Daily Artificial Gravity is Associated with Greater Neural Efficiency during Sensorimotor Adaptation
Bibliographic record
Abstract
Abstract Altered vestibular signaling and body unloading in microgravity results in sensory reweighting and adaptation. Microgravity effects are well-replicated in head-down tilt bed rest (HDBR). Artificial gravity (AG) is a potential countermeasure to mitigate effects of microgravity. We examined the effectiveness of daily AG for mitigating brain and/or behavioral changes in 60 days of HDBR. One group received AG for 30 minutes daily (AG; n=16) and a control group spent the same time in HDBR but received no AG (CTRL; n=8). All participants performed a sensorimotor adaptation task 5 times during fMRI scanning: twice prior to HDBR twice during HDBR, and once following HDBR. The AG group showed similar behavioral adaptation effects compared with the CTRLs. We identified decreased brain activation in the AG group from pre to late HDBR in the cerebellum for the task baseline portion and in the thalamus, calcarine, cuneus, premotor cortices, and superior frontal gyrus in the AG group during the early adaptation phase. The two groups also exhibited differential brain-behavior correlations. Together, these results suggest that AG may result in a reduced recruitment of brain activity for basic motor processes and sensorimotor adaptation. These effects may stem from somatosensory and vestibular stimulation from AG.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".