Temporal dynamics of hippocampal activity predict stable patterns of sensitization or habituation to noxious stimulation across sessions
Bibliographic record
Abstract
Abstract Acute pain serves to warn an organism of potential damage. When nociceptive stimulation persists, two possible responses emerge: If no risk of harm is anticipated, habituation may occur. If harm is considered possible, pain sensitization is likely. An individual’s adaptation to prolonged pain may provide insight into their ability to manage resources, and possibly their likelihood of developing chronic pain. Yet, little is known about the stability of these individual differences or their underlying neural mechanisms. Eighty-five participants undertook a repetitive noxious stimulation task and a resting-state scan in an MRI scanner, in a first session. They then completed the same task outside the scanner on three separate days. Pain adaptation was operationalized as the slope of change in pain ratings within session. Intraclass correlations were calculated between slopes across the four sessions, which demonstrated high stability and association with emotional disposition. Individuals who habituated to repeated stimuli showed increasing activity in the anterior hippocampus and amygdala, while individuals who sensitized showed increasing activity in the sensorimotor cortices. These clusters were then used as seeds in resting state analysis, with habituation associated with higher functional connectivity between hippocampus/amygdala and ventromedial prefrontal cortex(vmPFC), and higher connectivity between sensorimotor regions and the hippocampus, amygdala and insula cortex. Our findings suggest that pain adaptation is a stable phenotypic trait, which may have implications for the prediction of chronic pain. This study implicates neural sensory and appraisal systems in these stable responses, offering insight into the mechanisms underlying trait-like responses to prolonged nociceptive input.
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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) | 0.001 | 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".