1H-MRS Imaging in Patients with Non-Lesional Insular Cortex Epilepsy Characterized by Depth Electrode Recordings (P01.058)
Bibliographic record
Abstract
Objective: Identifying insular metabolic abnormalities using proton magnetic resonance spectroscopy imaging (1H-MRSI) during the presurgical evaluation of non-lesional insular cortex epilepsy (ICE). Background Seizures arising from the insular cortex can generate a wide variety of ictal symptoms potentially leading to a false diagnosis of temporal, frontal or parietal lobe epilepsy. In non-lesional ICE, standard non-invasive investigations such as video-EEG, SPECT and PET are often non-localizing or false-localizing potentially leading to inaccurate localization of the epileptogenic zone. Design/Methods: Using voxel-based 1H-MRSI, we studied 16 patients with suspected non-lesional ICE, 13 of which subsequently underwent insular and peri-insular depth electrode recordings, as well as 16 age-matched controls. Four voxels (8 ml each) were positioned to include bilateral anterior and posterior insular regions. Spectral analysis was performed to quantitate N-acetylaspartate (NAA) and creatinine (Cr). Comparisons of the NAA/Cr ratios between the normal and affected insula and between patients and controls were made using a one-way ANOVA. An asymmetry index (AI) was also calculated to evaluate intra-individual metabolic variations. Results: Insular interictal spikes (IIS) were recorded in 12 of 13 patients on intracerebral EEG recordings. Insular seizures were recorded in 6 of those patients (ICE). Ipsilateral and contralateral NAA/Cr ratios were found to be similar in all the groups. No difference in NAA/Cr ratios was found between ICE patients, IIS patients and healthy controls (2.05 ± 0.20, 2.16 ± 0.21 and 2.01 ± 0.16 respectively, P=0.06). Mean AIs in ICE and IIS patients were non-significant when compared to controls (P=0.17). Lateralization of the AI to the epileptic insular cortex was found in only one of 1 of 6 (17%) ICE patients. Conclusions: 1H-MRSI failed to identify significant metabolic abnormalities in patients with insular spikes and/or seizures and is hence unlikely to be useful during the presurgical evaluation of suspected ICE. Supported by: Quebec Bio-Imaging Network (QBIN) and the Savoy Foundation. Disclosure: Dr. Gibbs has nothing to disclose. Dr. Boulanger has nothing to disclose. Dr. Gilbert has nothing to disclose. Dr. Leroux has nothing to disclose. Dr. Bouthillier has nothing to disclose. Dr. Nguyen has nothing to disclose.
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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".