Pain Enhances Functional Connectivity of a Brain Network Evoked by Performance of a Cognitive Task
Bibliographic record
Abstract
Experimental and clinical evidence indicates that pain can affect cognitive processes, but the cortical networks involved in pain-cognition interactions are unclear. In this study, we determined the effect of pain on the activity of cortical areas involved in cognition acting as a whole (i.e., a network). Subjects underwent functional magnetic resonance imaging (fMRI) while engaged in an attention-demanding cognitive task (multisource interference task) of varying difficulty and simultaneously receiving painful stimuli at varying intensities. The control (baseline) condition was simple finger tapping that had minimal cognitive demands and without pain. Functional connectivity analysis revealed a cortical network consisting of two anti-correlated parts: a task-negative part (precuneus/posterior cingulate cortex, medial frontal and inferior parietal/temporal) the activity of which correlated negatively with the cognitive task and positively with the control baseline, and a task-positive part (inferior frontal, superior parietal, premotor, and anterior insula cortices) the activity of which correlated positively with the cognitive task and negatively with the baseline. Independent components analysis revealed these opposing networks were operating at a low frequency (0.03-0.08 Hz). The functional connectivity of the task-positive network was increased by cognitive demand and by pain. We suggest this attention-specific network balances the needs of general self-referential and environmental awareness versus focused attention to salient information. We postulate that pain affects cognitive ability by its reliance on this common attention-specific network. These data provide evidence that pain can modulate a network presumed to be involved in focused attention, suggesting a mechanism for the interference of pain on cognitive ability by the consumption of attentional resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".