Author response: Movement maintains forebrain neurogenesis via peripheral neural feedback in larval zebrafish
Bibliographic record
Abstract
Sensory experiences early in life help the brain to grow and develop. For example, raising animals in complete darkness stops the visual areas of their brain from forming properly. Previous studies have shown that sensory input helps to strengthen the connections between already existing brain cells, but it is unclear if it affects the actual creation of new brain cells. Conditions that reduce the mobility of young children, such as muscular disease, are often accompanied by learning difficulties. This suggests that physical movement may be important for healthy brain development. Scientists have previously found a link between exercise and an increased production of new brain cells in adults. However, such a link has not been established earlier in life, when the brain is developing the most. To address this, Hall and Tropepe studied how movement affects the brain development in zebrafish larvae, at an age when many of their brain cells are created. Restraining the larvae decreased their physical movement, while making them swim against a current increased their movement. Hall et al. looked at how this affected the larvae’s number of so called progenitor cells – the cells from which brain cells are created. When the larvae moved less, the number of progenitor cells decreased. But when they moved more frequently, the amount of progenitor cells increased. The experiments also showed that some sensory cells, which detect sensations associated with movement of the body during swimming, are linked to brain cell production. Blocking the development of these sensory cells prevented the rise in progenitor cells seen with increased movement in the larvae. However, activating these sensory cells in immobilised larvae increased the number of progenitor cells, even though the larvae could not move. These findings suggest that measures to increase physical movement in young children could be used to help the brain develop normally.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.069 | 0.012 |
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".