P.095 Functional neuroimaging signatures associated with analgesic effects of neuromodulation for chronic pain and their value in predicting treatment outcome
Bibliographic record
Abstract
Background: Responses to invasive neuromodulation therapy for chronic pain are highly variable after several months of sustained treatment, with some experiencing a complete loss of therapeutic effect. We sought to assess whether functional neuroimaging can provide a biomarker for treatment success and whether these biomarkers offer value in predicting treatment response. Methods: We searched Ovid MEDLINE and EMBASE from 1967 to 2022, including prospective studies correlating functional neuroimaging signatures with treatment response after surgical implantation. Results: After considering 355 studies for initial review, 22 studies were included. While there was significant heterogeneity in experimental design, preliminary findings suggest that differential regional cortical activation profiles and signatures can be employed to differentiate good from poor therapeutic responders. Three studies correlated pre-operative functional imaging with treatment effects post-implantation. For example, baseline activation patterns of specific brain regions on functional imaging modalities such as 11C-diprenorphrine PET and Tc-99m-SPECT significantly correlated with therapeutic response to motor cortex stimulation, and spinal cord stimulation (SCS), respectively. Conclusions: The included studies demonstrate the potential for functional imaging to predict the likelihood of successful neuromodulation treatment. The concept is relatively unexplored in the literature and could benefit from more studies with larger sample sizes to confirm clinical utility.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".