Convergent differential regulation of SLIT‐ROBO axon guidance genes in the brains of vocal learners
Bibliographic record
Abstract
Only a few distantly related mammals and birds have the trait of complex vocal learning, which is the ability to imitate novel sounds. This ability is critical for speech acquisition and production in humans, and is attributed to specialized forebrain vocal control circuits that have several unique connections relative to adjacent brain circuits. As a result, it has been hypothesized that there could exist convergent changes in genes involved in neural connectivity of vocal learning circuits. In support of this hypothesis, expanding on our related study (Pfenning et al. [2014] Science 346: 1256846), here we show that the forebrain part of this circuit that makes a relatively rare direct connection to brainstem vocal motor neurons in independent lineages of vocal learning birds (songbird, parrot, and hummingbird) has specialized regulation of axon guidance genes from the SLIT-ROBO molecular pathway. The SLIT1 ligand was differentially downregulated in the motor song output nucleus that makes the direct projection, whereas its receptor ROBO1 was developmentally upregulated during critical periods for vocal learning. Vocal nonlearning bird species and male mice, which have much more limited vocal plasticity and associated circuits, did not show comparable specialized regulation of SLIT-ROBO genes in their nonvocal motor cortical regions. These findings are consistent with SLIT and ROBO gene dysfunctions associated with autism, dyslexia, and speech sound language disorders and suggest that convergent evolution of vocal learning was associated with convergent changes in the SLIT-ROBO axon guidance pathway.
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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".