Plasticity in the dynamic pain connectome associated with ketamine-induced neuropathic pain relief
Bibliographic record
Abstract
Therapeutic interventions for neuropathic pain, such as the N-methyl-D-aspartate (NMDA) antagonist ketamine, can vary widely in effectiveness. In this study, we conducted a longitudinal functional MRI study to test the hypothesis that the pain-relieving effect of ketamine is the result of reversal of abnormalities in regional low-frequency brain oscillations (LFOs) and abnormal cross-network functional connectivity (FC) of the dynamic pain connectome. We found that (1) ketamine decreased regional LFOs in the posterior cingulate cortex of the default mode network, (2) a machine-learning algorithm demonstrated that treatment-induced brain changes could be used to make generalizable inferences about pain relief, (3) treatment responders exhibited a significant decrease in cross-network static FC between the posterior cingulate cortex and regions of the sensorimotor and salience networks following treatment, (4) the degree of reduced cross-network FC correlated with the amount of pain relief, and (5) ketamine treatment did not produce significant differences in static or dynamic FC within the ascending nociceptive or descending antinociceptive pathway. These findings support the proposition that regional LFOs contribute to cross-network connectivity that underlie the effectiveness of ketamine to produce significant relief from neuropathic pain. Together with our recent findings that pretreatment dynamic FC of the descending antinociceptive pathway can predict ketamine treatment outcomes, these new findings indicate that pain relief from ketamine arises from a combination of flexible pretreatment FC of the descending antinocieptive pathway together with plasticity (reduction) of cross-network connectivity of the default mode network with sensorimotor and salience networks.
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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 | 0.016 | 0.014 |
| 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 | 0.001 |
| 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".