Sleep quality and its association with the insular cortex in emotional empathy
Bibliographic record
Abstract
The human ability to vicariously share someone else's emotions (i.e., emotional empathy) relies on an extended neural network including regions in the anterior cingulate and insular cortex. Here, we tested the hypothesis that good sleep quality is associated with increased activation in the brain areas underlying emotional empathy. To this aim, we assessed subjective sleep quality in a large sample of healthy young volunteers, and asked participants to complete a computerized emotional empathy task. Then, we asked 16 participants to complete the same task while undergoing functional Magnetic Resonance Imaging (fMRI). After confirming the behavioral relationship between quality of sleep and emotional empathy in the large sample, we conducted a Region of Interest (ROI) analysis on selected ROIs involved in emotional empathy, and measured Blood Oxygen Level Dependent (BOLD) signal change in participants who performed the emotional empathy task in the MRI scanner; additionally, we assessed how the BOLD signal in different brain areas temporally correlated with performance throughout the task (i.e., task-based functional connectivity). We found increased BOLD signal change in a selective region within the left insula for individuals with better subjective sleep quality. These findings provide the very first evidence that individuals' sleep quality relates to emotional empathic responses through increased neural activation of a specific area within the insular cortex.
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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 | 0.001 |
| 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".