Correlating habenular subnuclei in rat and mouse by using topographic, morphological, and cytochemical criteria
Bibliographic record
Abstract
The mammalian habenulae consist of medial (MHb) and lateral (LHb) nuclear complexes. Especially the LHb has received much interest because it has been recognized as the potential center of an "anti-reward system." Subnuclear organization and connectivity of the LHb are well known. In contrast, criteria to classify habenular neurons into distinct groups with potentially different biological functions are missing, most likely as a result of the lack of appropriate marker proteins. Actually, a huge amount of data concerning the localization of more than 20,000 mouse protein genes is provided in the Allen Brain Atlas. Unfortunately, the immediate use of this information is prohibited by the fact that the subnuclear organization of the habenular complexes in mouse is not known so far. The present report, therefore, uses topographic, structural, and cytochemical information from the rat to recognize corresponding areas within the mouse habenulae. Taking advantage of the fact that the Klüver-Barrera technique allows simultaneous observation of neuronal cell bodies and myelinated fibers, we were able to correlate subnuclear areas in the mouse habenula to subnuclei, which had been rigorously identified by several criteria in the rat. Our data suggest that the topographic localization of habenular subnuclei is rather similar between mouse and rat and that they may be homologous in these two species. Consequently, our data may allow using the Allen Brain Atlas as a source of basal information, which should be helpful to select candidate molecular markers for functionally different neurons in the mouse and potentially in higher mammalian species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".