Cognitive behavioral training reverses the effect of pain exposure on brain network activity
Bibliographic record
Abstract
Repeated sensory exposures shape the brain's function and its responses to environmental stimuli. An important clinical and scientific question is how exposure to pain affects brain network activity and whether that activity is modifiable with training. We sought to determine whether repeated pain exposure would impact brain network activity and whether these effects can be reversed by cognitive behavioral therapy (CBT)-based training. Healthy subjects underwent 8 experimental sessions on separate days on which they received painful thermal stimuli. They were randomly assigned to groups receiving either CBT-based training (regulate group, n = 17) or a non-pain-focused treatment (control group, n = 13). Before and after these sessions, participants underwent functional magnetic resonance imaging (fMRI) during painful stimulation and at rest. The effect of repeated pain over time in the control group was a decrease in the neurotypical pain-evoked default mode network (DMN) deactivation. The regulate group did not show these DMN effects but rather had decreased deactivation of the right ventrolateral prefrontal cortex (R vlPFC) of the executive control network. In the regulate group, reduced pain-evoked DMN deactivation was associated with greater individual reduction in pain intensity and unpleasantness over time. Finally, the regulate group showed enhanced resting functional connectivity between areas of the DMN and executive control network over time, compared with the control group. Our study demonstrates that trainable cognitive states can alter the effect of repeated sensory exposure on the brain. The findings point to the potential utility of cognitive training to prevent changes in brain network connectivity that occur with repeated experience of pain.
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 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 | 0.007 | 0.003 |
| 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".