Transcutaneous Auricular Vagal Nerve Stimulation and Disorders of Consciousness: A Hypothesis for Mechanisms of Action
Bibliographic record
Abstract
Disorders of consciousness (DoC) are the hallmark of severe acquired brain injuries characterized by abnormal activity in important brain areas and disruption within and between brain networks. As DoC's therapeutic arsenal is limited, new potential therapies such as transcutaneous auricular vagal nerve stimulation (taVNS) have recently been explored. The potential of taVNS in the process of consciousness recovery has been highlighted in recent studies with DoC patients. However, it is not clear how taVNS plays a role in the recovery of consciousness. In this article, we first describe the neural correlates of consciousness, the vagus nerve anatomy and functions, along with the results of functional magnetic resonance imaging studies using taVNS. Based on consciousness recovery and taVNS mechanisms, we propose the Vagal Cortical Pathways model. This model highlights four consecutive pathways (A. Lower brainstem activation, B. Upper brainstem activation, C. Norepinephrine pathway, and D. Serotonin pathway) likely to have an impact on patients with a brain injury and DoC. Additionally, we suggest six different mechanisms of action: (1) Activation of the ascending reticular activating system; (2) Activation of the thalamus; (3) Re-establishment of the cortico-striatal-thalamic-cortical loop; (4) Promotion of negative connectivity between external and default mode networks by the activation of the salience network; (5) Increase in activity and connectivity within the external network through the norepinephrine pathway; and (6) Increase in activity within the default mode network through the serotonin pathway. This model aims to explain the potential therapeutic effects that taVNS has on brain activity in the process of consciousness recovery.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".