Galvanic vestibular stimulation in primates: recording vestibular afferents during transmastoid stimulation
Bibliographic record
Abstract
An increasingly popular tool to artificially activate the human vestibular system is galvanic vestibular stimulation (GVS), in which electrical stimulation is applied between surface electrodes on the mastoid processes behind the ears. To date, however, while the effects of GVS, including the perception of self-motion, eye movements, and postural sway have been well described, the neuronal correlates remain unknown. Specifically, how the vestibular system actually responds to GVS to drive perception and behaviour has not been established. Therefore, the focus of this thesis is to understand the effects of GVS on vestibular afferent activity and. in turn, correlate these neural responses to behavioural responses. To this end, I recorded the responses of individual vestibular afferent and eye movements evoked by different GVS protocols applied between surface electrodes on the mastoid processes of alert macaques. In response to sinusoidal stimulation, we show for the first time that otolith afferents, much like canal afferents, displayed an increase in both gain and phase lead as a function of frequency. In contrast, when recording eye velocity during sinusoidal GVS with the monkeys fixating on a target, the gain of torsional eye velocity relative to the peak GVS current amplitude remained relatively constant as a function of frequency. Thus far, the prevailing view is that the GVS activation of the peripheral vestibular system is linear. However, I provide evidence that suggests that afferent responses can show significant nonlinearities in response to GVS. Notably, vestibular afferents, primarily irregular afferents, displayed asymmetric responses to currents of opposite polarity. Furthermore, we found discrepancies in the traditional linear analyses between sinusoidal and stochastic stimulation. These results reveal nonlinearities in the vestibular afferent activity in response to GVS. Taken together, the findings presented in this thesis provide the neural correlates underlying GVS-evoked perceptual, ocular and postural responses – a fundamental step into understanding the effect of this technique required to advance its clinical and biomedical applications.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".