Volumetry of Hippocampus and Amygdala with High-resolution MRI and Three-dimensional Analysis Software: Minimizing the Discrepancies between Laboratories
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Within the medial temporal lobe, both the hippocampus and amygdala are frequently targeted by researchers and clinicians for volumetric analysis based on magnetic resonance imaging (MRI). However, different data acquisition techniques, analysis software and anatomical boundaries have in the past made it difficult to compare results of MRI studies from different laboratories. In order to reduce these differences, a segmentation protocol was established with 40 healthy normal control subjects recently scanned in our laboratory. Data acquisition was performed with a three-dimensional gradient echo technique, and scans were corrected for non-uniformity and registered into standard stereotaxic space prior to segmentation. Volumetric analysis was performed manually using three-dimensional software that allows simultaneous analysis of sagittal, coronal and horizontal images. Intra- and inter-rater coefficients yielded correlation coefficients comparable with other protocols. The hippocampal volume was larger in the right hemisphere (3324 versus 3208 mm(3)), while no interhemispheric differences for the amygdala (1154 versus 1160 mm(3)) could be observed. Most importantly, results from recent segmentation protocols for hippocampus and amygdala seem to approach each other with regard to mean volumes and interhemispheric differences. This indicates that the advances in scanning technique, volume preparation and segmentation protocols allow a more precise definition of medial temporal lobe structures with MRI, and that results for mean volumes for hippocampus and amygdala from different laboratories will eventually become comparable.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it