Psychological well-being modulates neural synchrony during naturalistic fMRI
Bibliographic record
Abstract
Abstract Psychological well-being (PWB) is a combination of self-acceptance, life purpose, personal growth, positive relationships, and autonomy, and has a significant relationship with physical and mental health. Previous studies using resting-state functional magnetic resonance imaging (fMRI) and static picture stimuli have implicated the anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), orbitofrontal cortex (OFC), insula and thalamus in PWB, however, the replication of associations across studies is scarce, both in strength and direction, resulting in the absence of a model of how PWB impacts neurological processing. Naturalistic stimuli better encapsulate everyday experiences and can elicit more “true-to-life” neurological responses, and therefore may be a more appropriate tool to study PWB. The current study seeks to identify how differing levels of PWB modulate neural synchrony in response to an audiovisual film. With consideration of the inherent variability of the literature, we aim to ascertain the validity of the regions previously mentioned and their association with PWB. We identified that higher levels of PWB were associated with heightened neural synchrony in the bilateral OFC and left PCC, and that lower levels of PWB were associated with heightened neural synchrony in the right temporal parietal junction (TPJ) and left superior parietal lobule (SPL), regions related to narrative processing. Taken together, this research confirms the validity of several regions in association with PWB and suggests that varying levels of PWB produce differences in the processing of a narrative during complex audiovisual processing.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".