Tractography of the amygdala and hippocampus: anatomical study and application to selective amygdalohippocampectomy
Bibliographic record
Abstract
OBJECT: The aim of this study was to evaluate, using diffusion tensor tractography, the white matter fibers crossing the hippocampus and the amygdala, and to perform a volumetric analysis and an anatomical study of the connections of these 2 structures. As a second step, the authors studied the white matter tracts crossing a virtual volume of resection corresponding to a selective amygdalohippocampectomy. METHODS: Twenty healthy right-handed individuals underwent 3-T MR imaging. Volumetric regions of interest were manually created to delineate the amygdala, the hippocampus, and the volume of resection. White matter fiber tracts were parcellated using the fiber assignment for continuous tracking tractography algorithm. All fibers were registered with the anatomical volumes. RESULTS: In all participants, the authors identified fibers following the hippocampus toward the fornix, the splenium of the corpus callosum, and the dorsal hippocampal commissure. With respect to the fibers crossing the amygdala, the authors identified the stria terminalis and the uncinate fasciculus. The virtual resection disrupted part of the fornix, fibers connecting the 2 hippocampi, and fibers joining the orbitofrontal cortex. The approach created a theoretical frontotemporal disconnection and also interrupted fibers joining the temporal pole and the occipital area. CONCLUSIONS: This diffusion tensor tractography study allowed for good visualization of some of the connections of the amygdala and hippocampus. The authors observed that the virtual selective amygdalohippocampectomy disconnected a large number of fibers connecting frontal, temporal, and occipital areas.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".