Cognitive modes involved in emotion regulation identified using Constrained Principal Component Analysis for fMRI (fMRI-CPCA)
Bibliographic record
Abstract
Abstract Meta-analyses of functional magnetic resonance imaging (fMRI) studies have identified networks of widely distributed brain regions supporting emotion regulation. These overlap with attentional or cognitive control brain networks. The literature is short on data speaking to specific neurocognitive functions of these broad brain networks in reappraisal - a key emotion regulatory strategy involving the reframing of an event according to a goal to increase or decrease experienced emotion. We address this gap by examining both the spatial configuration and temporal profile of event-related blood oxygenation level dependent (BOLD) responses during a task requiring reappraisal. We analysed fMRI datasets obtained from 84 participants (51% female) who were instructed to increase or decrease their emotional response to unpleasant images. We extracted spatial maps and their estimated temporal event-related BOLD signal changes of four components with the highest loadings. Neurocognitive functions were derived by mapping each component onto templates of previously identified task-based cognitive modes. This analysis yielded four cognitive modes: 1) “multiple demand” 2) “response”, 3) “re-evaluation”, and 4) “default mode". The temporal profiles showed particularly prominent patterns for the increase and decrease conditions in “multiple demand” (mode 1) and “re-evaluation” (mode 3) respectively. These findings highlight a central role for specific neurocognitive processes linked to attentional control (“multiple demand”) and switching (“re-evaluation”), as part of the broad brain networks supporting reappraisal. Moreover, the level of neural engagement of these cognitive modes varies depending on the regulatory goal. These findings provide tangible targets for neurocognitive interventions such as neurostimulation when emotion regulation is compromised.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".