Regional hippocampal diffusion abnormalities associated with subfield‐specific pathology in temporal lobe epilepsy
Bibliographic record
Abstract
OBJECTIVE: Hippocampal sclerosis (HS) is the most common pathology and best predictor of surgical outcome for medically refractory patients with temporal lobe epilepsy (TLE). Current clinical MRI methods can detect HS, but subfield pathology is poorly characterized, limiting accurate prediction of seizure-free outcomes after surgery. Diffusion tensor imaging (DTI) can probe regional microstructural changes associated with focal hippocampal pathology, but is typically limited by low-resolution whole-brain acquisitions. METHODS: ) DTI, T1, and quantitative T2 of the hippocampus was acquired in 18 preoperative TLE patients and 19 healthy controls. Diffusion images were qualitatively assessed for loss of internal architecture, and whole-hippocampus diffusion, volume, and quantitative T2 were compared across groups. Regional hippocampal diffusion abnormalities were examined in all subjects and compared to histology in four subjects who underwent anterior temporal lobectomy. RESULTS: High-resolution mean diffusion-weighted images enabled visualization of internal hippocampal architecture, used to visually identify HS with 86% specificity and 93% sensitivity. Mean diffusivity (MD) elevations were regionally heterogenous within the hippocampus and varied across TLE patients. The spatial location of diffusion abnormalities corresponded with the location of focal subfield neuron loss, gliosis, and reduced myelin staining abnormalities identified with postsurgical histology in four subjects who underwent anterior temporal lobectomy. Whole-hippocampus MD and T2 relaxation times were higher, and fractional anisotropy (FA) and volumes were lower in TLE patients relative to controls. Left hippocampus MD correlated with verbal memory in the TLE group. SIGNIFICANCE: Visualization of internal architecture and focal diffusion abnormalities on high-resolution diffusion imaging suggests potential clinical utility of diffusion imaging in TLE and may have significant implications for surgical planning and prediction of seizure-free outcomes in individual patients.
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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".