7T Magnetic Resonance Spectroscopy of Non-Lesional Temporal Lobe Epilepsy
Bibliographic record
Abstract
Temporal lobe epilepsy (TLE) is the most common form of focal epilepsy, and one that is generally amenable to surgical treatment when surgery is necessary. Unfortunately, roughly 25-30% of the patient population have no visible lesions on clinical MRI scans. Without anatomical abnormalities to help guide surgical resection, the success of surgical treatment decreases substantially. However, metabolic abnormalities may exist that could allow for accurate localization of epileptic tissue in this cohort. Magnetic resonance spectroscopy (MRS) is a technique that can detect and measure the concentration of metabolically important molecules within tissue, giving insight into the underlying cellular metabolism.\nIn this thesis, non-lesional TLE patients were studied and compared with control subjects using single voxel MRS at a magnetic field strength of 7T for the first time. We hypothesized that metabolite changes in the hippocampus would be associated with seizure lateralization. Non-lesional patients showed altered levels of creatine and choline when compared to healthy controls. These results were in agreement with prior work in the literature showing non-lesional TLE is primarily a result of glial cell proliferation without neuronal atrophy. However, in the patient cohort studied, these metabolites did not effectively lateralize seizure origin, potentially due to the varied underlying pathologies within the patient group.
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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) | 0.002 | 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".