It’s all connected! Multivariate pattern analysis of inter-network connectivity distinguishes between reappraisal and passive viewing of emotional scenes
Bibliographic record
Abstract
Down-regulation using reappraisal is often associated with negative connectivity between prefrontal areas such as the dorsolateral prefrontal cortex (dlPFC) and areas associated with emotion such as the insula and amygdala, though a network perspective is often lacking in emotion regulation research. Whereas the dlPFC is associated with the attentional control network (ACN), the insula and amygdala are associated with the salience and limbic networks, respectively. The default mode network (DMN), including the ventromedial PFC, also contributes to emotion regulation. The present study sought to determine if inter-network functional connectivity can dissociate reappraising from passively viewing a negative image using multivariate pattern analysis (MVPA). Thirty-one participants completed a functional magnetic resonance imaging task in which they reappraised and viewed negative images. Behavioral and skin conductance response results indicated that reappraisal was associated with reductions in negative affect compared to viewing. The univariate connectivity analysis revealed that connections between aspects of the DMN and ACN differed between reappraising versus viewing negative images. Notably, the inter-network connectivity MVPA results demonstrated that whether one was reappraising versus viewing an image could be predicted better than chance, with several connections reliably contributing to the model, including those between ACN and DMN.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".