Alpha and Theta Oscillations Associated With Behavioral Phenotypes of Pain–Attention Interaction
Bibliographic record
Abstract
PURPOSE: Pain is inherently salient and so draws our attention in addition to impacting performance on attention-demanding tasks. Individual variability in pain-attention interactions can be assessed by two kinds of behavioral phenotypes that quantify how individuals prioritize pain versus attentional needs. The intrinsic attention to pain (IAP) measure quantifies the degree to which a person attends to pain (high-IAP) or mind-wanders away from pain (low-IAP). The A/P categorization quantifies how pain impacts cognitive performance during an attention-demanding task classifying individuals into P type (pain dominates, worse performance during pain in comparison to no pain) and A type (attention to task dominates, better performance during pain in comparison to no pain). Although previous MRI-based studies have linked these phenotypes with the dynamic pain connectome (DPC), the underlying neural oscillations are not known. This paper aims to examine the brain-behavior relationship between alpha and theta oscillations within nodes of the DPC and pain-attention phenotypes. METHOD: Fifty participants (27 F, 23 M) underwent resting-state magnetoencephalography (MEG). Individual IAP scores were determined by assessing mind-wandering during pain and A/P type was based on interference of pain with cognitive task performance. FINDING: The main findings were: (1) peak alpha frequency (PAF) power did not differ between low/high-IAP individuals or A/P-type individuals within the nodes of the DPC; (2) compared to high-IAP individuals, those with low-IAP have slower PAF in the left primary somatosensory cortex, posterior cingulate cortex and precuneus and higher theta power in the ascending nociceptive pathway and default mode network; (3) males with low-IAP, compared to females, had higher PAF power throughout the DPC. CONCLUSION: Alpha and theta oscillations within the DPC may underlie aspects of attentional focus and pain-attention interactions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".