Proton MR Spectroscopy in Patients with Nonlesional Insular Cortex Epilepsy Confirmed by Invasive EEG Recordings
Bibliographic record
Abstract
ABSTRACT BACKGROUND AND PURPOSE Recent studies suggest that a nonnegligible proportion of drug‐resistant epilepsy surgery candidates have an epileptogenic zone that involves the insula. We aimed to examine the value of proton magnetic resonance spectroscopy (1H‐MRS) in identifying patients with insular cortex epilepsy. METHODS Patients with possible nonlesional drug‐refractory insular epilepsy underwent a voxel‐based 1H‐MRS study prior to an intracranial electroencephalographic (EEG) study. Patients were then divided into two groups based on invasive EEG findings: the insular group with evidence of insular seizures and the noninsular group with no evidence of insular seizures. Sixteen age‐matched healthy controls were also scanned for normative data. RESULTS Twenty‐two epileptic patients were recruited, 12 with insular seizures and 10 with extra‐insular seizures. Ipsilateral and contralateral insular N‐acetyl‐aspartate concentrations ([NAA]) and NAA/Cr ratios were found to be similar in both patient groups. No significant differences in [NAA] or NAA/Cr ratios were found between the insular group, noninsular group, and healthy controls. [NAA] and NAA/Cr asymmetry indices correctly lateralized the seizure focus in only 16.7% and 0% of patients, respectively. CONCLUSIONS Our preliminary findings suggest that 1H‐MRS fares poorly in identifying patients with nonlesional insular epilepsy.
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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 | 0.000 | 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".