Investigation of hippocampal substructures in focal temporal lobe epilepsy with and without hippocampal sclerosis at 7T
Bibliographic record
Abstract
PURPOSE: To provide a more detailed investigation of hippocampal subfields using 7T magnetic resonance imaging (MRI) for the identification of hippocampal sclerosis in temporal lobe epilepsy (TLE). MATERIALS AND METHODS: Patients (n = 13) with drug-resistant TLE previously identified by conventional imaging as having hippocampal sclerosis (HS) or not (nine without HS, four HS) and 20 age-matched healthy controls were scanned and compared using a 7T MRI protocol. Using a manual segmentation scheme to delineate hippocampal subfields, subfield-specific volume changes and apparent transverse relaxation rate ( R2*) were studied between the two groups. In addition, qualitative assessment at 7T and clinical outcomes were correlated with measured subfield changes. RESULTS: Volumetry of the hippocampus at 7T in HS patients revealed significant ipsilateral subfield atrophy in CA1 (P = 0.001) and CA4+DG (P < 0.001). Volumetry also uncovered subfield atrophy in 33% of patients without HS, which had not been detected using conventional imaging. R2* was significantly lower in the CA4+DG subfields (P = 0.001) and the whole hippocampus (P = 0.029) of HS patients compared to controls but not significantly lower than the group without HS (P = 0.077, P = 0.109). No correlation was found between quantitative volumetry and qualitative assessment as well as surgical outcomes (Sub, P = 0.495, P = 0.567, P = 0.528; CA1, P = 0.104 ± 0.171, P = 0.273, P = 0.554; CA2+CA3, P = 0.517, P = 0.952, P = 0.130 ± 0.256; CA4+DG, P = 0.052 ± 0.173, P = 0.212, P = 0.124 ± 0.204; WholeHipp, P = 0.187, P = 0.132 ± 0.197, P = 0.628). CONCLUSION: These preliminary findings indicate that hippocampal subfield volumetry assessed at 7T is capable of identifying characteristic patterns of hippocampal atrophy in HS patients; however, difficulty remains in using imaging to identify hippocampal pathologies in cases without HS. LEVEL OF EVIDENCE: 2 J. MAGN. RESON. IMAGING 2017;45:1359-1370.
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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 | 0.000 |
| 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".