Analysis of neuroanatomical differences in mice with genetically modified serotonin transporters assessed by structural magnetic resonance imaging
Bibliographic record
Abstract
The serotonin (5-HT) system has long been implicated in autism spectrum disorder (ASD) as indicated by elevated whole blood and platelet 5-HT, altered platelet and brain receptor and transporter binding, and genetic linkage and association findings. Based upon work in genetically modified mice, 5-HT is known to influence several aspects of brain development, but systematic neuroimaging studies have not previously been reported. In particular, the 5-HT transporter (serotonin transporter, SERT; 5-HTT) gene, Slc6a4 , has been extensively studied. Using a 7-T MRI and deformation-based morphometry, we assessed neuroanatomical differences in an Slc6a4 knockout mouse on a C57BL/6 genetic background, along with an Slc6a4 Ala56 knockin mouse on two different genetic backgrounds (129S and C57BL/6). Individually (same sex, same background, same genotype), the only differences found were in the female Slc6a4 knockout mouse; all the others had no significant differences. However, an analysis of variance across the whole study sample revealed a significant effect of Slc6a4 on the amygdala, thalamus, dorsal raphe nucleus, and lateral and frontal cortices. This work shows that an increase or decrease in SERT function has a significant effect on the neuroanatomy in 5-HT relevant regions, particularly the raphe nuclei. Notably, the Slc6a4 Ala56 knockin alone appears to have an insignificant, but suggestive, effect compared to the KO, which is consistent with Slc6a4 function. Despite the small number of 5-HT neurons and their localization to the brainstem, it is clear that 5-HT plays an important role in neuroanatomical organization.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".