Improving vocal communication with a ketogenic diet in a mouse model of autism
Bibliographic record
Abstract
Abstract Background Deficits in social communication and language development is a hallmark of autism spectrum disorder currently with no cure. Interventional studies using animal models have been very limited in demonstrating improved vocal communication. Autism has been proposed to involve metabolic dysregulation. Ketogenic diet (KD) is a metabolism-based therapy for medically intractable epilepsy, and its applications in other neurological conditions have been increasingly tested. However, how it would affect vocal communication has not been explored. The BTBR mouse strain is considered a model of idiopathic autism. They display robust deficits in vocalization during social interaction, and have metabolic changes implicated in autism. Methods We investigated the effects of KD on ultrasonic vocalizations (USVs) in juvenile and adult BTBR mice during male-female social encounters. Results After a brief treatment with KD, the amount, spectral bandwidth, and much of the temporal structure of USVs were robustly improved in both juvenile and adult BTBR mice. Composition of call categories and transitioning between individual call subtypes was more effectively improved in juvenile BTBR mice. Limitations Although sharing certain attributes, mouse vocalization is unlikely to model all aspects in the development and deficits of human language. KD is highly restrictive and can be difficult to administer, especially for many people with autism who have narrow food selections. Side effects and potential influence on development should also be considered. Future studies are required to tease apart the molecular mechanisms of KD’s effects on vocalization. Conclusions Together, our data provide further support to the hypothesis that metabolism-based dietary intervention could modify disease expression, including core symptoms, in autism.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".