Neuromodulation for Pain: A Comprehensive Survey and Systematic Review of Clinical Trials and Connectomic Analysis of Brain Targets
Bibliographic record
Abstract
BACKGROUND: Chronic pain is a debilitating condition that imposes a tremendous burden on health-care systems around the world. While frontline treatments for chronic pain involve pharmacological and psychological approaches, neuromodulation can be considered for treatment-resistant cases. Neuromodulatory approaches for pain are diverse in both modality and target and their mechanism of action is incompletely understood. OBJECTIVES: The objectives of this study were to (i) understand the current landscape of pain neuromodulation research through a comprehensive survey of past and current registered clinical trials (ii) investigate the network underpinnings of these neuromodulatory treatments by performing a connectomic mapping analysis of cortical and subcortical brain targets that have been stimulated for pain relief. METHODS: A search for clinical trials involving pain neuromodulation was conducted using 2 major trial databases (ClinicalTrials.gov and the International Clinical Trials Registry Platform). Trials were categorized by variables and analyzed to gain an overview of the contemporary research landscape. Additionally, a connectomic mapping analysis was performed to investigate the network connectivity patterns of analgesic brain stimulation targets using a normative connectome based on a functional magnetic resonance imaging dataset. RESULTS: In total, 487 relevant clinical trials were identified. Noninvasive cortical stimulation and spinal cord stimulation trials represented 49.3 and 43.7% of this count, respectively, while deep brain stimulation trials accounted for <3%. The mapping analysis revealed that superficial target connectomics overlapped with deep target connectomics, suggesting a common pain network across the targets. CONCLUSIONS: Research for pain neuromodulation is a rapidly growing field. Our connectomic network analysis reinforced existing knowledge of the pain matrix, identifying both well-described hubs and more obscure structures. Further studies are needed to decode the circuits underlying pain relief and determine the most effective targets for neuromodulatory treatment.
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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 | 0.015 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".