Spatial-topographic nestedness of interoceptive regions within the networks of decision making and emotion regulation: Combining ALE meta-analysis and MACM analysis
Bibliographic record
Abstract
Prominent theories propose that interoception modulates our behavioral and emotional responses involving decision-making and emotion regulation. Are the regions implicated in interoception also spatially related to and possibly nested within the networks of decision making and emotion regulation? Addressing this question, we performed three meta-analyses of functional magnetic resonance imaging studies to identify the regions that are commonly activated by the three domains using activation likelihood estimation (ALE). Additionally, we assessed the coactivation pattern of identified common regions using meta-analytic connectivity modeling (MACM). The results showed major overlaps of interoception with both decision making and emotion regulation in specifically the right dorsal anterior insula. The pairwise contrast analyses confirmed this finding and revealed conjunction-based activities in decision making and emotion regulation in the dorsal anterior cingulate cortex (dACC). MACM based on the identified insula revealed a widespread convergent coactivation pattern with the left anterior insula, dACC, and bilateral thalamus which, together, constitute the salience network. Among these co-activated regions, bilateral insula and the dACC were shared among all three domains. These results suggest that the regions mediating interoception including intero-exteroceptive integration and salience attribution are contained and thus spatially nested within the more extensive networks recruited during decision making and emotion regulation.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.012 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".