Daily artificial gravity partially mitigates vestibular processing changes associated with head-down tilt bedrest
Bibliographic record
Abstract
Microgravity alters vestibular signaling and reduces body loading, driving sensory reweighting. The unloading effects can be modelled using head-down tilt bedrest (HDT). Artificial gravity (AG) has been hypothesized to serve as an integrated countermeasure for the declines associated with HDT and spaceflight. Here, we examined the efficacy of 30 min of daily AG to counteract brain and behavior changes from 60 days of HDT. Two groups received 30 min of AG delivered via short-arm centrifuge daily (n = 8 per condition), either in one continuous bout, or in 6 bouts of 5 min. To improve statistical power, we combined these groups (AG; n = 16). Another group served as controls in HDT with no AG (CTRL; n = 8). We examined how HDT and AG affect vestibular processing by collecting fMRI scans during vestibular stimulation. We collected these data prior to, during, and post-HDT. We assessed brain activation initially in 12 regions of interest (ROIs) and then conducted an exploratory whole brain analysis. The AG group showed no changes in activation during vestibular stimulation in a cerebellar ROI, whereas the CTRL group showed decreased activation specific to HDT. Those that received AG and showed little pre- to post-HDT changes in left vestibular cortex activation had better post-HDT balance performance. Whole brain analyses identified increased pre- to during-HDT activation in CTRLs in the right precentral gyrus and right inferior frontal gyrus, whereas AG maintained pre-HDT activation levels. These results indicate that AG could mitigate activation changes in vestibular processing that is associated with better balance performance.
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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".