The vestibular system in pain and embodiment: cortical overlap, modulatory potential, and therapeutic perspectives
Bibliographic record
Abstract
Musculoskeletal pain is increasingly understood as a product of disrupted multisensory integration rather than a direct consequence of tissue damage alone. Among the sensory systems involved in shaping body representation and modulating pain, the vestibular system remains largely overlooked. Beyond its classical role in balance and spatial orientation, vestibular input contributes to embodiment, self-location, and bodily self-consciousness-processes that are frequently altered in chronic pain conditions. Neuroimaging and clinical evidence reveal a striking overlap between vestibular integration regions and the so-called pain neuromatrix, suggesting shared cortical substrates for vestibular and nociceptive/pain processing. Moreover, vestibular dysfunction is associated with disembodiment phenomena such as depersonalization and derealization, which mirror sensory distortions observed in chronic pain syndromes. Experimental studies demonstrate that vestibular stimulation-via caloric or electric modalities-can modulate pain perception, influence somatosensory integration, and recalibrate distorted body representations. This perspective paper synthesizes current findings at the intersection of vestibular neuroscience, pain modulation, and embodiment, proposing that the vestibular system could constitute a critical but underrecognized component in musculoskeletal health. Incorporating vestibular pathways into pain models may, therefore, improve our understanding of chronicity and open novel therapeutic avenues for neuromodulation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".