Analysis of Clinical Utility of Functional MRI in Neurosurgical Decision-Making in Focal Epilepsy
Bibliographic record
Abstract
BACKGROUND: Functional MRI (fMRI) has proven valuable in presurgical planning for people with brain tumors. However, it is underutilized for patients with epilepsy, likely due to less data on its added clinical value in this population. We reviewed clinical fMRI referrals at the QEII Health Sciences Center (Halifax, Nova Scotia) to determine the impact of fMRI on surgical planning for patients with epilepsy. We focused on reasons for fMRI referrals, findings and clinical decisions based on fMRI findings, as well as postoperative cognitive outcomes. METHODS: We conducted a retrospective chart review of patients who underwent fMRI between June 2015 and March 2021. RESULTS: Language lateralization represented the primary indication for fMRI (100%), with 7.7% of patients also referred for motor and sensory mapping. Language dominance on the side of resection was observed in 12.8% of patients; in 20.5%, activation was adjacent to the proposed resection site. In 18% of patients, fMRI provided an indication for further invasive testing due to the risk of significant cognitive morbidity (e.g., anterograde amnesia). Further invasive testing was avoided based on fMRI findings in 69.2% of patients. Cognitive outcomes based on combined neuropsychological findings and fMRI-determined language dominance were variable. CONCLUSION: fMRI in epilepsy was most often required to identify hemispheric language dominance. Although fMRI-determined language dominance was not directly predictive of cognitive outcomes, it helped identify patients at low risk of catastrophic cognitive morbidity and those at high risk who required additional invasive testing.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".