Psychosocial stress reactivity is associated with decreased whole-brain network efficiency and increased amygdala centrality.
Bibliographic record
Abstract
Cognitive and emotional functions are supported by the coordinated activity of a distributed network of brain regions. This coordinated activity may be disrupted by psychosocial stress, resulting in the dysfunction of cognitive and emotional processes. Graph theory is a mathematical approach to assess coordinated brain activity that can estimate the efficiency of information flow and determine the centrality of brain regions within a larger distributed neural network. However, limited research has applied graph-theory techniques to the study of stress. Advancing our understanding of the impact stress has on global brain networks may provide new insight into factors that influence individual differences in stress susceptibility. Therefore, the present study examined the brain connectivity of participants that completed the Montreal Imaging Stress Task (Goodman et al., 2016; Wheelock et al., 2016). Salivary cortisol, heart rate, skin conductance response, and self-reported stress served as indices of stress, and trait anxiety served as an index of participant's disposition toward negative affectivity. Psychosocial stress was associated with a decrease in the efficiency of the flow of information within the brain. Further, the centrality of brain regions that mediate emotion regulation processes (i.e., hippocampus, ventral prefrontal cortex, and cingulate cortex) decreased during stress exposure. Interestingly, individual differences in cortisol reactivity were negatively correlated with the efficiency of information flow within this network, whereas cortisol reactivity was positively correlated with the centrality of the amygdala within the network. These findings suggest that stress reduces the efficiency of information transfer and leaves the function of brain regions that regulate the stress response vulnerable to disruption. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".