Sensorimotor, Attention, and Default-Mode Networks for the Perception and Regulation of Thermal Pain: A Functional Network Analysis
Bibliographic record
Abstract
Prior functional magnetic resonance imaging (fMRI) studies have proposed a key set of brain regions involved in pain, and have demonstrated their tendency to cluster into distinct functional networks, or groups of co-activated regions. However, the functional contributions of these networks—to behaviour, cognition, and pain perception—have yet to be fully understood. Here we analyzed open-source fMRI Blood Oxygen Level-Dependent (BOLD) data collected from 31 healthy participants during a thermal pain task, in which various temperatures were administered across different types of pain regulation (i.e., regulate-up, regulate-down and no regulation), eliciting both bottom-up nociceptive and top-down regulatory aspects of pain perception. We extracted functional brain networks constrained to the variance predictable from the timing of heat stimulation under different combinations of stimulus temperature and regulation type. Estimated hemodynamic response (HDR) shapes were interpreted to determine each network’s role in pain perception under these conditions. Three functional brain networks involved in pain perception were identified: a sensorimotor response network, a frontoparietal attention network, and the default-mode network. Based on estimated HDRs, these networks appeared to contribute to pain perception by enabling stimulus-informed responses to pain, directing attentional resources, and regulating pain through ego-driven valuation processes, respectively. These findings provide new information about the cognitive functions of the brain networks involved in the perception of pain.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".