Multimodal integration of magnetic resonance imaging and intracranial electroencephalographic abnormalities in temporal lobe epilepsy surgery
Bibliographic record
Abstract
OBJECTIVE: Precise localization of epileptogenic tissue is critical for successful surgery in drug-resistant temporal lobe epilepsy (TLE) but is challenging in those requiring intracranial electroencephalography (iEEG). A range of modalities are used for localization, including magnetic resonance imaging (MRI) and EEG, which are typically integrated qualitatively by the clinical team. METHODS: This study quantitatively performed retrospective analysis of three modalities in 40 individuals with TLE who underwent subsequent resective surgery: preoperative diffusion-weighted MRI, T1-weighted MRI, and iEEG. Brain abnormalities in gray matter (GM) volume, superficial white matter (SWM) mean diffusivity, and interictal iEEG band power were derived by comparison to 97 MRI controls and 247 subjects with iEEG. We hypothesized that combined abnormalities in GM and SWM could differentiate postsurgical outcomes and adding iEEG abnormalities would improve outcome differentiation. RESULTS: MRI (union of GM and SWM) abnormalities were primarily concentrated in the ipsilateral hippocampus and inferior temporal regions. Resection of these abnormal regions effectively differentiated seizure-free outcomes (area under the curve [AUC] = .76, area under the precision-recall curve [AUPRC] = .78, p < .01), corroborating previous results from larger TLE cohorts. Adding iEEG abnormalities improved outcome differentiation (AUC = .92, AUPRC = .89, p < .01; z = 2.01, p < .05). MRI abnormalities were more likely to colocalize with iEEG implantation sites (z = 6.26, p < .01) and iEEG abnormalities (z = 4.34, p < .01) in individuals with favorable outcomes (International League Against Epilepsy [ILAE] class 1 and 2), but not in those with less favorable outcomes (ILAE class 3+). SIGNIFICANCE: Combining quantitative MRI-derived GM and SWM abnormalities with interictal iEEG data improves localization of epileptogenic tissue and postsurgical outcome differentiation. Multimodal approaches may offer added value for surgical planning in complex situations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".