed during the initial stress response or stress recovery, firstly, Pearson’s correlation was calculated between centrality change scores (i.e. RS2-RS1 and RS3-RS1) and the cortisol/subjective stress response. Next, regional centrality and within- and between-network connectivity were investigated per network using 2 × 2 LMMs (condition × time).

To identify the clinical relevance of our findings, the centrality of the networks that related to stress in HCs were investigated in healthy sibling and BD patient groups as well. LMMs were ran per group over all timepoints to see whether centrality changed dissimilarly over time between conditions (2 × 3, condition × time). Thereafter, centrality at RS1 was used to identify differences between conditions before stress exposure (2 × 1, condition), and at RS1-RS2 and RS2-RS3 to investigate the initial stress response and stress recovery, respectively (2 × 2, condition × time). Finally, models were performed using three-way interactions (condition × time × group) to identify potential differences between groups.

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