A high-resolution in vivo magnetic resonance imaging atlas of the human hypothalamic region
Bibliographic record
Abstract
The study of the hypothalamus and its topological changes provides valuable insights into underlying physiological and pathological processes. Owing to technological limitations, however, in vivo atlases detailing hypothalamic anatomy are currently lacking in the literature. In this work we aim to overcome this shortcoming by generating a high-resolution in vivo anatomical atlas of the human hypothalamic region. A minimum deformation averaging (MDA) pipeline was employed to produce a normalized, high-resolution template from multimodal magnetic resonance imaging (MRI) datasets. This template was used to delineate hypothalamic (n = 13) and extrahypothalamic (n = 12) gray and white matter structures. The reliability of the atlas was evaluated as a measure for voxel-wise volume overlap among raters. Clinical application was demonstrated by superimposing the atlas into datasets of patients diagnosed with a hypothalamic lesion (n = 1) or undergoing hypothalamic (n = 1) and forniceal (n = 1) deep brain stimulation (DBS). The present template serves as a substrate for segmentation of brain structures, specifically those featuring low contrast. Conversely, the segmented hypothalamic atlas may inform DBS programming procedures and may be employed in volumetric studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".