Functional brain imaging of space motion sickness
Bibliographic record
Abstract
Motion sickness (MS) has been experienced for thousands of years, yet much is still unknown about this disorder. For example, the purpose of MS is not yet understood, nor is the underlying neuroanatomy and neurophysiology of the disorder known.A recent theory states that MS is a mechanism that limits inappropriate, self-generated motor strategies that can cause inadvertent changes in the function of the vestibular system and therefore lead to disordered postural, locomotor, and gaze control (Watt et al., 1992). In light of this theory, MS may best be studied under actively- rather than passively-generated conditions. For that reason, self-generated coriolis stimulation (CS) was used to induce MS in susceptible subjects in this thesis. An important feature of CS is that for a given head movement, the pattern of vestibular stimulation depends on the direction of whole-body of rotation. The thesis consists of three parts. First, a method had to be devised to reposition subjects accurately within the positron emission tomography (PET) scanner after they performed the active, MS-inducing stimulus. Secondly, the effects of CS were assessed in a functional brain imaging study. Positron emission tomography was used to determine which brain areas are active when a person experiences the signs and symptoms of, and emotional reactions to, MS. Thirdly, the consequences of the direction-specific vestibular stimulation patterns of CS were studied by determining the effect of direction of rotation on adaptation to CS.As a result of these experiments, a safe and effective head holder was developed, some of the brain structures involved in MS were revealed, and a unique method for producing MS in a laboratory setting was further characterized.
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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 | 0.000 |
| 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".