Low-Frequency rTMS Ameliorates Autistic-Like Behaviors in Rats Induced by Neonatal Isolation Through Regulating the Synaptic GABA Transmission
Bibliographic record
Abstract
Patients with autism spectrum disorder (ASD) display abnormalities in neuronal development, synaptic function and neural circuits. The imbalance of excitatory and inhibitory (E/I) synaptic transmission has been proposed to cause the main behavioral characteristics of ASD. Repetitive transcranial magnetic stimulation (rTMS) can directly or indirectly induce excitability and synaptic plasticity changes in the brain noninvasively. However, whether rTMS can ameliorate autistic-like behaviors in animal model via regulating the balance of E/I synaptic transmission is unknown. By using our recent reported animal model with autistic-like behaviors induced by neonatal isolation (postnatal days 1-9), we found that low-frequency rTMS (LF-rTMS, 1 Hz) treatment for 2 weeks effectively alleviated the acquired autistic-like symptoms, as reflected by an increase in social interaction and decrease in self-grooming, anxiety- and depressive-like behaviors in young adult rats compared to those in untreated animals. Furthermore, the amelioration in autistic-like behavior was accompanied by a restoration of the balance between E/I activity, especially at the level of synaptic transmission and receptors in synaptosomes. These findings indicated that LF-rTMS may alleviate the symptoms of ASD-like behaviors caused by neonatal isolation through regulating the synaptic GABA transmission, suggesting that LF-rTMS may be a potential therapeutic technique to treat ASD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".