Seven‐tesla susceptibility‐weighted analysis of hippocampal venous structures: Application to magnetic‐resonance–normal focal epilepsy
Bibliographic record
Abstract
OBJECTIVE: Vascular structures may play a significant role in epileptic pathology. Although previous attempts to characterize vasculature relative to epileptogenic zones and hippocampal sclerosis have been inconsistent, an in vivo method of analysis would assist in resolving these inconsistencies and facilitate a comparison against healthy controls in a human model. Magnetic resonance imaging is a noninvasive technique that provides excellent soft tissue contrast, and the relatively recent development of susceptibility-weighted imaging has dramatically improved the visibility of small veins. METHODS: We built and tested a Hessian-based segmentation technique, which takes advantage of the increased signal and contrast available at 7 T to detect venous structures in vivo. We investigate the ability of this technique to quantify vessels in the brain and apply it to an asymmetry analysis of vessel density in the hippocampus in patients with mesial temporal lobe epilepsy (MTLE) and neocortical epilepsy. RESULTS: Vessel density was highly symmetric in the hippocampus in controls (mean asymmetry = 0.080 ± 0.076, median = 0.05027), whereas average vessel density asymmetry was greater in neocortical (mean asymmetry = 0.23 ± 0.17, median = 0.14) and MTLE (mean asymmetry = 0.37 ± 0.46, median = 0.26) patients, with the decrease in vessel density ipsilateral to the suspected seizure onset zone. Post hoc testing with one-way analysis of variance and Tukey post hoc test indicated significant differences in the group means (P < .02) between MTLE and the control group only. SIGNIFICANCE: Asymmetry in vessel density in the hippocampus is visible in patients with MTLE, even when qualitative and quantitative measures of hippocampal asymmetry show little volumetric difference between epilepsy patients and healthy controls.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".